Vascular risk profile and white matter hyperintensity volume among Mexican Americans and non‐Hispanic Whites: The HABLE study

Abstract

Introduction Among vascular risk factors we hypothesized that an increased prevalence of diabetes in Hispanics would be associated with greater white matter hyperintensity (WMH) volume, which may contribute to cognitive decline. Methods A total of 1318 participants (60% female; 49% Hispanic, 51% non‐Hispanic White; age 66.2 ± 8.9 years) underwent clinical evaluation and brain magnetic resonance imaging (MRI). WMH volume associations were assessed with age, sex, and ethnicity and then with vascular risk factors in a selective regression model. Results WMH volume was greater with older age ( P < .0001), Hispanic ethnicity ( P = .02), and female sex ( P = .049). WMH volume was best predicted by age, diastolic blood pressure, hypertension history, hemoglobin A1c (HbA1c), white blood cell count, and hematocrit ( P < .01 for all). Elevated HbA1c was associated with greater WMH volume among Hispanics (parameter estimate 0.08 ± 0.02, P < .0001) but not non‐Hispanic Whites (parameter estimate 0.02 ± 0.04, P = .5). Discussion WMH volume was greater in Hispanics, which may be partly explained by increased WMH volume related to elevated HbA1c among Hispanics but not non‐Hispanic Whites.

White matter hyperintensities in diverse populations: A systematic review of literature in the United States

“…For example, one study showed that differences in WMH between White and African Americans in a minimally adjusted model attenuated after the adjustment for vascular and socioeconomic factors [15] . Other studies showed a stronger association between cardiovascular risk factors and WMH in African Americans [ 17 , 22 , 31 , 33 ] and Hispanics [ 14 , 29 , 31 ] compared to White Americans, which are believed to be driving the observed WMH differences. A WHICAP study conducted in 2008 examined racial differences in the distribution of WMH in White, Hispanic and African Americans adjusting for group differences in age, sex, education and self-reported history of vascular risk factors and disease [22] .…”

“…These 23 articles comprised of 16 unique study cohorts. These cohorts include the Healthy Aging Brain in Latino Elders (HABLE) study [14] , Multi-Ethnic Study of Atherosclerosis (MESA) [15] , Atherosclerosis Risk in Communities (ARIC) study [16] , [17] , [18] , [19] , [20] , [21] , Washington Heights-Inwood Columbia Aging Project (WHICAP) [22] , [23] , [24] , Offspring Study of Racial and Ethnic Disparities in Alzheimer Disease (WHICAP Offspring) [24] , Diabetes Heart Study(DHS)-Mind [25] , Genetic Study of Atherosclerosis Risk (GeneSTAR) [26] , Harvard Aging Brain Study [27] , University of California Davis (UC Davis) Aging Diversity Cohort [ 28 , 29 ], Emory Alzheimer's Disease Research Center(ADRC) sample [30] , Northern Manhattan Study (NOMAS) [31] , Knight ADRC sample [32] , Dallas Heart Study [33] , Action to Control Cardiovascular Risk in Diabetes Memory in Diabetes (ACCORDION MIND) trial [34] , Cardiovascular Health Study (CHS) [35] , and Alzheimer's Disease Neuroimaging Initiative (ADNI) [36] .…”

“… Study Selection of participants Confounding variables Measurement of exposure Blinding of outcome assessments Incomplete outcome data Selective outcome reporting King KS et al. [14] Low Low Low Low Low Low Austin TR et al. [15] Low Low Low Low Low Low Gottesman RF et al.…”

Top Alzheimer's disease risk allele frequencies differ in HABS‐HD Mexican‐ versus Non‐Hispanic White Americans

“…The HABS‐HD team has published literature on the differences in AD endophenotype and how there may be racial/ethnicity‐specific etiological subcategories of AD depending on the genetics, ancestry, and disease progression 53 . MAs tend to have a higher prevalence of diabetes, metabolic dysfunction, and immune dysregulation 14,24,26,28,53–55 . Black Americans also tend to have a higher prevalence of hypertension and cardiovascular disease 2,21,56–58 .…”

Hyperparameter Tuning with High Performance Computing Machine Learning for Imbalanced Alzheimer’s Disease Data

“…Collecting AD data while staying as close as possible to the prevalence rate in the population may create imbalanced data, which makes ML challenging [4]. For example, the Health and Aging Brain Study-Health Disparities (HABS-HD) data used here contained 1328 normal controls and 377 mild cognitive impairments (MCIs) and ADs [5][6][7][8][9][10][11][12][13][14]. In our preliminary analysis, all positive samples in the testing set were classified wrongly as normal, although our training set had almost 100% sensitivity and specificity.…”