Background: Obesity and metabolic syndrome are being recognized as major risk factors of chronic disease, and their prevalence has been showing a steady increase over the past decade in Korea. In obesity and metabolic syndrome management, diet has bee...
Background: Obesity and metabolic syndrome are being recognized as major risk factors of chronic disease, and their prevalence has been showing a steady increase over the past decade in Korea. In obesity and metabolic syndrome management, diet has been emphasized as a key modifiable lifestyle factor, including the time aspect of nutritional intake. Chrononutrition is an emerging research area within nutritional science that explores the connection among nutritional intake, circadian rhythms, and health. The timing of eating is a potent synchronizing factor that is able to reset circadian clocks. Therefore, the mistiming of eating can result in circadian disruption, which in turn, can trigger adverse metabolic health. Despite growing evidence, current research on time-related eating behaviors is focused on its individual aspects, when time-related eating behaviors are interrelated.
Objectives: Here we identify different time-related eating patterns through cluster analysis, and examine the association of time-related eating patterns with obesity and metabolic syndrome.
Methods: Data from 17808 participants aged 19 or older from the Korea National Health and Nutrition Examination Survey (KNHANES) 2016–2021 were analyzed in this cross-sectional study.
Dietary information was obtained through a 24-h dietary recall, and obesity and metabolic syndrome were defined using anthropometric measurements, blood pressure measurements, and laboratory tests of blood samples that were part of the KNHANES. Time-related eating patterns were derived by applying k-means cluster analysis with four dietary exposures (morning energy proportion, evening energy proportion, number of eating occasions, and eating window). Multivariable-adjusted logistic regression models were used to investigate associations of time-related eating patterns with obesity and metabolic syndrome.
Results: The k-means cluster analysis revealed three time-related eating patterns: “early,” “late-short,” and “grazing.” The “late-short” pattern was associated with higher odds of metabolic syndrome (OR = 1.30, 95% CI 1.13–1.49), elevated blood pressure (OR = 1.20, 95% CI 1.06–1.36), elevated triglyceride (OR = 1.25, 95% CI 1.12–1.40), and elevated fasting blood glucose (OR = 1.22, 95% CI 1.09–1.36) compared with the “early” pattern, after adjusting confounders. The associations between the “late-short” pattern and metabolic syndrome, elevated triglyceride, and elevated fasting blood glucose were consistent in both men and women, while the association with elevated blood pressure remained significant only in men. The “grazing” pattern was associated with a higher prevalence of elevated fasting blood glucose (OR = 1.17, 95% CI 1.05–1.32), and a lower prevalence of obesity (OR = 0.86, 95% CI 0.77–0.96) and abdominal obesity (OR = 0.86, 95% CI 0.79–0.96) than the “early” pattern. However, when stratified by sex, the associations of the “grazing” pattern were only consistent with lower prevalence of abdominal obesity in men and higher prevalence of elevated fasting blood glucose in women only.
Conclusion: In summary, time-related eating patterns were associated with metabolic health, implicating that shifting energy intake earlier to the day rather than during late evening hours may be beneficial for preventing and managing obesity and metabolic syndrome.