HomeLarge Type Edition
HOME ARCHIVE SEARCH TABLE OF CONTENTS
This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Services
Right arrow Download to citation manager
Citing Articles
Right arrow Citing Articles via HighWire
PubMed
Right arrow PubMed Citation
The Journals of Gerontology Series A: Biological Sciences and Medical Sciences 62:1382-1388 (2007)
© 2007 The Gerontological Society of America

Fat Mass But Not Fat-Free Mass Is Related to Physical Capacity in Well-Functioning Older Individuals: Nutrition as a Determinant of Successful Aging (NuAge)—The Quebec Longitudinal Study

Danielle R. Bouchard, Serge Beliaeff, Isabelle J. Dionne and Martin Brochu

Health and Social Services Centre, Sherbrooke University Institute of Geriatrics, Faculty of Physical and Sports Education, University of Sherbrooke, Quebec, Canada.

Address correspondence to Martin Brochu, PhD, Centre de recherche sur le vieillissement, Institut universitaire de gériatrie de Sherbrooke, 1036, rue Belvédère Sud, Sherbrooke (Québec) Canada J1H 4C4. E-mail: martin.brochu{at}usherbrooke.ca


   Abstract
Top
 Abstract
Methods
 Results
 Discussion
 References
 
Background. Aging is associated with increases in fat mass (FM) and decreases in fat-free mass (FFM) that may affect physical capacity. However, it is not clear whether high FM or low FFM contribute more to a reduction in physical capacity.

Methods. A structural equation modeling strategy was used to test an explanatory model of the association between body composition and physical capacity. The concept of physical capacity was assessed by walking speed at a normal pace and the one leg stand test. To test the model, 904 men and women between 67 and 84 years old were studied. Body composition was measured by dual-energy x-ray absorptiometry (DXA). Confounding factors related to body composition and physical capacities were included in the explanatory model (physical activity level, age, gender, and number of reported diseases).

Results. The final model showed that physical capacity can be represented by a factorial first-order model including generic measures of walking speed and balance. Moreover, our results showed that percentage of FM was significantly associated with physical capacity (p <.01), whereas no such association was observed with FFM. Other variables such as physical activity level, number of self-reported diseases, and age were associated with physical capacity (all p <.01). Overall, the proposed model explained 48% and 57% of the variance observed in men and women when using the one leg stand and the walking speed at normal pace tests as measures of physical capacity.

Conclusion. FM was significantly and inversely correlated with physical capacity, whereas FFM was not associated when controlled for other potential confounding variables. More studies are needed to investigate the effect of different levels of obesity on physical capacity in older individuals.

The population is aging, which is associated with an increased prevalence of people reporting physical limitations (1). In fact, data from Statistics Canada showed that 40% of individuals older than 65 years reported at least one limitation in 2001 (1). Furthermore, low physical capacity (PC) can predict disability, institutionalization, and death (2).

Aging is also accompanied by significant changes in body composition. We now know that fat mass (FM) increases and fat-free mass (FFM) decreases with age, even when body weight (3) and physical activities (4) remain stable. Variations in FM and FFM have been found to be related to lower PC in elderly persons (5). However, there is some uncertainty regarding whether high FM or low FFM is more related to declines in PC. Some studies reported that low FFM was associated with incapacity (6,7), whereas others reported that FM is the major contributor (8,9).

It is difficult to get a clear picture of the association between body composition and PC because of differences in methodologies used. For example, studies have used self-reported measures such as activities of daily living (ADL), instrumental activities of daily living (IADL), and the Medical Outcomes Study 36-item survey (SF-36) to evaluate PC limitations (10,11). In contrast, other studies have used direct measures such as walking speed, leg extension power, and the chair stand test to measure PC (12,13). Despite the fact that questionnaires have been shown to be easy to administer and generally useful for measuring PC at a population level (14), tasks related to PC are complex and known to be influenced by psychological status, judgment, mood state, fitness level, and strength (15). These latter factors may explain the low correlations observed (r < 0.60) between self-reported and direct measures of PC (16,17). One other major problem relates to the methods used to determine body composition. Studies have used body mass index (BMI) (9), prediction equations (18), bioimpedance (7), or waist circumference (19).

The aim of this study was to investigate the contribution of FM and FFM to PC when taking into account other confounding variables such as physical activity level, age, and the number of self-reported diseases (13). To overcome limitations in the literature, we used (i) direct measures of body composition and PC with a large cohort of older men and women, and (ii) structural equations with latent variables (two or more measures combined to build a specific concept analyzed with Linear Structural Relationship [LISREL] software).


   METHODS
Top
 Abstract
 Methods
Results
 Discussion
 References
 
Study Sample
NuAge is a 5-year observational study of 1793 community-dwelling men and women aged 68–82 years in general good physical and mental health, and functionally independent at recruitment in 2003. The study sample was drawn from a random sample from Montreal (N = 889) and Sherbrooke sites (N = 904) stratified by age and sex obtained from the "Régie de l'assurance maladie du Québec." The participants recruited in each age strata are as follow: 70 ± 2 years: 337 women, 329 men; 75 ± 2 years: 305 women, 289 men; 80 ± 2 years: 298 women, 235 men. Participants are tested annually using rigorous standardized procedures over the 5-year longitudinal study. Recruitment was carried out in two phases. The two phases were completed between December 2003 and April 2005; first by an introductory letter and a phone call to do the preselection questionnaire, and second by a clinical examination done at the Institut Universitaire de Gériatrie de Montréal or the Institut Universitaire de Gériatrie de Sherbrooke.

Inclusion criteria were: French or English speaking, willing to commit for a 5-year period, able to walk without help, free of disabilities in ADLs, no cognitive impairment, able to walk 300 meters and to climb 10 stairs without rest, and able to sign an informed consent form. Exclusion criteria were: class II heart failure; chronic obstructive pulmonary disease requiring home oxygen therapy or oral steroids; inflammatory digestive disease; and cancer treated by radiation therapy, chemotherapy, or cancer surgery in the 5 years prior to enrollment. All participants signed an informed consent document approved by the ethics committees of both institutions.

For the purpose of the present study, data from 465 women and 439 men were analyzed because only participants from the Sherbrooke area underwent measurement using dual-energy x-ray absorptiometry (DXA). Two objectives tests were used to measure PC: walking speed at normal pace and one leg stand test. This article uses baseline data of the longitudinal study.

Assessment of PC
PC measurements used in this study were adapted from a previous study that had used similar validated tests (20). The tests used in the present study were chosen for their validity and accessibility and were supervised by trained research assistants.

Walking speed (normal pace).-- Participants walked twice at their usual pace over a 4-meter course (21). Time in milliseconds was recorded between the 1st and the 4th meter with a sport watch. The best trial was used for data analysis. Distance (m) divided by time (ms) was used to calculate walking speed.

Balance (one leg stand).-- Without shoes, participants stood 1 meter from the wall with one foot raised about 6 inches off the ground (22). During the procedure, they kept their hands on their hips and stood on one leg. The maximum time for the test was 60 seconds. The time was stopped when participants used the wall to maintain their balance or when they put their foot down. The mean performance on each side after two attempts was recorded, and the mean score for both legs was used in the analysis.

Body Composition
FM and FFM were measured using DXA (GE Lunar Prodigy; Madison, WI). Test–retest measures in our laboratory yielded a mean absolute difference of 5.7% and 1.1% for FM and FFM, respectively. Percent body fat and appendicular skeletal mass index (ASMI, kg/m2) were then calculated. ASMI (muscle mass of both legs and arms divided by height [m2]) was used to assess ASMI. This index has been reported to be a good way to quantify the association between muscle mass and functional limitations (23).

Physical Activity Level
Physical activity level was assessed using the Physical Activity Scale for the Elderly (PASE), which is validated in older populations (24). Daily activity was then recorded as a global performance according to the intensity and time of reported activities.

Sum of Self-Reported Diseases
Diseases were reported using a modified version of the Older American Resources and Services questionnaire (25). Participants were asked to answer "yes" (1) or "no" (0) if they had been diagnosed by a physician for 25 common diseases (e.g., diabetes, osteoporosis, high blood pressure). Participants also had the possibility of adding other diseases at the end of the questionnaire. The total number of diseases was recorded by adding the number of positive answers.

Data Analysis
Data are presented as means ± standard deviation (SD). Unpaired t tests were used to compare men and women. LISREL was used to estimate coefficients in a set of structural equations to test the basic hypothesis, which is the fit of our model with the present literature. In simple terms, this software allows simultaneous analysis of two or more dependent variables (such as walking speed and balance) as only one concept (PC, as proposed in the present study), and control for confounding variables found in the scientific literature.

First, we tested the homogeneity of variances and the normality of the distribution across variables. Correlation matrices were calculated with PRELIS, a LISREL utility program. Second, we tested the invariance hypothesis ({eta} = {Gamma}{xi} + {zeta}) to determine if there a relationship between the study variables and the literature data with a chi-square test. The plausibility of the hypothetical model was evaluated by the strategy of structural equation proposed by Jöreskog and Sörbom (26). The ML (maximum likelihood) method was used to estimate the results as proposed by Rivera and Satorra (27). Root mean square error of approximation (RMSEA) was used to guide the overall fit of the model (28). Finally, the 95% statistical threshold was used in our analyses to evaluate the structural coefficient. All lambdas x ({lambda}x) and theta-delta ({theta}{delta}) were fixed at 1, whereas theta-epsilon ({theta}{epsilon}), lambda y ({lambda}y) were set free, except for Models 1 and 2, in which they were fixed at 1.


   RESULTS
Top
 Abstract
 Methods
 Results
Discussion
 References
 
Participant characteristics are presented in Table 1. Participants were between 67 and 84 years old, and women represented 51.4% of the cohort. Men performed better than women in both PC tests (p <.01).


View this table:
[in this window]
[in a new window]

  		Table 1. Descriptive Characteristics of the Cohort.

 
Hypothetical Model
Model 1.-- The fit of the model was investigated, with the balance test as the independent variable including potential confounders such as percentage of FM, ASMI, age, physical activity level, and the sum of self-reported diseases. In both men (Figure 1a) and women (Figure 1b), ASMI was the only variable not significantly related to the one leg stand test result. Physical activity level was positively related to the result, whereas the other variables were negatively related. Age was the most important contributor to the result in men (r = –0.30, p <.01) and women (r = –0.37 p <.01). Overall, the first model shows an R2 of 0.24 in men and 0.29 in women (Table 2).


Figure 01
View larger version (16K):
[in this window]
[in a new window]

  		Figure 1. Hypothetical model for balance in men (a) and women (b). {theta}{delta} (theta delta) = reliability coefficient errors of the income variables; {delta} (delta) = error of income variables; x = income variable; {lambda} (lambda) = relationship between income variable and {eta}; {xi} (ksi) = latent construct; {gamma} (gamma) = relationship between {eta} and outcome variable); {eta} (eta) = instrumental latent construct; y = outcome variable; {epsilon} (epsilon) = error of outcome variables; {theta}{epsilon} (theta epsilon) = reliability coefficient errors of the outcome variables; DXA = dual-energy x-ray absorptiometry; ASMI = appendicular skeletal mass index (kg/m2); PASE = Physical Activity Scale for the Elderly

 

View this table:
[in this window]
[in a new window]

  		Table 2. Statistical Characteristics of the Proposed Models.

 
Model 2.-- In the second model, the fit of the model was tested with the mean walking speed at normal pace using the same potential confounders as in Model 1. In both men (Figure 2a) and women (Figure 2b), ASMI was the only variable not significantly related to the walking speed. Physical activity level was positively related to walking speed, whereas the other variables were negatively related. The second model shows an R2 of 0.15 in men and 0.23 in women (Table 2).


Figure 02
View larger version (16K):
[in this window]
[in a new window]

  		Figure 2. Hypothetical model for walking speed in men (a) and women (b). {theta}{delta} (theta delta) = reliability coefficient errors of the income variables; {delta} (delta) = error of income variables; x = income variable; {lambda} (lambda) = relationship between income variable and {eta}; {xi} (ksi) = latent construct; {gamma} (gamma) = relationship between {eta} and outcome variable); {eta} (eta) = instrumental latent construct; y = outcome variable; {epsilon} (epsilon) = error of outcome variables; {theta}{epsilon} (theta epsilon) = reliability coefficient errors of the outcome variables; DXA = dual-energy x-ray absorptiometry; ASMI = appendicular skeletal mass index (kg/m2); PASE = Physical Activity Scale for the Elderly

 
Model 3.-- In the final model (Figure 3a and b), both tests were combined as an overall index of PC. In men, age (r = –0.61, p <.001) and percentage of FM (r = –0.42, p <.001) showed the strongest correlations with PC, whereas ASMI was not significantly associated (Figure 3a). PC was positively associated with physical activity level (r = 0.29, p <.001) and negatively with the number of self-reported diseases (r = –0.24, p <.001). Overall, in men, the model explained 48% of the variance observed (Table 2).


Figure 03
View larger version (17K):
[in this window]
[in a new window]

  		Figure 3. Hypothetical model for physical capacity in men (a) and women (b). {theta}{delta} (theta delta) = reliability coefficient errors of the income variables; {delta} (delta) = error of income variables; x = income variable; {lambda} (lambda) = relationship between income variable and {eta}; {xi} (ksi) = latent construct; {gamma} (gamma) = relationship between {eta} and outcome variable); {eta} (eta) = instrumental latent construct; y = outcome variable; {epsilon} (epsilon) = error of outcome variables; {theta}{epsilon} (theta epsilon) = reliability coefficient errors of the outcome variables; DXA = dual-energy x-ray absorptiometry; ASMI = appendicular skeletal mass index (kg/m2); PASE = Physical Activity Scale for the Elderly

 
In women, the same pattern was observed (Figure 3b). Age (r = –0.73) and percentage of FM (r = –0.46) showed the strongest correlations (p <.001), whereas ASMI was not significantly associated with PC. PC was also positively correlated with physical activity level (r = 0.40, p <.001) and negatively with the number of self-reported diseases (r = –0.28, p <.001). Overall, in women, the model explained 57% of the variance observed in PC (Table 2).

Based on the LISREL procedure, the chi square was not significant in all models (p >.05). This result indicates that the three models studied fit with the literature. Furthermore, the adjustment index RMSEA also indicates that the three models are in agreement with the literature (p <.05). Models 1 and 2 were completely saturated (p = 1.00) because there was only 1 "y" variable analyzed (Table 2).


   DISCUSSION
Top
 Abstract
 Methods
 Results
 Discussion
References
 
To our knowledge, this is the first study that explores the relationship between direct measures of body composition and PC in a large cohort of men and women. First, as previously proposed (7), age, physical activity level, and number of self-reported diseases were all related to PC in our cohort. However, contrary to previous reports (6,7), ASMI was not associated with PC. In fact, when considering body composition variables, the best correlate of PC was the level of obesity, as expressed by the percentage of FM. In their study, Ferraro and Booth (29) also reported that the level of obesity was the best correlate of PC. However, conclusions derived from this latter study must be interpreted with caution because it relies on proxy measures of obesity such as BMI, which was not the case in the present study. Actually, BMI has been reported to be a nonspecific measure of body composition that does not discriminate between muscle and fat tissues, especially in older individuals (30).

Our observations also showed that FM had a comparable effect on PC in both men and women, as shown by the correlation reported. This observation seems to be in agreement with those of Buchner and colleagues (31), who also reported no relationship between gender and walking speed in a population with characteristics similar to those in our population. However, contrary to the present study, others reported that percentage of FM affects physical capacities differently in men and women (32). For example, Jensen and Friedmann (33) showed (when using self-reported questionnaires) that only obese women had an increased risk of having physical limitations. Knowing that correlations between questionnaires and direct measures of PC are generally weak (16,34), discrepancies between our results and those of other published studies must be interpreted with caution. Furthermore, in the present study, adiposity level was measured with DXA, whereas others used BMI (35,36).

Some limitations of the present study need to be addressed. First, our cohort was composed of independent and generally healthy older individuals, which limits our ability to generalize to other populations. This may lead to underestimation of the observed correlations between FFM and PC impairment previously reported by Buchner and colleagues (31). In fact, based on the walking speed threshold of 0.8 m/s proposed by Lauretani and colleagues (37) to define poor PC, only 5.4% of our participants had a slower average walking speed. Similarly, Janssen and colleagues (38) proposed a threshold of 7.26 kg/m2 in men and 5.45 kg/m2 in women as a minimum ASMI to have normal functional capacities. Despite the fact that PC was assessed with self-reported questionnaires in their study, if we consider those gender thresholds, only 16 men and 9 women in the present study were below the proposed values. Second, the cross-sectional design precludes conclusions regarding cause and effect relationships. Finally, some may argue that the use of percentage of FM to assess obesity levels may not be appropriate because two individuals can present similar relative values despite large differences in absolute terms. However, we obtained the same results when using other measures of body composition in the model such as %FFM as well as total FFM and FM.

Despite the limitations reported, the present study used a robust study design in a large and well-characterized cohort composed of men and women between 68 and 82 years old. In addition, instead of using questionnaires, we used valid, reliable, and direct measures of PC. Furthermore, the two physical tests studied are reliable (39) and commonly used to measure PC (6,7,40). Finally, body composition was measured with DXA. Taken together, we consider that the methodology used strengthens our results.

Conclusion
After controlling for potential confounding variables reported in the literature, percentage of FM was significantly associated with the PC concept whereas FFM was not when both measures of body composition were included in the same model. Our results may suggest that weight management interventions would be helpful to maintain or improve PC in older men and women. However, because obesity and aging are associated with long-term disability (41,42), institutionalization (43), and premature death (2), intervention and prospective studies should be conducted to investigate the long-term effects of the level of obesity on PC in older obese individuals.


   Acknowledgments
Top
 Abstract
 Methods
 Results
 Discussion
 References
 
D. R. Bouchard is supported by the Danone Institute. M. Brochu is supported by the Réseau québécois de recherche sur le vieillissement (FRSQ) and the Universty of Sherbrooke. I. J. Dionne and NuAge (The Quebec Longitudinal Study) are supported by the Canadian Institutes of Health Research (CIHR).


   Footnotes
Top
 Abstract
 Methods
 Results
 Discussion
 References
 
Decision Editor: Luigi Ferrucci, MD, PhD

Received November 13, 2006

Accepted March 9, 2007


   References
Top
 Abstract
 Methods
 Results
 Discussion
 References
 

  1. Statistique Canada. A Profile of Disability in Canada, 2001. N 89-577-XIF. 2001.
  2. Inouye SK, Peduzzi PN, Robison JT, Hughes JS, Horwitz RI, Concato J. Importance of functional measures in predicting mortality among older hospitalized patients. JAMA. 1998;279:1187-1193.[Abstract/Free Full Text]
  3. Zamboni M, Mazzali G, Zoico E, et al. Health consequences of obesity in the elderly: a review of four unresolved questions. Int J Obes (Lond). 2005;29:1011-1029.[Medline]
  4. Roubenoff R. Origins and clinical relevance of sarcopenia. Can J Appl Physiol. 2001;26:78-89.[Medline]
  5. Haight T, Tager I, Sternfeld B, Satariano W, van der Laan M. Effects of body composition and leisure-time physical activity on transitions in physical functioning in the elderly. Am J Epidemiol. 2005;162:607-617.[Abstract/Free Full Text]
  6. Evans WJ, Campbell WW. Sarcopenia and age-related changes in body composition and functional capacity. J Nutr. 1993;123:465-468.[Free Full Text]
  7. Sternfeld B, Ngo L, Satariano WA, Tager IB. Associations of body composition with physical performance and self-reported functional limitation in elderly men and women. Am J Epidemiol. 2002;156:110-121.[Abstract/Free Full Text]
  8. Zamboni M, Zoico E, Scartezzini T, et al. Body composition changes in stable-weight elderly subjects: the effect of sex. Aging Clin Exp Res. 2003;15:321-327.[Medline]
  9. Villareal DT, Banks M, Siener C, Sinacore DR, Klein S:. Physical frailty and body composition in obese elderly men and women. Obes Res. 2004;12:913-920.[Medline]
  10. Sulander T, Martelin T, Rahkonen O, Nissinen A, Uutela A. Associations of functional ability with health-related behavior and body mass index among the elderly. Arch Gerontol Geriatr. 2005;40:185-199.[Medline]
  11. He XZ, Baker DW. Body mass index, physical activity, and the risk of decline in overall health and physical functioning in late middle age. Am J Public Health. 2004;94:1567-1573.[Abstract/Free Full Text]
  12. Rolland Y, Lauwers-Cances V, Cesari M, Vellas B, Pahor M, Grandjean H. Physical performance measures as predictors of mortality in a cohort of community-dwelling older French women. Eur J Epidemiol. 2006;21:113-122.[Medline]
  13. Cesari M, Onder G, Russo A, et al. Comorbidity and physical function: results from the aging and longevity study in the Sirente geographic area (ilSIRENTE study). Gerontology. 2006;52:24-32.[Medline]
  14. Applegate WB, Blass JP, Williams TF. Instruments for the functional assessment of older patients. N Engl J Med. 1990;322:1207-1214.[Abstract]
  15. Jette DU, Downing J. Health status of individuals entering a cardiac rehabilitation program as measured by the Medical Outcomes Study 36-item short-form survey (SF-36). Phys Ther. 1994;74:521-527.[Abstract/Free Full Text]
  16. Bohannon RW, Brennan PJ, Pescatello LS, Marschke L, Hasson S, Murphy M. Adiposity of elderly women and its relationship with self-reported and observed physical performance. J Geriatr Phys Ther. 2005;28:10-13.[Medline]
  17. Nybo H, Gaist D, Jeune B, McGue M, Vaupel JW, Christensen K. Functional status and self-rated health in 2,262 nonagenarians: the Danish 1905 Cohort Survey. J Am Geriatr Soc. 2001;49:601-609.[Medline]
  18. Baumgartner RN. Body composition in healthy aging. Ann N Y Acad Sci. 2000;904:437-448.[Medline]
  19. Chen H, Bermudez OI, Tucker KL. Waist circumference and weight change are associated with disability among elderly Hispanics. J Gerontol Med Sci. 2002;57A:M19-M25.[Abstract/Free Full Text]
  20. Guralnik JM, Simonsick EM. Physical disability in older Americans. J Gerontol. 1993;48 Spec No:3–10.
  21. Guralnik JM, Simonsick EM, Ferrucci L, et al. A short physical performance battery assessing lower extremity function: association with self-reported disability and prediction of mortality and nursing home admission. J Gerontol Med Sci. 1994;49A:M85-M94.
  22. Dauty M, Bazin P, Prioux J, Grandet MJ, Potiron-Josse M, Dubois C. [Is it possible to propose the abolition of crutches according to the gait speed in patients with total knee arthroplasty?]. Ann Readapt Med Phys. 2003;46:91-96.[Medline]
  23. Zoico E, Di Francesco V, Guralnik JM, et al. Physical disability and muscular strength in relation to obesity and different body composition indexes in a sample of healthy elderly women. Int J Obes Relat Metab Disord. 2004;28:234-241.[Medline]
  24. Washburn RA, McAuley E, Katula J, Mihalko SL, Boileau RA. The Physical Activity Scale for the Elderly (PASE): evidence for validity. J Clin Epidemiol. 1999;52:643-651.[Medline]
  25. Fillenbaum GG, Smyer MA. The development, validity, and reliability of the OARS multidimensional functional assessment questionnaire. J Gerontol. 1981;36:428-434.
  26. Jöreskog KG, Sörbom D. LISREL 8: User's Reference Guide. Mooresville, IN: Scientific Software, Inc.; 1996.
  27. Rivera PE, Satorra SA. Analysing Group Differences: A Comparison of SEM Approaches. Mahwah, NJ: Lawrence Erlbaum Associates Inc.; 2002:85–104.
  28. Bollen K. Structural equations with latent variables. New York: Wiley; 1989.
  29. Ferraro KF, Booth TL. Age, body mass index, and functional illness. J Gerontol Soc Sci. 1999;54B:S339-S348.[Abstract]
  30. Corrada MM, Kawas CH, Mozaffar F, Paganini-Hill A. Association of body mass index and weight change with all-cause mortality in the elderly. Am J Epidemiol. 2006;163:938-949.[Abstract/Free Full Text]
  31. Buchner DM, Larson EB, Wagner EH, Koepsell TD, de Lateur BJ. Evidence for a non-linear relationship between leg strength and gait speed. Age Ageing. 1996;25:386-391.[Abstract/Free Full Text]
  32. Santillan AA, Camargo CA. Body mass index and asthma among Mexican adults: the effect of using self-reported vs measured weight and height. Int J Obes Relat Metab Disord. 2003;27:1430-1433.[Medline]
  33. Jensen GL, Friedmann JM. Obesity is associated with functional decline in community-dwelling rural older persons. J Am Geriatr Soc. 2002;50:918-923.[Medline]
  34. Peeters A, Bonneux L, Nusselder WJ, De Laet C, Barendregt JJ. Adult obesity and the burden of disability throughout life. Obes Res. 2004;12:1145-1151.[Medline]
  35. Davis JW, Ross PD, Preston SD, Nevitt MC, Wasnich RD. Strength, physical activity, and body mass index: relationship to performance-based measures and activities of daily living among older Japanese women in Hawaii. J Am Geriatr Soc. 1998;46:274-279.[Medline]
  36. Larrieu S, Peres K, Letenneur L, et al. Relationship between body mass index and different domains of disability in older persons: the 3C study. Int J Obes Relat Metab Disord. 2004;28:1555-1560.[Medline]
  37. Lauretani F, Russo CR, Bandinelli S, et al. Age-associated changes in skeletal muscles and their effect on mobility: an operational diagnosis of sarcopenia. J Appl Physiol. 2003;95:1851-1860.[Abstract/Free Full Text]
  38. Janssen I, Baumgartner RN, Ross R, Rosenberg IH, Roubenoff R. Skeletal muscle cutpoints associated with elevated physical disability risk in older men and women. Am J Epidemiol. 2004;159:413-421.[Abstract/Free Full Text]
  39. Curb JD, Ceria-Ulep CD, Rodriguez BL, et al. Performance-based measures of physical function for high-function populations. J Am Geriatr Soc. 2006;54:737-742.[Medline]
  40. Baumgartner RN, Wayne SJ, Waters DL, Janssen I, Gallagher D, Morley JE. Sarcopenic obesity predicts instrumental activities of daily living disability in the elderly. Obes Res. 2004;12:1995-2004.[Medline]
  41. Guralnik JM, Ferrucci L, Simonsick EM, Salive ME, Wallace RB. Lower-extremity function in persons over the age of 70 years as a predictor of subsequent disability. N Engl J Med. 1995;332:556-561.[Abstract/Free Full Text]
  42. Tinetti ME, Ginter SF. Identifying mobility dysfunctions in elderly patients. Standard neuromuscular examination or direct assessment? JAMA. 1988;259:1190-1193.[Abstract/Free Full Text]
  43. Studenski S, Perera S, Wallace D, et al. Physical performance measures in the clinical setting. J Am Geriatr Soc. 2003;51:314-322.[Medline]



This article has been cited by other articles:


Home page
 AMERICAN JOURNAL OF LIFESTYLE MEDICINEHome page
D. X. Marquez, E. E. Bustamante, B. J. Blissmer, and T. R. Prohaska
Health Promotion for Successful Aging
American Journal of Lifestyle Medicine, January 1, 2009; 3(1): 12 - 19.
[Abstract] [PDF]

Home page
 Journals of Gerontology Series A: Biological Sciences and Medical SciencesHome page
D. E. Alley, L. Ferrucci, M. Barbagallo, S. A. Studenski, and T. B. Harris
A Research Agenda: The Changing Relationship Between Body Weight and Health in Aging
J. Gerontol. A Biol. Sci. Med. Sci., November 1, 2008; 63(11): 1257 - 1259.
[Full Text] [PDF]

This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Services
Right arrow Download to citation manager
Citing Articles
Right arrow Citing Articles via HighWire
PubMed
Right arrow PubMed Citation

HOME ARCHIVE SEARCH TABLE OF CONTENTS