Aims/hypothesis Ideal cardiovascular health (CVH) is associated with lower diabetes risk. However, it is unclear whether this association is similar across glycaemic levels (normal [<5.6 mmol/l] vs impaired fasting glucose [IFG] [5.6-6.9 mmol/l]). Methods A secondary data analysis was performed in the REasons for Geographic and Racial Differences in Stroke (REGARDS) study. Incident diabetes was assessed among 7758 participants without diabetes at baseline (2003-2007) followed over 9.5 years. Baseline cholesterol, blood pressure, diet, smoking, physical activity and BMI were used to categorise participants based on the number (0-1, 2-3 and ≥4) of ideal CVH components. Risk ratios (RRs) were calculated using modified Poisson regression, adjusting for cardiovascular risk factors. Results Among participants (mean age 63.0 [SD 8.4] years, 56% female, 73% white, 27% African-American), there were 891 incident diabetes cases. Participants with ≥4 vs 0-1 ideal CVH components with normal fasting glucose (n = 6004) had 80% lower risk (RR 0.20; 95% CI 0.10, 0.37), while participants with baseline IFG (n = 1754) had 13% lower risk (RR 0.87; 95% CI 0.58, 1.30) (p for interaction by baseline glucose status <0.0001). Additionally, the magnitude of the association of ideal CVH components with lower diabetes risk was stronger among white than African-American participants (p for interaction = 0.0338). Conclusions/interpretation A higher number of ideal CVH components was associated with a dose-dependent lower risk of diabetes for participants with normal fasting glucose but not IFG. Tailored efforts that take into account observed differences by race and glycaemic level are needed for the primordial prevention of diabetes.

Background Higher cardiovascular health scores, using American Heart Association's (AHA) Life's Simple 7 (LS7), have been associated with lower risk of cardiovascular disease, type 2 diabetes, cancer, and mortality among all racial/ethnic groups. Nationally, Black men have the lowest levels of LS7. Thus, a study was conducted to evaluate the impact of a community-based team lifestyle change program on LS7 among Black men. Methods Black adult males ( n = 74) from a large Midwestern city participated in Black Impact, a 24-week community-based team lifestyle change program adapted from the Diabetes Prevention Program and AHA's Check, Change, Control Blood Pressure Self-Management Program, which incorporates AHA's LS7 framework. The change in a LS7 score (range 0–14) from baseline to 12 and 24 weeks was evaluated using a linear mixed-effects model adjusted for age, education, and income. Results The mean age of participants was 52 ± 10 years. The men were sociodemographically diverse, with annual income ranging from <$20,000 (7%) to ≥$75,000 (25%). Twenty-five percent were college graduates, 73% had private insurance, and 84% were employed. In fully adjusted models, LS7 score at baseline was 7.12 and increased 0.67 (95%CI: 0.14, 1.20, p = 0.013) and 0.93 (95%CI: 0.40, 1.46, p <0.001) points at 12 and 24 weeks, respectively, compared to baseline. Sensitivity analysis evaluating 5 components (excluding diet and physical activity) and 6 components (excluding diet) also showed significant increases at 12 and 24 weeks (all p <0.01). Conclusions The Black Impact lifestyle change single-arm pilot program showed that a community-based lifestyle intervention has the potential to improve LS7 in Black men. Further randomized studies are urgently needed to improve cardiovascular health and advance cardiovascular health equity in Black men.

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BACKGROUND: Serum cortisol levels have been associated with type 2 diabetes (T2D). However, the role of cortisol in T2D and glycemia is not fully elucidated among African Americans (AAs). We hypothesized that among AAs morning serum cortisol would be positively associated with glycemic measures and prevalent T2D. METHODS:We examined the cross-sectional association of baseline morning serum cortisol with fasting plasma glucose (FPG), Hemoglobin A1c (HbA1c), homeostasis model assessment of insulin resistance (HOMA-IR), β-cell function (HOMA-β), and prevalent T2D in the Jackson Heart Study. Linear regression models were used to examine the association of log-transformed cortisol with glycemic traits, stratified by T2D status. Logistic regression was used to examine the association of log-transformed cortisol with prevalent T2D. Models were adjusted for age, sex,

Summary1. Conservationists routinely use species distribution models to plan conservation, restoration and development actions, while ecologists use them to infer process from pattern. These models tend to work well for common or easily observable species, but are of limited utility for rare and cryptic species. This may be because honest accounting of known observation bias and spatial autocorrelation are rarely included, thereby limiting statistical inference of resulting distribution maps. 2. We specified and implemented a spatially explicit Bayesian hierarchical model for a cryptic mammal species (pygmy rabbit Brachylagus idahoensis). Our approach used two levels of indirect sign that are naturally hierarchical (burrows and faecal pellets) to build a model that allows for inference on regression coefficients as well as spatially explicit model parameters. We also produced maps of rabbit distribution (occupied burrows) and relative abundance (number of burrows expected to be occupied by pygmy rabbits). The model demonstrated statistically rigorous spatial prediction by including spatial autocorrelation and measurement uncertainty. 3. We demonstrated flexibility of our modelling framework by depicting probabilistic distribution predictions using different assumptions of pygmy rabbit habitat requirements. 4. Spatial representations of the variance of posterior predictive distributions were obtained to evaluate heterogeneity in model fit across the spatial domain. Leave-one-out cross-validation was conducted to evaluate the overall model fit. 5. Synthesis and applications. Our method draws on the strengths of previous work, thereby bridging and extending two active areas of ecological research: species distribution models and multistate occupancy modelling. Our framework can be extended to encompass both larger extents and other species for which direct estimation of abundance is difficult.