Mielke, Michelle M.

AbstractBackgroundBrief self‐administered cognitive assessments deployed remotely and combined with plasma biomarkers of Alzheimer’s disease (AD) are well suited to assess clinical symptoms and underlying biology in large‐scale decentralized studies. We investigated whether associations of self‐administered cognitive measures and plasma biomarkers were comparable to associations of in‐person cognitive measures and plasma biomarkers.MethodParticipants included 327 adults from the Mayo Clinic Study of Aging (mean age = 70.8; 48% female; mean education = 16.0 years; 96.9% white). Most (94.5%) were cognitively unimpaired; 5.5% had consensus diagnosis of mild cognitive impairment. Participants completed Mayo Test Drive (MTD; Stricker et al., 2022), a remote, self‐administered, multi‐device compatible web‐based cognitive assessment comprised of the Stricker Learning Span (SLS) and Symbols test that are combined into a MTD composite. Participants also completed in‐person neuropsychological assessment (global‐z; Mayo Preclinical Alzheimer’s disease Cognitive Composite [Mayo‐PACC]) and fasting venipuncture. Plasma was assayed via HD‐X simoa platform for amyloid‐beta (Aβ)‐42 and Aβ‐40 to create an Aβ42/40 ratio, glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL) using the N4PE Quanterix kit as well as phosphorylated‐tau protein 181 (p‐tau181). P‐tau181, GFAP, and NfL were log10‐transformed. Multiple regression analyses adjusting for age, sex, and education examined associations between plasma biomarkers and composite and subtest scores from remote and in‐person measures. Davidson‐Mackinnon J‐test compared R‐squared values of models with remote versus in‐person composites.ResultLower performances on MTD, global‐z, and Mayo‐PACC (Table 1) were associated with higher GFAP (p’s≤.02), NfL (p’s≤.003), and p‐tau181 (p’s = .04). For memory, worse SLS and AVLT sum of trials were associated with higher GFAP (p’s≤.01) and NfL (p’s≤.03). For processing speed/executive function, worse Symbols and Trails B were associated with higher p‐tau181 (p’s≤.02). Worse Trails B and Digit Symbol Coding were associated with higher NfL (p’s≤.02), and worse Digit Symbol Coding was associated with lower Aβ42/40 (p = 0.03; Table 2). A multivariable model with MTD explained significantly more variance (p = .04) for NfL (adjusted R‐squared = 0.461) than a multivariable model with Mayo‐PACC (adjusted R‐squared = 0.447); remote and in‐person composite measures were comparable for other associations (p’s>.05).ConclusionRemote and in‐person composite measures showed comparable associations with AD‐related plasma biomarkers, supporting MTD’s criterion validity.

AbstractBackgroundFor blood‐based biomarkers to fulfill their potential to revolutionize diagnosis and management of Alzheimer’s disease (AD), additional research in diverse populations remains critical. Here, we analyzed a biracial sample to assess the profiles of blood‐based AD biomarkers and their relationships to cognition and medical comorbidities.MethodsSelf‐reported African‐American (AA; n = 267) and non‐Hispanic White (NHW; n = 268) participants evaluated through the Mayo Jacksonville Alzheimer’s Disease Research Center were matched on age, sex, and clinical diagnosis. Plasma AD biomarkers (Aβ42/40, p‐tau181, GFAP, NfL) were measured using the Quanterix SiMoA HD‐X analyzer. Cognition was assessed with the Mini Mental State Examination. Wilcoxon rank sum tests were used to assess for differences in plasma biomarker levels by sex. Linear models tested for associations of race, chronic kidney disease (CKD), and vascular risk factors with plasma AD biomarker levels, covarying for age and sex. Additional models assessed for interactions between race and plasma biomarkers in predicting cognition, further covarying for education.ResultsThe sample included adults aged 43‐100 years, with 69% women. Education was higher in NHW participants (median 16 vs. 12 years, p<0.001). APOE (apolipoprotein E) ε4 positivity was higher in AA participants (43% vs. 34%; p = 0.04). There were no differences in plasma AD biomarker levels between AA and NHW participants. These findings were unchanged after stratifying by cognitive status (unimpaired versus impaired). Although the association between p‐tau181 and cognition appeared stronger in NHW participants while the association between Aβ42/40 and cognition appeared stronger in AA participants, these findings did not survive after excluding individuals with CKD. Women displayed higher GFAP (p = 0.002) and lower p‐tau181 (p = 0.001) levels than men. Diabetes was inversely associated with GFAP levels (p<0.001).ConclusionsIn a biracial, community‐based sample, we observed that sex differences, CKD, and vascular risk factors contributed to variation in plasma AD biomarkers, while self‐reported race did not. Our results reinforce the critical need to account for sex and systemic medical comorbidities in equitably deploying blood‐based AD biomarkers in the general population.

AbstractBackgroundPlasma biomarkers for Alzheimer’s disease (AD) such as phosphorylated tau protein 181 (p‐tau 181), neurofilament light chain (NfL), amyloid‐β 42/40 ratio (Aβ42/40), and glial fibrillary acidic protein (GFAP) have shown high performance in the discrimination of those with clinical mild cognitive impairment (MCI) or dementia from cognitively unimpaired individuals, but few population‐based autopsy studies have evaluated the neuropathological correlates of plasma biomarkers.MethodWe utilized a population‐based prospective study of 350 participants with autopsy and antemortem plasma biomarker data. Plasma biomarkers Aβ42/40 ratio, p‐tau181, GFAP, and NfL were measured using Quanterix Simoa assays. We predicted AD neuropathological change (ADNC), Braak staging, and neuritic plaque score using these plasma biomarkers and investigated the added utility of cognitive and demographic information. We utilized a variable selection procedure in cross‐validated (CV) logistic regression models by minimizing the Bayesian Information Criterion to identify the best set of predictors for a given outcome among plasma biomarkers, demographic variables, and neuropsychological tests included in the Mayo Clinic Preclinical Alzheimer Cognitive Composite (Mayo‐PACC).ResultADNC was best predicted with plasma GFAP, NfL, p‐tau181 biomarkers along with APOE ε4 carrier status and Mayo‐PACC cognitive score (CV AUC = 0.821). Braak staging was best predicted using plasma GFAP, p‐tau181, and cognitive scores (CV AUC = 0.788). Neuritic plaque score was best predicted using plasma Aβ42/40 ratio, p‐tau181, GFAP, and NfL biomarkers (CV AUC = 0.778). Inclusion of plasma biomarkers demonstrated significantly better prediction of Braak staging (p<0.01) and neuritic plaque score (p<0.01), but not ADNC (p = 0.13), when compared to models that included only cognitive and demographic information. Univariate models consistently underperformed in comparison to multivariate models.ConclusionPlasma biomarkers can provide valuable predictive information about the presence of AD pathology, which can facilitate earlier recognition and diagnosis of AD. Incorporation of demographic and cognitive variables along with plasma variables in multivariable models will improve prediction accuracy of AD pathology, specifically Braak staging and ADNC.

AbstractBackgroundPlasma biomarkers of Alzheimer’s disease (AD) and neurodegeneration have demonstrated diagnostic utility and associations with AD pathology. Our objective was to map the average trajectories over age of plasma biomarkers and more established biomarkers including cognition, amyloid PET, and hippocampal volume.MethodWe included 1731 cognitively unimpaired (CU) and 226 mild cognitive impairment (MCI) participants from the Mayo Clinic Study of Aging (mean age of 70.3 years; 46.3% female; 14.8 years of mean education) with one or more measurements of amyloid‐PET, MRI with hippocampal volume measurements adjusted for head size (HVa), and plasma biomarkers (Aβ42/40 ratio, p‐tau181, GFAP, and NfL measured using Quanterix Simoa assays). A global cognitive z‐score was created by averaging the z‐scores of memory, language, executive function, and visuospatial domains. Smoothing spline curves were calculated for the values of each variable over age and placed on the same plot. Since each variable has a different scale, the smoothed curves were centered at the predicted value at age 50 and scaled so that a value of 1 would be one standard deviation of the fitted values from ages 50‐80.ResultAt age 50, GFAP and NfL start to increase, and Aβ42/40 was observed to decrease. At age 55, amyloid‐PET and p‐tau181 had a smooth and upward‐curving increase. After age 70, the average values of p‐tau181 increased at a higher rate. Average cognition started declining after 55 years. HVa began to decline after age 60 and then showed a steeper decline at age 72.ConclusionThis study demonstrates that predicted values of plasma GFAP, NfL, and Aβ42/40 across participants begin to change at younger ages than cognitive decline or increases observed in amyloid PET. The changes observed during middle age in GFAP and NfL likely reflect non‐AD pathologies and aging processes, while Aβ42/40 may be a leading indicator of future cerebral amyloidosis. Predicted plasma neurodegenerative biomarkers (GFAP and NfL) begin to change at younger ages than a traditional biomarker of neurodegeneration (HVa). Predicted ptau181 increases were observed in tandem with amyloid PET likely reflecting current amyloid status.

AbstractBackgroundTwo major risk factors of Alzheimer’s disease (AD) are advanced age and the APOE‐ε4 risk allele. However, despite having these risk factors, some individuals escape from developing AD or have slower cognitive decline. In this study we hypothesized that “resilient” individuals, defined as those who remain cognitively normal despite being >80 years‐old and carrying at least one copy of the APOE‐ε4 allele, have concerted changes in their blood transcriptomes that are associated with resilience to AD.MethodWe generated blood RNA‐sequence data from 105 APOE‐ε4 carriers from the Mayo Clinic Study of Aging (MCSA) who remained cognitively normal after age 80 (resilient), or who developed mild cognitive impairment (MCI) or AD (non‐resilient). Weighted gene co‐expression network analysis (WGCNA) was performed to evaluate the association of the blood co‐expression network with primary phenotypes of resilience and memory scores. To validate our findings, we constructed a consensus co‐expression network using both MCSA and a replication cohort of blood microarray data from 91 Alzheimer’s Disease Neuroimaging Initiative (ADNI) study participants who had a similar demographic profile. We annotated the consensus modules for their enrichment of cell types, biological processes, genes within previously published genome‐wide significant (GWS) AD‐associated loci, and genes that reside in consensus modules built from seven brain regions across three different autopsy cohorts. To identify genetic variants regulating blood consensus module co‐expression, we performed module quantitative trait locus (modQTL) analysis.ResultWe found co‐expression modules significantly (q<0.05) correlated with the memory phenotype, logical memory delayed recall, across both MCSA and ADNI cohorts. Some of these modules were enriched with blood cell types and biological processes, including telomere maintenance, mitochondrial functions, and immune response. We found 1 module significantly (q<0.05) enriched with genes near GWS AD‐associated loci and 4 modules with genes in brain consensus modules. Lastly, we found 8 modQTLs significantly (Bonferroni‐p<0.05) associated with the co‐expression of 5 different modules.ConclusionWe identified blood co‐expression modules, biological pathways and specific genetic variants associated with cognitive resilience in the presence of AD risk factors. This study provides novel insights into the potential role of peripheral blood in the development of and resilience to AD.

Abstract Background Easily accessible and self‐administered cognitive assessments that can aide early identification of individuals at risk for Alzheimer’s disease (AD) dementia are critical for timely intervention. Mayo Test Drive (MTD) is a remote, self‐administered, multi‐device compatible, web‐based cognitive assessment (Stricker et al., 2022). We investigated continuous associations between MTD and amyloid and tau PET biomarkers and compared the strength of association to PET biomarkers and in‐person cognitive measures. Method 556 adults from the Mayo Clinic Study of Aging participated (mean age = 70.6; mean education = 16.0 years; 50.7% female; 97.5% white). Participants were predominantly cognitively unimpaired (95.3%; 4.7% had a consensus diagnosis of mild cognitive impairment). Participants completed (1) brain amyloid and tau PET scans, (2) in‐person neuropsychological assessment including Mayo Preclinical Alzheimer’s disease Cognitive Composite (Mayo‐PACC) and Global‐z, and (3) remote MTD comprised of the Stricker Learning Span (SLS) and the Symbols test, which are combined into an MTD composite. Multiple regressions adjusted for age, sex, and education queried associations between imaging biomarkers [amyloid meta‐region of interest (ROI), tau meta‐ROI, and tau entorhinal cortex (EC)‐ROI)] and composite and subtest scores from remote and in‐person measures. Davidson‐Mackinnon J‐test compared R‐squared values of models with remote versus in‐person composites. Result Lower performances on MTD composite and Mayo‐PACC were associated with higher amyloid meta‐ROI (p’s<.05; Table 1). MTD composite was also negatively associated with tau meta‐ROI and EC‐ROI (p’s<.05). For memory measures, SLS sum of trials was negatively associated with tau meta‐ROI (p = .01) and EC‐ROI (p<.001) with no associations between AVLT sum of trials and PET (p’s = .06; Table 2). Among processing speed/executive function measures, worse Trails B performance was associated with higher amyloid meta‐ROI (p<.001). Symbols and Digit Symbol Coding were not associated with PET (p’s = .29). A multivariable model with MTD explained significantly less variance (p = .04) for amyloid meta‐ROI (adjusted R‐squared = 0.165) than a multivariable model with Mayo‐PACC (adjusted R‐squared = 0.176); remote and in‐person composite measures were comparable for other associations (p’s>.05). Conclusion MTD is associated with continuous measures of amyloid and tau PET biomarkers of AD, demonstrating criterion validity for MTD. Several associations were comparable to relationships between gold‐standard in‐person neuropsychological tests and imaging biomarkers.

Abstract Background Brief self‐administered cognitive assessments deployed remotely and combined with plasma biomarkers of Alzheimer’s disease (AD) are well suited to assess clinical symptoms and underlying biology in large‐scale decentralized studies. We investigated whether associations of self‐administered cognitive measures and plasma biomarkers were comparable to associations of in‐person cognitive measures and plasma biomarkers. Method Participants included 327 adults from the Mayo Clinic Study of Aging (mean age = 70.8; 48% female; mean education = 16.0 years; 96.9% white). Most (94.5%) were cognitively unimpaired; 5.5% had consensus diagnosis of mild cognitive impairment. Participants completed Mayo Test Drive (MTD; Stricker et al., 2022), a remote, self‐administered, multi‐device compatible web‐based cognitive assessment comprised of the Stricker Learning Span (SLS) and Symbols test that are combined into a MTD composite. Participants also completed in‐person neuropsychological assessment (global‐z; Mayo Preclinical Alzheimer’s disease Cognitive Composite [Mayo‐PACC]) and fasting venipuncture. Plasma was assayed via HD‐X simoa platform for amyloid‐beta (Aß)‐42 and Aß‐40 to create an Aß42/40 ratio, glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL) using the N4PE Quanterix kit as well as phosphorylated‐tau protein 181 (p‐tau181). P‐tau181, GFAP, and NfL were log10‐transformed. Multiple regression analyses adjusting for age, sex, and education examined associations between plasma biomarkers and composite and subtest scores from remote and in‐person measures. Davidson‐Mackinnon J‐test compared R‐squared values of models with remote versus in‐person composites. Result Lower performances on MTD, global‐z, and Mayo‐PACC (Table 1) were associated with higher GFAP (p’s = .02), NfL (p’s = .003), and p‐tau181 (p’s = .04). For memory, worse SLS and AVLT sum of trials were associated with higher GFAP (p’s = .01) and NfL (p’s = .03). For processing speed/executive function, worse Symbols and Trails B were associated with higher p‐tau181 (p’s = .02). Worse Trails B and Digit Symbol Coding were associated with higher NfL (p’s = .02), and worse Digit Symbol Coding was associated with lower Aß42/40 (p = 0.03; Table 2). A multivariable model with MTD explained significantly more variance (p = .04) for NfL (adjusted R‐squared = 0.461) than a multivariable model with Mayo‐PACC (adjusted R‐squared = 0.447); remote and in‐person composite measures were comparable for other associations (p’s>.05). Conclusion Remote and in‐person composite measures showed comparable associations with AD‐related plasma biomarkers, supporting MTD’s criterion validity.

Introduction: US rural areas have a disproportionate burden of CVD and hypertension. Physical inactivity is a major risk factor, with 25% prevalence in US adults. However, rural disparities in physical inactivity are incompletely described. The aim was to evaluate differences in prevalence of physical inactivity based on county-level rurality. Methods: We used CDC county-level data for adults aged ≥20 years, 2004–2019, available for 97.6% of US counties (n=3148/3226). CDC defined physical inactivity as self-reported lack of leisure time physical activity in the month preceding the survey. Rurality was based on six categories ordered from least rural (large central metro) to most rural (noncore). We used weighted least squares regression to compare prevalence by rurality level, adjusting for 13 county-level sociodemographic factors; results reported as prevalence ratios (95% CI) with partial F -tests and P -values. Results: In age and sex ratio adjusted analysis (model 1), we observed a gradient in the prevalence of physical inactivity across levels of rurality, with significantly higher rates in small metro (1.03 [1.00–1.06]), micropolitan (1.05 [1.02–1.07]), and noncore (1.05 [1.02–1.08]) compared with large central metro counties ( F -statistic=7.5; P <.001). With further adjustment for other sociodemographic factors (model 2), the relative differences were negated in noncore counties (0.99 [0.97-1.02]), attenuated in intermediate counties, and accentuated in large fringe metro counties (1.06 [1.04-1.08]) ( F -statistic=14.3; P <.001). Further adjustment for obesity (model 3) largely attenuated the association of rurality with physical inactivity, resulting in no significant differences relative to large central metro counties ( F -statistic=4.8; P <.001). Conclusions: County-level rurality was associated with prevalence of physical inactivity. These disparities were largely accounted for by obesity, which may be targeted for intervention.

Introduction: US rural, compared with nonrural, counties have a higher burden of CVD. Obesity is a major CVD risk factor with 40% prevalence in adults. However, the rural-nonrural disparities in obesity are incompletely described. We aimed to evaluate differences in prevalence of obesity by county-level rurality. Methods: We used CDC county-level data for adults aged ≥20 years, 2004–2019, available for 97.6% of US counties (n=3148/3226). CDC defined obesity (body mass index ≥30 kg/m 2 ) using self-reported weight and height. Rurality was based on six levels from large central metro (most urban) to noncore (most rural). We compared prevalence by rurality level using weighted least squares regression, adjusting for 13 county-level sociodemographic measures, and reported prevalence ratios (95% CI). Results: Counties with increasing rurality reflected older populations, as indicated by higher median age (from 35.6 years to 42.4 years). Also, increasing rurality was associated with differences in income and proportion uninsured. In age and sex ratio adjusted analysis (model 1), obesity prevalence was 1.05- to 1.06-fold higher in medium metro, small metro, and micropolitan counties compared with large central metro counties (global test, P <.001). With further adjustment for sociodemographic measures (model 2; model 1 covariates plus race, state, income, proportion uninsured) these relative differences were preserved but attenuated (each 1.03-fold higher), while a greater difference was revealed for large fringe metro (1.06-fold higher) counties. In both models, differences in obesity prevalence between noncore and large central metro counties were minimal. (Table) Conclusion: Counties with intermediate rurality were associated with higher obesity prevalence. The association was partly accounted for by nonmodifiable and modifiable sociodemographic measures, which may be targeted to reduce rural-nonrural disparities in obesity.