Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Original Article
  • Published:

The effect of obesity combined with low muscle strength on decline in mobility in older persons: results from the InCHIANTI Study

International Journal of Obesity volume 33, pages 635–644 (2009)Cite this article

Abstract

Objective:

Both obesity and muscle impairment are increasingly prevalent among older persons and negatively affect health and physical functioning. However, the combined effect of coexisting obesity and muscle impairment on physical function decline has been little studied. We examined whether obese persons with low muscle strength experience significantly greater declines in walking speed and mobility than persons with only obesity or low muscle strength.

Design:

Community-dwelling adults aged ⩾65 years (n=930) living in the Chianti geographic area (Tuscany, Italy) were followed for 6 years in the population-based InCHIANTI study.

Measurements:

On the basis of baseline measurements (1998–2000), obesity was defined as body mass index (BMI) ⩾30 kg/m2 and low muscle strength as lowest sex-specific tertile of knee extensor strength. Walking speed and self-reported mobility disability (ability to walk 400 m or climb one flight of stairs) were assessed at baseline and at 3- and 6-year follow-up.

Results:

At baseline, obese persons with low muscle strength had significantly lower walking speed compared with all other groups (P⩽0.05). In longitudinal analyses, obese participants with low muscle strength had steeper decline in walking speed and high risk of developing new mobility disability over the 6-year follow-up compared with those without obesity or low muscle strength. After the age of 80, the differences between groups were substantially attenuated. The differences seen in walking speed across combination of low muscle strength and obesity groups were partly explained by 6-year changes in muscle strength, BMI and waist circumference.

Conclusions:

Obesity combined with low muscle strength increases the risk of decline in walking speed and developing mobility disability, especially among persons <80 years old.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1
Figure 2

Similar content being viewed by others

References

  1. Wang YC, Colditz GA, Kuntz KM . Forecasting the obesity epidemic in the aging US Population. Obesity (Silver Spring) 2007; 15: 2855–2865.

    Article  Google Scholar 

  2. WHO. Obesity: Preventing and Managing the Global Epidemic. Report of a WHO Consultation. WHO: Geneva, Switzerland, 2000.

  3. Stenholm S, Rantanen T, Alanen E, Reunanen A, Sainio P, Koskinen S . Obesity history as a predictor of walking limitation at old age. Obesity 2007; 15: 929–938.

    Article  Google Scholar 

  4. Mendes de Leon CF, Hansberry MR, Bienias JL, Morris MC, Evans DA . Relative weight and mobility: a longitudinal study in a biracial population of older adults. Ann Epidemiol 2006; 16: 770–776.

    Article  Google Scholar 

  5. Alley DE, Chang VW . The changing relationship of obesity and disability, 1988–2004. JAMA 2007; 298: 2020–2027.

    Article  CAS  Google Scholar 

  6. Angleman SB, Harris TB, Melzer D . The role of waist circumference in predicting disability in periretirement age adults. Int J Obes 2006; 30: 364–373.

    Article  CAS  Google Scholar 

  7. Rantanen T, Harris T, Leveille SG, Visser M, Foley D, Masaki K et al. Muscle strength and body mass index as long-term predictors of mortality in initially healthy men. J Gerontol A Biol Sci Med Sci 2000; 55: M168–M173.

    Article  CAS  Google Scholar 

  8. Stenholm S, Rantanen T, Heliövaara M, Koskinen S . The mediating role of C-reactive protein and handgrip strength between obesity and walking limitation. J Am Geriatr Soc 2008; 56: 462–469.

    Article  Google Scholar 

  9. Baumgartner RN . Body composition in healthy aging. Ann NY Acad Sci 2000; 904: 437–448.

    Article  CAS  Google Scholar 

  10. 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.

    Article  Google Scholar 

  11. Ferrucci L, Bandinelli S, Benvenuti E, Di Iorio A, Macci C, Harris TB et al. Subsystems contributing to the decline in ability to walk: bridging the gap between epidemiology and geriatric practice in the InCHIANTI study. J Am Geriatr Soc 2000; 48: 1618–1625.

    Article  CAS  Google Scholar 

  12. 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.

    Article  CAS  Google Scholar 

  13. Guralnik JM, Ferrucci L, Pieper CF, Leveille SG, Markides KS, Ostir GV et al. Lower extremity function and subsequent disability: consistency across studies, predictive models, and value of gait speed alone compared with the short physical performance battery. J Gerontol A Biol Sci Med Sci 2000; 55: M221–M231.

    Article  CAS  Google Scholar 

  14. Cesari M, Kritchevsky SB, Penninx BW, Nicklas BJ, Simonsick EM, Newman AB et al. Prognostic value of usual gait speed in well-functioning older people—Results from the Health, Aging and Body Composition Study. J Am Geriatr Soc 2005; 53: 1675–1680.

    Article  Google Scholar 

  15. Guralnik JM, Simonsick EM, Ferrucci L, Glynn RJ, Berkman LF, Blazer DG 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 1994; 49: M85–M94.

    Article  CAS  Google Scholar 

  16. Reuben DB, Seeman TE, Keeler E, Hayes RP, Bowman L, Sewall A et al. Refining the categorization of physical functional status: the added value of combining self-reported and performance-based measures. J Gerontol A Biol Sci Med Sci 2004; 59A: M1050–M1061.

    Google Scholar 

  17. Bandinelli S, Benvenuti E, Del Lungo I, Baccini M, Benvenuti F, Di Iorio A et al. Measuring muscular strength of the lower limbs by hand-held dynamometer: a standard protocol. Aging (Milano) 1999; 11: 287–293.

    CAS  Google Scholar 

  18. Wareham NJ, Jakes RW, Rennie KL, Mitchell J, Hennings S, Day NE . Validity and repeatability of the EPIC-Norfolk physical activity questionnaire. Int J Epidemiol 2002; 31: 168–174.

    Article  Google Scholar 

  19. Guralnik J, Fried L, Simonsick E, Kasper J, Lafferty M . The Women's Health and Aging Study: Health and Social Characteristics of Older Women with Disability. National Institute on Aging, NIH Publication No. 95-4009: Bethesda, MD, 1995.

    Google Scholar 

  20. Cesari M, Kritchevsky SB, Baumgartner RN, Atkinson HH, Penninx BW, Lenchik L et al. Sarcopenia, obesity, and inflammation—results from the Trial of Angiotensin Converting Enzyme Inhibition and Novel Cardiovascular Risk Factors study. Am J Clin Nutr 2005; 82: 428–434.

    Article  CAS  Google Scholar 

  21. Schrager MA, Metter EJ, Simonsick E, Ble A, Bandinelli S, Lauretani F et al. Sarcopenic obesity and inflammation in the InCHIANTI study. J Appl Physiol 2007; 102: 919–925.

    Article  Google Scholar 

  22. Verbeke G, Molenberghs G . Linear Mixed Models for Longitudinal Data, 2nd edn, Springer-Verlag: New York, 2001.

    Google Scholar 

  23. Molenberghs G, Verbeke G . Models for Discrete Longitudinal Data, 2nd edn, Springer: New York, 2005.

    Google Scholar 

  24. Davison KK, Ford E, Cogswell M, Dietz W . Percentage of body fat and body mass index are associated with mobility limitations in people aged 70 and older from NHANES III. J Am Geriatr Soc 2002; 50: 1802–1809.

    Article  Google Scholar 

  25. Zoico E, Di Francesco V, Guralnik JM, Mazzali G, Bortolani A, Guariento S 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.

    Article  CAS  Google Scholar 

  26. Visser M, Goodpaster BH, Kritchevsky SB, Newman AB, Nevitt M, Rubin SM et al. Muscle mass, muscle strength, and muscle fat infiltration as predictors of incident mobility limitations in well-functioning older persons. J Gerontol A Biol Sci Med Sci 2005; 60: M324–M333.

    Article  Google Scholar 

  27. Visser M, Newman AB, Nevitt MC, Kritchevsky SB, Stamm EB, Goodpaster BH et al. Reexamining the sarcopenia hypothesis. Muscle mass versus muscle strength. Health, Aging, and Body Composition Study Research Group. Ann NY Acad Sci 2000; 904: 456–461.

    Article  CAS  Google Scholar 

  28. Lauretani F, Russo C, Bandinelli S, Bartali B, Cavazzini C, Di Iorio A 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.

    Article  Google Scholar 

  29. Goodpaster BH, Park SW, Harris TB, Kritchevsky SB, Nevitt M, Schwartz AV et al. The loss of skeletal muscle strength, mass, and quality in older adults: the health, aging and body composition study. J Gerontol A Biol Sci Med Sci 2006; 61A: M1059–M1064.

    Article  Google Scholar 

  30. Doherty T . Aging and sarcopenia. J Appl Physiol 2003; 95: 1717–1727.

    Article  CAS  Google Scholar 

  31. Morley JE, Baumgartner RN, Roubenoff R, Mayer J, Nair KS . Sarcopenia. J Lab Clin Med 2001; 137: 231–243.

    Article  CAS  Google Scholar 

  32. Roubenoff R . Sarcopenia: effects on body composition and function. J Gerontol A Biol Sci Med Sci 2003; 58A: M1012–M1017.

    Article  Google Scholar 

  33. Elia M, Ritz P, Stubbs RJ . Total energy expenditure in the elderly. Eur J Clin Nutr 2000; 54: S92–S103.

    Article  Google Scholar 

  34. Zamboni M, Mazzali G, Fantin F, Rossi A, Di Francesco V . Sarcopenic obesity: a new category of obesity in the elderly. Nutr Metab Cardiovasc Dis 2008; 18: 388–395.

    Article  CAS  Google Scholar 

  35. Stenholm S, Harris TB, Rantanen T, Visser M, Kritchevsky SB, Ferrucci L . Sarcopenic obesity—definition, cause and consequences. Curr Opin Clin Nutr Metab Care 2008; 11: 693–700.

    Article  Google Scholar 

  36. Lee JS, Kritchevsky SB, Tylavsky F, Harris T, Simonsick EM, Rubin SM et al. Weight change, weight change intention, and the incidence of mobility limitation in well-functioning community-dwelling older adults. J Gerontol A Biol Sci Med Sci 2005; 60A: M1007–M1012.

    Article  Google Scholar 

  37. Al Snih S, Raji MA, Markides KS, Ottenbacher KJ, Goodwin JS . Weight change and lower body disability in older Mexican Americans. J Am Geriatr Soc 2005; 53: 1730–1737.

    Article  Google Scholar 

  38. Messier SP, Loeser RF, Miller GD, Morgan TM, Rejeski WJ, Sevick MA et al. Exercise and dietary weight loss in overweight and obese older adults with knee osteoarthritis: the Arthritis, Diet, and Activity Promotion Trial. Arthritis Rheum 2004; 50: 1501–1510.

    Article  Google Scholar 

  39. Villareal DT, Banks M, Sinacore DR, Siener C, Klein S . Effect of weight loss and exercise on frailty in obese older adults. Arch Intern Med 2006; 166: 860–866.

    Article  Google Scholar 

  40. Wang X, Miller GD, Messier SP, Nicklas BJ . Knee strength maintained despite loss of lean body mass during weight loss in older obese adults with knee osteoarthritis. J Gerontol A Biol Sci Med Sci 2007; 62: 866–871.

    Article  Google Scholar 

  41. Inelmen EM, Sergi G, Coin A, Miotto F, Peruzza S, Enzi G . Can obesity be a risk factor in elderly people? Obes Rev 2003; 4: 147–155.

    Article  CAS  Google Scholar 

  42. Ferrucci L, Alley D . Obesity, disability, and mortality: a puzzling link. Arch Intern Med 2007; 167: 750–751.

    Article  Google Scholar 

  43. Diggle P, Heagerty P, Liang K-Y, Zeger S . Analysis of Longitudinal Data, 2nd edn, Oxford University Press: New York, USA, 2002.

    Google Scholar 

Download references

Acknowledgements

The InCHIANTI study baseline (1998–2000) was supported as a ‘targeted project’ (ICS110.1/RF97.71) by the Italian Ministry of Health and, in part, by the US National Institute on Aging (Contracts: 263 MD 9164 and 263 MD 821336); the InCHIANTI Follow-up 1 (2001–2003) was funded by the US National Institute on Aging (Contracts: N.1-AG-1-1 and N.1-AG-1-2111); the InCHIANTI Follow-up 2 (2004–2006) was financed by the US National Institute on Aging (Contract: N01-AG-5-0002); supported, in part, by the Intramural Research Program of the National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA. This work was also supported by grants from the Finnish Academy (no. 125494 SS) and the Robert Wood Johnson Foundation (DA). None of the sponsoring institutions interfered with the collection, analysis, presentation or interpretation of the data reported here.

Author information

Authors and Affiliations

  1. Longitudinal Studies Section, Clinical Research Branch, National Institute on Aging, Baltimore, MD, USA

    S Stenholm & L Ferrucci

  2. Division of Welfare and Health Policies, National Institute for Health and Welfare, Helsinki, Finland

    S Stenholm & S Koskinen

  3. Department of Epidemiology and Preventive Medicine, University of Maryland School of Medicine, Baltimore, MD, USA

    D Alley

  4. Geriatric Unit, Azienda Sanitaria di Firenze, Florence, Italy

    S Bandinelli

  5. The Johns Hopkins University, Bloomberg School of Public Health, Baltimore, MD, USA

    M E Griswold

  6. Department of Health Sciences, The Finnish Center For Interdisciplinary Gerontology, University of Jyväskylä, Jyväskylä, Finland

    T Rantanen

  7. Laboratory of Epidemiology, Demography, and Biometry, National Institute on Aging, Bethesda, MD, USA

    J M Guralnik

Authors
  1. S Stenholm
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. D Alley
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. S Bandinelli
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. M E Griswold
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. S Koskinen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. T Rantanen
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. J M Guralnik
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. L Ferrucci
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S Stenholm.

Appendix

Appendix

This appendix contains a more detailed description of the models specified in the statistical analysis section. Let ‘i’ denote the ith participant, ‘1’ denote the baseline evaluation, ‘j’ the jth longitudinal evaluation (j=1, 2, 3), and SO, the four categories of combination of low muscle strength and obesity (1=low strength and obesity, 2=obesity, 3=low strength, 4=neither low strength nor obesity) defined for each participant at baseline. We decomposed the overall information on age effects (Ageij) into the baseline ‘cross-sectional’ between-participant age effect (Agei1) and the effect of ‘longitudinal’ changes in age within the same person (Δageij=Ageij−Agei1).43 To analyze the effects of these conditions on Walking Speed (WS), we formulate the following linear mixed model:

Where the β(SO) notation denotes a term specific to each SO category, b0i is a participant-specific random intercept and b1i is a random slope over age. This model allows the primary longitudinal age effects of interest (changes in outcomes within a person as that person ages over time) to depend on a participant's age at first measurement (that is, letting age effects differ for younger-old versus older-old). Furthermore, the implied marginal model at the centered adjustors is:

Within a SO category, the effect of aging 3 years (Δageij=3) from, say, 60–63 years is:

whereas the effect of aging 3 years from, say, 80 to 83 years is:

and thus, the interaction term β3 models the difference in within-participant aging across different baseline ages. Nonlinear effects could be estimated similarly with additional data, but with three observations per person the linear terms were adequate.

The generalized linear mixed (logistic) models used to explore the effect of combination of low muscle strength and obesity categories on mobility disability were similar; however, we did not estimate random slope terms or the Agei1 × Δageij interaction term as there were only three binary outcomes per person to support the model and the inclusion of these terms led to non-convergence.

Line plots of the age-related trajectories of walking speed were drawn on the basis of the estimates of linear mixed-effect regression models (Figure 1). To show the differences between age groups, and low strength and obesity groups, the line plots were drawn separately for five 5-year age groups between 65 and 85 years. For all regression models, continuous covariates were centered against their mean values and categorical variables against the mode values (Table 1).

Rights and permissions

Reprints and permissions

About this article

Cite this article

Stenholm, S., Alley, D., Bandinelli, S. et al. The effect of obesity combined with low muscle strength on decline in mobility in older persons: results from the InCHIANTI Study. Int J Obes 33, 635–644 (2009). https://doi.org/10.1038/ijo.2009.62

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ijo.2009.62

Keywords

This article is cited by

Search

Advanced search

Quick links