Klinger, Hannah M

Abstract Background pTau217 is a well validated plasma biomarker for Alzheimer's disease (AD), sensitive to the accumulation of amyloid pathology in the preclinical stages of disease. We leveraged whole blood RNA sequencing and plasma pTau217 measures of amyloid burden to identify blood transcriptomic gene‐network changes that relate to plasma pTau217 levels in cognitively normal older adults. We then quantified the abundance of cell populations from the transcriptomic data to identify blood cell abundance alterations that correlate with plasma ptau217 levels. Method Whole blood RNA sequencing and plasma pTau217 data were obtained from 1739 participants in the Anti‐Amyloid Treatment in Asymptomatic Alzheimer's Disease (A4) Study. RNAseq data were aligned, sorted, counted, batch normalized, and iteratively adjusted for covariates. 20,718 genes were available for analysis following quality control. Plasma samples ( n = 1597) were analyzed for pTau217 using the Eli Lilly and Company Diagnostics Laboratory immunoassay. Samples from 724 participants (63% females, mean age 71, 61% amyloid+) with RNA sequencing and biomarker data were used for analysis. Cell fractions were quantified using CIBERSORTx (LM22 used as reference) and WGCNA was used to calculate gene co‐expression modules. Linear regression related gene module expression or cell fraction to mean pTau217 levels covarying for age, sex, education, APOE ε2 and ε4 status. Correction for multiple comparisons used the false discovery rate. Gene enrichment was performed using Gene Ontology. Result Five gene modules were associated with plasma pTau217. For the turquoise module (β=‐0.81, p.fdr=0.005), higher expression was associated with higher p ‐tau217 while higher expression in the blue (β=‐0.70, p.fdr=0.01), lightcyan (β=‐0.68, p.fdr=0.01), grey60 (β=‐0.64, p.fdr=0.02), and brown (β=‐0.55, p.fdr=0.04) modules were associated with lower p ‐tau217. These modules were enriched for phagocytosis, ribonucleoprotein complex biogenesis, immunoglobulin mediated immune response, nucleosome assembly, and cytoplasmic translation pathway genes. In cell fraction analysis, higher predicted abundance of neutrophils related to ptau217 levels (β=0.33, p.fdr=0.03). Conclusion Our results demonstrate an association between immune response and epigenetic pathway genes with plasma pTau217 levels in preclinical AD. Future work will seek to clarify whether these signatures also relate to brain amyloid PET to evaluate blood transcription networks as complementary biomarkers to improve preclinical prediction.

Importance Alzheimer disease (AD) predominates in females at almost twice the rate relative to males. Mounting evidence in adults without AD indicates that females exhibit higher tau deposition than age-matched males, particularly in the setting of elevated β-amyloid (Aβ), but the evidence for sex differences in tau accumulation rates is inconclusive. Objective To examine whether female sex is associated with faster tau accumulation in the setting of high Aβ (as measured with positron emission tomography [PET]) and the moderating influence of sex on the association between APOE ε4 carrier status and tau accumulation. Data Sources This meta-analysis used data from 6 longitudinal aging and AD studies, including the Alzheimer’s Disease Neuroimaging Initiative, Berkeley Aging Cohort Study, BioFINDER 1, Harvard Aging Brain Study, Mayo Clinic Study of Aging, and Wisconsin Registry for Alzheimer Prevention. Longitudinal data were collected between November 2004 and May 2022. Study Selection Included studies required available longitudinal [ 18 F]flortaucipir or [ 18 F]-MK-6240 tau-PET scans, as well as baseline [ 11 C] Pittsburgh Compound B, [ 18 F]flutemetamol or [ 18 F]florbetapir Aβ-PET scans. Recruitment criteria varied across studies. Analyses began on August 7, 2023, and were completed on February 5, 2024. Data Extraction and Synthesis In each study, primary analyses extracted estimates for the sex (female or male) and the sex by baseline Aβ-PET status (high or low) association with longitudinal tau-PET using a series of mixed-effects models. Secondary mixed-effects models extracted the interaction estimate for the association of sex by APOE ε4 carrier status with longitudinal tau-PET. Study-specific estimates for each mixed-effects model were then pooled in a meta-analysis, and the global fixed effect (β) and total heterogeneity ( I 2 ) across studies were estimated. This study is reported following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline. Main Outcomes and Measures Seven tau-PET outcomes that showed cross-sectional sex differences were examined across temporal, parietal, and occipital lobes. Results Among 6 studies assessed, there were 1376 participants (761 [55%] female; mean [range] age at first tau scan, 71.9 [46-93] years; 401 participants [29%] with high baseline Aβ; 412 APOE ε4 carriers [30%]). Among individuals with high baseline Aβ, female sex was associated with faster tau accumulation localized to inferior temporal (β = −0.14; 95% CI, −0.22 to −0.06; P = .009) temporal fusiform (β = −0.13; 95% CI, −0.23 to −0.04; P = .02), and lateral occipital regions (β = −0.15; 95% CI, −0.24 to −0.06; P = .009) compared with male sex. Among APOE ε4 carriers, female sex was associated with faster inferior-temporal tau accumulation (β = −0.10; 95% CI, −0.16 to −0.03; P = .01). Conclusions and Relevance These findings suggest that sex differences in the pathological progression of AD call for sex-specific timing considerations when administrating anti-Aβ and anti-tau treatments.

AbstractBackgroundThe residual approach has found wide application in researching cognitive resilience, a phenomenon conceptually understood as cognitive performance being better‐than‐typical for an individual, despite apparent AD pathology. The standard residual approach extracts information about an individual’s resilience from the residuals of a linear model predicting cognition. This approach is subject to several limiting assumptions. Here, we explore the implications of these assumptions and propose an alternative predictive modeling approach which circumvents them.MethodsOur alternative approach relies on training a machine learning model on a sample of participants who follow a “typical” clinical progression (Fig. 1A). Residuals are then extracted for a heterogeneous dataset that potentially includes resilient participants by comparing observed cognitive outcomes to those predicted by the “typical” model (Fig. 1B). Additionally, our estimate can be refined by effectively estimating and removing model error from the extracted residual by leveraging a second predictive model (Fig. 1C). Using simulated data, we explore the assumptions made by the standard approach and the consequences of their violation. We then contrast this against our approach.ResultsOur simulations demonstrated that the standard residual approach only extracted accurate resilience estimates if the predictors used in the model explained cognition sufficiently well, while being unrelated to resilience itself. That is, as the predictors explain more of the resilience, the residuals became increasingly determined by model error (Fig. 2). We found that residuals achieved with our approach are more precise and provide individualized estimates that are unaffected by the aforementioned trade‐off (Fig. 3).ConclusionsOur approach holds several advantages over the standard approach: The definition of resilience can be freely adjusted and enforced via different definitions of “typical”, it allows for incorporating complex, non‐linear models without removing relevant information from the residual, the error component of the residual can be estimated through cross‐validation, and all predictors that potentially explain cognition can be included into the model without losing resilience information from the residual. Our approach can be used to measure any phenomenon that can be operationalized as a deviation from a typical outcome and, thus, is also applicable for researching resistance to pathology.