MagicalRsq-X: A cross-cohort transferable genotype imputation quality metric.

Since genotype imputation was introduced, researchers have been relying on the estimated imputation quality from imputation software to perform post-imputation quality control (QC). However, this quality estimate (denoted as Rsq) performs less well for lower-frequency variants. We recently published MagicalRsq, a machine-learning-based imputation quality calibration, which leverages additional typed markers from the same cohort and outperforms Rsq as a QC metric. In this work, we extended the original MagicalRsq to allow cross-cohort model training and named the new model MagicalRsq-X. We removed the cohort-specific estimated minor allele frequency and included linkage disequilibrium scores and recombination rates as additional features. Leveraging whole-genome sequencing data from TOPMed, specifically participants in the BioMe, JHS, WHI, and MESA studies, we performed comprehensive cross-cohort evaluations for predominantly European and African ancestral individuals based on their inferred global ancestry with the 1000 Genomes and Human Genome Diversity Project data as reference. Our results suggest MagicalRsq-X outperforms Rsq in almost every setting, with 7.3%-14.4% improvement in squared Pearson correlation with true R2, corresponding to 85-218 K variant gains. We further developed a metric to quantify the genetic distances of a target cohort relative to a reference cohort and showed that such metric largely explained the performance of MagicalRsq-X models. Finally, we found MagicalRsq-X saved up to 53 known genome-wide significant variants in one of the largest blood cell trait GWASs that would be missed using the original Rsq for QC. In conclusion, MagicalRsq-X shows superiority for post-imputation QC and benefits genetic studies by distinguishing well and poorly imputed lower-frequency variants.

Whole genome sequencing based analysis of inflammation biomarkers in the Trans-Omics for Precision Medicine (TOPMed) consortium.

Inflammation biomarkers can provide valuable insight into the role of inflammatory processes in many diseases and conditions. Sequencing based analyses of such biomarkers can also serve as an exemplar of the genetic architecture of quantitative traits. To evaluate the biological insight, which can be provided by a multi-ancestry, whole-genome based association study, we performed a comprehensive analysis of 21 inflammation biomarkers from up to 38 465 individuals with whole-genome sequencing from the Trans-Omics for Precision Medicine (TOPMed) program (with varying sample size by trait, where the minimum sample size was n = 737 for MMP-1). We identified 22 distinct single-variant associations across 6 traits-E-selectin, intercellular adhesion molecule 1, interleukin-6, lipoprotein-associated phospholipase A2 activity and mass, and P-selectin-that remained significant after conditioning on previously identified associations for these inflammatory biomarkers. We further expanded upon known biomarker associations by pairing the single-variant analysis with a rare variant set-based analysis that further identified 19 significant rare variant set-based associations with 5 traits. These signals were distinct from both significant single variant association signals within TOPMed and genetic signals observed in prior studies, demonstrating the complementary value of performing both single and rare variant analyses when analyzing quantitative traits. We also confirm several previously reported signals from semi-quantitative proteomics platforms. Many of these signals demonstrate the extensive allelic heterogeneity and ancestry-differentiated variant-trait associations common for inflammation biomarkers, a characteristic we hypothesize will be increasingly observed with well-powered, large-scale analyses of complex traits.

Rare variants in long non-coding RNAs are associated with blood lipid levels in the TOPMed whole-genome sequencing study.

Long non-coding RNAs (lncRNAs) are known to perform important regulatory functions in lipid metabolism. Large-scale whole-genome sequencing (WGS) studies and new statistical methods for variant set tests now provide an opportunity to assess more associations between rare variants in lncRNA genes and complex traits across the genome. In this study, we used high-coverage WGS from 66,329 participants of diverse ancestries with measurement of blood lipids and lipoproteins (LDL-C, HDL-C, TC, and TG) in the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) program to investigate the role of lncRNAs in lipid variability. We aggregated rare variants for 165,375 lncRNA genes based on their genomic locations and conducted rare-variant aggregate association tests using the STAAR (variant-set test for association using annotation information) framework. We performed STAAR conditional analysis adjusting for common variants in known lipid GWAS loci and rare-coding variants in nearby protein-coding genes. Our analyses revealed 83 rare lncRNA variant sets significantly associated with blood lipid levels, all of which were located in known lipid GWAS loci (in a ±500-kb window of a Global Lipids Genetics Consortium index variant). Notably, 61 out of 83 signals (73%) were conditionally independent of common regulatory variation and rare protein-coding variation at the same loci. We replicated 34 out of 61 (56%) conditionally independent associations using the independent UK Biobank WGS data. Our results expand the genetic architecture of blood lipids to rare variants in lncRNAs.

Proteomics, Human Environmental Exposure, and Cardiometabolic Risk.

BACKGROUND: The biological mechanisms linking environmental exposures with cardiovascular disease pathobiology are incompletely understood. We sought to identify circulating proteomic signatures of environmental exposures and examine their associations with cardiometabolic and respiratory disease in observational cohort studies. METHODS: We tested the relations of >6500 circulating proteins with 29 environmental exposures across the built environment, green space, air pollution, temperature, and social vulnerability indicators in ≈3000 participants of the CARDIA study (Coronary Artery Risk Development in Young Adults) across 4 centers using penalized and ordinary linear regression. In >3500 participants from FHS (Framingham Heart Study) and JHS (Jackson Heart Study), we evaluated the prospective relations of proteomic signatures of the envirome with cardiovascular disease and mortality using Cox models. RESULTS: Proteomic signatures of the envirome identified novel/established cardiovascular disease-relevant pathways including DNA damage, fibrosis, inflammation, and mitochondrial function. The proteomic signatures of the envirome were broadly related to cardiometabolic disease and respiratory phenotypes (eg, body mass index, lipids, and left ventricular mass) in CARDIA, with replication in FHS/JHS. A proteomic signature of social vulnerability was associated with a composite of cardiovascular disease/mortality (1428 events; FHS: hazard ratio, 1.16 [95% CI, 1.08-1.24]; P=1.77×10-5; JHS: hazard ratio, 1.25 [95% CI, 1.14-1.38]; P=6.38×10-6; hazard ratio expressed as per 1 SD increase in proteomic signature), robust to adjustment for known clinical risk factors. CONCLUSIONS: Environmental exposures are related to an inflammatory-metabolic proteome, which identifies individuals with cardiometabolic disease and respiratory phenotypes and outcomes. Future work examining the dynamic impact of the environment on human cardiometabolic health is warranted.

Development and Validation of the American Heart Association's PREVENT Equations.

BACKGROUND: Multivariable equations are recommended by primary prevention guidelines to assess absolute risk of cardiovascular disease (CVD). However, current equations have several limitations. Therefore, we developed and validated the American Heart Association Predicting Risk of CVD EVENTs (PREVENT) equations among US adults 30 to 79 years of age without known CVD. METHODS: The derivation sample included individual-level participant data from 25 data sets (N=3 281 919) between 1992 and 2017. The primary outcome was CVD (atherosclerotic CVD and heart failure). Predictors included traditional risk factors (smoking status, systolic blood pressure, cholesterol, antihypertensive or statin use, and diabetes) and estimated glomerular filtration rate. Models were sex-specific, race-free, developed on the age scale, and adjusted for competing risk of non-CVD death. Analyses were conducted in each data set and meta-analyzed. Discrimination was assessed using the Harrell C-statistic. Calibration was calculated as the slope of the observed versus predicted risk by decile. Additional equations to predict each CVD subtype (atherosclerotic CVD and heart failure) and include optional predictors (urine albumin-to-creatinine ratio and hemoglobin A1c), and social deprivation index were also developed. External validation was performed in 3 330 085 participants from 21 additional data sets. RESULTS: Among 6 612 004 adults included, mean±SD age was 53±12 years, and 56% were women. Over a mean±SD follow-up of 4.8±3.1 years, there were 211 515 incident total CVD events. The median C-statistics in external validation for CVD were 0.794 (interquartile interval, 0.763-0.809) in female and 0.757 (0.727-0.778) in male participants. The calibration slopes were 1.03 (interquartile interval, 0.81-1.16) and 0.94 (0.81-1.13) among female and male participants, respectively. Similar estimates for discrimination and calibration were observed for atherosclerotic CVD- and heart failure-specific models. The improvement in discrimination was small but statistically significant when urine albumin-to-creatinine ratio, hemoglobin A1c, and social deprivation index were added together to the base model to total CVD (ΔC-statistic [interquartile interval] 0.004 [0.004-0.005] and 0.005 [0.004-0.007] among female and male participants, respectively). Calibration improved significantly when the urine albumin-to-creatinine ratio was added to the base model among those with marked albuminuria (>300 mg/g; 1.05 [0.84-1.20] versus 1.39 [1.14-1.65]; P=0.01). CONCLUSIONS: PREVENT equations accurately and precisely predicted risk for incident CVD and CVD subtypes in a large, diverse, and contemporary sample of US adults by using routinely available clinical variables.

Regional and rural-urban patterns in the prevalence of diagnosed hypertension among older U.S. adults with diabetes, 2005-2017.

BACKGROUND: Hypertension prevalence among the overall US adult population has been relatively stable during the last two decades. However, whether this stabilization has occurred across rural-urban communities and across different geographic regions is unknown, particularly among older adults with diabetes who are likely to have concomitant cardiovascular risk factors. METHODS: This serial cross-sectional analysis used the 5% national sample of Medicare administrative claims data (n = 3,516,541) to examine temporal trends (2005-2017) in diagnosed hypertension among older adults with diabetes, across urban-rural communities and US census regions (Northeast, Midwest, South, and West). Joinpoint regression was used to obtain annual percent change (APC) in hypertension prevalence across rural-urban communities and geographic regions, and multivariable adjusted regression was used to assess associations between rural-urban communities and hypertension prevalence. RESULTS: The APC in the prevalence of hypertension was higher during 2005-2010, and there was a slowdown in the increase during 2011-2017 across all regions, with significant variations across rural-urban communities within each of the regions. In the regression analysis, in the adjusted model, older adults living in non-core (most rural) areas in the Midwest (PR = 0.988, 95% CI: 0.981-0.995) and West (PR = 0.935, 95% CI: 0.923-0.946) had lower hypertension prevalence than their regional counterparts living in large central metro areas. CONCLUSIONS: Although the magnitudes of these associations are small, differences in hypertension prevalence across rural-urban areas and geographic regions may have implications for targeted interventions to improve chronic disease prevention and management.

Racial difference in the association between non-alcoholic fatty liver disease and incident type 2 diabetes: findings from the CARDIA study.

AIMS/HYPOTHESIS: Type 2 diabetes and non-alcoholic fatty liver disease (NAFLD) are prevalent diseases of metabolic origin. We examined the association between NAFLD and the development of type 2 diabetes among non-Asian adults, and whether the association differs by race. METHODS: We analysed data from the Coronary Artery Risk Development in Young Adults (CARDIA) study, a population-based prospective cohort study. Participants underwent non-contrast abdominal computed tomography (CT) at baseline (2010-2011) and assessment of glucose measures at the follow-up exam (2015-2016). NAFLD was defined as liver attenuation ≤51 Hounsfield units on CT images after exclusion for other liver fat causes. Race was self-reported. We used targeted maximum likelihood estimation (TMLE) with machine-learning algorithms to estimate difference in type 2 diabetes risk between the NAFLD and non-NAFLD groups. RESULTS: Of the 1995 participants without type 2 diabetes at baseline (mean age±SD, 50.0±3.6 years; 59% women; 55.0% White and 45.0% Black), 21.7% of White and 16.8% of Black participants had NAFLD at baseline, and 3.7% of White and 8.0% of Black participants developed type 2 diabetes at follow up. After multivariable adjustment, risk difference for type 2 diabetes associated with NAFLD vs no NAFLD was 4.1% (95% CI 0.3%, 7.9%) among White participants and -1.9% (95% CI -5.7%, 2.0%) in Black participants. CONCLUSIONS/INTERPRETATION: NAFLD was associated with a higher risk of type 2 diabetes among White participants but not among Black participants. This finding suggests that the effect of liver fat on impaired glucose metabolism may be smaller in Black than in White individuals.

Heart Disease and Stroke Statistics-2022 Update: A Report From the American Heart Association.

BACKGROUND: The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs). METHODS: The American Heart Association, through its Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update. The 2022 Statistical Update is the product of a full year's worth of effort by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. This year's edition includes data on the monitoring and benefits of cardiovascular health in the population and an enhanced focus on social determinants of health, adverse pregnancy outcomes, vascular contributions to brain health, and the global burden of cardiovascular disease and healthy life expectancy. RESULTS: Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics. CONCLUSIONS: The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Heart Disease and Stroke Statistics-2021 Update: A Report From the American Heart Association.

BACKGROUND: The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs). METHODS: The American Heart Association, through its Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update. The 2021 Statistical Update is the product of a full year's worth of effort by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. This year's edition includes data on the monitoring and benefits of cardiovascular health in the population, an enhanced focus on social determinants of health, adverse pregnancy outcomes, vascular contributions to brain health, the global burden of cardiovascular disease, and further evidence-based approaches to changing behaviors related to cardiovascular disease. RESULTS: Each of the 27 chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics. CONCLUSIONS: The Statistical Update represents a critical resource for the lay public, policy makers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Persistence of Depressive Symptoms and Risk of Incident Cardiovascular Disease With and Without Diabetes: Results from the REGARDS Study.

BACKGROUND: Baseline depressive symptoms are associated with subsequent adverse cardiovascular (CV) events in subjects with and without diabetes but the impact of persistent symptoms vs. improvement remains controversial. OBJECTIVE: Examine long-term changes in depressive symptoms in individuals with and without diabetes and the associated risk for adverse CV events. DESIGN: REGARDS is a prospective cohort study of CV risk factors in 30,000 participants aged 45 years and older. PARTICIPANTS: N = 16,368 (16.5% with diabetes mellitus) who remained in the cohort an average of 11.1 years later and who had complete data. MAIN MEASURES: Depressive symptoms were measured using the 4-item Centers for Epidemiologic Study of Depression (CES-D) questionnaire at baseline and again at a mean follow-up of 5.07 (SD = 1.66) years. Adjudicated incident stroke, coronary heart disease (CHD), CV mortality, and a composite outcome were assessed in a subsequent follow-up period of 6.1 (SD = 2.6) years. METHODS: The association of changes in depressive symptoms (CES-D scores) across 5 years with incident CV events was assessed using Cox proportional hazards modeling. KEY RESULTS: Compared to participants with no depressive symptoms at either time point, participants without diabetes but with persistently elevated depressive symptoms at both baseline and follow-up demonstrated a significantly increased risk of incident stroke (HR (95% CI) = 1.84 (1.03, 3.30)), a pattern which was substantially more prevalent in blacks (HR (95% CI) = 2.64 (1.48, 4.72)) compared to whites (HR (95% CI) = 1.06 (0.50, 2.25)) and in those not taking anti-depressants (HR (95% CI) = 2.01 (1.21, 3.35)) in fully adjusted models. CONCLUSIONS: The persistence of depressive symptoms across 5 years of follow-up in participants without diabetes identifies individuals at increased risk for incident stroke. This was particularly evident in black participants and among those not taking anti-depressants.

Comparing Global and Spatial Composite Measures of Neighborhood Socioeconomic Status Across US Counties.

Area-level neighborhood socioeconomic status (NSES) is often measured without consideration of spatial autocorrelation and variation. In this paper, we compared a non-spatial NSES measure to a spatial NSES measure for counties in the USA using principal component analysis and geographically weighted principal component analysis (GWPCA), respectively. We assessed spatial variation in the loadings using a Monte Carlo randomization test. The results indicated that there was statistically significant variation (p = 0.004) in the loadings of the spatial index. The variability of the census variables explained by the spatial index ranged from 60 to 90%. We found that the first geographically weighted principal component explained the most variability in the census variables in counties in the Northeast and the West, and the least variability in counties in the Midwest. We also tested the two measures by assessing the associations with county-level diabetes prevalence using data from the CDC's US Diabetes Surveillance System. While associations of the two NSES measures with diabetes did not differ for this application, the descriptive results suggest that it might be important to consider a spatial index over a global index when constructing national county measures of NSES. The spatial approach may be useful in identifying what factors drive the socioeconomic status of a county and how they vary across counties. Furthermore, we offer suggestions on how a GWPCA-based NSES index may be replicated for smaller geographic scopes.

Psychosocial profiles and longitudinal achievement of optimal cardiovascular risk factor levels: the Coronary Artery Risk Development in Young Adults (CARDIA) study.

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.

Call to Action: Structural Racism as a Fundamental Driver of Health Disparities: A Presidential Advisory From the American Heart Association.

Structural racism has been and remains a fundamental cause of persistent health disparities in the United States. The coronavirus disease 2019 (COVID-19) pandemic and the police killings of George Floyd, Breonna Taylor, and multiple others have been reminders that structural racism persists and restricts the opportunities for long, healthy lives of Black Americans and other historically disenfranchised groups. The American Heart Association has previously published statements addressing cardiovascular and cerebrovascular risk and disparities among racial and ethnic groups in the United States, but these statements have not adequately recognized structural racism as a fundamental cause of poor health and disparities in cardiovascular disease. This presidential advisory reviews the historical context, current state, and potential solutions to address structural racism in our country. Several principles emerge from our review: racism persists; racism is experienced; and the task of dismantling racism must belong to all of society. It cannot be accomplished by affected individuals alone. The path forward requires our commitment to transforming the conditions of historically marginalized communities, improving the quality of housing and neighborhood environments of these populations, advocating for policies that eliminate inequities in access to economic opportunities, quality education, and health care, and enhancing allyship among racial and ethnic groups. Future research on racism must be accelerated and should investigate the joint effects of multiple domains of racism (structural, interpersonal, cultural, anti-Black). The American Heart Association must look internally to correct its own shortcomings and advance antiracist policies and practices regarding science, public and professional education, and advocacy. With this advisory, the American Heart Association declares its unequivocal support of antiracist principles.

Heart Disease and Stroke Statistics-2020 Update: A Report From the American Heart Association.

BACKGROUND: The American Heart Association, in conjunction with the National Institutes of Health, annually reports on the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs). METHODS: The American Heart Association, through its Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update. The 2020 Statistical Update is the product of a full year's worth of effort by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. This year's edition includes data on the monitoring and benefits of cardiovascular health in the population, metrics to assess and monitor healthy diets, an enhanced focus on social determinants of health, a focus on the global burden of cardiovascular disease, and further evidence-based approaches to changing behaviors, implementation strategies, and implications of the American Heart Association's 2020 Impact Goals. RESULTS: Each of the 26 chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics. CONCLUSIONS: The Statistical Update represents a critical resource for the lay public, policy makers, media professionals, clinicians, healthcare administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Lifecourse socioeconomic position and diabetes incidence in the REasons for Geographic and Racial Differences in Stroke (REGARDS) study, 2003 to 2016.

Low socioeconomic position (SEP) across the lifecourse is associated with Type 2 diabetes (T2DM). We examined whether these economic disparities differ by race and sex. We included 5448 African American (AA) and white participants aged ≥45 years from the national (United States) REasons for Geographic and Racial Differences in Stroke (REGARDS) cohort without T2DM at baseline (2003-07). Incident T2DM was defined by fasting glucose ≥126 mg/dL, random glucose ≥200 mg/dL, or using T2DM medications at follow-up (2013-16). Derived SEP scores in childhood (CSEP) and adulthood (ASEP) were used to calculate a cumulative (CumSEP) score. Social mobility was defined as change in SEP. We fitted race-stratified logistic regression models to estimate the association between each lifecourse SEP indicator and T2DM, adjusting for covariates; additionally, we tested SEP-sex interactions. Over a median of 9.0 (range 7-14) years of follow-up, T2DM incidence was 167.1 per 1000 persons among AA and 89.9 per 1000 persons among white participants. Low CSEP was associated with T2DM incidence among AA (OR = 1.61; 95%CI 1.05-2.46) but not white (1.06; 0.74-2.33) participants; this was attenuated after adjustment for ASEP. In contrast, low CumSEP was associated with T2DM incidence for both racial groups. T2DM risk was similar for stable low SEP and increased for downward mobility when compared with stable high SEP in both groups, whereas upward mobility increased T2DM risk among AAs only. No differences by sex were observed. Among AAs, low CSEP was not independently associated with T2DM, but CSEP may shape later-life experiences and health risks.

Long-Term Levels of LDL-C and Cognitive Function: The CARDIA Study.

OBJECTIVES: It is uncertain if long-term levels of low-density lipoprotein-cholesterol (LDL-C) affect cognition in middle age. We examined the association of LDL-C levels over 25 years with cognitive function in a prospective cohort of black and white US adults. METHODS: Lipids were measured at baseline (1985-1986; age: 18-30 years) and at serial examinations conducted over 25 years. Time-averaged cumulative LDL-C was calculated using the area under the curve for 3,328 participants with ≥3 LDL-C measurements and a cognitive function assessment. Cognitive function was assessed at the Year 25 examination with the Digit Symbol Substitution Test [DSST], Rey Auditory Visual Learning Test [RAVLT], and Stroop Test. A brain magnetic resonance imaging (MRI) sub-study (N = 707) was also completed at Year 25 to assess abnormal white matter tissue volume (AWMV) and gray matter cerebral blood flow volume (GM-CBFV) as secondary outcomes. RESULTS: There were 15.6%, 32.9%, 28.9%, and 22.6% participants with time-averaged cumulative LDL-C <100 mg/dL, 101-129 mg/dL, 130-159 mg/dL, and ≥160 mg/dL, respectively. Standardized differences in all cognitive function test scores ranged from 0.16 SD lower to 0.09 SD higher across time-averaged LDL-C categories in comparison to those with LDL-C < 100 mg/dL. After covariate adjustment, participants with higher versus lower time-averaged LDL-C had a lower RAVLT score (p-trend = 0.02) but no differences were present for DSST, Stroop Test, AWMV, or GM-CBFV. CONCLUSION: Cumulative LDL-C was associated with small differences in memory, as assessed by RAVLT scores, but not other cognitive or brain MRI measures over 25 years of follow-up.

Impact of Multiple Social Determinants of Health on Incident Stroke.

BACKGROUND AND PURPOSE: Social determinants of health (SDOH) have been previously associated with incident stroke. Although SDOH often cluster within individuals, few studies have examined associations between incident stroke and multiple SDOH within the same individual. The objective was to determine the individual and cumulative effects of SDOH on incident stroke. METHODS: This study included 27 813 participants from the REGARDS (Reasons for Geographic and Racial Differences in Stroke) Study, a national, representative, prospective cohort of black and white adults aged ≥45 years. SDOH was the primary exposure. The main outcome was expert adjudicated incident stroke. Cox proportional hazards models examined associations between incident stroke and SDOH, individually and as a count of SDOH, adjusting for potential confounders. RESULTS: The mean age was 64.7 years (SD 9.4) at baseline; 55.4% were women and 40.4% were blacks. Over a median follow-up of 9.5 years (IQR, 6.0-11.5), we observed 1470 incident stroke events. Of 10 candidate SDOH, 7 were associated with stroke (P<0.10): race, education, income, zip code poverty, health insurance, social isolation, and residence in one of the 10 lowest ranked states for public health infrastructure. A significant age interaction resulted in stratification at 75 years. In fully adjusted models, among individuals <75 years, risk of stroke rose as the number of SDOH increased (hazard ratio for one SDOH, 1.26 [95% CI, 1.02-1.55]; 2 SDOH hazard ratio, 1.38 [95% CI, 1.12-1.71]; and ≥3 SDOH hazard ratio, 1.51 [95% CI, 1.21-1.89]) compared with those without any SDOH. Among those ≥75 years, none of the observed effects reached statistical significance. CONCLUSIONS: Incremental increases in the number of SDOH were independently associated with higher incident stroke risk in adults aged <75 years, with no statistically significant effects observed in individuals ≥75 years. Targeting individuals with multiple SDOH may help reduce risk of stroke among vulnerable populations.

Risk factor control among Black and White adults with diabetes onset in older adulthood: The Reasons for Geographic and Racial Differences in Stroke (REGARDS) study.

The objective of this study was to determine whether attainment of clinical and lifestyle targets varied by race and sex among adults with diabetes onset in older adulthood. This study included 1420 black and white adults from the REGARDS study without diabetes at baseline (2003-07) but with diabetes onset at the follow-up exam (2013-16). Attainment of clinical targets (A1c <8%; blood pressure < 140/90 mmHg; and statin use) and lifestyle targets (not smoking; physical activity≥ 4 times/week; and moderate/no alcohol use) was assessed at the follow-up exam. Modified Poisson regression was used to obtain prevalence ratios (PR) for meeting clinical and lifestyle targets stratified by race and sex, separately. The mean age was 71.5 years, 53.6% were female, and 46.1% were black. The majority were aware of their diabetes status (85.7%) and used oral or injectable hypoglycemic medications (64.8%). Overall, 39.4% met all 3 clinical targets and 18.8% met all 3 lifestyle targets. Meeting A1c and blood pressure targets were similar by race and sex. Statin use was more prevalent for men than women among white adults (PR = 1.13; 95% CI = 0.99-1.29) and black adults (PR = 1.23; 95% CI = 1.06-1.43). For lifestyle factors, the non-smoking prevalence was similar by race and sex, while white men were more likely than white women to be physically active. Although the attainment of each clinical and lifestyle target separately was generally high among adults with diabetes onset in older adulthood, race and sex differences were apparent. Comprehensive management of clinical and lifestyle factors in people with diabetes remains suboptimal.

Ideal cardiovascular health, glycaemic status and incident type 2 diabetes mellitus: the REasons for Geographic and Racial Differences in Stroke (REGARDS) study.

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.

Fasting glucose variability in young adulthood and incident diabetes, cardiovascular disease and all-cause mortality.

AIMS/HYPOTHESIS: The aim of this study was to determine whether long-term intra-individual variability in fasting glucose (FG) during young adulthood is associated with incident diabetes, cardiovascular disease (CVD) and mortality. METHODS: We included participants from the Coronary Artery Risk Development in Young Adults (CARDIA) study, ages 18-30 years at baseline (1985-1986) and followed with eight examinations for up to 30 years. Long-term glucose variability was assessed using the CV (CV-FG) and the absolute difference between successive FG measurements (average real variability; ARV-FG). For participants who developed any event (diabetes, CVD or mortality), FG variability measurement was censored at the examination prior to event ascertainment. We estimated HRs for incident diabetes, CVD and mortality with adjustment for demographics, baseline FG, change in FG (censor - baseline) and time-varying education, smoking, alcohol consumption, BMI, physical activity, systolic BP, BP medications, LDL-cholesterol and cholesterol medications (and incident diabetes and diabetes medications for CVD and mortality outcomes). RESULTS: Among 3769 black and white participants, there were 317 incident diabetes cases (102,677 person-years), 159 incident CVD events (110,314 person-years) and 174 deaths (111,390 person-years). After adjustment, HRs per 1 SD higher ARV-FG were 1.64 (95% CI 1.52, 1.78) for diabetes, 1.15 (95% CI 1.01, 1.31) for CVD and 1.25 (95% CI 1.11, 1.40) for mortality. The HRs per 1 SD higher CV-FG were 1.39 (95% CI 1.21, 1.58) for diabetes, 1.32 (95% CI 1.13, 1.54) for CVD and 1.08 (95% CI 0.92, 1.27) for mortality, after adjustment. The cause-specific HRs per 1 SD higher ARV-FG were 1.29 (95% CI 1.14, 1.47) for non-CVD death and 1.05 (95% CI 0.76, 1.45) for CVD death. We did not observe evidence for effect modification of any association by sex or race. CONCLUSIONS/INTERPRETATION: Our results suggest that higher intra-individual FG variability during young adulthood before the onset of diabetes is associated with incident diabetes, CVD and mortality.