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Objective We investigated potential racial disparities in the effects of audiometric hearing loss and its treatment on dementia and mortality among 3,602 older adults aged 68–89 years, 22% of whom were self-identified Black race. Methods Adjudicated all-cause dementia was determined using neurocognitive test data, proxy reports, and surveillance of hospital records and death certificates. Audiometric hearing loss, defined as the better-ear averaged pure-tone threshold (0.5–4 kHz), was categorized using clinical cutpoints. Multivariable-adjusted Cox proportional hazards models included hearing loss–race interaction terms. Results Dementia risk associated with moderate-to-severe hearing loss did not differ by race [Black participants: hazard ratio (HR): 1.66; 95% confidence interval (CI): 1.05, 2.61; White participants: HR: 1.71; 95% CI: 1.16, 2.51; P -interaction = 0.92]. However, moderate-to-severe hearing loss was associated with a 2.3-fold increase in mortality among Black participants only (95% CI: 1.17, 4.60). Conclusions Our findings emphasize the importance of including minoritized populations in hearing treatment research to build an evidence base for policy development and clinical decision-making. Hearing loss affects the health of both Black and White Americans. Racial disparities in hearing healthcare should be addressed to advance health equity for all older adults.

Abstract Reported prevalence estimates of Lewy body pathology (LBP) vary widely, often without considering brain regional distributions or demographic influences. Large, population-representative autopsy cohorts are needed to provide estimates and clarify the distribution and clinical implications of LBP. Neuropathological, genetic, and clinical data were pooled from nine community- or population-based brain autopsy cohorts in the USA (n=6), Brazil, Austria, and Finland (n=7309 total; 59% women; mean age of death 84.2 years). Cognitive status was available for 6166 participants: dementia (38.5%), mild cognitive impairment (14.8%), and cognitively unimpaired (47.0%). Frequency of LBP was examined by anatomical distribution (neocortical, limbic, brainstem, amygdala, olfactory) and stratified by covariates. Prevalence was calculated in meta-analysis, and mixed-effect logistic regression examined associations with sex, cognitive status, Alzheimer’s disease neuropathological change (ADNC), and limbic-predominant age-related TDP-43 encephalopathy neuropathologic change (LATE-NC) co-pathology. Overall, 27.3% of participants had LBP: neocortical 8.4%, limbic 6.8%, brainstem 4.8%, amygdala 3.7%, and olfactory 3.6%. Neocortical LBP was associated with dementia (present in 15%; OR=4.06 [95%CI 3.24-5.10]) and with increased odds of ADNC (OR=2.30 [95%CI 1.88–2.81]) and LATE-NC (OR=2.03 [95%CI 1.69-2.44]). Sex differences were not observed in the frequencies of neocortical or limbic LBP. However, amygdala-predominant LBP was more frequent in women (OR=1.53 [95%CI 1.06-2.21]) whereas brainstem-predominant LBP was more common in men (OR=0.64 [95%CI 0.49-0.84]). Additionally, amygdala-predominant LBP was associated with increased odds of comorbid ADNC (OR=12.7 [95%CI 5.30–30.6]) while brainstem-predominant LBP was not associated with greater odds of ADNC co-pathology (OR=0.88 [95%CI 0.67–1.16]). Pooling data from large, international community-based autopsy cohorts allows for more robust estimates of region-specific LBP prevalence and their associations with cognitive status. The observed sex- and co-pathology–specific differences in brainstem and amygdala-predominant LBP highlight potential biological heterogeneity and suggest that distinct disease pathways may underlie these patterns.

BACKGROUND: Cerebral microbleeds (CMBs) are associated with dementia, but the impact of specific microbleed patterns (with distinct pathophysiologies) is unclear. Lobar CMBs commonly result from cerebral amyloid angiopathy (sometimes with cortical superficial siderosis [cSS]), while subcortical microbleeds indicate a hypertensive cause. This study investigates the association of CMB presence, patterns, and frequency with dementia risk. METHODS: Participants from the ARIC-NCS (Atherosclerosis Risk in Communities-Neurocognitive Study), a community-based longitudinal cohort, with a 3T research magnetic resonance imaging scan at visit 5 (2011–2013) and without prior intracerebral hemorrhage or dementia were included. CMB and cSS presence and location were evaluated using a T2* gradient-recalled echo sequence. Each individual’s pattern of CMBs was classified as no CMBs, subcortical only, mixed (subcortical and lobar±cSS), lobar±cSS only, or cSS. Dementia adjudication was determined by blinded review of cognitive testing, informant interviews, and hospital discharge codes or death certificates (from follow-up visits and ongoing surveillance). Cox proportional hazards models assessed associations between CMBs and incident dementia through 2020, adjusted for demographics, vascular risk factors, and white matter hyperintensities. RESULTS: Among 1583 participants (mean age, 76 years; 24.7% cumulative dementia incidence over a median of 23.6 follow-up years), 361 had CMBs. Compared with individuals without CMBs, those with mixed (hazard ratio, 1.99 [95% CI, 1.40–2.83]) or lobar±cSS (hazard ratio, 1.96 [95% CI, 1.30–2.97]) CMB patterns had increased dementia risk, whereas individuals with a subcortical-only pattern did not (hazard ratio, 1.04 [95% CI, 0.78–1.39]). cSS presence (n=21; versus absence) showed an increased risk of dementia (hazard ratio, 2.57 [95% CI, 1.36–4.89]). Greater numbers of microbleeds across all CMB subtypes exhibited an increased risk of dementia. CONCLUSIONS: Lobar and mixed CMB patterns and increasing numbers of CMBs are associated with incident dementia. Certain CMB patterns have differential associations with dementia, but severe enough disease burden, regardless of CMB distribution, is associated with increased dementia risk.

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.

Abstract Background Plasma biomarkers of Alzheimer's disease (AD) pathology and neurodegeneration provide a non‐invasive method to assess brain pathology associated with dementia and cognitive decline. However, it remains unclear whether these biomarkers are differentially associated with changes in specific cognitive domains or how they relate to subsequent cognitive change in participants with or without cognitive impairment. Methods Plasma biomarkers were assayed in a subset of ARIC participants using Quanterix SiMoA technology on stored specimens collected in midlife (visit 3: 1993‐95, mean age: 59) and late‐life (visit 5: 2011‐13, mean age: 77). Biomarkers included amyloid‐β 42 to amyloid‐β 40 (Aβ42:Aβ40) ratio, phosphorylated tau at threonine 181 ( p ‐Tau181), neurofilament light (NfL), and glial fibrillary acidic protein (GFAP). Factor scores for global cognition and cognitive domains (memory, language, executive function) were calculated from ten neuropsychological tests administered at visits 5 (2011‐13), 6 (2016‐17), 7 (2018‐19), 8 (2020), and 9 (2021‐22). Linear mixed effects models with multiple imputation of pre‐death cognitive change estimated the association between biomarkers in midlife and late‐life, and biomarker change from midlife to late‐life, with 5‐year change in late‐life global and domain‐specific cognition, adjusting for demographics, lifestyle and cardiovascular risk factors, and APOE‐ε4 status. Exploratory analyses examined effect modification of the biomarker‐global cognition associations by visit 5 adjudicated cognitive impairment. Results Among 1515 participants with plasma biomarkers (60.7% female, 25.5% identified as Black), 39% had late‐life cognitive impairment (Table 1). Higher midlife NfL and GFAP were associated with faster 5‐year declines in global cognition and language during late‐life (Table 2). Only midlife NfL was associated with faster late‐life memory decline. No midlife biomarkers were associated with executive function. In late‐life, lower Aβ42:Aβ40 and higher p ‐Tau 181, NfL, and GFAP were associated with faster declines in global cognition, memory, and language. Midlife to late‐life changes in p ‐Tau 181, NfL, and GFAP were associated with declines in global cognition and language. No consistent effect modification by cognitive impairment was observed (Figure 1). Conclusions Plasma biomarkers of AD pathology, neuronal injury, and astrogliosis, measured at different life epochs (midlife vs. late‐life) show distinct association patterns with subsequent changes in cognitive function across domains.

Abstract Background Plasma biomarkers show promise as a noninvasive method for measuring Alzheimer's disease pathology and neurodegeneration throughout the lifecourse. However, the relationship between longitudinal changes in these biomarkers and late‐life brain morphology in community‐dwelling populations requires further investigation. Method Between 2011 and 2013, 1,977 participants from the Atherosclerosis Risk in Communities Study received an adjudicated cognitive diagnosis and underwent 3 Tesla magnetic resonance imaging scans. Whole‐brain cortical thickness was quantified by Freesurfer. Stored plasma samples collected in midlife (1993‐95, mean age 59.0 years) and late‐life (2011‐13, mean age 76.8 years) from a subsample of 1,515 participants were assayed in 2022 using Quanterix SiMoA. The assay quantified amyloid‐β (Aβ)42/40, phosphorylated tau at threonine 181 ( p ‐Tau181), neurofilament light (NfL), and glial fibrillary acidic protein (GFAP). Linear regression models estimated the association of plasma biomarkers from midlife, late‐life, and the change from midlife to late‐life with cortical thickness in late‐life. Interactions and effect modification by age were examined in exploratory analyses. Result The sample (Table 1) included 920 (60.7%) women, 387 (25.5%) Black participants, 513 (33.9%) participants with mild cognitive impairment, and 85 (5.6%) participants with dementia. The average age in late‐life was 76.8 (5.3 SD) years and the average cortical thickness was 2.27 (0.11 SD) millimeters. Cortical thickness was lower in individuals with adjudicated mild cognitive impairment or dementia compared to individuals without cognitive impairment (2.25 mm versus 2.29 mm, p <0.0001). In models adjusted for demographics, lifestyle factors, cardiovascular factors, and the presence of APOE ε4 alleles, higher midlife measures of p ‐Tau181 and increases from midlife to late‐life in NfL and GFAP were associated with lower cortical thickness in late‐life (Table 2). Concurrent measures of plasma biomarkers in late‐life were associated with lower cortical thickness (Table 2) and exhibited a stronger association among participants ≥75 years compared to <75 years (Table 3). Conclusion Plasma biomarkers of Alzheimer's disease pathology and neurodegeneration obtained in midlife and late‐life were associated with late‐life differences in cortical thickness. Additional longitudinal research is needed to determine whether plasma biomarkers predict cortical thinning over time and may serve as an early indicator of accelerated brain atrophy.

Abstract Blood-based biomarkers show promise as a noninvasive method for measuring Alzheimer’s disease pathology and neurodegeneration throughout the lifecourse. However, the relationship between longitudinal changes in these biomarkers and late-life brain morphology in community-dwelling populations requires further investigation. Between 2011 and 2013, 1,977 participants from the Atherosclerosis Risk in Communities Study received underwent 3 Tesla magnetic resonance imaging scans. Whole-brain cortical thickness was quantified by Freesurfer. Stored plasma samples collected in midlife (1993-95, mean age 59.0 years) and late-life (2011-13, mean age 76.8 years) from a subsample of 1,515 participants (60.7% female, 25.5% Black adults) were assayed in 2022 using Quanterix SiMoA. The assay quantified amyloid-β (Aβ)42/40, phosphorylated tau at threonine 181 (p-Tau181), neurofilament light (NfL), and glial fibrillary acidic protein (GFAP). Linear regression models estimated the association of blood-based biomarkers from midlife, late-life, and the change from midlife to late-life with cortical thickness in late-life. In models adjusted for demographics, lifestyle factors, cardiovascular factors, and the presence of APOE ε4 alleles, higher midlife measures of p-Tau181 and increases from midlife to late-life in NfL and GFAP were associated with lower cortical thickness in late-life. Concurrent measures of p-Tau181, NfL, and GFAP were associated with lower cortical thickness in late-life and exhibited a stronger association among participants ≥75 years old compared to participants &lt;75 years old. Additional longitudinal research is needed to determine whether blood-based biomarkers predict cortical thinning over time and may serve as an early indicator of accelerated brain atrophy.