The journal of nutrition, health & aging

, Volume 22, Issue 6, pp 645–654 | Cite as

Latent Profiles of Macronutrient Density and their Association with Mobility Limitations in an Observational Longitudinal Study of Older U.S. Adults

  • Nicholas Joseph Bishop
  • K. E. Zuniga
  • A. L. Lucht



Our first objective was to estimate empirically-derived subgroups (latent profiles) of observed carbohydrate, protein, and fat intake density in a nationally representative sample of older U.S. adults. Our second objective was to determine whether membership in these groups was associated with levels of, and short term change in, physical mobility limitations.

Design and Setting

Measures of macronutrient density were taken from the 2013 Health Care and Nutrition Study, an off-year supplement to the Health and Retirement Study, which provided indicators of physical mobility limitations and sociodemographic and health-related covariates.


3,914 community-dwelling adults age 65 years and older.


Percent of daily calories from carbohydrate, protein, and fat were calculated based on responses to a modified Harvard food frequency questionnaire. Latent profile analysis was used to describe unobserved heterogeneity in measures of carbohydrate, protein, and fat density. Mobility limitation counts were based on responses to 11 items indicating physical limitations. Poisson regression models with autoregressive controls were used to identify associations between macronutrient density profile membership and mobility limitations. Sociodemographic and health-related covariates were included in all Poisson regression models.


Four latent subgroups of macronutrient density were identified: “High Carbohydrate”, “Moderate with Fat”, “Moderate”, and “Low Carbohydrate/High Fat”. Older adults with the lowest percentage of daily calories coming from carbohydrate and the greatest percentage coming from fat (“Low Carbohydrate/High Fat”) were found to have greater reported mobility limitations in 2014 than those identified as having moderate macronutrient density, and more rapid two-year increases in mobility limitations than those identified as “Moderate with Fat” or “Moderate”.


Older adults identified as having the lowest carbohydrate and highest fat energy density were more likely to report a greater number of mobility limitations and experience greater increases in these limitations than those identified as having moderate macronutrient density. These results suggest that the interrelation of macronutrients must be considered by those seeking to reduce functional limitations among older adults through dietary interventions.

Key words

Macronutrient density mobility limitations latent profile analysis Health Care and Nutrition Study (HCNS) Health and Retirement Study (HRS) 

Supplementary material

12603_2017_986_MOESM1_ESM.pdf (75 kb)
Supplementary Table 1 Descriptive statistics for macronutrient density and mobility limitations by analytic sample inclusion
12603_2017_986_MOESM2_ESM.pdf (83 kb)
Supplementary Table 2 (continued) Descriptive statistics for covariates by analytic sample inclusion
12603_2017_986_MOESM3_ESM.pdf (104 kb)
Supplementary Table 3 Descriptive statistics for fat type as percentage of daily calories and fat type as percentage of daily fat intake by analytic sample inclusion


  1. 1.
    den Ouden ME, Schuurmans MJ, Arts IE, van der Schouw YT. Association between physical performance characteristics and independence in activities of daily living in middle-aged and elderly men. Geriatr Gerontol Int 2013;13:274–280. doi:10.1111/j.1447-0594.2012.00890.xCrossRefGoogle Scholar
  2. 2.
    Seeman TE, Merkin SS, Crimmins EM, Karlamangla AS. Disability trends among older Americans: National Health and Nutrition Examination surveys, 1988–1994 and 1999–2004. Am J Public Health 2010;100:100–107. doi:10.2105/AJPH.2008.157388CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Luppa M, Luck T, Weyerer S, Konig H-H, Brahler E, Riedel-Heller SG. Prediction of institutionalization in the elderly. A systematic review. Age Ageing 2010;39:31–38. doi:10.1093/ageing/afp202CrossRefPubMedGoogle Scholar
  4. 4.
    Verbrugge LM, Jette AM. The disablement process. Soc Sci Med 1994;38(1):1–14. doi:10.1016/0277-9536(94)90294-1CrossRefPubMedGoogle Scholar
  5. 5.
    Inzitari M, Doets E, Bartali B, et al. Nutrition in the age-related disablement process. J Nutr Health Aging 2011;15:599–604.doi:10.1007/s12603-011-0053-1CrossRefPubMedGoogle Scholar
  6. 6.
    Leon-Munoz LM, Garcia-Esquinas E, Lopez-Garcia E, Banegas JR, Rodriguez-Artalejo F. Major dietary patterns and risk of frailty in older adults: a prospective cohort study. BMC Med 2015;13:11. doi:10.1186/s12916-014-0255-6CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Beasley JM, Lacroix AZ, Neuhouser ML, et al. Protein intake and incident frailty in the Women’s Health Initiative Observational Study. J Am Geriatr Soc 2010;58:1063–1071. doi:10.1111/j.1532-5415.2010.02866.xCrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Gregorio L, Brindisi J, Kleppinger A, et al. Adequate dietary protein is associated with better physical performance among post-menopausal women 60–90 years. J Nutr Heal Aging 2014;18:155–160. doi:10.1007/s12603-013-0391-2CrossRefGoogle Scholar
  9. 9.
    Houston DK, Nicklas BJ, Ding J, 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;87:150–155CrossRefPubMedGoogle Scholar
  10. 10.
    Bartali B, Frongillo EA, Stipanuk MH, et al. Protein intake and muscle strength in older persons: does inflammation matter? J Am Geriatr Soc 2012;60:480–484. doi:10.1111/j.1532-5415.2011.03833.xCrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Mulla UZ, Cooper R, Mishra GD, Kuh D, Stephen AM. Adult macronutrient intake and physical capability in the MRC National Survey of Health and Development. Am J Clin Nutr 2013;42:81–87. doi:10.1093/ageing/afs101Google Scholar
  12. 12.
    De Groot C, Van Staveren W, De Graaf C. Determinants of macronutrient intake in elderly people. Eur J Clin Nutr 2000;54:s70–s76. doi: Scholar
  13. 13.
    Hauser RM, Weir D. Recent developments in longitudinal studies of aging in the United States. Demography 2010;47:s111–s131. doi: Scholar
  14. 14.
    Willett WC, Sampson L, Stampfer MJ, et al. Reproducibility and validity of a semiquantitative food frequency questionnaire. Am J Epidemiol 1985;122:51–65. doi: 10.1093/oxfordjournals.aje.a114086CrossRefPubMedGoogle Scholar
  15. 15.
    Weir DR, Langa KM, Ofstedal MB, Hurd MD, Sharon R, Kardia LR, et al (n.d.) Health and Retirement Study Institutional Review Board Information. Accessed 01 September 2017Google Scholar
  16. 16.
    Chien S, Campbell N, Chan C, et al. 2015. RAND HRS Data Documentation, Version P. Santa Monica, CA: RAND Center for the Study of Aging. http://hrsonline. Accessed 01 September 2017Google Scholar
  17. 17.
    Nagi SZ. Disability and rehabilitation: legal, clinical, and self-concepts and measurement. Oxford: Ohio State U. Press, 1969.Google Scholar
  18. 18.
    Rosow I, Breslau N. A Guttman health scale for the aged. J Gerontol 1966;21:556–559. doi:10.1093/geronj/21.4.556CrossRefPubMedGoogle Scholar
  19. 19.
    Fonda S, Herzog AR, 2004. Documentation of physical functioning measured in the Health and Retirement Study and the Asset and Health Dynamics among the Oldest Old Study. HRS/AHEAD Doc Reports. Accessed 01 September 2017Google Scholar
  20. 20.
    Martin LG, Freedman VA, Schoeni RF, Andreski PM. Health and functioning among baby boomers approaching 60. Journals Gerontol Ser B Psychol Sci Soc Sci 2009;64B:369–377. doi:10.1093/geronb/gbn040CrossRefGoogle Scholar
  21. 21.
    Haas S. Trajectories of functional health: the “long arm” of childhood health and socioeconomic factors. Soc Sci Med 2008;66:849–861. doi:10.1016/j. socscimed.2007.11.004CrossRefPubMedGoogle Scholar
  22. 22.
    McLachlan G, Peel D. Finite mixture models. John Wiley & Sons, 2004.Google Scholar
  23. 23.
    Cameron AC, Trivedi PK. Regression analysis of count data, second ed. Cambridge: Cambridge University Press, 2013.CrossRefGoogle Scholar
  24. 24.
    Allison P. Change scores as dependent variables in regression analysis. Sociol Methodol 1992;20:93–114. doi: 10.2307/271083CrossRefGoogle Scholar
  25. 25.
    Muthén LK, Muthén BO, 1998–2017. Mplus user’s guide, eighth ed. Los Angeles, CA.Google Scholar
  26. 26.
    SAS Institute Inc. SAS/STAT® 14.1 User’s Guide. Cary, NC: SAS Institute Inc, 2015.Google Scholar
  27. 27.
    Institute of Medicine. Dietary reference intakes for energy, carbohydrate, fiber, fat, fatty acids, cholesterol, protein, and amino acids. Washington, DC: The National Academies Press 2005. doi:10.17226/10490.Google Scholar
  28. 28.
    O’brien RM. A caution regarding rules of thumb for variance inflation factors. Qual Quant 2007;41:673–690. doi:10.1007/s11135-006-9018-6CrossRefGoogle Scholar
  29. 29.
    Cohen J, Cohen P, West SG, Aiken LS. Applied multiple correlation/regression analysis for the behavioral sciences, third ed. Lawrence Erlbaum Associates, 2003.Google Scholar
  30. 30.
    ter Borg S, Verlaan S, Mijnarends DM, Schols JM, de Groot LC, Luiking YC. Macronutrient intake and inadequacies of community-dwelling older adults, a systematic review. Ann Nutr Metab 2015;66:242–255. doi:10.1159/000435862CrossRefPubMedGoogle Scholar
  31. 31.
    Bauer J, Biolo G, Cederholm T, et al. Evidence-based recommendations for optimal dietary protein intake in older people: a position paper From the PROT-AGE Study Group. J Am Med Dir Assoc 2013;14:542–559. doi:10.1016/j.jamda.2013.05.021CrossRefPubMedGoogle Scholar
  32. 32.
    Tomey KM, Sowers MFR, Crandall C, Johnston J, Jannausch M, Yosef M. Dietary intake related to prevalent functional limitations in midlife women. Am J Epidemiol 2008;167:935–943. doi:10.1093/aje/kwm397CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Granic A, Jagger C, Davies K, et al. Effect of dietary patterns on muscle strength and physical performance in the very old: findings from the Newcastle 85+ Study. PLoS One 2016;11:1–18. doi:10.1371/journal.pone.0149699Google Scholar
  34. 34.
    Houston DK, Stevens J, Cai J, Haines PS. Dairy, fruit, and vegetable intakes and functional limitations and disability in a biracial cohort: the Atherosclerosis Risk in Communities Study. Am J Clin Nutr 2005;81:515–522. doi:10.1038/sj.ijo.0803043CrossRefPubMedGoogle Scholar
  35. 35.
    Alipanah N, Varadhan R, Sun K, Ferrucci L, Fried LP, Semba RD. Low serum carotenoids are associated with a decline in walking speed in older women. J Nutr Health Aging 2009;13:170–175.CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Semba RD, Varadhan R, Bartali B, Ferrucci L, Ricks MO, Blaum C, Fried LP. Low serum carotenoids and development of severe walking disability among older women living in the community: the Women’s Health and Aging Study I. Age Ageing 2007;36:62–67. doi:10.1093/ageing/afl122CrossRefPubMedGoogle Scholar
  37. 37.
    Michelon E, Blaum C, Semba RD, Xue Q-L, Ricks MO, Fried LP. Vitamin and carotenoid status in older women: associations with the frailty syndrome. Journals Gerontol Ser A Biol Sci Med Sci 2006;61:600–607. doi:10.1093/gerona/61.6.600CrossRefGoogle Scholar
  38. 38.
    Wu I-C, Chang H-Y, Hsu C-C, et al. Association between dietary fiber intake and physical performance in older adults: a nationwide study in Taiwan. PLoS One 2013;8:e80209. doi:10.1371/journal.pone.0080209CrossRefGoogle Scholar
  39. 39.
    de Souza Vasconcelos KS, Domingues Dias JM, de Carvalho Bastone A, et al. Handgrip strength cutoff points to identify mobility limitation in communitydwelling older people and associated factors. J Nutr Health Aging 2016;20:306–315. doi:10.1007/s12603-015-0584-yCrossRefGoogle Scholar

Copyright information

© Serdi and Springer-Verlag France SAS, part of Springer Nature 2017

Authors and Affiliations

  • Nicholas Joseph Bishop
    • 1
  • K. E. Zuniga
  • A. L. Lucht
  1. 1.Texas State UniversitySan MarcosUSA

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