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To identify early changes in brain structure and function that are associated with cardiovascular risk factors (CVRF). Cross-sectional brain Magnetic Resonance I (MRI) study. Community based cohort in three U.S. sites. A Caucasian and African-American sub-sample (n= 680; mean age 50.3 yrs) attending the 25 year follow-up exam of the Coronary Artery Risk Development in Young Adults Study. 3T brain MR images processed for quantitative estimates of: total brain (TBV) and abnormal white matter (AWM) volume; white matter fractional anisotropy (WM-FA); and gray matter cerebral blood flow (GM-CBF). Total intracranial volume is TBV plus cerebral spinal fluid (TICV). A Global Cognitive Function (GCF) score was derived from tests of speed, memory and executive function. Adjusting for TICV and demographic factors, current smoking was significantly associated with lower GM-CBF and TBV, and more AWM (all <0.05); SA with lower GM-CBF, WM-FA and TBV (p=0.01); increasing BMI with decreasing GM-CBF (p<0003); hypertension with lower GM-CBF, WM-FA, and TBV and higher AWM (all <0.05); and diabetes with lower TBV (p=0.007). The GCS was lower as TBV decreased, AWM increased, and WM-FA (all p<0.01). In middle age adults, CVRF are associated with brain health, reflected in MRI measures of structure and perfusion, and cognitive functioning. These findings suggest markers of mid-life cardiovascular and brain health should be considered as indication for early intervention and future risk of late-life cerebrovascular disease and dementia.

Marijuana is a widely used psychoactive substance in the US and medical and recreational legalization has risen over the past decade. Despite the growing number of individuals using marijuana, studies investigating the association between epigenetic factors and recent and cumulative marijuana use remain limited. We therefore investigated the association between recent and cumulative marijuana use and DNA methylation levels. Participants from the Coronary Artery Risk Development in Young Adults Study with whole blood collected at examination years (Y) 15 and Y20 were randomly selected to undergo DNA methylation profiling at both timepoints using the Illumina MethylationEPIC BeadChip. Recent use of marijuana was queried at each examination and used to estimate cumulative marijuana use from Y0 to Y15 and Y20. At Y15 ( n  = 1023), we observed 22 and 31 methylation markers associated (FDR P  ≤ 0.05) with recent and cumulative marijuana use and 132 and 16 methylation markers at Y20 ( n  = 883), respectively. We replicated 8 previously reported methylation markers associated with marijuana use. We further identified 640 cis -meQTLs and 198 DMRs associated with recent and cumulative use at Y15 and Y20. Differentially methylated genes were statistically overrepresented in pathways relating to cellular proliferation, hormone signaling, and infections as well as schizophrenia, bipolar disorder, and substance-related disorders. We identified numerous methylation markers, pathways, and diseases associated with recent and cumulative marijuana use in middle-aged adults, providing additional insight into the association between marijuana use and the epigenome. These results provide novel insights into the role marijuana has on the epigenome and related health conditions.

Steps per day is a meaningful metric for physical activity promotion in clinical and population settings. To guide promotion strategies of step goals, it is important to understand the association of steps with clinical end points, including mortality. To estimate the association of steps per day with premature (age 41-65 years) all-cause mortality among Black and White men and women. This prospective cohort study was part of the Coronary Artery Risk Development in Young Adults (CARDIA) study. Participants were aged 38 to 50 years and wore an accelerometer from 2005 to 2006. Participants were followed for a mean (SD) of 10.8 (0.9) years. Data were analyzed in 2020 and 2021. Daily steps volume, classified as low (<7000 steps/d), moderate (7000-9999 steps/d), and high (≥10 000 steps/d) and stepping intensity, classified as peak 30-minute stepping rate and time spent at 100 steps/min or more. All-cause mortality. A total of 2110 participants from the CARDIA study were included, with a mean (SD) age of 45.2 (3.6) years, 1205 (57.1%) women, 888 (42.1%) Black participants, and a median (interquartile range [IQR]) of 9146 (7307-11 162) steps/d. During 22 845 person years of follow-up, 72 participants (3.4%) died. Using multivariable adjusted Cox proportional hazards models, compared with participants in the low step group, there was significantly lower risk of mortality in the moderate (hazard ratio [HR], 0.28 [95% CI, 0.15-0.54]; risk difference [RD], 53 [95% CI, 27-78] events per 1000 people) and high (HR, 0.45 [95% CI, 0.25-0.81]; RD, 41 [95% CI, 15-68] events per 1000 people) step groups. Compared with the low step group, moderate/high step rate was associated with reduced risk of mortality in Black participants (HR, 0.30 [95% CI, 0.14-0.63]) and in White participants (HR, 0.37 [95% CI, 0.17-0.81]). Similarly, compared with the low step group, moderate/high step rate was associated with reduce risk of mortality in women (HR, 0.28 [95% CI, 0.12-0.63]) and men (HR, 0.42 [95% CI, 0.20-0.88]). There was no significant association between peak 30-minute intensity (lowest vs highest tertile: HR, 0.98 [95% CI, 0.54-1.77]) or time at 100 steps/min or more (lowest vs highest tertile: HR, 1.38 [95% CI, 0.73-2.61]) with risk of mortality. This cohort study found that among Black and White men and women in middle adulthood, participants who took approximately 7000 steps/d or more experienced lower mortality rates compared with participants taking fewer than 7000 steps/d. There was no association of step intensity with mortality.

Previous reports have noted associations between infertility in women and increased risk of cardiovascular disease (CVD) events in later life. However, reports conflict regarding the associations between infertility and CVD risk factors. Using data from a population-based cohort of Black and White women, we examined the association between longitudinal assessments of CVD risk factors and infertility. The Coronary Artery Risk Development in Young Adults (CARDIA) study is a prospective cohort of Black and White women who have undergone repeated assessment of CVD risk factors beginning at study baseline (1985-1986). Risk factors included cigarette smoking, body mass index (BMI), blood pressure, lipid levels, glucose, and C-reactive protein. At approximately 40 years of age, an ancillary study assessed histories of infertility. We used generalized estimating equations with a logit link model to examine associations between infertility (dependent variable) and repeated CVD risk factors (independent variables), with adjustment for age, race, center, and education level in 1107 women. Cigarette use and higher levels of BMI, glucose, and triglycerides and lower levels of high-density lipoprotein cholesterol (HDL) were associated with infertility after adjustment for age, race, and education. Cigarette use had the strongest associations with self-reported infertility in multivariable models (odds ratio 1.85, 95% confidence interval 1.64, 2.14). Women with infertility histories have adverse CVD risk factors across the reproductive lifespan, but cigarette use is the primary CVD risk factor for women's self-reported infertility.

Depressive symptoms may either be a risk factor or prodromal to dementia. Investigating this association in midlife may help clarify the role of depression in cognitive aging. We aimed to identify trajectories in depressive symptoms in early to mid-life and related cognitive and brain outcomes in midlife. This study includes 3944 Black and White participants (ages 26−45 years at baseline) from the Coronary Artery Risk Development in Young Adults (CARDIA) study with 20 years of follow-up. Depressive symptoms were assessed using the Center for Epidemiological Studies Depression scale at five time points over 20 years. Growth mixture modeling (GMM) was used to identify depressive symptom trajectories. Participants completed a neuropsychological battery 20 years after baseline, including the Digit Symbol Substitution Test (DSST), Rey-Auditory Verbal Learning Test (RAVLT), Stroop Test, Montreal Cognitive Assessment (MoCA), and category and letter fluency tests. A sub-sample of participants ( n  = 662) underwent brain magnetic resonance imaging (MRI) to characterize gray matter volumes and white matter hyperintensities (WMHs). We identified four classes of depressive symptom trajectories: a “declining” class ( n  = 286, 7.3%) with initially high symptoms and subsequent decline, a class with consistently high symptoms (“steady high”; n  = 264, 6.7%), a class with late increases in symptoms (“increasing”; n  = 277, 7%), and a class with consistently low symptoms (“steady low”; n  = 3117, 79.0%). The steady high and the increasing classes had poorer performance on all cognitive tests, while the declining class had poorer performance on the DSST, verbal fluency, and MoCA. Compared to the steady low symptom class, the steady high class had lower volumes in the entorhinal cortex (β: −180.80, 95% CI: −336.69 to −24.91) and the amygdala (β: −40.97, 95% CI: −74.09 to −7.85), the increasing class had more WMHs (β: 0.55, 95% CI: 0.22 to 0.89), and the declining class was not significantly different in any brain measures. Trajectories in depressive symptoms in young to mid-adulthood show distinct cognitive and brain phenotypes in midlife. Steady high depressive symptoms may represent a group that is at risk for dementia, whereas increasing symptoms in midlife may be associated with white matter damage.

Background Animal and human studies suggest the gut microbiome is linked to diabetes but additional data are needed on the associations of the gut microbiome to specific diabetes characteristics. The aim of this study was to examine the associations of gut microbiome composition to insulin resistance [Homeostatic Model Assessment of Insulin Resistance (HOMA-IR)], duration of diabetes, and 4 stages of diabetes [normoglycemia, pre-diabetes, and diabetes with (+) and without (−) medication for diabetes]. Methods Data are from a sub-sample (n = 605) of Black and White participants from the 30-year follow-up exam of the prospectively followed community-based Coronary Artery Risk Development in Young Adults cohort (2015–2016; aged 48–60 years). Stool samples were collected and sequenced using the 16S ribosomal RNA method. Microbial measures included: α diversity (within-person), β diversity (between-person), and taxonomies. All analyses were adjusted for demographic, clinical, lifestyle factors, and use of relevant medications (full adjustment). Multivariate linear regression models were used to assess the association of diabetes characteristics with α diversity and genera abundance, while the association with β diversity was analyzed using permutational multivariate analysis of variance. Statistical significance was set to p -value < 0.05 for α and β diversity analyses and to q-value < 0.1 for genera abundance analyses. Results There were 16.7% of participants with pre-diabetes, and 14.4% with diabetes (9% diabetes+) with median (interquartile range) diabetes duration of 5 (5–10) years. In the fully adjusted models, compared to those with no diabetes, longer diabetes duration and the diabetes + group had a lower α diversity. There were significant differences in β diversity across diabetes-related characteristics. A significantly reduced abundance of butyrate-producing genera was associated with higher HOMA-IR (ex., Anaerostipes and Lachnospiraceae_UCG.004 ), longer diabetes duration (ex., Agathobacter and Ruminococcus ), and diabetes + (ex., Faecalibacterium and Romboutsia ). Conclusions Our results suggest that an adverse alteration of gut microbiome composition is related to higher insulin resistance, longer diabetes duration, and is present in those persons with diabetes using medications. These diabetes-related characteristics were also associated with lower levels of certain butyrate-producing bacteria that produce health-promoting short‐chain fatty acids. Understanding the role of gut microbiota in glucose regulation may provide new strategies to reduce the burden of diabetes.

This study investigates correlates of anti-S1 antibody response following COVID-19 vaccination in a U.S. population-based meta-cohort of adults participating in longstanding NIH-funded cohort studies. Anti-S1 antibodies were measured from dried blood spots collected between February 2021-August 2022 using Luminex-based microsphere immunoassays. Of 6245 participants, mean age was 73 years (range, 21-100), 58% were female, and 76% were non-Hispanic White. Nearly 52% of participants received the BNT162b2 vaccine and 48% received the mRNA-1273 vaccine. Lower anti-S1 antibody levels are associated with age of 65 years or older, male sex, higher body mass index, smoking, diabetes, COPD and receipt of BNT16b2 vaccine (vs mRNA-1273). Participants with a prior infection, particularly those with a history of hospitalized illness, have higher anti-S1 antibody levels. These results suggest that adults with certain socio-demographic and clinical characteristics may have less robust antibody responses to COVID-19 vaccination and could be prioritized for more frequent re-vaccination. The antibody response to COVID-19 vaccines varies among individuals. Here the authors find that older age, male sex, smoking, higher BMI, vaccine type, and certain comorbidities are associated with lower anti-S1 antibody levels after COVID-19 vaccinations, indicating that certain groups might benefit from higher frequency or doses of vaccination.

AimsIt is unclear if the presence of type-2 diabetes in one spouse is associated with the development of diabetes in the other spouse. We studied the concordance of diabetes among black and white participants in the Atherosclerosis Risk in Communities (ARIC) study and summarized existing studies in a meta-analysis.MethodsWe conducted a prospective cohort analysis of ARIC data from 8077 married men and women (mean age 54 years) without diabetes at baseline (1987–1989). Complementary log–log models that accounted for interval censoring was used to model the hazard ratio (HR) for the association of spousal diabetes status with the incidence of diabetes. For the meta-analysis, we searched MEDLINE and EMBASE for observational studies published through December 2018 that evaluated spousal concordance for diabetes.ResultsDuring a median follow-up of 22 years, 2512 incident cases of diabetes were recorded. In models with adjustment for general adiposity, spousal cardiometabolic factors and other diabetes risk factors, adults who had a spouse with diabetes had elevated risk for incident diabetes compared to those without a spouse diagnosed with diabetes (HR 1.20, 95% confidence interval 1.02–1.41). This association did not differ by sex or race. Summarized estimates from the 17 studies (489,798 participants from 9 countries) included in the meta-analysis showed a positive association between spousal diabetes status and the development of diabetes (Pooled odds ratio 1.88, 95% CI 1.52–2.33).ConclusionsResults from this large prospective biracial cohort and meta-analysis provides evidence that spouses of persons with diabetes are a high-risk group for diabetes.

Background Multivariate longitudinal data are under-utilized for survival analysis compared to cross-sectional data (CS - data collected once across cohort). Particularly in cardiovascular risk prediction, despite available methods of longitudinal data analysis, the value of longitudinal information has not been established in terms of improved predictive accuracy and clinical applicability. Methods We investigated the value of longitudinal data over and above the use of cross-sectional data via 6 distinct modeling strategies from statistics, machine learning, and deep learning that incorporate repeated measures for survival analysis of the time-to-cardiovascular event in the Coronary Artery Risk Development in Young Adults (CARDIA) cohort. We then examined and compared the use of model-specific interpretability methods (Random Survival Forest Variable Importance) and model-agnostic methods (SHapley Additive exPlanation (SHAP) and Temporal Importance Model Explanation (TIME)) in cardiovascular risk prediction using the top-performing models. Results In a cohort of 3539 participants, longitudinal information from 35 variables that were repeatedly collected in 6 exam visits over 15 years improved subsequent long-term (17 years after) risk prediction by up to 8.3% in C-index compared to using baseline data (0.78 vs. 0.72), and up to approximately 4% compared to using the last observed CS data (0.75). Time-varying AUC was also higher in models using longitudinal data (0.86–0.87 at 5 years, 0.79–0.81 at 10 years) than using baseline or last observed CS data (0.80–0.86 at 5 years, 0.73–0.77 at 10 years). Comparative model interpretability analysis revealed the impact of longitudinal variables on model prediction on both the individual and global scales among different modeling strategies, as well as identifying the best time windows and best timing within that window for event prediction. The best strategy to incorporate longitudinal data for accuracy was time series massive feature extraction, and the easiest interpretable strategy was trajectory clustering. Conclusion Our analysis demonstrates the added value of longitudinal data in predictive accuracy and epidemiological utility in cardiovascular risk survival analysis in young adults via a unified, scalable framework that compares model performance and explainability. The framework can be extended to a larger number of variables and other longitudinal modeling methods. Trial registration ClinicalTrials.gov Identifier: NCT00005130, Registration Date: 26/05/2000.

OBJECTIVE:--The prevalence of type 2 diabetes among Hispanic and Asian Americans is increasing. These groups are largely comprised of immigrants who may be undergoing behavioral and lifestyle changes associated with development of diabetes. We studied the association between acculturation and diabetes in a population sample of 708 Mexican-origin Hispanics, 547 non-Mexican-origin Hispanics, and 737 Chinese participants in the Multi-Ethnic Study of Atherosclerosis (MESA). RESEARCH DESIGN AND METHODS--Diabetes was defined as fasting glucose >=126 mg/dl and/or use of antidiabetic medications. An acculturation score was calculated for all participants using nativity, years living in the U.S., and language spoken at home. The score ranged from 0 to 5 (0 = least acculturated and 5 = most acculturated). Relative risk regression was used to estimate the association between acculturation and diabetes. RESULTS:--For non-Mexican-origin Hispanics, the prevalence of diabetes was positively associated with acculturation score, after adjustment for sociodemographics. The prevalence of diabetes was significantly higher among the most acculturated versus the least acculturated non-Mexican-origin Hispanics (prevalence ratio 2.49 [95% CI 1.14-5.44]); the higher the acculturation score is, the higher the prevalence of diabetes (P for trend 0.059). This relationship between acculturation and diabetes was partly attenuated after adjustment for BMI or diet. Diabetes prevalence was not related to acculturation among Chinese or Mexican-origin Hispanics. CONCLUSIONS:--Among non-Mexican-origin Hispanics in MESA, greater acculturation is associated with higher diabetes prevalence. The relation is at least partly mediated by BMI and diet. Acculturation is a factor that should be considered when predictors of diabetes in racial/ethnic groups are examined.