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M. Machón1,2,3, M. Mateo-Abad2,3, K. Vrotsou1,2,3, I. Vergara1,2,3


1. Instituto de Investigación Sanitaria Biodonostia, Grupo de Atención Primaria, San Sebastián, Spain; 2. Red de Investigación en Servicios de Salud en Enfermedades Crónicas (REDISSEC), Baracaldo, Spain; 3. Instituto de Investigación en Servicios de Salud Kronikgune, Baracaldo, Spain.
Corresponding author: Mónica Machón Sobrado, Instituto de Investigación Sanitaria Biodonostia, Paseo Doctor Beguiristian s/n, 20014 San Sebastián (Gipuzkoa), Spain, Tel. +34943006086; Fax: +34943006250; e-mail: monica.machonsobrado@osakidetza.eus

J Frailty Aging 2020;in press
Published online March 30, 2021, http://dx.doi.org/10.14283/jfa.2021.12



This study evaluated the health status and lifestyle habits of vulnerable, community-dwelling older adults during the first COVID-19 lockdown in Spain. A telephone assessment was carried out in 38 individuals (71% women), with a Barthel index ≥85 who were frail or had a high risk of falls. Data were compared with those from an assessment performed 9 months earlier. In the latter part of the lockdown, a high percentage of the studied individuals showed difficulties in walking up 10 steps and reported sleep problems (66%) and pain (74%). On the other hand, participants were not anxious/depressed (71%) and the majority did not report loneliness (60%). Compared to the earlier assessment, we identified a decline in functional capacity and worsening of nutritional status, but an increase in family support. Efforts should be made to implement intervention programs seeking to avoid accelerated decline under the current pandemic situation, and especially during possible new lockdowns.

Key word: COVID-19, older people, lockdown, health, lifestyle habits.



The SARS-Cov-2 virus has caused a worldwide pandemic. By September 7th 2020, the total number of confirmed cases in Spain were 525,549 (1). The highest severity and mortality rates of the coronavirus disease 2019 (COVID-19) are seen in older people (2). To deal with this health crisis, the Spanish Government declared the state of alarm from March 14th 2020 to June 21st 2020 (3). During the first part of this lockdown, until May 2nd 2020, people were only allowed to leave their homes to buy food or medicines, attend to a medical center, go to a bank or insurance company, go to work, care for vulnerable individuals or complete other activities considered essential (3). After that, a four-phase plan for easing restrictions towards the so-called “new normality” was asymmetrically applied across Spain.
A prolonged stay indoors alters lifestyle habits, with individuals potentially reducing their physical activity, eating a less healthy diet and/or experiencing a lack of social contact (4). Consequently, the health of the population may be affected. Relatively few studies so far have explored the changes in health status and lifestyle habits during a period of confinement in different age groups (5-7).
The objective of this study was to assess the health status and lifestyle habits of vulnerable, community-dwelling older adults during the first COVID-19 lockdown in Spain, comparing results with data collected at a previous time point.



Design and study population

This study was carried out on a sample of individuals who participated in a previous study (8).The initial sample included community-dwelling people aged ≥70 years, living in Guipúzcoa (Basque Country, Spain), with a Barthel index ≥85 (9, 10) who met one of the following criteria: frailty, based on the Timed up and go test ≥20 seconds (11,12) or a high risk of falls (12).

Data collection

For this study, a telephone assessment was performed by a nurse and a social worker between April 27th and May 14th 2020. In addition, we used data that had been collected 9 months earlier through face-to-face interviews, conducted by another nurse and social worker in a primary care center and at the participants´ home. During the interviews, information was collected on individuals´ health status, lifestyle habits, social life, and home and environment conditions.


The variables assessed during the telephone interviews were the following: age, sex, ability to perform basic (Barthel index (9,10)), and instrumental (Lawton test (13)) activities of daily living, body mass index, frequency of consumption of white meat, fish and eggs (adequate: daily and ≥3 times/week; inadequate: 1-2 times/week, <1 time/week and never or almost never), Mini Nutritional Assessment-Short Form (MNA, score 0-14 points) (14), physical activity (based on the question, “Do you regularly do at least 30 minutes of physical activity each day or 4 hours a week?”), hours of sleep per day, living arrangements and family support (based on the question, “Do you have enough family support?”). The previous assessment collected information on the aforementioned variables, and also level of education and cognitive status (Memory Alteration Test, score 0-50 points (15)).
Furthermore, the telephone interview included two questions from the FRAIL scale (16): “How much of the time during the past 4 weeks have you felt tired?” and “By yourself and without using aids, do you have any difficulty walking up 10 steps without resting?”. Participants were also asked to report any falls experienced in the previous 4 weeks; self-perceived health (assessed with one item, grouping response options as, poor [fair, poor and very poor] and good [very good and good]; diet in relation to frequency of fruit, vegetable and legumes intake; and sleep habits, with two questions: “Do you take pills for sleep?” and “How many times in the last 4 weeks have you had difficulties falling asleep?”. Lastly, health related quality of life was explored with the EuroQol EQ-5D-5L scale (17) and overall loneliness with the six-item De Jong Gierveld Loneliness scale (not lonely: 0-1; lonely: 2-6) (18, 19).

Statistical analysis

Categorical variables were described as frequencies with percentages and continuous variables as means with standard deviations (SD). Paired comparisons were carried out using McNemar´s test for categorical variables and Student´s paired t-test for continuous variables. Statistical analyses were performed with the free statistical software R, version 3.4.0.

Ethical aspects

The study was approved by the Comité de Ética de la Investigación con medicamentos de Euskadi (CEIm-E, 12/2020). Verbal consent was requested for participation in this research.



Of the 48 individuals (69% women, mean age: 82.0 years, SD=5.8) previously assessed, 10 were excluded from this study due to: death (n=1), hospitalization (n=1), refusal to participate (n=8).The final sample consisted of 38 individuals (71% women).
During the latter part of the lockdown, a 35% of the individuals reported having felt often tired in the previous 4 weeks, and most had difficulties in walking up 10 steps without resting (Table 1). Although a high percentage woke up rested in the morning, 46% took sleeping pills and 66% had had difficulties sleeping in the previous 4 weeks. Forty-two percent of participants reported poor self-perceived health. The majority reported slight or moderate pain or discomfort but they were not anxious or depressed (71%) and did not experience loneliness (60%).

Table 1
Characteristics of the sample during the latter part of the lockdown

Numbers are n (%) unless otherwise stated; *These variables were collected only during the initial assessment.


Functional capacity declined over time, for both basic and instrumental activities of daily living. Based on the Barthel index, fewer were able to walk independently without assistance (92% vs. 74%, p=0.008) or climb stairs (92% vs. 71%, p<0.001) after the confinement. Regarding nutritional status, the participants´ BMI decreased (p<0.001). At the same time, the normal MNA seen in all cases (100%) during the first assessment worsened to a risk of malnutrition and malnourishment in 32% and 3% of the sample, respectively. Participants also reported having fewer hours of sleep (p=0.001). At the same time, family support was higher than in the pre-lockdown period (p=0.005).

Table 2
Comparison between the initial assessment and the latter part of the lockdown

Numbers are n (%) unless otherwise stated.



The assessment of a group of vulnerable, community-dwelling older individuals in the latter part of the first COVID-19 lockdown period revealed several issues that merit discussion. Our data show more sleep and mobility problems than in a recent previous assessment. Most of the individuals also experienced a certain level of pain, but they were neither anxious/depressed nor lonely. Similarly, in Losada-Baltar et al. (5), older people reported lower levels of anxiety than middle aged adults and younger group. The lowest loneliness levels were reported by middle aged adults, followed by older adults and younger participants. The older population seems to have greater resilience and capacity to adapt to this kind of situation (5). Nonetheless, overall, the health status of the participants had worsened, compared to the first assessment. The decline in functional capacity, both for basic and instrumental activities of daily living was particularly striking. This deterioration may be associated with a higher risk of adverse events, such as hospitalization or death. Furthermore, we observed changes in dietary patterns, with a lower consumption of protein. As a result, nutritional status worsened from normal to at risk of malnutrition or malnourished in some cases. Regarding the Mediterranean diet, Rodriguez-Perez et al.(7) found that adherence in people >51 years, slightly increased during the confinement in Spain, while in Di Renzo et al., detected a higher adherence in 18-to 30-year-olds than in younger and older populations during the first COVID-19 lock-down in Italy (6).
This study highlights the risk that a long period at home may pose for vulnerable populations. Maintaining the health status of such individuals and helping them to avoid rapid deterioration under the current pandemic situation is a challenging task. There is a clear necessity for programs specifically designed to meet their needs. Promoting physical activity and helping individuals maintain healthy dietary and sleep patterns should be the focus of such programs. In particular, a multicomponent exercise routine, with aerobic, resistance, balance, coordination and mobility training exercises, easily performed at home, would be recommended for older people (11, 20). Regarding diet, efforts should be made to ensure an adequate protein intake (21). Sleep problems could be addressed by a combination of non-pharmacological treatments, such as sleep hygiene education and relaxation techniques (22). These and similar interventions would be useful in the current context, as well as in case of new lockdowns.
The main limitation of this study is that it is not possible to know whether the changes observed were due to the impact of the confinement or the time elapsed between the assessments. Frailty is a dynamic process, with functional status commonly worsening (23, 24). The sample studied was composed of initially independent but vulnerable individuals, with frailty or a high risk of falls. Therefore, the decline observed could have been spontaneous. Nonetheless, the lockdown situation is unlikely to have a positive impact on the condition of the participants. Another limitation is that the sample size is very small. Moreover, weight at the time of the telephone assessment was self-reported. This may have introduced some bias. On the other hand, during the telephone interview participants were also asked about their weight in the previous year, and values reported were similar to those measured at the initial assessment. Self-reported weight has been used in other studies (6, 7).



Functional and nutritional status and sleep patterns worsened during a COVID-19 lockdown in a group of vulnerable older individuals. The needs of this population should be considered and incorporated into interventions designed to avoid rapid decline under the current pandemic situation, and especially during any future period of confinement.


Funding: This study was supported by the Gipuzkoa Provincial Council, though the 2018 and 2019 Adinberri program (Grants number: DFG18/201 and FADIN19/002, respectively).
Conflict of interest: The authors have no conflicts of interest to declare.



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C.P. Launay1,2,3, L. Cooper-Brown2,3, V. Ivensky2,4, O. Beauchet1,2,4,5,6,7

1. Department of Medicine, Division of Geriatric Medicine, Sir Mortimer B. Davis – Jewish General Hospital and Lady Davis Institute for Medical Research, McGill University, Montreal, Quebec, Canada; 2. Centre of Excellence on Longevity of McGill Integrated University Health and Social Services Network, Quebec, Canada; 3. Faculty of Medicine, McGill University, Montreal, Quebec, Canada; 4. Faculty of Medicine, University of Montreal, Montreal, Quebec, Canada; 5. Departments of Medicine and Geriatrics, University of Montreal, Montreal, Quebec, Canada; 6. Research Center of the Geriatric University institute of Montreal, Montreal, Quebec, Canada; 7. Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore.

Corresponding Author: Cyrille Launay, MD, PhD; Department of Medicine, Division of Geriatric Medicine, Sir Mortimer B. Davis – Jewish General Hospital, McGill University, 3755 chemin de la Côte-Sainte-Catherine, Montréal, QC H3T 1E2, Canada; E-mail: cyrille.launay@mcgill.ca; Phone: (+1) 514-340-8222, #23837; Fax: (+1) 514-340-7547

J Frailty Aging 2021;in press
Published online March 18, 2021, http://dx.doi.org/10.14283/jfa.2021.9


Key words: Aged, epidemiology, community dwelling, frailty, COVID-19.

Dear Editor,

The COVID-19 pandemic had severe consequences for older adults. First, COVID-19 was associated with more severe medical complications and an increased mortality rate in older compared to younger adults (1). Second, home confinement, an intervention that reduces the spread of COVID-19, was associated with adverse consequences for the older community-dwelling population (2). It broke down social networks and the continuum of primary care, resulting in medication or food delivery issues, psychological fallout and increasing frailty risks (3). Frailty assessment provides insight into the degree of older community dwellers’ health status vulnerability, social isolation and adverse health event risks, and it should be assessed before interventions are proposed (3). We designed a short assessment tool known as “Evaluation SOcio-GERiatrique” (ESOGER) for Montreal’s homebound community-dwelling older adults (3). In a phone call, ESOGER briefly assessed frailty and social isolation and provided recommendations, facilitating contact with health or social care providers who initiate appropriate health and social care plans (3). This study aims to examine the longitudinal effects of ESOGER on frailty and social isolation in Montreal’s homebound community-dwelling older adults.
Adopting a pre-post intervention, single arm, prospective and longitudinal design, this experimental study enrolled 119 community-dwelling older adults (70.0% female) for a mean follow-up period of 23.2±13.6 days. Selection criteria were being age 70 and over, being homebound, living in Montreal, not being involved in an experimental study and agreeing to participate. The assessments (i.e., baseline and follow-up) were performed using ESOGER. ESOGER assessed frailty using the 6-item brief geriatric assessment (BGA), with scores ranging from 0 (no frailty) to 14 (severe frailty) (4), as well as social isolation through accessibility to essential services (i.e., medication and food delivery, home care) and contact with individuals (family, neighbours, friends, healthcare or social professionals) over the phone or in person. When services and/or social contact were absent, ESOGER provided recommendations addressing social isolation and/or care disruption. Evaluated outcomes were: 1) the difference between baseline and follow-up BGA scores (/14) calculated as (follow-up score – baseline score) / ((follow-up score + baseline score) /2); 2) new-onset moderate to severe frailty staging defined by scores ≥5/14 at follow-up, but not baseline, assessment; and 3) unsuccessful or successful implementation of ESOGER recommendations, using two proxies: a) new-onset social isolation and care disruption (i.e., no social isolation and no care disruption at baseline assessment with social isolation and care disruption at follow-up assessment) when ESOGER recommendations were not implemented; or b) addressed social isolation and care disruption (i.e., social isolation and care disruption at baseline assessment with no social isolation and no care disruption at follow-up assessment) when ESOGER recommendations were implemented. Linear and logistic regressions were used to examine the association between frailty (mean BGA score and moderate-to-severe frailty stage used as dependent variables in separate models) and ESOGER recommendations (used as independent variables) adjusted to participants’ baseline characteristics (Age≥ 85, sex, polypharmacy, length of follow-up). P-values less than 0.05 were considered statistically significant. All statistics were performed using SPSS (version 24.0; SPSS, Inc., Chicago, IL). The protocol received Jewish General Hospital (McGill University, Quebec, Canada) Research Ethics Committee approval.
Baseline characteristics of participants are shown in the Table 2. In our study, social isolation decreased significantly from baseline to follow-up assessments (62.2% versus 38.7% with P≤0.001), whereas frailty increased significantly (BGA score 4.1±3.2 and moderate-to-severe frailty 47.9% versus BGA score 5.1±3.5 and moderate-to-severe frailty 58.0% with P≤0.001). As shown in Table 1, the implementation of ESOGER recommendations was not associated with significant variations in frailty (P>0.166), whereas the absence of the implementation of ESOGER recommendations was associated with a significant increase in frailty (P≤0.038).

Table 1. Multiple linear and regression models showing the association between frailty (mean BGA score and moderate-to-severe frailty stage used as separated dependent variables) and ESOGER recommendations (used as independent variables), adjusted for participants’ baseline characteristics (n=119)

ESOGER: “Evaluation SOcio-GERiatrique”; ß: Coefficient or regression beta; CI: Confidence interval; *: Difference between baseline and follow-up score ranged from 0 (no frailty) to 14 (severe frailty) and calculated from the formula: ((follow-up score – baseline score) / ((follow-up score + baseline score) /2); †: Brief geriatric assessment score ≥5/14; significant P-values (i.e., ≤0.05) in bold.

Table 2. Baseline characteristics of participants (n=119)

BGA: brief geriatric assessment; SD: standard deviation; CI: Confidence interval; *: no formal (i.e., health and/or social professional) or informal (i.e., family and/or friends) help; †: inability to give the current year and/or month; ‡: Number of different medications taken daily ≥5; §: Score 6-item BGA >6 with score ranging from 0 (no frailty) to 14 (severe frailty)


Home confinement was associated with increased frailty in the studied sample of older community dwellers, and this change in frailty was dependent on ESOGER recommendation implementation. There was a significant increase in frailty when ESOGER recommendations were not implemented, whereas no change was observed when they were implemented. The negative impact of social isolation on older adults’ health condition has previously been reported (5,6). This effect could explain the increased frailty observed among participants in this study for whom ESOGER recommendations were not implemented. The absence of significant changes to frailty when ESOGER recommendations were implemented shows that telemedicine may be an effective approach to sustaining the continuum of care in vulnerable homebound patients during crises like COVID-19. The main limitation of our study is the small sample size of participants, and thus studies recruiting greater number of participants are needed to confirm the results of our pilot study. In addition, further developing such interventions may help to remotely stabilize patients and to avoid seeing them in consultation.


Conflicts of Interest: None declared by the authors.



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2. https://blogs.bmj.com/bmj/2020/06/15/covid-19-will-be-followed-by-a-deconditioning-pandemic/ (July 07, 2020 date last accessed)
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4. Launay CP, de Decker L, Kabeshova A, Annweiler C, Beauchet O. Screening for older emergency department inpatients at risk of prolonged hospital stay: the brief geriatric assessment tool. PLoS One. 2014;9(10): e110135.
5. Brooke J, Jackson D. Older people and COVID-19: Isolation, risk and ageism. J Clin Nurs. 2020;29(13‑14): 2044‑2046
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Q. Roquebert1,4, J. Sicsic1, B. Santos-Eggimann2, N. Sirven1, T. Rapp1,3


1. LIRAES (EA4470), Université de Paris; Paris, France; 2. Centre universitaire de médecine générale et santé publique (Unisanté), Université de Lausanne, Switzerland; 3. LIEPP, Sciences Po, France; 4. Université de Strasbourg, Université de Lorraine, CNRS, BETA, 67000 Strasbourg, France
Corresponding author: Jonathan Sicsic (PhD), LIRAES, 45 rue des Saints-Pères, Université de Paris 75006 Paris, Jonathan.sicsic@u-paris.fr. +33(0)177219361

J Frailty Aging 2021;in press
Published online March 15, 2021, http://dx.doi.org/10.14283/jfa.2021.7



This systematic literature review documents the link between frailty or sarcopenia, conceptualized as dimensions of physical health, and the use of long-term care services by older individuals. Long-term care services include formal and informal care provided at home as well as in institutions. A systematic review was performed according to PRISMA requirements using the following databases: PubMed-Medline, Embase, CINAHL, Web of Science, and Academic Search Premier. We included all quantitative studies published in English between January 2000 and December 2018 focusing on individuals aged 50 or more, using a relevant measurement of sarcopenia or physical frailty and a long-term care related outcome. A quality assessment was carried out using the questionnaire established by the Good Practice Task Force Report of the International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Five subsets of long-term care outcome were considered: 1/ nursing home placement (NHP), 2/ nursing home short stay (NHSS), 3/ formal personal care (FPC), 4/ formal home help (FHH), 5/ informal care (IC). Out of 1943 studies, 17 were finally included in the review. With some studies covering several LTC outcomes, frailty and / or sarcopenia were associated with increased LTC use in 17 out of 26 cases (NHP: 5/6, NHSS: 3/4, FPC: 5/7, FHH: 1/4, IC: 3/5) The association was not consistent in 5 cases (NHP: 1/6, NHSS: 1/4, FPC: 2/7, FHH: 0/4, IC: 1/5) and the association was either not significant or the results inconclusive in the remaining 9 cases. Overall, while results on sarcopenia are scarce, evidence support a positive association between frailty and LTC use. The evidence is stronger for the association of physical frailty with nursing home placement / short stay as well as on FPC. There is less (more heterogeneous) evidence regarding the correlation between physical frailty and FHH or IC use. Results need to be confirmed by more advanced statistical methods or design based on longitudinal data.

Key words: Frailty, sarcopenia, long-term care, systematic review, nursing home, informal care, formal care.



In a context of aging populations, the demand for long-term care (LTC) is increasing rapidly. Referring to the relatively large range of services needed in the long run to individuals because of their functional limitations, LTC can be provided in the community by relatives (informal care), professional nurses or care workers (formal care), or in specific institutions such as in nursing homes. LTC spending represents an increasing share of Western countries’ Gross Domestic Product (GDP) and most of these expenditures are funded by public mechanism schemes involving taxes and/or social insurance (1, 2). Projections suggest that LTC public expenditures should increase by 69% at least (in a healthy aging scenario) reaching 2.7 % of GDP in the European Union in 2070 (3).
Beyond disability status, increasing attention is being paid to the pre-disability condition as conceptualized by frailty and sarcopenia. While frailty is defined as a vulnerable health status resulting from the reduction of individuals’ physiological reserve, sarcopenia indicates the loss of muscle mass and muscle strength (4). In practice, frailty and sarcopenia are closely related, and it may be difficult to empirically distinguish these concepts. Indeed, frailty and sarcopenia often overlap; most frail older people exhibit sarcopenia, and some older people with sarcopenia are also frail (5). Frailty is relatively easy to measure in the clinical practice. Some readily accessible screening tests also exist for sarcopenia, such as the SARC-F (6). Yet precise measurement of sarcopenia involves more complex instruments such as dual x-ray absorptiometry scan to measure appendicular lean mass (ALM) and classify individuals according to pre-defined cut-points (7, 8). Overall, both are prevalent in old age and associated with adverse health events. Efforts to adapt healthcare and social systems to aging populations might benefit from taking into account the effects of frailty and sarcopenia on health and long-term care services utilization.
A growing literature has been exploring the relationship between frailty or sarcopenia and LTC use since the early 2000’s. However, it is difficult to have a clear perspective of the overall findings and the quality of evidence stemming from this literature. Indeed, publications used various measures of frailty, explored sometimes different LTC outcomes (formal care use, informal care use, nursing home use etc.), applied different statistical modelling techniques, sampling methods etc. Therefore, there is need for a systematic review to assess the quality of the methods, classify findings according to different outcomes, and provide an overall perspective of the relationship between frailty, sarcopenia and LTC use. To the best of our knowledge, such an analysis of the literature has not been done yet.
Three prior reviews of the literature have provided evidence that frailty is associated with a higher risk of hospitalization (9, 10) and nursing home placement (10, 11). These studies have found moderate evidence of the association between frailty and hospitalization or institutionalization. Results on sarcopenia are more limited: it was positively correlated with hospitalization in one study (12) but no study examined its link with nursing home entry.
The present review fills a gap in the literature by focusing on the link between frailty or sarcopenia and LTC, not only including nursing home admission but also formal and informal care provided in the community. Moreover, we evaluated the quality of evidence and summarized the current knowledge on frailty and sarcopenia as correlates of LTC use in populations aged 50 years and over. This article addresses the following question: are frailty and sarcopenia, conceptualized as dimensions of physical health, associated with the use of long-term care services by older individuals? Our systematic review is the first to focus on various LTC outcomes, and to provide an evaluation of both the results of the literature and the quality of the published evidence. The following aspects of LTC utilization are investigated: first, informal care, referring to the unpaid care provided by relatives; second, formal care, referring to the paid care provided by professionals, either nurses or personal care workers; finally, we also consider LTC provided in nursing homes.



The protocol for our systematic review was prepared and registered on the International Prospective Register of Systematic Reviews (PROSPERO; Ref CRD42020137212). The systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), and with close adherence to the Cochrane Handbook for Systematic Reviews of Interventions. The resulting PRISMA checklist (see Table A.1, Appendix A.1, in Supplemental Materials), and the flowchart (see Figure 1) depict the stages involved in the selection process. The following subsections briefly discuss the methodology.

Figure 1
Flowchart of screening phases


Data Sources and Search Strategy

The search was conducted from April 2019 to June 2019 on the following databases:
PubMed-Medline, Embase, CINAHL (Academic Search Premier/EconLit), Web of Science. A preliminary search was conducted in order to develop a search strategy. The final search strategy is described in Appendix A.2. We employed some methodological components of the Effective Practice and Organisation of Care (EPOC) group’s search strategy, combined with selected MeSH terms and free text terms related to the PICOS (i.e. population, intervention, comparator, outcome and study design) elements. Studies published in English between January 2000 and December 2018 were collected. Also, the reference lists of the retrieved articles were examined to look for further relevant literature (through a ‘pearling procedure’).

Study Selection

Two researchers independently screened the titles and abstracts of papers published in English between 2000-2018. The selection of relevant papers was completed according to predefined eligibility criteria: i) the population of interest was composed of people aged 50 or more; ii) sarcopenia or physical frailty were explicitly measured by an identified tool (phenotype, index, scale, indicator) measuring a physical or biomedical component (thus excluding papers focusing only on psychological or social conceptions of frailty); iii) the comparators were expected to be populations with different levels of frailty or sarcopenia, including the comparison of a frail (respectively sarcopenic) population and a non-frail (respectively non sarcopenic) population; iv) the outcome should be a long-term care outcome (formal care, informal care, nursing home admission); v) we focused on quantitative research papers and excluded case reports, qualitative works or other studies without design (discussion, commentary, workshop report, systematic review).
A pilot phase was conducted to check that these criteria were precise enough. In the selection process, the information regarding the paper (authorship, institutions, journal titles and year of publication) were not blinded: even though inclusion decisions could be affected by these parameters, blind assessment has been shown to be costly and have a limited value in the study selection (13). Differences in screening results were solved by dialogue between the five members of the research team involved in the preparation of the manuscript.

Study design

We searched for cross-sectional, cohort, and pre-post studies, using a validated measure of physical frailty or sarcopenia. There were no restrictions in terms of study setting, or characteristics of sample population (except the age condition).

Typology of LTC outcomes

The main outcomes were categorized as utilization of any type of LTC services, at the intensive (i.e., probability of use) or extensive (i.e., volume of use, volume of costs) margins. We considered five different categories: 1/ Nursing home placement (NHP), regrouping any long or permanent stay in a nursing home or LTC institution, 2/ Nursing home short stay (NHSS), 3/ Formal personal care (FPC), regrouping home care services explicitly stated (e.g., ‘home care’, ‘nursing care’), 4/ Formal home help (FHH), consisting in any type of non-care help (e.g., help with meals of household duties), and 5/ Informal care (IC), provided by spouse, children or relatives.

Data Extraction, Analysis and Synthesis

Two independent reviewers (QR, JS) screened each study to identify the abstract, title, keywords, and concepts reflecting both the study’s contribution and research context. Databases were searched for a combination of three groups of keywords, referring to the variables of interest (frailty and sarcopenia), the outcomes of interest (long-term care, included formal care provided by nurses and care workers, informal care and nursing homes), and a third group complementing the request with a condition relating to utilization (use, utilization, visits, consumption). The complete search strategy is described in Appendix A.2.
Once the search strategy was completed, the full text of the relevant studies was retrieved and assessed by the two review authors with respect to the inclusion/exclusion criteria. When consensus was difficult to reach, a third review author (TR) assessed the study. The data were extracted by one reviewer (QR or JS) and checked by a second reviewer (JS or QR). The raw data from each manuscript included authors’ names, year of publication, title, journal, country and regions (if specified), study setting, study design, study population, participant demographics (age, share of women), details on the physical frailty measure used, type of LTC outcome use, raw and adjusted estimates, and risk of bias assessment details. The collected data were organized manually and tabulated using standardized forms. Given the heterogeneity of studies in terms of type of outcome and reporting (i.e., odds ratio, risk ratio, hazard ratio, marginal effects, etc.) we were unable to pool the results and conduct a meta-analysis. Therefore, we provide a comparative summary of findings for the main outcomes, using measures of the association in the same way as they were reported.

Risk of bias and quality assessment

The risk of bias was assessed using the Report from the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) Task Force which provides a questionnaire to assess the relevance and credibility of observational studies (14). The credibility of each study was assessed by scoring each study across six quality domains: Design (8 items), Data (4 items), Analysis (3 items), Reporting (7 items), Interpretation (8 items), and Conflict of Interest (2 items). Two independent reviewers assessed each study (QR and JS). When a difference persisted between them, a third reviewer (TR) assessed the study and came to an agreement. Details regarding the grid and the coding strategy for each item are provided in Appendix A.3: Table A.3.1 assesses the credibility with respect to the study design domain, Table A.3.2 with respect to the data, Table A.3.3 with respect to the analysis, Table A.3.4 with respect to reporting, Table A.3.5 with respect to interpretation.
The Task Force Report (14) identified two specific criteria, where failure to address these criteria would indicate a “fatal flaw” and require particular caution in interpreting the results. One criterion concerned the Data: “was exposure outcome valid?” and the second concerned the Analysis: “was there a thorough assessment and control of confounding?”. Because the first criterion was already included as a study selection criterion, we only considered the second criteria as a potential fatal flaw. In case no multivariate regression analysis was conducted (adjusting for potential confounders), we classified the study as providing “not enough evidence” to conclude on the relationship being investigated.



Results of screening phases

The literature search yielded 2,534 titles in the preliminary phase. Figure 1 depicts the flow chart associated to the screening procedure. Removal of duplicates resulted in 1,933 potentially relevant papers. Then, at the screening stage, 1,920 studies were removed after applying the exclusion criteria. The pearling procedure allowed us to identify 4 more relevant papers. In the end, our systematic review comprised 17 studies on the relationship between frailty and /or sarcopenia and LTC use (16 studies using frailty, and 1 study using sarcopenia as main independent variable). Main outcomes, descriptions, and statistics (when available) are presented in Table 1. A complete summary of findings is provided in Appendix A.4, including information on the frailty/sarcopenia measurements, outcomes considered and statistical uncertainty around point estimates.

Table 1
Summary of study design and outcomes

a. Time frame only for longitudinal / cohort studies; *: Significant at the 5% level; b. OR calculated by authors based on raw coefficients (log OR); Abbreviations. HR: hazard ratio; OR: odds ratio; RR: risk ratio; ME: marginal effect; raw coefficient. NA: not available. ALM: appendicular lean mass; Note: details regarding measurements and statistical uncertainty around point estimates are provided in Appendix A.3.


As regard settings, 12 of the 17 reviewed studies took place in the general (community-dwelling) population, (15–26). Two studies considered residents of housing facilities or long-stay home care clients (27, 28) and two studies focused on the specific population of adults with intellectual disabilities (29) or cognitive impairments (30). One study focused on patients with diagnostic of Alzheimer disease (AD) (31). Interest in the association between physical frailty, sarcopenia, and LTC use seems to have arisen since 2008, with an increasing trend since. Most of the research was conducted in North America (n=4), Australia (n=2), and central Europe (n=11) (see Table A.5.1 and Figure A.5.1 for more details on the geographical distribution of studies).

Quality assessment

Overall, the mean relative score achievement (quality) was 84% (min= 67%, max= 100%) for the subdomain design, 97% (min= 67%, max= 100%) for the subdomain data, 28% (min= 0%, max= 67%) for the subdomain analyses, 84% (min= 57%, max= 100%) for the subdomain reporting, and 82% (min= 75%, max= 100%) for the subdomain interpretation. Appendix A.3 provides details on the scoring of each paper according to each domain. In the quality assessment, an important issue lies in the way potential confounders are dealt with: few studies used of an appropriate design (Design, Question 5, 2 studies coded 1 out of 17) and they were scarcely discussed (Interpretation, Question 4, 5 studies coded 1 out of 17). Overall, no study had all criteria 100% fulfilled. There were no large discrepancies in terms of quality according to the class of LTC outcome, though studies investigating FHH had lower scores on average, and studies focusing on LTC placements (short or permanent) tended to have higher quality (see Figures A.3.1 in Appendix A.3).
A “fatal flaw” (assessed by the absence of control or assessment of potential confounders) was diagnosed for four studies (19, 25, 26, 29). Two of the studies focused on formal home care (19, 29), among which one study additionally focused on home care (19), and two studies focused on informal care (25, 26).

Association between frailty, sarcopenia and LTC use

Frailty and LTC use

Overall, 10 studies reported a positive association between frailty and LTC use (15–18, 23, 24, 27–29, 31) (Table 2). Conversely, only two studies showed no significant association between frailty and LTC use (21, 22). In three studies, there was not enough evidence to conclude on the relationship between frailty and LTC use (e.g., because no multivariate regression was run) (19, 25, 26). Among the 10 studies reporting positive associations, three found mixed results (17, 24, 31), which varied according to the frailty measure used (see Table 1 for a summary of results according to each frailty measure used).

Table 2
Summary of results per class of outcome

Legend: NS nonsignificant; (+) Outcome increased significantly; NEE: not enough evidence (e.g., no adjusted multivariate regression conducted); (*) inconsistent association (the association varies according to the frailty measure considered); In each study, the LTC outcomes were groups into one of the five categories: 1/ Nursing home placement (NHP), 2/ Nursing home short stay (NHSS), 3/ Formal personal care (FPC), 4/ Formal home care (FHC), 5/ Informal care (IC). Several outcomes per study are possible.


Sarcopenia and LTC use

Only one study focused on the relationship between sarcopenia and LTC use [20]. Using a 3-classes characterization of sarcopenia defined as sarcopenia I (i.e., low appendicular lean mass / ALM alone), sarcopenia II (low ALM with weakness), or sarcopenia III (low ALM with weakness and poor gait speed), the authors found a positive association between sarcopenia (either I, II, or III) and NHP. Moreover, the relationship was stronger when sarcopenia severity was higher (the effect size was higher for sarcopenia III compared to sarcopenia I or sarcopenia II).

Heterogeneity of results according to the LTC outcome

The number of studies having focused on each type of LTC outcome was distributed as follows (one study may have analysed multiple LTC outcomes): six studies on nursing home placement (NHP) (15, 20, 22, 27, 28, 31), four studies on nursing home short stay (NHSS) (16, 17, 21, 23), seven studies on formal personal care (FPC) (16–19, 24, 25, 29), four studies on formal home help (FHH) (16, 19, 25, 26), and five studies on informal care (IC) (17, 24–26, 30). Among the six studies investigating the association between frailty and NHP, 5 studies found a significant positive association, including one with mixed results (i.e., depending on frailty measurement) (see Table 1). Regarding NHSS, three studies confirmed a positive significant association, including one with mixed results. Regarding FPC, five studies confirmed a positive significant association, including two with mixed results. The evidence was less compelling for the two other LTC outcomes. Regarding FHH, only one study found a positive significant association; it was not possible to conclude for the other three studies because no multivariate regression models were estimated. Last, concerning IC, three studies found a positive significant association with frailty, including one with mixed results.



The purpose of this systematic review was to assess the association between physical frailty or sarcopenia and LTC use. Our paper is the first to systematically review the results of these studies and assess their strength and weaknesses using a validated quality assessment grid (14). We found that most of the reviewed studies reported a positive association of physical frailty and LTC outcomes. Moreover, there is evidence showing an association between frailty and LTC use for each class of LTC outcome. Specifically, there seems to be already reasonable evidence to conclude on a positive relationship of physical frailty with nursing home placement / short stay as well as on FPC. However, there is less (more heterogeneous) evidence regarding the association between physical frailty and FHH or IC use. Besides, only one study investigated the association between sarcopenia and LTC use, and found a positive association for NHP. Further studies should thus focus on the particular LTC use outcomes with insufficient evidence, and on sarcopenia.
We found important heterogeneity across studies in terms of i) type of frailty indicator used, ii) statistical analysis, and iii) reporting of results. This heterogeneity made it difficult to compare or transpose the results from one study to another. Overall, 12 different physical frailty measures and three sarcopenia indicators were used (altogether in one study). Almost all studies used multidimensional frailty indexes (most of which based on self-reported data), with a majority using either the original 5 items Fried frailty index (with categorisation into robust, pre-frail, frail) (16, 18, 21–24, 30), the Tilburg frailty indicator (17, 22, 26) or composite frailty indexes counting the number (or percentage) of deficits accumulated (27, 28, 31). In one study, sarcopenia was defined using clinical assessment of appendicular lean mass, muscle strength, and gait speed (20).
Most studies used multivariate regression analyses controlling for several of the following covariates: age, gender, morbidity, disability, and some basic sociodemographic characteristics such as education, living arrangements, or income. However, none of the studies included exactly the same number and types of controls, making the comparing of results difficult. Moreover, in only few instances the rationale for including specific controls was provided. One study performed sensitivity analysis with respect to the model specification. We conclude that future studies should carefully explain and justify their model, and perform sensitivity analyses regarding model specification (inclusion or exclusion of covariates). Despite the fact that a majority of studies relied on cohort data, very few used statistical methods designed to reduce unobserved confounding using longitudinal or panel data models (e.g. fixed effects or correlated random effects models). Specifically, only two studies analysed LTC outcome in relation with frailty transitions (19, 24), among which only one used fixed effect model to control for time invariant confounders (24). Future studies investigating the association between frailty and LTC use should consider using more advanced designs and more robust statistical methods such as the one suggested.
Finally, the studies were highly heterogeneous in terms of reporting of results. Studies using binary outcomes reported the results either in terms of raw estimates, odds ratio, or marginal effects. Studies using continuous outcomes (e.g., costs or time between events) reported either hazard ratio or risk ratio. We thus recommend that future studies report results that can be compared across settings, such as reporting average marginal effects, which can be computed using linear or non-linear (e.g., logit, probit) models.
In conclusion, our article is the first systematic literature review to focus on the relationship between frailty and sarcopenia, operationalized using existing validated measures, on various LTC outcomes among people worldwide. Overall, there is evidence suggesting that frailty is a key correlate of LTC use. But there is still a lack of evidence on the relationship between sarcopenia and LTC use, due an insufficient number of studies that investigated that issue. However, the strength of evidence is heterogeneous and varies according to the type of LTC outcome considered. Overall, the quality of studies was judged weak to moderate. Very few studies were considered as providing strong evidence, in particular because of weaknesses in statistical analyses performed, although many studies relied on cohort data. Future studies should address potential confounding using longitudinal models allowing integrating the effect of within-individual heterogeneity and assessing the effect of transitions into frailty (or sarcopenia) on LTC use. Moreover, the studies should perform sensitivity analyses by varying the control variables or using stratified analyses. In particular, it is of importance to know if the effect of frailty varies according to predefined socio-economic categories (32). This information would be useful for the definition of appropriate LTC policies aiming at ensuring equity of access.
With regards to the implications, our systematic literature review supports three main orientations for aging policies. First, eligibility criteria for public allowances should include frailty measures because evidence shows that regardless of the measure used, frailty is associated with increased LTC use. Second, the challenges associated with frailty among older people are global, since correlations between frailty measures and LTC use are similar across countries. This calls for global aging initiatives, to learn from each country’s experience. Third, frailty prevention in older populations is a policy priority. While many countries (France, Germany, Netherlands etc.) implemented disability reforms, future policies should also target frail populations, and enhance innovations to detect frailty in the 65-75 age group.


Funding and acknowledgements: The research leading to these results has received support from the Innovative Medicines Initiative Joint Undertaking under grant agreement n° 115621, resources of which are composed of financial contribution from the European Union’ Seventh Framework Programme (FP7 / 2007-2013) and European Federation of Pharmaceutical Industries and Associations (EFPIA) companies’ in kind contribution.
Conflicts of Interest: None of the authors report any conflict of interest whatsoever.



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S.J. Wood1, J.S. Bell1,2,3, D.J. Magliano2,4, L. Fanning5, M. Cesari3,6, C.S. Keen1, J. Ilomäki1,2


1. Centre for Medicine Use and Safety, Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Melbourne, Australia; 2. Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia; 3. National Health and Medical Research Council (NHMRC) Centre of Research Excellence in Frailty and Healthy Ageing, Adelaide, Australia; 4. Baker Heart and Diabetes Institute, 75 Commercial Road, Melbourne, Australia; 5. Eastern Health Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia; 6. IRCCS Istituti Clinici Scientifici Maugeri, University of Milan, Milan, Italy

Corresponding Author: Stephen Wood, Centre for Medicine Use and Safety, Faculty of Pharmacy and Pharmaceutical Sciences, Monash University Melbourne, Australia, 3052, Tel: +61 423301741, E-mail: Stephen.Wood@monash.edu
J Frailty Aging 2021;in press
Published online February 29, 2021, http://dx.doi.org/10.14283/jfa.2021.6



Background: The risks of intensive blood glucose lowering may outweigh the benefits in vulnerable older people.
Objectives: Our primary aim was to determine whether age, frailty, or dementia predict discharge treatment types for patients with type 2 diabetes (T2D) and related complications. Secondly, we aimed to determine the association between prior hypoglycemia and discharge treatment types.
Design, Setting and Participants: We conducted a cohort study involving 3,067 patients aged 65-99 years with T2D and related complications, discharged from Melbourne’s Eastern Health Hospital Network between 2012 and 2016.
Measurements: Multinomial logistic regression was used to estimate odds ratios (ORs) with 95% confidence intervals (CI) for the association between age, frailty, dementia and hypoglycemia, and being prescribed insulin-only, non-insulin glucose-lowering drugs (GLDs) or combined insulin and non-insulin GLDs compared to no GLD. International Classification of Diseases-10 codes were used to identify dementia status and prior hypoglycemia; frailty was quantified using the Hospital Frailty Risk Score.
Results: Insulin-only, non-insulin GLDs, combined insulin and non-insulin GLDs, and no GLDs were prescribed to 19%, 39%, 20%, and 23% of patients, respectively. Patients >80 years were less likely than patients aged 65-80 to be prescribed any of the GLD therapies, (eg. non-insulin GLDs [OR 0.67; 95%CI 0.55-0.82]), compared to no GLD. Similarly, high vs. low frailty scores were associated with not being prescribed any of the three GLD therapies, (eg. non-insulin GLDs [OR 0.63; 95%CI 0.45-0.87]). However, dementia was not associated with discharge prescribing of GLD therapies. Patients with a hypoglycemia-related admission were more likely than those not hospitalized with hypoglycemia to receive insulin-only (OR 4.28; 95%CI 2.89-6.31).
Clinicians consider age and frailty when tailoring diabetes treatment regimens for patients discharged from hospital with T2D and related complications. There is scope to optimize prescribing for patients with dementia and for those admitted with hypoglycemia.

Key words: Type 2 diabetes, frailty, dementia.



The benefits of intensive glycemic control for preventing microvascular outcomes in middle and older age people with Type 2 diabetes (T2D) have been demonstrated in the ACCORD, ADVANCE, and VADT trials (1-3). However, intensive treatment is associated with an increased risk of hypoglycemia and does not improve survival or the incidence of macrovascular outcomes in people with limited life expectancies (1-3). The risks of intensive treatment may outweigh the benefits in frail older people (4). The guidelines of the American Diabetes Association (ADA) recommend less stringent glycemic targets of <8% and <8.5% (64 mmol/mol and 69 mmol/mol) for older individuals with complex and very complex health status (5). Similarly, Australian guidelines advise less intensive and individualized treatment for these patient groups (6). Nevertheless, UK data suggest that those who are frail and have dementia are treated with similar glucose-lowering drugs (GLDs) and with the aim to achieve similar glycemic targets as robust older people without dementia (7).
Frailty is an important complication of diabetes (8, 9), and is characterized by vulnerability to stressors and a reduced ability to maintain homeostasis (10). Frailty increases the risk of adverse drug events, including falls, disability and death (11). There are reciprocal relationships between hypoglycemia, dementia, and frailty (12). There have been calls for frailty status to guide treatment selection (13), with frail people with diabetes at 71% higher adjusted risk of all-cause hospitalization and twice the risk of mortality than non-frail people (14). Furthermore, older people with diabetes who develop dementia have three times the risk of hypoglycemia compared to those who do not develop dementia (15). The ACCORD-MIND study reported that cognitive decline over 20 months was associated with a higher risk of hypoglycemia regardless of treatment intensity (15).
Hospitalization represents an opportunity for clinicians to adjust T2D treatment regimens, although it is unclear to what extent hospital clinicians consider age, frailty and dementia in prescribing decisions. There is also a paucity of information about GLDs prescribed for older people who are frail and/or live with dementia, who may have different goals of care and treatment benefits and risks (5, 6). The primary aim of this study was to determine whether age, frailty, or dementia predict discharge treatment types for patients with T2D and related complications. Our secondary aim was to determine the association between prior hypoglycemia and discharge treatment types.



Data source, study design, and study population

The study was conducted at Eastern Health, a large metropolitan public hospital network in Melbourne, with three acute and four subacute hospitals (1,423 beds) (16). Eastern Health services a catchment area of 750,000 people and recorded 1,175,249 patient episodes between July 2015 and June 2016 (16). Eastern Health implemented an Electronic Medical Record (EMR) with electronic prescribing (e-prescribing) in 2011 (17). EMR discharge prescriptions record all medications intended for use by a patient after being discharged from the hospital (17). Demographic information and discharge diagnoses were extracted by the health service’s Decision Support Unit, which relies upon the standard practice of Clinical Coders within the Health information Unit (17). Diagnoses were recorded using International Classification of Diseases-10 (ICD-10) codes with up to 40 diagnoses per patient. Discharge medications were identified from the EMR using Anatomical Therapeutic Chemical (ATC) classification codes (18).
We conducted a cohort study of 3,067 adults aged between 65 and 99 years with T2D who were discharged from one of the Eastern Health hospital locations in Melbourne, Australia, between 2012 and 2016 with a principal diagnosis of T2D with a diabetes related complication.

Measures and definitions

Our study population included all patients with a principal diagnosis of T2D, identified using ICD-10 code E11, and an ICD-10 code (E11-E14) for a diabetes-related complication recorded at hospital discharge (index hospitalization) (18). Medications for T2D were broadly classified as insulins (ATC code A10A) or non-insulin GLDs (A10B). ATC codes used to identify GLDs classes are provided in Appendix A. A modified version of the Diabetes Complications Severity Index (DCSI) [19], which converts ICD-10 codes into a 13-level metric to quantify effects of diabetes on seven body systems, was used as an indicator of T2D severity. Although this version of the DCSI does not require laboratory data, validation studies have shown that its capacity to predict diabetes severity is comparable to other versions which do (19, 20). The DCSI is also likely to be indicative of diabetes duration as it has been shown that for every additional year of diabetes duration in people over 60 years, the adjusted odds of microvascular disease increases by 6% (p<0.001) (21).
We utilized a validated Hospital Frailty Risk Score, which categorizes people into three frailty categories based on the sum of weighted scores identified from International Classification of Diseases (ICD-10) codes (22). Gilbert et. al (2018) derived this score using 109 ICD-10 codes at least twice as prevalent in frail versus non-frail patients weighted according to how strongly they predict frailty (22). Codes used to derive the Hospital Frailty Risk Score (HFRS) reflect conditions linked to frailty (for example, volume depletion, cognitive impairment, and falls) or conditions overrepresented in frail populations such as lung disease, heart conditions and elective cataracts. Cut-point scores of <5, 5-15, and >15, as published by Gilbert et. al. indicated low, moderate, and high degrees of frailty, respectively. ICD-10 codes used to identify dementia and hypoglycemia are given in Appendix B.

Statistical analysis

Baseline characteristics were presented as means with standard deviations (SDs), medians with interquartile ranges (IQRs) or as frequencies and percentages. Predictors of treatment initiation were estimated using multinomial logistic regression. Variables were included in the final model if the unadjusted p-value associated with the odds ratio (OR) was <0.25. We included age (65-80 and >80), frailty (low, moderate or high) and dementia in our regression model and estimated adjusted odds ratios (ORs) with 95% confidence intervals (CI), adjusted for sex, index year, DCSI score, congestive cardiac failure (CCF), myocardial infarction (MI), renal disease, transient ischemic attack (TIA) or stroke, and hypoglycemia, (ICD-10 codes for comorbidities given in Appendix B). Variance Inflation Factors (VIF) with a cut-off of 2 were used to assess collinearity between the variables in the model. Statistical differences were evaluated using Pearson’s chi-squared test and ANOVA for categorical and continuous variables, respectively. We excluded the Charlson Comorbidity Index (CCI) from our adjusted model because it was collinear with several comorbidities in our model, though it is included in Table 1 for completeness. Comorbidities and concomitant medications were not included in the same model because concomitant medications were conceptualized as intermediate variables in the causal pathway between the comorbidity and the diabetes treatment regimen.
All analyses were conducted using the statistical software package SAS version 9.4 (SAS Institute Inc., Cary, NC, USA). This study was approved by the Eastern Health and Monash University Human Research Ethics Committees (study number LR41/2017).



Cohort Characteristics

Of the 3,067 people hospitalized with T2D, 19% were prescribed insulin-only, 39% non-insulin GLDs, 20% insulin and non-insulin combinations and 23% no GLDs (Table 1). Slightly less than half of the cohort were female (48%), and the mean age of the cohort was 78.6 years (SD 7.8). Patients not prescribed GLDs were older (81.0, SD 8.1) than those prescribed non-insulin GLDs (78.3, SD 7.7), insulin only (78.2, SD 7.3), or combination therapy (76.5, SD 7.3). Based on ICD-10 codes, 9% of the cohort had a dementia diagnosis, and 11% had been hospitalized with hypoglycemia.
Frailty scores were non-normally distributed, therefore medians and interquartile ranges (IQR) were reported. The median frailty score for the study population was 5.8 (IQR 2.5-10.2), with median frailty scores being higher amongst those who were not prescribed GLDs (6.9, IQR 3.0-11.5) and lower amongst those prescribed combinations (5.3, IQR 2.3-9.3), (Table 1). The Pearson correlation coefficient between age and frailty scores was 0.23 (p<0.0001). Figure 1a) shows that 69.7% of patients prescribed insulin-only therapy had a DCSI score >1, p<0.0001. Figure 1b) indicates that 21.6% and 16.0% of the insulin-only and combination therapy groups had a documented prior hypoglycemia during their index hospitalization. In contrast, 5.7% and 6.7% of individuals receiving no GLD and non-insulin hypoglycemic agents had a documented episode of hypoglycemia, p<0.0001. Patients in the combination group were least likely (9.5%), to have a HFRS >15, p<0.0001 and to have dementia (4.3%), p=0.0002, (Figure 1c). Those with HFRS >15 were most likely (12.5%) to have had an episode of hypoglycemia, but this was not significantly higher than the other groups, p=0.16 (Figure 1d).

Figure 1a. Proportion of patients in each treatment group with baseline DCSI scores ≤1 or >1

DCSI Diabetes Complications Severity Index; GLD Glucose Lowering Drug; p<0.0001 (Pearson’s chi-squared test)

Figure 1b. Proportions of patients in each treatment group with a diagnosis of hypoglycemia recorded during index hospitalization

GLD Glucose Lowering Drug; p<0.0001 (Pearson’s chi-squared test)

Figure 1c. Proportions of patients in each treatment group within each of the three frailty categories or with a diagnosis of dementia at baseline

HFRS Hospital Frailty Risk Score; GLD Glucose Lowering Drug; p<0.0001 for HFRS categories, p=0.0002 for dementia (Pearson’s chi-squared test)

Figure 1d. Proportion of patients in frailty categories with hypoglycemia diagnosis at index discharge

HFRS Hospital Frailty Risk Score; p=0.16 (Pearson’s chi-squared test)


Predictors of Prescribed Anti-Hyperglycemic Therapy

People aged >80 versus those aged 65-80 were less likely to be prescribed insulin only (OR 0.54 95%CI 0.42-0.69), non-insulin GLDs only (OR 0.67 95%CI 0.55-0.82) or combinations of the two (OR 0.37 95%CI 0.29-0.47), compared to no GLDs (Table 2, Figure 2a). People with high frailty scores, compared to low scores, were less likely to be prescribed insulin only (OR 0.62 95%CI 0.42-0.91), non-insulin GLDs (OR 0.63 95%CI 0.45-0.87), or combinations of the two (OR 0.65 95%CI 0.43-0.96), compared to no GLDs (Table 2, Figure 2b).
People with dementia were less likely to be prescribed non-insulin GLDs (OR 0.73 95%CI 0.53-1.01) or insulin and non-insulin GLD combinations (OR 0.72 95%CI 0.47-1.10) compared to no GLDs, although these results were non-statistically significant (Table 2). People hospitalized with hypoglycemia, were more likely to receive insulin only (OR 4.28 95%CI 2.89-6.31) or combinations of insulin and non-insulin GLDs, (OR 3.15 95%CI 2.11-4.69), compared to no GLDs.

Table 1. Demographic and clinical characteristics of people over 65 years with Type 2 diabetes and a primary diagnosis of a diabetes related complication, by prescribed discharge medication

SD Standard deviation; IQR Inter Quartile Range; DCSI Diabetes Complications Severity Index; CCI Charlson Comorbidity Index. Data are presented as n(%). Non-insulin therapy included: metformin, sulfonylureas, acarbose, thiazolidinediones, dipeptidyl peptidase-4 inhibitors (DPP-4Is), glucagon-like peptide-1 agonists (GLP-1As), sodium-glucose cotransporter-2 inhibitors (SGLT-2Is), and fixed-dose combinations (FDC); *P-values were calculated using the Pearson’s chi-squared test and ANOVA for categorical and continuous variables, respectively.

Table 2. Odds ratios for being prescribed Glucose Lowering Drugs (GLDs) versus No GLD at discharge amongst people with Type 2 diabetes and a primary diagnosis of a diabetes-related complication

DCSI: Diabetes Complications Severity Index; bold indicates a statistically significant result.


Types of T2D Therapy Prescribed

The most commonly prescribed insulin types within the group receiving insulin-only therapy were mixed (64.7%), fast-acting (30.0%) and long-acting (29.8%), with most individuals being prescribed either 1 (71.6%) or 2 (28.1%) different insulin products (Appendix C). Within the group receiving combination therapy, mixed (51.4%), long-acting (41.4%), and fast-acting (17.1%) insulins were most likely to be prescribed. All individuals in this group were prescribed either one (83.5%) or two (16.5%) types of insulin.
People in the non-insulin GLD group were most likely to be prescribed either metformin (69.9%) or a sulfonylurea (57.8%), with the majority being issued with either 1 (59.6%) or 2 (34.3%) non-insulin GLDs (Appendix C). Metformin (74.0%) and sulfonylureas (47.1%) were also the most commonly prescribed non-insulin GLDs in the combination group, and people in this group were most likely to receive either 1 (69.7%) or 2 (28.6%) non-insulin GLDs.

Figure 2a. Forest plot of type of antihyperglycemic therapy prescribed for people over 65 years, hospitalised with Type 2 diabetes and a related complication, by age group

OR Odds Ratio; CI Confidence Interval; GLD Glucose Lowering Drug.

Figure 2b. Forest plot of type of antihyperglycemic therapy prescribed for people over 65 years, hospitalised with Type 2 diabetes and a related complication, by frailty score

OR Odds Ratio; CI Confidence Interval; GLD Glucose Lowering Drug; HFRS Hospital Frailty Risk Score.



This was the first study to investigate how age, frailty, and dementia predict hospital discharge prescribing for people with T2D. Older age and frailty predicted less intense treatment of T2D, people 80 and older were 63% less likely than those aged between 65-80 years to receive combinations of insulin and non-insulin GLDs, compared to no GLDs. Moreover, frail people were 35% less likely than robust people to be discharged on a combination of insulin and non-insulin GLDs versus no GLDs.
Our findings suggest clinicians consider age and frailty by tailoring diabetes treatment regimens. This is encouraging because frail older individuals are more vulnerable to adverse events, such as hypoglycemia and mortality (23). In addition, weight loss and sarcopenia associated with frailty (12) may be exacerbated by changes in the natural history of T2D, which shifts from a progressive to a regressive course in individuals who are frail (24). Older age is a well-known risk factor for hypoglycemia, and our findings demonstrate adherence to national and international prescribing guidelines, which advise that individuals with shorter life expectancy derive limited benefits from stringent glycemic targets (5, 6). Older people with T2D are also less likely to recognize early signs of hypoglycemia due to reduced awareness of hypoglycemic symptoms and slower reaction times than younger counterparts (25). Severe hypoglycemia can cause sudden cardiovascular death, and episodes of mild hypoglycemia can cause falls, fractures, cognitive impairment, seizures, coma, cardiovascular events, and arrhythmias (23). National estimates in the US indicate that insulin users >80 years are hospitalized for hypoglycemia or insulin-related errors at five times the rate of insulin users aged 45-64 years (26). Reasons postulated for this increase include reduced food intake and administration of the wrong insulin product (26).
People with dementia tended to be less likely to be discharged on insulin and non-insulin GLD combinations compared to no GLDs. Although not statistically significant, this result suggests possible increasing awareness of the need to align treatment with goals of care (27). It may also reflect prescribers’ awareness that individuals with dementia have a reduced capacity to manage complex regimens, particularly those involving insulin, due to difficulties in remembering dosage directions, to take doses on time or to take with food. Insulin and oral hypoglycemic agents such as sulfonylureas are considered high-risk medications and are associated with preventable hospitalizations, including among residents of nursing homes and long-term care facilities.
People hospitalized with hypoglycemia were over three times as likely to be prescribed insulin and non-insulin GLD combinations and over four times as likely to be prescribed insulin only compared to no GLDs. While we were not able to assess the clinical appropriateness of T2D regimens for individual patients, this suggests a possible opportunity for treatment de-intensification in ‘at risk’ population groups. It is also possible that there is scope for regimen simplification, as 28.5% of individuals prescribed insulin only and 16.5% prescribed combination treatment used at least two insulin products. It has been shown that simplification of multiple insulin regimens to basal insulin glargine only, reduced duration of hypoglycemia by 65% after eight months (28).

Strengths and limitations

Our study analyzed five years of discharge prescribing data from a large public hospital network in Melbourne. To our knowledge, this is the first study to investigate the impact of age, frailty, and dementia status as predictors of T2D discharge treatment intensity. One limitation of this study is that the Hospital Frailty Risk Score was validated for individuals >75 years, whereas we included individuals ≥65 years. The Hospital Frailty Risk Score was calculated using ICD-10 codes including dementia and, therefore, it is possible that there was overlap between dementia and frailty. Prescribing patterns may have evolved since 2016, particularly with the introduction of sodium-glucose cotransporter-2 inhibitors (SGLT-2Is). We considered age, dementia, and frailty status as categorical rather than continuous variables. However, age, frailty and dementia severity are continuous and there is no evidence for specific cut-points to define prescribing appropriateness in relation to these parameters. Lack of data on diabetes duration is a limitation. However, we presented the diabetes treatment according to less and more severe diabetes complications, which are related to diabetes duration (21). We did not have data on pre-admission treatment. However, we have presented the proportion of patients with documented prior hypoglycemia in each of the treatment groups. We hypothesized that prior hypoglycemia would prompt clinicians to modify treatment. Analyzing discharge prescribing is consistent with the treatment decision design in which cohorts are anchored at the point when treatment decisions are made (29). This is because medication regimens are typically evaluated during a hospital episode (29). Additionally, given that the sample comprised Australians who had been hospitalized, the results are not necessarily generalizable to all older patients with T2D across all clinical settings. We were not able to analyze data on HbA1C levels and ethnicity. Finally, a common limitation with the use of prescribing data, is that we do not know whether prescribed medications are actually taken by patients as directed.



Frail older people hospitalized with T2D and diabetes-related complications are less likely to be prescribed insulin-only GLDs, non-insulin GLDs or a combination of both, compared to no GLDs. Increasing age is also associated with receiving less intensive GLD regimens. Conversely, people hospitalized with hypoglycemia are considerably more likely to be discharged with a medication regimen which includes insulin. Clinicians appear to consider age and frailty when prescribing for people with T2D, but there is further opportunity for treatment de-intensification in ‘at risk’ groups.


Acknowledgements: S.W. is supported through an Australian Government Research Training Program Scholarship. J.S.B. is supported by a NHMRC Boosting Dementia Research Leadership Fellowship [grant number #1140298].

Conflicts of Interest: J.S.B. has received grant income paid to his employer from NHMRC, Australian Government Department of Health, Victorian Government Department of Health and Human Services, Dementia Australia Research Foundation, Yulgilbar Foundation, GSK Independent Medical Education and several aged care provider organizations. M.C reports personal fees from Nestlé, outside the submitted work. J.I. reports grants from AstraZeneca, Amgen, Dementia Australia Research Foundation, National Health and Medical Research Council, and National Breast Cancer Foundation, outside the submitted work. S.W., D.J.M., L.F., and C.K. have no competing interests to declare.

Ethical standards: This study was approved by the Eastern Health and Monash University Human Research Ethics Committees (study number LR41/2017).



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N.K. LeBrasseur1, R. de Cabo2, R. Fielding3, L. Ferrucci4, L. Rodriguez-Manas5, J. Viña6, B. Vellas7


1. Robert and Arlene Kogod Center on Aging and Department of Physical Medicine and Rehabilitation, Mayo Clinic, Rochester, MN, USA; 2. Translational Gerontology Branch, National Institute on Aging, Baltimore, MD, USA; 3. Tufts University, Boston, MA, USA; 4. Intramural Research Program, National Institute on Aging, Baltimore, MD, USA; 5. Servicio de Geriatría, Hospital Universitario de Getafe, Toledo, Spain; 6. Freshage Research Group, Department of Physiology, Faculty of Medicine, University of Valencia, CIBERFES-ISCIII, INCLIVA, 46010 Valencia, Spain; 7. Gerontopole, INSERM U1027, Alzheimer’s Disease Research and Clinical Center, Toulouse University Hospital, Toulouse, France
Corresponding author: Nathan K. LeBrasseur, Robert and Arlene Kogod Center on Aging and Department of Physical Medicine and Rehabilitation, Mayo Clinic, Rochester, MN, USA, lebrasseur.nathan@mayo.edu

Task Force members: Samuel Agus (Paris); Sandrine Andrieu (Toulouse, France); Mylène Aubertin-Leheudre (Montréal, Canada); Amos Baruch (South San Francisco, USA); Shalender Bhasin (Boston, USA); Louis Casteilla (Toulouse, France); Peggy Cawthon (San Francisco, USA); Matteo Cesari (Milan, Italy); Manu Chakravarthy (Cambridge, USA); Alfonso J. Cruz Jentoft (Madrid, Spain); Carla Delannoy (Vevey, Switzerland); Philipe De Souto Barreto (Toulouse, France) ; Waly Dioh (Paris, France); Françoise Forette (Paris, USA); Sophie Guyonnet (Toulouse); Joshua Hare (Miami) ; Darren Hwee (South San Francisco); Kala Kaspar (Vevey); Valérie Legrand (Nanterre, France); Roland Liblau (Toulouse, France); Yvette Luiking (Utrecht, The Netherland) ; Bradley Morgan (South San Francisco, USA) ; Eric Morgen (Richmond, USA); John Morley (St Louis, USA) ; Angelo Parini (Toulouse, USA); Suzette Pereira (Columbus, USA); Alfredo Ramirez (Cologne, USA); Jean-Yves Reginster (Liege, Belgium); Yves Rolland (Toulouse, France); Ricardo Rueda (Columbus, USA); Jorge Ruiz (Miami, USA); Peter Schüler (Langen, Germany); Alan Sinclair (London, United Kingdom); Nicolas Thevenet (Nanterre, France); Janneke Van Wijngaarden (Utrecht, The Netherlands); Jeremy Walston (Baltimore, USA); Debra L. Waters (Dunedin, New Zealand)

J Frailty Aging 2021;in press
Published online February 23, 2021, http://dx.doi.org/10.14283/jfa.2021.5



The International Conference on Frailty and Sarcopenia Research Task Force met in March 2020, in the shadow of the COVID-19 pandemic, to discuss strategies for advancing the interdisciplinary field of geroscience. Geroscience explores biological mechanisms of aging as targets for intervention that may delay the physiological consequences of aging, maintain function, and prevent frailty and disability. Priorities for clinical practice and research include identifying and validating a range of biomarkers of the hallmarks of aging. Potential biomarkers discussed included markers of mitochondrial dysfunction, proteostasis, stem cell dysfunction, nutrient sensing, genomic instability, telomere dysfunction, cellular senescence, and epigenetic changes. The FRAILOMICS initiative is exploring many of these through various omics studies. Translating this knowledge into new therapies is being addressed by the U.S. National Institute on Aging Translational Gerontology Branch. Research gaps identified by the Task Force include the need for improved cellular and animal models as well as more reliable and sensitive measures.

Key words: Aging, frailty, resilience, hallmarks of aging, translational research.



The International Conference on Frailty and Sarcopenia Research (ICFSR) Task Force met in Toulouse, France on March 10, 2020 to discuss geroscience. The timing could not have been more prescient: On the following day, March 11, the World Health Organization (WHO) declared the COVID-19 outbreak a pandemic (1). The disease, caused by a virus known as SARS-CoV2, had at that point already claimed the lives of more than 4,000 people in 114 countries, with the risk of morbidity and mortality especially elevated in older people and those with underlying medical conditions (2).
Why older adults are particularly vulnerable to this novel virus remains poorly understood. Age-related physiological changes including immune senescence, the high incidence of chronic illnesses, and frailty may decrease resilience and increase susceptibility to cardiovascular, pulmonary, and infectious diseases (3). Older adults may also present atypically, further complicating diagnosis and treatment (4).
Targeting the biology of aging to prevent and treat aging-associated diseases and geriatric syndromes as a group represents a fundamentally different approach to extending human health. Historically, drug development efforts have centered on evidence-based risk factors identified through epidemiological studies and specific molecular alterations that contribute to singular diseases. This approach has had marked success but has also revealed that interventions for a specific disease, whether it be heart disease, cancer, Alzheimer’s Disease, pneumonia, or COVID-19, have a limited impact on the emergence of the multitude of other age-related conditions. The promise and potential payoff of interventions for aging and compressing morbidity is high; however, a more in-depth understanding of the underlying biology is needed. In response, the interdisciplinary field of geroscience has emerged to explore biological mechanisms of aging and determine how these mechanisms lead to the vast collection of age-related chronic diseases and geriatric syndromes, including sarcopenia and frailty (5–8). Geroscience approaches are clearly and urgently needed as well to better understand the susceptibility of older adults to acute challenges, such as COVID-19, and develop novel treatments for the most vulnerable individuals, including frail older adults (3).
Specialties represented in the ICFSR Task Force play a critical role in advancing geroscience because they are expert in 1) the discovery and quantification of the biological mechanisms of aging; 2) the study of aging and aging-related diseases in preclinical models, and; 3) the design and execution of clinical trials of testing interventions (exercise, dietary modifications, drugs, and combinations thereof) to optimize the health and function or resilience of multiple physiological systems. Indeed, a multidisciplinary approach is necessary to expedite the translation of newly discovered therapies to clinical application.


What is healthy aging?

The WHO introduced the concept that healthy aging and disease prevention hinges on preventing declines in intrinsic capacity – a composite of physical and mental capacities that peak in early adulthood and tend to decline in later years. WHO developed a model for integrated care of older people (ICOPE) that focuses on maintaining intrinsic capacity through the adoption of a healthy lifestyle (9).
As individuals age, they transition between robustness to frailty, defined as increased vulnerability to endogenous and exogenous stressors and a decline in physiological reserve and function across multiple organ systems. Thus, frailty and intrinsic capacity represent distinct but related concepts, both with similar physiological underpinnings (10). Physiological mechanisms of resilience and reserve further impact the capacity of an individual to overcome adverse events (11). Geroscience resides at the intersection of these concepts (12).


Biological hallmarks of aging

Geroscience assumes that aging itself, defined as the accumulation of diverse forms of molecular and cellular damage and repair, ultimately drives the increased risk of chronic diseases among older people (13). López-Otin and colleagues proposed nine distinct yet interrelated forms, or “hallmarks”, of aging: genomic instability, telomere attrition, epigenetic alterations, loss of proteostasis, deregulated nutrient sensing, mitochondrial dysfunction, cellular senescence, stem cell exhaustion, and altered intercellular communication (14). These hallmarks have been incorporated into an emerging view of geroscience that resulted in the creation of a trans-National Institutes of Health (NIH) Geroscience Interest Group, GSIG (15).
Together, the biological mechanisms progressively result in loss of cellular homeostasis, dysregulation across multiple physiological systems and, ultimately, disease, disability, and death. Both the accumulation and the repair of aging hallmarks are strongly influenced by behavior, the environment, and genetics, resulting in substantial variation among individuals. This has led to the important concept that biological age differs from chronological age. Biological age, in contrast to chronological age, is difficult to quantify, which has led to inconsistent definitions in the literature. Herein, approaches to measure aging hallmarks are presented as a means to better define biological age of cells, tissues, and, ultimately, organisms.
One of the longest longitudinal studies of normative human aging, the Baltimore Longitudinal Study of Aging (BLSA), has been running for more than 60 years. The BLSA collected multidomain clinical and functional data from participants with increasing frequency as they aged: every 4 years for those under age 60 increasing to every year for those over age 80. Using these data, the investigative team recently proposed a roadmap to build a phenotypic metric of aging, which by systematically characterizing the continuum of aging in an individual, could advance understanding of the kinetics of aging, as well as discovery and development of effective interventions. The framework encompasses four domains: body composition, energetics, homeostatic mechanisms, and neurodegeneration/neuroplasticity (16).
Scientists in the NIA Intramural Research Program are also conducting a Study of Longitudinal Aging in Mice (SLAM) to better understand whether discoveries in mice can be translated to humans and which aging phenotypes share or do not share common traits in order to fine tune interventions to translatable outcomes. They are conducting the study in two strains of mice of both sexes, selecting the C57BL6/J and the UM-HET3 mice to better recapitulate both the genetic homogeneity of most aging studies and the heterogeneity found in humans respectively. SLAM investigators are conducting a broad range of clinically relevant assessments over time and across multiple domains. To assess frailty, they will apply two newly developed tools: the mouse clinical frailty index and mouse frailty phenotype assessment (17). For example, at 3-month intervals, they assess gait speed, fasting blood glucose, energetic cost on a metabolic treadmill, and frailty. They also perform imaging tests such as magnetic resonance imaging (MRI), nuclear magnetic resonance (NMR), and micro-computed tomography (micro-CT) to obtain organ images and spectra, body composition, and bone mass changes with age.


Implementing biomarkers of aging in clinical research

Metrics of aging span multiple domains and include biological hallmarks, organ impairments (e.g., muscle weakness), functional limitations (e.g., slow gait speed), and disease and deficit accumulation (e.g., frailty). Undoubtedly, as people age, the onset and progression of decline within each domain differs; and understanding whether biomarkers of these different domain are mechanistically connected and exploring temporal relationship between domains is critical to translate this science into effective interventions. Based on the geroscience premise that aging itself is the driver of the majority of chronic diseases and geriatric syndromes, quantifiable indicators or “biomarkers” of biological age would be of significant utility to target individuals who are experiencing accelerated aging as well tracking the effectiveness of interventions aimed at slowing down the aging process.
For clinical research, biomarkers of aging could enable identification of persons appropriate for and, potentially, most responsive to interventions targeting the biology of aging. In such trials, biomarkers may be used to verify target engagement and also serve as informative surrogate endpoints that may change well before clinical outcomes (e.g., frailty measures (18)). In clinical practice, biomarkers may help providers discern between chronological and biological age and, in turn, serve as determinants of risk and guide clinical decision making (e.g., medical versus surgical management of a condition). Implementing laboratory biomarkers of aging in clinical research and practice, however, will first require demonstrating that they can be reliably measured in blood or other accessible tissues and reflect clinical manifestations of aging. Recently, a candidate panel of senescence biomarkers was developed based on the secretome of senescent human endothelial cells, fibroblasts, preadipocytes, epithelial cells, and myoblasts in vitro. In older adults undergoing surgery, the senescence biomarkers were shown to correlate with chronological age and biological age, as defined by the frailty index, and to predict adverse events such as surgical complications and rehospitalizations (19).
Table 1 lists some potential biomarkers of the hallmarks of aging as well as the biological matrix in which they could be assessed.

Table 1
Potential Biomarkers of the Hallmarks of Aging (courtesy of Tamara Tchkonia and Nathan LeBrasseur)


Omics-based laboratory biomarkers have also been investigated in the FRAILOMIC initiative (20,21), an international consortium funded by the European Commission that aims to develop omics-based clinical instruments to assess frailty and predict the risk of frailty and subsequent disability. The consortium is analyzing data from four European cohorts: InCHIATIi (Tuscany, Italy), AMI (Gironde, France), the Three-City (3-C)Study (Bordeaux, France) (3C), and Toledo Study for Healthy Aging (TSHA, Toledo, Spain). The wide range of potential biomarkers investigated in the exploratory phase of this initiative is shown in Table 2.

Table 2
Exploratory phase biomarkers (courtesy of Prof. L. Rodriguez Mañas)


FRAILomic studies thus far have shown that biomarkers of frailty change according to clinical characteristics of participants, suggesting the existence of different clinical phenotypes of frailty. For example, omics biomarkers may be associated with disability, sarcopenia or other organ-specific diseases, and vary by sex, ethnicity, and race. Lab biomarkers appear to be modestly associated with classical biomarkers such as biomarkers of inflammation, hormonal changes, and glucose dysregulation (23), particularly among individuals with disability.
For example, in one study of FRAILomic participants, serum levels of the soluble receptor for advanced glycation end-products (sRAGE) was shown to be an independent predictor of mortality in frail individuals, suggesting that sRAGE levels may be useful for prognostic assessment and treatment stratification (24). Another study demonstrated that frail participants had higher plasma levels of 3-methylhistidine (3-MH) and higher ratios of 3-MH to creatine and 3-MH to estimated glomerular filtration rate, suggesting that these markers may be useful in identifying individuals at risk of frailty Finally, in this same regard, fat-soluble vitamins and carotenoids are biomarkers of frailty status (robust, pre-frail, frail) (26) but do not predict the risk of becoming frail (27)
Future studies of biomarkers of aging and their cross-sectional and longitudinal relationships with parameters of function (e.g., physical, cognitive, cardiovascular, pulmonary, renal metabolic, immune, and sensory) and resilience (e.g., to infection and consequences of SARS-CoV2) affected by advancing age are warranted. Longitudinal studies promise to provide greater insights into rates of biological aging and, potentially, in the context of clinical trials, the extent to which the molecular and cellular effects of aging can be attenuated and/or reversed. As novel interventions targeting the biology of aging emerge, biomarkers of aging will facilitate their testing and development.


Translation – developing agents that target fundamental aging processes

The Translational Gerontology Branch (TGB) at the National Institute on Aging (NIA) is part of the NIA’s intramural research program. Research conducted in TGB labs ranges from drug discovery using a variety of in vivo and in vitro models to clinical and longitudinal studies. For example, TGB researchers are studying the cellular and molecular mechanisms underlying aging, diseases of aging, and longevity, including the hallmarks mentioned earlier. Clinical studies have explored domains of the aging phenotype such as changes in body composition, energy imbalance, homeostatic dysregulation, and neurodegeneration and the impact of those changes on disease susceptibility, reduced functional reserve, impaired stress response and healing capacity, impaired physical function, disability, and dementia. The NIA also supported establishment of the Translational Geroscience Network (TGN) to develop, implement, and test standard operating procedures for translational early phase trials of agents that target fundamental aging processes and to select, optimize, and validate ancillary measures of fundamental aging processes for use across all trials (R33 AG061456). TGN provides statistical and data management support and has established a biobanking and repository network.



Healthy aging involves both delaying the physiological consequences of aging and maintaining functioning as aging progresses. Interventions thus need to focus on preventing frailty and disability. To develop effective and feasible interventions for healthy aging, whether drugs, exercise, diet, or combinations thereof, will require a deeper understanding of the mechanisms of aging as well as identifying biomarkers that track with biological, not simply chronological age, and predict when an individual is approaching a tipping point and nearing a threshold of irreversible decline.
The pathway to these biomarkers is through the interdisciplinary field of geroscience, which seeks to define the biological mechanisms of aging that give rise to age-related diseases and disorders and to identify targets that may be amenable to different kinds of interventions (15). Developing these biomarkers will require improved cellular and animal models and the capacity to translate discoveries from those models into humans. Also required will be reliable and sensitive measures to assess the hallmarks of aging, for example, assessments of mitochondrial dysfunction, and the impact of interventions on these biological mechanisms and, in turn, the health and functioning of older adults.
The interdisciplinarity of geroscience will be essential to define the complex interactions of the multiple biological, physiological, and behavioral pathways that contribute to age-related declines in health. Interdisciplinarity will also ensure that advances in geroscience are applied to other biomedical disciplines such as neuroscience and cardiology.


Acknowledgements: The authors thank Lisa J. Bain for assistance in the preparation of this manuscript. Dr. LeBrasseur would like to acknowledge the support of the National Institutes of Health, National Institute on Aging grants AG060907, AG055529, and AG061456. The authors are grateful to Dr. Tamara Tchkonia at Mayo Clinic for her thoughtful guidance on contents of Table 1.
Conflicts of interest: NKL- none, DLW-none, MAL-none.
Ethical Standards: None.
Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.



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N. Martínez-Velilla1,2,3, M.L. Saez de Asteasu1,2, R. Ramírez-Vélez1, I.D. Rosero1, A. Cedeño-Veloz1,3, I. Morilla1,4, R.V. García1,4, F. Zambom-Ferraresi1,2, A. García-Hermoso1,5, M. Izquierdo1,2

1. Navarrabiomed, Complejo Hospitalario de Navarra (CHN)-Universidad Pública de Navarra (UPNA), IdiSNA, Pamplona, Spain; 2. CIBER of Frailty and Healthy Aging (CIBERFES), Instituto de Salud Carlos III, Madrid, Spain; 3. Department of Geriatric Medicine, Complejo Hospitalario de Navarra, Irunlarrea 3, Pamplona, Spain; 4. Department of Medical Oncology, Complejo Hospitalario de Navarra, Pamplona, Spain; 5. Laboratorio de Ciencias de la Actividad Física, el Deporte y la Salud, Facultad de Ciencias Médicas, Universidad de Santiago de Chile, USACH, Santiago, Chile.
Corresponding author: Mikel Izquierdo, PhD, Department of Health Sciences, Public University of Navarra, Av. De Barañain s/n 31008 Pamplona (Navarra) Spain, Tel + 34 948 417876, mikel.izquierdo@gmail.com

J Frailty Aging 2021;in press
Published online February 7, 2021, http://dx.doi.org/10.14283/jfa.2021.2



Background: Lung cancer is the second most prevalent common cancer in the world and predominantly affects older adults. This study aimed to examine the impact of an exercise programme in the use of health resources in older adults and to assess their changes in frailty status. Design: This is a secondary analysis of a quasi-experimental study with a non-randomized control group. Setting: Oncogeriatrics Unit of the Complejo Hospitalario de Navarra, Spain. Participants: Newly diagnosed patients with NSCLC stage I–IV. Intervention: Multicomponent exercise programme that combined resistance, endurance, balance and flexibility exercises. Each session lasted 45–50 minutes, and the exercise protocol was performed twice a week over 10 weeks. Measurements: Mortality, readmissions and Visits to the Emergency Department. Change in frailty status according to Fried, VES-13 and G-8 scales. Results: 26 patients completed the 10-weeks intervention (IG). Mean age in the control group (CG) was 74.5 (3.6 SD) vs 79 (3 SD) in the IG, and 78,9% were male in the IG vs 71,4% in the CG. No major adverse events or health-related issues attributable to the testing or training sessions were noted. Significant between-group differences were obtained on visits to the emergency department during the year post-intervention (4 vs 1; p:0.034). No differences were found in mortality rate and readmissions, where an increasing trend was observed in the CG compared with the IG in the latter (2 vs 0; p 0.092). Fried scale was the unique indicator that seemed to be able to detect changes in frailty status after the intervention. Conclusions: A multicomponent exercise training programme seems to reduce the number of visits to the emergency department at one-year post-intervention in older adults with NSCLC during adjuvant therapy or palliative treatment, and is able to modify the frailty status when measured with the Fried scale.

Key words: Lung cancer, frailty, exercise, health-care resources.



Lung cancer is the second most prevalent common cancer in the world and predominantly affects older adults; 50% of the diagnoses are in patients aged 70 or older, and about 14% in over 80 years old (1, 2). Overall, the survival rate at 5 years is lower in the very old, and patients aged 80 years or older are less likely to receive local therapy than younger patients (2). Additionally, the incidence and mortality from lung cancer have decreased among individuals aged 50 years and younger but have increased among those aged 70 years and older (3). However, geriatric patients may be undertreated, and are routinely underrepresented on clinical trials for many reasons including frailty, doubts about the usefulness of therapy, or lower patient willingness to pursue aggressive therapy (4, 5).
The standard-of-care therapy for patients with stage III Non-small cell lung cancer (NSCLC) is concurrent chemotherapy and radiotherapy (CRT), but there is a lack of data regarding the use of CRT in octogenarians and nonagenarians. The goal for the treatment of patients with stage IV NSCLC is palliation, both through improvement in their quality of life (QOL) and in prolongation of survival. Few comparative studies have been conducted that are limited to older patients, and even in very recent research of older adults with NSCLC, the cut-off age was 65 or 70 years (6), and in some studies, even 62.7% of patients aged ≥80 years with stage III NSCLC received no cancer-directed care (7). Patient selection is a key factor in order to administer some treatments in older adults because they are more likely to have a poor performance status with comorbidities, which can lead to little benefit (8).
There is a growing interest in non-invasive interventions for patients with lung cancer, with the goal of maximising physical performance. Physical exercise can be beneficial at any stage of the disease through increasing strength, endurance and decreasing emotional issues (9). Multicomponent exercise programmes have demonstrated to be well tolerated and safe in patients with lung cancer, but there is still a paucity of data to draw conclusive and precise exercise guidelines. A recent Cochrane review failed to establish any conclusive evidence regarding efficiency of exercise training on physical fitness in patients with advanced lung cancer (10–12), and there is little information on what kind of benefits an exercise intervention can provide in the use of health-related resources or the impact on the ability to reverse frailty in the older population. To date, the clinical effectiveness of physical exercise in advanced cancer remains inconclusive.
This study aimed to examine the impact of this exercise programme in the use of health resources and its ability to reduce the number of visits to an emergency department at one-year post-intervention and to assess the changes in frailty status.



Study design, setting and ethical considerations

This is a secondary analysis of a non-randomised, opportunistic control, longitudinal trial designed to examine the effects of a multicomponent exercise programme on surrogate measures of health status in patients with lung cancer in real-world settings (12). Patients were treated at the Oncogeriatrics Unit of the Complejo Hospitalario de Navarra (CHN), Pamplona, Spain. The study ran from May 2018 to November 2019 and was approved by the CHN Research Ethics Committee (25 April, 2018, reference number Pyto2018/5#214) according to the World Medical Association Declaration of Helsinki Declaration.

Patient population

Newly diagnosed patients with NSCLC stage I–IV (TNM classification) were enrolled after histologically confirmation and screening for eligibility by their oncologist. The study included an initial exam at the first visit (baseline) and a final exam after 10-weeks. The inclusion criteria were: aged 70 years or older, have a diagnosis of confirmed lung cancer, with a life expectancy exceeding 3 months (prognosis), with multimorbidity, a Barthel score ≥60 points, and to be able to communicate and collaborate with the research team. Exclusion criteria were clinically unstable patients defined medically as having received active treatment (chemotherapy or radiotherapy) before inclusion in the study, moderate–severe cognitive impairment considered as a score ≥5 in the Reisberg Global Deterioration Scale, and contraindications to exercise or already engaged in high levels of physical training.

Outcome assessment

The primary outcomes of this study were mortality rate, readmissions and visits to the emergency department during the year after the intervention. The secondary outcomes were the changes in the level of frailty measured with G8 (14, 15), Vulnerable Elders Survey-13 (VES-13) (16, 17) and Fried scales (17). The G8 is an eight-item screening tool, developed for older cancer patients. The tool covers multiple domains usually assessed by the geriatrician when performing the geriatric assessment. A score of ≤14 is considered abnormal. The VES-13 is a 13-item self-administered tool, developed for identifying older people at increased risk of health deterioration in the community. A score of ≥3 identifies individuals as “vulnerable”, which is defined as an increased risk of functional decline or death over 2 years. The Fried Frailty Criteria includes five items: weight loss, handgrip strength, gait speed, exhaustion and physical performance and a score of ≥3 indicates “frailty”.
Members of the research team were able to access the medical records of each patient. The same assessments were repeated at 10-weeks after intervention or usual care, and we checked the medical records in order to assess the mortality, number of readmissions and visits to the emergency department during the year posterior to the intervention.


The intervention is described elsewhere (12). Briefly, the control group (CG) did not perform any kind of supervised physical exercises/activities during the intervention period but received habitual outpatient care, including comprehensive geriatric assessment and physical rehabilitation when needed.
The intervention group (IG) received a multicomponent exercise programme that combined resistance, endurance, balance and flexibility exercises. Each session lasted 45–50 minutes, and the exercise protocol was performed twice a week over 10 weeks (Table 1). EGYM Smart strength machines (eGym® GmbH, München, Germany) were used for both resistance training and maximum strength measurements of the lower and upper extremity muscles. Muscle power training including motivational gamification and maximum acceleration of constant weight from 30% to 60% of the maximun strength measurements were used during training (Explonic eGym® intelligent training program). The exercise programme was individualised and included measurements of vital signs at the beginning and end of each session. Patients were advised to carry out the «Vivifrail» programme (18) at home during the entire study period. The control group received the usual medical treatment and was advised to continue their usual activities without restriction in physical activity throughout the study period.

Table 1
Multi-component exercise program

Abbreviations: HR: Heart Rate; RM: Repetition Maximum.


Statistical analyses

All analyses were performed by a researcher who was not involved in the study’s participant assessments and interventions. The statistical data analysis was performed with the commercial software SPSS Statistics version 25.0 (IBM Corp., Chicago, IL, USA). The Shapiro–Wilk test was used to determine whether parametric tests were appropriate, and the normality of data was checked graphically. In the present study, descriptive data, including frequencies for categorical variables and means and standard deviation (SD) for continuous variables, were reported. Baseline differences and use of health resources (readmission and visits to the emergency department) were analysed using the chi-squared test and Mann–Whitney U test for nominal data and the Kruskal–Wallis test for ordinal data. A significance level of 5% (p <0.05) was adopted for all statistical analyses.



Characteristics of participants

Of the 42 volunteers, 34 attended the oncologic and geriatric clinics screening. Of these, 26 completed the 10-weeks intervention. Two patients from the IG did not complete the programme due to death or oesophagal surgery. Data from the 19 remaining patients from the IG were analysed. A total of 6 of the 13 CG subjects dropped out of the study and did not take the final exam due to the progression of the disease (n = 3) or death (n = 3). Data from the 7 remaining CG participants were analysed. A total of 19 participants (4 females, 15 males) were eligible for analysis in the IG and 7 participants (2 females, 5 males) in the CG (Figure 1). All subjects in the IG completed at least 86% of the planned training sessions. No major adverse events or health-related issues attributable to the testing or training sessions were noted.
Table 2 displays the baseline characteristics by group. No significant differences were found between the two groups, except for age. Patients in the IG had a mean (SD) age of 74.5 (3.6) years, range 70–81 years (78.9% males) and BMI 26.8 (4.5) kg/m2. In total, 41% underwent surgery, and 78.9% received adjuvant chemotherapy alone or in combination with other therapies. Participants in the CG had a mean (SD) age of 79.0 (3.0) years, range 75–83 years (71.4% males), and BMI 25.5 (2.5) kg/m2. Within this group, 14% were submitted to surgery, and 85.7% were receiving adjuvant chemotherapy alone or in combination with other therapies.

Figure 1
CONSORT Flow Diagram – modified for non-randomized
trial design

Table 2
Baseline characteristics of the participants

Abbreviations: BMI, body mass index; COPD, chronic obstructive pulmonary disease; TNM, tumor node metastasis; VATS, video-assisted thoracic surgery; VES-13, Vulnerable Elders Survey-13. aData are reported as mean ± standard deviation or number (%).


Mortality, readmissions and Visits to the Emergency Department

Significant between-group differences were obtained on visits to the emergency department during the year post-intervention (4 vs 1; p:0.034). Furthermore, no differences were found in mortality rate and readmissions, where an increasing trend was observed in the CG compared with the IG in the latter (2 vs 0; p 0.092) (Table 3).

Table 3
Mortality rate, readmissions and visits to the Emergency Department at one year post-intervention

Abbreviations: ED, Emergency Department; IQR, interquartile range.


Change in frailty status according to Fried, VES-13 and G-8

Although no significant between-group differences were obtained on frailty status changes assessed with the G-8, VES-13 and Fried scale, the unique indicator that seems to be able to detect changes in frailty status is the Fried Index after the intervention (Table 4).

Table 4
Changes in frailty status according to G-8, VES-13
and Frailty Index after the intervention

Abbreviations: VES, Vulnerable Elders Survey.



The main finding of this study was that supervised multicomponent exercise training can be beneficial for patients with lung cancer, by decreasing the number of visits to the emergency department. Previously, we showed that a multicomponent exercise programme in older patients with NSCLC under adjuvant therapy or palliative treatment positively affected measures of functional performance and quality of life (i.e., pain symptoms and dyspnea) (12), but this secondary analysis goes a step further, and analyses additional outcomes that may help when making decisions in relation to the use of healthcare resources.
Non-oncologic causes of readmission and death predominate in the first 90 days after pneumonectomy, after which oncologic causes prevail (19). Most previous studies have been related to readmissions after pulmonary resection (21, 22), but there is hardly any data on the influence of exercise programmes on the number of visits to the emergency department or on the influence of frailty in the use of health resources in cancer patients (17, 23). Older adults have been traditionally excluded from clinical trials, and clinical data obtained in a younger population cannot be automatically extrapolated to older patients with lung cancer (23). Older patients have more comorbidities and tend to tolerate aggressive chemotherapy and radiotherapy worse than younger patients. Much of the data available currently is based on retrospective studies of trials that included patients with good performance status and patients of all ages. Nonetheless, retrospective analyses of ordinary trials without age-specific entry criteria are potentially biased by the intrinsic selection that governs enrollment. In the present study, we did not find differences in the mortality rate, but this factor is very difficult to modify, especially in an older population as complex and frail as the one that participated in the study. However, we found that the IG had a non-significant lower number of readmissions (p = 0.09) and a lower number of visits to the emergency department (p = 0.034) at one-year post-intervention, which had at least a moderate impact on aspects related to the quality of life and use of health resources.
Chronological age alone should not be the only factor in the cancer treatment plan. Other factors should be taken into account and frailty assessment in older patients with primary lung cancer is increasingly being recognised as a very important tool (24), and it could be used even to prevent under- or overtreatment (25). In fact, a comprehensive geriatric assessment should be used together with an evaluation of the toxicity profile of each drug to guide the choice of the best treatment (26).
There is a big dilemma regarding the scales and the models to select the patients who most benefit of specific oncogeriatric approaches (15). Some studies suggest the VES-13 scale or G-8 scale, nevertheless, the only scale in our study that identified a possible reversal of the frailty status was the Fried Index. This could be because physical exercise modifies more parameters that are taken into account in Fried model of frailty (physical activity, grip strength and gait speed) compared to the G8 model (which has a vague and generic question about mobility), or the VES-13 (which has questions related more to basic activity rather than functional capacity). This has implications for future studies and helps to clarify which indices we should use in this population sector. In our study, a supervised exercise training programme was able to reverse frailty in 21.1% of patients (vs 0% in CG) using the Fried scale. This scale includes many functional aspects such as handgrip strength and gait velocity that could benefit from a physical exercise programme in comparison with G-8 and VES-13 scales.
The management of the older person with cancer should be based on the risk/benefit assessment, and in the multidisciplinary interventions (medical, psychological and social) it may improve the tolerance of the treatments (27). Exercise should be part of this multidisciplinary approach because it provides physiological and psychological benefits for cancer survivors Cancer rehabilitation as a part of clinical management is still underutilised, but older adults with lung cancer would welcome a proactive intervention. There are some barriers due to the psychosocial impact of diagnosis and the effects of cancer treatment, but the intervention must be tailored to individual need and address physical limitations, psychological and social welfare in addition to physical activity and nutritional advice (28). In this regard, the present study shows that these kind of programmes are feasible and may improve the quality of life of older patients with NSCLC.
This study had several limitations that should be considered. The most important was that the number of participants in our study was relatively small, but there are not many related studies with more patients, and so more extensive multicentre studies are encouraged to reinforce our findings. However, our study based on a supervised and individualized multicomponent physical exercise intervention including muscle power training and motivational gamification was beneficial and safe for patients with advanced NSCLC, under adjuvant therapy or palliative treatment. To our knowledge, none of the previous studies that have evaluated physical training in older adults with lung cancer reported serious adverse events, which is consistent with the findings of our study. We believe that the present study represents an important addition to the current body of knowledge on the safety of exercise interventions, particularly in the elderly with NSCLC under adjuvant therapy or palliative treatment. Well-designed randomized clinical trials should be performed to corroborate the current findings, with a larger sample size to detect a significant difference in the components studied.
In conclusion, a multicomponent exercise training programme seems to reduce the number of visits to the emergency department at one-year post-intervention in older adults with NSCLC during adjuvant therapy or palliative treatment for their disease, and is able to modify the frailty status measured with the Fried scale.


Funding: M.I. is funded in part by a research grant PI17/01814 from the Ministerio de Economía, Industria, y Competitividad (ISCIII, FEDER). R.R.-V. is funded in part by a Postdoctotal fellowship grant ID 420/2019 of the Universidad Pública de Navarra, Spain. N.M.-V. is funded in part by a research grant from Gobierno de Navarra: «Project prevención de deterioro funcional del anciano frágil con cáncer de pulmón mediante un programa de ejercicio tras valoración geriátrica integral” (Expediente 43/18), promovido por el Departamento de Salud.
Acknowledgments: We thank Fundacion Miguel Servet (Navarrabiomed) for its support during the implementation of the study, as well as Fundacion Caja Navarra and Fundacion La Caixa. Finally, we thank our patients and their families for their confidence in the research team.
Conflicts of Interest: The authors declare no conflicts of interest.
Ethical Standards: The study was approved by the CHN Research Ethics Committee (25 April, 2018, reference number Pyto2018/5#214) according to the World Medical Association Declaration of Helsinki Declaration.



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N.J. Krnavek1, S. Ajasin2, E.C. Arreola2, M. Zahiri1, M. Noun1, P.J. Lupo2, B. Najafi1, M.M. Gramatges2

1. Baylor College of Medicine, Department of Surgery, Division of Vascular Surgery and Endovascular Therapy, USA; 2. Baylor College of Medicine, Department of Pediatrics, Section of Hematology and Oncology, USA
Corresponding author: Maria Monica Gramatges, Baylor College of Medicine, Department of Pediatrics, Feigin Center, 1102 Bates St, Suite 1200, Houston, Texas, 77030, (p) 832-824-4678; (f) 832-825-4651, gramatge@bcm.edu

J Frailty Aging 2020;in press

Published online December 23, 2020, http://dx.doi.org/10.14283/jfa.2020.71



Background: Survivors of childhood cancer (CCS) are at risk for early aging and frailty. Frailty in CCS has been assessed with established clinical criteria, a time-intensive approach requiring specialized training. There is an unmet need for cost-effective, rapid methods for assessing frailty in at-risk adolescent and young adult (AYA) CCS, which are scalable to large populations. Objectives: To validate a sensor-based frailty assessment tool in AYA CCS, compare frailty status between CCS and controls, and assess the correlation between frailty and number of CCS comorbidities. Design, Setting, and Participants: Mean frailty index (MFI) was assessed by a frailty wrist sensor in 32 AYA CCS who were ≥1 year off therapy and in remission. Results were compared with 32 AYA controls without cancer or chronic disease. Measurements: Frailty assessments with and without a simultaneous cognitive task were performed to obtain MFI. Results were compared between cases and controls using a Student t test, and the number of pre-frail/frail subjects by Chi Square test. The contribution of radiation therapy (RT) exposure to MFI was assessed in a sub-analysis, and the correlation between the number of comorbidities and MFI was measured using the Pearson method. Results: MFI was strongly correlated with gait speed in AYA CCS. CCS were more likely to be pre-frail than controls without cancer history (p=0.032), and CCS treated with RT were more likely to be pre-frail than CCS not treated with RT (p<0.001). The number of comorbidities was strongly correlated with MFI (ρ=0.65), with a 0.028 increase in MFI for each added condition (p<0.001). Conclusions: Results from this study support higher risk for frailty among CCS, especially those with multiple comorbidities or who were treated with RT. A wrist-worn sensor-based method is feasible for application in AYA CCS, and provides an opportunity for cost-effective, rapid screening of at-risk AYA CCS who may benefit from early interventions.

Key words: Survivorship, frailty, slowness, weakness.



Over 80% of children diagnosed with cancer survive their disease (1), but survivors of childhood cancer (CCS) are at risk for late effects including early onset aging and frailty (2-10). An increase in frailty accompanies physiologic aging, affecting ~9% of persons >65 years (11) and 25-40% of persons >80 years (12). Frailty identifies individuals more vulnerable to adverse health outcomes (13) and predicts risk for early mortality (14, 15). Compared with sibling controls, CCS are more likely to report poor general health, functional impairment, and activity limitations with a prevalence that increases with age (16, 4, 3). Among 1,922 adult CCS with a mean age of 33.6 +/- 8.1 years, up to 13.1% were frail and up to 31.5% were pre-frail when assessed by the Fried clinical criteria, similar to the frailty prevalence in the general population that is at least three decades older (5). Frailty in CCS has been associated with exposure to cranial radiation and higher doses of abdominal or pelvic radiation (17).
In addition to the Fried clinical criteria, a number of methods assess frailty by clinical or survey-based assessment of factors such as weight loss, exhaustion, poor grip strength, slow gait speed, low physical activity, chronic health conditions, functional impairments, clinical symptoms, and behavior/psychological factors such as poor sleep quality or mood (18). These methods were largely developed for the frail older patient and validated in geriatric populations. Given the high prevalence of frailty in adolescent and young adult (AYA) CCSs, there is an unmet need for efficient, reliable, and low-cost methods that assess frailty across the lifespan.
We conducted a pilot study in AYA CCS leveraging a sensor-based upper extremity frailty assessment tool that has a strong correlation with well-established clinical and survey-based methods of frailty assessment in adults (sensitivity and specificity of 85% and 93% for predicting pre-frailty and 100% sensitivity and specificity for predicting frailty compared with the Fried criteria, sensitivity and specificity of 78% and 82% compared with a validated modified Rockwood questionnaire, and good correlation (ρ = 0.78) with the 6 minute walk test) (19-22). The Fried criteria and Rockwood questionnaire are considered gold standards for measuring frailty in elderly populations, but include components that are not appropriate for application to AYA populations. Therefore, our first objective was to validate the frailty assessment tool by determining correlation between mean frailty index and gait speed, a component of the Fried criteria, in AYA CCS. We then 1) tested feasibility of applying the frailty assessment tool in AYA CCS in an outpatient setting, 2) compared mean frailty index and frailty status between CCS and controls and CCS with or without high-risk treatment exposures (i.e. radiation), and 3) assessed the correlation between mean frailty index and the number of comorbidities present at the time of enrollment.



Study Population

For the frailty meter validation, eligible cases were recruited from the Texas Children’s Cancer and Hematology Centers (TCCC), and included CCS who were ≥15 years old, English-speaking, ≥1 year off therapy, and in remission.
For the feasibility assessment and comparisons between cases and controls, cases were recruited from CCS who met the same above-described eligibility criteria, but were non-overlapping with subjects recruited to the validation step. Controls were ≥15 years old, without cancer history, and recruited from routine well visit patients at a Texas Children’s Pediatric Clinic or Baylor Clinic. For CCS, comorbidities present at the time of enrollment were abstracted from the electronic medical record (EMR). Comorbidities were defined as chronic health conditions listed as an active problem in the EMR Problem List, present for at least one year (determined by problem start date) and requiring ongoing medical care (determined by scheduled subspecialty clinic visits). All cases and controls provided informed consent, as well as assent when applicable, for participation and were enrolled to an IRB-approved research protocol. This research was conducted in accordance with the ethical standards of the Baylor College of Medicine Institutional Review Board (H-38994) and with the 1964 Helsinki declaration and its later amendments.

Frailty Assessment

Mean frailty index (MFI) was determined for each arm by trained personnel using a wrist-worn frailty meter (Figure 1) (26). A wearable sensor is placed on the patient’s wrist, and, while seated, the participant performs a repetitive elbow flexion and extension task as quick and steadily as possible for 20 seconds (single task assessment). The dual task assessment adds a cognitive load to the single task, and in older adults leads to worse performance and higher MFI scores, an effect that is most pronounced among adults with cognitive impairment (27). During the dual task assessment, the subject counts down from a random number provided by the test administrator while performing the task (27). The sensor contains a tri-axial gyroscope that estimates three-dimensional angular velocity of the upper arm and forearm segments. Outcomes measures representing the kinematics and kinetics of elbow flexion, including speed, rise time, and flexion number per 20-second interval (indicators of slowness); flexibility (indicator of rigidity); power and moment (indicators of weakness); speed variability (indicator of steadiness); and speed reduction (indicator of exhaustion) are derived from the angular velocity, anthropometric data, and sex, and captured wirelessly through a tablet device (BioSensics, LLC, Newton, MA, USA). MFI is derived from these outcome measures using an optimized linear regression model previously described by Lee et al., with a numeric output on a continuous scale between 0 and 1 (26). In addition to the MFI, these outcome measures quantify slowness, weakness, and exhaustion (20). Participants completed the single and dual task assessments for both the dominant and non-dominant upper extremities in an average time of just under 5 minutes.

Figure 1
Upper extremity frailty meter sensor and example output. Mean frailty index (MFI) is derived from the measures obtained, in the context of sex, BMI, height, and weight.


Gait Speed Assessment

For the validation step, gait speed was determined by the Timed Up and Go (TUG) test (23), which is both reliable and reproducible in AYAs and for which normative values in this age range are available (24, 25). Briefly, the time (in seconds) required for a subject to rise from a standard armchair, walk a distance of 3 meters, turn, walk back to the chair, and sit down again is measured for each subject.

Quality of Life Assessment

Given the well-described inverse relationship between frailty and quality of life (28), and evidence for impact of frailty on quality of life among CCS (29), we included the PROMIS measure to assess this outcome in conjunction with frailty assessment in our case-control comparisons. Each participant completed the PROMIS questionnaire, a reliable measure of patient-reported health status for mental and physical well-being that has been validated in both children and adults (30). Metrics included in this study were the PROMIS Global Physical Health, Global Mental Health, and Global Health.

Data Analysis

For the validation step, the relationship between MFI and TUG time was determined by the Pearson correlation test. For the subsequent comparisons, participants were classified based on pre-defined MFI thresholds obtained from the dominant arm as robust (MFI <0.20), pre-frail (0.20≤ MFI <0.35), or frail (MFI ≥0.35), using the benchmarks proposed by Rockwood et al (31). After confirming that the data were normally distributed, the mean MFI and MFI subcomponents were compared between cases and controls using a Student t test, and the number of pre-frail/frail subjects by a Chi Square test. For CCS, the relationship between the number of comorbidities and MFI was determined by linear regression, and correlation was tested using the Pearson method. Factors previously associated with frailty include any exposure to cranial radiation in both sexes and abdominal/pelvic radiation in excess of 34Gy/40Gy, respectively, in males) (17). Therefore, we conducted a sub-analysis to compare outcomes between CCS who met criteria for at-risk RT exposure to CCS who did not meet criteria for at-risk RT. PROMIS data were categorized by T score and established cut points for excellent, very good, good, fair, and poor, and then analyzed by the Chi Square test (32).



Seven CCS were recruited for the validation step (two females and five males), mean age of 22.8 years (15-30 years). The mean for the Timed Up and Go (TUG) was 7.87 seconds (range, 5.23-10.12 seconds). Results were comparable to age-normative values for all but two subjects, who each exceeded the upper bound of the 95% confidence interval for the mean by 0.68 seconds and by 1.15 seconds. MFI was 0.19, range 0.13-0.25, and a strong correlation was observed between MFI and TUG time (ρ =0.83, p=0.02).
There were 48 potentially eligible CCS seen for an office visit in the TCCC Late Effects Clinic between June 29 and July 31, 2019. Subjects were recruited four days per week, so that 34 CCS were approached for participation, of which 32 consented and two declined (94% participation rate). All controls who were approached consented to study, so that there were 32 CCS and 32 age-comparable controls that were enrolled. The distribution of primary diagnoses among CCS were as follows: leukemia/lymphoblastic lymphoma, 20; germ cell tumor, 1; Hodgkin/non-Hodgkin lymphoma, 2; rhabdomyosarcoma, 2; Wilm’s tumor, 2; central nervous system tumor, 3; retinoblastoma, 1; ovarian cancer, 1. Participants were a mean of 9.8 years since completion of cancer treatment (median 8.0 years, range 1-36 years). Table 1 shows the distribution of baseline characteristics among CCS and controls.

Table 1
Distribution of demographic characteristics in CCSs and controls


The mean MFI for the dominant arm, both single and dual tasks, was higher among CCS than controls (p=0.002 and p<0.001, respectively, Table 2). The difference in MFI was primarily driven by the weakness and slowness components of MFI, rather than exhaustion. For both the single and dual task assessments in the dominant arm, CCS were more likely to be pre-frail than controls (p=0.032 and p=0.003, respectively). None of the participants met the pre-determined MFI cutoff criteria for frailty.

Table 2
Mean Frailty Index, frailty subcomponents, quality of life, fatigue assessment in CCSs compared with controls

* MFI thresholds for pre-frail and frail were pre-determined from the benchmarks proposed by Rockwood et al, (31) i.e. robust: MFI <0.20, pre-frail: 0.20≤ MFI <0.35, and frail: MFI ≥0.35. All results are displayed as a mean score ± SD


Out of 32 CCS, 13 had at-risk RT exposures: 12 were treated with cranial radiation (12-55.8 Gy), and one male was treated with pelvic RT (50.4 Gy). No females were treated with abdominal or pelvic RT. In the single task assessment, CCS with at-risk RT exposures had a higher MFI (p<0.001) and were more likely to be pre-frail (p<0.001) than CCS without RT exposure (Table 2). As expected, the 12 CCS whose treatment included CRT had a significantly higher MFI than CCS not exposed to CRT, and though there was some evidence of dose-dependence, this difference was not statistically significant (p=0.15, Table 3). No substantial difference in MFI was noted after the addition of a cognitive load (dual task assessment) in controls and cases, regardless of CRT exposure (Tables 2 and 3).

Table 3
Dominant arm, single and dual task MFI determined in controls compared with CCSs without (n=20) and with (n=12) history of exposure to cranial radiation (CRT). Mean MFI increased with increasing dose of CRT

All results are displayed as mean score ± SD


Eleven CCS had no comorbidities, 10 had one condition, 5 had two conditions, 4 had three conditions, 1 had four conditions, and 1 had six conditions, described in more detail in Figure 2. The number of comorbidities was correlated with MFI (ρ = 0.65), with a 0.028 increase in MFI for each added condition (p<0.001).
No significant differences in the proportion of subjects reporting ‘poor’ or ‘fair’ vs. ‘good,’ very good,’ or ‘excellent’ global health, global mental, or global physical health status were observed between controls and CCS.

Figure 2
Distribution of chronic health conditions by system among CCS (n=32)

Cardiometabolic: congestive heart failure (1), hypertension (2), obesity (6); Endocrine: hypogonadism (1), hypothyroidism (2), primary ovarian failure (2), growth hormone deficiency (3); Vision/hearing: cataracts (2), severe visual impairment (2), hearing loss or tinnitus (3); Peripheral/Central nervous system: peripheral neuropathy (1), hemiparesis (1), chronic migraine (1), epilepsy (2); Musculoskeletal: arthritis (1), osteopenia/osteonecrosis (4); Neuropsychological: generalized anxiety disorder (2) major depressive disorder (2); Pulmonary: chronic lung disease (1), obstructive sleep apnea (1); Renal: chronic kidney disease (1); Hematological: chronic pancytopenia (1)



There is considerable need for methods that rapidly, effectively, and inexpensively screen CCS for evidence of pre-frailty or frailty conferred by cancer treatment. To date, studies conducted in CCS have used the clinical Fried criteria, which is both time-consuming and requires training to administer. In elderly persons, the wrist-worn sensor-based method for frailty status determination has a strong correlation with frailty status determined by both the Fried criteria and the Rockwood Frailty Index (19-22), and is a strong predictor of adverse outcomes such as prolonged hospitalizations and prospective falls (35, 36, 27, 37). Given that this tool has largely been applied in older adult populations, we first validated its use in AYA CCS by demonstrating correlation between frailty status determined by wrist-worn sensor and gait speed.
Our study suggests that this method is feasible for application in the outpatient setting. In our pilot study, CCS were more likely to have a higher MFI and be pre-frail than controls, and CCS with at-risk RT exposures were more likely to have a higher MFI and be pre-frail than CCS without a history of at-risk RT, in line with prior reports (5). Of note, the mean age of CCS in this study was 20.5 years, so it is not surprising that no CCS met thresholds for frailty. CRT-exposed CCS showed no discernible difference in MFI with the addition of a cognitive load (dual task) compared with the single task, and compared with non-CRT-exposed CCS. The absence of an effect on MFI observed with addition of a cognitive load suggests that the dual task may be of less importance when assessing frailty in CCS. MFI in CCS was strongly correlated with the number of comorbidities, but the study was not powered to detect associations with diagnosis, treatment dose, or modality other than RT exposure.
Detection of pre-frailty is important, because it offers an opportunity for early intervention in an anticipated trajectory of continued physical decline. The frailty assessment tool described here is a safe, low cost, and time-efficient method, requiring less than 5 minutes to complete compared with the 10-20 minutes required for the Fried method (33, 34). Moreover, it is a stand-alone tool that requires minimal training to use, and does not require additional equipment such as a dynamometer, stopwatch, or 15 foot floor tape. Our pilot data suggest higher MFI in CCS, especially CCS treated with RT compared with controls, and support prospective application of this method to predict risk for morbidity and mortality in CCS, correlated with objective functional and biological measures.


Acknowledgements: The authors would like to thank the patients who participated in this study as well as their families.
Funding: This work was supported by the Texas Children’s Cancer and Hematology Centers.
Conflicts of Interest: None declared by the Authors.
Ethical Standards: This study was approved by the Baylor College of Medicine institutional review board, and conducted in accordance with the Helsinki Declaration of 1975, as revised in 2000.



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C.P. Launay1,2, L. Cooper-Brown2,3, V. Ivensky2,4, O. Beauchet1,2,5,6

1. Department of Medicine, Division of Geriatric Medicine, Sir Mortimer B. Davis – Jewish General Hospital and Lady Davis Institute for Medical Research, McGill University, Montreal, Quebec, Canada; 2. Centre of Excellence on Longevity of McGill integrated University Health and social services Network, Quebec, Canada; 3. Faculty of Medicine, McGill University, Montreal, Quebec, Canada; 4. Faculty of Medicine, University of Montreal, Montreal, Quebec, Canada; 5. Dr. Joseph Kaufmann Chair in Geriatric Medicine, Faculty of Medicine, McGill University, Montreal, Quebec, Canada; 6. Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore

Corresponding Author: Olivier Beauchet, MD, PhD; Department of Medicine, Division of Geriatric Medicine, Sir Mortimer B. Davis – Jewish General Hospital, McGill University, 3755 chemin de la Côte-Sainte-Catherine, Montréal, QC H3T 1E2, Canada; E-mail: olivier.beauchet@mcgill.ca; Phone: (+1) 514-340-8222, #24741; Fax: (+1) 514-340-7547
J Frailty Aging 2020;
Published online December 22, 2020, http://dx.doi.org/10.14283/jfa.2020.69


Dear Editor,
Recently, Aubertin-Leheurdre & Rolland underscored issues and challenges related to the insufficient physical activity levels observed in the frail older population due to social distancing during the Coronavirus disease 2019 (COVID-19) pandemic (1). Social distancing is an effective intervention to limit the spread of COVID-19 (2). However, for older community dwellers social distancing implies homebound which may lead to a decline in physical activity, increased gait and balance disorders, cardiovascular disease burden and morality risk (1, 3, 4).
Frailty refers to a condition of vulnerability to physical and psychological stressors that exposes individuals to incident adverse health events, disabilities and death (5-7). There are two frailty phenotypes: physical and mental (6, 7). We suggested that frailty phenotypes may confer distinct risks for adverse outcomes to homebound older community dwellers. To explore this research hypothesis, there is a need to first gather information about homebound older adults’ physical and mental frailty phenotypes at the onset of confinement. Montreal (Quebec, Canada) is an urban area that is particularly affected by the COVID-19 pandemic, being, as of May 2020, the city with the highest number of confirmed COVID-19 cases in Canada (8). The confinement of Montreal’s older adults will be therefore lasting longer than initially anticipated.
A short assessment tool, known as “Évaluation SOcio-GÉRiatrique” (ESOGER), for Montreal’s older community-dwelling population has been designed in March 2020. ESOGER allows to screen both physical and mental frailty. The objective of the present study is to describe the clinical characteristics and frailty phenotype of Montreal’s older community dwellers, assessed with ESOGER after one month of being homebound.
Between April 20 and May 08, 2020, 879 older community dwellers were recruited to participate in this cross-sectional study. Selection criteria were: age ≥70 years, being homebound, understanding French and/or English and agreeing to participate in the study. ESOGER is a clinical assessment consisting of a digital questionnaire that includes close-ended questions exploring five complementary subdomains, including: 1) COVID-19 clinical symptomatology (i.e., fever ≥38°C/100F, cough, shortness of breath and other symptoms); 2) a frailty assessment performed using the 6-item brief geriatric assessment (BGA), 3) psychological stress using a verbal analogue scale (VAS) of anxiety ranging from 0 (i.e., no anxiety) to 10 (i.e., severe anxiety) (9). ESOGER can be filled out by health and social professionals, as well as by trained volunteers, through a phone call with older community dwellers or their caregivers. Participants were separated into 4 groups based on their frailty phenotype as per the 6-item BGA: no frailty, physical frailty (i.e., use of a walking aid), mental frailty (i.e., temporal disorientation associated with anxiety), and a combination of physical and mental frailty. Participants’ characteristics were summarized using frequencies and percentages. Between-group comparisons were performed using Chi-squared tests. P-values less than 0.001 were considered statistically significant because of multiple comparisons (n = 42). All statistics were performed using SPSS (version 24.0; SPSS, Inc., Chicago, IL). The study was approved by the Ethics Committee Of thé Jewish General Hospital (Montreal, Quebec, Canada).
The overall prevalence of frailty was 65.0%; the most prevalent type was physical frailty 38.3%, whereas the prevalence of mental frailty was 12.5%, and that of both frailties combined was 14.1% (Table 1). Participants identified as physically frail were older, used home support, and experienced polypharmacy at a higher rate than those with no frailty (P≤0.001) and those with mental frailty (P≤0.001). Participants combining both types of frailty took more medications compared to those with no frailty (P≤0.001) and had home support more often than those with mental frailty only (P≤0.001). There were no significant differences between groups for the other characteristics.

Table 1
Characteristics of participants separated into four groups based on their frailty phenotype (n=879)

*: based on Chi-squared test; †: Formal (i.e., health and/or social professionals) or informal (i.e., family and/or friends); ‡: Number of different medications taken daily ≥5; §: Older adults with at least 3 COVID-19 symptoms among fever ≥ 38°C /100°F, cough, shortness of breath and other symptoms; ¶: cane and rolling walker; #: Inability to give the month and/or year; **: Highest tertile of the verbal analogue anxiety scale score ranging from 0 (no anxiety) to 10 (severe anxiety).

These findings illustrate the high prevalence of frailty, especially of the physical phenotype, among homebound older community dwellers in Montreal. This result provides insight into the importance of prioritising preventive interventions that target insufficient physical activity in times of social distancing. Indeed, it is well known that older community dwellers with physical frailty are at increased risk for motor deconditioning and related adverse outcomes, which include muscle mass decline and increasingly unstable gait and balance, ultimately increasing the risk of falls and fractures (1). In this population, patient-centred care should include offering physical activity programs that take into account physical distancing measures (10). As suggested by Aubertin-Leheurdre & Rolland, innovative gerontechnology solutions such as exergames or web-based exercise programs may address the risk being homebound poses to older community dwellers (1).

Conflict of interest: The authors CPL, LCB, VI and OB report no disclosures relevant to the manuscript.



1. Aubertin-Leheurdre, Rolland Y. The importance of physical activity to care for frail older adults during the COVID-19 pandemic. J Am Med Dir Assoc. 2020 [Epub ahead of print].
2. Cudjoe TKM, Kotwal AA. «Social distancing» amidst a crisis in social isolation and loneliness. J Am Geriatr Soc. 2020 [Epub ahead of print]
3. WHO https://www.who.int/news-room/fact-sheets/detail/physical-activity (May 13, 2020 date last accessed)
4. Buttar HS, Li T, Ravi N. Prevention of cardiovascular diseases: Role of exercise, dietary interventions, obesity and smoking cessation. Exp Clin Cardiol. 2005;10:229-249.
5. Hubbard RE, Maier AB, Hilmer SN, Naganathan V, Etherton-Beer C, Rockwood K. Frailty in the Face of COVID-19. Age Ageing. 2020 [Epub ahead of print]
6. Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, et al. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci. 2001;56:M146-156.
7. Kelaiditi E, Cesari M, Canevelli M, van Kan GA, Ousset PJ, Gillette-Guyonnet S, et al. Cognitive frailty: rational and definition from an (I.A.N.A./I.A.G.G.) international consensus group. J Nutr Health Aging. 2013;17:726-734.
8. Coronavirus Disease 2019 (COVID-19). Daily Epidemiology Update (Updated May 15, 2020, 2:00 PM ET). https://www.canada.ca/en/public-health/services/diseases/2019-novel-coronavirus-infection.html?utm_campaign=gc-hc-sc-coronavirus2021-ao-2021-0005-9834796012&utm_medium=search&utm_source=google_grant-ads-107802327544&utm_content=text-en-434601690158&utm_term=%2Bcoronavirus#a1
9. Beauchet O, Chabot J, Fung S, Launay CP. Update of the 6-Item Brief Geriatric Assessment Screening Tool of Older Inpatients at Risk for Long Length of Hospital Stay: Results From a Prospective and Observational Cohort Study. J Am Med Dir Assoc. 2018;19:720-721.
10. Bonadias Gadelha A, Lima RM. Letter to the Editor: COVID-19 Quarantine in Older People: The Need to Think about Sarcopenia-Related Phenotypes. J Frailty Aging. 2020;9(4):244-245.



K. Kinoshita1,2, R. Otsuka3, C. Tange3, Y. Nishita1, M. Tomida3, F. Ando3,4, H. Shimokata3,5, H. Arai6
1. Department of Epidemiology of Aging, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, Aichi Japan; 2. Department of Community Healthcare and Geriatrics, Nagoya University Graduate School of Medicine, Aichi, Japan; 3. Section of NILS-LSA, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, Aichi, Japan; 4. Faculty of Health and Medical Sciences, Aichi Shukutoku University, Aichi, Japan; 5. Graduate School of Nutritional Sciences, Nagoya University of Arts and Sciences, Aichi, Japan; 6. National Center for Geriatrics and Gerontology, Aichi, Japan
Corresponding author: Rei Otsuka, Section of NILS-LSA, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, 7-430 Morioka, Obu, Aichi 474-8511, Japan, Tel: +81-562-46-2311; FAX: +81-562-46-2373; E-mail: otsuka@ncgg.go.jp

J Frailty Aging 2020;in press
Published online December 22, 2020, http://dx.doi.org/10.14283/jfa.2020.67



Polyunsaturated fatty acids help maintain insulin sensitivity, mitochondrial function, and anti-inflammation. It is well known that deterioration in these areas can cause frailty. However, little is known about the differences in serum polyunsaturated fatty acid levels among frailty components. We investigated the cross-sectional relationship between frailty and serum fatty acids in 1,033 community-dwelling older adults aged 60–88 years. Polyunsaturated fatty acid concentrations were measured from fasting blood samples. The modified phenotype criteria defined frailty. Polyunsaturated fatty acid levels were compared among each component using general linear modeling after controlling for sex, age, body mass index, smoking status, household income, and medical history. Lower polyunsaturated fatty acid levels were associated with the modified frailty criteria, including shrinking and weakness (p < 0.05). Our findings suggest that serum polyunsaturated fatty acid levels differ depending on the frailty status of older adults.

Key words: Cross-sectional study, polyunsaturated fatty acids, n-3 polyunsaturated fatty acids.



Fatty acids, especially polyunsaturated fatty acids (PUFA), have gained attention from many researchers and clinicians because they may have beneficial effects on the health of older adults (1). Besides being produced from food intake, some fatty acids are synthesized in the body. Previous studies have reported that the serum levels of fatty acids are affected by age and are independent of their intake (2).
PUFA can help maintain insulin sensitivity, mitochondrial function, and anti-inflammation (1). This is significant, since deterioration in these areas is known to cause frailty (3). Thus, frail older adults may have lower PUFA serum levels than healthy older adults. However, little is known about differences in fatty acid serum levels associated with frailty in community-dwelling older adults.
Investigating the relationship between serum fatty acids and frailty signs could be useful for future research and medical treatments, especially towards maintaining the health of older adults. Therefore, we aimed to verify the differences of fatty acid serum levels associated with frailty components in community-dwelling older adults.



Study participants

This study was conducted as a part of the National Institute for Longevity Sciences – Longitudinal Study of Aging (NILS-LSA) (4). Participants were recruited using a stratified random sampling method, by age (≥ 40 years) and sex, from the community-dwelling population of Obu City and Higashiura Town in Aichi Prefecture, Japan (4).
We cross-sectionally analyzed the fifth study wave data (between July 2006 and July 2008). Of the 2,419 participants in the fifth study, we excluded participants who: were aged < 60 years (n = 1,140), had incomplete frailty diagnoses (n = 178), had missing data for serum samples (n = 38), and had missing data for covariates (n = 30). Finally, 1,033 participants (males, n = 513, 49.7%) were analyzed in this study.
All participants provided written informed consent before participation. This study protocol was approved by the Ethics Committee of Human Research of the National Center for Geriatrics and Gerontology, Japan (No. 1115-3).

Blood sampling and fatty acid measurement

Blood samples were collected in the morning after fasting at least 12 hours. The NILS-LSA measurement details for fatty acids have been reported elsewhere (2). This study assessed total fatty acids (TFA), saturated fatty acids (SFA), monosaturated fatty acids (MUFA), and PUFA. Regarding PUFA, the n-3 series PUFA (n-3 PUFA) including alpha-linoleic acid (ALA), eicosapentaenoic acid (EPA), and docosahexaenoic acid (DHA); and the n-6 series PUFA (n-6 PUFA) including gamma-linoleic acid (GLA), linoleic acid (LA), arachidonic acid (AA) were also assessed.

Physical frailty assessment

Frailty was assessed using frailty phenotype modified for Japanese, based on the original criteria outlined by Fried (5, 6). Frailty was indicated if participants met three or more components; meeting one or two components indicated pre-frailty. Shrinking was defined by ≥ 5% weight loss in the prior two years, since the NILS-LSA survey is biennial (4, 6). Weakness was defined by a maximum grip strength of < 26 kg in males and < 18 kg in females (6). Slowness was defined by a 10-m general gait speed of < 1.0 m/sec (6). Low activity was defined as scoring in the lower 20% for physical activity on a modified Minnesota Leisure-time Physical Activity Questionnaire (6). Exhaustion was assessed by determining whether participants experienced either of the following conditions: “I felt that everything I did was an effort” and “I could not get ‘going,’” The responses, with regard to the previous week, were: “less than 1 day,” “1-2 days,” “3-4 days,” and “5-7 days.” Participants were defined as exhausted if they did not answer “less than 1 day” for either of these questions (6).


Body mass index (BMI, kg/m2) was calculated from anthropometric data. Smoking status (current smoker or not), annual household income (less than 5.5 million yen or more), and medical history (hypertension, ischemic heart disease, dyslipidemia, diabetes mellitus, and stroke) were assessed using self-report questionnaires.

Statistical analysis

Serum fatty acid levels were estimated with logarithmic conversion. Mean and standard deviations were calculated for continuous variables.
Participant characteristics, according to frailty status, were analyzed using the Chi-squared test and analysis of variance.
Using general linear modeling after controlling for covariates, we compared serum fatty acid levels between participants in three categories according to frailty phenotype (i.e., robust, pre-frail, and frail), and in participants with or without each of the five frailty components.
In the additional analysis, we included the overall energy intake (kcal/day) as covariates. The energy intake was assessed using a three-day dietary record that has been reported in detail elsewhere (7).
All analyses were performed using the Statistical Analysis System version 9.3 (SAS Institute, Cary, NC, USA); a two-sided p-value < 0.05 indicated statistical significance.



Table 1 shows participant characteristics. Participant age ranged from 60 to 88 years. There were 68 (6.6%) frail, 561 (54.3%) pre-frail, and 404 (39.1%) robust participants.

Table 1
Characteristics of participants according to physical frailty (n=1,033)

*Chi-squared test for proportion variables and ANOVA for continuous variables; †5.5 million yen = $51,882.32 USD, which was calculated according to the mean conversion rate during the study period (i.e., July 2006 to July 2008); SD, standard deviation; BMI, body mass index; ANOVA, analysis of variance.


In comparing fatty acid serum levels in robust, pre-frail, and frail participants, PUFA levels (μg/ml) were highest in the robust group and lowest in the frail group (least squares mean ± standard error; 1416.1 ± 1.0, 1378.1 ± 1.0, and 1341.5 ± 1.0; in respectively, p = 0.020 for between-group difference, p = 0.026 for trend).
Table 2 shows fatty acid serum levels according to the five frailty components. Participants with shrinking had significantly lower levels of TFA, SFA, MUFA, and PUFA than those without (p < 0.01 in all). Those with weakness had significantly lower EPA and DHA levels, than those without (p < 0.05 in all). Those with slowness had significantly lower EPA levels (p = 0.032); those with low activity had significantly lower DHA levels (p = 0.048). However, we found no significant differences in fatty acid serum levels for participants with and without exhaustion.

Table 2
Serum levels of fatty acids according to the five components of physical frailty (n=1,033)

Values are least squares mean (standard error). General linear modeling was performed after controlling for sex, age, BMI, current smoker, annual household income (< 5.5 million yen), and history of stroke, ischemic heart disease, hypertension, dyslipidemia, diabetes mellitus. Serum fatty acids levels were estimated after logarithmic conversion. TFA, total fatty acids; SFA, saturated fatty acids; MUFA, monounsaturated fatty acids; PUFA, polyunsaturated fatty acids; ALA, alpha-linoleic acid; EPA, eicosapentaenoic acid; DHA, docosahexaenoic acid, GLA, gamma-linoleic acid; LA, linoleic acid; AA, arachidic acid.



This study investigated the serum fatty acid levels according to frailty components in community-dwelling older adults. Our findings suggest that serum fatty acid concentrations, especially PUFA levels, differ depending on the frailty signs in older adults.
Both PUFA and MUFA are suggested to be effective in preventing muscle loss through suppressing reductions in insulin sensitivity (1). This may be because insulin accelerates muscle synthesis by activating the mammalian target of rapamycin complex 1 (8). PUFA is believed to prevent muscle atrophy and physical dysfunction by improving mitochondrial function and anti-inflammatory effects (1, 9). Mitochondrial dysfunction overproduces reactive oxygen species, leading to muscle atrophy due to induced protein degradation through the accelerated ubiquitin-proteasome system (10). Inflammation is suggested to be associated with components of frailty; however, this needs to be further explored in future studies (9, 3).
Participants with shrinking had significantly lower serum SFA levels. SFA may have an association with muscle loss (1). However, participants with shrinking also showed significantly lower TFA levels. Previous research observed that TFA serum levels were significantly lower in thin people compared with obese people (11). To account for the effect of consumption on shrinking, we additionally analyzed and controlled participants’ energy intake (kcal/day); however, this association remained even after the adjustment (data not shown).
We determined that lower PUFA levels, especially n-3 PUFA, were associated with signs of frailty. Based on a review article, randomized controlled trials (RCTs) of n-3 PUFA supplementation for muscle function were conducted with healthy older adults. However, the beneficial effects were shown only during sufficient supplementation (12). Intriguingly, Guerville and colleagues conducted an RCT in community-dwelling older adults with regard to slow gait speeds or any limitations in the activities of daily living; they reported that the n-3 PUFA supplementation and lifestyle intervention resulted in a lower frailty incidence than the placebo group, when they excluded participants who became frail within one year (13). They suggested that earlier intervention may be crucial (13). Considering our findings together, PUFA requirements might vary according to the conditions of frailty.
There are several limitations to this study. First, the participants may have been healthier than those in other cohorts because the mean frailty prevalence is 11.2% in Japan, as reported from seven community-based studies (14). However, our study participants were younger than in those seven studies. Second, although shrinking status was measured as the change after two years, whether these weight losses were intentional was not clarified. The original criteria measured the weight change during one year (5). Thus, it is possible that some participants with shrinking displayed more gradual weight loss than the original criteria accounted for, or they lost weight intentionally (5). Third, we repeated the analysis for each component; thus, the possibility of an alpha error cannot be ruled out. However, we judged this according to the statistical results, and our interpretation coincides with the view of the American Statistical Association (15).
In conclusion, our findings suggest that older adults with frailty components show lower serum levels of PUFA, especially EPA and DHA. PUFA may prevent frailty signs, such as skeletal muscle catabolism and physical dysfunction, through the beneficial effects of suppressing insulin resistance, maintaining the mitochondrial function, and anti-inflammation. These results can be useful for designing studies and treatment strategies regarding the improvement of physical frailty in older age.


Acknowledgments: We truly appreciate all participants and staff of the NILS-LSA for their cooperation and contributions to this study. We would like to thank Editage (www.editage.com) for English language editing.
Funding: This study was supported in part by the Food Science Institute Foundation, and Research Funding for Longevity Sciences from the National Center for Geriatrics and Gerontology, Japan (19-10 to R.O.); and the Japanese Ministry of Education, Culture, Sports, Science and Technology (20H04114 to H.S.).
Author contributions: Kaori Kinoshita conceived the study design, performed the data analysis, interpreted the results, and drafted the initial manuscript; Rei Otsuka collected data, conceived the study design, performed the data analysis, interpreted the results, and contributed to discussions; Chikako Tange collected data, interpreted the results and contributed to discussions; Yukiko Nishita collected data, interpreted the results, and contributed to discussions; Makiko Tomida collected data, interpreted the results, and contributed to discussions; Fujiko Ando designed the NILS-LSA, collected data, interpreted the results, and contributed to discussions; Hiroshi Shimokata designed the NILS-LSA, collected data, interpreted the results, and contributed to discussions; and Hidenori Arai supervised the study, conceived the study design, interpreted the results, and contributed to discussions.
Conflict of Interest: None declared by the authors.
Ethical standards: This study was carried out in accordance with the ethical standards.



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H. Amieva, J.-A. Avila-Funes, S. Caillot-Ranjeva, J.-F. Dartigues, M. Koleck, L. Letenneur, M. Pech, K. Pérès, N. Raoux, N. Rascle, C. Ouvrard, M. Tabue-Teguo, R. Villeneuve, V. Bergua

INSERM, Bordeaux Population Health Research Center, UMR 1219, Univ. Bordeaux, F-33000 Bordeaux, France
Corresponding author: Professor Helene Amieva, Inserm U 1219 Bordeaux Population Health, University of Bordeaux, 146 Rue Léo Saignant, 33076 Bordeaux cedex, France, Phone: +33 5 57 57 15 10 / Fax: +33 5 57 57 14 86, Helene.Amieva@u-bordeaux.fr
J Frailty Aging 2020;in press
Published online November 18, 2020, http://dx.doi.org/10.14283/jfa.2020.60



The health crisis we are facing is challenging seniors’ resources and capacities for adaptation and resilience. The PACOVID survey, set up a few days after containment, investigates their psychological and social experiences with regard to the COVID-19 crisis and to what extent these characteristics, representations and attitudes have an impact on health and mortality. A telephone survey is being carried out on 935 people already followed up in the framework of ongoing epidemiological studies. As we are writing this article, the interviews conducted during the containment have just ended. Even though we will have to wait for the analysis of the results to draw conclusions, words collected by the psychologists during the interviews already illustrate a great heterogeneity in the way older adults lived this experience: social isolation, anxiety, the importance of family and the difficulty of being deprived of it, but also remarkable coping skills and resilience capacities.

Key words: Containment, behaviors, vulnerability, coping, resilience.

In the face of health crises, the older population is one of the most vulnerable. The heat wave episode in France in August 2003 exceptional in terms of intensity, duration and geographical extent, clearly demonstrated this. The excess mortality related to this episode (15,000 additional deaths compared to the usual mortality) was explained by an increase in excess mortality with age particularly marked among people living alone at home or in nursing homes (1). While the world is still facing the COVID-19 epidemic, there is every reason to believe that older people will once again be the most affected, as shown by the mortality curves provided by the countries affected by the pandemic. This excess mortality can be explained by the physiological particularities of the older persons (a greater state of immuno-depression, a propensity to over-activate the inflammatory response, and the frequent co-morbidities such as heart failure or chronic obstructive bronchopneumopathy, favoring complications), but the specificities related to the psychological and social functioning of the older adults also contribute to this issue. Apart from any crisis situation, the consequences of a disease are very different from one older person to another depending on psychological resources, lifestyle, level of social support, home facilities, accessibility of services and shops, etc. Numerous studies have shown that socially isolated older adults present higher mortality, independently of many confounding factors (2–4). Depression also has a major impact on mortality of the older persons suffering from diseases such as cancer (5), as depressed people adhere less to preventive screening procedures, good health behaviors, and treatments. In a major health crisis, do these factors carry more weight? As few studies explored in depth this question, it is difficult to answer this question.
In the days following the containment measures in China, a literature review was undertaken to take stock of what is known about the psychological impact of quarantine (6). Based on 24 studies, the conclusions highlight the psychological impact, the most frequent consequences being post-traumatic stress symptoms, confusion and anger. These symptoms persist for several months or even years after quarantine. For example, in Wu et al.’s study (7) conducted during the SARS epidemic, quarantine predicted post-traumatic stress up to 3 years later Brooks et al. (6) also highlight the factors that contribute to the negative impact: duration of quarantine, level of fear of infection, feelings of frustration, boredom, supply problems, lack of information, loss of income, and stigmatization.
No study specifically focused on the older population particularly at risk at least at three levels: with regard to the response to the infectious agent itself because of the physiological characteristics of this population; with regard to the psycho-social characteristics of the older persons which make part of this population even more exposed to the risk of severe repercussions of the infection (elders with dependency, cognitive disorders, depression, social isolation or living in institutions); and possibly with regard to the situation of containment due to reduced adjustment capacities (8,9).
The PACOVID (Personnes Agées face au COVID-19) survey was set up in Bordeaux region a few days after containment. Through a 2-step telephone survey carried out during and after the confinement on 935 people (living at home or in institutions) already followed up in the framework of ongoing epidemiological studies, this project addresses the following questions:
1) What are the attitudes, psychological and social experiences of the older persons with regard to the COVID-19 crisis and the containment measures: level of stress, coping strategies, social support, access to information, instructions and measures put in place by government authorities, understanding and compliance to such measures, representations of the epidemic, access to digital communication tools?
2) To what extent do these characteristics, representations and attitudes have an impact on mortality and health events related and unrelated to COVID-19?
As we are writing this article, the first wave of the survey conducted during the period of containment has just ended (the second wave will take place away from the containment). Even though we will have to wait for the analysis of the results to draw conclusions, words collected by the psychologists during the interviews already illustrate a great heterogeneity in the way older adults live this experience. First of all, this survey reminds us to what extent loneliness and social isolation are worrying issues among the older persons, such as this 98-year-old woman, pleasantly surprised by our telephone call and who told us that she had not seen anyone since Christmas, «I lost my husband at the beginning of the year, I’ve been in conflict with my only son for many years, I don’t have any friends left, your call is very touching to me». For those who have more people around them, the importance of family and the difficulty of being deprived of it come out from numerous comments: «Usually, my children and grandchildren visit me at lunchtime, but now I feel alone and I miss them, we lose our appetite with my husband,» said one participant. Actually, several people told us that they apply the containment measures very strictly, even though it turned out during the interview that they continued to receive regular visits from their family, «Containment is for outsiders, the family is not the same. My children come to see me every day to bring me groceries, watch TV, help me with household chores. By the way, today my daughter-in-law is coming to mow the lawn!» says one participant. As we expected, anxiety was very present, as in this 103-year-old woman who told us: «I suffer from rheumatism to the legs, my physiotherapist doesn’t come anymore, I would have to walk but nobody can accompany me, I can’t do anything!». This other 98-year-old participant even tells us: «You know, I pray every day, maybe I won’t wake up tomorrow». Although frequent, these examples do not cover the whole range of experiences. Older people are not only vulnerable, they also have remarkable coping skills and resilience. A striking fact is that many people spontaneously referred to the parallel between this crisis and the war. «We old people know what it’s like, we’ve been through war! It’s the young people we worry about; they’re not used to it». Another participant said: «When I see the long queues in front of the shops, the difficulty in finding certain foods, I feel like I am back in wartime! But you know, we know what it’s like, we’ll survive!». Another man makes the connection with his past as a soldier: «You know, this epidemic reminds me of the time when I was in the trenches, there was a huge epidemic, I fell through the cracks, I hope I will do it again this time!». For some, this containment is lived in the greatest serenity, like this older man, passionate about flowers, a former nurseryman who spends his days in his garden where he grows more than 50 varieties of rare flowers to the admiration of his neighbours. «COVID-19 has no impact on me, I’ve been confined to my garden since I retired, and I’m very happy that way!». In the same vein, this woman says: «I have my chickens, my garden, cleaning, knitting, I don’t have time to be bored!». Another interesting testimony is that of a 97-year-old man who has settled for the confinement at his niece’s house: «I live the best of my life, I enjoy it, I am taken care of, I eat delicious meals, I hope the containment will keep going for a while yet!» As may be seen here, lifestyle, self-esteem and the meaning one gives to one’s life are determining factors in the way one experiences this confinement. In addition to the use of the telephone to keep in touch with beloved ones, digital tools equipped with simultaneous vision seem to have been new allies for some seniors during containment. For example, an older lady who was hosted in her daughter’s home tolds us: «I have a great-granddaughter who was just born, my daughter hooked us up to the camera, you know with the phone, if you had seen how she looked at me, I’m so happy to be able to see her». Other participants told us that they wished they had known how to use these tools like this centenary lady: «You know if my children showed me how it works, I would have liked to use these tools, I’m not against these things, it would have helped me.»
The crisis we are facing is challenging seniors’ resources and capacities for adaptation and resilience. While for some, maintaining balance seems easy, for others, containment will have serious consequences for psychological, cognitive and physical health. Isolation, anxiety and the absence of certain home care professionals may have increased certain risk situations such as frailty, falls, cognitive decline or lack of care. Thanks to the follow-up of participants enrolled in prospective cohorts, the results gathered from PACOVID will help characterizing the most vulnerable people, which is important to anticipate in the future medico-social actions to be implemented rapidly to support them.


Conflict of Interest: The authors declare they have no conflict of interest
Funding: The PACOVID study is supported by the National Agency Research « Agence Nationale de la Recherche » [ANR-20-COVI-0010-01]. The sponsor had no role in the design and conduct of the study; in the collection, analysis, and interpretation of data; in the preparation of the manuscript; or in the review or approval of the manuscript.
Ethical standards: This study is in accordance with the international ethical standards of research and with the 1964 Helsinki Declaration.



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