jfa journal

AND option

OR option

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.

SUPPLEMENTARY MATERIAL1

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.

PRELIMINARY STUDY ON PREVALENCE AND ASSOCIATED FACTORS WITH SARCOPENIA IN A GERIATRIC HOSPITALIZED REHABILITATION SETTING

 

K. PONGPIPATPAIBOON1, I. KONDO2, K.ONOGI1, S. MORI1, K. OZAKI2, A. OSAWA2, H. MATSUO2, N. ITOH2, M. TANIMOTO2

 

1. Department of Rehabilitation I, School of Medicine, Fujita Health University, Aichi, Japan; 2. Department of Rehabilitation Medicine, National Center for Geriatrics and Gerontology (NCGG), Aichi, Japan
Corresponding author: Izumi Kondo, Department of Rehabilitation Medicine, National Hospital for Geriatric Medicine, National Center for Geriatrics and Gerontology (NCGG), 7-430 Morioka-cho, Obu City, Aichi Prefecture, Japan, Fax: +81-562-44-8518, Phone: +81-562-46-2311, E-mail: ik7710@ncgg.go.jp

J Frailty Aging 2018;7(1):47-50
Published online October 4, 2017, http://dx.doi.org/10.14283/jfa.2017.40

 


Abstract

The reported prevalence of sarcopenia has shown a wide range, crucially based on the diagnostic criteria and setting. This cross-sectional study evaluated the prevalence of sarcopenia and sought to identify factors associated with sarcopenia on admission in a specialized geriatric rehabilitation setting based on the newly developed the Asian Working Group for Sarcopenia algorithm. Among 87 participants (mean age, 76.05 ± 7.57 years), 35 (40.2%) were classified as showing sarcopenia on admission. Prevalence was high, particularly among participants ≥80 years old, with tendencies toward lower body mass index, smoking habit, lower cognitive function, and greater functional impairment compared with the non-sarcopenic group. Identification of sarcopenia in elderly patients before rehabilitation and consideration of risk factors may prove helpful in achieving rehabilitation outcomes.

Key words: Elderly, hospitalization, prevalence, rehabilitation, sarcopenia.


 

 

Introduction

Sarcopenia is understood as a geriatric syndrome characterized by progressive and generalized loss of skeletal muscle mass, muscle strength and integrity (1). The adverse consequences of sarcopenia include an increased risk of falling, decreased ability in activities of daily living, mobility disorders, and higher morbidity and mortality among the elderly (2). The significant clinical impact of sarcopenia on the elderly increases the burden on the healthcare system (3).
The prevalence of sarcopenia as defined according to the European Working Group on Sarcopenia (EWGSOP) criteria is high among older inpatients, at 21.4-25.3% in acute geriatric units 4, 5) and around 46% in post-acute geriatric units (6). Although the sarcopenic prevalence among geriatric hospitalized patients has been relatively well examined, research data on sarcopenia in geriatric settings dedicated to rehabilitation remains scant. In addition, the sarcopenic prevalence as reported from epidemiological and interventional studies have differed markedly, making comparisons and interpretation difficult (7).
In 2014, The Asian Working Group for Sarcopenia (AWGS) proposed the AWGS diagnostic criteria of sarcopenia for Asian populations. The few studies that have examined sarcopenia mainly in community-dwelling elderly individuals based on AWGS criteria have shown a prevalence of around 7-10% (8, 9). The aim of this study was to determine the prevalence of sarcopenia based on the AWGS algorithm and to identify factors associated with sarcopenia among elderly patients admitted for specific rehabilitation treatment.

 

Methods

This study was conducted in elderly patients admitted to the acute geriatric ward dedicated for rehabilitation. Participants comprised individuals ≥60 years old with a stable medical condition, and sufficient cognitive function to perform tests (37 males, 50 females; underlying pathology: 39.1% fracture, 21.8% stroke, 20.7% postsurgical osteoarthritis, 18.4% others). Patients with severe medical or psychiatric illness, bedridden status, peripheral vascular disease with intermittent claudication, acute arthritis, severe pulmonary disease, heart failure, or specific muscular disease were excluded. All participants provided written informed consent. The institutional review board approved this study.

Measures

Baseline characteristics were collected. All tests and assessments for the diagnosis of sarcopenia and nutritional screening as well as functional assessments, including the Functional Independence Measure (FIM), Mini Mental State Examination (MMSE) were conducted within 2 days of admission or once a stable condition was achieved. Nutritional status was assessed using prealbumin and transferrin, which have been proposed as earlier nutritional markers due to shorter half-lives than albumin, and offer sensitive parameters for the efficacy of nutritional support as well as the preferred marker for malnutrition (10). All participants were managed with proper dietary nutrition by the rehabilitation team and nutritionists.

Diagnostic criteria for sarcopenia

The AWGS criteria were used to diagnose sarcopenia according to low muscle mass, low muscle strength, and/or low physical performance (11). Measurement of muscle strength and physical performance were utilized for screening. In cases where patients could not perform either activity, the score was taken as zero and assessment continued to the next step. Muscle mass was measured using bioelectrical impedance analysis (BIA), obtained using a Multi-Frequency Body Composition Analyzer (MC-180; Tanita, Japan). Suggested cut-off values were <7.0 kg/m2 for men and <5.7 kg/m2 for women, defined by appendicular skeletal muscle mass/height2. Muscle strength was assessed using a handgrip dynamometer. Three measurements with 1-min rest intervals were taken using the dominant hand unless this was unusable, and the mean was calculated. Strength cut-offs of <26 kg and <18 kg were classified as indicating low muscle strength for men and women, respectively. Physical performance was identified by 6-m usual gait speed. Timing was started on initiation of foot movement and stopped when one foot contacted the ground crossing the 6-m end line. The fastest of two trials was used for analysis. A cut-off of ≤0.8 m/s was used to identify low performance. All data were analyzed using SPSS 19.0 (SPSS Inc., Chicago, IL). Chi-square test was applied to compare the associated risk factors between patients with and without sarcopenia, and the diagnostic criteria of sarcopenia among three age groups. P-values <0.05 were considered statistically significant.

 

Results

The mean age of participants was 76.05 ± 7.5 years (range, 18.4% at 60-69 years, 49.4% at 70-79 years, 32.2% at ≥80 years). The prevalence of sarcopenia using AWGS criteria was 40.2% (Figure 1).

Figure 1 Diagnosis of sarcopenia according to the AWGS algorithm

Figure 1
Diagnosis of sarcopenia according to the AWGS algorithm

 

Participants were stratified into three age groups to compare the effects of age on sarcopenia (Table 1). Significant differences in sarcopenic prevalences, low muscle mass and low muscle strength (p = 0.024, 0.018, 0.047, respectively) were seen among age groups. Compared to non-sarcopenic patients, sarcopenic patients were significantly older from the age of 70 (40% at 70-79 years, and 48.6% at ≥80 years; p = 0.024).

Table 1 Prevalence of sarcopenia and diagnostic criteria in each age group

Table 1
Prevalence of sarcopenia and diagnostic criteria in each age group

P-values were obtained using the chi-square test.

 

Furthermore, mean BMI was lower in sarcopenic patients (19.55 ± 3.1 kg/m2) than in non-sarcopenic patients (24.49 ± 3.2 kg/m2; p < 0.001). In the sarcopenic group, 34.3% were underweight (<18.5 kg/m2), compared to 0% in the non-sarcopenic group (p < 0.001). In contrast, overweight (25-29 kg/m2; 30.8%) and obese (≥ 30 kg/m2; 3.8%) patients were only seen in the non-sarcopenic group. Patients with sarcopenia showed lower cognitive function as measured by the MMSE and cognitive FIM (p = 0.002 and < 0.001 respectively), and lower functional ability as measured by total FIM (p = 0.012). Smoking was more frequent among sarcopenic patients (p = 0.035). No significant differences in prealbumin or transferrin levels were seen.

 

Discussion

This report identified a high prevalence of sarcopenia according to AWGS criteria in elderly patients admitted for rehabilitation, particularly among those >80 years old, and demonstrated significant differences in age-dependent prevalence.
The AWGS proposed a similar approach for sarcopenia diagnosis, but, unlike the EWGSOP, recommended measuring both muscle strength and physical performance as screening tests with different cutoff criteria based on current evidence from Asian populations, which may differ from Caucasian populations in terms of body size, lifestyle, and cultural background (11). After establishing the AWGS algorithm in 2014 (11), emerging evidence based on the AWGS was published. In a recent cohort of community-dwelling elderly individuals (12), the sarcopenic prevalence according to the AWGS was 9.6% in men and 7.7% in women, supporting studies by Yu et al.8 and Lee et al. (9) (9.4% and 7.6% overall, respectively). Compared to this report, the lower rate of sarcopenia could be due to enrollment of community-living participants with no acute illness, representing a healthier group than hospitalized elderly individuals.
Regarding sarcopenia among elderly inpatients, previous studies have reported prevalences over a wide range (6.6-46%), and those studies were conducted using EWGSOP criteria (4-6). In this present result, the prevalence of sarcopenia based on AWGS criteria was high (40.2%). Further study would be useful for determining the consistency of sarcopenic prevalence according to AWGS criteria.
Sarcopenia appears to increase with advancing age, particularly in patients ≥80 years old (Table 1). This confirmed that age acts as a determinant of risk factors associated with sarcopenia. While focusing on age groups individually, one-third of patients aged 70-79 years and one-quarter of those aged 60-69 years were identified with sarcopenia. Furthermore, up to 60.7% of patients ≥80 years old had sarcopenia. Compared to prior studies using the AWGS algorithm, the prevalence of sarcopenia in similar age ranges was higher in this report. One study8 reported that community-dwelling elderly individuals ≥65 years old showed a 9.4% prevalence of sarcopenia. Another study9 divided participants into 2 groups (≥75 and <75 years), revealing a higher rate of sarcopenia in the older group (17.6% vs. 5.6%). Although sarcopenic prevalences in previous studies appeared to intensify with aging, as in this report, the percentages were obviously lower. This could be due to hospitalized elderly individuals having complicated health problems and therefore a higher risk of sarcopenia, suggesting the importance of early recognition and consideration of specialized healthcare, especially for hospitalized elderly patients.
The significantly lower BMI in sarcopenic patients was seen alongside a high percentage showing underweight status (34.3%), compared to none in the non-sarcopenic group. Conversely, overweight status was found only in the non-sarcopenic group. This suggests that increasing weight might contribute to a lower risk of sarcopenia, and declines in BMI could represent a manifestation of insufficient nutritional intake, leading to muscle loss, and increasing risk of sarcopenia. Prealbumin and transferrin concentrations tended to be lower in sarcopenic patients, although no significant differences were identified. Prealbumin and transferrin might not be sensitive enough to capture actual changes in nutritional condition. Further research using other nutritional parameters should be examined to confirm the correlation between sarcopenia and malnutrition.
Sarcopenia was associated with smoking, consistent with previous findings (13, 14). Comparisons also identified lower cognitive function and functional status in sarcopenic elderly individuals. While sarcopenia is understood to play a major role in contributing to physical decline, the relationships with cognitive declines remain unclear (15).
The limitations of this study were the relatively small sample sizes recruited from a single institute. Thus, the results may not be representative of geriatric rehabilitation inpatients. Nevertheless, the present findings depicted a high rate of sarcopenia among hospitalized elderly individuals and contributed insights into age-related sarcopenia. Initial screening for sarcopenia may be warranted before starting rehabilitation treatment.

 

Funding: This study was supported by research grants from the National Center for Geriatrics and Gerontology (NCGG), Japan.
Acknowledgments: We wish to thank all the study participants and health professionals involved in obtaining clinical measurements.
Disclosure statement: The authors declare no conflicts of interest.

 

References

1.     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.
2.      Rolland YCS, Abellan Van Kan G, Morley JE, et al. Sarcopenia: Its assessment, etiology, pathogenesis, consequences and future perspectives. J Nutr Health Aging 2008;12:433-450.
3.      Janssen I, Shepard DS, Katzmarzyk PT, Roubenoff R. The healthcare costs of sarcopenia in the United States. J Am Geriatr Soc 2004;52:80-85.
4.     Cerri AP, Bellelli G, Mazzone A, et al. Sarcopenia and malnutrition in acutely ill hospitalized elderly: Prevalence and outcomes. Clin Nutr 2015;34:745-751.
5.     Smoliner C, Sieber CC, Wirth R. Prevalence of sarcopenia in geriatric hospitalized patients. J Am Med Dir Assoc 2014;15:267-272.
6.     Sánchez-Rodríguez D, Calle A, Contra A, et al. Sarcopenia in post-acute care and rehabilitation of older adults: A review. Eur Geriatr Med 2016;7(3),224–231.
7.     Cruz-Jentoft AJ, Landi F, Schneider SM, et al. Prevalence of and interventions for sarcopenia in ageing adults: a systematic review. Report of the International Sarcopenia Initiative (EWGSOP and IWGS). Age Ageing 2014;43:748-759.
8.     Yu R, Leung J, Woo J. Incremental predictive value of sarcopenia for incident fracture in an elderly Chinese cohort: results from the Osteoporotic Fractures in Men (MrOs) Study. J Am Med Dir Assoc 2014;15:551-558.
9.     Lee ES, Park HM. Prevalence of sarcopenia in healthy Korean elderly women. J Bone Metab 2015;22:191-195.
10.     Neyra NR, Hakim RM, Shyr Y, Ikizler TA. Serum transferrin and serum prealbumin are early predictors of serum albumin in chronic hemodialysis patients. J Ren Nutr 2000;10:184-190.
11.      Chen LK, Liu LK, Woo J, et al. Sarcopenia in Asia: consensus report of the Asian Working Group for Sarcopenia. Journal of the American Medical Directors Association 2014;15:95-101.
12.     Yuki A, Ando F, Otsuka R, Matsui Y, Harada A, Shimokata H. Epidemiology of sarcopenia in elderly Japanese. J Phys Fitness Sports Med, 2015;4(1):111-115.
13.     Rom O, Kaisari S, Aizenbud D, Reznick AZ. Lifestyle and sarcopenia-etiology, prevention, and treatment. Rambam Maimonides Med J 2012;3:e0024:1-12.
14.     Rom O, Kaisari S, Aizenbud D, Reznick AZ. Sarcopenia and smoking: a possible cellular model of cigarette smoke effects on muscle protein breakdown. Ann N Y Acad Sci 2012;1259:47-53.
15.     Hsu YH, Liang CK, Chou MY, et al. Association of cognitive impairment, depressive symptoms and sarcopenia among healthy older men in the veterans retirement community in southern Taiwan: a cross-sectional study. Geriatr Gerontol Int 2014;14(Suppl1):102-108.

VARIOUS DIAGNOSTIC MEASURES OF FRAILTY AS PREDICTORS FOR FALLS, WEIGHT CHANGE, QUALITY OF LIFE, AND MORTALITY AMONG OLDER FINNISH MEN

 

N.M. PERTTILA1,2, K.H. PITKALA1, H. KAUTIAINEN1, R. TILVIS3, T. STRANDBERG3,4

 

1. University of Helsinki, Department of General Practice and Unit of Primary Health Care, Helsinki University Hospital, Helsinki, Finland; 2. City of Vantaa, Finland; 3. University of Oulu, Center for Life Course Health Research, Oulu, Finland; 4. University of Helsinki, Clinicum, and Helsinki University Hospital, Helsinki, Finland.
Corresponding author: N.M. Perttila, University of Helsinki, Department of General Practice and Unit of Primary Health Care, Helsinki University Hospital, P.O. Box 20 (Tukholmankatu 8 B), FI-00014 University of Helsinki, Finland. E-mail: niko.perttila@helsinki.fi  

J Frailty Aging 2017;in press
Published online August 16, 2017, http://dx.doi.org/10.14283/jfa.2017.26

 


Abstract

Background: Frailty predisposes individuals to a variety of complications. However, there is no consensus on the definition of frailty. Objectives: To examine whether various frailty measures are equivalent in identifying the same individuals as being frail and whether the measures also predict similar outcomes. Design, Setting and Participants: The Helsinki Businessmen Study cohort, which is a long-term observational study of men born in 1919-1934, was used as the population. We investigated these men by their postal questionnaire responses in 2000 and 2005. The mean age of the men (N=480) was 73 years at the start of follow-up. Measurements: We compared two phenotypic frailty measures, the Helsinki Businessmen Study measure (HBS), the modified Women’s Health Initiative Observational Study (WHI-OS), and the Frailty Index (FI) comprising 20 items. All three measurements were applied to Helsinki Businessmen Study cohort data collected via simple postal questionnaire from 480 men. We investigated how effectively these three measures distinguished between the not frail, prefrail, and frail individuals, and predicted mortality, falls, weight change, and health-related quality of life (HRQoL, 15D instrument) during a 5-year follow-up. Results: The HBS and the modified WHI-OS identified 35 persons (7.3%) each as frail but their respective sets comprised different groupings of individuals that partly overlapped. The FI identified 86 persons (17.9%) as frail. One-hundred-and-two (21.3%) men were classified as frail by at least one of the measures. All three measures significantly predicted higher mortality, higher number of fallers, and lower HRQoL for frail participants. None of the measures showed different results for weight change between the frailty groups or frailty stages. Conclusions: All three measures identified somewhat different sets of participants as frail. They all predicted increased mortality, falls and reduced HRQoL for the frail groups.

Key words: Prevalence, frail elderly, life expectancy, falls, quality of life.


 

Introduction

The term frailty describes a condition that is common in older people with deteriorated health that may not be a direct consequence of diseases (1). Frailty has been considered as a syndrome itself (2, 3). Frailty and its prognostic value have been intensively studied during the last decade. However, the lack of consensus on the definition has caused confusion in this field (1, 4). There are several definitions of frailty, which are used in scales, and studies on the validity of these scales have been published (2, 5-7).
The Fried criteria (2) are the most widely used (8) to define frailty. The Fried criteria stand for a phenotype of frailty and are based on physical features (2). Moreover, the Fried criteria also have been slightly modified to enable questionnaire data to be evaluated in the Helsinki Businessmen Study (HBS) measure (5) and also in the Women’s Health Initiative Observational Study (WHI-OS) measure (6). Physical frailty measures limit the definition to cover only the individual’s physical dimension and have been criticized for failing to take into consideration social, psychological, and biological dimensions (4).
Another widely used definition is the Frailty Index (FI). FI is quantitative and comprises many different aspects such as physical, cognitive, psychological, and social dimensions (4, 7). Compared with the phenotype represented by the Fried criteria, the FI also takes into consideration other features besides the physical dimensions that lead to frailty (9).
The reported prevalences of frailty have varied greatly due to the numerous definitions applied (3, 9, 10) and differences in age, sex, socioeconomic class, disabilities, diseases, and institutionalization of study populations (2, 9-11).
Irrespective of its definition, frailty has a poor prognosis. Frailty is associated with disabilities, makes people vulnerable to multiple complications, and increases the risk of death (1-3, 7, 12-15). Furthermore, frailty predisposes to falls (16, 17). Various measures have investigated frailty: the Fried criteria (2, 16, 18), the FI (7, 12, 19, 20), the WHI-OS measure (6) and the HBS measure (5) have predicted poor prognosis.
There are also studies that compare various scales in the same population (21, 22). All four of the compared measures (frailty phenotype, simplified frailty phenotype, FI, prognostic frailty scale) predicted significantly increased risk for adverse outcomes for participants classified as frail in a recent study (21). The prevalence varied from 2% for the simplified frailty phenotype to 49% for the prognostic frailty scale in that study (21). Another study compared four frailty measures: two phenotype-based measures, FI and one based on both phenotype and deficit accumulation (FRAIL) (22). The prevalence of frailty was greatest for the FI (22.6%), whereas the other three measures were close to each other (range 6.3% to 9.2%) (22). All four of the compared measures predicted new disability but only FI and the FRAIL scale predicted mortality (22).
The aim of this study was to compare how equivalent the three measures of frailty (HBS, modified WHI-OS, and FI) are at identifying frail individuals amongst a population of older men by analyzing the responses of a simple postal questionnaire sent to participants of the Helsinki Businessmen Study cohort. A further aim was to find what prognostic values of the three compared measures are regarding falls, weight loss, health-related quality of life (HRQoL), and mortality during a five-year follow-up.

 

Methods

Participants

The cohort profile of the Helsinki Businessmen Study has been described in detail elsewhere (23). Briefly, the Finnish Institute of Occupational Health organized health check-ups in the 1960s and 1970s to diminish cardiovascular risk. The participants (N=3490) were all men who had been born in 1919-1934 period and had worked as business executives. They received health education and their risk factors were evaluated by clinical and laboratory examinations and questionnaires. Their follow-up was conducted by postal questionnaires from the year 2000 onwards (23).
In 2000, a postal questionnaire was sent to the survivors of the Helsinki Businessmen Study cohort (N=1390). The majority (N=996, 72%) responded. We used the year 2000 questionnaire data as a baseline and included all participants that had all information available concerning frailty measures and follow-up data (until the year 2005) (N=480). Those with missing items (N=516) did not differ from those responding to frailty measures in respect to age, comorbidities, or baseline distribution of frailty measures (HBS, modified WHI-OS, FI). The characteristics of those defined as frail at baseline are described according to frailty status according to HBS, modified WHI-OS, and FI frailty measures, separately.
Details on demographics including age, marital status, weight (kg), body mass index (BMI, the person’s weight in kilograms (kg) divided by their height in meters squared (m²)), and the weight change from 1974 to 2000 were retrieved from the 1974 data and the 2000 questionnaire data. Diseases were inquired about with yes/no questions and the Charlson comorbidity index was constructed as described (24). Current smoking status (yes/no), regular use of statins (yes/no), and exercise status (exercises regularly (yes/no) and exercise hours per week) are presented. The RAND-36 (25) was embedded in the 2000 questionnaire.

Frailty measures

The HBS frailty measure (5) and the modified WHI-OS frailty measure (6) are phenotypic and based on the Fried criteria (2). The FI measure comprises physical, cognitive, psychological, and social dimensions (4, 7), and the number and extent of the dimensions taken into account between studies have varied (11). The FI was calculated as the number of conditions present divided by 20, which was the total number of items measured in this study. We used the cut-off scores of ≥0.25 for frail, 0.08-0.249 for prefrail and <0.08 for not frail. These cut-off points have been used earlier (11). Table 1 summarizes the frailty measures examined in this study.

Table 1 Comparison of frailty measures (Helsinki Businessmen Study (HBS), modified Women’s Health Initiative Observational Study (WHI-OS), Frailty Index (FI)) for this study

Table 1
Comparison of frailty measures (Helsinki Businessmen Study (HBS), modified Women’s Health Initiative Observational Study (WHI-OS), Frailty Index (FI)) for this study

Outcomes

The study population was classified as frail, prefrail, and not frail in 2000 according to these three compared frailty measures. We compared how the frailty measures identified participants as frail and constructed a Venn diagram (Figure 1) to illustrate the overlap of frail participants defined according to various measures.
The participants were followed-up until 2005. The participants received a questionnaire in 2005 that contained questions on current weight (kg), falls during the past year (yes, several times per year/yes, 1-2 times per year/no). The fallers were defined as those having fallen at least once during the past year. Weight change from 2000 to 2005 was calculated.
We used the 15D instrument (embedded in the 2005 questionnaire) to assess the health related quality of life (HRQoL) status of the participants in 2005 (26). The 15D is a comprehensive and generic instrument for measuring adult HRQoL. The 15 dimensions of the 15D instrument are: mobility, hearing, vision, breathing, eating, sleeping, speech, elimination, usual activities, mental function, discomfort and symptoms, distress, depression, vitality, and sexual activity. The 15D score is generated by using a set of utility or preference weights. The index score ranges between 0 (lowest HRQoL) and 1 (highest HRQoL). The 15D instrument compares favourably with other preference-based generic measures (27, 28). There has been systematic validation for the 15D instrument since the 1980s in different population samples and its development has been based on the feedback of both experts and patients (26). The 15D instrument is usually filled in by the participant himself, but it can also be filled in via an interview with the subject or their proxy. A difference of 0.02 to 0.03 for the 15 D instrument score between patient groups has been considered clinically significant (26).
Mortality data were retrieved from the Population Information System, which maintains a registry of all Finnish citizens, thus giving 100% complete coverage on mortality.

Statistical analyses

Difference between three frailty definitions were evaluated by using generalized linear models with appropriate distribution and link function; significance tests for estimates from all models were based on robust standard errors to account for the clustering of participant. The linearity across the three frailty stages was tested by using the Cochran-Armitage test and analysis of variance (ANOVA) with orthogonal polynomial contrast. The bootstrap method was used when the theoretical distribution of the test statistics was unknown or in the case of violation of the assumptions (e.g. non-normality). The normalities of variables were analyzed by Shapiro-Wilk statistics. Time-to-event analysis was based on the product limit estimate (Kaplan-Meier) of the cumulative “survival” function. The Cox proportional hazard model was used to estimate the age-adjusted risk for mortality. All analyses were performed using STATA 14.1 software (StataCorp LP, College Station, TX).

 

Results

Baseline characteristics in the year 2000

A total of 480 elderly men participated in this study after excluding all those with any missing values in 2000 or 2005. The baseline characteristics of the study group are shown in Table 2. The baseline is shown for all participants and separately for frail participants according to the HBS, modified WHI-OS, and FI measures. The mean age of sample was 73 years. There were some significant differences between the frail groups of the three frailty measures in the baseline. The FI frail group had greater mean weight and BMI and also less weight loss than the HBS frail group and modified WHI-OS frail group. Also, the FI frail group exercised more than HBS frail group and modified WHI-OS frail group.

Table 2 Baseline characteristics in the year 2000 of all participants (ALL), and of frail participants according to the Helsinki Businessmen Study (HBS), modified Women’s Health Initiative Observational Study (WHI-OS), and Frailty Index (FI) frailty measures

Table 2
Baseline characteristics in the year 2000 of all participants (ALL), and of frail participants according to the Helsinki Businessmen Study (HBS), modified Women’s Health Initiative Observational Study (WHI-OS), and Frailty Index (FI) frailty measures

* Difference between three frailty definitions were evaluated by using generalized linear models.

Identification of frailty by various measures

The results of the HBS, modified WHI-OS, and FI-based frailty measures are presented in a Venn diagram in Figure 1. A total of 102 participants (21.3%) were classified as frail according to at least one of the measures. Persons identified as being frail numbered 35 (7.3%) for the HBS, 35 (7.3%) for the modified WHI-OS, and 86 (17.9%) for the FI. Only 21 (4.4%) of the same participants were found to be frail by all three measures. HBS and modified WHI-OS identified 29 (6.0%) of the same people as frail. HBS and FI identified 22 (4.6%) of the same individuals as frail. The modified WHI-OS and FI identified 24 (5%) of the same group of individuals as frail. Five (1.0%) people were exclusively found to be frail by HBS, three (0.6%) people exclusively by only modified WHI-OS, and 61 (12.7%) people by only FI.

Figure 1 A Venn diagram showing the overlap of the Helsinki Businessmen Study (HBS) measure, the modified Women’s Health Initiative Observational Study (WHI-OS) measure, and the Frailty Index (FI) measure in identifying frailty. A total of 480 participants were evaluated according to these three measures and 102 were classified as frail by at least one measure

Figure 1
A Venn diagram showing the overlap of the Helsinki Businessmen Study (HBS) measure, the modified Women’s Health Initiative Observational Study (WHI-OS) measure, and the Frailty Index (FI) measure in identifying frailty. A total of 480 participants were evaluated according to these three measures and 102 were classified as frail by at least one measure

 

Fallers in 2005

The numbers of fallers are presented in Figure 2, panel A. There were higher proportions of fallers in the frail groups than in the prefrail or not frail groups in 2005 for all three compared measures. The difference was significant with all the measures. The proportions of fallers for the HBS was 20.7% [95% CI: 14.9 to 27.6] for the not frail group, 26.4% [95% CI: 20.4 to 33.2] for the prefrail group, and 47.1% [95% CI: 23.0 to 72.2] for the frail group (p=0.027).  The corresponding values for the WHI-OS were 21.5% [95% CI: 16.0 to 28.0] for the not frail group, 25.8% [95% CI: 19.3 to 33.1] for the prefrail group, and 52.9% [95% CI: 27.8 to 77.0] for the frail group (p=0.023). For FI, they were 21.5% [95% CI: 15.6 to 28.4] for the not frail group, 22.9% [95% CI: 16.5 to 30.4] for prefrail group, and 40.7% [95% CI: 27.6 to 55.0] for the frail group (p=0.016).

Weight change from 2000 to 2005

Weight changes are presented in Figure 2, panel B. All the groups tended to lose weight from 2000 to 2005. However, no significant differences in weight change existed between the various frailty stages according to any of the three frailty measures. The mean weight change for HBS was -0.8 kg [95% CI: -1.4 to -0.1] for the not frail group, -1.0 kg [95% CI: -1.5 to -0.4] for the prefrail group, and -0.6 kg [95% CI: -2.6 to 1.4] for the frail group (p=0.79). The corresponding values for the modified WHI-OS measure were -0.7 kg [95% CI: -1.3 to -0.2] for the not frail group, -1.0 kg [95 % CI: -1.6 to -0.4] for the prefrail group, and -0.5 kg [95% CI: -2.5 to 1.5] for the frail group (p=0.69). The mean weight changes for the FI measure were -0.9 kg [95% CI: -1.4 to -0.3] for the not frail group, -0.7 kg [95% CI: -1.3 to -0.0] for prefrail group, and -1.4 kg [95% CI: -2.5 to -0.3] for the frail group (p=0.62).

Figure 2 Participants divided by their status of non-frail/prefrail/frail according to the Helsinki Businessmen Study (HBS) measure, the modified Women’s Health Initiative Observational Study (WHI-OS) measure, and the Frailty Index (FI) measure. The figures show how these three groups according to various criteria are related to falls, weight change, quality of life (15D instrument), and mortality at different frailty stages

Figure 2
Participants divided by their status of non-frail/prefrail/frail according to the Helsinki Businessmen Study (HBS) measure, the modified Women’s Health Initiative Observational Study (WHI-OS) measure, and the Frailty Index (FI) measure. The figures show how these three groups according to various criteria are related to falls, weight change, quality of life (15D instrument), and mortality at different frailty stages

 

Quality of life in 2005

The quality of life data obtained by the 15D instrument in the year 2005 are presented in Figure 2, panel C. The HRQoL was found to be the lowest in the frail groups with all measures. The mean 15D scores for the HBS measure were 0.92 [95% CI: 0.91 to 0.93] for the not frail group, 0.87 [95% CI: 0.86 to 0.88] for the prefrail group, and 0.76 [95% CI: 0.71 to 0.81] for the frail group (p<0.001, adjusted for age). The mean 15D scores for modified WHI-OS, were 0.92 [95% CI: 0.91 to 0.93] for the not frail group, 0.86 [95 % CI: 0.85 to 0.88] for the prefrail group, and 0.74 [95% CI: 0.70 to 0.78] for the frail group (p<0.001, adjusted for age). The mean 15D scores for the FI was 0.92 [95% CI: 0.92 to 0.93] for the not frail group, 0.88 [95% CI: 0.87 to 0.90] for prefrail group, and 0.78 [95% CI: 0.75 to 0.81] for frail group (p<0.001, adjusted for age).

Mortality from 2000 to 2005

Mortality in different stages of frailty by different measures is shown in Figure 2, panel D. The mortality was the greatest in the frail groups as established by all three measures. The mortality in the HBS measure was 8.8% [95% CI: 5.5 to 13.7] for the not frail group, 19.5% [95% CI: 15.1 to 25.0] for the prefrail group, and 51.4% [95% CI: 36.3 to 68.6] for the frail group (p<0.001, adjusted for age). The mortality for modified WHI-OS, was 8.5% [95% CI: 5.5 to 13.0] for the not frail group, 21.3% [95% CI: 16.4 to 27.3] for prefrail group, and 51.4% [95% CI: 36.3 to 68.6] for the frail group (p<0.001, adjusted for age). The mortality for FI was 8.0% [95% CI: 5.0 to 12.7] for the not frail group, 19.1% [95% CI: 14.2 to 25.3] for the prefrail group, and 36.1% [95% CI: 26.9 to 47.1] for the frail group (p<0.001, adjusted for age).

 

Discussion

Both physical measures (HBS and modified WHI-OS) identified 7.3% of the participants as frail though they were not entirely the same set of individuals. The Venn diagram (Figure 1) shows the overlap of the identified participants. FI identified significantly more participants as frail (17.9%). The FI consequently predicted a smaller mortality percentage for the frail group than those predicted by the HBS or WHI-OS. The results suggest that variety of frailty measures, which are based on simple questionnaire, can be used to identify people at risk and to predict important outcomes such as mortality, falls, and quality of life.
This study has several strengths. HBS ranks among the longest cohort studies and has a good characterization of participants and the member of the cohort also give good response rates. The homogeneity of the participants reduces confounding when investigating mechanisms in which gender, race, and socioeconomic status may play important roles. Our study also has some limitations. The homogeneity and characteristics of participants also limit any generalizations that can be made about the results regarding other populations. This study is based solely on responses to mailed questionnaires, with their inherent limitations. However, the participants were highly educated, which probably indicates that the answers they gave are more reliable than those given by the general population. The number of falls was asked and based only on participants’ memories of the previous year. On the other hand, we defined fallers as those having fallen at least once in the previous year and recalled it as the criterion. Thus, whether or not the actual number of falls within the previous year in excess of one was remembered does not change the outcome measure in this study. The FI score was determined using fewer items (n=20) than in several previous studies (12). However, there is no absolute threshold of items and the current study actually helps to determine how low that threshold can be. The FI is a continuous score (7) but we used it in groups of not frail, prefrail and frail with cut-off points of 0.08 for prefrail and 0.25 for frail as used previously (11). Using the categories of not frail, prefrail and frail enables the comparison of phenotype and FI measures. Because of the small sample size some of the groups are quite small. Especially in the Venn diagram showing the overlapping of the frail groups of the three measures there are small groups of participants. On the other hand, with only small number of participants it is still possible to detect differences in the frailty measures.
The baseline characteristics show that the frail individuals according to the HBS (5), the modified WHI-OS (6), and the FI measure (7) had several diseases and low exercise levels. These baseline characteristics show similar trends of frailty to those described in previous studies (2, 6, 29). This suggests that these questionnaire-based measures can be used to identify frail individuals.
The prevalence of frailty in this study was higher with FI than with other two measures. The prevalence of frailty was 7.3% as determined by both phenotype-based measures, i.e. HBS and modified WHI-OS, and is consistent with earlier studies (10). HBS and modified WHI-OS had only small differences in measures. However, overlapping of the participants identified as frail by either measurement was only 70.7%. This suggests that frailty is a very challenging concept to define and it is very important to carefully choose the appropriate measure to investigate frailty. FI yielded a prevalence of 17.9% for frailty, which is similar to the result in a previous study (29). As in our study, the FI yielded greater prevalence value than the physical frailty measures in an earlier study (10). A recent systematic review found the prevalence of frailty varied from 4.0% to 59.1% among home-dwelling people according to various definitions (10). The prevalence was lowest in those studies that used a physical frailty definition such as the Fried criteria (2) and its modifications (10). Two recent studies that compared various frailty measures reported that the FI measure identified more participants as frail than phenotype based measures (21, 22), which was consistent with our study. The prevalence that was obtained by FI with only 20 items in this study is similar to those studies in which FI with 25 (22) or 39 (21) items were used. This similarity in prevalences between these studies suggests that FI can also be used with only 20 items.
All three measures compared in the present study predicted greater mortality, more fallers, and poorer quality of life among frail people (Figure 2). These findings are similar to previous studies, as found separately with the Fried criteria (2, 16, 18, 21), with the FI (7, 12, 19-21), with the WHI-OS (6), and with the HBS (5). None of the compared measures in the present study found differences in weight change among the different frailty stages. Weight change has been shown to be associated with frailty (2), but as far as we are aware only Sirola’s study (5) has investigated how frailty predicts weight change, and they found no significant differences.
Our findings indicate that there might not be a single definition of frailty. On the contrary, these measures approach this concept from different perspectives and predict important outcomes. However, our study does not recommend any of these frailty measures over the others. All of them show prognostic validity albeit identify different participants as frail. Depending on the intended use any of them can be applied. When needing sensitive measure, FI might be the choice whereas screening for those in need for exercise intervention the other two measures could be the better options.

 

Conclusion

This present study’s results indicate that various frailty measures based on simple questionnaires can be used to identify frailty and to predict important outcomes including mortality, falls, and the quality of life.

Acknowledgments: This work was supported by King Gustaf V and Queen Victoria’s Foundation of Freemasons, the Paulo Foundation and EVO (VTR) funding via Helsinki University Hospital. The sponsors had no role in the design or execution of the study, in the collection, management, analyses, or interpretation of the data, or in the preparation, review, or approval of the manuscript.
Conflict of interest: The authors declare that they have no competing interests. The study process complied with the current laws of Finland. All authors read and approved the final manuscript.
Ethical standard: The Ethics Committee of Helsinki University Hospital approved this study protocol, and all patients provided their informed consent.

 

References

1.     Fulop T, Larbi A, Witkowski JM, et al. Aging, frailty and age-related diseases. Biogerontology. 2010 Oct;11(5):547-563.
2.     Fried LP, Tangen CM, Walston J, et al. Frailty in older adults: Evidence for a phenotype. J Gerontol A Biol Sci Med Sci. 2001 Mar;56(3):M146-56.
3.     Fried LP, Ferrucci L, Darer J, Williamson JD, Anderson G. Untangling the concepts of disability, frailty, and comorbidity: Implications for improved targeting and care. J Gerontol A Biol Sci Med Sci. 2004 Mar;59(3):255-263.
4.     Fisher AL. Just what defines frailty? J Am Geriatr Soc. 2005 Dec;53(12):2229-2230.
5.     Sirola J, Pitkala KH, Tilvis RS, Miettinen TA, Strandberg TE. Definition of frailty in older men according to questionnaire data (RAND-36/SF-36): The helsinki businessmen study. J Nutr Health Aging. 2011 Nov;15(9):783-787.
6.     Woods NF, LaCroix AZ, Gray SL, et al. Frailty: Emergence and consequences in women aged 65 and older in the women’s health initiative observational study. J Am Geriatr Soc. 2005 Aug;53(8):1321-1330.
7.     Mitnitski AB, Mogilner AJ, Rockwood K. Accumulation of deficits as a proxy measure of aging. ScientificWorldJournal. 2001 Aug 8;1:323-336.
8.     Strandberg TE, Pitkala KH. Frailty in elderly people. Lancet. 2007 Apr 21;369(9570):1328-1329.
9.     Strandberg TE, Pitkälä KH, Tilvis RS. Frailty in older people. European Geriatric Medicine. 2011 12;2(6):344-355.
10.     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 Aug;60(8):1487-1492.
11.     Song X, Mitnitski A, Rockwood K. Prevalence and 10-year outcomes of frailty in older adults in relation to deficit accumulation. J Am Geriatr Soc. 2010 Apr;58(4):681-687.
12.     Rockwood K, Mitnitski A. Frailty in relation to the accumulation of deficits. J Gerontol A Biol Sci Med Sci. 2007 Jul;62(7):722-727.
13.     Bortz WM,2nd. A conceptual framework of frailty: A review. J Gerontol A Biol Sci Med Sci. 2002 May;57(5):M283-8.
14.     Morley JE, Haren MT, Rolland Y, Kim MJ. Frailty. Med Clin North Am. 2006 Sep;90(5):837-847.
15.     Rolland Y, Czerwinski S, Abellan Van Kan G, et al. Sarcopenia: Its assessment, etiology, pathogenesis, consequences and future perspectives. J Nutr Health Aging. 2008 Aug-Sep;12(7):433-450.
16.     Ensrud KE, Ewing SK, Taylor BC, et al. Frailty and risk of falls, fracture, and mortality in older women: The study of osteoporotic fractures. J Gerontol A Biol Sci Med Sci. 2007 Jul;62(7):744-751.
17.     Perttila NM, Öhman H, Strandberg TE, et al. Severity of frailty and the outcome of exercise intervention among participants with alzheimer disease: A sub-group analysis of a randomized controlled trial. European Geriatric Medicine. 2016 4;7(2):117-121.
18.     Kojima G, Iliffe S, Jivraj S, Walters K. Association between frailty and quality of life among community-dwelling older people: A systematic review and meta-analysis. J Epidemiol Community Health. 2016 Jan 18.
19.     Hubbard RE, Goodwin VA, Llewellyn DJ, Warmoth K, Lang IA. Frailty, financial resources and subjective well-being in later life. Arch Gerontol Geriatr. 2014 May-Jun;58(3):364-369.
20.     Fang X, Shi J, Song X, et al. Frailty in relation to the risk of falls, fractures, and mortality in older chinese adults: Results from the beijing longitudinal study of aging. J Nutr Health Aging. 2012 Oct;16(10):903-907.
21.     Widagdo IS, Pratt N, Russell M, Roughead EE. Predictive performance of four frailty measures in an older australian population. Age Ageing. 2015 Nov;44(6):967-972.
22.     Malmstrom TK, Miller DK, Morley JE. A comparison of four frailty models. J Am Geriatr Soc. 2014 Apr;62(4):721-726.
23.     Strandberg TE, Salomaa V, Strandberg AY, et al. Cohort profile: The helsinki businessmen study (HBS). Int J Epidemiol. 2015 Dec 24.
24.     Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. J Chronic Dis. 1987;40(5):373-383.
25.     Hays RD, Morales LS. The RAND-36 measure of health-related quality of life. Ann Med. 2001 Jul;33(5):350-357.
26.     Sintonen H. The 15D instrument of health-related quality of life: Properties and applications. Ann Med. 2001 Jul;33(5):328-336.
27.     Hawthorne G, Richardson J, Day NA. A comparison of the assessment of quality of life (AQoL) with four other generic utility instruments. Ann Med. 2001 Jul;33(5):358-370.
28.     Stavem K, Froland SS, Hellum KB. Comparison of preference-based utilities of the 15D, EQ-5D and SF-6D in patients with HIV/AIDS. Qual Life Res. 2005 May;14(4):971-980.
29.     Rockwood K, Howlett SE, MacKnight C, et al. Prevalence, attributes, and outcomes of fitness and frailty in community-dwelling older adults: Report from the canadian study of health and aging. J Gerontol A Biol Sci Med Sci. 2004 Dec;59(12):1310-1317.

PREVALENCE OF FRAILTY IN NURSING HOME RESIDENTS ACCORDING TO VARIOUS DIAGNOSTIC TOOLS

 

F. BUCKINX1,2, J.-Y. REGINSTER1,2, S. GILLAIN3, J. PETERMANS3, T. BRUNOIS1,2, O. BRUYÈRE1,2

 

1. Department of Public Health, Epidemiology and Health Economics, University of Liège, Belgium; 2. Support Unit in Epidemiology and Biostatistics, University of Liège, Belgium; 3. Department of geriatrics, University Teaching Hospital of Liège, Belgium; 4. Department of Sports Sciences, University of Liège, Belgium
Corresponding author: Fanny Buckinx, PhD Student, University of Liège, Belgium, Quartier Hôpital, avenue Hippocrate, 13, 4000 Liège, Belgium, Tel.: +32 4 366 49 33, Fax: +32 43 66 28 12, E-mail: fanny.buckinx@ulg.ac.be

J Frailty Aging 2017;6(3):122-128
Published online June 14, 2017, http://dx.doi.org/10.14283/jfa.2017.20

 


Abstract

Background: Although the theoretical foundations of frailty are well established in the literature, it remains an evolving concept lacking any unique definition or diagnostic criteria for use in clinical practice and epidemiological research. No consensus exists about the accurate prevalence rates of frailty. The various operational definitions of frailty can at least partly explain such discrepancies. Objective: To compare the prevalence of frailty, measured with different diagnostic tools, among elderly nursing home residents. Design: This is an analysis of baseline data collected among the SENIOR (Sample of Nursing home Elderly Individuals: an Observational Research) cohort. Setting: Nursing homes. Population: A total of 662 volunteer subjects from 28 nursing homes were included in this analysis. Among them, the mean age was 83.2 ± 8.99 years and 484 (72.5%) of them were women. Measurement: The percentages of frail and non-frail subjects were calculated according to 10 different definitions. Results: Prevalence of frailty varies from 1.70% (Frailty Index) to 76.3% (Groningen Frailty Indicator) depending on the tool used. Conclusions: The prevalence of frailty is highly dependent on the diagnostic tool used. It would be necessary to reach a consensus on which diagnostic tools to use if one wishes to have comparable data obtained in epidemiological studies.

Key words: Diagnostic tool, epidemiology, frailty, nursing homes, prevalence.


 

 

Introduction

With an ageing population, there is a growing interest in frailty. It may be regarded as a multidimensional geriatric syndrome of decreased resilience and resistance to stressors, resulting from cumulative decline across multiple physiological systems, causing vulnerability to adverse health outcomes such as falls, hospitalisation, institutionalisation and mortality (1). These adverse health effects in turn contribute to an increased demand for medical and social care and are associated with increased financial costs (2). Thus, one of the major challenges of geriatric medicine is to recognise these conditions as soon as possible and to halt (or slow) the downward spiral of increasing comorbidity and frailty. Although the theoretical foundations of frailty are well established in the literature, and the concept almost universally accepted, the practical effects and solutions remain controversial (3, 4). It remains an evolving concept lacking any unique definition or diagnostic criteria for use in clinical practice and epidemiological research (4). Multiple tools have been developed in recent years in order to diagnose this geriatric syndrome (5) and some of these tools have been widely used in epidemiological studies. Taking account of all such studies, the prevalence of frailty seems to increase with age, appears to be greater in women than in men and would appear to be more prevalent in people with  any combination of lower education or income, poorer health and higher rates of comorbid chronic disease and disability. However, no consensus exists about the accurate prevalence rates of frailty (6, 7). The various operational definitions of frailty used in these studies can at least partly explain such discrepancies (8). However, and to the best of our knowledge, no single study has investigated the impact of all these definitions of frailty on its prevalence in the same population. In nursing home populations, some studies have suggested that the prevalence of frailty is high, compared with non-institutionalised subjects (6, 7). The prevalence of frailty also depends on the countries (9). Indeed, a recent survey of 7510 community-dwelling older adults in 10 European countries found that the prevalence of frailty, according to frailty phenotype defined by Fried, was higher in southern than in northern Europe consistent with an unexplained north-south health risk gradient (10). African Americans are more likely to be frail than Caucasians (11). For these reasons, it is difficult to compare the results obtained in different studies, given the difference observed in the prevalence of frailty, which can be due to the inclusion of people living in different places, with different degrees of dependence or a different age range. However, it should be acknowledged that there is no specific operational definition of frailty validated for nursing home residents.  To the best of our knowledge, all existing tools to assess frailty have not been tested in this specific population. Indeed, only a few tools such as the frailty phenotype (12) or Clinical frailty Scale (13) have sometimes been used in studies performed in nursing homes, but a comparison between various tools has never been carried out. Therefore, the aim of this study was to compare the prevalence of frailty with regards to different diagnostic tools among elderly nursing home residents. Moreover, the differences in demographic and clinical characteristics of subjects diagnosed as frail according to the various definition of frailty are poorly understood and were also investigated in the present study.

 

Methods

Study design

This is an analysis of baseline data collected among the SENIOR (Sample of Elderly Nursing home Individuals: an Observational Research) cohort. The protocol was approved by the Ethics Committee of the University Teaching Hospital of Liège, under the number 2013/178.

Study subjects and setting

Residents of 28 nursing homes in the area of Liège, Belgium, were eligible for the study if they agreed to participate (i.e. informed consent). Subjects disoriented or unable to stand and walk (authorised technical support) were excluded from this research.

Data collection

Assessment of frailty

For each subject, frailty was measured using the 10 different diagnostic tools described below:
A) Clinical Frailty Scale (CFS) (14): this is based on a clinical evaluation in the domains of mobility, energy, physical activity and function, using descriptors and figures to stratify elderly adults according to their level of vulnerability. The score ranges from 1 (robust health) to 7 (complete functional dependence on others).
To measure the prevalence of frailty, all persons included in categories “terminally ill”, “very severely frail”, “severely frail”, “moderately frail” and “mildly frail”, were considered as “frail”.
B) Edmonton Frail Scale (EFS) (15): this samples 8 domains (Cognitive impairment, health attitudes, social support, medication use, nutrition, mood, continence, functional abilities). A score range between 0-3 is a robust state, 4-5 is a slightly frail state, 6-8 is a moderately frail state and 9-17 is a severely frail state.
All persons included in categories “severely frail”, “moderately frail” and “slightly frail” were considered as “frail”.
C) Frail Scale Status (16): this has 5 components: Fatigue, Resistance, Ambulation, Illness, and Loss of weight. Scores range from 0-5 and represent frail (3-5), pre-frail (1-2), and robust (0) health states.
D) Frailty index (17): this is expressed as a ratio of deficits present to the total number of deficits considered.  Frailty index includes 40 variables and the calculation was performed on the maximum number of deficits collected. Thus, participants were considered as frail when the ratio of deficits present to the total number of deficits considered was 0.25 (i.e. lowest quartile) or more (18, 19).
E) Frailty phenotype (7): this is a deficit across five domains. Thus, phenotype of frailty was identified by the presence of three or more of the following components: shrinking, weakness, poor endurance and energy, slowness and a low level of physical activity. The presence of one or two deficits indicates a pre-frail condition, and a total of three or more deficits indicates frailty while the absence of deficits indicates a robust state.
F) Groningen Frailty Indicator (GFI) (20): this consists of 15 self-report items and screens for loss of functions and resources in four domains: physical, cognitive, social, and psychological. Scores range from zero (not frail) to fifteen (very frail). A GFI score of 4 or higher was regarded as frail.
G) Sega grid (21): this establishes a risk profile of frailty and provides
reporting of problems and factors that may influence functional decline, including age, provenance, drugs, mood, perceived health, history of falls, nutrition, comorbidities, IADL, mobility, continence, feeding and cognitive functions. A score of 0, 1 or 2 is given for each item and a total over 11 points indicates a “very frail” condition, a score between 8 and 11 points indicates a frail condition while a score below 8 is a slightly frail condition.
All persons included in categories “frail” and “very frail” were considered as “frail”.
H) Share Frailty Instrument (Share-FI) (22): Using the five SHARE frailty variables (fatigue, loss of appetite, grip strength, functional difficulties & physical activity), D-Factor scores (DFS) were determined using the SHARE-FI formula and based on the DFS value, the subject could then be categorised as non-frail, pre-frail, or frail.
I) Strawbridge questionnaire (23): this defines frailty as difficulty in two or more functional domains (physical, cognitive, sensory, and nutritive). A score greater than or equal to 3 in more than one domain is considered vulnerable.
J) Tilburg Frailty Indicator (TFI) (24): The TFI consists of 2 parts. Part A contains 10 questions on determinants of frailty and diseases (multimorbidity); part B contains 3 domains of frailty (quality of life, disability, and healthcare utilisation) with a total of 15 questions on components of frailty. The threshold above which the participant is considered as frail is 5 points.
The objectives and the validation criteria of these various tools are shown in Appendix 1.

Other data collected

Other variables collected were socio-demographic data such as age or sex, anthropometric measurements such as weight, height, from which body mass index (BMI) was calculated, abdominal circumferences, type of institution, technical assistance for walking, drug consumption and medical history. The following clinical measurements were also collected:
–    Daily energy expenditure evaluated by the Minnesota Leisure Time Activities Questionnaire;
–    Cognitive skills assessed with the Mini Mental State Examination;
–    Nutritional status estimated by the Mini Nutritional Assessment;
–    Quality of life assessed by both the EQ-5D and the SF-36 questionnaires;
–    Activities of Daily Living estimated by the Katz index;
–    Comorbidities collected from the CIRS-G questionnaire;
–    Gait and body balance assessed using the Tinetti, the “Timed Up and Go” and the “Short Physical Performance Battery” tests  and gait speed

These data were collected during a face-to-face appointment with the patient. The same observer conducted all the tests in all nursing homes. The data were completed using the medical records.

Statistical analyses

Quantitative variables that were normally distributed were expressed as means ± standard deviation (SD), and quantitative variables that were not normally distributed were reported as medians and interquartile ranges (percentile 25, percentile 75). A Shapiro–Wilk test verified the normal distribution for all parameters. Qualitative variables were reported as numbers and frequencies (%). Participantss were defined as frail, or not, according to each of these 10 diagnostic tools. Then, the percentage of frail subjects for each definition was estimated. Afterwards, the degree of concordance between each definition was calculated by Cohen’s Kappa coefficient; the closer the value to 1, the better the concordance (i.e. k<0: disagreement, 0-0.2: very low agreement, 0.21-0.40: low agreement, 0.41-0.60: moderate agreement, 0.61-0.80: strong agreement, 0.81-1: excellent agreement). The percentage of pre-frail subjects was also assessed by 3 of these 10 definitions, that propose this intermediate state. The association between the different diagnostic tools and subject characteristics was assessed by multiple regression or logistic regression. All analyses were performed with Statistica 10 software and SAS Statistical package (version 9.3 for Windows). Results were considered statistically significant when 2-tailed p values were less than 0.05.

 

Results

Baseline characteristics of the population

A total of 662 subjects were included in this study. The mean age of the population was 83.2 ± 8.99 years and the population was predominantly women (72.5%). Participants’ demographic and clinical characteristics are shown in Table 1.

Table 1 Baseline characteristics of the population (n=662)

Table 1
Baseline characteristics of the population (n=662)

Prevalence of frailty according to different definitions

The prevalence of frailty varied from 15.2% (Frail Scale Status) and Frailty Index (83.7%) depending on the definition used. The percentage of pre-frail subjects varied from 28.0% (Clinical Frailty Scale) to 60.8% (Frailty phenotype) according to the definitions which propose this intermediate state. (Table 2).

Table 2 Number of frail subjects using the different definitions (n=662)

Table 2
Number of frail subjects using the different definitions (n=662)

Concordance between the different definitions of frailty

Table 3 presents the concordance between definitions. The concordance between the definitions was low (Overall Kappa Coefficient: 0.014 (-0.057 – 0.085)), with a Cohen’s Kappa coefficient which ranged from -0.77 (-0.85- -0.69), observed between Frailty Index and Sega gird, to 0.67 (0.61-0.73), observed between Frail Scale Status and Clinical Frailty Scale. Thus, participants diagnosed as frail with one definition are rarely diagnosed as frail with another definition. Nevertheless, reporting the Spearman’s correlation among the operational definitions without their categorization (i.e. continuous variables), these definitions follow similar patterns of increase in the risk of deficits. The correlations ranged between 0.13 (i.e. Edmonton frail scale and Strawbridge questionnaire) and 0.68 (i.e. Frailty Index and Frailty phenotype) and were all statistically significant.

Table 3 Concordance between definitions of frailty, estimated by Kappa Cohen’s coefficient (95% CI)

Table 3
Concordance between definitions of frailty, estimated by Kappa Cohen’s coefficient (95% CI)

A= Clinical Frailty Scale; B = Edmonton frail Scale, C= Frail Scale Status, D= Frailty Index, E= Frailty phenotype, F= Groningen Frailty Indicator, G= Sega Grid, H= Share Frailty Instrument, I= Strawbridge questionnaire, J= Tilburg Frailty indicator

Clinical characteristics of frail subjects

Depending on the tool, clinical characteristics of frail subjects appears to be different.Significant differences are observed regarding the age of participants, their sex, their walking support, their nutritional status evaluated by the Mini Nutritional Assessment, their quality of life assessed by the EQ-5D and by the SF-36, their functional abilities assessed by the Tinetti test, by the SSPB test and by gait speed (p<.0001 for all these data).

 

Discussion

In this study it was found, as expected, that the prevalence of frailty is highly dependent on the diagnostic tool used.  However, the ratios observed differ very widely, ranging from 1.70% to 76.3%, and this could have important consequences for clinicians, researchers and public health decision-makers.
Clearly, the diversity and the breadth of definition of frailty criteria would appear to have contributed to the wide range of prevalence found (6). Indeed, there are two main kinds of definition for frailty (one broad and the other physical) and a recent systematic literature review showed that studies using a physical definition consistently reported lower prevalence of frailty than those using a broad frailty definition (6). Frailty measurements can be grouped into three categories: subjective (i.e. self-reported, reported by participant or by a researcher), objective (i.e. directly measured components) or mixed (i.e. subjective and objective combined) (25). This may also have an impact on the prevalence of frailty.
A systematic review highlighted that the prevalence of frailty in community-dwelling elderly adults varied from 4.0% to 59.1% according to the diagnostic tool used (6). Contrary to the systematic review that compared various tools but in different populations, the present study evaluates the differences in prevalence of frailty using the different operational definitions within the same population. The results presented in the review are somewhat different from those obtained in this study, which could be explained by the difference in the populations studied. Nevertheless, the results presented here are more consistent with a recent meta-analysis which showed that the mean prevalence of frailty in nursing homes differed widely from study to study, ranging from 19.0% to 75.6% (26). One study, published in 2015, compared how different frailty measures predict short-term adverse outcomes (27). The results highlighted that, over a time interval of 10 months and among a sample of community-dwelling elderly individuals, the Groningen Frailty Index predicted an increase in IADL disability, and the Tilburg Frailty Indicator predicted a decline in quality of life. Actually, no study has yet investigated the predictive value, in a nursing home setting, of different operational definitions of frailty for the occurrence of different adverse health outcomes, and, to our knowledge, no operational definition of frailty has been validated among institutionalised people. And yet this would seem to be an important aspect to be explored in prospective studies to identify the best operational definition adapted to this particular population. This definition could then be considered as the gold standard among nursing home residents and could be used in clinical practice and research to make studies more comparable.  It is also important to point out that gait speed at usual pace was found to be a consistent risk factor for disability, cognitive impairment, institutionalisation, falls, and/or mortality. (28) In the population under study here, gait speed seemed significantly different according to different operational definitions of frailty used. It would be interesting to clarify the predictive value of this variable in future prospective studies in a nursing home setting.
In the present study, the prevalence of pre-frailty was between 28% and 60.8% and similar with other studies (6). It is important to note that people included in this study were volunteers, not disoriented and had to be able to move. Because of this selection, the most frail people have probably not been included in the study and, therefore, the prevalence of frailty in this study may be underestimated. Anyway, it is important to identify pre-frail people because preventive intervention programs can be implemented, thus modifying the rates of associated events (9).
Otherwise, the agreement between the definitions was very low. This means that the people diagnosed as frail are different depending on the diagnostic tool used. Nevertheless, the definitions seem to be correlated with each other.  This means that the frailest subjects, according one definition, are also the frailest ones, according to the other definitions; but the threshold between frail and robust is different depending on the operational definition used. Moreover, significant differences were found regarding the clinical characteristics of frail subjects diagnosed according to these 10 definitions. Indeed, depending on the diagnostic tool used, it seems that significant differences are observed concerning the age of the participants. Also, nutrition status is different depending on the definition used, and this could be explained because the different definitions do not evaluate systematically nutritional status or, at best, do it differently (anamnesis, weight loss). In addition, the quality of life of frail subjects according to the different tools seems different. This can also be explained because the quality of life is not always considered in the various diagnostic tools for frailty or based on a simple question.
Investigators use multiple scales to assess frailty, all of which count deficits in health. Frailty scales differ in the nature and number of deficits they count, which could explain the heterogeneity of frail persons according to different definitions. Because the characteristics of frail subjects are different depending on the tools used for the diagnosis of frailty, the long-term clinical consequences of frailty may also differ. Therefore therapeutic strategies will not be easily evaluated and implemented as long as studies do not use the same diagnostic tool.
Consensus does not yet exist regarding the component element of frailty (29)  and there is no validated operational definition for nursing home residents. From a clinical and Public Health point of view, further investigations identifying the best model of frailty in this specific population are needed in order to obtain comparable data in epidemiological studies. In clinical practice, it would improve the management of frailty. An unambiguous definition of frailty is of great importance for clinicians to identify those at an increased risk of adverse health outcomes, but also for policy makers to make cost-effective decisions in health care. In conclusion, the prevalence of frailty is highly dependent on the definition used. In addition, the concordance between the different modalities of diagnosis is low and this research reveals that the clinical characteristics of frail subjects diagnosed with varied definitions are different. As long as no consensus has been reached about the operationalisation of frailty, clinicians and policy-makers should be aware that differences between definitions exist and that it should have important consequences, at least in epidemiological research.

 

Acknowledgments: We thank all nursing homes who agreed to participate in this study.
Funding: Fanny Buckinx is supported by a Fellowship from the FNRS (Fonds National de la Recherche Scientifique de Belgique — FRSFNRS. www.frs-fnrs.be).
Conflict of Interest: No conflict of interest to disclose.

Appendix 1

References

1.    Bauer JM, Sieber CC. Sarcopenia and frailty: a clinician’s controversial point of view. Experimental gerontology. 2008;43:674-8.
2.    Lally F, Crome P. Understanding frailty. Postgraduate medical journal. 2007;83:16-20.
3.    Buckinx F, Rolland Y, Reginster J-Y, Ricour C, Petermans J, Bruyère O. Burden of frailty in the elderly population: perspectives for a public health challenge. Archives of Public Health. 2015;73:19.
4.    Bergman H, Ferrucci L, Guralnik J, et al. Frailty: an emerging research and clinical paradigm–issues and controversies. J Gerontol A Biol Sci Med Sci. 2007;62:731-7.
5.    Cesari M, Gambassi G, van Kan GA, Vellas B. The frailty phenotype and the frailty index: different instruments for different purposes. Age and ageing. 2014;43:10-2.
6.    Collard RM, Boter H, Schoevers RA, Oude Voshaar RC. Prevalence of frailty in community-dwelling older persons: a systematic review. Journal of the American Geriatrics Society. 2012;60:1487-92.
7.    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:M146-56.
8.    Castell MV, Sanchez M, Julian R, Queipo R, Martin S, Otero A. Frailty prevalence and slow walking speed in persons age 65 and older: implications for primary care. BMC family practice. 2013;14:86.
9.    Jurschik P, Nunin C, Botigue T, Escobar MA, Lavedan A, Viladrosa M. Prevalence of frailty and factors associated with frailty in the elderly population of Lleida, Spain: the FRALLE survey. Archives of gerontology and geriatrics. 2012;55:625-31.
10.    Santos-Eggimann B, Cuenoud P, Spagnoli J, Junod J. Prevalence of frailty in middle-aged and older community-dwelling Europeans living in 10 countries. J Gerontol A Biol Sci Med Sci. 2009;64:675-81.
11.    Xue QL. The frailty syndrome: definition and natural history. Clinics in geriatric medicine. 2011;27:1-15.
12.    Gonzalez-Vaca J, de la Rica-Escuin M, Silva-Iglesias M, et al. Frailty in INstitutionalized older adults from ALbacete. The FINAL Study: rationale, design, methodology, prevalence and attributes. Maturitas. 2014;77:78-84.
13.    Matusik P, Tomaszewski K, Chmielowska K, et al. Severe frailty and cognitive impairment are related to higher mortality in 12-month follow-up of nursing home residents. Arch Gerontol Geriatr. 2012;55(1):22-4.
14.    Rockwood K, Mitnitski A. Frailty defined by deficit accumulation and geriatric medicine defined by frailty. Clinics in geriatric medicine. 2011;27:17-26.
15.    Rolfson DB, Majumdar SR, Tsuyuki RT, Tahir A, Rockwood K. Validity and reliability of the Edmonton Frail Scale. Age and ageing. 2006;35:526-9.
16.    Morley JE, Malmstrom TK, Miller DK. A simple frailty questionnaire (FRAIL) predicts outcomes in middle aged African Americans. J Nutr Health Aging. 2012;16:601-8.
17.    Searle SD, Mitnitski A, Gahbauer EA, Gill TM, Rockwood K. A standard procedure for creating a frailty index. BMC geriatr. 2008;8:24.
18.    K. Rockwood AM. How might deficit accumulation give rise to frailty? J Frailty Aging 2012;1(1):8-12.
19.    Mitnitski A, Collerton J, Martin-Ruiz C, Jagger C, von Zglinicki T, Rockwood K, et al. Age-related frailty and its association with biological markers of ageing. BMC medicine. 2015;13:161.
20.    Baitar A, Van Fraeyenhove F, Vandebroek A, et al. Evaluation of the Groningen Frailty Indicator and the G8 questionnaire as screening tools for frailty in older patients with cancer. Journal of geriatric oncology. 2013;4:32-8.
21.    Schoevaerdts didier bs, Malhomme brigitte, Rezette céline, Gillet jean-bernard, Vanpee dominique, Cornette pascale, Swine christian. Identification précoce du profil gériatrique en salle d’urgences : présentation de la grille SEGA. La Revue de Gériatrie. 2004;29:169-78.
22.    Romero-Ortuno R, Walsh CD, Lawlor BA, Kenny RA. A frailty instrument for primary care: findings from the Survey of Health, Ageing and Retirement in Europe (SHARE). BMC geriatrics. 2010;10:57.
23.    Strawbridge WJ, Shema SJ, Balfour JL, Higby HR, Kaplan GA. Antecedents of frailty over three decades in an older cohort. The journals of gerontology Series B, Psychological sciences and social sciences. 1998;53:S9-16.
24.    Gobbens RJ, van Assen MA, Luijkx KG, Wijnen-Sponselee MT, Schols JM. The Tilburg Frailty Indicator: psychometric properties. Journal of the American Medical Directors Association. 2010;11(5):344-55.
25.    Bouillon K, Kivimaki M, Hamer M, et al. Measures of frailty in population-based studies: an overview. BMC geriatrics. 2013;13:64.
26.    Kojima G. Prevalence of Frailty in Nursing Homes: A Systematic Review and Meta-Analysis. J Am Med Dir Assoc. 2015.
27.    Coelho T, Paul C, Gobbens RJ, Fernandes L. Frailty as a predictor of short-term adverse outcomes. PeerJ. 2015;3:e1121.
28.    Abellan van Kan G, Rolland Y, Andrieu S, et al. Gait speed at usual pace as a predictor of adverse outcomes in community-dwelling older people an International Academy on Nutrition and Aging (IANA) Task Force. J Nutr Health Aging. 2009;13:881-9.
29.    Buckinx F, Rolland Y, Reginster JY, Ricour C, Petermans J, Bruyere O. Burden of frailty in the elderly population: perspectives for a public health challenge. Archives of public health = Archives belges de sante publique. 2015;73:19.
30.    Rockwood K, Song X, MacKnight C, et al. A global clinical measure of fitness and frailty in elderly people. CMAJ. 2005;173:489-95.

ASSESSING THE APPROPRIATENESS OF ORAL ANTICOAGULATION FOR ATRIAL FIBRILLATION IN ADVANCED FRAILTY: USE OF STROKE AND BLEEDING RISK-PREDICTION MODELS

 

R. O’CAOIMH1,2, E. IGRAS3, A. RAMESH3, B. POWER4, K. O’CONNOR5, R. LISTON3

 

1. Centre for Gerontology and Rehabilitation, University College Cork, St Finbarrs Hospital, Douglas road, Cork City, Ireland; 2. Health Research Board, Clinical Research Facility Galway, National University of Ireland, Galway, Geata an Eolais, University Road, Galway, Ireland; 3. Kerry General Hospital, Rathass, Tralee, Co Kerry, Ireland; 4. St. Columbanus Home & Killarney Community Hospital, Killarney, Co Kerry, Ireland; 5. Mercy University Hospital, Grenville place, Cork City, Ireland.
Corresponding author: Dr Rónán O’Caoimh, Centre for Gerontology and Rehabilitation, University College Cork, St Finbarrs Hospital, Douglas road, Cork City, Ireland, Email: rocaoimh@hotmail.com, Telephone: +353214901461, Facsimile: +3534901635

J Frailty Aging 2017;in press
Published online December 8, 2016, http://dx.doi.org/10.14283/jfa.2016.118

 


Abstract

Background: Atrial fibrillation (AF) is common among frail older adults. Oral anticoagulation (OAC) is particularly challenging for these due to overlapping stroke and bleeding risk factor profiles. Objective: To compare the utility of stroke and haemorrhage risk-prediction instruments in the treatment of AF among frail older adults. Design: Cross-sectional study. Settings and participants: Frail residents in four nursing homes with a Clinical Frailty Scale score ≥5 (median 7±0). Measurements: The prevalence of AF was assessed by ECG and chart review. Stroke (CHADS2 and CHA2DS2-VASc) and bleeding (HASBLED  and HEMORR2HAGES) risk-prediction scores were then applied. A validated, risk-based, colour-coded decision support tool, incorporating these instruments, was then used to create a risk matrix and assess the appropriateness of OAC. Results: In total, 225 patients were included. The distribution of CFS scores was similar irrespective of AF status. In all, 86/225 (38%) had any history of AF. Of these, only 15/86 (17%) were prescribed OAC. All those in AF scored ≥2 on the CHA2DS2-VASc. One-third also scored high-risk of bleeding using HAS-BLED or HEMORR2HAGES. Risk-prediction scores were similar between those with ‘known’ (documented) and occult (only on ECG) AF. The colour-coded decision tree suggested that OAC would be recommended for the majority in AF when HAS-BLED (60/86, 70%) was used as the bleeding risk-prediction instrument. Despite this, only 12/60 (20%) were anticoagulated. When HEMORR2HAGES was incorporated instead, one patient was advised OAC, the remainder no treatment (57%) or an antiplatelet (42%). Discussion: Stroke risk was high and bleeding risk levels comparatively low, suggesting that the balance of risk may favor OAC for AF in this cohort of patients with advanced frailty. Despite this and the high prevalence of AF, OAC prescription rates were low. The decision-support tool used showed mixed results, depending on the bleeding-risk score incorporated, suggesting that while useful, they should not replace clinical judgement.

Key words: Frailty, atrial fibrillation, prevalence, stroke, haemorrhage, risk prediction instruments, long-term care.


 

 

Introduction

Stroke due to atrial fibrillation (AF) is best prevented using oral anticoagulation (OAC) (1) with either warfarin or one of the newer novel oral anticoagulants (NOAC) (2). Although the prescription of OAC for stroke prevention has increased (3), OAC remains under-utilized among older adults (4), despite clear benefits (5). The decision to initiate OAC is particularly complex in frail patients. While some studies suggest that these are less likely to receive OAC compared to non-frail older adults (6), others indicate that it may not be frailty itself that determines the decision (7, 8). Irrespective, they are more likely to have adverse events with or without OAC (6).
Residents in long-term care (LTC), by virtue of age and comorbidity, have a high prevalence of frailty (9) and co-morbid AF (10). AF is under-diagnosed for these patients who are rarely re-evaluated for AF once admitted. The prevalence of AF in LTC varies from 7.5% (11) to 19% (12), although the exact figure is unknown. Managing stroke risk in LTC is important because of the impact it has on resident quality of life (13). Despite this, only half of those deemed ideal candidates are anticoagulated (10), similar to other, older frail hospital (7) and community-based samples (14). Reasons for this may include personal choice, multiple potential contra-indications, increased risk of bleeding (15) and a lack of evidence for benefit in this population with frail and or institutionalised older adults excluded from most clinical trials (16). For physicians, experience is one of the most important factors with negative experiences prescribing OAC weighing upon future decisions; physicians place more emphasis on bleeding risk than stroke avoidance (17).
Current guidelines do not provide specific guidance for using OAC in frailty (7). To aid management and decision-making, a growing number of risk-prediction instruments for risk of stroke with AF and bleeding with OAC have been developed (18), although their utility in frail older adults has yet to be established. Two of the most widely used stroke risk-predictors are the CHADS2 (19) and the CHA2DS2-VASc scores (20). Instruments to predict bleeding risk with OAC are also available including the HEMORR2HAGES (21) and HAS-BLED scores (22). Although these have limited accuracy, HAS-BLED performed best in a low-risk community sample (23). As the results of combined stroke and haemorrhagic risk-prediction tools are often contradictory, it is suggested that decision analysis may assist particularly where complexity and uncertainty exist (24). Such algorithms may allow clinicians to tailor OAC according to patient characteristics and to improve decision-making in frail older adults (25). These also increase quality-adjusted life expectancy based upon improvements in anti-thrombotic therapy (26). To date, no studies have examined the use of such models in patients with AF and advanced frailty. Given this and the paucity of information concerning the management of AF in LTC, the objective of this study was to examine the potential to manage AF in advanced frailty in LTC institutions in Ireland, using stroke and bleeding risk-prediction models and a colour-coded decision tree algorithm (24).

 

Methods

Design and data collection

This is a secondary analysis of a cross-sectional observational study originally conducted between May 2009 and January 2010, in four LTC units in a single county in southern Ireland (27). LTC referred to state managed high-dependency units providing nursing care, excluding private facilities, sheltered accommodation and residential care homes. At the time of sampling there were 277 residents available in these units. Those who declined consent (n=16), were agitated (n=17), actively dying (n=2), off-site (n=5), or on respite (n=12) were excluded. After a 12-lead ECG was performed a chart review was carried out. Residents were then classified into: ‘known persistent’, occult, paroxysmal and ‘never known’ AF using American College of Cardiology guidelines (28). ‘Known persistent’ AF i.e. chronic AF, lasts more than seven days (29). In this study it referred to AF that was both documented in the medical records and present on the ECG performed on the assessment day. Paroxysmal AF is a heterogeneous group of disorders, characterised by different frequency, duration, symptoms and mode of termination typically lasting less than seven days and separated by prolonged periods of sinus rhythm (29). In this study, paroxysmal AF was defined as AF that was documented previously in the medical records but not present on the ECG at assessment. It also included any patients with a past history of paroxysmal AF in their records who were found to be in sinus rhythm when assessed. ‘Never known’ AF describes residents that had no documented history of AF and were not in AF on the assessment ECG. An ‘ever known’ (‘Ever AF’) category was also included for those with any documented history of AF or if their assessment day ECG revealed AF. This included those with both chronic AF and paroxysmal AF, both of which should be considered for OAC (30). Risk-prediction models were then applied. All ECGs were independently reviewed to confirm the diagnosis. Ethics approval was obtained from the Clinical Research Ethics Committee of the Cork Teaching Hospitals. All patients signed informed consent where possible. Assent was obtained from those deemed to lack capacity.

Sample size

A power calculation, performed a priori, assuming a precision of 5% and a true prevalence of approximately 20%, estimated that 150 residents would be required. Residents were sampled using a quota approach and where possible all residents in the units sampled were included to determine the prevalence of AF and OAC.

Outcome measures

Frailty was measured using the Clinical Frailty Scale (CFS), scored from one (very fit) to nine (terminally ill), based on function and cognition, which has already been validated in LTC (31). A consultant geriatrician scored the CFS. Stroke risk was assessed with the CHADS2 and CHA2DS2-VASc scores. CHADS2 incorporates congestive cardiac failure, hypertension, age >75, diabetes and a history of stroke or transient ischaemic attack (TIA). Stroke or TIA is given a weighting of two, the others one, producing a maximum score of six. The CHA2DS2-VASc score sub-stratifies risk by age and sex with two points for age ≥75, an additional point for females and one point for vascular disease, giving a maximum score of nine. European Society of Cardiology (ESC) guidelines (32) suggest to treat with OAC if the CHA2DS2-VASc score is ≥2, with either an antiplatelet or preferably OAC if =1 and an antiplatelet or no treatment if =0.
Bleeding risk was measured using the HEMORR2HAGES and HAS-BLED scores. HEMORR2HAGES incorporates 11 different predictors of bleeding: one point for hepatic or renal disease, ethanol use, malignancy, age >75, reduced platelets, hypertension, anaemia, genetics, fall risks, stroke and two for re-bleeding. HAS-BLED includes seven variables: hypertension, abnormal renal/liver function, stroke, bleeding history or predisposition, labile INR, age >65, drugs/alcohol, each scoring one point. A HEMORR2HAGES score of ≥4 (21) and a HAS-BLED score ≥3 is considered high-risk (18, 22).
A validated, clinical decision-tree (24) was then used to make a colour-coded risk matrix, to compare all possible permutations of combining these stroke and bleeding risk-prediction instruments and maximise the combined probability of no stroke and no bleed per resident per year: green (balance of risk and benefit advises no treatment), amber (balance supports an antiplatelet) and red, (risks justify OAC). This decision-tree, while acknowledging that a HAS-BLED score ≥3 is high-risk, uses a cut-off score ≥4 as high-risk (24), adhering to the clinical principle that the net benefit of OAC is greater in those with untreated risk of stroke (33). Given its reported superiority over CHADS2, (34) CHA2DS2-VASc was used, with HAS-BLED and then HEMORR2HAGES in turn.

Statistical analysis

Data were analysed using SPSS version 20.0. The Shapiro-Wilk test was used to test normality. The Chi Squared test was used to assess differences between the distributions of categorical variables. The Mann-Whitney U test was used to assess for differences between non-parametric continuous variables. Agreement was measured with Cohen’s kappa.

 

Results

In all, 225 residents were included, median age 85 years, interquartile range (IQR) of 77-89= +12. The majority, 134 (60%), were female. The median Mini-Mental State Examination score available (usually on admission) was 18/30 (IQR 8-22), median Barthel Index was 30/100 (IQR 10-50). All residents were frail (minimum CFS 5/9) with a median CFS score of 7/9 (IQR 7-7). The distribution of CFS scores, indicated an advanced level of frailty with 68% (n=152) scored as severely frail (7/9) and 18% (n=41) as very severely frail (8/9) or terminally ill. In total, 101 (45%) residents had hypertension, 52 (23%) had a history of stroke and 45 (20%) had a diagnosis of ischaemic heart disease (IHD). In total, 86/225 (38%) residents were ‘ever known’ to be in AF (i.e. ‘Ever AF’) including 59 with documented AF. In all, 70 (31%) were in AF on ECG of whom 27/70 (39%) had occult AF. Of those in sinus rhythm, 139 (90%) had no documented history of AF and were therefore ‘never known’ to be in AF. The remainder (10%) had established or probable paroxysmal AF. The characteristics of residents according to their history of AF are presented in Table 1. There was no significant difference in the distribution of AF according to patient frailty level, χ2=2.4, p=0.3.

Table 1 Characteristics of all residents, including a comparison between those with (‘Ever AF’) and without (‘Never known AF’) a history of atrial fibrillation (AF)

Table 1
Characteristics of all residents, including a comparison between those with (‘Ever AF’) and without (‘Never known AF’) a history of atrial fibrillation (AF)

IQR: Interquartile range

 

Table 2 presents the prevalence rates of anticoagulation and antiplatelet prescription according to AF subclassifications. Of the 59 with documented AF, 15 (25%) were currently receiving OAC, all warfarin. None of the occult AF cases were on warfarin. No residents were receiving combined OAC and anti-platelet therapy. Only nine (33%) residents with occult AF were on any treatement compared to 46 (78%) of those with documented AF, p<0.001. Almost half (7/15) of those currently on OAC had documented bleeding events.

Table 2 Proportion of residents prescribed antiplatelet and oral anticoagulation (OAC) along with the median and interquartile range (IQR) stroke and bleeding risk-prediction scores including the proportion of residents reaching the cut-off scores for these instruments for different sub-classifications of atrial fibrillation (AF)

Table 2
Proportion of residents prescribed antiplatelet and oral anticoagulation (OAC) along with the median and interquartile range (IQR) stroke and bleeding risk-prediction scores including the proportion of residents reaching the cut-off scores for these instruments for different sub-classifications of atrial fibrillation (AF)

 

Risk-prediction scores were then applied to all residents with any history of AF according to their AF subclassification, see Table 2. The median CHADS2 score for ‘Ever AF’ was 2 (IQR 1-3), the median CHA2DS2-VASc score was 4 (IQR 3-5). All residents with ‘Ever AF’ had a CHA2DS2-VASc score ≥2 compared to only 74% with CHADS2. All residents with documented AF had a CHA2DS2-VASc  ≥2, 25% of whom were on warfarin. A further 31/59 (53%) were receiving at least one antiplatelet and 46/59 (78%) either an antiplatelet or warfarin. No resident with occult AF was receiving OAC. The median HAS-BLED scores for ‘Ever AF’ was 2 (IQR 1-3), the median HEMORR2HAGES score was 3 (2-4). Irrespective of instrument used, almost one third that were ever known to be in AF scored high-risk for bleeding. There was no statistically significant difference in the distribution of stroke or bleeding risk prediction scores between residents with documented and occult AF (Table 2).
Table 3 shows the distribution of residents with ‘Ever AF’ according to the results of the colour-coded decision tree using the CHA2DS2-VASc score and either the HAS-BLED or HEMORR2HAGES scores. Using HEMORR2HAGES most residents (49/86, 57%) were coded ‘green’, advising no treatment. Of these, only 22/49 (45%) were untreated compared with 5/25 (20%) of those coded ‘green’ using HAS-BLED. Using HAS-BLED, 60/86 (70%) were coded as ‘red’, suggesting that OAC would be appropriate, of whom 12/60 (20%) were anticoagulated. Only 1/86 (1%) were coded ‘red’ using HEMORR2HAGES.

Table 3 Distribution of colour-coded risk scores and proportion receiving appropriate treatment according to ESC guidelines for those ‘Ever AF’ to be in atrial fibrillation (n=86) using combined stroke (CHA2DS2-VASc) and bleeding (a. HAS-BLED or b. HEMORR2HAGES) risk-prediction instruments: green advises no treatment; amber advises aspirin; red advises anticoagulation (n=number)

Table 3
Distribution of colour-coded risk scores and proportion receiving appropriate treatment according to ESC guidelines for those ‘Ever AF’ to be in atrial fibrillation (n=86) using combined stroke (CHA2DS2-VASc) and bleeding (a. HAS-BLED or b. HEMORR2HAGES) risk-prediction instruments: green advises no treatment; amber advises aspirin; red advises anticoagulation (n=number)

 

Using Cohen’s kappa, there was good agreement as to who was high-risk using the established cut-off scores for HAS-BLED (≥3) and HEMORR2HAGES (≥4), κ=0.62, p=<0.001. However, there was a poor level of agreement between HAS-BLED and HEMORR2HAGES, when the colour-coded algorithm was applied to determine who was high-risk (κ=0.15, p=<0.001) and whether patients were on appropriate treatment, (κ=-0.18, p=0.04).

 

Discussion

This paper examines the utility of different stroke and bleeding risk-prediction instruments for AF in a sample of high-dependency, frail (median CFS score 7, indicating severe frailty), LTC residents in southern Ireland. The results show that there was a high prevalence of AF but low OAC prescription rates. The risk of stroke was high, particularly using the CHA2DS2-VASc score, with all residents reaching the threshold to be considered for OAC according to ESC guidelines (32). Fewer (74%) reached this threshold using CHADS2, which may underestimate stroke risk (35). This supports studies showing that the CHA2DS2-VASc score increases the number of patients for whom OAC is recommended (36). Median HAS-BLED and HEMORR2HAGES scores were below the cut-off for high-risk of bleeding with OAC. Irrespective of instrument used, only one third of residents with AF were scored high-risk of bleeding.
Although these results suggest that the balance should favour anticoagulation, use of OAC was low, even in those with documented AF in their medical charts. Approximately half were receiving an antiplatelet, which may relate to the high prevalence of IHD. Reassuringly, no residents were receiving both OAC and an antiplatelet. Although the prevalence of OAC prescription was low, it is reasonable in the context of the level of frailty, disability and comorbidity of the residents and is similar to other studies reporting rates of between 20% (37) and 46% (38). The risk matrices constructed using the colour-coded decision tool showed that OAC is recommended for the majority of residents ever known to be in AF (‘Ever AF’) when HAS-BLED (70%) but not HEMORR2HAGES (1%) was used as the bleeding risk-prediction instrument. When HEMORR2HAGES was used, most residents were advised no treatment or an antiplatelet. Only one resident was advised aspirin with HAS-BLED. Comparing the recommendations of the decision tree to the residents current treatment, revealed that 20% coded ‘red’ (i.e. appropriate for OAC) using HAS-BLED were anticoagulated. When HEMORR2HAGES was used more residents coded ‘green’ were appropriately not given treatment compared with HAS-BLED (45% v 20% respectively), although the absolute number advised against treatment was smaller with HAS-BLED. As current guidelines suggest OAC is preferable and many experts suggest that antiplatelets should be avoided for stroke prevention (34), this suggests that in this severely frail population that HAS-BLED is superior. It also supports evidence that decision tools, provided they incorporate the most accurate instruments, improve prescribing in AF (26).
As there was no statistically significant difference in the distribution of risk scores between residents with documented AF and those not previously known to be in AF (occult AF, found on the study ECG), the study suggests that these residents could also be considered for OAC. Although half of residents on OAC had experienced bleeding events, these were minor and treatment continued. That bleeding events are infrequent among such residents in LTC (39), suggests that screening for AF with ECGs may be useful even for these frail older patients, particularly if supported with clinical decision algorithms. However, there are other reasons to avoid anticoagulation in such a frail cohort that are not incorporated into these risk-prediction instruments, particularly in high-dependency LTC units. These include the limited evidence base, the ethics of prescribing medications for persons with limited life expectancy and until the introduction of NOACs, the need for monitoring. Thus, while useful, these instruments, alone or included in decision trees, should not replace clinical judgement.
This study has a number of limitations. The units were selected by convenience (though consecutive sampling was performed) and residents were severely frail limiting generalisability. Similar to other studies conducted in such highly selected settings e.g. hospitalised older adults (8), the results are unlikely to be representative of the majority of frail older adults i.e. community dwellers. Research in primary care is required to overcome this. Further, much of the data collection depended upon correct chart documentation, which may have created bias. For example, while the algorithm suggests that few patients were receiving appropriate treatment, irrespective of bleeding risk-prediction model applied with CHA2DS2-VASc (Table 3), it is not possible to determine if these patients had specific indications or contraindications to OAC or antiplatelet treatment if these were not documented in the charts. The prevalence of AF was high compared to other studies in similar cohorts, which have demonstrated rates of between 7.5% (11) and 19% (12). However, these patients from units in the United States of America may have had lower levels of frailty as nursing homes in Ireland usually provide care to those with already established functional impairment. Other possible reasons for this include variations in definitions of LTC, the sampling strategy and the homogenous study sample: older (median 85 years), Irish Caucasians. This may reduce the generalisability of the findings. Another limitation is that the risk-prediction instruments and decision tree used in this study were not developed for use in frail older adults or those in LTC, were not designed for use with NOACs and are based upon the premise that stroke avoidance is paramount (i.e. a HASBLED cut-off of ≥4). That said, these risk-prediction instruments are increasingly used and validated with NOACs (20). Further, the algorithm used to determine if current treatment was appropriate, incorporates these instruments and is consistent with ESC guidelines (32), albeit this does not include recommendations for frail older adults.
In summary, this study presents the first exploration of stroke and bleeding risk-prediction instruments in those with advanced frailty (older adults in LTC in Ireland) and is the first to determine the appropriateness of treatment using a clinical decision tree. The results show that stroke risk was high and bleeding risk comparatively low using these instruments and that a combination of the CHA2DS2-VASc and HAS-BLED scores may be optimal. The results also suggest that there is a high prevalence of occult AF in LTC and that these residents equally merit consideration for OAC, supporting the use of ECG screening in this setting. Further study is now required to measure the predictive validity of these instruments and decision support tools in frailty, particularly as NOACs become more widely prescribed.

 

Disclosure: The authors report no conflict of interest.

 

References

1.     Connolly SJ and the ACTIVE Writing Group on behalf of the ACTIVE Investigators. Clopidogrel plus aspirin versus oral anticoagulation for atrial fibrillation in the Atrial fibrillation Clopidogrel Trial with Irbesartan for prevention of Vascular Events (ACTIVE W): a randomised controlled trial. Lancet 2006;367:1903–12.
2.     Eikelboom JW, Weitz JI. New Anticoagulants. Circulation 2010;121:1523-32.
3.     Rowan SB, Bailey DN, Bublitz CE, Anderson RJ. Trends in Anticoagulation for Atrial Fibrillation in the U.S. An Analysis of the National Ambulatory Medical Care Survey Database. J Am College Cardiology 2007;49:1561-5.
4.     Ho SF, O’Mahony MS, Steward JA, Burr ML, Buchalter M. Left ventricular systolic dysfunction and atrial fibrillation in older people in the community—a need for screening? Age and Ageing 2004;33:488–92.
5.     Mant J, Hobbs FDR, Fletcher K, et al. Aspirin verses warfarin for stroke prevention in an elderly population with atrial fibrillation, (The Birmingham Atrial Fibrillation Treatment of the Aged study, BAFTA: a randomised controlled trial. Lancet 2007;370:493–503.
6.     Perera V, Bajorek BV, Matthews S, Hilmer SN. The impact of frailty on the utilisation of antithrombotic therapy in older patients with atrial fibrillation. Age Ageing 2009; 38:156–62.
7.     Nguyen TN, Cumming RG, Hilmer SN. Atrial fibrillation in older inpatients: are there any differences in clinical characteristics and pharmacological treatment between the frail and the non-frail? Intern Med J 2016;46:86-95.
8.     Lefebvre MC, St-Onge M, Glazer-Cavanagh M, et al. The effect of bleeding risk and frailty status on anticoagulation patterns in octogenarians with atrial fibrillation: the FRAIL-AF study. Can J Cardiol. 2016;32:169-76.
9.      Kojima G. Prevalence of Frailty in Nursing Homes: A Systematic Review and Meta-Analysis. J Am Med Dir Assoc. 2015;16:940-5.
10.     McCormick D, Gurwitz JH, Goldberg RJ, et al. Prevalence and Quality of Warfarin Use for Patients With Atrial Fibrillation in the Long-term Care Setting. Arch Intern Med 2001;161:2458–63.
11.     Gurwitz JH, Monette J, Rochon PA, et al. Atrial Fibrillation and Stroke Prevention With Warfarin in the Long-term Care Setting. Arch Intern Med 1997;157:978-84.
12.     Quilliam BJ, Lapane KL, Leibson C. Clinical Correlates and Drug Treatment of Residents With Stroke in Long-Term. Stroke 2001;32:1385-93.
13.     Das AK, Willcoxen PD, Corrado OJ, West RM. The impact of long-term warfarin on the quality of life of elderly people with atrial fibrillation. Age Ageing 2007;36:95–7.
14.     Frewen J, Finucane C, Rice C, Kearney P, Kenny RA, Harbison JA. The use of anticoagulation therapy in subjects with atrial fibrillation in the Irish longitudinal study of ageing (TILDA). Cerebrovasc Dis 2012;33:822–3.
15.     Dharmarajan TS, Varma S, Akkaladevi S, Lebelt AS, Norkus EP. To anticoagulate or not to anticoagulate? A common dilemma for the provider: physicians’ opinion poll based on a case study of an older long-term care facility resident with dementia and atrial fibrillation. J Am Med Dir Assoc 2006;7:23-8.
16.     Herrera AP, Snipes SA, King DW, Torres-Vigil I, Goldberg DS, Weinberg AD. Disparate Inclusion of Older Adults in Clinical Trials: Priorities and Opportunities for Policy and Practice Change. Am J Public Health 2010;100:105–12.
17.     Choudhry NK, Anderson GM, Laupacis A, et al. Impact of adverse events on prescribing warfarin in patients with atrial fibrillation: matched pair analysis. BMJ 2006;332:141–5.
18.     Lane DA, Lip GY. Use of the CHA2DS2-VASc and HAS-BLED scores to aid decision making for thromboprophylaxis in nonvalvular atrial fibrillation. Circulation. 2012;126:860-5.
19.     Gage BF, Waterman AD, Shannon W, Boechler M, Rich MW, Radford MJ. Validation of clinical classification schemes for predicting stroke: Results From the National Registry of Atrial Fibrillation. JAMA 2001;285:2864–70.
20.     Lip GY, Nieuwlaat R, Pisters R, et al. Refining clinical risk stratification for predicting stroke and thromboembolism in atrial fibrillation using a novel risk factor-based approach: the euro heart survey on atrial fibrillation. Chest 2010;137:263-72.
21.     Gage BF, Yan Y, Milligan PE, et al. Clinical classification schemes for predicting hemorrhage: Results from the National Registry of Atrial Fibrillation (NRAF). Am Heart J 2006;151:713-9.
22.     Pisters R, Lane  DA, Nieuwlaat  R, et al.  A novel user-friendly score (HAS-BLED) to assess 1-year risk of major bleeding in patients with atrial fibrillation: the Euro Heart Survey. Chest 2010;138:1093-100.
23. Apostolakis S, Lane DA, Guo Y, et al. Performance of the HEMORR2HAGES, ATRIA, and HAS-BLED Bleeding Risk–Prediction Scores in Patients With Atrial Fibrillation Undergoing Anticoagulation. JACC 2012;60:861-867.
24.     Romero-Ortuno R, O’Shea D. Aspirin versus warfarin in atrial fibrillation: decision analysis may help patients’ choice. Age Ageing 2012;41:250–4.
25.     Granziera S, Cohen AT, Nante G, Manzato E, Sergi G.Thromboembolic prevention in frail elderly patients with atrial fibrillation: a practical algorithm. J Am Med Dir Assoc. 2015;16:358-64.
26.     Eckman MH, Wise RE, Speer B, et al. Integrating real-time clinical information to provide estimates of net clinical benefit of antithrombotic therapy for patients with atrial fibrillation. Circ Cardiovasc Qual Outcomes 2014;7:680-6.
27.     O’Caoimh R, Igras E, Ramesh A, Power B, Liston R. The management of Atrial Fibrillation and the Use of Oral Anticoagulation for Stroke Prevention in long-term care. Irish Journal of Medical Science 2014;183 S(7):298-9.
28.     Fuster V, Rydén LE, Cannom DS et al. 2011 ACCF/AHA/HRS focused updates incorporated into the ACC/AHA/ESC 2006 guidelines for the management of patients with atrial fibrillation: a report of the American College of Cardiology Foundation/American Heart Association Task Force on practice guidelines. Circulation 2011;123:e269-367.
29.     Lévy S, Maarek M, Coumel P, et al. Characterization of different subsets of atrial fibrillation in general practice in France: the ALFA study. The College of French Cardiologists. Circulation 1999;99:3028-35.
30.     Lip GYH, Li Saw Hee FL. Paroxymal Atrial Fibrillation. QJM 2001;94:665-78.
31.     Rockwood K, Abeysundera MJ, Mitnitski A. How should we grade frailty in nursing home patients? J Am Med Dir Assoc. 2007;8:595-603.
32.     Camm AJ, Lip GY, De Caterina  R, et al.  2012 focused update of the ESC Guidelines for the management of atrial fibrillation: an update of the 2010 ESC Guidelines for the management of atrial fibrillation. Europace. 2012;14:1385-413.
33.     Singer DE, Chang Y, Fang MC et al. The net clinical benefit of warfarin anticoagulation in atrial fibrillation. Ann Intern Med 2009;151:297–305.
34.     Lip GYH, Lane DA. Stroke Prevention in Atrial Fibrillation A Systematic Review. JAMA, 2015;313:1950-62.
35.     Zhu W-G, Xiong  Q-M, Hong  K. Meta-Analysis of CHADS2 versus CHA2DS2-VASc for predicting stroke and thromboembolism in atrial fibrillation patients independent of anticoagulation. Tex Heart Inst J 2015;42:6-15.
36.     Winkle RA, Mead RH, Engel G, et al. Comparison of CHADS2 and CHA2DS2-VASC anticoagulation recommendations: evaluation in a cohort of atrial fibrillation ablation patients. Europace. 2014;16:195-201.
37.     Lackner TE, Battis GN. Use of warfarin for nonvalvular atrial fibrillation in nursing home patients. Arch Family Medicine 1995;4(12):1017-26.
38.     Abdel-Latif KA, Peng X, Messinger-Rapport BJ. Predictors of anticoagulation prescription in nursing home residents with atrial fibrillation. J Am Med Direct Assoc 2005;6:128–31.
39.     Kagansky N, Knobler H, Rimon E, Ozer Z, Levy S. Safety of Anticoagulation Therapy in Well-informed Older Patients. Arch Intern Med 2004;164:2044–50.
24.    Pedersen BK, Febbraio MA. Muscles, exercise and obesity: skeletal muscle as a secretory organ. Nat Rev Endocrinol 2012;8:457-65.
25.    Yarrow JF, White LJ, McCoy SC, Borst SE. Training augments resistance exercise induced elevation of circulating brain derived neurotrophic factor (BDNF). Neurosci Lett 2010;479:161-5.
26.    Ferris LT, Williams JS, Shen CL. The effect of acute exercise on serum brain-derived neurotrophic factor levels and cognitive function. Med Sci Sports Exerc 2007;39:728-34.
27.    Kelly ME, Loughrey D, Lawlor BA, Robertson IH, Walsh C, Brennan S. The impact of exercise on the cognitive functioning of healthy older adults: a systematic review and meta-analysis. Ageing Res Rev 2014;16:12-31.

DETERMINING THE CUT-OFF VALUES FOR SARCOPENIA IN THE KOREAN ELDERLY POPULATION USING BIOIMPEDANCE ANALYSIS

 

E.-J. CHANG1,3,4, H.-W. JUNG1,3,4, S.-W. KIM1,3, N.-J. HEO2,3, H.-J. CHIN1,3, C.-H. KIM1,3, K.-I. KIM1,3

1. Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea; 2. Department of Internal Medicine, Seoul National University Gangnam Center, Seoul, Republic of Korea; 3. Seoul National University College of Medicine, Seoul, Republic of Korea; 4. The first two authors equally contributed to the work.

Corresponding author: Kwang-il Kim, MD, PhD, Department of Internal Medicine, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Gumi-ro 166, Bundang-gu, Seongnam-si, Kyeongi-do 463-707, Republic of Korea, E-mail: kikim907@snu.ac.kr, Tel: +82-31-787-7032, Fax: +82-31-787-4052

J Frailty Aging 2015;4(1):34-40
Published online February 3, 2015, http://dx.doi.org/10.14283/jfa.2015.38


Abstract

Background: Bioimpedance analysis (BIA) is known to be a useful method for assessing sarcopenia because cost-effective and not involving radiation exposure. However, the cut-off values for sarcopenia using BIA have not yet been determined in the Korean population. Objectives: To establish the cut-off values for sarcopenia in the Korean elderly population with the use of BIA. Methods: Body composition assessed by BIA was obtained in 7,641 participants aged 20–34 years and 3,902 participants aged ≥65 years from data routinely collected during health examinations at Seoul National University Gangnam Center. Appendicular skeletal muscle mass was adjusted for height and weight. Gender-specific cut-points for class I and class II sarcopenia were defined as 1 and 2 standard deviations below the mean in the reference group aged 20–34 years, respectively. In addition, the gender-specific, lowest 20th percentile cut-offs for muscle mass in participants aged ≥65 years were determined. Results: The cut-offs for class I and class II sarcopenia in men for height-adjusted appendicular skeletal mass were 6.74 kg/m2 and 5.96 kg/m2 and for weight-adjusted appendicular skeletal mass were 29.4% and 27.4%, respectively; those in women for height-adjusted appendicular skeletal mass were 4.93 kg/m2 and 4.35 kg/m2, and for weight-adjusted appendicular skeletal mass were 25.6% and 23.9%, respectively. The lowest 20th percentile cut-offs for height-adjusted and weight-adjusted appendicular skeletal mass were 6.69 kg/m2 and 28.9% in men, and 5.76 kg/m2 and 24.5% in women, respectively. Based on the derived cut-offs, prevalence of class II sarcopenia in participants ≥65 years of age for height-adjusted and weight-adjusted appendicular skeletal mass was 3.7% and 3.5% in men, and 0.2% and 11.2% in women, respectively. Among the above-mentioned definitions, sarcopenia by height-adjusted appendicular skeletal mass was significantly associated with 2-year mortality in older participants. Conclusions: Muscle mass deficit in the Korean population can be assessed based on the cut-offs determined in this study using BIA.

 

Key words: Aged, sarcopenia, prevalence, Korean population.


 

Introduction

Sarcopenia, defined as loss of muscle mass and strength, is an important health concern because its prevalence increases with age, and it can lead to limitations in daily activities and adverse consequences from major diseases in older adults (1). Ultimately, it can contribute to morbidity and mortality in community-dwelling elderly or nursing home residents (2-4). However, evidence suggests that sarcopenia may be treatable (5). It is therefore important to identify older adults with sarcopenia.

Uniform diagnostic criteria for sarcopenia are not yet established, but various methods to evaluate sarcopenia have been introduced. Initially, studies focused on the state of the decreased muscle mass. Thereafter, many studies and consensus guidelines suggested that decreased muscle mass, strength or performance are essential requisites for a diagnosis of sarcopenia (6, 7).

Among the various methods used for an accurate muscle mass measurement, which is essential for diagnosis, computed tomography (CT) and magnetic resonance imaging (MRI) have been considered the gold standard techniques because of their ability to detect intramuscular fat infiltration (8). However, due to the high cost and time involved, they are not widely used for clinical trials and practice. Dual energy X-ray absorptiometry (DXA) and bioimpedance analysis (BIA) are commonly used alternative methods (8). Of these, BIA is the cheaper and safer method with no radiation exposure compared to DXA. Furthermore, on account of its portability, BIA can be used to measure muscle mass in epidemiological field studies and for both ambulatory and bed-ridden patients.

The cut-off values and prevalence of sarcopenia in the Korean population have been derived using the DXA data from a nationwide cross-sectional study (9). However, considering the advantages of BIA in clinical field and in large-scale studies, establishing valid cut-off values of muscle mass for sarcopenia with BIA is warranted. Moreover, a valid method that allows adjustment for anthropometric differences and selection of an adequate reference group is required to evaluate muscle mass deficit. However, controversy exists in adjusting for height or weight in the Korean population. Although mortality was associated with height-adjusted lean mass in a study (2), an extremely low prevalence of sarcopenia in women with height adjustment was observed in another study (9). Furthermore, only percent muscle mass rather than height-adjusted muscle mass was found to be associated with metabolic syndrome in a study (10).

This study is thus aimed to determine cut-off values for sarcopenia in the Korean population, using BIA with different adjustment methods and reference populations, based on large-scale health examination data.

Methods

Study population

We retrospectively reviewed routine health examination data of 66,737 consecutive participants aged ≥20 years at the Seoul National University Gangnam Center from 2003 to 2009. Body composition data including muscle mass and fat mass determined using BIA and routine laboratory analysis data were available for 53,290 (28,763 men and 24,527 women) participants. Among them, 2,201 men and 1,701 women were aged ≥65 years, while 3,625 men and 4,016 women were aged 20–35 years.

From the 20–35 year age group, 2,963 men and 3,728 women (excluding 15 outliers with residual muscle mass of more than 3 standard deviations [SD] from the mean) with no history of diseases such as hypertension, diabetes, coronary artery diseases, stroke, dyslipidemia, chronic liver or renal diseases, thyroid diseases, tuberculosis, and cancer, were included in the gender-specific reference groups (Figure 1).

Measurement of muscle mass

We used BIA data that was obtained using the Inbody 720 (Biospace, Seoul, Korea) to estimate lean body mass (LBM). The validity and reproducibility of the device was previously verified (11, 12). The device measured impedances of 8 electrodes from all 4 limbs of participants while they were standing, and segmental fat and lean mass were calculated. Because the participants presented for the routine health examination in the morning after fasting for 8 hours, the effect of hydration status was considered to be attenuated.

Various methods that adjust for body composition and height have been used to define a significantly decreased state of muscle mass (7). Baumgartner and colleagues (13) suggested adjustment of appendicular skeletal mass (ASM) by height (ASM/ht2, kg/m2), while Janssen and colleagues (14) suggested adjusting by weight (ASM/weight X 100, %). On the basis of these previous studies, we selected the values of height- and weight-adjusted ASM as markers of sarcopenia for this study. The class I and class II cut-off values of muscle mass indices were calculated as 1 and 2 SD below the mean, respectively, of the young, gender-specific reference group. Means and SDs of height- or weight-adjusted ASM were calculated from these gender-specific groups.

Covariates and outcome measures

Patients’ medical history was obtained and physical examinations were performed. Anthropometric parameters and blood pressure were measured. Routine laboratory examinations for complete blood cell count, C-reactive protein, albumin, creatinine, serum cholesterol, hemoglobin A1C, and uric acid levels were performed, after at least 8 h of fasting, by venipuncture. The National Statistical Office provided the date and cause of all deaths occurring until December 31, 2009. We included this mortality data in our dataset using each individual’s unique identifier.

Statistical analyses 

Descriptive statistics included calculation of mean values and SDs. In those over 65 years of age, the gender-specific, lowest 20th percentile cut-off points of muscle mass indices were determined. Anthropometric measures and muscle mass indices were compared by different age using plots and linear regression analysis. Gender-specific prevalence of sarcopenia in each age-group was calculated according to the cut-off values.

We used the Cox proportional hazard model to assess the effect of each muscle mass index on mortality, with non-sarcopenic status as the reference. The appropriateness of the model was checked by the log-log plot, and hazard ratios (HR) and 95% confidence intervals (CI) were calculated. Covariates for the adjusted Cox proportional hazard model included age, sex, C-reactive protein (15), white blood cell count (16), albumin (17), cholesterol (18), and systolic blood pressure (16), which had been reported to be associated with muscle mass or physical performance in the elderly or shown significant association with ASM in the study dataset. To attenuate the effect of baseline cachexia in participants who were initially diagnosed with malignancy and showed short-term mortality, we performed a sensitivity analysis by removing participants who died of malignancy that was newly diagnosed during the health examination and those who died within 1 year from the examination. All statistical analyses were two-sided, and a P-value <0.05 was considered statistically significant. The data were analyzed using SPSS 18.0 (SPSS Inc., Chicago, IL), and the figures were drawn using STATA 12.0 (StataCorp, College Station, TX).

Results

Baseline anthropometric measures

Of the 53,290 participants (mean age: 47.6 [SD: 12.0] years) with available body composition data, 28,403 (53.3%) were men. Baseline characteristics including demographic, anthropometric, and clinical variables of the young reference and older groups are shown in Table 1.

Table 1 Baseline characteristics of the young reference groups and elderly participants

Data are presented as mean±standard deviation; Abbreviation: ASM-appendicular skeletal muscle mass 

Correlations between the baseline anthropometric measures and age were assessed (Figure 2). As determined by linear regression analysis, in men, body weight (β −0.16, P<0.001) and height (β −0.19, P<0.001) decreased with age, and in women, body weight (β 0.12, P<0.001) increased and height (β −0.17, P<0.001) decreased with age (Figure 2). Body mass index (BMI) was negatively associated with age in men (β −0.01, P<0.001) and positively associated with age in women (β 0.10, P<0.001).

Figure 1 Diagram showing selection of 2 groups (young reference group and older age group) included in the final analysis

Adjusted ASM showed differing trends in men and women of different age (Figure 3). As determined by linear regression analysis, the height-adjusted ASM decreased with age in men (β −0.01, P<0.001) and increased in women (β 0.02, P<0.001), and the weight-adjusted ASM decreased with age in both sexes (β −0.02, P<0.001 in men, β −0.03, P<0.001 in women).

Figure 2 Changes in height, body weight, and body mass index (BMI) based on age and gender (F: women, M: men)

 

Cut-off values for sarcopenia in the young reference group

The mean values of height- and weight-adjusted ASM were 7.52 kg/m2 and 31.4% in the young male reference group and 5.51 kg/m2 and 27.3% in the young female reference group, respectively. Cut-off points for class I and II sarcopenia were 6.74 kg/m2 and 5.96 kg/m2 in men and 4.93 kg/m2 and 4.35 kg/m2 in women, respectively, for height-adjusted ASM. Cut-off points for class I and II sarcopenia were 29.4% and 27.4% in men and 25.6% and 23.9% in women, respectively, for weight-adjusted ASM (Table 1).

Figure 3 Scatter plot and fitted line of muscle indices based on age and sex

Cut-off values for sarcopenia in the older age group

The mean values of height-adjusted ASM were 7.26 kg/m2 and 6.24 kg/m2 and those for weight-adjusted ASM were 30.3% and 26.0% in men and women ≥65 years of age, respectively. The lowest 20th percentile cut-off points for men and women in this group were 6.69 kg/m2 and 5.8 kg/m2 for height-adjusted ASM and 28.9% and 24.5% for weight-adjusted ASM, respectively (Table 1).

Prevalence of sarcopenia by different cut-offs in the older age group

Prevalence of class I sarcopenia in the older age group was 18.0% (N=397) for men and 1.7% (N=29) for women based on height-adjusted ASM, whereas it was 23.8% (N=524) for men and 32.2% (N=547) for women based on weight-adjusted ASM. In addition, 3.5% (N=77) men and 0.2% (N=4) women in the older age group were classified as having class II sarcopenia based on height-adjusted ASM, and 3.5% (N=77) men and 11.2% (N=191) women were included in class II sarcopenia based on weight-adjusted ASM.

Prevalence of sarcopenia by age

The prevalence of class I and II sarcopenia in men, based on height-adjusted ASM, increased dramatically with age, revealing 31.4% (class I) and 7.0% (class II) in those aged >80 years (Figure 4). However, the proportion of participants with class II sarcopenia was very low in all age-groups excepting aged >80 years for women, and that of class I sarcopenia was 13.8% in the young reference group, decreased until 50–64 years of age, and increased after that. The prevalence rates of class I and II sarcopenia based on weight-adjusted ASM increased with age to 36.0% and 14.0%, respectively, in women aged ≥80 years.

Figure 4 Prevalence of class I and class I+II sarcopenia based on age and sex (Panel A: height-adjusted ASM; Panel B: weight-adjusted ASM)

 

Association of sarcopenia by each definition with mortality

Among the 3,902 participants aged ≥65 years, 64 died during the observation period of 27.2 (SD 13.5) months. We analyzed the association of sarcopenia with mortality using the Cox proportional hazards model with non-sarcopenic status as the reference variable. As shown in Table 2, sarcopenia by the height-adjusted definition was associated with subsequent mortality. Class II sarcopenia and sarcopenia in the lowest quintile of ASM/ht2 were associated with mortality (HR = 3.29, 95% CI 1.41–7.68; HR = 2.36, 95% CI 1.38–4.03, respectively) even after adjusting for variables that were associated with muscle mass. However, sarcopenia by the weight-adjusted definition was not associated with mortality, by either unadjusted or adjusted analysis. Diagnosis of class II sarcopenia by ASM/ht2 and by lowest quintile of height-adjusted ASM also predicted mortality (HR = 4.1, 95% CI 1.78–9.55; HR = 2.75, 95% CI 1.63–4.62) even after excluding those who died of newly found malignancy (N=14, and 8 of 14 died within 1 year) or died within 1 year of causes other than malignancy (N=7) after initiation of the examinations.

Table 2 Hazard ratios for death during the follow-up period of participants with sarcopenia compared to those without sarcopenia, according to each definition in the older age group

Abbreviations: HR-hazard ratio, ASM/ht2-appendicular skeletal muscle mass adjusted by height, ASM/wt: appendicular skeletal muscle mass adjusted by weight. The reference for Cox proportional hazards model analysis was non-sarcopenic status by each definition; * Covariates in the adjusted model included age, sex, serum C-reactive protein level, white blood cell count, serum albumin level, serum cholesterol level, and systolic blood pressure. 

Discussion

In this study, we proposed the cut-off values for sarcopenia using BIA with different adjustment methods and reference ages in the Korean elderly, based on large-scale health examination data. Additionally, height-adjusted ASM was found to be associated with subsequent mortality in the older participants.

BIA has many advantages over DXA despite the dominance of DXA in the assessment of muscle mass for research. BIA does not involve radiation exposure and is a simple method for repeated measurements. Furthermore, the cost of the required equipment and examinations is lower than that with DXA (8). It is therefore of interest to determine cut-off points of sarcopenia for BIA as an alternative to DXA.

The study based on the Fourth Korean National Health and Nutritional Examination Surveys previously suggested cut-off values for sarcopenia using DXA. For the definition of height-adjusted ASM, cut-off points for class I sarcopenia were 7.50 kg/m2 and 5.38 kg/m2, and those for class II sarcopenia were 6.58 kg/m2 and 4.59 kg/m2 in men and women, respectively (9). In Taiwan, the cut-off values for class II sarcopenia using BIA were reported to be 6.76 kg/m2 in men and 5.28 kg/m2 in women (19). Additionally, 7.26 kg/m2 and 5.45kg/m2 were suggested as cut-off values for class II sarcopenia by DXA in men and women, respectively, in Mexico (13). According to these values, the cut-offs for individuals in another Asian country, using both BIA and DXA, were similar to those in our study, but our values were smaller than those for individuals in a Western country.

Because mortality is of clinical consequence and is the final result of frailty and sarcopenia, considering the cycle of frailty (20), the association of sarcopenia with mortality is of relevance. In our previous study, only height-adjusted ASM had a meaningful relationship with mortality, and the relationship was not observed in weight-adjusted ASM (3). Furthermore, a Chilean research showed that the lowest gender-specific quartile of height-adjusted ASM was significantly associated with mortality (21). Sarcopenia defined by height-adjustment tends to identify frail individuals with lower BMI compared to sarcopenia defined by weight-adjustment. On the other hand, sarcopenia by weight adjustment reflects a lower muscle to fat ratio, rather than decreased absolute muscle mass. In this respect, weight adjustment might be associated with metabolic syndrome and obesity rather than frailty. In addition, in a study conducted in Taiwan, height-adjusted ASM rather than weight-adjusted ASM was significantly associated with hand grip strength (22). Therefore, with sarcopenia being a significant prognostic factor for falls, functional decline, and mortality in the elderly, the height-adjusted definition of sarcopenia may be more relevant clinically.

It is noteworthy that the prevalence of class II sarcopenia based on height-adjusted ASM in Korean women is very small. This phenomenon was also previously observed in another study conducted in Korea that used DXA (9), making clinical use of this adjustment less meaningful. Before analyzing the present data, we considered that the decreased prevalence of sarcopenia based on height-adjusted ASM might be due to the decreased sensitivity of DXA in detecting peripheral intramuscular fatty infiltration. However, the lower sarcopenia prevalence based on height-adjusted ASM in women was maintained even with the analysis of the BIA data. This phenomenon may be possibly explained by different changes in BMI based on gender and birth cohorts. In Korea, due to dramatically rapid economic development and changes in the society after the Korean War, the mean weight, height, and BMI of the population have increased steadily for the past 40 years. In men, the younger generations showed a steeper BMI increment than the older generations. Therefore, mean BMI in men is generally maintained through all ages in the cross-sectional graphs (23). On the contrary, in women, the young and old generations showed almost the same degree of BMI growth. Subsequently, the average BMI in women has increased with age in cross-sectional graphs (23), which was also observed in our data (Figure 2). This phenomenon may be due to the preference for a slim figure, less motor vehicle use, and more housekeeping activity among younger women than men of a similar age (24, 25). Accordingly, the mean height-adjusted ASM is raising with age as height-adjusted ASM is proportional to BMI (26). In order to exclude birth cohort effects, sarcopenia may be better diagnosed on the basis of the Z-score (definition based on comparison with people of the same generation) concept rather than the T-score (definition based on mean and SD of the young reference group).

This study may be clinically relevant since it was based upon large-scale data and established cut-off values for sarcopenia using BIA. In addition, the data extracted from the health examination data obtained at Seoul National University Gangnam Center from 2003 to 2009 included complete mortality statistics and several clinical covariates. Therefore, we were able to assess the association of decreased muscle mass with mortality.

Our study has several limitations. Muscle strength, physical performance, functional and nutritional status assessments were not performed during the examination. Sarcopenia is the loss of muscle mass and reduced muscle force or physical performance (6, 7); therefore, muscle force and physical performance assessment is important for accurately diagnosing sarcopenia. However, we focused on defining cut-off values for muscle mass deficit with BIA rather than diagnosing clinical sarcopenia with BIA alone. Moreover, functional outcomes in the frail elderly were not assessed, and the follow-up duration for mortality analysis was relatively short. However, even after excluding participants who had died within one year from the initiation of this study or in whom cancer had been detected from the health examination in mortality analysis, sarcopenia by height-adjusted ASM was associated with mortality. Although an 8-h fast before health examination was routine practice, possible inter-individual differences in the volume state cannot be completely excluded. Furthermore, only height rather than lower limb or tibial length was measured; this allowed the influence of possible osteoporosis on decreased height in older women. This limitation may have caused a possible over-estimation of ASM/ht2 among the older women in our study. Additionally, we did not incorporate other existing adjustment methods including the residuals method suggested in literature (6). Generalizability of data used in this study should be limited because of possible discrepancies between the general population and those participating in the health checkup. However, the height, body weight, BMI, and muscle mass patterns by age in our data were similar to the data of the Korean National Health and Nutritional Examination Surveys, a nationwide cross-sectional study in Korea (unpublished data).

The limitations of our study should be addressed with additional research. Further studies on estimation of muscle strength or physical performance and the verification of clinical association between the lowest quintile cut values of sarcopenia and muscle strength or physical activity are needed.

In conclusion, muscle mass deficit in the Korean population can be assessed based on the cut-off values proposed in this study using BIA.

Funding:  Any sponsor had no role in the design and conduct of the study; in the collection, analysis, and interpretation of data; in the preparation of the manuscript; or in the review or approval of the manuscript.

Acknowledgements:  The authors are indebted to J. Patrick Barron, Professor Emeritus, Tokyo Medical University and Adjunct Professor, Seoul National University Bundang Hospital for his editing of the final manuscript.

Conflict of interest: All authors declared no conflict of interest.  

References

1. Roubenoff R. Sarcopenia and its implications for the elderly. Eur J Clin Nutr 2000;54:S40-47.

2. Han SS, Kim KW, Kim KI, et al. Lean mass index: a better predictor of mortality than body mass index in elderly Asians. J Am Geriatr Soc 2010;58:312-317.

3. Jung HW, Kim SW, Chin HJ, Kim KI, Kim CH. Skeletal muscle mass as a predictor of mortality in the elderly population. J Korean Med Assc 2013;85:381-386.

4. 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 in press.

5. Roubenoff R. Sarcopenia: A major modifiable cause of frailty in the elderly: Sarcopenia in aging. J Nutr Health Aging 2000;4:140-142.

6. 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.

7. Chen LK, Liu LK, 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.

8. Pahor M, Manini T, Cesari M. Sarcopenia: clinical evaluation, biological markers and other evaluation tools. J Nutr Health Aging 2009;13:724-728.

9. Kim Y-S, Lee Y, Chung Y-S, et al. Prevalence of sarcopenia and sarcopenic obesity in the Korean population based on the Fourth Korean National Health and Nutritional Examination Surveys. J Gerontol A biol Sci Med Sci 2012;67:1107-1113.

10. Kim TN, Yang SJ, Yoo HJ, et al. Prevalence of sarcopenia and sarcopenic obesity in Korean adults: the Korean sarcopenic obesity study. Int J Obes (Lond) 2009;33:885-892.

11. Jensky-Squires NE, Dieli-Conwright CM, Rossuello A, et al. Validity and reliability of body composition analysers in children and adults. Br J Nutr 2008;100:859-865.

12. Kim JH, Choi SH, Lim S, et al. Assessment of appendicular skeletal muscle mass by bioimpedance in older community-dwelling Korean adults. Arch Gerontol Geriatr 2014;58:303-307.

13. Baumgartner RN, Koehler KM, Gallagher D, et al. Epidemiology of sarcopenia among the elderly in New Mexico. Alm J Epidemiol 1998;147:755-763.

14. Janssen I, Heymsfield SB, Ross R. Low relative skeletal muscle mass (sarcopenia) in older persons is associated with functional impairment and physical disability. J Am Geriatr Soc 2002;50:889-896.

15. Taaffe DR, Harris TB, Ferrucci L, Rowe J, Seeman TE. Cross-sectional and prospective relationships of interleukin-6 and C-reactive protein with physical performance in elderly persons: MacArthur studies of successful aging. J Gerontol A Biol Sci Med Sci 2000;55:M709-715.

16. Han K, Park YM, Kwon HS, et al. Sarcopenia as a determinant of blood pressure in older Koreans: findings from the Korea National Health and Nutrition Examination Surveys (KNHANES) 2008-2010. PLoS One 2014;9:e86902.

17. Visser M, Kritchevsky SB, Newman AB, et al. Lower serum albumin concentration and change in muscle mass: the Health, Aging and Body Composition Study. Am J Clin Nutr 2005;82:531-537.

18. Chin SO, Rhee SY, Chon S, et al. Sarcopenia is independently associated with cardiovascular disease in older Korean adults: the Korea National Health and Nutrition Examination Survey (KNHANES) from 2009. PLoS One 2013;8:e60119.

19. Chang C-I, Chen C-Y, Huang K-C, et al. Comparison of three BIA muscle indices for sarcopenia screening in old adults. Eur Geriatr Med 2013;4:145-149.

20. 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.

21. Bunout D, de la Maza MP, Barrera G, Leiva L, Hirsch S. Association between sarcopenia and mortality in healthy older people. Austral J Ageing 2011;30:89-92.

22. Liu LK, Lee WJ, Liu CL, et al. Age-related skeletal muscle mass loss and physical performance in Taiwan: implications to diagnostic strategy of sarcopenia in Asia. Geriatr Gerontol Int 2013;13:964-971.

23. Kwon JW, Song YM, Sung J, Sohn Y, Cho SI. Varying patterns of BMI increase in sex and birth cohorts of Korean adults. Obesity 2007;15:277-282.

24. Berg C, Rosengren A, Aires N, et al. Trends in overweight and obesity from 1985 to 2002 in Göteborg, West Sweden. Int J Obes 2005;29:916-924.

25. Lahti-Koski M, Jousilahti P, Pietinen P. Secular trends in body mass index by birth cohort in eastern Finland from 1972 to 1997. Int J Obes Relat Metab Disord 2001;25:727-734.

26. Iannuzzi-Sucich M, Prestwood KM, Kenny AM. Prevalence of sarcopenia and predictors of skeletal muscle mass in healthy, older men and women. J Gerontol A Biol Sci Med Sci 2002;57:M772-M777.

IMPACT OF DIFFERENT DIAGNOSTIC CRITERIA ON THE PREVALENCE OF SARCOPENIA IN AN ACUTE CARE GERIATRIC WARD

W.M.W.H. SIPERS1, J.M.M. MEIJERS2, R.B. VAN DIJK1,3, R.J.G. HALFENS2, J.M.G.A. SCHOLS2

 

1. Department of Geriatric Medicine, Orbis Medical Center, Sittard-Geleen, The Netherlands; 2. School CAPHRI, Department of Health Services Research, Maastricht University, Maastricht, The Netherlands; 3. Elderly Care De Zorggroep, location Roermond, Roermond, The Netherlands; 4. School CAPHRI, Department of Family Medicine, Maastricht University, Maastricht, The Netherlands.

Corresponding author: Walther M.W.H. Sipers, MD, Department of Geriatric Medicine, Orbis Medical Center, Dr. H. van der Hoffplein 1, 6162 BG Sittard-Geleen, The Netherlands, Phone:0031884597783, Fax: 0031884597344, E-mail: w.sipers@orbisconcern.nl

J Frailty Aging 2014;3(4):222-229
Published online January 12, 2015, http://dx.doi.org/10.14283/jfa.2014.28

 


Abstract

Background: Sarcopenia is probably an important causal factor for functional decline in acutely ill hospitalized geriatric patients. Low skeletal muscle mass, low gait speed and low grip strength are hallmarks of diagnosing sarcopenia. However there are many different diagnostic criteria to assess sarcopenia. Objectives: In this study the influence of different criteria for sarcopenia was studied on sarcopenia prevalence in geriatric patients admitted to an acute care hospital. Design: Cross sectional study design. Setting: A geriatric ward of a large Dutch hospital. Participants: Geriatric patients. Measurements: Skeletal muscle mass measured using bio impedance analysis (BIA), gait speed using the 4 meter walking test and grip strength. The sarcopenia prevalence was investigated according to criteria of: muscle mass, grip strength, the European Working Group on Sarcopenia in Elderly People, the International Working Group on Sarcopenia and the Special Interest Group of Society of Sarcopenia, Cachexia and Wasting Disorders. Results: 85 geriatric patients were included (61 women). Applying the 17 different criteria, the sarcopenia prevalence varied from 26-75% for women and from 42-100% for men. Comparing the Janssen calculation with the Maltron calculation sarcopenia prevalence ranged from respectively 26-67% and 67-70% for women and from 42-71% and 75-100% for men. Almost all patients (96%) had a low gait speed. Conclusions: Sarcopenia is highly prevalent in an acute hospitalized geriatric population, although the prevalence varies widely depending on the diagnostic criteria applied. A prospective study is needed to discover which criteria of sarcopenia can predict best adverse outcomes.

 

Key words: Sarcopenia, diagnostic criteria, prevalence, hospitalized geriatric patients.


Introduction

Acute hospitalization is a hazardous event with a high mortality rate for older people and high rates of post-discharge disability (1). This is especially the case for geriatric patients, the frailest elderly people (1, 2). Accelerated muscle loss due to acute illness, bed rest and malnutrition, combined with a decline in physical performance, is probably an important causal factor for functional decline and adverse outcomes (3-5). Reduced muscle strength has been found to be associated with dependency in daily living activities and with mortality (6-8).

It was Rosenberg, in 1989, who first described this loss of muscle mass with aging as sarcopenia (9). It was more than another 10 years before the European Working Group on Sarcopenia in Elderly People (EWGSOP) developed the first diagnostic criteria with a diagnostic algorithm (10). This group indicated that muscle strength, physical performance and muscle mass have to be determined to assess sarcopenia. In addition, the International Working Group on Sarcopenia (IWGS) and Special Interest Group (SIG) of the Society of Sarcopenia, Cachexia and Wasting Disorders developed additional definitions and criteria for sarcopenia (11, 12). The IWGS defined sarcopenia as the loss of muscle mass with a physical performance measure (6). The SIG defined sarcopenia with limited mobility if a patient had low muscle mass and low physical performance (12). The debate about the definition of sarcopenia continues and there is still no consensus.

In addition to various definitions, there are different methods with several techniques, equations, reference populations and devices for measuring different domains of sarcopenia such as muscle mass, gait speed and grip strength (10). For example, when applying Bioelectrical Impedance Analysis (BIA), there are devices with single-frequency or multi-frequency multi- segmental measurement and with different BIA calculations (13, 14).

Although sarcopenia seems to be an important and prevalent clinical topic in geriatric medicine, to our knowledge there are only a few publications specifically about geriatric patients in acute hospital geriatric wards. Rossi and colleagues found a prevalence of 26% for sarcopenia among acutely ill persons in a geriatric unit in an academic medical hospital (15). Other researchers found a prevalence of 25.3% in an acute geriatric ward of a general hospital (16). A prevalence of 28% was found in a cohort from the CRIME project in geriatric and internal medicine acute care wards at seven Italian hospitals (17).

In this study, we investigated the prevalence of sarcopenia according to skeletal muscle mass with BIA using different calculations related to height and weight. We applied the EWGSOP, IWGS and SIG criteria to geriatric patients admitted to an acute care hospital (10-12). Our research questions were:

  • What is the prevalence of sarcopenia according to skeletal muscle mass, EWGSOP, IWGS and SIG criteria among geriatric patients admitted to a geriatric ward of an acute care hospital?
  • What is the influence of different BIA equations, Maltron or Janssen calculation, on the prevalence of sarcopenia in the geriatric patients?
  • How many patients are sarcopenic according to all the definitions tested in this study?

 

Methods

Design

This study followed a cross-sectional design.

Study sample

All geriatric patients admitted to the acute geriatric ward of a large Dutch general hospital were included in the study. For organizational reasons, there were two periods of recruitment: the first from March through June 2012 and the second for the same months in 2013. The inclusion criteria were an age above 70 years, the ability to walk prior to admission and being physical frail according to the Fried criteria (18).

Patients were excluded if they had a pacemaker or an implantable cardioverter defibrillator (ICD), were not able to perform instructions because of a severe confusional state or dementia, or had a terminal condition. Patients were also excluded if they were transferred from another hospital department or if there was no informed consent by patient or proxy.

The Ethics committee of Sittard-Heerlen approved the study (number 13-N-60) and participants signed an informed consent.

Measurements

Patient characteristics

Patient characteristics were retrieved from the medical and nursing files. These included sex, age, living situation, diagnosed medical conditions, medical history and activities of daily living prior to the acute illness that led to hospital admission. Height was estimated to the nearest centimeter by measuring ulna length because many patients were temporarily bedridden (19). The physical frailty score was assessed according to the Fried criteria, which ranges from 0 to 5: a score of 3 or higher indicates physical frailty (18). Weight was measured on a sitting weight scale (SECA, Model 959). Measurements of handgrip strength, muscle mass and gait speed were made on the fourth day after hospital admission.

A number of scales were used to evaluate patient characteristics. The cumulative illness rating scale (CIRS) was used to calculate the number and severity of chronic illnesses of the patients’ comorbid diseases. The score ranges from 0, which corresponds to the absence of disorders, to a maximum of 56 (20). Malnutrition was measured using the Short Nutritional Assessment Questionnaire (SNAQ), which is a validated screening instrument for malnutrition. Scores range from 0 to 5; a score of 3 or higher indicates that the patient is malnourished (21). The Katz ADL-6, a validated instrument for screening daily living activities, was used to assess ADL. Scores range from 0 (totally independent) to 6 (completely dependent) (22).

Handgrip strength

Handgrip strength was assessed using the Jamar dynamometer (Sammons Preston, Inc., Bolingbrook, IL, USA), a frequently used and validated tool for assessing healthy elderly people. We applied the Southampton protocol, which is described in detail in the review by Roberts et al (2011) (23, 24).

Muscle mass

The Maltron BioScan 920-II, a multi-frequency multi- segmental bio-impedance (mf-ms BIA) device, was used to measure muscle mass. Mf-ms BIA has been validated for the assessment of whole body composition and segmental lean mass in elderly people (6, 25). The Maltron BioScan 920-II has an eight-point electrode system, which separately measures impedance of the patient’s trunk, arms and legs at four different frequencies (5 kHz, 50 kHz 100 Hz and 200 Hz) for each body segment. The Maltron BioScan 920-II calculates appendicular skeletal muscle mass according to the Janssen calculation and a device-specific calculation called the Maltron calculation (25). Patients were measured early in the morning before breakfast, wearing only the pajamas described in the user’s manual.

Gait speed

The four-meter walking test has been validated for measuring gait speed in elderly people. The faster of two trials was used and the test was started from a standing still position. Patients were instructed to walk at an easy usual speed and were allowed to use a walking aid if necessary (26).

Diagnostic criteria for sarcopenia

Table 1 shows the different diagnostic criteria for sarcopenia that are described in the literature and were used as reference criteria in this study. They are based on measurements of muscle mass by BIA, handgrip strength and gait speed.

Statistics

Data were analyzed using IBM SPSS Statistics 20. Due to the aim of the study, descriptive statistics were used. As the prevalence of sarcopenia is different for men and women, data were analyzed for men and woman separately (27). Categorical data are presented as frequencies and percentages, and discrete and continuous data are presented as means with standard deviations.

Table 1: Cut-off points for sarcopenia based on different definitions

RMM*= Relative Muscle Mass; SMI†=Skeletal Muscle Mass Index; BMI‡= Body Mass Index

 

Results

In the two three-month periods, 205 older patients were admitted and asked to participate. At baseline, 77 patients were excluded. Of that number, 13 patients or proxies refused to participate. Another 64 patients were excluded because they did not meet the inclusion criteria: 10 patients had a severe confusional state or dementia, 7 had a pacemaker or ICD, 13 were unable to walk, 24 had a terminal condition and 10 were transferred from another hospital department.

After exclusion, 128 patients were eligible to participate. However, there was an incomplete data set for 34 patients and there were technical problems with the BIA or Jamar dynamometer when measuring 9 patients. In the end, 85 patients with complete datasets were included in the analyses.

The mean age of participants was 84 years and 72% were female. The mean Fried score was 3.8 for men (SD=0.7) and 4.0 for women (SD=0.7). Sixty-nine percent of the women and 92% of the men lived in the community surrounding the hospital. The mean CIRS score was 19.3 (SD=5.1) for women and 20.9 (SD=5.7) for men. Thirty-nine percent of the participants were malnourished, with SNAQ scores of 3 or higher. Forty-seven percent were highly ADL dependent, with a Katz ADL-6 score of 5.

Figure 1: Flow chart describing patient recruitment

 

Sarcopenia prevalence

Table 2 shows sarcopenia prevalence using the different diagnostic criteria.

Sarcopenia prevalence differed when various diagnostic criteria were applied. According to definitions that only take muscle mass into account (A1, A2, A3, and A4), prevalence ranged from 26-70% for women and from 46-100% for men. When sarcopenia was defined as low grip strength (B), the prevalence was 75% for both men and women. According to definitions that include a combination of low muscle mass and low gait speed (IWGS: C1-C4 and SIG: C5-C6), sarcopenia prevalence ranged from 26-69% for women and from 42-96% for men. According to the EWGSOP diagnostic criteria (D1- D6) with different cut-off points for low grip strength and low RMM or SMI for muscle mass, sarcopenia prevalence ranged from 26-70% for women and from 42-92% for men. The prevalence of sarcopenia according to the EWGSOP algorithm used with the Maltron and Janssen calculations (D5-D6) was 67% and 26% for women and 92% and 71% for men, respectively. Figure 2 shows the influence of both the Jansen and Maltron calculations for estimating muscle mass on the prevalence of sarcopenia.

The prevalence of sarcopenia was higher when the Maltron calculation was applied.

Twelve (14%) geriatric patients (5 men and 7 women) were sarcopenic according to all 17 definitions. When only the Janssen calculation was applied for measuring muscle mass, 15 (18%) patients were sarcopenic according to all definitions; when only the Maltron calculation was applied, 30 (35%) were sarcopenic.

Table 2: Prevalence of sarcopenia in study population (n=85), based on different criteria: skeletal muscle mass, EWGSOP, IWGS and SIG

* Code as defined in Table 1

 

Discussion

We found a huge variation in sarcopenia prevalence when applying different criteria. This variation is consistent with results from the Leiden Longevity Study, in which sarcopenia prevalence also varied when different criteria were used. In the Leiden community cohort, who were aged 70 years and older, the prevalence varied from 0-45.2% for men and 0-25.8% for women (28).

Analysis of 119 acutely ill older patients (mean age of 80.4 years and 34.4% female) who were admitted to a geriatric unit in an academic medical department showed a sarcopenia prevalence of 26%. In this study the EWGSOP algorithm with assessment of muscle mass by bio-impedance analysis, muscle strength by handheld dynamometer and gait speed by the four- meter walking test was used (15). Other researchers found a similar prevalence: 25.3% in 198 patients (mean age of 82.8 years and 70.2% female) from an acute care geriatric ward of a general hospital. They used the cut-off points of the EWGSOP algorithm, although they used the Short Physical Performance Battery instead of the four-meter walking test (16). A similar sarcopenia prevalence (28%) was found in 770 acutely ill older patients (mean age of 81.7 years and 56% female) who were admitted to geriatric and internal medicine acute care wards at seven Italian hospitals (17). In that study the EWGSOP algorithm, with assessment of muscle mass by bio-impedance analysis (including the Janssen calculation), grip strength and gait speed (four-meter walking test) was used.

The higher sarcopenia prevalence we found compared to the above findings might be explained because our geriatric patients had a higher average age (84 years, SD=5.8) and were more physically frail (mean Fried score of 4). The higher prevalence of sarcopenia in our study might also be explained because of the impact of relatively acute loss of muscle mass due to acute illness, as shown in earlier research (5).

Another possible explanation for the big variance in sarcopenia prevalence might be the impact of the different calculations used in the BIA estimation of muscle mass as shown in Figure 2. Overall, the rate of sarcopenia prevalence assessed by applying definitions that use the Janssen calculation is lower than the rate assessed by applying definitions using the Maltron calculation. It is possible that the Janssen calculation might be overrating skeletal muscle mass, since it draws a direct relationship between resistance and skeletal muscle mass (13). In contrast, the Maltron calculation uses other frequencies which allow it to calculate intracellular and extracellular water and body mass. Many hospitalized frail elderly people have hydration problems that have a direct influence on the estimation of muscle mass, especially when using the Janssen calculation. As Figure 2 shows, there is little variance in sarcopenia prevalence (67% versus 70%) for women when using the Maltron calculation and different definitions. As far as we know, this is the second study to use BIA with the Maltron calculation. A high correlation between BIA with Maltron calculation and MRI was shown in an aged community population (25).The higher prevalence of sarcopenia in men compared to women in all criteria (with the exception of criterion with code D4; Table 1) is consistent with results from earlier studies (27).

In our study, few geriatric patients (12; 14%) were sarcopenic according to all 17 different criteria (as shown in Table 1). This finding is consistent with the results of the Leiden Longevity Study, in which only 1 of 130 participants (0.2%) was identified as sarcopenic according to seven different diagnostic criteria (28). It is relevant to know which of the criteria or components of sarcopenia are related with adverse outcomes in acutely hospitalized frail elderly people, such as those in this study’s target group. McLean and colleagues found among 1869 women (mean age of 76 years) and 4411 men (mean age of 74 years), pooled from 6 cohort studies from the Foundation for the National Institutes of Health (FNIH) Sarcopenia Project, that low grip strength (<26kg men and <16kg women) and low appendicular lean mass to Body Mass Index ratio (<0.789 men and <0.512 women) were associated with increased likelihood for incident mobility impairment (29, 30,31). However validation of these candidate criteria for clinically relevant weakness and low lean mass (measured with BIA instead of dual-energy x-ray absorptiometry) in acutely hospitalized frail elderly people is needed.

Our study had some limitations. First, it was a single center study, limited to one acute care geriatric ward of a Dutch general hospital and only included geriatric patients who were mobile prior to hospitalization. Second, participants were only recruited between March and June, so it is possible that seasonal variations of disease manifestations might have affected the study findings. The influence of season has been previously demonstrated for variation in disease, vitamin D level, physical activity, ankle dorsiflexion strength and hours spent outside (32). A third limitation of this study is the fact that we were missing values for 43 (33%) of the eligible patients. Although they had similar ages and were equally physically frail (data not shown) compared to the included patients, we cannot exclude possible influences their inclusion may have had on our findings. Finally, we were not able to differentiate between sarcopenia and cachexia in this study. These two conditions may overlap in acutely ill geriatric patients because they share common etiological pathways. Therefore we have to be cautious with generalizing our prevalence results.

Figure 2: Prevalence of sarcopenia in study population (n=85) according to different criteria with Janssen and Maltron calculations

 

Conclusion

Sarcopenia is highly prevalent in an acute hospitalized geriatric population, although the prevalence varies widely depending on the diagnostic criteria applied. In this study, almost all the geriatric patients (96%) had a low gait speed. Furthermore, using the Maltron calculation to estimate skeletal muscle mass found a higher prevalence of sarcopenia and a higher rate of sarcopenic patients according to all definitions than did the Janssen calculation. Sarcopenia prevalence was almost 70% for female geriatric patients, according to all definitions including muscle mass. The prevalence varied more widely for men when the SMI, RMM, SIG, IWGS and EWGSOP criteria were applied.

We clearly found that current definitions of sarcopenia are identifying different patients. In accordance with the findings from McLean and colleagues it might be possible to diagnose sarcopenia in acutely ill geriatric patients based only on grip strength and a muscle mass index, because almost all the patients in our study sample had low gait speed (29). Since acute conditions are often related to momentary functional impairments, gait speed may be less suitable as a criterion for diagnosing sarcopenia in acutely ill geriatric patients. From a clinical point of view and in accordance with the findings from the FNIH Sarcopenia Project, it is important to select sarcopenic geriatric patients with adverse outcomes like loss of mobility or functional decline, which can be prevented with adequate treatment (29,30,31). A prospective study is needed to discover which specific criteria of sarcopenia, for example the new candidate criteria from the FNIH Sarcopenia Project, can predict these adverse outcomes. This may be the starting point for treating sarcopenic geriatric patients and preventing unnecessary and undesired functional loss.

 

Acknowledgments: We want to thank Leopold Engels for his critical review of the manuscript.

Funding: There was no funding.

 

References

1. Covinsky K, Pierluissi E, Johnston C. Hospitalization Associated Disability. JAMA 2011;306:1782-1793.

2. Merino Martín S, Cruz Jentoft AJ. Impact of hospital admission on functional and cognitive measures in older subjects. Eur Geriatr Med 2012;3:208-212.

3. Janssen I, Heymsfield SB, Ross R. Low relative skeletal muscle mass(sarcopenia) in older persons is associated with functional impairment and physical disability. J Am Geriatr Soc 2002;50:889-896.

4. Gale CR, Martyn CN, Cooper C, Sayer AA (2007), Grip strength, body composition, and mortality. Int J Epidemiol 36:228-235

5. Kortebein et al. Effect of 10 Days of Bed Rest on Skeletal Muscle in Healthy Older Adults JAMA 2007; 297:16;1772- 1774

6. Ling CHY et al. Accuracy of direct segmental multi-frequency bio impedance analysis in the assessment of total body and segmental body composition in middle aged adult population. Clin Nutr 2011;30:610-615

7. Janssen I, Baumgartner R, Ross R et al. Skeletal muscle cut points associated with elevated physical disability risk in older men and women. Am J Epidemiol 2004;159:413–421.

8. Cesari M, Kritchevsky SB, Newman AB et al. Added value of physical performance measures in predicting adverse health-related events: results from the health, aging and body composition study. J Am Geriatr Soc 2009 57:251–259.

9. Rosenberg I. Summary comments: epidemiological and methodological problems in determining nutritional status of older persons. Am J Clin Nutr 1989;50:1231–1233

10. 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.

11. Fielding RA, Vellas B, Evans WJ, et al. Sarcopenia: an undiagnosed condition in older adults. Current consensus definition: prevalence, etiology, and consequences. International Working Group on Sarcopenia. J Am Med Dir Assoc 2011;12:249-256.

12. Morley JE, Abbatecola AM, Argiles JM, et al. Sarcopenia with limited mobility: an international consensus. J Am Med Dir Assoc 2011;12:403- 409.

13. Janssen I, Heymsfield SB, Baumgartner RN, Ross R. Estimation of skeletal muscle mass by bioelectrical impedance analysis. J Appl Physiol 2000;89:465-471.

14. Kyle UG, Genton L, Hans D, Pichard C. Validation of a bioelectrical impedance analysis equation to predict appendicular skeletal muscle mass (ASMM). Clin Nutr 2003;22:537-543.

15. Rossi AP, Fantin F, Micciolo R, et al. Identifying sarcopenia in acute care setting patients. J Am Med Dir Assoc 2014;15:303.e7-303.e12.

16. Smoliner C, Sieber CC, Wirth R. Prevalence of sarcopenia in geriatric hospitalized patients. J Am Med Dir Assoc 2014;15:267-272.

17. Vetrano DL, Landi F, Volpato S, et al. Association of Sarcopenia With Short- and Long-term Mortality in Older Adults Admitted to Acute Care Wards: Results From the CRIME Study J Gerontol A Biol Sci Med Sci 2014 in press. doi: 10.1093/gerona/glu034.

18. 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.

19. Malnutrition Advisory Group, British Association of Parenteral and Enteral Nutrition. October 2008. Malnutrition Universal Screening Tool. The Malnutrition Universal Screening Tool (MUST) is reproduced here with the kind permission of BAPEN.

20. Nagaratnam N, Gayagay G. Validation of the Cumulative Illness Rating Scale (CIRS) in hospitalized nonagenarians. Arch Gerontol Geriatr 2007;44:29-36.

21. Kruizenga HM, Seidell JC, Vet H de, et al. Development and validation of a hospital screening tool for malnutrition: the short nutritional assessment questionnaire (SNAQ). Clin Nutr 2005;24:75–82.

22. Katz S, Ford AB, Moskowitz RW, Jackson BA, Jaffe MW. Studies of illness in the aged. The index of ADL: a standardized measure of biological and psychosocial function. JAMA 1963;185:914–919.

23. Roberts H et al. A review of the measurement of grip strength in clinical and epidemiological studies: towards a standardized approach. Age and Ageing 2011;40:423-429

24. Oldfields OD. The assessment and analysis of handedness: The Edinburgh Inventory. Neuropsychology 1971;9:97-113

25. Chien MY, Ta-Yi Huang, Ying-Tai Wu. Prevalence of sarcopenia estimated using a bioelectric impedance analysis. Prediction equation in community-dwelling elderly people in Taiwan. J Am Geriatr Soc 2008;56:1710-1715.

26. Graham JE, Ostir GV, Fisher SR, Ottenbacher KJ. Assessing walking speed in clinical research: A systematic review. Journal of Evaluation in Clinical Practice 2008;14:552-562.

27. Thomas D. Sarcopenia. Clin Geriatr Med 2010;26:331–346.

28. Bijlsma AY, Meskers CGM, Ling CHY et al. Defining sarcopenia: the impact of different diagnostic criteria on the prevalence of sarcopenia in a large middle aged cohort. Age (Dordr.) Epub 2012 Febr 8

29. McLean RR, Shardell MD, Alley DE et al. Criteria for Clinically Relevant Weakness and Low Lean Mass and Their Longitudinal Association With Incident Mobility Impairment and Mortality: The Foundation for the National Institutes of Health (FNIH) Sarcopenia Project. J Gerontol A Biol Sci Med Sci 2014;69:576–583

30. 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

31. Dam TT, Peters KW, Fragala M et al. An Evidence-Based Comparison of Operational Criteria for the Presence of Sarcopenia. J Gerontol A Biol Sci Med Sci. 2014;69:584–590.

32. Bird ML, Hill KD, Robertson IK, Ball MJ, Pittaway J, Williams AD. Serum [25(OH)D] status, ankle strength and activity show seasonal variation in older adults: relevance for winter falls in higher latitudes. Age Ageing 2013;42:181-185.