jfa journal

AND option

OR option



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.



1. Dickinson MH, Farley CT, Full RJ, Koehl MA, Kram R, Lehman S. How animals move: an integrative view. Science. 2000 Apr 7;288(5463):100–6.
2. Studenski S, Perera S, Patel K, Rosano C, Faulkner K, Inzitari M, et al. Gait speed and survival in older adults. JAMA. 2011 Jan 5;305(1):50–8.
3. Fried LP, Xue Q-L, Cappola AR, Ferrucci L, Chaves P, Varadhan R, et al. Nonlinear multisystem physiological dysregulation associated with frailty in older women: implications for etiology and treatment. J Gerontol A Biol Sci Med Sci. 2009 Oct;64(10):1049–57.
4. Frisoli A, Chaves PH, Ingham SJM, Fried LP. Severe osteopenia and osteoporosis, sarcopenia, and frailty status in community-dwelling older women: results from the Women’s Health and Aging Study (WHAS) II. Bone. 2011 Apr 1;48(4):952–7.
5. Kirk B, Al Saedi A, Duque G. Osteosarcopenia: A case of geroscience. Aging Med (Milton). 2019 Sep;2(3):147–56.
6. Greco EA, Pietschmann P, Migliaccio S. Osteoporosis and Sarcopenia Increase Frailty Syndrome in the Elderly. Front Endocrinol (Lausanne). 2019;10:255.
7. Li G, Thabane L, Papaioannou A, Ioannidis G, Levine MAH, Adachi JD. An overview of osteoporosis and frailty in the elderly. BMC Musculoskelet Disord [Internet]. 2017 Jan 26 [cited 2020 Apr 24];18. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5270357/
8. Johnell O, Kanis JA. An estimate of the worldwide prevalence and disability associated with osteoporotic fractures. Osteoporos Int. 2006 Dec;17(12):1726–33.
9. Hiligsmann M, Cornelissen D, Vrijens B, Abrahamsen B, Al-Daghri N, Biver E, et al. Determinants, consequences and potential solutions to poor adherence to anti-osteoporosis treatment: results of an expert group meeting organized by the European Society for Clinical and Economic Aspects of Osteoporosis, Osteoarthritis and Musculoskeletal Diseases (ESCEO) and the International Osteoporosis Foundation (IOF). Osteoporos Int. 2019 Nov;30(11):2155–65.
10. Magaziner J, Hawkes W, Hebel JR, Zimmerman SI, Fox KM, Dolan M, et al. Recovery from hip fracture in eight areas of function. J Gerontol A Biol Sci Med Sci. 2000 Sep;55(9):M498-507.
11. Cashman KD. Diet, Nutrition, and Bone Health. J Nutr. 2007 Nov 1;137(11):2507S-2512S.
12. Beaudart C, Sanchez-Rodriguez D, Locquet M, Reginster J-Y, Lengelé L, Bruyère O. Malnutrition as a Strong Predictor of the Onset of Sarcopenia. Nutrients [Internet]. 2019 Nov 27 [cited 2020 Apr 28];11(12). Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6950107/
13. Díaz de Bustamante M, Alarcón T, Menéndez-Colino R, Ramírez-Martín R, Otero Á, González-Montalvo JI. Prevalence of malnutrition in a cohort of 509 patients with acute hip fracture: the importance of a comprehensive assessment. European Journal of Clinical Nutrition. 2018 Jan;72(1):77–81.
14. Yeung SSY, Reijnierse EM, Pham VK, Trappenburg MC, Lim WK, Meskers CGM, et al. Sarcopenia and its association with falls and fractures in older adults: A systematic review and meta-analysis. J Cachexia Sarcopenia Muscle. 2019;10(3):485–500.
15. Kirk B, Phu S, Brennan-Olsen SL, Bani Hassan E, Duque G. Associations between osteoporosis, the severity of sarcopenia and fragility fractures in community-dwelling older adults. Eur Geriatr Med. 2020 Jun;11(3):443–50.
16. Sale JEM, Frankel L, Thielke S, Funnell L. Pain and fracture-related limitations persist 6 months after a fragility fracture. Rheumatol Int. 2017 Aug;37(8):1317–22.
17. Gheorghita A, Webster F, Thielke S, Sale JEM. Long-term experiences of pain after a fragility fracture. Osteoporos Int. 2018 May 1;29(5):1093–104.
18. Landi F, Calvani R, Ortolani E, Salini S, Martone AM, Santoro L, et al. The association between sarcopenia and functional outcomes among older patients with hip fracture undergoing in-hospital rehabilitation. Osteoporos Int. 2017;28(5):1569–76.
19. Kua J, Ramason R, Rajamoney G, Chong MS. Which frailty measure is a good predictor of early post-operative complications in elderly hip fracture patients? Arch Orthop Trauma Surg. 2016 May;136(5):639–47.
20. Marottoli RA, Berkman LF, Cooney LM. Decline in physical function following hip fracture. J Am Geriatr Soc. 1992 Sep;40(9):861–6.
21. Kerr C, Bottomley C, Shingler S, Giangregorio L, de Freitas HM, Patel C, et al. The importance of physical function to people with osteoporosis. Osteoporos Int. 2017;28(5):1597–607.
22. Ong T, Yong BKA, Shouter T, Shahrokhi N, Sahota O. Optimising bone health among older people with hip fractures and co-existing advanced chronic kidney disease. Eur Geriatr Med. 2020 Jun 1;
23. Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, et al. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci. 2001 Mar;56(3):M146-156.
24. European Medicines Agency. Reflection paper on physical frailty: Instruments for baseline characterisation of older populations in clinical trials [Internet]. 2015. Report No.: EMA/CHMP/778709/2015. Available from: https://www.ema.europa.eu/en/documents/scientific-guideline/reflection-paper-physical-frailty-instruments-baseline-characterisation-older-populations-clinical_en.pdf
25. Talevski J, Sanders KM, Duque G, Connaughton C, Beauchamp A, Green D, et al. Effect of Clinical Care Pathways on Quality of Life and Physical Function After Fragility Fracture: A Meta-analysis. J Am Med Dir Assoc. 2019 Jul;20(7):926.e1-926.e11.
26. Hawley S, Javaid MK, Prieto-Alhambra D, Lippett J, Sheard S, Arden NK, et al. Clinical effectiveness of orthogeriatric and fracture liaison service models of care for hip fracture patients: population-based longitudinal study. Age Ageing. 2016 Mar;45(2):236–42.
27. Nuti R, Brandi ML, Checchia G, Di Munno O, Dominguez L, Falaschi P, et al. Guidelines for the management of osteoporosis and fragility fractures. Intern Emerg Med. 2019 Jan;14(1):85–102.
28. Lems WF, Dreinhöfer KE, Bischoff-Ferrari H, Blauth M, Czerwinski E, da Silva J, et al. EULAR/EFORT recommendations for management of patients older than 50 years with a fragility fracture and prevention of subsequent fractures. Ann Rheum Dis. 2017 May;76(5):802–10.
29. Compston J, Cooper A, Cooper C, Gittoes N, Gregson C, Harvey N, et al. UK clinical guideline for the prevention and treatment of osteoporosis. Arch Osteoporos. 2017 Dec;12(1):43.
30. Landi F, Sieber C, Fielding RA, Rolland Y, Guralnik J, the ICFSR Task Force A. Nutritional intervention in sarcopenia: report from the international conference on frailty and sarcopenia research task force. Journal of Frailty & Aging. 2018 Dec 1;J Frailty Aging 20187(4):247–52.
31. Cruz-Jentoft AJ, Kiesswetter E, Drey M, Sieber CC. Nutrition, frailty, and sarcopenia. Aging Clin Exp Res. 2017 Feb;29(1):43–8.
32. Wei K, Thein FS, Nyunt MSZ, Gao Q, Wee SL, Ng TP. Nutritional and Frailty State Transitions in the Singapore Longitudinal Aging Study. J Nutr Health Aging. 2018;22(10):1221–7.
33. Vellas B, Guigoz Y, Garry PJ, Nourhashemi F, Bennahum D, Lauque S, et al. The mini nutritional assessment (MNA) and its use in grading the nutritional state of elderly patients. Nutrition. 1999 Feb 1;15(2):116–22.
34. Lilamand M, Kelaiditi E, Cesari M, Raynaud-Simon A, Ghisolfi A, Guyonnet S, et al. Validation of the Mini Nutritional Assessment-Short Form in a Population of Frail Elders without Disability. Analysis of the Toulouse Frailty Platform Population in 2013. J Nutr Health Aging. 2015 May;19(5):570–4.
35. Lorenzo-López L, Maseda A, de Labra C, Regueiro-Folgueira L, Rodríguez-Villamil JL, Millán-Calenti JC. Nutritional determinants of frailty in older adults: A systematic review. BMC Geriatr. 2017 15;17(1):108.
36. Wei K, Nyunt MSZ, Gao Q, Wee SL, Ng T-P. Frailty and Malnutrition: Related and Distinct Syndrome Prevalence and Association among Community-Dwelling Older Adults: Singapore Longitudinal Ageing Studies. J Am Med Dir Assoc. 2017 Dec 1;18(12):1019–28.
37. Verlaan S, Ligthart-Melis GC, Wijers SLJ, Cederholm T, Maier AB, de van der Schueren MAE. High Prevalence of Physical Frailty Among Community-Dwelling Malnourished Older Adults-A Systematic Review and Meta-Analysis. J Am Med Dir Assoc. 2017 May 1;18(5):374–82.
38. Aaldriks AA, Maartense E, le Cessie S, Giltay EJ, Verlaan H a. CM, van der Geest LGM, et al. Predictive value of geriatric assessment for patients older than 70 years, treated with chemotherapy. Crit Rev Oncol Hematol. 2011 Aug;79(2):205–12.
39. Malafarina V, Reginster J-Y, Cabrerizo S, Bruyère O, Kanis JA, Martinez JA, et al. Nutritional Status and Nutritional Treatment Are Related to Outcomes and Mortality in Older Adults with Hip Fracture. Nutrients. 2018 Apr 30;10(5).
40. Kim CO. Predicting the Efficacy of Protein-Energy Supplementation in Frail Older Adults Living in Community. J Frailty Aging. 2014;3(2):126–31.
41. Luger E, Dorner TE, Haider S, Kapan A, Lackinger C, Schindler K. Effects of a Home-Based and Volunteer-Administered Physical Training, Nutritional, and Social Support Program on Malnutrition and Frailty in Older Persons: A Randomized Controlled Trial. J Am Med Dir Assoc. 2016 01;17(7):671.e9-671.e16.
42. Volkert D, Beck AM, Cederholm T, Cruz-Jentoft A, Goisser S, Hooper L, et al. ESPEN guideline on clinical nutrition and hydration in geriatrics. Clin Nutr. 2019;38(1):10–47.
43. Soysal P, Veronese N, Arik F, Kalan U, Smith L, Isik AT. Mini Nutritional Assessment Scale-Short Form can be useful for frailty screening in older adults. Clin Interv Aging. 2019;14:693–9.
44. Dent E, Visvanathan R, Piantadosi C, Chapman I. Use of the Mini Nutritional Assessment to detect frailty in hospitalised older people. J Nutr Health Aging. 2012;16(9):764–7.
45. Kanis JA, Harvey NC, McCloskey E, Bruyère O, Veronese N, Lorentzon M, et al. Algorithm for the management of patients at low, high and very high risk of osteoporotic fractures. Osteoporos Int. 2020 Jan;31(1):1–12.
46. Kanis JA, Cooper C, Rizzoli R, Reginster J-Y, Scientific Advisory Board of the European Society for Clinical and Economic Aspects of Osteoporosis (ESCEO) and the Committees of Scientific Advisors and National Societies of the International Osteoporosis Foundation (IOF). European guidance for the diagnosis and management of osteoporosis in postmenopausal women. Osteoporos Int. 2019 Jan;30(1):3–44.
47. Cruz-Jentoft AJ, Baeyens JP, Bauer JM, Boirie Y, Cederholm T, Landi F, et al. Sarcopenia: European consensus on definition and diagnosis: Report of the European Working Group on Sarcopenia in Older People. Age Ageing. 2010 Jul;39(4):412–23.
48. Cruz-Jentoft AJ, Bahat G, Bauer J, Boirie Y, Bruyère O, Cederholm T, et al. Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing. 2019 Jul;48(4):601.
49. Dent E, Morley JE, Cruz-Jentoft AJ, Arai H, Kritchevsky SB, Guralnik J, et al. International Clinical Practice Guidelines for Sarcopenia (ICFSR): Screening, Diagnosis and Management. J Nutr Health Aging. 2018;22(10):1148–61.
50. Vellas B, Fielding RA, Bens C, Bernabei R, Cawthon PM, Cederholm T, et al. Implications of icd-10 for sarcopenia clinical practice and clinical trials: report by the international conference on frailty and sarcopenia research task force. Journal of Frailty & Aging. 2018 Mar 1;J Frailty Aging 20187(1):2–9.
51. Broy SB. The Vertebral Fracture Cascade: Etiology and Clinical Implications. Journal of Clinical Densitometry. 2016 Jan 1;19(1):29–34.
52. Kanis JA, Johansson H, Odén A, Harvey NC, Gudnason V, Sanders KM, et al. Characteristics of recurrent fractures. Osteoporos Int. 2018 Aug;29(8):1747–57.
53. Kopperdahl DL, Aspelund T, Hoffmann PF, Sigurdsson S, Siggeirsdottir K, Harris TB, et al. Assessment of incident spine and hip fractures in women and men using finite element analysis of CT scans. J Bone Miner Res. 2014 Mar;29(3):570–80.
54. Lloyd BD, Williamson DA, Singh NA, Hansen RD, Diamond TH, Finnegan TP, et al. Recurrent and injurious falls in the year following hip fracture: a prospective study of incidence and risk factors from the Sarcopenia and Hip Fracture study. J Gerontol A Biol Sci Med Sci. 2009 May;64(5):599–609.
55. Dempster DW, Chines A, Bostrom MP, Nieves JW, Zhou H, Chen L, et al. Modeling-Based Bone Formation in the Human Femoral Neck in Subjects Treated With Denosumab. J Bone Miner Res. 2020 Mar 12;
56. Ferrari S, Libanati C, Lin CJF, Brown JP, Cosman F, Czerwiński E, et al. Relationship Between Bone Mineral Density T-Score and Nonvertebral Fracture Risk Over 10 Years of Denosumab Treatment. J Bone Miner Res. 2019 Jun;34(6):1033–40.
58. McClung MR, Grauer A, Boonen S, Bolognese MA, Brown JP, Diez-Perez A, et al. Romosozumab in postmenopausal women with low bone mineral density. N Engl J Med. 2014 Jan 30;370(5):412–20.
59. Cosman F, Crittenden DB, Adachi JD, Binkley N, Czerwinski E, Ferrari S, et al. Romosozumab Treatment in Postmenopausal Women with Osteoporosis. N Engl J Med. 2016 20;375(16):1532–43.
60. Cosman F, Nieves JW, Roimisher C, Neubort S, McMahon DJ, Dempster DW, et al. Administration of teriparatide for four years cyclically compared to two years daily in treatment Naïve and alendronate treated women. Bone. 2019;120:246–53.
61. Watts NB, Hattersley G, Fitzpatrick LA, Wang Y, Williams GC, Miller PD, et al. Abaloparatide effect on forearm bone mineral density and wrist fracture risk in postmenopausal women with osteoporosis. Osteoporos Int. 2019 Jun;30(6):1187–94.
62. McClung MR, Harvey NC, Fitzpatrick LA, Miller PD, Hattersley G, Wang Y, et al. Effects of abaloparatide on bone mineral density and risk of fracture in postmenopausal women aged 80 years or older with osteoporosis. Menopause. 2018;25(7):767–71.
63. Reginster J-Y, Bianic F, Campbell R, Martin M, Williams SA, Fitzpatrick LA. Abaloparatide for risk reduction of nonvertebral and vertebral fractures in postmenopausal women with osteoporosis: a network meta-analysis. Osteoporos Int. 2019 Jul;30(7):1465–73.
64. Kim TY, Bauer DC, McNabb BL, Schafer AL, Cosman F, Black DM, et al. Comparison of BMD Changes and Bone Formation Marker Levels 3 Years After Bisphosphonate Discontinuation: FLEX and HORIZON-PFT Extension I Trials. Journal of Bone and Mineral Research. 2019;34(5):810–6.
65. Wallner C, Jaurich H, Wagner JM, Becerikli M, Harati K, Dadras M, et al. Inhibition of GDF8 (Myostatin) accelerates bone regeneration in diabetes mellitus type 2. Sci Rep [Internet]. 2017 Aug 29 [cited 2020 May 2];7. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5575348/
66. Rooks D, Praestgaard J, Hariry S, Laurent D, Petricoul O, Perry RG, et al. Treatment of Sarcopenia with Bimagrumab: Results from a Phase II, Randomized, Controlled, Proof-of-Concept Study. J Am Geriatr Soc. 2017 Sep;65(9):1988–95.
67. Cosman F, Crittenden DB, Ferrari S, Lewiecki EM, Jaller-Raad J, Zerbini C, et al. Romosozumab FRAME Study: A Post Hoc Analysis of the Role of Regional Background Fracture Risk on Nonvertebral Fracture Outcome. J Bone Miner Res. 2018;33(8):1407–16.
68. Saag KG, Petersen J, Brandi ML, Karaplis AC, Lorentzon M, Thomas T, et al. Romosozumab or Alendronate for Fracture Prevention in Women with Osteoporosis. N Engl J Med. 2017 12;377(15):1417–27.
69. Bone HG, Cosman F, Miller PD, Williams GC, Hattersley G, Hu M-Y, et al. ACTIVExtend: 24 Months of Alendronate After 18 Months of Abaloparatide or Placebo for Postmenopausal Osteoporosis. J Clin Endocrinol Metab. 2018 01;103(8):2949–57.
70. Cosman F, Miller PD, Williams GC, Hattersley G, Hu M-Y, Valter I, et al. Eighteen Months of Treatment With Subcutaneous Abaloparatide Followed by 6 Months of Treatment With Alendronate in Postmenopausal Women With Osteoporosis: Results of the ACTIVExtend Trial. Mayo Clin Proc. 2017 Feb;92(2):200–10.
71. Leder BZ, Tsai JN, Uihlein AV, Wallace PM, Lee H, Neer RM, et al. Denosumab and teriparatide transitions in postmenopausal osteoporosis (the DATA-Switch study): extension of a randomised controlled trial. Lancet. 2015 Sep 19;386(9999):1147–55.
72. Kojima G. Frailty as a Predictor of Future Falls Among Community-Dwelling Older People: A Systematic Review and Meta-Analysis. Journal of the American Medical Directors Association. 2015 Dec;16(12):1027–33.
73. Dent E, Morley JE, Cruz-Jentoft AJ, Woodhouse L, Rodríguez-Mañas L, Fried LP, et al. Physical Frailty: ICFSR International Clinical Practice Guidelines for Identification and Management. J Nutr Health Aging. 2019;23(9):771–87.
74. Kritchevsky SB, Lovato L, Handing EP, Blair S, Botoseneanu A, Guralnik JM, et al. Exercise’s effect on mobility disability in older adults with and without obesity: The LIFE study randomized clinical trial. Obesity (Silver Spring). 2017;25(7):1199–205.
75. Marzetti E, Cesari M, Calvani R, Msihid J, Tosato M, Rodriguez-Mañas L, et al. The “Sarcopenia and Physical fRailty IN older people: multi-componenT Treatment strategies” (SPRINTT) randomized controlled trial: Case finding, screening and characteristics of eligible participants. Exp Gerontol. 2018;113:48–57.
76. Lebrasseur NK, Achenbach SJ, Melton LJ, Amin S, Khosla S. Skeletal muscle mass is associated with bone geometry and microstructure and serum insulin-like growth factor binding protein-2 levels in adult women and men. J Bone Miner Res. 2012 Oct;27(10):2159–69.
77. Houston DK, Nicklas BJ, Ding J, Harris TB, Tylavsky FA, Newman AB, et al. Dietary protein intake is associated with lean mass change in older, community-dwelling adults: the Health, Aging, and Body Composition (Health ABC) Study. Am J Clin Nutr. 2008 Jan;87(1):150–5.
78. Englund DA, Kirn DR, Koochek A, Zhu H, Travison TG, Reid KF, et al. Nutritional Supplementation With Physical Activity Improves Muscle Composition in Mobility-Limited Older Adults, The VIVE2 Study: A Randomized, Double-Blind, Placebo-Controlled Trial. J Gerontol A Biol Sci Med Sci. 2017 Dec 12;73(1):95–101.
79. Fielding RA, Travison TG, Kirn DR, Koochek A, Reid KF, von Berens Å, et al. Effect of Structured Physical Activity and Nutritional Supplementation on Physical Function in Mobility-Limited Older Adults: Results from the VIVE2 Randomized Trial. J Nutr Health Aging. 2017;21(9):936–42.
80. Breen L, Phillips SM. Interactions between exercise and nutrition to prevent muscle waste during ageing. Br J Clin Pharmacol. 2013 Mar;75(3):708–15.
81. Deutz NEP, Bauer JM, Barazzoni R, Biolo G, Boirie Y, Bosy-Westphal A, et al. Protein intake and exercise for optimal muscle function with aging: recommendations from the ESPEN Expert Group. Clin Nutr. 2014 Dec;33(6):929–36.
82. Batsis JA, Villareal DT. Sarcopenic obesity in older adults: aetiology, epidemiology and treatment strategies. Nat Rev Endocrinol. 2018;14(9):513–37.
83. Das SK, Roberts SB, Bhapkar MV, Villareal DT, Fontana L, Martin CK, et al. Body-composition changes in the Comprehensive Assessment of Long-term Effects of Reducing Intake of Energy (CALERIE)-2 study: a 2-y randomized controlled trial of calorie restriction in nonobese humans. Am J Clin Nutr. 2017;105(4):913–27.
84. Villareal DT, Fontana L, Weiss EP, Racette SB, Steger-May K, Schechtman KB, et al. Bone Mineral Density Response to Caloric Restriction–Induced Weight Loss or Exercise-Induced Weight Loss: A Randomized Controlled Trial. Arch Intern Med. 2006 Dec 11;166(22):2502–10.
85. Villareal DT, Chode S, Parimi N, Sinacore DR, Hilton T, Armamento-Villareal R, et al. Weight loss, exercise, or both and physical function in obese older adults. N Engl J Med. 2011 Mar 31;364(13):1218–29.
86. Shah K, Armamento-Villareal R, Parimi N, Chode S, Sinacore DR, Hilton TN, et al. Exercise training in obese older adults prevents increase in bone turnover and attenuates decrease in hip bone mineral density induced by weight loss despite decline in bone-active hormones. J Bone Miner Res. 2011 Dec;26(12):2851–9.
87. Adamo ML, Farrar RP. Resistance training, and IGF involvement in the maintenance of muscle mass during the aging process. Ageing Res Rev. 2006 Aug;5(3):310–31.
88. Castaneda C, Gordon PL, Fielding RA, Evans WJ, Crim MC. Marginal protein intake results in reduced plasma IGF-I levels and skeletal muscle fiber atrophy in elderly women. J Nutr Health Aging. 2000;4(2):85–90.
89. Schürch MA, Rizzoli R, Slosman D, Vadas L, Vergnaud P, Bonjour JP. Protein supplements increase serum insulin-like growth factor-I levels and attenuate proximal femur bone loss in patients with recent hip fracture. A randomized, double-blind, placebo-controlled trial. Ann Intern Med. 1998 May 15;128(10):801–9.
90. Alehagen U, Johansson P, Aaseth J, Alexander J, Brismar K. Increase in insulin-like growth factor 1 (IGF-1) and insulin-like growth factor binding protein 1 after supplementation with selenium and coenzyme Q10. A prospective randomized double-blind placebo-controlled trial among elderly Swedish citizens. PLoS One [Internet]. 2017 Jun 13 [cited 2020 May 4];12(6). Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5469470/
91. Houston DK, Tooze JA, Davis CC, Chaves PHM, Hirsch CH, Robbins JA, et al. Serum 25-hydroxyvitamin D and physical function in older adults: the Cardiovascular Health Study All Stars. J Am Geriatr Soc. 2011 Oct;59(10):1793–801.
92. Ceglia L, Niramitmahapanya S, da Silva Morais M, Rivas DA, Harris SS, Bischoff-Ferrari H, et al. A randomized study on the effect of vitamin D3 supplementation on skeletal muscle morphology and vitamin D receptor concentration in older women. J Clin Endocrinol Metab. 2013 Dec;98(12):E1927-1935.
93. Shea MK, Fielding RA, Dawson-Hughes B. The effect of vitamin D supplementation on lower-extremity power and function in older adults: a randomized controlled trial. Am J Clin Nutr. 2019 01;109(2):369–79.
94. Smith GI, Julliand S, Reeds DN, Sinacore DR, Klein S, Mittendorfer B. Fish oil-derived n-3 PUFA therapy increases muscle mass and function in healthy older adults. Am J Clin Nutr. 2015 Jul;102(1):115–22.
95. Pahor M, Anton SD, Beavers DP, Cauley JA, Fielding RA, Kritchevsky SB, et al. Effect of Losartan and Fish Oil on Plasma IL-6 and Mobility in Older Persons. The ENRGISE Pilot Randomized Clinical Trial. J Gerontol A Biol Sci Med Sci. 2019 Sep 15;74(10):1612–9.
96. Cole ZA, Dennison EM, Cooper C. Osteoporosis epidemiology update. Curr Rheumatol Rep. 2008 Apr;10(2):92–6.
97. Cooper C, Campion G, Melton LJ. Hip fractures in the elderly: a world-wide projection. Osteoporos Int. 1992 Nov;2(6):285–9.
98. Rolland Y, Abellan Van Kan G, Gillette-Guyonnet S, Roux C, Boonen S, Vellas B. Strontium ranelate and risk of vertebral fractures in frail osteoporotic women. Bone. 2011 Feb;48(2):332–8.
99. Laskou F, Dennison E. Interaction of Nutrition and Exercise on Bone and Muscle. Eur Endocrinol. 2019 Apr;15(1):11–2.
100. Sambrook PN, Cameron ID, Chen JS, Cumming RG, Lord SR, March LM, et al. Influence of fall related factors and bone strength on fracture risk in the frail elderly. Osteoporos Int. 2007 May;18(5):603–10.
101. Lai S-W, Cheng K-C, Lin C-L, Liao K-F. Furosemide use and acute risk of hip fracture in older people: A nationwide case-control study in Taiwan. Geriatr Gerontol Int. 2017 Dec;17(12):2552–8.
102. Torstensson M, Hansen AH, Leth-Møller K, Jørgensen TSH, Sahlberg M, Andersson C, et al. Danish register-based study on the association between specific cardiovascular drugs and fragility fractures. BMJ Open. 2015 Dec 29;5(12):e009522.
103. Seppala LJ, van der Velde N, Masud T, Blain H, Petrovic M, van der Cammen TJ, et al. EuGMS Task and Finish group on Fall-Risk-Increasing Drugs (FRIDs): Position on Knowledge Dissemination, Management, and Future Research. Drugs Aging. 2019;36(4):299–307.
104. Seppala LJ, Wermelink AMAT, de Vries M, Ploegmakers KJ, van de Glind EMM, Daams JG, et al. Fall-Risk-Increasing Drugs: A Systematic Review and Meta-Analysis: II. Psychotropics. J Am Med Dir Assoc. 2018;19(4):371.e11-371.e17.
105. de Vries M, Seppala LJ, Daams JG, van de Glind EMM, Masud T, van der Velde N, et al. Fall-Risk-Increasing Drugs: A Systematic Review and Meta-Analysis: I. Cardiovascular Drugs. J Am Med Dir Assoc. 2018;19(4):371.e1-371.e9.
106. Seppala LJ, van de Glind EMM, Daams JG, Ploegmakers KJ, de Vries M, Wermelink AMAT, et al. Fall-Risk-Increasing Drugs: A Systematic Review and Meta-analysis: III. Others. J Am Med Dir Assoc. 2018;19(4):372.e1-372.e8.
107. Tchkonia T, Zhu Y, van Deursen J, Campisi J, Kirkland JL. Cellular senescence and the senescent secretory phenotype: therapeutic opportunities. J Clin Invest. 2013 Mar;123(3):966–72.
108. Khosla S, Farr JN, Kirkland JL. Inhibiting Cellular Senescence: A New Therapeutic Paradigm for Age-Related Osteoporosis. J Clin Endocrinol Metab. 2018 01;103(4):1282–90.
109. Hopewell S, Copsey B, Nicolson P, Adedire B, Boniface G, Lamb S. Multifactorial interventions for preventing falls in older people living in the community: a systematic review and meta-analysis of 41 trials and almost 20 000 participants. Br J Sports Med. 2019 Aug 21;
110. Andrieu S, Guyonnet S, Coley N, Cantet C, Bonnefoy M, Bordes S, et al. Effect of long-term omega 3 polyunsaturated fatty acid supplementation with or without multidomain intervention on cognitive function in elderly adults with memory complaints (MAPT): a randomised, placebo-controlled trial. Lancet Neurol. 2017 May;16(5):377–89.
111. Pérez LM, Enfedaque-Montes MB, Cesari M, Soto-Bagaria L, Gual N, Burbano MP, et al. A Community Program of Integrated Care for Frail Older Adults: +AGIL Barcelona. J Nutr Health Aging. 2019;23(8):710–6.
112. Kito N, Matsuo K, Ogawa K, Izumi A, Kishima M, Itoda M, et al. Positive Effects of “Textured Lunches” Gatherings and Oral Exercises Combined with Physical Exercises on Oral and Physical Function in Older Individuals: A Cluster Randomized Controlled Trial. J Nutr Health Aging. 2019;23(7):669–76.
113. Ruan Q, Xiao F, Gong K, Zhang W, Zhang M, Ruan J, et al. Prevalence of Cognitive Frailty Phenotypes and Associated Factors in a Community-Dwelling Elderly Population. J Nutr Health Aging. 2020;24(2):172–80.
114. Ge M, Zhang Y, Zhao W, Yue J, Hou L, Xia X, et al. Prevalence and Its Associated Factors of Physical Frailty and Cognitive Impairment: Findings from the West China Health and Aging Trend Study (WCHAT). J Nutr Health Aging. 2020;24(5):525–33.
115. Chye L, Wei K, Nyunt MSZ, Gao Q, Wee SL, Ng TP. Strong Relationship between Malnutrition and Cognitive Frailty in the Singapore Longitudinal Ageing Studies (SLAS-1 and SLAS-2). J Prev Alzheimers Dis. 2018;5(2):142–8.
116. Shimada H, Makizako H, Tsutsumimoto K, Doi T, Lee S, Suzuki T. Cognitive Frailty and Incidence of Dementia in Older Persons. J Prev Alzheimers Dis. 2018;5(1):42–8.
117. Halil M, Cemal Kizilarslanoglu M, Emin Kuyumcu M, Yesil Y, Cruz Jentoft AJ. Cognitive aspects of frailty: mechanisms behind the link between frailty and cognitive impairment. J Nutr Health Aging. 2015 Mar;19(3):276–83.
118. Vellas B, Scrase D, Rosenberg GA, Andrieu S, Araujo de Carvalho I, Middleton LT. Editorial: WHO Guidelines on Community-Level Interventions to Manage Declines in Intrinsic Capacity: The Road for Preventing Cognitive Declines in Older Age? J Prev Alzheimers Dis. 2018;5(3):165–7.
119. Tavassoli N, Piau A, Berbon C, De Kerimel J, Lafont C, De Souto Barreto P, et al. Framework implementation of the inspire icope-care program in collaboration with the world health organization (who) in the occitania region. Journal of Frailty & Aging [Internet]. 2019 Mar 1 [cited 2020 May 28]; Available from: https://www.jfrailtyaging.com/all-issues.html
120. Takeda, C., Guyonnet, S., Sumi, Y., Vellas. B., Araujo De Carvalho, I. Integrated Care for Older People and the Implementation in the INSPIRE Study. 2020;7(2):70-74.doi: 10.14283/jpad.2020.8.
121. Beard JR. Editorial: Linking Geroscience and Integrated Care to Reinforce Prevention. J Prev Alzheimers Dis. 2020;7(2):68–9.
122. Guerville F, de Souto Barreto P, Giudici KV, Rolland Y, Vellas B, MAPT/DSA Group. Association of 3-Year Multidomain Intervention and Omega-3 Supplementation with Frailty Incidence. J Am Geriatr Soc. 2019 Aug;67(8):1700–6.
123. Muscedere J, Kim PM, Afilalo J, Balion C, Baracos VE, Bowdish D, et al. Proceedings of the Canadian Frailty Network Workshop: Identifying Biomarkers of Frailty to Support Frailty Risk Assessment, Diagnosis and Prognosis. Toronto, January 15, 2018. J Frailty Aging. 2019;8(3):106–16.
124. Rodriguez-Mañas L, Araujo de Carvalho I, Bhasin S, Bischoff-Ferrari HA, Cesari M, Evans W, et al. ICFSR Task Force Perspective on Biomarkers for Sarcopenia and Frailty. J Frailty Aging. 2020;9(1):4–8.
125. Bray NW, Jones GJ, Rush KL, Jones CA, Jakobi JM. Multi-Component Exercise with High-Intensity, Free-Weight, Functional Resistance Training in Pre-Frail Females: A Quasi-Experimental, Pilot Study. J Frailty Aging. 2020;9(2):111–7.
126. Cruz-Jentoft AJ, Woo J. Nutritional interventions to prevent and treat frailty. Curr Opin Clin Nutr Metab Care. 2019;22(3):191–5.
127. Dicks ND, Kotarsky CJ, Trautman KA, Barry AM, Keith JF, Mitchell S, et al. Contribution of Protein Intake and Concurrent Exercise to Skeletal Muscle Quality with Aging. J Frailty Aging. 2020;9(1):51–6.



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

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

J Frailty Aging 2021;in press
Published online January 18, 2021, http://dx.doi.org/10.14283/jfa.2021.2



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

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



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



Study design, setting and ethical considerations

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

Patient population

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

Outcome assessment

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


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

Table 1
Multi-component exercise program

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


Statistical analyses

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



Characteristics of participants

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

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

Table 2
Baseline characteristics of the participants

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


Mortality, readmissions and Visits to the Emergency Department

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

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

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


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

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

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

Abbreviations: VES, Vulnerable Elders Survey.



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


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



1. Casaluce F, Sgambato A, Maione P, Spagnuolo A, Gridelli C. Lung cancer, elderly and immune checkpoint inhibitors. J Thorac Dis. 2018;10(1):S1474-S1481. doi:10.21037/jtd.2018.05.90.
2. Owonikoko TK, Ragin CC, Belani CP, et al. Lung cancer in elderly patients: An analysis of the surveillance, epidemiology, and end results database. J Clin Oncol. 2007;25(35):5570-5577. doi:10.1200/JCO.2007.12.5435.
3. Wingo PA, Cardinez CJ, Landis SH, et al. Long-term trends in cancer mortality in the United States, 1930-1998. Cancer. 2003;97(12 SUPPL.):3133-3275. doi:10.1002/cncr.11380.
4. Hutchins LF, Unger JM, Crowley JJ, Coltman C. A. J, Albain KS. Underrepresentation of patients 65 years of age or older in cancer-treatment trials. N Engl J Med. 1999;341(27):2061-2067. doi:10.1056/NEJM199912303412706.
5. Sacher AG, Le LW, Leighl NB, Coate LE. Elderly patients with advanced NSCLC in phase III clinical trials: Are the elderly excluded from practice-changing trials in advanced NSCLC? J Thorac Oncol. 2013;8(3):366-368. doi:10.1097/JTO.0b013e31827e2145.
6. Carmichael JA, Wing-San Mak D, O’Brien M. A review of recent advances in the treatment of elderly and poor performance NSCLC. Cancers (Basel). 2018;10(7). doi:10.3390/cancers10070236.
7. Cassidy RJ, Zhang X, Switchenko JM, et al. Health care disparities among octogenarians and nonagenarians with stage III lung cancer. Cancer. 2018;124(4):775-784. doi:10.1002/cncr.31077.
8. Takigawa N, Ochi N, Nakagawa N, et al. Do elderly lung cancer patients aged ≥75 years benefit from immune checkpoint inhibitors? Cancers (Basel). 2020;12(7):1-12. doi:10.3390/cancers12071995.
9. Michaels C. The importance of exercise in lung cancer treatment. Transl Lung Cancer Res. 2016;5(3):235-238. doi:10.21037/tlcr.2016.03.02.
10. Peddle-McIntyre CJ, Singh F, Thomas R, Newton RU, Galvao DA, Cavalheri V. Exercise training for advanced lung cancer. Cochrane Database Syst Rev. 2019;2019(2). doi:10.1002/14651858.CD012685.pub2.
11. Rosero ID, Ramírez-Vélez R, Lucia A, et al. Systematic review and meta-analysis of randomized, controlled trials on preoperative physical exercise interventions in patients with non-small-cell lung cancer. Cancers (Basel). 2019;11(7). doi:10.3390/cancers11070944.
12. Rosero ID, Ramírez-Vélez R, Martínez-Velilla N, Cedeño-Veloz BA, Morilla I, Izquierdo M. Effects of a Multicomponent Exercise Program in Older Adults with Non-Small-Cell Lung Cancer during Adjuvant/Palliative Treatment: An Intervention Study. J Clin Med. 2020;9(3):862. doi:10.3390/jcm9030862.
13. Soubeyran P, Bellera C, Goyard J, et al. Screening for Vulnerability in Older Cancer Patients: The ONCODAGE Prospective Multicenter Cohort Study. PLoS One. 2014;9(12):e115060. doi:10.1371/journal.pone.0115060.
14. Bellera CA, Rainfray M, Mathoulin-Pélissier S, et al. Screening older cancer patients: First evaluation of the G-8 geriatric screening tool. Ann Oncol. 2012;23(8):2166-2172. doi:10.1093/annonc/mdr587.
15. Decoster L, Van Puyvelde K, Mohile S, et al. Screening tools for multidimensional health problems warranting a geriatric assessment in older cancer patients: an update on SIOG recommendations†. Ann Oncol. 2015;26(2):288-300. doi:10.1093/annonc/mdu210.
16. Saliba D, Elliott M, Rubenstein LZ, et al. The vulnerable elders survey: A tool for identifying vulnerable older people in the community. J Am Geriatr Soc. 2001;49(12):1691-1699. doi:10.1046/j.1532-5415.2001.49281.x.
17. Fried LP, Tangen CM, Walston J, et al. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci. 2001;56(3):M146-56. https://www.ncbi.nlm.nih.gov/pubmed/11253156.
18. Izquierdo M, Casas-Herrero A, Zambom-Ferraresi F, Martínez-Velilla N, Alonso-Bouzón C, Rodriguez-Mañas L. Multicomponent physical exercise program vivifrail. A practical guide for prescribing a Multicomponent Physical training program to prevent weakness and falls in people over 70 [Internet]. 2017 [cited 2019 Aug 4]. Available from: http://vivifrail.com/wp-content/uploads/2019/11/VIVIFRAIL-ENG-Interactivo.pdf
19. Jones GD, Tan KS, Caso R, et al. Time-varying analysis of readmission and mortality during the first year after pneumonectomy. In: Journal of Thoracic and Cardiovascular Surgery. Vol 160. Mosby Inc.; 2020:247-255.e5. doi:10.1016/j.jtcvs.2020.02.086.
20. Handy JR, Child AI, Grunkemeier GL, et al. Hospital readmission after pulmonary resection: Prevalence, patterns, and predisposing characteristics. Ann Thorac Surg. 2001;72(6):1855-1860. doi:10.1016/S0003-4975(01)03247-7.
21. Hu Y, McMurry TL, Isbell JM, Stukenborg GJ, Kozower BD. Readmission after lung cancer resection is associated with a 6-fold increase in 90-day postoperative mortality. J Thorac Cardiovasc Surg. 2014;148(5):2261-2267.e1. doi:10.1016/j.jtcvs.2014.04.026.
22. Min L, Yoon W, Mariano J, et al. The vulnerable elders-13 survey predicts 5-year functional decline and mortality outcomes in older ambulatory care patients. J Am Geriatr Soc. 2009;57(11):2070-2076. doi:10.1111/j.1532-5415.2009.02497.x.
23. Ludmir EB, Subbiah IM, Mainwaring W, et al. Decreasing incidence of upper age restriction enrollment criteria among cancer clinical trials. J Geriatr Oncol. 2020;11(3):451-454. doi:10.1016/j.jgo.2019.11.001.
24. Wang Y, Zhang R, Shen Y, Su L, Dong B, Hao Q. Prediction of chemotherapy adverse reactions and mortality in older patients with primary lung cancer through frailty index based on routine laboratory data. Clin Interv Aging. 2019;14:1187-1197. doi:10.2147/CIA.S201873.
25. Tsubata Y, Shiratsuki Y, Okuno T, et al. Prospective clinical trial evaluating vulnerability and chemotherapy risk using geriatric assessment tools in older patients with lung cancer. Geriatr Gerontol Int. 2019;19(11):1108-1111. doi:10.1111/ggi.13781.
26. Gridelli C, Aapro M, Ardizzoni A, et al. Treatment of advanced non-small-cell lung cancer in the elderly: Results of an International Expert Panel. J Clin Oncol. 2005;23(13):3125-3137. doi:10.1200/JCO.2005.00.224.
27. Balducci L, Extermann M. Management of Cancer in the Older Person: A Practical Approach. Oncologist. 2000;5(3):224-237. doi:10.1634/theoncologist.5-3-224.
28. Swan F, Chen H, Forbes CC, Johnson MJ, Lind M. CANcer BEhavioural nutrition and exercise feasibility trial (CanBenefit); phase I qualitative interview findings. J Geriatr Oncol. 2020. doi:10.1016/j.jgo.2020.09.026.




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.




1. Chen X, Mao G, Leng SX. Frailty syndrome: an overview. Clin Interv Aging 2014;9:433-41. doi: 10.2147/CIA.S45300
2. Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, et al. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci. 2001;56(3):M146-56. doi: 10.1093/gerona/56.3.m146
3. Ensrud KE, Ewing SK, Taylor BC, Fink HA, Cawthon PM, Stone KL, et al. Comparison of 2 frailty indexes for prediction of falls, disability, fractures, and death in older women. Arch Intern Med 2008;168(4):382-9. doi: 10.1001/archinternmed.2007.113
4. Clegg A, Young J, Iliffe S, Rikkert MO, Rockwood K. Frailty in elderly people. Lancet. 2013;381(9868):752-62. doi: 10.1016/S0140-6736(12)62167-9
5. Pereira AA, Borim FSA, Aprahamian I, Neri AL. Comparison of two models of frailty for the prediction of mortality in Brazilian community-dwelling older adults: The FIBRA study. J Nutr Health Aging 2019;23(10):1004-10. doi: 10.1007/s12603-019-1264-0
6. Dent E, Morley JE, Cruz-Jentoft AJ, Woodhouse L, Rodriguez-Manas L, Fried LP, et al. Physical frailty: ICFSR international clinical practice guidelines for identification and management. J Nutr Health Aging 2019;23(9):771-87. doi: 10.1007/s12603-019-1273-z
7. Collard RM, Boter H, Schoevers RA, Oude Voshaar RC. Prevalence of frailty in community-dwelling older persons: A systematic review. J Am Geriatr Soc. 2012;60(8):1487-92. doi: 10.1111/j.1532-5415.2012.04054.x
8. Choi J, Ahn A, Kim S, Won CW. Global prevalence of physical frailty by Fried’s criteria in community-dwelling elderly with national population-based surveys. J Am Med Dir Assoc 2015;16(7):548-50. doi: 10.1016/j.jamda.2015.02.004
9. Kojima G, Iliffe S, Taniguchi Y, Shimada H, Rakugi H, Walters Prevalence of frailty in Japan: A systematic review and meta-analysis. J Epidemiol 2017;27(8):347-53. doi: 10.1016/j.je.2016.09.008
10. Shimada H, Makizako H, Doi T, Yoshida D, Tsutsumimoto K, Anan Y, et al. Combined prevalence of frailty and mild cognitive impairment in a population of elderly Japanese people. J Am Med Dir Assoc 2013;14(7):518-24. doi: 10.1016/j.jamda.2013.03.010
11. Makizako H, Shimada H, Doi T, Tsutsumimoto K, Suzuki T. Impact of physical frailty on disability in community-dwelling older adults: A prospective cohort study. BMJ open 2015;5(9):e008462. doi: 10.1136/bmjopen-2015-008462
12. Fukutomi E, Okumiya K, Wada T, Sakamoto R, Ishimoto Y, Kimura Y, et al. Relationships between each category of 25-item frailty risk assessment (Kihon Checklist) and newly certified older adults under Long-Term Care Insurance: A 24-month follow-up study in a rural community in Japan. Geriatr Gerontol Int 2015;15(7):864-71. doi: 10.1111/ggi.12360
13. Chen LK, Liu LK, Woo J, Assantachai P, Auyeung TW, Bahyah KS, et al. Sarcopenia in Asia: Consensus report of the Asian Working Group for Sarcopenia. J Am Med Dir Assoc 2014;15(2):95-101. doi: 10.1016/j.jamda.2013.11.025
14. Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ 2003;327(7414):557-60. doi: 10.1136/bmj.327.7414.557
15. Ofori-Asenso R, Chin KL, Mazidi M, Zomer E, Ilomaki J, Zullo AR, et al. Global incidence of frailty and prefrailty among community-dwelling older adults: A systematic review and meta-analysis. JAMA Netw Open 2019;2(8):e198398. doi: 10.1001/jamanetworkopen.2019.8398
16. He B, Ma Y, Wang C, Jiang M, Geng C, Chang X, et al. Prevalence and risk factors for frailty among community-dwelling older people in China: A systematic review and meta-analysis. J Nutr Health Aging 2019;23(5):442-50. doi: 10.1007/s12603-019-1179-9
17. Siriwardhana DD, Hardoon S, Rait G, Weerasinghe MC, Walters KR. Prevalence of frailty and prefrailty among community-dwelling older adults in low-income and middle-income countries: A systematic review and meta-analysis. BMJ open 2018;8(3):e018195. doi: 10.1136/bmjopen-2017-018195
18. Kojima G. Prevalence of frailty in nursing homes: A systematic review and meta-analysis. J Am Med Dir Assoc 2015;16(11):940-5. doi: 10.1016/j.jamda.2015.06.025
19. Da Mata FA, Pereira PP, Andrade KR, Figueiredo AC, Silva MT, Pereira MG. Prevalence of frailty in Latin America and the Caribbean: A systematic review and meta-analysis. PLoS One 2016;11(8):e0160019. doi: 10.1371/journal.pone.0160019
20. United Nations. Department of Economic and Social Affaris, Population Dynamics. World Population Prospects. http://esa.un.org/unpd/wpp/DataQuery/ Accessed 18 December 2019.
21. Tamiya N, Noguchi H, Nishi A, Reich MR, Ikegami N, Hashimoto H, et al. Population ageing and wellbeing: Lessons from Japan’s long-term care insurance policy. Lancet 2011;378(9797):1183-92. doi: 10.1016/S0140-6736(11)61176-8
22. Suzuki T, Nishita Y, Jeong S, Shimada H, Otsuka R, Kondo K, et al. Are Japanese older adults rejuvenating? Changes in health-related measures among older community dwellers in the last decade. Rejuvenation Res 2020; in press. doi: 10.1089/rej.2019.2291
23. Langa KM, Larson EB, Crimmins EM, Faul JD, Levine DA, Kabeto MU, et al. A comparison of the prevalence of dementia in the United States in 2000 and 2012. JAMA Intern Med 2017;177(1):51-58. doi: 10.1001/jamainternmed.2016.6807
24. Hoogendijk EO, Afilalo J, Ensrud KE, Kowal P, Onder G, Fried LP. Frailty: implications for clinical practice and public health. Lancet 2019;394(10206):1365-75. doi: 10.1016/S0140-6736(19)31786-6
25. Shimada H, Makizako H, Doi T, Tsutsumimoto K, Suzuki T. Incidence of disability in frail older persons with or without slow walking speed. J Am Med Dir Assoc 2015;16(8):690-6. doi: 10.1016/j.jamda.2015.03.019
26. Suzuki T, Kwon J. Health status of older adults living in the community in Japan: Recent changes and significance in the super-aged society. Geriatr Gerontol Int 2018;18(5):667-677. doi: 10.1111/ggi.1326627. Suzuki T. Health status of older adults living in the community in Japan: Recent changes and significance in the super-aged society. Geriatr Gerontol Int. 2018 May;18(5):667-77.
28. Tomioka K, Kurumatani N, Hosoi H. Age and gender differences in the association between social participation and instrumental activities of daily living among community-dwelling elderly. BMC Geriatr 2017;17(1):99. doi: 10.1186/s12877-017-0491-7
29. Sialino LD, Schaap LA, van Oostrom SH, Nooyens ACJ, Picavet HSJ, Twisk JWR, et al. Sex differences in physical performance by age, educational level, ethnic groups and birth cohort: The Longitudinal Aging Study Amsterdam. PLoS One 2019;14(12):e0226342. doi: 10.1371/journal.pone.0226342
30. Seino S, Shinkai S, Fujiwara Y, Obuchi S, Yoshida H, Hirano H, et al. Reference values and age and sex differences in physical performance measures for community-dwelling older Japanese: A pooled analysis of six cohort studies. PLoS One 2014;9(6):e99487. doi: 10.1371/journal.pone.0099487



C. Fompeyrine1,2, L.A. Abderhalden2, N. Mantegazza2, N. Hofstetter2, G. Bieri-Brüning3, H.A. Bischoff-Ferrari1,2,4, M. Gagesch1,2


1. Department of Geriatrics and Aging Research, University Hospital Zurich, Zurich, Switzerland; 2. Centre on Aging and Mobility, University of Zurich, Zurich, Switzerland
3. Zurich Geriatric Services and Nursing Homes, Zurich, Switzerland; 4. University Clinic for Acute Geriatric Care, City Hospital Waid and Triemli, Zurich, Switzerland.
Corresponding author: Michael Gagesch, MD, Dept. of Geriatrics and Aging Research, University Hospital Zurich, Raemistrasse 100, 8091 Zurich, Switzerland, michael.gagesch@usz.ch

J Frailty Aging 2020;in press
Published online October 20, 2020, http://dx.doi.org/10.14283/jfa.2020.58



Frail older adults with ongoing care needs often require post-acute care (PAC) following acute hospitalization when not eligible for specific rehabilitation. Long-term outcomes of PAC in this patient group have not been reported for Switzerland so far. In the present report, we investigated 12-month mortality in regard to frailty status upon admission to PAC in a nursing home setting. In our sample of 140 patients (mean age 84 [±8.6] years) 4.3% were robust, 37.1% were pre-frail, 54.3% were frail and 4.3% were missing frailty status. Mortality at 12-months follow-up stratified by baseline frailty was 0% (robust), 11.5% (pre-frail) and 31.6% (frail). Kaplan-Meier analysis stratified by frailty status showed a decreased probability of 12-months survival for frail individuals compared to their pre-frail and robust counterparts (P = 0.0096). Being frail was associated with more than 4-fold increased odds of death at follow-up (OR 4.19; 95% CI 1.53-11.47).

Key words: Frailty, long-term mortality, post-acute care, nursing homes.


Health in older age comprises a broad spectrum from robustness to vulnerability and frailty (1). The latter is associated with multiple negative outcomes in various care settings, from general practice to acute hospital care (2). With their often complex health status, older adult patients frequently require longer lengths of stay in the hospital and often remain at an increased level of care, impeding a prompt discharge home after an acute illness (3). At the same time, standard rehabilitation programs do not always appear suitable for many frail older patients (4).
Post-acute care (PAC) programs aim to bridge the gap between acute care and returning home for older adults, not otherwise eligible for rehabilitation. While earlier studies from different countries and specific settings (i.e. heart-failure patients) have demonstrated positive effects of PAC, such as reduced readmissions and decreased mortality rates (5, 6), its potential benefits and outcomes, particularly in frail older adults are still understudied (7). In addition, healthcare systems and PAC programs appear to have major differences between countries, hampering direct comparisons (8).
In a prior analysis, clinically significant improvements of physical function and ADL were reported in robust and frail Swiss older adults after a PAC program in nursing homes with a mean duration of 31 days (9). However, no research on the long-term outcomes of PAC in Swiss nursing homes exists so far (4). Therefore, the aim of our study was to investigate the association of frailty status upon admission to PAC with 12-month mortality in a real-world sample of Swiss older adults.



Study Design

We conducted a one-year follow-up study at designated PAC units of three municipal nursing homes within the City of Zurich, Switzerland. Written informed consent was obtained before study enrolment. The competent ethics committee of the Canton of Zurich approved our study (BASEC 2016-01069).

PAC Setting and Patients

Our study recruited consecutive patients 60 years and older referred to a PAC unit after acute care hospitalization between August and September 2016. An interdisciplinary team under the supervision of a board-certified geriatrician completed a comprehensive geriatric assessment (CGA) for each patient within one week upon admission, performed the PAC program and held bi-weekly team meetings. PAC consisted of activating nursing care (i.e. goal-directed instruction and training of ADL), five sessions of individual physical therapy per week and additional occupational therapy as needed, based upon the initial CGA. The maximum length of stay at PAC units was usually limited to 10 weeks duration and the effective date of discharge was based on the accomplishment of specific goals, derived from the individual care plan (9).
For our follow-up investigation, we matched the initial list of PAC patients with the death registry of the City of Zurich at one year after discharge. Living status and mortality date (if applicable) was recorded. We utilized frailty status from CGA at admission to a PAC unit according to the Fried frailty phenotype (items: unintentional weight loss, fatigue, slowness, weakness, low activity level) (10). Among numerous proposed frailty definitions, the Fried frailty phenotype is one of the most recognized and highly cited concepts and has been validated in various healthcare settings (2, 11). Patients with zero positive criteria were classified as robust, patients with 1-2 positive criteria as pre-frail and patients with ≥3 positive criteria were considered frail (10). In addition, we utilized further patient characteristics recorded at admission (Barthel-Index, Short physical performance battery (SPPB), Mini-Mental State Examination (MMSE) score, number of drugs and number of diagnoses) to describe the functional status and comorbidity burden.

Statistical Analysis

Three months and one year mortality rate after PAC discharge as well as further patient characteristics recorded at admission were calculated and stratified by level of frailty (robust, pre-frail, frail). Kaplan-Meier curves for visual representation were constructed for the overall sample to compare frail vs. robust and pre-frail at admission to PAC. Fisher’s exact test was used to evaluate whether mortality rate one year after discharge from PAC was independent of frailty status at admission. ANOVA and Chi-square test were used to evaluate whether there was a difference in mortality rate between frailty levels, as well as age and gender. Furthermore, a logistic regression model predicting mortality was evaluated to determine a possible association between frailty status upon admission to PAC and mortality rate on follow-up. The model was adjusted for age and gender. Statistical significance was determined as P<0.05 using 2-sided tests. All statistical analyses were performed using R v3.5.0 (The R Foundation for Statistical Computing, Vienna, Austria) and SAS v9.4 (SAS Institute, Inc. Cary, USA).



Baseline Population

Our baseline sample consisted of n=140 patients, including 62.9% (n=88) women. Mean age at admission to PAC was 84 years (± 8.57). Mean length of stay at PAC was 31 days (± 16.5). In all, the most frequent diagnoses on admission to PAC were fractures (n=29), infections except pulmonary manifestations (n=18), mobility disorders (n=17), cognitive impairment (n=15), and heart disease (n=11), as reported earlier (12).

Mortality and Frailty Status

For n=139 patients, mortality status and mortality date at 3 and 12 months after discharge from PAC were applicable. At admission to PAC, 4.3% (n=6) of patients were robust, 37.1% (n=52) were pre-frail, 54.3% (n=76) were frail and 4.3% (n=6) were missing information on frailty status. The one-year mortality rate for the overall sample was 22.9% (32/140). One-year mortality rate stratified according to the different levels of frailty was 0% (robust, 0/6), 11.5% (pre-frail, 6/52) and 31.6% (frail, 24/76). Frailty status in relation to mortality, functional status and comorbidity burden is summarized in Table 1.

Table 1 Baseline characteristics and mortality after PAC stratified by frailty status

a. n=6 missing frailty status at admission; b. testing the difference between frailty levels; c. n=5 missing patients; d. SPPB, Short physical performance battery, n=10 missing patients; e. n=3 missing patients; f. n=1 missing patients; g. MMSE, Mini-Mental State Examination, n=7 missing patients; h. n=1 missing patient; i. n=2 deceased patients were missing frailty status at admission


For further analysis, we combined the group of robust and pre-frail patients, as none of the robust group deceased in the year following discharge from PAC. Our logistic regression model showed significantly increased odds of death for being frail (OR 4.19; 95% CI 1.53-11.47), and male gender (OR 3.19; 95% CI 1.28-8.0), but not for older age (OR 1.06, 95% CI 1.00-1.13 for each additional year).
Estimating survival with a Kaplan Meier analysis stratified by frailty status at admission to PAC showed a decreased probability of one-year survival for frail individuals, compared to patients classified as pre-frail or robust (P = 0.0096), Figure 1. In addition, each point increment on the frailty score at admission to PAC was associated with a decreasing one-year survival (P = 0.014).

Figure 1 Kaplan Meier estimates stratified for frailty status



With more than one in two patients being frail and more than one in three being at risk for the condition (i.e. pre-frail) in our sample, frailty appears to be highly prevalent in Swiss older adults undergoing PAC in a nursing home setting. In comparison, the estimated prevalence of frailty in community-dwelling older adults in Switzerland is 5.8% (13). In our analysis, male gender and prevalent frailty were significantly associated with decreased survival at 12 months follow-up. In particular, frail patients had a greater than 4-fold increased odds for long-term mortality compared to their robust and pre-frail counterparts.
Our findings are in line with results from a prior study in older adults from Spain, where age, male gender and worse functional status were associated with higher 12-month mortality after acute illness (14). Our overall mortality rate of 22.9% is comparable to reports from earlier studies in former hospitalized geriatric patients from Germany and Italy (20.3% in Ritt et al. (15); 24.9% in Pilotto et al. (16)). However, those studies investigated one-year mortality after acute hospitalization without reporting on the utilization of PAC. Notably, patients in one of the aforementioned studies had a lower frailty prevalence at admission to acute care than our patient group (e.g. 43.3% vs. 54.3% in this study) (15).
When comparing the 12-month mortality rate of 31.6% in our frail patients with the aforementioned studies from acute care settings in Germany and Italy, it appears consistent with those reported by Ritt et al. (36.1%) and Pilotto et al. (24.9%) (15, 16). Of note, the higher mean age of patients in our study was more comparable to the first study (mean age >80 years), while Pilotto and colleagues investigated a sample with a mean age <80 years. Therefore, this difference is probably due to the influence of age in relation to the difference in mortality and warrants further investigation.
As a strength, our study is the first to report on the long-term outcomes of PAC in Swiss nursing homes and its association with frailty status. Further, we used a standardized operationalization of the Fried frailty phenotype, a derivation of the original version by Fried et al. (10). Our study also has its limitations. First, our sample size and short duration of patient recruitment limit the generalizability of our results. We also lack information on causes of death during follow-up. Furthermore, we had to cluster robust and pre-frail patients for our analysis, which might hinder comparisons to other studies. In addition, the frailty phenotype may not be the best frailty instrument to predict 12-month mortality in this patient group (15). Finally, our study did not include a control group of “standard” nursing home care residents to compare with our results regarding potential recovery time in the absence of specific interventions.



Our study in 140 former geriatric inpatients 12 months after discharge from PAC suggests that male gender and frailty status upon admission to PAC are significantly associated with increased long-term mortality in this group of Swiss older adults. While in line with prior studies from other populations, our study adds important knowledge on the specific situation in Switzerland. More studies are needed to further investigate the impact of PAC programs on short and long-term outcomes in Switzerland, including older adults affected by frailty.


Acknowledgments: We like to thank Marion Thalmann and Thomas Tröster for performing the initial data collection. In addition we thank Dr. Wei Lang for his statistical advice. Furthermore, we would like to thank all involved staff members at the participating municipal nursing homes in the City of Zurich.
Conflicts of Interest: The authors declare no conflict of interest.
Ethical Standards: The authors declare that the study porcedures comply with current ethical standards for research involving human participants in Switzerland. The study protocol has been approved by the Cantonal Ethics Committee of the Canton of Zurich, Switzerland (BASEC 2016-01069);
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.
Funding section: Open Access funding provided by University Hospital Zurich.



1. Buchner DM, Wagner EH. Preventing frail health. Clin Geriatr Med. 1992;8(1):1-17.
2. Dent E, Kowal P, Hoogendijk EO. Frailty measurement in research and clinical practice: A review. Eur J Intern Med. 2016;31:3-10.
3. Covinsky KE, Palmer RM, Fortinsky RH, Counsell SR, Stewart AL, Kresevic D, et al. Loss of independence in activities of daily living in older adults hospitalized with medical illnesses: increased vulnerability with age. J Am Geriatr Soc. 2003;51(4):451-8.
4. Kone I, Zimmermann B, Wangmo T, Richner S, Weber M, Elger B. Hospital discharge of patients with ongoing care needs: a cross-sectional study using data from a city hospital under SwissDRG. Swiss medical weekly. 2018;148:w14575.
5. Feltner C, Jones CD, Cené CW, Zheng ZJ, Sueta CA, Coker-Schwimmer EJ, et al. Transitional care interventions to prevent readmissions for persons with heart failure: a systematic review and meta-analysis. Ann Intern Med. 2014;160(11):774-84.
6. Prvu Bettger J, Alexander KP, Dolor RJ, Olson DM, Kendrick AS, Wing L, et al. Transitional care after hospitalization for acute stroke or myocardial infarction: a systematic review. Ann Intern Med. 2012;157(6):407-16.
7. Roberts PS, Goud M, Aronow HU, Riggs RV. Frailty in a Post-Acute Care Population: A Scoping Review. PM & R : the journal of injury, function, and rehabilitation. 2018.
8. Abrahamsen J, Rozzini R, Boffelli S, Cassinadri A, Ranhoff A, Trabucchi M. Comparison of Italian and Norwegian postacute care settings for older patients in need of further treatment and rehabilitation after hospitalization. The Journal of Aging Research and Clinical Practice (JARCP). 2015.
9. Thalmann M, Troster T, Fischer K, Bieri-Bruning G, Patrick B, Bischoff-Ferrari HA, et al. Do older adults benefit from post-acute care following hospitalisation? A prospective cohort study at three Swiss nursing homes. Swiss medical weekly. 2020;150:w20198.
10. Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, et al. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci. 2001;56(3):M146-56.
11. Hoogendijk EO, Afilalo J, Ensrud KE, Kowal P, Onder G, Fried LP. Frailty: implications for clinical practice and public health. Lancet. 2019;394(10206):1365-75.
12. Tröster T, Thalmann M, Fischer K, Bieri-Brüning G, Beeler PE, Bischoff-Ferrari HA, et al. Frailty, underweight and impaired mobility are associated with institutionalisation after post-acute care. Swiss medical weekly. 2020;150:w20276.
13. Santos-Eggimann B, Cuenoud P, Spagnoli J, Junod J. Prevalence of frailty in middle-aged and older community-dwelling Europeans living in 10 countries. J Gerontol A Biol Sci Med Sci. 2009;64(6):675-81.
14. Baztan JJ, Galvez CP, Socorro A. Recovery of functional impairment after acute illness and mortality: one-year follow-up study. Gerontology. 2009;55(3):269-74.
15. Ritt M, Bollheimer LC, Sieber CC, Gaßmann KG. Prediction of one-year mortality by five different frailty instruments: A comparative study in hospitalized geriatric patients. Arch Gerontol Geriatr. 2016;66:66-72.
16. Pilotto A, Rengo F, Marchionni N, Sancarlo D, Fontana A, Panza F, et al. Comparing the prognostic accuracy for all-cause mortality of frailty instruments: a multicentre 1-year follow-up in hospitalized older patients. PLoS One. 2012;7(1):e29090.



B. Everaars1,2, K. Jerković – Ćosić2, N. Bleijenberg3,4, N. J. de Wit4, G.J.M.G. van der Heijden1

1. Department of Social Dentistry, Academic Center for Dentistry Amsterdam (ACTA), University of Amsterdam and VU University, Amsterdam, The Netherlands; 2. University of Applied Sciences Utrecht, Research Group Innovations in Preventive Care; Utrecht, The Netherlands; 3. University of Applied Sciences Utrecht, Institute of Nursing Studies, Research Group Chronic Diseases; Utrecht, The Netherlands; 4. Utrecht University, University Medical Center Utrecht, Julius Center for Health Sciences and Primary Care, Utrecht, The Netherlands.
Corresponding author: B. Everaars, University of Applied Sciences Utrecht, Research Group Innovations in Preventive Care, Heidelberglaan 7, 3512 CS, Utrecht, The Netherlands, Email: babette.everaars@hu.nl, Telephone: 0614317567
J Frailty Aging 2021;10(1)56-62
Published online October 12, 2020, http://dx.doi.org/10.14283/jfa.2020.55



Background: In frail older people with natural teeth factors like polypharmacy, reduced salivary flow, a decrease of oral self-care, general healthcare issues, and a decrease in dental care utilization contribute to an increased risk for oral complications. On the other hand, oral morbidity may have a negative impact on frailty. Objective: This study explored associations between oral health and two frailty measures in community-dwelling older people. Design: A cross-sectional study. Setting: The study was carried out in a Primary Healthcare Center (PHC) in The Netherlands. Participants: Of the 5,816 persons registered in the PHC, 1,814 persons were eligible for participation at the start of the study. Measurements: Two frailty measures were used: 1. Being at risk for frailty, using Electronical Medical Record (EMR) data, and: 2. Survey-based frailty using ‘The Groningen Frailty Indicator’ (GFI). For oral health measures, dental-record data (dental care utilization, dental status, and oral health information) and self-reported oral problems were recorded. Univariate regression analyses were applied to determine the association between oral health and frailty, followed by age- and sex-adjusted multivariate logistic regressions. Results: In total 1,202 community-dwelling older people were included in the study, 45% were male and the mean age was 73 years (SD=8). Of all participants, 53% was at risk for frailty (638/1,202), and 19% was frail based on the GFI (222/1,202). A dental emergency visit (Odds Ratio (OR)= 2.0, 95% Confidence Interval (CI)=1.33;3.02 and OR=1.58, 95% CI=1.00;2.49), experiencing oral problems (OR=2.07, 95% CI=1.52;2.81 and OR=2.87, 95% CI= 2.07;3.99), and making dietary adaptations (OR=2.66, 95% CI=1.31;5.41 and OR=5.49, 95% CI= 3.01;10.01) were associated with being at risk for frailty and survey-based frailty respectively. Conclusions: A dental emergency visit and self-reported oral health problems are associated with frailty irrespective of the approach to its measurement. Healthcare professionals should be aware of the associations of oral health and frailty in daily practice.

Key words: Dental care for aged, frailty, oral health, primary health care.



After decades of a decline in the prevalence of edentulous people of older age, this decline seems to have stalled (1, 2). In older people with natural teeth factors like polypharmacy, reduced salivary flow, a decrease of oral self-care, general healthcare issues, and a decrease in dental care utilization contribute to an increased risk for oral complications (3-5). These clinical and lifestyle factors together with demographic, social and biological factors are present in the onset of frailty (6). Frailty is a progressive condition mostly at a higher age, that is associated with adverse health outcomes including functional decline, long-term care and a higher risk of mortality (7, 8).
Few studies investigated the association between oral health and frailty (or a domain thereof). The most recent review of Hakeem et al. (2019) including only longitudinal studies, showed associations between number of teeth and masticatory function on the one hand, and frailty on the other hand (9). Another review including only cross-sectional studies reported associations between, on the one hand, the need for a dental prosthesis, self-reported oral health, dental service use, oral health-related quality of life, and on the other hand frailty (its physical component). But associations for frailty with number of teeth, masticatory function and periodontitis were not found (10).
The above-mentioned reviews used the unidimensional Fried frailty phenotype to determine frailty. However, studies exploring the association between oral health and multi-dimensional frailty measures are lacking (9, 11). Therefore, the aim of this study was to explore the association between oral health and frailty using two frailty measures: (1) based on Electronical Medical Record (EMR) data and (2) frailty based on survey data in community-dwelling older people.



For reporting this cross-sectional study we applied the relevant items of Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement (12).

Design and setting

This cross-sectional study was carried out in a Primary Healthcare Center (PHC) in The Netherlands. The multidisciplinary PHC team consists of general practitioners (GP), practice nurses, health care assistants, a pharmacist, dentists, and dental hygienists.

Participants and study sample

Of the 5,816 persons registered in the PHC, 1,842 were 60 years of age or older at the start of the study. The GP or practice nurse considered 28 persons unable to participate because of cognitive or physical constraints, whereby 1,814 persons were considered eligible for participation in the study.

Data collection procedures

All 1,814 persons were invited to participate in the study in April 2016. They received an information letter from the GP and were asked for informed consent to extract dental record data and to match these data with their routine healthcare data. We asked if and in which dental clinic the participants were registered, in order to retrieve dental record data. In addition, participants were asked to complete a questionnaire on self-reported frailty. If individuals agreed to participate in the study but information on their dental clinic was missing, this was obtained by phone. After receiving informed consent, dental record data from 1.5 years prior to the consent was collected. This period of 1.5 years was arbitrary chosen because it is considered as a dental consultation gap: around 75% of the Dutch citizens visit the dentist every year (13).
For participants registered in the dental clinic within the same primary healthcare center (81%), the research assistant manually collected the dental record data. For participants registered in other dental clinics in the Netherlands (19%), data were obtained from the dentist via a structured data extraction form.

Frailty indicators

Frailty identification

Frailty was assessed by two validated measures. First, we used the Utrecht Periodic Risk Identification and Monitoring system (U-PRIM). The U-PRIM was designed to classify older people at risk for frailty. The U-PRIM extracts data from the EMR on multi-morbidity to calculate the Frailty Index (FI) (14), polypharmacy and a possible GP consultation gap. The FI is calculated out of the proportion of 50 potential health deficits defined by the presence of one or more International Classification of Primary Care (ICPC) and diagnosis and prescription (Anatomic Therapeutic Chemical (ATC)) codes (ranging from zero (fit) to one (frail)) (14-16). A cut-off of 0.2 distinguished between a positive or negative FI score (see Table 1). Polypharmacy was defined as the chronic use of five or more different kinds of medications according to the ATC coding. A GP consultation gap was denoted as present when someone did not visit the GP for 3 or more years (with the exception of the annual influenza vaccination). For the purpose of analyses, frailty based on the U-PRIM variables was dichotomized: older people at risk for frailty vs. older people not at risk for frailty. People were considered at risk for frailty if they scored positive on either the FI, polypharmacy or GP consultation gap. The only possibility to be classified as not at risk for frailty was to score negative on all three variables. For a detailed coding scheme, see Table 1.
Second, a self-administered survey on self-reported frailty was filled out: i.e. frailty based on survey data. For this, the Groningen Frailty Indicator (GFI) was used. The GFI is a Dutch validated questionnaire consisting of 15 questions regarding the physical, cognitive, social and psychological domains (17). Each question was rated as 0 (negative) or 1 (positive) with a total score ranging from 0 to 15. Results were dichotomized in frail based on survey data (a total GFI score of 4 or higher) vs. not frail based on survey data (a GFI score lower than 4) (see Table 1) (17).

Table 1
Scoring overview of frailty measures

† Someone was not potentially frail if scoring negative on all three variables (FI, Polypharmacy, and GP consultation gap).


Oral health indicators

Dental record data and self-reported oral problems were collected. The information on self-reported oral problems was collected together with the GFI questions and concerned two questions: 1. Do you experience pain, a dry mouth or other discomforts in your mouth? 2. Did you change your food choices because of this discomfort in your mouth? Response categories ‘’sometimes’’ and ‘’yes’’ were dichotomized into positive (1), and answers ‘’no’’ were scored negative (0).
Participants who stated to not visit/being registered in a dental clinic the past 1.5 years, were categorized as having a dental consultation gap. If participants had no dental consultation gap, additional dental information within the timeframe of 1.5 years was extracted: the dental status (natural teeth (reference), partial prosthesis, full prosthesis), dental treatments: extraction (yes=1/no=0), caries treatment (yes=1/no=0), emergency treatment (yes=1/no=0) and periodontal problems were extracted. Periodontal status was categorized according to the Dutch Periodontal Index (DPSI) (18). Category A+B indicated no/minor periodontal disease (0) whereas category C referred to periodontal disease (1). Dental visit was recoded into a dental consultation gap: visited the dentist within the past 1.5 years (0) and did not visit the dentist within the past 1.5 years (1).

Statistical analysis

Descriptive data were calculated separately for participants being at risk for frailty or not, and for those who were frail versus non-frail according to the GFI questionnaire. The following descriptive statistics were calculated: mean and standard deviation (SD) (continuous and normally distributed data), median and interquartile range (IQR) (non-normally distributed data), and percentages (nominal scale) were reported. To compare the groups, t-tests, chi-square tests and a Mann-Whitney U test were performed (see footnote in Table 2).
To explore the associations between oral health and both frailty measures, univariate logistic regression analyses were performed on all oral health variables and the covariates. Subsequently, a multivariate logistic regression analysis was performed adjusted for age and sex. For all statistical analyses, a p-value of ≤0.05 was considered significant. Analyses were performed with SPSS version 24.0.

Table 2
Descriptive baseline data on general health and oral health distinguished for people at risk for frailty (based on EMR data) and frailly based on survey data (GFI)

Note: SD=standard deviation; IQR=inter quartile range; Statistical tests: for the variables: Sex, Dental consultation gap, Self-reported oral problems and all dental record data (nominal scale) a chi-square test was performed; For: Age, Frailty Index, Number of medications, and GFI score (ratio scale) a t-test was performed. For the variable: consultation gap (compare medians) the Mann-Whitney U test was performed; §. Self-reported oral problems had 8 missing cases for experiences of oral problems and 9 missing cases for food adaptation. Analysis have been performed on full data: respectively N=1194 and N=1193. †. From 908 participants we retrieved dental record data. In case of a consultation gap or if people were not registered in a dental practice, no dental record was available from the past 1.5 years. The proportion of dental record data and statistical test are calculated based on the total N=908. ‡. Periodontal information was not available for people with full prosthesis (on implants) and were excluded for analysis. The proportion of periodontal information and statistical tests are calculated based on the total N=878.


Missing data

Participants with five or more missing items on their GFI questionnaire, were excluded for analysis. For dental record data, full data analysis was performed: participants without a registration in a dental clinic or with a dental consultation gap no additional dental record data were available and were excluded for analysis.
Twelve participants had missing data on either one or two self-perceived oral health questions. For these participants, full data-availability analysis was performed.
Regarding periodontal health, the DPSI score was not documented by dentists in many cases. A sensitivity analysis showed that the inclusion of these missing variables as an individual risk factor provided us with the most informative results.


The Medical ethics Review board of UMC Utrecht decided to provide a waiver for the study from full assessment according to the Medical Research Involving Human Subjects Act (WMO) (reference: WAG/mb/16/013553). During the study, the study team adhered to the General Data Protection Regulation (GDPR).


Of the 1,814 persons eligible for study participation, 1,378 provided consent. In total, 1,202 were included in the study. A flowchart for in- and exclusion is shown in Figure 1.

Figure 1
Flowchart of in- and excluded persons and availability of dental record data


Demographics and General health factors

Of the 1,202 participants, 545 (45%) were male. The mean age of participants was 73 years (SD=8). Based on EMR data 638 (53%) of the older people were at risk for frailty: 554 (46%) scored positive on the FI, 397 (33%) scored positive on polypharmacy and 22 (2%) scored positive on a GP consultation gap. Based on survey data, 222 (18%) were considered frail. People at risk for frailty according to EMR data were generally older, had a higher FI score, used more medications, had a higher GFI mean score and had a shorter GP consultation gap compared to participants not at risk (Table 2). Frail older people based on survey data according to the GFI showed comparable characteristics (Table 2).

Oral health

Out of 1,202 participants, we retrieved from 908 (76%) participants dental record data (Figure 1). From 294 (24%) participants we could not retrieve dental record data because 121 (41%) of them were not registered in a dental clinic and 173 (59%) of them did not visit their dentist the past 1.5 years (see Figure 1). In 264 (29%) participants, no DPSI score had been registered by the dental professional in the timeframe of 1.5 years (missing data).
We found significant differences in oral health between participants at risk for frailty based on EMR data and participants not at risk. For those at risk for frailty compared to those not at risk, larger proportions had a dental consultation gap (27% vs. 21%), experienced oral problems (29% vs. 14%), made dietary adaptations because of oral problems (7% vs. 2%), had a dental emergency visit (19% vs. 10%) and had partial- (32% vs. 20%) or full prosthesis (4% vs. 2%) (see Table 2).
Frail participants based on survey data according to the GFI showed also significant differences in oral health compared to the non-frail participants. For frail participants compared to non-frail participants, larger proportions experienced oral problems (42% vs. 17%), made dietary adaptations because of oral problems (15% vs. 2%), had a dental emergency visit (21% vs.13%), had a partial (36% vs. 24%) or full prosthesis (8% vs. 2%), and missing periodontal information (39% vs. 28%) (see Table 2). No significant differences were found for caries treatment and tooth extraction between the groups, with respectively risk of frailty based on EMR data and survey based frailty (according to the GFI) (see Table 2).

Associations between oral health and frailty

Associations (adjusted for age and sex) between frailty based on EMR data and oral health were found. Participants at risk for frailty, compared to participants not at risk had a higher chance on experiencing oral discomfort (OR=2.07, 1.52;2.81), making dietary adaptations (OR=2.66, 1.31;5.41) and consulting an emergency dental visit (OR=2.00, 1.33;3.02) (see Table 3).
Similar associations were found for frail participants based on survey data according to the GFI (including adjustment for age and sex) compared to non-frail participants: they had a higher chance of experiencing oral discomfort, making dietary adaptations and an emergency dental visit. In addition, they had a higher chance of having a partial or full prosthesis. The strongest associations were found between frailty based on survey data according to the GFI and making dietary adaptations and wearing a full prosthesis (see Table 3). Frail participants were 5.5 times more likely (OR 5.49, 95% CI 3.01; 10.01) of making dietary adaptations because of oral problems and 3.3 times more likely wearing full prosthesis (OR 3.33, CI 1.49;7.44).

Table 3
Unadjusted and adjusted associations between frailty and oral health

§. self-reported oral problems had 8 missing cases for experiences of oral problems and 9 missing cases for food adaptation. Analysis have been performed on full data: respectively N=1194 and N=1193; †. Analysis were performed on 908 participants. In case of a consultation gap or if people were not registered in a dental practice, no dental record was available from the past 1.5 years. The proportion of dental record data and statistical test are calculated based on the total N=908; ‡. Periodontal information was not available for people with full prosthesis (on implants) and were excluded for analysis. The analysis were performed on N=878 participants; * Significant association P≤0.05



This study identifies the associations between oral health and two frailty measures in community-dwelling older people. A dental consultation gap, an emergency dental visit, wearing a (partial) prosthesis and self-reported oral health problems are associated with one or both frailty measures. The strongest associations were found between frailty based on survey data according to the GFI on the one hand and making dietary adaptations and wearing a full prosthesis on the other hand.
Our findings are supported by studies showing similar patterns regarding oral health and frailty measures. Although the association between risk of frailty and having less natural teeth has been reported for most studies included in the review by Torres et al. (2015) (10) and three cross-sectional studies (18-20), the definitions and measures that have been used among these studies on oral health and frailty differ to a large extent. Therefore it remains difficult to compare the results of these studies. This accounts also for the associations between frailty and periodontal information. In contrast to other studies, we extracted information on periodontal status from dental records and did not perform a clinical periodontal assessment (10).
Moreover, it has been reported that frail people in the Netherlands tend to seek less dental care because of giving higher priority to other health care issues than oral health problems (3, 21).

Strengths and limitations

In this study, we strived to use data that is objective and easy to be extracted in daily practice. However, some limitations need to be considered to appreciate our findings. First, the dental record data were extracted manually from the dental records. While in some instances availability of dental record data was limited and registration was poor, data collection was successful in the majority of persons consenting for participation. Second, by collecting self-reported oral health problems we obtained information that is not routinely reported by dentists, like xerostomia (5). Last, this research was performed in an area with a high density of people with high socio-economic status (SES), with a mean score of 0.89 compared to the rest of the Netherlands (mean 0.17) (23). Since a low socio-economic status has shown to negatively impact oral health and frailty, we need to take into account an underestimation of the prevalence of oral health problems and frailty in our results compared to the general population (24).

Implications for practice, policy, and research

Based on the findings of our study as well as other recent studies (10, 25, 26) we suggest incorporating dental record data in the frailty screening of older community-dwelling people.
Besides the self-reported oral health problems, including dental record data might be useful in predicting frailty, as this is an easy and low-cost way to gather patients’ oral health information. However, to do so, it is necessary for dental clinic to systematically record the patients’ dental care utilization, oral health status, and problems. Standardized documentation of this information is needed, to enable healthcare workers to use multi-disciplinary information in frailty detection and proactive care programs. The same accounts for future research.
The World Dental Federation (FDI) has published a uniform definition of oral health and currently is working on a standardized set of oral health measures (27), which could be adopted in the context of dental care for frail older people. However, to date, the predictive prognostic value of oral health in the early frailty risk detection of community-dwelling older persons has not been shown and the development of such a prognostic prediction model is warranted.
In conclusion, an emergency visit at the dentist and self-reported oral health problems are associated with frailty irrespective of the approach to its measurement. To improve understanding of the relationship between oral health and frailty in community-dwelling older persons, follow-up research with large study populations is needed. The data-collection of these studies should stay close to what healthcare professionals routinely document and it is recommended to include dental record data and self-reported oral health problems in the prognostic prediction models derived thereof.


Acknowledgments: We gratefully acknowledge the professionals from the Primary Healthcare Center for collaboration during the research project, the student-assistants who participated in the data collection procedures, and all dental clinics who participated in extracting dental record data on their patients.
Funding: This study was performed without funding.
Conflict of interest: Ms. Babette Everaars has nothing to disclose. Dr. K. Jerković – Ćosić has nothing to disclose. Dr. N. Bleijenberg has nothing to disclose. Dr. N.J. de Wit has nothing to disclose. Dr. G.J.M.G. van der Heijden has nothing to disclose.
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.



1. Slade GD, Akinkugbe AA, Sanders AE. Projections of U.S. Edentulism Prevalence Following 5 Decades of Decline. Journal of Dental Research. 2014 Oct;93(10):959-65.
2. Wu B, Liang J, Plassman BL, Remle C, Luo X. Edentulism trends among middle-aged and older adults in the United States: comparison of five racial/ethnic groups. Community Dent Oral Epidemiol. 2012 Apr;40(2):145-53.
3. Niesten D, Witter DJ, Bronkhorst EM, Creugers NHJ. Oral health care behavior and frailty-related factors in a care-dependent older population. J Dent. 2017 Jun;61:39-47.
4. Ohi T, Sai M, Kikuchi M, Hattori Y, Tsuboi A, Hozawa A, et al. Determinants of the utilization of dental services in a community-dwelling elderly Japanese population. Tohoku J Exp Med. 2009 Jul;218(3):241-9.
5. Ramsay SE, Papachristou E, Watt RG, Tsakos G, Lennon LT, Papacosta AO, et al. Influence of Poor Oral Health on Physical Frailty: A Population-Based Cohort Study of Older British Men. J Am Geriatr Soc. 2018 Mar;66(3):473-9.
6. Hoogendijk EO, Afilalo J, Ensrud KE, Kowal P, Onder G, Fried LP. Frailty: implications for clinical practice and public health. Lancet. 2019 Oct 12;394(10206):1365-75.
7. Slaets JPJ. Vulnerability in the elderly: frailty. Med Clin North Am. 2006 Jul;90(4):593-601.
8. Rockwood K. Frailty and its definition: a worthy challenge. J Am Geriatr Soc. 2005 Jun;53(6):1069-70.
9. Hakeem FF, Bernabe E, Sabbah W. Association between oral health and frailty: A systematic review of longitudinal studies. Gerodontology. 2019 Sep;36(3):205-15.
10. Torres LH, Tellez M, Hilgert JB, Hugo FN, de Sousa MD, Ismail AI. Frailty, Frailty Components, and Oral Health: A Systematic Review. J Am Geriatr Soc. 2015 Dec;63(12):2555-62.
11. Roppolo M, Mulasso A, Gobbens RJ, Mosso CO, Rabaglietti E. A comparison between uni- and multidimensional frailty measures: prevalence, functional status, and relationships with disability. Clin Interv Aging. 2015 Oct 22;10:1669-78.
12. von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. PLoS Med. 2007 Oct 16;4(10).
13. Ongeveer drie kwart bezoekt jaarlijks huisarts en tandarts. 2013. Available from: https://www.cbs.nl/nl-nl/nieuws/2013/27/ongeveer-drie-kwart-bezoekt-jaarlijks-huisarts-en-tandarts. Accessed 7 May 2019
14. Drubbel I, Bleijenberg N, Kranenburg G, Eijkemans RJ, Schuurmans MJ, de Wit NJ, et al. Identifying frailty: do the Frailty Index and Groningen Frailty Indicator cover different clinical perspectives? a cross-sectional study. BMC Fam Pract. 2013 May 21;14:64-.
15. Bleijenberg N, Drubbel I, Schuurmans MJ, Dam HT, Zuithoff NP, Numans ME, et al. Effectiveness of a Proactive Primary Care Program on Preserving Daily Functioning of Older People: A Cluster Randomized Controlled Trial. J Am Geriatr Soc. 2016 Sep;64(9):1779-88.
16. Rockwood K, Song X, Macknight C, Bergman H, Hogan DB, McDowell I, Mitnitski A. A global clinical measure of fitness and frailty in elderly people. Can Med Assoc J. 2005:9-13.
17. Schuurmans H, Steverink N, Lindenberg S, Frieswijk N, Slaets JP. Old or frail: what tells us more? J Gerontol A Biol Sci Med Sci. 2004 Sep;59(9):962.
18. Van der Velden U. The Dutch periodontal screening index validation and its application in The Netherlands. J Clin Periodontol. 2009 Dec;36(12):1018-24.
19. Watanabe Y, Hirano H, Arai H, Morishita S, Ohara Y, Edahiro A, et al. Relationship Between Frailty and Oral Function in Community-Dwelling Elderly Adults. J Am Geriatr Soc. 2017 Jan;65(1):66-76.
20. Hasegawa Y, Sakuramoto A, Sugita H, Hasegawa K, Horii N, Sawada T, et al. Relationship between oral environment and frailty among older adults dwelling in a rural Japanese community: a cross-sectional observational study. BMC Oral Health. 2019 Jan 22;19(1):23-8.
21. de Andrade FB, Lebrao ML, Santos JL, Duarte YA. Relationship between oral health and frailty in community-dwelling elderly individuals in Brazil. J Am Geriatr Soc. 2013 May;61(5):809-14.
22. Niesten D, van Mourik K, van der Sanden W. The impact of frailty on oral care behavior of older people: a qualitative study. BMC Oral Health. 2013 Nov 1;13:61-.
23. Ontwikkeling Sociaal Economische Status (SES) Utrechtse Heuvelrug, GGD Utrecht en Nederland 1998-2010.1998. https://www.rivm.nl/media/profielen/profile_1581_Utrechtse_Heuvelrug_omgeving.html. Accessed 22 Feb 2019
24. Donaldson AN, Everitt B, Newton T, Steele J, Sherriff M, Bower E. The Effects of Social Class and Dental Attendance on Oral Health. J Dent Res. 2008;87(1):60-4.
25. Tanaka T, Takahashi K, Hirano H, Kikutani T, Watanabe Y, Ohara Y, et al. Oral Frailty as a Risk Factor for Physical Frailty and Mortality in Community-Dwelling Elderly. The journals of gerontology. Series A, Biological sciences and medical sciences. 2018 Nov 10,;73(12):1661-7.
26. Castrejon-Perez RC, Jimenez-Corona A, Bernabe E, Villa-Romero AR, Arrive E, Dartigues JF, et al. Oral Disease and 3-Year Incidence of Frailty in Mexican Older Adults. J Gerontol A Biol Sci Med Sci. 2017 Jul 1;72(7):951-7.
27. FDI and ICHOM present Standard Set of Adult Oral Health Measures. 2018. https://www.fdiworlddental.org/news/20180908/fdi-and-ichom-present-standard-set-of-adult-oral-health-measures. Accessed 2 April 2019



A. Takaoka1, D. Heels-Ansdell1, D.J. Cook1,2, M.E. Kho3


1. Department of Health Research Methods, Evidence, and Impact, Faculty of Health Sciences, McMaster University, Canada; 2. Department of Medicine, Faculty of Health Sciences, McMaster University, Canada; 3. School of Rehabilitation Science, Faculty of Health Sciences, McMaster University, Physiotherapy Department, St. Joseph’s Healthcare Hamilton, Canada.
Corresponding author: Michelle E. Kho, PhD, PT, School of Rehabilitation Science, Faculty of Health Sciences, McMaster University, Physiotherapy Department, St. Joseph’s Healthcare Hamilton, Institute of Applied Health Sciences, 1400 Main St. W. Hamilton, ON L8S 1C7, Email: khome@mcmaster.ca, Telephone: (905) 525-9140 x28221,
Fax Number: (905) 524-0069

J Frailty Aging 2021;10(1)49-55
Published online October 5, 2020, http://dx.doi.org/10.14283/jfa.2020.52



Background: Physical therapy initiated early in an ICU stay may reduce functional deficits in critically ill patients; however, the association of frailty with outcomes in those receiving early in-ICU rehabilitation is unknown. Objective: To estimate the association between frailty and 3 outcomes in patients enrolled in an ICU randomized clinical trial (RCT). Design: Exploratory secondary analyses of the CYCLE pilot RCT (NCT02377830). Setting: 7 Canadian ICUs. Participants: Previously ambulatory critically ill adults. Intervention: Participants were randomized to early in-bed cycling plus routine physiotherapy versus early routine physiotherapy alone. Measurements: Using regression analyses, we modelled the association between pre-hospital Clinical Frailty Scale (CFS) scores, Physical Function in ICU Test-scored (PFIT-s), muscle strength, and mortality at hospital discharge, adjusting for illness severity (APACHE II) and the randomized intervention. We explored the influence of imputing mean PFIT-s and strength scores for decedents, and with listwise deletion of decedents in a sensitivity analysis. Results: Of 66 patients, 2 had missing data, 2 had incomplete data, and 21 died by hospital discharge. At hospital discharge for 66 patients, frailty was not associated with PFIT-s (mean difference (MD) [95% CI]=0.20, [-2.08, 2.74]) or muscle strength (1.96, [-12.6, 16.6]). A sensitivity analysis yielded consistent results. Frailty was also not associated with hospital mortality (odds ratio 0.91, [0.28 to 2.93]). Conclusion: We found no association between pre-hospital frailty, physical function, strength, or mortality at hospital discharge in critically ill patients enrolled in an early rehabilitation trial. Larger sample sizes are needed to further explore the association of frailty with these outcomes at hospital discharge.

Key words: ICU, rehabilitation, frailty, outcomes, mechanical ventilation.




Frailty is a patient health state characterized by losses in one or more domains of function (1, 2). In critically ill patients, a systematic review identified a 30% (95% CI: 29 to 32) baseline prevalence of frailty across 10 studies and 3030 participants (3). Regardless of frailty instrument used, patients with baseline frailty are consistently at a greater risk of functional dependence, disability, and mortality following critical illness (4–6). As the number of mechanically ventilated patients are projected to increase due to an aging baby boomer population (7), the impact of frailty is an urgent health concern across the continuum of care.
Rehabilitation initiated early in an ICU stay is a promising intervention to improve outcomes in critically ill adults (8). Increasing evidence has demonstrated that preserved physical fitness may be associated with lower 1-year mortality in elderly patients with frailty (9); however, to our knowledge, no studies have examined the association of frailty on the outcomes of patients receiving early rehabilitation in the ICU.
We recently completed a 7-centre pilot study of early leg cycle ergometry with mechanically ventilated patients who were ambulatory and independent prior to critical illness (10, 11). Using the study database, we conducted an exploratory analysis to evaluate the association between pre-hospital frailty status and hospital discharge measures of physical function, muscle strength, and mortality. We hypothesized that patients with frailty would have worse physical function, less muscle strength, and higher mortality at hospital discharge.




This study was approved by the Hamilton Integrated Research Ethics Board (#14-531).

Design, Patients and Settings

We conducted a preliminary, exploratory multivariable regression analyses of the CYCLE (Critical Care Cycling to Improve Lower Extremity Strength) pilot randomized controlled trial (RCT) (NCT02377830) that enrolled 66 critically ill patients across 7 Canadian ICUs. The methods and results of the RCT are described elsewhere (10, 11). Briefly, patients were included if they were >18 years old, admitted within the first 4 days of mechanical ventilation and first 7 days of ICU, and independently ambulated with or without a gait aid before their critical illness. Primary exclusion criteria were any conditions impairing cycling, proven or suspected neuromuscular weakness, inability to follow commands in English, a temporary pacemaker, expected risk of hospital mortality >90%, palliative goals of care, or persistent exemptions precluding cycling. Enrolled patients were randomized to receive early in-bed cycle ergometry (30 minutes, 5 days/week, up to 28 days or ICU discharge) plus routine physiotherapy or early routine physiotherapy alone for the duration of their ICU stay.

Dependent Variables

At hospital discharge, trained physiotherapists blinded to treatment allocation measured function using the Physical Function in ICU Test-scored (PFIT-s) (12) and strength using the Medical Research Council Sum Score (MRC-SS) (13). Research coordinators documented hospital vital status (dead/alive).

Independent Variable

Research coordinators evaluated frailty status in the 1-2 weeks before current hospital admission using the Clinical Frailty Scale (CFS) (2). These scores were generated at trial enrollment through family member and/or patient interviews and comprehensive chart reviews.


We included covariates in our models to adjust for potential confounders. To address sample size limitations and to avoid overfitting models, we strategically limited the number of predictors in our models (>10 participants per predictor in linear models; >10 events per predictor in logistic models (14)). We purposefully selected 2 covariates a priori based on possible confounders of the relationships between pre-hospital frailty and our 3 outcomes. Our first covariate was illness severity (15–17) measured using the APACHE II (Acute Physiology and Chronic Health Evaluation II) score (18). We considered age as a covariate because of its association with both frailty and our outcomes of interest; however, since age contributes to overall APACHE II scores, we did not include it as a separate variable to avoid redundancy. Our second covariate was the randomized intervention, cycling plus routine physiotherapy versus routine physiotherapy alone, given the context of this analysis nested within the CYCLE pilot RCT.
Detailed descriptions of variables and covariates are provided in Table e1 (e-supplemental appendix).


We tabulated descriptive statistics of baseline variables (e.g., age, sex, BMI, admission type, APACHE II scores (18), Charlson Comorbidity Index (19), Functional Comorbidity Index (20), pre-ICU Functional Status Score for the ICU (FSS-ICU) (21), pre-ICU Katz Independence in Activities of Daily Living (Katz ADL) scores (22)) and trial-related characteristics (e.g., group allocation, time to first session, total days of rehabilitation, length of stay in ICU and hospital, outcomes) according to dichotomized frailty status, with frailty defined as a CFS score >5. For continuous variables, we reported means and standard deviations (SD), or medians and interquartile ranges (IQR) if data were not normally distributed. We compared characteristics of patients with and without frailty using Student’s t-tests or Mann-Whitney U tests as appropriate. We reported categorical variables as counts and proportions, and compared groups using Pearson’s chi-square test.
We performed confirmatory multivariable linear regression to estimate the association between pre-hospital CFS scores, PFIT-s, and MRC-SS. We used binary logistic regression to model the association between pre-hospital CFS scores and hospital survival. In both models, we dichotomized patients by CFS scores for enhanced clinical interpretability. Linear regression results are presented as mean difference (MD) and 95% confidence intervals (CI). Overall model statistics are reported as R2 and F values with degrees of freedom (df numerator, df denominator) in the e-supplemental appendix. Logistic regression odds ratios (OR) are presented with 95% confidence intervals. We considered a p-value <0.05 statistically significant for all tests. All analyses were performed using SPSS (IBM Corp. Released 2016. IBM SPSS Statistics for Macintosh, Version 25.0. Armonk, NY: IBM Corp.).

Missing Data

For patients with missing PFIT-s or MRC-SS data, when possible, we used ICU discharge scores under the rationale that ICU scores were based on the patient’s own data and would provide a conservative estimate of outcome data at hospital discharge. For patients who died, we assigned PFIT-s and MRC-SS of 0 under the assumption that those who died would have little to no function or muscle strength. We conducted sensitivity analyses to explore the influence of these imputations for decedents (23).

Sensitivity analyses

Based on methodology adapted from Murphy et al. (23), we assessed our continuous outcome models with 1) listwise deletion, wherein only complete cases were included, and 2) imputed data using mean scores.



We enrolled 66 patients in this pilot RCT (cycle intervention: n=36, control: n=30) with a mean (SD) age of 61.6 (16.9) years and APACHE II score of 23.5 (8.6) (Table 1). The prevalence of frailty (CFS>5) in our cohort was 26% (17/66) (Figure 1). Baseline characteristics were similar between those with and without frailty, with the exception of more surgical admissions (p=0.019) and unexpectedly higher Katz ADL scores (p<0.001), and higher FSS-ICU (p<0.001) in those with frailty (Table 1). Twenty-two (33%) patients died in hospital (36% with frailty, 33% without frailty) (Table 2). There were no differences in trial-related physiotherapy characteristics, including time to first physiotherapy session or total days of rehabilitation, between those with frailty and those without (Table 2).

Figure 1
Clinical Frailty Scale (CFS) scores

Distribution of Clinical Frailty Scale (CFS) scores. Overall prevalence of frailty (CFS>5) was 26% (17/66).

Table 1
Baseline characteristics of patients enrolled in the CYCLE pilot RCT, by frailty status

α. Pearson Chi Squared Test; β. Mann-Whitney U Test; γ. n=48 (one missing value); δ. equal variances not assumed; BMI- Body Mass Index; APACHE II- Acute Physiology and Chronic Health Index II Score; FSS-ICU- Functional status score for ICU; Katz ADL- Katz Activities of Daily living


Table 2
Trial and outcome characteristics of patients enrolled in the CYCLE pilot RCT, by frailty status

α. Excludes ICU discharge scores for 2 patients with missing hospital discharge assessments due to unexpected discharge; β. Excludes ICU discharge scores for 2 patients with incomplete hospital discharge assessments; Pearson’s Chi Squared Test for categorical variables; Mann-Whitney U Test for continuous variables


One patient completed PFIT-s and MRC-SS assessments while waiting to be discharged from hospital, but subsequently deteriorated, was re-admitted to ICU and died during the index hospitalization. The remaining 21 decedents were assigned PFIT-s and MRC-SS of 0. Four patients survived, but had some missing data. For the 2 (3%) patients with missing PFIT-s and MRC-SS due to unexpected hospital discharge, and 2 (3%) patients with partially completed MRC-SS (Figure 2), we used the corresponding ICU discharge measures in place of hospital discharge scores.

Figure 2
Flow diagram of patients enrolled in CYCLE Pilot RCT by frailty status


Patient flow diagram by frailty status. PFIT-s – Physical Function in ICU Test-scored; d/c – discharge; ax – assessment; MRC-SS – Medical Research Council Sum Score. N=23 patients missed PFIT-s assessments in hospital due to death. N=2 patients had missed PFIT-s and MRC-SS assessments due to unexpected discharge from hospital. N=2 patients had only partial MRC-SS scores completed.


At hospital discharge, frailty was not associated with PFIT-s scores (MD= 0.20, 95%CI: -2.08 to 2.74) or muscle strength (MD=1.96, 95% CI: -12.6 to 16.6). These results were consistent in the sensitivity analyses. Frailty was not associated with in-hospital mortality (OR= 0.91, 95% CI: 0.28 to 2.93). We report full details of each model in eTable 2 and eTable 3, and results of the sensitivity analyses in eTable 4, and eFigures 1 and 2 in the e-supplemental appendix.



In this cohort of previously ambulatory critically ill patients enrolled in a trial of early rehabilitation, our exploratory analyses demonstrated that pre-hospital frailty status measured using the CFS was not associated with physical function, muscle strength, nor mortality at hospital discharge, after adjusting for severity of illness and randomized assignment.
Our baseline frailty prevalence was 26% (95% CI: 15.4 to 36.6), which was similar to the 30% (95% CI: 29 to 32) prevalence reported in previous prospective ICU studies summarized in a systematic review (3). Although the wide confidence interval surrounding our estimate indicates a high degree of imprecision, our slightly lower observed prevalence may reflect our inclusion criteria which required patients to ambulate independently before their critical illness (10, 11). The high level of baseline independence in this cohort may also explain the unexpectedly higher Katz ADL and FSS-ICU scores in those who were frail; however, these differences may also be due to chance, given our small sample size. Our results may also differ from this systematic review because the pooled estimate in the review included several distinct measures of frailty, including the CFS, Frailty Index (24), and Frailty Phenotype (1). Both the Frailty Index and Frailty Phenotype tend to report a higher frailty prevalence compared to the CFS (25, 26).
We found no association between frailty measured using the CFS and hospital mortality in our small cohort of patients. Our results are similar to 3 studies in critically ill patients that did not find an association between frailty and mortality at hospital discharge (5, 27, 28). In contrast, 3 prospective studies demonstrated associations between higher CFS scores and hospital mortality (4, 25, 26). Bagshaw et al. conducted a 6-center prospective cohort study enrolling 421 medical-surgical patients with a frailty prevalence of 32.8% and demonstrated higher in-hospital mortality among patients with frailty (adjusted OR 1.81, 95% CI: 1.09 to 3.01) (4). Of the remaining two studies, patients with frailty were also more likely to die in hospital (25, 26). Compared to our cohort, differences in previous study results could be due to patient population (high proportion of trauma patients), or use of unadjusted analyses (univariate logistic regression and Chi square) (25, 26).
Our results also differ from previous studies examining the relationship between frailty and function in ICU survivors. Three studies reported different results over time for the association between frailty and function (4, 5, 28). Hope et al. reported an association between pre-ICU frailty disability in activities of daily living (ADLs) at 6-months after hospital discharge, but not at the time of hospital discharge (28). Brummel et al. demonstrated an association between higher CFS scores and greater odds of disability in instrumental activities of daily living (iADL), but not ADLs at 3- and 12- months post-hospital discharge (5). Bagshaw et al. demonstrated an independent association between pre-ICU frailty (CFS ≥5) and the odds of self-reported new functional dependence at 6- and 12-months after hospital discharge (OR 2.25, 95% CI 1.03 to 4.89) (4). Our results may differ from previous research because of different measurement methods (patient self-report vs. performance-based measures), timing of measurements, or the possibility of type-II error due to small sample size.
The previously cited studies did not document receipt of rehabilitation during the ICU stay. Our physical function results are similar to a single-centre retrospective study of 264 patients who received early progressive mobilization in a cardiovascular ICU (CVICU) (29). Patients ≥60 years old, admitted to a 12-bed CVICU and meeting eligibility criteria, received early mobilization activities. Mobilization activities varied from bed/cardiac chair (Level 1) to independent/modified independent walking >50 feet (Level 4). The prevalence of frailty measured by the CFS was 34.1% (90/264). In a multivariable model, after adjusting for age, sex, and severity of illness (APACHE III score), there was no difference in change in level of function at CVICU discharge between patients with or without frailty. Similar to other studies, patients with frailty had higher hospital mortality (8.9%) than those without (5.7%), however the authors did not conduct an adjusted analysis (29).
Differences in patient population, analysis methods, outcome measurement, exposure to ICU rehabilitation interventions, and study design may account for discordant results between the current study and previous research. Prospective and historical cohort studies may be limited by confounding as well as availability and quality of data. Previous studies had broad inclusion criteria, whereas our study focused on patients who could ambulate before their critical illness. Few studies documented receipt of ICU rehabilitation interventions. Our study included a sample of medical-surgical critically ill patients from 7 institutions, both the intervention group and control group started rehabilitation within a median (IQR) of 3 (2-4) days from ICU admission, and patients completed performance-based measures (10, 11). Rehabilitation in ICU is a promising intervention to improve muscle strength, functional capacity, and walking distance at ICU discharge. It may also shorten length of stay in both ICU and hospital, and improve health related quality of life at hospital discharge (30) and 6-months post discharge (31–33). We hypothesize that rehabilitation interventions could have a moderating effect on the functional deficits experienced by ICU survivors with frailty meeting strict inclusion criteria in clinical trials.
Our study had limitations. Our small sample size restricted the number of covariates that could be included in models and rendered our results underpowered and at risk of residual confounding. With a larger sample, we would have controlled for other known confounders including the functional comorbidity index, Katz-ADL, or body mass index. We dichotomized CFS scores for clinical interpretability. Furthermore, missing dependent variables due to death were imputed based on clinical rationale which may have created biased estimates (34); however, our sensitivity analyses explored the robustness of our imputation decisions.
Strengths of this study included the use of known confounders in regression models regardless of their statistical significance in the model (35). Trained physiotherapists, blinded to treatment allocation and frailty assessment conducted our performance-based function and strength measures. We had limited missing outcome data due to loss to follow up and managed these missing values using conservative estimates. Finally, this was the first prospective study of the association between frailty and outcomes of physical function, muscle strength, and mortality in a cohort of critically ill patients enrolled in an early ICU rehabilitation trial.
There is a projected future increase in our aging population and subsequently the number of mechanically ventilated patients (7). These findings support a larger research effort towards developing and studying interventions which aim to decrease healthcare system burden and resource utilization associated with the growing population of individuals living with frailty (36). To facilitate evaluation of the association of frailty with function, we suggest that future studies include common measures at similar time points. Recent papers on core outcome sets for studies of patients with acute respiratory failure (37), mechanical ventilation (38), and critical care rehabilitation studies (39) and frailty (in progress) support this premise.



We found no association between pre-hospital frailty and physical function, muscle strength, or mortality at hospital discharge in previously ambulatory critically ill patients enrolled in an early rehabilitation trial. Larger sample sizes are needed to further explore the influence of frailty on short-term outcomes after hospitalization.


Declaration of Author(s) Competing Interests: None.
Clinical Trials Registration Number: NCT02377830
Funding Statement: This work was supported by grants from Technology Evaluation in
the Elderly Network Catalyst (now Canadian Frailty Network; CAT2014-05), Canadian Respiratory Research Network Emerging Research Leaders Initiative, Ontario Thoracic Society Grant-in-Aid and Canadian Institutes of Health Research Transitional Operating Grant (Award #142327), Canada Foundation for Innovation, and the Ontario Ministry of Research and Innovation. MEK and DJC are each funded by a Canada Research Chair. AT was supported by Canadian Frailty Network Interdisciplinary Fellowship Award (IFP-2018). Restorative Therapies (Baltimore, MD) provided 2 RT-300 supine cycle ergometers for Toronto General Hospital and London Health Sciences sites for this research.
Acknowledgments: We would like to acknowledge the CYCLE Pilot RCT participating centers: St. Joseph’s Healthcare Hamilton, Juravinski Hospital, Hamilton General Hospital, Toronto General Hospital, London Health Sciences – Victoria, St. Michael’s Hospital, and Ottawa General Hospital. http://icucycle.com/cycle-rct/
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.




1. Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, et al. Frailty in Older Adults: Evidence for a Phenotype. J Gerontol Ser A. 2001 Mar 1;56(3):M146–57.
2. Rockwood K, Song X, MacKnight C, Bergman H, Hogan DB, McDowell I, et al. A global clinical measure of fitness and frailty in elderly people. CMAJ. 2005 Aug 30;173(5):489–95.
3. Muscedere J, Waters B, Varambally A, Bagshaw SM, Boyd JG, Maslove D, et al. The impact of frailty on intensive care unit outcomes: a systematic review and meta-analysis. Intensive Care Med. 2017 Aug;43(8):1105–22.
4. Bagshaw SM, Stelfox HT, McDermid RC, Rolfson DB, Tsuyuki RT, Baig N, et al. Association between frailty and short- and long-term outcomes among critically ill patients: a multicentre prospective cohort study. CMAJ Can Med Assoc J J Assoc Medicale Can. 2014 Feb 4;186(2):E95-102.
5. Brummel NE, Bell SP, Girard TD, Pandharipande PP, Jackson JC, Morandi A, et al. Frailty and Subsequent Disability and Mortality among Patients with Critical Illness. Am J Respir Crit Care Med. 2017 01;196(1):64–72.
6. Ferrante LE, Pisani MA, Murphy TE, Gahbauer EA, Leo-Summers LS, Gill TM. The Association of Frailty With Post-ICU Disability, Nursing Home Admission, and Mortality: A Longitudinal Study. Chest. 2018;153(6):1378–86.
7. Needham DM, Bronskill SE, Calinawan JR, Sibbald WJ, Pronovost PJ, Laupacis A. Projected incidence of mechanical ventilation in Ontario to 2026: Preparing for the aging baby boomers. Crit Care Med. 2005 Mar;33(3):574–9.
8. Tipping CJ, Harrold M, Holland A, Romero L, Nisbet T, Hodgson CL. The effects of active mobilisation and rehabilitation in ICU on mortality and function: a systematic review. Intensive Care Med. 2017;43(2):171–83.
9. Åhlund K, Ekerstad N, Bäck M, Karlson BW, Öberg B. Preserved physical fitness is associated with lower 1-year mortality in frail elderly patients with a severe comorbidity burden. Clin Interv Aging. 2019;14:577–86.
10. Kho ME, Molloy AJ, Clarke F, Herridge MS, Koo KKY, Rudkowski J, et al. CYCLE pilot: a protocol for a pilot randomised study of early cycle ergometry versus routine physiotherapy in mechanically ventilated patients. BMJ Open. 2016 Apr 1;6(4):e011659.
11. Kho ME, Molloy AJ, Clarke FJ, Reid JC, Herridge MS, Karachi T, et al. Multicentre pilot randomised clinical trial of early in-bed cycle ergometry with ventilated patients. BMJ Open Respir Res. 2019;6(1):e000383.
12. Denehy L, de Morton NA, Skinner EH, Edbrooke L, Haines K, Warrillow S, et al. A physical function test for use in the intensive care unit: validity, responsiveness, and predictive utility of the physical function ICU test (scored). Phys Ther. 2013 Dec;93(12):1636–45.
13. Kleyweg RP, Meché FGAVD, Schmitz PIM. Interobserver agreement in the assessment of muscle strength and functional abilities in Guillain-Barré syndrome. Muscle Nerve. 1991;14(11):1103–9.
14. Harrell FE, Lee KL, Matchar DB, Reichert TA. Regression models for prognostic prediction: advantages, problems, and suggested solutions. Cancer Treat Rep. 1985 Oct;69(10):1071–7.
15. Fan E. Physical complications in survivors of acute lung injury: A 2-year longitudinal prospective study. [Internet]. Johns Hopkins University; 2013. Available from: http://search.ebscohost.com/login.aspx?direct=true&db=cin20&AN=109863169&site=ehost-live
16. Pfoh ER, Wozniak AW, Colantuoni E, Dinglas VD, Mendez-Tellez PA, Shanholtz C, et al. Physical declines occurring after hospital discharge in ARDS survivors: a 5-year longitudinal study. Intensive Care Med. 2016;42(10):1557–66.
17. Hodgson CL, Denehy L. Measuring physical function after ICU: one step at a time. Intensive Care Med. 2017;43(12):1901–3.
18. Knaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: a severity of disease classification system. Crit Care Med. 1985 Oct;13(10):818–29.
19. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373–83.
20. Groll DL, To T, Bombardier C, Wright JG. The development of a comorbidity index with physical function as the outcome. J Clin Epidemiol. 2005 Jun;58(6):595–602.
21. Zanni JM, Korupolu R, Fan E, Pradhan P, Janjua K, Palmer JB, et al. Rehabilitation therapy and outcomes in acute respiratory failure: an observational pilot project. J Crit Care. 2010;25(2):254–62.
22. Katz S, Ford AB, Moskowitz RW, Jackson BA, Jaffe MW. Studies of Illness in the Aged. The index of ADL: A Standardized measure of Biological and Psychosocial Function. JAMA. 1963 Sep 21;185:914–9.
23. Murphy TE, Gill TM, Leo-Summers LS, Gahbauer EA, Pisani MA, Ferrante LE. The Competing Risk of Death in Longitudinal Geriatric Outcomes. J Am Geriatr Soc. 2019;67(2):357–62.
24. Mitnitski A, Mogilner A, Rockwood K. Accumulation of deficits as a proxy measure of aging. Eur BMC. 2001;(1):232–336.
25. Le Maguet P, Roquilly A, Lasocki S, Asehnoune K, Carise E, Saint Martin M, et al. Prevalence and impact of frailty on mortality in elderly ICU patients: a prospective, multicenter, observational study. Intensive Care Med. 2014 May;40(5):674–82.
26. Tipping CJ, Hodgson CL, Harrold M, Chan T, Holland AE. Frailty in Patients With Trauma Who Are Critically Ill: A Prospective Observational Study to Determine Feasibility, Concordance, and Construct and Predictive Validity of 2 Frailty Measures. Phys Ther. 2019 01;99(8):1089–97.
27. Fisher C, Karalapillai DK, Bailey M, Glassford NG, Bellomo R, Jones D. Predicting intensive care and hospital outcome with the Dalhousie Clinical Frailty Scale: a pilot assessment. Anaesth Intensive Care. 2015 May;43(3):361–8.
28. Hope AA, Hsieh SJ, Petti A, Hurtado-Sbordoni M, Verghese J, Gong MN. Assessing the Usefulness and Validity of Frailty Markers in Critically Ill Adults. Ann Am Thorac Soc. 2017 Jun;14(6):952–9.
29. Goldfarb M, Afilalo J, Chan A, Herscovici R, Cercek B. Early mobility in frail and non-frail older adults admitted to the cardiovascular intensive care unit. J Crit Care. 2018;47:9–14.
30. Burtin C; Clerckx B; Robbeets C; Ferdinande P; Langer D; Troosters T; Hermans G; Decramer M; Gosselink R. Early exercise in critically ill patients enhances short-term functional recovery. Crit Care Med 2009 Sep3792499-2505. 2009;
31. Kayambu G, Boots R, Paratz J. Early physical rehabilitation in intensive care patients with sepsis syndromes-a randomised controlled trial. Physiother U K. 2015;101:eS735-.
32. Arias-Fernández P, Romero-Martin M, Gómez-Salgado J, Fernández-García D. Rehabilitation and early mobilization in the critical patient: systematic review. J Phys Ther Sci. 2018 Sep;30(9):1193–201.
33. Zang K, Chen B, Wang M, Chen D, Hui L, Guo S, et al. The effect of early mobilization in critically ill patients: A meta-analysis. Nurs Crit Care. 2019;(9808649, c3k).
34. Graham JW. Missing data analysis: making it work in the real world. Annu Rev Psychol. 2009;60:549–76.
35. Lederer DJ, Bell SC, Branson RD, Chalmers JD, Marshall R, Maslove DM, et al. Control of Confounding and Reporting of Results in Causal Inference Studies. Guidance for Authors from Editors of Respiratory, Sleep, and Critical Care Journals. Ann Am Thorac Soc. 2019;16(1):22–8.
36. Kokorelias KM, Munce S, McGilton KS, Cronin SM, Wang CD, Eftekhar P, et al. Frailty in the context of rehabilitation interventions for adults: protocol for a scoping review. BMJ Open. 2019 Feb 1;9(2):e024838.
37. Needham DM, Sepulveda KA, Dinglas VD, Chessare CM, Friedman LA, Bingham CO, et al. Core Outcome Measures for Clinical Research in Acute Respiratory Failure Survivors. An International Modified Delphi Consensus Study. Am J Respir Crit Care Med. 2017 01;196(9):1122–30.
38. Blackwood B, Ringrow S, Clarke M, Marshall JC, Connolly B, Rose L, et al. A Core Outcome Set for Critical Care Ventilation Trials. Crit Care Med. 2019 Oct;47(10):1324–31.
39. Connolly B, Denehy L, Hart N, Pattison N, Williamson P, Blackwood B. Physical Rehabilitation Core Outcomes In Critical illness (PRACTICE): protocol for development of a core outcome set. Trials. 2018 May 25;19(1):294.



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


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

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



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

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



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


Material and methods

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


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

Study design and procedures

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

Statistical analyses

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



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

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

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


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

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


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



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


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



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



M. Almada1,*, P. Brochado1,*, D. Portela2, L. Midão1,3, E. Costa1

1. UCIBIO/REQUIMTE, PORTO4AGEING – Competences Centre on Active and Healthy Ageing of the University of Porto, Faculty of Pharmacy, University of Porto, Porto, Portugal; 2. ACES Entre Douro e Vouga I – Feira Arouca, Faculty of Medicine, University of Porto, Porto, Portugal; 3. Institute of Biomedical Sciences Abel Salazar, University of Porto, Porto, Portugal. *Both authors contributed equal to the manuscript.
Corresponding author: Elísio Costa, Laboratory of Biochemistry, Department of Biological Sciences, University of Porto, Rua de Jorge Viterbo Ferreira, 228, 4050-313 Porto, Portugal, Email: emcosta@ff.up.pt
J Frailty Aging 2021;10(1)10-16
Published online August 8, 2020, http://dx.doi.org/10.14283/jfa.2020.44



Aim: As a person ages, the risk of fall increases, which affects quality of life and represents a financial burden to health- and social-systems, and a greater morbidity and mortality risk. Fall leads to decreased social contact, anxiety, long-term physical disability, severe dependency and hospitalizations. Currently, few studies address this phenomenon using a uniform methodology; therefore, this study aims to explore the prevalence of fall and associated-variables in older adults across Europe. Methods: In this cross-sectional analysis, we used data from Wave 6 of SHARE. The prevalence of fall was assessed through the answer “falling down” to the question “For the past six months at least, have you been bothered by any of the health conditions on this card?”. Multilevel logistic regression was used, using fall as a dependent variable. Multilevel univariable logistic regression models were made to identify potential associated factors. Results: From the 41,098 participants, 56.3% were female, and the average age was of 70.0 ± 8.9 years. The prevalence of fall was 8.2% (CI 8.0% to 8.4%), being higher in women (10.1% vs. 5.8%) and increasing with age. Age, female gender, being frail or pre-frail, higher scores on the EURO-D scale, polypharmacy and fear of falling were found to be significantly associated with fall. Conclusions: We found that fall is prevalent in the European community-dwelling population, with variations between countries. As a public health priority, identification of the variables associated with fall is important in order to identify/monitor the risk in older groups and develop tailored and cost-effective interventions for fall prevention.

Key words: Ageing, fall prevalence, frailty.



Population ageing is a contemporary demographic phenomenon. As a person ages, the risk of fall exponentially increases, being nowadays a major public health issue in older people. Fall is the leading cause of morbidity and mortality due to injury in people over 65 years (1). In fact, around 90% of hip fractures, one of the most debilitating injuries in older people, are the result of fall (2). Besides, fall represents a great financial burden and is predicted to substantially increase with the rise of life expectancy. Falling is associated with decreased functioning, higher hospitalization rates and use of health services as well as loss of productivity. In the EU, health care expenditure for the treatment of fall-related injuries is approximately 25 billion euros per year (3). In the U.S., 25 – 30% of community-dwelling adults are reported to fall each year, and the total cost of fatal and non-fatal fall is nearly 50 billion dollars (4). Although it is assumed that falling could be prevented through the implementation of personalized prevention plans that target patient-specific risk factors, older people continue to experience falling and its life-threatening and disabling sequela (1).
Risk factors that may lead to fall are demographic (age, ethnicity, sex), biological (history of fall, neuromuscular conditions, gait disturbances, sensory impairments), environmental (home or surroundings hazards, slippery surface), behavioural (balance confidence, medication, alcohol consumption or sedentary behaviour), socio-economic (low education, lack of social interactions) and cognitive-related factors (5, 6). In addition, fear of falling has been shown to impact quality of life, and leads to negative consequences such as the avoidance of daily living activities and social contact, and the promotion of anxiety and depression, causing long-term physical disability (7). A multidimensional risk factor assessment, along with targeted interventions (exercise strategies that include strength, gait, and balance), environment/home hazard assessment and other factors like vision assessment, medication review and withdrawal, or vitamin D supplementation, are advocated to reduce the risk of falling (8), but their efficacy is still controversial. Indeed, fall risk-reduction programs have received significant funding in public health initiatives, nonetheless their efficacy is still below expectation, and the accurate identification of those requiring intervention to reduce fall risk is still a challenge for health professionals (9).
A prospective study from 12 European countries from 2010-2013, described that the variation rate of falling among community-dwelling adults over 65 was associated with the prevalence of intrinsic fall risk factors, including low self-rated health, mobility limitations, difficulties in performing activities of daily-living, dizziness and depression (10). Currently, only a few studies address the phenomenon of fall at the European level using a uniform and universal methodology; thus, the Survey of Health, Ageing and Retirement in Europe (SHARE) project is a great tool for this purpose (11-13).
Falling is a major cause of disability and mortality among older people. Moreover, fall prevention is among the most complicated branch of medicine nowadays, which requires multidimensional, time-consuming and expensive interventions, which are still ineffective. To reduce fall rates and improve health outcomes, it is important to identify new variables associated with falling, including frailty status, social network satisfaction, polypharmacy and factors related with falling (fear of falling down, dizziness/faints/blackouts and fatigue) which may underpin improved services and tailored-interventions to overcome this overwhelming situation. Therefore, in this study, we reviewed the prevalence of fall over the last 6 months, across 17 European countries plus Israel, using SHARE wave 6 (2015), and identified the associated variables in the population aged 55 years and above.


Materials and Methods

In this cross-sectional analysis, we used data from participants aged 55 and over, from Wave 6 of SHARE, which is a multidisciplinary and international database on health, social and economic status, and social and family networks of representative samples of community-based populations from 17 European countries (Austria, Belgium, Croatia, Czech Republic, Denmark, Estonia, France, Germany, Greece, Italy, Luxembourg, Poland, Portugal, Spain, Sweden, Switzerland and Slovenia) and Israel. This project became a pillar of European research on ageing. Wave 6 of this survey collected data between January and November 2015, involving approximately 68,231 individuals, aged between 24 and 106 years. Due to the limitation of information available with regard to the question used to assess the prevalence of fall, no discrimination was performed between indoor and outdoor fall, nor with or without mobility.

Prevalence of Fall

Fall prevalence was assessed through the answer “falling down” to the question “For the past six months at least, have you been bothered by any of the health conditions on this card?”. A showcard was presented to the participants with the following health conditions: 1 – falling down; 2 – fear of falling down; 3 – dizziness, faints or blackouts; 4 – fatigue; 5 – none. Fall prevalence was calculated based on those who selected “falling down”.

Exploratory variables

Association of fall and age, gender, polypharmacy, frailty status, fall related variables such as fear of falling down, dizziness/faints/blackouts, fatigue, social network satisfaction and depressive symptomatology was explored. “Age” was calculated according to the answer “Year of birth” and the year 2015, and four age classes were set (55-64, 65-74, 75-84 and 85+ years). “Gender” had “male” or “female” as possible answers. “Polypharmacy” was assessed with the question: “Do you take at least five different drugs on a typical day?”, and had “yes” and “no” as possible answers. Frailty status was assessed using a SHARE operationalized Fried phenotype, and individuals were categorized as “robust”, “pre-frail” or “frail” (14). Fear of falling down, dizziness/faints/blackouts and fatigue were assessed through the question “For the past six months at least, have you been bothered by any of the health conditions on this card?”. A showcard was presented to the participants with the following health conditions: 1 – falling down; 2 – fear of falling down; 3 – dizziness, faints or blackouts; 4 – fatigue; 5 – none, and those variables were dichotomized in “yes” or “no”. “Social network satisfaction”, used as a continuous variable, was a result from the question “Overall, how satisfied are you with the [relationship that you have with the person/relationships that you have with people] we just talked about? Please answer on a scale from 0 to 10, where 0 means completely dissatisfied and 10 means completely satisfied”. “Depressive symptomatology”, also used as a continuous variable, between 0 “not depressed” and 12 “very depressed”, was defined as the total score on the EURO-D scale, included in the SHARE database.

Statistical analysis

We performed a descriptive result analysis to estimate the proportion of individuals who have fallen in the 18 countries. Age- and gender-standardized prevalence of fall by country, and the 95% confidence intervals (95% CI), were also assessed. All results related to the prevalence of fall were standardized using the standard European population of 2013 (15). Given the multilevel structure of data, a multilevel logistic regression was used, with falling as the dependent variable. We have assessed for multicollinearity, confounding factors, correlations and potential interactions. Correlational analyses were performed between the independent variables, and fall are examined for inclusion in the multiple regression model. Multilevel univariable logistic regression models were made, considering each covariate, to identify potential factors associated with falling. Significant covariates were included in a multilevel multivariate logistic regression model. Odds ratios (OR) and their 95% CI were reported. Analyses were performed using IBM SPSS (version 25).



Prevalence study

From all the 68,231 participants included on SHARE wave 6, we included the participants, aged 55 and above, that answered all the questions related to gender, frailty status, social network satisfaction, depressive symptomatology, polypharmacy and factors associated with falling (fear of falling down, dizziness/faints/blackouts and fatigue). A final sample of 41,098 participants from 17 European countries and Israel was studied. From these participants, 56.3% were female, with ages ranging between 55 and 103, with an average age of 70.0 ± 8.9 years. The geographical distribution of fall was assessed in the different countries (Figure 1 and Table 1).
As reported in Table 1, the fall prevalence over the last 6 months was 8.2% (CI 8.0% to 8.4%) and significantly varied between countries. Overall, the proportion of individuals reporting fall was lower in Greece (3.4%; CI 3.2% to 3.5%), Slovenia (4.9%; CI 4.7% to 5.0%), Sweden (5.9%; CI 5.7% to 6.1%) and Switzerland (5.9%; CI 5.8% to 6.1%). The highest rate of fall was observed in Portugal (16.3%; CI 16.0% to 16.6%) followed by the Czech Republic (11.6%; CI 11.3% to 11.9%), France (11.3%; CI 11.0% to 11.5%) and Spain (11.0%; CI 10.7% to 11.3%) (Table 1).

Prevalence of self-reported falling detailed by country, age and gender, among the 18 countries included in SHARE’s wave 6


Figure 1
Geographical distribution of the prevalence of fall across Europe


Moreover, we showed that the proportion of those who had fallen during the last 6 months was significantly higher in women compared with men (10.1% vs 5.8%, respectively, p < 0.001) and increased with age. In addition, the prevalence of fall was about 2-fold higher in women in comparison to men in all age groups. Overall, the rate of fall among 75 – 85 years and ≥ 85 years was, respectively, 12.0% and 19.0%, in comparison to 55 – 64 years and 65 – 74 years, which was 4.7% and 6.7%, respectively.

Association of fall with explanatory variables

Due to the absence of multicollinearity effects, all covariates were included in the models. Analysing all countries together and using unadjusted models, we found an association between fall and all included exploratory variables except for social network satisfaction (Table 2). For the adjusted model, we found that fall increase with age (65 – 74 years [OR = 1.257 (1.128–1.402)]; 75 – 84 years [OR = 1.710 (1.531–1.911)] and 85 or older [OR = 2.011 (1.743–2.321)], compared with those aged 55-64 years old). Moreover, we found that fall are more frequent in females than in males [OR = 1.313 (1.208–1.426)], in those individuals taking at least 5 different drugs per day [OR = 1.428 (1.315-1.550)], as well as in those with a pre-frail [OR = 1.327 (1.195–1.473)] and frail status [OR = 2.114 (1.836–2.434)] rather than robust individuals. Regarding fall related factors, fear of falling down [OR = 7.235 (6.707–7.804)], suffering from dizziness, faints or blackouts [OR = 1.712 (1.569–1.866)], as well as depressive symptomatology [OR = 1.135 (1.115–1.155)], were also significantly associated with falling.

Table 2
Association of age, gender, frailty status, social network satisfaction, depression and polypharmacy with falling: unadjusted and adjusted models



In 2017, the older population of the EU (aged 65 years and above) made up 19.4% of the population. By 2080, the share of people aged ≥ 80 years is predicted to be more than double, reaching 13% of the whole population, whereas those aged ≥ 65 years will account for 29.1% (16). Population ageing is a current trend that began several decades ago, all over Europe. This phenomenon brings novel social, economic and cultural challenges that extend far beyond individuals, to society, family and community. In fact, it is estimated that, every year, around 30% of community-dwelling people over age 65 fall (17). Moreover, falling is an important cause of morbidity and mortality, besides leading to mobility/activity avoidance, social isolation and to substantial financial costs.
In this study, we analysed the prevalence of fall on a community-dwelling population (assessed through the answer of whether a person had experienced fall over the past 6 months) in 17 Europe countries and Israel, and its correlation with different exploratory variables. Overall, the prevalence of fall is 8.2%, and the rate of fall significantly varied between-countries, ranging from 3.4% in Greece to 16.3% in Portugal. This may be explained by the different approaches taken by each country to address fall. Spain, France, Israel, the Czech Republic, Luxembourg and Portugal were the countries with the higher proportion of fall, whereas it was lower in Sweden, Slovenia and Greece. Due to the limitation of information available with the question used to assess the prevalence of fall, no discrimination was performed between indoor and outdoor fall nor with or without mobility. A previous study reported a variation between countries in the rate of falling after adjusting for socio-demographics, ranging between 7.9% in Switzerland to 16.2% in the Czech Republic. In addition, they identified a relationship between fall and the prevalence of intrinsic fall risk factors (age, gender, self-rated health, limitation with mobility and with activities of daily living, and depression) (10). Interestingly, for all countries, the rates of falling were higher, compared to our findings. This may reflect that, over recent years, policies are being pushed forward to establish fall-prevention plans, but this issue is still far from being resolved, and still more efforts are needed. Therefore, our study, besides bringing updated fall prevalence data at the European level, emphasizes the need to face population ageing and its associated challenges. It must be highlighted that the differences in both studies may also be related with the fact that, in our study, we analysed people of 55 years and above from 18 countries using SHARE wave 6 (2015), in order to study the impact of falling during the ageing process, in comparison to data from 12 countries for people aged 65 or older using SHARE waves 4/5 (2010/2013) which was explored in the previous study; thus, the methodologies used on both studies were different (10).
Overall, we report in this study that, among older people, 19.0% of participants aged 85 years and above, and 12.0% of those aged 75 – 84 had experienced fall. Moreover, 10.1% of women and 5.8% of men self-reported falling over the last 6 months. It is known that falling is associated with risk factors often found in older people, such as a history of fall, reduced mobility, weakness, unsteady gait, confusion and certain medications (18-20). Thus, as expected, we show that the prevalence of fall is higher in older people, but foremost we report that, at European level, it is about 2-fold higher in women compared to men. There is evidence that women have a higher likelihood of falling, although few studies consider sex-specific analyses in the rate of falling (20, 21). In 2016, an English longitudinal study addressed this question and highlighted that gender must be taken into account when designing fall-prevention strategies (22).
In addition, we found that from participants that reported fall over the last 6 months, 25.3% are frail; 15.0% take at least 5 drugs per day and 22.9% reported depressed symptomatology reported. Polypharmacy is a growing global trend with the ageing population, which affects patient safety and quality of life (23). Previous studies have reported a relationship between polypharmacy and fall (12, 24), although some studies found no association (25, 26). The difference may depend on small sample sizes or selective study populations (27). Yet, the definition of polypharmacy is still not standardized, which may lead to different interpretations between studies (28). In this study, we used the most widely accepted definition of “polypharmacy” as taking five or more medications per day (29). Our findings show that, among participants that report fall over the past 6 months, the frequency of those who report taking at least 5 drugs per day is about 3-fold higher than those who are not. Herein, we did not consider the type of medication, which might have been a recall bias, although by using a large data sample of the European population we show that the prevalence of polypharmacy among older people is high and is significantly associated with falling. In Ireland, a prospective study on a population of community-dwelling adults aged 50 years and older, associated antidepressant or benzodiazepine medicines with falling (30). In fact, central nervous system drugs, including antipsychotics, antiparkinsonian drugs, and narcotic analgesics have been strongly associated with falling (31). In this regard, we also found a relation between depression and falling. In fact, both depression and the fear of falling have been associated with impairment of gait, balance and risk of falling, which makes the management of depression challenging in fall-prone individuals (32).
Frailty is a geriatric multidimensional syndrome with signs and symptoms that are predictors of increased vulnerability for adverse health outcomes and poor quality of life. Moreover, since deteriorating mobility is one of the main risk factors for fall, the ability of older people to identify changes in mobility early could decrease the incidence of fall in older people (33), although current meta-analysis studies regarding the risk of fall in frail, older adults are inconclusive. It has been reported that, compared to robust older adults, frail older adults were more likely to experience fall (34). Herein, we report that the prevalence of fall is 3-fold higher for frail people than pre-frail, but about 7-fold higher when compared to robust people.
The fear of falling may be associated with impairment of gait, balance and risk of falling, which makes the management of depression in fall-prone individuals challenging (30). However, there are few studies that investigate fall prevalence with other disability conditions. In this study, we demonstrate that the fear of falling, dizziness/faints or blackouts and fatigue are significantly associated with suffering fall over the last 6 months.
There are some limitations in our study, including potential variables that were not considered, that should be addressed; therefore, caution must be taken while interpreting these results. All data collected in SHARE is self-reported, which might pose some questions as it is known that people who volunteer to participate in research surveys are more motivated and healthier and, therefore, a high number of older people with disabilities might have been excluded. Moreover, differences in wealth and expenditure on older patients care between countries may explain some of the results. Some strengths must be stressed too: the high number of subjects involved and the international cross-sectional structure of the study allow comparisons and suggestions for stakeholders with a wider perspective.
Falling is one of the major public health issues relating to community-dwelling older people. Due to the social and financial burden caused by falling, it is imperative to develop appropriate strategies to prevent this, and further identify current variables and/or potential risk factors associated with falling. In this study, we show that the occurrence of fall is prevalent and heterogeneous across Europe, and it is a priority to find tailored-preventive plans for older people among dwelling communities. We also describe novel intrinsic factors (depressive symptomatology, frailty and polypharmacy) and further associated variables (fear of falling, dizziness/faints or blackouts and fatigue) that must be monitored in older people, as that should be considered in the design of personalized interventions to prevent fall in the community dwelling population.
In conclusion, across Europe the rate of fall is about 8%, and it is estimated to increase with the phenomena of population ageing. Therefore, we emphasize the importance of addressing this condition and its associated variables among older people, to promote active and healthy ageing.

Conflict of interest: No potential conflicts of interest were disclosed
Acknowledgements: This work used data from the SHARE Project, which has been funded by the European Commission through the fifth framework program (Project QLK6-CT-2001-00360 in the thematic program Quality of Life). Further support by the European Commission through the sixth framework program (Projects SHARE-I3, RII-CT-2006-062193, as an Integrated Infrastructure Initiative, COMPARE, CIT5-CT-2005-028857, as a project in Priority 7, Citizens and Governance in a Knowledge Based Society, and SHARE-LIFE [CIT4-CT-2006-028812]), through the seventh framework program (SHARE-PREP [No 211909], SHARE-LEAP [No. 227822] and M4 [No. 261982], and through Horizon 2020 [SHARE-DEV3 No. 676536] and SERISS [No. 654221]) are gratefully acknowledged. This work received financial support FCT/MCTES through national funds UIDB/04378/2020, and FCT under the grant attributed to Luís Midão (SFRH/BD/137090/2018).
Ethical standards: The SHARE study is subjected to continuous ethics review, and from wave 4 onwards, it was reviewed and approved by the Ethics Council of the Max Planck Society. This study required no ethics approval once it was performed using publicly available data.


1. Schnock, K.O., P.H. E, and P.C. Dykes, Fall Prevention Self-Management Among Older Adults: A Systematic Review. Am J Prev Med, 2019.
2. De Laet, C.E., et al., Incremental cost of medical care after hip fracture and first vertebral fracture: the Rotterdam study. Osteoporos Int, 1999. 10(1): p. 66-72.
3. Kannus, P., et al., Prevention of falls and consequent injuries in elderly people. Lancet, 2005. 366(9500): p. 1885-93.
4. Florence, C.S., et al., Medical Costs of Fatal and Nonfatal Falls in Older Adults. J Am Geriatr Soc, 2018. 66(4): p. 693-698.
5. Shyamala, T., et al., Health Promotion Board-Ministry of Health Clinical Practice Guidelines: Falls Prevention among Older Adults Living in the Community. Singapore Med J, 2015. 56(5): p. 298-300; quiz 301.
6. Rajagopalan, R., I. Litvan, and T.P. Jung, Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions. Sensors (Basel), 2017. 17(11).
7. Whipple, M.O., A.V. Hamel, and K.M.C. Talley, Fear of falling among community-dwelling older adults: A scoping review to identify effective evidence-based interventions. Geriatr Nurs, 2018. 39(2): p. 170-177.
8. Al-Aama, T., Falls in the elderly: spectrum and prevention. Can Fam Physician, 2011. 57(7): p. 771-6.
9. Cameron, I.D., et al., Interventions for preventing falls in older people in care facilities and hospitals. Cochrane Database Syst Rev, 2018. 9: p. CD005465.
10. Franse, C.B., et al., A prospective study on the variation in falling and fall risk among community-dwelling older citizens in 12 European countries. BMJ Open, 2017. 7(6): p. e015827.
11. Gaudin, A.C., et al., Management and orientation of geriatric patients admitted to emergencies for a fall: results of the French prospective OREGoN cohort study. Geriatr Psychol Neuropsychiatr Vieil, 2019. 17(3): p. 254-260.
12. Dhalwani, N.N., et al., Association between polypharmacy and falls in older adults: a longitudinal study from England. BMJ Open, 2017. 7(10): p. e016358.
13. Stanmore, E.K., et al., The effectiveness and cost-effectiveness of strength and balance Exergames to reduce falls risk for people aged 55 years and older in UK assisted living facilities: a multi-centre, cluster randomised controlled trial. BMC Med, 2019. 17(1): p. 49.
14. Manfredi, G., et al., Prevalence of frailty status among the European elderly population: Findings from the Survey of Health, Ageing and Retirement in Europe. Geriatr Gerontol Int, 2019.
15. EUROSTAT, Revision of the European Standard Population – Report of Eurostat’s Task Force, P.O.o.t.E. Union, Editor. 2013: Luxembourg.
16. https://ec.europa.eu/eurostat/. [cited 2019 17/05].
17. Deandrea, S., et al., Risk factors for falls in community-dwelling older people: a systematic review and meta-analysis. Epidemiology, 2010. 21(5): p. 658-68.
18. Dionyssiotis, Y., Analyzing the problem of falls among older people. Int J Gen Med, 2012. 5: p. 805-13.
19. Rubenstein, L.Z., Falls in older people: epidemiology, risk factors and strategies for prevention. Age Ageing, 2006. 35 Suppl 2: p. ii37-ii41.
20. Janakiraman, B., et al., Falls among community-dwelling older adults in Ethiopia; A preliminary cross-sectional study. PLoS One, 2019. 14(9): p. e0221875.
21. Larsen, R., et al., Female risk-adjusted survival advantage after injuries caused by falls, traffic or assault: a nationwide 11-year study. Scand J Trauma Resusc Emerg Med, 2019. 27(1): p. 24.
22. Gale, C.R., C. Cooper, and A. Aihie Sayer, Prevalence and risk factors for falls in older men and women: The English Longitudinal Study of Ageing. Age Ageing, 2016. 45(6): p. 789-794.
23. Midao, L., et al., Polypharmacy prevalence among older adults based on the survey of health, ageing and retirement in Europe. Arch Gerontol Geriatr, 2018. 78: p. 213-220.
24. Zia, A., S.B. Kamaruzzaman, and M.P. Tan, Polypharmacy and falls in older people: Balancing evidence-based medicine against falls risk. Postgrad Med, 2015. 127(3): p. 330-7.
25. Tromp, A.M., et al., Fall-risk screening test: a prospective study on predictors for falls in community-dwelling elderly. J Clin Epidemiol, 2001. 54(8): p. 837-44.
26. Zia, A., S.B. Kamaruzzaman, and M.P. Tan, The consumption of two or more fall risk-increasing drugs rather than polypharmacy is associated with falls. Geriatr Gerontol Int, 2017. 17(3): p. 463-470.
27. Baranzini, F., et al., Fall-related injuries in a nursing home setting: is polypharmacy a risk factor? BMC Health Serv Res, 2009. 9: p. 228.
28. Hammond, T. and A. Wilson, Polypharmacy and falls in the elderly: a literature review. Nurs Midwifery Stud, 2013. 2(2): p. 171-5.
29. Bjerrum, L., et al., Methods for estimating the occurrence of polypharmacy by means of a prescription database. Eur J Clin Pharmacol, 1997. 53(1): p. 7-11.
30. Richardson, K., K. Bennett, and R.A. Kenny, Polypharmacy including falls risk-increasing medications and subsequent falls in community-dwelling middle-aged and older adults. Age Ageing, 2015. 44(1): p. 90-6.
31. Shuto, H., et al., Medication use as a risk factor for inpatient falls in an acute care hospital: a case-crossover study. Br J Clin Pharmacol, 2010. 69(5): p. 535-42.
32. Iaboni, A. and A.J. Flint, The complex interplay of depression and falls in older adults: a clinical review. Am J Geriatr Psychiatry, 2013. 21(5): p. 484-92.
33. Bongers, K.T., Y. Schoon, and M.G. Olde Rikkert, Feasibility of repeated self-measurements of maximum step length and gait speed by community-dwelling older persons. BMJ Open, 2016. 6(8): p. e011538.
34. Cheng, M.H. and S.F. Chang, Frailty as a Risk Factor for Falls Among Community Dwelling People: Evidence From a Meta-Analysis. J Nurs Scholarsh, 2017. 49(5): p. 529-536.



Y. Santin1, S. Lopez2, I. Ader5, S. Andrieu3,4, N. Blanchard6, A. Carrière5, L. Casteilla5, B. Cousin5, N. Davezac2, P. De Souto Barreto3,4, C. Dray1, N. Fazilleau6, D. Gonzalez-Dunia6, P. Gourdy1, S. Guyonnet3,4, N. Jabrane-Ferrat6, O. Kunduzova1, F. Lezoualc’h1, R. Liblau6, L.O. Martinez1, C. Moro1, P. Payoux7, L. Pénicaud5, V. Planat-Bénard5, C. Rampon2, Y. Rolland3,4, J.-P. Schanstra1, F. Sierra9, P. Valet1, A. Varin5, N. Vergnolle8, B. Vellas3,4, J. Viña10, B.P. Guiard2, A. Parini1


1. Institut des Maladies Métaboliques et Cardiovasculaires, Inserm, Université Paul Sabatier, UMR 1048 – I2MC, Toulouse, France; 2. Centre de Recherches sur la Cognition Animale (CRCA), Centre de Biologie Intégrative (CBI), Université de Toulouse, CNRS, UPS, Toulouse, France; 3. Gerontopole of Toulouse, Institute of Ageing, Toulouse University Hospital (CHU Toulouse), Toulouse, France; 4. UPS/Inserm UMR1027, University of Toulouse III, Toulouse, France; 5. STROMALab, CNRS ERL 5311, Etablissement Français du Sang-Occitanie (EFS), National Veterinary School of Toulouse (ENVT), Inserm U1031, University Toulouse III Paul Sabatier, Toulouse, France; 6. Centre de Physiopathologie Toulouse Purpan, INSERM/CNRS/UPS UMR 1043, University of Toulouse III, Toulouse, France; 7. ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, France
8. IRSD, Université de Toulouse, INSERM, INRA, ENVT, UPS, U1220, CHU Purpan, CS60039, 31024, Toulouse, France; 9. Division of Aging Biology, National Institute on Aging (NIA), National Institutes of Health (NIH), Bethesda, Maryland, USA; 10. Freshage Research Group-Dept. Physiology-University of Valencia, CIBERFES, INCLIVA, Valencia, Spain.
Corresponding author: Professor Angelo Parini, Institut des Maladies Métaboliques et Cardiovasculaires, Inserm/Université Paul Sabatier UMR 1048 – I2MC, 1 avenue Jean Poulhès BP 84225 31432 Toulouse Cedex 4 – France, Phone: (+33)561325601, e-mail: angelo.parini@inserm.fr

J Frailty Aging 2020;in press
Published online August 7, 2020, http://dx.doi.org/10.14283/jfa.2020.43



Aging is the major risk factor for the development of chronic diseases. After decades of research focused on extending lifespan, current efforts seek primarily to promote healthy aging. Recent advances suggest that biological processes linked to aging are more reliable than chronological age to account for an individual’s functional status, i.e. frail or robust. It is becoming increasingly apparent that biological aging may be detectable as a progressive loss of resilience much earlier than the appearance of clinical signs of frailty. In this context, the INSPIRE program was built to identify the mechanisms of accelerated aging and the early biological signs predicting frailty and pathological aging. To address this issue, we designed a cohort of outbred Swiss mice (1576 male and female mice) in which we will continuously monitor spontaneous and voluntary physical activity from 6 to 24 months of age under either normal or high fat/high sucrose diet. At different age points (6, 12, 18, 24 months), multiorgan functional phenotyping will be carried out to identify early signs of organ dysfunction and generate a large biological fluids/feces/organs biobank (100,000 samples). A comprehensive correlation between functional and biological phenotypes will be assessed to determine: 1) the early signs of biological aging and their relationship with chronological age; 2) the role of dietary and exercise interventions on accelerating or decelerating the rate of biological aging; and 3) novel targets for the promotion of healthy aging. All the functional and omics data, as well as the biobank generated in the framework of the INSPIRE cohort will be available to the aging scientific community. The present article describes the scientific background and the strategies employed for the design of the INSPIRE Mouse cohort.

Key words: INSPIRE program, biological aging, mouse cohort, frailty, biomarkers.



The improvement of medical care and living conditions has increased life expectancy. Although being a progress per se, the extension of life expectancy is associated with an elevated risk of all types of chronic diseases as well as the decline in intrinsic capacities (1). Research on the basic “biology of aging” aims to increase life expectancy and to improve the quality of life. In this context, geroscience has emerged as a new interdisciplinary field seeking to define the biological underpinnings of aging that lie at the crossroads of age-dependent biology, chronic disease and health (2, 3). The geroscience hypothesis postulates that, since aging plays a major role in most chronic diseases, addressing aging physiology will reduce or delay the onset of multiple age-associated defects.
Frailty is a clinical state of increased vulnerability resulting from aging-related decline in function and reserve across multiple physiological systems, that carries an increased risk for poor health outcomes including falls, incident disability, hospitalization, and mortality (4). Even though frailty is an age-associated syndrome, the idea that it is not a normal and inevitable part of aging is growing. Hence, frailty can be conceptualized as a result of accelerated biological aging (5), and elucidating its etiology is thus critical for its prevention and/or treatment. Therefore, there is a pressing need to discover markers to differentiate biological age from chronological age and to identify individuals at higher risk of developing chronic diseases, ultimately with the goal to propose pharmacological and non-pharmacological approaches targeting biological processes underlying aging.
According to this integrated view, the INSPIRE research program has been created to foster research in the field of geroscience and healthy aging. INSPIRE aims at promoting healthy aging and preventing dependency through, among other strategies, the constitution of a bio-resource platform going from animals to humans in order to provide clinical, biological and technological resources for research and development on aging (for a detailed review on the INSPIRE program, see [6]). Besides the implementation of digital medicine (ICOPE program from the WHO) and the constitution of an INSPIRE Human Translational Cohort (6, 7), the INSPIRE program will create a unique Mouse Cohort dedicated to basic research, whose setup and design will be described in the present article.


Overview of the INSPIRE Mouse cohort

The primary goal of the INSPIRE Mouse cohort is to foster an understanding of the close relationship between the molecular mechanisms of biological aging and the onset of clinical frailty. This approach will importantly lead to the identification of frailty biomarkers. Complementarily, the INSPIRE Mouse cohort will enable to better characterize frailty in mice by implementing already existing tools such as the “Valencia Score”, a frailty score mainly based on neuromuscular alterations (8), or the “Howlett and Rockwood frailty index” relying on a list of deficits that accumulate during aging (9). This will eventually lead to the creation of an “INSPIRE Frailty Score” suitable for mice and as close as possible to the clinical scenario in humans. Since multiple molecular pathways are involved in the aging process and can contribute to various aspects of frailty, a panel of valid biomarkers in combination with functional measures of frailty would allow both diagnosis and follow up in preclinical and clinical settings (10).
A major asset of the INSPIRE Mouse cohort is its “mirroring” of the INSPIRE Human cohort in order to facilitate the translation of results from basic science to humans (Figure 1). To further improve the extrapolation of the results to the clinic, “humanized” living conditions, i.e. high fat/high sucrose diet and sedentary lifestyle, will be studied as common risk factors of accelerated aging. A particular attention has also been paid to the selection of a mouse strain that congruently mimics human heterogeneity. Besides, as main studies on frailty have been done on either male or female mice, comparison of frailty between genders will also be a major advantage of the INSPIRE Mouse cohort. These important considerations should facilitate the crosstalk between humans and experimental models, therefore speeding up the discovery process (Figure 1).
Here, we provide detailed information on the INSPIRE Mouse cohort setup, by putting forward an innovative methodology ranging from the study design to comprehensive phenotyping. This will be done by integrating measures evaluating different dimensions of frailty including cognitive/motor capacities, cardiac function assessment, body composition, metabolic parameters, urinary incontinency and immune function as defined in humans (syndrome diagnosis). Importantly, tissue biobanking for frailty biomarkers identification is implemented.


Study design

The INSPIRE Mouse cohort was designed to be as close as possible to human lifestyle. As a major issue in aging studies in mice is that most are carried out in inbred strains, the INSPIRE Mouse cohort will gather a genetically heterogeneous mouse stock to better mimic human diversity. In addition, besides normal aging, physical activity/exercise we will be studied as a human-relevant paradigm of delayed aging, while obesity/overweight will be evaluated as a risk factor for accelerated aging in mice. These are well-known risk factors for frailty in humans (11–14) and, as compared to other experimental approaches, they are particularly suitable to promote cognitive (15, 16), cardiometabolic (17, 18) and immune dysfunctions (19), that are involved in progressive/long-term frailty (20, 21). These aspects are described below.

Selection of mouse strain

Animal models have been critical tools in biomedical research, and among them, the laboratory mouse is undoubtedly the most commonly used experimental non-human model. The prevalence of mouse models in biomedical research, in particular in the field of aging, is unsurprisingly considerable given that mice require relatively inexpensive care, reproduce quickly, and have a high genetic similarity to humans (22). Especially, inbred strains (like C57Bl/6J mice or BALB/c mice), transgenic and congenic mice with inbred backgrounds are most used. An inbred strain is defined as a strain that has been through at least 20 generations of sib-mating, making animals from the same inbred strain effectively genetically identical (i.e. isogenic) (23). However, such strains do not reflect genetically diverse human populations, and therefore constitute only a small part of the picture. Hence, outbred stocks (as contrary to inbred mice that are referred as strains, outbred mice are referred to as stocks) represent new research options that parallel or even exceed human genetic diversity, offering more generalizability of responses across populations. Unfortunately, unfamiliarity with outbred mice and concerns about difficulty, genetic variability and lack of reproducibility have impeded their widespread use by the research community. Nevertheless, while there is a common belief suggesting that inbred strains should present less variability of outcomes (24), presenting practical and ethical advantages, a recent review of the literature shows this to be erroneous. Indeed, several studies have shown that for a majority of readouts, inbred and outbred mice showed comparable phenotypic variations (25–27). In addition, Tuttle et al. performed a systematic review of the primary literature, and found that strain type (i.e. inbred or outbred) did not have any effect on within-strain variability regardless of trait category including anatomy, behavior, immune function, molecules and organ function (26). Therefore, except in cases where precise genotypic regulation or standardization is required, it appears that outbred stocks from heterogeneous backgrounds are more appropriate models in many biomedical research applications.
Among the available outbred stocks, the SWISS mice are commonly used. The initial stock was bred at the Centre Anti-Cancéreux Romand in Lausanne, Switzerland, in the 1920s and consisted of two male and seven female albino mice derived from a non-inbred stock. These mice have many advantages for long-term studies, as they are inexpensive, robust and commercially available. They have been used for mouse transgenesis experiments, principally due to efficient breeding and large litter sizes. Importantly, they have a large genetic diversity, which is similar to that found within and between human populations (28). In addition, SWISS mice are sensitive to high fat diets (29–33) and have been used in aging studies (34, 35), the latter being of primary importance for the INSPIRE project. Indeed, Antoch and collaborators reported that mean life expectancy of these mice was 121.1 ± 9.2 weeks for males and 109.6 ± 6.9 weeks for females, with maximum lifespan being of 150 weeks and 164 weeks respectively (35).
For the reasons stated above, the INSPIRE Mouse cohort will gather SWISS mice as a model mimicking the genetic heterogeneity of human populations. It is important to note that females are often underrepresented in animal studies, leading to a compromised understanding of female biology and resulting in poorer treatment outcomes for women. By looking primarily in males, important biological effects can be missed or misinterpreted, partly due to hormonal and genetic intrinsic differences. In addition, contrary to a common belief, recent analyses have found that variability in female performance without regard for the estrous phase is not higher than performance variability in males (36). We thus decided to include both male and female SWISS mice in the INSPIRE cohort in order to further improve the reliability and representativeness of our findings (Table 1). When planning a study that includes an advanced age group, it is important to provide extra animals to ensure sufficient statistical power as a result of early mortality. Therefore, the number of males and females in each group was statistically adjusted considering both spontaneous and high fat diet-induced mortality (35, 37) (Table 1). Finally, as the tracking of each mouse is critical to carry out an individual follow-up, microchips will be implanted in mice so they will be easily identified using a microchip reader.

Table 1
Mouse cohort organization

For the cross-sectional study, end-point analysis will be performed at 6, 12, 18 and 24 months which roughly correspond to 30, 42, 56 and 70 years in humans. Two conditions that affect human and mouse health will be studied: high fat high sucrose (HFHS) obesity and exercise. For the longitudinal study, mice will be allowed to live their natural lifespan, and mean and maximal lifespans will be then calculated. Both male (M) and female (F) SWISS mice will be included in the cohort.


High fat high sucrose (HFHS) diet-induced obesity as a model of accelerated aging

There is strong evidence that excessive adiposity contributes to the impairment of several parameters of frailty, notably reducing the ability of older adults to perform physical activities, impairing different forms of memory and increasing metabolic instability (38). Many obesity-related conditions including low-grade inflammation, insulin resistance, type 2 diabetes and low physical activity are risk factors for frailty. In order to study the biological and molecular changes that occur during aging, and to depict the differences between accelerated and normal aging, a model of diet-induced obesity will be used to induce accelerated aging.
At present, there is a range of commercial high-fat diets that have been demonstrated to make small rodents obese. However, some of these diets contain levels of dietary fat that are much higher than the levels that humans routinely consume. The typical American or European diet contains about 35–40% fat by energy, and a tolerable high-fat human diet might contain 50–60% of energy as fat. However, the 60% fat rodent diet often used in experimental paradigms presents a much greater distortion of the fat content of a normal rodent chow. Thus, rodent studies with a 60% fat content might not be as relevant to human physiology as those which use a 40-45% fat diet (39). Moreover, mice fed with 60% fat diet become more obese, and do so faster than the ones fed with 40-45% fat diet. Thus, while many researchers use the 60% rodent diet as a matter of economics and convenience, it is not the best option for long-term follow-up studies. It is noteworthy that fatty acid (FA) composition of the diet should also be considered besides the percentage of fat in the diet. Moreover, it has been suggested that HFD with high sugar content better mimic the human western diet (40).
For all the aforementioned reasons, we decided to use a customized high fat high sucrose (HFHS) diet containing 40 % energy from animal and vegetal fat (among which 41% saturated fatty acids, 45% monounsaturated fatty acids and 14% polyunsaturated fatty acids) and 25% by weight sucrose. This diet or its corresponding customized control diet will be given to the mice from 6 to 24 months (Table 1). Interestingly, comparable HFHS diets have been shown to promote sarcopenia, bone loss and impaired neurological function in mice (41, 42). These findings represent some of the major features observed in aging humans, suggesting that HFHS diet-fed mice represent a useful model for studying accelerated aging.

Voluntary activity through running wheel access as a model of decelerated aging

Behavioral paradigms that are commonly used to model human exercise training in mice include forced treadmill running, forced wheel running and voluntary wheel running. Mice running behavior in voluntary wheels is closer to the natural running pattern than forced exercise, as it is performed under non-stress conditions, does not require a negative stimulus, and does not interfere in the normal nocturnal-diurnal rhythmicity of the animal (43). Remarkably, laboratory mice run spontaneously when they have access to running wheels, and this behavior is also observed in feral mice when running wheels are placed in nature (44). Voluntary wheel running thus consists of a rewarding behavior and not a stereotypic behavior that can result from environmental restriction and devoid of any goal or function (45). Another advantage of voluntary wheel running is that, since no direct intervention from the experimenter is required, it can be easily used in long-term studies. Hence, voluntary activity will be assessed in the INSPIRE Mouse cohort by giving mice access to upright running wheels (Table 1). To obtain continuous recording throughout the lifespan, we will use a sophisticated method connected to an analysis software that will record detailed activity parameters, including the number and duration of each running period, as well as the number of revolutions, speed, total distance and time, and dark/light cycle activity patterns on running wheels. As mice will be identified by microchips, parameters will be obtained for each single mouse, and at the time of this writing, we are developing a “toll like” detection system to measure individual mouse voluntary activity. Of note, to avoid enrichment/steric hindrance-linked bias, wheels will be placed in all cages. Nevertheless, in the control groups for which the effect of «no physical exercise” will be assessed, running wheels will be blocked (Table 1).

Spontaneous mobility

Mobility is among the most studied and most relevant parameters affecting quality of life with strong prognostic value for disability and survival. Indeed, locomotor impairments in older adults represent a pre-clinical transitional stage towards disability (46). It is thus necessary to understand how aging-related changes in mobility in mice resemble changes in humans.
To this end, the INSPIRE program will provide a life-long measurement of aging-related locomotor activity in mice, through automated home cage monitoring. This technique enables to monitor animals over long periods of time without human intervention. The system we will use, known as Digital Ventilated Cages (DVC®), is designed to gather continuous animal activity data directly from the home cage while keeping cages into conventional Individual Ventilated Cages (IVC) racks (Supp material). It provides a reduction in animal distress thereby increasing welfare, minimization of biases and increased reproducibility of data (47, 48). Therefore, mice belonging to the INSPIRE cohort will be housed in DVC cages so locomotor activity of all mice will be continuously and automatically monitored throughout their life. This activity metric represents the overall in-cage activity generated by all mice in a cage from any electrode and is not tracking activity of individual group-housed animals. Therefore, this parameter will be complemented by the individual aforementioned measure, i.e. voluntary activity through running wheel, as well as neuromuscular function by Valencia Score and behavioral cognitive tests (see the following section).


Comprehensive phenotyping

In this section, we provide an overview of our methodology for the measurement of healthspan and frailty in naturally aging, diet-induced accelerated aging, and exercise-induced decelerated aging in mice. These methods cover a spectrum of highly relevant biological indicators of frailty including cognitive, neuromuscular, cardiac, metabolic and immune function as well as urinary incontinency (For precise timeline, see figure 2). The goal is to improve the currently available “Frailty Scores” with an extended “INSPIRE Frailty Score” suitable for mice, and taking into account accurate parameters to get closer to the human clinical settings (9, 49) (Figure 3).

Figure 1
Parallel between INSPIRE Mouse and Human cohorts

The animal cohort will mimic the human diversity in functional status by providing both healthy and frail animal models to investigations. Both cohorts will allow the normalization and optimization of clinical and biological parameters, and will provide common dataset with equivalent clinical (e.g., cognitive function, mobility) and biological tests. Running animal and human cohorts in parallel is expected to facilitate cross-talks between the experimental models and the clinic in order to 1) identify causal mechanisms of clinical frailty; 2) discover biomarkers associated with functional loss; and 3) develop new therapeutic strategies allowing healthy aging. Of note, the INSPIRE Research Initiative will also use Nothobranchius Furzeri (African Killifish) and pet dogs as additional cohorts to investigate aging process.

Figure 2
Representation of experimental timeline

For the cross-sectional study, the multiple tests at 6, 12, 18 and 24 months will be performed over a period of 3-4weeks. The nature of these tests is indicated below each end-point. One month before end-point analysis (5, 11, 17 and 23 months), bladder function will be assessed. At 9, 15 and 21 months, blood will be collected in a longitudinal way to mainly evaluate immune system modifications. Both mouse mobility and voluntary activity will be continuously recorded during the whole study. For the longitudinal study, mice will be allowed to live out their maximum natural lifespan. * indicates the start of the HFHS diet at 6 months. FBO: Feces, Blood, Organs.


Observational study: longitudinal vs cross-sectional

Both cross-sectional and longitudinal approaches are observational studies commonly used in aging research. In cross-sectional studies, data are collected as a whole to study a mouse population at a single point in time to examine the relationship between variables of interest. Conversely, in longitudinal studies, data are gathered from the same mouse repeatedly over an extended period of time.
In the case of age-related healthspan studies, data are collected at predetermined ages from multiple individuals within a population. The cross-sectional study design allows performing invasive or terminal procedures but precludes the evaluation of lifespan. In the case of the INSPIRE Mouse cohort, a major cross-sectional study will be conducted with endpoint analyses being performed in different groups of mice at the ages of 6, 12, 18 and 24 months-old (Table 1), which roughly correspond to ages from 30 to 70 years in humans. This will allow us to carry out a large number of tests to evaluate and characterize the onset of frailty in aging mice (Figure 2). Importantly, it will also enable to evaluate if some organs “age” prematurely compared to others, and to presume the role of different organ dysfunction in the onset and progression of frailty.
Conversely, longitudinal studies allow mice to live out their maximum natural lifespan, either dying naturally or being euthanized in case of major decline. Therefore, a longitudinal sub-cohort with 120 animals (60 males and 60 females) will be implemented to the INSPIRE cross-sectional study to evaluate the spontaneous mouse mortality, and to determine mean and maximal lifespans in our animal facilities (Table 1, figure 2).

Frailty evaluation by the “Valencia Score”

The development of frailty scores suitable for mice and which resemble those that are used in the clinical scenario has become an essential challenge in basic gerontological research. In pursuit of this goal, the “Valencia Score” has been recently developed to measure frailty in rodents (8). It is based on the human clinical parameters described by Linda Fried and co-workers [50], and thus facilitates the extrapolation to humans, as it relies on five robust clinical criteria including unintentional weight loss, weakness, poor endurance, slowness and low activity level, that can be easily measured in mice. According to this score, if a mouse fails three or more components out of five, it is considered as frail, if it fails one or two criteria, it is classified as prefrail, whereas if it does not fail any criteria it is considered as robust, which is equivalent to the clinical classification defined in the Fried Frailty Score. We decided to use the Valencia Score as a starting point to evaluate frailty, and the following parameters will be therefore primarily measured.

Body weight

Animals’ body weights will be recorded biweekly throughout their lifespan to have a precise follow-up of weight evolution. In order to have reliable and individual data, all the mice will be weighted. As suggested by Gomez-Cabrera and colleagues, a 5% weight loss over a one-month period will be considered positive for this frailty criterion (8), a parameter reflecting the unintentional weight loss commonly observed in frail people.
In order to avoid variability in locomotor activity and other parameters driven by differences in circadian rhythms, all testing will be done starting at the same time. Tests will be run in the order listed, from the least to the most stressful, thereby decreasing the chance that one test might affect the behavior evaluated in the subsequent paradigm.

Grip strength

The grip strength test is a simple non-invasive method designed to assess neuromuscular function through animal’s limb strength. It takes advantage of the animal’s tendency to grasp a horizontal metal bar or grid while suspended by its tail. It allows to determine the maximum force, or peak of force, developed by a mouse when the operator tries to move it away from the bar or grid. The measurement is carried out using a high-precision sensor and an electronic device, guaranteeing a perfect capture and display of the maximal force. As suggested by the “Valencia Score”, a cut-off point below which 20% of the observations may be found has to be calculated, and all the animals ranking below this 20th percentile will be considered to fulfill the frailty criterion of weakness, which is frequently measured in the clinical setting.

Motor coordination

The tightrope test is a method for evaluating neuromuscular coordination and vigor. It is positively correlated to lifespan in rodents and has been extensively validated as a behavioral marker of aging since it was first described in the seventies (51, 52). When animals are placed on a tightrope, they are able to grasp the string with the four legs and tail and move to reach a side pole. Mice are scored positive if they are unable to reach the side pole before a 60 sec time-limit or if they fall from the rope. Usually, obese and aged mice cannot lift their hind legs and, after hanging for a few seconds from the forepaws, fall on the cage bedding. In this case, mice are scored as “positive” for this frailty criterion.

Incremental treadmill test

Poor endurance and slowness are key components of the diagnosis of frailty in humans. These parameters can be evaluated in mice by measuring the running time and speed values when performing an incremental intensity test in a treadmill. For endurance, the running time values will be measured. Then, similar to the grip test, a 20th percentile will be calculated as a cut-off point. The animals that will report a running time under this “threshold” will fulfill this frailty criterion. Besides endurance, running speed will be measured as an index of “slowness”. The same aforementioned calculation will be performed to define a threshold under which mice will be considered as positive for the “slowness criterion”. Of note, very old animals are usually unable to keep even the lowest running intensities. In our study this is likely to be exacerbated in older mice fed the HFHS diet. As in clinical practice, subjects that are unable to perform any one test are categorized as positive for that criterion.
In the case of the INSPIRE Mouse cohort, the Valencia Score will be used as a primary indicator to evaluate frailty in mice. However, as this score is mainly based on neuromuscular alterations that are commonly observed in frail people, implementation of additional parameters would be of great value to better characterize frailty onset and progression. Therefore, in order to detect early signs of frailty that might not be detected by the Valencia Score, complementary measurements will be carried out on the INSPIRE Mouse cohort in order to propose an extended “INSPIRE Frailty Score”, including cognitive, cardiac, metabolic as well as other biological functions (Figure 2). These measurements are described in the following sections.

Behavioral cognitive tests

Behavioral indicators of healthspan in mice include gait/ataxia, motivated activity, cognition, and affective function (53). In the context of aging, we will primarily use the spontaneous alternation Y-maze, which assesses prefrontal cortex- and hippocampus-dependent spatial working and reference memory, reflecting changes in cognitive performance (54).
The Y-maze spontaneous alternation test is based on rodent’s innate curiosity to explore previously unvisited areas and is used to assess spatial working memory. When placed in a Y-shaped maze, a mouse will show a tendency to enter previously unexplored arms, thus showing alternation in the arm visits. The number of arm entries and the successive entry sequences in the 3 arms are recorded in order to calculate the percentage of alternation. An entry occurs when the four legs are in the arm.

Cardiac function

Cardiac dysfunction is a main issue in elderly people, and its assessment could be of great interest in the diagnosis and the better characterization of frailty. Despite the absence of underlying pathologies like hypertension or myocardial infarction which lead to heart failure with reduced ejection fraction (HFrEF), the ‘normal’ aged heart usually exhibits changes like arterial stiffening, increased myocardial stiffness, decreased diastolic myocardial relaxation, increased left ventricular (LV) mass and decreased peak contractility (55). In addition, aging and related comorbidities (obesity, hypertension, diabetes, chronic obstructive disease, anemia and chronic kidney disease) may initiate or aggravate chronic systemic inflammation that may further affect cardiac remodeling and dysfunction (56). Therefore, the majority of elderly patients exhibit heart failure but have a preserved systolic LV function, a syndrome known as heart failure with preserved ejection fraction (HFpEF). Patients with this syndrome have severe symptoms of exercise intolerance, frequent hospitalizations and increased mortality. Despite the importance of HFpEF, optimal treatments remain largely insufficient. The INSPIRE Mouse cohort thus represents a model to better understand HFpEF pathophysiology within a ‘systemic’ perspective. Of note, approximately 85% of elderly HFpEF patients are overweight or obese, and the HFpEF epidemic has largely paralleled the obesity epidemic (57). Therefore, HFHS diet-induced obesity also represents a congruent mouse model of HFpEF.
For the evaluation of cardiac function, we have selected echocardiography. In addition to traditional parameters reflecting systolic function (ejection fraction and ventricular wall thickness), particular attention will be given to the measurement of diastolic (dys)function. In particular, the evaluation of mitral inflow will be assessed, as it is very informative and plays an important role in grading diastolic dysfunction (Supp material). Of much interest, these parameters will be complemented with strain imaging to measure the regional and global deformation of the myocardium, which allows for early detection of subclinical LV dysfunction.
The combination of the aforementioned cardiac parameters will allow to better highlight HFpEF in mice and to upgrade the Valencia Frailty Score with the degree of diastolic dysfunction.

Metabolic function

During aging, there are changes in body composition, including a loss of lean body mass, bone mass, body water, and a relative increase of fat mass. The bone deteriorates in composition, structure and function, which predisposes to osteoporosis. Furthermore, the increase in fat mass is distributed more specifically in the abdominal region, which is associated with cardiovascular disease and diabetes (58). Changes in body composition often occur in the absence of weight fluctuations, being due to alterations in energy balance, with a positive balance leading to weight gain and a negative balance resulting in weight loss. These key parameters will thus be assessed in the INSPIRE Mouse cohort.

Body composition and bone analysis

Magnetic Resonance Imaging
Body composition analysis will be performed by Magnetic Resonance Imaging (MRI) which provides an accurate estimate of whole-body fat, lean, free water, and total water masses in live mice. This technology combines simplicity of use, short scan times, and the comfort of animals which do not need to be anesthetized.

X-ray micro computed tomography
Bone analysis will be done by micro-computed tomography (micro-CT), which can provide ultrahigh-resolution images with resolution of less than 10 µm. This analysis will be performed after bone collection following terminal anesthesia. This technique will evaluate key parameters of bone microarchitecture like cortical thinning, cortical porosity, thinning of the trabeculae and loss of trabecular connectivity.

Plasmatic metabolic profiling

In addition to the aforementioned parameters, key plasmatic markers will be measured in plasma collected 2h after fasting, at the time of euthanasia. The combination of biochemical and multiplex immunoassay analysis will allow us to determine a broad range of metabolic markers in mice. These markers include, but are not limited to, hepatic enzymes, lipids and lipoproteins, incretins, glycated proteins, glucose, lactic acid, glucagon, insulin, leptin, PYY, amylin, peptide C, ghrelin and others.
The consideration of metabolic function in the INSPIRE frailty score will be of great importance to correlate body and bone compositions, and plasmatic metabolic profiling with neuromuscular and cardiac alterations, which will allow a better characterization of the sequential progression of frailty.

Bladder function

Urinary incontinence is a major problem in the elderly population, especially among women (59). Affected individuals often make great efforts to deny or hide urinary incontinence, which can lead to psychosocial hindrance. Its consideration is thus important in the characterization of frailty, but unfortunately its measurement is often undervalued in aging research, in particular in animal cohorts.
We thus decided to measure urinary incontinence in the INSPIRE Mouse cohort in order to study lower urinary tract function during aging. To this end, a spontaneous void spot assay (VSA) will be performed (Supp material), so urinary spotting patterns will be used as an indirect way of measuring bladder function and outlet control (60). As urinary incontinence is usually considered as a feature of frailty in humans, its measurement in mice will improve the scoring of frailty to be closer to the clinical evaluation.

Immune function

A crucial component of aging is a set of alterations in the immune system that can manifest as a decreased ability to fight infection, diminished response to vaccination, increased incidence of cancer and constitutive low-grade inflammation (61). The latter, which has been called “inflammaging”, has drawn particular attention in the field of aging, as recent studies have provided evidence that a pool of molecules can be secreted by senescent cells, a process known as senescence-associated secretory phenotype (SASP). This SASP includes cytokines, chemokines, proteases and growth factors that can affect neighboring cells via autocrine/paracrine pathways.
Immunological markers will be assessed in the INSPIRE Mouse cohort at different time points, i.e. 9, 15 and 21 months through submandibular blood collection and 6, 12, 18 and 24 months through terminal blood collection in the posterior vena cava (Figure 2). These markers will be measured in plasma by multiplex immunoassays and include, but are not limited to, IL6, IL-1 beta, TNF alpha, IL-12, IFN gamma, IL-2, IL-10, TGF beta, IL-4, IL-13, IL-17, CCL2, CXCL9, CXCL10, CCL22, CCL17, CRP. In addition, end-point blood collection will also serve at determining the white blood cell count of mice.
Adding some key markers reflecting immune system modifications in the characterization of frailty would be of great interest, as this feature is not considered in the current evaluation of frailty in mice.

Organ collection, biobanking and multi-omics analysis

After phenotyping, mice will be sacrificed and urine, feces, blood and tissues will be collected for biobanking as appropriate. Mice will be fasted 2h before euthanasia. Urine will be collected after placing mice in metabolic cages for 12 hours, the day before euthanasia. Feces will be collected during mouse handling, just before euthanasia and directly frozen. Blood will be collected just after euthanasia from the posterior vena cava, which is recommended for terminal stage studies in order to collect a maximal volume of blood. The fluids will be prepared as appropriate (e.g. for plasma collection), aliquoted and stored at -80°C before further investigations. Concerning the tissues, as many tissues as possible will be collected. Each tissue will be then subdivided into two pieces: the first one will be included in Optimal Cutting Temperature (OCT) compound, paraformaldehyde (PFA) or glutaraldehyde as appropriate, and cut into ultrathin slices for complete anatomopathological analysis. The other piece will be flash frozen in liquid nitrogen and, shortly before analysis, tissues will be fragmented with a biopulverizer into tiny pieces the size of grains of sand or course powder. This technique was selected for different reasons: 1) it reduces the number of collector tubes; 2) it limits sampling bias during organ collection; and 3) it optimizes subsequent rapid and complete lysis using lytic solutions or mechanical homogenizers. All the samples will be stored in a Biological Resource Center dedicated to the conservation of biological resources according to strict criteria of ethics and quality.
Multi-Omics analysis will be performed on biological fluids, feces and tissues. To facilitate the transfer of the results to the Human cohort, priority will be given to the analyses in plasma, urine (in particular proteomics and metabolomics profiling) and feces (microbiota analysis). The goal of this approach is to define a set of robust and accurate biomarkers for normal, accelerated, and decelerated aging. The tissues will be then dedicated to the multi-Omics-designed identification of novel tissue-specific candidate biomarkers for frailty and accelerated/decelerated aging, and to the validation of the novel candidate targets for prevention and treatment of accelerated aging (Figure 3).

Figure 3
Graphical abstract of the proposed INSPIRE Frailty Score

The goal of the INSPIRE Mouse cohort is to propose a clinically relevant “INSPIRE Frailty Score”, combining both functional and biological parameters, which will bring important knowledge on frailty characterization, assessment and target identification.


Conclusions and perspectives

Belonging to the global INSPIRE platform on geroscience, the INSPIRE Mouse cohort represents a unique way to model and better characterize frailty in mice. Although excellent institutions like the Buck Institute and the National Institute on Aging also carry out comparable studies in mice dedicated to investigate biological aging, the main originality of the INSPIRE Mouse cohort relies on the focus on getting closer to the human lifestyle to define the time course and the mechanisms of frailty/accelerated aging onset. Within this line, the selection of outbred mice that better parallel human genetic diversity, is a determining parameter offering more generalizability of responses across populations. In addition, including both males and females, and mimicking “humanized” lifestyles through voluntary physical activity and HFHS-diet induced obesity further approach real human living conditions.
Through a large functional and biological phenotyping of mice, a first objective of the INSPIRE project is to define the age at which early signs of frailty arise. Indeed, frailty is considered as a clinical syndrome appearing in advanced ages (62), but this is because the definition of frailty is mainly based on clinical criteria becoming discriminating in old patients. However, it is likely that the biological mechanisms leading to frailty and accelerated aging may be induced and detectable much earlier than the actual clinical signs of frailty. The goal here is to define the early signs of premature aging and to correlate them to the normal/altered functional phenotype to 1/ define the age of frailty onset and 2/ identify the organ/system(s) primarily altered in the frailty process. To this aim, the development of a clinically relevant score for frailty in mice is essential. Within this line, the “Howlett and Rockwood frailty index” is a simple and noninvasive index, based on 31 health-related variables like alopecia, distended abdomen, hearing loss and breathing rate (9). Although this 31-item check list is based on deficit accumulation during aging, we believe that investigator bias may play a critical role in diagnosis of frailty, which may affect the comparison of results across studies. More recently, the Valencia Score has been developed to determine frailty in naturally aging mice, based on five clinical components previously reported for humans by Fried and co-workers (8, 50). Despite its undeniable interest, this approach is primarily focused on the in-depth study of aging-related neuromuscular alterations and does not evaluate other key aspects of frailty such as cognitive, cardiac or metabolic impairments. Therefore, for the INSPIRE Mouse cohort, mice will be initially labeled as ‘frail/pre-frail/robust’ based solely on the Valencia test. Then, functional phenotyping will allow us to know if other aspects of frailty that are currently undervalued (e.g. cardiac or metabolic alterations) are detectable earlier than neuromuscular defects, which could greatly refine frailty detection. Then, a cut-off will be empirically determined for each parameter in order to set a more accurate frailty score. This method will bring key information on frailty by 1/ evaluating the effect of HFHS-induced overweight and sedentarism on frailty onset and 2/ including clinically relevant criteria like cognitive, cardiac, metabolic, bladder and immune parameters in addition to the currently measured neuromuscular deficits (Figure 3). Importantly, all these parameters, which will be supplemented by the longitudinal follow up of mouse mobility and voluntary activity, closely reflect changes observed in humans and therefore better approach the human frailty criteria.
Besides phenotypic measures, molecular biomarkers will be highly valuable and complementary in the prediction of healthy/unhealthy aging. Through a better understanding of the close relationship between the molecular mechanisms of cell premature aging and the onset of frailty/accelerated aging, the INSPIRE Mouse cohort will foster the identification of a panel of robust and sensitive frailty biomarkers that have not been extensively studied so far. Multi-Omics analysis of blood, urine and feces will allow to rapidly identify such biomarkers’ profiles (that can be conceptualized as a “frailty ID”), which might inform timely pharmacological and non-pharmacological preventive strategies acting directly on aging and contributing to a healthy state even in late ages. Then, these multi-Omics approaches will be extended to tissues to eventually discover novel tissue-specific putative biomarkers and therapeutic targets of frailty/accelerated aging (Figure 3).
An important notion, tightly linked to frailty is resilience, which is defined as the capacity to respond to or recover from clinically relevant stresses (63). Therefore, resilience must be evaluated in aging studies and necessitates the development of new animal models, which would be of particular great value for testing the benefits of geroprotectors. However, modelling resilience in mice is challenging, as there is no consensus on its precise definition or on how best to measure it (64). Although some models are currently available, there is very little data related to the characterization of the multiple deficits caused, especially in aged animals. As of this writing, INSPIRE investigators (gathering physicians, pharmacists, epidemiologists, geriatricians, clinicians, molecular biologists and others interested in the process of aging) are working on the tremendous question of “resilience modelling”, aiming at reaching a consensus on the suitability of such models.
To sum up, the INSPIRE Mouse cohort will importantly lead to the precise functional characterization of frailty together with the identification of robust molecular biomarkers to predict healthy/unhealthy aging. The resulting INSPIRE Frailty Score, combining both functional and biological parameters, will thus allow to refine frailty characterization and detection in animal models (figure 3). Therefore, by belonging to the global INSPIRE platform on geroscience (6, 65) and through its interaction with the INSPIRE Human Translational cohort and the INSPIRE Icope Care Cohort (6, 7, 66), the INSPIRE Mouse cohort should speed up the discovery process in the field of aging, with the final goal to increase access to healthy aging for the current and next generations.


Acknowledgments: We thank Massimiliano Bardotti, Rémy Burcelin and Sarah Gandarillas for their help in the design of the cohort. The Inspire Program was supported by grants from the Region Occitanie/Pyrénées-Méditerranée (Reference number: 1901175), the European Regional Development Fund (ERDF) (Project number: MP0022856), and the Inspire Chairs of Excellence funded by: Alzheimer Prevention in Occitania and Catalonia (APOC), EDENIS, KORIAN, Pfizer, Pierre-Fabre.
Conflict of interest: All authors of the paper “Towards a large-scale assessment of relationship between biological and chronological aging: The INSPIRE Mouse cohort” declare no conflict of interest related to this manuscript.
Permissions: 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.
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.





1. Niccoli T, Partridge L. Ageing as a Risk Factor for Disease. Current Biology 2012;22:R741–R752
2. Kennedy BK, Berger SL, Brunet A, et al. Geroscience: linking aging to chronic disease. Cell 2014;159:709–713
3. Sierra F. The Emergence of Geroscience as an Interdisciplinary Approach to the Enhancement of Health Span and Life Span. Cold Spring Harb Perspect Med 2016;6
4. Cesari M, Calvani R, Marzetti E. Frailty in Older Persons. Clinics in Geriatric Medicine 2017;33:293–303
5. Wallace RG, Twomey LC, Custaud M-A, et al. Potential Diagnostic and Prognostic Biomarkers of Epigenetic Drift within the Cardiovascular Compartment. BioMed Research International. https://www.hindawi.com/journals/bmri/2016/2465763/. Accessed 28 May 2020
6. de Souto Barreto P, Guyonnet S, Ader I, et al. The Inspire Research Initiative: A Program for Geroscience and Healthy Aging Research going from Animal Models to Humans and the Healthcare System. J Frailty Aging 2020
7. Takeda C, Guyonnet S, Sumi Y, Vellas B, Araujo de Carvalho I. Integrated Care for Older People and the Implementation in the INSPIRE Care Cohort. J Prev Alzheimers Dis 2020;7:70–74
8. Gomez-Cabrera MC, Garcia-Valles R, Rodriguez-Mañas L, et al. A New Frailty Score for Experimental Animals Based on the Clinical Phenotype: Inactivity as a Model of Frailty. J Gerontol A Biol Sci Med Sci 2017;72:885–891
9. Whitehead JC, Hildebrand BA, Sun M, et al. A Clinical Frailty Index in Aging Mice: Comparisons With Frailty Index Data in Humans. J Gerontol A Biol Sci Med Sci 2014;69:621–632
10. Towards frailty biomarkers: Candidates from genes and pathways regulated in aging and age-related diseases. Ageing Research Reviews 2018;47:214–277
11. Herr M, Cesari M, Landre B, et al. Factors associated with changes of the frailty status after age 70: Findings in the MAPT study. Ann Epidemiol 2019;34:65-70.e1
12. Landré B, Czernichow S, Goldberg M, Zins M, Ankri J, Herr M. Association Between Life-Course Obesity and Frailty in Older Adults: Findings in the GAZEL Cohort. Obesity 2020;28:388–396
13. da Silva VD, Tribess S, Meneguci J, et al. Association between frailty and the combination of physical activity level and sedentary behavior in older adults. BMC Public Health 2019;19:709
14. Kehler DS, Hay JL, Stammers AN, et al. A systematic review of the association between sedentary behaviors with frailty. Exp Gerontol 2018;114:1–12
15. Miller AA, Spencer SJ. Obesity and neuroinflammation: a pathway to cognitive impairment. Brain Behav Immun 2014;42:10–21
16. Yeomans MR. Adverse effects of consuming high fat-sugar diets on cognition: implications for understanding obesity. Proc Nutr Soc 2017;76:455–465
17. Chung ST, Onuzuruike AU, Magge SN. Cardiometabolic risk in obese children. Ann N Y Acad Sci 2018;1411:166–183
18. Aronne LJ, Isoldi KK. Overweight and obesity: key components of cardiometabolic risk. Clin Cornerstone 2007;8:29–37
19. Andersen CJ, Murphy KE, Fernandez ML. Impact of Obesity and Metabolic Syndrome on Immunity12. Adv Nutr 2016;7:66–75
20. Tamura Y, Ishikawa J, Fujiwara Y, et al. Prevalence of frailty, cognitive impairment, and sarcopenia in outpatients with cardiometabolic disease in a frailty clinic. BMC Geriatr 2018;18
21. Hwang A-C, Liu L-K, Lee W-J, et al. Association of Frailty and Cardiometabolic Risk Among Community-Dwelling Middle-Aged and Older People: Results from the I-Lan Longitudinal Aging Study. Rejuvenation Res 2015;18:564–572
22. Yuan R, Peters LL, Paigen B. Mice as a mammalian model for research on the genetics of aging. ILAR J 2011;52:4–15
23. Johnson M. Laboratory Mice and Rats. Materials and Methods 2020
24. Festing MF. Warning: the use of heterogeneous mice may seriously damage your research. Neurobiol Aging 1999;20:237–244; discussion 245-246
25. Jensen VS, Porsgaard T, Lykkesfeldt J, Hvid H. Rodent model choice has major impact on variability of standard preclinical readouts associated with diabetes and obesity research. Am J Transl Res 2016;8:3574–3584
26. Tuttle AH, Philip VM, Chesler EJ, Mogil JS. Comparing phenotypic variation between inbred and outbred mice. Nat Methods 2018;15:994–996
27. Hsieh LS, Wen JH, Miyares L, Lombroso PJ, Bordey A. Outbred CD1 mice are as suitable as inbred C57BL/6J mice in performing social tasks. Neurosci Lett 2017;637:142–147
28. Aldinger KA, Sokoloff G, Rosenberg DM, Palmer AA, Millen KJ. Genetic Variation and Population Substructure in Outbred CD-1 Mice: Implications for Genome-Wide Association Studies. PLoS One 2009;4
29. Gao M, Ma Y, Liu D. High-fat diet-induced adiposity, adipose inflammation, hepatic steatosis and hyperinsulinemia in outbred CD-1 mice. PLoS ONE 2015;10:e0119784
30. Buttigieg A, Flores O, Hernández A, Sáez-Briones P, Burgos H, Morgan C. Preference for high-fat diet is developed by young Swiss CD1 mice after short-term feeding and is prevented by NMDA receptor antagonists. Neurobiol Learn Mem 2014;107:13–18
31. Chen K, Zheng X, Feng M, Li D, Zhang H. Gut Microbiota-Dependent Metabolite Trimethylamine N-Oxide Contributes to Cardiac Dysfunction in Western Diet-Induced Obese Mice. Front Physiol 2017;8:139
32. Zhang H, Meng J, Yu H. Trimethylamine N-oxide Supplementation Abolishes the Cardioprotective Effects of Voluntary Exercise in Mice Fed a Western Diet. Front Physiol 2017;8
33. Anderson NJ, King MR, Delbruck L, Jolivalt CG. Role of insulin signaling impairment, adiponectin and dyslipidemia in peripheral and central neuropathy in mice. Dis Model Mech 2014;7:625–633
34. Ramos-Ibeas P, Pericuesta E, Peral-Sanchez I, et al. Longitudinal analysis of somatic and germ-cell telomere dynamics in outbred mice. Mol Reprod Dev 2019;86:1033–1043
35. Antoch MP, Wrobel M, Kuropatwinski KK, et al. Physiological frailty index (PFI): quantitative in-life estimate of individual biological age in mice. Aging (Albany NY) 2017;9:615–626
36. Clayton JA, Collins FS. Policy: NIH to balance sex in cell and animal studies. Nature News 2014;509:282
37. Pettersson US, Waldén TB, Carlsson P-O, Jansson L, Phillipson M. Female Mice are Protected against High-Fat Diet Induced Metabolic Syndrome and Increase the Regulatory T Cell Population in Adipose Tissue. PLoS ONE 2012;7:e46057
38. Porter Starr KN, McDonald SR, Bales CW. Obesity and Physical Frailty in Older Adults: A Scoping Review of Intervention Trials. J Am Med Dir Assoc 2014;15:240–250
39. Speakman JR. Use of high-fat diets to study rodent obesity as a model of human obesity. Int J Obes 2019;43:1491–1492
40. Heydemann A. An Overview of Murine High Fat Diet as a Model for Type 2 Diabetes Mellitus. Journal of Diabetes Research. https://www.hindawi.com/journals/jdr/2016/2902351/. Accessed 8 Apr 2020
41. Burchfield JG, Kebede MA, Meoli CC, et al. High dietary fat and sucrose result in an extensive and time-dependent deterioration in health of multiple physiological systems in mice. J Biol Chem 2018;293:5731–5745
42. Rasool S, Geetha T, Broderick TL, Babu JR. High Fat With High Sucrose Diet Leads to Obesity and Induces Myodegeneration. Front Physiol 2018;9
43. Goh J, Ladiges W. Voluntary Wheel Running in Mice. Current Protocols in Mouse Biology 2015;5:283–290
44. Meijer JH, Robbers Y. Wheel running in the wild. Proceedings of the Royal Society B: Biological Sciences 2014;281:20140210
45. Manzanares G, Brito-da-Silva G, Gandra PG, Manzanares G, Brito-da-Silva G, Gandra PG. Voluntary wheel running: patterns and physiological effects in mice. Brazilian Journal of Medical and Biological Research 2019;52
46. Ferrucci L, Cooper R, Shardell M, Simonsick EM, Schrack JA, Kuh D. Age-Related Change in Mobility: Perspectives From Life Course Epidemiology and Geroscience. J Gerontol A Biol Sci Med Sci 2016;71:1184–1194
47. Iannello F. Non-intrusive high throughput automated data collection from the home cage. Heliyon 2019;5:e01454
48. Pernold K, Iannello F, Low BE, et al. Towards large scale automated cage monitoring – Diurnal rhythm and impact of interventions on in-cage activity of C57BL/6J mice recorded 24/7 with a non-disrupting capacitive-based technique. PLOS ONE 2019;14:e0211063
49. Martinez de Toda I, Garrido A, Vida C, Gomez-Cabrera MC, Viña J, De la Fuente M. Frailty Quantified by the “Valencia Score” as a Potential Predictor of Lifespan in Mice. J Gerontol A Biol Sci Med Sci 2018;73:1323–1329
50. Fried LP, Tangen CM, Walston J, et al. Frailty in Older AdultsEvidence for a Phenotype. J Gerontol A Biol Sci Med Sci 2001;56:M146–M157
51. Miquel J, Blasco M. A simple technique for evaluation of vitality loss in aging mice, by testing their muscular coordination and vigor. Experimental Gerontology 1978;13:389–396
52. Matheu A, Maraver A, Klatt P, et al. Delayed ageing through damage protection by the Arf/p53 pathway. Nature 2007;448:375–379
53. Ackert-Bicknell CL, Anderson L, Sheehan S, et al. Aging Research Using Mouse Models. Curr Protoc Mouse Biol 2015;5:95–133
54. Kraeuter A-K, Guest PC, Sarnyai Z. The Y-Maze for Assessment of Spatial Working and Reference Memory in Mice. Methods Mol Biol 2019;1916:105–111
55. Strait JB, Lakatta EG. Aging-associated cardiovascular changes and their relationship to heart failure. Heart Fail Clin 2012;8:143–164
56. Upadhya B, Kitzman DW. Heart Failure With Preserved Ejection Fraction in Older Adult. Heart Fail Clin 2017;13:485–502
57. Ndumele CE, Coresh J, Lazo M, et al. Obesity, subclinical myocardial injury, and incident heart failure. JACC Heart Fail 2014;2:600–607
58. St-Onge M-P, Gallagher D. Body composition changes with aging: The cause or the result of alterations in metabolic rate and macronutrient oxidation? Nutrition 2010;26:152–155
59. Vogel SL. Urinary Incontinence in the Elderly. Ochsner J 2001;3:214–218
60. Hill WG, Zeidel ML, Bjorling DE, Vezina CM. Void spot assay: recommendations on the use of a simple micturition assay for mice. Am J Physiol Renal Physiol 2018;315:F1422–F1429
61. Sadighi Akha AA. Aging and the immune system: An overview. J Immunol Methods 2018;463:21–26
62. Vellas B, Sourdet S. Prevention of Frailty in Aging. J Frailty Aging 2017;6:174–177
63. Kirkland JL, Stout MB, Sierra F. Resilience in Aging Mice. J Gerontol A Biol Sci Med Sci 2016;71:1407–1414
64. Schosserer M, Banks G, Dogan S, et al. Modelling physical resilience in ageing mice. Mech Ageing Dev 2019;177:91–102
65. Guerville F, De Souto Barreto P, Ader I, et al. Revisiting the Hallmarks of Aging to Identify Markers of Biological Age. J Prev Alzheimers Dis 2020;7:56–64
66. Beard JR. Linking geroscience and integrated care to reinforce prevention. Journal of Prevention of Alzheimer’s Disease 2020;J Prev Alz Dis 20207:68–69



M. Tosato1, F. Varone1, A. Ciccullo1, R. Calvani1, D. Moschese1, A. Potenza1, M. Siciliano1, M. Fantoni1


1. Fondazione Policlinico Universitario Agostino Gemelli IRCSS, Rome, Italy; ORCID: https://orcid.org/0000-0001-5750-9746.
Corresponding author: Matteo Tosato, Fondazione Policlinico Universitario Agostino Gemelli IRCSS, Rome, Italy, email: matteo.tosato@policlinicogemelli.it

J Frailty Aging 2021;10(1)70-71
Published online August 7, 2020, http://dx.doi.org/10.14283/jfa.2020.41



COVID-19, the disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, showed higher severity and lethality in male older adults . There are currently no specific treatments. Studies are evaluating the efficacy of monoclonal antibodies against interleukin-6 receptor. Here we present the case of a 98-years old man admitted to our COVID-Hospital with acute respiratory failure. Comprehensive geriatric assessment showed no signs of frailty. First-line therapy with hydroxychloroquine and anticoagulants was not effective. Patient was administered intravenous monoclonal antibodies, and he showed remarkable clinical improvement. This case suggests that age alone should not preclude access to new therapeutic approaches. Comprehensive, multisciplinary, multidomain approaches are needed to develop patient-tailored treatments against COVID-19.

Key words: Frailty, covid-19, comprehensive geriatric assesment, aging.



In late 2019, a novel betacoronavirus (SARS-CoV-2), emerged in the province of Hubei, China. The disease related to SARS-CoV-2, named COVID-19, rapidly spread all over the world and was declared a global pandemic by the World Health Organization (WHO). While the majority of cases present with no or mild respiratory symptoms, about 20% of the cases could develop severe forms of the disease, with life-threatening complications such as respiratory failure and multiorgan dysfunction (1). Fatality rates vary between studies, but large cohort observational studies reported a greater disease severity in older patients (1-2).
We have recently observed a demographic shift towards older ages in COVID-19 patients admitted to our COVID Hospital. This phenomenon was partly due to newly discovered outbreaks in nursing homes and rehabilitation units. Older patients present specific characteristics, such as frailty, multimorbidity, polypharmacotherapy, and other conditions that render their management particularly difficult. Their unmet clinical needs represent the “new challenge” in COVID-19 pandemic.
Hereby we report a case of severe COVID-19 pneumonia in an old man admitted to our COVID-hospital in Rome, Italy.


Case representation

A 98-years old man was admitted to our hospital on April 15th, following a 2-day history of cough and dyspnea. The patient came from a rehab-unit where he was admitted following hip fracture. He was independent in daily activities before hospital admission and not frail (Clinical Frailty Scale score was 3) (3). Chronic obstructive pulmonary disease, hypertension, chronic lymphocytosis and benign prostatic hypertrophy were also present in his medical history. Blood tests at admission showed C-Reactive Protein 221.7 mg/L (Reference Value [RV] <5.0), D-dimer 4425 ng/mL (RV <500), ferritin 1286 ng/mL (RV 21-275), IL-6 105.8 ng/L (RV <4.4). Chest X-rays documented signs of interstitial pneumonia (Figure 1). Patient presented in severe clinical conditions, hypoxemic, requiring oxygen supplementation with a FiO2 of 0.6. Patient was treated immediately with hydroxychloroquine and anticoagulant therapy with no clinical benefits. At day 5, he was administered intravenous sarilumab (400 mg as single dose). The day after, patient started showing remarkable clinical improvement. Over the following weeks, we observed a full consciousness recovery and improvement in respiratory function, with progressive reduction of oxygen supplementation. On May 8th he was discharged from the hospital after blowing out his 99 candles.

Figure 1
Patient’s chest X-rays at hospital admission



As of 9 May 2020, the current COVID-19 pandemic has already caused over 270,000 deaths worldwide (4). Older patients are more vulnerable to COVID-19 as witnessed by their higher hospitalization and mortality rate; in particular, institutionalized older persons appear particularly at risk of experiencing negative outcomes. COVID-19 has now become the main challenge in geriatric care. Thus, geriatricians are increasingly recognized as key figures in multidisciplinary hospital teams dealing with the COVID-19 pandemic (5). There are currently no specific treatments available for COVID-19. Several studies are underway to evaluate the efficacy of monoclonal antibodies against the interleukin-6 receptor (tocilizumab and sarilumab) in mitigating the cytokine cascade and improving the clinical course of the disease. However, older adults are usually excluded from clinical trials and therefore are at risk, during this pandemic phase, of not having access to the treatments being studied. In our case, a 98-years old patient optimally responded to off-label sarilumab with marked improvement in clinical conditions and no adverse reactions reported. Although clinical trials will be necessary to assess safety and efficacy of sarilumab in the treatment of COVID-19, our finding may be promising.



This case suggests that age alone should not preclude access to new therapeutic approaches. Comprehensive, multisciplinary, multidomain approaches assessing, among others, comorbidity burden and frailty status, are needed to develop patient-tailored treatments against COVID-19.


Funding: This case report received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
Conflict of interest: On behalf of all authors, the corresponding author states that there is no conflict of interest.
Ethical approval: Not applicable. No research study involved.
Statement of human and animal rights: All procedures performed in the study were in accordance with the ethical standards of the institutional or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards and conformed to the Declaration of Helsinki on human research.
Informed consent: The patient included in the study gave written informed consent for the publication.



1. Wu Z, McGoogan JM. Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72 314 Cases From the Chinese Center for Disease Control and Prevention [published online ahead of print, 2020 Feb 24]. JAMA. 2020;10.1001/jama.2020.2648.
2. Chen R, Liang W, Jiang M, et al. Risk Factors of Fatal Outcome in Hospitalized Subjects With Coronavirus Disease 2019 From a Nationwide Analysis in China [published online ahead of print, 2020 Apr 15]. Chest. 2020;S0012-3692(20)30710-8. doi:10.1016/j.chest.2020.04.010
3. Rockwood K, Song X, MacKnight C, Bergman H, et al. A global clinical measure of fitness and frailty in elderly people. CMAJ. 2005 Aug 30;173(5):489-95.
4. John Hopkins University – Coronavirus resource center. Accessed on May 9th. Available at https://coronavirus.jhu.edu/
5. Landi F, Barillaro C, Bellieni A, et al. The New Challenge of Geriatrics: Saving Frail Older People from the SARS-COV-2 Pandemic Infection. J Nutr Health Aging. 2020;24(5):466-470. doi:10.1007/s12603-020-1356-x