INTRODUCTION: Most previous studies on food insecurity and cardiovascular disease risk factors are cross-sectional. Without longitudinal data, it is unclear whether food insecurity precedes poor health and how exposure timing impacts these relationships. METHODS: Data from 2000 to 2001, 2005 to 2006, and 2010 to 2011 of the Coronary Artery Risk Development in Young Adults study were used. Food insufficiency-a screener measure related to food insecurity-was assessed in 2000-2001 and 2005-2006 using a single item. Cardiovascular disease risk factors were objectively assessed in 2010-2011. Impacts of food insufficiency patterns (food sufficient, food insufficient in 2000-2001 only, food insufficient in 2005-2006 only, food insufficient in both 2000-2001 and 2005-2006) on cardiovascular disease risk factors were estimated using inverse probability weighting of marginal structural models. Covariates that change over time were adjusted for using stabilized weights; baseline covariates were adjusted for in the marginal structural models. Analyses were conducted in 2020-2021. RESULTS: The baseline sample included 2,596 participants (56% women, 47% White). In unadjusted analyses, all food insufficiency patterns were associated with higher BMI, waist circumference, and blood pressure than food sufficiency. After accounting for covariates, estimates were attenuated but still consistent with adverse effects of food insufficiency, particularly among women. CONCLUSIONS: After covariate adjustment, food insufficiency was associated with several cardiovascular disease risk factors. Findings from this study should be replicated in other settings and populations. If verified, this evidence could provide justification for intervening in food insecurity to reduce future cardiovascular disease risk.

Psychosocial factors are associated with the achievement of optimal cardiovascular disease risk factor (CVDRF) levels. To date, little research has examined multiple psychosocial factors simultaneously to identify distinguishing psychosocial profiles among individuals with CVDRF. Further, it is unknown whether profiles are associated with achievement of CVDRF levels longitudinally. Therefore, we characterized psychosocial profiles of individuals with CVDRF and assessed whether they are associated with achievement of optimal CVDRF levels over 15 years. We included 1148 CARDIA participants with prevalent hypertension, hypercholesterolemia and/or diabetes mellitus in 2000-2001. Eleven psychosocial variables reflecting psychological health, personality traits, and social factors were included. Optimal levels were deemed achieved if: Hemoglobin A1c (HbA1c) < 7.0%, low-density lipoprotein (LDL) cholesterol < 100 mg/dl, and systolic blood pressure (SBP) < 140 mm Hg. Latent profile analysis revealed three psychosocial profile groups "Healthy", "Distressed and Disadvantaged" and "Discriminated Against". There were no significant differences in achievement of CVDRF levels of the 3 targets combined across profiles. Participants in the "Distressed and Disadvantaged" profile were less likely to meet optimal HbA1c levels compared to individuals in the "Healthy" profile after demographic adjustment. Associations were attenuated after full covariate adjustment. Distinct psychosocial profiles exist among individuals with CVDRF, representing meaningful differences. Implications for CVDRF management are discussed.

INTRODUCTION: There are known racial differences in cardiovascular health behaviors, including smoking, physical activity, and diet quality. A better understanding of these differences may help identify intervention targets for reducing cardiovascular disease disparities. This study examined whether socioeconomic, psychosocial, and neighborhood environmental factors, in isolation or together, mediate racial differences in health behaviors. METHODS: Participants were 3,081 men and women from the Coronary Artery Risk Development in Young Adults study who were enrolled in 1985-1986 (Year 0) and completed a follow-up examination in 2015-2016 (Year 30). A health behavior score was created at Years 0, 7, 20, and 30 using smoking, physical activity, and diet assessed that year. The race difference in health behavior score was estimated using linear regression in serial cross-sectional analyses. Mediation analyses computed the proportion of the race and health behavior score association attributable to socioeconomic, psychosocial, and neighborhood factors. RESULTS: Data analysis conducted in 2016-2017 found that blacks had significantly lower health behavior scores than whites across 30 years of follow-up. Individual socioeconomic factors mediated 48.9%-70.1% of the association between race and health behavior score, psychosocial factors 20.3%-30.0%, and neighborhood factors 22.1%-41.4% (p < 0.01 for all). CONCLUSIONS: Racial differences in health behavior scores appear to be mediated predominately by correspondingly large differences in socioeconomic factors. This study highlights the profound impact of socioeconomic factors, which are mostly not under an individual's control, on health behaviors. Policy action targeting socioeconomic factors may help reduce disparities in health behaviors.

OBJECTIVE: In 2010, the American Diabetes Association (ADA) added hemoglobin A1c (A1C) to the guidelines for diagnosing type 2 diabetes. However, existing models for predicting diabetes risk were developed prior to the widespread adoption of A1C. Thus, it remains unknown how well existing diabetes risk prediction models predict incident diabetes defined according to the ADA 2010 guidelines. Accordingly, we examined the performance of an existing diabetes prediction model applied to a cohort of African American (AA) and white adults from the Coronary Artery Risk Development Study in Young Adults (CARDIA). RESEARCH DESIGN AND METHODS: We evaluated the performance of the Atherosclerosis Risk in Communities (ARIC) diabetes risk prediction model among 2,456 participants in CARDIA free of diabetes at the 2005-2006 exam and followed for 5 years. We evaluated model discrimination, calibration, and integrated discrimination improvement with incident diabetes defined by ADA 2010 guidelines before and after adding baseline A1C to the prediction model. RESULTS: In the overall cohort, re-estimating the ARIC model in the CARDIA cohort resulted in good discrimination for the prediction of 5-year diabetes risk (area under the curve [AUC] 0.841). Adding baseline A1C as a predictor improved discrimination (AUC 0.841 vs. 0.863, P = 0.03). In race-stratified analyses, model discrimination was significantly higher in whites than AA (AUC AA 0.816 vs. whites 0.902; P = 0.008). CONCLUSIONS: Addition of A1C to the ARIC diabetes risk prediction model improved performance overall and in racial subgroups. However, for all models examined, discrimination was better in whites than AA. Additional studies are needed to further improve diabetes risk prediction among AA. long as the work is properly cited, the use is educational and not for profit, and the work is not altered.