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. 2024 Mar 7;16(6):760.
doi: 10.3390/nu16060760.

Circadian Syndrome Is Associated with Dietary Patterns among Middle-Older Americans: The Health and Retirement Study

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Circadian Syndrome Is Associated with Dietary Patterns among Middle-Older Americans: The Health and Retirement Study

Abeer Ali Aljahdali et al. Nutrients. .

Abstract

Population aging is a global demographic characteristic of the 21st century, and healthy eating is a core component of healthy aging. However, limited evidence is available among older adults for associations between diet quality and circadian syndrome (CircS). Thus, this study examined associations between dietary patterns and CircS among a representative sample of middle-older adults in the US. The sample comprised middle-older adults enrolled in the 2016 core wave of the Health and Retirement Study (HRS) and one of its sub-studies, the 2013 Health Care and Nutrition Study (HCNS). A food frequency questionnaire was used to quantify habitual food intake and identify dietary patterns using a factor analysis. CircS was defined based on the existence of ≥4 components of metabolic syndrome and indicators of sleep disorders and depression. A total of 4253 middle-older adults with a mean age (SD) of 65.4 (10.0) years were included in the study. The prevalence of CircS was 35.9%. Comparing extreme quartiles of the "Prudent Pattern", the odds ratio (95% CI) for CircS was 0.72 (0.55-0.94), and it was 1.47 (1.10-1.95) for the "Western Pattern". The "Western Pattern" was positively associated while the "Prudent Pattern" was inversely associated with the odds of CircS among middle-older adults.

Keywords: Health and Retirement Study; US; circadian rhythms; circadian syndrome; diet; dietary patterns; middle-older adults.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Associations (odds ratios (95% CI) between dietary patterns and components of circadian syndrome among adults participating in Health and Retirement Study. p-values were calculated using dietary pattern score as a continuous variable in multivariable models. Models adjusted for age, sex, race, energy intake, physical activity, education, smoking and alcohol drinking.

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