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Abstract The Atherosclerosis Risk in Communities (ARIC) study started in 1987‐1989 (visit 1), when participants from four U.S. communities were 45‐64yo. Participants have been seen at up to 10 additional in‐person visits with annual/semi‐annual telephone calls to ascertain clinical events, hospitalizations, and other health outcomes. In 2011‐2013, at ARIC visit 5, the ARIC Neurocognitive Study (ARIC‐NCS) was initiated for surviving participants, incorporating neuropsychological assessments at multiple in‐person visits and informant interviews to support cognitive adjudication. A subset of participants have undergone brain MRI with measurement of brain volumes, cortical thickness, arterial disease and vascular lesions, with a smaller subset having brain florbetapir PET. Blood‐based Alzheimer's disease (AD) biomarkers are available at multiple visits along with extensive measurement of vascular risk factors, vascular markers, and ‐omic (e.g.genomic and proteomic) data. This presentation will focus on the available data in ARIC, the methods underlying its collection and subsequent analysis, and some of the primary contributions of this study to the field. With 37 years of follow‐up, a great strength of ARIC is the ability to evaluate risk factors in midlife; ARIC studies have supported an association of midlife hypertension, diabetes, physical inactivity, and other vascular risk factors with reduced dementia risk, even among those with increased genetic risk for dementia. ARIC studies have demonstrated associations between stroke and dementia, and have linked vascular risk factors with blood‐based and imaging biomarkers of AD‐related dementias. ARIC has demonstrated the importance of hearing loss in dementia risk, and a subset of ARIC participants were recruited into (and were those who most benefited from the intervention of) the ACHIEVE study of hearing aid use for reduction of dementia. Proteomic studies have identified markers of dementia risk when evaluated in midlife. Recently, ARIC data informed updated estimates on lifetime risk of dementia, where it was estimated that among individuals surviving beyond age 55, 42% would develop dementia over their lifetime through age 95. ARIC studies continue to focus on risk factors for dementia, particularly now in the oldest old, and on contributors to cognitive reserve and resilience. ARIC follows a model of data sharing and broad collaboration.

ImportanceMidlife vascular risk factors are associated with an elevated risk of dementia. However, the total contribution of vascular risk factors in midlife and late life with incident dementia is uncertain.ObjectiveTo quantify the proportion of incident dementia attributable to modifiable vascular risk factors measured in midlife and late life and to examine differences by apolipoprotein ε4 genotype, self-reported race, and sex.Design, Setting, and ParticipantsThis was a prospective cohort analysis of the Atherosclerosis Risk in Communities (ARIC) study using 33 years of follow-up (1987-2020). The setting included ARIC field centers (Jackson, Mississippi; Forsyth County, North Carolina; Minneapolis suburbs, Minnesota; Washington County, Maryland). Study baseline in Black and White participants with complete exposure and covariate data was set by age at risk factor measurement (45-54 years, 55-64 years, and 65-74 years). Data were analyzed from August 2023 to December 2024.ExposuresHypertension (systolic blood pressure [BP] ≥130 mm Hg, diastolic BP ≥80 mm Hg, or use of medication for BP), diabetes (fasting glucose ≥126 mg/dL, nonfasting glucose ≥200 mg/dL, self-reported physician’s diagnosis, or use of any diabetes medication), and current smoking (self-reported).Main Outcomes and MeasuresIncident dementia. Population attributable fractions were estimated by age 80 years, and separately after 80 years, from having at least 1 vascular risk factor by age at risk factor measurement.ResultsA total of 7731 participants were included in analysis of risk factors measured at age 45 to 54 years (4494 female [58%]; 2207 Black [29%]; 5524 White [71%]), 12 274 contributed to analysis of risk factors measured at age 55 to 64 years (6698 female [55%]; 2886 Black [24%]; 9388 White [76%]), and 6787 contributed to analysis of risk factors measured at age 65 to 74 years (3764 female [56%], 1375 Black [20%]; 5412 White [80%]). There were 801, 995, and 422 dementia cases by 80 years, respectively. The fraction of dementia by 80 years attributable to at least 1 vascular factor at age 45 to 54 years was 21.8% (95% CI, 14.3%-29.3%), at 55 to 64 years was 26.4% (95% CI, 19.1%-33.6%), and at 65 to 74 years was 44.0% (95% CI, 30.9%-57.2%). Attributable fractions for these factors were higher in apolipoprotein ε4 noncarriers at age 55 years and older (range, 33.3%-61.4%), Black individuals at age 45 years and older (range, 25.5%-52.9%), and female individuals at age 55 years and older (range, 29.2%-51.3%). Only 2% to 8% of dementia cases after 80 years were attributable to these factors.Conclusions and RelevanceResults of this cohort study suggest that between 22% and 44% of incident dementia cases by 80 years in the ARIC study were attributed to midlife and late-life vascular risk factors. Assuming causal relationships, maintaining optimal vascular health across the life course could mitigate a sizeable proportion of dementia risk by 80 years.

Introduction: Physical activity (PA) has been identified as a protective factor for dementia, but the mechanisms linking PA to brain health are unclear. To elucidate potential pathways linking PA to brain health, we tested the hypothesis that higher levels of late-life PA are associated with a lower burden of Alzheimer’s disease (AD) pathology, cerebrovascular disease, and neurodegeneration measured through brain imaging and plasma biomarkers. Methods: ARIC-NCS participants without dementia in 2011-13 and with measures of PA, 3T brain magnetic resonance imaging (MRI), and stored plasma samples were examined (n=1,771; mean age: 76.5 years; 40.5% male; 27.6% Black). Participants were classified as meeting or not meeting 2018 aerobic PA guidelines based on a standardized interviewer-administered questionnaire. MRI biomarkers included white matter hyperintensity (WMH) volume (cm 3 ), total brain volume (TBV) (cm 3 ), and cortical thickness (CT) (mm). Plasma biomarkers of the ratio of amyloid beta 42 to 40 (Aβ42/Aβ40), phosphorylated Tau-181 (p-Tau181), neurofilament light chain (NfL), and glial fibrillary acidic protein (GFAP) were measured using Quanterix Simoa immunoassays. All biomarkers were standardized to measures in 2011-13 to facilitate comparisons. Covariate-adjusted linear regression models estimated cross-sectional associations of PA with MRI or plasma biomarkers. Covariate-adjusted linear mixed effects models quantified associations of PA with the 5-year rate of change in a subset of participants with MRI (n=669) or plasma (n=651) biomarkers at both baseline and follow-up from 2011-19 (median follow-up: 5.4 years). Results: Meeting versus not meeting aerobic PA guidelines was cross-sectionally associated with lower volumes of WMH (β = -0.16, 95% CI: -0.25, -0.07), higher TBV (β = 0.09; 95% CI: 0.05, 0.13), and lower plasma NfL (β = -0.102, 95% CI: -0.184, -0.021) ( Figure ). Late-life PA was not associated with 5-year rates of change in MRI or plasma biomarkers. Conclusions: Late-life PA was cross-sectionally associated with less neurodegeneration and cerebrovascular disease. Associations with 5-year changes in imaging and plasma biomarkers of AD pathology, cerebrovascular disease, or neurodegeneration were not statistically supported. Longitudinal biomarker studies with wearable devices that quantify other daily intensity-based movement metrics (e.g., light PA) and larger sample sizes are needed to further examine mechanisms linking PA to brain health.

Introduction: Physical activity (PA) has been identified as a modifiable factor inversely associated with cognitive decline and dementia risk. However, most longitudinal studies to date have short follow-up and report inconsistent results on the benefit of PA to support brain health among older adults. In this study, we hypothesized that higher levels of PA measured in late life would be associated with slower rates of global and domain-specific cognitive decline and a lower risk of dementia over ~10 years in late life. Methods: Dementia-free participants (n=5,982; mean age: 76 years; 42% male, 23% Black) who completed the interviewer-administered Modified Baecke PA Questionnaire at ARIC visit 5 (2011-2013) were included. Using the PA component of the American Heart Association’s Life’s Simple 7, participants’ PA levels were categorized as ideal, intermediate, or poor. Cognitive tests were administered at ARIC visits 5 (2011-13), 6 (2016-17), 7 (2018-19), 8 (2020), and 9 (2021-22), and were used to calculate factor scores for global cognitive function and the domains of memory, language, and executive function. Linear mixed models estimated the association of PA with change in cognitive function. Dementia cases were defined by adjudicated review, telephone interviews, informant interviews, hospitalization records, and death certificates. Dementia risk was estimated using Cox proportional hazards regression models. All models adjusted for age, sex, race-center, education, APOE-ε4 allele, smoking, hypertension, body mass index, and diabetes. Results: Participants with ideal or intermediate PA, compared to poor PA, had slower rates of decline in global cognition, but no significant differences in rates of decline across individual cognitive domains. Over a median follow-up of 7.8 years, participants with ideal (adjusted hazard ratio [HR] = 0.62, 95% CI: 0.60, 0.67) or intermediate (adjusted HR = 0.87, 95% CI: 0.80, 0.91) PA had a reduced risk of dementia compared to participants with poor PA. Conclusions: Higher PA levels in late life demonstrated a protective association with long-term global cognitive decline and incident dementia. Although further research with more frequent and objective measures of PA is needed to determine the duration, frequency, and intensity of PA recommended to promote brain health in older adults, our findings suggest that promoting PA among aging populations offers a non-invasive and accessible approach to support brain health.

Introduction: Vascular risk factors of hypertension, diabetes, and smoking in midlife (45-64 years) and possibly early late-life (65-74 years) are associated with increased risk of dementia. Current estimates of population attributable risk for dementia assume vascular factors are independent and do not interact to increase risk of dementia, which may underestimate the combined impact of vascular risk reduction on dementia incidence. In this study, we evaluated the joint association and interaction of vascular risk factors on risk of dementia. Methods: We performed a prospective cohort analysis using the Atherosclerosis Risk in Communities Neurocognitive Study using up to 33 years of follow-up (1987-2020). We used age as baseline (45-54 years, n=7,731; 55-64 years, n=12,273; and 65-74 years, n=6,660). We defined hypertension as systolic blood pressure ≥130 mm Hg, diastolic blood pressure ≥80 mm Hg, or anti-hypertension medication use. We defined diabetes as fasting glucose level ≥126 mg/dL, non-fasting glucose ≥200 mg/dL, self-reported physician diagnosis, or use of any diabetes medication. Current smoking was self-reported. We investigated additive and multiplicative interactions for combinations of vascular risk factors on incident (adjudicated) dementia by age 80 using joint effect variables in Cox regression models stratified by age of risk factor measurement (45-54 years, 55-64 years, or 65-74 years). For interaction analyses, parameter values >1 indicate positive interaction (synergy) and values <1 indicate negative interaction (antagonism). Results: In participants with risk factors measured in these baseline age strata, there were 801 (10%), 995 (8%), and 422 (6%) cases of incident dementia by age 80, respectively. Hypertension and diabetes exhibited positive interactions on the additive and multiplicative scales in midlife (Figure 1). Diabetes and smoking in midlife had positive additive interaction (value >1, Figure 2). All vascular risk factor combinations in late-life had negative interactions on the multiplicative scale (values <1, Figures 1-3). Conclusions: Hypertension with diabetes and diabetes with smoking, all measured in midlife, interact to increase risk of dementia by age 80. The overall impact of reducing midlife vascular risk factors on incident dementia could be sizeable. These findings highlight the importance of multifactor interventions addressing combinations of vascular risk factors for dementia prevention strategies.

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.

AbstractBackgroundThe residual approach has found wide application in researching cognitive resilience, a phenomenon conceptually understood as cognitive performance being better‐than‐typical for an individual, despite apparent AD pathology. The standard residual approach extracts information about an individual’s resilience from the residuals of a linear model predicting cognition. This approach is subject to several limiting assumptions. Here, we explore the implications of these assumptions and propose an alternative predictive modeling approach which circumvents them.MethodsOur alternative approach relies on training a machine learning model on a sample of participants who follow a “typical” clinical progression (Fig. 1A). Residuals are then extracted for a heterogeneous dataset that potentially includes resilient participants by comparing observed cognitive outcomes to those predicted by the “typical” model (Fig. 1B). Additionally, our estimate can be refined by effectively estimating and removing model error from the extracted residual by leveraging a second predictive model (Fig. 1C). Using simulated data, we explore the assumptions made by the standard approach and the consequences of their violation. We then contrast this against our approach.ResultsOur simulations demonstrated that the standard residual approach only extracted accurate resilience estimates if the predictors used in the model explained cognition sufficiently well, while being unrelated to resilience itself. That is, as the predictors explain more of the resilience, the residuals became increasingly determined by model error (Fig. 2). We found that residuals achieved with our approach are more precise and provide individualized estimates that are unaffected by the aforementioned trade‐off (Fig. 3).ConclusionsOur approach holds several advantages over the standard approach: The definition of resilience can be freely adjusted and enforced via different definitions of “typical”, it allows for incorporating complex, non‐linear models without removing relevant information from the residual, the error component of the residual can be estimated through cross‐validation, and all predictors that potentially explain cognition can be included into the model without losing resilience information from the residual. Our approach can be used to measure any phenomenon that can be operationalized as a deviation from a typical outcome and, thus, is also applicable for researching resistance to pathology.