Morrill, Valerie

Abstract INTRODUCTION Understanding comorbidities’ combined impacts on dementia risk may offer a more comprehensive understanding of individuals’ risk. Using machine‐learning, we grouped individuals with similar midlife risk profiles into clusters and explored associations with dementia risk. METHODS Participants without dementia at baseline (1987–1989) from the prospective Atherosclerosis Risk in Communities (ARIC) study were included (ages 45–64 years; N = 15,250). Using unsupervised hierarchical cluster analysis, nine clusters were created and defined based on 14 midlife morbidities. The associations with incident dementia ( N = 3272 cases, median follow‐up 25 years) and deaths ( N = 9099) were evaluated using time‐to‐event regression models. RESULTS Compared with the healthiest cluster (Cluster 1), Clusters 2 (smoking) (hazard ratio [HR](95% confidence interval [CI]) = 1.62 (1.08, 2.43)), 5 (obesity, diabetes, hypertension, and hypertriglyceridemia) (HR(95%CI) = 1.91 (1.35,2.70)), and 7/8 (atrial fibrillation/heart failure) (HR(95%CI) = 2.69 (1.59,4.57)) were associated with dementia. Accounting for competing risk of death in the Fine‐Gray subdistribution model negated the cluster‐dementia association. DISCUSSION Midlife morbidity clusters are important for dementia and mortality risk.

Objective: Epidemiological data have suggested that late-life depression (LLD) occurs as a consequence of cerebrovascular and neurodegenerative disorders, but there is also some evidence suggesting that the relationship may occur in the other direction, namely that LLD may be a risk factor for cerebrovascular and neurodegenerative disorders. However, the neurobiological basis of these associations remain unclear. This study investigated the associations of LLD with markers of cerebrovascular pathology, brain volumes, and cortical thickness from 3-Tesla brain magnetic resonance imaging (MRI). Methods: Participants from the Atherosclerosis Risk in Communities Study received brain MRI scans in late-life (ages 67-90). The following neuroimaging outcomes were assessed: cortical infarcts, lacunar infarcts, any microhemorrhages, lobar microhemorrhages, subcortical microhemorrhages, white matter hyperintensity volume, total and regional brain volumes (z-scores), and total average and regional cortical thickness (z-scores). At the same visit, LLD was evaluated with the 11-item Center for Epidemiological Studies Depression Scale (CES-D) and defined as CES-D score≥9. Associations between LLD and neuroimaging outcomes were examined using logistic and linear regression models, and were adjusted for age, race-center, sex, education level, vascular risk factors, stroke, cognitive status, APOE e4 carrier status, and total intracranial volume when assessing volumes. Results: Among 1,857 participants, the median age was 76 years, 59.5% were female, 28.3% were Black, and 6.8% had LLD. LLD was not associated with infarcts or microhemorrhages. There was no evidence of an association between LLD and total white matter hyperintensity volume or total average cortical thickness. However, LLD was independently associated with smaller volume (SD) of the rostral anterior cingulate (b 1 : -0.18; 95% CI: -0.33, -0.04) and greater cortical thickness (SD) in the medial orbitofrontal cortex (b 1 : 0.19; 95% CI: 0.01, 0.38), though effect sizes were small and did not survive correction for multiple comparisons. Conclusion: Findings from this study suggest LLD is not cross-sectionally associated with neuroimaging markers of cerebrovascular disease. While LLD may be linked to subtle volume and cortical thickness changes in specific regions of interest, future longitudinal research is warranted to validate and clarify these associations and explore their potentially bidirectional relationship.

Background: Stroke is a leading cause of death and disability despite advances in risk factor management. Many risk factors for stroke have also been implicated as risk factors for mild cognitive impairment (MCI) and dementia. Given these shared risk factors, cognitive impairment (CI) may represent an accumulation of neurological injury that is associated with an elevated risk of stroke. In ARIC-NCS, we aimed to 1) understand the independent relationship between pre-stroke CI (MCI or Dementia) and incident stroke; 2) explore mortality differences in stroke patients with vs without pre-stroke CI; and 3) evaluate modifiers of these relationships. Methods: 5434 stroke-free participants were evaluated for cognitive status at an in-person visit in 2011-13 (visit 5; ages 66-90), based on cognitive testing and informant interviews. Incident definite or probable strokes (ischemic or hemorrhagic) from visit 5 through 2022 were identified via ongoing surveillance (semi-annual telephone calls, with expert classification). Using Cox proportional hazards regression models adjusted for demographics and vascular risk factors, we evaluated associations between CI (MCI or dementia) and incident stroke, and between CI and death among individuals with stroke. Finally, we tested effect modification by age at visit 5, sex, education, and race, via stratified models and formal interaction terms. Results: Compared to those without CI, individuals with CI (23%) were older (77 vs 75 years, p<0.001), and more frequently male (48% vs 40%). Over 10+ years, CI was independently associated with an increased risk of incident stroke (HR:1.52, 95% CI:1.18-1.96). Among individuals with stroke, risk of death was also higher in CI vs non-CI patients (HR 1.78, 95% CI 1.27-2.50). In stratified analysis, we found a significant interaction by sex (p-interaction=0.04): in males, CI was associated with elevated stroke risk (HR:2.01, 95% CI:1.39-2.91) without a similar risk in females (HR:1.22, 95% CI:0.85-1.75). Interactions by race, age, and educational level were not statistically significant, but effect sizes were qualitatively larger in White participants and individuals with more education. Conclusion: CI is associated with incident stroke independent of shared risk factors, especially in men. This work adds to our understanding of populations at risk for stroke, thus aiding efforts to optimize stroke prevention in at-risk populations.