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C. Udina1, J. Ars1, A. Morandi1, J. Vilaró2, C. Cáceres1, M. Inzitari1

1. REFiT Barcelona research group, Parc Sanitari Pere Virgili and Vall d’Hebron Institut de Recerca (VHIR), Barcelona, Spain; 2. Blanquerna School of Health Sciences, Global Research on Wellbeing (GRoW), Universitat Ramon Llull. Barcelona, Spain.
Corresponding author: Cristina Udina, MD, Parc Sanitari Pere Virgili, C/ Esteve Terradas, 30, 08023 Barcelona, Spain, cudina@perevirgili.cat, ORCID ID: 0000-0002-0140-669X

J Frailty Aging 2021;in press
Published online February 7, 2021, http://dx.doi.org/10.14283/jfa.2021.1



COVID-19 patients may experience disability related to Intensive Care Unit (ICU) admission or due to immobilization. We assessed pre-post impact on physical performance of multi-component therapeutic exercise for post-COVID-19 rehabilitation in a post-acute care facility. A 30-minute daily multicomponent therapeutic exercise intervention combined resistance, endurance and balance training. Outcomes: Short Physical Performance Battery; Barthel Index, ability to walk unassisted and single leg stance. Clinical, functional and cognitive variables were collected. We included 33 patients (66.2±12.8 years). All outcomes improved significantly in the global sample (p<0.01). Post-ICU patients, who were younger than No ICU ones, experienced greater improvement in SPPB (4.4±2.1 vs 2.5±1.7, p<0.01) and gait speed (0.4±0.2 vs 0.2±0.1 m/sec, p<0.01). In conclusion, adults surviving COVID-19 improved their functional status, including those who required ICU stay. Our results emphasize the need to establish innovative rehabilitative strategies to reduce the negative functional outcomes of COVID-19.

Key words: COVID-19, older adults, therapeutic exercise, rehabilitation, post-ICU rehabilitation.



COVID-19’s impact increases with age (1). Besides mortality, patients may experience relevant disability, related to serious complications requiring Intensive Care Unit (ICU) admission, which has been linked to physical impairments (2). Less severe cases might experience functional decline due to immobilization due to the disease and isolation measures to prevent transmission. Early and effective rehabilitation interventions are urgent, despite healthcare systems may be overwhelmed and rehabilitation may be disrupted. Therapeutic exercise (TE) is a physical therapy technique used to improve or maintain a person’s physical condition through resistance, endurance, flexibility and balance training. The intensity, volume, progression and type of exercise must be individualized based on the physical condition and tolerance during the execution of TE. Previous research shows the benefits of supervised TE in acutely ill patients to improve their physical condition and autonomy through exercise (3). During the early weeks of the pandemic our post-acute care facility had to adapt in order to provide care for COVID-19 patients (4). In addition to maintaining the usual physical therapy interventions for more impaired patients, we created an intensive rehabilitation pathway through TE to facilitate a quick recovery and faster discharge at home. Our aim is to describe the pre-post impact on physical performance of multi-component therapeutic exercise for post-COVID-19 rehabilitation.



We performed a cohort study of post-acute care patients that overcame COVID-19 and were included in a rehabilitation protocol based on multi-component therapeutic exercise. The inclusion criteria were: 1) ability to walk unassisted pre-COVID-19 (use of cane or walker was allowed); 2) able to stand after the resolution of acute COVID-19; 3) social situation allowed discharge in 10 days. We collected demographics, COVID-19 related variables, comorbidities (sum of hypertension, diabetes, arrhythmia, myocardial infarction, Chronic Obstructive Pulmonary Disease/Asthma, mild cognitive impairment, dementia, other neurodegenerative diseases, stroke, depression, osteoarthritis and low back pain) and prevalence of polypharmacy (5 or more drugs) at admission. Our comprehensive assessment included: pre-COVID functional status with the Barthel Index and Lawton Index and frailty status with the Clinical Frailty Scale (CFS); cognitive function at post-acute admission with the Montreal Cognitive Assessment (MoCA) for global cognition and the Symbol Digit Modalities Test (SDMT) for attention and processing speed. SDMT scores are age-adjusted (5), considering a score of 7 or higher as normal range. The Confusion Assessment Method (CAM) was used to screen for Delirium. These covariates were collected based on clinical and functional aspects that might impact physical function as well as the response to physical exercise. We assessed physical function at day 1 and 10 of the intervention. Those patients who were discharged before day 10 were evaluated at discharge. We performed the Short Physical Performance Battery (SPPB) as a measure of gait performance (time to walk 4 meters), balance (stand for 10 seconds with feet side-by-side and in semi-tandem and tandem positions) and lower limb strength (time required to stand up and sit down 5 times from a chair without using the arms). Furthermore, we assessed independence for the basic activities of daily living with the Barthel Index, need of assistance to walk with the Functional Ambulation Category (FAC) (6) and the single leg stance test (7). We evaluated exercise capacity with the 6-minute walk test (6MWT) in a sub-sample (for logistical reasons).
The 30-minute 7 days/week multi-component therapeutic exercise intervention (summarized in Figure 1) was led by an expert physical therapist and combined: a) resistance training [1-2 sets with 8-10 repetitions each (intensity between 30-80% of the Repetition Maximum (8) )]; b) endurance training (up to 15-minutes aerobic training with a cycle ergometer, steps or walking) and c) balance training (walking with obstacles, changing directions or on unstable surfaces). Additionally, recommendations were provided to decrease daily sedentary behavior. Each session was individualized to each patient’s physical condition.
Outcome measures included: SPPB global score, gait speed (m/s), balance score and chair-stand time (seconds), Barthel Index score, ability to walk unassisted (FAC score 4 or higher) and maintain single leg stance for 10 seconds and distance walked during the 6MWT (meters). We used descriptive statistics with mean and Standard Deviation (SD) or frequencies as required. We assessed differences between the initial and final values in the outcome variables with Wilcoxon signed rank test and McNemar test for continuous and categorical variables, respectively. We calculated the mean pre-post change for each continuous outcome variable: Variable POST – VARIABLE PRE. We used Mann-Whitney U test to compare the mean change in the outcomes between patients treated or not in the ICU as well as to compare baseline characteristics in both groups. All statistical analysis was performed with statistical software: IBM SPSS Statistics for Windows, Version 21.0. (Armonk, NY: IBM Corp).



We included 33 patients (66.2±12.8 years, 57.6% women), of whom 90.9% (n=30) presented with pneumonia and 60.6% (n=20) were admitted to the ICU, all (n=20) requiring mechanical ventilation, with a mean ICU stay of 10.3±9.9 days. The sample consisted of pre-COVID-19 well-functioning adults (Barthel Index 98.5±5.8 and Lawton Index 6.7±2.1) with low frailty (CFS score 2.5±1.3) and comorbidity (sum of comorbidities 1.5±1.6) but high polypharmacy at admission (72.7% (n=24)). Post-ICU patients were younger, with lower comorbidity, better pre-COVID-19 functional status and lower frailty, compared to non-ICU patients (Table 1). Although none of the patients had delirium according to CAM scores at admission, post-COVID-19 cognitive function was mildly impaired in the whole cohort and within both groups. After the intervention (mean duration=8.2±1.7 days), all physical performance measures showed a statistically significant improvement when comparing the initial and final values in the global sample and among post-ICU patients, while non-ICU patients did not improve in balance-related variables. Furthermore, post-ICU patients experienced a greater improvement in SPPB and gait speed mean change compared to non-ICU (4.4±2.1 vs 2.5±1.7, p<0.01 and 0.4±0.2 vs 0.2±0.1, p<0.01, respectively). None of the patients died during the intervention and all were discharged home. In a subsample of 22 participants (61.9±12.1 years, 63.6% women, 81.8% admitted to the ICU and 95.5% with pneumonia), mean 6MWT walked distance improved from 158.7±154.1 to 346.3±111.5 m (p<0.001).

Table 1
Baseline characteristics and functional outcomes, in the total sample and stratified by previous ICU admission

Abbreviations: ICU: Intensive Care Unit. MoCA: Montreal Cognitive Assessment. CFS: Clinical Frailty Scale. SDMT: Symbol Digit Modalities Test. SPPB: Short Physical Performance Battery. FAC: Functional Ambulation Category. SDMT normal range ≥ 7. Legend: (*) Pre-post comparison within group with Wilcoxon rank test and McNemar test (significance at a p-level < 0.05 marked with †). (‡) Comparison of the mean change between the ICU and the non-ICU groups with Mann-Whitney U Test (significance at a p-level < 0.05 marked with †)

Figure 1
Scheme of the individualized multi-component therapeutic exercise intervention, combining 3 or more modalities daily

Abbreviation: RM: repetition maximum



In summary, in our sample of post-COVID-19 adults and older adults, physical function improved after a relatively short therapeutic exercise intervention. This improvement seems clinically meaningful, according to previous studies (9). Compared to the non-ICU group, post-ICU patients showed higher improvements, possibly due to their younger age and better functional, clinical and frailty status pre-COVID-19. Noteworthy, our sample showed mild cognitive impairment post-COVID-19 according to a brief cognitive assessment, which we might speculate as non-preexisting, especially in the ICU group, due to their relatively young age and preserved functional status. This cognitive dysfunction could be related to delirium during COVID-19’s acute phase or even be a neurological feature of COVID-19’s infection (10). Further research is needed to support these findings and to study long-term effects of COVID-19 on cognition.
Evidence about post-COVID-19 rehabilitation is still scarce, although there is a growing body of literature highlighting the need of rehabilitation strategies. To our knowledge, this is the first study on the effects of intensive rehabilitation through a structured therapeutic exercise intervention of post-COVID-19 patients in post-acute care, a setting able to combine the acute management of these patients with rehabilitative interventions (4). Improving physical function in post-ICU patients is crucial as previous research has shown long-term negative outcomes (11). However, the type of exercise intervention previously reported in post-ICU rehabilitation so far seems not comparable to our intensive and multimodal protocol (12). Previous research shows the efficacy of similar therapeutic exercise strategies tested in acute geriatric units, demonstrating functional benefits of short-term supervised exercise during acute medical illnesses: the reported magnitude of change of 2.4 points in the total SPPB (13) is similar to the change in our non-ICU group, which is indeed older and with a slightly pre-COVID-19 worse clinical and functional profile, compared to the ICU group. According to studies performed with Acute Respiratory Distress Syndrome survivors, the improvement in exercise capacity experienced in the small subsample seems also clinically relevant (14). The cognitive impairment detected among the post-ICU patients is also in line with the findings reported in Acute Respiratory Distress Syndrome survivors (15), however in our opinion the impairment detected in non-ICU patients, deserves further research to shed some light into the potential neurological manifestations of COVID-19.
Main limitations of the study are the small sample size and the absence of a control group to assess the effect of the intervention. Among the strengths, we enrolled adults and older adults post-COVID-19 with different acute care pathways during the acute phase, with a comprehensive assessment of clinical and functional variables.
In conclusion, adults and older adults surviving COVID-19 seem to improve their functional status, despite previous admission to ICU, through a short, individualized, multi-component therapeutic exercise intervention. Further research with controlled, larger samples and longer treatment periods might help elucidate the role of rehabilitation interventions in the reduction of negative functional outcomes of COVID-19, hence mitigating the potential increase in COVID-19-related disability and health care costs.


Funding: This research did not receive any funding from agencies in the public, commercial, or not-for-profit sectors.
Conflicts of interests: The authors (CU, JA, AM, JV, CC) state that they have no financial nor non-financial conflict of interests. MI received from Nestlé a fee for scientific advice, not related to the work or the topic of the current manuscript.
Ethics approval: The study procedures were approved by the institutional ethics committee. The authors declare that all study’s procedures are according to the 1964 Helsinki Declaration and that personal participant’s information was treated to ensure complete privacy. Furthermore, all procedures performed during the study were in the context of usual care of patients admitted to post-acute care.



1. Borobia A, Carcas A, Arnalich F, et al. A Cohort of Patients with COVID-19 in a Major Teaching Hospital in Europe. J Clin Med. 2020;9(6):1733. doi:10.3390/jcm9061733
2. Ohtake PJ, Lee AC, Scott JC, et al. Physical Impairments Associated With Post–Intensive Care Syndrome: Systematic Review Based on the World Health Organization’s International Classification of Functioning, Disability and Health Framework. Phys Ther. 2018;98(8):631-645. doi:10.1093/ptj/pzy059
3. Valenzuela PL, Morales JS, Castillo-García A, et al. Effects of exercise interventions on the functional status of acutely hospitalised older adults: A systematic review and meta-analysis. Ageing Res Rev. 2020;61(xxxx):101076. doi:10.1016/j.arr.2020.101076
4. Inzitari M, Udina C, Len O, et al. How a Barcelona post-acute facility became a referral center for comprehensive management of subacute patients with COVID-19. J Am Med Dir Assoc. June 2020. doi:10.1016/j.jamda.2020.06.015
5. Peña-Casanova J, Quiñones-Úbeda S, Quintana-Aparicio M, et al. Spanish multicenter normative studies (NEURONORMA project): Norms for verbal Span, visuospatial Span, letter and number sequencing, trail making test, and symbol digit modalities test. Arch Clin Neuropsychol. 2009;24(4):321-341. doi:10.1093/arclin/acp038
6. Martin BJ, Cameron M. Evaluation of walking speed and functional ambulation categories in geriatric day hospital patients. Clin Rehabil. 1996;10(1):44-46. doi:10.1177/026921559601000109
7. Bohannon RW, Tudini F. Unipedal balance test for older adults: a systematic review and meta-analysis of studies providing normative data. Physiother (United Kingdom). 2018;104(4):376-382. doi:10.1016/j.physio.2018.04.001
8. Serra-Rexach JA, Bustamante-Ara N, Hierro Villarán M, et al. Short-term, light- to moderate-intensity exercise training improves leg muscle strength in the oldest old: A randomized controlled trial. J Am Geriatr Soc. 2011;59(4):594-602. doi:10.1111/j.1532-5415.2011.03356.x
9. Perera S, Mody SH, Woodman RC, Studenski SA. Meaningful change and responsiveness in common physical performance measures in older adults. J Am Geriatr Soc. 2006;54(5):743-749. doi:10.1111/j.1532-5415.2006.00701.x
10. Helms J, Kremer S, Merdji H, et al. Neurologic Features in Severe SARS-CoV-2 Infection. N Engl J Med. 2020;382(23):2268-2270. doi:10.1056/nejmc2008597
11. Herridge MS, Tansey CM, Matté A, et al. Functional Disability 5 Years after Acute Respiratory Distress Syndrome. N Engl J Med. 2011;364(14):1293-1304. doi:10.1056/NEJMoa1011802
12. Connolly B, Denehy L, Brett S, Elliott D, Hart N. Exercise rehabilitation following hospital discharge in survivors of critical illness: an integrative review. Crit Care. 2012;16(3):226. doi:10.1186/CC11219
13. Martínez-Velilla N, Casas-Herrero A, Zambom-Ferraresi F, et al. Effect of Exercise Intervention on Functional Decline in Very Elderly Patients During Acute Hospitalization. JAMA Intern Med. 2019;179(1):28. doi:10.1001/jamainternmed.2018.4869
14. Chan KS, Pfoh ER, Denehy L, et al. Construct validity and minimal important diff erence of 6-minute walk distance in survivors of acute respiratory failure. Chest. 2015;147(5):1316-1326. doi:10.1378/chest.14-1808
15. Hopkins RO, Weaver LK, Collingridge D, Parkinson RB, Chan KJ, Orme JF. Two-year cognitive, emotional, and quality-of-life outcomes in acute respiratory distress syndrome. Am J Respir Crit Care Med. 2005;171(4):340-347. doi:10.1164/rccm.200406-763OC




A.T. Olagunju1,2,3, J.A. Morgan2, A. Aftab4, J.R. Gatchel5, P. Chen6, A. Dols7,8, M. Sajatovic6, W.T. Regenold9


1. Department of Psychiatry and Behavioral Neurosciences, McMaster University/St Joseph’s Healthcare Hamilton, Hamilton, ON, Canada; 2. Discipline of Psychiatry, The University of Adelaide, Adelaide, SA, Australia; 3. Department of Psychiatry, College of Medicine, University of Lagos, Lagos, Nigeria; 4. Department of Psychiatry, University of California, San Diego, CA, USA; 5. Division of Geriatric Psychiatry, McLean Hospital, Harvard Medical School, Boston, MA, USA; 6. Departments of Psychiatry & Neurology, Case Western Reserve University School of Medicine, University Hospitals Cleveland Medical Centre, Cleveland, OH, USA; 7. GGZ inGeest Specialized Mental Health Care, Department of Old Age Psychiatry, 1081 HJ, Amsterdam, The Netherlands; 8. Amsterdam UMC, Vrije Universiteit Amsterdam, Psychiatry, Amsterdam Public Health research institute and Neuroscience Amsterdam, 1081 HV, Amsterdam, The Netherlands; 9. Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, Division of Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA.
Corresponding author: Andrew T. Olagunju, Department of Psychiatry and Behavioral Neurosciences, McMaster University/St Joseph’s Healthcare Hamilton, 100 West 5th Street, Hamilton, ON, L8N 3K7 Canada, Email:olagunja@mcmaster.ca

J Frailty Aging 2020;in press
Published online December 9, 2020, http://dx.doi.org/10.14283/jfa.2020.64


Objectives: To better understand the role of nutrition in older adults (aged 50 years or older) with bipolar disorders (OABD), we conducted a systematic review of the literature and appraise existing evidence. Methods: Following PRISMA guidelines, we searched databases including Medline/PubMed, PsychINFO, EMBASE, CINAHL, Scopus, Web of Science, Cochrane Register, FDA website, and clinical trial registries through 2019 for eligible reports. The search string combined MeSH terms for bipolar disorder, nutrition and older adults. This was supplemented by snowball searching of references in relevant studies and authors were contacted to request their work where necessary. All included studies were rated with the National Institutes of Health Study Quality Assessment Tools based on study designs. Results: Of 2280 papers screened, ten studies including eight observational and two interventional studies. The topic foci of the papers examined several nutrients, (including vitamin B12, vitamin D, coenzyme Q10, homocysteine, and folate), nutritional deficiencies and biochemical correlates. The prevalence rates of deficiencies varied with specific nutrients (3.7% to 71.6% for Vitamin B12 and 34.6% for Vitamin D), and between inpatient versus outpatient populations. While nutritional interventions appeared to be associated with improvement in both affective and cognitive outcomes, the sample sizes of OABD varied and were generally small. Conclusion: While there is evidence for the benefits of nutritional interventions on affective, cognitive and overall outcome in OABD, the quality of the evidence is limited. Our findings underscore the need for high quality studies to inform evidence-based guidelines for nutritional assessment and supplemention in OABD.

Key words: Bipolar disorder, depression, geriatrics, nutrition, older adults, mania.



Recent international collaborative research indicates that suboptimal diet contributes significantly to the global burden of diseases with 11 million deaths and loss of 255 million disability-adjusted life-years attributable to dietary risk factors in 2017 (1-3). Despite the evidence linking poor nutrition to disease, nutrition and nutritional supplements are often overlooked in the assessment and treatment of patients generally, and in individuals with bipolar disorder (BD) in particular.
Older adults with bipolar disorder (OABD) may be especially prone to nutritional deficiencies due to various age-related factors that can diminish nutrient intake and absorption. Reduced dietary intake of nutrients may occur due to decreased sensory function, decreased appetite and dysphagia with advanced age (4). Causes of decreased nutrient absorption that are more common in older adults include: age related changes in the gut or atrophic gastritis; peptic ulcer disease with Helicobacter pylori infection; gastric and intestinal resections and taking medications that can interfere with nutrient absorption. Many of these interfering medications are commonly prescribed and include inhibitors of gastric acid secretion such as proton pump inhibitors and histamine-2 receptor antagonists; anti-epileptics, metformin, methotrexate, triamterene and trimethoprim (5, 6). Importantly, nutritional status, particularly vitamin levels, can influence cognitive function and affective symptoms in older adults with mood disorders, including BD (7-10).
In addition to the lack of awareness of diet and nutritional deficiencies in the care of OABD, clinicians are often unaware of the increasing use of nutritional supplements by their patients. Herbal and nutritional compounds are used widely by older adults and have been reported to be taken by nearly one in three older adults with bipolar disorder or major depression (11). Most individuals who use these supplements do not mention this to their clinicians, putting them at risk for potential drug-supplement interactions with adverse health effects.
Given the clinical importance of nutritional deficiencies and nutritional supplement use, a working group within the International Society for Bipolar Disorders (ISBD) (12) OABD taskforce undertook a systematic review with the following objectives in mind: 1) to synthesize literature evidence on nutritional deficiencies and supplements in OABD; 2) to review the quality of research evidence on nutritional deficiencies and supplements in relation to the epidemiology, pathophysiology, clinical treatment, outcome and wellbeing of OABD, and 3) to make recommendations regarding further research that will promote evidence-based assessment and management of OABD.



Eligibility criteria

We followed the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines in conducting the literature search in this review (13, 14). All literature for all years through 2019 was searched. The age cut-off for OABD for eligible studies in this review is 50 years and older based on the knowledge that around 50 years adults already have decreased absorption of nutrients, which is a primary age-related cause of nutritional deficiency (15, 16). Screening for nutrient deficiencies has been recommended in people aged 50 years and older in the United States and elsewhere in the world (15-18). We included all study designs including retrospective, observational, cross-sectional, and intervention trial but excluded case reports. Other inclusion criteria were availability of study full-text in the English language,19 and a clearly described representation of OABD among the study participants.

Information sources

We searched databases including Medline/PubMed, PsychINFO, EMBASE, Web of Science, Cochrane Register, the Food and Drug Administration (FDA) website, and international clinical trial registries for all years through 2019 for eligible reports. The bibliographies of included studies and relevant reviews were snow-ball searched for additional studies. Study authors were also contacted to request their work where necessary.

Search strategy

We used a search strategy addressing the following key terms in various permutations: nutrition, older adults, and bipolar disorder combined with OR and AND. Search strings included “diet* OR Diet, Food, and Nutrition OR food OR nutrition* OR “nutritional status” OR “nutritional physiological phenomenon” OR micronutrient* vitamin* OR multivitamin* OR vitamin B OR folate OR B6 OR B12 OR niacin OR vitamin C OR vitamin D OR vitamin E OR calcium OR chromium OR iron OR magnesium OR zinc OR artemisin OR caffeine OR dehydroepiandrosterone OR DHEA OR echinacea OR fish oil OR GABA OR gamma-amino butyric acid OR garlic OR ginkgo OR glucosamine OR karaela OR melatonin OR methylsulfonylmethane OR primrose oil OR probiotics OR S-adenosylmethionine OR St. Johns wort OR tryptophan OR tyrosine OR valerian AND “old age” OR aged OR elder* OR geriatric* OR geriatric psychiatry OR older OR aging OR ageing AND “bipolar disorder” OR manic depressi* OR bipolar depression. Search strings were performed in titles, abstracts, major topic/subject headings, and MeSH headings.

Study selection

Study selection, review of included studies and data extraction were conducted by Andrew T. Olagunju (ATO), Julie A. Morgan (JAM), Awais Aftab (AA), and supervised by William T. Regenold (WTR). Titles and abstracts were screened independently by at least two authors to shortlist studies for further review. The full texts of the shortlisted studies were reviewed by at least two authors independently according to the inclusion criteria. Inconsistencies about inclusion or exclusion of studies were resolved by discussion between authors or in consultation with WTR or other members of the task force to reach consensus.

Quality/bias assessment

As studies with different designs and representation of older adults were eligible, the Study Quality Assessment Tools of the National Institutes of Health (NIH) for the assessment of Observational Cohort and Cross-Sectional Studies, Case-Control, and Controlled Intervention Studies were used to assess the quality of included studies.20 We rated individual studies on a range of 12-14 items depending on their designs to produce a comprehensive overview of the risk of bias in individual studies and highlighted relevant overall quality limitations.

Data collection process

Data items collected from eligible reports included study characteristics, nutrients, main findings and implications for OABD (Table 1). In total, we screened 2280 titles and abstracts to produce a shortlist of 92 potential reports for full-text review. Of these 92 reports, 10 studies were selected for inclusion in the final review. (See Figure 1)

Table 1
Studies included in the review examining nutrition in older adults with bipolar disorder

Legend: BD- bipolar disorders, BCAA- branched-chain amino acid, C-cross-sectional, CC-case control, MTHFR- Methylene tetrahydrofolate reductase, N-total number of sample, NR-not reported, OABD- Older adults with bipolar disorders, R-retrospective chart review, RCT-randomized controlled clinical trial, YMRS- Young Mania Rating Scale

Figure 1
Flow of studies through the systematic review



Study characteristics

Ten studies were included in the review (5, 7, 8, 10, 11, 21-24, 25). The publication dates of these reports spanned three decades from 1990 to 2015. Of the ten studies, six were retrospective chart reviews, two were cross-sectional studies, and the remaining two were clinical trials. Considering the study participants, in two studies all participants were adults diagnosed with BD with a representation of OABD, (21, 22) while in the remaining studies (n=8), participants with BD represented a subset of the total sample. In general, the topic foci in the included studies addressed different aspects of nutrition and nutritional deficiencies, including biochemical, epidemiological and clinical factors, and nutritional supplements in OABD. All were able to specify the sample sizes of OABD in the included studies aged 50 years or older, ranging from 3-50. Follow-up was limited as the majority of the studies reported cross-sectional observations. (See table 1)

Biochemical, clinical and epidemiological findings on nutrients

Table 2 presents findings on specific nutrients and nutritional supplements covered in the ten studies included in this review. In total, four classes of nutrients including vitamins, minerals, and dietary herbs and nutritional supplements are covered in the studies and described below.

Table 2
Quality assessment with NIH scale-tool based on study design

1-14 are quality assessment items/criteria; CCS- case-controlled studies; CD- cannot determine; CIS- controlled intervention studies, NIH- National Institute of Health; NA- not applicable; NR-not reported; OCCSS- observational cohort and cross-sectional studies; OR-overall rating; SD-study design.



Five retrospective chart reviews (5, 7, 8, 10, 23) focused on vitamin B12 and/or folate. Importantly, several reports looked at vitamin B12 deficiency in mixed populations of older adults including patients with BD. The prevalence rates of Vitamin B12 deficiency ranged from 3.7%8 to 71.6%.10 Lower levels of vitamin B12 correlated strongly with memory loss and poor cognitive performance in psychotic depression (8). Approximately one quarter of geriatric clinic outpatients with B12 deficiency had neuropsychiatric symptoms with behavioural disturbance, memory loss and sensorimotor disorders being most common (10). Folate deficiency was reported to be 1.3% in acute geropsychiatric inpatients (7). One study concluded that the biochemically interrelated vitamins, B12 and folate, may exert both separate and concomitant influences on affect and cognition and that poorer vitamin status may contribute to certain geropsychiatric disorders that have later life onset and lack a familial predisposition (7).
Another study investigated the relationships between Vitamin B12 and folate levels as well as homocysteine (HCY) levels on brain volume changes and brain white matter disease in geriatric patients with psychiatric disorders (23). These authors found that, low serum concentrations of folate, but not of B12, were associated with magnetic resonance imaging (MRI) evidence of brain white-matter disease and smaller hippocampus and amygdala brain-volume measures. Elevated HCY, which can result from folate deficiency, was also associated with MRI evidence of brain white matter disease (23). Finally, one study found that among older adult psychiatric inpatients, B12 serum levels and percentages of probable and possible B12 deficiency did not differ significantly between cognitive disorder patients and non-cognitive disorder patients, including OABD, suggesting that it was reasonable to routinely screen older adult psychiatric inpatients for B12 deficiency whether or not cognitive disorder symptoms were present (5). One retrospective chart review study (24) and cross-sectional study(25) addressed vitamin D in OABD. Vitamin D deficiency prevalence rates ranged from 3.7 % to 34.6%. Vitamin D insufficiency (less severe and defined as levels <30 ng/mL) was reported in approximately 75% of psychiatric inpatients in two cross sectional assessments (25). However, no associations were found between vitamin d levels and a screen of global cognitive function or psychiatric diagnoses (24, 25).

Dietary, Herbs and Nutritional Supplements

In one cross sectional study of OABD and older adults with major depression (11), herbs and nutraceutical (HNC) products were ingested by 30% of individuals. About 40% incorrectly believed that HNC products were FDA-regulated at that time. The majority (64%) had not discussed the use of HNC with treating physicians and some preferred to take HNC compared to physician-prescribed psychotropic medications (14-20%). The use of HNC was more common in those with BD (44%) than those with unipolar major depression (16%).
Two open label trials (21, 22) reported significant reduction in the severity of depressive symptoms with high-dose CoEnzyme Q10 in OABD (21). These findings could support a role of therapy targeted at mitochondrial function in affective disorders (22).

Study Quality Assessment

Quality assessment of all included studies (n = 10) was rated fair (n=8) and good (n=2) with risk of bias items included in the tool. The limited representation of OABD represented a risk of bias. Notwithstanding the limitations in the quality of the studies, the reported outcomes on nutritional deficiencies and supplements in OABD were considered the best available evidence for the recommendations made. (See table 2)



Clinical implications

Most of the studies reviewed—seven of ten studies (70%) focused on three vitamins—vitamin B12, vitamin D and folate and the consequences of their deficiencies. These studies support a relationship between vitamin B12 deficiency and both affective symptoms and cognitive impairment in OABD (7, 8). They also provide evidence for associations between folate levels and hippocampus and amygdala volumes and brain MRI white matter hyper- intensities (WMHs) in older adult psychiatric patients (23). Vitamin D deficiency was reported to be common in older adult psychiatric patients. Generally, vitamin deficiencies did not differ across psychiatric disorders, indicating that screening for these deficiencies in older adults should not be limited to particular diagnostic groups such as cognitive disorders. Evidence for a therapeutic role for vitamin supplementation was limited. There is some evidence for a benefit from folate supplementation for OABD taking lithium.
Tissue levels of homocysteine can be elevated due to vitamin B12 or folate deficiencies. Four studies examined the relationships among vitamin B12, folate and homocysteine levels and their associations with psychiatric symptoms or evidence of brain disease (23). These studies support an inverse relationship between B12 and folate blood levels on one hand and homocysteine blood levels on the other in individuals with bipolar depression. They also suggest that HCY blood levels are elevated in individuals with BD and support an association between elevated levels and impairment in executive function and in the extent of brain WMHs. These studies provide evidence for measuring HCY blood levels along with vitamin B12 and folate levels in OABD.
Studies of diet and nutritional supplements revealed that supplement use is very common among OABD (11). Furthermore, supplement use was typically not discussed with health providers (11).
Clinical trials of adjunctive nutritional supplements found preliminary evidence for a benefit from Coenzyme Q10 on depressive symptoms in OABD (21, 22). The positive results in these supplement studies require replication in a larger group of OABD prior to their consideration for routine clinical use in OABD.

Study limitations

There are several study limitations of this review. The included studies were not homogenous nor entirely focused on OABD. Of the ten articles included in the review, only two reported study results exclusively from a sample or population of OABD. By expanding our inclusion criteria to capture studies with some significant number of OABD participants, we were able to report a more comprehensive review of studies. The majority of these studies were retrospective chart reviews and cross-sectional studies, while the number of RCTs was limited. Consequently, inferences on causal relationships between the nutrients studied and the pathophysiology or treatment of OABD is limited. Furthermore, none of the included studies (n=10) looked at the potential contributions of gender and the type of bipolar disorder (I and II) to their study findings despite the clinical importance of these two factors (26).



Scientific literature on nutrition and nutritional supplements in OABD exists but is limited in both quantity and quality. However, we can draw several conclusions from this review. First, because older adults are prone to nutritional deficits and suffer from several vitamin deficiencies that influence cognition and affective symptoms, there should be consideration given to better research to develop clinical guidelines for routine screening for deficiencies of vitamin B12, folate and vitamin D in OABD. Second, screening for elevated blood levels of HCY that result from vitamin deficiencies should also be considered. Notably there is need for well-designed and powered clinical trials to examine the effects of nutritional interventions including dietary questionnaires, vitamin deficiency screening, HCY blood level screening, and adjunctive nutrient supplementation in OABD.


• Nutrition is critical to physical health and general well-being of older adults with bipolar disoder (OABD), however nutritional deficiencies are common albeit varies for specific nutrients.
• While there is evidence for the benefits of nutritional interventions on affective, cognitive and overall outcome in OABD, the quality of the evidence is limited.
• Findings in this review underscore the need for high quality studies for development of evidence-based guideline for assessing and supplementing nutrition in OABD.


Acknowledgements: The authors thank the International Society for Bipolar Disorders executives, and staff for their support for this task force. Our gratitude also goes to Ms Maureen Bell of the University of Adelaide library for her support and advice.
Conflicts of interest: The authors declare no conflicts of interest to the content of the manuscript.
Disclosure information: Financial Disclosure information for Martha Sajatovic MD are stated below: Research grants within past 3 years: Otsuka, Alkermes, Janssen, International Society for Bipolar Disorders, Reuter Foundation, Woodruff Foundation, Reinberger Foundation, National Institutes of Health (NIH), Centers for Disease Control and Prevention (CDC); Consultant: Alkermes, Bracket, Otsuka, Janssen, Neurocrine, Health Analytics; Royalties: Springer Press, Johns Hopkins University Press, Oxford Press, UpToDate; CME activities: American Physician’s Institute, MCM Education, CMEology, Potomac Center for Medical Education, Global Medical Education, Creative Educational Concepts
Financial Disclosure information: Dr. Gatchel reports other relevant financial activities from Merck, grants from Alzheimer’s Association, grants from BrightFocus Foundation, outside the submitted work.



1. GBD 2017 Diet Collaborators. Health effects of dietary risks in 195 countries, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet (London, England). 2019;393(10184):1958-72.
2. GBD 2016 Risk Factors Collaborators. Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet (London, England). 2017;390(10100):1345-422.
3. GBD 2017 Risk Factor Collaborators. Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet (London, England). 2018;392(10159):1923-94.
4. Schiffman SS. Intensification of sensory properties of foods for the elderly. J Nutr. 2000;130(4S Suppl):927S-30S.
5. Lachner C, Martin C, John D, Nekkalapu S, Sasan A, Steinle N, et al. Older adult psychiatric inpatients with non-cognitive disorders should be screened for vitamin B12 deficiency. Journal of Nutrition, Health and Aging. 2014;18(2):209-12.
6. Langan RC, Goodbred AJ. Vitamin B12 Deficiency: Recognition and Management. Am Fam Physician. 2017;96(6):384-9.
7. Bell IR, Edman JS, Marby DW, Satlin A, Dreier T, Liptzin B, et al. Vitamin B12 and folate status in acute geropsychiatric inpatients: affective and cognitive characteristics of a vitamin nondeficient population. Biol Psychiatry. 1990;27(2):125-37.
8. Bell IR, Edman JS, Miller J, Hebben N, Linn RT, Ray D, et al. Relationship of normal serum vitamin B12 and folate levels to cognitive test performance in subtypes of geriatric major depression. J Geriatr Psychiatry Neurol. 1990;3(2):98-105.
9. Coppen A, Chaudhry S, Swade C. Folic acid enhances lithium prophylaxis. J Affect Disord. 1986;10(1):9-13.
10. Issac TG, Soundarya S, Christopher R, Chandra SR. Vitamin B12 deficiency: an important reversible co-morbidity in neuropsychiatric manifestations. Indian journal of psychological medicine. 2015;37(1):26-9.
11. Keaton D, Lamkin N, Cassidy KA, Meyer WJ, Ignacio RV, Aulakh L, et al. Utilization of herbal and nutritional compounds among older adults with bipolar disorder and with major depression. Int J Geriatr Psychiatry. 2009;24(10):1087-93.
12. (ISBD) ISoBD. https://www.isbd.org/active-task-forces. 2019.
13. Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gotzsche PC, Ioannidis JP, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. Annals of internal medicine. 2009;151(4):W65-94.
14. Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Annals of internal medicine. 2009;151(4):264-9, w64.
15. . Hughes CF, Ward M, Hoey L, McNulty H. Vitamin B12 and ageing: current issues and interaction with folate. Ann Clin Biochem. 2013 Jul;50(Pt 4):315-29.
16. Langan RC, Goodbred AJ. Vitamin B12 Deficiency: Recognition and Management. Am Fam Physician. 2017 Sep 15;96(6):384-389.
17. Government of South Wales, Australia: A Practical Guide to Effective Engagement with Older People. Retrieved on 27/01/2020 from: https://www.sahealth.sa.gov.au/wps/wcm/connect/efc56a004efc69f1b7ccf79ea2e2f365/Better+Together+-+A+Practical+Guide+to+Effective+Engagement+with+Older+People.pdf?MOD=AJPERES&CACHEID=ROOTWORKSPACE-efc56a004efc69f1b7ccf79ea2e2f365-mMADUnH
18. Institute of Medicine Panel on folate, other B vitamins and choline. Dietary Reference Intake: Thiamin, Riboflavin, Niacin, Vitamin B6, Vitamin B12, Pantothenic Acid, Biotin and CholineWashington DC: National Academy Press, 1998, pp.306–56.
19. Bauer M, Glenn T, Conell J, Rasgon N, Marsh W, Sagduyu K, et al. Common use of dietary supplements for bipolar disorder: a naturalistic, self-reported study. Int J Bipolar Disord. 2015;3(1):29.
20. NIH National Heart LaBIS, quality assessment tools [cited 2017 M. Available from: https://wwwnhlbinihgov/health-pro/guidelines/in-develop/cardiovascular-risk reduction/tools. 2017
21. Forester BP, Harper DG, Georgakas J, Ravichandran C, Madurai N, Cohen BM. Antidepressant effects of open label treatment with coenzyme Q10 in geriatric bipolar depression. Journal of clinical psychopharmacology. 2015;35(3):338-40.
22. Forester BP, Zuo CS, Ravichandran C, Harper DG, Du F, Kim S, et al. Coenzyme Q10 effects on creatine kinase activity and mood in geriatric bipolar depression. J Geriatr Psychiatry Neurol. 2012;25(1):43-50.
23. Scott TM, Tucker KL, Bhadelia A, Benjamin B, Patz S, Bhadelia R, et al. Homocysteine and B vitamins relate to brain volume and white-matter changes in geriatric patients with psychiatric disorders. Am J Geriatr Psychiatry. 2004;12(6):631-8.
24. Lapid MI, Drake MT, Geske JR, Mundis CB, Hegard TL, Kung S, et al. Hypovitaminosis D in psychogeriatric inpatients. J Nutr Health Aging. 2013;17(3):231-4.
25. Gronli O, Kvamme JM, Jorde R, Wynn R. Vitamin D deficiency is common in psychogeriatric patients, independent of diagnosis. BMC Psychiatry. 2014;14.
26. Müller JK, Leweke FM. Bipolar disorder: clinical overview. Med Monatsschr Pharm. 2016 Sep;39(9):363- 9.



E. Patrizio1, R. Calvani2, E. Marzetti2,3, M. Cesari4

1. Azienda di Servizi alla Persona Istituti Milanesi Martinitt e Stelline e Pio Albergo Trivulzio, Milan, Italy; 2. Fondazione Policlinico Universitario «Agostino Gemelli» IRCCS, Rome, Italy; 3. Università Cattolica del Sacro Cuore, Institute of Internal Medicine and Geriatrics, Rome, Italy; 4. Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy.
Corresponding author: Enrica Patrizio, Azienda di Servizi alla Persona Istituti Milanesi Martinitt e Stelline e Pio Albergo Trivulzio, Milan, Italy, Email: patrizio.enrica@gmail.com

J Frailty Aging 2020;in press
Published online November 24, 2020, http://dx.doi.org/10.14283/jfa.2020.61



The evaluation of the physical domain represents a critical part of the assessment of the older person, both in the clinical as well as the research setting. To measure physical function, clinicians and researchers have traditionally relied on instruments focusing on the capacity of the individual to accomplish specific functional tasks (e.g., the Activities of Daily Living [ADL] or the Instrumental ADL scales). However, a growing number of physical performance and muscle strength tests has been developed in parallel over the past three decades. These measures are specifically designed to: 1) provide objective results (not surprisingly, they are frequently timed tests) taken in standardized conditions, whereas the traditional physical function scales are generally self- or proxy-reported measures; 2) be more sensitive to changes; 3) capture the real biology of the function through the assessment of standardized tasks mirroring specific functional subdomains; and 4) mirror the quality of specific mechanisms underlying more complex and multidomain functions. Among the most commonly used instruments, the usual gait speed test, the Short Physical Performance Battery, the handgrip strength, the Timed Up-and-Go test, the 6-minute walk test, and the 400-meter walk test are widely adopted by clinicians and researchers. The clinical and research importance of all these instruments has been demonstrated by their predictive capacity for negative health-related outcomes (i.e., hospitalization, falls, institutionalization, disability, mortality). Moreover, they have shown to be associated with subclinical and clinical conditions that are also not directly related to the physical domain (e.g., inflammation, oxidative stress, overall mortality). For this reason, they have been repeatedly indicated as markers of wellbeing linked to the burden of multiple chronic conditions rather than mere parameters of mobility or strength. In this work protocols of the main tests for the objective assessment of physical function in older adults are presented.

Key words: Physical function, physical performance, gait speed, muscular strength, comprehensive geriatric assessment, older adults.



The aging of the global population is accompanied by an epidemiological transition from infectious and communicable diseases to a growing burden of chronic diseases. Health status in older persons is determined by the complex interaction of multiple factors (multiple chronic diseases, psychological, social, and environmental factors), that is not captured by traditional paradigms based on the concept of standalone diseases. The most common manifestation of poor health status in this population is represented by the loss of functioning, decrease in the autonomy of mobility and activities of daily living (ADLs), till the onset of disability and dependence (1, 2).
The Comprehensive Geriatric Assessment (CGA), evaluating not only the presence of diseases, but also the individual’s functions (intended both as physical and cognitive abilities), psychological factors, and social aspects, is a diagnostic and therapeutic process able to objectively define the health status of the frail older individual and support the design of tailored plans of intervention (3).
Loss of muscle strength and decline of physical performance are critical elements to consider in the detection of important age-related conditions. The definition of physical frailty usually includes measurements of handgrip strength and gait speed. Consistently, physical performance measures, as gait speed, Timed Up-and-Go (TUG) test, and the Short Physical Performance Battery (SPPB) are useful instruments for the screening of frailty in the general population (4). The European Working Group On Sarcopenia In Older People (EWGSOP2) recently published the updated diagnostic criteria for sarcopenia, that include poor muscle strength, and reduced physical performance to define the presence of sarcopenia and quantify its severity, respectively (5–7).
A recent position paper of the European Society for Clinical and Economic Aspects of Osteoporosis and Osteoarthritis (ESCEO) working group on frailty and sarcopenia proposed a standardization of the clinical assessment of muscle function and physical performance in order to promote the diagnosis of these conditions and facilitate the design of care programs (8). Muscle strength was defined as “the amount of force a muscle can produce with a single maximal effort”. Physical performance, on the other side, is considered a rather multidimensional concept, a function of the whole body, resulting from the functioning of multiple organs and systems (e.g., musculoskeletal, cardiovascular, respiratory, central and peripheral nervous systems). Hence, poor physical performance can be considered an early marker of frailty and subclinical diseases. Muscular strength and physical performance measures are related to pathophysiological conditions (i.e., atherosclerosis, inflammation, reduction of aerobic capacities), and are proved to predict the risk of healthcare use (i.e., hospitalizations, institutionalization) and adverse events (i.e., falls, cognitive decline, disability, death) (9–12). At the same time, these measures may represent a target and marker of efficacy for preventive interventions, such as rehabilitative and physical activity programs, being sensitive to changes of the health status. Such predictive capacity is confirmed across large and different populations. Furthermore, being these tests very clinical-friendly, they have been gradually embraced in the daily routine by different disciplines and settings, from the primary care to oncology, from cardiology to surgery, in order to support the diagnostic and therapeutic process (13–16).
In the last years, a number of measures and tools have been developed for the assessment of physical function. The aim of this paper is to describe standard procedures guiding the administration of the most important measures of strength and physical performance. In particular, we here present how to administer the handgrip strength test, the gait speed test, and the 400-meters walking test (17–19). Other important and well-established instruments, as the SPPB and the TUG test are not object of the present work as detailed instructions and video tutorials are already available (https://sppbguide.com/, and https://youtu.be/BA7Y_oLElGY respectively) (20, 21).



The protocols here presented are routinely part of a standard geriatric evaluation and do not require any authorization by institutional human research ethics committees. All patients can undergo the following tests. Exclusion criteria are specified within each protocol.

Handgrip strength test

To perform the test, a regularly calibrated, handheld hydraulic dynamometer is recommended (the JAMAR dynamometer is considered the gold standard). Use the same chair for every measurement. Subjects reporting current flare-up of pain in the wrist or hand or has recently undergone to surgery of the hand or wrist should not be tested on the affected side.

1 Seat the participant in a standard chair. Instruct to place forearms resting on the arms of the chair, with wrist just above the end of the arm of the chair. Tell to keep the hand in a neutral position, with the thumb pointing upwards.
2 Show the subject how to use the dynamometer. Place your hand around the handle, putting the base with the thumb on one side, and the other four fingers on the other. Tell the participant that he/she will not feel the grips moving when squeezing but the device is working and measuring his/her strength. Demonstrate the subject that the tighter the grip, the better score is registered by the instrument. Caution the participant that the dynamometer is quite heavy.
3 Place the adjustable handle of the dynamometer in the second position from the inside, unlocking the clip located on the lower post and fitting the adjustable handle on the second space between the teeth of the post.
4 Give the dynamometer to the participant and make sure that the grip bars are at the correct distance (with the fixed handle resting in the middle of the palm and the movable part in the center of the four fingers). If the handle seems to be too large or narrow to allow the patient to squeeze comfortably, remove the dynamometer and adjust, widening or tightening the handle.
5 Support the base with the palm of the hand while the subject holds the dynamometer, being careful not to limit its movement. Allow the participant to try to squeeze to familiarize with the instrument.
6 Check that the peak needle is set to zero. If it is not, rotate counter-clockwise the small caster in the middle of the gauge to move it to zero.
7 Start the test with the right hand. Invite the participant to squeeze the handle as strongly as possible. Use standard encouragement to highly motivate the participant during the test (e.g. «Squeeze, squeeze, harder!»). Ensure the participant maintains the maximum isometric effort for at least 3-5 seconds. When the needle stops rising, invite the participant to stop squeezing.
8 Read grip strength in kilograms from the outside dial and record the result to the nearest 1 kg. After each reading, reset the peak needle to zero. Repeat the measurement at the left hand. Obtain three readings in total for each hand, alternating the sides. Allow 10 seconds rest between each measurement.
Note: the peak-hold needle automatically records the maximum result. The gauge presents two dials, the inner one registers the value in lb, the outside in kg.
9 The highest reading of the 6 measurements is reported as the final result. Ask for and report about the hand dominance (i.e. right, left or ambidextrous).
10 If the participant complains about pain, discontinue the test and repeat the assessment only on the other side. If pain appears at both hands, stop the test.

Gait speed

To perform the test, the subject should wear comfortable clothes and shoes (with low heels for women). Only a single straight cane may be used during the walk. If the person can walk a short distance without it, should be encouraged to do so. If a person is unable or uses a walker, he/she should be considered as presenting mobility disability. As such, although the test can still be conducted with the use of the device, the meaning of the results for this specific geriatric outcome might be of limited value.
1 To perform the test, get a stopwatch and mark a 4-meter track along a flat floor. Ensure that the walking course is devoid of obstacles and include at least an extra meter at each end (Figure 1).
2 Encourage the person to walk without using any assisting devices. During the test watch particularly closely participants who normally use them, to prevent falls.
3 Instruct the participant to stand with both feet touching the starting line, and to start walking at usual pace over the 4-meter course, after a verbal command (“Go”). The assessor will then show how to perform the test to the participant, again stressing the request of walking at usual pace without running.
Note: The individual should not be aware where the goal line is placed in order to avoid a possible reduction of the pace when approaching to it. However, the tape on the floor might provide an implicit goal to the participant. For this reason, the participant might be instructed at walking well past the line on the floor.
4 During the walk stay to the side and slightly behind the subject, outside of the participant’s visual field, in order not to set the pace, but remaining in a good position for the safety of the person.
5 Begin timing when the first foot starts to move across the starting line, and stop when the first foot crosses the 4-meter mark. Do not start the watch when saying the verbal command, but when the participant actually begins to move. Do not stop timing if the foot lands on the line but does not completely cross it.
6 Report the time of execution of the test and calculate the gait speed. Repeat the test a second time and use the fastest time as result.
Note: If there is a problem with the stopwatch or the examiner is not sure of the timing, the test should be repeated.

Figure 1
4-meter walking test. The walking course should be unobstructed. Timing begins when the first foot starts to move across the starting line, and should be stopped when the first foot crosses the 4-meter mark


400-meter walking test

To perform the test, the subject should wear comfortable clothes and shoes (with low heels for women). During testing, the use of walk assistive devices, other than a single straight cane, is not allowed. If the subject does not feel safe attempting the walking course without aids (i.e. walker, quad cane, crutch), do not administer the test.
The assessor must be completely familiar with the test procedures and practice before attempting to administer the test to a participant. Procedures should be clearly demonstrated to the participants before performing the test and they should be queried to ensure that they understand the instructions. To ensure reproducibility, it is imperative that all participants are given the same instructions and that quantitative measurements associated with the tests are made in a uniform manner.
1 Identify a 20-meter long track by marking it with small traffic cones. Make sure that the walking path is not obstructed and include at least an extra meter at each end. Get a stopwatch and position two standard chairs along the walking course in order to allow the subject to rest during the test, if necessary (Figure 2).
2 Conduct the subject to the starting line and instruct to stand in a still position behind the line. It is important to clarify the goal of the test to the participant, i.e. to complete the 400-meter course.
3 Before starting the test measure the radial pulse for 30 seconds and blood pressure.
4 Instruct the subject to walk at usual pace, without overexerting, back-and-forth the 20-meter track for ten times, in order to allow the participant to plan the activity and consequently organize the walking pace according to his/her own reserves.
5 When participant indicates to feel ready to begin, proceed with the test. Instruct the individual to start to walk down the corridor at the command “Go”, and turn around the traffic cones, generating a continuous loop. Start timing when the participant takes the first step.
6 Stay by the side and just behind the participant, outside the subject’s visual field, during the walk. Be close enough to be able to support the participant if manifests difficulty or risks to fall, but not so close to dictate the pace.
7 When the 4rth lap is completed, ask the participant to report the perceived exert, and record the corresponding score of the Borg index for dyspnea.
Note: The test should be conducted at usual pace and the final goal is to complete the 400-meter course and not to reach the maximal effort. The participant should not overexert, therefore, if the participant reports “hard” or “very hard” should be invited to reduce the effort. The measurement of vital signs (radial pulse and blood pressure) before starting and at the end of the test, as well as the administration of the Borg scale after 4 laps and at the end of the test provide additional information useful to guarantee the participant’s safety and provide insights about the undergoing aerobic stress.
8 At the end of each lap (20-meter back and forth), encourage the subject with standardized phrases and count the number of completed and remaining laps.
9 Provide the participant the cane if he/she asks for it during the test, or has the evident necessity to use it to complete the walk.
10 Allow the participant to stop the walk to rest at any time, but not to lean against the wall, other surface (desk, counter, etc.), or sit. After 30 seconds, ask the participant if he/she can continue walking. If it is possible, continue the test, otherwise another 30 seconds of rest, in standing position, are allowed. If the subject is unable to continue after a 60-second rest or needs to sit down, stop the test.
Note: There is no limit to the number of rest stops as long as they can complete the walk without sitting.
11 Stop the stop-watch when the participant completes 400 meters (10 laps, first foot touching the floor beyond the finish line) or after 15 minutes, even if the participant has not covered all the distance. Record the time or, in the second case, measure the accomplished distance.
12 Immediately stop the test if participant reports chest pain or tightness, dyspnea, feeling faint, dizzy, or lower limbs pain.
13 At the end of the test, record the Borg index score, the sitting radial pulse for 30 seconds and blood pressure. Record the number, timing, and reasons for the rest stops (fatigue, chest pain, feeling faint or dizzy, shortness of breath, or other).

Figure 2
400-meter walking test. A 20-meter long track should be identified by marking it with small traffic cones. The participant has to walk at usual pace back-and-forth the 20-meter track for ten times, turning around the traffic cones in a continuous loop. The two chairs should be positioned by the side of the walking course, in order to not obstruct the track, but close enough to be rapidly reached if the subject needs to rest and sit during the test


Importance and predictive value of the presented tests

Low grip strength was found to be associated with slow gait speed, incident dismobility, disability, functional dependence, cognitive impairment, depression, cardiovascular diseases, hospital admission, and mortality (all-cause mortality, cardiovascular and non-cardiovascular mortality), in both sexes and independently of age and comorbidities (22–25). This relationship is confirmed across different populations and times of follow-up. Rantanen and colleagues studied the relationship between the handgrip strength with incident mobility and functional limitations in a large population (8,006 men from the Honolulu Heart Program and the Honolulu Asia Aging Study) aged at baseline 45-68 years old, with a 25 years follow-up (26). They found a strong association between the muscle strength in midlife and the risk of becoming disabled over the long-term follow-up. The strongest participants (i.e., >42.0 kg) had a significantly better risk profile when compared with those with poorest results at the handgrip, even after adjustment for a number of confounding conditions (Table 1). These results may be explained by greater physiological reserves in these subjects. Dodds and colleagues recently pooled data of grip strength from 8 different studies conducted in Great Britain on the general population. A total of 49,964 persons were considered to produce life-course nomograms of handgrip strength. The generated curves described a three period-evolution, with an increase to peak in early adulthood (i.e., 51 kg between 29-39 years old for men, and 31 kg between 26-42 years old for women), broad maintenance through to midlife, and a declining phase at older age (27). Different cut points were identified in the literature for poor handgrip strength, ranging from 16 to 21 kg for women and 26 to 30 kg for men, defining the risk of adverse events. Values adjusted for BMI or height also exists (23). The EWGSOP proposed values for poor grip strength <27 kg for men and <16 kg for women (7). Data on sensitivity to change in grip strength are still limited and inconsistent, a change of 6 kg was proposed to be significant. Some studies considered also the effect size (difference between the mean/median values of grip strength at baseline and after an intervention, divided by the standard deviation/inter-quartile range of the baseline measurement), and a value of 0.2–0.5 has been considered as indicative of low responsiveness, 0.51–0.8 of moderate responsiveness, and >0.8 of high responsiveness (17).

Table 1
Relationship between the handgrip strength with incident mobility and functional limitations. Adapted from Rantanen
et al. JAMA 1999

Results from multiple logistic regressions testing the predictive capacity of midlife grip strength for functional limitation and disability at advanced age (n=3,218); highest tertile used as reference group. Adjusted for age, weight, height, education, occupation, smoking, physical activity, and chronic conditions (i.e., arthritis, chronic obstructive pulmonary disease, coronary heart disease, stroke, diabetes, and angina).


Gait speed is a strong predictor of negative health outcomes, independently of the presence of common medical conditions and disease risk factors. Many studies demonstrated a strong association with incident disability (intended both as loss of ADL independency and dismobility), cognitive decline and dementia, falls and related fractures, mortality, and healthcare utilization (e.g., hospitalization and institutionalization) (11, 18, 28). Although tested in very different populations (e.g., inpatients and outpatients, independent, frail, and disabled subjects), different walking distances, and studied outcomes, the prognostic value is very consistent (Table 2). Studenski and colleagues studied the relationship between gait speed and mortality in a pooled population of 34,485 community-dwelling older people derived from 9 studies (Cardiovascular Health Study, Health, Established Populations for the Epidemiological Study of the Elderly, Aging and Body Composition study, Hispanic Established Populations for Epidemiological Study of the Elderly, InCHIANTI Study, Osteoporotic Fractures in Men, Third National Health and Nutrition Examination Survey, Predicting Elderly Performance, Study of Osteoporotic Fractures). The Authors found an overall HR for survival per each 0.1 m/s faster gait speed of 0.88 (95% CI, 0.87-0.90; P <0.001) confirmed after further adjustment for sex, BMI, smoking status, systolic blood pressure, diseases, prior hospitalization, and self-reported health (overall HR 0.90; 95% CI, 0.89-0.91; P <0.001). They also estimated the median life expectancy based on sex, age, and gait speed, providing a sort of nomograms (29). The value of 0.8 m/s for a 4-meter distance was found to identify frail patients with a high sensitivity (0.99), moderate specificity (0.64), and a high negative predictive value (0.99), and has been chosen by the EWGSOP2 as cut-off to diagnose severe sarcopenia (7, 30). At the same time, in their systematic review of the literature, Abellan van Kan and colleagues identified multiple cut-points of gait speed related to adverse outcomes, categorizing older people as slow (<0.6 m/s), intermediate (0.6-1.0 m/s), and fast (>1.0 m/s) walkers, demonstrating a continuum gradient of risk ranging from very fit to mobility impaired subjects (11). Furthermore, gait speed at usual pace in a 4-meter walk demonstrated also to be sensible to changes, with 0.05 m/s defining a minimally significant change and 0.1 m/s indicating a substantial change, with a corresponding reduction of 17.7% in absolute risk of death when increases of this value (31).

Table 2
Gait speed and ADL or mobility disability. Adapted from G. Abellan Van Kan et al.
The Journal of Nutrition, Health & Aging, 2009

* These studies analyzed the risk of disability considering gait speed variation rather than a specific cutpoint, as mentioned in the last column of the table; Health ABC study: Health Aging and Body Composition study, CHS: Cardiovascular Health Study, WHAS-I: Women’s Health and Aging Study, Hispanic EPESE: Hispanic Established Population for the Epidemiological Study of the Elderly, RR: relative risk, HR: hazard ratio, OR: odds ratio, ADL: activity of daily living.


The ability to perform the 400-meter walking test in less than 15 minutes defines the presence of mobility disability. This distance, corresponding to the length of about two blocks in the United States, is considered the minimum walking distance needed to have an independent life. The limit of 15 minutes, corresponds to a gait speed of 0.4 m/s, proven to be incompatible with functional autonomy. This instrument has a strong predictive capacity for development of negative health events (disability, mortality). Although this test is mostly used as a dichotomous indicator (presence/absence of mobility disability), its predictive capacity has been established also in relation to some of the parameters characterizing specific aspects of the performance (e.g., mean gait speed, number of stops to rest). Vestergaard and colleagues studied the differences in mortality and functional impairment rates during a 3- and 6-year follow-up period in the InCHIANTI Study population, analyzing the walking time and the variability in lap time. They found these factors to be both a short- and long-term predictors of mortality, and rest stopping mostly a long-term predictor of mortality (Table 3) (32). In a second study, based on the LIFE-P study, they found that the risk of mobility disability at follow-up was higher in those taking longer to complete the baseline 400-MWT and among those who needed to rest during the test (risk adjusted for age, sex, and clinic site: OR 5.4; CI 2.7–10.9) (33).

Table 3
Risk of death according to 400-meter walk test characteristics. Adapted from Vestergaard et al. Rejuvenation research, 2009

The model is adjusted for age, sex, Mini Mental State Examination score, symptoms of depression, education, smoking, body mass index, being sedentary, number of comorbid conditions (max 10, hypertension, coronary heart disease, congestive heart failure, stroke, peripheral artery disease, diabetes, pulmonary disease, hip fracture, cancer, arthritis), and SPPB score; HR: Hazard Ratio, CI: Confidence interval



The protocols presented in this paper are an attempt to standardize the methods of administration of these measures, in order to provide comparable results. Although there is not a unique way to conduct the here described assessments, given the different clinical settings and research protocols in which they can be applied, some steps are recognized as critical and able to affect results.
The absolute values and precision of grip strength measurements can be influenced by aspects of the protocol, such as hand size and dominance, posture (of the whole body and position of joints of the upper limb), provided encouragement, and the use of the maximum or the mean grip strength values (17). The observance of definite instructions in these steps, as already highlighted in the review of Roberts and colleagues, is crucial to ensure homogeneous measures and the training of the examiner assume a special importance to guarantee the reliability of the test (17). Taken with a handheld hydraulic dynamometer, the handgrip strength test demonstrated to have a good test–retest reliability (Intraclass Correlation Coefficient, ICC ≥ 0.85) and an excellent inter-rater reliability (ICC 0.95–0.98) (8, 17). The use of a handheld hydraulic dynamometer (units in Kg) is, therefore, considered the gold standard, but, for patients with upper extremity impairment or musculoskeletal deformations or diseases (as rheumatoid arthritis, osteoarthritis, or carpal tunnel syndrome), may not guarantee an accurate measure of muscle strength and may lead to underestimations, because it can cause stress on weak joints. Other available instruments are pneumatic, which measure grip pressure, mechanical, and strain dynamometer. The dynamometer should be calibrated at least once per year.
Elements of variability in the execution of gait speed test are walk distances (4, 6, or 10 meters, 8 or 15 foots), a static or dynamic start for walking, the usual or maximal gait speed, and the use of walking aids. A distance of 4 meters has been demonstrated to be feasible in different clinical settings, with a better accuracy compared to shorter walks. Moreover, the same distance is one of the components of the SPPB, allowing to deduce comparable measures from the whole battery (11). However, the test is characterized by a ceiling effect in high functioning persons with a high baseline walking speed (8). For these reasons, longer versions of the gait speed test (e.g., using 6- or 10-meter tracks) have been developed and validated in the literature for allowing a better discrimination of results in very fit individuals. Given the growing use of photocell-based systems of measurement, the method here proposed give the chance to have comparable results. Timing can also be measured differently from how we presented in the protocol, as starting and stopping the watch when the foot lands beyond the starting and finish lines. Moreover, some studies report measures of gait speed in full movement, starting the time measurement after the first two meters of walking. However, including the phase of acceleration provides information regarding subject’s abilities of coordination and movement planning, that are influenced by conditions frequently affecting older persons (i.e. Parkinson disease and other movement disorders). In the systematic review of the literature conducted by Peels and colleagues, the use of a moving start showed no significant difference in gait speed compared to a static start. They also found in a single study that subjects using a walking aid (cane) have a slower gait speed compared to those without (34).
To ensure the reproducibility of the 400-meter walking test, the training of the examiner is critical, in order to provide the same instructions and encouragement to participants, and to avoid to affect the results of the test dictating the pace during the walk. To ensure the correct execution the assessment, is also important to respect the provided timing for the stop rest and for the whole test. Moreover, the possibility to use walking aids and to warm up can influence the performance. The 400-meter walking test also demonstrated a high test-retest reliability, but it is mostly applied in research setting, requiring a higher administration time and a bigger space to be performed. On the other hand, being a dichotomous measure able to identify mobility disability, it provides an important and easy-to-understand indication of fitness of the subject, useful to address treatments or other tailored interventions (35).
The hand-grip strength test has been found to correlate with strength of other muscle groups, thus a good indicator of overall strength. The sensitivity to changes of the gait speed, together with an excellent test-retest and inter-rater reliability (ICC, 0.96-0.98) make gait speed a good marker for efficacy of intervention programs and treatments. Test-retest reliability of gait speed has been confirmed across different populations, from healthy older adults, people with comorbidities, to patients affected by stroke, cardiovascular disease, COPD. Compared to other tests (SPPB, chair stand test), gait speed has a stronger or similar predictive capacity for adverse events (ADL and mobility disability, hospitalization, health decline). Composite measures, as the SPPB, may have a better prognostic value, especially for high performance subjects (10, 11). Moreover, a walking speed less than 0.5 m/s is highly predictive of inability to perform the 400-meter walking test. Being very easy to perform, even in restricted places, and with minimal risk, it may be used as an alternative indicator of mobility disability when the performance of the 400-meters walking test is not possible (35).
In conclusion, the strong predictive capacity for adverse outcomes of muscle strength and physical performance measures as well as the reliability and the high feasibility of these tools make them suitable for supporting clinical and research decisions. In particular, the assessment of these measures may support the development of person-tailored interventions aimed at preventing/managing age-related conditions, as frailty and sarcopenia. These measures can both identify subjects at risk (who may benefit from tailored interventions), especially in primary care, but also serve as markers for monitoring the efficacy of the decisions. The dissemination of their use in clinical and research setting with a standard procedure may permit an early application and monitoring of critical aspects of the wellbeing of older persons.


Funding: None.
Conflcit of interest: The authors have no conflicts of interest to disclose.



1. Katz S, Ford A, Moskowitz R, Jackson B, Jaffe M. Studies of Illness in the Aged: The Index of ADL: A Standardized Measure of Biological and Psychosocial Function. JAMA. 1963;185(12):914–919. doi:10.1001/jama.1963.03060120024016
2. Lawton MP, Brody EM. Assessment of Older People: Self-Mantaining and Instrumental Activities of Daily Living. Gerontologist. 1969;9:179-186.
3. Ellis G, Gardner M, Tsiachrista A, et al. Comprehensive geriatric assessment for older adults admitted to hospital. Cochrane Database Syst Rev. 2017;(9). doi:10.1002/14651858.CD006211.pub3.www.cochranelibrary.com
4. Lee H, Lee E, Jang IY. Frailty and comprehensive geriatric assessment. J Korean Med Sci. 2020;35(3):1-13. doi:10.3346/jkms.2020.35.e16
5. Morley J, Vellas B, Abellan van Kan G, et al. Frailty Consensus: A Call to Action. J Am Med Dir Assoc. 2014;14(6):392-397. doi:10.1016/j.jamda.2013.03.022.Frailty
6. Fried LP, Tangen CM, Walston J, et al. Frailty in Older Adults: Evidence for a Phenotype Linda. J Gerontol. 2001;56(3):M146-M157. doi:10.1093/gerona/56.3.M146
7. Cruz-Jentoft AJ, Bahat G, Bauer J, et al. Sarcopenia: Revised European consensus on definition and diagnosis. Age Ageing. 2019;48(1):16-31. doi:10.1093/ageing/afy169
8. Beaudart C, Rolland Y, Cruz-Jentoft AJ, et al. Assessment of Muscle Function and Physical Performance in Daily Clinical Practice: A position paper endorsed by the European Society for Clinical and Economic Aspects of Osteoporosis, Osteoarthritis and Musculoskeletal Diseases (ESCEO). Calcif Tissue Int. 2019;105(1):1-14. doi:10.1007/s00223-019-00545-w
9. Studenski S, Perera S, Wallace D, et al. Physical performance measures in the clinical setting. J Am Geriatr Soc. 2003;51(3):314-322. http://ovidsp.ovid.com/ovidweb.cgi?T=JS&PAGE=reference&D=emed6&NEWS=N&AN=2003125471.
10. 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(2):251-259. doi:10.1111/j.1532-5415.2008.02126.x
11. 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 Heal Aging. 2009;13(10):881-889. doi:10.1007/s12603-009-0246-z
12. Cesari M, Penninx BWJH, Pahor M, et al. Inflammatory Markers and Physical Performance in Older Persons : The InCHIANTI Study. J Gerontol Med Sci. 2004;59(3):242-248.
13. Lee L, Fm M, Coe C, et al. Screening for frailty in primary care. Can Fam Physician. 2017;27(1):e51-e57.
14. Mohile SG, Dale W, Somerfield MR, et al. Practical assessment and management of vulnerabilities in older patients receiving chemotherapy: Asco guideline for geriatric oncology. J Clin Oncol. 2018;36(22):2326-2347. doi:10.1200/JCO.2018.78.8687
15. Lilamand M, Dumonteil N, Nourhashémi F, et al. Gait speed and comprehensive geriatric assessment: Two keys to improve the management of older persons with aortic stenosis. Int J Cardiol. 2014;173(3):580-582. doi:10.1016/j.ijcard.2014.03.112
16. Prestmo A, Hagen G, Sletvold O, et al. Comprehensive geriatric care for patients with hip fractures: A prospective, randomised, controlled trial. Lancet. 2015;385(9978):1623-1633. doi:10.1016/S0140-6736(14)62409-0
17. Roberts HC, Denison HJ, Martin HJ, et al. A review of the measurement of grip strength in clinical and epidemiological studies: Towards a standardised approach. Age Ageing. 2011;40(4):423-429. doi:10.1093/ageing/afr051
18. Cesari M, Kritchevsky SB, Penninx BWHJ, et al. Prognostic value of usual gait speed in well-functioning older people – Results from the health, aging and body composition study. J Am Geriatr Soc. 2005;53(10):1675-1680. doi:10.1111/j.1532-5415.2005.53501.x
19. Simonsick EM, Montgomery PS, Newman AB, Bauer DC, Harris T. Measuring fitness in healthy older adults: The health ABC long distance corridor walk. J Am Geriatr Soc. 2001;49(11):1544-1548. doi:10.1046/j.1532-5415.2001.4911247.x
20. Guralnik JM, Simonsick EM, Ferrucci L, et al. A Short Physical Performance Battery assessing lower extremity function. J Gerontol. 1994;49(2):M85-M94.
21. Podsiadlo, D; Richardson S. The timed ”up & go”: A test of basic functional mobility for frail elderly persons. J Am Geriatr Soc. 1991;39(2):142-148.
22. Stessman J, Rottenberg Y, Fischer M, Hammerman-rozenberg A, Jacobs JM. Handgrip Strength in Old and Very Old Adults: Mood, Cognition, Function, and Mortality. J Am Geriatr Soc. 2017;(65):526-532. doi:10.1111/jgs.14509
23. Alley DE, Shardell MD, Peters KW, et al. Grip Strength Cutpoints for the Identification of Clinically Relevant Weakness. Journals Gerontol Med Sci. 2014;69(5):559-566. doi:10.1093/gerona/glu011
24. Leong DP, Teo KK, Rangarajan S, et al. Prognostic value of grip strength: Findings from the Prospective Urban Rural Epidemiology (PURE) study. Lancet. 2015;386(9990):266-273. doi:10.1016/S0140-6736(14)62000-6
25. Giampaoli S, Ferrucci L, Cecchi F, et al. Hand-grip strength predicts incident disability in non-disabled older men. Age Ageing. 1999;28(1):283-288.
26. Rantanen T, Guralnik JM, Foley D, et al. Midlife Hand Grip Strength as a Predictor of Old Age Disability. JAMA. 1999;281(6):558-560.
27. Dodds RM, Syddall HE, Cooper R, et al. Grip Strength across the Life Course : Normative Data from Twelve British Studies. PLoS One. 2014:1-15. doi:10.1371/journal.pone.0113637
28. Woo J, Suzanne CHO, Yu ALM. Dependency, Mortality, and Institutionalization in Chinese Aged 70 And Older. J Am Geriatr Soc. 1999;47:1257-1260.
29. Studenski S, Perera S, Patel K, et al. Gait Speed and Survival in Older Adults. JAMA. 2011;305(1):50-58.
30. Clegg A, Rogers L, Young J. Diagnostic test accuracy of simple instruments for identifying frailty in community-dwelling older people: a systematic review. Age Ageing. 2015;44(1):148-152. doi:10.1093/ageing/afu157
31. Perera S, Mody SH, Woodman RC, Studenski SA. Meaningful change and responsiveness in common physical performance measures in older adults. J Am Geriatr Soc. 2006;54(5):743-749. doi:10.1111/j.1532-5415.2006.00701.x
32. Vestergaard S, Patel K V., Bandinelli S, Ferrucci L, Guralnik JM. Characteristics of 400-meter walk test performance and subsequent mortality in older adults. Rejuvenation Res. 2009;12(3):177-184. doi:10.1089/rej.2009.0853
33. Vestergaard S, Patel K V, Walkup MP, et al. Stopping to Rest during a 400-meter Walk and Incident Mobility Disability in Older Persons with Functional Limitations. J Am Geriatr Soc. 2010;57(2):260-265. doi:10.1111/j.1532-5415.2008.02097.x.Stopping
34. Peel NM, Kuys SS, Klein K. Gait speed as a measure in geriatric assessment in clinical settings: A systematic review. Journals Gerontol – Ser A Biol Sci Med Sci. 2013;68(1):39-46. doi:10.1093/gerona/gls174
35. Rolland YM, Cesari M, Miller ME, Penninx BW, Atkinson HH, Pahor M. Reliability of the 400-M usual-pace walk test as as assessment of mobility limitation in older adults. J Am Geriatr Soc. 2004;52(6):972-976. doi:10.1111/j.1532-5415.2004.52267.x



E. González-Bautista1, P. de Souto Barreto1,2, K. Virecoulon Giudici1, S. Andrieu1,2, Y. Rolland1,2, B. Vellas1,2, for the MAPT/DSA group*


1. Gerontopole of Toulouse, Institute of Aging, Toulouse University Hospital (CHU Toulouse), Toulouse, France; 2. UPS/Inserm UMR1027, University of Toulouse III, Toulouse, France; *The members are listed at the end of the manuscript.
Corresponding author: Emmanuel González-Bautista. Gerontopole of Toulouse, Institute of Ageing, Toulouse University Hospital (CHU Toulouse), 37 Allée Jules Guesde, 31000 Toulouse, France. Mobile 06 22 10 14 96 emmanuel.scout@gmail.com

J Frailty Aging 2020;in press
Published online August 7, 2020, http://dx.doi.org/10.14283/jfa.2020.42



Background: The screening tool of the Integrated Care for Older People (ICOPE Step 1), designed to detect declines in the domains of intrinsic capacity, has been incipiently investigated in older adult populations. Objectives: To retrospectively estimate the frequency of priority conditions associated with declines in intrinsic capacity according to an adaptation of the screening tool ICOPE Step 1 among participants of the Multidomain Alzheimer Preventive Trial (MAPT). Design: A cross-sectional retrospective analysis from the baseline assessment of the MAPT. Setting: The data was gathered during a preventive consultation for cardiovascular risk factors in memory clinics in France. Participants : Seven hundred fifty-nine older adults aged 70-89 years with memory complaints, allocated to the multidomain groups of the MAPT study. Measurements: Five domains of intrinsic capacity (cognition, locomotion, nutrition, sensorial, and psychological) were assessed using a screening tool similar to the ICOPE Step 1 (MAPT Step 1). The frequency of six conditions associated with declines in intrinsic capacity (cognitive decline, limited mobility, malnutrition, visual impairment, hearing loss, and depressive symptoms) was obtained for older adults with memory complaints participating in the MAPT study. Results: Overall, 89.3% of the participants had one or more conditions associated with declines in intrinsic capacity. The overall frequency of each condition was: 52.2% for cognitive decline, 20.2% for limited mobility, 6.6% for malnutrition, 18.1% for visual impairment, 56.2% for hearing loss, and 39% for depressive symptoms. Conclusion: After being screened with an adaptation of the ICOPE step 1 (MAPT step 1) tool, 9/10 older adults had one or more conditions associated with declines in intrinsic capacity. The relative frequency differs across conditions and could probably be lower in a population without memory complaints. The frequency of screened conditions associated with declines in IC highlights how relevant it is to develop function-centered care modalities to promote healthy aging.

Key words: Intrinsic capacity, screening, physical functions, integrated care, older adults.

Abbreviations: CCHA: Clinical Consortium for Healthy Ageing; COPD: Chronic obstructive pulmonary disease; FA: Functional ability; IC: Intrinsic capacity; ICOPE: Integrated Care for Older People; MAPT: Multidomain Alzheimer Preventive Trial; NCD: Non-communicable disease; OA: Older adults; WHO: World Health Organization.



Screening for early declines of intrinsic capacity (IC) is crucial for the implementation of the Integrated Care for Older People (ICOPE)(1). The World Health Organization (WHO) Clinical Consortium for Healthy Ageing (CCHA) and other public health and aging experts developed the ICOPE guidelines. The objective of the ICOPE guidelines is to help key stakeholders in the health and social care arenas to design and implement integrated person-centered models of care (1–3).
The ICOPE approach might prevent care-dependency by timely detecting and managing conditions associated with declines in intrinsic capacity (IC). IC is the composite of all physical and mental capacities of an individual, organized in five domains: cognition, mobility, nutrition, sensorial, and psychological. The interaction between the IC and the environment determines functional ability and healthy aging (4,5). The WHO ICOPE approach has, thus, the goal of helping health systems support healthy aging (4) through the maintenance of optimal functional ability levels during aging.
The clinical care pathways proposed by the ICOPE (1) start with a screening process (ICOPE Step 1). The purpose of the screening is to detect conditions associated with declines in IC at the community level, namely cognitive decline, limited mobility, malnutrition, visual impairment, hearing loss, and depressive symptoms. People identified as having normal IC levels will receive general health advice (i.e., physical activity, nutrition). In contrast, those with IC declines will receive an in-depth assessment (ICOPE Step 2) to confirm or rule such declines. Afterward, they will follow the next steps in the care pathway (i.e., searching the causes of low IC levels, designing a person-centered care plan).
For health service providers, ICOPE Step 1 has a strategic role because it opens the door for the subsequent steps in the healthcare pathway. By managing the adequate «filter» to identify the individuals who can benefit the most from health and social interventions, the healthcare system could enhance the effective use of the available resources.
We have recently reviewed the literature on the topic (in press) and observed only ten original studies on the topic. None of those studies investigated the frequency of low levels of IC according to the screening tool. To our knowledge, measurements of conditions associated with declines in intrinsic capacity, according to ICOPE Step 1 have not been reported so far. Therefore, this study aimed to retrospectively estimate the frequency of conditions associated with declines in intrinsic capacity according to an adaptation of the screening tool ICOPE Step 1 among participants of the Multidomain Alzheimer Preventive Trial (MAPT).



This study uses cross-sectional data to describe the baseline frequency of conditions associated with IC declines among the participants of the Multidomain Alzheimer Preventive Trial (MAPT). The MAPT was not designed to assess the ICOPE screening; thus, we used a retrospective approach to define the variables of interest according to the availability of data. The detailed methodology of MAPT has been described elsewhere(6, 7). Briefly, MAPT was a 3-year randomized controlled trial on the effect of a multidomain intervention (nutritional and physical activity counseling, cognitive training, and annual preventive consultations for the management of cardiovascular risk factors and the detection of functional impairments) with and without supplementation of omega-3 polyunsaturated fatty acids (PUFA) on the prevention of cognitive decline among community-dwelling adults aged 70 years and older. The trial protocol (ClinicalTrials.gov identifier: NCT00672685) was approved by the French Ethical Committee located in Toulouse (CPP SOOM II) and was authorized by the French Health Authority. All participants signed their consent before any study assessment.


Inclusion criteria for the MAPT study were meeting at least one of three conditions: a) spontaneous memory complaint expressed to their physician, b) limitation in one instrumental activity of daily living (IADL), or c) slow gait speed (≤0.8 m/s). Exclusion criteria comprised participants with a Mini-Mental State Examination (MMSE) score < 24, diagnosis of dementia, the limitation for any of the basic activities of daily living, and those taking PUFA supplements at baseline.
The 759 subjects allocated to the multidomain intervention groups of MAPT constitute our study sample. Data on the five domains of IC was available only for them because they underwent a preventive consultation with a physician who assessed for the hearing and vision capacities. The rest of the 1,679 participants initially enrolled in MAPT were lacking data on the sensorial domain.

IC domains assessment – Step 1 (screening)

We followed the recommendations from the WHO ICOPE Handbook to operationalize the ICOPE Step 1 tool(1). To be consistent with the terms used in the Handbook, we used «cognitive decline,» «limited mobility,» «malnutrition,» «visual impairment,» «hearing loss» and «depressive symptoms» to refer to the conditions associated with declines in IC. These terms are not equivalent to clinical diagnoses.
The same items recommended by the WHO were used to evaluate three domains: cognition, locomotion, and vitality/nutrition. Nevertheless, due to data availability, we adapted the operationalization of the following conditions associated with declines in IC: visual impairment: was assessed by self-reported visual acuity items; hearing loss: was measured with item number 3 of the screening version of the hearing handicap inventory for the elderly (HHSE-S(8,9)); depressive symptoms: were defined according to items 2 and 7 of the Geriatric Depression Scale (GDS-15) (10), which were judged by three experts (one geriatrician, one general practitioner, and one researcher in clinical gerontology) as being the most similar items compared to those recommended by WHO. This resulting adapted screening tool was then called «MAPT Step 1» (Table 1).
Specifically, for hearing loss, we used the validated HHIE-S(8) because studies have validated this instrument against pure tone audiometry. Sindhusake et al. (11) concluded that HHIE-S has adequate sensitivity and specificity for detecting moderate hearing loss (audiometry hearing threshold of >40 dB). The HHIE-S cut-off >8 points is established in the guidelines of the American Speech-Language-Hearing Association(12) as a criterion for a referral to further audiological testing.
More sophisticated measurements of IC have been published but are not feasible in a clinical routine setting(13,14). A screening procedure like ICOPE Step 1 may help to target an at-risk population that would, then, receive in-depth assessments and a closer follow-up.

Table 1
Comparison of the operationalization of the conditions associated with declines in IC between the ICOPE handbook and the definitions applied in MAPT study

*Participants were explicitly asked for each of the items in the time and spatial orientation, and not only an open-ended question. The participant was recorded as with cognitive decline if he/she was wrong to tell the date (number and name of the day, month, year), or wrong to tell the name of the hospital, the level of the building, department and region.
†We used item number 3 of the HHIE-S because of its similarity with the whisper test.


IC domains in-depth assessment

The following tests were performed for an in-depth assessment of the IC domains:
• Cognition: Mini-Mental State Examination (MMSE) (15).
• Locomotion: Short Performance Physical Battery (SPPB) (16)
• Vitality/nutrition: Mini Nutritional Assessment (full version MNA)(17).
• Vision: Monoyer vision chart (18).
• Hearing: Hearing Handicap Inventory for the Elderly – Screening version (HHIE-S) (8).
• Psychological: 15-item Geriatric Depression Scale (GDS-15) (10).

Statistical Analysis

We used percentages to report the frequency of the declines in IC in our study population and by age and sex subgroups. Scores of the in-depth assessments were described using means and standard deviation (SD). Data were analyzed using STATA 14®.



The mean age of our study population was 75.2 years (SD=4.3), 63.6% of them were women, and 28.9% reported 12 years or more of formal education (Table 2). Overall, 89.3% of the population presented one or more conditions associated with declines in IC (87.4% among females, 92.8% among males). Table 3 shows the frequency of conditions associated with declines in IC by domain. Relative frequency of the conditions of interest and mean scores of the tests used for in-depth assessment for sex and age-groups are reported in Supplementary Table S1.

Table 2
Sex, frailty status, number of instrumental activities impaired and mean values of functional performance tests by age group among participants of the MAPT Study

MMSE= mini-mental state examination. SPPB= short performance physical battery. MNA= mini nutritional assessment full version. HHIE-S= Hearing Handicap Inventory for the Elderly – Screening version. GDS= Geriatric Depression Scale; * provided in decimal acuity. For reference, 0.8 decimal = 20/25 imperial= 6/7.5 metric = 0.1 LogMAR.

Table 3
Frequency of conditions associated with declines in IC according to the ICOPE step 1 screening tool by age group among participants of the MAPT Study

*Maximum of possible conditions is six because the sensorial domain includes visual impairment and hearing loss


Cognitive domain

Half of the studied population presented signs of cognitive decline. Table 4 shows the details of the cognitive items of MAPT Step 1. Failing in the word recall section of the MMSE was three times more frequent than failing in the orientation section. In all age groups, the most frequently mistaken word recall was for the last word in the list given to the participants (i.e., cigarette, flower, door – participants less often recalled the word «door»).

Table 4
Prevalence of cognitive sub-domains and mistaken item from the Mini-Mental State Examination (MMSE) including alternative definitions for cognitive decline by age group among participants of the MAPT Study

* Areas of political division. †MAPT participants used the French version during the study.


Mobility domain

Overall, the mean of the time to perform five chair rises was 11.9 seconds (SD=4.5), with age-specific averages ranging from 11.0 (70-74 years) to 16.0 seconds (85-89 years). For the group aged 85-89 years (n=22), the cut-off of 14 seconds proposed by ICOPE Step 1 was at the percentile 52. Further details about the distribution of the sex- and age-specific chair rise times are provided in Supplementary Table S1.

Vitality/Nutrition domain

The mean score in the MNA was 27.6 points, ranging from 27.7 in the youngest group to 26.8 in the oldest group. The frequency of self-reported weight loss or appetite loss was lower than 5% for all the age groups, except for the appetite loss in those aged 80-84 years (7.3%).

Sensorial domain

Vision. In our study population, 92.1% of participants used glasses, contact glasses, implants, or magnifiers at the time of the interview. Even with their supportive devices, up to 14.5% of the participants found it hard to read a newspaper or watch television.
Hearing. Among the participants, 18.2% were using a hearing assistive device at the time of the interview. The screening question for hearing loss identified 55% of those aged 70-74 years and 68% of those aged 85-89 years as positive for hearing loss.



Our study is the first to describe the frequency of the IC declines according to an adaptation of the ICOPE Step 1 screening tool (MAPT step 1) in a selected cohort of memory clinic attendees. Overall, 89.3% of the participants had one or more conditions associated with declines in IC, according to MAPT Step 1 (87.4% among females, 92.8% among males). Nine in every ten screened older adults would be referred to a specialized, in-depth evaluation. It should be noted, however, that MAPT participants expressed memory complaints at recruitment.
We found a high demand for an in-depth assessment. Consider that the MAPT population is more fit than others reported in French studies (except perhaps for cognitive function due to the inclusion criteria). Compared to a random sample used in the French Three-City Study published by Avila-Funes et al. (19), our population was slightly older (mean age 74.1, SD=5.2 vs. 75.2 years, SD=4.3), reported higher levels of educational attainment (>12 years: 17.0% vs. 28.9%) and a lower frequency of frailty (7.0% vs. 3.2%). Therefore, our findings might be underestimating the frequency of IC declines detected by MAPT Step 1 in a real-world population of users of the healthcare system (except for cognitive decline). Our results highlight the need for adapting our health care system to improve the assessment of functions to prevent functional decline. For example, people detected with signs of cognitive decline using the screening tool ICOPE Step 1 could benefit from multidomain interventions (20–22).
Half of the study population showed signs of cognitive decline. Interestingly, participants more frequently failed in selected items (i.e., recalling the name of the day or the last word in a list of three). Therefore, it will be interesting to explore the domain and item’s capacity to predict health events such as frailty incidence in future studies.
Regarding locomotion, the cut-off time to perform five chair rises deserves further investigation (the ICOPE handbook suggests 14 seconds). A cut-off of 15 sec was used in a study measuring the time to complete ten rather than five chair-rises (23). A meta-analysis by Bohannon concluded that 11.4 and 12.7 seconds are suitable cut-off values among subjects aged 60-69 and 80-89, respectively (24). In the SPPB validation study, Guralnik et al. (16) found that 13.7 sec corresponded to the 50th percentile of performance in the chair rise test in a population of more than 5,000 American people aged 71 years or older in 1981. However, current generations might have a better physical performance than those assessed 30 years ago. Establishing age- tailored cut-offs for the chair rise test would allow for a better classification of the performance levels. They would not add difficulty to the implementation of the ICOPE care pathway.
Malnutrition was the least frequent condition. The average score for the full MNA was 27.6, which is higher than the cut-off often used to define the risk of malnutrition (23.5) (17). We think that clinicians should address anorexia and weight loss even if the MNA test if above the cut-off designed for malnutrition. According to the Global Leadership Initiative on Malnutrition (GLIM) criteria for malnutrition in adults (25), reduced food intake/assimilation and weight loss are sufficient to integrate the diagnosis of malnutrition. Also, the Beck depression inventory (26) and by the Center for Epidemiologic Studies Depression (CES-D) scale (27) consider the loss of appetite as a depressive symptom. Moreover, those who leave alone are at higher nutritional risk (28).
For the psychological domain, we did not use the exact questions suggested in the ICOPE screening tool Step 1 because they were not available in MAPT Study. Therefore, we selected from the GDS-15 the two items that more closely matched the ICOPE definition (Table 1). The frequency of depressive symptoms in our study was high (39%), compared to a previous investigation among French older adults (13.8% evaluated with the CES-D in people aged 75 years; SD=6.8) (29). Shared risk factors for cognitive decline and depressive symptoms can explain this difference. Due to MAPT inclusion criteria, participants were at increased risk for cognitive decline, and more than 40% presented mild cognitive impairment (MCI). For instance, using the MAPT Step 1 tool resulted in an overlap of 72% of the participants according to their cognitive decline and depressive symptoms status (7 out of 10 were simultaneously free from or afflicted by both conditions). The connexions across the IC domains are a hallmark of the ICOPE approach (30, 31). Timely interventions targeting these interactions can prevent further losses of ADL ADLs (32).
In the visual domain, we used three questions related to self-reported problems for vision, even when using correction devices (Table 1). Of note, the frequency of visual impairment in our study may have been underestimated because the questions used in MAPT are more specific than the general question proposed in ICOPE Step 1. Furthermore, we did not consider if the person had hypertension or diabetes, as suggested in the ICOPE tool. Evidence suggests that more than half of the cases of visual impairment in older ages are due to cataract and refractive errors, with less than 5% due to diabetic retinopathy (33–35). Perceived difficulty in reading a journal or watching television was reported in 15% of the population wearing vision aids. We consider this ratio as an indicator of unsatisfied demand for visual correction adjustment.
Our hearing loss estimates show that the age-specific frequency of hearing handicap was higher than the ones reported in the study of Wiley et al. performed with 3,471 non-Hispanic whites in Wisconsin, USA. Our figures were similar to those reported by Tomioka et al. (36), with 1,731 community-dwelling older adults from Nara and Osaka, Japan. The different age distribution could explain the dissimilarities.
Our study has strengths, such as being among the first to report the frequency of conditions associated with declines in the five IC domains in a selected cohort of memory clinic attendees. Moreover, the MAPT Step 1 and the ICOPE Step 1 use the same items for most of the IC domains. Therefore, the frequency of declines on visual, hearing, and psychological domains might have been different from our findings if the WHO ICOPE screening tool had been used. On the other hand, some limitations should be mentioned. There was a potential selection bias towards cognitive decline, given that having a spontaneous memory complaint was one of the inclusion criteria in MAPT. However, in a sensitivity analysis removing participants with a cognitive decline in the MAPT Step 1, 78% of the remaining population still had one or more conditions associated with declines in IC (Supplementary Table S2). Our data should not be generalized to other populations. Compared to the age and sex distribution of French older adults, our population overrepresented women (57.1% vs. 63.6%) and adults aged 70-79 years (39.1% vs. 49.8%) (37).
In summary, almost 90% of adults aged 70 years and older in a selected cohort of memory clinic attendees had at least one condition associated with declines in IC. Frequencies varied from 52.2% in the cognitive domain to 6.2% in the nutrition domain. These findings suggest that implementing the ICOPE Step 1 at the community level will help screen for conditions associated with declines in intrinsic capacity. The frequency of declines in IC can provide health systems managers with an estimation of the amount and the type of resources needed to implement the ICOPE clinical pathways. For example, satisfying the demand for visual aids adjustment, or recruitment of health workforce with psychological training. Interesting questions emerged from this descriptive study. For example, should some items be changed to increase the chances of detecting most of the people at risk? Also, are age-specific cut-offs the most suitable approach for some IC domains in the ICOPE screening tool (notably, locomotion)?


MAPT/DSA group: Principal investigator: Bruno Vellas (Toulouse); Coordination: Sophie Guyonnet; Project leader: Isabelle Carrié; CRA: Lauréane Brigitte; Investigators: Catherine Faisant, Françoise Lala, Julien Delrieu, Hélène Villars; Psychologists: Emeline Combrouze, Carole Badufle, Audrey Zueras; Methodology, statistical analysis and data management: Sandrine Andrieu, Christelle Cantet, Christophe Morin; Multidomain group: Gabor Abellan Van Kan, Charlotte Dupuy, Yves Rolland (physical and nutritional components), Céline Caillaud, Pierre-Jean Ousset (cognitive component), Françoise Lala (preventive consultation) (Toulouse). The cognitive component was designed in collaboration with Sherry Willis from the University of Seattle, and Sylvie Belleville, Brigitte Gilbert and Francine Fontaine from the University of Montreal. Co-Investigators in associated centres: Jean-François Dartigues, Isabelle Marcet, Fleur Delva, Alexandra Foubert, Sandrine Cerda (Bordeaux); Marie-Noëlle-Cuffi, Corinne Costes (Castres); Olivier Rouaud, Patrick Manckoundia, Valérie Quipourt, Sophie Marilier, Evelyne Franon (Dijon); Lawrence Bories, Marie-Laure Pader, Marie-France Basset, Bruno Lapoujade, Valérie Faure, Michael Li Yung Tong, Christine Malick-Loiseau, Evelyne Cazaban-Campistron (Foix); Françoise Desclaux, Colette Blatge (Lavaur); Thierry Dantoine, Cécile Laubarie-Mouret, Isabelle Saulnier, Jean-Pierre Clément, Marie-Agnès Picat, Laurence Bernard-Bourzeix, Stéphanie Willebois, Iléana Désormais, Noëlle Cardinaud (Limoges); Marc Bonnefoy, Pierre Livet, Pascale Rebaudet, Claire Gédéon, Catherine Burdet, Flavien Terracol (Lyon), Alain Pesce, Stéphanie Roth, Sylvie Chaillou, Sandrine Louchart (Monaco); Kristelle Sudres, Nicolas Lebrun, Nadège Barro-Belaygues (Montauban); Jacques Touchon, Karim Bennys, Audrey Gabelle, Aurélia Romano, Lynda Touati, Cécilia Marelli, Cécile Pays (Montpellier); Philippe Robert, Franck Le Duff, Claire Gervais, Sébastien Gonfrier (Nice); Yannick Gasnier and Serge Bordes, Danièle Begorre, Christian Carpuat, Khaled Khales, Jean-François Lefebvre, Samira Misbah El Idrissi, Pierre Skolil, Jean-Pierre Salles (Tarbes). MRI group: Carole Dufouil (Bordeaux), Stéphane Lehéricy, Marie Chupin, Jean-François Mangin, Ali Bouhayia (Paris); Michèle Allard (Bordeaux); Frédéric Ricolfi (Dijon); Dominique Dubois (Foix); Marie Paule Bonceour Martel (Limoges); François Cotton (Lyon); Alain Bonafé (Montpellier); Stéphane Chanalet (Nice); Françoise Hugon (Tarbes); Fabrice Bonneville, Christophe Cognard, François Chollet (Toulouse). PET scans group: Pierre Payoux, Thierry Voisin, Julien Delrieu, Sophie Peiffer, Anne Hitzel, (Toulouse); Michèle Allard (Bordeaux); Michel Zanca (Montpellier); Jacques Monteil (Limoges); Jacques Darcourt (Nice). Medico-economics group: Laurent Molinier, Hélène Derumeaux, Nadège Costa (Toulouse). Biological sample collection: Bertrand Perret, Claire Vinel, Sylvie Caspar-Bauguil (Toulouse). Safety management: Pascale Olivier-Abbal. DSA Group: Sandrine Andrieu, Christelle Cantet, Nicola Coley.
Funding: The present work was performed in the context of the Inspire Program, a research platform supported by grants from the Region Occitanie/Pyrénées-Méditerranée (Reference number: 1901175) and the European Regional Development Fund (ERDF) (Project number: MP0022856). The sponsors 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: NA
Authors contributions: PB, BV and EG conceived the study. EG, statistics and manuscript writing. KV, PB, LM, BV and SA provided inputs and reviewed the manuscript. BV and SA are PIs in MAPT.
Funding section: The present work was performed in the context of the Inspire Program, a research platform supported by grants from the Region Occitanie/Pyrénées-Méditerranée (Reference number: 1901175) and the European Regional Development Fund (ERDF) (Project number: MP0022856). The sponsors 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. “This study received funds from Alzheimer Prevention in Occitania and Catalonia (APOC Chair of Excellence – Inspire Program). The MAPT study was supported by grants from the Gérontopôle of Toulouse, the French Ministry of Health (PHRC 2008, 2009), Pierre Fabre Research Institute (manufacturer of the omega-3 supplement), ExonHit Therapeutics SA, and Avid Radiopharmaceuticals Inc. The promotion of this study was supported by the University Hospital Center of Toulouse. The data sharing activity was supported by the Association Monegasque pour la Recherche sur la maladie d’Alzheimer (AMPA) and the INSERM-University of Toulouse III UMR 1027 Unit».
Conflict of interest: The authors declare no competing interest relevant to this article.
Ethical standard: This study did not include any experiments involving humans or other animals.





1. World Health Organization. Integrated care for older people (ICOPE): Guidance for person-centred assessment and pathways in primary care. [Internet]. Geneva: WHO; 2019 [cited 2019 Nov 14]. 87 p. Available from: https://apps.who.int/iris/bitstream/handle/10665/326843/WHO-FWC-ALC-19.1-eng.pdf?sequence=17
2. Thiyagarajan JA, Araujo de Carvalho I, Peña-Rosas JP, Chadha S, Mariotti SP, Dua T, et al. Redesigning care for older people to preserve physical and mental capacity: WHO guidelines on community-level interventions in integrated care. PLOS Med [Internet]. 2019 Oct 18 [cited 2020 Jan 8];16(10):e1002948. Available from: http://dx.plos.org/10.1371/journal.pmed.1002948
3. World Health Organisation. WHO Clinical Consortium on Healthy Ageing. Report of consortium meeting 1-2 December 2016 in Geneva, Switzerland. 2017;(December).
4. World Health Organization. World Report on Ageing and Health. Geneva: WHO Press; 2015.
5. Cesari M, De Carvalho IA, Thiyagarajan JA, Cooper C, Martin FC, Reginster JY, et al. Evidence for the domains supporting the construct of intrinsic capacity. Vol. 73, Journals of Gerontology – Series A Biological Sciences and Medical Sciences. Oxford University Press; 2018. p. 1653–60.
6. Vellas B, Carrie I, Gillette-Guyonnet S, Touchon J, Dantoine T, Dartigues JF, et al. MAPT study: A multidomain approach for preventing Alzheimer’s disease: design and baseline data. J Prev Alzheimer’s Dis [Internet]. 2014 Jun [cited 2020 Feb 5];1(1):13–22. Available from: http://www.ncbi.nlm.nih.gov/pubmed/26594639
7. Andrieu S, Guyonnet S, Coley N, Cantet C, Bonnefoy M, Bordes S, et al. Effect of long-term omega 3 polyunsaturated fatty acid supplementation with or without multidomain intervention on cognitive function in elderly adults with memory complaints (MAPT): a randomised, placebo-controlled trial. Lancet Neurol. 2017 May 1;16(5):377–89.
8. Ventry IM, Weinstein BE. The hearing handicap inventory for the elderly: A new tool. Ear Hear. 1982;3(3):128–34.
9. Bagai A. Does This Patient Have Hearing Impairment ? JAMA. 2008;295(4):416–28.
10. Yesavage JA, Sheikh JI. Geriatric Depression Scale (GDS). Clin Gerontol [Internet]. 1986 Nov 18;5(1–2):165–73. Available from: https://doi.org/10.1300/J018v05n01_09
11. Sindhusake D, Mitchell P, Smith W, Golding M, Newall P, Hartley D, et al. Validation of self-reported hearing loss. The blue mountainshearing study. Int J Epidemiol. 2001;30(6):1371–8.
12. Association AS-L-H. Guidelines for Audiologic Screening. www.asha.org/policy [Internet]. 1997 [cited 2020 Feb 7];1–64. Available from: http://www.asha.org/policy/GL1997-00199/
13. Beard JR, Jotheeswaran AT, Cesari M, Araujo de Carvalho I. The structure and predictive value of intrinsic capacity in a longitudinal study of ageing. BMJ Open [Internet]. 2019 Nov 2 [cited 2019 Nov 7];9(11):e026119. Available from: http://www.ncbi.nlm.nih.gov/pubmed/31678933
14. Gutiérrez-Robledo LM, García-Chanes RE, Pérez-Zepeda MU. Allostatic Load as a Biological Substrate to Intrinsic Capacity: A Secondary Analysis of CRELES. J Nutr Health Aging [Internet]. 2019 [cited 2019 Nov 5];23(9):788–95. Available from: http://www.ncbi.nlm.nih.gov/pubmed/31641727
15. Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975;12(3):189–98.
16. Guralnik JM, Simonsick EM, Ferrucci L, Glynn RJ, Berkman LF, Blazer DG, et al. A short physical performance battery assessing lower extremity function: Association with self-reported disability and prediction of mortality and nursing home admission. Journals Gerontol. 1994;49(2).
17. Vellas B, Guigoz Y, Garry PJ, Nourhashemi F, Bennahum D, Lauque S, et al. The mini nutritional assessment (MNA) and its use in grading the nutritional state of elderly patients. Nutrition. 1999 Feb;15(2):116–22.
18. World Health Organization. World report on vision. Vol. 214, World report on vision. Geneva: WHO Press; 2019. 180–235 p.
19. Ávila-Funes JA, Helmer C, Amieva H, Barberger-Gateau P, Le Goff M, Ritchie K, et al. Frailty among community-dwelling elderly people in france: The three-city study. Journals Gerontol – Ser A Biol Sci Med Sci. 2008;63(10):1089–96.
20. Rosenberg A, Mangialasche F, Ngandu T, Solomon A, Kivipelto M. Multidomain Interventions to Prevent Cognitive Impairment, Alzheimer’s Disease, and Dementia: From FINGER to World-Wide FINGERS. J Prev Alzheimer’s Dis [Internet]. 2020 [cited 2020 Mar 23];7(1):29–36. Available from: http://www.ncbi.nlm.nih.gov/pubmed/32010923
21. Cherian L, Wang Y, Fakuda K, Leurgans S, Aggarwal N, Morris M. Mediterranean-Dash Intervention for Neurodegenerative Delay (MIND) Diet Slows Cognitive Decline After Stroke. J Prev Alzheimer’s Dis [Internet]. 2019 [cited 2020 Mar 23];6(4):267–73. Available from: http://www.ncbi.nlm.nih.gov/pubmed/31686099
22. Udeh-Momoh C, Price G, Ropacki MT, Ketter N, Andrews T, Arrighi HM, et al. Prospective Evaluation of Cognitive Health and Related Factors in Elderly at Risk for Developing Alzheimer’s Dementia: A Longitudinal Cohort Study. J Prev Alzheimer’s Dis [Internet]. 2019 [cited 2020 Mar 23];6(4):256–66. Available from: http://www.ncbi.nlm.nih.gov/pubmed/31686098
23. Hardy R, Cooper R, Shah I, Harridge S, Guralnik J, Kuh D. Is chair rise performance a useful measure of leg power? Aging Clin Exp Res. 2010;22(5–6):412–8.
24. BOHANNON RW. Reference Values for the Five-Repetition Sit-To-Stand Test: a Descriptive Meta-Analysis of Data From Elders. Percept Mot Skills. 2006;103(5):215.
25. Cederholm T, Jensen GL, Correia MITD, Gonzalez MC, Fukushima R, Higashiguchi T, et al. GLIM criteria for the diagnosis of malnutrition – A consensus report from the global clinical nutrition community. J Cachexia Sarcopenia Muscle [Internet]. 2019 Feb 1 [cited 2020 Feb 27];10(1):207–17. Available from: https://onlinelibrary.wiley.com/doi/abs/10.1002/jcsm.12383
26. Beck AT, Ward CH, Mendelson M, Mock J, Erbaugh J. An Inventory for Measuring Depression. Arch Gen Psychiatry. 1961;4(6):561–71.
27. Radloff LS. The CES-D scale: A self-report depression scale for research in the general population. Appl Psychol Meas. 1977;1(3):385–401.
28. Suthutvoravut U, Tanaka T, Takahashi K, Akishita M, Iijima K. Living with Family yet Eating Alone is Associated with Frailty in Community-Dwelling Older Adults: The Kashiwa Study. J frailty aging [Internet]. 2019 [cited 2020 Mar 23];8(4):198–204. Available from: http://www.ncbi.nlm.nih.gov/pubmed/31637406
29. Montagnier D, Barberger-Gateau P, Jacqmin-Gadda H, Dartigues JF, Rainfray M, Pérès K, et al. Evolution of prevalence of depressive symptoms and antidepressant use between 1988 and 1999 in a large sample of older French people: Results from the personnes agées quid study. J Am Geriatr Soc [Internet]. 2006 Dec [cited 2020 Feb 13];54(12):1839–45. Available from: http://www.ncbi.nlm.nih.gov/pubmed/17198488
30. Kiiti Borges M, Oiring de Castro Cezar N, Silva Santos Siqueira A, Yassuda M, Cesari M, Aprahamian I. The Relationship between Physical Frailty and Mild Cognitive Impairment in the Elderly: A Systematic Review. J frailty aging [Internet]. 2019 [cited 2020 Mar 23];8(4):192–7. Available from: http://www.ncbi.nlm.nih.gov/pubmed/31637405
31. Kwan RYC, Leung AYM, Yee A, Lau LT, Xu XY, Dai DLK. Cognitive Frailty and Its Association with Nutrition and Depression in Community-Dwelling Older People. J Nutr Health Aging [Internet]. 2019 Dec 1 [cited 2020 Mar 23];23(10):943–8. Available from: http://www.ncbi.nlm.nih.gov/pubmed/31781723
32. McGrath R, Erlandson KM, Vincent BM, Hackney KJ, Herrmann SD, Clark BC. Decreased Handgrip Strength is Associated With Impairments in Each Autonomous Living Task for Aging Adults in the United States. J frailty aging. 2019;8(3):141–5.
33. Zhu RR, Shi J, Yang M, Guan HJ. Prevalences and causes of vision impairment in elderly chinese: A socioeconomic perspective of a comparative report nested in Jiangsu Eye Study. Int J Ophthalmol. 2016 Jul 18;9(7):1051–6.
34. Murthy GVS, Vashist P, John N, Pokharel G, Ellwein LB. Prevalence and causes of visual impairment and blindness in older adults in an area of India with a high cataract surgical rate. Ophthalmic Epidemiol. 2010 Aug;17(4):185–95.
35. Bourne RRA, Jonas JB, Bron AM, Cicinelli MV, Das A, Flaxman SR, et al. Prevalence and causes of vision loss in high-income countries and in Eastern and Central Europe in 2015: Magnitude, temporal trends and projections. Br J Ophthalmol. 2018 May 1;102(5):575–85.
36. Tomioka K, Ikeda H, Hanaie K, Morikawa M, Iwamoto J, Okamoto N, et al. The Hearing Handicap Inventory for Elderly-Screening (HHIE-S) versus a single question: reliability, validity, and relations with quality of life measures in the elderly community, Japan. Qual Life Res [Internet]. 2013 Jun [cited 2020 Feb 12];22(5):1151–9. Available from: http://www.ncbi.nlm.nih.gov/pubmed/22833152
37. United Nations Department of Economic and Social Affairs. Population Division. Ageing Profiles [Internet]. Ageing Profiles. 2019 [cited 2020 Feb 13]. Available from: https://population.un.org/ProfilesOfAgeing2019/index.html



D. Angioni1, M. Hites2, F. Jacobs2, S. De Breucker1

1. Department of Geriatric Medicine, CUB-Erasme, Brussels, Belgium; 2. Infectious Diseases Clinic, CUB-Erasme, Brussels, Belgium.
Corresponding author: Davide Angioni, Hopital Erasme, Gériatrie, Belgium, davideangioni2@gmail.com

J Frailty Aging 2020;9(4)232-237
Published online January 6, 2020, http://dx.doi.org/10.14283/jfa.2019.45



Objectives: To assess the prevalence of intra-hospital mortality and associated risk factors in older people aged 75+, admitted with blood stream infections (BSI). Design: Single center retrospective study performed in an 850-bed of the academic hospital of the Université Libre de Bruxelles. Setting and Participants: From January 2015 to December 2017, all inpatients over 75 years old admitted with BSI were included. Measures: Demographical, clinical and microbiological data were collected. Results: 212 patients were included: median age was 82 [79-85] years and 60 % were female. The in-hospital mortality rate was 19%. The majority of microorganisms were Gram-negative strains, of which Escherichia coli was the most common, and urinary tract infection was the most common origin of BSI. Compared to patients who survived, the non-survivor group had a higher SOFA score (6 versus 3, p<0.0001), a higher comorbidity score (5 versus 4, p<0.0001), more respiratory tract infections (28 vs 6 %, p < 0.0001) and fungal infections (5 vs 1 %, p = 0.033), bedridden status (60 vs 25 %, p < 0.0001), and healthcare related infections (60 vs 40 %, p = 0.019). Using Cox multivariable regression analysis, only SOFA score was independently associated with mortality (HR 1.75 [95%IC 1.52-2.03], p<0.0001).Conclusions and Implications: BSI in older people are severe infections associated with a significant in-hospital mortality. Severity of clinical presentation at onset remains the most important predictor of mortality for BSI in older people. BSI originating from respiratory source and bedridden patients are at greater risk of intra-hospital mortality. Further prospective studies are needed to confirm these results.

Key words: Blood stream infections, older adults, mortality, bacteremia, SOFA.




Bloodstream infection (BSI) is a common cause of hospitalization and mortality in older people (1). The incidence of BSI increases with age (2), due to multiple factors such as immune senescence, comorbidity, malnutrition, and environmental factors (3). The diagnosis of BSI in frail old people remains a challenge because of the high frequency of atypical clinical presentations (4). Geriatric symptoms such as delirium, drowsiness, loss of appetite, weight loss, falls, and incontinence may be in the foreground in the absence of specific symptoms of infection (5). Inflammatory biomarkers like C – reactive protein may be useful but lacks specificity and the use of procalcitonin as a specific biomarker of infection is still debated (6). Urinary tract infections are the most common source of bacteremia in the majority of studies, followed by respiratory infections. Gram-negative are more common than Gram-positive organisms. They are responsible for 40% to 60% of BSI in older people (1). Among them, Escherichia coli spp is the most common pathogen found, accounting for 40% of community-acquired BSI, and 10-20% of healthcare-associated BSI (7-11). In the last 20 years, the incidence of BSI has increased in the general population, and in-hospital case-fatality ratio has decreased (12). Four studies evaluated intra-hospital mortality rate in patients over 75 years of age, ranging from 15 to 56%. (11, 13-15). None of these studies assessed the mortality rate as a primary objective, and population and clinical characteristics varied considerably from one study to another.
The main objective of this study was to assess the prevalence of intra-hospital mortality in patients older than 75 years old admitted with BSI. The secondary objectives were to evaluate the characteristics of BSI and to identify risk factors for in-hospital mortality.


Materials and methods

Setting and design

We performed a single center retrospective study in the 850 beds of the academic hospital of the Université Libre de Bruxelles, Brussels, Belgium. From January 2015 to December 2017, we included all inpatients over 75 years admitted for BSI (bacteremia and fungemia), in whom positive blood cultures were obtained within the first 48 hours after admission. We identified the patients on the basis of laboratory reports generated by the microbiology department. We excluded patients whom blood cultures were positive for a germ considered as a contaminant, patients having signed an opt-out declaration (written declaration of refusal to participate in a clinical study) and patients for whom the medical files were incomplete. Source of infection was determined according to CDC definitions (16). The local Ethical Committee (Comité d’Ethique Hospitalo-Facultaire Erasme-ULB) approved the study (P2017/125) but waived the need for informed consent because of its retrospective nature.

Clinical data

We evaluated patient-related risk factors, BSI-related risk factors and environmental risk factors of BSI.
Patient’s factors were age (years), gender (Male = 1, Female = 0), Sequential Organ Failure Assessment (SOFA) score (from 0 to 24 points) (17), Charlson Comorbidity Index (from 0 to 37 points) (18), and immunosuppression (Yes = 1; No = 0). Immunosuppression was defined as HIV patients with lymphocytes CD4+<200/mm³, patients taking immunosuppressive drugs in a context of organ transplant or autoimmune pathology, patients taking at least 7.5 mg of prednisolone for more than 3 months, and/or neutropenia with <500 neutrophils/mm3. Others factors were: active solid or hematological tumor (Yes = 1; No = 0), chronic renal insufficiency (Yes = 1; No = 0), defined in patients with a glomerular filtration rate (GFR) < 60 mL/min according to CKD- EPI and/or under dialysis, severe dementia (Yes = 1; No = 0), defined according to medical data recorded, and bedridden patients (Yes = 1; No = 0), defined as patients unable to get out of bed for more than 3 days during hospitalization.
Factors related to BSI were: adequate antibiotic therapy initiated in the first 48h of BSI onset (according to the antibiogram) (Yes = 1; No = 0); Health care-associated bloodstream infection (HCA-BSI) (Yes = 1; No = 0) defined as those having at least one of the following characteristics (19): having been discharged from an acute care hospital within the last 30 days, receiving hemodialysis or any kind of intravenous therapy provided by a hospital-dependent facility within 30 days prior to the BSI, residence in a long-term care facility. Other factors were: BSI with multi-resistant bacteria (BMR- BSI) (Yes = 1; No = 0), defined as a bacterium resistant to at least 3 classes of antibiotics including a third-generation cephalosporin (20), the microorganisms responsible for the BSI divided in four categories (GRAM-positive strains, GRAM-negative strains, fungal and polymicrobial infections) (Yes = 1; No = 0), the source of BSI divided in five categories (urinary, respiratory, intra-abdominal, other sources and unknown sources) (Yes = 1; No = 0) and the need for a surgical or endoscopic treatment for source control (Yes = 1; No =0).
Environmental factors were: residence in a nursing home before hospitalization (Yes = 1; No = 0), recent hospitalization in the last 30 days (Yes = 1; No = 0) and treatment with any antibiotic in the previous 30 days (Yes = 1; No = 0). The survivor group was defined as patients who were discharged from the hospital alive. The non-survivor group was defined as patients who died during hospitalization, regardless of the cause and the length of hospitalization.

Statistical analysis

Analyses were conducted using Stata-12 software (Stata Corp LLC, College Station, TX, USA). Descriptive results were reported as number and percentage (categorical variables). Continuous variables were expressed as mean ± SD or median (interquartile range, IQR). Comparison of the clinical characteristics differences in both groups (Survivors and Non-survivors patients) were performed using Chi2 test or Fischer’s test for categorical variables, non-paired Student’s t test or Mann Whitney test respectively for parametric and non-parametric continuous variables.
Patient survival was calculated by the Kaplan Meier method. The association between patient mortality and independent variables was estimated by univariate Cox proportional hazards model.
Thereafter, factors significantly associated with mortality were identified using univariable Cox’s regression. Multivariable models were built using all significant variables detected in the univariable Cox’s regression. Because of the number of variables allowed in the final model (cases=40), a first model was selected by using a forward stepwise procedure. Only one variable was selected and the final model was presented with the significant variable (SOFA) and isolates.
We have presented the HRs with 95% confidence interval derived from the Cox model and p-value corresponding to the Wald’s test. The proportional hazards assumption for variables in the final Cox model was tested graphically for categorical variables and by using interaction with time for quantitative variable.
Two-sided p-values < 0.05 were considered as statistically significant.



Two hundred and twelve (60 %) patients with BSI were included, and 143 patients (40 %) were excluded because of positive blood cultures considered as contaminants. The most common pathogen considered as a contaminant was Staphylococcus epidermidis spp (48%) found in single bottle. The median age of the study group was 82 [79-85] years and two thirds were female. Forty patients died (19%) after an average of 11 [5-10] days after admission. Figure 1 shows the survival curve: sixty (40 %) died within the first week of hospitalization, twenty-five (62%) within the first 15 days, and thirty- five (87%) within the first 30 days of hospitalization. Table 1 shows the characteristics of survivor et non-survivor groups. SOFA score and comorbidity according to the Charlson Comorbidity Index was higher in the non-survivor group. Fourteen (35%) of them were admitted at least once to Intensive Care Unit. Twenty-five (62%) died while receiving antibiotic therapy. The survival rate was equal with or without source control of bacteremia, either by endoscopy (p=0.628), or by surgical treatment (p=0.103). No difference was seen between groups for the number of adequate empirical antibiotherapy (p=0.194).

Table 1 Descriptive results in total, Non Survivor and Survivor groups

Table 1
Descriptive results in total, Non Survivor and Survivor groups

SOFA : Sequential Organ Failure Assessment. Continuous data are expressed in medians ([IQR] or means ± SD. Categorical data are expressed in total numbers (percentages)

Figure 1 Kaplan-Meier curve

Figure 1
Kaplan-Meier curve


The majority of causative microorganisms were Gram-negative strains with E. coli as the most frequently isolated bacteria; urinary tract infection was the most common origin of BSI (Tables 1, 2). Cox univariate regression analysis identified the following risk factors for in- hospital mortality: the SOFA score, the Charlson Comorbidity Index, the status of being bedridden, the healthcare related infections, and the respiratory source (Table 3). On the other hand, infections caused by Escherichia coli (HR 0.36 [CI 95% 0.16-0.77], p=0.009) were found to be protective factor in terms of mortality. Using Cox multiple regression analysis, only the SOFA score was independently significantly associated with mortality.

Table 2 List of microbiological isolates found in blood cultures

Table 2
List of microbiological isolates found in blood cultures

ESBL = Extended-Spectrum ß-Lactamase

Table 3 Univariate Cox regression model

Table 3
Univariate Cox regression model

HR = Hasard Ratio. 95%CI = 95% Confidence Interval; SOFA : Sequential Organ Failure Assessment.

Table 4 Multiple cox regression model (cases=40/ n=212)

Table 4
Multiple cox regression model (cases=40/ n=212)

aHR = adjusted Hasard Ratio. 95%CI = 95% Confidence Interval; SOFA : Sequential Organ Failure Assessment


We described the factors associated with mortality inpatients older than 75 years old with community-acquired bloodstream infections, hospitalized in medical and surgical units in an academic center. The patient population included in our study has similar characteristics to patients older than 75 years old hospitalized in acute care units in Belgium, in terms of age, sex and length of stay (30). The most common source of BSI was urinary tract infection, as has been shown in many studies (7-10, 13-15). Urinary tract infection (UTI) is the most frequent bacterial infection in old people (21). Although bacteremia is classically considered as a marker of severe disease (22), two studies demonstrated that the prognosis of UTI associated with bacteremia is not worse than UTI without bacteremia in old patients (23, 24). We found multi-drug resistant gram-negative strains in 16% of cases, mainly E. Coli and K. Pneumoniae producing extended-spectrum ß-lactamase (ESBL). Only 2% were associated with Methicillin-resistant Staphylococcus Aureus and no patients presented BSI related to Vancomycin-resistant Enterococci (VRE) or Carbapenemase-producing Klebsiella species (KPC). The presence of multi drug resistant and classically healthcare associated bacteria in this population is due to the the fact that Gram negative strains producing ESBL are an emerging cause of community infections (25) and to the fact that many patients included in our study presented one or more risk factors like living in a nursing home (18%), a history of recent hospitalization (23%) or recent antibiotherapy (18%). The source of anaerobic BSI, essentially Clostridium species (1%) and Bacteroides species (2%), and polymycrobial BSI (8%) originated from intra-abdominal in almost all cases.
We also found a high prevalence of BSI due to abdominal infections, partially due to a large and active medico-surgical digestive department in our hospital. The prevalence of BSI from unknown source varies from one study to another, depending on the definitions used to describe the presence of an infection (1). In our study, the source was identified in 87% of cases, which is equivalent to other studies (7, 9, 13-15). The unknown source might also reflect the fact that clinicians limit investigations and privilege an empirical strategy, because of old age itself or because of pre-defined therapeutic limitations. Gram-negative strains were responsible of two thirds of BSI. In the literature, Gram-negative strains are more common than Gram-positive pathogens in BSI of older patients (15). The risk of colonization with Gram-negative microorganisms increases with age, functional status, nursing home residency, hospitalization and respiratory disease.

The mortality associated with BSI was significant (19%) but lower than what is reported in other studies (11, 13-15). Mortality varies according to the characteristics of the population studied. For example, Blots et al. described a mortality rate of 56% but they considered only nosocomial BSI in patients hospitalized in intensive care units (11). The low mortality rate might also be due to the systematic co-management of patients with BSI in our hospital: the infectious diseases physician is immediately informed by the microbiology laboratory if a patient has positive blood cultures. Patients are then examined and antibacterial treatment is immediately reviewed: treatment is started, maintained if considered appropriate, or adapted to the pathogen identified in blood cultures (26).
More than one third of the non-survivor group died after the end of the antibiotic treatment, suggesting that the risk of mortality from BSI is not only a direct consequence of infection but also the consequence of the complications from the infection during hospitalization (anorexia, weakness, bedbound status, cardiac failure, altered neurological status, etc.). Although we found different patient-related, BSI-related and environmental-related risk factors associated with hospital mortality in Cox univariate analysis like bedridden status, respiratory infection, only SOFA score was found to be an independent risk factor of mortality. The SOFA score is an organ dysfunction/failure and morbidity estimation tool predicting the clinical outcomes in critically ill patients (17). In our study, a median score of 6 in the non-survivor group means a significant dysfunction of at least 2 systems. We hypothesize that the severity of the clinical presentation at the onset remains the most important predictor of mortality for BSI in older people, as already described in other studies (1, 7, 11, 13). Since this score has been validated for critically ill ICU patients, future studies are needed to assess the prognosis of patients with BSI.
The association between poor functional status and mortality in BSI has already been described; it may reflect both the poor condition of the patient prior to the infection and the severity of the infection itself (10, 27, 28). Gavazzi et al. demonstrated that an ADL score <2 was associated with 30-day mortality in nosocomial BSI (27). In Belgium, Reunes et al. found that increased age and bedridden status were independent risk factors for death in nosocomial BSI (10). Based on this, we suggest that early mobilization in case of bacteremia could influence the rate of mortality of these patients.
The respiratory source has also been described in other studies as an independent risk of mortality in BSI (7, 11). In case of pneumonia, the yield of blood cultures increases significantly with the severity of pneumonia (29).
There are several study limitations that should be acknowledged. First, this is a retrospective study; geriatric syndromes like depression, malnutrition or functional status were therefore not systematically assessed. For the same reason, information on therapeutic limitations and vaccination status were lacking in the medical files. Second, it is a single center study, limiting its external validity.


Conclusion and implications

Our study confirms that BSI in older people are severe infections associated with a significant in-hospital mortality. The severity of clinical presentation assessed by the SOFA score at admission remains the most important predictor of mortality for BSI in older people. We highlight that BSI originating from pneumonia are the most lethal and that bedridden patients are at greater risk of in-hospital mortality. On the other hand, urinary BSI are the most common but are less dangerous. Further multi-centric, long-term prospective studies are needed to better identify the patients older than 75 years old with a BSI at risk of dying during their hospitalization.


Acknowledgements: Professor Doctor Christian Melot, (Emergency Department, CUB Erasme, Brussels, Belgium) and Professor Judith Racapé (Biostatistics Department, CUB Erasme, Brussels, Belgium)
Conflicts of Interest: The author(s) declare(s) that there is no conflict of interest regarding the publication of this article.
Ethical standards: All procedures followed were in accordance with the ethical standards.





1.    Yahav D, Eliakim-Raz N, Leibovici L, Paul M. Bloodstream infections in older patients. Virulence 2016;7:341–352. doi: 10.1080/21505594.2015.1132142.
2.    Skogberg K., Lyytikainen O., Ollgren J., Nuorti J.P., Ruutu P. Population-based burden of bloodstream infections in Finland. Clin Microbiol Infect. 2012;18(6):E170–E176.
3.    Htwe T.H., Mushtaq A., Robinson S.B., Rosher R.B., and Khardori N. Infection in the elderly. Infect. Dis. Clin. North Am 2007; 21:711–743: ix. 10.1016/j.idc.2007.07.006.
4.    Gavazzi G, Krause KH. Ageing and infection. Lancet Infect Dis 2002; 2(11):659-66; PMID:12409046; http:// dx. doi.org/10.1016/S1473-3099(02)00437-1.
5.    Berman P, Hogan DB, Fox RA. The atypical presentation of infection in old age. Age Ageing 1987; 16:201– 207. doi:10.1093/ageing/16.4.201.
6.    van Duin D. Diagnostic challenges and opportunities in older adults with infectious diseases. Clin Infect Dis. 2012; 54:973–978.
7.    Hernandez C, Feher C, Soriano A, Marco F, Almela M, Cobos-Trigueros N, De La Calle C, Morata L, Mensa J, Mart ınez JA. Clinical characteristics and outcome of elderly patients with community-onset bacteremia. J Infect 2015; 70(2):135-43; http://dx.doi. org/10.1016/j.jinf.2014.09.002
8.    Lee CC, Chen SY, Chang IJ, Chen SC, Wu SC. Comparison of clinical manifestations and outcome of community-acquired bloodstream infections among the oldest old, elderly, and adult patients. Med 2007; 86(3): 138-44; http://dx.doi.org/10.1097/SHK.0b013e318067da56.
9.     Sogaard M, Schonheyder HC, Riis A, Sorensen HT, Norgaard M. Short-term mortality in relation to age and comorbidity in older adults with community-acquired bacteremia: a population-based cohort study. J Am Geriatrics Society 2008; 56(9):1593-600; http://dx.doi.org/ 10.1111/j.1532-5415.2008.01855.x.
10.     Reunes S, Rombaut V, Vogelaers D, Brusselaers N, Lizy C, Cankurtaran M, Labeau S, Petrovic M, Blot S. Risk factors and mortality for nosocomial bloodstream infections in elderly patients. Eur J Int Med 2011; 22(5):e39-44; http://dx.doi.org/10.1016/j.ejim.2011.02.004.
11.     Blot S, Cankurtaran M, Petrovic M, Vandijck D, Lizy C, Decruyenaere J, Danneels C, Vandewoude K, Piette A, Vershraegen G, et al. Epidemiology and outcome of nos- ocomial bloodstream infection in elderly critically ill patients: a comparison between middle-aged, old, and very old patients. Critical Care Med 2009; 37(5): 1634-41; http://dx.doi.org/10.1097/CCM.0b013e31819da98e.
12.     Goto M, Al-Hasan MN. Overall burden of bloodstream infection and nosocomial bloodstream infection in North America and Europe. Clin Microbiol Infect. 2013;19(6):501–509. doi: 10.1111/1469-0691.12195.
13.     Leibovici L,Pitlik SD,Konisberger H,Drucker M. Bloodstream infections in patients older than eighty years. Age Ageing 1993; 22(6):431-42; http://dx.doi. org/10.1093/ageing/22.6.431.
14.     Wester AL, Dunlop O, Melby KK, Dahle UR, Wyller TB. Age-related differences in symptoms, diagnosis and prognosis of bacteremia. BMC Infect Dis 2013; 13:346; http://dx.doi.org/ 10.1186/1471-2334-13-346.
15.     Greenberg BM, Atmar RL, Stager CE, Greenberg SB. Bacteraemia in the elderly: predictors of out- come in an urban teaching hospital. J Infect 2005; 50(4):288-95; http://dx.doi.org/ 10.1016/j.jinf. 2004.06.014.
16.     Horan T. C., Andrus M., Dudeck M. A. (2008). CDC/NHSN surveillance definition of health care-associated infection and criteria for specific types of infections in the acute care setting. Am J Infect Control 2008 36, 309– 332. Doi :10.1016/j.ajic.2008.03.002
17.     Vincent J-L, Moreno R, Takala J, Willatts S, De Mendonça A, Bruining H, et al. The SOFA (sepsis-related organ failure assessment) score to describe organ dysfunction/failure. Intensive Care Med. 1996;22:707–710. doi: 10.1007/BF01709751.
18.     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):373-83.
19.     Cardoso T., Almeida M., Friedman N. D., et al. Classification of healthcare-associated infection: a systematic review 10 year after the first proposal. BMC Medicine 2014;12, article 40.
20.     Magiorakos A.P., Srinivasan A., Carey R.B., Carmeli Y., Falagas ME., Giske CG., et al. Multidrug-resistant, extensively drug-resistant and pandrug-resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance. Clin. Microbiol. Infect 2012; 18: 268–281. 10.1111/j. 1469-0691.2011.03570.x.
21.     Nicolle LE. Urinary Tract Infections in the Older Adult. Clin Geriatr Med. 2016; 32:523–538. https://doi. org/ 10.1016/j.cger.2016.03.002.
22.     Jerkeman M, Braconier JH. Bacteremic and non-bacteremic febrile urinary tract infection, a review of 168 hospital-treated patients. Infection 1992; 20:143–145. PMID: 1644489.
23.     Artero A, Esparcia A, Eiros JM, Madrazo M, Alberola J, Nogueira JM. Effect of Bacteremia in Elderly Patients with Urinary Tract Infection. Am J Med Sci. 2016; 352:267–271. https://doi.org/10.1016/j. amjms. 2016.05.031
24.     Artero A, Inglada L, Gómez-Belda A, Capdevila JA, Diez LF, Arca A, Romero JM, Domínguez-Gil M, Serra- Centelle SC , de la Fuente. The clinical impact of bacteremia on outcomes  in  elderly  patients  with  pyelonephritis  or  urinary  sepsis:  A prospective multicenter study. PLoS One. 2018; 24;13(1):e0191066. doi: 10.1371/journal.pone.0191066.
25.     Rodriguez-Bano J, Picon E, Gijon P, et al. Community-onset bacteremia due to extended-spectrum beta-lactamase-producing Escherichia coli: risk factors and prognosis. Clin Infect Dis 2010;50:40–8.
26.     Byl B, Clevenbergh P, Jacobs F, Struelens MJ, Zech F, Kentos A, et al. Impact of infectious diseases specialists and microbiological data on the appropriateness of antimicrobial therapy for bacteremia. Clin Infect Dis (1999) 29:60–6.10.1086/520182
27.     Gavazzi G, Escobar P, Olive F, Couturier P, Franco A. Nosocomial bacteremia in very old patients: predictors of mortality. Aging Clin Exp Res 2005; 17(4):337-42. http://dx.doi.org/10.1007/BF03324619
28.     Deulofeu F, Cervello B, Capell S, Marti C, Mercade V. Predictors of mortality in patients with bacteremia: the importance of functional status. J Am Geriatrics Society 1998; 46(1):14-8; http://dx.doi.org/10.1111/j.1532- 5415.1998.tb0100
29.     Waterer G.W., Wunderink R.G. The influence of the severity of community-acquired pneumonia on the usefulness of blood cultures. Respir. Med. 2001;95:78–82. doi: 10.1053/rmed.2000.0977
30.     https://his.wiv-isp.be/fr/Documents%20partages/HO_FR_2013.pdf

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J. Ferri-Guerra1,2, R. Aparicio-Ugarriza1,2, D. Salguero1,2, D. Baskaran1, Y.N. Mohammed1,2, H. Florez1,2, J.G. Ruiz1,2


1. Miami VA Healthcare System Geriatric Research, Education and Clinical Center (GRECC), Miami, USA; 2. University of Miami Miller School of Medicine, Miami, USA.
Corresponding author: Corresponding author: Jorge G. Ruiz, MD, VA GRECC Associate Director for Clinical Affairs, Bruce W. Carter Miami VAMC, GRECC (11GRC), 1201 NW 16th Street, Miami, Florida 33125, Telephone: (305) 575-3388 /Fax: (305) 575-3365, Mail: j.ruiz@miami.edu, ORCID: 0000-0003-3069-8502

J Frailty Aging 2020;9(2)94-100
Published online October 4, 2019, http://dx.doi.org/10.14283/jfa.2019.31



Background: Diabetes (DM) is associated with an accelerated aging that promotes frailty, a state of vulnerability to stressors, characterized by multisystem decline that results in diminished intrinsic reserve and is associated with morbidity, mortality and utilization. Research suggests a bidirectional relationship between frailty and diabetes. Frailty is associated with mortality in patients with diabetes, but its prevalence and impact on hospitalizations are not well known. Objectives: Determine the association of frailty with all-cause hospitalizations and mortality in older Veterans with diabetes. Design: Retrospective cohort. Setting: Outpatient. Participants: Veterans 65 years and older with diabetes who were identified as frail through calculation of a 44-item frailty index. Measurements: The FI was constructed as a proportion of healthcare variables (demographics, comorbidities, medications, laboratory tests, and ADLs) at the time of the screening. At the end of follow up, data was aggregated on all-cause hospitalizations and mortality and compared non-frail (robust, FI≤ .10 and prefrail FI=>.10, <.21) and frail (FI≥.21) patients. After adjusting for age, race, ethnicity, median income, history of hospitalizations, comorbidities, duration of DM and glycemic control, the association of frailty with all-cause hospitalizations was carried out according to the Andersen-Gill model, accounting for repeated hospitalizations and the association with all-cause mortality using a multivariate Cox proportional hazards regression model. Results: We identified 763 patients with diabetes, mean age 72.9 (SD=6.8) years, 50.5% were frail. After a median follow-up of 561 days (IQR=172), 37.0% they had 673 hospitalizations. After adjustment for covariates, frailty was associated with higher all-cause hospitalizations, hazard ratio (HR)=1.71 (95%CI:1.31-2.24), p<.0001, and greater mortality, HR=2.05 (95%CI:1.16-3.64), p=.014. Conclusions: Frailty was independently associated with all-cause hospitalizations and mortality in older Veterans with diabetes. Interventions to reduce the burden of frailty may be helpful to improve outcomes in older patients with diabetes.

Key words: Frailty, diabetes mellitus, hospitalizations, mortality, older adults.



Frailty is a state of vulnerability to stressors, characterized by multisystem decline that results in a diminished intrinsic reserve (1). Frailty is associated with higher morbidity, mortality and healthcare utilization. Research evidence suggests bidirectional relationship between diabetes and frailty (2). Older adults with frailty demonstrate a high prevalence of risk factors associated with diabetes including obesity, inactivity, declining renal function (3, 4). On the other hand, diabetes may contribute to a higher risk for frailty as a result of the high prevalence of cardiovascular risk factors (5). The bidirectional association between frailty and diabetes and their combined effects may be particularly deleterious for older persons (2, 5).
Several explanations for the relationship between frailty and diabetes mellitus have been proposed. Frailty and diabetes share some of the same mechanisms: insulin resistance, low grade inflammation, oxidative stress, stem cell dysfunction, mitochondrial dysfunction, and sarcopenia (6-8). Comorbid medical and mental health conditions often coexist in both frailty and diabetes including but not limited to obesity, cardiovascular disease, sleep apnea, depression and cognitive impairment (5, 9, 10). Hypoglycemia in older people with diabetes may be a particularly important contributor to frailty risk and similarly frailty may predispose older people with diabetes to hypoglycemia (8).
Both frailty (11) and diabetes (12) are prevalent in the Veteran than the general US population. Diabetes (13) and frailty (14) are both independently associated with a higher risk for all-cause hospitalizations in older adults. Furthermore, mortality is also higher in frailty and diabetes. In these patients, the concurrence of frailty and diabetes may further increase the effects of individual conditions on clinical outcomes that may lead to higher healthcare utilization and mortality. Previous studies have shown that older adults with either frailty (14) or diabetes (15) are high utilizers of healthcare including hospitalizations. Although Veterans receiving care at Department of Veterans Affairs (VA) Medical Centers have an increased risk for hospitalizations, there is, however, no data regarding the effects on hospitalizations in Veterans with coexistent frailty and diabetes. Thus, the aim of this study was to determine the effects of frailty on all-cause hospitalization and mortality in older adults with diabetes at a VA medical center.



Study Setting

This research is a retrospective cohort study that was conducted at a tertiary care VA Medical Center, a US government-run healthcare institution. The study is part of a clinical demonstration quality improvement project looking at identifying Veterans with frailty.

Identification of Patients

Our project team identified community-dwelling Veterans 65 years and older with diabetes coming to the VA Medical Center outpatient clinics from January 2016 to August 2017, and patients were follow-up until October 2018. We identified patients with diabetes, diagnosed between October 2, 1996, and July 17, 2017. Trained research associates collected patient data from the electronic health record and the VA Corporate Data Warehouse (CDW) including demographic information; vital signs and BMI, physical and mental health conditions; laboratory data, sensory problems and functional status. We used the zip code and race to obtain the patients annual median household income based on 2014 Census tract data as a parameter for social status classification. Information on physical health conditions was used to calculate an age-adjusted Charlson comorbidity index (CCI) (16).

Frailty Assessment

Data collected for each patient from reviews of the VA electronic health record and the VA Corporate Data Warehouse (CDW) was used to calculate a frailty index (FI) which included 44 items. Each patient’s FI had a minimum of 30 of the 44 items. The 44 items in the FI belonged to 7 major categories (supplementary materials): socio-demographic (4 items), vital signs and other measurements (3 items), physical and mental health conditions (20 items), laboratory data (10 items), sensory problem (1 item) and functional status (6 items). The 44-item frailty index used in this study is based on the deficit accumulation conceptual framework that assumes that frailty is the result of interacting physical, functional psychological, and social factors (17). Unlike the frailty phenotype, which is the most widely used conceptual model in the field, the deficit accumulation approach does not rely on predetermined variables (18). Each patient’s FI was calculated by dividing the number of items present (19). We chose the cut-off of 0.21, which was most recently used by Orkaby et al. as part of a large VA study on the prevalence of frailty in the VA (11). This resulted in a score between 0-1, where higher scores represent higher frailty. The patients were stratified as non-frail (FI is <0.21) and frail (FI is ≥ 0.21).
Hospitalization Ascertainment
Patients were followed from January 2016 to August 2017 until October 2018 for VA all-cause hospitalizations following the initial assessment of frailty. We recorded the total number of hospital admissions during the previous one year and for the follow up period. The primary reasons for hospitalizations were assessed using ICD 9 and 10 codes assigned by trained staff after discharge.


All-cause mortality was identified through official sources including VHA facilities, death certificates, and National Cemetery Administration data available from the VA CDW. There is high agreement (91-99%) between dates of death recorded in the CDW and dates of death recorded in external sources that feed the VHA Vital Status File (20). The last day of follow-up was October 31th, 2018.

Data Analysis

Baseline characteristics are presented as frequency (percent) for categorical variables, and as mean+SD for continuous variables. We report descriptive statistics of age, race, ethnicity, median income, marital status, body-mass index (BMI), and age-adjusted CCI, duration of diabetes, DM with complication, number of medications, use of insulin or sulfonylureas, metformin, level of glycemia control,  previous and during follow-up hospitalizations. All variables were checked for normality of distribution using the Kolmogorov-Smirnov test. All values showed no-normal distribution. Mann-Whitney U and Kruskal-Wallis test (for non-normally distributed variables) and Chi-Square were run to evaluate the differences between non-frail and frail. The association of frailty with all-cause hospitalizations in older adults with diabetes was determined with the Andersen-Gill model, accounting for repeated hospitalizations. Patients were censored if they died without having a hospital admission. Univariate and multivariate analyses were conducted adjusting for age, race, ethnicity, median household income, BMI, age-adjusted CCI, diabetes complications, duration of diabetes, use of insulin or sulfonylureas, metformin, level of glycemia control, and all-cause hospitalizations in the previous year. Four models were constructed to assess the role of the covariates in the association between frailty and all-cause hospitalization: Model 1 was adjusted for age, race, ethnicity, BMI and Median Household Income. Model 2 was adjusted for the covariates in Model 1 and age-adjusted CCI. Model 3 was adjusted for the covariates in Models 1-2 and diabetes complications, duration of diabetes, use of insulin or sulfonylureas, metformin and level of glycemia control. Model 4 was adjusted for the covariates in the previous models and for hospitalizations in the previous year. The proportional hazard assumption was tested using scaled Schoenfeld residuals and was found to be valid. Cox regression analysis was performed to calculate the hazard ratios and 95% confidence intervals (CIs) of survival for frailty on all-cause mortality. We built 4 models to assess the role of the covariates in the association between frailty and all-cause mortality as described for all-cause hospitalizations. To assess the robustness of our results, sensitivity analyses were performed in which we dichotomized subgroups of older Veterans with frailty by age (<75 and ≥ 75 years old), race (White vs. African American), and with history of hospitalizations in the previous year (Yes vs. No). We did not have to exclude variables having a high collinearity among themselves. Associations were considered significant if p<0.05. Follow up duration was calculated as follows: (October 31th, 2018 – frailty assessment date)/365. All analyses were performed using the SPSS 25.0 for Windows (SPSS, Inc., Chicago, Illinois) and SAS for Windows version 3.71 (SAS Institute Inc., Cary, North Carolina). All statistical tests were two-tailed, and statistical significance was assumed for a p-value <0.05.



Patient Characteristics

Table 1 shows participant characteristics. 763 participants were included in the study. Patients were 98.3% male, 56.7% White, 77.1% non-Hispanic and the mean age was 72.9 (SD= 6.8) years. Compared with the non-frail, older adults with diabetes were less likely to be married, have more end-organ damage, longer duration of diabetes, more multimorbidity and use of medications, more likely to be taking insulin or sulfonylureas, less likely to be on metformin and have more hospitalizations in the previous year (Table 1).

Table 1 Participant Characteristics

Table 1
Participant Characteristics

*Diabetes with End organ damage: patients diagnosed with one or more of the following diagnosis: retinopathy, neuropathy and nephropathy. SD = standard deviation; n = number of participants. BMI= body mass index; FU= follow-up; Mann-Whitney U and Kruskal-Wallis test (for non-normally distributed variables) and Chi-Square for continuous variables and categorical variables, respectively. Significant differences between frailty groups are in bold (p< .05).



There were 673 all-cause hospitalizations over a median follow-up period of 561 days (IQR= 172) with the range between 0 and 12 hospitalizations. The leading causes for hospitalization were cardiovascular, infectious and renal diagnoses representing 137 (21%), 71 (11%) and 69 (10%) of the total respectively. The year before evaluation of frailty, 239 patients (31.3%) had at least one hospitalization and 524 (68.7%) did not have any hospitalizations. Over the follow up period, 481 participants (63.0%) did not have any hospitalizations; whereas, 282 (37.0%) had at least 1 hospitalization (data are not shown).
As shown in Table 2, using the Andersen-Gill model fully adjusted for covariates, frailty was significantly associated with higher risk for hospitalizations compared to non-frail patients, adjusted HR=1.71 (95%CI:1.31–2.24), p<.0001. There were some differences appeared after conducting sensitivity analysis in the subgroup of older Veterans with diabetes and frailty. In terms of age, there were no associations between frailty and all-cause hospitalizations in participants 75 years of age and older after adjustment for all covariates (Table 3), HR=.86 (95%CI:.59-1.23), p=.399. There were significant associations of frailty with lower risk for all-cause hospitalizations in African American participants after adjusting for covariates: HR=.61 (95%CI:.41-.91), p=.015 (Table 3). After dividing the groups into those with and those without hospitalizations in the previous year, there were significant differences in those participants with previous hospitalizations HR=3.37 (95%CI:2.43-4.66), p<.0001 (Table 3).

Table 2 Association of Frailty with All-Cause Hospitalizations and Mortality in Older Veterans with Diabetes (n = 763)

Table 2
Association of Frailty with All-Cause Hospitalizations and Mortality in Older Veterans with Diabetes (n = 763)

Model 1 was adjusted for age, race, ethnicity, BMI and Median Household Income. Model 2 was adjusted for the covariates in Model 1 and Charlson Comorbidity Index. Model 3 was adjusted for the covariates in Models 1-2 and diabetes complications, duration of diabetes, use of insulin or sulfonylureas, metformin and level of glycemia control. Model 4 was adjusted for the covariates in the previous models and for hospitalizations in the previous year. Significant associations are in bold (p< .05).

Table 4 Association of All-Cause Mortality with Age Group (American (n=165) and Prior Hospitalizations (No (n=207) vs. Yes (n=178)) in Patients with Frailty and Diabetes (n = 385)

Table 3
Association of All-Cause Mortality with Age Group (American (n=165) and Prior Hospitalizations (No (n=207) vs. Yes (n=178)) in Patients with Frailty and Diabetes (n = 385)

Model 1 was adjusted for age, (except age group: <75y and ≥75y), race (except for race group: White vs African American), ethnicity, BMI and Median Household Income. Model 2 was adjusted for the covariates in Model 1 and Charlson Comorbidity Index. Model 3 was adjusted for the covariates in Models 1-2 and diabetes complications, duration of diabetes, use of insulin or sulfonylureas, metformin and level of glycemia control. Model 4 was adjusted for the covariates in the previous models and for hospitalizations in the previous year (except for Prior hospitalizations: Yes Prior and No prior). Significant associations are in bold (p< .05).



Over the follow-up period, 81 deaths occurred. Table 2 displays the association between mortality and frailty in older Veterans with diabetes. After adjusting for all covariates, (Model 4), frailty increased the risk of all-cause mortality during follow up, HR=2.05 (95%CI:1.16-3.64), p=.014. During sensitivity analyses, frailty did not show association with all-cause mortality in participants 75 years of age and older after adjustment for covariates HR=1.39 (95%CI:.79-2.46), p=.248 (Table 3). There was not association of frailty with all-cause mortality in African Americans after adjustment for covariates: HR=.67 (95%CI:.34 – 1.32),  p=.244.  Furthermore, frailty was significantly associated with higher all-cause mortality in those with previous hospitalizations after adjustment: HR=3.36 (95%CI:1.87-6.06), p<.0001 (Table 3).

Table 4 Association of All-Cause Mortality with Age Group (<75 y (n=261) vs. ≥75 y (n=124)), Race (White (n=220) vs. African American (n=165) and Prior Hospitalizations (No (n=207) vs. Yes (n=178)) in Patients with Frailty and Diabetes (n = 385)

Table 4
Association of All-Cause Mortality with Age Group (American (n=165) and Prior Hospitalizations (No (n=207) vs. Yes (n=178)) in Patients with Frailty and Diabetes (n = 385)

Model 1 was adjusted for age, (except age group: <75y and ≥75y), race (except for race group: White vs African American), ethnicity, BMI and Median Household Income. Model 2 was adjusted for the covariates in Model 1 and Charlson Comorbidity Index. Model 3 was adjusted for the covariates in Models 1-2 and diabetes complications, duration of diabetes, use of insulin or sulfonylureas, metformin and level of glycemia control. Model 4 was adjusted for the covariates in the previous models and for hospitalizations in the previous year (except for Prior hospitalizations: Yes Prior and No prior). Significant associations are in bold (p< .05).



In this study, we investigated whether frailty was associated with risk for either all-cause hospitalizations or mortality in older adults with diabetes. The overall analysis showed an association between frailty in older adults with diabetes and a higher risk for all-cause hospitalization and mortality after adjustment for known confounders. There were, however, differences between subgroups of participants with frailty. African Americans with frailty had a lower risk for all-cause hospitalizations that whites. Older adults with frailty and history of hospitalizations in the previous year demonstrated a higher risk for both all-cause hospitalizations and mortality.
The independent contribution of frailty to a higher risk for all-cause hospitalizations and mortality in older people with diabetes may be related to several factors associated with this syndrome. Frailty may shape the presentation of type 2 diabetes by increasing the risk of hypoglycemia (8). Weight loss and sarcopenia which are often part of the frailty syndrome, may be further exacerbated by concurrent anorexia of aging potentially leading to the normalization of glycemic control and an increased risk for recurrent and sometimes severe hypoglycemia (21), which may lead to cardiovascular complications. Cardiovascular diagnoses represented the leading cause of hospitalization in our sample of older Veterans with diabetes. Older individuals with diabetes and frailty may be especially susceptible to the physiological effects of hypoglycemia on the cardiovascular system potentially contributing to a higher rate of hospitalizations. It has been proposed that older adults with diabetes and frailty may benefit from less aggressive targets for glycemic control (21). The increased inflammatory and coagulation abnormalities characteristic of frailty may also worsen the microvascular effects of diabetes (22) resulting in a higher rate of complications and in turn a risk for higher rate of subsequent hospitalizations and poor clinical outcomes including death. In our study complications of renal disease were amongst the leading causes of hospitalization. In older adults, frailty may mediate the link between diabetes and disability which in itself is associated with a higher risk for all-cause hospitalization (23) and mortality (24). Falls, delirium, dementia and other geriatric syndromes often coexist with frailty sharing mechanisms that may also jointly contribute to an increase risk for hospitalizationsand mortality (25). Cognitive impairment, which is often underrecognized, often occurs and coexists patients with both diabetes and frailty, and is a known risk factor for hospital admissions and readmissions (26) and mortality in in patients with diabetes (27). That frailty has an additive effect to that of diabetes on all-cause hospitalizations and mortality in older adults with an already increased risk for healthcare utilization and decreased survival is particularly noteworthy. Identifying frailty could facilitate clinical decision making and potentially contribute to the implementation of clinical interventions aimed at reducing poor clinical outcomes and hospitalization risk in older patients with diabetes.
Although some studies have addressed the issue of previous hospitalizations in older adults with frailty, none has specifically looked at the coexistence of both frailty and diabetes in subgroups of older adults with frailty. This analysis reveals some evidence of the level of heterogeneity in all-cause hospitalizations and mortality older people with diabetes and frailty.The lack of differences in hospitalizations between the two age subgroups may just be function of the smaller sample size. However, another explanation may be related to the characteristics of the subgroups (supplementary materials). The over 75 years old group shows characteristics that may explain the lower rate of hospitalizations we observed namely a higher proportion of Whites, a higher median household income, and a lower rate of diabetes complications. On the other hand, factors that may offset such advantages in the older group include a longer duration of diabetes and higher levels of multimorbidity. African American race is  independently associated with frailty (28) but differences in all-cause hospitalizations suggest that all things being equal, African Americans with frailty are less likely to be hospitalized. More research is needed regarding race-based differences in clinical and healthcare utilization outcomes of individuals with frailty and the specific factors that may contribute to this differential. Older adults with diabetes and a history of hospitalizations represent a high risk group for future hospitalizations (29). In hospitalized older adults, frailty was independently associated with a higher rate of complications and mortality risk (30). In frail older adults with diabetes hospitalization may further compromise their medical condition as the may be particularly vulnerable to the effects of hospitalization. In terms of mortality, our results are consistent with previous studies showing that African Americans with diabetes have similar (31) or even lower mortality that Caucasians (32, 33). The explanations vary and may include reporting bias, lack of adjustment for socio-economic status (lower for African Americans), better access to care in an integrated healthcare system such as the VA (33) and differences in the prevalence of diabetes-related complications between these two groups (32-34). Although several studies have shown that African Americans have higher rates of kidney disease related mortality (32), Whites have higher rates of coronary heart disease than African Americans (33, 34) potentially leading to competing risks that may offset the effects of ESRD-related mortality on African Americans effectively canceling out any possible mortality differences.
Strengths of this study include a relatively large sample of older adults with documented diabetes diagnoses, complete evaluation of frailty, inclusion of complete healthcare data from electronic health records, adjustment for multiple covariates associated with increased risk for all-cause hospitalization and mortality, and a long period of follow up. There are a few limitations. We used a convenience sample of predominantly male Veterans at one medical center, and the ethnic, racial, educational, and socio-economic composition as well as the structure of the healthcare system may be different from other healthcare settings in the US.  Future cohort studies should include larger, more diverse and randomly selected samples from varied geographic locations and healthcare systems.
This study indicated overall associations of frailty with higher risk for all-cause hospitalization and mortality in older adults with diabetes. Frailty appears to have an additive effect beyond that of diabetes on hospitalizations and mortality. Developing interventions aimed at reducing hospitalization risk in older adults with diabetes may start with the identification of frailty followed by the management of this syndrome in these individuals. Further research is needed with random sampling in a broader spectrum of healthcare settings to better understand what roles frailty might play in healthcare utilization, mortality and other clinical outcomes of older adults with diabetes.


Funding: This material is the result of work supported with resources and the use of facilities at the Miami VA Healthcare System GRECC.
Conflict of interest: The authors declare none.
Ethical standards: A protocol of this study was submitted to and approved by the Institutional Review Board as a VA quality improvement project.



1.    Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, et al. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci. 2001;56(3):M146-56.
2.    Sinclair AJ, Abdelhafiz A, Dunning T, Izquierdo M, Rodriguez Manas L, Bourdel-Marchasson I, et al. An International Position Statement on the Management of Frailty in Diabetes Mellitus: Summary of Recommendations 2017. J Frailty Aging. 2018;7(1):10-20.
3.    Hubbard RE, Lang IA, Llewellyn DJ, Rockwood K. Frailty, body mass index, and abdominal obesity in older people. J Gerontol A Biol Sci Med Sci. 2010;65(4):377-81.
4.    Veronese N, Stubbs B, Fontana L, Trevisan C, Bolzetta F, De Rui M, et al. Frailty Is Associated with an Increased Risk of Incident Type 2 Diabetes in the Elderly. J Am Med Dir Assoc. 2016;17(10):902-7.
5.    Bouillon K, Kivimaki M, Hamer M, Shipley MJ, Akbaraly TN, Tabak A, et al. Diabetes risk factors, diabetes risk algorithms, and the prediction of future frailty: the Whitehall II prospective cohort study. J Am Med Dir Assoc. 2013;14(11):851 e1-6.
6.    Morley JE, Malmstrom TK, Rodriguez-Manas L, Sinclair AJ. Frailty, sarcopenia and diabetes. J Am Med Dir Assoc. 2014;15(12):853-9.
7.    Garcia-Esquinas E, Graciani A, Guallar-Castillon P, Lopez-Garcia E, Rodriguez-Manas L, Rodriguez-Artalejo F. Diabetes and risk of frailty and its potential mechanisms: a prospective cohort study of older adults. J Am Med Dir Assoc. 2015;16(9):748-54.
8.    Abdelhafiz AH, Koay L, Sinclair AJ. The effect of frailty should be considered in the management plan of older people with Type 2 diabetes. Future Sci OA. 2016;2(1):FSO102.
9.    Garcia-Esquinas E, Jose Garcia-Garcia F, Leon-Munoz LM, Carnicero JA, Guallar-Castillon P, Gonzalez-Colaco Harmand M, et al. Obesity, fat distribution, and risk of frailty in two population-based cohorts of older adults in Spain. Obesity (Silver Spring). 2015;23(4):847-55.
10.    Adame Perez SI, Senior PA, Field CJ, Jindal K, Mager DR. Frailty, Health-Related Quality of Life, Cognition, Depression, Vitamin D and Health-Care Utilization in an Ambulatory Adult Population With Type 1 or Type 2 Diabetes Mellitus and Chronic Kidney Disease: A Cross-Sectional Analysis. Can J Diabetes. 2019;43(2):90-7.
11.    Orkaby AR, Nussbaum L, Ho YL, Gagnon D, Quach L, Ward R, et al. The Burden of Frailty Among U.S. Veterans and Its Association With Mortality, 2002-2012. J Gerontol A Biol Sci Med Sci. 2018.
12.     Liu Y, Sayam S, Shao X, Wang K, Zheng S, Li Y, et al. Prevalence of and Trends in Diabetes Among Veterans, United States, 2005-2014. Prev Chronic Dis. 2017; 14:170230.
13.     Benjamin SM, Wang J, Geiss LS, Thompson TJ, Gregg EW. The Impact of Repeat Hospitalizations on Hospitalization Rates for Selected Conditions Among Adults With and Without Diabetes, 12 US States, 2011. Prev Chronic Dis. 2015; 12:150274.
14.     Chang SF, Lin HC, Cheng CL. The Relationship of Frailty and Hospitalization Among Older People: Evidence From a Meta-Analysis. J Nurs Scholarsh. 2018;50(4):383-91.
15.     De Berardis G, D’Ettorre A, Graziano G, Lucisano G, Pellegrini F, Cammarota S, et al. The burden of hospitalization related to diabetes mellitus: a population-based study. Nutr Metab Cardiovasc Dis. 2012;22(7):605-12.
16.     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):373-83.
17.     Rockwood K, Song X, MacKnight C, Bergman H, Hogan DB, McDowell I, et al. A global clinical measure of fitness and frailty in elderly people. CMAJ. 2005;173(5):489-95.
18.     Cesari M, Gambassi G, van Kan GA, Vellas B. The frailty phenotype and the frailty index: different instruments for different purposes. Age Ageing. 2014;43(1):10-2.
19.     Searle SD, Mitnitski A, Gahbauer EA, Gill TM, Rockwood K. A standard procedure for creating a frailty index. BMC Geriatr. 2008;8:24.
20.     Maynard C. Ascertaining Veterans’ Vital Status: VA Data Sources for Mortality Ascertainment and Cause of Death. [Internet]. Washington, DC: VA Health Services Research & Development (HSRD); 2017 [updated March 6, 2017; cited 2019. Available from: https://www.hsrd.research.va.gov/for_researchers/cyber_seminars/ archives/video_archive.cfm?SessionID=1242.
21.     American Diabetes Association. 11. Older Adults: Standards of Medical Care in Diabetes-2018. Diabetes Care. 2018;41(Suppl 1):S119-S125.
22.     Walston J, McBurnie MA, Newman A, Tracy RP, Kop WJ, Hirsch CH, et al. Frailty and activation of the inflammation and coagulation systems with and without clinical comorbidities: results from the Cardiovascular Health Study. Arch Intern Med. 2002;162(20):2333-41.
23.     Miller EA, Weissert WG. Predicting elderly people’s risk for nursing home placement, hospitalization, functional impairment, and mortality: a synthesis. Med Care Res Rev. 2000;57(3):259-97.
24.     Torisson G, Stavenow L, Minthon L, Londos E. Importance and added value of functional impairment to predict mortality: a cohort study in Swedish medical inpatients. BMJ Open. 2017;7(5):e014464.
25.     Romero-Ortuno R, Forsyth DR, Wilson KJ, Cameron E, Wallis S, Biram R, et al. The Association of Geriatric Syndromes with Hospital Outcomes. J Hosp Med. 2017;12(2):83-9.
26.     Callahan KE, Lovato JF, Miller ME, Easterling D, Snitz B, Williamson JD. Associations Between Mild Cognitive Impairment and Hospitalization and Readmission. J Am Geriatr Soc. 2015;63(9):1880-5.
27.     McGuire LC, Ford ES, Ajani UA. The impact of cognitive functioning on mortality and the development of functional disability in older adults with diabetes: the second longitudinal study on aging. BMC Geriatr. 2006;6:8.
28.     Hirsch C, Anderson ML, Newman A, Kop W, Jackson S, Gottdiener J, et al. The association of race with frailty: the cardiovascular health study. Ann Epidemiol. 2006;16(7):545-53.
29.     Rubin DJ. Hospital readmission of patients with diabetes. Curr Diab Rep. 2015;15(4):17.
30.     Evans SJ, Sayers M, Mitnitski A, Rockwood K. The risk of adverse outcomes in hospitalized older patients in relation to a frailty index based on a comprehensive.
31.     Conway BN, May ME, Blot WJ. Mortality among low-income African Americans and whites with diabetes. Diabetes care. 2012;35(11):2293-9.
32.     Conway BN, May ME, Fischl A, Frisbee J, Han X, Blot WJ. Cause-specific mortality by race in low-income Black and White people with Type 2 diabetes. Diabet Med. 2015;32(1):33-41.
33.     Lynch CP, Gebregziabher M, Echols C, Gilbert GE, Zhao Y, Egede LE. Racial disparities in all-cause mortality among veterans with type 2 diabetes. J Gen Intern Med. 2010;25(10):1051-6.
34.     DeStefano F, Newman J. Comparison of coronary heart disease mortality risk between black and white people with diabetes. Ethn Dis. 1993;3(2):145-51.



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


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

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



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

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



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



Study Design

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

Subject Characteristics

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

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

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

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


Blood Collection and Cytokine Analysis

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

Skeletal Muscle Size and Composition

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

Skeletal Muscle Contractile Function

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

Physical Function

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

Statistical Analysis

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



Participant Characteristics

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

Baseline Circulating Inflammation Predicts Skeletal Muscle Characteristics

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

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

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

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


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

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

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


Circulating IL-6 Associates with Exercise Training Adaptation

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

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

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

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


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

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

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

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



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


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



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



J. Muscedere1, P.M. Kim2, J. Afilalo3, C. Balion4, V.E. Baracos5, D. Bowdish6, M. Cesari7, J. D. Erusalimsky8, T. Fülöp9, G. Heckman10, S.e. Howlett11, R.G. Khadaroo12, J.L. Kirkland13, L. Rodriguez Mañas14, E. Marzetti15, G. Paré4, P. Raina16, K. Rockwood17, A. Sinclair18, C. Skappak19, C. Verschoor16, S. Walter20 for the Canadian Frailty Network


1. Department of Critical Care Medicine, Queen’s University; 2. Canadian Frailty Network, Kingston, ON, Canada; 3. Division of Cardiology and Centre for Clinical Epidemiology, Jewish General Hospital, McGill University; 4. Department of Pathology and Molecular Medicine, McMaster University; 5. Department of Oncology, University of Alberta; 6. Department of Pathology and Molecular Medicine, Master University; 7. Department of Clinical Sciences and Community Health, Università di Milano; and Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy; 8. Department of Biomedical Sciences, Cardiff Metropolitan University, Cardiff, UK; 9 Research Center on Aging, University of Sherbrooke, Québec, Canada; 10. School of Public Health and Health Systems, Schlegel University of Waterloo Research Institute for Aging, University of Waterloo; 11. Departments of Pharmacology and Medicine (Geriatric Medicine), Faculty of Medicine, Dalhousie University; 12. Department of Surgery and Critical Care Medicine, University of Alberta; 13. Robert and Arlene Kogod Center on Aging, Mayo Clinic; 14. Department of Geriatrics, Hospital Universitario de Getafe, Madrid; 15. Fondazione Policlinico Universitario «Agostino Gemelli» IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy; 16. Department of Health Research Methods, Evaluation and Impact, McMaster University; 17. Division of Geriatric Medicine, Dalhousie University; 18. Foundation for Diabetes Research in Older People at Diabetes Frail Ltd; 19. Schwartz/Reisman Emergency Medicine Institute, Mount Sinai Hospital, University of Toronto, and the Division of Emergency Medicine, McMaster University; 20. Fundación de Investigación Biomédica Hospital Universitario de Getafe, Getafe, Spain. Dept. of Epidemiology and Biostatistics, University of California San Francisco, California, United States of America.
Corresponding author: Dr. John Muscedere, Kingston Health Sciences Centre, 76 Stuart Street, Kingston, Ontario, Canada, Email: john.muscedere@kingstonhsc.ca
J Frailty Aging 2019;in press
Published online April 30, 2019, http://dx.doi.org/10.14283/jfa.2019.12



The Canadian Frailty Network (CFN), a pan-Canadian not-for-profit organization funded by the Government of Canada through the Networks of Centres of Excellence Program, is dedicated to improving the care of older Canadians living with frailty. The CFN has partnered with the Canadian Longitudinal Study on Aging (CLSA) to measure potential frailty biomarkers in biological samples (whole blood, plasma, urine) collected in over 30,000 CLSA participants.  CFN hosted a workshop in Toronto on January 15 2018, bringing together experts in the field of biomarkers, aging and frailty. The overall objectives of the workshop were to start building a consensus on potential frailty biomarker domains and identify specific frailty biomarkers to be measured in the CLSA biological samples. The workshop was structured with presentations in the morning to frame the discussions for the afternoon session, which was organized as a free-flowing discussion to benefit from the expertise of the participants. Participants and speakers were from Canada, Italy, Spain, United Kingdom and the United States.   Herein we provide pertinent background information, a summary of all the presentations with key figures and tables, and the distillation of the discussions.  In addition, moving forward, the principles CFN will use to approach frailty biomarker research and development are outlined.  Findings from the workshop are helping CFN and CLSA plan and conduct the analysis of biomarkers in the CLSA samples and which will inform a follow-up data access competition.

Key words: CFN, CLSA, frailty, frailty index, seniors, older adults, biomarkers, aging.



Older adults living with frailty represent an increasing concern for health systems due to increased vulnerability to acute stressors, increased risk of functional impairment, increased healthcare utilization including emergency department visits, hospitalizations, and increased mortality (1-4). Therefore, it is critically important to identify, as early as possible, those at imminent risk of frailty and those living with frailty. In addition, there is a need to be able to assess the severity of frailty as objectively as possible for both prognostic purposes and monitoring of response to therapeutic interventions.
There are two main views of frailty; as a syndrome or as a state arising from an accumulation of deficits. Considerable progress and increased understanding of both approaches has occurred since their introduction (5). Frailty as a phenotype can be measured by Fried or Bergman phenotype criteria (1, 6). This model derives from an attempt to characterize the clinical manifestations of vulnerability outside of multimorbidity and disability. Frailty as an accumulation of deficits can be measured by the Frailty Index (FI), which is the number of physiological deficits affecting an individual divided by the total number of deficits measured (7, 8). The theoretical underpinning for the deficit model arises from the observation that deficits increase variably with age in humans and in animals and that the risk of morbidity, functional decline and death increases with an increasing number of deficits both individually and on a population basis (9, 10).
Although the concept of frailty is widely accepted, how to best detect and measure the severity of frailty remains controversial and as a consequence, there are many clinical frailty assessment instruments utilized in practice and research (11-13). Shortcomings of current instruments include the requirement for relatively large amounts of data, the use of specialized procedures, subjective assessments and the lack of responsiveness to therapeutic interventions (14-16). Frailty biomarkers have the potential to complement the clinical evaluation of frailty including aiding in its diagnosis, assessment of severity, and evaluation of prognosis (17, 18). Although clinical frailty assessment instruments may be more effective from a population-based approach, whereas frailty biomarkers, may be able to individualize diagnosis/prognosis and personalize care by determining an individual’s biological frailty profile.
The utility of biomarkers has been demonstrated in the diagnosis of some cancers and other disorders (19-21), and to assess treatment responses and disease progression in various diseases (22, 23). In addition, biomarkers offer the promise of precision medicine where a person’s care and treatment is based on one’s genetics and biology and there is a need to extend and explore this promise to the challenges posed by frailty.
A promising venue to study frailty biomarkers are longitudinal research initiatives that collect clinical data on aging in large numbers of participants coupled with biological samples. The Canadian Longitudinal Study on Aging (CLSA) (www.clsa-elcv.ca/) which studies the aging process in over 50,000 Canadians is one of the largest and most comprehensive initiatives in the world. Selected participants undergo comprehensive clinical evaluations including frailty assessments supplemented with hand grip-strength, timed up-and-go, chair rise, 4-metre walk and standing balance. Using a FI of 90 possible health deficits with 0.25 as the threshold for frailty, approximately 7% of CLSA participants are frail, increasing to approximately 11% in those over the age of 75 (24). Biological specimens are collected in a selected cohort every three years including whole blood, serum, plasma and buffy coat containing peripheral blood mononuclear cells (Table 1).  For a biomarker to be considered for inclusion in the CLSA, it needs to have at least preliminary evidence to link the biomarker to a pathophysiological frailty pathway or mechanism.
Here we report the proceedings of a symposium convened by the Canadian Frailty Network (CFN) (www.cfn-nce.ca) in collaboration with the CLSA. The overall objective of this meeting was to inform future efforts of CFN to improve the availability of frailty biomarkers and to guide further CFN funded analyses of biological samples held by the CLSA. Specific objectives of the symposium were to:
1.    Explore the current state of evidence of biomarkers for frailty.
2.    Obtain an understanding of other frailty biomarker initiatives around the world.
3.    Obtain guidance on biomarkers that could be measured in the CLSA biological samples, in addition to those already being assayed, to identify the biological, biochemical and genetic factors/markers associated with the onset and progression of frailty in order to develop predictive, prognostic and diagnostic tests to aid in the care and treatment of people living with frailty.

Table 1 Biomarkers currently being analysed in the CLSA samples

Table 1
Biomarkers currently being analysed in the CLSA samples

Workshop details

The workshop was held in Toronto, Canada on January 15th 2018. Participants for the workshop were international stakeholders, key opinion leaders and frailty and/or biomarker experts.  They were identified using one or more of the following criteria: they were leading large scale initiatives involving frailty and the measurement of biomarkers, they were investigators on CFN-funded research grants studying frailty and measuring biomarkers, they had published peer-reviewed studies on frailty and related biomarkers, were clinicians caring for older adults living with frailty and/or were in relevant decision-making roles. The twenty-two delegate attendees included basic researchers (e.g., biochemist, pharmacologist, immunologist), clinician researchers (e.g., intensivist, geriatrician, cardiologist) and health-care administrators/policy experts.

The current state of frailty biomarkers and frailty assessment

Multiple clinical frailty assessment instruments exist, each with advantages and disadvantages and it is not clear which instrument is optimal for a particular care setting (15). In addition, due to variability in their measurement characteristics, the prevalence of frailty depends on the instrument utilized (25, 26). This variability together with the subjective nature of some frailty assessments have generated increasing interest in integrating clinical assessments with objective laboratory-based biomarker tests.   The working definition for this discussion was that a biomarker is, “a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention.” (NIH working group) (27). Some potential uses of biomarkers conceived by the 2001 NIH working group are listed in Table 2.

Table 2 Potential utility of biomarkers

Table 2
Potential utility of biomarkers


Biomarkers may lead to the identification of mechanisms and pathophysiological pathways leading to or causing frailty, and the early identification of frailty which, importantly, may be reversible in early stages (28, 29). In addition, identification and modification of treatment plans in those identified as frail may ameliorate poor outcomes from healthcare interventions (30-32). Lastly biomarkers may help with selecting interventions, monitor response to treatments, and identify those for whom some interventions are unlikely to be of benefit.
Although no single biomarker has yet proven to be of sufficient diagnostic or prognostic utility to be clinically useful for frailty, there has been increasing work on integrating laboratory evaluation in the assessment of frailty (33). As an example, a FI composed of laboratory results has been developed and shown to be effective in identifying frailty in individuals living in the community and those in long-term care facilities (17). This index of biomarkers (the FI-Lab) predicts increased risk of death in community-dwelling and institutionalized individuals and it can be combined with a clinical FI to improve prediction of outcomes in older adults (17, 18, 34-36).
A recent systematic review of frailty biomarker studies in community-dwelling individuals of trials using a validated definition of frailty and comparing two or more biomarkers was recently reported (33). Biomarkers related to immune function, inflammation, endocrine function and metabolic syndrome were the most frequently reported. There have been conflicting results regarding the utility of single biomarkers for the detection and assessment of frailty. Associations with frailty have been shown for inflammatory markers (e.g., interleukin-6 (IL-6), Tumor necrosis factor-alpha (TNF-alpha), C-reactive protein (CRP)), reduced total lymphocyte count and other markers of immunocompetence, although this has not been observed with some markers that have been associated with aging such as telomere length and oxidative stress, when studied individually (37). Other systematic reviews have come to the same conclusion. The variety of biomarkers reported in recent systematic reviews are summarized in Table 3 (33, 38-40).

Table 3 Summary of biomarkers examined in prior studies

Table 3
Summary of biomarkers examined in prior studies


Specific Frailty Biomarker Considerations

Frailty Index (FI) approach to biomarkers

Consistent with the deficit accumulation approach, Mitnitski et al. (18) re-analysed the data in Collerton et al. (37) to create an FI composed of 40 biomarkers of cellular ageing, inflammation and haematology. Like the FI-Lab, this suggests that currently available single biomarkers have relatively low information value by themselves when compared to a combination of multiple markers and an FI approach with a panel of biomarkers may be a more promising avenue of investigation, and more closely correspond to how deficits propagate to give rise to frailty (41, 42).
The deficit accumulation approach has been validated in animal models of frailty, where both laboratory-based and clinical FI tools have been developed and behave similarly as in humans (9, 43-45). Animal studies also indicate that levels of pro-inflammatory cytokines increase in proportion to FI scores (45). Further studies suggest that mechanisms that give rise to frailty are present at the cellular/subcellular levels and scale up to impact function at the level of the organ and ultimately the organism (46, 47). The availability of animal models of frailty with biomarker and clinical criteria has led to the study of possible interventions for frailty in pre-clinical models using resveratrol, caloric restriction and an angiotensin converting enzyme inhibitor (48, 49). All this is consistent with the multifactorial nature of frailty, thus requiring the evaluation of a biological network (and not a standalone biomarker) for adequately capturing the complexity of the state.
In conclusion, measuring a larger set of biomarkers (e.g., 40) is likely to be better than measuring a smaller set. Given current evidence, inflammatory markers should be included in any frailty biomarker panel as should measures of metabolism (large panel metabolomics) and other measures related to aging processes (e.g., telomere length, oxidative stress, DNA damage and repair).  Following a standard procedure for creating a FI from clinical data, for a biomarker to be included as a deficit in a FI, the biomarker should have the following characteristics, as outlined by Searle et al. (8):
•    Abnormal biomarker levels should be associated with a negative health-related outcome.
•    The risk associated with abnormal levels of the biomarker should be higher as age increases.
•    The biomarker should be neither too rare (i.e., < 1%) nor too common (i.e., > 80% by age 80).
•    Biomarker data should be available for at least 80% of individuals.

Frailty and skeletal muscle

Muscle mass, has for the most part, been neglected in frailty assessment instruments, perhaps because there are no good practical tools to measure it.  Most current methods are crude (e.g., calipers, body mass index), complex or difficult to use or access (e.g., magnetic resonance imaging). Computerized tomography (CT) scans are of increasing promise due to their availability and growing evidence indicates that muscle mass seen on CT scans, as evidenced by psoas muscle area, correlates with outcomes including mortality (50-52). In particular, muscle mass analysis can be an important measure for frailty assessment in patients who are already undergoing CT scans for other medical reasons and can be of added prognostic value to other biochemical biomarkers/tests. Although CTs are of potential utility, further research is required before they can be recommended for routine clinical use. Bioelectrical impedance analysis (BIA) and dual-energy X-ray absorptiometry (DEXA) are also commonly used for the assessment of muscle mass but their availability are limited and their ability to guide clinical practice requires further evaluation (53, 54). Overall, skeletal muscle assessment should be tailored to the objectives desired and the availability of the diagnostic test. Also different tests may be required for different care settings (55).

Anemia and hypoalbuminemia

Anemia has been shown to increase rapidly with age and is associated with adverse geriatric outcomes such as disability, reduced physical function, cognitive dysfunction and dementia (56, 57). The presence of anemia is also associated with an increased likelihood of frailty (58). It is unknown whether this is due to reduced oxygen delivery, resulting in inactivity and fatigue, or if anemia is the result of other conditions linked to frailty, including chronic inflammation and/or malnutrition. In addition, it is unknown if the successful treatment of anemia can reverse or attenuate frailty?
Reduced serum albumin has been associated with disability and mortality (59). While hypoalbuminemia was previously thought to be a marker of malnutrition, it is now known that it is more likely due to systemic inflammation (60). The incorporation of albumin and anemia into clinical frailty criteria has been shown to be predictive of death and worsening disability in selected surgical populations (e.g., transcatheter aortic valve replacement) (www.frailtytool.com) (61, 62). Moreover, these two parameters are commonly measured clinically thereby increasing their availability for the assessment of frailty.

Other biomarkers

Other biomarkers such as vitamin D and androgen levels have also been linked to frailty.  A systematic review suggests that low levels of vitamin D are associated with an increase in the risk of frailty (63), although potential benefits of vitamin D supplementation on frailty requires additional evidence (64). There is no consensus in the literature with respect to the biological role of androgens in frailty. An earlier review concluded that more work needs to be done (65), although recent longitudinal studies show an association between testosterone and FI scores (66, 67). Evidence to date suggests that testosterone treatment may be beneficial in older people (68-70). However, further evidence is required before recommending that these biomarkers be incorporated into future frailty biomarker panels.


Current frailty studies and biomarker databases

Participants of this CFN sponsored workshop are leading a number of international initiatives investigating the potential utility of biomarkers for frailty assessment. These initiatives are discussed below.

Sarcopenia & Physical fRailty IN older people: multi-componenT Treatment strategies (SPRINTT); www.mysprintt.eu/en/wp

The SPRINTT project (5 years) was started in 2014 as a private/public partnership between the European Commission and the European Federation of Pharmaceutical Industries and Associations.  The overall goal of SPRINTT is to develop innovative therapeutic interventions against physical frailty and sarcopenia as a prototype geriatric indication. SPRINTT includes a definitive large phase III randomized clinical trial (RCT) (71) comparing the efficacy of a multi-component intervention in preventing mobility disability, based on long-term structured physical activity, personalised nutritional counselling/dietary intervention and an information and communication technology intervention, versus a healthy ageing lifestyle education program.  The RCT completed recruitment in November 2018 and follow-up is planned to be completed in 2019.  One of the accomplishments of SPRINTT was the operationalization of a new condition incorporating physical frailty and sarcopenia, whose methodology was endorsed by the European Medicines Agency (EMA) (72).
For each participant biological samples are collected (whole blood, serum, plasma and urine) for future analysis (Table 4).  The collection of these biospecimens has two objectives: 1) development of discriminant biomarkers that will allow for comparison of biomarker levels between physical frailty and sarcopenia (at baseline) versus physical frailty and no sarcopenia. 2) development of prognostic and predictive biomarkers for the identification of study participants at high risk for the development of mobility disability and prediction of response to the multi-component intervention.

Table 4 Candidate biomarkers being considered for SPRINTT

Table 4
Candidate biomarkers being considered for SPRINTT

FRAILOMIC; www.frailomic.org

The main goal of the FRAILOMIC initiative is to develop clinical instruments composed of clinical data, laboratory biomarkers and omics-based laboratory biomarkers. Specifically, the three objectives of FRAILOMIC are to develop clinical instruments that can: 1) improve diagnostic accuracy of frailty in day-to-day practice, 2) predict the risk of frailty and 3) assess the prognosis of frailty in terms of death, disability and other adverse outcomes.  FRAILOMIC has four main phases, with the first two phases now complete, which have identified the biomarkers listed in Table 5.  Preliminary results show that biomarkers potentially useful for the diagnosis of frailty are different from those useful for assessing frailty risk and prognosis.

Table 5 Biomarkers identified in exploratory phase. Biomarkers identified according to their relationship with function, muscle and longevity. Selection of biomarkers attempted to be as inclusive as practical

Table 5
Biomarkers identified in exploratory phase. Biomarkers identified according to their relationship with function, muscle and longevity. Selection of biomarkers attempted to be as inclusive as practical



Biomarkers, definition of frailty and fit within clinical practice

Key to the selection of biomarkers for frailty assessment will be an agreed upon definition of frailty. However, coming to a consensus definition of frailty and choosing the appropriate clinical instruments have proven to be difficult.  Current frailty instruments/tools in use today, whether based on a deficit accumulation model or a phenotypic model have varied sensitivity, specificity, and positive and negative predictive values (73-76). Moreover, frailty assessment tools may perform differently in different populations and settings (11-13). Overall, it is very likely that there will be no single best reference clinical frailty assessment instrument, so to proceed with biomarker work, a single measurement paradigm will need to be selected and used consistently.
In regards to the selection of the reference paradigm, it should be connected with the underlying biology of frailty. It is generally agreed that frailty is a result of the age-associated accumulation of deficits across multiple systems and therefore the FI paradigm seems to be a reasonable starting point. The FI is consistent in different care settings, countries and species because of its basis in systems biology. In addition, the FI is multi-dimensional and allows for grading of frailty severity, which is an important determinant of outcome. One drawback to the FI is that it may be impractical as a clinical assessment tool since it requires large amounts of data, stemming from the results of a comprehensive evaluation of the individual but this is less of a disadvantage when used for research purposes as a reference standard.
Any adopted biomarker needs to pair with the clinical instrument used to better diagnose, predict risk and determine therapeutic responsiveness (77). The utility of the FI is that it can utilize readily measured clinical deficits, many of which are currently recorded and even exist in electronic health records (10). The FI can utilize readily available and routine laboratory tests, that can also be augmented with novel biomarkers. This would apply to laboratory tests such hemoglobin, albumin, estimated glomerular filtration rate, low density lipoprotein and glycosylated hemoglobin. If a combination of these perform well in a frailty assessment tool such as the FI, the utility of more complex, more difficult to measure and more expensive biomarkers would need to be demonstrated before they are adopted. Overall, to be most applicable and advance the possibility of personalized medicine for people living with frailty, it would be better to start broadly with the FI and then tailor biomarkers to the specific needs of the person.

Clinical frailty assessment instruments versus biomarker-based frailty assessment tools

Biomarkers may direct clinicians, but do not replace geriatric expertise. Any biomarker(s) chosen will need to assist clinical activities beyond what already exists, be easier to use and be more cost-effective. Biomarkers that are difficult and expensive to measure or require specialized equipment should be reserved for environments dealing with complex cases or for research purposes.  Some assessments, such as gait speed combined with grip strength, may already provide data that biomarkers promise to do (74, 75). Biomarkers will need to increase the sensitivity, specificity, negative predictive value and positive predictive value of clinical evaluations or instruments, especially for risk prediction and this will need to be demonstrated in clinical use. Also, for their adoption, it will need to be demonstrated that their utilization is associated with better outcomes.
When a clinical frailty assessment tool is used in combination with biomarkers, it is important that the biomarker(s) increase the utility of the tool as follows: 1) It allows the tool to better identify a population as truly frail than the clinical tool alone, and 2) It allows for better selection of a care plan for an individual than the clinical tool alone. To determine if a biomarker has these key characteristics, further research will be required once candidate biomarkers are identified. As an example, if a biomarker(s) identifies a nutrient deficiency, we need to know that acting on that information would improve outcomes. This will likely only be answered by doing randomized clinical trials with the ability to detect changes in patient-centered outcomes such as quality of life. Overall, the identification of frailty in a more rigorous manner, with attendant care plans, would be a major step forward.

Generally available versus novel biomarkers

Biomarkers that are only associated with a particular disease may not be of high interest when considering frailty since they are likely specific for the disease. Biomarkers more applicable to frailty are likely those that are closer to the physiology and basic biology of aging. Studies of the basic biology of aging has led to a number of new interventions targeting fundamental aging processes that have been found to impact development of age-related disorders, including many chronic diseases, geriatric syndromes and loss of resilience/loss of ability to respond to stressors in animal models (78, 79). These new interventions will need to be tested in human clinical trials. Biomarkers may be better able to characterize the populations enrolled in these trials to avoid the heterogeneity that is characteristic of frailty and aging. If successful, these interventions may be able to fundamentally change geriatric medicine, with the ultimate goal of increasing health-span and not necessarily life-span.

Need for a variety of biomarkers

An ideal biomarker would present early in the course of frailty and correlate with its severity. There are indications that different biomarkers are needed for risk stratification, diagnosis and prognosis. Overall, it is unlikely that a single biomarker can address all these objectives (80). Moreover, the utility of a diagnostic biomarker is dependent on the prevalence of the condition which in frailty are care setting dependent increasing from a small percentage of the general population to as much as 60% in hospital, and majority in long-term care (81-84). In very high prevalence environments, diagnostic frailty biomarkers may have limited utility. For prognosis, we need to be as precise as possible as to what they predict to make them useful to clinicians; risk of poor functional outcomes may be as important as mortality.

Broader versus narrower sets of biomarkers

For discovery, there may be value in adopting a biomarker approach based on metabolomic profiling, understanding that this approach might not have clinical value as yet but will help to generate a wide range of markers, which from a research perspective may have great utility. The added benefit of utilizing metabolomics on whole blood, plasma or urine is that once mass spectrometry (MS) or nuclear magnetic resonance (NMR) data are captured, these data can be reanalyzed in the future for new markers. Metabolomics has already produced results that are leading to frailty interventions, such as ketogenic compounds, for which clinical trials are about to start (85, 86). There are at least 20 proteins or peptides in mice related to frailty where preliminary human trials are being considered (87, 88). Also with newer techniques, proteins can be measured in very small quantities of sample (e.g., 10 microlitres). The caveat is that although MS and NMR are both powerful technologies with great discovery (research) potential, at this time translating their application to clinical settings is more difficult than other approaches, such as determining inflammation by measuring interleukins or acute phase CRP.
There are large studies utilizing metabolomics but no such large study exists for frailty investigation. However, this work has already commenced in the CLSA with 1,000 people having metabolomics analysis. Further metabolomics analysis within the CLSA database will be a unique opportunity to significantly add to the evidentiary base.



This workshop brought together experts in the field of biomarkers, aging and/or frailty to discuss frailty biomarkers. This discussion will inform CFN and CLSA in planning for the analysis of a select group of biomarkers in the CLSA samples and a follow-up data access competition to utilize these new biomarker data in conjunction with the wealth of clinical data collected by the CLSA.
There was considerable discussion regarding the appropriate way to think about frailty and how the frame of reference will influence the choice of biomarkers to analyze.  Considerations discussed included probabilistic medicine and population-based care versus precision medicine or individualized medicine and how these overall approaches should influence the use of biomarkers and their incorporation into practice and ultimately influence policy.  There was debate about care settings and biomarker utility across care settings. In addition, the overall objective of using biomarkers as diagnostic tools for frailty versus their use to quantify frailty risk versus their use as frailty prognostic tools were topics of high interest and discussion. By the end of the workshop, there was a clear sense that research and clinical trials need to be conducted regarding frailty biomarkers and the need for the development of biomarker tools to be used either alone or with currently existing clinical frailty assessment instruments such as the FI.
Arising from the meeting, the list of principles to guide future CFN biomarker initiatives including its partnership with the CLSA, are as follows:
1)    Biomarkers should reflect a pathophysiological pathway or mechanism that is fundamental to frailty onset, development/progression and severity. Conceptually there maybe two categories of biomarkers:
i)    Biomarkers that are linked with frailty but are not causal to the pathophysiology of frailty. These would not be actionable.
ii)    Biomarkers that are a component of the pathophysiology of frailty and have a causal role. These would be actionable such that the modulation of the biomarker could directly affect the onset or severity of frailty and/or progression of frailty.
2)    The utility of biomarkers can be classified into two different types:
i)    Biomarkers to increase the utility of (or support) existing clinical frailty measures (e.g., FI).
ii)    Biomarkers to be used independently of clinical frailty measures.
3)    Biomarkers should be able to be embedded in clinical assessments and tools, but more research on how to best achieve this is needed. Concomitant use of both a clinical frailty assessment instrument and biomarkers is likely to be the optimal method to bring about personalized frailty assessment and individualized care plans.
4)    Biomarkers chosen for a clinical tool should be evaluated on their ability to accomplish the ultimate clinical purpose. For instance, biomarkers used for diagnosis may be different from those used for risk assessment, which may differ from those used for prognosis.
5)    Different care settings are likely to require different biomarkers due to variation in prevalence of both frailty and biomarkers in different populations.
6)    Any candidate biomarker should be validated in different populations, care settings and environments.
7)    An ideal frailty biomarker would be able to measure the effectiveness of an intervention.
8)    Practical considerations related to ease of measurement (i.e., special instruments and/or expertise required) and ease of securing biological samples (e.g., tissue biopsy vs blood sample collection) should be considered when selecting frailty biomarkers.


Acknowledgements: The Canadian Frailty Network (CFN) is a pan-Canadian network focused on the care of older citizens living with frailty. CFN is comprised of nearly 3,500 corporate and non-profit partners, researchers, scientists, health-care professionals, citizens, students, trainees, educators, and decision-makers. CFN supports and catalyzes original research and innovations to improve the care and quality of life of Canadians living with frailty across all settings of care. The Network also trains the next generation of health-care professionals and scientists. CFN is funded by the Government of Canada through the Networks of Centres of Excellence (NCE) Program. In early 2017, in recognition of the work done in its first five years of operation, the Government of Canada announced funding for a second five-term (2017-2022).

Conflict of interest disclosures: Matteo Cesari, Emanuele Marzetti and Leocadio Rodriguez Mañas are partners of the SPRINTT consortium, which is partly funded by the European Federation of Pharmaceutical Industries and Associations (EFPIA) . Kenneth Rockwood is President and Chief Science Officer of DGI Clinical, which in the last five years has contracts with pharma and device manufacturers (Baxter, Baxalta, Shire, Hollister, Nutricia, Roche, Otsuka) on individualized outcome measurement. In 2017 he attended an advisory board meeting with Lundbeck. He is Associate Director of the Canadian Consortium on Neurodegeneration in Aging, which is funded by the Canadian Institutes of Health Research, and with additional funding from the Alzheimer Society of Canada and several other charities, as well as, in first phase (2013-2018), from Pfizer Canada and Sanofi Canada.   He receives career support from the Dalhousie Medical Research Foundation as the Kathryn Allen Weldon Professor of Alzheimer Research, and research support from the Canadian Institutes of Health Research, the Nova Scotia Health Research Foundation, the Capital Health Research Fund and the Fountain Family Innovation Fund of the Nova Scotia Health Authority Foundation.



1.    Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, et al. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci. 2001;56(3):M146-56.
2.    Shamliyan T, Talley KM, Ramakrishnan R, Kane RL. Association of frailty with survival: a systematic literature review. Ageing Res Rev. 2013;12(2):719-36.
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;59(3):255-63.
4.    Bagshaw SM, Stelfox HT, McDermid RC, Rolfson DB, Tsuyuki RT, Baig N, et al. Association between frailty and short- and long-term outcomes among critically ill patients: a multicentre prospective cohort study. CMAJ. 2014;186(2):E95-102.
5.    Rockwood K, Howlett SE. Fifteen years of progress in understanding frailty and health in aging. BMC Med. 2018;16(1):220.
6.    Bergman H, Ferrucci L, Guralnik J, Hogan DB, Hummel S, Karunananthan S, et al. Frailty: an emerging research and clinical paradigm–issues and controversies. J Gerontol A Biol Sci Med Sci. 2007;62(7):731-7.
7.    Mitnitski AB, Mogilner AJ, Rockwood K. Accumulation of deficits as a proxy measure of aging. ScientificWorldJournal. 2001;1:323-36.
8.    Searle SD, Mitnitski A, Gahbauer EA, Gill TM, Rockwood K. A standard procedure for creating a frailty index. BMC Geriatr. 2008;8:24.
9.    Rockwood K, Blodgett JM, Theou O, Sun MH, Feridooni HA, Mitnitski A, et al. A Frailty Index Based On Deficit Accumulation Quantifies Mortality Risk in Humans and in Mice. Sci Rep. 2017;7:43068.
10.    Clegg A, Bates C, Young J, Ryan R, Nichols L, Ann Teale E, et al. Development and validation of an electronic frailty index using routine primary care electronic health record data. Age Ageing. 2016;45(3):353-60.
11.    Sternberg SA, Wershof Schwartz A, Karunananthan S, Bergman H, Mark Clarfield A. The identification of frailty: a systematic literature review. J Am Geriatr Soc. 2011;59(11):2129-38.
12.    Cesari M, Gambassi G, van Kan GA, Vellas B. The frailty phenotype and the frailty index: different instruments for different purposes. Age Ageing. 2014;43(1):10-2.
13.    Abbasi M, Rolfson D, Khera AS, Dabravolskaj J, Dent E, Xia L. Identification and management of frailty in the primary care setting. CMAJ. 2018;190(38):E1134-E40.
14.    Gilardi F, Capanna A, Ferraro M, Scarcella P, Marazzi MC, Palombi L, et al. Frailty screening and assessment tools: a review of characteristics and use in Public Health. Ann Ig. 2018;30(2):128-39.
15.    Dent E, Kowal P, Hoogendijk EO. Frailty measurement in research and clinical practice: A review. Eur J Intern Med. 2016;31:3-10.
16.    de Vries NM, Staal JB, van Ravensberg CD, Hobbelen JS, Olde Rikkert MG, Nijhuis-van der Sanden MW. Outcome instruments to measure frailty: a systematic review. Ageing Res Rev. 2011;10(1):104-14.
17.    Howlett SE, Rockwood MR, Mitnitski A, Rockwood K. Standard laboratory tests to identify older adults at increased risk of death. BMC Med. 2014;12:171.
18.    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 Med. 2015;13:161.
19.    Sylman JL, Mitrugno A, Atallah M, Tormoen GW, Shatzel JJ, Tassi Yunga S, et al. The Predictive Value of Inflammation-Related Peripheral Blood Measurements in Cancer Staging and Prognosis. Front Oncol. 2018;8:78.
20.    Yu IS, Cheung WY. A Contemporary Review of the Treatment Landscape and the Role of Predictive and Prognostic Biomarkers in Pancreatic Adenocarcinoma. Can J Gastroenterol Hepatol. 2018;2018:1863535.
21.    Shang J, Yamashita T, Fukui Y, Song D, Li X, Zhai Y, et al. Different Associations of Plasma Biomarkers in Alzheimer’s Disease, Mild Cognitive Impairment, Vascular Dementia, and Ischemic Stroke. J Clin Neurol. 2018;14(1):29-34.
22.    Chen RY, Via LE, Dodd LE, Walzl G, Malherbe ST, Loxton AG, et al. Using biomarkers to predict TB treatment duration (Predict TB): a prospective, randomized, noninferiority, treatment shortening clinical trial. Gates Open Res. 2017;1:9.
23.    Kostikas K, Brindicci C, Patalano F. Blood Eosinophils as Biomarkers to Drive Treatment Choices in Asthma and COPD. Curr Drug Targets. 2018;19(16):1882-96.
24.    Kanters DM, Griffith LE, Hogan DB, Richardson J, Patterson C, Raina P. Assessing the measurement properties of a Frailty Index across the age spectrum in the Canadian Longitudinal Study on Aging. J Epidemiol Community Health. 2017;71(8):794-9.
25.    van Iersel MB, Rikkert MG. Frailty criteria give heterogeneous results when applied in clinical practice. J Am Geriatr Soc. 2006;54(4):728-9.
26.    Kehler DS, Ferguson T, Stammers AN, Bohm C, Arora RC, Duhamel TA, et al. Prevalence of frailty in Canadians 18-79 years old in the Canadian Health Measures Survey. Bmc Geriatr. 2017;17(1):28.
27.    Atkinson A, Jr; Magnuson, Warren G.; Colburn, Wayne A.; DeGruttola, Victor G; DeMets, David L.; Downing, Gregory J; Hoth, Daniel F; Oates, John A; Peck, Carl C; Schooley, Robert T; Spilker, Bert A; Woodcock, Janet; Zeger, Scott L. . Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Ther. 2001;69(3):89-95.
28.    Jha SR, Hannu MK, Newton PJ, Wilhelm K, Hayward CS, Jabbour A, et al. Reversibility of Frailty After Bridge-to-Transplant Ventricular Assist Device Implantation or Heart Transplantation. Transplant Direct. 2017;3(7):e167.
29.    Gustavson AM, Falvey JR, Jankowski CM, Stevens-Lapsley JE. Public Health Impact of Frailty: Role of Physical Therapists. J Frailty Aging. 2017;6(1):2-5.
30.    Mosquera C, Spaniolas K, Fitzgerald TL. Impact of frailty on surgical outcomes: The right patient for the right procedure. Surgery. 2016;160(2):272-80.
31.    Sepehri A, Beggs T, Hassan A, Rigatto C, Shaw-Daigle C, Tangri N, et al. The impact of frailty on outcomes after cardiac surgery: a systematic review. J Thorac Cardiovasc Surg. 2014;148(6):3110-7.
32.    Fagard K, Leonard S, Deschodt M, Devriendt E, Wolthuis A, Prenen H, et al. The impact of frailty on postoperative outcomes in individuals aged 65 and over undergoing elective surgery for colorectal cancer: A systematic review. J Geriatr Oncol. 2016;7(6):479-91.
33.    Lee L, Patel T, Hillier LM, Maulkhan N, Slonim K, Costa A. Identifying frailty in primary care: A systematic review. Geriatr Gerontol Int. 2017;17(10):1358-77.
34.    Rockwood K, McMillan M, Mitnitski A, Howlett SE. A Frailty Index Based on Common Laboratory Tests in Comparison With a Clinical Frailty Index for Older Adults in Long-Term Care Facilities. J Am Med Dir Assoc. 2015;16(10):842-7.
35.    Blodgett JM, Theou O, Howlett SE, Rockwood K. A frailty index from common clinical and laboratory tests predicts increased risk of death across the life course. Geroscience. 2017.
36.    Ritt M, Jager J, Ritt JI, Sieber CC, Gassmann KG. Operationalizing a frailty index using routine blood and urine tests. Clin Interv Aging. 2017;12:1029-40.
37.    Collerton J, Martin-Ruiz C, Davies K, Hilkens CM, Isaacs J, Kolenda C, et al. Frailty and the role of inflammation, immunosenescence and cellular ageing in the very old: cross-sectional findings from the Newcastle 85+ Study. Mech Ageing Dev. 2012;133(6):456-66.
38.    Fernandez-Garrido J, Ruiz-Ros V, Buigues C, Navarro-Martinez R, Cauli O. Clinical features of prefrail older individuals and emerging peripheral biomarkers: a systematic review. Arch Gerontol Geriatr. 2014;59(1):7-17.
39.    Sanchez-Flores M, Marcos-Perez D, Costa S, Teixeira JP, Bonassi S, Pasaro E, et al. Oxidative stress, genomic features and DNA repair in frail elderly: A systematic review. Ageing Res Rev. 2017;37:1-15.
40.    Soysal P, Isik AT, Carvalho AF, Fernandes BS, Solmi M, Schofield P, et al. Oxidative stress and frailty: A systematic review and synthesis of the best evidence. Maturitas. 2017;99:66-72.
41.    Farrell SG, Mitnitski AB, Theou O, Rockwood K, Rutenberg AD. Probing the network structure of health deficits in human aging. Phys Rev E. 2018;98(3).
42.    Rutenberg AD, Mitnitski AB, Farrell SG, Rockwood K. Unifying aging and frailty through complex dynamical networks. Exp Gerontol. 2018;107:126-9.
43.    Parks RJ, Fares E, Macdonald JK, Ernst MC, Sinal CJ, Rockwood K, et al. A procedure for creating a frailty index based on deficit accumulation in aging mice. J Gerontol A Biol Sci Med Sci. 2012;67(3):217-27.
44.    Whitehead JC, Hildebrand BA, Sun M, Rockwood MR, Rose RA, Rockwood K, et al. A clinical frailty index in aging mice: comparisons with frailty index data in humans. J Gerontol A Biol Sci Med Sci. 2014;69(6):621-32.
45.    Kane AE, Keller KM, Heinze-Milne S, Grandy SA, Howlett SE. A Murine Frailty index based on Clinical and Laboratory Measurements: Links between Frailty and Pro-inflammatory Cytokines differ in a Sex-specific Manner. J Gerontol A Biol Sci Med Sci. 2018.
46.    Jansen HJ, Moghtadaei M, Mackasey M, Rafferty SA, Bogachev O, Sapp JL, et al. Atrial structure, function and arrhythmogenesis in aged and frail mice. Sci Rep. 2017;7:44336.
47.    Feridooni HA, Sun MH, Rockwood K, Howlett SE. Reliability of a Frailty Index Based on the Clinical Assessment of Health Deficits in Male C57BL/6J Mice. J Gerontol A Biol Sci Med Sci. 2015;70(6):686-93.
48.    Kane AE, Hilmer SN, Boyer D, Gavin K, Nines D, Howlett SE, et al. Impact of Longevity Interventions on a Validated Mouse Clinical Frailty Index. J Gerontol A Biol Sci Med Sci. 2016;71(3):333-9.
49.    Keller K, Kane A, Heinze-Milne S, Grandy SA, Howlett SE. Chronic treatment with the ACE inhibitor enalapril attenuates the development of frailty and differentially modifies pro-and anti-inflammatory cytokines in aging male and female C57BL/6 mice. J Gerontol A Biol Sci Med Sci. 2018.
50.    Mamane S, Mullie L, Piazza N, Martucci G, Morais J, Vigano A, et al. Psoas Muscle Area and All-Cause Mortality After Transcatheter Aortic Valve Replacement: The Montreal-Munich Study. Can J Cardiol. 2016;32(2):177-82.
51.    Zuckerman J, Ades M, Mullie L, Trnkus A, Morin JF, Langlois Y, et al. Psoas Muscle Area and Length of Stay in Older Adults Undergoing Cardiac Operations. Ann Thorac Surg. 2017;103(5):1498-504.
52.    Bibas L, Saleh E, Al-Kharji S, Chetrit J, Mullie L, Cantarovich M, et al. Muscle Mass and Mortality After Cardiac Transplantation. Transplantation. 2018;102(12):2101-7.
53.    Cruz-Jentoft AJ, Bahat G, Bauer J, Boirie Y, Bruyere O, Cederholm T, et al. Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing. 2019;48(1):16-31.
54.    Studenski SA, Peters KW, Alley DE, Cawthon PM, McLean RR, Harris TB, et al. The FNIH sarcopenia project: rationale, study description, conference recommendations, and final estimates. J Gerontol A Biol Sci Med Sci. 2014;69(5):547-58.
55.    Beaudart C, McCloskey E, Bruyere O, Cesari M, Rolland Y, Rizzoli R, et al. Sarcopenia in daily practice: assessment and management. Bmc Geriatr. 2016;16(1):170.
56.    Penninx BW, Pahor M, Cesari M, Corsi AM, Woodman RC, Bandinelli S, et al. Anemia is associated with disability and decreased physical performance and muscle strength in the elderly. J Am Geriatr Soc. 2004;52(5):719-24.
57.    Hong CH, Falvey C, Harris TB, Simonsick EM, Satterfield S, Ferrucci L, et al. Anemia and risk of dementia in older adults: findings from the Health ABC study. Neurology. 2013;81(6):528-33.
58.    Chaves PH, Semba RD, Leng SX, Woodman RC, Ferrucci L, Guralnik JM, et al. Impact of anemia and cardiovascular disease on frailty status of community-dwelling older women: the Women’s Health and Aging Studies I and II. J Gerontol A Biol Sci Med Sci. 2005;60(6):729-35.
59.    Corti MC, Guralnik JM, Salive ME, Sorkin JD. Serum albumin level and physical disability as predictors of mortality in older persons. JAMA. 1994;272(13):1036-42.
60.    Don BR, Kaysen G. Serum albumin: relationship to inflammation and nutrition. Semin Dial. 2004;17(6):432-7.
61.    Robinson TN, Eiseman B, Wallace JI, Church SD, McFann KK, Pfister SM, et al. Redefining geriatric preoperative assessment using frailty, disability and co-morbidity. Ann Surg. 2009;250(3):449-55.
62.    Afilalo J, Lauck S, Kim DH, Lefevre T, Piazza N, Lachapelle K, et al. Frailty in Older Adults Undergoing Aortic Valve Replacement: The FRAILTY-AVR Study. J Am Coll Cardiol. 2017;70(6):689-700.
63.    Zhou J, Huang P, Liu P, Hao Q, Chen S, Dong B, et al. Association of vitamin D deficiency and frailty: A systematic review and meta-analysis. Maturitas. 2016;94:70-6.
64.    Bruyere O, Cavalier E, Buckinx F, Reginster JY. Relevance of vitamin D in the pathogenesis and therapy of frailty. Curr Opin Clin Nutr Metab Care. 2017;20(1):26-9.
65.    Afilalo J. Androgen deficiency as a biological determinant of frailty: hope or hype? J Am Geriatr Soc. 2014;62(6):1174-8.
66.    Swiecicka A, Eendebak R, Lunt M, O’Neill TW, Bartfai G, Casanueva FF, et al. Reproductive Hormone Levels Predict Changes in Frailty Status in Community-Dwelling Older Men: European Male Ageing Study Prospective Data. J Clin Endocrinol Metab. 2018;103(2):701-9.
67.    Hsu B, Cumming RG, Naganathan V, Blyth FM, Le Couteur DG, Seibel MJ, et al. Associations between circulating reproductive hormones and SHBG and prevalent and incident metabolic syndrome in community-dwelling older men: the Concord Health and Ageing in Men Project. J Clin Endocrinol Metab. 2014;99(12):E2686-91.
68.    Srinivas-Shankar U, Roberts SA, Connolly MJ, O’Connell MD, Adams JE, Oldham JA, et al. Effects of testosterone on muscle strength, physical function, body composition, and quality of life in intermediate-frail and frail elderly men: a randomized, double-blind, placebo-controlled study. J Clin Endocrinol Metab. 2010;95(2):639-50.
69.    Toma M, McAlister FA, Coglianese EE, Vidi V, Vasaiwala S, Bakal JA, et al. Testosterone supplementation in heart failure: a meta-analysis. Circ Heart Fail. 2012;5(3):315-21.
70.    Neto WK, Gama EF, Rocha LY, Ramos CC, Taets W, Scapini KB, et al. Effects of testosterone on lean mass gain in elderly men: systematic review with meta-analysis of controlled and randomized studies. Age (Dordr). 2015;37(1):9742.
71.    Landi F, Cesari M, Calvani R, Cherubini A, Di Bari M, Bejuit R, et al. The “Sarcopenia and Physical fRailty IN older people: multi-componenT Treatment strategies” (SPRINTT) randomized controlled trial: design and methods. Aging Clin Exp Res. 2017;29(1):89-100.
72.    Cesari M, Marzetti E, Calvani R, Vellas B, Bernabei R, Bordes P, et al. The need of operational paradigms for frailty in older persons: the SPRINTT project. Aging Clin Exp Res. 2017;29(1):3-10.
73.    Warnier RM, van Rossum E, van Velthuijsen E, Mulder WJ, Schols JM, Kempen GI. Validity, Reliability and Feasibility of Tools to Identify Frail Older Patients in Inpatient Hospital Care: A Systematic Review. J Nutr Health Aging. 2016;20(2):218-30.
74.    Lee L, Patel T, Costa A, Bryce E, Hillier LM, Slonim K, et al. Screening for frailty in primary care: Accuracy of gait speed and hand-grip strength. Can Fam Physician. 2017;63(1):e51-e7.
75.    Lee LP, Tejal; Hillier, Loretta, M; Locklin, Jason; Milligan, James; Pefanis, John; Costa. Andrew; Lee, Joseph; Slonim, Karen; Giangregorio, Lora; Hunter, Susan; Keller, Heather; Boscart, Veronique Frailty Screening and Case-Finding for Complex Chronic Conditions in Older Adults in Primary Care. Geriatrics. 2018;3(3).
76.    van Loon IN, Goto NA, Boereboom FTJ, Bots ML, Verhaar MC, Hamaker ME. Frailty Screening Tools for Elderly Patients Incident to Dialysis. Clin J Am Soc Nephrol. 2017;12(9):1480-8.
77.    Rodriguez-Manas L, Fried LP. Frailty in the clinical scenario. Lancet. 2015;385(9968):e7-e9.
78.    Tchkonia T, Kirkland JL. Aging, Cell Senescence, and Chronic Disease: Emerging Therapeutic Strategies. JAMA. 2018;320(13):1319-20.
79.    Xu M, Pirtskhalava T, Farr JN, Weigand BM, Palmer AK, Weivoda MM, et al. Senolytics improve physical function and increase lifespan in old age. Nat Med. 2018;24(8):1246-56.
80.    Cardoso AL, Fernandes A, Aguilar-Pimentel JA, de Angelis MH, Guedes JR, Brito MA, et al. Towards frailty biomarkers: Candidates from genes and pathways regulated in aging and age-related diseases. Ageing Res Rev. 2018;47:214-77.
81.    Collard RM, Boter H, Schoevers RA, Oude Voshaar RC. Prevalence of frailty in community-dwelling older persons: a systematic review. J Am Geriatr Soc. 2012;60(8):1487-92.
82.    Le Maguet P, Roquilly A, Lasocki S, Asehnoune K, Carise E, Saint Martin M, et al. Prevalence and impact of frailty on mortality in elderly ICU patients: a prospective, multicenter, observational study. Intensive Care Med. 2014;40(5):674-82.
83.    Kojima G. Prevalence of Frailty in Nursing Homes: A Systematic Review and Meta-Analysis. J Am Med Dir Assoc. 2015;16(11):940-5.
84.    Chong E, Ho E, Baldevarona-Llego J, Chan M, Wu L, Tay L. Frailty and Risk of Adverse Outcomes in Hospitalized Older Adults: A Comparison of Different Frailty Measures. J Am Med Dir Assoc. 2017;18(7):638 e7- e11.
85.    Longo VD, Antebi A, Bartke A, Barzilai N, Brown-Borg HM, Caruso C, et al. Interventions to Slow Aging in Humans: Are We Ready? Aging Cell. 2015;14(4):497-510.
86.    Newman JC, Covarrubias AJ, Zhao M, Yu X, Gut P, Ng CP, et al. Ketogenic Diet Reduces Midlife Mortality and Improves Memory in Aging Mice. Cell Metab. 2017;26(3):547-57 e8.
87.    Lange KWL, Katharina M.; Makulska-Gertruda, Ewelina; Nakamura, Yukiko; Reissmann, Andreas; Kanaya, Shigehiko; Hausera, Joachim. Ketogenic diets and Alzheimer’s disease. Foo Science and Human Wellness. 2017;6(1):1-9.
88.    Han YM, Bedarida T, Ding Y, Somba BK, Lu Q, Wang Q, et al. beta-Hydroxybutyrate Prevents Vascular Senescence through hnRNP A1-Mediated Upregulation of Oct4. Mol Cell. 2018;71(6):1064-78 e5.



S. Satake1,2, H. Shimokata3,4, K. Senda5, I. Kondo6,7, H. Arai8,9, K. Toba10


1. Section of Frailty Prevention, Department of Frailty Research, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology; 2. Department of Geriatric Medicine, Hospital, National Center for Geriatrics and Gerontology; 3. Section of Longitudinal Study of Aging, National Institute for Longevity Science (NILS-LSA), National Center for Geriatrics and Gerontology; 4. Graduate School of Nutritional Sciences, Nagoya University of Arts and Sciences; 5. Department of Clinical Research Promotion, Innovation Center for Clinical Research, National Center for Geriatrics and Gerontology; 6. Director, Center of Assistive Robotics and Rehabilitation for Longevity and Good Health, National Center for Geriatrics and Gerontology; 7. Department of Rehabilitation Medicine, Hospital, National Center for Geriatrics and Gerontology; 8. Director, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology; 9. Director, Hospital, National Center for Geriatrics and Gerontology; 10. President, National Center for Geriatrics and Gerontology.
Corresponding author: Shosuke Satake, Section of Frailty Prevention, Department of Frailty Research, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, 7-430, Morioka-cho, Obu, Aichi 474-8511, Japan, Phone: +81-562-46-2311, FAX: +81-562-44-8518, e-mail address: satakes@ncgg.go.jp

J Frailty Aging 2019;in press
Published online February 20, 2019, http://dx.doi.org/10.14283/jfa.2019.3



The Kihon Checklist (KCL) is a structured questionnaire consisting of 7 domains to assess seniors’ function in daily living. The aim of this study was to examine which domains of the KCL can predict incident dependency and mortality. The municipality sent a KCL questionnaire to independent seniors in Higashi-ura Town and collected the answers of the 5542 seniors who provided complete answers. Their incident dependency and mortality were followed-up for 2.5 years. A Cox proportional hazard model indicated that meeting any of the criteria in instrumental activities of daily living, physical, nutrition, and mood domains significantly predicted the risk of dependency, whereas meeting any of the criteria in physical, nutrition and socialization domains significantly predicted the risk of mortality. Category assessment by the KCL could be useful to predict incident dependency and all-cause mortality..

Key words: Self-reported questionnaire, frailty, category assessment, long-term care, older adults.



The Kihon Checklist (KCL), which was developed by the Ministry of Health, Labor and Welfare (MHLW) in Japan, is extensively used to assess seniors’ physical, mental, and social functions in daily lives and to identify older adults who are at risk of requiring support or care in the near future (1). It is a self-reported questionnaire that consists of 25 yes/no questions regarding 7 domains, instrumental activities of daily living (IADL), physical, nutrition, eating, socialization, memory, and mood (2). Difficulty with any question is counted as a score in the KCL, with a higher score in each domain of the checklist indicating a higher risk of requiring support or care in that domain. The MHLW proposes original criteria to identify vulnerable seniors for disability and supplemental criteria for specialized supports (3).
In previous studies, several investigators reported how well the original and supplemental criteria of the KCL could predict incident dependency in community-dwelling older adults (3-5). However, there have been no reports in which the relationship between each domain of the KCL and all-cause mortality was analyzed, as far as we know.
The aim of this study was to examine which domains of the KCL were related to all-cause mortality in addition to new-onset dependency in the Japanese community-dwelling population.



Design and subjects

Of all senior residents aged 65 years and older in Higashi-ura Town in April, 2010, the municipal government identified 8091 independent older persons who were not certified as long-term care insurance (LTCI) service need. They sent a KCL questionnaire to the independent seniors and asked them to return it after answering all questions. Among the respondents, the independent older residents who filled in all of the questions of the questionnaire were selected as the subjects eligible for this study. The subjects’ baseline characteristics and KCL data were collected. Information about a new LTCI certification and death within 2.5 years was given by the municipal government. The Ethics Committee of the National Center for Geriatrics and Gerontology, Obu, Japan and Higashi-ura municipal assembly approved the study protocol.

Category assessments

Among the original criteria, a score of 3 or more in the physical domain (#6-10), a score of 2 in the nutrition domain (#11 and #12), and a score of 2 or more in the eating domain (#13-15) indicate physical decline, malnutrition, and oral dysfunction, respectively (3). In addition, a score of 1 or more in the memory domain (#18-20) and a score of 2 or more in the mood domain (#21-25) suggest cognitive impairment and depressive mood, respectively, based on the supplemental criteria (3). Homebound status was defined as an answer of “no” to question #16 in the socialization domain (#16 and #17). Because there is no published cut-off value for the IADL domain (#1-5), a cut-off value of a score higher than one point was used in this study. Subjects who did not meet each criterion were considered controls.

Definition of dependency

Dependency was defined as having a certification for needing the LTCI service in this study. In the LTCI service system, certification for LTCI service need is separately assessed by entrusted investigators from responsible municipal governments and medical doctors in charge of the senior who applied for certification by the LTCI. Then, based on their reports regarding dependency in activities of daily living and comorbidities, the examining committee composed of municipal staff, medical doctors, and community health nurses rich in experience in the geriatric field decides the need for certification and its grade (6). Information on the LTCI certification and death of all senior residents was collected by the municipal government.

Statistical analysis

The chi-squared test was used to analyze the differences in the baseline characteristics and the incidences of dependency and mortality within 2.5 years between cases meeting any of the criteria and controls. Cox proportional hazards model regression analyses were used to estimate hazard ratios (HRs) and construct 95% confidence intervals (CIs) of cases in each domain compared to controls, adjusting for age, sex, and all-domains except the target domain. All analyses were conducted using the R statistical package version 3.2.2. (R project for Statistical Computing, Vienna, Austria) (7). A p value of less than 0.05 was considered significant.



Among the 9367 residents who were 65 years and older, 1276 seniors who had already been certified as requiring care or support in their daily lives were excluded. The KCL questionnaire was sent to the remaining older residents. Although 5638 seniors replied and filled in the questionnaire, 5542 seniors (68.5%) filled it out completely and were eligible for this study.
The mean age of the study subjects was 72.6 years, and 46.4% were men. The subjects’ baseline characteristics and KCL data have been described elsewhere (8). The percentages of the subjects meeting each criterion of the KCL were 25.7%, 15.2%, 1.2%, 13.5%, 7.4%, 33.7%, and 17.4% for IADL, physical, nutrition, eating, socialization, memory, and mood domains, respectively. The percentages of seniors who had new LTCI certifications (Figure 1a) and died (Figure 1b) over the 2.5 years were significantly higher in cases meeting any of the criteria in each domain than in controls. The Cox proportional hazard model adjusted for age, sex, and 6 domains except the target domain indicated that IADL, physical, nutrition, and mood domains were significant predictors for the risk of dependency, compared to control, with HRs of 1.696 (95% CI: 1.371-2.099), 1.938 (95% CI: 1.548-2.426), 1.824 (95% CI: 1.047-3.175), and 1.892 (95% CI: 1.522-2.352), respectively (Figure 2a). On the other hand, the risk of all-cause mortality could be predicted by physical, nutrition, and socialization domains, with HRs of 1.875 (95% CI: 1.196-2.941), 2.645 (1.071-6.530), and 1.843 (1.123-3.024), respectively (Figure 2b).

Figure 1 Percentages of subjects (a) who had new certification for long-term care insurance service need, and (b) who died within 2.5 years in the case and control groups of each domain of the KCL

Figure 1
Percentages of subjects (a) who had new certification for long-term care insurance service need, and (b) who died within 2.5 years in the case and control groups of each domain of the KCL


Figure 2 Hazard ratios of incident dependency (a) and all-cause mortality for each domain of the KCL. Lines indicate 95% confidence intervals

Figure 2
Hazard ratios of incident dependency (a) and all-cause mortality for each domain of the KCL. Lines indicate 95% confidence intervals



In this study, both physical and nutrition domains in the KCL could significantly predict incident dependency and all-cause mortality within 2.5 years. In addition, IADL and mood domains were significant predictors of dependency, while the socialization domain significantly predicted mortality. From the view point of aging process, the deficits in the IADL or mood domain may be an early sign for predicting adverse health outcomes, whereas the deficits in the socialization domain may be a later sign or serious indication.
We previously reported that the total KCL score could be a useful index to assess frailty status and to predict new incidences of dependency and mortality (8, 9). However, it was unclear which domain could affect independent seniors’ health in the future. In this sense, our results of this study indicate that category assessment by the KCL could be useful to predict the impact on adverse health outcomes.
Several previous studies using the KCL showed that it could predict new certification for LTCI service need (3-5) or a deterioration in the Tokyo Metropolitan Institute of Gerontology Index of Competence (10). Recently, Kamegaya et al. reported the predictive ability of the 6 domains of the KCL, except the IADL domain in which the MHLW did not show the public cutoff point, for the risk of 3-year incident LTCI certification in 21,325 community-dwellers (5). They reported that physical, nutrition, memory, and mood domains showed significant odds ratios to predict new certification for LTCI service need on logistic regression analysis incorporating age, sex, and six domains into the model as covariates. On the other hand, we proposed a cutoff point of the IADL domain based on this community-based complete survey and analyzed new incident dependency incorporating all 7 domains of the KCL into the Cox proportional hazard model. Unlike the previous analysis, the present analysis did not indicate that the memory domain could significantly predict the new incidence of dependency. This difference is probably due to whether the IADL domain was simultaneously incorporated into the analytical model, because IADL is related to cognitive impairment (11), or due to a lack of analytic power in this study.
Furthermore, it was found that physical, nutrition, and socialization domains of the KCL could significantly predict all-cause mortality. In particular, seniors meeting the socialization domain criterion of the KCL, which means homebound status, had a higher risk of death independent of other domains. Mortality of subjects without LTCI certification within 2.5 years was significantly higher in people with problems in the socialization domain than in those without (data not shown). Recently, the co-existence of social isolation and homebound status was reported to increase the risk of all-cause mortality by Sakurai et al (12). The present result also supported their report, although homebound status was assessed in the present study by asking just one question, ‘Do you go out at least once a week?’.
The limitations of this study are 1) the limited number of subjects answering all questions, 2) insufficient medical information at baseline, and 3) indirect assessment of dependency, because new certification for LTCI service need was selected as indicating new incident dependency.
In conclusion, the results of the present study showed that the category assessment of the KCL could predict incident dependency and all-cause mortality in the near future. It is important to identify the problematic domains of seniors’ function in daily living.

Funding: This study was supported by the Research Fund for Longevity Science (22-1, 25-11, and 30-6) from the National Center for Geriatrics and Gerontology, Japan. The sponsor had no role in the design and conduct of the study; in the collection, analysis, and interpretation of the manuscript; or in the review or approval of the manuscript.
Acknowledgements: The authors greatly appreciate the municipal staff’s help and efforts.
Conflict of interest: None.



1.    Japanese Ministry of Health, Labor, and Welfare. The Manuals of the Evaluation for Ability to Perform Daily Activities on Preventive Care. 2005. https://www.mhlw.go.jp/topics/2009/05/dl/tp0501-1c_0001.pdf. Accessed 5 October 2012.
2.     Sewo Sampaio PY, Sampaio RA, Yamada M, Arai H. Systematic review of the Kihon Checklist: Is it a reliable assessment of frailty? Geriatr Gerontol Int. 2016; 16: 893-902
3.     Tomata Y, Hozawa A, Ohmori-Matsuda K, et al. Validation of the Kihon Checklist for predicting the risk of 1-year incident long-term care insurance certification: the Ohsaki Cohort 2006 Study. Nihon Koshu Eisei Zasshi. 2011; 58: 3-13.
4.     Fukutomi E, Okumiya K, Wada T, et al. Relationships between each category of 25-item frailty risk assessment (Kihon Checklist) and newly certified older adults under Long-Term Care Insurance: A 24-month follow-up study in a rural community in Japan. Geriatr Gerontol Int. 2015; 15: 864-871.
5.     Kamegaya T, Yamaguchi H, Hayashi K. Evaluation by the Basic Checklist and the risk of 3 years incident long-term care insurance certification. J Gen Fam Med. 2017; 18: 230-236.
6.     Japanese Ministry of Health, Labor, and Welfare. Long-term Care Insurance System of Japan. https://www.mhlw.go.jp/english/policy/care-welfare/care-welfare-eldely/dl/ltcisj_e.pdf. Accessed 21 April 2017.
7.     R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. 2015 Vienna, Austria. URL: https://www.R-project.org/.
8.     Satake S, Shimokata H, Senda K, Kondo I, Toba K. Validity of the Kihon Checklist score for the predicting the incidence of 3-year dependency and mortality in a community-dwelling older population. J Am Med Dir Assoc. 2017; 18: 552.e1-552.e6.
9.     Satake S, Senda K, Hong YJ, et al. Validity of the Kihon Checklist for assessing frailty status. Geriatr Gerontol Int. 2016: 16: 709-716.
10.     Fukutomi E, Okumiya K, Wada T, et al. Importance of cognitive assessment as part of the “Kihon Checklist” developed by the Japanese Ministry of Health, Labor, and Welfare for prediction of frailty at a 2-year follow-up. Geriatr Gerontol Int. 2013; 13: 654-662.
11.     Fieo R, Zahodne L, Tang MX, et al. The historical progression from ADL scrutiny to IADL to advanced ADL: Assessing functional status in the earliest stages of dementia. J Gerontol A Biol Sci Med Sci. 2017; Dec 13: doi: 10. 1093/Gerona/glx235.
12.     Sakurai R, Yasunaga M, Nishi M, et al. Co-existence of social isolation and homebound status increase the risk of all-cause mortality. Int Psychogeriatr. 2018; Jul 19: 1-9. doi: 10. 1017/S1041610218001047.



B. Treesattayakul1, T. Winuprasith2, B. Theeranuluk3, D. Trachootham2


1. Master Program in Nutrition and Dietetics, Institute of Nutrition, Mahidol University, Thailand; 2. Institute of Nutrition, Mahidol University, Nakhon Pathom, Thailand; 3. Dental Department, Phutthamonthon Hospital, Nakhon Pathom, Thailand.
Corresponding author: Dunyaporn Trachootham, Institute of Nutrition, Mahidol University, 999 Phutthamonthon Sai 4 Road, Salaya, Nakhon Pathom, Thailand, 73170. E-mail: dunyaporn.tra@mahidol.ac.th; dif.dunyaporn@gmail.com; Tel.: (66) 2 800 2380 ext.326; fax: (66) 2 441 9344

J Frailty Aging 2019;in press
Published online February 20, 2019, http://dx.doi.org/10.14283/jfa.2019.2



Contact between upper and lower posterior teeth is crucial for chewing. However, the influence of posterior occluding teeth loss on protein intake and muscle mass was unclear. This cross-sectional study compared consumption frequency of protein food, amount of protein and relevant micronutrient intakes and muscle mass indices among older adults with different Eichner indices (EI) of posterior occluding teeth loss. Ninety Thai healthy adults were divided into three groups (N=30 each) according EI with statistically comparable characters. Food frequency questionnaire, 4-days diet record, and bioelectrical impedance analysis were used for outcome measurement. Our findings suggested that loss of posterior occluding teeth on both sides was associated with less frequent consumption of meat, nut, egg, fish and dairy products, inadequate intakes of protein (< 0.8 g/kg body weight), iron and vitamin B12, and reduced muscle mass indices in older adults. Future large-scale cohort studies are warranted to confirm these findings.

Key words: Older adults, tooth loss, protein intake, muscle mass, Eichner index.



Tooth loss is common in elderly affecting chewing and swallowing, food intake and quality of life (1, 2). A large cohort study reported chewing and swallowing difficulties in 48% and 14% of the edentulous subjects, respectively (2). Another study showed association between tooth loss and reduced intakes of fruit, vegetable, meat and beans (1). Instead, main nutrients were from solid fat, alcohol, and added sugar (3). Thus, teeth loss altered food selection toward unhealthy food choice, imbalanced nutrient intake, malnutrition and poor quality of life (1, 2, 4). Most published works focused on the relationship between total number of missing teeth and diet quality (1-4). However, ability to chew food into small pieces (masticatory performance) primarily depends on occlusal contact between upper and lower posterior teeth (5, 6). Eichner Index (EI) classified the persons according to patterns of the remaining teeth (based on the number, location and sides of the mouth), and has been associated with reduced masticatory performance and bite force (6-8). People without occluding teeth tended to avoid hard food such as meat and nuts which were high in protein (9). Nevertheless, no studies investigated the association between loss of occluding teeth and adequacy of protein intake in older adults. A study showed the association between tooth loss and a decline in walking speed (10). However, the impacts of posterior occluding teeth loss on muscle mass were unclear. This study aimed to compare frequencies and amount of protein, iron, vitamin B12 intakes and muscle mass indices among older adults with different Eichner indices of posterior occluding teeth loss.


Materials and methods

Sample size calculation and statistical analyses are described in detail at the Online Appendix.

Study procedures

This was a cross-section observational study. Ninety Thai participants were recruited to Institute of Nutrition, Mahidol University. First, a dentist screened the participants by medical history taking and oral examination.  Inclusion criteria were 51-79 years old, no systemic diseases or having controlled systemic disease, able to eat by mouth, lost at least one molar tooth, Thai nationality, at least 5 years residence, and well-communicated. Exclusion criteria were clinical illness, such as acute inflection, brain problems; stroke, Alzheimer, psychosis, chronic renal failure, active cancer and high risk of aspiration pneumonia (choke of food into the lungs). Participants were divided into three groups (N=30 each) according to EI including group A (having posterior occluding pairs of teeth on both sides); group B (having anterior tooth contact with or without posterior occluding pairs), and group C (having no occlusal contacts) (7, 8). For categorization according to EI, only functional teeth being in occlusal contact with the opposite upper/lower teeth were included. Teeth with proper restoration or intact fixed prostheses (crown and bridge, implant with complete crown) were included if occluded with the opposite teeth. Occluded third molar teeth were included. To prevent confounders, age, gender, living areas, history of systemic diseases, and dry mouth signs (Challacombe Scale ≥ 4) were controlled variables. Statistical analysis showed no significant differences in those characters among three groups. The outcome variables included muscle mass, muscle mass index, frequency of protein food consumption, amount of protein, iron, and vitamin B12 intakes per day. The study was approved by Mahidol University Central Institutional Review Board (MU-CIRB) with the approval number 2017/177.0910. Fifty years old or above were defined as older persons, according to a World Health Organization’s project (11). It was also the starting age of tooth loss in Thai population based on the National Survey of Oral Health and the age range 51-79 years old was used as a category of Thai daily recommended intake (Thai DRI).

Outcome measures

Muscle mass (kg) was measured by Tanita BC-730, a bioelectrical impedance analysis (BIA) machine. BIA provided as estimated muscle mass based on relation between volume of a conductor (muscle) and its electrical resistance.  Muscle mass index was calculated as muscle mass/ height2 (kg/m2) (12).  Consumption frequencies of meat, nut, egg, fish and dairy products were evaluated by using Food frequency questionnaires (FFQ). The choices of frequency included 1 time/day, 2 times/day, 3 times/day, 1-2 times/week, 3-4 times/week, 5-6 times/week, and none. The categories were re-arranged as daily, weekly, and none for statistical analysis. Daily intakes of protein, iron, and vitamin B12 were analyzed by four-day dietary record (Thursday, Friday, Saturday and Sunday of the same week) followed by nutrient analysis using INMUCAL-Nutrient V.3 (13). Average daily intakes (g) were calculated by [(average of weekend days x 2) + (average of weekdays x 5)] / 7. The daily intake was compared with Thai DRI. Consumption of less than 0.8 g protein /kg body weight or less than 70% DRI of iron and vitamin B12 is considered inadequate.



The post-hoc power of 0.9 was achieved for comparison of protein intake among groups. The age of participants ranged from 51-78 years with the mean ages of three groups were relatively similar (62.2 ± 7.5 in Group A, 65.1 ± 6.3 in Group B, and 66.3 ± 6.4 in Group C). Most participants were female, living in central of Thailand, having controlled systemic diseases, and having no dry mouth. There were no significant differences in age, sex, living area, systemic disease, and dry mouth status among three groups (Appendix 2). Analysis of protein consumption revealed that consumptions of meat, nut, egg, fish, and dairy products in group C were significantly less frequent than other groups. Only nuts and eggs consumptions of group B were significantly less frequent than group A (Figure 1A-E). Correspondingly, more participants with inadequate intakes of protein; <0.8 g/kg bw (90%), vitamin B12 (80%), and iron (60%) were observed in Group C than other groups. (Figure 2A-C). Furthermore, participants in group C had significantly lower protein intakes (g) than other groups (Figure 2D). Consistently, group C participants had lower muscle mass indices (kg/m2) than those of group A (Figure 2E). Moreover, there was a significant correlation between muscle mass and protein intake (Figure 2F).

Figure 1 Frequencies of protein food group consumption

Figure 1
Frequencies of protein food group consumption

Comparison for frequencies of meat (A), nuts (B), eggs (C), fish (D) and dairy products (E) among participants in group A (GA: having occluding molar teeth on both sides), group B (GB: having occluding molar teeth only one side) and group C (GC: loss of molar teeth on both sides). Each stacked bar represents the percentage of participants who had daily (black), weekly (gray) or no (white) intakes of specified food types. (*) = p < 0.05, (**) = p < 0.01 (***) = p < 0.001, (****) = p < 0.0001 analyzed by Chi-square test.

Figure 2 Adequacy of protein, iron and vitamin B12 intakes and muscle mass

Figure 2
Adequacy of protein, iron and vitamin B12 intakes and muscle mass

Comparison for adequacy of protein (A), iron (B), and vitamin B12 (C) intakes among participants in group A (GA: intact molar teeth both sides), group B (GB: molar teeth loss one side) and group C (GC: molar teeth loss both sides). Each stacked bar represents the percentage of participants with inadequate (black) or adequate (white) intakes. (***) = p < 0.001, (****) = p < 0.0001 analyzed by Chi-square test. Comparison of daily protein intakes (D) and muscle mass indices (E) among participants in group A (GA), group B (GB) and group C (GC). Each bar represented mean ± SD. (*) = p < 0.05 analyzed by Kruskal-Wallis test. Correlation between protein intake and muscle mass of all participants shown in scatter plot (F). (r) = Spearman rank correlation coefficient, (*) = p < 0.05 analyzed by linear regression



This pilot study demonstrated that loss of posterior occluding teeth on both sides was associated with less frequent consumption of all protein food types, inadequate intakes of protein, iron, and vitamin B12 and lower muscle mass indices. The findings were consistent with previous reports showing the association between total number of tooth loss and lower intake of protein (1, 4). Positive correlation between protein intake and muscle mass suggested that there may be a link between reduced protein intake and lower muscle mass observed in the patients with bilateral posterior occluding teeth loss. Adequate protein consumption is essential to stimulate muscle protein synthesis and maintain muscle mass (14). Thus, this study could be a reference for further exploring the association between bilateral posterior occluding teeth loss and the risk of sarcopenia.

While bilateral loss of posterior occluding teeth was associated with inadequate protein, iron, and vitamin B12 intakes, unilateral loss of occluding teeth was not linked. Since unilateral loss of occluding teeth was associated with less frequent consumption of only nuts and eggs, consumption of other food including meat, dairy products and fish may compensate for total protein intakes.
The strength of this study included the use of Eichner Index to classify older adults based on posterior occluding teeth loss and the matched characteristics between three groups. Nevertheless, the study had some limitations. First, participant wearing denture were not excluded. A previous study showed the association between denture use and energy intake (15). Nevertheless, higher percent of proper denture use in group C participants of this study (Appendix 3) suggested that even with removable denture the participants with bilateral posterior occluding teeth loss still consumed less protein food. Second, this study collected data at one time. There may be seasonal variations of dietary intake. Third, categorization of adequate intakes of protein, iron and vitamin B12 was based on data from dietary record, which may be subjective. Furthermore, duration after tooth loss may influence adaptation to eat softer textured protein food. Future large-scale cohort studies are warranted to compare changes in frequency and amount of protein intakes, and muscle mass among elders with different EI. The study should cover all seasons of the year to better represent dietary intake. Also, the study should start immediately after the wound of tooth loss healed and finish before the elders start wearing denture.  Furthermore, blood analysis of nutrients (protein iron and vitamin B12) should be performed along with dietary record to evaluate adequacy of nutrient intake.


Funding: No funding. All participants received free oral examination and nutrition assessment.
Acknowledgements: The authors thanked Ms. Kanoknun Vittayakasemsont, Ms. Wassana Pookate, and Ms. Nattvara Nirdnoy for assistance; staffs of Phutthamonthon Hospital and Institute of Nutrition for facilitating data collection.
Conflict of Interests: All authors have no conflicts to disclose.
Ethical Standards: Mahidol University Central Institutional Review Board approved the study.
Thai Clinical Trials Registry (TCTR) study ID: 20180828009







1.    Zhu Y, Hollis JH. Tooth loss and its association with dietary intake and diet quality in American adults. J Dent 2014; 42(11):1428-1435.
2.    Hsu KJ, Lee HE, Wu YM, Lan SJ, Huang ST, Yen YY. Masticatory factors as predictors of oral health-related quality of life among elderly people in Kaohsiung City, Taiwan. Qual Life Res 2014; 23(4):1395-1405.
3.     Savoca MR, Arcury TA, Leng X et al. Severe tooth loss in older adults as a key indicator of compromised dietary quality. Public Health Nutr 2009; 20: 1–9.
4.     Sheiham A, Steele JG, Marcenes W, et al. The relationship among dental status, nutrient intake, and nutritional status in older people. J Dent Res 2001; 80:408–413.
5.     Okayasu H, Miura H, Okada D, Shin C. Relationship between upper and lower molars during mastication. J Med Dent Sci 2009; 56(2):91-99.
6.     Ikebe K, Matsuda K, Kagawa R, et al. Masticatory performance in older subjects with  varying degrees of tooth loss. J Dent 2012; 40(1):71-76.
7.     Eichner K. Renewed examination of the group classification of partially edentulous arches by Eichner and application advices for studies on morbidity statistics (article in German). Stomatol DDR 1990; 40:321-325.
8.     Ikebe K, Matsuda K, Murai S, Maeda Y, Nokubi T. Validation of the Eichner index in relation to occlusal force and masticatory performance. Int J Prosthodont 2010; 23:521-524.
9.     Osterberg T, Tsuga K, Rothenberg E, Carlsson GE, Steen B. Masticatory ability in 80-year-old subjects and its relation to intake of energy, nutrients and food items. Gerodontology 2002; 19(2):95-101.
10.     Welmer A-K, Rizzuto D, Parker MG, Xu W. Impact of tooth loss on walking speed  decline over time in older adults: a population-based cohort study. Aging Clin Exp Res 2017; 29(4):793–800.
11.     World Health Organization. Proposed working definition of an older person in Africa for the MDS Project. 2002. http://www.who.int/healthinfo/survey/ageingdefnolder/en/ Accessed 18 August 2018
12.     Kurinami N, Sugiyama S, Morita A, et al. Ratio of muscle mass to fat mass assessed by bioelectrical impedance analysis is significantly correlated with liver fat accumulation in patients with type 2 diabetes mellitus. Diabetes Res Clin Pract 2018; 139:122-130.
13.     Ivanovitch K, Klaewkla J, Chongsuwat R, Viwatwongkasem C, Kitvorapat W. The intake of energy and selected nutrients by Thai urban sedentary workers: an evaluation of   adherence to dietary recommendations. J Nutr Metabol.2014, Article ID 145182, 17 pages, doi:10.1155/2014/145182
14.     Franzke B, Neubauer O, Cameron-Smith D, Wagner KH. Dietary Protein, Muscle and  Physical Function in the Very Old. Nutrients 2018; 10(7). pii: E935. doi: 10.3390/nu10070935
15.     Witter DJ, Woda A, Bronkhorst EM, Creugers NH. Clinical interpretation of a        masticatory normative indicator analysis of masticatory function in subjects with  different occlusal and prosthodontic status. J Dent 2013; 41:443-448.