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Y. Rolland1, M. Cesari2, R.A. Fielding3, J.Y. Reginster4,5, B. Vellas7, A.J. Cruz-Jentoft6 and the ICFSR Task Force


1. Service de Médecine Interne et Gérontologie Clinique, Gérontopôle, CHU Toulouse, INSERM 1027, France; 2. IRCCS Istituti Clinici Scientifici Maugeri, University of Milan, Milan, Italy; 3. Tufts University, Boston, MA, USA; 4. Division of Epidemiology, Public Health and Health Economics, University of Liege, Liege, Belgium; 5. Chair for Biomarkers of Chronic Diseases, Biochemistry Department, College of Science, King Saud University, Riyadh, Kingdom of Saudi Arabia; 6. Servicio de Geriatría, Hospital Universitario Ramón y Cajal (IRYCIS), Madrid, Spain; 7. Gerontopole, INSERM U1027, Alzheimer’s Disease Research and Clinical Center, Toulouse University Hospital, Toulouse, France
Corresponding author: Yves Rolland, Service de Médecine Interne et Gérontologie Clinique, Gérontopôle, CHU Toulouse, INSERM 1027, France, rolland.y@chu-toulouse.fr


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) ; Manu Chakravarthy (Cambridge, USA); Rafael De Cabo (Baltimore, USA); Carla Delannoy (Vevey, Switzerland); Philipe De Souto Barreto (Toulouse, France) ; Waly Dioh (Paris, France); Luigi Ferrucci (Baltimore, USA); Françoise Forette (Paris, USA); Sophie Guyonnet (Toulouse); Joshua Hare (Miami) ; Darren Hwee (South San Francisco); Kala Kaspar (Vevey); Nathan LeBrasseur (Rochester, USA); 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); Leocadio Rodriguez Manas (Getafe (Madrid), Spain); 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); Bruno Vellas (Toulouse, France) ; José Viña (Valencia, Spain); Jeremy Walston (Baltimore, USA); Debra Waters (Dunedin, New Zealand)

J Frailty Aging 2021;in press
Published online January 18, 2021, http://dx.doi.org/10.14283/jfa.2021.4



Interactions among physiological pathways associated with osteoporosis and sarcopenia are thought to contribute to the onset of frailty. The International Conference on Frailty and Sarcopenia Research Task Force thus met in March 2020 to explore how emerging interventions to manage fracture and osteoporosis in older adults may reduce frailty, disability, morbidity, and mortality in the older population. Both pharmacological and non-pharmacological interventions (including nutritional intervention, exercise, and other lifestyle changes) were discussed, including nutritional intervention, exercise, and other lifestyle changes. Pharmacological treatments for osteoporosis include bone-forming and antiresorptive agents, which may optimally be used in sequential or combination regimens. Since similar mechanisms related to resorption underlie physiological changes in muscle and bone, these interventions may provide benefits beyond treating osteoporosis. Clinical trials to test these interventions, however, often exclude frail older persons because of comorbidities (such as mobility disability and cognitive impairment) or polypharmacy. The Task Force recommended that future clinical trials use harmonized protocols, including harmonized inclusion criteria and similar outcome measures; and that they test a range of multidomain therapies. They further advocated more high-quality research to develop interventions specifically for people who are frail and old. The ICOPE program recommended by WHO appears to be highly recommended to frail older adults with osteoporosis.

Key words: Frailty, osteoporosis, prevention, ICOPE.



All organisms show biologically driven declines in motor function as they age and these declines are closely linked to mortality (1, 2). In humans, these declines manifest as the frailty syndrome, which is defined by the overlapping characteristics of low physical activity, slowed motor performance, weakness, fatigue or exercise intolerance, and unintentional weight loss (3). Physiologically, frailty reflects a lowered resistance to stressors resulting from multi-systemic decline. Clinically, frailty is associated with diagnoses of sarcopenia, the age-related loss of muscle mass and strength, and osteoporosis, the loss of bone mass and the deterioration of bone tissue (4). When they occur together, the syndrome may be referred to as “osteosarcopenia” (5). Moreover, interactions between bone and muscle through multiple physiological pathways, including hormonal and inflammatory pathways, are thought to result in the frailty syndrome (6).
As it has done every year since 2014, the International Conference of Frailty and Sarcopenia Research (ICFSR) Task Force brought together researchers from academia and industry to discuss challenges and opportunities for managing frailty and sarcopenia. In 2020 the Task Force met in Toulouse, France, where it focused attention on emerging interventions to manage fracture and osteoporosis in frail older adults. This population group has often been excluded from recent osteoporosis drug trials due to comorbidities and polypharmacy, despite the fact that they may potentially benefit more from a treatment since they are more likely to have falls, fractures, disability and a poor prognosis.


Associations of frailty with osteoporosis, fragility fracture, and malnutrition

Bone fragility caused by osteoporosis occurs commonly in older adults and results in increased risk of fragility fracture (7). A systematic review of worldwide studies estimated that 9 million osteoporotic fractures occurred in 2000, resulting in substantial disability, morbidity, and mortality (8). However, osteoporosis may not be diagnosed until an individual has experienced multiple fragility fractures; and studies show that after diagnosis, treatment for osteoporosis is not routinely given in older adults and adherence to medical regimens is poor (9).
One of the most common and disabling fractures sustained by older persons is hip fracture, which may result in long-term mobility impairment, reduced ability to care for oneself or participate in everyday activities, pain, anxiety, and depression (10). Nutrition plays an important role in bone health and sarcopenia (11, 12), and malnutrition is common in individuals with hip fracture (13). Sarcopenia is also associated with an increased rate fractures in older adults (14, 15).
Most patients with hip fracture complain of pain and resulting functional limitations six months after the fracture (16), which can lead to a vicious cycle of self-medication and mistrust of clinicians (17). Recovery from hip fracture may be delayed in the presence of sarcopenia (18), and hip fracture may be particularly disabling in individuals with frailty (19). Nearly 30 years ago, Marottoli and colleagues showed that physical function before the fracture predicts functional recovery (20). Comorbidities, fear of falling, and other age-related conditions may further exacerbate hip fracture and its associated functional consequences (21, 22). Moreover, individuals over age 80 years, in addition to meeting the frailty phenotype proposed by Fried and colleagues (i.e., weight loss, fatigue, slow gait speed, weakness, sedentary lifestyle), often live alone, and often experience cognitive decline (23); thus they need special management for frailty. However, frail older persons are often excluded from clinical trials of fragility fracture interventions, in part because of comorbidities, sarcopenia, cognitive impairment, and polypharmacy (24).
The substantial impact of fragility fractures on functioning in frail older persons thus requires dedicated and multidisciplinary care pathways, which have been shown to improve quality of life and physical function and limit excessive costs (25,26). Intensive interventions including exercise and physical therapy immediately following hip fracture is essential. Preventive strategies also need to be widely implemented, including early identification of those at risk, increased prescribing of bone loss prevention treatments, and the introduction of care models based on the comprehensive geriatric assessment and personalization of interventions. Recently multidisciplinary, evidence-based guidelines for the management of osteoporosis and fragility fractures have been published (27–29).
Given the association of poor nutrition with sarcopenia and frailty (30, 31), assessment of the nutritional status of older adults provides a potential pathway to interventions that could delay or prevent these disabling conditions of aging (32). The Mini Nutritional Assessment (MNA) is a tool designed to rapidly assess nutritional status though a series of simple measurements and brief questions (33). The MNA has been validated in frail older persons (34) and in community-dwelling older adults, demonstrating that frailty and malnutrition are distinct but related conditions (35–37).
Using the MNA short form (MNA-SF), investigators showed that poor nutrition in combination with frailty was associated with an increased prevalence and incidence of poor functional outcomes in the Singapore Longitudinal Aging Study (32). In cancer patients, a low MNA score combined with a high Groningen Frailty Index (GFI) score was associated with an increased mortality risk (38). MNA score has also been used as a prognostic factor of adverse outcomes after hip fracture (39). Yet while there is mounting evidence about the importance of stratifying research populations for frailty, impaired nutritional status at baseline has been associated with greater benefits from the interventions (40, 41). The new ESPEN guidelines on the treatment of malnutrition in older people include a section on hip fracture, with the recommendation to incorporate nutrition intervention into a multidisciplinary approach (42).
As a screening tool in outpatients, the MNA-SF has been shown to have a sensitivity of 71.2% and specificity of 92.8% (AUC 0.906) for the detection of frailty, and a 45.7% sensitivity and 78.3% specificity (AUC 0.687) for the detection of pre-frailty (43). In hospitalized patients, the MNA-SF predicted frailty with good sensitivity but only marginal specificity (44). There is no evidence that the MNA can be used as an outcome measure in trials.


Pharmacological treatment for osteoporosis, sarcopenia, and frailty

Better targeting of therapeutic interventions for the management of osteoporosis starts with diagnosis, identification of risk factors, and an assessment of fracture risk (45). The International Osteoporosis Foundation and European Society for Clinical and Economic Aspects of Osteoporosis and Osteoarthritis published guidance for the diagnosis and management of osteoporosis in 2013, and recently updated such guidance (46). Diagnostic criteria for sarcopenia have also been recommended by other different groups. The European Working Group on Sarcopenia in Older People (EWGSOP) published a definition in 2010 and updated it in 2019 based on a better understanding of the condition (47,48); and the ICFSR published guidelines on the management of sarcopenia in 2018 (49). In 2017, sarcopenia also was assigned a diagnostic code in the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) code book, indicating recognition of sarcopenia as a separately reportable disease condition for clinical practice and drug development (50).
A fracture may trigger a downward spiral of recurrent fractures known as the “fracture cascade” (51). A study in Iceland showed that the first fracture dramatically increases the risk of a subsequent fractures, particularly during the first year following the first event and regardless of the site of it. The authors concluded that treatment should be started immediately to prevent recurrence of the problem (52). Bone fragility, determined by assessing bone mineral density (BMD) at the hip or spine by DXA scan, is associated with high fracture risk (53), suggesting that restoring bone density may significantly reduce the risk of a second fracture. Low muscle strength and low physical function (sarcopenia) also increase the risk of injurious falls and fractures after a first hip fracture (54).
Several bone-forming drugs are clinically available, including anti-resorptive agents such as denosumab (55–57); romosozumab, a monoclonal antibody that both increases bone formation and inhibits bone resorption (58,59); anabolic agents such as teriparatide (60) and abaloparatide (61–63); biphosphonates such as alendronate and zoledronic acid (64); and myostatin inhibitors, which are also under research as potential drugs to treat sarcopenia (65, 66).
Optimal treatment of osteoporosis may require sequential or combination therapies, for example starting with a bone forming agent then add an antiresorptive agent for maintenance. For example, in the phase 2 FRActure study in postmenopausal woMen with ostEoporosis (FRAME), romosuzumab followed by denosumab reduced the risk of fracture in postmenopausal women (67). Other sequential regimens that have shown promise in lowering fracture risk and/or increasing bone density include romosozumab followed by alendronate (68), abaloparatide followed by alendronate (69,70), and combination denosumab/teriparatide followed by denosumab alone (71).


Preventing frailty and its consequences through nutrition and exercise

The concept of frailty facilitates a better understanding of heterogeneity in the older population and promotes study of the aging process. It provides a possible target for preventive measures aimed at reducing the functional decline and the occurrence of negative events such as falls and fractures (72, 73). Frail patients present with weakness, fatigue, a sedentary lifestyle and mobility impairment. They may have anorexia and recent weight loss. All of these clinical signs increase the risk of falls and fractures. They are also accessible to interventions such as nutritional management and/or physical exercise (focused on strength training and balance), which reduce the risk of falling (74, 75).
Several mechanisms responsible for both growth and decline of muscles and bones are shared. It has been hypothesized that pharmacological, nutritional, and/or exercise-based interventions may also overlap and provide mutual/dual benefits (76). For example, both skeletal muscle and bone respond to treatment with androgens, and exercise is an essential element of treatment regimens for osteoporosis, sarcopenia, and frailty. Malnutrition plays an important role in the development of both sarcopenia and frailty (31). Decreased dietary protein intake has been shown to result in decreased lean muscle mass in the Health Aging and Body Composition (ABC) Study (77). The Vitality, Independence and Vigor Study (VIVE2) showed that a high protein, high vitamin D nutritional supplement added to a physical activity intervention led to improvements in muscle density and a loss of intermuscular fat in mobility-limited older adults (78), although these benefits seemed insufficient to improve functional measures such as gait speed (79). Other studies have shown that a combination of resistance exercise and increased protein intake prevented muscle wasting in older adults (80, 81).
Obesity is known to contribute to functional declines and frailty in older adults. Sarcopenia in combination with obesity – a condition known as sarcopenic obesity – increases the risk of functional decline through multiple synergistic pathways. Intervention strategies to combat sarcopenic obesity include weight reduction, calorie restriction, and exercise. Pharmacological strategies may also prove useful (82). Weight reduction through calorie restriction has been shown to have positive effects on longevity, yet it also may result in a loss of fat and lean mass and bone density (83,84). In a study of older frail obese adults, an intervention that combined weight loss and aerobic plus resistance exercise, Villareal and colleagues showed that in comparison to either approach alone, the combination resulted in greater physical function and aerobic capacity and attenuated the loss of bone mineral density (85, 86).
The mechanisms by which dietary changes and exercise influence muscle and bone provide clues that may help design better and more targeted intervention strategies. For example, evidence implicates age-related declines in muscle insulin-like growth factor 1 (IGF-1) in sarcopenia; and both exercise and injury increase IGF-1, IGF-1 receptors, and IGF-1 activated signaling pathways. Aging muscle may have less ability to synthesize IGF-1 or may be resistant to IGF-1, and aging may also be associated with attenuation of the ability of exercise to induce IGF-1 (87).
A small study of healthy older women fed with a low-protein diet for 10 weeks showed a decline in both muscle mass and IGF-1 (88). More than 20 years ago, Rizzoli and colleagues showed that protein supplementation in frail individuals post hip fracture restored levels of IGF-1 in the plasma and attenuated loss in bone mineral density compared to placebo (89). Supplementation with selenium and coenzyme Q10 have also been shown to increase levels of IGF-1 in older adults (90).
Skeletal muscle cells express the vitamin D receptor (VDR), and low levels of vitamin D have been associated with lower muscle strength, mobility impairments, and disability (91). In mobility-impaired older women, vitamin D supplementation increased VDR expression and improved skeletal muscle fiber size (92). However, another study in older adults with low baseline levels of serum 25(OH)D showed that while supplementation increased serum levels to more normal levels, there was no effect on lean mass, lower-extremity power, or strength (93).
Nutritional supplements that target inflammation have also been proposed as a strategy for improving muscle function in older adults. For example, omega-3 fatty acids derived from fish oil have also been shown to slow decline in muscle mass and function in older adults (94). However, a recent clinical trial, the ENabling Reduction of low-Grade Inflammation in SEniors (ENRGISE) Pilot study, which tested the efficacy of fish oil and the angiotensin receptor blocker losartan in older, mobility-impaired adults, showed no improvement of walking speed or serum level of the inflammatory marker IL-6 (95).
Demonstrating the efficacy of nutritional interventions is challenging for many reasons, including the difficulty of determining whether the baseline level of dietary intake is inadequate and capturing subtle effects of change from baseline. These challenges are exacerbated when nutritional interventions are superimposed on other interventions.


Designing clinical trials to target bone fracture in frail older adults

The burden of fracture is expected to increase worldwide as the population ages, yet few trials have assessed the benefit of treatments in the oldest old and even less in the frail population (96, 97). Thus, fracture prevention and optimizing bone health represent important public health goals. Interventions that target the frail population offer the potential for the greatest benefit, as was demonstrated in a study by Rolland and colleagues, which tested the ability of strontium ranelate to reduce vertebral fractures in osteoporotic women, independently of frailty status (98). Beyond pharmacological interventions, nutrition and exercise have been shown to act synergistically to improve bone and muscle health and thus should be incorporated into randomized clinical trials (99).
To increase the efficiency and maximizing learnings from clinical studies, sponsors and researchers should use harmonized protocols with similar outcome measures. The ICFSR Task Force suggested the following:

Possible Study Design

The placebo-controlled, parallel-arm, double-blind trial is the gold standard for assessing efficacy and effectiveness. Other elements of an optimal trial design include:
• A long run-in phase before initiating treatment, during which activity diaries could be monitored and dietary inadequacies or anemia corrected to ensure a stable baseline.
• 2 x 2 designs for studies testing multimodal approaches such as resistance exercise and/or combination of resistance and aerobic exercise and nutrition.
• Using assessment time points that have been harmonized with other studies to enable data pooling and meta-analyses of data.
• Use the gold standard of collecting falls incidence using monthly calendars.
• At least one-year of follow up. If studies aim to target bone fracture or prevent the progression from pre-sarcopenia to sarcopenia, long follow-up will be necessary.

Proposed Outcomes

• Primary outcome: fragility fractures at 24 months (hip and spine).
• Secondary outcomes:
o Physical performance and disability as measures of functional decline
o Injurious falls
o Patient-reported outcomes, including mobility assessments and quality of life
o Nursing home admissions
o Bone turnover biomarkers
o BMD assessment (hip and spine)
• Exploratory outcomes
o Cognitive function
o Comorbidities
o Survival

Note that Fragility fractures or injurious falls as the primary outcome will require a very large sample size. Benefit of pharmacological treatment has also needed a large sample size.

Potential Target Population

• Patients with low BMD, high rate of falls (such as ≥2 self-reported falls/year), and frailty.
• Inclusion criteria: ≥ 75 years old with osteoporosis defined by low BMD, FRAX, and/or history of osteoporotic fracture, and with frailty defined by variable proven predictive of falls (100). Patients in nursing homes and those with dementia should be included where possible.
• Exclusions: Projected life expectancy < 2 years or estimated glomerular filtration rate < 30 mL/min/1.73 m2, individuals who are bedridden or who have contraindications related to the drug being tested

Design of Interventions

Frailty is a complex syndrome requiring multidimensional interventions. Interventions should target two or more risk factors for falls. For example, polypharmacy and some specific medications have been associated with increase fracture risk (101, 102). The European Geriatric Medicine Society (EuGMS) Task and Finish group on Fall-Risk-Increasing Drugs (FRIDs) recently proposed practical recommendation and strategies to reduce the use of FRIDs (103). The increase risk of falls related to the use of psychotropics drugs (104), cardiovascular drugs (105) and other medications (106) is now well-known. As the field of geroscience continues to emerge, it may become possible to target aging itself (107). For example, cellular senescence represents a promising therapeutic paradigm for potentially preventing or even reversing age-related osteoporosis and simultaneously treating multiple aging comorbidities (108).
Multidomain interventions for preventing falls in older people living in the community typically include physical activity (strength and balance classes with walking practice), and deprescribing. A systematic review and meta-analysis concluded that such multidomain interventions may reduce the rate of falls and recurrent falls, although the impact on fracture reduction has not been clearly demonstrated (109).
To test an osteoporosis drug in combination with a multidomain intervention, four parallel groups are recommended: 1) osteoporosis drug alone, 2) multidomain intervention alone, 3) osteoporosis drug plus multidomain intervention, 4) placebo or active comparator.
The Multidomain Alzheimer’s Prevention Trial (MAPT) study is an example of a multidomain trial in frail older adults (110). This three-year, multicenter, randomized, placebo-controlled superiority trial enrolled community-dwelling persons aged 70 or older with spontaneous memory complaints, absence of dementia, and limitations in one instrumental activity of daily living or slow gait speed. They were randomly assigned to one of four groups: 1) a multidomain intervention comprising cognitive training, physical activity, and nutritional counseling plus omega-3 polyunsaturated fatty acids with a total daily dose of 800 mg docosahexaenoic acid and 225 mg eicosapentaenoic acid, 2) the multidomain intervention plus placebo, 3)omega-3 polyunsaturated fatty acids alone, or 4) placebo alone. The trial was registered with ClinicalTrials. gov (NCT00672685).


Conclusions and next steps

The ICFSR Task Force reached several conclusions. First, it recognized that the traditional care system is inadequate for dealing with complex health disorders of aging such as frailty, where multidisciplinarity is required (111, 112). Cognitive impairment is often associated with frailty and must be taken into consideration (113, 114). The links between frailty and cognition are now well described (115–117) and integrated care like the ICOPE program have to be promoted to prevent and treat fractures in frail older persons (118–121).
Second, the Task Force suggested that reducing fracture risk among older adults requires first intervening with a powerful agent to restore the strength of bone, and then switching to an anti-resorptive agent to maintain bone health. The need for treatment is especially true after a first major hip fracture. The high cost of many of these drugs imposes a barrier to such an approach and payers will require studies that document efficacy; yet fractures themselves are costly and health economics studies show that bone forming agents are cost-effective even over short time periods. Combination therapies were also recommended, not just for treating the bone but for other factors as well, particularly in individuals who are frail. Benefits of these drugs in frail populations with high risk of fracture, short life expectancy, and high risk of adverse events such as nursing home residents should be investigated. One problem is that these frail older adults often take many drugs due to co-morbidities, including cognitive impairment, undernutrition, depression, and loneliness, raising questions about the value of further adding drugs to treat osteoporosis versus decreasing drug consumption in frail older adults. Advances in the field of geroscience may help in the future to answer these questions by introducing new biomarkers and better targeted therapies (122–124).
Third, the Task Force noted that while pathophysiology of bone fracture is the same in frail and non-frail adults, the mechanisms that lead to bone fracture – poor balance, sarcopenia, poor physical performance, sedentary lifestyle, and poor nutritional status – differ. Given these differences, specific recommendations may be needed for interventions in people who are frail, for example by more routinely adopting multidimensional and comprehensive interventions (125). To develop these interventions, more studies are needed in people who are frail and old. In addition, high-quality research is needed to confirm the role of nutrition in reversing or preventing frailty and adverse outcomes in frail persons (126, 127). Moreover the ICOPE program developed by WHO appears to be most useful for the frail older adults with osteoporosis to maintain Intrinsic capacities, monitor functions with ICOPE MONITOR (119) and prevent further disabilities (Table 1).

Table 1
Screening Tool for the “Integrated Care for Older Persons” (ICOPE)


Acknowledgements: The authors thank Lisa J. Bain for assistance in the preparation of this manuscript.
Conflicts of interest: ACJ reports grants or personal fees from Fresenius Kabi, Abbott Nutrition, Nestlé, Nutricia, Sanofi, and Pfizer, all unrelated to the submitted article. MC is member of Advisory Board for Nestlé.
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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S. Dupuis-Blanchard1, C. Bigonnesse1, M.K. Andrew2, O. Gould3, D. Maillet1

1. Université de Moncton, Moncton, Canada; 2. Dalhousie University, Halifax, Canada; 3. Mount Allison University, Sackville, Canada.
Corresponding author: Suzanne Dupuis-Blanchard, School of Nursing, Université de Moncton, 18 Antonine Maillet Ave., Moncton, NB E1A 3E9, Canada,
Email : suzanne.dupuis-blanchard@umoncton.ca, Telephone : (506)858-4673, Fax : (506)858-4017

J Frailty Aging 2021;in press
Published online January 18, 2021, http://dx.doi.org/10.14283/jfa.2021.3



Background: The relationship between frailty and variables such as housing are the least included in models of frailty and research on frailty or social frailty and relocation is negligible. The decision to relocate is complex and demanding for older adults with a loss of independence but little is known about what makes older adults relocate to congregated housing designated for older adults, let alone in combination with social frailty, and how they navigate this transition. Objectives: This mixed method descriptive study aims to understand the influence of social frailty for a population of French-speaking semi-independent older adults relocating to a housing continuum community. Design: Semi-structured individual interviews including sociodemographic data and the PRISMA-7 Frailty Scale were conducted with recently relocated older adults. Setting: A newly opened French-speaking housing continuum community in Eastern Canada that offers luxury apartments for independent older adults, two assisted living facilities for semi-independent older adults along with a long-term care facility. Participants: Twenty-nine older adults with a mean age of 85 years, mostly female, married or widowed and highly educated. Measurements: Content analysis of the transcribed recorded interviews and descriptive statistical analyses to examine relationships between the frailty PRISMA-7 scale, answers to additional questions and the sociodemographic data. Results: There was not a significant difference in the scores for socialization before and after relocation nor between prior help and current help; however, there was a significant negative correlation between help and socialization before and after relocation. Three main themes included: imposed influences, push and pull factors and post relocation. Conclusions: The results indicate that several social factors contributed to relocation and that participants were experiencing social frailty. Participants were at the crossover point of being vulnerable to experiencing additional deficits which would potentially have led to higher frailty had they not relocated.

Key words: Social frailty, relocation, social support, community, official language minority.



Although frailty phenotypes have mostly ignored the notion of social frailty (1), the concept is slowly gaining interest in the literature. Defined as the absence of social resources, limited social activity and the inability to accomplish basic social needs (2), social frailty is touted as the precursor to physical frailty (3) or prefrailty (4, 5). Others who have explored the concept of social frailty have identified protective factors such as social support, engagement, living situation, self-esteem, sense of control, relations with others and contextual socio-economic status (6). Additional factors such as not living alone, going out more frequently, visiting friends, feeling helpful and talking with someone every day also had a strong impact on future disability in older adults living in the community (2, 7). Furthermore, despite the relevance of both frailty and social context on decision-making regarding housing and re-location (e.g. moving from rural to more urban areas, down-sizing or moving to supported living settings), the relationship between frailty and variables such as housing are the least included in models of frailty (1) and research on frailty or social frailty and relocation is negligible (8). Therefore, the goal of this mixed method descriptive study was to understand the influence of social frailty on decisions to relocate for a population of mostly well-educated and financially secure French-speaking semi-independent older adults.
The decision to relocate is complex and demanding for older adults with a loss of independence. Factors such as transportation, access to home maintenance services (especially in the language of choice), adequate income and level of education, attitude and resolve, self-perceived health, and choice of home/community have been determined to influence older adults’ ability to stay in their home (9) but little is known about what makes older adults relocate to congregated housing designated for older adults, whether social frailty plays a role in this decision, and how this transition is experienced (8). Although relocation is a common transition, it affects older adults much differently than younger adults. Recognizing components of social frailty in relocating older adults is important to prevent or delay a frailty diagnosis, prevent or lessen disability (10), reduce mortality (3) and improve the lives of families and caregivers (11).
This mixed method descriptive study was conducted at a newly opened Francophone housing continuum community in Eastern Canada that offers luxury apartments for independent older adults, two assisted living facilities for semi-independent older adults along with a long-term care facility. Given the influence of higher levels of education (12) and good socioeconomic status (13, 14, 15) on reducing the risk of frailty and the impact of identifying as a French-speaking older adult living in an official linguistic minority community (OLMC) on health inequalities (16), this study provides insight into the role of language as well as favourable social positioning on social frailty and relocation.



After receiving ethics approval, semi-independent older adults speaking and understanding French, aged 65 years or older, and living in a luxury housing continuum community were recruited. Recruitment strategies included brief presentations to older adults, advertisements in the community’s newsletter and electronic billboards, staff endorsing the study and the assistance of the Citizen Advisory Committee (CAC) which consisted of older adults and employees from the housing community.
The purpose of the CAC was to include stakeholders in the study to provide different perspectives and understanding to the research project to maximize the relevance of the results. CAC members informed and advised the research team and members included five older adults living at the study site, a community representative of an older adult organization, three researchers, one student research assistant and two employees from the housing continuum community. A total of three meetings were held with CAC members throughout the 12 months’ study.
A purposive sample of 29 older adults participated in semi-structured individual interviews of an average 40 minutes in duration. Interviews were conducted at a date and time convenient to the participant and most participants chose to have the interview in their apartment unit (in their new home). Sociodemographic data were recorded at the beginning of the interview and the PRISMA-7 Frailty Scale (17) was administered at the end of the interview process. The PRISMA-7 Frailty Scale is meant for early detection and management of frailty and is composed of seven yes and no questions addressing risk factors for frailty. Three or more “yes” answers are used as the cut-off for being at risk. In addition, corresponding questions related to the scale were administered with the goal of better understanding frailty of study participants. These Likert scale questions explored such components as help needed prior to relocation and after relocation, social activities prior and post relocation as well as asking for help from family and friends. For these items, a 7-point scale was used, with higher scores indicating higher levels of vulnerability.
Qualitative data analysis consisted of conventional content analysis (18) of the transcribed recorded interviews using NVivo 11 software to develop initial codes derived from the data, categories and defining themes. Descriptive statistical analyses for small sample sizes (19, 20) were performed to examine relationships between the frailty PRISMA-7 scale, answers to the additional questions and the sociodemographic data. Study results were discussed with the CAC for context and clarification as well as with the research team.



Participant Characteristics

Most of the 29 participants were female (62.1%) with an average age of 85 years old. Most were either married (38%) or widowed (41%) and 35% had no children living in proximity (20 km radius). Participants had relocated to the study site from a single dwelling (52%), an apartment or condo (28%) or directly from the hospital (14%). At the time of interview, most had relocated within 1-12 months. Participants were highly educated with 62% having a university degree and 17% a college education. Participants self rated their health as very good (35%) and good (41%) although 48% reported health problems that limited their activities.

Frailty Scale

Of the 29 study participants, 17 participants scored 3 or below (58.6 %) on the PRISMA-7 Frailty Scale with a group average score of 3.1 out of 7. Table 1 presents participants scores.

Table 1
PRISMA-7 Scores


Given the PRISMA-7 Frailty Scale scores for questions 3 (related to activities) and 5 (related to health), two corresponding Likert scale questions were analyzed: finding someone to help prior and post relocation as well as socializing before and after relocation. A paired-samples t-test was conducted to compare socialization prior to relocation and post relocation. There was not a significant difference in the scores for socialization before relocation (M = 5.14, SD = 1.38) and after relocation (M = 4.48, SD = 1.70), t(28) = 2.03, p = .052; d = .43 although a larger sample may have yielded statistical significance. Moreover, there was not a significant difference in the scores of prior help (M = 1.79, SD = 1.29) and current help (M = 1.36, SD = .78), t(27) = 1.80, p = .08; d = .40. A Pearson correlation indicated a significant negative correlation between help obtained and socialization both before relocation (r(28) = -.41, p = .03) and after relocation (r(27) = -.42, p = .03).

Imposed Influences

Results from the qualitative analysis indicated that two main life events seem to have compelled participants to move: health deterioration and capacity to source reliable support.

Health deterioration

Declining health happened over time but when a chronic health problem became overly challenging or that a new health issue arose, either for the study participant or their spouse, this was often a trigger factor that made participants decide to relocate. One participant explained: “My concern was mainly falling and finding myself alone.” Another participant shared: “I could see my health failing in terms of mobility, so sooner or later, it was better for me to initiate the move myself.”

Formal social support

Most participants described challenges in receiving formal home support services but also questioned the quality of the services once these were received. Many inconsistencies were identified such as arriving late, employee not staying for the contracted time, and tasks not completed. Even for those participants using private services, it remained challenging to receive the appropriate assistance. One participant shared, “I could have paid someone, but there’s no one reliable. I don’t mind paying $30 an hour, but they have to do the work.”

Informal social support

The majority of participants voiced strong opinions of not wanting to ask for help from family members, especially their children, but also from friends and neighbours: “It’s always trouble because you have to find someone to do your housework and other things, there’s too many things.” One reality shared by many participants was the impact of the loss of a spouse or primary caregiver as a trigger to relocation: “I have no one, I have no one anymore, they’re all deceased.”

Push and Pull Factors

Participants also identified other factors contributing to social frailty and pushing them out of their home: transportation and feelings of insecurity.
The loss of one’s ability to drive had an important impact on aging in place. Being able to drive was deemed an aspect of independence that is irreplaceable by public transit. One participant explained:
I didn’t feel vulnerable, but in a condo, without a car…you need milk, well you have to call a taxi or a friend who has…In that sense, it didn’t make sense anymore. [If] I had been able to keep my car, I would still be there you know.

Feeling of insecurity

Feelings of insecurity were mentioned by many participants and was described as: “I wasn’t feeling well, I didn’t feel safe where I was.” Others explained that they were aware that they were aging and that they needed to make changes to facilitate life: “We knew that sooner or later, we would have to move. We wouldn’t be able to keep up with our activities. Especially since it was a lot more work for me to, to maintain the house.”

Pull factors

There were also reasons for wanting to relocate to the study location that facilitated participants’ decision to relocate. Some of these factors include the location of the housing continuum, near the university and cultural centre, as well as the language spoken in the study location. Many shared: “We wanted to be somewhere Francophone; my English is not too good.” Despite wanting to stay where French was spoken, some expressed difficulties with the different accents and words used by other residents. Other pull factors included the quality of services, the continuity of care options and the ability to be close to family members. Additionally, participants could financially afford to relocate to this relatively expensive housing complex.

Post Relocation

Participants explained that once relocated, they had to adapt to their new home and that this process was different for everyone. In fact, adapting to the new home seemed more difficult for those who had relocated without having made the decision to relocate or were forced by circumstances (or triggers), and while most had made their own decision, attitude towards their relocation seemed to impact their ability to adapt. Like this participant, many shared:
I meet people who ask me how I like my new apartment. It’s too small, but I tell myself, I can’t change it, I have to adapt. There you go. My head speaks to me a lot, I need to have patience. It’s not tomorrow when everything will fall into place. It will take time.
Establishing a routine also seemed like an important step in developing feelings of belonging. This included socializing with others but not overstepping boundaries. One participant explained: “I noticed here that people don’t go from apartment to apartment, and I like that.” Another explained how easy it was to be with others: “At 7PM, if no one calls, I go downstairs, and there’s someone there to play cards. You know, I think we’re a group, it seems like we can talk to each other if we need to talk and we play cards.” For those with hearing or sight impairments, socialization can be challenging and still for others, they feel like they don’t belong. One important element is that the move was not just a move but much more. One participant explained: “I knew it would be a major upheaval. It’s not a move, it’s a life change. It’s not really a move, I can’t count how many times I’ve moved in my life, but this is a major upheaval. I know when I leave here it’s probably going to be feet first.” This followed with a discussion about adapting to an aging self and the realities of aging as part of their relocation. Table 2 presents additional illustrative quotes from study participants.

Table 2
Additional Participant Quotes



Even though 79% of participants had a post-secondary education and all had an adequate income to access private supports before relocating, results indicate that social frailty may have been present before relocation and may have played a role in deciding to relocate. Even for this relatively advantaged group, access to services for aging in place remained challenging and inadequate which resulted in relocation. The post relocation administration of the PRISMA-7 Frailty Scale with a mean group score result of 3.1 suggests that prior to relocation, participants were at the crossover point of being vulnerable to experiencing additional deficits which would potentially have led to higher frailty had they not relocated, as a score of 3 or higher indicates a need for further assessment (17). By addressing social frailty through relocation, participants potentially alleviated multiple factors leading to the social frailty experienced prior to relocation (21). Two important factors that participants identified as forcing them to relocate were loss of social support networks, described as difficulties accessing services and death of a partner/primary caregiver, as well as a sudden change in health status in the self or partner. Other factors mentioned included transportation issues (loss of a driver’s licence) and feelings of insecurity, both previously recognized as components of aging in place (9).
Most of the participants in this study made the choice to relocate, and their new home offered services in their preferred language, a location close to friends and family, and the availability of a continuum of housing and care. Although the transition to this more supportive environment seemed to alleviate social frailty it remained that participants were required to adapt to a new environment, and establish new routines, new relationships and new patterns of socialization. Participants’ ongoing appraisal of their own resiliency, such as strong communication skills, affiliative personalities and favourable health status, combined with the unpredictability of the residential environment could influence their coping mechanism (22). This could potentially explain why participants failed to socialize more since relocation despite previous findings stating that one pull factor in relocation is increased socialization (23). Of particular interest is the finding that both before and after relocating to a supportive environment, those who require more help with daily activities tend to socialize less. If replicated, this finding may suggest that even in supportive environments, enhanced social support and opportunities for socializing may need to be provided when care needs increase, even with relatively independent older adults.
The results of this study provide a better understanding of the concept of social frailty. Specifically, social support networks, formal support services, transportation, and feeling safe were identified as determining factors of social frailty leading to relocation to a housing continuum community. Moreover, the use of a highly educated and financially comfortable sample of older adults allowed us to explore how decisions to relocate are made when options are relatively unconstrained by socioeconomic concerns. Study participants would have had the financial resources to pay for increased supports in their prior home as well as the education and social privilege to advocate for themselves. While recognizing these contributions, limitations of the study include a non-representative sample, limited statistical power due to the small sample size, and the use of limited and self-reported measures. Further research on social frailty is needed to better understand the relationship between social frailty and physical pre-frailty/frailty. In addition, a longitudinal study of older adults with data collections beginning before a relocation transition and continuing to a few years post relocation in congregated housing would provide additional understanding of both social frailty and the transition process, and how these two constructs interact. Clearly, the role that social frailty plays in older adults’ ability to age in place and the decision to relocate is worthy of future study.


Ethical standards: REB approval from Université de Moncton #1920-011.
Conflcit of interest: No conflict of interest.
Funding: This research was supported by funding from the Canadian Frailty Network (CAT2018-42) and the New Brunswick Health Research Foundation (2018-CFN-1775).



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

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

J Frailty Aging 2021;in press
Published online January 18, 2021, http://dx.doi.org/10.14283/jfa.2021.1



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

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



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



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



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

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

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

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

Abbreviation: RM: repetition maximum



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


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



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3. Valenzuela PL, Morales JS, Castillo-García A, et al. Effects of exercise interventions on the functional status of acutely hospitalised older adults: A systematic review and meta-analysis. Ageing Res Rev. 2020;61(xxxx):101076. doi:10.1016/j.arr.2020.101076
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15. Hopkins RO, Weaver LK, Collingridge D, Parkinson RB, Chan KJ, Orme JF. Two-year cognitive, emotional, and quality-of-life outcomes in acute respiratory distress syndrome. Am J Respir Crit Care Med. 2005;171(4):340-347. doi:10.1164/rccm.200406-763OC




R. McGrath1, P.J. Carson2, D.A. Jurivich3

1. Department of Health, Nutrition, and Exercise Sciences, North Dakota State University, Fargo, ND, USA;
2. Department of Public Health, North Dakota State University, Fargo, ND, USA; 3. Department of Geriatrics, University of North Dakota, Grand Forks, ND, USA

Corresponding Author: Ryan McGrath, Department of Health, Nutrition, and Exercise Sciences, North Dakota State University, NDSU Dept. 2620, PO Box 6050, Fargo, ND 58108-6050, Phone: 701-231-7474, Fax: 701-231-8872, Email: ryan.mcgrath@ndsu.edu

J Frailty Aging 2020;
Published online January 6, 2020, http://dx.doi.org/10.14283/jfa.2020.73


Dear Editor,
SARS-CoV-2, the cause of COVID-19, remains a novel infectious virus that has led to millions of cases globally. While COVID-19 infection and death prevention remain a top public health priority, post-hospitalization COVID-19 recovery is also important and emerging in adults surviving infection. For example, persons that survived a COVID-19 hospitalization have persisting mobility impairments and morbidities several months post-hospitalization (1). Given that the initial months after a critical illness hospitalization are crucial for functional recovery, monitoring physical functioning and related biomarkers after discharge from a COVID-19 hospitalization could be vital for functional recovery, and the proper deployment of relevant interventions.
The pathophysiology related to functional recovery after a COVID-19 hospitalization may provide insights into improving the recovery process. For example, respiratory virus infections, such as COVID-19, trigger inflammatory responses at both the site of the infection and systemically (2). “Cytokine storms” have been observed in those with COVID-19 infections, which is linked to the same tissue damage and organ system failures that contributes to poor physical functioning (3). The hyperinflammatory states seen in COVID-19 patients are related to the inflammaging that leads to functional declines during aging, even in younger individuals (4). Interleukin-6 is a prognosticator in patients with COVID-19 (5), and is likewise an important contributor to declines in muscle function (6). Thus, the inflammatory responses observed in middle-aged and older adults with COVID-19, especially after hospitalization, could predict functional trajectories.
Clinical assessments of physical functioning help to identify the onset and progression of the disabling cascade (7). Although physical performance assessments such as gait speed may provide useful information regarding functional recovery from COVID-19 after hospitalization, these assessments have limitations because they require full body movements. Alternatively, muscle function assessments present organ level insights for the initial stages of physical function deficits. Muscle dysfunction precedes the physical performance limitations that lead to mobility impairments and morbidities such as sarcopenia (8).
Handgrip strength is a convenient assessment of strength capacity and reliable measure of muscle function that requires patients to squeeze a relatively inexpensive isometric dynamometer with maximal effort for a short duration (e.g., 3-5 seconds). As such, handgrip strength measurements are commonly used in clinical and research settings to examine muscle function. However, protocols for handgrip strength focus exclusively on maximal strength, and other muscle function characteristics that may better elucidate muscle dysfunction remain overlooked.
Utilizing digital handgrip dynamometers and attaching a triaxial accelerometer on the top of a dynamometer may help in evaluating the additional aspects of muscle function that are not otherwise ascertained with traditional handgrip dynamometers (9). For example, digital handgrip dynamometry and accelerometry have the ability to not only measure maximal strength, but also strength asymmetry, explosiveness, coordination, force steadiness, fatigability, and muscle contraction induced tremoring. Some of these aspects, such as fatigability, could similarly extend into the use of handgrip dynamometers for the concept of resilience, which may be important for COVID-19 survival and recovery. Utilizing digital handgrip dynamometry and accelerometry also maintains procedural ease and overall test inclusiveness for persons recovering from a COVID-19 hospitalization.

Figure 1
Conceptual Model for Monitoring Inflammatory Responses and Muscle Function for Predicting Functional Recovery After COVID-19 Hospitalization


Figure 1 presents a conceptual model for observing inflammatory responses and muscle function to predict functional recovery in persons recovering from a COVID-19 hospitalization. Overall, inflammation is an important biomarker for muscle function (10), and inflammatory responses occurring during COVID-19 infections could be linked to diminished physical functioning after COVID-19 recovery. Very little is known about how serum biomarkers and the several attributes of muscle function could be impacted after a COVID-19 hospitalization. Inflammatory responses occurring during COVID-19 critical illness hospitalizations that influence functional recovery could be worse than non-COVID-19 illness hospitalizations. Monitoring inflammatory responses and physical functioning in patients that are recovering from COVID-19 at discharge and intermittently thereafter may help to predict their functional trajectories and allow for timely interventions that foster recovery. Therefore, healthcare providers should strongly consider measuring inflammatory responses and physical functioning in COVID-19 patients sustaining a hospitalization if they are not already, and research efforts may likewise provide additional insights. Similar work may also have generalizability to other relevant hospitalizations.
Given the ongoing novelty of COVID-19, examining inflammatory responses and physical functioning is important for patients recovering from COVID-19 not only regain independent living, but also provide new information into the recovery process. While COVID-19 prevention and treatment are still of the utmost importance, surveilling the health of the growing number of persons recovering from a COVID-19 hospitalization will also emerge as a necessary implication.

Conflict of interest: No conflicts of interest.


1. Garrigues E, Janvier P, Kherabi Y, et al. Post-discharge persistent symptoms and health-related quality of life after hospitalization for COVID-19. J Infect. 2020;81(6):e4-e6. doi:10.1016/j.jinf.2020.08.029.
2. Dutta A, Das A, Kondziella D, Stachowiak MK. Bioenergy Crisis in Coronavirus Diseases? Brain Sci. 2020;10(5):277. Published 2020 May 2. doi:10.3390/brainsci10050277.
3. Ragab D, Salah Eldin H, Taeimah M, Khattab R, Salem R. The COVID-19 Cytokine Storm; What We Know So Far. Front Immunol. 2020;11:1446. Published 2020 Jun 16. doi:10.3389/fimmu.2020.01446.
4. Bektas A, Schurman SH, Franceschi C, Ferrucci L. A public health perspective of aging: do hyper-inflammatory syndromes such as COVID-19, SARS, ARDS, cytokine storm syndrome, and post-ICU syndrome accelerate short- and long-term inflammaging?. Immun Ageing. 2020;17:23. Published 2020 Aug 24. doi:10.1186/s12979-020-00196-8.
5. Grifoni E, Valoriani A, Cei F, et al. Interleukin-6 as prognosticator in patients with COVID-19. J Infect. 2020;81(3):452-482. doi:10.1016/j.jinf.2020.06.008.
6. Grosicki GJ, Barrett BB, Englund DA, et al. Circulating Interleukin-6 Is Associated with Skeletal Muscle Strength, Quality, and Functional Adaptation with Exercise Training in Mobility-Limited Older Adults. J Frailty Aging. 2020;9(1):57-63. doi:10.14283/jfa.2019.30.
7. Patrizio E, Calvani R, Marzetti E, Cesari M. Physical Functional Assessment in Older Adults. J Frailty Aging. 2020 (In Press). https://doi.org/10.14283/jfa.2020.61.
8. Crosigani S, Sedini C, Calvani R, et al. Sarcopenia in Primary Care: Screening, Diagnosis, Management. J Frailty Aging. 2020 (In Press). https://doi.org/10.14283/jfa.2020.63.
9. Mahoney S, Klawitter L, Hackney KJ, et al. Examining Additional Aspects of Muscle Function with a Digital Handgrip Dynamometer and Accelerometer in Older Adults: A Pilot Study. Geriatrics (Basel). 2020;5(4):86. Published 2020 Oct 31. doi:10.3390/geriatrics5040086.
10. Rodriguez-Mañas L, Araujo de Carvalho I, Bhasin S, et al. ICFSR Task Force Perspective on Biomarkers for Sarcopenia and Frailty. J Frailty Aging. 2020;9(1):4-8. doi:10.14283/jfa.2019.32.



M.J. Benton, A.L. Silva-Smith, J.M. Spicher

University of Colorado Colorado Springs, Colorado Springs, CO, USA.
Corresponding author: Melissa J. Benton, PhD, RN, Helen & Arthur E. Johnson Beth-El College of Nursing & Health Sciences, University of Colorado Colorado Springs, 1420 Austin Bluffs Parkway, Colorado Springs, CO 80918, phone: +1-719-255-4140, email: mbenton@uccs.edu

J Frailty Aging 2020;in press
Published online December 23, 2020, http://dx.doi.org/10.14283/jfa.2020.72


Background: Muscle provides a reservoir for water to maintain fluid volume and blood pressure, so older adults may be at risk for orthostatic hypotension due to muscle loss with age. Objectives: To evaluate the association between muscle loss with age and postural blood pressure. Design: Longitudinal comparison of overnight changes in hydration, postural blood pressure, and strength. Setting: Community field study. Participants: Sixty-nine men and women (76.0 ± 0.8 years) with low (Low) or normal (Normal) muscle based on the Lean Mass Index. Measurements: Body composition was measured with bioelectrical impedance analysis. Postural blood pressure was measured sequentially (lying, sitting, standing). Strength was measured with a handgrip dynamometer, Arm Curl test, and Chair Stand test. Results: On Day 1, Low had less hydration and a significant drop in postural systolic blood pressure compared to Normal (lying to standing: -11.06 ± 2.36 vs. +1.14 ± 2.20 mmHg, p < 0.001). Overnight, both groups lost significant total body water, while fluid volume was unchanged. On Day 2, both groups experienced significant drops in postural systolic blood pressure, although the drop in Low was more profound and significantly greater than Normal (lying to standing: -16.85 ± 2.50 vs. -3.89 ± 2.52 mmHg, p = 0.001). On both days, Normal compensated for postural changes with increases in postural diastolic blood pressure not observed in Low. Only Low experienced significant overnight decreases in all strength measures. Conclusions: In older men and women, muscle loss with age is accompanied by loss of hydration and less stable early morning postural systolic blood pressure that increase risk for orthostatic hypotension and can also increase risk for falls.

Key words: Orthostatic hypotension, postural blood pressure, lean mass index, hydration, strength.



Orthostatic hypotension (OH) increases risk for cognitive impairment, disability, and mortality (1-4). Generally, among older adults, the prevalence of OH is greater than 20% (5). However, among those with a history of falls, prevalence can exceed 50% (6). Finally, frailty and OH are associated, and among frail older adults, prevalence can be as high as 66% (4).
The criteria for OH are a decrease in postural systolic blood pressure of ≥ 20 mmHg or a decrease in postural diastolic blood pressure of ≥ 10 mmHg within three minutes of standing (7). Older adults who meet this criteria have been found to be weaker, slower, and have diminished physical function compared to those without OH (8). Notably, these characteristics are diagnostic of frailty (9), and related to sarcopenia or muscle loss with age (10).
Muscle provides a reservoir for water to maintain fluid volume and blood pressure (11), so older adults with low muscle mass may be at risk for OH, especially when oral intake is diminished, such as the overnight period. We previously demonstrated that older women with less muscle mass also have less total body water and intramuscular water compared to those with greater muscle mass, and these differences are reflected by more unstable systolic and diastolic postural blood pressure that meets the criteria for OH (12). By comparison, men have greater muscle mass, experience less loss with age, and have a lower prevalence of OH (2, 13). Nonetheless, men are at risk for OH and to our knowledge, the association between muscle, hydration, and postural blood pressure has not yet been evaluated among older men. Therefore, the objective of this study was to answer the question: Is muscle loss with age associated with risk of OH in both older men and women? To do so, we compared overnight changes in hydration, postural blood pressure, and strength in community-dwelling older men and women.




Men and women were recruited who were age 65 years or older, non-smokers, able to stand up independently, and able to ambulate independently or with an assistive device such as a cane or walker. Because our variable of interest was hydration, participants were excluded if they were currently using diuretic medications, or had any condition that could influence hydration including fever, nausea, vomiting or diarrhea; hemodialysis or peritoneal dialysis; or had been hospitalized within the last month.


The university Institutional Review Board approved the study and all participants gave written informed consent prior to enrollment.


Participants completed two identical measurement sessions in their own homes on consecutive days. Baseline (Day 1) measurements were completed in a euhydrated state (normal food and fluid intake) between 10:00 am – 4:00 pm when older adults are most well hydrated based on their 24-hour fluid consumption patterns (14). The second (Day 2) measurement session was completed the next morning, within 30 minutes of waking with participants in a fasted state (no food or fluids for at least 8 hours). All data were collected by the same researcher.


Lean mass and hydration were measured using multifrequency bioelectrical impedance analysis (Quadscan 4000, Bodystat, UK). Participants remained supine for at least 5 minutes prior to measurement. Bioelectrical impedance analysis is valid, reliable, and accurate despite hydration status (15).
Postural blood pressure was measured using a digital blood pressure monitor (Omron Healthcare, Japan). Three sequential measurements were taken (lying, sitting, standing). The initial (lying) measurement was taken after completion of lean mass and hydration measurement, so participants had been resting for at least 5 minutes. The second (sitting) measurement was taken 1 minute after sitting upright with both feet flat on the floor. The third (standing) measurement was taken 1 minute after standing erect.
Handgrip strength was measured to the nearest 0.1 kg using a digital grip dynamometer (Takei Scientific Instruments, Japan). Participants sat with the device in their dominant hand, their arm supported on a stable surface, their wrist in a neutral position, and their elbow at a 90° angle. They squeezed the device one time as hard as possible for 3 seconds. Handgrip strength has been validated using manual upper extremity muscle strength testing as the criterion measure (16), and reliability has been established in multiple studies with intra-class correlation coefficients exceeding 0.80 (17). Moreover, handgrip dynamometry has strong predictive validity for cognitive, physical, and functional decline in older adults (18).
Upper and lower body strength was measured using the Arm Curl and Chair Stand tests. For the Arm Curl, participants sat with a 5-lb (women) or 8-lb (men) dumbbell in their dominant hand. They repeatedly raised and lowered it through a full range of motion for 30 seconds. For the Chair Stand test, participants remained seated with both arms folded across their chest. They repeatedly stood up to a fully erect position and sat down again for 30 seconds. Criterion validity for the 30-second Arm Curl and Chair Stand tests has been determined using laboratory measurement of maximal upper and lower body strength (chest and leg press), and test-retest reliability has been well established with intra-class correlation coefficients exceeding 0.80 (19-21).

Grouping for Analysis

Participants were grouped by lean mass relative to height (kg/m2) using the Lean Mass Index (LMI). Low muscle mass (Low) was defined as women <15.0 kg/m2 and men <19.0 kg/m2, and normal muscle mass (Normal) was defined as women ≥15.0 kg/m2 and men ≥19.0 kg/m2, 22).

Statistical Analysis

Data were analyzed using SPSS version 25 (IBM, USA). Analysis of variance (ANOVA) was used to identify individual between-group differences, and repeated measures ANOVA was used to evaluate between and within-group differences over time (Day 1 vs. Day 2 measurements). An additional multivariate analysis using age as a covariate was conducted to assess the influence of age on postural blood pressure changes. Chi-square analysis was used to evaluate between-group differences in gender, and Spearman correlation analysis was used to evaluate the influence of gender on postural blood pressure changes. Significance was determined as p < 0.05 and data were reported as mean ± standard error with 95% confidence intervals. Effect sizes were calculated as eta squared (η2) and interpreted as small (≥ 0.01), medium (≥ 0.06), and large (≥ 0.14) effects. Sample size calculation determined that 64 participants were adequate for a two-group ANOVA with a significance level of 0.05, 80% power, and a medium effect size.



Sixty-nine men (n = 37) and women (n = 32) completed the study. Overall, they were 76 ± 0.8 years of age with an average body mass index (BMI) of 26.0 ± 0.5 kg/m2 (Table 1). There were no differences between genders for age, BMI, or resting (lying) blood pressure, and correlation analysis identified no influence of gender on postural blood pressure changes, so men and women were combined for analysis based on LMI. In total, 34 participants met the criteria for Low muscle mass. Nineteen (55%) were males and 15 (45%) were females.

Table 1
Participant characteristics at baseline (Day 1)

Note: Data reported as Mean ± SE, [95% CI], (η2) = effect size (eta squared); Low = Low muscle group; Normal = Normal muscle group; M = Male; F = Female; *Between-group differences in co-morbidities calculated using Fisher’s Exact Test.

At baseline (Day 1), participants in the Low group were significantly older, with lower body mass and BMI. They also had less lean mass that was accompanied by significantly less total body water, fluid volume, and intramuscular water. Although resting systolic blood pressure did not differ between groups, those in the Low group had significantly lower resting diastolic blood pressure, as well as significantly lower systolic and diastolic blood pressure when repositioned to sitting and standing postures (Table 1). In addition, during postural changes the Low group experienced a significant decrease in systolic blood pressure compared to the Normal group, which remained relatively stable (lying to standing: -11.06 ± 2.36 vs. +1.14 ± 2.20 mmHg, p < 0.001, η2 = 0.18) (Figure 1A). At the same time, a difference in diastolic blood pressure was observed. Specifically, the Normal group compensated for postural changes by increasing diastolic blood pressure, while the Low group did not (lying to standing: +5.14 ± 1.38 mmHg vs. +0.56 ± 1.49 mmHg; p = 0.027, η2 = 0.07). When age was included as a covariate, between-group differences in postural systolic blood pressure were somewhat attenuated (adjusted p = 0.003), while between-group differences in postural diastolic blood pressure were no longer significant.

Figure 1
Postural blood pressure changes on Day 1 (A) when participants were normally hydrated and Day 2 (B) when participants had fasted overnight. On both days, participants with low muscle had significant drops in systolic BP (Day 1: -11.06 ± 2.36 mmHg, p < 0.001; Day 2: -16.85 ± 2.50 mmHg, p < 0.001) that were not observed in those with normal muscle (Day1: +1.14 ± 2.20 mmHg; Day 2: -3.89 ± 2.52 mmHg). In contrast, participants in the normal muscle group compensated for postural changes with increases in diastolic BP (Day 1: +5.14 ± 1.38 mmHg, p = 0.027; Day 2: +5.37 ± 1.17 mmHg, p = 0.009) that were not observed in the low muscle group (Day 1: +0.56 ± 1.49 mmHg; Day 2: -0.03 ± 1.67 mmHg)


Overnight, both groups lost significant but similar amounts of total body water (p < 0.001), although only the Normal group lost significant amounts of intramuscular water (p = 0.038). Fluid volume remained stable in both groups (Table 2, Figure 2). This change in hydration was manifested as significant (p < 0.001) overnight decreases in both body mass (Low: -0.85 ± 0.07 kg; Normal: -0.97 ± 0.26 kg) and lean mass (Low: -1.08 ± 0.13 kg; Normal: -1.09 ± 0.16 kg). However, there were no between-group differences in any of these overnight changes. By comparison, significant between and within-group differences were observed in postural blood pressure (Figure 1B). On Day 2, systolic blood pressure decreased significantly (p < 0.001) during postural changes from lying to standing in both groups. However, the decrease in the Low group was even more profound than on Day 1 and significantly greater than that observed in the Normal group (lying to standing: -16.85 ± 2.50 vs. -3.89 ± 2.52 mmHg, p = 0.001, η2 = 0.18). Furthermore, as was observed on Day 1, the Normal group again compensated for postural changes with an increase in diastolic blood pressure that was significantly greater than the Low group that again remained stable (lying to standing: +5.37 ± 1.17 vs. -0.03 ± 1.67 mmHg, p = 0.009, η2 = 0.17). When age was included as a covariate, between-group differences in postural blood pressure were again somewhat attenuated (systolic blood pressure: adjusted p = 0.004; diastolic blood pressure: adjusted p = 0.040). Overall, on Day 2, 44.1% (n = 15) of the Low group met the criteria for OH (decrease in postural systolic blood pressure of ≥ 20 mmHg or decrease in postural diastolic blood pressure of ≥ 10 mmHg) compared to only 8.6% (n = 3) of the Normal group (p = 0.001).

Table 2
Overnight changes in mass, hydration, and strength in participants with Low and Normal muscle

Note: Data reported as Mean ± SE, [95% CI], (η2) = effect size (eta squared); Low = Low muscle group; Normal = Normal muscle group

Significant overnight changes in strength that favored older adults in the Normal group also occurred. On Day 1, handgrip strength was similar between groups (Low: 24.5 ± 1.6 kg; Normal: 22.5 ± 1.6 kg) (Table 1). Overnight, a significant between-group difference was observed (Table 2). Participants in the Low group experienced a significant decrease in handgrip strength (-2.42 ± 0.48 kg; p = 0.001) that was not observed in the Normal group (-0.76 ± 0.77 kg). A similar pattern was also observed in lower body strength. For lower body strength measured as the Chair Stand test, both groups were initially similar (Low: 11.6 ± 0.8 repetitions; Normal: 10.6 ± 0.7 repetitions). Overnight, a significant decrease in lower body strength was observed in the Low group (-1.42 ± 0.41 repetitions, p = 0.001) that was not observed in the Normal group (-0.43 ± 0.22 repetitions), and that resulted in a statistically significant difference between groups (p = 0.034). Finally, Arm Curl scores were initially similar between groups (Low: 14.5 ± 0.5 repetitions; Normal: 14.8 ± 0.8 repetitions). Overnight, a significant decrease was observed in both groups (Low: -2.67 ± 0.40 repetitions; p < 0.001; Normal: -0.91 ± 0.37 repetitions, p = 0.017), although the decrease in the Low group was statistically greater than that observed in the Normal group (p = 0.002).

Figure 2
Overnight changes in hydration between participants with Low and Normal muscle. Both groups lost similar and significant amounts of total body waster (p < 0.001), while only the Normal group lost significant amounts of intramuscular water (p = 0.038). Fluid volume remained stable in both groups



To our knowledge, this is the first study to evaluate postural blood pressure changes in men and women using muscle as the criterion for evaluation. Based on our findings, muscle loss with age is associated with risk for OH in both men and women. Although the average drop in postural systolic blood pressure of 17 mmHg that we observed in participants with low muscle mass was less than the ≥ 20 mmHg decrease needed to meet the definition of OH, the differences between older men and women with low compared to normal muscle mass were statistically significant with large effect sizes. Hence, we believe our findings reflect a clear association with muscle loss, especially as we found a statistically greater prevalence of participants that met the diagnostic criteria for OH among participants with low muscle mass (44.1%) compared to those with normal muscle mass (8.6%). Furthermore, we found no relationship with gender, indicating that both men and women are equally susceptible despite differences in absolute and relative lean mass.
Poor nutrition, which increases the risk for muscle loss and frailty in older adults, has previously been found to be associated with OH (23). This is consistent with the differences in body composition observed among our participants, in which those with low muscle were also observed to have significantly less fat and an overall lower BMI compared to those with normal muscle. Some previous research has demonstrated a negative association between BMI and OH, such that older adults with OH had lower BMI levels than those without OH (4, 24). However, average BMI values were in the overweight category and in one study, differences were not statistically significant (4). The association between OH and BMI is not clear. In other previous reports of normal (25) and overweight (2) older adults with and without OH, there were no differences based on BMI. Furthermore, in a comparison of OH prevalence among robust, pre-frail, and frail older adults, the prevalence of OH and participant age increased significantly with level of frailty, but there was no difference in BMI, which was in the overweight category for all participants (26). We believe the link between frailty and OH may lie in the influence of lean (muscle) mass. This is a gap in the literature and should be explored.
In addition to greater instability in postural blood pressure, participants with low muscle experienced greater overnight losses of strength compared to those with normal muscle. When loss of strength, especially in the lower body, is accompanied by severe early morning drops in postural blood pressure, this increases risk for falls in the early morning when between 30 to 50% of falls are reported to occur (27). Falls are of concern among older adults with OH, who have a greater than 50% higher risk of a first fall than older adults without OH (28). Treatment of OH is frequently driven by concerns regarding potential injuries due to falls. Unfortunately, first line treatment often focuses on medication reduction, including cardiovascular medications such as antihypertensives (29). This creates a burden for patients who must choose between the risks associated with OH and potential risks associated with discontinuance of medications. Cardiovascular medications optimize blood pressure control and reduce the risk of stroke, myocardial infarction, renal dysfunction, and complications of diabetes (30). Furthermore, abrupt discontinuation of blood pressure-lowering medications, can place patients at risk for an acute stroke or cardiovascular event (31). Non-pharmacological strategies for management of OH are available, but evidence indicates that they are minimally effective (32), and not generally acceptable to older adults with OH (33).
Increasing muscle mass may represent a novel strategy for OH that has not previously been considered. Although frailty is associated with OH (4), we can find no studies in which body composition has been included in the assessment of older adults with OH. Nonetheless, decreased fluid volume and deconditioning are recognized factors in the etiology of OH (34). Muscle, as a repository for body fluids, enhances fluid volume. As observed in our participants, those with greater muscle had significantly greater reserves of water. Furthermore, there was a non-selective loss of fluid overnight of approximately one liter that did not differ between those with low and normal muscle mass. This non-selective fluid loss is consistent with what we previously observed in older women (12). Our interpretation is that individuals with limited fluid reserves due to reduced amounts of muscle cannot compensate for fluids losses during periods of low intake. Hence, in our study, when muscle tissue apparently “donated” intramuscular water to maintain overall fluid volume during the overnight period, those with low muscle mass were seen to be at greater risk for unstable postural blood pressure and loss of strength compared to those with greater muscle mass and the fluid reserves that accompany it.
Resistance training may represent a feasible non-pharmacologic approach to blood pressure management in the context of OH. However, evidence is limited. We identified only one resistance training program for patients with OH by Zion and colleagues (35), and it did not successfully improve blood pressure. However, the duration was only 8 weeks and elastic bands were used for training. While elastic bands can provide adequate resistance to stimulate muscle hypertrophy, their use in research has been limited. Krause and colleagues reported use of elastic bands to increase muscle mass in healthy older adults, but the training program was 12 weeks long and all training was supervised (36). Unfortunately, Zion and colleagues did not measure body composition (35), but it seems likely that their shorter, unsupervised program was not sufficient to increase muscle mass and therefore had no effect on blood pressure. Evidently, more research is needed.
We recognize that the fact that we did not control for medications other than diuretics may be considered a study weakness. However, we also recognize that use of medications is increasing, especially use of multiple medications. In the United States, approximately 90% of older adults report use of at least one medication, while approximately 40% report polypharmacy (use of 5 or more medications) (37, 38). For the current study, we used a pragmatic approach with the intent of evaluating older adults under real-world conditions in their own homes, and those conditions include use of regularly prescribed medications. Furthermore, previous research demonstrates that medications do not have a significant influence on OH (39, 40). In older men and women no association has been found between OH and either the number or type of medications, including antihypertensives, diuretics, antipsychotics, antidepressants, and drugs for Parkinson’s disease (39, 40). Nonetheless, there are numerous other types of medications that may influence OH that have not been evaluated.
In conclusion, our findings support a role for muscle in maintaining stable postural blood pressure and decreasing risk for OH. Although more research is evidently needed, in these older adults, muscle loss with age was accompanied by loss of hydration and less stable early morning systolic blood pressure that may increase risk for falls. Resistance exercise to increase muscle mass may provide a novel therapeutic strategy that should be explored.

Acknowledgements: The authors thank Andrew Quinonez for assistance with graphic design to format the figures for publication.
Funding: No funding was received for this study.
Conflicts of Interest: All authors declare no conflict of interest.


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C.S. Kow1, S.S. Hasan2,3
1. School of Postgraduate Studies, International Medical University, Kuala Lumpur, Malaysia; 2. School of Applied Sciences, University of Huddersfield, Huddersfield, United Kingdom; 3. School of Biomedical Sciences & Pharmacy, University of Newcastle, Callaghan, Australia

Corresponding Author: Chia Siang Kow, International Medical University, Kuala Lumpur, Malaysia, Email ID: chiasiang_93@hotmail.com

Dear Editor,
We read with great interest the consensus recommendations issued by Boreskie et al. (1) to prevent older adults from frailty progression and health declines during COVID-19 pandemic. The effort by authors to disseminate recommendations which had been summarized in the SAVE model is commendable since it serves to remind the clinicians not to neglect the frail older individuals even amid strained health care resources from the COVID-19 pandemic. In fact, we agree with authors that these strategies should be implemented to maintain and/or improve the functional status of frail individuals since frail individuals may be at risk for a worse prognosis shall they acquire COVID-19. In addition, due to declined health state, frail individuals may be more susceptible to the acquisition of COVID-19 (2). Our preliminary investigation had demonstrated that the frail individuals have been overrepresented among population with COVID-19, with the following are illustrative of our findings.
We perform a comprehensive literature search in PubMed, Scopus, Google Scholar, and preprint repositories (medRxiv and Research Square) from December 1, 2019 up to November 26, 2020, using the following keywords which included “COVID-19” or “SARS-CoV-2” or “severe acute respiratory syndrome coronavirus 2” and “frailty” or “frail” with no language restriction. Studies eligible for inclusion were those with any design, assessed frailty with any validated frailty assessment tools, and reported prevalence of frailty in patients with COVID-19. We excluded article types such as comments, narrative reviews, conference papers, and case reports without reporting original data.
After removing duplicates, pair of reviewers (CSK and SSH) independently reviewed the titles and abstracts to identify the articles meeting eligibility criteria. The full-text screening was subsequently used to identify a final list of studies that corresponded to the inclusion and exclusion criteria. If multiple studies were available from the same cohort of patients, the study with the largest sample was included in the review. Disagreement between the two reviewers was resolved through discussions. Two investigators (CSK and SSH) independently extracted relevant data from included studies: family name of the first author, publication year, study design, study setting (single centre, multicentre, or database review), age of participants, sample size, the prevalence of frailty, and frailty assessment scale. We employed a random-effects model to estimate the pooled prevalence of frailty and 95% confidence interval (CI). We examined the heterogeneity between studies using the I2 statistics with 50% and using the χ2 test with P <0.10, as the thresholds for statistically significant heterogeneity.
In total, 22 studies (list of reference as Supplementary Data) representing data from 25,246 patients with COVID-19 were included. Majority of the studies were from the European countries (the United Kingdom [n=12], Italy [n=1], Germany [n=1], Sweden [n=1], France [n=1], Belgium [n=1], Turkey [n=1]), while the remaining studies were from Brazil (n=1), the United States of America [n=1], Ecuador [n=1], and China [n=1]. The pooled prevalence of frailty across these 22 studies in a random-effect model of meta-analysis was 45% (Figure 1; 95% CI 35% to 56%; I2=99%; P <0.001). The pooled prevalence of frailty was much higher than the recent meta-analysis by O’Caoimh et al. (3) which reported an overall estimated worldwide frailty prevalence of 18% (95% CI 17% to 19%) among community-dwelling adults.

Figure 1
Pooled prevalence of frailty among patients with COVID-19


Such indirect comparison has indicated that frail individuals may be overrepresented among the COVID-19 patient population and given a rather strong hint that the presence of frailty may lead to a higher risk of acquisition of COVID-19. Therefore, the strategies put forth by Boreskie et al. (1)to overcome frailty and its associated complications become more important than before amid the COVID-19 pandemic since frailty could also predict a worse prognosis in patients with COVID-19. Furthermore, the frail individuals should be prioritized for prophylactic measures against COVID-19, either pharmacologically or non-pharmacologically. Administration of vitamin D proposed by authors as one of the measures to tackle frailty may in fact kill two birds with one stone: vitamin D could lead to improvement in the physical performance among older adults (4), and may be beneficial to prevent poor disease outcomes shall patients acquire COVID-19, since vitamin D deficiency (as suggested by serum 25 (OH)D concentration <20 ng/mL) had been associated with a severe course of COVID- 19 (5). Last but not least, the fact that almost 1 in 2 patients with COVID-19 was frail should alert the clinicians for standardized screening of frailty status in this patient population, at least among older individuals (≥60 years of age). Nevertheless, a limitation of our meta-analysis is related to the timing of the assessment of frailty in the included studies. Since the assessment of frailty was performed at baseline upon hospital admission, the prevalence of frailty could have been overestimated in these studies. Therefore, future studies could perform assessment of frailty in a longitudinal manner, without limited to assessment upon admission.

Conflict of interest: CSK and SSH have no conflicts of interest to declare.



1. Boreskie KF, Hay JL, Duhamel TA. Preventing Frailty Progression during the COVID-19 Pandemic. J Frailty Aging. 2020;9(3):130-131.
2. Xue QL. Frailty as an integrative marker of physiological vulnerability in the era of COVID-19. BMC Med. 2020;18(1):333.
3. O’Caoimh R, Sezgin D, O’Donovan MR, et al. Prevalence of frailty in 62 countries across the world: a systematic review and meta-analysis of population-level studies [published online ahead of print, 2020 Oct 17]. Age Ageing. 2020;afaa219.
4. Ju SY, Lee JY, Kim DH. Low 25-hydroxyvitamin D levels and the risk of frailty syndrome: a systematic review and dose-response meta-analysis. BMC Geriatr. 2018;18(1):206.
5. Jain A, Chaurasia R, Sengar NS, Singh M, Mahor S, Narain S. Analysis of vitamin D level among asymptomatic and critically ill COVID-19 patients and its correlation with inflammatory markers. Sci Rep. 2020;10(1):20191.




1. Department of Physical Therapy, Faculty of Medicine, School of Health Sciences, Kagoshima University, Kagoshima, Japan; 2. Department of Epidemiology, National Center for Geriatrics and Gerontology, Obu, Japan; 3. Department Community Welfare, Niimi University, Niimi, Japan; 4. Section of NILS-LSA, National Center for Geriatrics and Gerontology, Obu, Japan; 5. Department of Preventive Gerontology, National Center for Geriatrics and Gerontology, Obu, Japan; 6. Institute of Gerontology, The University of Tokyo, Bunkyo-ku, Japan; 7. Research Team for Human Care, Tokyo Metropolitan Institute of Gerontology, Itabashi-ku, Japan; 8. Research Team for Promoting Independence and Mental Health, Tokyo Metropolitan Institute of Gerontology, Itabashi-ku, Japan; 9. Research Team for Social Participation and Community Health, Tokyo Metropolitan Institute of Gerontology, Itabashi-ku, Japan; 10. Department of Joint Disease Research, 22nd Century Medical and Research Center, The University of Tokyo, Bunkyo-ku, Japan; 11. Graduate School of Comprehensive Human Sciences, University of Tsukuba, Tsukuba, Japan; 12. Tokyo Metropolitan Institute of Gerontology, Itabashi-ku, Japan; 13. National Center for Geriatrics and Gerontology, Obu, Japan & Institute of Gerontology, J.F. Oberlin University, Machida, Japan.
Corresponding author: Hyuma Makizako, epartment of Physical Therapy, Faculty of Medicine, School of Health Sciences, Kagoshima University, Kagoshima, Japan, makizako@health.nop.kagoshima-u.ac.jp

J Frailty Aging 2020;in press
Published online December 22, 2020, http://dx.doi.org/10.14283/jfa.2020.68



Objective: To examine whether age-specific prevalence of frailty in Japan changed between 2012 and 2017. Design: This study performed meta-analyses of data collected from 2012 to 2017 using the Integrated Longitudinal Studies on Aging in Japan (ILSA-J), a collection of representative Japanese cohort studies. Setting: The ILSA-J studies were conducted on community-living older adults. Participants: ILSA-J studies were considered eligible for analysis if they assessed physical frailty status and presence of frailty in the sample. Seven studies were analyzed for 2012 (±1 year; n = 10312) and eight studies were analyzed for 2017 (±1 year; n = 7010). Five studies were analyzed for both 2012 and 2017. Measurements: The study assessed the prevalence of frailty and frailty status according to 5 criteria: slowness, weakness, low activity, exhaustion, and weight loss.Results: The overall prevalence of physical frailty was 7.0% in 2012 and 5.3% in 2017. The prevalence of frailty, especially in people 70 years and older, tended to decrease in 2017 compared to 2012. Slight decreases were found in the prevalence of frailty subitems including weight loss, slowness, exhaustion, and low activity between 2012 and 2017, but change in the prevalence of weakness was weaker than other components. Conclusions: The prevalence of physical frailty decreased from 2012 to 2017. There are age- and gender-related variations in the decrease of each component of frailty.

Key words: Frailty, aging, cohort study, older.



Frailty is defined as a clinically recognizable state of increased vulnerability in older adults resulting from age-associated declines in physiologic reserves and function across multiple organ systems (1). Although it is recognized as a multidimensional construct, comprising psychological and social conditions and symptoms in addition to physical, the physical frailty phenotype is well defined and its impact on adverse health outcomes such as disability, hospitalization, and death has been examined in many prior studies (2-5). Clinical practice guidelines based on the current evidence base provide recommendations for identifying and managing frailty in older adults (6). Reducing the risk and prevalence of frailty may play an important role in extending healthy life expectancy in the aged population.
The most common components used to assess physical frailty are the frailty phenotype proposed by Fried et al. using data from the Cardiovascular Health Study (CHS) (2). Based on the Fried criteria, a wide prevalence of frailty has been reported among community-dwelling people aged 65 years and older, ranging from 4% to 27% (7, 8). In Japan, with a rapidly increasing aging population, the overall prevalence of frailty was 7.4%, with a similar prevalence in men (7.6%) and women (8.1%) (9). These prevalence rates increased with advancing age (1.9%, 3.8%, 10.0%, 20.4%, and 35.1% for people aged 65 to 69, 70 to 74, 75 to 79, 80 to 84, and 85 or older, respectively) (9).
In the past several decades, both life and health expectancy have increased in many countries. In Japan, the average life expectancy was 81.3 years for men and 87.3 years for women in 2018, according to data from the Ministry of Health. There may be improvement in physical health status among older adults based on increased life and health expectancy. Although previous studies indicated the prevalence of frailty in a large cohort or meta-analysis, no studies focused on trends in the prevalence of frailty and assessment years.
This study performed meta-analyses using data from the National Center for Geriatrics and Gerontology’s Integrated Longitudinal Studies on Aging in Japan (ILSA-J), a collection of 13 longitudinal cohort studies on aging in Japan involving community-dwelling older adults, to test whether the age-specific prevalence of frailty changed in Japan between 2012 and 2017.



Data Sources

This study performed meta-analyses using ILSA-J data on frailty. The ILSA-J included a total of 13 longitudinal cohort studies conducted throughout Japan (Table 1). Studies were considered eligible for inclusion in the present analysis if they assessed physical frailty status and prevalence of frailty in the sample using the Fried criteria (2) (e.g., slowness, weakness, exhaustion, low activity, and weight loss). Of the 13 cohort studies, 7 (total n = 10312; 4611 men and 5701 women) were analyzed for 2012 (±1 year), and 8 (total n = 7010; 2662 men and 4348 women) were analyzed for 2017 (±1 year). Finally, only 10 of the 13 cohort studies in the ILSA-J project were included in this meta-analysis, because 3 cohort studies did not provide data on frailty status in 2012 and 2017.

Characteristics of the cohort studies included in the meta-analysis


Main Outcome Measures and Operational Definition of Frailty

The main outcome measures in this study were the prevalence of frailty status and the five frailty sub-items (%). This study determined physical frailty status according to the 5 criteria of physical frailty suggested by the Japanese version of the CHS (J-CHS) (10, 11) and a slightly revised criterion: weight loss, slowness, weakness, exhaustion, and low activity. Participants whose responses did not correspond to any of these target criteria were considered to be robust; those who responded positively for 1 or 2 criteria were considered pre-frail; and those with 3 or more positive criteria responses were considered frail.
Although all the cohort studies included in the current meta-analysis used the same 5 criteria to assess frailty status, there were differences in the subcriteria (Appendix table 1). The 5 criteria defining physical frailty were assessed as follows. Weight loss was identified by a response of “yes” to the question (12), “Have you lost 2 kg or more in the past 6 months?” Slowness was identified by a normal walking speed of <1.0 m/s (10). Weakness was identified according to grip strength of the subject’s dominant hand: <26 kg for men and <18 kg for women (13). Exhaustion was identified by a response of “yes” to the question (12), “In the last 2 weeks, have you felt tired for no reason?” Low activity was identified by a response of “no” to both the following questions (10): “Do you engage in moderate levels of physical exercise or sports aimed at health?” and “Do you engage in low levels of physical exercise aimed at health?”

Data Collection

All ILSA-J cohort studies were approved by the ethics committee of the relevant university or institute. Among the 13 total cohort studies, those that assessed frailty provided data on the prevalence of frailty status (frailty, pre-frailty, and robust) and the 5 frailty subitems for meta-analyses. Thus, no author of the present study could access participants’ individual data.

Statistical Analysis

A two-step approach was used in the current study. First, we obtained the frailty prevalence in each cohort study separately, then, we calculated the combined prevalence using meta-analysis. The prevalence rates of frailty and pre-frailty for the years 2012 and 2017 were calculated by age group and gender. The 5 frailty items were also included to calculate prevalence. The present meta-analysis used a two-step approach. First, Cochran’s Q test was used to assess the presence of heterogeneity across cohorts, indicated by p<0.05, and I2 statistic values of 25%, 50%, and 75% indicated low, moderate, and high degrees of heterogeneity, respectively (14). Then, prevalence and 95% confidence intervals (CIs) were calculated for frailty and pre-frailty using a random-effects model if heterogeneity was present (p<0.05) and a fixed-effects model if heterogeneity was absent based on Cochran’s Q test (9). In addition, we performed a sensitivity analysis restricting the meta-analysis to surveys performed at both time-points, 2012 and 2017. Statistical analyses were completed using Comprehensive Meta-Analysis software (Version 3; Biostat, Englewood, NJ, USA).



Table 2 presents the data on the presence of heterogeneity across cohorts and the prevalence of physical frailty among each age group in 2012 and 2017. There was a slight decrease (1.7%) in overall prevalence of physical frailty between 2012 and 2017. The overall prevalence of physical frailty was 7.0% (95% CI 5.4-9.0%) in 2012 and 5.3% (95% CI 4.3-6.6%) in 2017. The sensitivity analysis restricted to surveys with data at both time-points (2012 and 2017) provided similar results to the main analysis (Appendix table 2). Greater decreases in the prevalence of frailty were found in adults aged 75 years and older. Specifically, in 2012, the prevalence of frailty was 7.4% in the 75-79 age group, 12.6% in the 80-84 group, and 23.2% in the 85-89 group. In 2017, a 3.0% decrease was found in the 75-79 age group, a 4.2% decrease in the 80-84 group, and a 6.2% decrease in the 85-89 group.

Table 2
Prevalence of physical frailty by age group


Among men, frailty prevalence increased with advancing age in both 2012 and 2017. In 2012, prevalence was 6.3% in the 75-79 age group, 9.9% in the 80-84 group, and 24.6% in the 85-89 group ; in 2017, a decrease of 3.1% was found in the 75-79 age group (prevalence of 3.2%), 3.1% in the 80-84 group (prevalence of 6.8%), and 8.2% in the 85-89 group (prevalence of 16.4%).
Similar trends were observed in women. The prevalence of frailty in 2012 was 8.1% in the 75-79 age group, 14.8% in the 80-84 group, and 26.3% in the 85-89 group. In 2017, a decrease of 3.1% was found in the 75-79 age group (prevalence of 5.0%), 5.4% in the 80-84 group (prevalence of 9.4%), and 8.7% in the 85-89 group (prevalence of 17.6%).
The gender-stratified prevalence of physical frailty subitems is shown in Tables 3 and 4. Regardless of gender, slight decreases (less than 5%) in the subitems were found between 2012 and 2017 among young old groups (ages 65-69 and 70-74), with the exception of low activity in men aged 65-69 and women aged 70-74. Differing trends between men and women were found among old groups (ages 75-79, 80-84, and 85-89). In men, subitems with greater decreases (more than 5%) included exhaustion, which decreased 6.0% in the 75-79 age group, 5.2% in the 80-84 group, and 8.9% in the 85-89 group; slowness, which decreased 7.7% in the 85-59 group; and low activity, which decreased 7.2% in the 85-89 group).

Table 3
Prevalence of physical frailty components (Men)

Note. Sample sizes for 2012 age groups were as follows: 65-69, n=1540 (6 studies); 70-74, n=1434 (6 studies); 75-79, n=942 (6 studies); 80-84, n=519 (6 studies); 85-89, n=176 (6 studies). Sample sizes for 2017 age groups were as follows: 65-69, n=357 (5 studies); 70-74, n=629 (6 studies); 75-79, n=882 (7 studies); 80-84, n=565 (7 studies); 85-89, n=229 (7 studies).

Table 4
Prevalence of physical frailty components (Women)

Note. Sample sizes for 2012 age groups were as follows: 65-69, n=1808 (6 studies); 70-74, n=1518 (6 studies); 75-79, n=1205 (7 studies); 80-84, n=892 (7 studies); 85-89, n=278 (7 studies). Sample sizes for 2017 age groups were as follows: 65-69, n=835 (6 studies); 70-74, n=1115 (7 studies); 75-79, n=1400 (8 studies); 80-84, n=756 (8 studies); 85-89, n=242 (7 studies).


Compared with men, women were found to have decreased prevalence in many components. In the 75-79 age group, all components expect for weakness decreased (weight loss, 9.7%; slowness, 5.8%; exhaustion, 7.3%; low activity, 6.4%). All components decreased in the 80-84 and 85-89 groups (weight loss, 7.5% and 8.1%, respectively; slowness, 12.1% and 16.6%; weakness, 6.1% and 5.5%; exhaustion, 9.3% and 5.8%; low activity, 5.4% and 5.9%).



This study performed meta-analyses using data from ILSA-J cohort studies and showed that the prevalence of frailty tended to decrease in 2017 compared to 2012, especially in adults 75 years and older. The sensitivity analysis confirmed the main findings and indicates that this increases the robustness of the findings.
A recent systematic review of articles published in 28 countries estimated the global incidence of frailty among community-dwelling adults (15). Among robust individuals who survived a median follow-up of 3.0 years, 13.6% became frail, with a pooled incidence rate of 43.4 cases per 1000 person-years (15); incidence rates varied by diagnostic criteria and country income level. Previous systematic review and meta-analysis studies have also suggested variation in the prevalence of frailty based on diagnostic criteria (16), country income level (17), and residential environment (18, 9). Additionally, the prevalence of frailty among community-dwelling older adults has been reported to differ based on race (9, 19). Therefore, the influences of those characteristics should be considered when discussing the prevalence of frailty and prevention strategies.
Most systematic review and meta-analysis studies that examine the prevalence of frailty include articles published after 2000. Worldwide, there were 901 million people aged 60 years or over in 2015, an increase of 48% over the global total of 607 million older people in 2000 (20). The global number of people aged 60 years or over increased by 68% in urban areas, compared to 25% in rural areas, from 2000 to 2015 (20). In Japan, approximately 12% of the population was 65 years or older in 1990, about the same as the total in the USA in 1990 (21). By 2010, the 65 and older population in Japan doubled, rising from 15 million to 29 million and comprising 23% of the total population, the highest proportion in the world (21). The percentage rose to over 28% in 2019. Although the number of older people in Japan is increasing rapidly, their latent capabilities and background factors can be changed. Health-related measures among Japanese community-dwelling older adults from 2007 to 2017 indicate that a phenomenon of “rejuvenation” is occurring among the new generation of older Japanese adults (22). In the United States, dementia declined significantly between 2000 and 2012, and one associated factor was an increase in educational attainment (23). Thus, better change in older adults’ latent capabilities and background factors may lead to a decrease in the prevalence of frailty.

Several important factors, such as comorbidities, low socioeconomic position, poor diet, and sedentary lifestyle, increase the risks of frailty (24). Some of these are modifiable. Therefore, it may be possible to reduce the prevalence of frailty by controlling or improving risk factors. Although this study’s meta-analyses had a relatively short observational term of 5 years, decreasing trends in the prevalence of frailty may become clearer based on long-term observation.
Among 5 components of frailty, weakness and slowness may have greater impacts on increased risk of disability (11, 25). In this study, there was a decreasing trend in the prevalence of almost all items, however there was less change in the prevalence of weakness compared with other items. No change or a slight increase in the prevalence of weakness was observed in men in all age groups, whereas for women, only a decrease in the 80 years and older group was observed.
This study found significant differences in frailty prevalence between men and women. Older women, especially in the old-old population (aged 75 years and over), were found to have decreased prevalence in almost all frailty items. Recently, the ILSA-J reported differences between the years 2007 (± 2 years) and 2017 (± 2 years) in several indices (e.g., body composition, walking speed, and grip strength) that are related to the health and functioning of older adults (22). Better health status and a slower decline in most of the health-related measures were observed in 2017 compared with a decade ago. Japanese older adults living in the community have been consistently increasing their walking speed over the past 25 years, and the improvement in walking speed is especially striking in women (26, 27). In a previous study that analyzed IADL performance in 17,680 older adults with dependency in basic ADL, the men were found to have 3 times higher prevalence of poor performance of IADL compared with the women (28). Older adult women may reduce age-related decline in functional level by increasing or maintaining the multidimensional aspects of their lives, such as social and leisure time activities. In addition, our data showed higher study participation rates in women than in men for both 2012 and 2017. These findings may indicate that women have more interest in their health compared with men. Increased interest in personal health may prevent or delay the progression of frailty.
Consistent “female disadvantage” in physical performance among older adults has been demonstrated (29). One previous study with 4683 Japanese nondisabled community-dwelling older adults demonstrated increasing significant gender differences in one-legged stance performance and gait speed with age. In contrast, gender differences significantly decreased in hand-grip strength with increasing age (30). In other words, strength may be more affected by advancing age in older adult men than in older adult women. Thus, preventing or delaying the progression of weakness with age may be difficult in men. Weakness was determined according to grip strength of the subject’s dominant hand, with cutoff values of 26 kg for men and 18 kg for women. Although the average values of grip strength may decrease slightly in new generation of Japanese older adults (22), the changes may not reach sufficient levels, indicating that this component is less susceptible to generation changes than the others.
Several limitations of the present study should be noted. First, the meta-analyses in the present study used cross-sectional data from 7 cohort studies in 2012 (±1 year) and 8 cohort studies in 2017 (±1 year). Therefore, the study design was not longitudinal, following the same individuals and cohorts. Second, the current study used data from 2012 and 2017, analyzing the trends in prevalence over a period of 5 years. This may be too short to fully examine trends of change. Third, the number of participants varied widely by age group, especially participants in the 85 and older group, which had a relatively small sample size (fewer than 200 men in 2012). Finally, assessment protocols were dependent on each cohort study, not unified across all cohorts. We believe that the cohort studies included in the current meta-analysis had high data quality, but not all of the studies were designed using probabilistic samples. For instance, recruitment methods (e.g., random sampling, direct mail to all citizen, and volunteers) varied. In addition, more knowledge on the prevalence of the risk factors for frailty and those components, such as comorbidities, nutritional status, and cognitive function will support the phenomenon of decreasing frailty in the new generation of Japanese older adults.
Although this study examines a relatively short period of time (5 years), it has several strengths. First, it is, to our knowledge, the first study to describe trends in the prevalence of frailty. Second, the prevalence of frailty and subitems were assessed through a meta-analysis of 10 Japanese cohort studies, which provided data from 287 to 4779 older adults. Third, frailty status was assessed not only by questionnaires but also by objective measures such as grip strength and walking speed; therefore, are results may reflect functional status.
In conclusion, the current meta-analyses suggested that the prevalence of frailty has shown a decreasing trend in the new generation of Japanese older adults, especially in adults aged 75 years and older. This finding may indicate physical rejuvenation in older adults. Progression of this trend may improve health expectancy and shorten the gap between life expectancy and health expectancy. Future studies with more long-term follow-up period and a larger sample will be needed to clarify the trends in the prevalence of frailty among community-dwelling older adults.


Acknowledgement: This work was funded by the National Center for Geriatrics and Gerontology (Choujyu 29-42). We are grateful to all the participants for their valuable contribution to this study. The authors thank Dr. Katsunori Kondo of the National Center for Geriatrics and Gerontology & Chiba University, Dr. Yoshinori Fujiwara of the Tokyo Metropolitan Institute of Gerontology, and Dr. Shuichiro Watanabe of J.F. Oberlin University who are members of the ILSA-J project for their contributions in study progression. We also thank to Dr. Takehiko Doi of the National Center for Geriatrics and Gerontology, Dr. Tomoki Tanaka of the Institute of Gerontology, The University of Tokyo, Dr. Hisashi Kawai, Dr. Yu Nofuji, Dr. Takumi Abe and Dr. Susumu Ogawa of the Tokyo Metropolitan Institute of Gerontology, Dr. Yu Taniguchi of National Institute for Environmental Studies, and Dr. Yutaka Watanabe of the Faculty of Dental Medicine, Hokkaido University for their helpful supporting data sharing process and Ms. Shiho Fujii of the National Center for Geriatrics and Gerontology for her help in preparing the tables.
Conflicts of Interest: None declared.
Ethics Statement: This study was conducted in compliance with the current laws of Japan.
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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K.L. Aguirre-Amaya1, M. Palomares-Custodio1, C. Quispe-Vicuña2, S. Abanto-Urbano3, D. Urrunaga-Pastor4


1. Sociedad Científica de Estudiantes de Medicina de la Universidad Nacional del Santa, Universidad Nacional del Santa, Ancash, Peru; 2. Sociedad Científica San Fernando, Universidad Nacional Mayor de San Marcos, Lima, Peru;
3. Sociedad Científica de Estudiantes de Medicina Villarealinos (SOCEMVI), Universidad Nacional Federico Villareal, Lima, Peru; 4. Universidad Científica del Sur, Lima, Peru.

Corresponding Author: Diego Urrunaga-Pastor, Escuela de Medicina, Universidad Científica del Sur, Lima, Peru, Carretera Panamericana Sur 19, Distrito de Villa El Salvador, 15067; Email: diego.urrunaga.pastor1@gmail.com

J Frailty Aging 2020;
Published online December 12, 2020, http://dx.doi.org/10.14283/jfa.2020.66


Dear Editor,
In December 2019, the first cases of COVID-19 were reported in Wuhan, China and it rapidly spread around the world to more than 188 countries. Among these, Latin American countries such as Brazil, Peru, Colombia, Mexico and Argentina are part of the ten countries with more COVID-19 confirmed cases, placing Peru in the first place with the highest number of deaths registered per 100 000 inhabitants due to this disease(1).
The Ministry of Health of Peru(MINSA, by its initials in Spanish) confirmed the first COVID-19 case on March 6, 2020 and registered 657 129 cases and 29 068 deaths until August 31, 2020(2). Comorbidities such as high blood pressure, diabetes mellitus, cardiovascular diseases, and cancer have been described as severity and mortality predictors for COVID-19, predominantly affecting older adults(3). In addition, with the increase of older adults’ population in low-and middle-income countries and the higher prevalence of chronic diseases in this age group(4), they require better monitoring in health systems. However, health systems have collapsed during the context of the COVID-19 pandemic, generating negative consequences. For this reason, it is relevant to assess mortality in older adults during the COVID-19 pandemic in Peru.
We carried out a secondary analysis of the National Computer System of Deaths registry(SINADEF, by its initials in Spanish), obtained from the National Repository of Health Information(REUNIS, by its initials in Spanish), an open-access Peruvian system(5). We performed data analysis and graphs using the Microsoft Excel program. Excess deaths were defined by the difference between observed deaths and expected deaths according to previous years before the COVID-19 pandemic. We calculated the expected mortality(EM) as the average of the registered deaths in the SINADEF from January to August 2018 and 2019(6). We included these years because, during this period, there was greater coverage of the virtual death registry. We only analyzed the non-violent deaths reported.
The older adult’s death excess from March to August in 2020 was 54337 and 54411 deaths compared to 2018 and 2019, respectively. The older adults’ death represented 70.2%, 71.8% and 73.1% of the total deaths from March to August in 2018, 2019 and 2020, respectively. Furthermore, the excess of deaths in this group during 2020 was 802(13%) in March, 3426(59%) in April, 12121(196%) in May, 12795(191%) in June, 13559(193%) in July and 11672(168%) in August(Figure 1).

Figure 1
Death excess in older adults (60 years or above) during COVID-19 pandemic in Peru

The results showed that there was an increase in older adults’ mortality during the COVID-19 pandemic period compared to previous years. We have estimated a death excess of 139.7% regardless of the cause of death reported. However, deaths attributed to COVID-19 in this age group represented only one-fifth of deaths registered in older adults’ during this period(2). This circumstance would explain the approach to other possible causes that justify this excess of death.
Physiological changes associated with aging, a decrease in immune system response, the presence of disability, chronic diseases and polypharmacy are characteristic conditions of frail older adults. Thus, they require periodic control of their chronic diseases, to receive their medication and prevent complications(7). However, in Peru, due to the state of emergency, the health services restricted their attention only to emergencies, suspending the outpatient consulting service. Consequently, complications of uncontrolled chronic diseases could be one of the leading causes of this mortality increase in older adults.
Similarly, health services narrowed the COVID-19 attention in this age group and given the lack of intermediate and intensive care beds, the younger population without comorbidities and a greater probability of recovery was prioritized(7). In addition, the absence of outpatient services and the health services collapse could have led to an increase in self-medication and over-medication in older adults(7). These practices could have been harmful considering their comorbidities and the necessity for more specialized attention, leading to more complications and deaths in their homes or nursing homes.
It is necessary to improve health care services for older adults in Peru. The Peruvian health system is currently collapsed, fragmented, and under-resourced(8). Despite the increase in universal health coverage, there are only approximately 0.2 intensive care units(ICU) beds per 100 000 inhabitants(9). However, the health budget for 2019 was 2.3% of the Gross National Product and only less than half was executed(10). The national budget designated to provide home health care services for this population should be strengthened. In addition, follow-up programs for patients with chronic diseases should be prioritized to supply them with their medication and prevent complications. The COVID-19 pandemic has evidenced a lack of resources and health care services for older adults, a vulnerable population that requires more attention and effort to maintain healthy aging.

Funding: The study was self-funded.
Conflict of interests: The authors disclose no conflict of interest.


1. Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect Dis. 2020;20(5):533-4.
2. Ministerio de Salud. Sala Situacional COVID-19 Perú. MINSA: Lima, Perú. 2020 https://covid19.minsa.gob.pe/sala_situacional.asp. Accessed 3 October 2020.
3. Potere N, Valeriani E, Candeloro M, Tana M, Porreca E, Abbate A, et al. Acute complications and mortality in hospitalized patients with coronavirus disease 2019: a systematic review and meta-analysis. Critical care. 2020;24(1):1-12.
4. Melorose J, Perroy R, Careas S. World population prospects. United Nations. 2015;1(6042):587-92.
5. Ministerio de Salud. Sistema Informático Nacional de Defunciones – SINADEF. 2020. https://www.minsa.gob.pe/defunciones. Accessed 3 October 2020.
6. Wu J, McCann A, Katz J, Peltier E. 60,000 Missing Deaths: Tracking the True Toll of the Coronavirus Outbreak. New York Times. 2020.
7. Lloyd-Sherlock P, Ebrahim S, Geffen L, McKee M. Bearing the brunt of covid-19: older people in low and middle income countries. BMJ. 2020; 368:m1052.
8. Alcalde-Rabanal JE, Lazo-González O, Nigenda G. Sistema de salud de Perú. Salud Públ Méx. 2011;53:s243-s54.
9. Almeida F. Exploring the impact of COVID-19 on the sustainability of health critical care systems in South America. Int J Health Policy Manag. 2020.
10. García E. Comex: Perú gasta en salud por debajo del promedio en América Latina. Diario Gestión. 2019. https://gestion.pe/economia/comex-peru-gasta-salud-debajo-promedioamerica-latina-268172. 2019. Accessed 3 October 2020.



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

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

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



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

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

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



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




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


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


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


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

Cognitive Assessment

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

Body Composition

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

Functional Performance

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

Sarcopenia and Sarcopenic Obesity

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

Statistical Analysis

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



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

Figure 1
Participant flowchart


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

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

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


Participant Characteristics and Sarcopenic Obesity

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

Table 2
Participant characteristics and sarcopenic obesity statuses

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


Muscle Function (ANOVA)

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

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

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


Multiple Linear Regression for SPPB

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

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

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



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



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


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




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


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

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


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

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



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



Eligibility criteria

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

Information sources

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

Search strategy

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

Study selection

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

Quality/bias assessment

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

Data collection process

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

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

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

Figure 1
Flow of studies through the systematic review



Study characteristics

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

Biochemical, clinical and epidemiological findings on nutrients

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

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

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



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

Dietary, Herbs and Nutritional Supplements

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

Study Quality Assessment

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



Clinical implications

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

Study limitations

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



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


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


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



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