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OBJECTIVE: To evaluate the association between meal timing and type 2 diabetes risk in U.S. Hispanic/Latino adults. RESEARCH DESIGN AND METHODS: The Hispanic Community Health Study/Study of Latinos (HCHS/SOL) is a multicenter, community-based, prospective cohort study. This study included 8,868 HCHS/SOL adults without diabetes at baseline (2008–2011) and attending the visit 2 examination (2014–2017). Energy intake and glycemic load (GL) in each meal timing were assessed at baseline using two 24-h dietary recalls. Incident diabetes was identified through annual follow-up calls or at visit 2. Hazard ratios (HRs) for incident diabetes were estimated using Cox models, accounting for the complex survey design. RESULTS: The study population (50.9% female) had a baseline mean age of 39.0 (95% CI, 38.4–39.5) years. Over a median (range) follow-up of 5.8 (0.8–9.6) years, 1,262 incident diabetes cases were documented. Greater energy intake and GL in late morning (9:00–11:59 a.m.) were associated with a lower diabetes risk, whereas greater energy intake and GL in other meal timings were not. After accounting for diet quantity and quality, sociodemographic characteristics, lifestyle factors, and chronic conditions, the HRs were 0.94 (95% CI, 0.91–0.97) per 100-kcal energy intake increment and 0.93 (0.89–0.97) per 10-unit GL increment in late morning. Replacing energy intake or GL from early morning (6:00–8:59 a.m.), afternoon (12:00–5:59 p.m.), or evening (6:00–11:59 p.m.) with late-morning equivalents was associated with a comparably lower diabetes risk. CONCLUSIONS: This study identified late morning as a favorable meal timing in Hispanic/Latino adults, providing a novel perspective on type 2 diabetes prevention that warrants confirmation. [ABSTRACT FROM AUTHOR]

In one-way analysis of variance models, performing simultaneous multiple comparisons of treatment groups with a control group may be of interest. Dunnett's test is used to test such differences and assumes equal variances of the response variable for each group. This assumption is not always met even after transformation. A parametric bootstrap (PB) method is developed here for comparing multiple treatment group means against the control group with unequal variances and unbalanced data. In simulation studies, the proposed method outperformed Dunnett's test in controlling the type I error under various settings, particularly when data have heteroscedastic variance and unbalanced design. Simulations show that power is often lower for the PB method than for Dunnett's test under equal variance, balanced data, or smaller sample size, but similar to or higher than for Dunnett's test with unequal variance, unbalanced data and larger sample size. The method is applied to a dataset concerning isotope levels found in elephant tusks from various geographical areas. These data have very unbalanced group sizes and unequal variances. This example illustrates that the PB method is easy to implement and avoids the need for transforming data to meet the equal variance assumption, simplifying interpretation of results. [ABSTRACT FROM AUTHOR]

The issue of unmet equal variance assumption in multi-factor ANOVA has been addressed in the literature with several methods, and parametric bootstrap (PB) has been found in the one-way and two-way cases to outperform other methods. We extend previously developed PB procedures for one- and two-way ANOVA, and illustrate with a three-way ANOVA model with unequal group variances (heteANOVA model). We develop a framework for working with these models, analogous to usual multi-factor ANOVA procedures, where F-tests and Tukey's simultaneous multiple comparison procedures are replaced by PB procedures. Using simulation, we compare these methods to F-tests for each step in model selection, as well as to Tukey's test for multiple comparison procedures (MCP). The results of our simulations indicate that the PB methods outperform F-tests and Tukey's test in terms of Type I error when data are unbalanced. [ABSTRACT FROM AUTHOR]