Black and White individuals differ in dementia prevalence, risk factors, and symptomatic presentation

Introduction: Health disparities remain pervasive in US and eliminating such disparities is one of the overarching goals of the Healthy People 2020 agenda. Previous studies have assessed the disparities in risk of coronary heart disease (CHD) mortality by race/ethnicity, but most of them only focused on the average CHD risk without taking into account the full risk distribution which would enable analysis of specific high-risk sub-groups. In this study, we estimated the 10-year risk distribution of CHD mortality based on 5 leading modifiable risk factors in US (i.e. smoking, adiposity, high blood pressure, serum cholesterol and blood glucose). We quantified the racial disparities in absolute CHD risk while accounting for full risk distribution. Methods: We included 3866 individuals aged 45 to 74 years, who were black or white, non-pregnant, free of CHD and had measurements of all 5 risk factors from 6 consecutive 2-year cycles of the National Health and Nutrition Examination Survey 1999-2010. We used mortality data from National Center for Health Statistics to estimate the cause-age-sex-race specific mortality in 2010. We also obtained hazard ratios of the selected 5 risk factors on CHD mortality from large meta-analyses of epidemiological studies. We predicted the 10-year risk of CHD death for each individual by simulating their survival process from 2010 to 2020 incorporating competing risks by death from other correlated causes. To assess health disparities, we compared the 5 th , 25 th , 50 th , 75 th and 95 th percentile of the predicted risks between black and white by age and sex. Results: More than half of the black and white population aged 45 to 74 years had a low 10-year risk of CHD death (< 2%). The age-sex-race specific distributions of 10-year CHD risk were right-skewed with a large proportion of population on the low risk tail. Comparing to white, black had similar shape of CHD risk distributions, but higher risk levels at all percentiles across age and sex groups. In 55-64 ages where CHD was the major cause of death, the median of CHD risk for black males was 2.9% (interquartile range (IQR) 1.7% - 4.4%), which was 0.7% larger than that for white males (2.2%, IQR 1.4% - 3.3%). This risk difference was similar in females: the median CHD risk for black females was 1.6% (IQR 0.9% - 2.4%) and 0.9% for white females (IQR 0.5% - 1.5%). The disparities became larger on the high risk tail (95 th percentile of predicted risk), where black had 2.7% higher risk for male and 2.3% for female in 55-64 ages. In older age groups (65-74 ages), such difference increased to 3.5% for both male and female. Conclusions: This analysis showed a skewed 10-year CHD risk distribution in US. The racial disparities are larger in the high risk sub-groups compared to those in the center of the risk distribution, indicating that the high risk subgroups should be the target population of intervention that aims to reduce health disparities in US.

Download Full-text

Obesity and cardiovascular disease risk factors in black and white girls: the NHLBI Growth and Health Study.

American Journal of Public Health ◽

10.2105/ajph.82.12.1613 ◽

1992 ◽

Vol 82 (12) ◽

pp. 1613-1620 ◽

Cited By ~ 211

Keyword(s):

Risk Factors ◽

Cardiovascular Disease ◽

Disease Risk ◽

Cardiovascular Disease Risk ◽

Cardiovascular Disease Risk Factors ◽

Health Study ◽

Black And White ◽

White Girls

Download Full-text

Abstract P118: Machine Learning Algorithm For Improving Classification Of Myocardial Infarction Across A Diverse Population Using US Veteran Electronic Health Record Data

Circulation ◽

10.1161/circ.143.suppl_1.p118 ◽

2021 ◽

Vol 143 (Suppl_1) ◽

Author(s):

Kelly Cho ◽

Nicholas Link ◽

Petra Schubert ◽

Zeling He ◽

Jacqueline P Honerlaw ◽

...

Keyword(s):

Machine Learning ◽

Risk Factors ◽

Myocardial Infarction ◽

Density Lipoprotein ◽

Health Record ◽

Female Veterans ◽

Black And White ◽

Code Algorithm ◽

Billing Code

Introduction: The majority of population-based studies of myocardial infarction (MI) rely on billing codes for classification. Classification algorithms employing machine learning (ML) increasingly used for phenotyping using electronic health record (EHR) data. Hypothesis: ML algorithms integrating billing and information from narrative notes extracted using natural language processing (NLP) can improve classification of MI compared to billing code algorithms. Improved classification will improve power to compare risk factors across population subgroups. Methods: Retrospective cohort study of nationwide Veterans Affairs (VA) EHR data. MI classified using 2 approaches: (1) published billing code algorithm, (2) published phenotyping pipeline incorporating NLP and ML. Results compared against gold standard chart review of MI outcomes in 308 Veterans. We also tested known association between high density lipoprotein cholesterol (HDL-C) and MI outcomes classified using the 2 approaches among Black and White Veterans, stratified by sex and race; prior study showed HDL-C less protective for Black compared to White individuals. Results: We studied 17,176,658 million Veterans, mean age 69 years, 94% male, 12% self-report Black, 71% White. The billing code algorithm classified MI at positive predictive value (PPV) 0.64 compared to the published ML approach, PPV 0.90; the latter classified a modestly higher percentage of non-White Veterans. Using ML algorithm for MI, we replicated a reduced protective effect of HDL-C in Black vs White male and female Veterans (Table); with the billing code algorithm no association was observed between low density lipoprotein cholesterol (LDL-C) or HDL-C with MI among Black female Veterans. Conclusions: Using nationwide VA data, application of an ML approach improved classification of MI particularly among non-White Veterans, resulting in improved power to study differences in association for MI risk factors among Black and White Veterans.

Download Full-text

Abstract 196: Development and Validation of Machine Learning-based Race-specific Models to Predict 10-year Risk of Heart Failure: A Multi-cohort Analysis

Circulation ◽

10.1161/circ.142.suppl_3.196 ◽

2020 ◽

Vol 142 (Suppl_3) ◽

Author(s):

Matthew W Segar ◽

Byron Jaeger ◽

Kershaw V Patel ◽

Vijay Nambi ◽

Chiadi E Ndumele ◽

...

Keyword(s):

Machine Learning ◽

Risk Factors ◽

Heart Failure ◽

Risk Prediction ◽

Prediction Models ◽

External Validation ◽

Superior Performance ◽

Risk Models ◽

Black And White ◽

White Adults

Introduction: Heart failure (HF) risk and the underlying biological risk factors vary by race. Machine learning (ML) may improve race-specific HF risk prediction but this has not been fully evaluated. Methods: The study included participants from 4 cohorts (ARIC, DHS, JHS, and MESA) aged > 40 years, free of baseline HF, and with adjudicated HF event follow-up. Black adults from JHS and white adults from ARIC were used to derive race-specific ML models to predict 10-year HF risk. The ML models were externally validated in subgroups of black and white adults from ARIC (excluding JHS participants) and pooled MESA/DHS cohorts and compared to prior established HF risk scores developed in ARIC and MESA. Harrell’s C-index and Greenwood-Nam-D’Agostino chi-square were used to assess discrimination and calibration, respectively. Results: In the derivation cohorts, 288 of 4141 (7.0%) black and 391 of 8242 (4.7%) white adults developed HF over 10 years. The ML models had excellent discrimination in both black and white participants (C-indices = 0.88 and 0.89). In the external validation cohorts for black participants from ARIC (excluding JHS, N = 1072) and MESA/DHS pooled cohorts (N = 2821), 131 (12.2%) and 115 (4.1%) developed HF. The ML model had adequate calibration and demonstrated superior discrimination compared to established HF risk models (Fig A). A consistent pattern was also observed in the external validation cohorts of white participants from the MESA/DHS pooled cohorts (N=3236; 100 [3.1%] HF events) (Fig A). The most important predictors of HF in both races were NP levels. Cardiac biomarkers and glycemic parameters were most important among blacks while LV hypertrophy and prevalent CVD and traditional CV risk factors were the strongest predictors among whites (Fig B). Conclusions: Race-specific and ML-based HF risk models that integrate clinical, laboratory, and biomarker data demonstrated superior performance when compared to traditional risk prediction models.

Download Full-text

The interplay between microglial states and major risk factors in Alzheimer’s disease through the eyes of single-cell RNA-sequencing: beyond black and white

Journal of Neurophysiology ◽

10.1152/jn.00395.2019 ◽

2019 ◽

Vol 122 (4) ◽

pp. 1291-1296 ◽

Cited By ~ 2

Author(s):

Djuna von Maydell ◽

Mehdi Jorfi

Keyword(s):

Risk Factors ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Single Cell ◽

Rna Sequencing ◽

Cellular Mechanisms ◽

Black And White ◽

Single Cell Rna Sequencing ◽

Sequencing Studies ◽

Integral Role

Microglia constitute ~10–20% of glial cells in the adult human brain. They are the resident phagocytic immune cells of the central nervous system and play an integral role as first responders during inflammation. Microglia are commonly classified as “HM” (homeostatic), “M1” (classically activated proinflammatory), or “M2” (alternatively activated). Multiple single-cell RNA-sequencing studies suggest that this discrete classification system does not accurately and fully capture the vast heterogeneity of microglial states in the brain. In fact, a recent single-cell RNA-sequencing study showed that microglia exist along a continuous spectrum of states. This spectrum spans heterogeneous populations of homeostatic and neuropathology-associated microglia in both healthy and Alzheimer’s disease (AD) mouse brains. Major risk factors, such as sex, age, and genes, modulate microglial states, suggesting that shifts along the trajectory might play a causal role in AD pathogenesis. This study provides important insight into the cellular mechanisms of AD and underlines the potential of novel cell-based therapies for AD.

Download Full-text