Browse Subject Areas

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Association between Frailty, Osteoporosis, Falls and Hip Fractures among Community-Dwelling People Aged 50 Years and Older in Taiwan: Results from I-Lan Longitudinal Aging Study

  • Li-Kuo Liu,

    Affiliations Aging and Health Research Center, National Yang Ming University, Taipei, Taiwan, Center for Geriatrics and Gerontology, Taipei Veterans General Hospital, Taipei, Taiwan

  • Wei-Ju Lee,

    Affiliations Aging and Health Research Center, National Yang Ming University, Taipei, Taiwan, Institute of Public Health, National Yang Ming University, Taipei, Taiwan, Department of Family Medicine, Taipei Veterans General Hospital Yuanshan Branch, I-Lan County, Taiwan

  • Liang-Yu Chen,

    Affiliations Aging and Health Research Center, National Yang Ming University, Taipei, Taiwan, Institute of Public Health, National Yang Ming University, Taipei, Taiwan, Center for Geriatrics and Gerontology, Taipei Veterans General Hospital, Taipei, Taiwan

  • An-Chun Hwang,

    Affiliations Aging and Health Research Center, National Yang Ming University, Taipei, Taiwan, Institute of Public Health, National Yang Ming University, Taipei, Taiwan, Center for Geriatrics and Gerontology, Taipei Veterans General Hospital, Taipei, Taiwan

  • Ming-Hsien Lin,

    Affiliations Aging and Health Research Center, National Yang Ming University, Taipei, Taiwan, Center for Geriatrics and Gerontology, Taipei Veterans General Hospital, Taipei, Taiwan

  • Li-Ning Peng,

    Affiliations Aging and Health Research Center, National Yang Ming University, Taipei, Taiwan, Institute of Public Health, National Yang Ming University, Taipei, Taiwan, Center for Geriatrics and Gerontology, Taipei Veterans General Hospital, Taipei, Taiwan

  • Liang-Kung Chen

    Affiliations Aging and Health Research Center, National Yang Ming University, Taipei, Taiwan, Center for Geriatrics and Gerontology, Taipei Veterans General Hospital, Taipei, Taiwan

Association between Frailty, Osteoporosis, Falls and Hip Fractures among Community-Dwelling People Aged 50 Years and Older in Taiwan: Results from I-Lan Longitudinal Aging Study

  • Li-Kuo Liu, 
  • Wei-Ju Lee, 
  • Liang-Yu Chen, 
  • An-Chun Hwang, 
  • Ming-Hsien Lin, 
  • Li-Ning Peng, 
  • Liang-Kung Chen



Association of frailty with adverse clinical outcomes has been reported in Western countries, but data from the Asian population are scarce. This study aimed to evaluate the epidemiology of frailty among community-dwelling middle-aged and elderly population and to explore its association with musculoskeletal health in Taiwan.


I-Lan Longitudinal Aging Study (ILAS) data were retrieved for this study. Frailty was defined by the Fried’s criteria; a comparison of demographic characteristics, physical performance, and body composition, including skeletal muscle mass and bone mineral density (BMD), as well as recent falls, history of hip fractures and the functional status of subjects with different frailty statuses were accomplished.


Overall, the data of 1,839 participants (mean age: 63.9±9.3 years, male 47.5%) were obtained for analysis. The prevalence of pre-frailty was 42.3% in men and 38.8% in women, whereas the prevalence of frailty was 6.9% and 6.7% in men and women, respectively. Frailty was significantly associated with older age, the male gender, larger waist circumference, lower skeletal muscle index, lower hip BMD, poorer physical function, poorer nutritional status, and poorer cognitive function. Also, frailty was significantly associated with osteoporosis (OR: 7.73, 95% CI: 5.01–11.90, p<0.001), history of hip fractures (OR: 8.66, 95% CI: 2.47–30.40, p = 0.001), and recent falls (O.R: 2.53, 95% CI: 1.35–4.76, p = 0.004).


Frailty and pre-frailty, in Taiwan, was closely associated with recent falls, history of hip fractures and osteoporosis among community-dwelling people 50 years of age and older. Furthermore, frailty intervention programs should take an integrated approach towards strengthening both and muscle mass, as well as prevention of falls.


Frailty is a well-recognized geriatric syndrome,[1] which features the loss of function, loss of physiologic reserve, and an increased vulnerability to diseases and death.[2] In addition, frailty is also associated with cognitive impairment,[3] multimorbidity, impaired functional status,[4] risk of falls and fractures,[5] medical and surgical outcomes,[6,7] hospitalizations, institutionalization and mortality.[8] Moreover, frailty is closely associated with body compositional changes and osteoporosis,[9] and may overlap with the pathogenesis of sarcopenia.[10] Despite extensive reports regarding frailty and related adverse health outcomes, the association of frailty and the changes of body composition has not been well understood.[11]

The prevalence of frailty varies greatly from study to study in its use of different diagnostic criteria in different settings.[2,12,13] Although the epidemiology may vary greatly, the age-related increasing trend of frailty prevalence has been clearly shown indifferent studies. As one of the fastest aging countries in the world, Taiwan needs to face the challenges related to population aging as most Western countries are doing.[14,15] Among all health care challenges, the impact of frailty to health and health care outcomes is of great importance. Previous studies have disclosed that frailty was associated with the decline in lean muscle mass, bone mass and the presence of sarcopenia,[1618] which may result in a greater negative impact on older people. Although these associations have been reported in previous studies, little is known regarding the association among Asian populations. Therefore, this study aimed to evaluate the prevalence and clinical characteristics of frailty among the community-dwelling middle aged and elderly population in Taiwan, and to explore the associations of frailty and musculoskeletal health.

Materials and Methods

Study subjects

The I-Lan Longitudinal Aging Study (ILAS) is a community-based aging cohort study in I-Lan County of Taiwan, which aimed to evaluate the complex interrelationship between aging, frailty, sarcopenia and cognitive decline. Community-dwelling people aged 50 years and older were randomly selected for study from the I-Lan County of Taiwan.[19] Selected inhabitants were invited via mail or telephone to participate with the research team, and were enrolled when they signed the consent forms as study participations. The inclusion criteria for ILAS were: (1) inhabitants who presently live in I-Lan County without a plan of moving in the near future, and (2) residents 50 years of age or older. Subjects with the following conditions were excluded: (1) those who were unable to adequately communicate with the research nurses, (2) those unable to complete all evaluation tests due to poor functional status, (3) those who had a limited life expectancy due to major illnesses, and (4) current residents in long-term care facilities. Overall, the data of 1,839 participants of ILAS were retrieved for study. All participants signed a written informed consent. The whole study and the consent procedure had been approved by the Institutional Review Board of National Yang Ming University.

Demography, physical examinations and laboratory examinations

A questionnaire consisting of demographic information, socioeconomic condition, medical history and the burden of chronic diseases was evaluated using Charlson’s Comorbidity Index.[20] Tobacco usage was categorized into three classes: non-smoker, ex-smoker (quit in past 6 months) and current smoker. Participants who consumed alcohol were categorized as drinkers and non-drinkers. A comprehensive functional assessment was performed on all participants by using the following: the Functional Autonomy Measurement System for physical function test,[21] the Center for Epidemiologic Studies Depression Scale (CES-D) for measuring the mood status,[22] the Mini-Nutrition Assessment (MNA) for nutritional status measurement,[23] and the Mini–Mental State Examination (MMSE) for cognitive function measurement.[24]

All subjects underwent anthropometric measurements by research nurses, including height and body weight, and the body mass index (BMI) was calculated accordingly. Baseline blood samples were obtained for each participant in the morning after an overnight fasting of at least 10 hours. Serum levels of albumin and total cholesterol were measured using an automatic analyzer (ADVIA 1800, Siemens, Malvern, PA, USA). Whole-blood glycated hemoglobin A1c (HbA1c) was measured by an enzymatic method using the Tosoh G8 HPLC Analyzer (Tosoh Bioscience, Inc., San Francisco, CA, USA). Serum levels of intact-parathyroid hormone (i-PTH) (Siemens Advia Centaur) and 25-hydroxyvitamin D (25(OH)D) (Diasorin Liaison) were also measured by ELISA methods. High-sensitivity C-reactive protein (hs-CRP) was determined by an immunoturbidimetric assay (Siemens Advia 1800) for further analysis.

Muscle strength and physical performance

For all participants, handgrip strength of the dominant hand was measured using digital dynamometers (Smedlay’s Dynamo Meter; TTM, Tokyo, Japan), with participants standing in an upright position with both arms down on their sides. The best results of three tests were used for further analysis. Moreover, participants performed a timed 6-meter walk for each participant to evaluate their physical performance.

Bone mineral density (BMD) and body composition

A whole body dual-energy X-ray absorptiometry (DXA) scan was performed on each participant to measure their total body fat mass and fat-free lean body mass (LBM) by using a Lunar Prodigy instrument (GE Healthcare, Madison, WI, USA). Appendicular skeletal muscle mass (ASM) was calculated as the sum of the lean soft tissue mass of all four limbs. In this study, height-adjusted muscle index, or relative appendicular skeletal muscle (RASM),[25] was calculated by appendicular skeletal muscle mass divided by height (m) square (ASM/height2, kg/m2). BMD at the lumbar spine and bilateral hip joints were measured for analysis.

Definition of Frailty

In this study, frailty is defined by Fried’s criteria, which includes exhaustion, weakness, slowness, physical inactivity and weight loss.[26] Exhaustion was defined using the 2 statements by the Center for Epidemiologic Studies-Depression scale (CES-D). Weakness was defined by low handgrip strength, and slowness was defined by slow gait speed. Physical inactivity was evaluated by using the International Physical Activity Questionnaire (IPAQ).[27,28] Weight loss was defined as having involuntary weight loss of >5% in the past year or 3kgs within past 3 months. Weakness, slowness and physical inactivity referred to those who performed lower than the gender-specific lowest quintile of the study population. A participant was classified as frail if he/she was positive for three of more items on the Fried’s criteria, and those who were positive for one or two items were classified as pre-frail. Those who were negative on all 5 items of Fried’s criteria were considered robust.

Statistical analysis

In this study, continuous variables were expressed as the mean ± standard deviation, and the categorical data was expressed by percentages. Comparisons of continuous data between groups were done by Student’s t test and comparisons of categorical data were done by Chi square test when appropriate. Comparisons between groups of different frailty statuses were performed by one-way ANOVA. To study the cross-sectional association between bone health, muscle quality and the frailty syndrome, multinomial logistic regression was used, allowing the modeling of the prefrail and frail states by using robust as reference group. Further gender-specific analysis was also performed for the above-mentioned conditions.

The covariates of interest, waist circumference, muscle index, bone mineral density were also analyzed. Other covariates included age, gender, functional status, cognition status, nutrition, and comorbid conditions. Finally, serum 25(OH)D and i-PTH level were added to the model because they were closely related to bone mineral density and fall.

The first sequential model included basic characteristics, bone density and muscle quality. The second model added functional confounders, and the serum markers related to bone and fall were added to the third model.


Overall, data of 1,839 participants 50 years of age and older (mean age: 63.9±9.3 years, 47.5% males) from ILAS were retrieved for study. Table 1 summarized the comparisons of demographic characteristics of study participants between genders. In this study, BMI was similar between men and women, but men had significantly higher lean body mass, appendicular skeletal muscle mass, and skeletal muscle index (RASM) than women. In contrast, the women had higher total body fat percentage and more total body fat mass than men. Also, men had significantly stronger handgrip strength (35.1±8.3 Kg vs. 21.8±5.4 Kg, P<0.001), and faster gait speed (1.6±0.5 vs. 1.4±0.4 m/s, P<0.001) than women. Moreover, men also had significantly higher bone mineral density in both their lumbar spine and femoral neck (Table 1).

Table 1. Demographic characteristics of participants of the I-Lan Longitudinal Aging Study.

Table 2 summarized the comparisons of clinical characteristics between subjects in different frailty statuses. The prevalence of pre-frailty was 42.3% in men and 38.8% in women, while frailty was 6.9% and 6.7% in men and women, respectively. Overall, frail subjects were significantly older but pre-frail and frail participants had higher waist circumference than those robust subjects, although the BMI did not differ significantly between them. Also, smoking was not significantly different between frailty groups but frail people were less likely to consume alcohol habitually.

Table 2. Baseline association of demographic and health characteristics with frailty: the I-Lan Longitudinal Aging Study.

In the body composition analysis, frail people had significantly lower lean body mass, appendicular skeletal muscle mass, RASM and BMD when compared with other groups. However, the serum levels of total 25-OH vitamin D were similar in robust and pre-frail groups, but significantly lower in the frail group. The serum levels of i-PTH were similar between subjects with different frailty statuses. Comparisons of functional status, depressive symptoms, nutritional status and cognitive function showed a declining trend between different frailty statuses. Also, frail people had the highest CCI scores, followed by pre-frail and robust subjects, which was statistically significant. Comparisons of serum markers for protein-energy nutrition such as albumin and total cholesterol and total lymphocyte counts showed no statistical differences between subjects with different frailty statuses.

Table 3 showed the odds ratios for frailty status in association with poor medical conditions. Frailty was significantly associated with osteoporosis (OR: 7.73, 95% CI: 5.01–11.90, p<0.001), history of hip fractures (OR: 8.66, 95% CI: 2.47–30.40, p = 0.001), and recent falls (O.R: 2.53, 95% CI: 1.35–4.76, p = 0.004). Gender differences were only found in the association between osteoporosis and frailty status. In women, worse frail conditions were found to be at a higher risk of osteoporosis. The odds ratio was 2.62 in the prefrail group and 8.25 in the frail group of women compared with their robust college. Robust and prefrail men had a lower risk of osteoporosis compared with robust women, but the odds ratio increased to 2.85 when it came to frail men.

Table 3. Odds ratios for frailty status in association with poor medical conditions.

In multinomial logistic regression analysis, we found that older, male, with larger waist circumference, lower muscle mass index, lower hip BMD, lower SMAF scores (poorer functional status), lower MNA score (higher malnutrition or undernutrition risk), and lower MMSE score (poorer cognition) were all independent risk factors for pre-frailty and frailty (Table 4).

Table 4. Association between frailty, physical performance and body composition as described by Odds Ratios (ORs) for multinomial logistic regression models.


The prevalence of frailty in different epidemiological studies varied from 4% to 13% by using different diagnostic criteria,[3,2931] whereas the prevalence of pre-frailty ranged from 28% to 44%.[12,26] In this study, the prevalence of frailty and pre-frailty was 6.8%, and 40.5%, respectively. The results were compatible to the report from the CHS study,[26] and the prevalence was in between the two previous Taiwanese studies.[32,33] However, the inclusion/exclusion criteria of the study participants for ILAS were more similar to the CHS in that both studies focused on otherwise healthy community-dwelling older people. Therefore, we considered the prevalence of frailty in ILAS to be more feasible for international comparisons than those studies carried out Taiwan.

Obesity paradox of older people is a challenging public health issue in the aging society, which should be managed by a life course approach.[34,35] In this study, frailty was not associated with BMI and the percentage of body fat, but the waist circumferences of pre-frail and frail subjects were significantly larger than the robust subjects. Some studies suggested that central obesity and fat redistribution were important predictors of frailty,[3638] rather than general body mass or fat mass. On the other hand, pre-frail and frail subjects had lower lean body mass, appendicular skeletal mass and lower skeletal muscle index than the robust subjects despite having similar BMI between the groups. Overall, frailty is significantly associated with the decline of physical function and changes of body composition, which may be mainly due to loss of bone and muscle mass without the significant increase in fat mass.

In this study, a strong association between frailty and lower BMD, in both the lumbar spine and hips of older adults, was identified, even after adjusting for age, gender and functional status so that they were compatible with previous studies.[18,39,40] A significant health hazard of frailty was falls and related fragility fractures.[41,42] In this study, frail subjects were more likely to fall, and to have osteoporosis, as well as sarcopenia and a history of hip fractures. Newton et al. demonstrated that the BMD was significantly lower in frail elderly people, especially among those with recurrent falls.[43] Also, the higher fracture risk of frailty was independent of BMD measurements among the elderly population.[44] Hence, a comprehensive survey of the musculoskeletal health and implementation of fall prevention was of great importance while frailty is identified in clinical practice.[45] Similar to previous studies,[46,47] frailty was associated with lower serum levels of vitamin D in this study, but the serum levels of i-PTH were similar between groups. Besides musculoskeletal health, frailty was also associated with poorer functional status, poorer cognitive function, higher malnutrition risk, and higher burden of chronic conditions as in some of the previous studies.[4,41,48] Moreover, frail elderly people also had higher serum levels of HbA1c and hs-CRP, which was related to chronic inflammation and insulin resistance.[4951]

Despite all the effort that went into the research, there were some limitations in this study. First, the cross-sectional study design may have limited the possibilities of exploring the causal relationship of frailty and poorer musculoskeletal health of the elderly. However, since ILAS is a longitudinal cohort study, we believe that the follow-up data will facilitate in building the causal relationship between frailty and its adverse health impacts. Second, the determination of cut-offs for individual items of the frailty definition, including low physical activity, low handgrip strength and low walking speed were obtained from the study sample from the original frailty definition. Since ILAS excluded subjects with disabilities, determination of the diagnostic cutoffs may not be applied to the general population. As a result, the study may underestimate the actual prevalence of pre-frailty and frailty. Third, participants were only included when they were able to complete their physical tests. Hence, those who were unable to complete the physical function assessments were excluded, which may underestimate the true condition in the general population.

In conclusion, frailty is closely associated with lower bone mineral density, lower skeletal muscle mass, recent falls and history of hip fractures, which denotes a strong risk of further fragility fractures and associated adverse clinical outcomes. Therefore, a frailty intervention programs should take an integrated approach to strengthen both bone and muscle mass, as well as fall prevention.

Supporting Information


We thank our colleagues from the Aging and Health Research Center, National Yang Ming University; Center for Geriatrics and Gerontology, Taipei Veterans General Hospital, as well as the Ministry of Science and Technology of Taiwan (MOST 103–2633-B-400–002).

Author Contributions

Conceived and designed the experiments: LKC MHL. Performed the experiments: LKL WJL. Analyzed the data: LKL LYC. Contributed reagents/materials/analysis tools: ACH LNP. Wrote the paper: LKL LKC.


  1. 1. Ahmed N, Mandel R, Fain MJ. Frailty: an emerging geriatric syndrome. Am J Med. 2007;120: 748–53. pmid:17765039
  2. 2. van Iersel MB, Rikkert MG. Frailty criteria give heterogeneous results when applied in clinical practice. J Am Geriatr Soc. 2006;54: 728–9. pmid:16686901
  3. 3. 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: 518–24. pmid:23669054
  4. 4. Fried LP, Ferrucci L, Darer J, Williamson JD, Anderson G. Untangling the concepts of disability, frailty, and comorbidity: implications for improved targeting and care. J Gerontol A Biol Sci Med Sci. 2004;59: 255–63. pmid:15031310
  5. 5. Fang X, Shi J, Song X, Mitnitski A, Tang Z, Wang C, et al.-Frailty in relation to the risk of falls, fractures, and mortality in older Chinese adults: results from the Beijing Longitudinal Study of Aging. J Nutr Health Aging. 2012;16: 903–7. pmid:23208030
  6. 6. Yamada M, Arai H, Nagai K, Uemura K, Mori S, Aoyama T. Differential determinants of physical daily activities in frail and nonfrail community-dwelling older adults. J Clin Gerontol Geriatr. 2011;2: 42–6.
  7. 7. Makary MA, Segev DL, Pronovost PJ, Syin D, Bandeen-Roche K, Patel P, et al. Frailty as a predictor of surgical outcomes in older patients. J Am Coll Surg. 2010;210: 901–8. pmid:20510798
  8. 8. Shamliyan T, Talley KM, Ramakrishnan R, Kane RL. Association of frailty with survival: a systematic literature review. Ageing Res Rev. 2013;12: 719–36. pmid:22426304
  9. 9. Blain H, Rolland Y, Beauchet O, Annweiler C, Benhamou CL, Benetos A, et al. Usefulness of bone density measurement in fallers. Joint Bone Spine. 2014;81: 403–8. pmid:24703626
  10. 10. 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;39: 412–23. pmid:20392703
  11. 11. Delgado C, Doyle JW, Johansen KL. Association of frailty with body composition among patients on hemodialysis. J Ren Nutr. 2013;23: 356–62. pmid:23648049
  12. 12. Cawthon PM, Marshall LM, Michael Y, Dam TT, Ensrud KE, Barrett-Connor E, et al. Frailty in older men: prevalence, progression, and relationship with mortality. J Am Geriatr Soc. 2007;55: 1216–23. pmid:17661960
  13. 13. Gobbens RJ, Luijkx KG, Wijnen-Sponselee MT, Schols JM. Toward a conceptual definition of frail community dwelling older people. Nurs Outlook. 2010;58: 76–86. pmid:20362776
  14. 14. Chen LK, Rockwood K. Planning for frailty. J Clin Gerontol Geriatr; 2012;3: 3–4.
  15. 15. Chen LK, Inoue H, Won CW, Lin CH, Lin KF, Tsay SF, et al. Challenges of urban aging in Taiwan: Summary of urban aging forum. J Clin Gerontol Geriatr. 2013;4: 97–101.
  16. 16. Jung HW, Kim SW, Lim JY, Kim KW, Jang HC, Kim CH, et al. Frailty status can predict further lean body mass decline in older adults. J Am Geriatr Soc. 2014;62: 2110–7. pmid:25370293
  17. 17. Cooper C, Dere W, Evans W, Kanis JA, Rizzoli R, Sayer AA, et al. Frailty and sarcopenia: definitions and outcome parameters. Osteoporos Int. 2012;23: 1839–48. pmid:22290243
  18. 18. Rolland Y, Abellan van Kan G, Benetos A, Blain H, Bonnefoy M, Chassagne P, et al. Frailty, osteoporosis and hip fracture: causes, consequences and therapeutic perspectives. J Nutr Health Aging. 2008;12: 335–46. pmid:18443717
  19. 19. Liu LK, Lee WJ, Chen LY, Hwang AC, Lin MH, Peng LN, et al. Sarcopenia, and its association with cardiometabolic and functional characteristics in Taiwan: results from I-Lan Longitudinal Aging Study. Geriatr Gerontol Int. 2014;14 Suppl 1: 36–45. pmid:24450559
  20. 20. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. Journal of Chronic Diseases. 1987;40: 373–83. pmid:3558716
  21. 21. Hebert R, Carrier R, Bilodeau A. The Functional Autonomy Measurement System (SMAF): description and validation of an instrument for the measurement of handicaps. Age Ageing. 1988;17: 293–302. pmid:2976575
  22. 22. LS R. The CES-D Scale: a self-report depression scale for research in the general population. Appl Psychol Meas. 1977;1: 385–401.
  23. 23. Guigoz Y. The Mini Nutritional Assessment (MNA) review of the literature—What does it tell us? J Nutr Health Aging. 2006;10: 466–85; discussion 485–7. pmid:17183419
  24. 24. Folstein MF, Folstein SE, McHugh PR. "Mini-mental state". A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1075;12: 189–98. pmid:1202204
  25. 25. Liu LK, Lee WJ, Liu CL, Chen LY, Lin MH, Peng LN, et al. Age-related skeletal muscle mass loss and physical performance in Taiwan: Implications to diagnostic strategy of sarcopenia in Asia. Geriatr Gerontol Int. 2013;13: 964–71. pmid:23452090
  26. 26. Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, et al. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci. 2001;56: M146–56. pmid:11253156
  27. 27. Qu NN, Li KJ. [Study on the reliability and validity of international physical activity questionnaire (Chinese Vision, IPAQ)]. Zhonghua Liu Xing Bing Xue Za Zhi. 2004;25: 265–8. pmid:15200945
  28. 28. Liou YM, Jwo CJ, Yao KG, Chiang LC, Huang LH. Selection of appropriate Chinese terms to represent intensity and types of physical activity terms for use in the Taiwan version of IPAQ. J Nurs Res. 2008;16: 252–63. pmid:19061172
  29. 29. Moreira VG, Lourenco RA. Prevalence and factors associated with frailty in an older population from the city of Rio de Janeiro, Brazil: the FIBRA-RJ Study. Clinics (Sao Paulo). 2013;68: 979–85.
  30. 30. Jurschik P, Nunin C, Botigue T, Escobar MA, Lavedan A, Viladrosa M. Prevalence of frailty and factors associated with frailty in the elderly population of Lleida, Spain: the FRALLE survey. Arch Gerontol Geriatr. 2012;55: 625–31. pmid:22857807
  31. 31. Jung HW, Kim SW, Ahn S, Lim JY, Han JW, Kim TH, et al. Prevalence and outcomes of frailty in Korean elderly population: comparisons of a multidimensional frailty index with two phenotype models. PLoS One. 2014;9: e87958. pmid:24505338
  32. 32. Lin CC, Li CI, Meng NH, Lin WY, Liu CS, Lin CH, et al. Frailty and its associated factors in an elderly taiwanese metropolitan population. J Am Geriatr Soc. 2013;61: 292–4. pmid:23405925
  33. 33. Chen CY, Wu SC, Chen LJ, Lue BH. The prevalence of subjective frailty and factors associated with frailty in Taiwan. Arch Gerontol Geriatr. 2010;50 Suppl 1: S43–7. pmid:20171456
  34. 34. Strandberg TE, Stenholm S, Strandberg AY, Salomaa VV, Pitkala KH, Tilvis RS. The "obesity paradox," frailty, disability, and mortality in older men: a prospective, longitudinal cohort study. Am J Epidemiol. 2013;178: 1452–60. pmid:24008903
  35. 35. Kim YP, Kim S, Joh JY, Hwang HS. Effect of interaction between dynapenic component of the European working group on sarcopenia in older people sarcopenia criteria and obesity on activities of daily living in the elderly. J Am Med Dir Assoc. 2014;15: 371 e1–5.
  36. 36. Shah K, Hilton TN, Myers L, Pinto JF, Luque AE, Hall WJ. A new frailty syndrome: central obesity and frailty in older adults with the human immunodeficiency virus. J Am Geriatr Soc. 2012;60: 545–9. pmid:22315957
  37. 37. Hubbard RE, Lang IA, Llewellyn DJ, Rockwood K. Frailty, body mass index, and abdominal obesity in older people. J Gerontol A Biol Sci Med Sci. 2010;65: 377–81. pmid:19942592
  38. 38. Goulet ED, Hassaine A, Dionne IJ, Gaudreau P, Khalil A, Fulop T, et al. Frailty in the elderly is associated with insulin resistance of glucose metabolism in the postabsorptive state only in the presence of increased abdominal fat. Exp Gerontol. 2009;44: 740–4. pmid:19723576
  39. 39. Crepaldi G, Maggi S. Sarcopenia and osteoporosis: A hazardous duet. J Endocrinol Invest. 2005;28(10 Suppl): 66–8. pmid:16550726
  40. 40. Sternberg SA, Levin R, Dkaidek S, Edelman S, Resnick T, Menczel J. Frailty and osteoporosis in older women—a prospective study. Osteoporos Int. 2014;25: 763–8. pmid:24002542
  41. 41. Tom SE, Adachi JD, Anderson FA Jr., Boonen S, Chapurlat RD, Compston JE, et al. Frailty and fracture, disability, and falls: a multiple country study from the global longitudinal study of osteoporosis in women. J Am Geriatr Soc. 2013;61: 327–34. pmid:23351064
  42. 42. Womack JA, Goulet JL, Gibert C, Brandt CA, Skanderson M, Gulanski B, et al. Physiologic frailty and fragility fracture in HIV-infected male veterans. Clin Infect Dis. 2013;56: 1498–504. pmid:23378285
  43. 43. Newton JL, Kenny RA, Frearson R, Francis RM. A prospective evaluation of bone mineral density measurement in females who have fallen. Age Ageing. 2003;32: 497–502. pmid:12957998
  44. 44. Cheung EY, Bow CH, Cheung CL, Soong C, Yeung S, Loong C, et al. Discriminative value of FRAX for fracture prediction in a cohort of Chinese postmenopausal women. Osteoporos Int. 2012;23: 871–8. pmid:21562875
  45. 45. Frisoli A Jr., Chaves PH, Ingham SJ, 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;48: 952–7. pmid:21195216
  46. 46. Hirani V, Naganathan V, Cumming RG, Blyth F, Le Couteur DG, Handelsman DJ, et al. Associations between frailty and serum 25-hydroxyvitamin D and 1,25-dihydroxyvitamin D concentrations in older Australian men: the Concord Health and Ageing in Men Project. J Gerontol A Biol Sci Med Sci. 2013;68: 1112–21. pmid:23657973
  47. 47. Wong YY, McCaul KA, Yeap BB, Hankey GJ, Flicker L. Low vitamin D status is an independent predictor of increased frailty and all-cause mortality in older men: the Health in Men Study. J Clin Endocrinol Metab. 2013;98: 3821–8. pmid:23788685
  48. 48. Fried LP, Kronmal RA, Newman AB, Bild DE, Mittelmark MB, Polak JF, et al. Risk factors for 5-year mortality in older adults: the Cardiovascular Health Study. JAMA. 1998;279: 585–92. pmid:9486752
  49. 49. Morley JE, Haren MT, Rolland Y, Kim MJ. Frailty. Medical Clinics of North America. 2006;90: 837–47. pmid:16962845
  50. 50. Morley JE, Baumgartner RN. Cytokine-related aging process. J Gerontol A Biol Sci Med Sci. 2004;59: M924–9. pmid:15472157
  51. 51. Abbatecola AM, Ferrucci L, Grella R, Bandinelli S, Bonafe M, Barbieri M, et al. Diverse effect of inflammatory markers on insulin resistance and insulin-resistance syndrome in the elderly. J Am Geriatr Soc. 2004;52: 399–404. pmid:14962155