Objective: The incidence and prevalence of cognitive decline and dementia are significantly higher among African Americans compared with non-Hispanic Whites. The aim of this study was to determine whether inheritance of the sickle cell trait (SCT) i.e. heterozygosity for the sickle cell mutation increases the risk of cognitive decline or dementia Among African Americans. Methods: We studied African American participants enrolled in the Atherosclerosis Risk in Communities study. SCT genotype at baseline and outcome data from cognitive assessments at visits 2, 4 and 5, and an MRI performed at visit 5 were analyzed for the association between SCT and risk of cognitive impairment and/or dementia. Results: There was no significant difference in risk factors profile between participants with SCT (N = 176) and those without SCT (N = 2532). SCT was not independently associated with a higher prevalence of global or domain-specific cognitive impairment at baseline or with more rapid cognitive decline. Participants with SCT had slightly lower incidence of dementia (HR = 0.63 [0.38, 1.05]). On the other hand, SCT seems to interact with the apolipoprotein E ε4 risk allele resulting in poor performance on digit symbol substitution test at baseline (z-score = −0.08, Pinteraction = 0.05) and over time (z-score = −0.12, Pinteraction = 0.04); and with diabetes mellitus leading to a moderately increased risk of dementia (HR = 2.06 [0.89, 4.78], Pinteraction = 0.01). Conclusions: SCT was not an independent risk factor for prevalence or incidence of cognitive decline or dementia, although it may interact with and modify other putative risk factors for cognitive decline and dementia.

Background: Brain age is an MRI-derived estimate of brain tissue loss that has a similar pattern to aging-related atrophy. White matter hyperintensities (WMHs) are neuroimaging markers of small vessel disease and may represent subtle signs of brain compromise. We tested the hypothesis that WMHs are independently associated with premature brain age in an original aging cohort. Methods: Brain age was calculated using machine-learning on whole-brain tissue estimates from T1-weighted images using the BrainAgeR analysis pipeline in 166 healthy adult participants. WMHs were manually delineated on FLAIR images. WMH load was defined as the cumulative volume of WMHs. A positive difference between estimated brain age and chronological age (BrainGAP) was used as a measure of premature brain aging. Then, partial Pearson correlations between BrainGAP and volume of WMHs were calculated (accounting for chronological age). Results: Brain and chronological age were strongly correlated (r(163)=0.932, p<0.001). There was significant negative correlation between BrainGAP scores and chronological age (r(163)=-0.244, p<0.001) indicating that younger participants had higher BrainGAP (premature brain aging). Chronological age also showed a positive correlation with WMH load (r(163)=0.506, p<0.001) indicating older participants had increased WMH load. Controlling for chronological age, there was a statistically significant relationship between premature brain aging and WMHs load (r(163)=0.216, p=0.003). Each additional year in brain age beyond chronological age corresponded to an additional 1.1mm3 in WMH load. Conclusions: WMHs are an independent factor associated with premature brain aging. This finding underscores the impact of white matter disease on global brain integrity and progressive age-like brain atrophy