Gottesman, Rebecca F.

Abstract Background Prospective investigations on blood biomarkers for Alzheimer's disease and related‐dementias (ADRDs) and cognitive impairment provide important insights into their clinical utility in early disease diagnosis and risk stratification, but have been primarily limited to AD pathologies (e.g. amyloid‐beta and hyperphosphorylated‐tau aggregation). Studies of biomarkers reflecting other pathogenic processes that contribute to ADRDs, especially in community‐based cohorts, are lacking. We examined associations of blood biomarkers reflecting glial and neuronal dysfunction and neuroinflammation, including soluble cluster of differentiation‐14 (sCD14), chitinase‐3‐like protein‐1 (YKL‐40), glial‐fibrillary acidic protein (GFAP), neurofilament‐light chain (NfL), total‐Tau, and Ubiquitin C‐terminal hydrolase L1 (UCH‐L1) with cognitive decline and incident dementia in ARIC study participants. Methods Blood biomarkers were measured in 1,864 ARIC Visit 3 (1993‐1995) participants using the Meso Scale Discovery assay (YKL‐40), the Quanterix Simoa Neurology 4‐Plex assay (total‐Tau, NfL, GFAP, UCH‐L1), and a commercial ELISA assay (sCD14, R&D Systems); global cognition z‐scores were derived from three cognitive tests (measuring memory, language, and executive function) administered up to six times over 25 years, and incident dementia cases were ascertained via in‐person cognitive assessments (expert‐adjudicated), informant‐based interviews, and hospitalization and death records through 2018. We used shared parameter models to simultaneously fit linear mixed‐effect models and proportional hazard models to estimate 25‐year cognitive decline and dementia rates separately for each biomarker (log‐base2‐transformed), adjusting for covariates (Table). Results Among 1,864 participants (mean±SD age:63±4.5 years, 60% female, 49% self‐reported Black), 27% developed dementia. Higher levels of each biomarker were associated with faster cognitive decline. All but UCH‐L1 were associated with elevated dementia risk (Table). For example, one standard deviation (SD) higher log‐base2 YKL‐40 was associated with ‐0.10 SD steeper 25‐year cognitive decline (95%CI:‐0.15,‐0.04) and 59% higher dementia hazard (hazard ratio (HR):1.59 (95%CI:1.20,2.10); likewise, sCD14 was associated with an additional cognitive decline of ‐0.06 (95%CI:‐0.11,‐0.01) and 38% higher dementia hazard (HR:1.38 (95%CI:1.08,1.76)). Conclusions Blood biomarkers reflecting neuropathological processes, including microglial activation, astrocytic reactivity, and neuronal degeneration, were associated with faster cognitive decline and increased dementia risk, suggesting that they may aid in risk stratification and intervention strategies for ADRDs. Future analyses will examine whether these associations differ by race and APOE‐ε4 s tatus.

Abstract Background Even among cognitively unimpaired (CU) older adults, specific patterns of neuropsychological performance impart differential risk for progression to Alzheimer's disease and related dementias (ADRD). Early identification in the prodromal phase may inform early intervention efforts. We used a data‐driven approach to classify neuropsychological phenotypes within CU older adults from the Atherosclerosis Risk in Communities (ARIC) study and tested for differences in ADRD and cerebrovascular outcomes. Method Participants included 4737 CU (via a previously published algorithm plus consensus diagnosis protocol) older adults (mean age=75.1 years, 60.8% female, 23.1% Black) with neuropsychological data from Visit 5 (2011‐2013) of the ARIC study. Latent profile analysis was leveraged to identify unique cognitive phenotypes using 10 demographically adjusted (age, sex/gender, education, race‐center, WRAT Reading) neuropsychological z‐scores. Cognitive phenotypes were then compared on demographics, depressive symptoms, and vascular risk factors (VRFs). In a subset with available neuroimaging and biofluid data ( n range=1102‐1678), white matter hyperintensity (WMH) and bilateral hippocampal (HV) volumes normalized for intracranial volume and plasma biomarkers (i.e., Aβ42/40, ptau181, NfL, and GFAP) were examined by phenotype. Phenotype comparisons adjusted for demographic variables, VRFs, and an indicator of kidney function (i.e., eGFR‐creatinine) for plasma biomarkers. We applied Tukey adjustment for pairwise comparisons. Result A 6‐class solution of cognitive phenotypes emerged as the best fitting model: Average Overall (28%), Low Overall/Very Low Memory (7%), Average Overall/Super Memory (11%), Subtle Low Overall/Low Executive (22%), Subtle Low Executive/High Memory (13%), and Average Overall/Low Memory (19%; Figure 1). Phenotypes differed on education, race, and word reading, despite prior adjustments for these variables when creating the neuropsychological z‐scores (Table 1). Additionally, phenotypes differed on depressive symptoms, VRFs, WMH, HV, Aβ42/40, and ptau181 (all p s < .05). The Subtle Low Overall/Low Executive group had the highest WMH volumes, the Subtle Low Executive/High Memory group had the lowest Aβ42/40, and the Low Overall/Very Low Memory group had the lowest HV and highest levels of plasma ptau181 (Table 2). Conclusion Cognitive heterogeneity is observed even among CU older adults, with lower executive performance associated with cerebrovascular changes and lower memory associated with certain AD‐related changes, possibly indicating risk for cognitive progression through distinct pathways.

Abstract Background The Aging and Cognitive Health Evaluation in Elders (ACHIEVE) randomized trial ( n = 977;ClinicalTrials.gov:NCT03243422) demonstrated that hearing intervention slowed 3‐year cognitive decline by 48% among a subgroup of participants. To further investigate differences by treatment with respect to cognitive benefit, we tested the hypothesis that hearing intervention is associated with improved neurodegeneration blood biomarkers 3‐years post‐randomization and assessed whether combining biomarkers and cognitive results improved power in the trial design. Method The ACHIEVE study is a multicenter, parallel‐arm, randomized trial (hearing intervention vs health education control) on 3‐year cognitive decline among adults 70–84years with untreated hearing loss and without substantial cognitive impairment. Participants were recruited from two populations: (1) a long‐standing cardiovascular health observational study (Atherosclerosis Risk in Communities [ARIC]), and (2) de novo community volunteers. Plasma was collected in year 3 in a subsample ( n = 540) while ARIC participants ( n = 164) also had baseline plasma. Glial fibrillary acidic protein (GFAP) and neurofilament light (NfL) neurodegenerative biomarkers were derived using the Alamar CNS protein panel. Regression models estimated the association of treatment with 3‐year biomarkers and global cognitive differences in the combined sample and by recruitment source and annualized change from baseline in the ARIC sample. We included a simple composite score of the standardized outcomes (Table 2). Result Among the subsample, baseline characteristics were balanced by treatment. There were no significant differences on 3‐year neurodegeneration biomarker levels by treatment in the combined (Table 1) or de novo groups (Table 2). Among ARIC participants, intervention resulted in lower 3‐year GFAP (mean:‐0.415;95%CI:‐0.712,‐0.118) and NfL (mean:‐0.349;95%CI:‐0.652,‐0.046) (Table 2). Annualized change from baseline analyses among ARIC participants (Table 3) revealed intervention was associated with a slower rate of cognitive decline (difference‐in‐means:0.059;95%CI:0.018,0.100) and a slower rate of increase in GFAP (difference‐in‐means: ‐0.060;95%CI:‐0.112,‐0.009). Differences in estimates suggest combined score, relative to cognition alone, may increase power in clinical trials that show a positive effect (ARIC subgroup: p = 0.02vs. 0.08[Table 2] and p = 0.0009vs.0.048[Table 3]). Conclusion Hearing intervention was associated with positive 3‐year effects on neurodegeneration blood biomarkers in ARIC participants which parallels cognitive decline results. Results provides objective evidence of brain changes following hearing intervention and potential signal of a more powerful combined outcome approach for future brain health clinical trials.

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.

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.

AbstractBackgroundCerebral small vessel disease (cSVD), as defined by neuroimaging characteristics such as white matter hyperintensities (WMHs), cerebral microhemorrhages (CMHs), and lacunar infarcts, is highly prevalent and has been associated with dementia risk and other clinical sequelae. Although risk factors for cSVD have been identified, little is known about the biological processes and molecular mediators that influence cSVD development and progression.MethodsWithin the Atherosclerosis Risk in Communities (ARIC) study, we used SomaScan Multiplexed Proteomic technology to relate 4,877 plasma proteins to concurrently measured MRI‐defined cSVD characteristics, including WMHs, CMHs, and lacunar infarcts, in late‐life (n=1508; mean age: 76). Candidate protein associations with WMH volume were replicated in the Cardiovascular Health Study (n=765; mean age: 73) and Baltimore Longitudinal Study of Aging (n=902; mean age: 66). Cohort‐specific effect estimates were pooled using a fixed‐effect, inverse variance weighted meta‐analysis. Within 4228 non‐demented older adults in ARIC, we examined the association of WMH‐associated proteins with eight‐year dementia risk (641 incident dementia cases) using multivariate Cox proportional hazard models. All multivariate regression models were adjusted for demographic, kidney function, and cardiovascular risk variables.ResultsThis proteome‐wide analysis of older adults identified 13 WMH‐associated plasma proteins involved in synaptic function, endothelial integrity, and angiogenesis (Figures 1a, 2a). Nine of these proteins replicated in one or more external cohort (Figures 1b, 2b) and two proteins, oligodendrocyte myelin glycoprotein (OMG) and neuronal pentraxin receptor (NPTXR), remained associated with late‐life WMH volume when measured nearly two decades earlier during midlife (Figures 1c, 2c). Eight of the 13 WMH‐associated proteins were associated with lacunar infarcts, 6 with subcortical CMHs, and 4 with lobar CMHs (Figures 1d, 2d). Although none of the genes coding for WMH‐associated proteins have been nominated by WMH GWAS, 7 of these genes have been previously linked to cardiometabolic traits known to influence cSVD, such as blood pressure and cholesterol (Figures 1e, 2e). Finally, 11 candidate proteins were associated with incident dementia, underscoring their clinical relevance (Figures 1f, 2f).ConclusionsThis large‐scale proteomic analysis within longitudinal community‐based samples identified several plasma proteins, including OMG and NPTXR, as top candidate biomarkers for elevated WMH volume and its clinical manifestations.