Eur J Clin Nutr. 2021 May;75(5):759-767. doi: 10.1038/s41430-020-00802-4. Epub 2020 Nov 16.

Longitudinal associations of dietary patterns with sociodemographic and lifestyle factors in older adults: the TASOAC study.

European journal of clinical nutrition

Hoa H Nguyen, Feitong Wu, Wendy H Oddy, Karen Wills, Sharon L Brennan-Olsen, Graeme Jones, Tania Winzenberg

PMID: 33199850 DOI: 10.1038/s41430-020-00802-4

Abstract

BACKGROUND/OBJECTIVES: To derive dietary patterns and examine their longitudinal associations with sociodemographic and lifestyle factors in the Tasmanian Older Adult Cohort.

SUBJECTS/METHODS: This is a corrected analysis of a retracted paper. We followed 1098 adults aged ≥50 years for 5 years. Dietary intake was assessed using a validated food frequency questionnaire. Baseline dietary patterns were identified using exploratory factor analysis and scores at each time point calculated using the weighted sum score method. Associations of energy-adjusted dietary pattern scores with participant characteristics were assessed using linear mixed-effects models.

RESULTS: The four dietary patterns identified were: fruit and vegetable (vegetables, potatoes, fruits); animal protein (poultry, red meats, fish); snack (snacks, sweets, nuts); western (meat pies, hamburgers, pizzas). Fruit and vegetable pattern scores were lower in men and current smokers at baseline. Animal protein scores were lower in older and retired people but higher in men and smokers at baseline. The sex difference in animal protein score increased over time (p = 0.012). At baseline, snack score was positively associated with age and physical activity, but lower in men and current smokers. The effect of age on snack score lessened over time (p = 0.035). Western scores were lower in older people but higher in men, current smokers and those living in disadvantaged areas at baseline. The effect of age on western score reduced over time (p = 0.001).

CONCLUSIONS: The higher scores for healthy and/or lower scores for unhealthy patterns in men, smokers, retirees and those experiencing social disadvantage suggest these could be target groups for interventions to improve diet quality in older adults.

References

1. Iburg KM. Global, regional, and national disability-adjusted life-years (DALYs) for 333 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet. 2017;390:1260–344. - PubMed
2. World Health Organization. Nutrition. https://www.who.int/nutrition/topics/2_background/en/ . - PubMed
3. Lim SS, Vos T, Flaxman AD, Danaei G, Shibuya K, Adair-Rohani H, et al. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet. 2012;380:2224–60. - PubMed
4. Newby P, Tucker KL. Empirically derived eating patterns using factor or cluster analysis: a review. Nutr Rev. 2004;62:177–203. - PubMed
5. Kim J, Yu A, Choi BY, Nam JH, Kim MK, Oh DH, et al. Dietary patterns and cognitive function in Korean older adults. Eur J Nutr. 2015;54:309–18. - PubMed
6. Cade JE, Taylor EF, Burley VJ, Greenwood DC. Does the Mediterranean dietary pattern or the Healthy Diet Index influence the risk of breast cancer in a large British cohort of women? Eur J Clin Nutr. 2011;65:920–8. - PubMed
7. Ocké MC. Evaluation of methodologies for assessing the overall diet: dietary quality scores and dietary pattern analysis. Proc Nutr Soc. 2013;72:191–9. - PubMed
8. Jomaa L, Hwalla N, Chamieh MC, Naja F, Itani L, Mehio-Sibai A. A Lebanese dietary pattern promotes better diet quality among older adults: Findings from a national cross-sectional study. BMC Geriatr. 2016;16:85. - PubMed
9. Markussen MS, Veierød MB, Kristiansen AL, Ursin G, Andersen LF. Dietary patterns of women aged 50–69 years and associations with nutrient intake, sociodemographic factors and key risk factors for non-communicable diseases. Public Health Nutr. 2016;19:2024–32. - PubMed
10. Andreeva VA, Allès B, Feron G, Gonzalez R, Sulmont-Rossé C, Galan P, et al. Sex-specific sociodemographic correlates of dietary patterns in a large sample of French elderly individuals. Nutrients. 2016;8:484. - PubMed
11. Hsiao PY, Mitchell DC, Coffman DL, Allman RM, Locher JL, Sawyer P, et al. Dietary patterns and diet quality among diverse older adults: the University of Alabama at Birmingham study of aging. J Nutr Health Aging. 2013;17:19–25. - PubMed
12. Chan R, Chan D, Woo J. Associations between dietary patterns and demographics, lifestyle, anthropometry and blood pressure in Chinese community-dwelling older men and women. J Nutr Sci. 2012;1:e20. - PubMed
13. Thorpe MG, Milte CM, Crawford D, McNaughton SA. A comparison of the dietary patterns derived by principal component analysis and cluster analysis in older Australians. Int J Behav Nutr Phys Act. 2016;13:1–14. - PubMed
14. Abdollahi S, Zeinali F, Azam K, Toupchian O, Djafarian K. Identifying major dietary patterns among the elderly in Tehran Health Homes, Jundishapur. J Health Sci. 2015;7:e30395. - PubMed
15. Harrington JM, Dahly DL, Fitzgerald AP, Gilthorpe MS, Perry IJ. Capturing changes in dietary patterns among older adults: a latent class analysis of an ageing Irish cohort. Public Health Nutr. 2014;17:2674–86. - PubMed
16. Nguyen HH, Wu F, Oddy WH, Wills K, Brennan-Olsen SL, Jones G, et al. Dietary patterns and their associations with socio-demographic and lifestyle factors in Tasmanian older adults: a longitudinal cohort study. Eur J Clin Nutr. 2019;73:714–23. https://doi.org/10.1038/s41430-018-0264-1 . Epub 7 Aug 2018. - PubMed
17. Nguyen HH, Wu F, Oddy WH, Wills K, Brennan-Olsen SL, Jones G, et al. Retraction Note: Dietary patterns and their associations with socio-demographic and lifestyle factors in Tasmanian older adults: a longitudinal cohort study. Eur J Clin Nutr. 2020;74:214. - PubMed
18. Gardener SL, Rainey-Smith SR, Barnes MB, Sohrabi HR, Weinborn M, Lim YY, et al. Dietary patterns and cognitive decline in an Australian study of ageing. Mol Psychiatry. 2015;20:860–6. - PubMed
19. Keogh JB, Lange K, Syrette J. Comparative analysis of two FFQ. Public Health Nutr. 2010;13:1553–8. - PubMed
20. Ding C, Quinn S, Jones G, Cicuttini F, Parameswaran V, Burgess J. Serum levels of vitamin D, sunlight exposure, and knee cartilage loss in older adults: the Tasmanian older adult cohort study. Arthritis Rheum. 2009;60:1381–9. - PubMed
21. Balogun S, Aitken D, Winzenberg T, Wills K, Scott D, Callisaya M, et al. Longitudinal associations of serum 25-hydroxyvitamin d, physical activity, and knee pain and dysfunction with muscle loss in community-dwelling older adults. J Gerontol A Biol Sci Med Sci. 2017;1–6. - PubMed
22. Brennan S, Winzenberg T, Pasco J, Wluka A, Dobbins A, Jones G. Social disadvantage, bone mineral density and vertebral wedge deformities in the Tasmanian Older Adult Cohort. Osteoporos Int. 2013;24:1909–16. - PubMed
23. Australian Bureau of Statistics. Census of population and housing: socio-economic indexes for areas; Australia 2001 number 2039.0. 2001. - PubMed
24. Scott D, Blizzard L, Fell J, Jones G. Prospective associations between ambulatory activity, body composition and muscle function in older adults. Scand J Med Sci Sports. 2011;21:e168–75. - PubMed
25. Osborne JW. What is rotating in exploratory factor analysis. Pract Assess Res Eval. 2015;20:1–7. - PubMed
26. Beavers AS, Lounsbury JW, Richards JK, Huck SW, Skolits GJ, Esquivel SL. Practical considerations for using exploratory factor analysis in educational research. Pract Assess Res Eval. 2013;18:1–13. - PubMed
27. Williams B, Onsman A, Brown T. Exploratory factor analysis: a five-step guide for novices. J Emerg Prim Health Care. 2010;8:1–13. - PubMed
28. Little RJ, D’Agostino R, Cohen ML, Dickersin K, Emerson SS, Farrar JT, et al. The prevention and treatment of missing data in clinical trials. N Engl J Med. 2012;367:1355–60. - PubMed
29. Vansteelandt S, Carpenter J, Kenward M.Analysis of incomplete data using inverse probability weighting and doubly robust estimators. Methods Eur J Res Methods Behav Soc Sci.2010;6:37–48. - PubMed
30. StataCorp. Texax 77845 USA ed: College Station; 2016. - PubMed
31. FDA US Food and Drug Administration. Tobacco use and men’s health. https://www.fda.gov/tobacco-products/health-information/tobacco-use-and-mens-health#references . - PubMed
32. Mayén A-L, Marques-Vidal P, Paccaud F, Bovet P, Stringhini S. Socioeconomic determinants of dietary patterns in low- and middle-income countries: a systematic review. Am J Clin Nutr. 2014;100:1520–31. - PubMed
33. Bartholomew DJ, Knott M, Moustaki I. Latent variable models and factor analysis: a unified approach: John Wiley & Sons; 2011. - PubMed
34. Reedy J, Subar AF, George SM, Krebs-Smith SM. Extending methods in dietary patterns research. Nutrients. 2018;10:571. - PubMed
35. Krok-Schoen JL, Archdeacon Price A, Luo M, Kelly OJ, Taylor CA. Low dietary protein intakes and associated dietary patterns and functional limitations in an aging population: a NHANES analysis. J Nutr Health Aging. 2019;23:338–47. - PubMed
36. Ni Lochlainn M, Bowyer RCE, Steves CJ. Dietary protein and muscle in aging people: the potential role of the gut microbiome. Nutrients. 2018;10:929. - PubMed
37. Bauer J, Biolo G, Cederholm T, Cesari M, Cruz-Jentoft A, Morley J, 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;14:542–59. - PubMed

Publication Types

• Journal Article
• Research Support, Non-U.S. Gov't
• Corrected and Republished Article

Grant support

• APP1158661/Department of Health | National Health and Medical Research Council (NHMRC)
• 1117037/Department of Health | National Health and Medical Research Council (NHMRC)