Rachel F. Buckley

Background and ObjectivesBlood-based biomarkers offer a cost-effective, minimally invasive alternative to neuroimaging and CSF biomarkers. Newly developed fluid biomarkers, including the N-terminal...

Background: High-resolution digital cognitive tools administered at frequent intervals enable the study of age-related cognitive change at a previously unattainable level of granularity. Computational modeling further enables the simultaneous examination of multiple aspects of cognitive performance. Objective: The objective of this study was to examine whether repeated digital multiday learning assessments can detect age-related cognitive decline over short time frames in cognitively unimpaired older adults and to quantify age-related differences in process-based cognitive markers derived from computational modeling. Methods: A total of 209 cognitively unimpaired older adults (mean age 73.6, SD 8.2 years; n=136, 65.1% female) from the Harvard Aging Brain Study and affiliated studies were administered the Boston Remote Assessment for Neurocognitive Health, which includes the face-name task, a paired associative learning task. Daily median response times for correct responses were used as the primary outcome. A new version of the face-name task was administered across up to 4 testing intervals over a mean of 2.8 (SD 0.4) years. Using computational modeling, we estimated longitudinal changes in peak performance, a practice-adjusted marker of an individual’s best expected response time, as an indicator of age-related cognitive decline. We further examined age-related differences in peak performance, stability (within-person variability), learning within and across testing intervals, and between-interval forgetting, with embedded linear regressions for age, sex, years of education, and device type. Results: Peak performance slowed by 0.04 seconds per year (95% credible interval 0.02-0.06; approximately 2% per year), indicating age-related cognitive decline. Higher age (+1-SD) was associated with slower peak performance (+0.19 seconds, 95% credible interval 0.12-0.26), steeper annual slowing in peak performance (+0.036 seconds per year), greater within-person variability (+0.043 seconds), reduced learning across testing intervals, and more forgetting. Conclusions: Our findings indicate that a digital multiday learning paradigm combined with computational modeling of response times sensitively detects age-related cognitive decline over short timescales (<3 years), which are difficult to capture in conventional study designs relying on infrequent assessments. In addition, this approach identifies specific process-based cognitive markers associated with aging, including slower performance, reduced stability, and diminished learning across testing intervals.

Introduction: Early Alzheimer’s disease (AD) staging remains challenging because clinical categories and global amyloid PET measures provide only coarse characterization of disease progression. We developed a continuous amyloid staging score from regional amyloid PET to complement global amyloid burden. Methods: Using longitudinal florbetapir PET from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), we trained an end-to-end deep learning model to directly estimate a continuous amyloid staging score. We evaluated longitudinal consistency, its relationship with global amyloid burden, regional contributors to early-stage variation, and whether the staging score stratified subsequent memory decline among participants matched for baseline global amyloid burden. Cognitive analyses were replicated in A4+LEARN. Results: The staging score better preserved longitudinal visit order than existing amyloid staging approaches. During early disease stages, it captured regional amyloid variation despite relatively stable global amyloid burden. Among participants with similar baseline global amyloid burden, higher staging scores were associated with faster subsequent memory decline in both ADNI and A4+LEARN. Discussion: A continuous amyloid staging score complements global amyloid burden for early AD staging and cognitive risk stratification.

Detection of Alzheimer's disease (AD) before the development of clinical symptoms is critical for enabling the use of new treatments. Circular RNAs (circRNAs) are highly stable non-coding RNAs enriched in the brain that can cross the blood-brain barrier. Here, analyzing blood data from 1,221 individuals with AD and healthy individuals, we identified 34 circRNAs associated with AD status. A predictive model including these 34 circRNAs was comparable to plasma phosphorylated Tau-217 (pTau217) in classifying AD based on biomarker-confirmed (amyloid-β and Tau) status and replicated in independent samples from the Knight-Alzheimer Disease Research Center (n = 551: 76 AD, 475 cognitively unimpaired) and preclinical A4 (n = 1,767) cohorts. Classification of biomarker-confirmed status by blood circRNAs (area under the curve (AUC) = 0.945) had a higher predictive ability than plasma pTau217 (AUC = 0.877) and was further improved in the integrated model (circRNA+pTau217 AUC = 0.977). This model showed high AD specificity with low predictive power for Parkinson's disease, frontotemporal dementia, and other neurodegenerative diseases. In the Knight-Alzheimer Disease Research Center discovery cohort, these circRNAs (hazard ratio = 2.92) outperformed pTau217 (hazard ratio = 1.81) and amyloid-positron emission tomography when predicting progression to symptomatic AD. Although prospective validation in larger cohorts is needed, these results propose blood circRNAs as potential biomarkers for AD diagnosis and disease progression.

Histopathological assessment has served as the gold standard for diagnosing Alzheimer’s disease (AD). Emerging technological advancements, including the development of amyloid positron emission tomography (PET), have enabled early detection of amyloid pathology, one of the neuropathological hallmarks of AD. Genome-wide association study (GWAS) across cohorts of aging and AD, leveraging different measurements of amyloid burden, may facilitate the identification of novel genetic variants that drive the earliest neuropathological changes in AD. This study presents the largest GWAS of brain amyloidosis to date, leveraging amyloid β (Aβ) measured by in vivo amyloid PET and postmortem histopathology from 13,555 individuals of European ancestry. Amyloid positivity was defined as moderate or frequent neuritic plaques according to the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) staging scores for each postmortem cohort. A Gaussian mixture model (GMM) was applied to each amyloid PET cohort to identify the cohort and tracer-specific cut-offs that differentiate amyloid positive and negative populations. In silico and ex vivo analyses further characterized implicated loci, including interrogating the association between bulk and single-nucleus gene expression profiles and AD-related traits. Genetic covariance analysis assessed the extent amyloid PET and postmortem measures reflect the shared genetic architecture of brain amyloidosis. Our combined amyloidosis GWAS identified three established AD risk loci: BIN1 (rs6733839, OR = 1.20, 95% CI 1.14-1.26, P = 1.32 × 10−11), CR1 (rs4844610, OR = 1.24, 95% CI = 1.16-1.32, P = 4.21 × 10−10), APOE (rs429358, OR = 4.01, 95% CI = 3.66-4.38, P = 4.54 × 10−201), and a newly identified brain amyloidosis-associated variant on chromosome 17 (rs35635959, OR = 1.18, 95% CI = 1.12-1.25, P = 1.47 × 10−8). SuSiE fine-mapping identified a single credible set of 15 putative causal variants with rs35635959 as the lead variant. Subsequent eQTL and SuSiE-based colocalization analyses prioritized rs35635959 as a strong eQTL for TUBG2, encoding tubulin gamma 2, which is involved in microtubule organization and synaptic plasticity. Further cell-type-specific characterization of this gene in neurons from dorsolateral prefrontal cortex tissue indicated that decreased TUBG2 expression was associated with increased Aβ burden and AD case status (PFDR < 0.045). Furthermore, our study is the first to report a modest genetic covariance (covariance=0.17, P < 6.54 × 10−8) between the genetic architecture of amyloid burden captured by different modalities. While APOE showed a strong association with both amyloid endophenotypes, the observed genetic covariance was not substantially attenuated after excluding variants within the APOE region (covariance=0.16, P < 1.32 × 10−7). Our results highlight the benefits of leveraging compatible, harmonized AD endophenotypes to increase power to uncover new molecular insights into the etiology of AD neuropathology. Wang et al. present the largest GWAS of brain amyloidosis to date, analysing 13,555 individuals using amyloid PET and postmortem Aβ measures. They identify a novel amyloidosis-associated variant on chromosome 17, demonstrate genetic covariance between modalities, and highlight complex traits sharing genetic architecture with Aβ burden.

Plasma phosphorylated tau 217 (pTau217) is an excellent biomarker of Alzheimer's disease (AD) pathology, but it remains uncertain whether pTau217 can predict amyloid-β (Aβ) and tau accumulation prior to Aβ positron emission tomography (PET) positivity. Here, we leverage data from a well-characterized prospective cohort of cognitively unimpaired older adults to examine mass spectrometry-based plasma %pTau217 (pTau217/non-phosphorylated-Tau217×100) relative to changes in Aβ/tau PET and cognition. A higher baseline %pTau217 was associated with faster Aβ and tau accumulation on PET, which then led to greater cognitive decline. Among individuals Aβ PET-negative at baseline, higher %pTau217 levels presaged increases in Aβ and tau PET signals. Together, our results suggest that very low %pTau217 in cognitively unimpaired older adults is associated with a minimal risk of AD pathology accumulation and cognitive decline.

Background: Female‐specific reproductive factors have been associated with stroke risk, although evidence for some factors (eg, live births) remains conflicting. We determined the association between number of live births and other female‐specific reproductive factors and subsequent risk of stroke and magnetic resonance imaging markers of vascular brain injury in a community‐based cohort. Methods: This was a prospective cohort study of 1882 (mean age, 61.3±9.6 years) women from the FHS (Framingham Heart Study) Offspring cohort who were stroke free at the baseline examination (1998–2001). Reproductive factors included number of live births, age at menopause, postmenopausal hormone replacement therapy use and serum estradiol and estrone levels. The primary outcome was incident all‐cause stroke, with covert brain infarcts and white matter hyperintensity volume on brain magnetic resonance imaging as cross‐sectional secondary outcomes. Results: During a median 18‐year follow‐up, 126 women had a stroke. On multivariable Cox proportional hazards models controlling for vascular risk factors, ≥3 live births (versus 0, reference) was associated with a reduced risk of stroke (hazard ratio, 0.51 [95% CI, 0.31–0.85]; P P =0.03). No significant association was detected between other reproductive factors and incident stroke or magnetic resonance imaging markers of vascular brain injury. Conclusions: A greater number of live births was associated with a decreased risk of stroke and covert brain infarct in women and may be an important factor to include in female‐specific clinical prediction rules for stroke.

INTRODUCTION: Early biological pathways explaining the risk for Alzheimer's disease (AD)–related cognitive decline remain poorly understood. METHODS: Using linear mixed‐effects models, we investigated whether whole blood gene expression (RNA sequencing) moderates the relationship between AD biomarkers measured by amyloid beta (Aβ) and tau‐PET (positron emission tomography) imaging and longitudinal cognition in 770 cognitively unimpaired older adults (Age _mean  = 71.3, 62% female) from Anti‐Amyloid Treatment in Asymptomatic Alzheimer's (A4) and Longitudinal Evaluation of Amyloid Risk and Neurodegeneration (LEARN) (A4/LEARN). RESULTS: We identified protective and AD risk–related gene expression signatures on the autosome and X chromosome. Six genes (n _genes (%); 2(33%) X‐linked) interacted with Aβ‐PET, whereas 103 genes (3(3%) X‐linked) interacted with neocortical tau‐PET, to influence cognitive decline. A total of 110 genes (17(15%) X‐linked) and 3156 genes (121(4%) X‐linked) were moderated by both sex and Aβ‐ or tau‐PET, respectively. Pathway enrichment analyses reflected immunity, protein synthesis, and lipid metabolism. DISCUSSION: These findings underscore the importance of peripheral transcriptomic markers in identifying sex‐differentiated pathways related to risk of and protection from cognitive decline in preclinical AD.