Abstract INTRODUCTION Neuronal health as a potential underlying mechanism of the beneficial effects of exercise has been understudied in humans. Furthermore, there has been limited consideration of potential moderators (e.g., cardiovascular health) on the effects of exercise. METHODS Clinically normal middle‐aged and older adults completed a validated questionnaire about exercise engagement over a 10‐year period ( n = 75; age 63 ± 8 years). A composite estimate of neuronal injury was formulated that included cerebrospinal fluid‐based measures of visinin‐like protein‐1, neurogranin, synaptosomal‐associated protein 25, and neurofilament light chain. Cardiovascular risk was estimated using the Framingham Risk Score. RESULTS Cross‐sectional analyses showed that greater exercise engagement was associated with less neuronal injury in the group with lower cardiovascular risk ( p = 0.008), but not the group with higher cardiovascular risk ( p = 0.209). DISCUSSION Cardiovascular risk is an important moderator to consider when examining the effects of exercise on cognitive and neural health, and may be relevant to personalized exercise recommendations. Highlights We examined the association between exercise engagement and neuronal injury. Vascular risk moderated the association between exercise and neuronal injury. Cardiovascular risk may be relevant to personalized exercise recommendations.
Abstract Introduction One goal of the Longitudinal Early Onset Alzheimer's Disease Study (LEADS) is to define the fluid biomarker characteristics of early‐onset Alzheimer's disease (EOAD). Methods Cerebrospinal fluid (CSF) concentrations of Aβ1‐40, Aβ1‐42, total tau (tTau), pTau181, VILIP‐1, SNAP‐25, neurogranin (Ng), neurofilament light chain (NfL), and YKL‐40 were measured by immunoassay in 165 LEADS participants. The associations of biomarker concentrations with diagnostic group and standard cognitive tests were evaluated. Results Biomarkers were correlated with one another. Levels of CSF Aβ42/40, pTau181, tTau, SNAP‐25, and Ng in EOAD differed significantly from cognitively normal and early‐onset non‐AD dementia; NfL, YKL‐40, and VILIP‐1 did not. Across groups, all biomarkers except SNAP‐25 were correlated with cognition. Within the EOAD group, Aβ42/40, NfL, Ng, and SNAP‐25 were correlated with at least one cognitive measure. Discussion This study provides a comprehensive analysis of CSF biomarkers in sporadic EOAD that can inform EOAD clinical trial design.
Machine learning can be used to create "biologic clocks" that predict age. However, organs, tissues, and biofluids may age at different rates from the organism as a whole. We sought to understand how cerebrospinal fluid (CSF) changes with age to inform the development of brain aging-related disease mechanisms and identify potential anti-aging therapeutic targets. Several epigenetic clocks exist based on plasma and neuronal tissues; however, plasma may not reflect brain aging specifically and tissue-based clocks require samples that are difficult to obtain from living participants. To address these problems, we developed a machine learning clock that uses CSF proteomics to predict the chronological age of individuals with a 0.79 Pearson correlation and mean estimated error (MAE) of 4.30 years in our validation cohort. Additionally, we analyzed proteins highly weighted by the algorithm to gain insights into changes in CSF and uncover novel insights into brain aging. We also demonstrate a novel method to create a minimal protein clock that uses just 109 protein features from the original clock to achieve a similar accuracy (0.75 correlation, MAE 5.41). Finally, we demonstrate that our clock identifies novel proteins that are highly predictive of age in interactions with other proteins, but do not directly correlate with chronological age themselves. In conclusion, we propose that our CSF protein aging clock can identify novel proteins that influence the rate of aging of the central nervous system (CNS), in a manner that would not be identifiable by examining their individual relationships with age.
Acquired and heritable traits are associated with dementia risk; however, how these traits are associated with age at symptomatic onset (AAO) of Alzheimer disease (AD) is unknown. Identifying the associations of acquired and heritable factors with variability in intergenerational AAO of AD could facilitate diagnosis, assessment, and counseling of the offspring of parents with AD.
Objective
To quantify the associations of acquired and heritable factors with intergenerational differences in AAO of AD.
Design, Setting, and Participants
This nested cohort study used data from the Knight Alzheimer Disease Research Center that included community-dwelling participants with symptomatic AD, parental history of dementia, and available DNA data who were enrolled in prospective studies of memory and aging from September 1, 2005, to August 31, 2016. Clinical, biomarker, and genetic data were extracted on January 17, 2017, and data analyses were conducted from July 1, 2017, to August 20, 2019.
Main Outcomes and Measures
The associations of acquired (ie, years of education; body mass index; history of cardiovascular disease, hypertension, hypercholesterolemia, diabetes, active depression within 2 years, traumatic brain injury, tobacco use, and unhealthy alcohol use; and retrospective determination of AAO) and heritable factors (ie, ethnicity/race, paternal or maternal inheritance, parental history of early-onset dementia,APOE ε4 allele status, and AD polygenic risk scores) to intergenerational difference in AAO of AD were quantified using stepwise forward multivariable regression. Missense or frameshift variants within genes associated with AD pathogenesis were screened using whole-exome sequencing.
Results
There were 164 participants with symptomatic AD, known parental history of dementia, and available DNA data (mean [SD] age, 70.9 [8.3] years; 90 [54.9%] women) included in this study. Offspring were diagnosed with symptomatic AD a mean (SD) 6.1 (10.7) years earlier than their parents (P < .001). The adjustedR2for measured acquired and heritable factors for intergenerational difference in AAO of AD was 0.29 (F8,155 = 9.13;P < .001). Paternal (β = −9.52 [95% CI, −13.79 to −5.25]) and maternal (β = −6.68 [95% CI, −11.61 to −1.75]) history of dementia, more years of education (β = −0.58 [95% CI −1.08 to −0.09]), and retrospective determination of AAO (β = −3.46 [95% CI, −6.40 to −0.52]) were associated with earlier-than-expected intergenerational difference in AAO of AD. Parental history of early-onset dementia (β = 21.30 [95% CI, 15.01 to 27.59]), presence of 1APOE ε4 allele (β = 5.00 [95% CI, 2.11 to 7.88]), and history of hypertension (β = 3.81 [95% CI, 0.88 to 6.74]) were associated with later-than-expected intergenerational difference in AAO of AD. Missense or frameshift variants within genes associated with AD pathogenesis were more common in participants with the greatest unexplained variability in intergenerational AAO of AD (19 of 48 participants [39.6%] vs 26 of 116 participants [22.4%];P = .03).
Conclusions and Relevance
Acquired and heritable factors were associated with a substantial proportion of variability in intergenerational AAO of AD. Variants in genes associated with AD pathogenesis may contribute to unexplained variability, justifying further study.
Abstract Alzheimer’s disease biomarkers are widely accepted as surrogate markers of underlying neuropathological changes. However, few studies have evaluated whether preclinical Alzheimer’s disease biomarkers predict Alzheimer’s neuropathology at autopsy. We sought to determine whether amyloid PET imaging or CSF biomarkers accurately predict cognitive outcomes and Alzheimer’s disease neuropathological findings. This study included 720 participants, 42–91 years of age, who were enrolled in longitudinal studies of memory and aging in the Washington University Knight Alzheimer Disease Research Center and were cognitively normal at baseline, underwent amyloid PET imaging and/or CSF collection within 1 year of baseline clinical assessment, and had subsequent clinical follow-up. Cognitive status was assessed longitudinally by Clinical Dementia Rating®. Biomarker status was assessed using predefined cut-offs for amyloid PET imaging or CSF p-tau181/amyloid-β42. Subsequently, 57 participants died and underwent neuropathologic examination. Alzheimer’s disease neuropathological changes were assessed using standard criteria. We assessed the predictive value of Alzheimer’s disease biomarker status on progression to cognitive impairment and for presence of Alzheimer’s disease neuropathological changes. Among cognitively normal participants with positive biomarkers, 34.4% developed cognitive impairment (Clinical Dementia Rating > 0) as compared to 8.4% of those with negative biomarkers. Cox proportional hazards modelling indicated that preclinical Alzheimer's disease biomarker status, APOE ɛ4 carrier status, polygenic risk score and centred age influenced risk of developing cognitive impairment. Among autopsied participants, 90.9% of biomarker-positive participants and 8.6% of biomarker-negative participants had Alzheimer's disease neuropathological changes. Sensitivity was 87.0%, specificity 94.1%, positive predictive value 90.9% and negative predictive value 91.4% for detection of Alzheimer's disease neuropathological changes by preclinical biomarkers. Single CSF and amyloid PET baseline biomarkers were also predictive of Alzheimer’s disease neuropathological changes, as well as Thal phase and Braak stage of pathology at autopsy. Biomarker-negative participants who developed cognitive impairment were more likely to exhibit non-Alzheimer's disease pathology at autopsy. The detection of preclinical Alzheimer's disease biomarkers is strongly predictive of future cognitive impairment and accurately predicts presence of Alzheimer's disease neuropathology at autopsy.
Proteomic studies for Alzheimer’s disease (AD) are instrumental in identifying AD pathways but often focus on single tissues and sporadic AD cases. Here, we present a proteomic study analyzing 1305 proteins in brain tissue, cerebrospinal fluid (CSF), and plasma from patients with sporadic AD, TREM2 risk variant carriers, patients with autosomal dominant AD (ADAD), and healthy individuals. We identified 8 brain, 40 CSF, and 9 plasma proteins that were altered in individuals with sporadic AD, and we replicated these findings in several external datasets. We identified a proteomic signature that differentiated TREM2 variant carriers from both individuals with sporadic AD and healthy individuals. The proteins associated with sporadic AD were also altered in patients with ADAD, but with a greater effect size. Brain-derived proteins associated with ADAD were also replicated in additional CSF samples. Enrichment analyses highlighted several pathways, including those implicated in AD (calcineurin and Apo E), Parkinson’s disease (α-synuclein and LRRK2), and innate immune responses (SHC1, ERK-1, and SPP1). Our findings suggest that combined proteomics across brain tissue, CSF, and plasma can be used to identify markers for sporadic and genetically defined AD.
Nearly all individuals with Down Syndrome (DS) will develop Alzheimer Disease (AD) neuropathology by the age of 40, and up to 80% will develop cognitive decline consistent with AD dementia. Characterization of CSF biomarkers in DS is critical for the advancement of diagnostic tools and therapeutic interventions for AD in these individuals. To this end, CSF biomarkers were analyzed in a cohort from the Alzheimer's Biomarkers Consortium-Down Syndrome (ABC-DS) study. Forty-two participants with DS (mean age 48±6 years) underwent CSF collection and a clinical evaluation that assessed for the presence or absence of dementia. Established CSF biomarkers for AD (Aβ40, Aβ42, total tau [tTau], and phosphorylated tau-181 [pTau]) were measured with the LUMIPULSE G1200 automated assay platform. Additional emerging biomarkers of neuronal and synaptic injury (VILIP-1, NfL, SNAP-25, neurogranin) and gliosis/neuroinflammation (sTREM2, YKL-40) will be measured via Erenna or plate-based ELISAs. Mean analyte levels were compared between asymptomatic (no MCI, no dementia) and symptomatic (MCI or dementia) individuals by Student's t-test and ANCOVA, and the relationship between biomarkers and age was evaluated by Pearson correlation. Twenty-seven individuals were asymptomatic and 15 were symptomatic (8 MCI, 7 AD dementia). In the cohort as a whole, CSF Aβ42 (r=−0.44, p=0.003) and Aβ42/Aβ40 (r=−0.50, p=0.0005) decreased significantly with older age, whereas pTau (r=0.44, p=0.003), tTau/Aβ42 (r=0.35, p=0.02) and pTau/Aβ42 (r=0.45, p=0.002) increased with age. Symptomatic individuals had significantly lower CSF Aβ42 (p=0.003) and Aβ42/Aβ40 (p=0.02) and higher pTau (p=0.02), tTau/Aβ42 (p=0.01) and pTau/Aβ42 (p=0.005). After controlling for age, CSF Aβ42 remained significantly lower in the symptomatic group (p<0.05), whereas only trends (p=0.05-0.15) remained for differences in the other biomarkers, likely due to inadequate statistical power in this small cohort. Individuals with DS exhibit patterns of established AD-related CSF biomarkers similar to those observed in studies of late-onset AD and autosomal-dominant AD, although the patterns are influenced by age in this cohort. Additional studies of prevalent and incident cases with larger numbers are needed to confirm the potential utility of these and other CSF biomarkers for the detection of AD pathology in DS in clinical practice and clinical trials.
Abstract Background The objective of this study was to examine the relationship between cerebrospinal fluid (CSF) neurofilament light (NfL) chain levels and established biomarkers of neurofibrillary tangle (NFT) pathophysiology. Secondarily, we assessed the discordance of beta‐amyloid, NFT, and neurodegeneration markers. Method 236 individuals (207 cognitively normal and 29 cognitively impaired) were selected based on completion of CSF collection and AV1451 and AV45 PET imaging within 1 year, and measurement of CSF NfL, beta‐amyloid‐42, total‐tau, and phosphorylated‐tau181 (p‐tau). Using covariate‐adjusted linear regressions we compared log‐transformed CSF NfL to AV1451 summary SUVRs and CSF p‐tau. Using Gaussian mixture models, we determined positivity thresholds in markers of beta‐amyloid (CSF beta‐amyloid‐42 and AV45), NFT (CSF p‐tau and AV1451), and neurodegeneration (CSF total‐tau and CSF NfL), and assessed discordant results between related biomarkers. Result CSF NfL was highly associated with CSF p‐tau (p=9.83e‐10), whereas CSF NfL was marginally associated with AV1451 (p=0.006). CSF p‐tau and AV1451 were only moderately correlated (r=0.457). The differences in relationship between tau and NfL markers may be due to effects of age on these three biomarkers of interest. Age was minimally correlated with AV1451 (r=0.326) and moderately correlated with CSF p‐tau and NfL (r=0.410 and 0.512, respectively). Secondarily, we found that our sample was 18.2% discordant between beta‐amyloid markers, 18.7% discordant between NFT markers, and 17.8% discordant between neurodegeneration markers. Conclusion The relationship between CSF NfL and NFT pathophysiology differed depending on the NFT biomarker that was considered and may be related to elevations in certain CSF biomarkers as a function of age. Currently, CSF p‐tau and AV1451 are often considered interchangeable measures of NFT pathophysiology. These results suggest more careful consideration and assessment of biomarkers should be taken when choosing markers of beta‐amyloid, NFT, and neurodegeneration.
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