Objectives: To examine the joint association of hydration status and sleep quality with prevalent type 2 diabetes mellitus (T2DM) and to quantify their additive and multiplicative interactions. Methods: This cross-sectional study included 4762 participants from the baseline Hainan Prospective Cohort Study. Logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for the four combinations of hydration status and sleep quality. Additive interaction was assessed with the relative excess risk due to interaction (RERI), the attributable proportion (AP), and the synergy index (S); multiplicative interaction was assessed with a product term. Predicted probabilities were estimated by g-computation. Results: The prevalence of T2DM was 8.13% (387/4762). Compared with non-dehydration and good sleep quality, the combination of dehydration and poor sleep quality was associated with substantially higher odds of prevalent T2DM (adjusted OR 9.92, 95% CI 7.14 to 14.00). All three additive interaction measures were statistically significant (RERI 7.74, 95% CI 4.99 to 10.50; AP 78.04%, 95% CI 69.28 to 86.80; S 7.57, 95% CI 3.49 to 16.42), as was the multiplicative interaction (OR 3.96, 95% CI 2.30–6.90). On the absolute risk scale, the interaction contrast was 14.53 percentage points (95% CI: 11.01 to 18.34), with an attributable proportion of 80.5% (95% CI: 66.3 to 94.6) and a synergy index of 5.14 (95% CI: 2.94 to 17.25). The predicted probability of prevalent T2DM was highest in the combined dehydration and poor sleep group. Conclusions: The combination of dehydration and poor sleep quality was associated with higher odds of prevalent T2DM than either condition alone, and a statistically significant additive interaction was observed. Because these additive interaction measures were derived from odds ratios for a non-rare outcome, their absolute magnitude may be overestimated on the odds-ratio scale; the risk-difference-scale measures confirmed the interaction. Given the cross-sectional design, reverse causation cannot be excluded, and longitudinal data are needed to clarify the temporal sequence of these associations.

Objectives: The objectives of this study were to investigate the association between dietary patterns and dyslipidemia among middle-aged and older adults in Hainan Province and to provide a scientific reference for regional dyslipidemia prevention strategies. Methods: A multi-stage cluster random sampling method was used to conduct questionnaire surveys and physical examinations among 4748 middle-aged and older adults from five regions in Hainan Province. Dietary patterns were derived using a food frequency questionnaire and factor analysis. Dietary-pattern scores were categorized into quartiles from lowest (Q1) to highest (Q4) adherence, with Q1 as the reference group. Logistic regression models were used to examine the associations between dietary patterns and four types of dyslipidemia. Results: A total of 4748 participants were enrolled, of whom 3321 (69.9%) had dyslipidemia. After adjusting for multiple confounders, compared with Q1, the meat-based dietary pattern at Q4 was positively associated with dyslipidemia (OR = 1.380, 95% CI: 1.102–1.729). The fruit-and-vegetable pattern was inversely associated with dyslipidemia at Q2 (OR = 0.800, 95% CI: 0.677–0.945), Q3 (OR = 0.790, 95% CI: 0.663–0.941), and Q4 (OR = 0.630, 95% CI: 0.519–0.765). The cooking-oil and wheat pattern was inversely associated with dyslipidemia at Q2 (OR = 0.843, 95% CI: 0.682–0.980) and Q4 (OR = 0.818, 95% CI: 0.715–0.993). Conclusions: The meat-based dietary pattern was positively associated with dyslipidemia, whereas the fruit-and-vegetable pattern and the cooking-oil and wheat pattern were inversely associated with dyslipidemia. These findings suggest that dietary patterns characterized by higher consumption of fruits, vegetables, and unsaturated fatty acids were inversely associated with dyslipidemia in this population.