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OBESITY MEASURES AND DEFINITIONS OF SARCOPENIC OBESITY IN SINGAPOREAN ADULTS – THE YISHUN STUDY

 

B.W.J. Pang1,*, S.-L. Wee1,2,*, L.K. Lau1, K.A. Jabbar1, W.T. Seah1, D.H.M. Ng1, Q.L.L. Tan1, K.K. Chen1, M.U. Jagadish1,3, T.P. Ng1,4

1. Geriatric Education and Research Institute (GERI), Singapore; 2. Faculty of Health and Social Sciences, Singapore Institute of Technology, Singapore; 3. Geriatric Medicine, Khoo Teck Puat Hospital, Singapore; 4. Department of Psychological Medicine, National University of Singapore, Singapore.
Corresponding author: Shiou-Liang Wee, Geriatric Education and Research Institute (GERI), 2 Yishun Central 2, Tower E Level 4 GERI Admin, 768024, Singapore, Phone: +65 6807 8011, weeshiouliang@gmail.com; Benedict Wei Jun Pang, Geriatric Education and Research Institute (GERI), 2 Yishun Central 2, Tower E Level 4 GERI Admin, 768024, Singapore, Phone: +65 6807 8030, L3enanapang@gmail.com (B.W.J. Pang)

J Frailty Aging 2020;in press
Published online December 11, 2020, http://dx.doi.org/10.14283/jfa.2020.65

 


Abstract

Objectives: Due to the lack of a uniform obesity definition, there is marked variability in reported sarcopenic obesity (SO) prevalence and associated health outcomes. We compare the association of SO with physical function using current Asian Working Group for Sarcopenia (AWGS) guidelines and different obesity measures to propose the most optimal SO diagnostic formulation according to functional impairment, and describe SO prevalence among community-dwelling young and old adults. Design: Obesity was defined according to waist circumference (WC), percentage body fat (PBF), fat mass index (FMI), fat mass/fat-free mass ratio (FM/FFM), or body mass index (BMI). SO was defined as the presence of both obesity and AWGS sarcopenia. Muscle function was compared among phenotypes and obesity definitions using ANOVA. Differences across obesity measures were further ascertained using multiple linear regressions to determine their associations with the Short Physical Performance Battery (SPPB). Setting: Community-dwelling adults 21 years old and above were recruited from a large urban residential town in Singapore. Participants: 535 community-dwelling Singaporeans were recruited (21-90 years old, 57.9% women), filling quotas of 20-40 participants in each sex- and age-group. Measurements: We took measurements of height, weight, BMI, waist and hip circumferences, body fat, muscle mass, muscle strength, and functional assessments. Questionnaire-based physical and cognitive factors were also assessed. Results: Overall prevalence of SO was 7.6% (WC-based), 5.1% (PBF-based), 2.7% (FMI-based), 1.5% (FM/FFM-based), and 0.4% (BMI-based). SO was significantly associated with SPPB only in the FMI model (p<0.05), and total variance explained by the different regression models was highest for the FMI model. Conclusions: Our findings suggest FMI as the most preferred measure for obesity and support its use as a diagnostic criteria for SO.

Key words: Sarcopenic obesity, sarcopenia, obesity, prevalence, Singapore.

Abbreviation: ALMI: Appendicular Lean Mass Index; AWGS: Asian Working Group for Sarcopenia; FM/FFM: Fat Mass to Fat-Free Mass ratio; FMI: Fat Mass Index; GPAQ: Global Physical Activity Questionnaire; GS: Gait Speed; HGS: Handgrip Strength; KES: Knee Extensor Strength; LASA: Longitudinal Aging Study Amsterdam; MNA: Mini Nutritional Assessment; PBF: Percentage Body Fat; RBANS: Repeatable Battery for the Assessment of Neuropsychological Status; SO: Sarcopenic Obesity; SPPB: Short Physical Performance Battery; TUG: Timed Up-and-Go; WC: Waist Circumference.


 

Introduction

The rising tide of obesity prevalence is currently a global public health problem of epidemic proportions in all ages. At the same time, population ageing will double the number of persons aged 65 years and over worldwide in the next three decades, reaching 1.5 billion in 2050 (1), many of whom will be physically frail and disabled from the progressive loss of muscle mass and function (sarcopenia) (2). Obesity, the excessive accumulation of body fat, is an important factor in the development of metabolic syndrome and cardiovascular disease (3), and is also an important contributing cause of muscle loss. The two conditions have overlapping causes and feedback mechanisms that are interconnected and mutually-aggravating (3). Coexistence of both, a condition known as sarcopenic obesity (SO), has been shown to act synergistically to exacerbate metabolic impairment, disability, cardiovascular disease and mortality more so than either condition alone (3,4).
Although the diagnosis of sarcopenia has been increasingly harmonized by consensus working groups such as the Asian Working Group for Sarcopenia (AWGS) (2), the lack of a uniform obesity definition in the context of SO has led to great variation in the methods and cut-offs applied to define obesity, resulting in marked variability in reported SO prevalence as well as conflicting data on observed adverse health outcomes (5). Obesity is officially considered a disease that requires clinical treatment, however, there are currently no universally-accepted definitions for it (3). Commonly used measures include the body mass index (BMI), waist circumference (WC), percentage body fat (PBF), fat mass index (FMI) and fat mass to fat-free mass (FM/FFM) ratio. BMI provides a good indication of disease risk, but does not distinguish between fat and fat-free mass, thus making its clinical value questionable (3,5,6). WC indicates central obesity and serves as a surrogate measure of visceral adiposity (5-7), while PBF gives an objective indication of total body fat and its distribution (5). FM and FFM are the most frequently used adiposity indexes for SO classification (7), with the FM/FFM ratio deemed clinically-suitable in the diagnosis of SO (6). However, each of these measures assesses a different construct of obesity and are not interchangeable (5). To better understand its underlying physiological processes, and to determine disease prevalence and design clinical interventions, it is necessary to progress towards a unified criteria for the diagnosis and classification of obesity.
Aside from the wide heterogeneity of obesity measures used in studies, inconsistent observations of associations between SO and disease risk (7) may also result from the criteria used for defining sarcopenia in most studies, which other than muscle mass did not always consider muscle strength and physical function (3,5). Muscle strength and function have been shown to decline more rapidly with age and contribute more significantly to physical decline and frailty than muscle mass.
The primary aim of the present study was to propose the most optimal SO diagnostic formulation by comparing the association of SO with physical function using different obesity measures (WC, PBF, FMI, FM/FFM and BMI). We hypothesize that the most optimal diagnostic formulation would be one that is most significantly associated with physical functional impairment. The secondary aim was to compare and describe estimates of SO prevalence among community-dwelling younger and older adults in the Singapore population using AWGS guidelines for sarcopenia diagnosis and the different obesity definitions.

 

Methods

Setting

Community-dwelling adults (≥21 years) were recruited from the town of Yishun, one of the largest north-residential towns in Singapore, residential population of 220,320 (50.6% females), with 12.2% older adults (≥65 years), similar to the overall Singapore residential population of 4,026,210 (51.1% females), with 14.4% older adults (≥65 years) (8).

Participants

Random sampling was employed to obtain a representative sample of approximately 300 male and 300 female participants, filling quotas of 20-40 participants in each sex- and age-group (10-year age-groups between 21-60; 5-year age-groups after 60). Detailed recruitment methods and exclusion criteria have been reported previously (9). Ethics approval was obtained from the National Healthcare Group DSRB (2017/00212). All respondents signed informed consent before participating in the study.

Questionnaires

Participants answered questionnaires pertaining to education level, housing type (a proxy for socio-economic status), living arrangement, marital status, smoking and drinking (more than four days a week), a health and medical questionnaire indicating medical conditions and comorbidities, a mini nutritional assessment (MNA) (10), a global physical activity questionnaire (GPAQ) (11) and the LASA physical activity questionnaire (12).

Anthropometry

Body weight to the nearest 0.1 kg and height to nearest millimeter were measured using a digital balance and stadiometer (Seca, GmbH & Co. KG, Hamburg, Germany). Waist and hip circumferences were measured using a non-elastic, flexible measuring tape around the navel and widest part of the hips respectively. Body mass index (BMI) was calculated as weight (kg) divided by height (m) squared. ‘Obesity’ according to waist circumference (WC) was defined as ≥90 and ≥80 cm for men and women respectively (13). A BMI of ≥27.5 kg/m2 was used to define obesity as recommended by the World Health Organization for Asian populations (14).

Cognitive Assessment

Global cognition and cognitive domains including immediate and delayed memory, visuospatial, language and attention were assessed using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) (15).

Body Composition

Bone mineral density, total percentage body fat (PBF), fat mass (FM), fat-free mass (FFM) and appendicular lean mass (ALM) were measured using DXA (Discovery WI, Hologic, Inc., Marlborough, USA). Fat mass index (FMI) and appendicular lean mass index (ALMI) were calculated as FM (kg) and ALM (kg) divided by height (m) squared, where FM equals to total body fat mass and ALM equals to the sum of lean mass in the upper and lower limbs. FM/FFM ratio was calculated by taking FM (kg) divided by FFM (kg). ‘Obesity’ according to PBF, FMI and FM/FFM were defined using the upper two quintiles of PBF and FMI, and a ratio of >0.80 for FM/FFM (6).

Functional Performance

Muscle function, as a gauge of functional deterioration and impairment, was measured using objective and validated assessments including handgrip strength (HGS) (16), knee extensor strength (KES) (17), usual gait speed (GS) (18), the Short Physical Performance Battery (SPPB) (19) and the Timed Up-and-Go (TUG) (20). HGS was assessed using the Jamar Plus+ Digital Hand Dynamometer (Patterson Medical, Evergreen Boulevard, Cedarburg, USA), and the highest of four readings (two trials per arm) recorded. KES was assessed using a spring gauge strapped 10 cm above the ankle joint, and the highest of four readings (two trials per leg) recorded. GS was measured using the 6 m GAITRite Walkway (CIR Systems Inc., Sparta. New Jersey, USA) with a 2 m lead in and out phase, and the average speed (three trials) recorded. TUG measures the progress of balance, sit-to-stand and walking. The average timing (two trials) was recorded. A composite score was calculated for SPPB, which comprises three components: balance, gait speed and repeated chair stands.

Sarcopenia and Sarcopenic Obesity

Sarcopenia was assessed using the AWGS criteria (2). Poor physical function was defined as GS <1.0 m/s, low muscle mass as ALMI <7.0 and <5.4 kg/m2, and muscle strength by HGS <28 and <18 kg for men and women respectively. Presence of low muscle mass and poor muscle strength and/or physical performance constitutes ‘sarcopenia’ (2). Participants with both sarcopenia and obesity were classified as ‘sarcopenic obese (SO)’. Those who had neither were classified as ‘normal’.

Statistical Analysis

SPSS version 22 (Chicago, Illinois, USA) was used for analysis. Prevalence of SO were extrapolated to the general population weights by age groups. In statistical analyses, the sarcopenia component was defined according to low muscle mass and strength only, and one-way analysis of variance (ANOVA) with Bonferroni correction for post-hoc comparisons were performed to compare the four phenotypes – ‘Normal’, ‘Obese’, ‘Sarcopenic’ and ‘Sarcopenic Obese’ – against muscle functions for those 50 years and older. To further ascertain the impact of obesity definitions on physical function, univariate and multiple linear regressions were performed to determine their associations with SPPB. Statistical significance was set at p<0.05.

 

Results

A total of 542 participants (57.9% females) aged 21-90 years were recruited. Due to incomplete data from seven participants, data from 535 participants were analyzed (Figure 1.). Of these, 81.9% were Chinese, 8.6% Malays, 6.7% Indians, and 2.8% from other races. Mean age was 58.6 (18.8) years. Reference values and descriptive statistics are presented in Supplementary Tables S1. and S2.

Figure 1
Participant flowchart

 

Cut-off values for obesity using the sex-specific upper two quintiles of PBF and FMI were 31.0% and 7.63 kg/m2 for men, and 41.4% and 9.93 kg/m2 for women. Overall population-adjusted prevalence of sarcopenic obesity (SO) was 7.6% (WC-based: men 7.2%; women 7.9%), 5.1% (PBF-based: men 4.4%; women 5.7%), 2.7% (FMI-based: men 2.2%; women 3.2%), and 1.5% (FM/FFM-based; men 0%; women 2.9%). Population-adjusted prevalence of SO for older adults (≥60 years) was 21.6% (WC-based), 16.1% (PBF-based), 9.5% (FMI-based) and 3.7% (FM/FFM-based; Table 1.).

Table 1
Prevalence estimates in study sample and adjusted to the Singapore general population age groups weights

SO: Sarcopenic obese. Values are presented as percentages (%)

 

Participant Characteristics and Sarcopenic Obesity

Across all five obesity measures, the SO phenotypes had higher age compared to the overall sample (Table 2.). Individuals with the lowest education levels, smallest housing types, lived alone, were widowed, had diabetes, hypertension or high cholesterol, or had one or more medical conditions were more likely to have SO. Individuals who smoked were more likely to have SO using the PBF, FMI and BMI phenotypes, while individuals who drank were more likely to have SO using the WC, PBF, FMI and BMI phenotypes. The FM/FFM-based definition did not identify any males with SO (0%).

Table 2
Participant characteristics and sarcopenic obesity statuses

SO: Sarcopenic Obesity; WC: Waist Circumference; PBF: Percentage Body Fat; FMI: Fat Mass Index; FM: Fat Mass; FFM: Fat-Free Mass. BMI: Body Mass Index. Values are presented as mean (SD) or number (%)

 

Muscle Function (ANOVA)

The SO phenotype consistently performed poorer than the normal group in functional measures for the WC, PBF and FMI obesity definitions (p<0.05, Table 3.). The SO phenotype also persistently performed poorer than the obese group for the PBF definition (p<0.05), and in HGS, KES, GS and TUG for the WC and FMI definitions (p<0.05). Compared to the sarcopenic group, the SO phenotype performed poorer in HGS for the WC and FM/FFM definitions, and in TUG for the PBF definition (p<0.05).

Table 3
Comparison of muscle function among phenotypes and obesity definitions using ANOVA (≥50 years old)

P value (<0.05); 1 denotes a significant post-hoc Bonferroni test between Normal and Obese (P<0.05); 2 denotes a significant post-hoc Bonferroni test between Normal and Sarcopenic (P<0.05); 3 denotes a significant post-hoc Bonferroni test between Normal and SO (P<0.05); 4 denotes a significant post-hoc Bonferroni test between Obese and Sarcopenic (P<0.05); 5 denotes a significant post-hoc Bonferroni test between Obese and SO (P<0.05); 6 denotes a significant post-hoc Bonferroni test between Sarcopenic and SO (P<0.05)

 

Multiple Linear Regression for SPPB

We adjusted for age, gender, education level, housing type, diabetes, GPAQ activity level and RBANS global (Table 4.). Small sample sizes for SO as defined by FM/FFM (n=9) and BMI (n=2) precluded multiple linear regression analysis using these definitions. Using the WC-based, PBF-based and FMI-based definitions, the total variance explained by the regression models were 22.6% [F(8, 352)=14.147, p<0.001], 23.1% [F(8, 352)=14.534, p<0.001] and 23.6% [F(8, 352)=14.867, p<0.001] respectively. SO was significantly associated with SPPB only in the FMI model (p<0.05).

Table 4
Multiple linear regression analysis for Short Physical Performance Battery (≥50 years old)

β: Standardized Coefficient; B: Unstandardized Coefficient; SE: Standard Error; GPAQ: Global Physical Activity Questionnaire; MET: Metabolic Equivalent of Task; RBANS: Repeatable Battery for the Assessment of Neuropsychological Status SO: Sarcopenia Obesity; * denotes a significant P value (<0.05)

 

Discussion

In this study, we present reference values for waist circumference (WC), percentage body fat (PBF), fat mass index (FMI), fat mass to fat-free mass (FM/FFM) ratio, and body mass index (BMI), as well as sex-specific cut-off values for PBF and FMI, to define alternative phenotypic representations of sarcopenic obesity (SO). We describe the corresponding SO prevalences using AWGS guidelines for sarcopenia, across the age groups of healthy adults. Our estimated prevalence of SO for adults aged ≥21 years and ≥60 years were 7.6% and 21.6% (WC-based), 5.1% and 16.1% (PBF-based), 2.7% and 9.5% (FMI-based), 1.5% and 3.7% (FM/FFM-based), and 0.4% and 1.6% (BMI-based) respectively.
Comparatively, a recent study on 200 cognitively-intact and functionally-independent community-dwelling adults in Singapore (≥50 years) reported a prevalence of 10.5% and 10.0% based on WC-based and PBF-based definitions of SO respectively (5), slightly lower than the 15.4% and 10.7% in the present study (≥50 years). Notably, the authors used the original AWGS criteria to define sarcopenia (21), with lower cut-offs for muscle strength and gait speed and thus lower detection rates compared to the updated AWGS criteria (2). Another study involving 591 healthy volunteers in Korea found a prevalence of 10.9% (40-59 years) and 18.0% (≥60 years) using the PBF-based definition (4), higher than the 2.1% (40-59 years) and 16.1% (≥60 years) in the present study. However, their criteria did not include the functional components of sarcopenia (2), and their population-derived cut-offs for low muscle mass were much higher at 8.81 and 7.36 kg/m2 compared to AWGS’ 7.0 and 5.4 kg/m2 used in the present study (2, 21).
Using one-way analysis of variance (ANOVA) with Bonferroni correction for post-hoc comparisons, the SO phenotype consistently performed poorer than the normal group in functional measures for the WC, PBF and FMI obesity definitions. The SO phenotype also persistently performed poorer than the obese group for the PBF definition, and in HGS, KES, GS and TUG for the WC and FMI definitions. Compared to the sarcopenic group, the SO phenotype performed poorer in HGS for the WC and FM/FFM definitions, and in TUG for the PBF definition.
Multiple linear regression results for SPPB revealed that only the FMI-based definition of SO was significantly associated with poorer SPPB scores, and the total variance explained by the different regression models was highest for the FMI definition, followed by PBF and WC. Physical function impairment in the absence of disability likely represents the shared core of sarcopenia and physical frailty. Such functional deterioration with deficits in gait speed, balance, and muscle strength, can be objectively assessed through the SPPB (22). Given that the SPPB is considered one of the most reliable and valid assessments for functional performance (5,19,22), our findings suggest FMI to be the most preferred obesity measure for defining SO.
Although PBF gives an objective indication of total body fat, it does not discern between visceral and subcutaneous fat (5). While WC provides an estimate of visceral adiposity which is associated with higher morbidity than its subcutaneous counterpart (23), it is not adjusted for height and is thus insensitive to body size (5-7). BMI gives a good indication of disease risk, but does not differentiate fat from fat-free mass (3, 5, 6). In addition, the BMI definition led to a noticeably much lower SO detection rate (0.4%) compared to the other definitions, similar to what was reported in a previous study.5 In corroboration with the literature, fat mass was previously reported to be the most frequently used adiposity index for the classification of SO, and its adjustment to height squared (FMI) has been the preferred method to account for differences in body size across age and between the sexes (6). In terms of physical performance, FMI is also considered an accurate indicator of total body adiposity that could improve the predictive value of SO in functional deterioration (6, 7). In addition, FMI was found to be a better screening tool in the prediction of metabolic syndrome in Chinese men and women (24) and more accurately assessed obesity in Mexican Americans (25) compared to BMI or PBF.
The FM/FFM definition did not identify any men with SO. This is similar to the findings of previous studies, where using the FM/FFM definition led to markedly disproportionate low numbers of men identified with SO (6, 7, 26). Women inherently have much higher relative fat mass than men (27), and conversely, men have higher relative fat-free mass (total body water, muscle and bone mass) than women at all ages (27,28). This is primarily due to the hormonal differences between men and women; men have higher testosterone levels which exhibits anabolic effects on muscle and bone (29), while higher estrogen levels in women promote subcutaneous fat deposition especially in the hips, thighs and chest (30). In addition, approximately 75% of skeletal muscle tissue is composed of water (31). Thus, with higher muscle mass, men inadvertently hold more total body water, further contributing to the discrepancy in fat mass and fat-free mass between men and women. To address the underlying gender-bias of the FM/FFM criteria and improve its accuracy in identifying gender-specific obesity and SO prevalence, different cut-off values for men and women (lower cut-off values for men) should be explored.
A recent study on 1235 adults with type 2 diabetes (T2D) in Singapore (≥45 years) reported a SO prevalence of 19.4% using the FM/FFM-based definition, higher than the 2.3% reported in this study, although the criteria for diagnosing SO in that study did not include the AWGS functional components for sarcopenia, which could possibly have inflated the proportions identified with SO (6). Furthermore, previous studies have shown a close link between sarcopenia and obesity through insulin resistance (3). Visceral fat accumulation (which promotes secretion of pro-inflammatory cytokines) is a contributing factor to the loss of skeletal muscle (which is the largest insulin-responsive tissue). Obesity and sarcopenia have a synergistic effect on promoting insulin resistance which could exacerbate T2D (4). In addition, patients with T2D exhibit insulin resistance, systemic inflammation and metabolic complications that could in turn perpetuate excess adiposity accumulation and loss of muscle mass (3), leading to a vicious cycle of worsening insulin resistance, T2D, sarcopenia and obesity (4).
The strengths of this study are its population-based nature, thoroughness of data collection and application of up-to-date and evidence-based consensus. It also has a few limitations. It presents cross-sectional data on obesity, muscular health and function of Singaporeans, which precludes inferences on causality. Agreement amongst the obesity definitions was also not investigated, though it has previously been established that different obesity definitions intrinsically measure different constructs and are therefore not interchangeable (5). While the AWGS criteria is for older adults, we also applied the same criteria to estimate prevalence of SO in younger adults, and so this might have been an underestimate, though we only included those 50 years and older in our statistical analyses. Finally, the participants were community-dwelling adults; thus, the findings may not be generalizable to hospitalized, institutionalized or disabled individuals.

 

Conclusions

This study presents new and much-needed data that help to better define and document sarcopenic obesity across age groups of healthy, community-dwelling Asian adults. To address the variability in sarcopenic obesity prevalence and conflicting data on its associations with adverse health consequences, a universally-accepted obesity definition is of utmost importance. Our findings suggest that FMI is the most preferred method for measuring obesity, and support its use as a diagnostic criteria for sarcopenic obesity.

 

Disclosure statement: The authors declare no conflict of interest. The research work conducted for this study comply with the current laws of the country in which they were performed.
Acknowledgements: This research was supported as part of a core funding from the Ministry of Health of Singapore to GERI. The authors gratefully acknowledge the strong support of Prof. Pang Weng Sun in making this Yishun Study possible, and the support of Dr. Lilian Chye, Sylvia Ngu Siew Ching, Aizuriah Mohamed Ali, Mary Ng Pei Ern, Chua Xing Ying and Shermaine Thein in this study. BWJP, SLW, MUJ, TPN contributed to the research design. BWJP, LKL, KAJ, WTS, DHMN, QLLT, KKC conducted the research. BWJP, LKL, KAJ, WTS, DHMN, QLLT, KKC analyzed data and performed statistical analysis. BWJP, SLW, TPN wrote the paper. BWJP, SLW, TPN had primary responsibility for final content. All authors have read and approved the final version.

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SUPPLEMENTARY MATERIAL2

References

1. United Nations, Department of Economic and Social Affairs, Population Division. World Population Prospects 2019: Highlights. New York: United Nations. 2019.
2. Chen L, Woo J, Assantachai P, Auyeung T, Chou M, Iijima K, Jang H, Kang L, Kim M, Kim S et al. Asian Working Group for Sarcopenia: 2019 Consensus Update on Sarcopenia Diagnosis and Treatment. Journal of the American Medical Directors Association. 2020;21(3):300-307.e2. https://doi.org/10.1016/j.jamda.2019.12.012.
3. Kalinkovich A, Livshits G. Sarcopenic obesity or obese sarcopenia: A cross talk between age-associated adipose tissue and skeletal muscle inflammation as a main mechanism of the pathogenesis. Ageing Research Reviews. 2017;35:200-221. https://doi.org/10.1016/j.arr.2016.09.008.
4. Kim T, Yang S, Yoo H, Lim K, Kang H, Song W, Seo J, Kim S, Kim N, Baik S et al. Prevalence of sarcopenia and sarcopenic obesity in Korean adults: the Korean sarcopenic obesity study. International Journal of Obesity. 2009;33(8):885-892. https://doi.org/10.1038/ijo.2009.130.
5. Khor E, Lim J, Tay L, Yeo A, Yew S, Ding Y, Lim W. Obesity Definitions in Sarcopenic Obesity: Differences in Prevalence, Agreement and Association with Muscle Function. The Journal of Frailty of Aging. 2019;000-000. https://doi.org/10.14283/jfa.2019.28.
6. Low S, Goh K, Ng T, Ang S, Moh A, Wang J, Ang K, Subramaniam T, Sum C, Lim S. The prevalence of sarcopenic obesity and its association with cognitive performance in type 2 diabetes in Singapore. Clinical Nutrition. 2019; 000-000. https://doi.org/10.1016/j.clnu.2019.10.019.
7. Prado C, Wells J, Smith S, Stephan B, Siervo M. Sarcopenic obesity: A Critical appraisal of the current evidence. Clinical Nutrition. 2012;31(5):583-601. https://doi.org/10.1016/j.clnu.2012.06.010.
8. Singapore Department of Statistics (DOS) [Internet]. Base. 2020 [cited 5 May 2020]. Available from: https://www.singstat.gov.sg.
9. Pang B, Wee S, Lau L, Jabbar K, Seah W, Ng D et al. Prevalence and Associated Factors of Sarcopenia in Singaporean Adults—The Yishun Study. Journal of the American Medical Directors Association. 2020;000-000. https://doi.org/10.1016/j.jamda.2020.05.029.
10. Kaiser M, Bauer J, Ramsch C, Uter W, Guigoz Y, Cederholm T, Thomas D, Anthony P, Charlton K, Maggio M et al. Validation of the Mini Nutritional Assessment short-form (MNA®-SF): A practical tool for identification of nutritional status. The Journal of Nutrition, Health and Aging. 2009;13(9):782-788. https://doi.org/10.1007/s12603-009-0214-7.
11. Armstrong T, Bull F. Development of the World Health Organization Global Physical Activity Questionnaire (GPAQ). Journal of Public Health. 2006;14(2):66-70. https://doi.org/10.1007/s10389-006-0024-x.
12. Stel V, Smit J, Pluijm S, Visser M, Deeg D, Lips P. Comparison of the LASA Physical Activity Questionnaire with a 7-day diary and pedometer. Journal of Clinical Epidemiology. 2004;57(3):252-258. https://doi.org/10.1016/j.jclinepi.2003.07.008.
13. Alberti K, Zimmet P, Shaw J. International Diabetes Federation: a consensus on Type 2 diabetes prevention. Diabetic Medicine. 2007;24(5):451-463. https://doi.org/10.1111/j.1464-5491.2007.02157.x.
14. WHO Expert Consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. The Lancet. 2004;363(9403):157-163. https://doi.org/10.1016/s0140-6736(03)15268-3.
15. Collinson S, Fang S, Lim M, Feng L, Ng T. Normative Data for the Repeatable Battery for the Assessment of Neuropsychological Status in Elderly Chinese. Archives of Clinical Neuropsychology. 2014;29(5):442-455. https://doi.org/10.1093/arclin/acu023.
16. Rantanen T. Midlife Hand Grip Strength as a Predictor of Old Age Disability. JAMA. 1999;281(6):558. https://doi.org/10.1001/jama.281.6.558.
17. Guralnik J, Ferrucci L, Simonsick E, Salive M, Wallace R. Lower-Extremity Function in Persons over the Age of 70 Years as a Predictor of Subsequent Disability. New England Journal of Medicine. 1995;332(9):556-562. https://doi.org/10.1056/NEJM199503023320902.
18. Studenski S. Gait Speed and Survival in Older Adults. JAMA. 2011;305(1):50. https://doi.org/10.1001/jama.2010.1923.
19. Puthoff M. Research Corner Outcome Measures in Cardiopulmonary Physical Therapy: Short Physical Performance Battery. Cardiopulmonary Physical Therapy Journal. 2008;19(1):17-22. https://doi.org/10.1097/01823246-200819010-00005.
20. Shumway-Cook A, Brauer S, Woollacott M. Predicting the Probability for Falls in Community-Dwelling Older Adults Using the Timed Up & Go Test. Physical Therapy. 2000;80(9):896-903. https://doi.org/10.1093/ptj/80.9.896.
21. Chen L, Liu L, Woo J, Assantachai P, Auyeung T, Bahyah K, Chou M, Chen L, Hsu P, Krairit O et al. Sarcopenia in Asia: Consensus Report of the Asian Working Group for Sarcopenia. Journal of the American Medical Directors Association. 2014;15(2):95-101. https://doi.org/10.1016/j.jamda.2013.11.025.
22. Calvani R, Marini F, Cesari M, Tosato M, Anker S, von Haehling S, Miller R, Bernabei R, Landi F, Marzetti E et al. Biomarkers for physical frailty and sarcopenia: state of the science and future developments. Journal of Cachexia, Sarcopenia and Muscle. 2015;6(4):278-286. https:doi.org/10.1002/jcsm.12051.
23. Lee Y, Biddle S, Chan M, Cheng A, Cheong M, Chong Y, Foo L, Lee C, Lim S, Ong W et al. Health Promotion Board–Ministry of Health Clinical Practice Guidelines: Obesity. Singapore Medical Journal. 2015;57(06):292-300. https://doi.org/10.11622/smedj.2016103.
24. Liu P, Ma F, Lou H, Liu Y. The utility of fat mass index vs. body mass index and percentage of body fat in the screening of metabolic syndrome. BMC Public Health. 2013;13(1). https://doi.org/10.1186/1471-2458-13-629.
25. Peltz G, Aguirre M, Sanderson M, Fadden M. The role of fat mass index in determining obesity. American Journal of Human Biology. 2010;22(5):639-647. https://doi.org/10.1002/ajhb.21056.
26. Biolo G, Di Girolamo F, Breglia A, Chiuc M, Baglio V, Vinci P, Toigo G, Lucchin L, Jurdana M, Praznikar Z et al. Inverse relationship between “a body shape index” (ABSI) and fat-free mass in women and men: Insights into mechanisms of sarcopenic obesity. Clinical Nutrition. 2015;34(2):323-327. https://doi.org/10.1016/j.clnu.2014.03.015.
27. Cheng Q, Zhu X, Zhang X, Li H, Du Y, Hong W, Xue S, Zhu H. A cross-sectional study of loss of muscle mass corresponding to sarcopenia in healthy Chinese men and women: reference values, prevalence, and association with bone mass. Journal of Bone and Mineral Metabolism. 2013;32(1):78-88. https://doi.org/10.1007/s00774-013-0468-3.
28. Roberts S, Williamson D. Causes of Adult Weight Gain. The Journal of Nutrition. 2002;132(12):3824S-3825S. https://doi.org/10.1093/jn/132.12.3824S.
29. Flöter A, Nathorst-böös J, Carlström K, Ohlsson C, Ringertz H, von Schoultz B. Effects of combined estrogen/testosterone therapy on bone and body composition in oophorectomized women. Gynecological Endocrinology. 2005;20(3):155-160. https://doi.org/10.1080/09513590400021193.
30. Lizcano F, Guzmán G. Estrogen Deficiency and the Origin of Obesity during Menopause. BioMed Research International. 2014;2014:1-11. https://doi.org/10.1155/2014/757461.
31. Listrat A, Lebret B, Louveau I, Astruc T, Bonnet M, Lefaucheur L, Picard B, Bugeon J. How Muscle Structure and Composition Influence Meat and Flesh Quality. The Scientific World Journal. 2016;2016:1-14. https://doi.org/10.1155/2016/3182746.

METHODOLOGICAL ISSUES AND THE IMPACT OF AGE STRATIFICATION ON THE PROPORTION OF PARTICIPANTS WITH LOW APPENDICULAR LEAN MASS WHEN ADJUSTING FOR HEIGHT AND FAT MASS USING LINEAR REGRESSION: RESULTS FROM THE CANADIAN LONGITUDINAL STUDY ON AGING

 

A.J. Mayhew1,2,3, S.M. Phillips4, N. Sohel1,2,3, L. Thabane1,5, P.D. McNicholas6, R.J. de Souza1,7, G. Parise4, P. Raina1,2,3

 

1. Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada; 2. Labarge Centre for Mobility in Aging, Hamilton, Ontario, Canada; 3. McMaster Institute for Research on Aging, Hamilton, Ontario, Canada; 4. Department of Kinesiology, McMaster University, Hamilton, Ontario, Canada; 5. Biostatistics Unit, Research Institute at St Joes, St. Joseph’s Healthcare Hamilton, Hamilton, Ontario, Canada; 6. Department of Mathematics & Statistics, McMaster University, Hamilton, Ontario, Canada; 7. Population Genomics Program, Chanchlani Research Centre, McMaster University, Hamilton, Ontario, Canada.
Corresponding author: Parminder Raina, PhD, Department of Health Research Methods, Evidence, and Impact, McMaster University, MIP 309A, 175 Longwood Road South, Hamilton, Ontario, L8P 0A1, Canada, Tel: 905 525 9140 x 22197, e-mail: praina@mcmaster.ca

J Frailty Aging 2020;in press
Published online September 29, 2020, http://dx.doi.org/10.14283/jfa.2020.48

 


Abstract

Background: Using residual values calculated from models regressing appendicular lean mass on fat mass and height is one of several suggested strategies for adjusting appendicular lean mass for body size when measuring sarcopenia. However, special consideration is required when using this technique in different subgroups in order to capture the correct individuals as sarcopenic. Objectives: To provide guidance about how to conduct stratified analyses for the regression adjustment technique using age groups as an example. Design: Cross-sectional study. Setting: Data collected at baseline (2012-2015) for the Canadian Longitudinal Study on Aging. Participants: Community dwelling participants of European descent aged 45 to 85 years (n=25,399). Measurements: Appendicular lean mass, height, and weight were measured. Sex-specific residuals were calculated in participants before and after stratifying participants by age group (45-54, 55-64, 65-74, 75-85 years). Cut offs corresponding to the sex-specific 20th percentile residual values in participants ≥65 years were determined first in the residuals calculated in all participants and residuals calculated in only those aged ≥65 years. For each set of cut offs, the percentage of age and sex-stratified participants with low appendicular lean mass were compared for the residuals calculated in all participants and the residuals calculated after stratifying by age. Results: In 12,622 males and 12,737 females, regardless of the cut off used, the percentage of participants with low appendicular lean mass decreased with age when residuals were calculated after age stratification. When the residuals were calculated in all participants, the percentage of participants with sarcopenia increased from the youngest to the oldest age groups. Conclusions: Sex-specific residuals in all participants should be calculated prior to stratifying the sample by age group, or other stratification variables, for the purposes of developing appendicular lean mass cut offs or subgroup analyses.

Key words: Appendicular lean mass, CLSA, muscle, residuals, sarcopenia, skeletal muscles.


 

Introduction

Sarcopenia refers to the decline in muscle mass, muscle strength, and muscle function that occurs with age (1). It is associated with an increased risk of falls and fractures, activities of daily living limitations, and mortality (2–5). Given the profound individual and societal costs of sarcopenia, there has been substantial interest in finding ways to prevent and treat sarcopenia. However, the field of sarcopenia research has been hindered by the lack of a clear definition and standardized diagnostic criteria (6).
Four expert-group definitions for sarcopenia define sarcopenia as the combination of low muscle mass, typically measured as appendicular lean mass (ALM), with either low muscle strength or impaired physical performance (6–10). There is a consensus among the definitions that ALM should be adjusted for body size due to the strong correlation between ALM with height and weight, however there is little agreement about which measure of body size should be utilized (6, 11). Four techniques are recommended; dividing by height squared, body mass, body mass index (BMI), and regressing ALM on height and fat mass (6–10). Of these methods, regressing ALM on height and fat mass may most accurately identify individuals with low ALM as it adjusts for two measures of body size whereas the other techniques only adjust for one measure of body size (12). This technique involves creating a regression model (ALM = intercept + height (m2) + fat mass (kg)) in a sample of individuals. For each individual, a predicted value of ALM is calculated based on the regression equation. Subtracting the estimated value of ALM from the actual value of ALM for each person provides a residual value. Positive residual values indicate that the individual has more ALM than would be expected given their height and weight and negative residual values indicate the individual has less ALM than would be expected given their height and weight.
Unlike adjusting ALM by height, weight, or BMI which are done at the individual level and are not influenced by other participants, calculating residuals is dependent on the sample. For height, weight, and BMI adjustment, the adjusted values refer to the same amount of ALM relative to the anthropometric measure adjusted for regardless of the person or sample. In contrast, the residual value for each person is dependent on the regression equation which in turn is dependent on the distribution of the variables in the sample. Consequently, even if low ALM offs are developed in a random, population-based sample, they cannot be appropriately applied to another population unless the two samples have identical joint distributions of ALM, fat mass, and height. Due to the unavailability of cut offs, studies that have investigated sarcopenia using the residual adjustment technique have considered the lowest quintile of sex-specific residual values as sarcopenic (13–19). However, a consequence of using the lowest quintile is that sarcopenia prevalence is the same for all studies, regardless of age, which is problematic for a condition for which the prevalence increases with age. This poses additional challenges for studies with a wide range of ages which want to conduct age stratified analyses.
To our knowledge, there has not been any discussion in the literature about the implications of stratifying a sample by age when applying the residual technique. We aimed to provide the necessary guidance for how to handle age stratification when calculating residual values for ALM adjusted for height and fat mass.

 

Methods

Setting and study population

We used data from the Canadian Longitudinal Study on Aging (CLSA), a national longitudinal research platform. There were 51,338 participants aged 45 to 85 years recruited from the ten Canadian provinces at baseline. Participants had to be physically and cognitively able to participate on their own as well as not living in institutions such as long term care to be eligible for the study. The participants were recruited in to one of two cohorts, the Tracking cohort and the Comprehensive cohort. Participants from all ten provinces were randomly selected for the Tracking cohort (n=21,241) and were interviewed by telephone. The Comprehensive cohort participants (n=30,097) lived within 25-50kg of one of 11 Data Collection Sites located in seven provinces. The Comprehensive cohort participants were interviewed in-person and also completed in-depth physical assessments and provided blood and urine samples. Details on the study design have been described elsewhere (20). Only participants from the Comprehensive cohort (n=30,097) were included in these analyses as the physical assessment data was required. The sample was further limited to those identifying as European as ALM, muscle strength, and physical function have shown to vary by ethnicity (21–23). This project uses data collected at baseline (September 2011 to May 2015). Ethics approval was received by the Hamilton Research Ethics Board (#2686).

Clinical measurements

Trained research assistants collected data on height, weight, and muscle mass. Height was measured twice using a stadiometer and the mean value of the two measurements was used in the analyses. The Hologic Discovery ATM DXA machine was calibrated daily using a spine phantom, weekly using a whole body step phantom, and yearly using a gold standard phantom. DXA provides a valid measures of ALM and fat mass when compared to the gold standards of computerized tomography (CT) and magnetic resonance imaging (MRI) scans (24, 25).
All analyses were stratified by sex. We used multiple linear regression models with ALM as the dependent variable and height (m2) and fat mass (kg) as the independent variables to estimate the predicted value of ALM for each participant. The residual values were calculated as the predicted value of ALM subtracted from the actual value of ALM. To test the impact of age stratification on the residual values, we first calculated residuals based on the regression model including participants aged 45 to 85 years. We then calculated residuals based on regression models run separately for each age strata (45 to 54, 55 to 64, 65 to 74, and 75 to 85 years). We followed the EWGSOP recommendation of using the lowest sex-specific 20th percentile of residual values as the cut off for low ALM (7). We chose to limit the sample for calculating cut offs to participants ≥65 years based on guidance from the literature (7). To explore the impact of age stratification on the values of the residual cut offs, we determined the cut offs for the residuals in the model that included all participants aged 45 to 85 years, as well as for residual values based on a model that only included participants 65 years and older.
The cut-offs detertmined using the non-age stratified residuals and the residuals calculated in just participants aged ≥65 years were applied to the residuals calculated in the whole sample and the age-stratified residuals. Therefore, there were four different strategies used to identify participants: Strategy 1: all residuals calculated in all participant; Strategy 2: individual residuals calculated in all participants, cut offs developed in participants ≥65 years; Strategy 3: individual residuals calculated in specific age groups, cut offs developed in all participants; Strategy 4: individual residuals calculated in specific age groups, cut offs developed in participants ≥65 years.

Statistical anaylses

Of the 30,097 participants at baseline, 1324 were excluded as they were non-European, 3356 were excluded for missing ALM, grip strength, gait speed, or BMI data resulting in a final sample size of 25,399 participants. All statistical analyses were completed using SAS (version 12.3).
The percentage of age and sex-stratified participants categorized as having low ALM by each of the four strategies for handling age-stratification for the development of cut offs and individual residual values were determined. Bootstrap percentile confidence intervals were calculated for each estimate. This technique involves resampling with replacement and calculating the proportion of participants with sarcopenia for each resample (26). We resampled 10,000 times and identified the values corresponding to the 2.5th and 97.5th percentiles of the 10,000 resamples in order to estimate the 95% confidence interval. This technique has the advantage of only including valid values of parameter estimates in the confidence interval (26).

 

Results

Participant characteristics

The mean (SD) age of the participants was 62.8 (10.2) years and 49.9% of the sample were males (Table 1). Younger males and females had greater mean (SD) ALM: 27.2kg (4.2) and 17.9kg (3.4), grip strength: 47.3kg (9.1) and 28.6kg (5.6), and gait speed: 1.03m/s (0.18) and 1.02m/s (0.19) compared to older males and females (ALM: 24.4kg (3.7) and 16.3kg (2.9), grip strength: 39.4kg (8.5) and 23.6kg (5.2), and gait speed: 0.94m/s (0.19) and 0.90m/s (0.19).

Table 1
Participant characteristics

1. Heart disease includes angina, myocardial infarction, and heart disease; 2. Cardiovascular disease includes stroke and transient ischemic attack; 3. Neurological conditions include multiple sclerosis, epilepsy, migraine headaches, and Parkinson’s Disease
 

Distribution of residuals

The overall distribution of the residual values was calculated in all participants versus calculating the residuals in age-stratified groups. In males, the mean (SD) for all participants was 0 (2.90), while the mean of the residuals for all age-stratified residuals pooled together was 0 (3.13). The corresponding values were 0 (2.08) and 0 (2.16) in females. However, the distribution of the data within each age group was markedly different. In both males and females, when the residuals were calculated after stratifying the sample by age, the residuals of each age group had a mean of 0. In contrast, when the residuals were calculated in the whole sample, there was a gradient of mean values when stratified by age group. The mean residual value for males 45 to 54 years was 1.36 and for females was 0.84 which decreased to -1.95 in males and -0.67 in females aged 75 to 85 years (Supplementary Appendix 1).

Muscle mass cut off estimates

The lowest 20th percentile cut offs corresponded to -3.51 for males and -2.15 for females when the residual values were calculated all participants, then restricted to participants aged ≥65 years. When the residuals were calculated in only participants ≥65 years, the 20th percentile cut offs were -2.23 for males and -1.58 for females.

Low muscle mass prevalence

The lower cut offs determined using the non-age stratified residual values of -3.51 for males and -2.23 for females identified fewer participants as having low muscle mass compared to the age-stratified residual values of -2.15 for males and -1.58 for females (Figure 1). For these cut offs, the prevalence of low muscle mass was 12.3% for males and 14.6% for females when the individual residuals were not age stratified (Strategy 1) and 10.3% for males and 13.8% for females when the individual residuals were age stratified (Strategy 3). The cut offs developed using residual values calculated in only participants ≥65 years, identified 23.8% of males and 22.8% of females as having low ALM when the non-age stratified residual values (Strategy 2) and 21.7% of males and 21.9% of females as having low ALM when the age-stratified values were used (Strategy 4).
When looking at the percentage of people with low muscle mass within each age group, the percentage of males and females with low muscle mass increased with age when the individual residuals were not age-stratified, regardless of the cut offs used (Strategy 1 and Strategy 2). In contrast, the percentage of males and females with low muscle mass decreased with age when the age-stratified residuals were used (Strategy 3 and Strategy 4).

Figure 1
Percentage of participants with low ALM adjusted for height and fat mass stratified by age group and sex

 

Discussion

To our knowledge, this is the first study to investigate the implications of age stratification when using the residual values for ALM after regressing on height and fat mass. We determined that residual values should be calculated in all participants before stratifying by age for the purposes of subgroup analyses or developing muscle mass cut offs (Strategy 1).
Stratifying the sample by age prior to calculating residuals for the purpose of subgroup analyses based on age or for developing cut offs proved problematic. When the sample was stratified by age before calculating the residuals (Strategy 3 and Strategy 4), the percentage of participants with low ALM decreased from the youngest to the oldest age groups (Figure 1) because of how the residuals are calculated. The maximum likelihood estimation technique used in linear regression to calculate the residuals requires that the sum of the residuals for the sample to equal zero. When the sample was stratified by age before calculating the residuals, the mean value of the residuals for each age group was zero. However, the standard deviation decreased with age (Supplementary Appendix 1). The greater the standard deviation for the age group, the more participants were below the low ALM cut off and therefore the higher the percentage of people with low ALM.
The problems we encountered stratifying our sample by age before calculating the residuals extend to any situation in which residuals calculated in one sample are combined or applied to another sample. Residual values are sample dependent and therefore unless two groups of participants have identical joint distributions of ALM, height, and fat mass, the residuals from one study will not identify people with the same amount of ALM relative to height and fat mass. This means that cut offs for the residual technique, even if developed in a population-based random sample with cut offs validated against relevant health outcomes, cannot be meaningfully applied to another sample. For this reason, in our analyses Strategy 1 which calculates the residuals in all participants before limiting to those ≥65 years to determine the lowest quintile is the appropriate strategy.
To resolve the issue of residual values and corresponding cut offs not being comparable between studies, prediction equations, similar to those that have been used for lung function can be developed (27). A sample of representative older adults could be used to create sex-specific prediction equations for ALM based on height and fat mass. Variables such as age, ethnicity, and other body composition variables could be explored for inclusion in the equation, as well as possible interactions between variables. These equations would allow for results to be meaningfully compared between studies and would also allow clinicians to use this technique to diagnose low ALM in individuals. Low ALM cut offs, ideally determined by assessing which cut offs best predict health outcomes relevant to sarcopenia, could be established and used differents studies.
To our knowledge, only one study has assessed the relationship between low ALM operationalized using the residual adjustment technique with health (12, 28). Cawthon et al. observed that low ALM adjusted for height and fat mass was significantly associated with risk of functional limitations and mortality, but not recurrent falls or hip fractures (12). Studies operationalizing sarcopenia as low ALM only often do not find significant associations with health, therefore the associations found with functional limitations and mortality are particularly notable (12, 29, 30). Given this evidence as well as the strong face validity for adjusting ALM simultaneously for height and fat mass, future studies are required to determine if adjusting ALM for height and fat mass, alone and in combination with muscle strength or function, better identifies people at poor risk for health compared to the other adjustment techniques.
In conclusion, adjusting ALM for height and fat mass using the regression technique is a promising method of operationalizing low ALM that warrants greater inclusion in future sarcopenia studies. In this study, we show that to appropriately apply the residual technique to a stratified sample, the regression equation must be calculated in all participants before stratifying the sample in order to identify the correct individuals as sarcopenic.

 
Acknowledgements: This research was made possible using the data/biospecimens collected by the Canadian Longitudinal Study on Aging (CLSA). Funding for the Canadian Longitudinal Study on Aging (CLSA) is provided by the Government of Canada through the Canadian Institutes of Health Research (CIHR) under grant reference: LSA 94473 and the Canada Foundation for Innovation. This research has been conducted using the CLSA dataset, Baseline Comprehensive Dataset version 4.0, under Application Number 160608. The CLSA is led by Drs. Parminder Raina, Christina Wolfson and Susan Kirkland. The opinions expressed in this manuscript are the author’s own and do not reflect the views of the Canadian Longitudinal Study on Aging.
Funding: No funding to report.
Conflict of interest: None declared
Author contributions: AJM, SMP, and PR conceptualized this project with feedback from NS, LT, PDM, RJd and GP. AJM and NS completed the analysis of the data. AJM, NS, and PR interpreted the results. AJM completed the draft of the manuscript with revisions from the remaining authors. All authors provided approval for the final version to be published and agree to be accountable for all aspects of the work.
Ethical standards: Ethics approval for this project was received by the Hamilton Research Ethics Board (#2686).
 
SUPPLEMENTARY MATERIAL
 

References

1. Rosenberg IH. Sarcopenia: origins and clinical relevance. J Nutr 1997;127:990S–911S
2. Kim JH, Lim S, Choi SH, et al. Sarcopenia: an independent predictor of mortality in community-dwelling older Korean men. J Gerontol A Biol Sci Med Sci 2014;69:1244–1252
3. Landi F, Cruz-Jentoft AJ, Liperoti R, et al. Sarcopenia and mortality risk in frail older persons aged 80 years and older: results from ilSIRENTE study. Age Ageing 2013;42:203–209
4. Janssen I, Heymsfield SB, Ross R. Low relative skeletal muscle mass (sarcopenia) in older persons is associated with functional impairement and physical disability. J Am Geriatr Soc 2002;50:889–896
5. Yeung SSY, Reijnierse EM, Pham VK, et al. Sarcopenia and its association with falls and fractures in older adults: A systematic review and meta-analysis. J Cachexia Sarcopenia Muscle 2019;10:485–500
6. Cruz-Jentoft AJ, Bahat G, Bauer J, et al. Sarcopenia: Revised European consensus on definition and diagnosis. Age Ageing 2019;48:16–31
7. Cruz-Jentoft A, Baeyens J, Bauer J, et al. Sarcopenia: European consensus on definition and diagnosis: Report of the European Working Group on Sarcopenia in Older People. Age Ageing 2010;39:412–423
8. Studenski SA, Peters KW, Alley DE, et al. The FNIH sarcopenia project: Rationale, study description, conference recommendations, and final estimates. Journals Gerontol Med Sci 2014;69:547–558
9. Fielding RA, Vellas B, Evans WJ, et al. Sarcopenia: An undiagnosed condition in older adults. Current consensus definition: Prevalence, etiology, and conseuqences. International Work Group on Sarcopenia. Am Med Dir Assoc 2011;12:249–256
10. Chen L-K, Liu L-K, Woo J, et al. Sarcopenia in Asia: Consensus report of the Asian Working Group for Sarcopenia. J Am Med Dir Assoc 2014;15:95–101
11. Gallagher D, Visser M, De Meersman RE, et al. Appendicular skeletal muscle mass: effects of age, gender, and ethnicity. J Appl Physiol 2017;83:229–239
12. Cawthon PM, Blackwell TL, Francisco S, et al. An evaluation of the usefulness of consensus definitions of sarcopenia in older men: results from the observational Osteoporotic Fractures in Men (MrOS) cohort study. J Am Diet Assoc 2016;63:2247–2259
13. Menant JC, Weber F, Lo J, et al. Strength measures are better than muscle mass measures in predicting health-related outcomes in older people: time to abandon the term sarcopenia? Osteoporos Int 2016;1–12
14. Delmonico MJ, Harris TB, Lee J-S, et al. Alternative definitions of sarcopenia, lower extremity performance, and functional impairment with aging in older men and women. J Am Geriatr Soc 2007;55:769–774
15. Newman AB, Kupelian V, Visser M, et al. Sarcopenia: Alternative Definitions and Associations with Lower Extremity Function. J Am Geriatr Soc 2003;51:1602–1609
16. Figueiredo CP, Domiciano DS, Lopes JB, et al. Prevalence of sarcopenia and associated risk factors by two diagnostic criteria in community-dwelling older men: The São Paulo Ageing & Health Study (SPAH). Osteoporos Int 2014;25:589–596
17. Domiciano DS, Figueiredo CP, Lopes JB, et al. Discriminating sarcopenia in community-dwelling older women with high frequency of overweight/obesity: The São Paulo Ageing & Health Study (SPAH). Osteoporos Int 2013;24:595–603
18. Chalhoub D, Cawthon PM, Ensrud KE, et al. Risk of nonspine fractures in older adults with sarcopenia, low bone mass, or both. J Am Geriatr Soc 2015;63:1733–1740
19. Scott D, Chandrasekara SD, Laslett LL, Cicuttini F, Ebeling PR, Jones G. Associations of sarcopenic obesity and dynapenic obesity with bone mineral density and incident fractures over 5 – 10 years in community-dwelling older adults. Calcif Tissue Int 2016;99:30–42
20. Raina PS, Wolfson C, Kirkland SA, et al. Cohort profile: The Canadian Longitudinal Study on Aging (CLSA). Can J Aging 2009;28:221–229
21. Capistrant BD, Glymour MM, Berkman LF. Assessing mobility difficulties for cross-national comparisons: Results from the World Health Organiation Study on Global AGEing and Adult Health. J Am Geriatr Soc 2015;62:329–335
22. Leong DP, Teo KK, Rangarajan S, et al. Prognostic value of grip strength: findings from the Prospective Urban Rural Epidemiology (PURE) study. Lancet 2015;386:266–273
23. Silva AM, Shen W, Heo M, et al. Ethnicity-related skeletal muscle differences across the lifespan. Am J Hum Biol 2010;22:76–82
24. Buckinx F, Landi F, Cesari M, et al. Pitfalls in the measurement of muscle mass: a need for a reference standard. J Cachexia Sarcopenia Muscle 2018;9:269–278
25. Kullberg J, Brandberg J, Angelhed JE, et al. Whole-body adipose tissue analysis: Comparison of MRI, CT and dual energy X-ray absorptiometry. Br J Radiol 2009;82:123–130
26. Carpenter J, Bithell J. Bootstrap confidence intervals: when, which, what? A practical guide for medical statisticians. Stat Med 2000;19:1141–1164
27. Falaschetti E, Laiho J, Primatesta P, Purdon S. Prediction equations for normal and low lung function from the health survey for England. Eur Respir J 2004;23:456–463
28. Beaudart C, Zaaria M, Reginster J. Health outcomes of sarcopenia: A systematic review and meta-analysis. PLoS One 2017;12:e0169548
29. Schaap LA, Schoor NM Van, Lips P, Visser M. Associations of sarcopenia definitions, and their components, with the incidence of recurrent falling and fractures: The Longitudinal Aging Study Amsterdam. Journals Gerontol Med Sci 2018;73:1199–1204
30. Bischoff-Ferrari HA, Orav JE, Kanis JA, et al. Comparative performance of current definitions of sarcopenia against the prospective incidence of falls among community-dwelling seniors age 65 and older. Osteoporos Sarcopenia 2015;26:2793–2802

DO MALNUTRITION, SARCOPENIA AND FRAILTY OVERLAP IN NURSING-HOME RESIDENTS?

 

G. Faxén-Irving1, Y. Luiking2, H. Grönstedt3, E. Franzén4, Å. Seiger5, S. Vikström6, A. Wimo7, A.-M. Boström8, T. Cederholm9

 

1. Department of Neurobiology, Care science and Society, Division of Clinical Geriatrics, Karolinska Institutet, Stockholm and Allied Health Professionals, Functional Area Clinical Nutrition, Karolinska University Hospital, Sweden; 2. Danone Nutricia Research, Utrecht, the Netherlands; 3. Stockholms Sjukhem R&D unit, Stockholm; Allied Health Professionals, Functional Area Occupational Therapy & Physiotherapy, Karolinska University Hospital, Stockholm, Sweden; 4. Stockholms Sjukhem R&D unit, Stockholm, Department of Neurobiology, Care science and Society, Division of physiotherapy, Karolinska Institutet, Stockholm & Allied Health Professionals, Function Area Occupational Therapy & Physiotherapy, Karolinska University Hospital, Stockholm, Sweden; 5. Department of Neurobiology, Care Science and Society, Division of Clinical Geriatrics, Karolinska Institutet, Sweden; 6. Department of Neurobiology, Care Science and Society, Division of Occupational Therapy, Karolinska Institutet, Stockholm, Sweden; 7. Department of Neurobiology, Care Science and Society, Division of Neurogeriatrics, Karolinska Institutet, Stockholm, Sweden; 8. Stockholms Sjukhem R&D unit, Stockholm, Department of Neurobiology, Care science and Society, Division of nursing, Karolinska Institutet, Stockholm, and Theme Aging, Karolinska University Hospital, Stockholm, Sweden; 9. Department of Public Health and Caring Sciences, Division of Clinical Nutrition and Metabolism and Division of Geriatrics, Uppsala University, Uppsala, Sweden. Trial Registration: ClinicalTrials.gov Identifier: NCT02702037
Corresponding author: Gerd Faxén-Irving, Department of Neurobiology, Care science and Society, Division of Clinical Geriatrics, Karolinska Institutet, Stockholm and Allied Health Professionals, Functional Area Clinical Nutrition, Karolinska University Hospital, Sweden, gerd.faxen.irving@ki.se

J Frailty Aging 2021;10(1)17-21
Published online August 12, 2020, http://dx.doi.org/10.14283/jfa.2020.45

 


Abstract

Objectives: To study the prevalence and overlap between malnutrition, sarcopenia and frailty in a selected group of nursing home (NH) residents. Design: Cross-sectional descriptive study. Setting: Nursing homes (NH). Participants: 92 residents taking part in an exercise and oral nutritional supplementation study; >75 years old, able to rise from a seated position, body mass index ≤30 kg/m2 and not receiving protein-rich oral nutritional supplements. Measurements: The MNA-SF and Global Leadership Initiative on Malnutrition (GLIM) criteria were used for screening and diagnosis of malnutrition (moderate or severe), respectively. Sarcopenia risk was assessed by the SARC-F Questionnaire (0-10p; ≥4=increased risk), and for diagnosis the European Working Group of Sarcopenia in Older People (EWGSOP2) criteria was used. To screen for frailty the FRAIL Questionnaire (0-5p; 1-2p indicating pre-frailty, and >3p indicating frailty), was employed. Results: Average age was 86 years; 62% were women. MNA-SF showed that 30 (33%) people were at risk or malnourished. The GLIM criteria verified malnutrition in 16 (17%) subjects. One third (n=33) was at risk for sarcopenia by SARC-F. Twenty-seven (29%) subjects displayed confirmed sarcopenic according to EWGSOP2. Around 50% (n=47) was assessed as pre-frail or frail. Six people (7%) suffered from all three conditions. Another five (5%) of the residents were simultaneously malnourished and sarcopenic, but not frail, while frailty coexisted with sarcopenia in 10% (n=9) of non-malnourished residents. Twenty-nine (32%) residents were neither malnourished, sarcopenic nor frail. Conclusions: In a group of selected NH residents a majority was either (pre)frail (51%), sarcopenic (29%) or malnourished (17%). There were considerable overlaps between the three conditions.

Key words: Nursing home, older person, malnutrition, frailty, sarcopenia.


 

Introduction

Malnutrition and sarcopenia, commonly occurring in older adults, are associated with negative outcomes (1). Loss of muscle mass and function combined with poor nutrition contributes to an increased risk of frailty; i.e. a state of vulnerability and decreased resilience against stressors (2). Estimated prevalence of physical frailty in the community is around 15% and 25% in adults aged >65 years and >85 years, respectively (3). A review and meta-analysis found frailty to be a significant predictor of all-cause mortality in older NH residents (4).
The prevalence of malnutrition and risk of malnutrition in NH residents depends on multiple factors, including the tools and criteria for assessment used. Recently the Global Leadership Initiative for Malnutrition (GLIM) suggested a two-step process starting with screening for malnutrition and then assessment for diagnosis and grading the severity of malnutrition (5).
Sarcopenia, i.e. loss of muscle strength and mass, occurs with aging and is accelerated by inactivity and disease. Sarcopenia leads to impaired ability to perform activities of daily living (ADL), i.e. walking, toileting, eating and socializing, and subsequently results in increased dependence (6). In addition, it increases the risk of falls and pressure ulcers (7). According to a recently published systematic review and meta-analysis, using the European Working Group of Sarcopenia in Older People (EWGSOP) definition from 2010, the prevalence of sarcopenia was 41% in older NH residents (8). The recent EWGSOP2 criteria (9) focuses on low muscle strength as the key characteristic of probable sarcopenia and uses detection of low muscle quantity and quality to confirm the sarcopenia diagnosis. Subsequently, poor physical performance indicates severe sarcopenia. The SARC-F Questionnaire was developed to facilitate screening in clinical practice, and it shows a strong capacity to predict poor physical performance and muscle function in older adults (10).
Malnutrition, sarcopenia and frailty frequently interact and coexist in older people. The main objectives of this study were to determine the prevalence of these three catabolic conditions in a selected group of NH residents, and to assess how they overlap. Moreover, we wanted to apply the recently accepted screening and diagnostic tools for sarcopenia and malnutrition in a NH-setting.

 

Material and methods

This report is based on baseline data from the Older People Exercise and Nutrition (OPEN) study, a two-arm randomized controlled trial performed in NH at two municipalities in the Stockholm area (Sweden) (11). Out of 120 residents participating in the OPEN study, 92 had complete data at baseline regarding nutritional status, sarcopenia and frailty and were analyzed in this cross-sectional study.

Participants

Inclusion criteria for participation were age ≥75 years and ability to rise from a seated position. Exclusion criteria were BMI >30 kg/m2, use of protein-rich oral nutritional supplements, severe dysphagia, tube feeding, bedridden, severe kidney disease, terminal stage of life, and inability to give informed consent. Two clinically experienced physiotherapists from the research group performed the data collection.

Study design and procedures

Occurrence of malnutrition was assessed in a two-step procedure starting with screening as suggested by the GLIM consortium (5). For screening, the Mini Nutritional Assessment Short Form (MNA-SF) (0-14; 12-14 = normal nutritional status; 8-11 = at risk for malnutrition; 0-7 = malnourished) was used. The diagnosis of malnutrition was set according to the GLIM format that requires at least one phenotypic criterion; i.e. weight loss, underweight or low muscle mass, combined with at least one etiologic criterion; i.e. reduced food intake or severe disease burden. Severity of malnutrition grades as Stage 1 (moderate) or Stage 2 (severe) malnutrition (5) according to the degree of aberration of the phenotypic criteria. Underweight was indicated by BMI <22 kg/m2, and BMI <20 kg/m2 indicated severe malnutrition.
Bioelectrical impedance analysis (BIA) (ImpediMed SFB7) was performed to estimate body composition into fat free mass index (FFMI in kg/m2) and fat mass index (FMI in kg/m2). A FFMI of 17 kg/m2 for men and 15 kg/m2 for women were thresholds for reduced muscle mass (5).
Sarcopenia was assessed by the EWGSOP2 algorithm for case-finding, diagnosis and severity determination (9). SARC-F Questionnaire was used in parallel to assess risk of sarcopenia. The SARC-F questions reflect strength, assistance with walking, rise from a chair, climb stairs and accidental falls; (0-10p; ≥4=increased risk) (10). According to EWGSOP2 sarcopenia was diagnosed as probable by an impaired chair stand test, and subsequently confirmed when combined with low FFMI. The residents performed a modified timed chair stand test with arms folded over the chest or with support from the chair arms or walking aid (11), and considered impaired when <10 chair stands in 30 sec (<85 years) or <8 chair stands in 30 sec (≥85 years) (11). Severity of sarcopenia was graded by using gait speed (in m/sec, measured over a distance of 10 m indoors), with a gait speed below ≤0.8 m/sec as an indicator of severe sarcopenia.
The FRAIL questionnaire (0-5p; 0=robust; 1-2= pre-frail and 3-5= frail) was used to screen for frailty (12).

Statistical analyses

Data is presented using descriptive statistics, i.e. mean and SD for continuous variables or median and interquartile range (IQR). The Statistica® 10.0 software package (Statsoft) Tulsa, OK, USA) was used for the statistical calculations.

 

Results

The residents were on average 86 years old (Table 1). A majority suffered from an average of three diagnoses; cognitive and cardiac disorders were most common (data not shown).
Table 1 shows that mean BMI was around 25 (kg/m2). BMI <22 and <20 were found in 19 (21%) and seven (8%) of the residents, respectively. BIA revealed a low FFMI (kg/m2) in 17 (49%) men and 22 (39%) women.
One third of the residents was assessed by the MNA-SF screening tool to be at risk of malnutrition or malnourished. Subsequently, the GLIM criteria confirmed malnutrition in a total of 16 (17%) of the participants; i.e. 12 and 4 were graded as moderately and severely malnourished, respectively (Table 1).

Table 1
Nutritional status, sarcopenia and frailty by gender in selected nursing-home residents

Mean ± SD, median (interquartile range, IQR). MNA-SF=Mini Nutritional Assessment-Short Form (0-14 points). GLIM=Global Leadership of Malnutrition. EWGSOP=European Working Group on Sarcopenia in Older People. SARC-F is a screening tool for sarcopenia; 0-10 points, ≥4 points= increased risk. FRAIL is a screening tool for frailty; 5)Cederholm T et al. GLIM criteria for the diagnosis of malnutrition – A consensus report from the global clinical nutrition community. 9)Cruz-Jentoft et al. Sarcopenia: revised European consensus on definition and diagnosis.

 

The SARC-F Questionnaire depicted around 1/3 of the residents to be at risk of sarcopenia. The EWGSOP2 criteria indicated altogether 40 (44%) to have “probable” sarcopenia, while three (3%) and 24 (26%) residents had confirmed and severe sarcopenia, respectively (Table 1). One of four was not sarcopenic. Nineteen out of the 33 residents assessed as at risk by SARC-F were diagnosed as probable and 12 as confirmed sarcopenia according to EWGSOP2.

Figure 1
Prevalence and overlaps of malnutrition, sarcopenia and frailty in a selected group of nursing-home residents.

 

The FRAIL Questionnaire screening indicated a prevalence of pre-frailty (only) and frailty of 38% and 13%, respectively (Table 1).
The Venn diagram (Fig 1) shows how malnutrition, sarcopenia and prefrail/frailty overlapped. Six (7%) residents suffered from all three conditions. Malnutrition and sarcopenia co-existed in five non-frail subjects (5%), and sarcopenia and (pre-)frailty in nine (10%) non-malnourished subjects. One of the 47 residents identified as pre-frail or frail was also malnourished, but not sarcopenic. Twenty-nine (32%) residents were neither malnourished, sarcopenic nor frail.

 

Discussion

The aim of this paper is to present prevalence and overlap of malnutrition, sarcopenia and frailty in a selected group of NH residents and to apply the recently accepted criteria to screen and diagnose sarcopenia (EWGSOP2) and malnutrition (GLIM) in a NH setting. Almost one-third of the residents was sarcopenic (confirmed or severe) (Table 1), one out of five malnourished and half were pre-frail or frail. About one in five displayed an overlap between sarcopenia and malnutrition, in line with a recent report [8]. Frailty and sarcopenia showed overlap in one of ten, also in line with a previous report (13). Pre-frailty and frailty overlapped with malnutrition in seven persons; i.e. six were also sarcopenic.
Regarding SARC-F, there was a good agreement between the number of residents screened as at risk of sarcopenia, and those diagnosed as probable and confirmed sarcopenia according to EWGSOP2.
Among the one third (n=30) of the participants who were assessed as being at least at risk of malnutrition according to MNA-SF, about half were diagnosed as malnourished according to the GLIM criteria. Thus, malnutrition was confirmed in altogether 17% of the residents.
The FRAIL Questionnaire identified close to half of the participants as pre-frail, but only 12 persons (13%) as frail. This result may indicate that the study group was more robust than the average NH population.
The ICFSR international expert group recently published guidelines for identification and management of physical frailty and sarcopenia. To treat sarcopenia it is recommended to use resistance-based physical activity and to consider protein-rich oral nutritional supplementation/or protein-rich diet even in the older population living in NH (14). To manage frailty, a multicomponent physical activity program including resistance-based training and protein/energy supplementation (in case of weight loss or undernutrition) is recommended (3).
There are limitations of the study that need to be considered. One is that the selection of NH-residents was based on the capacity to take part in an intervention study, thus reducing the generalizability of the results. Another potential limitation is that we used a modified 30-s timed chair stand; e.g. the participants were allowed to use the upper extremities for support when rising from the chair. This modification may ensure that individuals with low physical function can complete the test and to eliminate the floor effect demonstrated with other sit-to-stand protocols. This chair stand protocol also deviates from the timed five chair stands that EWGSOP recommends, and that is also affected by floor effects in frail sarcopenic older people.
We may conclude that even among a group of fairly robust NH residents, two thirds suffered from any of the three catabolic conditions sarcopenia (confirmed or severe) (29%), malnutrition (17%) and pre-frailty/frailty (51%). There were substantial overlaps between malnutrition and sarcopenia and between frailty and sarcopenia. We suggest that screening and diagnosis of these three conditions should be integrated in NH care and should be followed by intervention and monitoring.

 

Funding: The study was financially supported by Danone Nutricia Research. Representatives from Nutricia have been involved in the study design, but the company was not involved in data collection and analyses. The final interpretation of the study results, review, and decision to submit the manuscript was performed by independent researchers with no affiliation to the funding source. The study is also funded by Gamla Tjänarinnor.
Authors’ contributions: Gerd Faxén-Irving, Tommy Cederholm, Åke Seiger, Anders Wimo, Anne-Marie Boström were responsible for the design of the protocol and the methodology of the study. Gerd Faxén Irving, Tommy Cederholm, Åke Seiger, Anne-Marie Boström, Erika Franzén, Helena Grönstedt, Yvette C Luiking, Sofia Vikström, Anders Wimo contributed to the writing of the manuscript. All authors read and approved the final manuscript.
Acknowledgements: The authors would like to thank all participating residents and staff in the eight NHs. We are grateful to our Canadian collaborators, Dr. Susan E Slaughter and her research group at University of Alberta, Edmonton, Canada for advice in developing the study protocol. We are also grateful to Dr. Sara Runesdotter for statistical support, Ms. Elin Linde for support regarding data collection and Ms. Frida Eriksson for data management.
Ethical considerations: The study has been approved by the Regional Ethical Review Board in Stockholm, EPN, D no. 2013/1659-31/2, 2015/1994-32 and 2016/1223-32.
Conflict of interest: The authors have received grants from Gamla Tjänarinnor charitable fund, grants from Nutricia Global, during the conduct of the study.
Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

 

References

1. Vanderwoude MFJ, Alish CJ, Sauer AC, Hegazi RA. Malnutrition-sarcopenia syndrome: is this the future of nutrition screening and assessment for older adults? J Aging Res 2012;1-8.
2. Abellan van Kan G, Rolland YM, Morley JE, Vellas B. Frailty: toward a clinical definition. J Am Med Dir Assoc 2008;9:71-72.
3. Dent E, Morley JE, Cruz-Jentoft AJ, Woodhouse L, Rodriguez-Manas L, Fried LP et al. Physical frailty: ICFSR international clinical practice guidelines for identification and management. J Nutr Health Aging 2019;23(9):771-787.
4. Zhang X, Dou Q, Zhang W, Wang C, Xie X, Yang Y,Zeng Y. Frailty as a Predictor of All-Cause Mortality Among Older Nursing Home Residents: A Systematic Review and Meta-analysis. J Am Med Dir Assoc 2019;20(6):657-663.
5. Cederholm T, Jensen GL, Correia MITD, Gonzalez MC, Fukushima R et al. GLIM Core Leadership Committee, GLIM Working Group. GLIM criteria for the diagnosis of malnutrition – A consensus report from the global clinical nutrition community. Clin Nutr 2018:1-9.
6. Correa-de Araujo R, Hadley E. Skeletal muscle function deficit: A new terminology to embrace the evolving concepts of sarcopenia and age-related muscle dysfunction. J Gerontol A Biol Sci Med Sci 2014;69:591-594.
7. Landi F, Cruz-Jentoft AJ, Liperoti R, Russo A, Giovannini S, Tosato M et al. Sarcopenia: Sarcopenia and mortality risk in frail older persons aged 80 years and older: Results from ilSIRENTE study. Age Ageing 2013;42:203-209.
8. Shen Y, Chen J,Chen X, Hou L, Lin X and Yang M. Prevalence and associated factors of sarcopenia in nursing home residents: A systematic review and meta-analysis. J Am Med Dir Assoc 2019;20:5-13.
9. Cruz-Jentoft AJ, Bahat G, Bauer J, Boirie Y, Bruyère O, Cederholm T et al. Writing group for the European Working Group on Sarcopenia in older people2 (EWGSOP2), and the extended group for EWGSOP2: Sarcopenia: revised European consensus on definition and diagnosis. Age and Ageing 2018;0:1-16.
10. Woo J, Leung J, Morley JE. Validating the SARC-F: A suitable community screening tool for sarcopenia? J Am Med dir Assoc 2014;15:630-634.
11. Grönstedt H, Vikström S, Cederholm T, Franzén E, Seiger Å, Wimo A et al. Effect of sit-to-stand exercises combined with protein-rich oral supplementation in older persons. JAMDA 2020 in press.
12. Morley JE, Malmstrom TK, Miller DK: A simple frailty questionnaire (FRAIL) predicts outcomes in middle aged African Americans. J Nutr Health Aging 2012, 16 (7):601-608.
13. Mijnarends DM, Schols J MGA, Meijers J MM, Tan F ES, Verlaan S, Luiking Y C et al. Instruments to assess sarcopenia and physical frailty in older people living in a community (care) setting: Similarities and discrepancies. J Am Med Dir Assoc 2015;16:301-308.
14. Dent E, Morley JE, Cruz-Jentoft AJ, Arai H, Kritchevsky SB, Guralnik J et al. International clinical practice guidelines for sarcopenia (ICFSR): Screening, diagnosis and management. J Nutr Health Aging 2018;1-13.

SARCOPENIA AND ADVERSE POST-SURGICAL OUTCOMES IN GERIATRIC PATIENTS: A SCOPING REVIEW

 

M. Hossain1,2, D. Yu1,2, B. Bikdeli1,2,3, S. Yu1,2,3

 

1. Aged and Extended Care Services, The Queen Elizabeth Hospital. Central Adelaide Local Health Network, South Australia; 2. Adelaide Medical School, Faculty of Health and Medical Science, University of Adelaide, South Australia; 3. Centre of Research Excellence in Frailty and Healthy Ageing, University of Adelaide, South Australia.
Corresponding author: Monowar Hossain, Aged and Extended Care Services, The Queen Elizabeth Hospital. Central Adelaide Local Health Network, South Australia, ssmcmono@gmail.com

J Frailty Aging 2021;10(1)63-69
Published online May 19, 2020, http://dx.doi.org/10.14283/jfa.2020.27

 


Abstract

Background: Sarcopenia is associated with adverse outcomes in cancer, chemotherapy, solid organ transplants, intensive care and medical patients. It has also been proven to increase perioperative mortality, hospital length of stay and complications in patients of various age groups. However, a limited number of studies have examined the association of post-surgical outcomes and sarcopenia inclusively in patients aged 65 years and older. Objective: This scoping review aimed to examine the relationship between adverse post-surgical outcomes and sarcopenia in patients aged 65 years and older. Methodology: EMBASE and Medline databases were searched for sarcopenia, perioperative period and post-surgical outcomes. The articles were screened based on exclusion and inclusion criteria and were reviewed systematically as per the Joanna Briggs Institute (JBI) Methodology for Scoping Reviews. Results: After duplicates removal and application of the inclusion and exclusion criteria, eight articles were included for this study from a total of nine hundred initially identified articles. All studies defined sarcopenia as low muscle mass but did not include physical function or muscle strength as the parameter of sarcopenia. Low muscle mass was associated with higher mortality in emergency surgeries, reduced long term survival in open elective surgeries, and increased length of hospital stay in endoscopic surgeries. Conclusion: The current review suggests that low muscle mass is associated with higher mortality and various adverse post-surgical outcomes in the elderly. It remains to be determined if applying the definition of sarcopenia as per the international consensus/guidelines will affect the association of adverse post-surgical outcomes and sarcopenia.

Key words: Sarcopenia, adverse post-surgical outcomes, 65 years and older.


 

Introduction

The proportion of older people is rapidly growing in the world. As per the US National Institute of Health (NIH) funded census bureau report in March 2016, six hundred seventeen million (8.5%) people worldwide were aged 65 and over (1). This number is expected to increase to nearly 1.6 billion (17%) by 2050 (1). With ageing, there is an increased prevalence of age-related health issues, one of which is sarcopenia. Sarcopenia is defined as progressive loss of skeletal muscle strength, mass and function. Sarcopenia is common amongst the older people aged 65 and above with the estimated prevalence ranging from 7.5% to 29% in community dwellers, up to 32% in residential care and 10% in hospital settings (2, 3, 4). The prevalence is even higher, more than 50%, above the age of 80 years (5).
Sarcopenia can lead to physical disability, functional impairment and even mortality (5, 7). It is associated with high health care cost, and it has negative impacts on different medical conditions and ICU settings (3, 7, 8, 9, and 10). Sarcopenia has now been recognised as a disease entity and been given an ICD-10-CM code to increase awareness among physicians to make this diagnosis, and also to stimulate drug development for its treatment (7). It is a treatable and preventable condition. Many studies have confirmed the positive effects of resistance training and dietary interventions in the treatment of sarcopenia (3, 11-15). Early identification and initiation of therapy are critical in preventing further decline.
A growing number of complex surgeries are now being performed even above the age of 80 years (16). A significant number of these elderly surgical patients are frail and sarcopenic who are vulnerable to major physiologic stressors, including major surgery or surgical complications (17). Extensive studies in various surgical fields have attempted to find the associations of sarcopenia and adverse post-surgical outcomes, and the majority of these studies have found some associations. Firstly, perioperative hospital costs are significantly higher for sarcopenic patients (18, 19). More importantly, sarcopenia has now been proven to be an independent predictive factor of adverse outcomes in adult patients with gastric, pancreatic, hepatobiliary, colorectal and urological cancer surgeries (20-25). In the liver transplant setting sarcopenia has been associated with an increased wait time mortality, 12 months mortality, increased ICU stay, and longer hospital stays (26). A similar result was also observed in adult vascular surgery patients showing lower readmission-free survival rates in patients with sarcopenia (27). Systematic reviews on patients of 18 years and above undergoing abdominal surgeries revealed higher postoperative complications, higher mortality, increased length of stay, lower overall survival, and lower disease-free survival in sarcopenic patients (28, 29). Sarcopenia has also been found to be related to adverse post-surgical outcomes in spinal and cardiovascular surgeries in adult patients (30, 31). Therefore, the early recognition and treatment of sarcopenia are essential for elderly patients in surgical settings. For elective surgery, there is a chance to optimise function before surgery, even though at this point, it is unclear if intervention before surgery leads to a changed outcome. For emergency surgery, sarcopenia can be used as a predictive tool and a guide to decide the suitability of patients for surgery.
Although the association of sarcopenia and various adverse outcomes has been established in various surgical and non-surgical settings, there are limited sarcopenia studies in orthopaedic settings, and in spinal surgeries, the results are conflicting. The study by Inose et al. has demonstrated inferior spinal surgery recovery rate at the final follow up in patients with sarcopenia but Charest-Morin et al. have found no relation of adverse post-surgical outcomes with sarcopenia (31, 32). Moskven et al. in their systematic review concluded that frailty does predict adverse post-surgical outcomes but not sarcopenia (33). The jury is still out in understanding the association between sarcopenia and post-surgical outcomes in the elderly.

 

Aim of the review

This scoping review aimed to examine the relationship between sarcopenia and post-surgical outcomes in patients aged 65 years and above.

 

Methodology

This scoping review followed the six steps of Joanna Briggs Institute (JBI) Methodology for Scoping Reviews which includes 1) identification of the research question, 2) identification of relevant studies, 3) study selection, 4) charting the data obtained, 5) collating, summarising and reporting the results and 6) further consultation (34).

Literature search

A professional librarian performed the literature search at Medline (1946 to 12 September 2018) and EMBASE (1974 to 12 September 2018) databases. The following keywords were initially used: “sarcopenia”, “perioperative period” and “adverse surgical outcomes”. The search was further expanded as follows:
Sarcopenia: “muscle mass”, “low handgrip’, or “weak hand grip”, or “low muscle strength”, or “weak muscle strength”, or “low gait speed” or “slow gait speed.”
Type of surgery: “surgery”, or “surgical procedure” or, “orthopaedic surgery”, or “orthopaedic procedure”, or “vascular surgery”, or “aneurysm surgery”, or “abdominal surgery”, or “spinal surgery.”
Perioperative period: “Pre-operative”, or “intraoperative”, or “perioperative”, or “postoperative.”
Adverse outcomes: “Mortality”, or morbidity”, “length of stay”, or “delayed healing”, or “risks”, “prognosis”, or “negative outcome”, or “negative prediction.”

Inclusion criteria

This review included retrospective and prospective cohort studies. Studies that were included met the following criteria:
1. Perioperative studies on patients with sarcopenia
2. Studies that examined the association of sarcopenia and adverse post-post-surgical outcomes
3. Actual age 65 years and above
4. Studies reported in English

The exclusion criteria

1. Sarcopenia related to malignancy, chemotherapy or transplants
2. Sarcopenia related to systemic diseases: IBD, chronic liver disease, neurological conditions or myopathies
3. Sarcopenic obesity
4. Review articles, case reports, commentaries and editorials.

Data Extraction and statistical analysis

All the articles identified with the above search criteria were independently screened by the first and second reviewers. Firstly, articles were screened from titles, then from abstracts and finally from the full review of the shortlisted articles. Both reviewers discussed their findings with each other at each step for clarification. Articles were included for this review if both reviewers agreed-upon criteria provided. The third reviewer was involved in any conflict or disagreement between the first and second reviewers at any stage.
All data obtained from the articles were extracted and tabulated. The tabulation includes the following characteristics: authors; year of publication; country of origin; publication type; aims and purpose; subject characteristics; description of articles; definition of sarcopenia; and the key findings of post-surgical outcomes. For the ease of description and comparison, surgical interventions were further divided into Endoscopic, open and emergency categories. Data were analysed as per the JBI scoping review and qualitative research guidelines (34)

 

Results

Altogether nine hundred articles were identified from the EMBASE and Medline database in which 216 articles were duplicates, hence removed from the study. After screening and full-text review of the remaining 684 articles, eight articles were selected for this review. The characteristics and outcomes of the eight studies are summarised in table 1.

Table 1
Studies characteristics

Abbreviations: N: total number, M: male, F: female, S- patients with low muscle mass, NS- no sarcopenia, CCI: Charlson comorbidity index (CCI), SMI: skeletal muscle index, LOS: length of stay, ASA- American Society of anaesthesiologist, NTPA- normalised psoas area mFI- modified frailty index, MACCE- major adverse cardiac and cerebrovascular events, HTN- hypertension, DM- diabetes mellitus, IHD- ischaemic heart disease, CVA-cerebrovascular accident, CKD- chronic kidney disease, CHF- chronic heart failure, COPD- chronic obstructive pulmonary disease, AFatrial fibrillation, PVD- peripheral vascular disease, CABG- coronary artery bypass graft, SD- standard deviation. PCI: percutaneous coronary intervention, BMI- Body Mass Index, Hb- Haemoglobin, CT- computed tomography, L – lumbar, HR- hazard ratio, ICU- intensive care Unit

 

In this review, three studies assessed endoscopic procedures: two on endovascular aneurysm repair (EVAS) and one on trans catheter aortic valve implantation (TAVI) (39-41). The remaining five studies investigated open surgical procedures: three studies examining elective surgeries and the other two studies examining emergency surgeries (32, 35-38). The open operations included the elective total arch replacement, open heart valve surgeries for aortic arch aneurysm, primary elective spine surgery for degenerative disease, as well as various abdominal surgeries. About 4% of patients in an endoscopic study underwent open aneurysmal repair, on the other hand, one study in the open category included patients with endoscopic (laparoscopic) procedures as well but for the ease of data analysis they were not described separately (38, 41).
In the open elective category, there were three studies: two retrospective and one ambispective. Both retrospective studies investigated cardiovascular surgeries; one on heart valve surgery and the other on aortic arch replacement (35, 36). In both two studies, the lower 5-year survival rate, higher late mortality and higher perioperative complications were observed in patients with low muscle mass. Besides, vascular events at five years were lower in both studies. The ambispective study investigated non-complex spinal surgery in a small number of a relatively healthy group of patients (102), and interestingly this study did not find any association of sarcopenia and adverse post-surgical outcomes (32).
There were two studies in the emergency surgery category and both assessed abdominal surgeries for acute conditions (37, 38). The emergency procedures were small bowel resection, laparotomy, colon resection, cholecystectomy, hernia & peptic ulcer disease repair. Both studies showed higher inpatient mortality and higher rates of transfer to rehabilitation and other acute-care services for patients with low muscle mass. One study not only showed higher inpatient mortality in the low muscle mass group of patients but also showed higher mortality across all time points up to one year after surgery (38). This study also demonstrated more patients in the low muscle mass group requiring palliation. Some conflicting results were also noted in the emergency studies: one study found the longer length of stay and more ICU care need for the patients of low muscle mass (38), but the other study did not find any such outcome (37). Moreover, one study reported higher rates of postoperative complications and higher home service requirements for low muscle mass group of patients (37), but the other study did not report any such outcome (38).
All three endoscopic studies were retrospective, and they all assessed vascular procedures. All three studies found low muscle mass to be associated with longer length of hospital stay. One study also found the statistically significant low median and overall survivals (39) and another study found a trend towards higher mortality (P 0.08) for patients of low muscle mass (41). Only one study assessed the association of low muscle mass and postoperative complications and found no association between them (39). One study was insufficiently powered to determine complications (40), and the third study did not comment on the association of low muscle mass and post-operative complications (41).

Sarcopenia international definition and definition in this review

Over the years, there have been significant variations in the definition of sarcopenia. To date, there have been six widely acceptable consensus definitions of sarcopenia proposed by various groups. The main differences between the consensuses lie in the different proposed cut-off points for low muscle mass and low muscle strength. Despite the differences, there was an overall agreement that the diagnosis of sarcopenia will require two or more muscle parameters to be below the predetermined cut-off values. European Working Group on Sarcopenia in Older People (EWGSOP) is the most commonly used definition of sarcopenia which was first published in 2010 (low muscle mass and low grip strength or low physical function) and updated in October 2018 (2, 6). In the current definition, muscle strength has been considered as the most reliable measure of muscle function; hence the primary parameter of sarcopenia (7). In the EWGSOP current operational definition, sarcopenia is confirmed if low muscle quantity or quality is present with low muscle strength, sarcopenia is severe if low physical performance is added with a confirmed diagnosis, and sarcopenia is probable if only low muscle strength is present without other parameters (7).
All studies included in this study have defined sarcopenia using only one parameter of muscle measurement. All studies utilised CT scan to measure muscle mass. Majority of the studies used psoas muscle as the surrogate marker of overall muscle mass. Two studies used total skeletal muscles (instead of psoas muscle alone) at L3/L4 level as the marker of sarcopenia (37, 40).
There were several methods of determining cut-offs for low muscle mass: three studies used lowest sex-specific quartile of psoas-muscle/lumbar-muscles as low muscle mass (32, 35, 38), whereas four studies defined low muscle mass as > 2 SD below the mean psoas muscle index of healthy adult (36, 37, 39, 40). One study did not clarify the method of determining the cut-off value for low muscle mass (41). No study defined sarcopenia using the definition as proposed by international consensus.

 

Discussion

This is the first scoping/systematic review which assessed the relation of sarcopenia and adverse post-surgical outcomes in non-malignant older persons aged sixty-five years and older. Sarcopenia definition in the majority of the articles of this scoping review was not consistent with the international consensus definition. All the studies in this review defined sarcopenia as low muscle mass and did not include muscle strength or muscle function as a measurement parameter of sarcopenia. Low muscle mass can have various causes and sarcopenia is only of them. Therefore a conclusion about sarcopenia and adverse post-surgical outcomes cannot be drawn from this scoping review.
Low muscle mass has already been established as a predictor of adverse post-surgical outcomes in malignant, non-surgical, as well as various surgical patients (3, 5, 7-10, 18-31). The current review supports these findings for elderly patients as well. The majority of the studies included in this scoping review found some associations of low muscle mass with various adverse post-surgical outcomes. For patients with low muscle mass, three studies demonstrated decreased long term survival (35, 36, 39), five studies showed increased mortality (35, 36, 37, 38, 41), three studies showed at least one higher short or long term complication (35, 36, 37) and four studies showed the longer length of stay (38, 39, 40, 41). Only one study did not find any association of adverse post-surgical outcomes and low muscle mass; however, this was a small study performed for relatively simple pathologies with straightforward operative techniques in a relatively healthy group of patients (32).
Some inconsistencies were noted in the method of determining the cut-off values for low muscle mass, and the cut-off values were also different for different studies. For cut-off value, one study used total psoas/skeletal muscle area (35), one study used adjusted muscle area for body surface area (36) and other studies used adjusted muscle area for body height (32, 37-40).
Some inconsistencies were also noted for adjustment of outcomes against standard variables. The outcome measures were adjusted for age, sex, BMI and comorbidities in most studies, but other variables were different for different studies. Therefore, the relation of low muscle mass and adverse post-surgical outcomes might have been over or underestimated in some of the studies, and hence, the actual association might have been missed. For instance, results were adjusted for the frailty index in only two studies (32, 40) but not by others. Similarly, serum albumin, haemoglobin and renal impairment were found to be predictors of adverse outcomes in some of the studies (35, 36, 41) but outcome measures were not adjusted against some of these variables in some studies (32, 37-40). Frailty and sarcopenia overlap in their clinical aspects, and malnutrition plays a vital role both in frailty and sarcopenia (44). Frailty and undernutrition are often present in sarcopenic patients, and they can also result in worse prognosis in surgical patients, but they are not the same thing as sarcopenia. However, most studies in this review did not adjust clinical outcomes for undernutrition and frailty, which is a big drawback of this study.
In this review, the association of low muscle mass and adverse post-surgical outcomes were assessed for various high-risk surgeries. Three studies investigated vascular surgeries (36, 39, 41), two assessed abdominal surgeries (37, 38), two studies examined valvular heart disease (35, 40), and one study assessed spinal surgeries (32). There are potential scopes for further sarcopenia studies in future orthopaedic and hip surgeries.
Finally, the majority of the studies in this review were retrospective and single centred small studies.

Limitations of this review

This review only included the articles reported in English. It excluded studies on patients with sarcopenic obesity. It also excluded studies that assessed the association of sarcopenia and adverse post-surgical outcomes in the elderly but also involved patients below the age of sixty-five years (42, 43). Therefore, this review has missed the findings of these studies and hence, it might have lost correct associations of sarcopenia and adverse post-surgical outcomes in the elderly.

 

Conclusion

The current review suggests that for older patients, we have evidence of post-surgical adverse events for low muscle mass but not for sarcopenia. Low muscle mass is associated with increased early or late mortality, and various other adverse post-surgical outcomes: it is associated with high mortality in emergency surgeries, reduced long term survival rate in open elective surgeries and increased length of hospital stay in endoscopic surgeries. Further extensive multi-centred prospective studies with international consensus definitions will be needed to delineate the associations between sarcopenia and surgical outcomes in older adults. This will be a key step before planning for experimental studies in assessing targeted interventions.

 

Conflicts of Interest: None.
Ethical standards: None ethical approved required.

 

References

1. Cire, B. 2016. World’s older population grows dramatically [Press release]. Retrieved from https://www.nih.gov/news-events/news-releases/worlds-older-population-grows-dramatically
2. Yu, S., Umapathysivam, K., & Visvanathan, R. Sarcopenia in older people. Int J Evid Based Healthc, 2014;12(4), 227-243. doi:10.1097/XEB.0000000000000018
3. Landi, F., Calvani, R., Cesari, M., Tosato, M., Martone, A. M., Ortolani, E., . . . Marzetti, E. Sarcopenia: An Overview on Current Definitions, Diagnosis and Treatment. Curr Protein Pept Sci, 2018;19(7), 633-638. doi:10.2174/1389203718666170607113459
4. Cruz-Jentoft, A. J., Landi, F., Schneider, S. M., Zuniga, C., Arai, H., Boirie, Y., Cederholm, T. Prevalence of and interventions for sarcopenia in ageing adults: a systematic review. Report of the International Sarcopenia Initiative (EWGSOP and IWGS). Age Ageing, 2014;43(6), 748-759. doi:10.1093/ageing/afu115
5. Kim, T. N., & Choi, K. M. Sarcopenia: definition, epidemiology, and pathophysiology. J Bone Metab, 2013;20(1), 1-10. doi:10.11005/jbm.2013.20.1.1
6. Cruz-Jentoft, A. J., Bahat, G., Bauer, J., Boirie, Y., Bruyere, O., Cederholm, T., . . . the Extended Group for, E. Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing, 2019;48(1), 16-31. doi:10.1093/ageing/afy169
7. Anker, S. D., Morley, J. E., & von Haehling, S. Welcome to the ICD-10 code for sarcopenia. J Cachexia Sarcopenia Muscle, 2016;7(5), 512-514. doi:10.1002/jcsm.12147
8. Kizilarslanoglu, M. C., Kuyumcu, M. E., Yesil, Y., & Halil, M. Sarcopenia in critically ill patients. J Anesth, 2016;30(5), 884-890. doi:10.1007/s00540-016-2211-4
9. Ryan, E., McNicholas, D., Creavin, B., Kelly, M. E., Walsh, T., & Beddy, D. Sarcopenia and Inflammatory Bowel Disease: A Systematic Review. Inflammatory Bowel Diseases, 2018;25(1), 67-73. doi:10.1093/ibd/izy212
10. Song, M., Xia, L., Liu, Q., Sun, M., Wang, F., & Yang, C. Sarcopenia in Liver Disease: Current Evidence and Issues to Be Resolved. In J. Xiao (Ed.), Muscle Atrophy, 2018 (pp. 413-433). Singapore: Springer Singapore
11. Fragala, M. S., Dam, T. T., Barber, V., Judge, J. O., Studenski, S. A., Cawthon, P. M., Kenny, A. M. Strength and function response to clinical interventions of older women categorized by weakness and low lean mass using classifications from the Foundation for the National Institute of Health sarcopenia project. J Gerontol A Biol Sci Med Sci, 2015;70(2), 202-209. doi:10.1093/gerona/glu110
12. Denison, H. J., Cooper, C., Sayer, A. A., & Robinson, S. M. Prevention and optimal management of sarcopenia: a review of combined exercise and nutrition interventions to improve muscle outcomes in older people. Clin Interv Aging, 2015;10, 859-869. doi:10.2147/CIA.S55842
13. Waters, D. L., Baumgartner, R. N., Garry, P. J., & Vellas, B. Advantages of dietary, exercise-related, and therapeutic interventions to prevent and treat sarcopenia in adult patients: an update. Clin Interv Aging, 2010;5, 259-270. doi:10.2147/cia.s6920
14. Komar, B., Schwingshackl, L., & Hoffmann, G. Effects of leucine-rich protein supplements on anthropometric parameter and muscle strength in the elderly: A systematic review and meta-analysis. The journal of nutrition, health & aging, 2015;19(4), 437-446. doi:10.1007/s12603-014-0559-4
15. Padilla Colon, C. J., Molina-Vicenty, I. L., Frontera-Rodriguez, M., Garcia-Ferre, A., Rivera, B. P., Cintron-Velez, G., & Frontera-Rodriguez, S. Muscle and Bone Mass Loss in the Elderly Population: Advances in diagnosis and treatment. J Biomed (Syd), 2018;3, 40-49. doi:10.7150/jbm.23390
16. Karakoc, D. (2016). Surgery of the Elderly Patient. International Surgery, 2016;101(3-4), 161-166. doi:10.9738/intsurg-d-15-00261.1
17. Friedman, J., Lussiez, A., Sullivan, J., Wang, S., & Englesbe, M. Implications of sarcopenia in major surgery. Nutr Clin Pract, 2015;30(2), 175-179. doi:10.1177/0884533615569888
18. Gani, F., Buettner, S., Margonis, G. A., Sasaki, K., Wagner, D., Kim, Y., Pawlik, T. M. Sarcopenia predicts costs among patients undergoing major abdominal operations. Surgery, 2016;160(5), 1162-1171. doi:10.1016/j.surg.2016.05.002
19. Kirk, P. S., Friedman, J. F., Cron, D. C., Terjimanian, M. N., Wang, S. C., Campbell, D. A., Werner, N. L. One-year postoperative resource utilization in sarcopenic patients. J Surg Res, 2015;199(1), 51-55. doi:10.1016/j.jss.2015.04.074
20. Chindapasirt, J. Sarcopenia in Cancer Patients. Asian Pac J Cancer Prev, 2015;16(18), 8075-8077. doi:10.7314/apjcp.2015.16.18.8075
21. Mintziras, I., Miligkos, M., Wachter, S., Manoharan, J., Maurer, E., & Bartsch, D. K. Sarcopenia and sarcopenic obesity are significantly associated with poorer overall survival in patients with pancreatic cancer: Systematic review and meta-analysis. Int J Surg, 2018;59, 19-26. doi:10.1016/j.ijsu.2018.09.014
22. Ongaro, E., Buoro, V., Cinausero, M., Caccialanza, R., Turri, A., Fanotto, V., . . . Aprile, G. Sarcopenia in gastric cancer: when the loss costs too much. Gastric Cancer, 2017;20(4), 563-572. doi:10.1007/s10120-017-0722-9
23. Harimoto, N., Shirabe, K., Yamashita, Y. I., Ikegami, T., Yoshizumi, T., Soejima, Y. Yamanaka, T. Sarcopenia as a predictor of prognosis in patients following hepatectomy for hepatocellular carcinoma. Br J Surg, 2013;100(11), 1523-1530. doi:10.1002/bjs.9258
24. Nakanishi, R., Oki, E., Sasaki, S., Hirose, K., Jogo, T., Edahiro, K., Maehara, Y. Sarcopenia is an independent predictor of complications after colorectal cancer surgery. Surg Today, 2018;48(2), 151-157. doi:10.1007/s00595-017-1564-0
25. Fukushima, H., & Koga, F. Impact of sarcopenia in the management of urological cancer patients. Expert review of anticancer therapy, 2017;17, 1-12. doi:10.1080/14737140.2017.1301209
26. Meeks, A. C., & Madill, J. Sarcopenia in liver transplantation: A review. Clin Nutr ESPEN, 2017;22, 76-80. doi:10.1016/j.clnesp.2017.08.005
27. Chowdhury, M. M., Ambler, G. K., Al Zuhir, N., Walker, A., Atkins, E. R., Winterbottom, A., & Coughlin, P. A. Morphometric Assessment as a Predictor of Outcome in Older Vascular Surgery Patients. Ann Vasc Surg, 2018;47, 90-97. doi:10.1016/j.avsg.2017.08.002
28. Hasselager, R., & Gogenur, I. Core muscle size assessed by perioperative abdominal CT scan is related to mortality, postoperative complications, and hospitalization after major abdominal surgery: a systematic review. Langenbecks Arch Surg, 2014;399(3), 287-295. doi:10.1007/s00423-014-1174-x
29. Jones, K., Gordon-Weeks, A., Coleman, C., & Silva, M. Radiologically Determined Sarcopenia Predicts Morbidity and Mortality Following Abdominal Surgery: A Systematic Review and Meta-Analysis. World J Surg, 2017;41(9), 2266-2279. doi:10.1007/s00268-017-3999-2
30. Yamashita, M., Kamiya, K., Matsunaga, A., Kitamura, T., Hamazaki, N., Matsuzawa, R., Miyaji, K. Prognostic Value of Psoas Muscle Area and Density in Patients Who Undergo Cardiovascular Surgery. Can J Cardiol, 2017;33(12), 1652-1659
31. Inose, H., Yamada, T., Hirai, T., Yoshii, T., Abe, Y., & Okawa, A. The impact of sarcopenia on the results of lumbar spinal surgery. Osteoporos Sarcopenia, 2018;4(1), 33-36. doi:10.1016/j.afos.2018.02.003
32. Charest-Morin, R., Street, J., Zhang, H., Roughead, T., Ailon, T., Boyd, M., Flexman, A. M. Frailty and sarcopenia do not predict adverse events in an elderly population undergoing non-complex primary elective surgery for degenerative conditions of the lumbar spine. Spine J, 2018;18(2), 245-254. doi:10.1016/j.spinee.2017.07.003
33. Moskven, E., Bourassa-Moreau, E., Charest-Morin, R., Flexman, A., & Street, J. The impact of frailty and sarcopenia on postoperative outcomes in adult spine surgery. A systematic review of the literature. Spine J, 2018;18(12), 2354-2369
34. Institute, T. J. B. Joanna Briggs Institute Reviewers’ Manual: 2015 edition / Supplement.
35. Okamura, H., Kimura, N., Tanno, K., Mieno, M., Matsumoto, H., Yamaguchi, A., & Adachi, H. The impact of preoperative sarcopenia, defined based on psoas muscle area, on long-term outcomes of heart valve surgery. J Thorac Cardiovasc Surg. 2018;doi:10.1016/j.jtcvs.2018.06.098
36. Ikeno, Y., Koide, Y., Abe, N., Matsueda, T., Izawa, N., Yamazato, T., Okita, Y. Impact of sarcopenia on the outcomes of elective total arch replacement in the elderlydagger. Eur J Cardiothorac Surg, 2017;51(6), 1135-1141. doi:10.1093/ejcts/ezx050
37. Du, Y., Karvellas, C. J., Baracos, V., Williams, D. C., Khadaroo, R. G., Acute, C., & Emergency Surgery, G. Sarcopenia is a predictor of outcomes in very elderly patients undergoing emergency surgery. Surgery, 2014;156(3), 521-527. doi:10.1016/j.surg.2014.04.027
38. Rangel, E. L., Rios-Diaz, A. J., Uyeda, J. W., Castillo-Angeles, M., Cooper, Z., Olufajo, O. A., Sodickson, A. D. Sarcopenia increases risk of long-term mortality in elderly patients undergoing emergency abdominal surgery. J Trauma Acute Care Surg, 2017;83(6), 1179-1186. doi:10.1097/TA.0000000000001657
39. Thurston, B., Pena, G. N., Howell, S., Cowled, P., & Fitridge, R. Low total psoas area as scored in the clinic setting independently predicts midterm mortality after endovascular aneurysm repair in male patients. J Vasc Surg, 2018;67(2), 460-467. doi:10.1016/j.jvs.2017.06.085
40. Dahya, V., Xiao, J., Prado, C. M., Burroughs, P., McGee, D., Silva, A. C., . . . Batchelor, W. Computed tomography-derived skeletal muscle index: A novel predictor of frailty and hospital length of stay after transcatheter aortic valve replacement. Am Heart J, 2016;182, 21-27. doi:10.1016/j.ahj.2016.08.016
41. Shah, N., Abeysundara, L., Dutta, P., Christodoulidou, M., Wylie, S., Richards, T., & Schofield, N. The association of abdominal muscle with outcomes after scheduled abdominal aortic aneurysm repair. Anaesthesia, 2017;72(9), 1107-1111. doi:10.1111/anae.13980
42. Drudi, L. M., Phung, K., Ades, M., Zuckerman, J., Mullie, L., Steinmetz, O. K., Afilalo, J. Psoas Muscle Area Predicts All-Cause Mortality After Endovascular and Open Aortic Aneurysm Repair. Eur J Vasc Endovasc Surg, 2016;52(6), 764-769. doi:10.1016/j.ejvs.2016.09.011
43. Newton, D. H., Kim, C., Lee, N., Wolfe, L., Pfeifer, J., & Amendola, M. Sarcopenia predicts poor long-term survival in patients undergoing endovascular aortic aneurysm repair. J Vasc Surg, 2018;67(2), 453-459. doi:10.1016/j.jvs.2017.06.092
44. Cruz-Jentoft, A.J., Kiesswetter, E., Drey, M. et al. Nutrition, frailty, and sarcopenia. Aging Clin Exp Res 2017;29, 43–48. https://doi.org/10.1007/s40520-016-0709-0

 

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PREVALENCE OF PREFRAILTY AND FRAILTY IN SOUTH AMERICA: A SYSTEMATIC REVIEW OF OBSERVATIONAL STUDIES

 

H.J. Coelho-Junior1,2, E. Marzetti2,3, A. Picca3, R. Calvani3, M. Cesari4,5, M.C. Uchida1

 

1. Applied Kinesiology Laboratory–LCA, School of Physical Education, University of Campinas, Campinas, SP, Brazil; 2. Università Cattolica del Sacro Cuore, Institute of Internal Medicine and Geriatrics, Rome, Italy; 3. Center for Geriatric Medicine (Ce.M.I.), Fondazione Policlinico Universitario “Agostino Gemelli” IRCCS, Rome, Italy; 4. Department of Clinical Sciences and Community Health, Università di Milano, Milan, Italy; 5. Geriatric Unit, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy.
Corresponding author: Hélio J. Coelho-Junior, Department of Geriatrics, Neuroscience and Orthopedics, Fondazione Policlinico Universitario «Agostino Gemelli» IRCCS, Università Cattolica del Sacro Cuore. L.go F. Vito 1, Rome 00168, Italy. Tel.: +39 (06) 3015-5559. Fax: +39 (06) 3051-911, E-mail: coelhojunior@hotmail.com.br

J Frailty Aging 2020;9(4)197-213
Published online April 29, 2020, http://dx.doi.org/10.14283/jfa.2020.22

 


Abstract

Objectives: The present study aimed at investigating the prevalence of prefrailty and frailty in South American older adults according to the setting and region. Design: A literature search combining the terms “frailty”, “South America” or a specific country name was performed on PubMed, EMBASE, Lilacs, and Scielo to retrieve articles published in English, Portuguese or Spanish on or before August 2019. Participants: Older adults aged 60+ years from any setting classified as frail according to a validated scale were included in the study. Measurements: Frailty assessment by a validated scale. Results: One-hundred eighteen reports (98 performed from Brazil, seven from Chile, five from Peru, four from Colombia, two from Ecuador, one from Argentina, and one from Venezuela) were included in the study. The mean prevalence of prefrailty in South America was 46.8% (50.7% in older in-patients, 47.6% in the community, and 29.8% in nursing-home residents). The mean prevalence of frailty in South America was 21.7% (55.8% in nursing-home residents, 39.1% in hospitalized older adults, and 23.0% in the community). Conclusions: Prefrailty and frailty are highly prevalent in South American older adults, with rates higher than those reported in Europe and Asia. In the community, almost one-in-two is prefrail and one-in-five is frail, while hospitalized persons and nursing-home residents are more frequently affected. These findings indicate the need for immediate attention to avoid frailty progression toward negative health outcomes. Our findings also highlight the need for specific guidelines for the management of frailty in South America.

Key words: Latin America, low-income countries, elderly, sarcopenia, mobility, nursing-home.


 

 

Introduction

Frailty is a potentially reversible state of increased vulnerability to stressful events (1) that occurs as a result of multisystem biological derangements (2–5) and socioeconomic inequalities (6–8). Frailty progression increases the risk of several negative health-related outcomes, including disability, loss of independence, institutionalization, and death (9–11). Noticeably, frailty is associated with greater healthcare utilization and costs (12), making this condition a top public health priority (1).
Since the operationalization of the frailty phenotype by Fried et al. (13), considerable research has been devoted to explore its incidence (14), prevalence (15–18), associated factors (19, 20), and main outcomes (21). These efforts have allowed generation of recommendations and guidelines for the identification and management of frailty across healthcare settings (22–24). Yet, the majority of studies upon which guidelines are based were conducted in high-income countries, while very few publications have been produced in South America (14–16). Hence, epidemiological characteristics of frailty in this region are poorly described. This is especially concerning since South America, in spite of the image of a «young» region, is aging at a faster pace than Europe (25). Furthermore, risk factors for frailty development, such as socioeconomic disadvantages, chronic diseases and disabilities, are highly prevalent in South America (6).
To increase the knowledge of the epidemiology of frailty in South America, the present systematic review explored the prevalence of prefrailty and frailty in South American older adults according to settings, regions, and frailty assessment tools.

 

Methods

We conducted a systematic review of observational studies to investigate the prevalence of prefrailty and frailty in South America. The study was fully performed by investigators and no librarian was part of the team. This study complies with the criteria of the Primary Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) Statement (supplementary material 1) (26). All data are available in the Open Science Framework at https://doi.org/10.17605/OSF.IO/XZ2S8.

Eligibility criteria

The following criteria were used for inclusion: (a) observational studies, including cross-sectional, cohort, case-control and longitudinal studies, which described or supplied data to calculate the prevalence of prefrailty and/or frailty in older adults from any setting (e.g., community, institutions); (b) age 60 years or more; (c) frailty assessment by a validated scale; and (d) published studies (English, Portuguese, and Spanish languages). There was no restriction on sample size or study population, and studies that investigated disease-specific populations were also included and analyzed accordingly. Studies that did not report the prevalence of robust older adults in addition to frailty prevalence or that classified participants as frail only according to reduced physical/or cognitive function were excluded.

Search strategy and selection criteria

Studies published on or before August 2019 were retrieved from the following four electronic databases by one investigator: (1) PubMed, (2) EMBASE, (3) Lilacs, and (4) Scielo. Reference lists for reviews and retrieved articles for additional studies were checked and citation searches on key articles were performed on Google Scholar and ResearchGate for additional reports. A search strategy was designed using keywords, MeSH terms, and free text words such as frailty, South America, Latin America, and the name of all South American countries. Keywords and MeSH terms (for PubMed), or its corresponding in Lilacs and Scielo (i.e., DeCS) were combined using Boolean operators. The complete search strategy used for the PubMed is shown in supplementary material 2. Only eligible full-texts in English, Portuguese or Spanish languages were considered for review.

Data extraction and quality assessment

Titles and abstracts of retrieved articles were screened for eligibility by two researchers. If an abstract did not provide enough information for evaluation, the full-text was retrieved. Disagreements were solved by a third reviewer. Reviewers were not blinded to authors, institutions, or manuscript journals. Data extraction was independently performed by two reviewers using a standardized coding form. Disagreements were solved by a third reviewer. Coded variables included methodological quality and the characteristics of studies. If two or more studies shared the same sample, the largest sample size was considered in the analysis (15, 18). The prevalence of prefrail and frailty were calculated according to the cutoff values used in the studies (supplementary material 3), so that no changes were performed when frailty identification was made using the Fried frailty phenotype (13), Tilburg frailty indicator (TFI) (27), Fatigue, Resistance, Ambulation, Illnesses, & Loss of Weight (FRAIL) scale (28), Kihon checklist (KCL) (29), and Study of Osteoporotic Fracture (SOF) (30) instruments. When participants were identified as visible vulnerable with the Edmonton frailty scale (EFS) (31) and apparently vulnerable with the Clinical Frailty Scale (CFS) (32), they were considered prefrail, as well as they were considered frail when were identified as Mild, Moderate, and Severe Frailty using the Edmonton frailty scale (CFS) (31) and mildly, moderate, and severely frail using the CFS (32).The quality of reporting for each study was assessed by two researchers using the Newcastle Ottawa Quality Assessment Scale (NOS) for non-randomized studies (33, 34). The agreement rate between reviewers for quality assessment was κ=0.93.

Results

Literature search
Of 20,229 records recovered from electronic databases and hand search, 19,612 were excluded based on duplicate data, title or abstract. Six-hundred seventeen records were fully reviewed and assessed for eligibility. Eventually, 118 studies met inclusion criteria (Figure 1).

Figure 1
Flowchart of the study

 

Characteristics of included studies

Table 1 provides a general description of included studies. Overall, a total of 53,134 older adults (mean age ± standard deviation [SD]= 80.1 ± 3.8 years; women= 32,006 [60.2%]) from seven countries (Argentina, Brazil, Chile, Colombia, Ecuador, Peru, and Venezuela) were studied between 2008 and 2019. Studies were based on cross-sectional, longitudinal, and cohort designs. Of the 118 included studies, 98 (83.0%; n=36,786) were performed in Brazil (6, 31, 35–130), seven (5.9%; n=6,091) in Chile (100, 131–136), five (4.2%; n=4,052) in Peru (137–141), four (3.3%; n=3,836) in Colombia (142–145), two (1.7%; n=304) in Ecuador(146,147), one (0.8%; n=100) in Argentina (148), and one (0.8%; n=1,965) in Venezuela (139).
The frailty phenotype (13) was the most commonly used tool for frailty assessment (66.6%), followed by the EFS (23.6%), the TFI (4.9%), the FRAIL scale (3.3%), the KCL (2.4%), the SOF index (0.8%), and the CFS (0.8%). Most studies (n=104; 91.5%) were conducted in community-dwellers, while nursing-home residents were evaluated in nine studies, hospitalized persons were investigated in five studies, and three studies were performed with population data. Seven studies reported the prevalence of frailty using the same sample two or more times, while three studies used more than two tools to assess frailty.
Participants were recruited in different places, including urban, rural, and areas of social vulnerability, primary and secondary healthcare centers, and community centers, to quote a few. The most common comorbidities were hypertension (33 studies), diabetes (25 studies), osteoarthritis (19 studies), cancer (17 studies), stroke (15 studies), chronic pulmonary obstructive disease (12 studies), chronic kidney disease (CKD) (10 studies), and heart failure (HF) (10 studies). Dyslipidemia, obesity, coronary heart disease, myocardial infarction, atrial fibrillation, cognitive impairment, and disability were reported in less than five studies each.

Table 1
Characteristics of the included studies

CHD= Coronary heart disease; CKD= Chronic kidney disease; CPOD= Chronic pulmonary obstructive disease; CVD= Cardiovascular diseases; DS= Depressive symptoms; EFS= Edmonton frail scale; FIBRA= Fragilidade em idosos brasileiros; FRAIL= Fatigue, resistance, ambulation, illnesses, & loss of weight; HF= Heart failure; HTN= Hypertension; IHG= Isometric handgrip strength; KCL= Kihon checklist; MCI= Mild-cognitive impairment; MI= Myocardial infarction; OA= Osteoarthritis; SABE= Saúde, bem-estar e envelhecimento; SOF= Study of osteoporotic fracture; TFI= Tilburg frailty indicator; TUG= Timed “Up and Go” ; WS= Walking speed. a, b, c, d, e, f= These studies used the same sample; h, i, j, k= The same study reported the prevalence with different assesment tools

 

Quality assessment

The overall score and the point-by-point analysis of quality assessment of cross-sectional and cohort studies are shown in Table 2. The overall score of cross-sectional studies ranged from 2 to 10 (maximum value: 11). All studies used a validated instrument for frailty assessment (item 4). Regarding selection criteria (item 1), 38.9% of studies used a representative sample from a random population, 21.2% did not describe the sampling strategy, 20.3% used a selected group of participants (e.g., institutionalized older adults), and 15.3% used a somewhat representative sample selected using a non-random method. The sample size (item 2) was justified in 49.5% of studies. Comparisons between respondents and non-respondents in the main characteristics (item 3) were only performed in 5.0% of the studies. Age was selected as the most important confounder factor (item 5) and it was controlled for in less than half of the studies (47.5%). Similarly, only 46.6% of studies controlled for additional factors (i.e., gender or body mass index [BMI]) (item 5). Outcomes (item 6) were assessed using an independent blind method in 56.8% of studies, self-reported scales or questionnaires in 35.2%, record linkage in 1.7%, while 1.7% did not describe the method. Finally, appropriate statistical analysis (item 7) was used in 57.8% of studies.

Table 2
Quality assessment of the included studies

α= Cross-sectional study; β= Cohort study; *Max= 11 points for α and 9 points for β. Cross-sectional studies: 1) Representativeness of the sample: a) Truly representative of the average in the target population, b) Somewhat representative of the average in the target population, c) Selected group of users, d) No description of the sampling strategy; 2) Sample size: a) Justified and satisfactory, b) Not justified; 3) Non-respondents: a) Comparability between respondents and non-respondents characteristics, b) The response rate is unsatisfactory, or the comparability between respondents and non-respondents is unsatisfactory, c) No description of the response rate or the characteristics of the responders and the non-responders; 4) Ascertainment of the exposure: a) Validated measurement tool, b) Non-validated measurement tool, but the tool is available or described, c) No description of the measurement tool; 5) Comparability: a) The study controls for the most important factor, b) The study control for any additional factor; 6) Outcome: a) Independent blind assessment, b) Record linkage, c) Self report, d) No description; 7) Statistical test: a) The statistical test used to analyze the data is clearly described and appropriate, and the measurement of the association is presented, including confidence intervals and the probability level (p value), b) The statistical test is not appropriate, not described or incomplete. Cohort studies: 1) Representativeness of the exposed cohort: a) truly representative, b) somewhat representative, c) selected group of users, d) no description of the derivation of the cohort; 2) Selection of the non exposed cohort: a) drawn from the same community as the exposed cohort, b) drawn from a different source, c) no description of the derivation of the non exposed cohort; 3) Ascertainment of exposure: a) secure record (eg surgical records), b) structured interview, c) written self report, d) no description; 4) Demonstration that outcome of interest was not present at start of study: a) yes, b) no; 5) Comparability: a) study controls for age; b) study controls for any additional factor; 6) Assessment of outcome: a) independent blind assessment, b) record linkage, c) self report, d) no description; 7) Was follow-up long enough for outcomes to occur: a) yes (select an adequate follow up period for outcome of interest) b) no; 8) Adequacy of follow up of cohorts: a) complete follow up, b) subjects lost to follow up, c) no description of those lost, d) no statement

 

Regarding cohort studies, all of them used a structured interview to assess exposure (item 3), recruited the non-exposed cohort from the same setting as the exposed cohort (item 2), demonstrated that the outcome of interest was not present at the beginning of the study (item 4), and evaluated the outcome using an independent blind method (item 6). Seventy-five percent of the studies used a truly representative sample, and 25% a somewhat representative sample (item 1). One study did not control for any main (item 5) or additional factors. The follow-up period (item 7) was not long enough in one study and a representative sample completed the follow-up period in 75% of studies.

Prevalence of prefrailty and frailty in South America

Overall, the mean prevalence of prefrailty was 46.8%, ranging from 23.0% in Ecuador to 55.9% in Peru (Figure 2). When data were analyzed according to the assessment tool, the prevalence of prefrailty was 50.7%, 44.8%, and 18.4% for Fried, FRAIL, and EFS, respectively. The highest prevalence of prefrailty was observed in hospitalized older adults (50.7%), followed by community-dwelling persons (47.6%), and nursing-home residents (29.8%). Regarding older adults with specific conditions, people with cardiovascular disease (CVD) and CKD showed a prevalence of prefrailty of 51.2% and 26.7%, respectively.

Figure 2
Mean prevalence of prefrailty according to country in South America

 

Overall, the mean prevalence of frailty was 21.7%, ranging from 10.6% in Colombia to 31.3% in Chile (Figure 3). When data were analyzed according to the assessment tool, the prevalence of frailty was 48.8%, 38.0%, 34.7%, 26.9%, 26.0%, 18.4%, 18,2% according to TFI, SOF, Fried, KCL, CFS, EFS, and FRAIL, respectively. The highest prevalence of frailty was observed in nursing-home residents (55.8%) (supplementary Figure 4), followed by hospitalized older adults (39.1%) (supplementary Figure 5), and community-dwellers (23.0%) (supplementary Figure 6). Regarding older people with specific conditions, persons with cancer showed the highest prevalence (54.9%), followed by those with CVD (37.8%) and CKD (37.5%). The prevalence of frailty increased progressively with age (21.4% at 60-69 years, 24.5% at 70-79 years, 30.3% at 80+ years). Most studies reported a higher prevalence of frailty in women than in men.

Figure 3
Mean prevalence of frailty according to country in South America

 

Prevalence of prefrailty and frailty according to country

Argentina

The mean prevalence of frailty in Argentina was 26.0%. Data were exclusively based on older patients with HF. Frailty was assessed using the CFS.

Brazil

The mean prevalence of prefrailty in Brazil was 46.9%, ranging from 4.8% to 71.1%. When data were analyzed according to the assessment tool, the prevalence of prefrailty was 49.1%, 45.6%, and 19.4% for Fried, FRAIL, and EFS, respectively. The highest prevalence of prefrailty was observed in hospitalized older adults (51.0%), followed by community-dwellers (47.1%) and nursing-home residents (29.8%). Regarding older adults with specific conditions, people with CVD and CKD showed a prevalence of prefrailty of 51.2% and 26.7%, respectively.
The mean prevalence of frailty in Brazil was 26.1%, ranging from 1.9% to 75.0%. When data were analyzed according to the assessment tool, the prevalence of frailty was 48.3%, 38.0%, 34.8%, 33.1%, 26.9%, and 19.3% for TFI, SOF, FRAIL, EFS, KCL, and Fried, respectively. The highest prevalence of frailty was observed in nursing-home residents (55.8%), followed by hospitalized persons (39.6%) and community-dwellers (24.8%). Regarding older adults with specific conditions, people with cancer had the highest prevalence of frailty (57.7%), while those with CVD and CKD showed a prevalence of frailty of 37.8% and 37.5%, respectively.

Chile

The mean prevalence of prefrailty in Chile was 54.3%, ranging from 38.9% to 69.0%. The highest prevalence of prefrailty was observed in hospitalized older adults (51.0%), followed by community-dwellers (47.1%) and nursing-home residents (29.8%).
The mean prevalence of frailty in Chile was 31.3%, ranging from to 4.5% to 80.0%. When data were analyzed according to the assessment tool, Fried criteria identified a mean of 23.2% of older adults with frailty, while 80% were identified by TFI. The highest prevalence of frailty was observed in hospitalized older adults (50.0%), followed by community-dwellers (28.1%).

Colombia

The mean prevalence of prefrailty and frailty in Colombia was 49.3% (12.9-53.0%) and 10.6% (7.9-12.1%), respectively. When data were analyzed according to the assessment tool, Fried criteria (44.0% and 9.6%) identified a larger number of prefrail and frail older adults compared with EFS (12.9% and 8.9%).

Ecuador

The mean prevalence of prefrailty and frailty in Ecuador was 57.4% and 31.2%, respectively. Data were exclusively based on older adults from the Atahualpa region. Frailty status was assessed using the TFI.

Peru

The mean prevalence of prefrailty and frailty in Chile was of 55.9% (47.3-64.6%) and 19.9% (7.7-27.7%), respectively. Older people with cancer showed a frailty prevalence of 23.8%, while 22.1% of community-dwelling older adults were frail.

Venezuela

The mean prevalence of frailty in Venezuela was 12.4%. Data were exclusively based on older adults from Caracas. Frailty status was assessed using the Fried criteria.

 

Discussion

The present study investigated the prevalence of prefrailty and frailty in older adults from different settings in South America. Results from our systematic review show that about 46.8% of older people living in Brazil, Chile, Colombia, Ecuador, and Peru are prefrail. The highest prevalence of prefrailty was observed in hospitalized older adults (50.7%), followed by community-dwellers (47.6%) and nursing-home residents (29.8%). The cumulative prevalence of frailty in South America was 21.7%. The prevalence of frailty across settings differed from that of prefrailty, with the highest rate observed in nursing-home residents (55.8%), followed by hospitalized (39.1%) and community-dwelling persons (23.0%). When data were analyzed according to the geographic area, most countries showed a mean prevalence of prefrailty ~50% and a mean prevalence of frailty ~20%, with the notable exceptions of Colombia (10.6%) and Chile (31.3%).
Only one systematic review investigated the prevalence of frailty (19.6%) in South America, but results were based on a limited number of search terms, South America and Caribbean countries, and only studies with representative samples of community-dwellers were included (15). Our findings add to the existing literature by reporting the prevalence of prefrailty and frailty in older South Americans according to setting, country, and assessment tools.
Based on our results, the prevalence of frailty in the community in South America (23.0%) is almost twofold higher in comparison to Europe (12.0%)(16) and more than threefold higher than in Japan (7.4%)(18). Similarly, a higher prevalence of frailty was observed in South American nursing-home residents (55.8%) when compared with European peers (45.0%) (16). These findings are consistent with previous investigations that showed a higher prevalence of prefrailty and frailty in low- and middle-income countries compared with high-income regions (14, 15, 149). A possible explanation for this phenomenon may reside in the fact that disadvantaged socioeconomic conditions are frequently associated with inequalities in healthcare access, lower dietary quality, physical inactivity, multimorbidity and disability (150, 151), all of which contribute to the development and progression of frailty (6–8, 20).
Divergent prevalence rates of prefrailty and frailty were observed across settings, which may reflect different patterns of healthcare utilization in South America depending on the frailty status. As people progress from robustness to prefrailty, they show increased prevalence of multimorbidity (152, 153), disability (154), and risk of adverse health-related events (153), leading to higher healthcare utilization (153) and possibly hospitalization (155). In addition, muscle strength, gait speed, and balance (155, 156) are reduced in prefrail persons compared with robust older adults, which may account for increased incidence of falls (152, 154) and fractures (154) and related hospitalizations in these individuals (155).
On the other hand, frail older people show worse overall health status compared with their prefrail counterparts (152), which make them need more time to recover from stressful events, increasing the use of critical care services (157) and frequent hospital readmission (158). Mortality is a frequent outcome in hospitalized frail older adults (158), and nursing-home allocation is a common discharge disposition for survivors (158). Indeed, frailty is highly prevalent in nursing-homes (11, 159), possibly reflecting the increased need of medical attention (157) as well as cognitive decline (155, 159), and disabilities of residents (160).
According to Ofori-Asenso et al. (14), the 3-year frailty incidence rate among prefrail individuals worldwide is 62.7 cases per 1000 person-years, which might suggest that more than one million new cases of frailty may be expected in South America each year. This figure has relevant public health implications and calls for immediate actions against frailty in South America. Indeed, the early detection of prefrailty and frailty may reduce the risk for negative health-related outcomes and healthcare utilization through the design and implementation of person-tailored interventions (161).
Strategies to reverse frailty should be devised according to frailty status and setting. Community-dwellers showed the lowest prevalence of frailty (23.0%), while almost one-in-two (47.6%) was classified as prefrail. Older adults living in the community are commonly able to perform activities of daily living (ADL) and, consequently, might benefit from long-term interventions that need more engagement, such as exercise programs (162, 163) and dietary counseling (164–166). Hence, public health policies for this population may include group-based multicomponent exercise programs aimed at improving physical performance (167–169) and dietary support. Personalized interventions may be required by hospitalized older adults and nursing-home residents, given the complexity of their clinical conditions, the high prevalence of multimorbidity and disability, and the high mortality rates (170–173).
Quality assessment analysis indicates a high prevalence of selecting, inclusive, and reporting biases. The main limitations included small sample size, sampling strategy, and lack of clinical information. Future observational studies should be conducted taking into account the above-mentioned issues.
Our study is not free of limitations. First, although our findings are based on the majority of Latin American countries, limited evidence was available for most of them, except for Brazil. Indeed, no studies were retrieved that investigated the prevalence of prefrailty and frailty in Bolivia, Paraguay, Uruguay, Guyana and Suriname, and only few reports were available for Argentina, Venezuela and Ecuador. Second, although unlikely, it is possible that more studies could be available in other databases than those used for the present study. However, selected databases have wide coverage without losing the quality of journals. Third, the cross-sectional design of included studies limits extrapolation and interpretation of findings.

 

Conclusions

Prefrailty and frailty are highly prevalent in South American older adults, with rates higher than in Europe and Asia. Among community-dwellers, almost one-in-two is prefrail and one-in-five is frail, while hospitalized older adults and nursing-home residents are more often affected. These findings call for immediate actions to ensure sustainability of healthcare systems. Hence, our report may provide basic information for healthcare authorities and policy makers to devise novel models of care responsive to emerging medical needs of older South Americans.

 

Conflicts of Interest: Authors report no conflict of interests.
Acknowledgements: The authors are grateful to the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES; Finance Code 001) for a scholarship granted to Hélio José Coelho Júnior.

 

SUPPLEMENTARY MATERIAL1

SUPPLEMENTARY MATERIAL2

SUPPLEMENTARY MATERIAL3

SUPPLEMENTARY MATERIAL4

SUPPLEMENTARY MATERIAL5

SUPPLEMENTARY MATERIAL6

 

References

1. Collard RM, Boter H, Schoevers RA, Oude Voshaar RC. Prevalence of Frailty in Community-Dwelling Older Persons: A Systematic Review. J Am Geriatr Soc. 2012;60(8):1487–92.
2. van Kan GA, Rolland Y, Houles M, Gillette-Guyonnet S, Soto M, Vellas B. The Assessment of Frailty in Older Adults. Clin Geriatr Med. 2010;26(2):275–86.
3. Clegg A, Young J, Iliffe S, Rikkert MO, Rockwood K. Frailty in elderly people. Lancet. 2013;381(9868):752–62.
4. Morley JE, Malmstrom TK. Frailty, sarcopenia, and hormones. Endocrinol Metab Clin North Am. 2013;42(2):391–405.
5. Choi J, Ahn A, Kim S, Won CW. Global Prevalence of Physical Frailty by Fried’s Criteria in Community-Dwelling Elderly With National Population-Based Surveys. J Am Med Dir Assoc. 2015;16(7):548–50.
6. Gomes CDS, Guerra RO, Wu YY, Barbosa JFS, Gomez F, Sousa ACPA, et al. Social and Economic Predictors of Worse Frailty Status Occurrence Across Selected Countries in North and South America and Europe. Innov Aging. 2018;2(3):igy037.
7. Franse CB, van Grieken A, Qin L, Melis RJF, Rietjens JAC, Raat H. Socioeconomic inequalities in frailty and frailty components among community-dwelling older citizens. PLoS One. 2017;12(11):e0187946.
8. Hanlon P, Nicholl BI, Jani BD, Lee D, McQueenie R, Mair FS. Frailty and pre-frailty in middle-aged and older adults and its association with multimorbidity and mortality: a prospective analysis of 493 737 UK Biobank participants. Lancet Public Health. 2018;3(7):e323–32.
9. Kojima G. Frailty as a predictor of hospitalisation among community-dwelling older people: a systematic review and meta-analysis. J Epidemiol Community Health. 2016;70(7):722–9.
10. Kojima G. Frailty significantly increases the risk of fractures among middle-aged and older people. Evid Based Nurs. 2017;20(4):119–20.
11. Kojima G. Prevalence of Frailty in Nursing Homes: A Systematic Review and Meta-Analysis. J Am Med Dir Assoc. 2015;16(11):940–5.
12. Hajek A, Bock J-O, Saum K-U, Matschinger H, Brenner H, Holleczek B, et al. Frailty and healthcare costs-longitudinal results of a prospective cohort study. Age Ageing. 2018;47(2):233–41.
13. Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, et al. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci. 2001;56(3):M146-56.
14. Ofori-Asenso R, Chin KL, Mazidi M, Zomer E, Ilomaki J, Zullo AR, et al. Global Incidence of Frailty and Prefrailty Among Community-Dwelling Older Adults. JAMA Netw Open. 2019;2(8):e198398.
15. Da Mata FA, Pereira PP, Andrade KR, Figueiredo AC, Silva MT, Pereira MG. Prevalence of Frailty in Latin America and the Caribbean: A Systematic Review and Meta-Analysis. PLoS One. 2016;11(8):e0160019.
16. O’Caoimh R, Galluzzo L, Rodríguez-Laso Á, Van der Heyden J, Ranhoff AH, Lamprini-Koula M, et al. Prevalence of frailty at population level in European ADVANTAGE Joint Action Member States: a systematic review and meta-analysis. Ann Ist Super Sanita. 54(3):226–38.
17. Manfredi G, Midão L, Paúl C, Cena C, Duarte M, Costa E. Prevalence of frailty status among the European elderly population: Findings from the Survey of Health, Aging and Retirement in Europe. Geriatr Gerontol Int. 2019;9(8):723–29.
18. Kojima G, Iliffe S, Taniguchi Y, Shimada H, Rakugi H, Walters K. Prevalence of frailty in Japan: A systematic review and meta-analysis. J Epidemiol. 2017;27(8):347–53.
19. de Carvalho Mello A, Montenegro Engstrom E, Correia Alves L. Health-related and socio-demographic factors associated with frailty in the elderly: a systematic literature review. Cad Saude Publica. 2014;30(6):1143–68.
20. Feng Z, Lugtenberg M, Franse C, Fang X, Hu S, Jin C, et al. Risk factors and protective factors associated with incident or increase of frailty among community-dwelling older adults: A systematic review of longitudinal studies. PLoS One. 2017;12(6):e0178383.
21. Vermeiren S, Vella-Azzopardi R, Beckwée D, Habbig A-K, Scafoglieri A, Jansen B, et al. Frailty and the Prediction of Negative Health Outcomes: A Meta-Analysis. J Am Med Dir Assoc. 2016;17(12):1163.e1-1163.e17.
22. Morley JE, Vellas B, Abellan van Kan G, Anker SD, Bauer JM, Bernabei R, et al. Frailty Consensus: A Call to Action. J Am Med Dir Assoc. 2013;14(6):392–7.
23. Turner G, Clegg A, British Geriatrics Society, Age UK, Royal College of General Practioners. Best practice guidelines for the management of frailty: a British Geriatrics Society, Age UK and Royal College of General Practitioners report. Age Ageing. 2014;43(6):744–7.
24. Dent E, Lien C, Lim WS, Wong WC, Wong CH, Ng TP, et al. The Asia-Pacific Clinical Practice Guidelines for the Management of Frailty. J Am Med Dir Assoc. 2017;18(7):564–75.
25. WHO. Global Health and Aging. 2015 (accessed on March 5, 2020), available at https://www.who.int/ageing/publications/global_health.pdf.
26. Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gøtzsche PC, Ioannidis JPA, et al. The PRISMA Statement for Reporting Systematic Reviews and Meta-Analyses of Studies That Evaluate Health Care Interventions: Explanation and Elaboration. PLoS Med. 2009;6(7):e1000100.
27. Santiago LM, Luz LL, Mattos IE, Gobbens RJJ, van Assen MALM. Psychometric properties of the Brazilian version of the Tilburg frailty indicator (TFI). Arch Gerontol Geriatr. 2013;57(1):39–45.
28. Morley JE, Malmstrom TK, Miller DK. A simple frailty questionnaire (FRAIL) predicts outcomes in middle aged African Americans. J Nutr Health Aging. 2012;16(7):601–8.
29. Sewo Sampaio PY, Sampaio RAC, Yamada M, Arai H. Systematic review of the Kihon Checklist: Is it a reliable assessment of frailty? Geriatr Gerontol Int. 2016;16(8):893–902.
30. Ensrud KE, Ewing SK, Taylor BC, Fink HA, Cawthon PM, Stone KL, et al. Comparison of 2 Frailty Indexes for Prediction of Falls, Disability, Fractures, and Death in Older Women. Arch Intern Med. 2008;168(4):382.
31. Fabrício-Wehbe SCC, Schiaveto FV, Vendrusculo TRP, Haas VJ, Dantas RAS, Rodrigues RAP. Cross-cultural adaptation and validity of the &quot;Edmonton Frail Scale – EFS&quot; in a Brazilian elderly sample. Rev Lat Am Enfermagem. 2009;17(6):1043–9.
32. Rockwood K, Song X, MacKnight C, Bergman H, Hogan DB, McDowell I, et al. A global clinical measure of fitness and frailty in elderly people. CMAJ. 2005;173(5):489–95.
33. Modesti PA, Reboldi G, Cappuccio FP, Agyemang C, Remuzzi G, Rapi S, et al. Panethnic Differences in Blood Pressure in Europe: A Systematic Review and Meta-Analysis. PLoS One. 2016;11(1):e0147601.
34. Wells GA, Shea B, O’Connell DO, Peterson J, Welch V, Losos M, et al. Newcastle-Ottawa Scale (NOS). 2019 (accessed on March 5, 2020), available at http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp.
35. Bôas NCRV, Salomé GM, Ferreira LM. Frailty syndrome and functional disability among older adults with and without diabetes and foot ulcers. J Wound Care. 2018;27(7):409–16.
36. Fernandes Bolina A, Rodrigues RAP, Tavares DM dos S, Haas VJ. Factors associated with the social, individual and programmatic vulnerability of older adults living at home. Rev da Esc Enferm da USP. 2019;53:e03429.
37. Brigola AG, Luchesi BM, Alexandre TDS, Inouye K, Mioshi E, Pavarini SCI. High burden and frailty: association with poor cognitive performance in older caregivers living in rural areas. Trends Psychiatry Psychother. 2017;39(4):257–63.
38. Pavarini SCI, Neri AL, Brigola AG, Ottaviani AC, Souza ÉN, Rossetti ES, et al. Elderly caregivers living in urban, rural and high social vulnerability contexts. Rev da Esc Enferm. 2017;51:e03254.
39. Carneiro JA, Ramos GCF, Barbosa ATF, Medeiros SM, de Almeda Lima C, da Costa FM, et al. Prevalência e fatores associados à incontinência urinária em idosos não institucionalizados. Cad Saúde Coletiva. 2017;25(3):268–77.
40. Carneiro JA, Ramos GCF, Barbosa ATF, de Mendonça JMG, da Costa FM, Caldeira AP, et al. Prevalência e fatores associados à fragilidade em idosos não institucionalizados. Rev Bras Enferm. 2016;69(3):435–42.
41. de Carvalho Mello A, Carvalho MS, Correia Alves L, Gomes VP, Engstrom EM. Consumo alimentar e antropometria relacionados à síndrome de fragilidade em idosos residentes em comunidade de baixa renda de um grande centro urbano. Cad Saude Publica. 2017;33(8):e00188815.
42. Cezar NOC, Izbicki R, Cardoso D, Almeida JG, Valiengo L, Camargo MVZ, et al. Frailty in older adults with amnestic mild cognitive impairment as a result of Alzheimer’s disease: A comparison of two models of frailty characterization. Geriatr Gerontol Int. 2017;17(11):2096–102.
43. da Silva Coqueiro R, de Queiroz BM, Oliveira DS, das Merces MC, Oliveira Carneiro JA, Pereira R, et al. Cross-sectional relationships between sedentary behavior and frailty in older adults. J Sports Med Phys Fitness. 2017;57(6):825–30.
44. Cordeiro LM, de Lima Paulino J, Bessa MEP, Borges CL, Leite SFP, Cordeiro LM, et al. Qualidade de vida do idoso fragilizado e institucionalizado. Acta Paul Enferm. 2015;28(4):361–6.
45. Alencar MA, Dias JMD, Figueiredo LC, Dias RC. Frailty and cognitive impairment among community-dwelling elderly. Arq Neuropsiquiatr. 2013;71(6):362–7.
46. Corona LP, Pereira de Brito TR, Nunes DP, da Silva Alexandre T, Ferreira Santos JL, de Oliveira Duarte YA, et al. Nutritional status and risk for disability in instrumental activities of daily living in older Brazilians. Public Health Nutr. 2014;17(2):390–5.
47. da Graça Oliveira Crossetti M, Antunes M, Waldman BF, Unicovsky MAR, de Rosso LH, Lana LD, et al. Factors that contribute to a NANDA nursing diagnosis of risk for frail elderly syndrome. Rev Gaúcha Enferm. 2018;39:e2017-0233.
48. Cruz DTD, Vieira MT, Bastos RR, Leite ICG. Factors associated with frailty in a community-dwelling population of older adults. Rev Saude Publica. 2017;51:106.
49. da Silva SLA, Vieira RA, Arantes P, Dias RC. Avaliação de fragilidade, funcionalidade e medo de cair em idosos atendidos em um serviço ambulatorial de geriatria e gerontologia. Fisioter Pesqui. 2009;16(2):120–5.
50. da Silva V, Tribess S, Meneguci J, Sasaki J, Santos D, Carneiro J, et al. Time Spent in Sedentary Behaviour as Discriminant Criterion for Frailty in Older Adults. Int J Environ Res Public Health. 2018;15(7):1336.
51. de Andrade FB, Lebrão ML, Santos JLF, de Oliveira Duarte YA. Relationship Between Oral Health and Frailty in Community-Dwelling Elderly Individuals in Brazil. J Am Geriatr Soc. 2013;61(5):809–14.
52. de Amorim JSC, da Silva SLA, Ude Viana J, Trelha CS. Factors associated with the prevalence of sarcopenia and frailty syndrome in elderly university workers. Arch Gerontol Geriatr. 2019;82:172–8.
53. Mendonça De Melo D, Falsarella GR, Neri AL. Autoavaliação de saúde, envolvimento social e fragilidade em idosos ambulatoriais. Self-rated health, social involvement and frailty in elderly outpatients. Rev. Bras. Geriatr. Gerontol. 2014;17(3):471–84.
54. de Sousa JAV, Lenardt MH, Grden CRB, Kusomota L, Dellaroza MSG, Betiolli SE, et al. Physical frailty prediction model for the oldest old. Rev Lat Am Enfermagem. 2018;26:e3023.
55. dos Santos AA, Ceolim MF, Pavarini SCI, Neri AL, Rampazo MK. Associação entre transtornos do sono e níveis de fragilidade entre idosos. Acta Paul Enferm. 2014;27(2):120–5.
56. Aprahamian I, Lin SM, Suemoto CK, Apolinario D, Oiring de Castro Cezar N, Elmadjian SM, et al. Feasibility and Factor Structure of the FRAIL Scale in Older Adults. J Am Med Dir Assoc. 2017;18(4):367.e11-367.e18.
57. Duarte MCS, das Graças Melo Fernandes M, Rodrigues RAP, da Nóbrega MML. Prevalência e fatores sociodemográficos associados à fragilidade em mulheres idosas. Rev Bras Enferm. 2013;66(6):901–6.
58. Falsarella GR, Gasparotto LPR, Barcelos CC, Coimbra IB, Moretto MC, Pascoa MA, et al. Body composition as a frailty marker for the elderly community. Clin Interv Aging. 2015;10:1661.
59. Farías-Antúnez S, Fassa AG, Farías-Antúnez S, Fassa AG. Prevalência e fatores associados à fragilidade em população idosa do Sul do Brasil, 2014*. Epidemiol e Serviços Saúde. 2019;28(1):e2017405.
60. Fluetti MT, Roberto J, Fhon S, De Oliveira PA, Martins L, Chiquito O, et al. Síndrome da fragilidade em idosos institucionalizados The frailty syndrome in institutionalized elderly persons. Rev. Bras. Geriatr. Gerontol. 2018:21(1):60–9,
61. Filippin LI, Miraglia F, Carvalho Leite JC, Chakr R, Cardoso Oliveira N, Berwanger DD. Identifying frailty syndrome with TUG test in home-dwelling elderly. Identificação da síndrome da fragilidade com o teste TUG em idosos residentes na comunidade. Geriatr Gerontol Aging. 2017;11(2):80.
62. Freitas CV, do Socorro Nascimento Falcão Sarges E, Santana Moreira KEC, Carneiro SR. Evaluation of frailty, functional capacity and quality of life of the elderly in geriatric outpatient clinic of a university hospital. Rev Bras Geriatr Gerontol. 2016;19(1):119–28.
63. Frisoli A, Ingham SJM, Paes ÂT, Tinoco E, Greco A, Zanata N, et al. Frailty predictors and outcomes among older patients with cardiovascular disease: Data from Fragicor. Arch Gerontol Geriatr. 2015;61(1):1–7.
64. da Costa Lima Fernandes H, Gaspar JC, Yamashita CH, Amendola F, Alvarenga MRM, de Campos Oliveira MA. Avaliação da fragilidade de idosos atendidos em uma unidade da Estratégia Saúde da Família. Texto Context – Enferm. 2013;22(2):423–31.
65. Fhon JRS, Diniz MA, Leonardo KC, Kusumota L, Rodrigues RAP, Haas VJ. Frailty syndrome related to disability in the elderly. Acta Paul Enferm. 2012;25(4):589–94.
66. Gesualdo GD, Zazzetta MS, Say KG, de Souza Orlandi F. Fatores associados à fragilidade de idosos com doença renal crônica em hemodiálise. Cien Saude Colet. 2016;21(11):3493–8.
67. Aprahamian I, Cezar NOC, Izbicki R, Lin SM, Paulo DLV, Fattori A, et al. Screening for Frailty With the FRAIL Scale: A Comparison With the Phenotype Criteria. J Am Med Dir Assoc. 2017;18(7):592–6.
68. Gross CB, Kolankiewicz ACB, Schmidt CR, Berlezi EM, Gross CB, Kolankiewicz ACB, et al. Níveis de fragilidade de idosos e sua associação com as características sociodemográficas. Acta Paul Enferm. 2018;31(2):209–16.
69. Grden CRB, Lenardt MH, de Sousa JAV, Kusomota L, Dellaroza MSG, Betiolli SE, et al. Associations between frailty syndrome and sociodemographic characteristics in long-lived individuals of a community. Rev Lat Am Enfermagem. 2017;25:e2886.
70. Holanda CM, Guerra RO, Nóbrega PV, Costa HF, Piuvezam MR, Maciel ÁC. Salivary cortisol and frailty syndrome in elderly residents of long-stay institutions: A cross-sectional study. Arch Gerontol Geriatr. 2012;54(2):e146–51.
71. de Jesus ITM, dos Santos Orlandi AA, Grazziano E da S, Zazzetta MS. Fragilidade de idosos em vulnerabilidade social. Acta Paul Enferm. 2017;30(6):614–20.
72. de Jesus ITM, Orlando FS, Zazzetta MS. Frailty and cognitive performance of elderly in the context of social vulnerability. Dement Neuropsychol. 2018;12(2):173–80.
73. de Jesus ITM, Diniz MAA, Lanzotti RB, Orlandi FS, Pavarin SCI, Zazzetta MS. Fragilidade e qualidade de vida de idosos em contexto de vulnerabilidade social. Texto Context – Enferm. 2018;27(4):e4300016.
74. Lealdini V, Trufelli DC, da Silva FBF, Normando SRC, Camargo EW, Matos LL, et al. Applicability of modified Glasgow Prognostic Score in the assessment of elderly patients with cancer: A pilot study. J Geriatr Oncol. 2015;6(6):479–83.
75. Lenardt MH, Carneiro NHK, Binotto MA, Setoguchi LS, Cechinel C, Lenardt MH, et al. The relationship between physical frailty and sociodemographic and clinical characteristics of elderly. Esc Anna Nery – Rev Enferm. 2015;19(4):585–92.
76. Lenardt MH, Kozlowski Cordeiro Garcia AC, Binotto MA, Hammerschmidt Kolb Carneiro N, Lourenço TM, Cechinel C. Non-frail elderly people and their license to drive motor vehicles. Ancianos no frágiles y la habilitación para conducir vehículos automotores. Rev Bras Enferm. 2018;71(2):373–82.
77. Lin SM, Aliberti MJR, Fortes-Filho SQ, Melo JA, Aprahamian I, Suemoto CK, et al. Comparison of 3 Frailty Instruments in a Geriatric Acute Care Setting in a Low-Middle Income Country. J Am Med Dir Assoc. 2018;19(4):310-314.e3.
78. Aprahamian I, Suemoto CK, Aliberti MJR, de Queiroz Fortes Filho S, de Araújo Melo J, Lin SM, et al. Frailty and cognitive status evaluation can better predict mortality in older adults? Arch Gerontol Geriatr. 2018;77:51–6.
79. de Llano PMP, Lange C, Nunes DP, Pastore CA, Pinto AH, Casagranda LP. Fragilidade em idosos da zona rural: proposta de algoritmo de cuidados. Acta Paul Enferm. 2017;30(5):520–30.
80. Lustosa LP, Marra TA, dos Santos Pessanha FPA, de Carvalho Freitas J, de Cássia Guedes R. Fragilidade e funcionalidade entre idosos frequentadores de grupos de convivência em Belo Horizonte, MG. Rev Bras Geriatr Gerontol. 2013;16(2):347–54.
81. de Albuquerque Melo EM, de Oliveira Marques AP, Leal MCC, de Albuquerque Melo HM. Síndrome da fragilidade e fatores associados em idosos residentes em instituições de longa permanência. Saúde em Debate. 2018;42(117):468–80.
82. Medeiros SM, Silva LSR, Carneiro JA, Ramos GCF, Barbosa ATF, Caldeira AP, et al. Fatores associados à autopercepção negativa da saúde entre idosos não institucionalizados de Montes Claros, Brasil. Cien Saude Colet. 2016;21(11):3377–86.
83. de Morais D, Terassi M, Inouye K, Luchesi BM, Pavarini SCI. Dor crônica de idosos cuidadores em diferentes níveis de fragilidade. Rev Gaúcha Enferm. 2016;37(4):e60700.
84. de Souza Moreira B, Dos Anjos DM, Pereira DS, Sampaio RF, Pereira LS, Dias RC, et al. The geriatric depression scale and the timed up and go test predict fear of falling in community-dwelling elderly women with type 2 diabetes mellitus: a cross-sectional study. BMC Geriatr. 2016;16(1):56.
85. Nascimento CMC, Zazzetta MS, Gomes GAO, Orlandi FS, Gramani-Say K, Vasilceac FA, et al. Higher levels of tumor necrosis factor β are associated with frailty in socially vulnerable community-dwelling older adults. BMC Geriatr. 2018;18(1):268.
86. Neri AL, Yassuda MS, de Araújo LF, do Carmo Eulálio M, Cabral BE, de Siqueira MEC, et al. Metodologia e perfil sociodemográfico, cognitivo e de fragilidade de idosos comunitários de sete cidades brasileiras: Estudo FIBRA. Cad Saude Publica. 2013;29(4):778–92.
87. de Queiroz Neves A, da Silva AMC, Cabral JF, Mattos IE, Santiago LM. Prevalence of and factors associated with frailty in elderly users of the Family Health Strategy. Rev Bras Geriatr e Gerontol. 2018;21(6):680–90.
88. Nóbrega PV, Maciel AC, de Almeida Holanda CM, Oliveira Guerra R, Araújo JF. Sleep and frailty syndrome in elderly residents of long-stay institutions: A cross-sectional study. Geriatr Gerontol Int. 2014;14(3):605–12.
89. Augusti ACV, Falsarella GR, Coimbra AMV. Análise da síndrome da fragilidade em idosos na atenção primária – Estudo transversal. Rev Bras Med Família Comunidade. 2017;12(39):1–9.
90. de Sousa Orlandi F, Gesualdo GD. Assessment of the frailty level of elderly people with chronic kidney disease undergoing hemodialysis. Acta Paul Enferm. 2014;27(1):29–34.
91. Oliveira DR, Bettinelli LA, Pasqualotti A, Corso D, Brock F, Erdmann AL. Prevalence of frailty syndrome in old people in a hospital institution. Rev Lat Am Enfermagem. 2013;21(4):891–8.
92. Parentoni AN, Lustosa LP, dos Santos KD, Sá LF, Ferreira FO, Mendonça VA. Comparação da força muscular respiratória entre os subgrupos de fragilidade em idosas da comunidade. Fisioter Pesqui. 2013;20(4):361–6.
93. Pegorari MS, Ruas G, Patrizzi LJ. Relationship between frailty and respiratory function in the community-dwelling elderly. Brazilian J Phys Ther. 2013;17(1):9–16.
94. Pegorari MS, Tavares DM. Factors associated with the frailty syndrome in elderly individuals living in the urban area. Rev Lat Am Enfermagem. 2014;22(5):874–82.
95. Ramos GCF, Carneiro JA, Barbosa ATF, Mendonça JMG, Caldeira AP. Prevalência de sintomas depressivos e fatores associados em idosos no norte de Minas Gerais: um estudo de base populacional. J Bras Psiquiatr. 2015;64(2):122–31.
96. Ricci N, Silva Pessoa G, Ferrioli E, Correa Dias R, Rodrigues Perracini M. Frailty and cardiovascular risk in community-dwelling elderly: a population-based study. Clin Interv Aging. 2014;1677.
97. Rossetti ES, Terassi M, Ottaviani AC, dos Santos-Orlandi AA, Pavarini SCI, Zazzetta MS. Fragilidade, sintomas depressivos e sobrecarga de idosos cuidadores em contexto de alta vulnerabilidade social. Texto Context – Enferm. 2018;27(3):e3590016.
98. Santiago LM, Mattos IE, Santiago LM, Mattos IE. Prevalência e fatores associados à fragilidade em idosos institucionalizados das regiões Sudeste e Centro-Oeste do Brasil. Rev Bras Geriatr e Gerontol. 2014;17(2):327–37.
99. Santiago LM, Luz LL, Mattos IE, Gobbens RJJ, van Assen MALM. Psychometric properties of the Brazilian version of the Tilburg frailty indicator (TFI). Arch Gerontol Geriatr. 2013;57(1):39–45.
100. Alvarado BE, Zunzunegui M-V, Béland F, Bamvita J-M. Life course social and health conditions linked to frailty in Latin American older men and women. J Gerontol A Biol Sci Med Sci. 2008;63(12):1399–406.
101. Santiago LM, Gobbens RJJ, van Assen MALM, Carmo CN, Ferreira DB, Mattos IE. Predictive validity of the Brazilian version of the Tilburg Frailty Indicator for adverse health outcomes in older adults. Arch Gerontol Geriatr. 2018;76:114–9.
102. Santiago LM, Gobbens RJJ, Mattos IE, Ferreira DB. A comparison between physical and biopsychosocial measures of frailty: Prevalence and associated factors in Brazilian older adults. Arch Gerontol Geriatr. 2019;81:111–8.
103. Sampaio LS, Carneiro JAO, da Silva Coqueiro R, Fernandes MH. Indicadores antropométricos como preditores na determinação da fragilidade em idosos. Cien Saude Colet. 2017;22(12):4115–24.
104. dos Santos Tavares DM, Nader ID, de Paiva MM, Dias FA, Pegorari MS. Association of socioeconomic and clinical variables with the state of frailty among older inpatients. Rev Lat Am Enfermagem. 2015;23(6):1121–9.
105. de Souza Santos PL, Fernandes MH, Santos PHS, Santana TDB, Cassoti CA, Coqueiro RDS, et al. Indicadores de desempenho motor como preditores de fragilidade em idosos cadastrados em uma Unidade de Saúde da Família. Motricidade. 2016;12(2):88.
106. Sewo Sampaio PY, Sampaio RAC, Yamada M, Ogita M, Arai H. Validation and translation of the Kihon Checklist (frailty index) into Brazilian Portuguese. Geriatr Gerontol Int. 2014;14(3):561–9.
107. Sewo Sampaio PY, Sampaio RAC, Coelho Júnior HJ, Teixeira LFM, Tessutti VD, Uchida MC, et al. Differences in lifestyle, physical performance and quality of life between frail and robust Brazilian community-dwelling elderly women. Geriatr Gerontol Int. 2016;16(7).
108. Sampaio PYS, Sampaio RAC, Yamada M, Ogita M, Arai H. Comparison of frailty among Japanese, Brazilian Japanese descendants and Brazilian community-dwelling older women. Geriatr Gerontol Int. 2015;15(6):762–9.
109. dos Santos-Orlandi AA, de Brito TRP, Ottaviani AC, Rossetti ES, Zazzetta MS, Pavarini SCI, et al. Elderly who take care of elderly: a study on the Frailty Syndrome. Rev Bras Enferm. 2017;70(4):822–9.
110. de Albuquerque Sousa ACP, Dias RC, Maciel ÁCC, Guerra RO. Frailty syndrome and associated factors in community-dwelling elderly in Northeast Brazil. Arch Gerontol Geriatr. 2012;54(2):e95–101.
111. Belisário MS, Dias FA, Pegorari MS, de Paiva MM, dos Santos Ferreira PC, Corradini FA, et al. Cross-sectional study on the association between frailty and violence against community-dwelling elderly people in Brazil. Sao Paulo Med J. 2017;136(1):10–9.
112. Storti LB, Fabrício-Whebe SCC, Kusumota L, Rodrigues RAP, Marques S. Fragilidade de idosos internados na clínica médica da unidade de emergência de um hospital geral terciário. Texto Context – Enferm. 2013;22(2):452–9.
113. Silveira T, Pegorari MS, de Castro SS, Ruas G, Novais-Shimano SG, Patrizzi LJ. Association of falls, fear of falling, handgrip strength and gait speed with frailty levels in the community elderly. Med (Ribeirao Preto Online). 2015;48(6):549.
114. dos Santos Tavares DM, Colamego CG, Pegorari MS, dos Santos Ferreira PC, Dias FA, Bolina AF, et al. Cardiovascular risk factors associated with frailty syndrome among hospitalized elderly people: a cross-sectional study. Sao Paulo Med J. 2016;134(5):393–9.
115. dos Santos Tavares DM, Freitas Corrêa TA, Aparecida Dias F, dos Santos Ferreira PC, Sousa Pegorari M. Frailty syndrome and socioeconomic and health characteristics among older adults. Colomb Med. 2017;v48(i3):126–31.
116. dos Santos Tavares DM, Faria PM, Pegorari MS, dos Santos Ferreira PC, Nascimento JS, Marchiori GF. Frailty Syndrome in Association with Depressive Symptoms and Functional Disability among Hospitalized Elderly. Issues Ment Health Nurs. 2018;39(5):433–8.
117. Teixeira-Gasparini E, Partezani-Rodrigues R, Fabricio-Wehbe S, Silva-Fhon J, Aleixo-Diniz M, Kusumota L. Uso de tecnologías de asistencia y fragilidad en adultos mayores de 80 años y más. Enfermería Univ. 2016;13(3):151–8.
118. Zukeran MS, Ritti-Dias RM, Franco FGM, Cendoroglo MS, de Matos LDN, Lima Ribeiro SM. Nutritional Risk by Mini Nutritional Assessment (MNA), but not Anthropometric Measurements, has a Good Discriminatory Power for Identifying Frailty in Elderly People: Data from Brazilian Secondary Care Clinic. J Nutr Health Aging. 2019;23(2):217–20.
119. Viana JU, Silva SLA, Torres JL, Dias JMD, Pereira LSM, Dias RC, et al. Influence of sarcopenia and functionality indicators on the frailty profile of community-dwelling elderly subjects: a cross-sectional study. Brazilian J Phys Ther. 2013;17(4):373–81.
120. Zazzetta MS, Gomes GAO, Orlandi FS, Gratão ACM, Vasilceac FA, Gramani-Say K, et al. Identifying Frailty Levels and Associated Factors in a Population Living in the Context of Poverty and Social Vulnerability. J Frailty Aging. 2017;6(1):29–32.
121. Vieira GÂCM, Costa EP, Medeiros ACT, Costa MML, Rocha FAT. Avaliação da fragilidade em idosos participantes de um centro de convivência Evaluation of fragility in elderly participants of a community center. Rev Pesqui Cuid Fundam Online. 2017;9(1):114.
122. Binotto MA, Lenardt MH, Carneiro NHK, Lourenço TM, Cechinel C, del Carmen Rodríguez-Martínez M, et al. Fatores associados à velocidade da marcha em idosos submetidos aos exames para habilitação veicular. Rev Lat Am Enfermagem. 2019;27: e3138.
123. da Costa Alves EV, Flesch LD, Cachioni M, Neri AL, Batistoni SST. The double vulnerability of elderly caregivers: multimorbidity and perceived burden and their associations with frailty. Rev Bras Geriatr Gerontol. 2018;21(3):301–11.
124. Borges CL, da Silva MJ, Clares JWB, Bessa MEP, de Freitas MC. Avaliação da fragilidade de idosos institucionalizados. Acta Paul Enferm. 2013;26(4):318–22.
125. da Silva VD, Tribess S, Meneguci J, Sasaki JE, Garcia-Meneguci CA, Carneiro JAO, et al. Association between frailty and the combination of physical activity level and sedentary behavior in older adults. BMC Public Health. 2019;19(1):709.
126. Borges CL, da Silva MJ, Clares JWB, Nogueira JDM, de Freitas MC. Características sociodemográficas e clínicas de idosos institucionalizados: contribuições para o cuidado de enfermagem. Rev Enferm UERJ. 2015;23(3):381–7.
127. Almeida J, Rodrigues R, Durães S, Clara M, Guedes A, Santos FL. Fragilidade em idosos: prevalência e fatores associados. Rev Bras Enferm. 2017;70:780–5.
128. Almeida Carneiro J, Carmen Fagundes Ramos G, Teresa Fernandes Barbosa A, Débora Souza Vieira E, Santos Rocha Silva J, Prates Caldeira A. Quedas em idosos não institucionalizados no norte de Minas Gerais: prevalência e fatores associados. Rev Bras Geriatr. Gerontol. 2016;19(4):613–25.
129. Carvalho TC, do Valle AP, Jacinto AF, de Sá Mayoral VF, Boas PJFV. Impact of hospitalization on the functional capacity of the elderly: A cohort study. Rev Bras Geriatr e Gerontol. 2018;21(2):134–42.
130. Santos PHS, Fernandes MH, Casotti CA, da Silva Coqueiro R, Carneiro JAO. Perfıl de fragilidade e fatores associados em idosos cadastrados em uma Unidade de Saúde da Família. Cien Saude Colet. 2015;20(6):1917–24.
131. Albala C, Lera L, Sanchez H, Angel B, Márquez C, Arroyo P, et al. Frequency of frailty and its association with cognitive status and survival in older Chileans. Clin Interv Aging. 2017;12:995–1001.
132. Araya AX, Herrera MS, Iriarte E, Rioja R. Evaluación de la funcionalidad y fragilidad de las personas mayores asistentes a centros de día. Rev Med Chil. 2018;146(8):864–71.
133. Bustamante-Ara N, Villarroel L, Paredes F, Huidobro A, Ferreccio C. Frailty and health risks in an agricultural population, Chile 2014–2017. Arch Gerontol Geriatr. 2019;82:114–9.
134. Díaz-Toro F, Nazzal Nazal C, Verdejo H, Rossel Ví, Castro P, Larrea R, et al. Factores asociados a fragilidad en pacientes hospitalizados con insuficiencia cardiaca descompensada. Rev Med Chil. 2017;145(2):164–71.
135. Palomo I, Giacaman RA, León S, Lobos G, Bustamante M, Wehinger S, et al. Analysis of the characteristics and components for the frailty syndrome in older adults from central Chile. The PIEI-ES study. Arch Gerontol Geriatr. 2019;80:70–5.
136. Tapia P C, Valdivia-Rojas Y, Varela V H, Carmona G A, Iturra M V, Jorquera C M. Indicadores de fragilidad en adultos mayores del sistema público de salud de la ciudad de Antofagasta. Rev Med Chil. 2015;143(4):459–66.
137. At J, Bryce R, Prina M, Acosta D, Ferri CP, Guerra M, et al. Frailty and the prediction of dependence and mortality in low- and middle-income countries: a 10/66 population-based cohort study. BMC Med. 2015;13(1):138.
138. Varela Pinedo L, Ortiz Saavedra PJ, Chávez Jimeno H. Velocidad de la marcha como indicador de fragilidad en adultos mayores de la comunidad en Lima, Perú. Rev Esp Geriatr Gerontol. 2010;45(1):22–5.
139. Llibre Rodriguez JJ, Prina AM, Acosta D, Guerra M, Huang Y, Jacob KS, et al. The Prevalence and Correlates of Frailty in Urban and Rural Populations in Latin America, China, and India: A 10/66 Population-Based Survey. J Am Med Dir Assoc. 2018;19(4):287-295.e4.
140. Runzer-Colmenares FM, Urrunaga-Pastor D, Aguirre LG, Reategui-Rivera CM, Parodi JF, Taype-Rondan A. Frailty and vulnerability as predictors of radiotoxicity in older adults: A longitudinal study in Peru. Med Clínica. 2017;149(8):325–30.
141. Runzer-Colmenares FM, Samper-Ternent R, Al Snih S, Ottenbacher KJ, Parodi JF, Wong R. Prevalence and factors associated with frailty among Peruvian older adults. Arch Gerontol Geriatr. 2014;58(1):69–73.
142. Curcio C-L, Henao G-M, Gomez F. Frailty among rural elderly adults. BMC Geriatr. 2014;14(1):2.
143. Ocampo-Chaparro JM, de J. Zapata-Ossa H, Cubides-Munévar ÁM, Curcio CL, Villegas JDD, et al. Prevalence of poor self-rated health and associated risk factors among older adults in Cali, Colombia. Prevalencia de factores de riesgo de la autopercepción de salud y asociados pobres entre los adultos mayores en Cali, Colombia. Colomb. Med. 2013;44(4):224–31.
144. Ramírez Ramírez JU, Cadena Sanabria MO, Ochoa ME. Aplicación de la Escala de fragilidad de Edmonton en población colombiana. Comparación con los criterios de Fried. Rev Esp Geriatr Gerontol. 2017;52(6):322–5.
145. Samper-Ternent R, Reyes-Ortiz C, Ottenbacher KJ, Cano CA. Frailty and sarcopenia in Bogotá: results from the SABE Bogotá Study. Aging Clin Exp Res. 2017;29(2):265–72.
146. Del Brutto OH, Mera RM, Brown DL, Nieves JL, Milla-Martinez MF, Fanning KD, et al. The association of frailty with abnormal ankle-brachial index determinations is related to age: Results from the Atahualpa Project. Int J Cardiol. 2016;202:366–7.
147. Del Brutto OH, Mera RM, Cagino K, Fanning KD, Milla-Martinez MF, Nieves JL, et al. Neuroimaging signatures of frailty: A population-based study in community-dwelling older adults (the Atahualpa Project). Geriatr Gerontol Int. 2017;17(2):270–6.
148. Costa D, Aladio M, Girado CA, Pérez de la Hoz R, Sara Berensztein C. Frailty is independently associated with 1-year mortality after hospitalization for acute heart failure. IJC Hear Vasc. 2018;21:103–6.
149. Siriwardhana DD, Hardoon S, Rait G, Weerasinghe MC, Walters KR. Prevalence of frailty and prefrailty among community-dwelling older adults in low-income and middle-income countries: a systematic review and meta-analysis. BMJ Open. 2018;8(3):e018195.
150. Banks LM, Kuper H, Polack S. Poverty and disability in low- and middle-income countries: A systematic review. PLoS One. 2017;12(12):e0189996.
151. Singer L, Green M, Rowe F, Ben-Shlomo Y, Morrissey K. Social determinants of multimorbidity and multiple functional limitations among the ageing population of England, 2002-2015. SSM Popul Health. 2019;8:100413.
152. Buttery AK, Busch MA, Gaertner B, Scheidt-Nave C, Fuchs J. Prevalence and correlates of frailty among older adults: findings from the German health interview and examination survey. BMC Geriatr. 2015;15:22.
153. Chao C-T, Wang J, Chien K-L, group Co of GeN in N (COGENT) study. Both pre-frailty and frailty increase healthcare utilization and adverse health outcomes in patients with type 2 diabetes mellitus. Cardiovasc Diabetol. 2018;17(1):130.
154. Tom SE, Adachi JD, Anderson FA, Boonen S, Chapurlat RD, Compston JE, et al. Frailty and fracture, disability, and falls: a multiple country study from the global longitudinal study of osteoporosis in women. J Am Geriatr Soc. 2013;61(3):327–34.
155. Yu R, Morley JE, Kwok T, Leung J, Cheung O, Woo J. The Effects of Combinations of Cognitive Impairment and Pre-frailty on Adverse Outcomes from a Prospective Community-Based Cohort Study of Older Chinese People. Front Med. 2018;5:50.
156. Vieira ER, Da Silva RA, Severi MT, Barbosa AC, Amick Iii BC, Zevallos JC, et al. Balance and Gait of Frail, Pre-Frail, and Robust Older Hispanics. Geriatrics. 2018;3(3):42.
157. Muscedere J, Waters B, Varambally A, Bagshaw SM, Boyd JG, Maslove D, et al. The impact of frailty on intensive care unit outcomes: a systematic review and meta-analysis. Intensive Care Med. 2017;43(8):1105–22.
158. Bernabeu-Mora R, García-Guillamón G, Valera-Novella E, Giménez-Giménez LM, Escolar-Reina P, Medina-Mirapeix F. Frailty is a predictive factor of readmission within 90 days ofhospitalization for acute exacerbations of chronic obstructive pulmonarydisease: a longitudinal study. Ther Adv Respir Dis. 2017;11(10):383.
159. Kojima G. Frailty as a Predictor of Nursing Home Placement Among Community-Dwelling Older Adults. J Geriatr Phys Ther. 2018;41(1):42–8.
160. Kojima G. Frailty as a predictor of disabilities among community-dwelling older people: a systematic review and meta-analysis. Disabil Rehabil. 2017;39(19):1897–908.
161. Cesari M, Marzetti E, Thiem U, Pérez-Zepeda MU, Abellan Van Kan G, Landi F, et al. The geriatric management of frailty as paradigm of “The end of the disease era”. Eur J Intern Med. 2016;31:11–4.
162. Lopez P, Pinto RS, Radaelli R, Rech A, Grazioli R, Izquierdo M, et al. Benefits of resistance training in physically frail elderly: a systematic review. Aging Clin Exp Res. 2018;30:889–99.
163. Fiatarone MA, Marks EC, Ryan ND, Meredith CN, Lipsitz LA, Evans WJ. High-Intensity Strength Training in Nonagenarians. JAMA. 1990;263(22):3029.
164. Coelho-Júnior HJ, Rodrigues B, Uchida M, Marzetti E. Low Protein Intake Is Associated with Frailty in Older Adults: A Systematic Review and Meta-Analysis of Observational Studies. Nutrients. 2018;10(9):1334.
165. Coelho-Júnior HJ, Calvani R, Picca A, Gonçalves IO, Landi F, Bernabei R, et al. Protein-related dietary parameters and frailty status in older community-dwellers across different frailty instruments. Nutrients. 2020;12(2):508.
166. Calvani R, Miccheli A, Landi F, Bossola M, Cesari M, Leeuwenburgh C, et al. Current nutritional recommendations and novel dietary strategies to manage sarcopenia. J Frailty Aging. 2013;2(1):38–53.
167. Cadore EL, Sáez de Asteasu ML, Izquierdo M. Multicomponent exercise and the hallmarks of frailty: Considerations on cognitive impairment and acute hospitalization. Exp Gerontol. 2019;122:10–4.
168. Rodriguez-Larrad A, Arrieta H, Rezola C, Kortajarena M, Yanguas JJ, Iturburu M, et al. Effectiveness of a multicomponent exercise program in the attenuation of frailty in long-term nursing home residents: Study protocol for a randomized clinical controlled trial. BMC Geriatr. 2017;17(1):60.
169. Tarazona-Santabalbina FJ, Gómez-Cabrera MC, Pérez-Ros P, Martínez-Arnau FM, Cabo H, Tsaparas K, et al. A Multicomponent Exercise Intervention that Reverses Frailty and Improves Cognition, Emotion, and Social Networking in the Community-Dwelling Frail Elderly: A Randomized Clinical Trial. J Am Med Dir Assoc. 2016;17(5):426–33.
170. Tabue-Teguo M, Dartigues J-F, Simo N, Kuate-Tegueu C, Vellas B, Cesari M. Physical status and frailty index in nursing home residents: Results from the INCUR study. Arch Gerontol Geriatr. 2018;74:72–6.
171. Zhang XM, Dou QL, Zhang WW, Wang CH, Xie XH, Yang YZ, et al. Frailty as a Predictor of All-Cause Mortality Among Older Nursing Home Residents: A Systematic Review and Meta-analysis. J Am Med Dir Assoc. 2019;30:657-663.e4.
172. Lin SM, Aliberti MJR, Fortes-Filho S de Q, Melo JA, Aprahamian I, Suemoto CK, et al. Comparison of 3 Frailty Instruments in a Geriatric Acute Care Setting in a Low-Middle Income Country. J Am Med Dir Assoc. 2018;19(4):310-314.e3.
173. Åhlund K, Ekerstad N, Öberg B, Bäck M. Physical Performance Impairments and Limitations among Hospitalized Frail Older Adults. J Geriatr Phys Ther. 2018;41(4):230–5.

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REPORTED WEIGHT CHANGE IN OLDER ADULTS AND PRESENCE OF FRAILTY

 

R.S. Crow1,2, C.L. Petersen3,4, S.B. Cook5, C.J. Stevens1, A.J. Titus6,4, T.A. Mackenzie1,2,3, J.A. Batsis1,2,3

1. Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, United States; 2. Department of Medicine, Dartmouth Hitchcock Medical Center, Lebanon, New Hampshire, United States; 3. The Dartmouth Institute for Health Policy, Lebanon, New Hampshire, United States; 4 . Quantitative Biomedical Sciences Program, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, United States; 5. Department of Kinesiology, University of New Hampshire, Durham, NH, United States; 6. Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon NH, United States.
Corresponding author: Rebecca Crow DO, Section of General Internal Medicine, Dartmouth-Hitchcock Medical Center, 1 Medical Center Drive, Lebanon, NH 03756, Telephone: (603) 653-9500, Facsimile: (603) 650-0915, E-mail: Rebecca.s.crow@hitchcock.org

J Frailty Aging 2020;9(2)74-81
Published online January 6, 2020, http://dx.doi.org/10.14283/jfa.2019.44

 


Abstract

Objective: A 5% change in weight is a significant predictor for frailty and obesity. We ascertained how self-reported weight change over the lifespan impacts rates of frailty in older adults. Methods: We identified 4,984 subjects ≥60 years with body composition measures from the National Health and Nutrition Examination Survey. An adapted version of Fried’s frailty criteria was used as the primary outcome. Self-reported weight was assessed at time current,1 and 10 years earlier and at age 25. Weight changes between each time point were categorized as ≥ 5%, ≤5% or neutral. Logistic regression assessed the impact of weight change on the outcome of frailty. Results: Among 4,984 participants, 56.5% were female, mean age was 71.1 years, and mean BMI was 28.2kg/m2. A weight loss of ≥ 5% had a higher association with frailty compared to current weight, age 25 (OR 2.94 [1.72,5.02]), 10 years ago (OR 1.68 [1.05,2.69]), and 1 year ago (OR 1.55 [1.02,2.36]). Weight gain in the last year was associated with increased rate of frailty (1.59 [1.09,2.32]). Conclusion: There is an association between frailty and reported weight loss over time while only weight gain in the last year has an association with frailty.

Key words: Obesity, frailty, sarcopenia, pre-frailty.

Abbreviations: BMI: body mass index; NHANES: National Health and Nutrition Examination Survey.


 

Introduction

Frailty is the result of physical, psychological and social factors that contribute to a decline in the body’s physiological reserve and its reduced ability to maintain homeostasis among life’s stressors (1, 2). While a standardized pragmatic definition of frailty is still debated, Fried’s landmark study operationalized frailty as a phenotype defined by a set of variables: unintentional weight loss of ≥10 lbs, self-reported exhaustion, slow gait speed, low energy expenditure and weak grip strength (frail ≥3, pre-frail 1 or 2, robust =0) (3). Frailty is strongly associated with functional losses, disability, increased healthcare utilization and higher cost of healthcare (1, 4-8).
Weight status is commonly assessed in healthcare settings; however, it may be underutilized as a metric to indicate current and future adverse health outcomes (9, 10). Changes in body composition occur with each decade of life and include a peaking of fat mass in the 7th decade followed by subsequent decline in both skeletal muscle and fat (11, 12) making the most commonly used metric, body mass index (BMI), a highly insensitive measure of weight status in older adults(13). Some older adults are at risk for what is known as sarcopenic obesity or a disproportionate loss of lean mass to gain of fat mass which is shown to be associated with higher rates of morbidity and mortality outside of both sarcopenia or obesity alone (14, 15).  Long-term changes of loss or gain in body weight are associated with the highest mortality rates among persons in the general population while mortality is the lowest among those with modest weight changes(16). Weight cycling, or gaining/losing a similar amount of weight repeatedly, is known to be associated with higher disability and mortality rates(17). This harmful cycle emphasizes the importance of obtaining a weight history in clinical practice as even lower percent changes may be significant in those with frailty (18-22).
Gaining an understanding of longitudinal weight measures over a lifespan could be helpful for predicting future risk of disease and functional status. Such measures are easily captured using most outpatient electronic medical records. As both frailty and obesity are associated with similar adverse outcomes (4-8, 23-25), weight change trajectories may help clinicians assess for the development of frailty and other adverse health outcomes in older adulthood. The purpose of this study was to evaluate the relationship between self-reported weight change over a lifespan and frailty in a representative sample of US older adults.

 

Methods

Study Design and Participants

Participants included in the analysis were community dwelling older adults identified from 1999-2004 National Health and Nutrition Survey (NHANES) data. NHANES is a multistage probability survey conducted by the National Center for Health Statistics designed to assess the health and nutritional status of adults and children in the United States. The survey oversamples Non-Hispanic blacks, Mexican Americans, persons greater than 60 years of age. Results are therefore generalizable to the United States population. All manuals, procedures and data files are publicly available at http://www.cdc.gov/nchs/nhanes.html.
NHANES screened 38 077 individuals, interviewed 31 125, and then examined 29 402 in a mobile examination unit, with exams conducted by trained medical personnel. For this secondary analysis of data, we included participants aged 60 years older with body composition measures and frailty variables for a final analytical cohort of N =4 984. The local Institutional Review Board at Dartmouth College exempted this study from review due to the de-identified nature of all NHANES data in the database.

Baseline Characteristics

Self-reported sociodemographic characteristics including age, race, sex, physical activity levels, smoking status and co-morbid conditions were obtained from questionnaires completed by participants or their primary caregivers. Age was stratified into three categories as performed in our previous analyses: 60-69, 70-79, 80+ years (13, 26, 27). Race was reported as non-Hispanic White, non-Hispanic Black, and Hispanic American. Co-morbid conditions were self-reported using the question, “Has a doctor or other health professional ever told you that you have [medical condition]?” Smoking status was classified as current smoker, former smoker or never smoker. Physical activity was categorized as sitting, walking, performing light loads or heavy work using the question, “Please tell me which of these four sentences best describes your usual daily activities?”

Study Variables

Frailty: We defined frailty according to the phenotypic model (3), using participant self-reported and objectively measured data. This phenotypic definition consists of five criteria derived from the Cardiovascular Health Study (3, 28) as follows: unintentional weight loss of 10 pounds or more in a year; self-reported exhaustion; weakness defined by grip strength; slow walking speed; and low physical activity. We adapted the criteria to define each variable(26), respectively, using data available in NHANES: low body mass index (BMI)<18.5kg/m2 [59 (1.3%)]; difficulty walking between rooms [586 (10%)]; difficulty lifting or carrying 10 pounds [1,455 [27.1%)], gait speed <0.8 m/s [1,865 (31.13%)]; and self-reported perception of reduced physical activity compared to others [735 (14.1%)]. Frailty was defined as meeting three or more of the five following criteria and pre-frailty was defined as meeting 1 or 2 criteria. Individuals not meeting any criteria were classified as robust.
Anthropometric Measures: Weight was assessed using a self-reported questionnaire. Participants were asked to report their current weight, weight one year ago, weight 10 years ago, and weight at age 25. Participants were asked to provide their specific weight in kg and if not known to give their best guess. Additional questions included “During the past 12 months, have you tried to lose weight?” and “During the past 12 months have you done anything to keep from gaining weight?”  Objective weight was measured on the right side of the body to the nearest tenth of a centimeter on an electronic digital scale (calibrated in kilograms), and height was measured using a stadiometer.

Statistical Analysis

All data were downloaded in September 2015 into a single dataset. Weight history data was combined in November 2017 following NHANES standard operating procedures, accounting for weighting, strata, primary sampling unit, and cluster. Descriptive statistics are presented as means ± standard errors, and counts (weighted percentages). Comparisons between groups were conducted using t-tests and chi-square tests of independence. We calculated self-reported percent weight change as the quotient of the difference between baseline year and year in question (1 year prior, 10 years prior or at age 25 years old). Meaningful weight loss/gain is categorized as ± a change of 5% or more (29). We created three categories: ≥5% weight loss; ≥5% weight gain; or no change in weight (-5 to +5% weight change). The latter category is represented as the referent in our models. A 5% change in weight loss or gain was used since it has been used as a significant predictor for both frailty and obesity in past studies (30, 31). Slope for each individual change was calculated as the participant’s age at each of the three time points (quotient of Weight Time1.- Weight Time2 and Δ Age) and is represented as the change in weight per year. Multiple models were constructed to evaluate the effect of weight change (primary predictor – gain/loss of 5%) on the presence of frailty (primary outcome). Gait speed was not assessed in NHANES 2003-2004 therefore imputation by mean was used conditional on covariates to account for missing values using R (v 3.3.2) and the package mice for 3,645 participants(www.r-project.org). The package creates plausible data values from a distribution specifically designed for each data point; five imputed data sets are generated using predictive mean matching. The correction variables used were age, sex, education, race, diabetes, arthritis, congestive heart failure, cancer, and lean mass percent. The five data sets were averaged, resulting in a final imputed data set used for analysis. Analyses were run on the full imputed data set as well as a subset excluding the imputed variables to test the quality. The data presented in our results is based on full imputed data alone; data excluding imputed variables is not shown and presented elsewhere (26).
We constructed three incremental logistic regression models adjusting for co-variates: age, gender (Model 1); Model 1 co-variates plus race, education, smoking (Model 2); Model 2 co-variates plus diabetes, arthritis, coronary artery disease, and cancer (Model 3). Data are not shown for Model 1 and Model 2 but results did not change as we adjusted for additional variables. All logistic regression models assessed the impact of weight change (gain, loss, no change) for two different frailty outcomes (Frailty vs. Pre-Frailty/Robust, and Pre-Frailty vs. Robust). Visual representation of change in weight over time was plotted per individual with a LOESS local regression line (span = 0.7). All analyses were conducted using STATA v.14 (College Station, Texas) and R v3.5 (www.r-project.org). P values were considered statistically significant if they were less than the criterion level of 0.05.

 

Results

Table 1 presents the baseline characteristics of participants. Of 4,984 participants, 56.5% were female, mean age was 71.1 years and BMI was 28.2 kg/m2. Prevalence of pre-frailty and frailty was 40.1% and 9.1%, respectively. Robust participants were more likely to be non-Hispanic white and have a higher education level (p=<0.001). Frail patients were more likely to have comorbidities such as arthritis, diabetes and coronary artery disease (p=<0.001) but not more likely to have cancer (p=0.48). Nearly all frail patients met the criterion of weakness (96.9%) and the most common criterion identified for pre-frailty was slowness of gait (59.1%).

Table 1 Baseline Characteristics of Participants

Table 1
Baseline Characteristics of Participants

Values represented are means± standard errors or counts (weighted percentages).

 

Table 2 outlines participants’ self-reported weights and weight changes over time. Weight increased in all groups over time, with individuals with frailty losing weight, as compared to the pre-frail or robust groups within the past year or 10 years. Weight gain of ≥5% in the past year was associated with higher rates of frailty among those classified in other categories. When comparing each participant’s weight at age 25 years old to his/her current weight, fewer individuals with frailty gained clinically significant weight and most had a greater than 5% weight loss. Changes in weight and rates of combination of frailty, pre-frailty and robust percentages can be found in Supplementary Table 1.

Table 2 Weight Change and Rates of Classification Along Frailty Spectrum

Table 2
Weight Change and Rates of Classification Along Frailty Spectrum

This was an ANOVA test and all values represented are mean ± standard error or counts (weighted percentage).

Multivariable logistic regression models evaluating the relationship between weight change during a specific time interval, and presence of frailty or pre-frailty are presented in Table 3.  Weight loss of ≥5% was strongly associated with presence of frailty compared to pre-frailty/robust and the relationship strengthens with longer time intervals. A ≥5% weight gain only was associated with frailty if the gain occurred within the last year. Similar associations were seen with pre-frailty when comparing pre-frailty to robust status, albeit weaker than the association seen with frailty.

Table 3 Association of Weight Change and Frailty Status

Table 3
Association of Weight Change and Frailty Status

Data are represented as odds ratio [95% Confidence interval]. Multivariable logistic regression models (referent category: no change in weight) are represented as odds ratios (95% confidence intervals). The primary predictor was weight change (gain ≥5%, loss of 5%, or no change in weight) during the time period. Separate multivariable models were created for the outcomes of Frailty vs. Pre-Frailty/Robust, and Pre-Frailty vs. Robust (each yes/no). Models presented were adjusted for: age (years as continuous variable), sex (female=1 male=0), race (non-Hispanic white (ref), non-Hispanic black, Hispanic and Other) education status >12 years (yes=1, no=0), smoking status (former, current=1, never=0) self-reported diabetes (yes=1, no=0) , arthritis (yes=1, no=0), coronary artery disease (yes=1, no=0), cancer (yes=1, no=0).

 

Figure 1 represents the change in weight as a function of age plotted with a LOESS smoothed line by frailty status. Graphically, a period of increased rate of weight gain is noted prior to a steep decline in weight for individuals who were frail at time of study inclusion; conversely, this was not observed in those with pre-frail or robust status. Additionally, frail individuals had a higher peak weight (87.3 kg), occurring earlier in life (58.4 years), relative to both pre-frail (peak weight 79.9 kg at age 59.9 years) and robust (79.0 kg peak weight at 63.0 years) individuals. This suggests that the earlier one reaches their peak weight in life, the higher incident frailty.

Figure 1 Change in Weight by Frailty Status. This figure represents a LOESS smoothed line demonstrating the change in weight as a function of age by frailty status

Figure 1
Change in Weight by Frailty Status. This figure represents a LOESS smoothed line demonstrating the change in weight as a function of age by frailty status

 

Discussion

Our findings demonstrate the importance of dynamic weight changes over a lifetime in the future development of frailty. While frailty is classically defined by weight loss (3), here we show that significant changes in weight gain or loss over a lifetime were associated with frailty progression making this a more complicated relationship than was previously thought. We created two separate analyses looking at frailty versus pre-frail and robust participants and pre-frail vs robust participants alone. Pre-frailty has individually been associated with an increase in overall mortality and cardiovascular mortality (26). Here we demonstrate that significant weight loss or weight gain is strongly associated with frailty and has a weaker, but statistically significant, association with pre-frailty supporting the significance of each step along the frailty spectrum.
While weight loss’ association with frailty is well accepted, the relationship of weight gain with frailty is less understood. A 22 year follow-up study demonstrated obesity was associated with higher rates of pre-frailty and frailty compared to robust individuals, suggesting obesity could be a contributing factor to progression along the frailty spectrum (32, 33). Unsurprisingly, we showed that older adults with a ≥5% weight loss compared to 1 year ago, 10 years ago, and age 25 have significantly increased odds of having frailty suggesting that weight loss is a strong indicator of potential frailty development. Yet, a ≥5% weight gain only increased odds of frailty when this occurred within the last year, but not compared to 10 years ago or age 25. This seems to suggest that a sudden increase in weight may not be marker of health, but a weight trajectory trending more toward the concept of sarcopenic obesity (34).
Prior work has shown that when evaluating weight gain and loss, more lean mass is lost than is gained over repeated fluctuations suggesting weight cycling could accelerate sarcopenia in older adults and contribute to sarcopenic obesity (35). Weight cycling leads to greater central body fat and increased mortality (22). Some theorize that the trajectory of pathological aging is to move from robust status to sarcopenic obesity to frailty then to disability and mortality (36). Our previous data suggest that late adulthood weight changes over a 10 year period are predictive of sarcopenia development and that there is a healthy, natural propensity to gain weight over a life course (37). Our current findings demonstrate a similar trend as robust participants were more likely than those pre-frail or frail to have gained ≥5% weight over the course of the prior 10 years.
A natural, healthy trend toward weight gain could explain why the classification of overweight status in older adults, in part, has been noted as somewhat “protective” (38). Overweight status could be beneficial for reducing disability and functional loss with reduced osteoporosis and injuries from falls (39). An ability to gain or maintain weight demonstrates a reduced vulnerability to stressors from comorbid health conditions (40).
This study is not without limitations. First, the analysis relied on self-reported weights which may be impacted by recall bias but studies looking at self-reported weight accuracy and actual weight are reasonably comparable (41), however we were not able to assess the accuracy of recall in those with cognitive impairment included in the study. This was not done due to concurrent limitations in how those with cognitive impairment would be identified from NHANES variables (41). In clinical practice, weight 10 years prior and at age 25 would most likely be ascertained by recall, and therefore, use of self-report is ecologically valid. Second, the sample was comprised of community dwelling adults. Individuals living in facilities were not included, which limits the ability to generalize these findings to the older adult population as a whole. To operationalize Fried’s frailty criteria in NHANES some of the original definitions were modified. Those labeled frail could be considered those at “high likelihood of frailty” by our modified metrics. However the accuracy of our results are further supported by the fact the prevalence of each component is comparable to those observed in other studies who did not require modification (3, 42, 43). As walking speed was missing for 3,645 patients, multiple imputations needed to be performed to maximize the number of participants with appropriate data. Multivariate imputation by chained equations, a robust method that generates multiple predictions for each missing value, taking the uncertainty of the imputations into account and yielding accurate standard errors (44), was used to handle missing data. The reasons for unintentional weight loss in participants was unknown therefore the implications of these factors behind their weight loss are also unknown. As with all cross-sectional studies, we are not able to make causal inferences. Lastly, the true relationship between weight change and incident to frailty may not necessarily be linear.

 

Conclusions

The results demonstrate an association between frailty and weight change over time. Weight loss over a lifespan is strongly associated with frailty while weight gain in the last year also is association with higher rates of frailty and can therefore be a marker of declining health. A natural trend toward weight gain and overweight status in a lifetime could actually be a sign of metabolic health and longevity. These findings demonstrate the clinical value in weight trends obtained at most clinical visits and highlight potential trends that may warrant closer evaluation for syndromes like frailty that require additional intervention to help curb poorer health outcomes.

 

Acknowledgements: The authors would like to acknowledge Meaghan A. Kennedy MD, MPH who helped with editing and reviewing the transcript as well as support from the National Institute on Aging of the National Institutes of Health, Burroughs-Wellcome/Dartmouth Big Data in the Life Sciences Training Program, National Institute of Mental Health, Dartmouth Health Promotion and Disease Prevention Research Center, The Dartmouth Center for Health and Aging and the Dartmouth Hitchcock Medical Center Department of Medicine.
Disclosures: There are no conflicts of interest pertaining to this manuscript
Funding: Dr. Batsis receives funding from the National Institute on Aging of the National Institutes of Health under Award Number K23AG051681. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Dr. Batsis has also received honoraria from the Royal College of Physicians of Ireland, Endocrine Society, and Dinse, Knapp, McAndrew LLC, legal firm. Dr. Mackenzie:  none. Dr. Cook: none. Alexander Titus’ research reported in this publication was supported in part by the National Institutes of Health under Award Number T32LM012204. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Mr. Petersen’s research reported in this publication was supported by the Burroughs-Wellcome/Dartmouth Big Data in the Life Sciences Training Program. Dr. Crow’s research reported in this publication was supported by The Dartmouth Center for Health and Aging and the Department of Medicine. Dr. Stevens’ research is supported by National Institute of Mental Health (T32 MH073553, PI: Bruce, Fellow: Stevens). Support was also provided by the Dartmouth Health Promotion and Disease Prevention Research Center supported by Cooperative Agreement Number U48DP005018 from the Centers for Disease Control and Prevention. The findings and conclusions in this journal article are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention. The authors acknowledge Friends of the Norris Cotton Cancer Center at Dartmouth and NCI Cancer Center Support Grant 5P30 CA023108-37 Developmental Funds.

SUPPLEMENTARY MATERIAL

 

References

1.    Chong E, Ho E, Baldevarona-Llego J, Chan M, Wu L, Tay L. Frailty and Risk of Adverse Outcomes in Hospitalized Older Adults: A Comparison of Different Frailty Measures. J Am Med Dir Assoc. 2017;18(7):638.e7-.e11.
2.    Clegg A, Young J, Iliffe S, Rikkert MO, Rockwood K. Frailty in elderly people. Lancet. 2013;381(9868):752-62.
3.    Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, et al. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci. 2001;56(3):M146-56.
4.    Robinson TN, Wu DS, Stiegmann GV, Moss M. Frailty predicts increased hospital and six-month healthcare cost following colorectal surgery in older adults. Am J Surg. 2011;202(5):511-4.
5.    Bock JO, Konig HH, Brenner H, Haefeli WE, Quinzler R, Matschinger H, et al. Associations of frailty with health care costs–results of the ESTHER cohort study. BMC Health Serv Res. 2016;16:128.
6.    Makizako H, Shimada H, Doi T, Tsutsumimoto K, Suzuki T. Impact of physical frailty on disability in community-dwelling older adults: a prospective cohort study. BMJ Open. 2015;5(9):e008462.
7.    Comans TA, Peel NM, Hubbard RE, Mulligan AD, Gray LC, Scuffham PA. The increase in healthcare costs associated with frailty in older people discharged to a post-acute transition care program. Age Ageing. 2016;45(2):317-20.
8.    Avila-Funes JA, Amieva H, Barberger-Gateau P, Le Goff M, Raoux N, Ritchie K, et al. Cognitive impairment improves the predictive validity of the phenotype of frailty for adverse health outcomes: the three-city study. J Am Geriatr Soc. 2009;57(3):453-61.
9.    Meltzer AA, Everhart JE. Unintentional weight loss in the United States. Am J Epidemiol. 1995;142(10):1039-46.
10.    Willett WC. Weight loss in the elderly: cause or effect of poor health? Am J Clin Nutr. 1997;66(4):737-8.
11.    Beaufrere B, Morio B. Fat and protein redistribution with aging: metabolic considerations. Eur J Clin Nutr. 2000;54 Suppl 3:S48-53.
12.    Gallagher D, Visser M, De Meersman RE, Sepulveda D, Baumgartner RN, Pierson RN, et al. Appendicular skeletal muscle mass: effects of age, gender, and ethnicity. J Appl Physiol (1985). 1997;83(1):229-39.
13.    Batsis JA, Mackenzie TA, Bartels SJ, Sahakyan KR, Somers VK, Lopez-Jimenez F. Diagnostic accuracy of body mass index to identify obesity in older adults: NHANES 1999-2004. Int J Obes (Lond). 2016;40(5):761-7.
14.    Choi KM. Sarcopenia and sarcopenic obesity. Korean J Intern Med. 2016;31(6):1054-60.
15.    Cauley JA. An Overview of Sarcopenic Obesity. J Clin Densitom. 2015;18(4):499-505.
16.    Andres R, Muller DC, Sorkin JD. Long-term effects of change in body weight on all-cause mortality. A review. Ann Intern Med. 1993;119(7 Pt 2):737-43.
17.    Oh TJ, Moon JH, Choi SH, Lim S, Park KS, Cho NH, et al. Body-Weight Fluctuation and Incident Diabetes Mellitus, Cardiovascular Disease, and Mortality: A 16-Year Prospective Cohort Study. J Clin Endocrinol Metab. 2019;104(3):639-46.
18.    Payette H, Coulombe C, Boutier V, Gray-Donald K. Weight loss and mortality among free-living frail elders: a prospective study. J Gerontol A Biol Sci Med Sci. 1999;54(9):M440-5.
19.    Somes GW, Kritchevsky SB, Shorr RI, Pahor M, Applegate WB. Body mass index, weight change, and death in older adults: the systolic hypertension in the elderly program. Am J Epidemiol. 2002;156(2):132-8.
20.    Tully CL, Snowdon DA. Weight change and physical function in older women: findings from the Nun Study. J Am Geriatr Soc. 1995;43(12):1394-7.
21.    Alibhai SM, Greenwood C, Payette H. An approach to the management of unintentional weight loss in elderly people. CMAJ. 2005;172(6):773-80.
22.    Murphy RA, Patel KV, Kritchevsky SB, Houston DK, Newman AB, Koster A, et al. Weight change, body composition, and risk of mobility disability and mortality in older adults: a population-based cohort study. J Am Geriatr Soc. 2014;62(8):1476-83.
23.    Dee A, Kearns K, O’Neill C, Sharp L, Staines A, O’Dwyer V, et al. The direct and indirect costs of both overweight and obesity: a systematic review. BMC Res Notes. 2014;7:242.
24.    Visscher TL, Seidell JC. The public health impact of obesity. Annu Rev Public Health. 2001;22:355-75.
25.    Hirani V, Naganathan V, Blyth F, Le Couteur DG, Seibel MJ, Waite LM, et al. Longitudinal associations between body composition, sarcopenic obesity and outcomes of frailty, disability, institutionalisation and mortality in community-dwelling older men: The Concord Health and Ageing in Men Project. Age Ageing. 2016.
26.    Crow RS, Lohman MC, Titus AJ, Bruce ML, Mackenzie TA, Bartels SJ, et al. Mortality Risk Along the Frailty Spectrum: Data from the National Health and Nutrition Examination Survey 1999 to 2004. J Am Geriatr Soc. 2018;66(3):496-502.
27.    Rippberger PL, Emeny RT, Mackenzie TA, Bartels SJ, Batsis JA. The association of sarcopenia, telomere length, and mortality: data from the NHANES 1999-2002. Eur J Clin Nutr. 2018;72(2):255-63.
28.    Kulminski AM, Ukraintseva SV, Kulminskaya IV, Arbeev KG, Land K, Yashin AI. Cumulative deficits better characterize susceptibility to death in elderly people than phenotypic frailty: lessons from the Cardiovascular Health Study. J Am Geriatr Soc. 2008;56(5):898-903.
29.    Jensen MD, Ryan DH, Apovian CM, Ard JD, Comuzzie AG, Donato KA, et al. 2013 AHA/ACC/TOS guideline for the management of overweight and obesity in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and The Obesity Society. J Am Coll Cardiol. 2014;63(25 Pt B):2985-3023.
30.    Painter SL, Ahmed R, Hill JO, Kushner RF, Lindquist R, Brunning S, et al. What Matters in Weight Loss? An In-Depth Analysis of Self-Monitoring. J Med Internet Res. 2017;19(5):e160.
31.    Bieniek J, Wilczynski K, Szewieczek J. Fried frailty phenotype assessment components as applied to geriatric inpatients. Clin Interv Aging. 2016;11:453-9.
32.    Stenholm S, Strandberg TE, Pitkala K, Sainio P, Heliovaara M, Koskinen S. Midlife obesity and risk of frailty in old age during a 22-year follow-up in men and women: the Mini-Finland Follow-up Survey. J Gerontol A Biol Sci Med Sci. 2014;69(1):73-8.
33.    Strandberg TE, Sirola J, Pitkala KH, Tilvis RS, Strandberg AY, Stenholm S. Association of midlife obesity and cardiovascular risk with old age frailty: a 26-year follow-up of initially healthy men. Int J Obes (Lond). 2012;36(9):1153-7.
34.    Batsis JA, Mackenzie TA, Lopez-Jimenez F, Bartels SJ. Sarcopenia, sarcopenic obesity, and functional impairments in older adults: National Health and Nutrition Examination Surveys 1999-2004. Nutr Res. 2015;35(12):1031-9.
35.    Newman AB, Lee JS, Visser M, Goodpaster BH, Kritchevsky SB, Tylavsky FA, et al. Weight change and the conservation of lean mass in old age: the Health, Aging and Body Composition Study. Am J Clin Nutr. 2005;82(4):872-8; quiz 915-6.
36.    Cederholm T. Overlaps between Frailty and Sarcopenia Definitions. Nestle Nutr Inst Workshop Ser. 2015;83:65-9.
37.    Batsis JA, Petersen CL, Crow RS, Cook SB, Stevens CJ, Lillian SM, et al. Weight Change and Risk of Sarcopenia: Data from the National Health and Nutrition Examination Surveys 1999-2004 [Abstract]. 2018 International Frailty & Sarcopenia Conference, Miami Beach, FL, https://frailty-sarcopeniacom/docs/abstracts-2018pdf [accessed 18 September 2018]. 2018.
38.    Flegal KM, Kit BK, Orpana H, Graubard BI. Association of all-cause mortality with overweight and obesity using standard body mass index categories: a systematic review and meta-analysis. JAMA. 2013;309(1):71-82.
39.    Bowen ME. The relationship between body weight, frailty, and the disablement process. J Gerontol B Psychol Sci Soc Sci. 2012;67(5):618-26.
40.    Villareal DT, Banks M, Siener C, Sinacore DR, Klein S. Physical frailty and body composition in obese elderly men and women. Obes Res. 2004;12(6):913-20.
41.    Luo J, Thomson CA, Hendryx M, Tinker LF, Manson JE, Li Y, et al. Accuracy of self-reported weight in the Women’s Health Initiative. Public Health Nutr. 2019;22(6):1019-28.
42.    Shamliyan T, Talley KM, Ramakrishnan R, Kane RL. Association of frailty with survival: a systematic literature review. Ageing Res Rev. 2013;12(2):719-36.
43.    Fernandez-Garrido J, Ruiz-Ros V, Buigues C, Navarro-Martinez R, Cauli O. Clinical features of prefrail older individuals and emerging peripheral biomarkers: a systematic review. Arch Gerontol Geriatr. 2014;59(1):7-17.
44.    Azur MJ, Stuart EA, Frangakis C, Leaf PJ. Multiple imputation by chained equations: what is it and how does it work? Int J Methods Psychiatr Res. 2011;20(1):40-9.

IMPACT OF FAT-FREE ADIPOSE TISSUE ON THE PREVALENCE OF LOW MUSCLE MASS ESTIMATED USING CALF CIRCUMFERENCE IN MIDDLE-AGED AND OLDER ADULTS

 

T. Abe1, S.J. Dankel2, Z.W. Bell1, E. Fujita3, Y. Yaginuma3, T. Akamine3, R.W. Spitz1, V. Wong1, R.B. Viana1,4, J.P. Loenneke1

 

1. Department of Health, Exercise Science, & Recreation Management, Kevser Ermin Applied Physiology Laboratory, The University of Mississippi, University, MS 38677, USA;
2. Department of Health and Exercise Science, Rowan University, Glassboro, NJ 08028, USA; 3. Department of Sports and Life Sciences, National Institute of Fitness and Sports in Kanoya, Kagoshima, Japan; 4. Faculty of Physical Education and Dance, Federal University of Goiás, Goiânia, Brazil.
Corresponding author: Takashi Abe, PhD, 224 Turner Center, University, MS 38677, USA, Phone: +1 (662) 915-5521, Fax: +1 (662) 915-5525, Email:  t12abe@gmail.com

J Frailty Aging 2020;9(2)90-93
Published online October 10, 2019, http://dx.doi.org/10.14283/jfa.2019.34

 


Abstract

Previous studies proposed calf circumference cutoff values for predicting dual-energy X-ray absorptiometry (DXA)-derived low muscle mass. However, DXA-derived appendicular lean mass (aLM) includes non-skeletal muscle components such as the appendicular fat-free component of adipose tissue fat cells (aFFAT). The purpose of this study was to compare the calf circumference method of classification before (Model #1) and after (Model #2) eliminating the influence of FFAT in healthy Japanese adults (50 to 79 years; mean age 70 (SD 7) years). Model 1, and Model 2 for classifying low muscle mass had a sensitivity of 78% and 64%, specificity of 76% and 75%, positive predictive value of 31% and 28%, and negative predictive value of 96% and 93%, respectively. Appendicular fat-free component of adipose tissue has the potential to influence the ability of calf circumference to accurately classify individuals with low muscle mass. Consideration should be made when using this as a screening tool for low muscle mass.

Key words: Aging, anthropometry, dual-energy x-ray absorptiometry, muscle mass, sarcopenia.


 

Introduction

Sarcopenia refers to low muscle mass and poor physical function in older men and women. In a recent consensus, low muscle mass has been defined by the amount of appendicular lean soft tissue mass (absolute or relative to height squared) an individual has as measured by dual-energy X-ray absorptiometry (DXA) (1). These definitions are diagnostically useful for healthcare professionals who treat individuals at risk for low muscle mass and provide clinically relevant target goals for those working with these individuals. However, DXA measurements are costly and the device has limited availability. Therefore, the DXA is impractical for screening large populations and creates a need for a simple, evidence-based, screening tool for identifying individuals who have low muscle mass (2, 3).
Anthropometric measurement is a simple method that can be used when it may be impractical or cumbersome to use more sophisticated measurements. When height is identical, body mass (fat mass plus fat-free body mass) may be positively related to limb and trunk circumferences of the body. Previous studies have reported that there is a moderate to strong correlation between calf circumference and DXA-derived appendicular lean mass (aLM) and aLM index in Japanese middle-aged and older men and women (4), Brazilian older men and women (5) and French old women (6). Further, two studies (4, 5) indicated that a calf circumference less than 34 cm in men and 33 cm in women is associated with low muscle mass. However, DXA-derived aLM includes non-skeletal muscle and non-organ components, such as fat-free adipose tissue (FFAT) in the extremities (7, 8). This FFAT, if not accounted for, could falsely inflate the aLM in individuals with a relatively high amount of adipose tissue mass (9). Therefore, it is unknown whether FFAT impacts the diagnostic utility of calf circumference for correctly classifying low muscle mass. In the present study, we compare the calf circumference method of classification before and after eliminating the influence of FFAT.

 

Methods

Participants

Two-hundred and sixty-five apparently healthy Japanese adults (108 men and 157 women) aged 50 to 79 years volunteered for this study (Table 1). Written informed consent was obtained from all participants after the explanation of the study purpose and potential risks. The participants were well-functioning middle-aged and older adults evaluated by using usual walking speed (>1.0 m/s), and were free of chronic health conditions (e.g. myocardial infarction, stroke, cancer, myositis, and neuromuscular disorders) as assessed by self-report. This study was approved by the Institutional Review Board of the National Institute of Fitness and Sports in Kanoya.

Anthropometric measurements

Body mass and standing height were measured to the nearest 0.1 kg and 0.1 cm, respectively, by using a height scale (Yagami YG-200, Tokyo, Japan) and an electronic weight scale (Tanita WB-260A, Tokyo, Japan). Body mass index was calculated as body mass (kg) / height squared (m2). Calf circumference was measured to the nearest 0.1 cm by using a tape measure at 30% proximal of lower leg length (between the lateral malleolus of the fibula and the lateral condyle of the tibia) on the right side of the body. This 30% position was nearly identical to the maximum circumference of the lower leg.

DXA measurements

A whole-body DXA scan was performed using a DXA scanner (Discovery A, Hologic Inc., Bedford, MA, USA) to determine percentage of body fat, total body fat mass, appendicular fat mass (aFM), total body lean tissue mass, and aLM. Quality assurance testing and calibration were performed the morning of data collection days to ensure that the DXA scanner was operating properly. Participants were asked to refrain from vigorous exercise for at least 24 hours prior to the scans. Also, participants were asked to refrain from eating for at least 3 hours prior to scans. Appendicular fat mass and aLM calculations were based on the sum of fat mass or lean mass in all four limbs. Test-retest reliability (minimal difference; 0.58 kg aLM) of the DXA measurements was reported previously (7).

Fat-free adipose tissue mass (FFAT) estimation

Appendicular FFAT (aFFAT) was calculated based on a model proposed by Heymsfield et al. (8). The model described that 85% of adipose tissue is fat and the remaining 15% of adipose tissue is calculated fat-free component.  According to the model, appendicular adipose tissue mass can be calculated as appendicular adipose tissue mass = DXA derived aFM ÷ 0.85. Then, aFFAT can be calculated as aFFAT = appendicular adipose tissue mass × 0.15. Adjusting for the influence of aFFAT on DXA determined aLM, we calculated an index: aLM minus aFFAT (aLM-aFFAT).

Diagnosing criterion of low muscle mass

We used the diagnosing criterion of low muscle mass proposed by the Asian Working Group for Sarcopenia (7.0 kg/m2 in men and 5.4 kg/m2 in women) for the aLM index. According to the previous study (10), we also used new cutoff values of low muscle mass which eliminated appendicular FFAT (6.7 kg/m2 in men and 4.9 kg/m2 in women) for the aLM-aFFAT index.

Cutoff values of calf circumference

Previous studies have proposed the optimal calf circumference cutoff values (34 cm in men and 33 cm in women) for predicting low muscle mass (4, 5). In this study, we used these cutoff values for the analysis.

Statistical analysis

Confusion matrices were constructed to test the ability of calf circumference to predict low muscle mass before (Model #1) and after (Model #2) correcting for aFFAT. Following construction of each confusion matrix, we determined the sensitivity, specificity, positive predictive value, and negative predictive value for each model.

 

Results

In model #1 (Table 2), 12% of the overall sample was classified as having low muscle mass (15% of men and 10% of women). The calf circumference method of assessing low muscle mass had an overall sensitivity of 78% (69% in men and 88% in women), specificity of 76% (84% in men and 71% in women), positive predictive value of 31% (42% in men and 25% in women), and negative predictive value of 96% (94% in men and 98% in women).

Table 1 Physical characteristics and body composition of the participants

Table 1
Physical characteristics and body composition of the participants

Values are means ± standard deviations (SD); aFFAT, appendicular fat free adipose tissue mass; aLM, appendicular lean tissue mass; aLM-aFFAT, appendicular lean mass minus aFFAT; SD, standard deviation

 

In model #2 (Table 2), 14% of the overall sample was classified as having low muscle mass (18% of men and 11% of women). The calf circumference method of assessing low muscle mass had an overall sensitivity of 64% (58% of men and 71% of women), specificity of 75% (83% of men and 69% of women), positive predictive value of 28% (42% of men and 22% of women), and negative predictive value of 93% (90% of men and 95% of women).

Table 2 Confusion matrix for the classification of low muscle mass using the calf circumference method. Model #1 is using appendicular lean mass (aLM) as the criterion and Model #2 is using appendicular lean mass following the elimination of fat free adipose tissue as the criterion (aLM-FFAT)

Table 2
Confusion matrix for the classification of low muscle mass using the calf circumference method. Model #1 is using appendicular lean mass (aLM) as the criterion and Model #2 is using appendicular lean mass following the elimination of fat free adipose tissue as the criterion (aLM-FFAT)

 

Discussion

In the last few years, several studies investigated the utility of anthropometric measurements in the prediction of skeletal muscle mass, especially for the lower extremity (4, 5, 11). Two of these studies proposed the same calf circumference cutoff values (34 cm in men and 33 cm in women) for predicting low muscle mass as measured by DXA. These studies reported that the calf circumference cutoff values had relatively high sensitivity (67-88%) and specificity (73-91%) for predicting low muscle mass in middle-aged and older men and women (4, 5). In the present study, we found that eliminating the influence of FFAT had minimal influence on specificity but did impact the sensitivity of the test (reduced from 78% to 64%). That is, the proportion of individuals with low muscle mass who test positive for having low muscle mass is reduced following FFAT correction. Further, the positive predictive value, the fraction of the positive predictions that are actually positive, was only 31% in model #1 and 28% in model #2. When interpreting the positive predictive value, it is important to consider the prevalence of low muscle mass. In contrast, the sensitivity and specificity are properties of the test and are not usually influenced by the prevalence of the condition (12).
A simple estimation method for diagnosing low muscle mass is desired, and anthropometric measurements are the most affordable method. However, the calf circumference may be affected not only by lower limb muscle mass but also by the structure of the ankle joint such as plantar flexor moment arm (13). In addition, daily physical activity at moderate and vigorous intensities measured by an accelerometer was associated with higher muscle size in the lower leg, but not in other body sites (14). For a simple measurement, the single site assessment of calf circumference may serve as a reasonable screening tool for identifying individuals who may need an in-depth evaluation using more sophisticated measurements.
In conclusion, eliminating the influence of aFFAT impacted the sensitivity of calf circumference in detecting low muscle mass; but not specificity. Prior to the elimination of aFFAT, the test correctly classified 8 out of every 10 individuals with low muscle mass. Following the elimination of aFFAT, calf circumference only correctly classified 6 out of every 10 individuals with low muscle mass. These findings suggest that aFFAT can influence the ability of calf circumference to accurately classify individuals with low muscle mass. Failure to correct for aFFAT may result in a false inflation of sensitivity.

 

Ethics declaration: All experimental procedures were conducted in accordance with the guidelines in the Declaration of Helsinki and approved by the university’s institutional review board.
Conflict of Interest: No conflicts of interest, financial or otherwise, are disclosed by the authors.
Funding: This study received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.
Acknowledgements: Our appreciation is extended to the volunteers who participated in this study. We also thank the graduate students for their assistance in the testing of this study.

 

References

1.    Cruz-Jentoft AJ, Bahat G, Bauer J, et al. Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing 2019;48:16–31.
2.    Paris M, Mourtzakis M. Assessment of skeletal muscle mass in critically ill patients: considerations for the utility of computed tomography imaging and ultrasonography. Curr Opin Clin Nutr Metab Care 2016;19:125–130.
3.    Abe T, Thiebaud RS, Loenneke JP. Simple chart for practical screening of low muscle mass in well-functioning middle-aged and older men and women. Geriatr Gerontol Int 2018;18:657–658.
4.    Kawakami R, Murakami H, Sanada K, et al. Calf circumference as a surrogate marker of muscle mass for diagnosing sarcopenia in Japanese men and women. Geriatr Gerontol Int 2015;15:969–976.
5.    Pagotto V, Santos KF, Malaquias SG, Bachion MM, Silveira EA. Calf circumference: clinical validation for evaluation of muscle mass in the elderly. Rev Bras Enferm 2018;71:322–328.
6.    Rolland Y, Lauwers-Cances V, Cournot M, et al. Sarcopenia, calf circumference, and physical function of elderly women: A cross-sectional study. J Am Geriatr Soc 2003;51:1120–1124.
7.    Abe T, Thiebaud RS, Loenneke JP, Fujita E, Akamine T. DXA-rectified appendicular lean mass: Development of ultrasound prediction models in older adults. J Nutr Health Aging 2018;22:1080–1085.
8.    Heymsfield SB, Gallagher D, Kotler DP, et al. Body-size dependence of resting energy expenditure can be attributed to nonenergetic homogeneity of fat-free mass. Am J Physiol Endo Metab 2002;282:E132–E138.
9.    Abe T, Dankel SJ, Loenneke JP. Body fat loss automatically reduces lean mass by changing the fat-free component of adipose tissue. Obesity (Silver Spring) 2019;27:357–358.
10.    Abe T, Loenneke JP, Thiebaud RS, Fijita E, Akamine T. The impact of DXA-derived fat-free adipose tissue on the prevalence of low muscle mass in older adults. Eur J Clin Nutr 2019;73:757–762.
11.    Diano D, Ponti F, Guerri S, et al. Upper and lower limbs composition: a comparison between anthropometry and dual-energy X-ray absorptiometry in healthy people. Arch Osteoporos 2017;12:78.
12.    Ranganathan P, Aggarwal R. Common pitfalls in statistical analysis: Understanding the properties of diagnostic tests – Part 1. Perspect Clin Res 2018;9:40–43.
13.    Baxter JR, Piazza SJ. Plantar flexor moment arm and muscle volume predict torque-generating capacity in young men. J Appl Physiol 2014;116:538–544.
14.    Abe T, Mitsukawa N, Thiebaud RS, et al. Lower body site-specific sarcopenia and accelerometer-determined moderate and vigorous physical activity: the HIREGASAKI study. Aging Clin Exp Res 2012;24:657–662.

CLINICALLY MEANINGFUL CHANGE FOR PHYSICAL PERFORMANCE: PERSPECTIVES OF THE ICFSR TASK FORCE

 

J. Guralnik1, K. Bandeen-Roche2, S.a.r. Bhasin3, S. Eremenco4, F. Landi5, J. Muscedere6, S. Perera7, J.-Y. Reginster8, L. Woodhouse9, B. Vellas10 and the ICFSR Task Force

 

1. University of Maryland School of Medicine, Baltimore, MD, USA; 2. Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; 3. Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA; 4 – Critical Path Institute, Tucson, AZ, USA; 5. Fondazione Policlinico A. Gemelli, Catholic University, Rome, Italy; 6. Queen’s University, Kingston, Ontario, Canada; 7. University of Pittsburgh, Pittsburgh, PA, USA; 8. University of Liege, Liege, Belgium; 9. University of Alberta, Edmonton, Alberta, Canada; 10. Gerontopole, INSERM U1027, Alzheimer’s Disease Research and Clinical Center, Toulouse University Hospital, Toulouse, France.
Corresponding author: Jack Guralnik, University of Maryland School of Medicine, Baltimore, MD, USA, jguralnik@epi.umaryland.edu.

Task force members: Samuel Agus (Paris, France), Islene Araujo de Carvalho (Geneva, Switzerland), Mylène Aubertin-Leheudre (Montréal, Canada), Karen Bandeen-Roche (Baltimore, USA), Ann Belien (Hesusden-Zolder, Belgium), Shalender Bhasin (Boston, USA), Heike Bischoff-Ferrari  (Zurich, Switzerland), Andreas Busch (Vevey, Switzerland), Ryne Carney (Washington, USA), Matteo Cesari (Milano, Italy), Caroline Couleur (Epalinges, Switzerland), Alfonso Cruz Jentoft (Madrid, Spain), Susanna Del Signore (London, United Kingdom), Carla Delannoy (Vevey, Switzerland), Waly Dioh (Paris, France), Sonya Eremenco (Tucson, USA), Bill Evans (Durham, USA), Toby Ferguson (Cambridge, USA), Roger Fielding (Boston, USA), Jack Guralnik (Baltimore, USA), Ludo Haazen (Hesusden-Zolder, Belgium), Joshua Hare (Miami, USA), Aaron Hinken (Collegeville, USA), Darren Hwee (South San Francisco, USA), Lori Janesko (Uniontown, USA), Kala Kaspar (Vevey, Switzerland),  Francesco Landi (Roma, Italy), Valérie Legrand (Nanterre, France), Bradley Morgan (South San Francisco, USA), John Morley (St Louis, USA), John Muscedere (Kingston, Canada), David Neil (Collegeville, USA),  Marco Pahor (Gainesville, USA),  Marika Paul (Columbus, USA), Subashan  Perera (Pittsburgh, USA), Suzette Pereira (Columbus, USA), John Rathmacher (Ames, USA), Jean Yves Reginster (Liège, Belgium), Leocadio Rodriguez Manas (Getafe (Madrid), Spain), Yves Rolland (Toulouse, France), Michelle Rossulek (Cambridge, USA), Jorge Ruiz (Miami, USA), Lisa Tarasenko (Cambridge, USA), Effie Tozzo (Cambridge, USA), Heber Varela (Miami, USA), Bruno Vellas (Toulouse, France), Jeremy Walston (Baltimore, USA), Debra Waters (Dunedin, New Zealand), Linda Woodhouse (Edmonton, Canada)

J Frailty Aging 2019 ;in press
Published online October 10, 2019, http://dx.doi.org/10.14283/jfa.2019.33

 


Abstract

For clinical studies of sarcopenia and frailty, clinically meaningful outcome measures are needed to monitor disease progression, evaluate efficacy of interventions, and plan clinical trials. Physical performance measures including measures of gait speed and other aspects of mobility and strength have been used in many studies, although a definition of clinically meaningful change in performance has remained unclear. The International Conference on Frailty and Sarcopenia Research Task Force (ICFSR-TF), a group of academic and industry scientists investigating frailty and sarcopenia, met in Miami Beach, Florida, USA in February 2019 to explore approaches for establishing clinical meaningfulness in a manner aligned with regulatory authorities. They concluded that clinical meaningful change is contextually dependent, and that both anchor- based and distribution-based methods of quantifying physical function are informative and should be evaluated relative to patient-reported outcomes. In addition, they identified additional research needed to enable setting criteria for clinical meaningful change in trials.

Key words: Sarcopenia, frailty, aging, disability, physical performance, clinically meaningful change, outcome measures.


 

 

Introduction

Clinical research studies in older populations have shifted over the last two decades from assessing biological indicators and disease status to measuring physical function as a primary endpoint. This shift reflects the World Health Organization’s (WHO’s) framework for health and disability, the International Classification of Functioning, Disability and Health (ICF) (1), which provides a multidimensional framework for conceptualizing and understanding functioning and disability by integrating medical and biopsychosocial models. Using a patient-focused approach, the ICF promotes the use of language that frames disablement not in terms of disease but in how people live with their conditions across three domains: body function and structure, activity, and participation, i.e., movement in three-dimensional space, interacting with other people, and socialization (2).
With new interventions for sarcopenia on the horizon, the concept of clinical meaningfulness has emerged as an important concern for researchers, clinicians, and regulators. Thus, the International Conference on Frailty and Sarcopenia Research Task Force (ICFSR-TF), a group of academic and industry scientists investigating frailty and age-related muscle loss (sarcopenia), convened a group of experts on February 19, 2019 to explore approaches for establishing clinical meaningfulness and related regulatory considerations.
Clinical meaningfulness, as defined by the U.S. Food and Drug Administration (FDA), requires that an outcome assessment measure something that is clinically important and that substantively affects how the patient feels, functions, or survives. Thus, clinically meaningful outcome measures for sarcopenia and frailty typically assess physical function, quality of life, and survival. Muscle strength and muscle mass may also be considered as outcome measures but only if they correlate with improved function or predict other relevant health outcomes such as reduced mortality, morbidity, institutionalization, and disability (3-7).
Clinically meaningful measures may be used to monitor adults in clinical settings and in observational studies, to evaluate efficacy in clinical trials, and to compute sample size and power when planning a clinical trial (8). However, meaningful change in an observational study may differ from meaningful change in an intervention trial where change can occur much more rapidly in the positive direction and must have both clinical and public health relevance. Since rapid changes may be perceived as being much greater in magnitude than those that occur more gradually, objective measurement is important.

 

Defining a clinically meaningful change in physical performance

Meaningful change can be defined as a change that has clinical or practical importance, has an impact on an individual’s self-perceived health status or quality of life, or as a fraction of the standard deviation representing a certain level of movement across the distribution of measurements in the population. Clinically meaningful change depends on the outcome on which it is based. Physical performance measures regularly used in clinical trials include various measures of gait and balance parameters and/or the Short Physical Performance Battery (SPPB), a composite measure of walking speed, standing balance, and sit-to-stand performance (9). Gait performance measures include the 4-meter gait speed test (4MGS), the 6-minute walk distance test (6MWD), the 10-meter walk test (10MWT), the timed 400-meter walk (400MW), and the 3-meter timed “Up & Go” test (TUG) (10), (11).  Other possible measures such as gait variability, unipodal balance, and stair negotiation performance may also be used to assess mobility impairments (12, 13). Most evidence has been gathered for the 4MGS, which can be performed in a reasonably small space. For example, in a prospective cohort study of older adults, Perera and colleagues showed that a decline in gait speed of 0.1 m/s on the 4MGS  or 1 point on the SPPB over a one-year period was associated with an increased risk of subsequent mortality (14).
Clinically meaningful changes in outcomes may be expressed as changes that exceed minimally clinically important differences (MCID), clinically meaningful differences (CMD), or minimally important changes (MIC) (15). To determine the MCID and Minimally Clinically Important Improvement (MCII), either distribution-based or anchor-based measures may be used. Distribution-based methods use statistical and psychometric properties of a measure to estimate effect size and standard error of measurement (SEM=σ(1-r)1/2, where σ=standard deviation and r=reliability (16)) as functions of variability and reliability, while anchor-based methods use a change in the patient’s or provider’s perception to identify the corresponding magnitude of change in a selected measure (8).
Preliminary work by Perera and colleagues estimated what constitutes a meaningful change for three performance measures: gait speed, SPPB, and 6MWD using data from varying populations enrolled in both observational and clinical studies: 1) a basic training data set of a 3-month clinical trial of strength training intervention in people with mild-to-moderate limitations; 2) 1-year data of participants in the Predicting Elderly Performance (PEP) study dataset; and 3) 3-month data from the Stroke Rehabilitation (REHAB) randomized clinical trial of a therapeutic exercise program (8). Using both distribution- and anchor-based approaches, they concluded that small but meaningful changes are near to 0.05 m/s for gait speed, 0.5 points for SPPB, and 20m for 6MWD; and that substantial changes were near to  0.10 m/s for gait speed, 1.0 point for SPPB, and 50m for 6MWD.
They also found that meaningful changes are not affected by gender, race, or baseline performance in the Health ABC study. While men tended to have greater magnitudes for meaningful change in 400MWT and there were health and disease differences (e.g. substantial change estimate for SPPB for those with greater body mass index (BMI) when the anchor of walking ¼ mile was used, but not using other anchors), they did not show a consistent pattern and were limited by dropout bias in 400MWT (17).
In the Lifestyle Interventions and Independence for Elders Pilot (LIFE-P) study of exercise as an intervention, investigators examined the relationship between self-reported and performance measures and estimated the magnitude of meaningful change in 400MWT, 4MGS, and SPPB (18). They used both distribution-based and anchor-based methods, two magnitudes of change, and multiple indicators of self-perceived mobility. Relationships between self-reported and performance measures were consistent between treatment arms. Minimally significant changes were 20-30 seconds in the 400MWT, 0.03-0.05 m/s in the 4MGS, and 0.3-0.8 points in the SPPB. Substantial changes were 50-60 seconds in the 400MWT, 0.08 m/s in the 4MGS, and 0.4-1.5 points in the SPPB.

 

A validation approach to define meaningful change

A crucial first step in defining meaningful change is to clarify what is meant by the concept of meaningful change. A clinically important change in physical functioning should be large enough that a person perceives the change or that participation (e.g., daily roles) is affected. In clinical trials, a clinically important change indicates a treatment effect large enough to support market authorization of a drug. The analytical approach chosen should be driven by how meaningful change is defined for a particular study depending on its main purpose.
Defining meaningful change may be challenging for several reasons. First, meaningful change varies according to context, including baseline level of function as well as demographic and disease considerations. Second, when no gold standard exists with which to make a comparison, the measures by which meaningful changes are judged may not reflect the true state.
One method for assessing the ability of a measure to discriminate individuals by their anchor status is to determine meaningful adverse change (MAC) that achieves both good sensitivity and specificity (19). The Women’s Health and Aging Study (WHAS), an observational study on the characteristics and progression of disability in older, functionally limited women (20) provides an example of a validation framework for evaluating change over the course of one year using usual pace walking speed as the performance measure and self-reported walking difficulties as the anchor. Participants were dichotomized into those who worsened in any one of seven categories of walking difficulty and those who did not worsen in any category, and walking speed change was compared for those two groups. The difference in mean change between those two groups was estimated at -0.091 meters/sec (95% confidence interval [CI] of -0.128 to -0.054), with a mean change among those not worsening of 0.011 (95% CI of -0.014 to 0.035). A decline of 0.10 m/sec (substantial change), however, had a sensitivity of .41 and specificity of 0.73 for self-perceived worsening, and receiver operating characteristic (ROC) analysis of the ability to discriminate clinical change yielded an area under the curve (AUC) of only 0.59, suggesting that other considerations would be needed to adjudicate whether this is good enough for clinical practice in the community-dwelling context of the WHAS. Reanalyzing the data by evaluating empirical cumulative probability distributions of walking speed stratified by decline in 3 categories of walking difficulty all the way to improving in 3 categories of walking difficulty yielded overlapping curves (except when contrasting perception changes transitioning across multiple categories), indicating that either the anchor is inappropriate or a more sensitive performance measure is needed. In such a context, building performance indices combining multiple measures simultaneously may prove useful for enhancing precision.

 

Combining performance and patient reported outcome measures

Patient reported outcome measures (PROMs) have been advocated by regulatory agencies because they provide information about what is meaningful to patients. For example, fear of falling is one possible patient-reported measure that might correlate well with balance, strength, and other mobility-related functions. Many studies combine PROMs with performance measures since they provide complementary information (21). In a prospective cohort study, Perera and colleagues showed that performance change and self-reported change were both independently associated with 5-year survival (14).
Studies comparing self-reported versus activity-based performance measures of function such as self-paced walk, TUG, and stair tests have shown moderate correlations (22-25), suggesting that the measures provide complementary information. Moreover, these studies show that the selection of measures is condition specific. For example, in these studies the TUG was shown to be the most sensitive measure to change in patients who have undergone total hip replacement, while in patients undergoing knee arthroplasty the stair measure was more responsive to change.

Case study: Determining meaningful change in physical function in testosterone trials in older men (TOM)

The Testosterone in Older Men with Mobility Limitations (TOM) trial was designed to determine the effect of testosterone administration on physical function and lower extremity strength in older men with mobility limitations and low serum levels of testosterone. The trial was terminated early as a result of an increase in adverse cardiovascular events among participants in the treatment group (26). The trial included both a self-reported measure, the Late-Life Function and Disability Instrument (LLFDI), and several performance-based measures including handgrip strength, bilateral leg and chest press (a measure of strength and power), 12-step stair climb, the 40-meter walk test, and the SPPB. The LLFDI assesses participants’ ability to complete discrete actions or activity and their performance of socially-defined tasks (activity and participation in the ICF framework).
Both anchor-based and distribution-based methods were used to determine the MCID for physical function. To assess anchor-based responsiveness, participants were grouped according to self-reported global rating of improvement (better versus no change or worse). The distribution-based responsiveness analysis provided an estimate of effect size, minimal detectable change based on a 90% CI (MDC90), and the percentage of participants exceeding MDC90 by group.
These analyses demonstrated that loaded walk and stair climb were the most sensitive, with anchor and distribution-based measures being similar. The SPPB balance assessment was the least sensitive measure. Handgrip strength and LLFDI were not responsive to change while both the Foundation of the National Institutes of Health (FNIH) and European guidelines advocate using handgrip strength to identify participants for sarcopenia trials (27, 28).  These results suggest that this measure may be less useful to measure responsiveness to an intervention.

 

Regulatory considerations of clinically meaningful change

Regulators prefer hard clinical endpoints to surrogate endpoints (e.g. biomarkers) when making decisions about market authorization. For example, in osteoporosis trials, a statistically significant difference in fracture rates – a hard clinical endpoint – is considered meaningful (29), whereas a surrogate endpoint such as bone mineral density would not in and of itself be considered meaningful, although it may be used to bridge studies for extension of indications.
The European Medicines Agency (EMA) guideline on clinical investigation of medicinal products used pain and function as co-primary endpoints in the treatment of osteoarthritis (30). The expert consensus committee that developed the guidelines suggested the threshold for minimal perceptible clinical improvement in pain as a 10 mm improvement on a 100 mm visual analog pain scale for drugs intended to improve symptoms or at least a 5 mm mean difference between placebo and active groups (31). These criteria were applied in a trial of chondroitin sulfate compared to placebo and the non-steroidal anti-inflammatory drug (NSAID) celecoxib, which showed that both drugs produced a statistically significant and clinically relevant improvement, yet whether the magnitude of the effect is sufficient to justify granting market approval remained an unanswered question (32).
A PROM, the SarQoL, has been developed to assess quality-of-life in sarcopenia patients (33). While it has demonstrated the ability to detect statistically significant change, the MIC has not yet been determined; thus, the clinical significance is not clear.
Whether to use continuous or dichotomous variables may also be discussed with regulators. For example, the FRActure in postmenopausal woMen with ostEoporosis (FRAME) study of the bone-forming agent romosozumab assessed percent change in BMD from baseline, demonstrating that the treatment results in a rapid increase in BMD in comparison to bone loss in the placebo group and at the same time reduces fracture risk (34). When using percent change the clinical significance of the observed absolute change must also be considered.
In addition to data on clinically meaningful change used to support marketing authorization for a treatment, payers and policy makers may require additional real-world data and cost-effectiveness studies to support reimbursement (35). For example, validation of the FRAX risk assessment tool was achieved by the Screening for Osteoporosis in Older Women for the Prevention of Fracture (SCOOP) study in the United Kingdom, which showed that screening with FRAX resulted in a reduced risk of hip fracture, i.e., that the tool is medically relevant (36). Another real-world study conducted by the French Ministry of Health – the Pharmaco-Epidemiology of GonArthroSis and coxarthrosis (PEGASus) study — assessed the ability of multiple symptomatic slow-acting drugs for osteoarthritis to reduce the consumption of NSAIDs, which are associated with substantial adverse reactions. Only glucosamine sulfate showed a significant reduction in consumption of NSAIDs.
The FDA has a somewhat different perspective on meaningful change in that they focus on within-patient anchor-based change. Distribution-based approaches may be used as supportive or supplementary information. Moreover, they require changes to be meaningful to the patient, using terms to which patients can relate. This has led them to incorporate patient preferences into their deliberations and selection of outcome measures.
The Aging in Motion (AIM) coalition has been working with FDA for several years on a project to qualify gait speed alone and the SPPB as acceptable and endorsed measures of function. However, the agency has stressed the need for both an objective measure such as SPPB and a self-report approach, which has increased the complexity of the qualification process.
PROMs present many potential challenges for sponsors.   The correlation between PROMs and objective performance measures is modest, and the FDA suggests using them together as joint outcomes. Multiple primary outcomes may increase trial sample size requirements. PROMs are also subject to differences in beliefs and behaviors, making them more difficult to compare across diverse populations. One suggested approach would be to use a goal attainment scale in which the patient sets goals as well as metrics for success.
PROMs, including QOL measures, also must be very specific to the indication. For sarcopenia, this means that PROMs should relate to how low muscle mass affects how patients feel, function, and survive. Used in combination with performance measures, they could provide a powerful way of demonstrating efficacy. While there has been a reluctance of pharmaceutical companies to embed context-specific PROMs in Phase 2 and 3 studies, doing so would produce an enormous body of data that could help establish relevant anchors to estimate MCID and validate other measures.

 

Moving Forward

One problem for research into how the ICF guidelines are interpreted is that structure and function are typically assessed with clinical measures applied in a controlled environment while assessment of activity and participation require capturing the patient perspective, which is heavily influenced by the environment, adaptation mechanisms, resilience, and coping. Moreover, meaningful change is context, perspective, and purpose dependent.
The Task Force identified several key areas for future research that should be considered when setting the criteria for a clinically meaningful change in a clinical trial:
•    Published estimates of MCID derived from study participants who are only mildly functionally limited may have limited value for studies that enroll participants at high risk of physical disability. In substantially impaired participants, a small improvement in a performance test may translate into a large benefit in daily life and be perceived by the participant. Future work should address MCID in subsets of the population stratified by ability, with the instruments chosen being appropriate for that level of ability.
•    The validation framework described above offers a paradigm for thinking carefully about the ideal definition of clinically meaningful change and then working backwards to identify how to measure and assess meaningful change.
•    To measure clinically meaningful changes in real-world performance, it may be appropriate to incorporate into trials continuous digital technologies such as accelerometers as well as novel analytical techniques to determine MCID, CMD, and MCII. Signal processing of accelerometer data may also identify additional features predictive of adverse or beneficial outcomes.

 

Acknowledgements: The authors thank Lisa J. Bain for assistance in the preparation of this manuscript.
Conflicts of interest:  The Task Force was partially funded by one educational grant from the Aging In Motion Coalition and registration fees from industrial participants (Biogen, Biophytis, Cytokinetics, Glaxosmithkline, Longeveron, Pfizer and Rejuvenate Biomed NV). These corporations placed no restrictions on this work. S. Eremenco, F. Landi declare there are no conflicts. Dr. Guralnik reports personal fees from Pluristem , personal fees from Viking Therapeutics, personal fees from Novartis Pharma, outside the submitted work. K. Bandeen-Roche reports grants from National Institutes of Health,  during the conduct of the study.
S.A.R.  Bhasin reports grants from AbbVie, grants from Alivegen, grants from MIB, other from FPT, other from AbbVie, outside the submitted work. J. Muscedere is Scientific Director for the Canadian Frailty Network, a non-for profit network funded by the Government of Canada. S. Perera has received Travel expenses to the International Conference on Frailty and Sarcopenia Task Force meeting in February 2019 in Miami Beach, FL paid by Alliance for Aging Research. J.Y. Reginster reports grants and personal fees from IBSA-GENEVRIER, grants and personal fees from MYLAN, grants and personal fees from RADIUS HEALTH, personal fees from PIERRE FABRE, grants from CNIEL, personal fees from DAIRY RESEARCH COUNCIL (DRC), outside the submitted work. B. Vellas reports grants from Nestle, Nutricia, Novartis outside the submitted work.
Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

 

References

1.    World Health Organization. The International Classification of Functioning, Disability and Health (ICF). Geneva2001.
2.    Jette AM. Toward a common language for function, disability, and health. Phys Ther 2006;86:726-734.
3.    Abellan van Kan G, Cameron Chumlea W, Gillette-Guyonet S, et al. Clinical trials on sarcopenia: methodological issues regarding phase 3 trials. Clin Geriatr Med 2011;27:471-482.
4.    Vellas B, Fielding R, Bhasin S, et al. Sarcopenia Trials in Specific Diseases: Report by the International Conference on Frailty and Sarcopenia Research Task Force. J Frailty Aging 2016;5:194-200.
5.    Vellas B, Pahor M, Manini T, et al. Designing pharmaceutical trials for sarcopenia in frail older adults: EU/US Task Force recommendations. J Nutr Health Aging 2013;17:612-618.
6.    Chiles Shaffer N, Fabbri E, Ferrucci L, Shardell M, Simonsick EM, Studenski S. Muscle Quality, Strength, and Lower Extremity Physical Performance in the Baltimore Longitudinal Study of Aging. J Frailty Aging 2017;6:183-187.
7.    Pahor M, Manini T, Cesari M. Sarcopenia: clinical evaluation, biological markers and other evaluation tools. J Nutr Health Aging 2009;13:724-728.
8.    Perera S, Mody SH, Woodman RC, Studenski SA. Meaningful change and responsiveness in common physical performance measures in older adults. J Am Geriatr Soc 2006;54:743-749.
9.    Guralnik JM, Simonsick EM, Ferrucci L, et al. A short physical performance battery assessing lower extremity function: association with self-reported disability and prediction of mortality and nursing home admission. J Gerontol 1994;49:M85-94.
10.    Podsiadlo D, Richardson S. The Timed “Up & Go”: A test of basic functional mobility for frail elderly persons. J Am Geriatr Soc 1991;39:142-148.
11.    Cesari M, Fielding R, Benichou O, et al. Pharmacological Interventions in Frailty and Sarcopenia: Report by the International Conference on Frailty and Sarcopenia Research Task Force. J Frailty Aging 2015;4:114-120.
12.    Brach JS, Perera S, Studenski S, Katz M, Hall C, Verghese J. Meaningful change in measures of gait variability in older adults. Gait Posture 2010;31:175-179.
13.    Oh-Park M, Perera S, Verghese J. Clinically meaningful change in stair negotiation performance in older adults. Gait Posture 2012;36:532-536.
14.    Perera S, Studenski S, Chandler JM, Guralnik JM. Magnitude and patterns of decline in health and function in 1 year affect subsequent 5-year survival. J Gerontol A Biol Sci Med Sci 2005;60:894-900.
15.    Page P. Beyond statistical significance: clinical interpretation of rehabilitation research literature. Int J Sports Phys Ther 2014;9:726-736.
16.    McHorney CA, Tarlov AR. Individual-patient monitoring in clinical practice: are available health status surveys adequate? Qual Life Res 1995;4:293-307.
17.    Perera S, Studenski S, Newman A, et al. Are estimates of meaningful decline in mobility performance consistent among clinically important subgroups? (Health ABC study). J Gerontol A Biol Sci Med Sci 2014;69:1260-1268.
18.    Kwon S, Perera S, Pahor M, et al. What is a meaningful change in physical performance? Findings from a clinical trial in older adults (the LIFE-P study). J Nutr Health Aging 2009;13:538-544.
19.    Terwee CB, Bot SD, de Boer MR, et al. Quality criteria were proposed for measurement properties of health status questionnaires. J Clin Epidemiol 2007;60:34-42.
20.    Simonsick EM, Guralnik JM, Volpato S, Balfour J, Fried LP. Just get out the door! Importance of walking outside the home for maintaining mobility: findings from the women’s health and aging study. J Am Geriatr Soc 2005;53:198-203.
21.    Studenski S. What are the outcomes of treatment among patients with sarcopenia? J Nutr Health Aging 2009;13:733-736.
22.    Finch E, Walsh M, Thomas SG, Woodhouse LJ. Functional ability perceived by individuals following total knee arthroplasty compared to age-matched individuals without knee disability. J Orthop Sports Phys Ther 1998;27:255-263.
23.    Jones LW, Cohen RR, Mabe SK, et al. Assessment of physical functioning in recurrent glioma: preliminary comparison of performance status to functional capacity testing. J Neurooncol 2009;94:79-85.
24.    Stratford PW, Kennedy D, Pagura SM, Gollish JD. The relationship between self-report and performance-related measures: questioning the content validity of timed tests. Arthritis Rheum 2003;49:535-540.
25.    Stratford PW, Kennedy DM, Woodhouse LJ. Performance measures provide assessments of pain and function in people with advanced osteoarthritis of the hip or knee. Phys Ther 2006;86:1489-1496.
26.    Basaria S, Coviello AD, Travison TG, et al. Adverse events associated with testosterone administration. N Engl J Med 2010;363:109-122.
27.    Cruz-Jentoft AJ, Baeyens JP, Bauer JM, et al. Sarcopenia: European consensus on definition and diagnosis: Report of the European Working Group on Sarcopenia in Older People. Age Ageing 2010;39:412-423.
28.    Studenski SA, Peters KW, Alley DE, et al. The FNIH sarcopenia project: rationale, study description, conference recommendations, and final estimates. J Gerontol A Biol Sci Med Sci 2014;69:547-558.
29.    Miller PD, Hattersley G, Riis BJ, et al. Effect of Abaloparatide vs Placebo on New Vertebral Fractures in Postmenopausal Women With Osteoporosis: A Randomized Clinical Trial. JAMA 2016;316:722-733.
30.    European Medicines Agency. Guideline on clinical investigation of medicinal products used in the treatment of osteoarthritis. In: (CHMP) CftMPfHU, ed. London2010.
31.    Reginster JY, Reiter-Niesert S, Bruyere O, et al. Recommendations for an update of the 2010 European regulatory guideline on clinical investigation of medicinal products used in the treatment of osteoarthritis and reflections about related clinically relevant outcomes: expert consensus statement. Osteoarthritis Cartilage 2015;23:2086-2093.
32.    Reginster JY, Dudler J, Blicharski T, Pavelka K. Pharmaceutical-grade Chondroitin sulfate is as effective as celecoxib and superior to placebo in symptomatic knee osteoarthritis: the ChONdroitin versus CElecoxib versus Placebo Trial (CONCEPT). Ann Rheum Dis 2017;76:1537-1543.
33.    Beaudart C, Biver E, Reginster JY, et al. Development of a self-administrated quality of life questionnaire for sarcopenia in elderly subjects: the SarQoL. Age Ageing 2015;44:960-966.
34.    Cosman F, Crittenden DB, Ferrari S, et al. FRAME Study: The Foundation Effect of Building Bone With 1 Year of Romosozumab Leads to Continued Lower Fracture Risk After Transition to Denosumab. J Bone Miner Res 2018;33:1219-1226.
35.    MacEwan JP, Gill TM, Johnson K, et al. Measuring Sarcopenia Severity in Older Adults and the Value of Effective Interventions. J Nutr Health Aging 2018;22:1253-1258.
36.    McCloskey E, Johansson H, Harvey NC, et al. Management of Patients With High Baseline Hip Fracture Risk by FRAX Reduces Hip Fractures-A Post Hoc Analysis of the SCOOP Study. J Bone Miner Res 2018;33:1020-1026.

ICFSR TASK FORCE PERSPECTIVE ON BIOMARKERS FOR SARCOPENIA AND FRAILTY

 

L. Rodriguez-Mañas1, I. Araujo de Carvalho2, S. Bhasin3, H.A. Bischoff-Ferrari4, M. Cesari5, W. Evans6, J.M. Hare7, M. Pahor8, A. Parini9, Y. Rolland10, R.A. Fielding11, J. Walston12, B. Vellas13 and the ICFSR Task Force

 

1. Servicio de Geriatría, Hospital Universitario de Getafe, Toledo, Spain; 2. World Health Organization, Geneva, Switzerland; 3. Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA; 4. University Hospital and University of Zurich, Zurich, Switzerland; 5. Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, University of Milan, Milan, Italy; 6. Duke University Medical Center, Durham NC, USA; 7. Interdisciplinary Stem Cell Institute, University of Miami Miller School of Medicine, Miami, FL, USA; 8. University of Florida Institute on Aging, Gainesville, FL, USA; 9. Institute of Cardiovascular and Metabolic Diseases, INSERM U1048, CHU Toulouse, Toulouse France; 10. Service de Médecine Interne et Gérontologie, Clinique Gérontopôle, Hôpital La Crave, Casselardit, Toulouse, France; 11. Tufts University, Boston, MA, USA; 12. Johns Hopkins Division of Geriatric Medicine and Gerontology, Baltimore, Maryland, USA; 13. Gerontopole, INSERM U1027, Alzheimer’s Disease Research and Clinical Center, Toulouse University Hospital, Toulouse, France.
Corresponding author: L. Rodriguez Mañas, Hospital Universitario de Getafe, Spain, leocadio.rodriguez@salud.madrid.org

Task force members: Samuel Agus (Paris, France), Islene Araujo de Carvalho (Geneva, Switzerland), Mylène Aubertin Leheudre (Montréal, Canada), Karen Bandeen-Roche (Baltimore, USA), Ann Belien (Hesusden-Zolder, Belgium), Shalender Bhasin (Boston, USA), Heike Bischoff-Ferrari  (Zurich, Switzerland), Andreas Busch (Vevey, Switzerland), Ryne Carney (Washington, USA), Matteo Cesari (Milano, Italy), Caroline Couleur (Epalinges, Switzerland), Alfonso Cruz Jentoft (Madrid, Spain), Susanna Del Signore (London, United Kingdom), Carla Delannoy (Vevey, Switzerland), Waly Dioh (Paris, France), Sonya Eremenco (Tucson, USA), Bill Evans (Durham, USA), Toby Ferguson (Cambridge, USA), Jack Guralnik (Baltimore, USA), Ludo Haazen (Hesusden-Zolder, Belgium), Joshua Hare (Miami, USA), Aaron Hinken (Collegeville, USA), Darren Hwee (South San Francisco, USA), Lori Janesko (Uniontown, USA), Kala Kaspar (Vevey, Switzerland),  Francesco Landi (Roma, Italy), Valérie Legrand (Nanterre, France), Bradley Morgan (South San Francisco, USA), John Morley (St Louis, USA), John Muscedere (Kingston, Canada), David Neil (Collegeville, USA),  Marco Pahor (Gainesville, USA),  Marika Paul (Columbus, USA), Subashan  Perera (Pittsburgh, USA), Suzette Pereira (Columbus, USA), John Rathmacher (Ames, USA), Reginster Jean Yves (Liège, Belgium), Leocadio Rodriguez Mañas (Getafe (Madrid), Spain), Michelle Rossulek (Cambridge, USA), Jorge Ruiz (Miami, USA), Lisa Tarasenko (Cambridge, USA), Effie Tozzo (Cambridge, USA), Heber Varela (Miami, USA), Bruno Vellas (Toulouse, France), Jeremy Walston (Baltimore, USA), Debra Waters (Dunedin, New Zealand), Linda Woodhouse (Edmonton, Canada)

J Frailty Aging 2019;in press
Published online October 7, 2019, http://dx.doi.org/10.14283/jfa.2019.32

 


Abstract

Biomarkers of frailty and sarcopenia are essential to advance the understanding of these conditions of aging and develop new diagnostic tools and effective treatments. The International Conference on Frailty and Sarcopenia Research (ICFSR) Task Force – a group of academic and industry scientists from around the world — met in February 2019 to discuss the current state of biomarker development for frailty and sarcopenia. The D3Cr dilution method, which assesses creatinine excretion as a biochemical measure of muscle mass, was suggested as a more accurate measure of functional muscle mass than assessment by dual energy x-ray absorptiometry (DXA). Proposed biomarkers of frailty include markers of inflammation, the hypothalamic-pituitary-adrenal (HPA) axis response to stress, altered glucose insulin dynamics, endocrine dysregulation, aging, and others, acknowledging the complex multisystem etiology that contributes to frailty. Lack of clarity regarding a regulatory pathway for biomarker development has hindered progress; however, there are currently several international efforts to develop such biomarkers as tools to improve the treatment of individuals presenting these conditions. .

Key words: Frailty, sarcopenia, biomarkers, consensus.


 

Introduction

Biomarkers have proven essential to advance understanding of the biological underpinnings of various diseases, as diagnostic tools, and in clinical trials as indicators of treatment effectiveness. For complex conditions such as sarcopenia and frailty, the multiplicity of phenotypes and pathogenic mechanisms makes the development of biomarkers particularly challenging, since biological markers associated with single aspects of the condition are only marginally associated with clinically relevant outcomes (1).
In February 2019, the International Conference on Frailty and Sarcopenia Research (ICSFR) Task Force convened a meeting to discuss the current status of biomarker development for sarcopenia and frailty. The ICFSR Task Force comprises academic and industry scientists from 13 countries in North America, Europe, Asia, and Australia/Oceania who are involved in the development of interventions to treat these disabling age-related conditions.
The term sarcopenia was coined by Rosenberg in the late ‘80s to describe age-related loss of muscle mass and was later revised to incorporate declines in muscle strength and physical function (2, 3). Assessment of muscle mass by dual energy x-ray absorptiometry (DXA), computed tomography (CT), and magnetic resonance imaging (MRI) have provided the most widely used biomarkers for sarcopenia (4). However, CT and MRI have limitations related to the high cost and complexity of the technology, and DXA has shown poor correlation with health-related quality of life (5).
Frailty is a syndrome characterized by progressive functional decline, decreased physiological reserve and resilience and increased vulnerability to a variety of stressors (6). Multiple operational definitions of frailty have been proposed (7-9). The phenotypic criteria proposed by Fried and colleagues, which define frailty by the presence of weakness, slowness, weight loss, declining physical function, and fatigue continue to be the most widely used (10).
For both frailty and sarcopenia, identification of biomarkers depends on the definition of the condition and the goal is to develop clinically relevant markers as diagnostic tools, to assess treatment effectiveness, to understand biological etiology, and to advance prevention efforts. In 2016, an International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) code was established for sarcopenia, which enabled it to be recognized by the US Food and Drug Administration (FDA) and European Medicines Agency (EMA) and the National Centre for Classification in Health (NCCH) in Australia as a separately reportable condition (11). By removing barriers for diagnosing sarcopenia, the ICD-10 code will enable standardized data collection and improve the efficiency of clinical trials (12). However, the presence of multiple, largely overlapping operational definitions and the multidimensional nature of frailty make it unlikely that an ICD code for frailty will be established in the near future.
Frailty is strongly associated with muscle mass and function; thus, sarcopenia has been proposed as the biological substrate for physical frailty (13). Merging the two conditions into a single entity – Physical Frailty and Sarcopenia (PF&S) – a condition that can be diagnosed and potentially treated has also been proposed (14, 15) and a core inflammatory profile with a gender-specific signature has been identified (16).

 

Biomarkers of sarcopenia and frailty

A definition of sarcopenia should take into account the role of muscle mass in the risk of disability and age-related risk of chronic disease; thus, for sarcopenia measures of muscle mass, quality, and function have been proposed as potential biomarkers. Plasma growth and differentiation factor-15 (GDF-15) has also been associated with sarcopenia-related outcomes and increases with age but has not been evaluated as a sarcopenia biomarker (17).  Since frailty has a complex multisystem etiology, biomarkers needed to assess multiple dysregulated systems. Proposed biomarkers of frailty include inflammatory markers such as interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α), C-reactive protein (CRP), neutrophil cell count (18), and others (Table 1).

Table 1 Possible Biomarkers of Frailty and Sarcopenia

Table 1
Possible Biomarkers of Frailty and Sarcopenia

CRP, C-reactive protein; DXA, dual X-ray absorptiometry; GDF-15, growth differentiation factor 15; IFNγ, interferon gamma; IL-6, interleukin 6; IL-8, interleukin 8; MCP-1, monocyte chemoattractant protein 1; MPO, myeloperoxidase; PDGF-BB, platelet derived growth factor BB; SIRT, silent mating-type information regulation 2 homolog 1; TNF-α, tumor necrosis factor alpha.

 

D3-Creatine (D3Cr) dilution – a biomarker for sarcopenia

Although dozens of papers measure lean mass and call it muscle mass, lean mass is not the same as muscle mass. Indeed, the Foundation of the National Institutes of Health (FNIH) Sarcopenia project concluded that low lean mass is poor predictor of functional impairment (19). The relationship between muscle mass measured by XX and fracture risk is highly significant; however appendicular lean mass (ALM) is not related to what? at all (20).
To assess the true effects of intrinsic, age-associated effects on skeletal muscle contractile function, an accurate measure of functional muscle mass undiluted by lipid, connective tissue, and fibrotic tissue is needed. Assessment of creatinine excretion provides such a measure of muscle mass (21). Creatine is irreversibly converted to creatinine and excreted in urine, where it can be measured by liquid chromatography-mass spectrometry (LCMS). Evans and colleagues developed a direct and accurate method for measuring creatine pool size by orally administering stable isotope-labelled creatine and then collecting a single fasted urine sample 48-96 hours after dosing for measurement of D3Cr (22, 23). This D3Cr dilution method uses an algorithm based on urine levels of creatine and creatinine to determine the dilution of the oral label in the whole-body creatine pool of skeletal muscle, thus providing an accurate measure of skeletal muscle mass.
In the Osteoporotic Fractures in Men (MrOS) study, a multi-site study of community dwelling men 80 years and older, the D3Cr dilution method was compared to DXA, high-resolution peripheral quantitative CT (HRpQCT), Short Physical Performance Batter (SPPB), the 400-meter walk test (400MW), and force plate for lower extremity power. Muscle mass by the D3Cr dilution method showed a moderate correlation with DXA total lean mass but no correlation with DXA ALM/ht2. It also demonstrated a strong relation between muscle mass determined by D3Cr dilution method with physical performance (SPPB, chair stands), incidence of falls, and mobility limitations (20). In assessing the relative importance of muscle versus fat in sarcopenic obesity, repeated assessment of multiple measures at 18-month intervals showed that muscle mass determined using the D3Cr dilution method correlated with grip strength and walking speed even though there was no change in total lean mass, ALM, or ALM/ht2. Muscle mass determined using the D3Cr dilution method also was shown to be a strong predictor of disability (24). These results suggest that muscle mass is a primary determinant of physical performance and adverse outcomes, and that the relative effects of higher body fatness are less important. However, results regarding the D3Cr dilution method need to be replicated in large representative cohorts.

Frailty biomarkers

Potential biological triggers of frailty in older adults may include increased inflammation and mitophagy (25); altered stress response systems mediated through the angiotensin system, the HPA axis, and the sympathetic nervous system; and decreased energy production. Chronic inflammatory markers such as IL-6, CRP, interleukin-1-receptor agonist, interleukin-18, and soluble TNF-α receptor 1 (sTNFR1), combined in an inflammation index score, appears to capture the magnitude of chronic inflammation in aging and was shown to be a better predictor of mortality compared to single measures (26). However, these markers are highly variable and non-specific and influenced by meals and time of day. Recent studies suggest that sTNFR1 is the least variable over weeks and months.
Salivary cortisol has been used as a marker of the hypothalamic-pituitary-adrenal (HPA) axis response to stress (27), and a diurnal pattern of cortisol levels (lower in the morning, higher in the evening) has been associated with frailty (28, 29). Frailty has also been associated with lower levels of serum insulin-like growth factor (30), lower levels of testosterone and high levels of estradiol (31, 32), elevated levels of silent mating-type information regulation 2 homolog 1 (SIRT1) (33), altered glucose-insulin dynamics (34), endocrine dysregulation (35), endothelial dysfunction (36), elevated clotting factors (37), mitochondrial dysfunction (38), and alterations in the metabolome (39).
Given that frailty is an aging-related syndrome, biomarkers of aging are also important and have been gaining increased attention with the emergence of the field of gerosciences (40-42). For example, possible biomarkers of frailty include a marker of nuclear membrane defects, which has been associated with aging (43), the expression of several mRNAs involved in the cell response to stress (44), and markers of mTOR activation, the adaptive immune system, and cell senescence.
Age-related changes in the adaptive and innate immune response including the chronic low-level proinflammatory state known as inflammaging, and immunosenescence, which is strongly driven by inflammaging, result in increased susceptibility to influenza and other disease and a decreased response to influenza vaccination (6, 45-47). Hare and colleagues have been developing mesenchymal stem cells (MSCs) as a treatment for many diseases of aging, including frailty. They have shown that MSCs improve immune potential by modulating T and B cell response. Moreover, their studies suggest that vaccine responsiveness may represent an ideal biomarker of aging in that it correlates with the frailty phenotype, changes with interventions that change the phenotype (such as MSC treatment), represents a biologically plausible mechanism of frailty, and provides medically meaningful information.

 

Regulatory considerations

While frailty is an acceptable concept in clinical care and for characterizing populations, it is not presently a “disease entity” recognized by and ICD-10 code. However, to adapt our health care system to an aging population, transitioning from a disease-centered to a function-centered approach will be necessary to maintain function in older adults and prevent dependency.  For these reasons, biomarkers of frailty are urgently needed. Frailty is an entity where several physiological systems are dysregulated or malfunctioning. Thus, an isolated biomarker of frailty would have limited usefulness in drug development whereas the search for panels of biomarkers seems promising.
Context of use is an important consideration for regulators. Biomarkers are useful as indicators of target engagement or for screening, diagnosis, or assessing outcomes in specifically-designated populations. Consensus from the field on what would represent an appropriate biomarker/set of biomarkers for proof of concept versus clinical trials could support efforts to achieve regulatory acceptance. However, it is necessary that the physiopathological mechanisms underlying the two conditions of interest are carefully defined and limited in order to propose unequivocal biomarkers of “disease”. Ongoing projects such as the Sarcopenia and Physical fRailty IN older people: multi-componenT Treatment strategies (SPRINTT), funded by the Innovative Medicines Initiative (IMI), are going in exactly this direction (48). Running in parallel with SPRINTT, the BIOmarkers associated with Sarcopenia and Physical frailty in EldeRly pErsons (BIOSPHERE) study analyzed 12 candidate serum biomarkers to identify and validate a panel of PF&S biomarkers that capture the multi-factorial nature of PF&S, identify potential intervention targets, and provide potential diagnostic tools and endpoints for use in clinical trials (49). In this same regard, FRAILOMICS is evaluating the role of sets of biomarkers in the prediction of the risk of developing physical frailty, its diagnosis and its prognosis in terms of incident disability and death (50, 51).

 

Conclusions

Recognizing that the field is in the early stages of developing biomarkers for sarcopenia and frailty, the Task Force identified several research gaps and barriers that need to be addressed to expedite this process and move biomarkers from research to clinical settings.
Part of the difficulty resides in the difficulty of applying the usual standards applicable to stand-alone diseases of young and adult individuals to the more complex and heterogeneous nature of age-related conditions of advanced age. In addition, it is important to improve our understanding of measurements able to capture the conditions of interest in order to promote their optimal  translation from research into clinical practice. Practical issues such as cost effectiveness also need to be considered.
Moreover, current biomarker discovery efforts have been limited by being based on predefined hypotheses. Broader screening of potential biomarkers through omics and an integrated bioinformatics approaches could advance discovery efforts. Since frailty is a failure of many systems, panels of biomarkers will likely be required. Machine learning and information technology innovation could thus be used to develop risk scores that could be used in clinical and research settings. Other technologies, such as induced pluripotent stem cells (iPSCs), could be used to study markers of senescence and could also enable a move towards personalized medicine.

 

Acknowledgements: The authors thank Lisa J. Bain for assistance in the preparation of this manuscript.
Conflicts of interest:  The Task Force was partially funded by one educational grant, Aging In Motion, and registration fees from industrial participants (Biogen, Biophytis, Cytokinetics, Glaxosmithkline, Longeveron, Pfizer and Rejuvenate Biomed NV). These corporations placed no restrictions on this work.
L. Rodriguez Mañas, M. Cesari, M Pahor, J. Walston declare there are no conflicts. S. Bhasin reports grants from AbbVie, grants from Alivegen, grants from MIB, grants from Abbott, other from FPT, other from AbbVie, outside the submitted work. He has a patent Free testosterone determination issued. Y. Rolland reports grants from Biophytis, Novartis, outside the submitted work. R. Fielding reports grants from National Institutes of Health (National Institute on Aging),  during the conduct of the study; grants, personal fees and other from Axcella Health, other from Inside Tracker, grants and personal fees from Biophytis, grants and personal fees from Astellas, personal fees from Cytokinetics, personal fees from Amazentis, grants and personal fees from Nestle’, personal fees from Glaxo Smith Kline, outside the submitted work. B. Vellas reports grants from Nestle, Nutricia, Novartis outside the submitted work.
Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

 

References

1.    Calvani R, Marini F, Cesari M, et al. Biomarkers for physical frailty and sarcopenia: state of the science and future developments. J Cachexia Sarcopenia Muscle 2015;6:278-286.
2.    Rosenberg IH. Sarcopenia: origins and clinical relevance. J Nutr 1997;127:990S-991S.
3.    Clark BC. Neuromuscular Changes with Aging and Sarcopenia. J Frailty Aging 2019;8:7-9.
4.    Vellas B, Pahor M, Manini T, et al. Designing pharmaceutical trials for sarcopenia in frail older adults: EU/US Task Force recommendations. J Nutr Health Aging 2013;17:612-618.
5.    Woo T, Yu S, Visvanathan R. Systematic Literature Review on the Relationship Between Biomarkers of Sarcopenia and Quality of Life in Older People. J Frailty Aging 2016;5:88-99.
6.    Pahor M, Kritchevsky SB, Waters DL, et al. Designing Drug Trials for Frailty: ICFSR Task Force 2018. J Frailty Aging 2018;7:150-154.
7.    Gobbens RJ, Luijkx KG, Wijnen-Sponselee MT, Schols JM. Towards an integral conceptual model of frailty. J Nutr Health Aging 2010;14:175-181.
8.    Rockwood K, Song X, MacKnight C, et al. A global clinical measure of fitness and frailty in elderly people. CMAJ 2005;173:489-495.
9.    Rodriguez-Manas L, Feart C, Mann G, et al. Searching for an operational definition of frailty: a Delphi method based consensus statement: the frailty operative definition-consensus conference project. J Gerontol A Biol Sci Med Sci 2013;68:62-67.
10.    Fried LP, Tangen CM, Walston J, et al. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci 2001;56:M146-156.
11.    Cao L, Morley JE. Sarcopenia Is Recognized as an Independent Condition by an International Classification of Disease, Tenth Revision, Clinical Modification (ICD-10-CM) Code. J Am Med Dir Assoc 2016;17:675-677.
12.    Vellas B, Fielding RA, Bens C, et al. Implications of ICD-10 for Sarcopenia Clinical Practice and Clinical Trials: Report by the International Conference on Frailty and Sarcopenia Research Task Force. J Frailty Aging 2018;7:2-9.
13.    Landi F, Calvani R, Cesari M, et al. Sarcopenia as the Biological Substrate of Physical Frailty. Clin Geriatr Med 2015;31:367-374.
14.    Cesari M, Landi F, Calvani R, et al. Rationale for a preliminary operational definition of physical frailty and sarcopenia in the SPRINTT trial. Aging Clin Exp Res 2017;29:81-88.
15.    Marzetti E, Calvani R, Cesari M, et al. Operationalization of the physical frailty & sarcopenia syndrome: rationale and clinical implementation. Transl Med UniSa 2015;13:29-32.
16.    Marzetti E, Picca A, Marini F, et al. Inflammatory signatures in older persons with physical frailty and sarcopenia: The frailty “cytokinome” at its core. Exp Gerontol 2019.
17.    Semba RD, Gonzalez-Freire M, Tanaka T, et al. Elevated Plasma Growth and Differentiation Factor-15 is Associated with Slower Gait Speed and Lower Physical Performance in Healthy Community-Dwelling Adults. J Gerontol A Biol Sci Med Sci 2019.
18.    Collerton J, Martin-Ruiz C, Davies K, et al. Frailty and the role of inflammation, immunosenescence and cellular ageing in the very old: cross-sectional findings from the Newcastle 85+ Study. Mech Ageing Dev 2012;133:456-466.
19.    Studenski SA, Peters KW, Alley DE, et al. The FNIH sarcopenia project: rationale, study description, conference recommendations, and final estimates. J Gerontol A Biol Sci Med Sci 2014;69:547-558.
20.    Cawthon PM, Orwoll ES, Peters KE, et al. Strong Relation between Muscle Mass Determined by D3-creatine Dilution, Physical Performance and Incidence of Falls and Mobility Limitations in a Prospective Cohort of Older Men. J Gerontol A Biol Sci Med Sci 2018.
21.    Heymsfield SB, Arteaga C, McManus C, Smith J, Moffitt S. Measurement of muscle mass in humans: validity of the 24-hour urinary creatinine method. Am J Clin Nutr 1983;37:478-494.
22.    Clark RV, Walker AC, O’Connor-Semmes RL, et al. Total body skeletal muscle mass: estimation by creatine (methyl-d3) dilution in humans. J Appl Physiol (1985) 2014;116:1605-1613.
23.    Evans WJ, Hellerstein M, Orwoll E, Cummings S, Cawthon PM. D3 -Creatine dilution and the importance of accuracy in the assessment of skeletal muscle mass. J Cachexia Sarcopenia Muscle 2019;10:14-21.
24.    Cawthon PM, Blackwell T, Cummings SR, et al. The association between muscle mass assessed by D3CR dilution with incident ADL and IADL disability in community dwelling older men. J Frailty Aging 2019;8:S8.
25.    Ko F, Abadir P, Marx R, et al. Impaired mitochondrial degradation by autophagy in the skeletal muscle of the aged female interleukin 10 null mouse. Exp Gerontol 2016;73:23-27.
26.    Varadhan R, Yao W, Matteini A, et al. Simple biologically informed inflammatory index of two serum cytokines predicts 10 year all-cause mortality in older adults. J Gerontol A Biol Sci Med Sci 2014;69:165-173.
27.    Hellhammer DH, Wust S, Kudielka BM. Salivary cortisol as a biomarker in stress research. Psychoneuroendocrinology 2009;34:163-171.
28.    Johar H, Emeny RT, Bidlingmaier M, et al. Blunted diurnal cortisol pattern is associated with frailty: a cross-sectional study of 745 participants aged 65 to 90 years. J Clin Endocrinol Metab 2014;99:E464-468.
29.    Varadhan R, Walston J, Cappola AR, Carlson MC, Wand GS, Fried LP. Higher levels and blunted diurnal variation of cortisol in frail older women. J Gerontol A Biol Sci Med Sci 2008;63:190-195.
30.    Doi T, Makizako H, Tsutsumimoto K, et al. Association between Insulin-Like Growth Factor-1 and Frailty among Older Adults. J Nutr Health Aging 2018;22:68-72.
31.    Carcaillon L, Blanco C, Alonso-Bouzon C, Alfaro-Acha A, Garcia-Garcia FJ, Rodriguez-Manas L. Sex differences in the association between serum levels of testosterone and frailty in an elderly population: the Toledo Study for Healthy Aging. PLoS One 2012;7:e32401.
32.    Carcaillon L, Garcia-Garcia FJ, Tresguerres JA, Gutierrez Avila G, Kireev R, Rodriguez-Manas L. Higher levels of endogenous estradiol are associated with frailty in postmenopausal women from the toledo study for healthy aging. J Clin Endocrinol Metab 2012;97:2898-2906.
33.    Ma L, Niu H, Sha G, Zhang Y, Liu P, Li Y. Serum SIRT1 Is Associated with Frailty and Adipokines in Older Adults. J Nutr Health Aging 2019;23:246-250.
34.    Kalyani RR, Varadhan R, Weiss CO, Fried LP, Cappola AR. Frailty status and altered glucose-insulin dynamics. J Gerontol A Biol Sci Med Sci 2012;67:1300-1306.
35.    Leng SX, Cappola AR, Andersen RE, et al. Serum levels of insulin-like growth factor-I (IGF-I) and dehydroepiandrosterone sulfate (DHEA-S), and their relationships with serum interleukin-6, in the geriatric syndrome of frailty. Aging Clin Exp Res 2004;16:153-157.
36.    Alonso-Bouzon C, Carcaillon L, Garcia-Garcia FJ, Amor-Andres MS, El Assar M, Rodriguez-Manas L. Association between endothelial dysfunction and frailty: the Toledo Study for Healthy Aging. Age (Dordr) 2014;36:495-505.
37.    Walston J, McBurnie MA, Newman A, et al. Frailty and activation of the inflammation and coagulation systems with and without clinical comorbidities: results from the Cardiovascular Health Study. Arch Intern Med 2002;162:2333-2341.
38.    Andreux PA, van Diemen MPJ, Heezen MR, et al. Mitochondrial function is impaired in the skeletal muscle of pre-frail elderly. Sci Rep 2018;8:8548.
39.    Corona G, Polesel J, Fratino L, et al. Metabolomics biomarkers of frailty in elderly breast cancer patients. J Cell Physiol 2014;229:898-902.
40.    Burch JB, Augustine AD, Frieden LA, et al. Advances in geroscience: impact on healthspan and chronic disease. J Gerontol A Biol Sci Med Sci 2014;69 Suppl 1:S1-3.
41.    Epel ES, Lithgow GJ. Stress biology and aging mechanisms: toward understanding the deep connection between adaptation to stress and longevity. J Gerontol A Biol Sci Med Sci 2014;69 Suppl 1:S10-16.
42.    Newgard CB, Pessin JE. Recent progress in metabolic signaling pathways regulating aging and life span. J Gerontol A Biol Sci Med Sci 2014;69 Suppl 1:S21-27.
43.    Scaffidi P, Misteli T. Lamin A-dependent nuclear defects in human aging. Science 2006;312:1059-1063.
44.    El Assar M, Angulo J, Carnicero JA, et al. Frailty Is Associated With Lower Expression of Genes Involved in Cellular Response to Stress: Results From the Toledo Study for Healthy Aging. J Am Med Dir Assoc 2017;18:734 e731-734 e737.
45.    Ciabattini A, Nardini C, Santoro F, Garagnani P, Franceschi C, Medaglini D. Vaccination in the elderly: The challenge of immune changes with aging. Semin Immunol 2018;40:83-94.
46.    Lopez-Otin C, Blasco MA, Partridge L, Serrano M, Kroemer G. The hallmarks of aging. Cell 2013;153:1194-1217.
47.    Simon AK, Hollander GA, McMichael A. Evolution of the immune system in humans from infancy to old age. Proc Biol Sci 2015;282:20143085.
48.    Marzetti E, Calvani R, Landi F, et al. Innovative Medicines Initiative: The SPRINTT Project. J Frailty Aging 2015;4:207-208.
49.    Calvani R, Picca A, Marini F, et al. The “BIOmarkers associated with Sarcopenia and PHysical frailty in EldeRly pErsons” (BIOSPHERE) study: Rationale, design and methods. Eur J Intern Med 2018;56:19-25.
50.    Erusalimsky JD, Grillari J, Grune T, et al. In Search of ‘Omics’-Based Biomarkers to Predict Risk of Frailty and Its Consequences in Older Individuals: The FRAILOMIC Initiative. Gerontology 2016;62:182-190.
51.    Rodriguez-Manas L. Use of Biomarkers. J Frailty Aging 2015;4:125-128.

CIRCULATING INTERLEUKIN-6 IS ASSOCIATED WITH SKELETAL MUSCLE STRENGTH, QUALITY, AND FUNCTIONAL ADAPTATION WITH EXERCISE TRAINING IN MOBILITY-LIMITED OLDER ADULTS

 

G.J. Grosicki1,2, B.B. Barrett1, D.A. Englund1, C. Liu1, T.G. Travison3,4, T. Cederholm5, A. Koochek6, Å. von Berens5, T. Gustafsson7, T. Benard1, K.F. Reid1, R.A. Fielding1

 

1. Nutrition, Exercise Physiology, and Sarcopenia Laboratory, Jean Mayer USDA Human, Nutrition Research Center on Aging, Tufts University, Boston, MA, USA; 2. Department of Health Sciences and Kinesiology, Biodynamics and Human Performance Center, Georgia Southern University (Armstrong Campus), Savannah, GA, USA; 3. Marcus Institute for Aging Research, Hebrew SeniorLife, Boston, MA, USA; 4. Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA; 5. Department of Public Health and Caring Sciences, Clinical Nutrition and Metabolism, Uppsala University, Uppsala, Sweden; 6. Department of Food Studies, Nutrition and Dietetics, Uppsala University, Uppsala Sweden; 7. Division of Clinical Physiology, Department of Laboratory Medicine, Karolinska Institutet, Stockholm, Sweden.
Corresponding author:  Gregory J. Grosicki, Ph.D., Department of Health Sciences and Kinesiology, Biodynamics and Human Performance Center, Georgia Southern University (Armstrong Campus), 11935 Abercorn Street, Savannah, GA, 31419. Phone: (912) 344-3317. Fax: (912) 344-3490. Email: ggrosicki@georgiasouthern.edu

J Frailty Aging 2019;in press
Published online September 13, 2019, http://dx.doi.org/10.14283/jfa.2019.30

 


Abstract

Background: Human aging is characterized by a chronic, low-grade inflammation suspected to contribute to reductions in skeletal muscle size, strength, and function. Inflammatory cytokines, such as interleukin-6 (IL-6), may play a role in the reduced skeletal muscle adaptive response seen in older individuals. Objectives: To investigate relationships between circulating IL-6, skeletal muscle health and exercise adaptation in mobility-limited older adults. Design: Randomized controlled trial. Setting: Exercise laboratory on the Health Sciences campus of an urban university. Participants: 99 mobility-limited (Short Physical Performance Battery (SPPB) ≤9) older adults. Intervention: 6-month structured physical activity with or without a protein and vitamin D nutritional supplement. Measurements: Circulating IL-6, skeletal muscle size, composition (percent normal density muscle tissue), strength, power, and specific force (strength/CSA) as well as physical function (gait speed, stair climb time, SPPB-score) were measured pre- and post-intervention. Results: At baseline, Spearman’s correlations demonstrated an inverse relationship (P<0.05) between circulating IL-6 and thigh muscle composition (r = -0.201), strength (r = -0.311), power (r = -0.210), and specific force (r = -0.248), and positive association between IL-6 and stair climb time (r = 0.256; P<0.05). Although the training program did not affect circulating IL-6 levels (P=0.69), reductions in IL-6 were associated with gait speed improvements (r = -0.487; P<0.05) in “higher” IL-6 individuals (>1.36 pg/ml). Moreover, baseline IL-6 was inversely associated (P<0.05) with gains in appendicular lean mass and improvements in SPPB score (r = -0.211 and -0.237, respectively). Conclusions: These findings implicate age-related increases in circulating IL-6 as an important contributor to declines in skeletal muscle strength, quality, function, and training-mediated adaptation. Given the pervasive nature of inflammation among older adults, novel therapeutic strategies to reduce IL-6 as a means of preserving skeletal muscle health are enticing.

Key words: Inflammation, IL-6, sarcopenia, older adults, aging.


 

Introduction

Age-related inflammation (also termed ‘inflamm-aging’) is characterized by reduced control over the production of pro-inflammatory cytokines, independent of comorbidities and cardiovascular risk factors (1). The endogenous immune mediator interleukin-6 (IL-6) is purported to be of particular relevance in modulating the relationship between aging and chronic disease, aptly receiving the designation “a cytokine for gerontologists” (2). Although an age-related increase in IL-6 levels is not ubiquitous across studies (3), there is substantial evidence for greater IL-6 levels in older people (4). While the biological mechanisms underpinning this relationship in humans are not fully understood, laboratory experiments demonstrating IL-6 mediated activation of proteolytic signaling and atrogene expression highlight the myocellular significance of this phenomenon (5). Elevated IL-6 levels have also been implicated in intramuscular adipose tissue accumulation (6), which may have an even greater impact on functional status than muscle loss itself (7). The translational significance of these IL-6 induced skeletal muscle changes is highlighted in epidemiological studies demonstrating relationships between IL-6, physical function, and disability onset in older persons (8).
Although exercise training is widely recognized as a safe and effective strategy to improve skeletal muscle size and function in older adults, whether these benefits are mediated by changes in IL-6 remains to be determined. Cross-sectional analyses frequently report lower IL-6 levels in active vs. inactive older individuals (9), possible stemming from the up-regulation of anti-inflammatory cytokines (IL-1ra and IL-10) seen with aerobic training (10). However, large-scale longitudinal investigations seeking to reduce IL-6 levels with structured physical activity have yielded conflicting results (11, 12). Recently, findings from a 6-month resistance training study in older adults demonstrated no effect on levels of inflammation, but found high baseline inflammation to attenuate strength gains (13). These finding support the premise that elevated inflammation with advancing age impedes muscle sensitivity to anabolic stimuli, a phenomenon known as “anabolic resistance”.
The present study intends to: 1) characterize the relationship between circulating IL-6 and skeletal muscle size, composition, and contractile and physical function in mobility-limited older adults, and 2) investigate the effects of a 6-month structured physical activity program (aerobic and resistance exercise) on IL-6 and possibly associated adaptations in skeletal muscle characteristics, the primary findings of which have been reported previously (14, 15). We hypothesize an inverse association between baseline IL-6 and skeletal muscle size and function, and that 6-months of structured physical activity will reduce IL-6 levels in a manner that is associated with beneficial morphological and functional skeletal muscle adaptations.

 

Methods

Study Design

This study was a secondary analysis on data collected as part of a multi-center (one center in each of the United States and Sweden) randomized control trial designed to examine the effects of 6-month structured physical activity with or without nutritional supplementation (150kcal, 20g whey protein, 800 IU vitamin D) in mobility-limited, vitamin D insufficient (serum 25(OH) D 9-24 ng/mL) older adults (14-16). Cognitively impaired individuals (mini-mental state examination score < 24), individuals unable to walk 400 meters within 15 minutes, or those with acute and/or terminal illness were excluded. Supervised training sessions were conducted three times per week for 6-months and included both aerobic (walking) and resistance training (using ankle weights), as previously described (16). Strength exercises included chair rises, knee extensions, side hip raises, knee flexion and calf raises, with flexibility and balance exercises to warm-up and cooldown. The overall goal of the physical activity program was for the participants to complete at least 150 min per week of physical activity at a moderate (13 of 20 perceived exertion using Borg’s scale) intensity. All participants provided written consent to participate and study protocol and procedures were approved by the Tufts University Health Sciences Institutional Review Board and the Regional Ethical Committee of Uppsala, Sweden.

Subject Characteristics

A total of 149 mobility-limited (Short Physical Performance Battery (SPPB) ≤ 9) older adults (≥ 70y) were recruited and randomized. Of these, 137 completed the 6-month intervention, 95 of which provided pre- and post- training blood samples for cytokine analysis (Table 1). Because skeletal muscle variables of interest (i.e., size, contractile function, and physical performance) and interleukin-6 levels were not differentially affected by the nutritional supplement (14, 16), placebo and supplement groups have been combined in the present analysis.

Table 1 Participant characteristics before and after 24-weeks of combined aerobic and resistance training (n = 95)

Table 1
Participant characteristics before and after 24-weeks of combined aerobic and resistance training (n = 95)

All data are Mean (SD) or Count (%).*P<0.05; †P<0.10; y, years; kg/m2, kilograms per meter squared; pg/ml, picograms per milliliter; Nm, Newton meters; cm, centimeters; s, seconds; m/s, meters per second;  CSA, cross-sectional area; SPPB, short physical performance battery; y, years; a. Muscle composition defined as ratio of normal density to whole muscle CSA; b. Peak torque (Nm) measured at 60°/s; c. Power (W) assessed at 180°/s; d. Defined as peak torque/CSA.

 

Blood Collection and Cytokine Analysis

Following an overnight fast, blood samples (50 mL) were collected at baseline and after 12 and 24 weeks of the physical activity intervention, at least 48 hours after the last training session. Participants were encouraged to forgo use of non-steroidal anti-inflammatory agents or aspirin 72h before the blood draw. Venipuncture was performed by a qualified healthcare professional, and samples were collected in EDTA-containing and serum tubes. EDTA-containing tubes were centrifuged at 1000g at 4˚C for 10 min and aliquots of plasma and serum were frozen in liquid nitrogen and stored at -80 ˚C until analysis. Subsequent laboratory analyses were performed by the Nutrition Evaluation Laboratory at the Jean Mayer USDA Human Nutrition Research Center on Aging to measure standard blood analytes, blood lipids and hematology, acute phase proteins, and circulating cytokines and growth factors. Interleukin 6 was measured by a high sensitivity quantitative sandwich enzyme linked immunoassay kit procedure (Quantikine HS Human IL-6 Immunoassay, Minneapolis, MN) with intra-assay CVs of 6.9-7.8% and inter-assay CVs of 6.5-9.6%.

Skeletal Muscle Size and Composition

Lean mass and body composition were measured by dual-energy x-ray absorptiometry (DXA) (Boston, Hologic, Discovery A (Bedford, MA); Sweden, GE Lunar (Madison, WI)) (17) at baseline and two days following completion of the intervention. The DXA system generates photons at two principal energy levels (40 and 70 KeV) which allow measurement of bone and soft tissue. All scans were centrally analyzed at Tufts by a single investigator in a blinded manner (16). Total-body mass, muscle mass, fat mass, and appendicular lean mass were derived (15).
Computed tomography (CT) scans of the non-dominant thigh were obtained at the midpoint of the femur for each subject pre- and post-intervention. The length of the femur was determined from a coronal scout image as the distance between the intercondylar notch and the trochanteric notch. All scans were obtained using a Siemens Somatom Scanner (Erlangen, Germany) operating at 120 KV and 100 mA. Technical factors included a slice width of 10 mm and a scanning time of 1 s. All scans were centrally analyzed at Tufts by a single investigator in a blinded manner using SliceOmatic v4.2 software (Montreal, Canada). Images were reconstructed on a 512 × 512 matrix with a 25-cm field of view. Thigh muscle cross-sectional area (CSA) was considered the total area of non-adipose and non-bone tissue within the deep fascial plane, quantified in the range of 0–100 Hounsfield units (HU). Further, thigh muscle CSA was partitioned into low-density muscle CSA (0–34 HU) and normal-density muscle CSA (35–100 HU). Muscle composition was defined as normal-density divided by total thigh muscle CSA, and is presented as a percent, with a higher value indicating a superior muscle composition.

Skeletal Muscle Contractile Function

Skeletal muscle strength and power of the knee extensors were determined using the Biodex System 3 Isokinetic Dynamometer (Biodex Medical Systems, Shirley, NY) at baseline and 3 days following completion of the intervention. Isokinetic strength was assessed at 60˚/s and measured in Newton meters (Nm). Isokinetic power was assessed at 180˚/s and measured in Watts (W). Specific force (i.e., muscle quality) was assessed as isokinetic muscle strength/thigh muscle CSA.

Physical Function

Physical function was assessed at baseline and after 6-months of the physical activity intervention, 3 days after the final training session. Functional variables of interest included 400 meter (m) walk speed (18), hand grip strength (19), quickest time to ascend a flight of 10 stairs (i.e., stair climb time), and Short Physical Performance Battery (SPPB) score (20), a functional evaluation consisting of three subtasks: standing balance, habitual walking, and repeated chair rise.

Statistical Analysis

Pre- and post-training participant characteristics were analyzed using descriptive statistics and are presented as means and standard deviations (SD). Normally distributed data were analyzed using an independent t-test and non-normally distributed data (i.e., IL-6; as determined by Shaprio-Wilk’s test for normality) were analyzed using a nonparametric Mann-Whitney U-test. Correlations between IL-6 (baseline and change; pg/ml) and skeletal muscle variables of interest (baseline and change; respective units) were analyzed using Spearman rank-order correlation coefficients. All analyses were performed using SPSS Version 24 with significance set at the P<0.05 level.

 

Results

Participant Characteristics

A total of 99 mobility-limited older (≥70y) adults (SPPB ≤9) provided baseline measures of IL-6 and completed skeletal muscle testing, 95 of which completed the 6-month physical activity intervention. Table 1 compares pre- and post- skeletal muscle characteristics of the 95 participants with baseline IL-6 measures who completed the exercise intervention. By in large, these findings mirror those from the larger cohort showing improvements in physical function (i.e., SPPB and walk speed) (14) that were not reflected by robust morphological (i.e., size) or contractile (e.g., strength) changes (15). Nonparametric testing demonstrated no relationship between IL-6 and age (P=0.724), sex (P=0.583), or body mass index (BMI; P=0.960).

Baseline Circulating Inflammation Predicts Skeletal Muscle Characteristics

Baseline circulating IL-6 explained ~4 and 10% of the variance in muscle composition and strength (P<0.05), respectively (Table 2). While the relationship between pre-intervention IL-6 and thigh CSA trended towards significance (P<0.10), significant associations (P<0.05) were observed between IL-6 and specific force (i.e., muscle quality; r2=.06) as well as stair climb time (r2=.07). To improve our understanding of the relationship between IL-6 and skeletal muscle mass (both absolute and relative to body weight), IL-6 levels were compared between sarcopenic and non-sarcopenic individuals using contemporary evidence-based cut-points for clinically relevant low lean mass (i.e., appendicular lean mass, ALM<19.75 or 15.02; ALMBMI<0.789 or 0.512, in men and women, respectively; Figure 1) (21, 22).

Table 2 Spearman’s rho correlation coefficients between baseline IL-6 values and muscle size, contractile function, and physical performance (n = 90-99)

Table 2
Spearman’s rho correlation coefficients between baseline IL-6 values and muscle size, contractile function, and physical performance (n = 90-99)

CSA, cross-sectional area; SPPB, short physical performance battery; a. Muscle composition defined as ratio of normal density to whole muscle CSA; b. Peak torque (Nm) measured at 60°/s; c. Power (W) assessed at 180°/s; d. Defined as peak torque/CSA.

 

Figure 1 Box plots comparing plasma interleukin-6 levels in sarcopenic (grey boxes) and non-sarcopenic (white boxes) mobility-limited older adults. Sarcopenia variables and cut-points from the Foundation for the National Institutes of Health Sarcopenia Project (21)

Figure 1
Box plots comparing plasma interleukin-6 levels in sarcopenic (grey boxes) and non-sarcopenic (white boxes) mobility-limited older adults. Sarcopenia variables and cut-points from the Foundation for the National Institutes of Health Sarcopenia Project (21)

Males: ALM<19.75, ALMBMI<0.789; Females: ALM<15.02, ALMBMI<0.512. *P<0.05; †P<0.10. ALM, appendicular lean mass; ALMBMI, appendicular lean mass/body mass index (kg/m2)

 

Circulating IL-6 Associates with Exercise Training Adaptation

Circulating IL-6 levels were not affected by prolonged (i.e., 6 months) aerobic and resistance exercise training (P=0.692). In all subjects, changes in IL-6 were inversely related to changes in walk speed (r = -0.285; P<0.05). To test the hypothesis that changes in IL-6 levels would be more beneficial in individuals with greater baseline inflammation, we used the IL-6 population mean (1.36 pg/ml) to divide our cohort into “lower” and “higher” IL-6 categories (Table 3). While no relationship was observed between changes in IL-6 and any skeletal muscle measures in the “lower” IL-6 group, alterations in IL-6 explained approximately a quarter of the variance in changes in SPPB-score and walk speed in the “higher” IL-6 individuals.

Table 3 Spearman’s rho correlation coefficients between change in IL-6 values and change in muscle size, contractile function, and physical performance (n = 95)

Table 3
Spearman’s rho correlation coefficients between change in IL-6 values and change in muscle size, contractile function, and physical performance (n = 95)

Interleukin-6 categories generated using cohort mean; CSA, cross-sectional area; SPPB, short physical performance battery. *p<0.05; a. Muscle composition defined as ratio of normal density to whole muscle CSA; b. Peak torque (Nm) measured at 60°/s; c. Power (W) assessed at 180°/s; d. Defined as peak torque/CSA.

 

As circulating inflammation may curtail the benefits of exercise training, we also compared baseline IL-6 with skeletal muscle adaptation following the 6-month intervention (Table 4). These analyses demonstrated an inverse association between baseline IL-6 and improvements in appendicular lean mass and SPPB-score (P<0.05).

Table 4 Spearman’s rho correlation coefficients between baseline IL-6 values and change in muscle size, contractile function, and physical performance (n = 84-94)

Table 4
Spearman’s rho correlation coefficients between baseline IL-6 values and change in muscle size, contractile function, and physical performance (n = 84-94)

CSA, cross-sectional area; SPPB, short physical performance battery; a. Muscle composition defined as ratio of normal density to whole muscle CSA; b. Peak torque (Nm) measured at 60°/s; c. Power (W) assessed at 180°/s; d. Defined as peak torque/CSA.

 

Discussion

Findings from this study contribute to a growing body of literature showing an inverse relationship between circulating IL-6 and lower-extremity muscle size, composition, contractile function, and physical performance in mobility-limited, vitamin D insufficient older adults. Although the anti-inflammatory benefits of exercise training may reduce pro-inflammatory markers, IL-6 levels were unaltered by 6-months of physical activity. Interestingly, individual changes in IL-6 were inversely associated with training-related improvements in gait speed, an important predictor of morbidity and mortality in older adults (23). Significant associations between baseline IL-6 and gains in lean mass and function over the course of the intervention further emphasize the probable influence of circulating IL-6 in mediating exercise training response in mobility-limited older adults.
Regardless of whether chronological aging is responsible for or simply associated with a heightened inflammatory profile (24), inverse relationships between pro-inflammatory cytokines (IL-6 and TNF-α) and lean tissue mass are consistently observed (25). The inverse relationship between appendicular lean mass and circulating IL-6 observed in the present cohort supports this paradigm. Less anticipated was the greater circulating levels of IL-6 in sarcopenic males, but not females, relative to non-sarcopenic same-sex counterparts (Figure 1), portending to the possibility of sex differences in inflammation-mediated skeletal muscle remodeling (26). In a seminal large-scale study using data from the Health, Aging, and Body Composition (Health ABC) cohort, Visser and colleagues were the first to demonstrate higher IL-6 levels to be associated not only with lower muscle mass but muscle strength (grip and knee extensor) in well-functioning older adults (27). Extending upon this work, findings from the present study suggest circulating IL-6 may contribute not only to quantitative but qualitative (i.e., muscle composition) changes in aging skeletal muscle that detrimentally effect contractile function and overall physical performance in mobility-limited older adults. These findings implicate IL-6-associated skeletal muscle deficiencies as a catalyst in the relationship between circulating inflammation and incident disability in older persons. With the number of mobility-limited older adults exponentially rising, illumination of effective therapeutic interventions to alleviate inflammatory burden in this population is highly desirable.
Tailored lifestyle programs involving dietary and/or physical activity interventions are increasingly realized as a safe and effective way to curtail disability onset. The functional benefits of exercise training may be at least partially mediated by an increased production of anti-inflammatory cytokines (e.g., IL-1ra, IL-10) (28), which work to suppress the pro-inflammatory milieu, characteristic of aging muscle. This supposition is supported by the lower levels of pro-inflammatory indices (IL-6 and CRP) and greater thigh muscle cross-sectional area in older lifelong endurance-trained individuals compared to their age-matched counter-parts (29). However, in the present study, 6-months of physical activity (aerobic and resistance exercise) failed to elicit an appreciable change in IL-6 levels in mobility-limited older adults, a finding in contrast to the reduction in IL-6 seen in older adults with greater baseline IL-6 (~3.4 pg/ml) enrolled in the LIFE study (12), but similar to more recent findings showing static IL-6 levels following 6-mo resistance training in frail and pre-frail older persons (13). These contrasting findings bring to light the likely relevance of exercise training mode (i.e., aerobic vs. resistance) in arbitrating inflammatory benefits; while aerobic exercise may help to alleviate inflammatory burden, resistance training studies, by in large, yield negative results (11). With this sentiment in mind, supplementing resistance exercise with aerobic training more vigorous than what was employed in the present investigation (i.e., 30-min of walking (16)) may be required if reducing inflammation is desired.
Despite the apparent lack of observed anti-inflammatory training-effect or significant associations between change in IL-6 levels and change in muscle size, composition, or contractile function, congruent inverse shifts in IL-6 and walk speed that were driven by individuals with greater baseline inflammation highlight the probable functional benefit of reducing IL-6 levels in this population. Previously, in conjunction with dietary intervention, a similar 18-month exercise program was proven to reduce IL-6 levels and improve walk speed in older (≥ 55y) adults with knee osteoarthritis (30). These findings, collected in distinct older cohorts, emphasize the functional significance of reducing IL-6 levels in mobility-limited older adults or other clinical populations, particularly if considering adoption of an exercise training program (Table 4). Furthermore, reducing circulating inflammation may help to combat age-related anabolic resistance, as is suggested by the inverse associations between baseline IL-6 and changes in ALM and SPPB in the present study, and TNF-α and strength gains shown previously (13). Moreover, oral ingestion of an anti-inflammatory agent (i.e., over-the-counter doses of acetaminophen or ibuprofen) in combination with resistance training appears to enhance muscle hypertrophy and strength gains in older adults (~65y) (31). More research to understand the mechanisms through which reducing inflammation seems to enhance proteostasis and to apprehend the complex interplay between exercise, inflammation and skeletal muscle in older adults is needed.
In conclusion, baseline IL-6 was inversely correlated with skeletal muscle size, strength, composition, contractile function, and physical performance in a well-characterized cohort of mobility-limited older adults. Although 6-months of physical activity (aerobic and resistance exercise) failed to reduce circulating IL-6 levels, changes in IL-6 were inversely associated with significant improvements in walking speed. Furthermore, training-mediated adaptations in skeletal muscle size and physical performance were inversely related to pre-training IL-6 levels. Whether the low baseline vitamin D levels of our participants influenced our findings is deserving of future exploration. These findings add to a growing body of literature demonstrating the multifarious skeletal muscle ramifications of elevated cytokine abundance in older adults.

 

Acknowledgements: We thank our participants for their time and efforts that made this study possible.
Funding: This work supported in part by Nestlé. In addition, this work was also supported by the U.S. Department of Agriculture (USDA), under agreement No. 58-1950-4-003 and the Boston Claude D. Pepper Center Older American Independence Centers (OAIC; 1P30AG031679). The sponsors had no role in the design and conduct of the study; I the collection, analysis, and interpretation of data; in the preparation of the manuscript; or in the review or approval of the manuscript. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the authors and do not necessarily reflect the view of the USDA. ClinicalTrials.gov Identifier: NCT03083275
Conflict of interest: Dr. Fielding reports grants from National Institutes of Health (National Institute on Aging) during the conduct of the study; grants, personal fees and other from Axcella Health, other from Inside Tracker, grants and personal fees from Biophytis, grants and personal fees from Astellas, personal fees from Cytokinetics, personal fees from Amazentis, grants and personal fees from Nestle’, personal fees from Glaxo Smith Kline, outside the submitted work; Dr. Cederholm reports grants from Nestle, during the conduct of the study. Dr. von Berens reports personal fees from Nestlé Health Science during the conduct of the study; Dr. Koochek reports personal fees and non-financial support from Nestlé Health Science, during the conduct of the study. Ms. Barrett, Mr. Benard and Mr. Englund have nothing to disclose. Dr.’s Grosicki, Liu, Reid, Travison, and Gustafsson have nothing to disclose..
Funding: This work is supported by the National Institutes of Health (R01 grant number AG032052-03 and K24 grant number HD070966-01) and the National Center for Research Resources (grant number UL1RR025758-01). Manuscript preparation was supported by the National Institutes of Health (K99 grant number AG051766) awarded to A.J.J.
Ethical standards: This study was reviewed and approved by the Tufts University Health Sciences Institutional Review Board.

 

References

1.    Franceschi C, Bonafe M, Valensin S, Olivieri F, De Luca M, Ottaviani E, et al. Inflamm-aging. An evolutionary perspective on immunosenescence. Ann N Y Acad Sci. 2000 Jun;908:244-54.
2.    Ershler WB. Interleukin-6: a cytokine for gerontologists. J Am Geriatr Soc. 1993 Feb;41(2):176-81.
3.    Beharka AA, Meydani M, Wu D, Leka LS, Meydani A, Meydani SN. Interleukin-6 production does not increase with age. J Gerontol A Biol Sci Med Sci. 2001 Feb;56(2):B81-8.
4.    Singh T, Newman AB. Inflammatory markers in population studies of aging. Ageing Res Rev. 2011 Jul;10(3):319-29.
5.    Haddad F, Zaldivar F, Cooper DM, Adams GR. IL-6-induced skeletal muscle atrophy. J Appl Physiol. 2005 Mar;98(3):911-7.
6.    Addison O, Drummond MJ, LaStayo PC, Dibble LE, Wende AR, McClain DA, et al. Intramuscular fat and inflammation differ in older adults: the impact of frailty and inactivity. J Nutr Health Aging. 2014 May;18(5):532-8.
7.    Grosicki GJ, Standley RA, Murach KA, Raue U, Minchev K, Coen PM, et al. Improved single muscle fiber quality in the oldest-old. J Appl Physiol. 2016 Oct 01;121(4):878-84.
8.    Ferrucci L, Harris TB, Guralnik JM, Tracy RP, Corti MC, Cohen HJ, et al. Serum IL-6 level and the development of disability in older persons. J Am Geriatr Soc. 1999 Jun;47(6):639-46.
9.    Jankord R, Jemiolo B. Influence of physical activity on serum IL-6 and IL-10 levels in healthy older men. Med Sci Sports Exerc. 2004 Jun;36(6):960-4.
10.    Brandt C, Pedersen BK. The role of exercise-induced myokines in muscle homeostasis and the defense against chronic diseases. J Biomed Biotechnol. 2010;2010:520258.
11.    Beavers KM, Brinkley TE, Nicklas BJ. Effect of exercise training on chronic inflammation. Clin Chim Acta. 2010 Jun 3;411(0):785-93.
12.    Nicklas BJ, Hsu FC, Brinkley TJ, Church T, Goodpaster BH, Kritchevsky SB, et al. Exercise training and plasma C-reactive protein and interleukin-6 in elderly people. J Am Geriatr Soc. 2008 Nov;56(11):2045-52.
13.    Hangelbroek RWJ, Knuiman P, Tieland M, de Groot L. Attenuated strength gains during prolonged resistance exercise training in older adults with high inflammatory status. Exp Gerontol. 2018 Jun;106:154-8.
14.    Fielding RA, Travison TG, Kirn DR, Koochek A, Reid KF, von Berens A, et al. Effect of structured physical activity and nutritional supplementation on physical function in mobility-limited older adults: Results from the VIVE2 randomized trial. J Nutr Health Aging. 2017;21(9):936-42.
15.    Englund DA, Kirn DR, Koochek A, Zhu H, Travison TG, Reid KF, et al. Nutritional Supplementation With Physical Activity Improves Muscle Composition in Mobility-Limited Older Adults, The VIVE2 Study: A Randomized, Double-Blind, Placebo-Controlled Trial. The journals of gerontology Series A, Biological sciences and medical sciences. 2018;73:95-101.
16.    Kirn DR, Koochek A, Reid KF, Von Berens Å, Travison TG, Folta S, et al. The Vitality, Independence, and Vigor in the Elderly 2 Study (VIVE2): Design and methods. Contemporary Clinical Trials. 2015;43:164-71.
17.    Visser M, Fuerst T, Lang T, Salamone L, Harris TB. Validity of fan-beam dual-energy X-ray absorptiometry for measuring fat-free mass and leg muscle mass. Health, Aging, and Body Composition Study–Dual-Energy X-ray Absorptiometry and Body Composition Working Group. J Appl Physiol (1985). 1999 Oct;87(4):1513-20.
18.    Newman AB, Simonsick EM, Naydeck BL, Boudreau RM, Kritchevsky SB, Nevitt MC, et al. Association of long-distance corridor walk performance with mortality, cardiovascular disease, mobility limitation, and disability. Jama. 2006 May 3;295(17):2018-26.
19.    Hamilton GF, McDonald C, Chenier TC. Measurement of grip strength: validity and reliability of the sphygmomanometer and jamar grip dynamometer. J Orthop Sports Phys Ther. 1992;16(5):215-9.
20.    Guralnik JM, Simonsick EM, Ferrucci L, Glynn RJ, Berkman LF, Blazer DG, et al. A short physical performance battery assessing lower extremity function: association with self-reported disability and prediction of mortality and nursing home admission. J Gerontol. 1994 Mar;49(2):M85-94.
21.    Studenski SA, Peters KW, Alley DE, Cawthon PM, McLean RR, Harris TB, et al. The FNIH sarcopenia project: Rationale, study description, conference recommendations, and final estimates. J Gerontol A Biol Sci Med Sci. 2014;69 A:547-58.
22.    Cawthon PM, Peters KW, Shardell MD, Mclean RR, Dam T-TL, Kenny AM, et al. Cutpoints for low appendicular lean mass that identify older adults with clinically significant weakness. J Gerontol A Biol Sci Med Sci. 2014;69:567-75.
23.    Studenski S, Perera S, Patel K, Rosano C, Faulkner K, Inzitari M, et al. Gait speed and survival in older adults. JAMA. 2011;305:50.
24.    Hager K, Machein U, Krieger S, Platt D, Seefried G, Bauer J. Interleukin-6 and selected plasma proteins in healthy persons of different ages. Neurobiol Aging. 1994 Nov-Dec;15(6):771-2.
25.    Cesari M, Kritchevsky SB, Baumgartner RN, Atkinson HH, Penninx BW, Lenchik L, et al. Sarcopenia, obesity, and inflammation–results from the Trial of Angiotensin Converting Enzyme Inhibition and Novel Cardiovascular Risk Factors study. Am J Clin Nutr. 2005 Aug;82(2):428-34.
26.    Stupka N, Lowther S, Chorneyko K, Bourgeois JM, Hogben C, Tarnopolsky MA. Gender differences in muscle inflammation after eccentric exercise. Journal of applied physiology (Bethesda, Md : 1985). 2000 Dec;89(6):2325-32.
27.    Visser M, Pahor M, Taaffe DR, Goodpaster BH, Simonsick EM, Newman AB, et al. Relationship of interleukin-6 and tumor necrosis factor-alpha with muscle mass and muscle strength in elderly men and women: the Health ABC Study. J Gerontol A Biol Sci Med Sci. 2002 May;57(5):M326-32.
28.    Petersen AM, Pedersen BK. The anti-inflammatory effect of exercise. J Appl Physiol (1985). 2005 Apr;98(4):1154-62.
29.    Mikkelsen UR, Couppe C, Karlsen A, Grosset JF, Schjerling P, Mackey AL, et al. Life-long endurance exercise in humans: circulating levels of inflammatory markers and leg muscle size. Mech Ageing Dev. 2013 Nov-Dec;134(11-12):531-40.
30.    Messier SP, Mihalko SL, Legault C, Miller GD, Nicklas BJ, DeVita P, et al. Effects of Intensive Diet and Exercise on Knee Joint Loads, Inflammation, and Clinical Outcomes Among Overweight and Obese Adults With Knee Osteoarthritis: The IDEA Randomized Clinical Trial. Jama. 2013 Sep 25;310(12):1263-73.
31.    Trappe TA, Carroll CC, Dickinson JM, LeMoine JK, Haus JM, Sullivan BE, et al. Influence of acetaminophen and ibuprofen on skeletal muscle adaptations to resistance exercise in older adults. Am J Physiol Regul Integr Comp Physiol. 2011 Mar;300(3):R655-62.