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.

Introduction: Plasma biomarkers of Alzheimer’s disease (AD), including amyloid-β (Aβ42/Aβ40), phosphorylated tau (p-tau181), neurofilament light (NfL), and glial fibrillary acidic protein (GFAP) have shown strong reliability in predicting dementia risk. White matter hyperintensities (WMH) represent a major vascular contributor to dementia. We investigated how plasma biomarkers of AD interact with WMH on MRI to influence incident dementia risk. Methods: We included dementia-free participants from the Atherosclerosis Risk in Communities (ARIC) Study who underwent MRI and plasma biomarker assessment at Visit 5 (2011–2013). WMH was quantified and considered continuously (log-transformed and standardized), and in tertiles. Plasma biomarkers were log-base-2-transformed. Cox proportional hazards models were used to estimate dementia risk over 10 years associated with plasma biomarkers and WMH, independently and multiplicatively. Models were adjusted for demographic variables (age, sex, race, and education), vascular risk factors (hypertension, diabetes, smoking status, body mass index, and estimated glomerular filtration rate), and APOE ε4 status. Results: Among 1,538 participants (aged 67- 90 years, 29% Black), 30% ( N = 462) developed dementia over the 10-year follow-up period. For every doubling of plasma Aβ42/Aβ40 , indicating lower amyloid burden, dementia risk decreased, while doubling of p-tau 181 , NfL, and GFAP were each associated with increased dementia risk, in separate models; each of these associations was independent of WMH (Table 1). Additionally, dementia risk increased per SD increase in WMH, independent of each plasma biomarker. Although no multiplicative interaction terms reached statistical significance (p<0.05), there was suggestive evidence that Aβ42/Aβ40 and GFAP have smaller effect sizes on dementia risk in subgroups at the highest WMH tertile (p-interactions<0.10; see Table 2). Discussion: Plasma biomarkers and WMH independently predict dementia risk over 10 years, though there remained limited evidence of multiplicative effects. Plasma biomarkers may be less important for dementia risk in individuals who also have extensive WMH. These findings emphasize the importance of incorporating both AD-specific plasma markers as well as indicators of cerebral small vessel disease, such as WMH, into dementia risk profiling.

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.

Background: Cerebral microbleeds (CMBs) are associated with incident dementia, but the impact of specific CMB patterns is unclear. CMBs in lobar regions suggest cerebral amyloid angiopathy (CAA), which is sometimes accompanied by superficial siderosis (SS), while subcortical CMBs indicate hypertensive origins. This study investigates the association between CMB patterns and dementia risk in the community-based longitudinal Atherosclerosis Risk in Communities Neurocognitive Study (ARIC-NCS). Methods: All ARIC-NCS participants with a 3T research MRI at visit 5 (2011-13; aged 67-90) without intracerebral hemorrhage or dementia were included. CMB and SS presence and location were coded from T2* GRE sequences. Individuals were classified into one of four patterns: no CMBs, only subcortical, mixed (lobar and/or SS + subcortical), and only lobar and/or SS. Incident dementia diagnoses were defined by cognitive testing (in-person and telephone), informant interviews, and hospital discharge codes or death certificates. Cox proportional-hazards models assessed the association between the presence, patterns, and frequency of CMBs (0,1,2,3+), presence of SS, and incident dementia from visit 5 through 2020. Model covariates included demographics, vascular risk factors, and imaging markers of small vessel disease. Results: Among 1609 participants, 364 had CMBs (Table 1). Participants with CMBs tended to be older and female compared to those without CMBs. Compared to individuals with no CMBs, presence of any CMBs was associated with an increased risk of dementia (Table 2). Compared to individuals without CMBs, individuals with only lobar CMBs and/or SS had an increased risk of incident dementia, as did individuals with mixed CMBs, but individuals with subcortical-only did not. Participants with ≥3 CMBs of any variant had an increased risk of incident dementia vs no CMBs. Although SS was infrequent, its presence (vs no SS) was associated with an elevated risk of dementia. Conclusion: CMBs, particularly in a mixed or lobar and/or SS-only pattern, are linked to an increased risk of incident dementia. The number of recurrent CMBs and any SS also showed an increased dementia risk. These data support that dementia risk is high in individuals with a CAA-type CMB pattern, but also emphasize that a mixed pattern, not typical of CAA alone, is associated with a high risk of dementia. Further studies should evaluate mechanisms by which these different patterns contribute to dementia.