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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.

AbstractBackgroundBlood‐based biomarkers show promise as a noninvasive, inexpensive method for measuring Alzheimer’s disease pathology throughout the lifecourse. However, the predictive and classification accuracy of these biomarkers at different stages of the lifecourse and among diverse, community‐dwelling populations requires further investigation.MethodBetween 2014 and 2015, 329 dementia‐free participants from the Atherosclerosis Risk in Communities Study underwent brain magnetic resonance imaging (MRI) and florbetapir positron emission tomography (PET). Amyloid positivity was defined as a global cortex standardized uptake value ratio greater than 1.2. Stored plasma samples collected in midlife (1993‐95, mean age 58.5 years) and late‐life (2011‐13, mean age 76.2 years) from a subsample of 259 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). Unadjusted and covariate‐adjusted logistic regression models estimated the association between plasma biomarkers from midlife and late‐life and amyloid positivity in late‐life. Unadjusted receiver operating characteristic curves documented the classification accuracy of the plasma biomarkers separately and collectively.ResultThe sample (Table 1) included 151 women (58.1%), 105 Black participants (40.4%), and 136 (52.3%) participants with amyloid positivity. In models adjusted for demographics, lifestyle factors, cardiovascular factors, and the presence of APOE ε4 alleles, assays measured in midlife were not associated with amyloid positivity in late‐life (Table 2). However, late‐life measurements of Aβ42/40, p‐Tau181, and GFAP and change per decade from midlife to late‐life in p‐Tau181 and GFAP were associated with greater odds of amyloid positivity. The greatest classification accuracy (Figure 1) was achieved by using all assays measured in midlife and late‐life (AUC = 0.754), although the accuracy when using only late‐life measures was comparable (AUC = 0.737).ConclusionLate‐life plasma biomarkers of Alzheimer's disease neuropathology and astrogliosis were associated with late‐life amyloid positivity, but midlife plasma biomarkers obtained 16 to 20 years earlier did not exhibit a statistically significant association. Additional longitudinal research is needed to determine whether changes in plasma biomarkers measured before an individual is classified as amyloid positive can be used to identify individuals at risk of developing Alzheimer's disease.

AbstractBackgroundOlder adults with dual cognitive and mobility impairments have higher financial costs and poorer quality of life than adults with either impairment alone. Blood biomarkers of Alzheimer Disease (AD) pathology (Aβ42, Aβ40 and p‐tau181) and neurodegeneration (neurofilament light (NFL) and glial fibrillary acidic protein (GFAP)) may identify individuals at risk for both mobility and cognitive impairment and provide novel insights into mechanistic underpinnings.MethodBlood biomarkers (SiMoA Quanterix N4PE, p‐tau181 single‐plex) were available in a subsample of 1751 ARIC study participants at Visit 5 (V5, 2011‐13, mean age 76.2 years (5.3), 41% men, 28% Black); Aβ42 was reversed and adjusted for Aβ40 (‐Aβ42|40) for consistent directions of associations (higher=worse). Usual gait speed and cognitive status were assessed over 8 years at V5, V6 (2016‐17), and V7 (2017‐18). Participants were categorized by V5 impairment: mobility‐only (gait speed <0.8m/s, n=430), cognitive‐only (expert‐adjudicated dementia, n=38), dual (n=54), or neither (referent, n=1229). Multinomial regression models examined relative risk ratios (RRR) of V5 log‐transformed biomarkers with prevalent impairments and, among those with neither impairment at V5, incident impairments through V7. All models adjusted for V5 age, sex, self‐reported race (Black or White), renal function, and body mass index.ResultHigher levels of all biomarkers were associated with greater risk of prevalent impairment in mobility, cognitive, or dual domains (Figure A). Over 8‐years of follow‐up, incident impairments included 471 participants with only mobility impairments, 39 with only cognitive impairments, 55 with dual impairments, and 168 died without developing any impairment. All biomarkers were associated with incident dual impairment per IQR higher biomarker: p‐tau181 (RRR=2.54; 95% CI: 1.45, 4.44), ‐Aβ42|40 (RRR=1.66; 1.31, 2.09), NfL (RRR=2.69; 1.22, 5.91) and GFAP (RRR=2.13; 1.07, 4.25). P‐tau181, ‐Aβ42|40, and GFAP were also associated with incident cognitive‐only impairment, while P‐tau181, NFL and GFAP were associated with incident mobility‐only impairment. (Figure B).ConclusionBlood biomarkers of brain health may be useful tools for earlier identification of those at risk for cognitive, mobility, or dual declines, facilitating earlier targeted interventions. Potential differences in AD‐pathology versus neurodegeneration blood biomarker relationships with outcomes of mobility and cognition may help to identify more targeted interventions for both.

Abstract INTRODUCTION We aimed to evaluate clinical interpretation cutpoints for two plasma phosphorylated tau (p‐tau)217 assays (ALZpath and Lumipulse) as predictors of amyloid status for implementation in clinical practice. METHODS Clinical performance of plasma p‐tau217 against amyloid positron emission tomography status was evaluated in participants with mild cognitive impairment or mild dementia ( n = 427). RESULTS Using a one‐cutpoint approach (negative/positive), neither assay achieved ≥ 90% in both sensitivity and specificity. A two‐cutpoint approach yielding 92% sensitivity and 96% specificity provided the desired balance of false positives and false negatives, while categorizing 20% and 39% of results as indeterminate for the Lumipulse and ALZpath assays, respectively. DISCUSSION This study provides a systematic framework for selection of assay‐specific cutpoints for clinical use of plasma p‐tau217 for determination of amyloid status. Our findings suggest that a two‐cutpoint approach may have advantages in optimizing diagnostic accuracy while minimizing potential harm from false positive results. Highlights Phosphorylated tau (p‐tau)217 cutpoints for detection of amyloid pathology were established. A two‐cutpoint approach exhibited the best performance for clinical laboratory use. p‐tau217 assays differed in the percentage of results categorized as intermediate.

Abstract Background Relationships of midlife inflammation with late‐life mobility and influences of chronic health conditions, race, and social determinants of health (SDoH) on these relationships are poorly understood. Methods Among 4758 community‐dwelling participants (41% men, 20% Black), high‐sensitivity C‐reactive protein (hsCRP) was measured over 20+ years: in midlife at study visit 2 (V2: 1990–1992, 47–68 years); at V4 (1996–1998, 53–74 years); and with concurrent late‐life 4‐m gait speed at V5 (2011–2013, 67‐88 years, mean 75 years). SDoH measures included race, the national‐rank area deprivation index, education, and income. We examined associations of late‐life gait speed with midlife hsCRP (V2 continuous and clinically high ≥3 mg/L), with 20‐year hsCRP history from midlife (V2–V5 average continuous hsCRP and clinically high ≥3 mg/L) and with inflammation accumulation (visits and years with high hsCRP). Regression models adjusted for demographic, cardiovascular, and SDoH measures; effect modification by the presence of other common chronic conditions (obesity, diabetes, hypertension) and race were examined, with and without accounting for SDoH. Results High midlife hsCRP was associated with slower late‐life gait speed, even among those without chronic conditions in midlife: −4.6 cm/s (95% CI: −6.4, −2.8). Importantly, sustained high hsCRP was associated with a 20‐year slowing of −10.0 cm/s (−14.9, −5.1) among those who never experienced obesity, diabetes, or hypertension over the 20‐year period. Associations were similar between Black participants, −3.8 cm/s (−6.9, −0.7) and White participants −3.3 (−4.5, −2.2) per interquartile range of midlife hsCRP; effect modifications by chronic conditions and race were unsupported throughout. Results were robust to accounting for SDoH or otherwise; however, worse SDoH was associated with higher inflammation and slower gait speed in both Black and White participants. Conclusions Inflammation in midlife may contribute to clinically meaningful late‐life slowing of gait speed, even among otherwise healthy‐appearing adults and regardless of race and socioeconomic disadvantage. Regular monitoring and interventions for inflammation may be warranted from midlife.

Background: Cardiovascular health may be used for prevention of cerebral vascular disease; however, data on the association of cardiovascular health across midlife and late-life with late-life cerebral vascular disease are lacking. Our aim was to examine whether midlife or late-life cardiovascular health as well as changes of cardiovascular health within midlife and between midlife and late-life were associated with prevalence of magnetic resonance imaging markers of cerebral vascular disease at late-life. Methods: Prospective cohort study including 1638 participants from the Atherosclerosis Risk in Communities Study who took part in 2 visits at midlife (mean ages, 53 and 59 years), and a late-life visit (mean age, 76 years). A cardiovascular health Life’s Simple 7 score (range, 0–12/0–14, depending on diet availability) including 6 out of 7 items was calculated at each visit, with weight assigned to each item as poor (0), intermediate (1), or ideal (2). Participants underwent 3T brain magnetic resonance imaging scans in late-life visit. Outcomes were white matter hyperintensity volume, microbleeds, and lacunar, subcortical, and cortical infarcts at late-life. Linear and logistic regression models were used to assess the associations of cardiovascular health in midlife and late-life, and improvement of cardiovascular health within midlife, and from midlife to late-life with magnetic resonance imaging markers of cerebral vascular disease, adjusting for potential confounders. Results: A higher cardiovascular health in midlife, improvement of cardiovascular health within midlife, higher cardiovascular health at late-life, and improvement of cardiovascular health from midlife to late-life were associated with a lower prevalence of cerebral vascular disease markers. For example, improvement in cardiovascular health (per point) from midlife to late-life was associated with smaller white matter hyperintensity volume (β, −0.07 [95% CI, −0.10 to −0.04]) and lower odds of microbleeds (odds ratio, 0.93 [0.90–0.97]), lacunar (odds ratio, 0.93 [0.89–0.97]), subcortical (odds ratio, 0.93 [0.89–0.97]), and cortical infarcts (odds ratio, 0.92 [0.87–0.97]). Conclusions: Improving cardiovascular health within midlife and from midlife to late-life may prevent development of cerebral vascular disease.

Introduction: Cardiovascular health may be used for prevention of cerebral vascular disease. We evaluated whether improvement of cardiovascular health within midlife and from midlife to late-life were each associated with lower prevalence of MRI markers of cerebral vascular disease at late-life. Methods: 1,638 participants from the Atherosclerosis Risk in Communities (ARIC) Study took part in two visits at midlife (mean ages 53 at ARIC visit 1 and 59 at visit 3), and a late-life visit (mean age 76). A cardiovascular health Life’s Simple 7 score (range 0-12/0-14, depending on diet availability) including six/seven items was calculated at each visit, with weight assigned to each item as poor (0), intermediate (1), or ideal (2). Participants underwent 3T brain MRI scans in 2011-2013 (late-life visit). Outcomes were white matter hyperintensity volume, microbleeds, and lacunar, subcortical, and cortical infarcts at late-life. Linear and logistic regression models were used to assess the associations of cardiovascular health with MRI markers of cerebral vascular disease. Results: Thirty nine percent of participants had high cardiovascular health at midlife visit 1, 32% at midlife visit 3, and 33% at late-life. Improvement of cardiovascular health within midlife and from midlife to late-life were each associated with a lower prevalence of cerebral vascular disease markers. For example, improvement in cardiovascular health (per point) from midlife to late-life was associated with smaller white matter hyperintensity volume (Beta:-0.07 [95%CI:-0.10,-0.04]), and lower odds of microbleeds (OR:0.93 [0.90,0.97]), lacunar (OR:0.93 [0.89,0.97)], subcortical (OR:0.93 [0.89,0.97])], and cortical infarcts (OR:0.92 [0.87,0.97)] (Figure). Conclusions: Improving cardiovascular health within midlife and from midlife to late-life may prevent development of cerebral vascular disease.

Abstract We hypothesized that poorer cerebral perfusion could impair mobility, particularly at lower systolic blood pressure (SBP). Cerebral perfusion was measured via arterial spin-labelled (ASL)-MRI in community-dwelling adults of the Mayo Clinic Study of Aging. Usual-pace gait speed was assessed over 5.6 meters on an electronic mat. Linear regression models estimated cross-sectional gait speed associations with ASL and modifying effects of age and SBP, adjusting for sex and body mass index. Results report differences in gait speed (meter/second) per standard deviation (SD) lower ASL or SBP (mean=139.1 mmHg, SD=19.7 mmHg). Among 479 participants (aged 31-94 years; mean age 67.6 years; 44% women; mean gait speed 1.17m/s), relationships of ASL with gait speed varied by age and SBP (ASL-x-age interaction: p&lt;.007; ASL-x-SBP interaction: p&lt;.007). At age 65, 75 or 85 years and a SBP of 120mmHg, each SD lower ASL was associated with a 0.04 m/s (0.01,0.07), 0.07 m/s (0.04,0.10), and 0.09 m/s (0.05,0.13) slower gait speed at respective ages. At age 65, 75 or 85 years with low ASL (1 SD below the mean), each SD lower SBP was associated with 0.06 (0.02,0.09) m/s, 0.03 (0.01,0.06) m/s or .01 (-0.02,0.04) m/s slower gait speed, respectively. With high ASL (mean ASL+1SD), lower SBP trended towards faster gait at older age, e.g. estimated gait speed was 0.04 m/s (0.00,0.09) faster for each SD lower SBP at age 85years. Poorer cerebral perfusion may contribute to slower gait, particularly with lower SBP. The interrelations of cerebral perfusion, SBP, and age with mobility merit further study.