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Abstract Background Decreased kidney function, indicated by a lower estimated glomerular filtration rate (eGFR), may affect interpretation of Alzheimer's disease (AD) blood biomarkers, including phosphorylated tau ( p ‐tau) and amyloid‐beta (Aβ) peptides, due to altered clearance. However, there is limited data available to determine the influence of eGFR on these biomarkers and whether it differs by biomarker or assay type, especially in racially diverse studies of community‐based older adults. Methods We used data from 557 participants in the Mayo Clinic Study of Aging (Rochester, MN n = 182; age 79±8 years; 36.3% Female, White participants) and the UMMC MIND Center‐Mayo Clinic Study of Aging (Jackson, MS n = 375; age 69±8 years; 73% Female 27% Black participants). eGFR was determined using the 2021 CKD‐EPI Creatinine equation. Plasma biomarkers were measured using the Lumipulse immunoassays for p ‐tau217, Aβ42, and Aβ40 (Fujirebio Diagnostics; n = 420); or the mass spectrometry‐based PrecivityAD2 assay (C2N Diagnostics; n = 253) for p ‐tau217, np‐tau217, Aβ42, and Aβ40. Common ratio values were calculated. Aβ42/40 measures were inverted so higher values are worse for all biomarkers. Log base‐2 transformed regression analysis related eGFR to each single analyte and ratio; coefficients (95% CI) reflect estimated doubling in biomarker per doubling in eGFR. Group specific [MN White ( n = 182), MS White ( n = 224), MS Black ( n = 151) participants] estimates are reported, as the distribution of social determinants of health can vary across populations and locations. Results Lower eGFR was associated with higher (worse) AD‐plasma biomarkers, and group stratified estimates were mostly consistent (Figure 1). Ratios of biomarkers diminished but did not entirely alleviate associations with eGFR. For example, MN White participants’ C2N p ‐tau217 association of ‐1.47 (‐1.94, ‐1.00), was attenuated to ‐0.67 (‐1.11, ‐0.22), when using p ‐tau217/np‐tau217. Conclusions Kidney function may be associated with AD‐plasma biomarker values, even with common ratio corrections. There was not strong evidence for highly differential associations of eGFR by race/site categories, though sample sizes are currently limited. The implications for diagnosis and risk classification need to be explored further to support health equity. Optimal consideration of comorbidities on blood biomarker concentrations must continue to be informed by investigation in large, racially and ethnically diverse, community‐based studies.

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 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 Diffusion MRI (dMRI) measures are increasingly being used as biomarkers for aging and dementia in multi‐center studies. Measurements in longitudinal studies are often acquired on different systems, which can result in systematic differences of machine origin (e.g. vendor, hardware, algorithms). This may warrant harmonization prior to pooling multi‐site data. However, harmonization algorithms may introduce a risk of removing valuable information content and reducing sensitivity to physiological factors (e.g. sex, age, disease). Here, we tested the hypothesis that protocol standardization may minimize machine‐origin differences, obviating a need for post‐acquisition harmonization in a two‐site (Mississippi‐MS and Minnesota‐MN) cohort of the longitudinal Mayo Clinic Study of Aging (MCSA). Methods Two data sets were analyzed: first, a crossover dataset with 80 participants (scanned within 2 days) bridging two different protocols on GE and Siemens scanners; second, participants scanned using standardized protocols from the ongoing MCSA ( N MN‐MCSA =204; N MS‐MCSA =52). Parameters are listed in Figure 1. For crossover data, analyses were repeated with GE data resampled to 2‐mm isotropic resolution, and discarding Siemens‐only b‐shells to artificially reduce protocol disparities. Analysis encompassed visualization of overlain subjectwise histogram‐derived densities obtained from whole‐brain masks for dMRI measures; and median dMRI measures from within the genu of the corpus callosum (GCC) with nonparametric statistical testing. When available, both DTI and NODDI measures were compared. Results Pronounced visual distributional differences seen between MD and FA in crossover data were reduced with resampling of GE data and including only b‐shells shared across acquisitions. For the resampled/reshelled data, significant differences remained for all GCC‐FA comparisons, but not all GCC‐MD (Figure 2). For the ongoing MCSA data from two sites, MD and FA whole‐brain densities were more closely aligned, with GCC‐FA and GCC‐MD showing no significant differences (Figure 3). NODDI measures showed more discordant densities than DTI measures, including shifts in most probable values and differences in distributional arrangement. Conclusions Our work provides further evidence supporting protocol and processing standardization for minimizing machine‐origin differences prior to considering harmonization. Differences in dMRI metrics seen when comparing across unstandardized protocols were reduced when protocols were standardized. NODDI metrics had more apparent sensitivity to site than DTI measures.

Abstract Background Chronic kidney disease (CKD) is associated with increased blood‐based biomarker concentrations, potentially confounding their utility in Alzheimer's disease (AD) evaluation. While plasma phosphorylated tau217 ( p ‐tau217) is elevated in the presence of amyloid related pathology, amyloid beta 1‐42 (Aβ42) is decreased. We investigated the association of plasma p ‐tau217, non‐phosphorylated tau217 (np‐tau217), Aβ42, Aβ40, as well as the Aβ42/Aβ40 and p ‐tau217/Aβ42 ratios, versus decreased estimated glomerular filtration rate (eGFR in mL/min/1.73 m 2 ) in individuals without amyloid pathology. Method This study included participants with CKD stages 1‐2 (eGFR 60‐98, n = 50), and stages 3‐4 (eGFR 25‐59, n = 33), who were Pittsburgh 11 C‐Compound B amyloid‐PET negative (Centiloid of < 25). Plasma p ‐tau217, np‐tau217, Aβ42, and Aβ40 were measured by immunoprecipitation mass spectrometry (C 2 N Diagnostics). %p‐tau217 [( p ‐tau217/np‐tau217) x100], Aβ42/Aβ40 ratio, and p ‐tau217/Aβ42 ratio were calculated. Log‐Log (power function) best‐fit regression analysis was used to quantitatively predict effects of changes in eGFR (2021 CKD‐EPI equation) versus each single analyte or ratio (Figure 1). Result Selected best‐fit analyte equations versus eGFR are shown in Table 1. The predicted percent increase of p ‐tau217 as eGFR decreases from 60 to 45 was 52% (95% CI: 30–78%). Significantly smaller increases of 16% were predicted for Aβ42 (95% CI: 12–21%), and 19% for Aβ40 (95% CI: 16–23%) over the same eGFR range. The best‐fit predicted increase for the p ‐tau217/Aβ42 ratio and %p‐tau217 as eGFR decreases from 60 to 45 was 31% (95% CI: 12–53%), and 19% (95% CI: 2–38%), respectively. While not recommended for assessment of amyloid pathology, the Aβ42/Aβ40 ratio did not exhibit a predicted increase (‐2.3% decrease, 95% CI: ‐5.2–0.6%) versus decreasing eGFR from 60 to 45. Conclusion The use of either %p‐tau217 or the p ‐tau217/Aβ42 ratio (to a lesser degree) reduced the confounding effects of decreasing eGFR as compared to p ‐tau217 alone. Aβ42 and Aβ40 exhibited similar reduced predicted increases versus decreasing eGFR as compared to p ‐tau217. Relative to the predicted effects of decreasing eGFR on p ‐tau217 measurements, the p ‐tau217/Aβ42 ratio relative increase was reduced by 40%, while the relative increase was reduced by 63% for %p‐tau217 measurements.

Background Many guidelines list migraine with aura (MwA) as a contraindication to estrogen-containing combined hormonal contraceptives (CHCs) due to vascular risks. However, current evidence is based on small sample studies with potential influence by confounding factors. Additionally, few studies have examined the vascular risk associated with modern CHCs with lower dose estrogen, particularly in relation to aura status. The present study aims to investigate the vascular risk of modern CHCs in women with migraine with and without aura. Methods We used a de-identified electronic medical record database with 120 million patients across multiple health systems in the United States of America. We included female patients aged 18–45 years who received a migraine diagnosis code, had at least three office visits within three years, and were prescribed at least one migraine-specific medication within 6 months following the first outpatient visit. All data after 2010 were included. Patients with prior cardiovascular events were excluded. Our composite endpoint consisted of acute ischemic stroke, acute myocardial infarction, deep vein thrombosis/pulmonary embolism and intravenous thrombolytic administration. We stratified our analysis according to CHC exposure and aura status and compared the incidence of the endpoint with high-dimensional propensity score-matching between CHC users and non-users in (i) the overall cohort, (ii) MwA and (iii) migraine without aura (MwoA); between MwA and MwoA in (iv) patients prescribed CHCs; and in (v) those without CHC prescriptions. Results We included 5535 patients who received CHC prescriptions and 21,520 who did not. 114 (2.06%) of CHC users and 547 (2.54%) of CHC non-users had at least one vascular event. With propensity score-matched comparison, the composite endpoint did not significantly differ between CHC and non-CHC in the overall migraine group, those with MwA and the MwoA group. In those prescribed CHC, MwA and MwoA did not differ in all the outcomes. For CHC non-users, MwA was associated with a higher incidence of acute ischemic stroke (hazard ratio = 2.45; 95% confidence interval = 1.58–3.78; p < 0.001; n = 6201 in each group) and the composite endpoint (hazard ratio = 1.34; 95% confidence interval = 1.08–1.67; p = 0.008). Conclusions Our real-world study showed that exposure to modern CHC was not associated with a significant increase in vascular risk in women aged 18–45 years with migraine, MwA or MwoA who have no prior cardiovascular events. However, in those who never received CHC, MwA was associated with higher vascular risks compared to MwoA. While limitations exist using large scale electronic medical record databases for analysis, our results suggest that carefully designed prospective studies should be conducted to reassess the vascular risk associated with CHC use in women with migraine, especially MwA.

Abstract Introduction Rapid eye movement (REM) sleep behavior disorder (RBD) is the single strongest predictor of Parkinson’s disease (PD) risk. Given neurotrauma and psychiatric morbidity, the aging post-9/11 Veteran cohort is a target population for understanding probable RBD frequency and neuroprotection opportunities. We previously reported over 50% of post-9/11 Veterans screened positive for probable RBD. We aimed to examine frequencies of prodromal PD markers and polysomnography (PSG)-confirmed RBD in post-9/11 Veterans. Methods Veterans with probable RBD based on the single-question RBD screen (n=12) were recruited from the Translational Research Center for Traumatic Brain Injury (TBI) and Stress Disorders (TRACTS) Houston cohort. Participants were assessed for subthreshold parkinsonism (Unified Parkinson’s Disease Rating Scale-III Motor score &gt;3, excluding action tremor), olfactory loss (12-item Sniffin’ Sticks), TBI exposures, autonomic symptoms, orthostasis, excessive daytime sleepiness, post-traumatic stress disorder (PTSD), and psychiatric comorbidities. PSG studies were conducted per American Academy of Sleep Medicine guidelines. A board-certified sleep physician confirmed the presence of REM sleep without atonia. We compared the frequency of prodromal marker positivity between participants with and without subthreshold parkinsonism. Results All 12 participants were male (6 [50%] Black, 3 [25%] Hispanic) with a mean (SD) age of 44.4 ± 6.7 years. PSG data (n=10) revealed REM sleep without atonia in 5 (50%), moderate obstructive sleep apnea (OSA) in 2 (20%), and severe OSA in 2 (20%) participants. Five (41.6%) participants had mild motor dysfunction suggestive of subthreshold parkinsonism on the UPDRS-III (M±SD score of 8.6 ± 5.9), while 7 participants had normal or low UPDRS-III ratings (2.3 ± 1.3; p=0.02). Veterans with subthreshold parkinsonism had lower Sniffin’ Sticks scores than those without (M±SD 9.4 ± 2.07 vs. 11.29 ± 0.76; 95% CI: 0.01–3.76, p=0.049). Conclusion This pilot study suggests that motor and olfactory prodromal PD markers are frequent in post-9/11 Veterans with probable RBD. Larger prospective cohort studies are required to further quantify REM sleep without atonia and assess synuclein-specific biomarkers in this population. Support (if any) Center for Alzheimer’s and Neurodegenerative Diseases at Baylor College of Medicine [MJ]; VA CSR&D (#IK2CX002363-01A1) [MJ]; VA RR&D (B9268-X) [RJ]; NIH/NIA/NINDS (U19-AG071754 & R34-AG056639) [ES]; and AASM [ES].

AbstractBackgroundThe NIA Health Disparities Research (NIA‐HD) Framework organizes factors in four domains (i.e., environmental, sociocultural, behavioral, and biological), which work together over the lifetime to influence health and health disparities.MethodIn previous work, we employed a 2‐stage approach using factor and principal component (PC) analyses to identify patterns of factors in the domains of the NIA‐HD Framework. We found three PCs that identified related groupings from more than one NIA‐HD domain. PC1: retirement status, residence type, multivitamin use, heart disease, Charlson comorbidity index, walking/balance difficulties, and Beck Depression Inventory (BDI)‐II questions – mainly from the somatic dimension; PC2: physical activity/exercise, social and group activities, education, occupation, cardiometabolic risk factors, and self‐rated health; PC3: national and state area deprivation index ranking and BDI‐II questions – mainly in the cognitive dimension. Age and sex‐adjusted linear regression models were used to assess the cross‐sectional association of each PC with AD‐plasma biomarkers measured using the Quanterix Simoa platform in the Mayo Clinic Study of Aging (MCSA) participants. AD‐plasma biomarkers included Aβ 42/40, phosphorylated‐tau 181 (p‐tau181), plasma glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL). All analyses were considered statistically significant at a p <0.05.ResultThere were 1300 cognitively unimpaired participants, 50 years old or older, with available plasma biomarkers (mean age (SD) 68.6 (9.5) years; 49.1% were female) at the current study baseline. PC1 and PC3 were associated with higher NfL and p‐tau 181 values (p<0.05). PC1 and PC2 were associated with higher GFAP values (p<0.05). No PCs were associated with the Aβ 42/40 ratio. Findings were mostly similar after additionally adjusting for global cognitive performance and chronic kidney disease.ConclusionWe found that patterns in a wide range of environmental, sociocultural, behavioral, and biological factors were associated with AD plasma biomarkers. Longitudinal studies are warranted to validate findings and support multi‐faceted approaches to address health disparities and achieve disease delay and prevention.

AbstractBackgroundPlasma Aβ42/40 and p‐tau217 can predict amyloid positivity in cross‐sectional studies. However, it is unclear how plasma biomarkers perform longitudinally, which is important to inform their utility in tracking disease progression. The goal of this study is to describe temporal evolutions of plasma Aβ42/40 and ptau217 ratio (p‐tau217r) measured via mass spectrometry, p‐tau217 measured via an immunoassay, and amyloid PET.MethodsWe included participants in the Mayo Clinic Study of Aging with amyloid PET (PiB) and plasma Aβ42/40 + ptau217r (mass spectrometry, C2N) and/or p‐tau217 (Lilly, MSD). We fit a joint non‐linear mixed effects model with Aβ42/40, p‐tau217r, p‐tau217 (Lilly), and amyloid PET as outcomes and age, sex, APOE ε4 carriership and education as covariates. The model included a per‐participant adjustment (time‐shift) for each biomarker indicating how much earlier or later in time the biomarker was estimated to progress relative to the average participant, a correlation (R) between adjustments for each pair of biomarkers, and the impact of covariates on each biomarker’s timing.ResultsThe study included 1459 participants, age 77 (10) years, 44% female, 82% cognitively unimpaired. Mean (SD) years of followup was 5.6(3.0) years for mass spectrometry plasma measures, 2.5(0.7) years for p‐tau217 (Lilly), and 4.6(3.3) for amyloid PET. Model fits are shown for each biomarker, with greater variability in serial measurements for plasma than PET (Figure 1). Individual adjustments between each of the plasma biomarkers and amyloid PET were highly correlated: p‐tau217r, R=0.73 (95%CI: 0.67, 0.78); p‐tau217 (Lilly), R=0.66 (0.62, 0.7); Aβ42/40, R=0.6 (0.52, 0.68) (Figure 2). The p‐tau217r and Aβ42/40 individual adjustments were modestly correlated, R=0.39 (0.28, 0.49). APOE ε4 carriership had the strongest covariate effect on all biomarkers, e.g. p‐tau217r progression 9.4 (6.7, 12) years earlier (Figure 3).ConclusionsPlasma Aβ42/40 and p‐tau217 measured with currently available assays may both be amenable to longitudinal modeling, although the degree of variability in Aβ42/40 over time may pose challenges. Findings suggest that both plasma Aβ42/40 and p‐tau217r may provide information regarding an individual’s timing of progression on amyloid PET; lower correlations with each other than amyloid PET suggests that each analyte may provide complimentary information.