Alzheimer’s disease (AD) is clinically characterized by progressive memory loss and cognitive decline, with aging as the primary risk factor. Early AD pathology includes accumulation of amyloid-beta (Aß) in plaques. This study aims to investigate the molecular mechanisms by which aging contributes to brain amyloidosis using a machine learning-based approach on large proteomic datasets from cerebrospinal fluid (CSF). To accomplish this, we trained a machine learning model to predict CSF Aß42/40, a key biomarker for amyloidosis. Our modified elastic net model using adaptive feature selection achieved robust accuracy (Pearson Correlation of 0.86) predicting CSF Aß42/40 in our validation cohort. Pathway analysis of the model-utilized proteins (and proteins highly correlated to them) revealed age-associated alterations potentially linked to amyloidosis, particularly highlighting dysregulated autophagy and membrane trafficking pathways. These findings suggest that impaired autophagosome-lysosome fusion and endosomal processing may drive the decline in Aß clearance with aging. Our study highlights the power of machine learning in biomarker approximation and biological prediction, enabling insights into multiple diseases.

INTRODUCTION:We recently identified a plasma-based seven-protein model with strong performance for Alzheimer's disease (AD) classification. Here, we evaluated whether these proteins, alone or combined with plasma phosphorylated tau 217 (p-tau217), predict progression from cognitively unimpaired to symptomatic AD. METHODS:Using longitudinal data from Knight-ADRC (Alzheimer's Disease Research Center) with replication in Alzheimer's Disease Neuroimaging Initiative (ADNI), we modeled time to progression using Cox regression. Models included p-tau217, the seven-protein panel, and their combination. RESULTS:The p-tau217 alone showed similar progression prediction (hazard ratio [HR] = 4.08) than the seven-protein model (HR = 4.85). Integrating the seven-protein model with ptau217 significantly improved risk, identifying a high-risk group (HR = 11.15) with two intermediate-risk groups. Simplified models retained prognostic value, with top-performing ratios, Complexin-2/Synaptic vesicle membrane protein VAT-1 homolog (CPLX2/VAT1) and Acetylcholinesterase/Neuronal pentraxin receptor (ACHE/NPTXR) also lead to a significantly better risk stratification than p-tau217 alone. DISCUSSION:Integrating p-tau217 with targeted plasma proteins enables graded risk stratification and identifies individuals at highest risk of progression, supporting clinically scalable approaches for early risk assessment.

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.

Neurodegenerative diseases (NDs), including Alzheimer's disease (AD), Parkinson's disease (PD), dementia with Lewy bodies (DLB), and frontotemporal dementia (FTD), share overlapping clinical and pathological features. We analyzed cerebrospinal fluid (CSF) and plasma proteomes from 2,705 and 3,009 samples, respectively, across these NDs, identifying disease-specific and shared molecular signatures. CSF showed more disease-associated proteins than plasma, with AD and DLB exhibiting the strongest cross-tissue similarity. Pathway analyses revealed shared dysregulation of immune-related processes in CSF and plasma across the NDs, as well as disease-specific impairment of glycosylation and apoptotic pathways in AD; ATF4 and PERK signaling in PD; fibroblast growth factor receptor (FGFR) and interleukin signaling in DLB; and glycoprotein hormones disruption in FTD. We developed disease-specific predictive models showing high accuracy (area under the curve [AUC]: 0.81-0.95 in CSF and 0.80-0.89 in plasma). These findings reveal distinct and convergent mechanisms across NDs, highlighting potential biomarkers and pathways for diagnostic and therapeutic strategies in neurodegeneration.

Abstract Cerebrospinal fluid amyloid beta 42, total tau, and phosphorylated tau 181 are well accepted markers of Alzheimer’s disease. These biomarkers better reflect disease pathogenesis compared to clinical diagnosis. Here, we perform a genome wide association study meta-analysis including 18,948 individuals of European ancestry and identify 12 genome-wide significant loci across all three biomarkers, eight of them novel. We replicate the association of biomarkers with APOE , CR1 , GMNC/CCDC50 and C16orf95/MAP1LC3B . Novel loci include BIN1 for amyloid beta and GNA12, MS4A6A, SLCO1A2 with both total tau and phosphorylated tau 181, as well as additional loci on chr. 8, near ANGPT1 and chr. 9 near SMARCA2 . We also demonstrate that these variants have significant association with Alzheimer’s disease risk, disease progression and/or brain amyloidosis. The associated genes are implicated in lipid metabolism independent of APOE , coupled with autophagy and brain volume regulation driven by total tau and phosphorylated tau 181 dysregulation.

It has become standard practice to visualize regional signals from genome-wide association studies (GWAS) using LocusZoom plots. Similarly, GWAS signals are compared to regionally matched quantitative trait loci (QTLs), i.e. variant-to-gene regulation data, using LocusCompare plots to aid assessment of candidate trait-related genes. Despite broad usage, these tools annotate variants by linkage disequilibrium (LD) to a single lead or index variant. This single-index representation has limitations for visualizing complex loci that contain multiple independent signals. We present LocusBlend, an interactive web application for multi-index LD-blended visualization of genomic loci. LocusBlend supports one or two genomic association summary-statistic datasets and one to three index variants, multi-index LocusZoom color-blended plots, and matching LocusCompare visualizations. Applications to Alzheimer's disease GWAS and QTL signals illustrate LocusBlend enables visualization and separation of independent signals despite shared LD and high genomic complexity. Overall, LocusBlend is aimed at supporting researchers handle the continuously expanding complexity of human genomics findings.

INTRODUCTION Accurate clinical diagnosis of neurodegenerative diseases remains challenging, particularly when individuals have mixed pathologies. We implemented the generalizable protein-based neurodegenerative disease artificial intelligence (GPND-AI) classifier using the NUcleic acid-Linked Immuno-Sandwich Assay (NULISA) central nervous system (CNS) panel to classify Alzheimer's disease, Parkinson's disease, frontotemporal dementia, dementia with Lewy bodies, and healthy controls, while disentangling mixed pathologies.METHODS Proteomic and clinical information from the Charles F. and Joanne Knight Alzheimer's Disease Research Center (Knight-ADRC) and Movement Disorder Clinic were used to train and test the GPND-AI classifier. External validation was performed in a Banner Sun Health Research Institute cohort and additional Knight-ADRC samples with neuropathologically confirmed diagnoses.RESULTS GPND-AI identified 15 proteins that achieve an area under the curve (AUC) of 0.955 and 92.3% accuracy across five diagnostic categories. In validation cohort, predicted co-pathologies significantly correlated with clinical characteristics.DISCUSSION GPND-AI identified a 15-protein panel that accurately classifies individuals across the four major neurodegenerative diseases. Validation against neuropathology-confirmed diagnoses supports the utility of proteomics-based approaches for mapping disease-specific and co-existing neurodegenerative processes.

INTRODUCTION:Few genetic studies on Alzheimer's disease (AD) have incorporated multiple ancestries and omic datasets to pinpoint actionable AD risk effectors for each ancestry. METHODS:Here, we first performed genetic colocalization between molecular phenotypes (proteomics and metabolomics) from two ancestral groups (European [EUR] and African [AFR]) and the two largest EUR AD genome-wide association studies. We next performed pathway enrichment analyses to identify biological mechanisms. RESULTS:We found 21 proteins and one metabolite colocalized with AD risk that were shared between the EUR and AFR ancestry groups. We also identified 25% AFR and 60% EUR proteins; 50% AFR and 10% EUR metabolites were unique. The pathway enrichment analyses nominated interleukin-1 production and lipid pathway were shared underlying proteomic and metabolomic findings, respectively. DISCUSSION:Our findings indicate that these four plasma datasets may pinpoint different effectors of AD risk in diverse populations; findings from AFR participants require validation with AFR-based genome-wide association study data. HIGHLIGHTS:For proteomics, 61% of findings for European (EUR) ancestry and 72% for African (AFR) ancestry were not previously reported. For metabolomics, 83% of findings for EUR ancestry and 50% AFR ancestry were not previously reported. Both convergent and divergent pathways were identified in EUR- and AFR-ancestry stratified analyses in either proteomics or metabolomics findings.

Plasma protein quantitative trait loci (pQTLs) have been integrated with genetic studies to prioritize proteins implicated in numerous human diseases. However, limited interaction between plasma and the central nervous system decreases the fluid's relevance for neurological disease. We compared the pQTL landscapes between plasma and cerebrospinal fluid (CSF), detecting widespread differences across fluids that translate to the identification and prioritization of proteins and pathways implicated in neurological disorders. Of almost 5000 CSF and plasma pQTLs, fewer than 30% were present in both fluids, demonstrating the importance of cross-context analyses to understand genetic regulation of protein abundance. We identified 427 associations between proteins and risk of 14 neurological traits, including 249 associations that were not found in previous studies. Only 69 of the associations were consistently detected in both fluids, demonstrating the information gained through the analysis of multiple bodily contexts. We further demonstrated that CSF proteogenomics captures more substantial disease overlap (for example, between Alzheimer's disease and dementia with Lewy bodies) and captures trait-relevant biology missed in plasma, including cell death and immune response signatures in Alzheimer's and multiple sclerosis. Through this work, we demonstrated the importance of analyzing less accessible but more trait-relevant contexts to fully understand human disease.

INTRODUCTION:Cerebral amyloidosis is a defining feature of Alzheimer's disease (AD), yet the molecular heterogeneity among amyloidbeta-positive (Aβ+) individuals remains poorly defined. We aimed to map the proteomic correlates of cerebral amyloidosis and link them to clinical variability within Aβ+ individuals. METHODS:We integrated quantitative amyloid PET with large-scale plasma proteomics (∼7000 proteins; SomaScan version 4.1) in Knight Alzheimer's Disease Research Center and Bio-Hermes cohorts (n = 1429). Proteome-wide association analyses identified proteins associated with amyloid load, followed by unsupervised clustering and pathway enrichment analyses. RESULTS:We identified 454 amyloid-associated proteins, of which 54 replicated cross-cohort. A derived 54-protein proteomic score correlated with amyloid burden, AD biomarkers, and clinical severity. Pathway analyses of clinically distinct protein clusters revealed coordinated enrichment of intracellular signaling, immune, and proteostasis modules. DISCUSSION:These findings delineate the circulating proteomic signature of cerebral amyloidosis and support plasma proteomics as a complementary approach to phosphorylated tau at threonine 217 and amyloid PET for biological stratification and characterization of disease heterogeneity in AD.

Summary:The X chromosome comprises approximately 5% of the human genome and encodes over 800 protein-coding genes, many of which exhibit sex-differentiated expression patterns due to escape from X chromosome inactivation (XCI) mechanisms. Despite its relevance to sex differences in complex traits, the X chromosome is routinely excluded from genome-wide association studies due to analytical challenges, and when analyzed, the impact of escape from XCI or sex is limitedly explored. No dedicated, publicly accessible browser for X chromosome-wide association study (XWAS) summary statistics currently exists, creating a barrier to systematic investigation of X-linked contributions to human traits. Here, we present geneXplore, an interactive web browser based on the PheWeb2 implementation, tailored for XWAS summary statistics across 1,944 phenotypes while distinguishing random XCI (rXCI), escape from XCI (eXCI), and sex-stratified analyses. Users can explore results via interactive plots (Manhattan and Miami, PheWAS and LocusZoom), searchable tables and access to cross-database lookup, with full summary statistics available for download. Availability and Implementation:geneXplore is freely available at https://genexplore.wustl.edu/ with no registration required and will be maintained for a minimum of two years following publication. Source code is available at https://github.com/Belloy-Lab/geneXplore_XWAS_Browser under an MIT license.

Many non-coding variants influence complex traits and diseases through gene regulation, yet the mechanisms linking these variants to downstream biology remain poorly understood. Here, we present eQTLGen Phase 2, a comprehensive genome-wide analysis of gene expression quantitative trait loci (eQTLs) in 43,301 blood samples from 52 datasets. Beyond local ciseffects, this sample size enabled the first systematic mapping of trans-eQTLs at scale. We identify cis-eQTLs for nearly all expressed genes (94.7%) and trans-eQTLs for over half (56.2%). Second, by colocalizing cis-eQTLs with trans-eQTLs, we infer a directed gene regulatory network comprising 47,554 directed gene regulatory relationships. These networks reveal how genetic perturbations in upstream regulators produce dose-dependent downstream effects, supported by Perturb-seq and ChIP-seq data. Third, integrating this network with 87 genome-wide association studies allows us to systematically prioritize trait-relevant pathways and candidate genes. Variants exerting both cis- and trans-effects are markedly more likely to colocalize with trait associations than cis-only variants, delineating a subset of functionally active cis-eQTLs from a large group with limited downstream impact. This distinction provides a conceptual framework for identifying regulatory variants that truly mediate complex trait biology. Together, these results provide a publicly available resource of cis- and trans-eQTLs and an in vivo scaffold for human gene-regulatory networks, elucidating how propagation of cis-effects modulates complex disease.

BACKGROUND: Individuals with autosomal dominant Alzheimer's disease (ADAD) arising from mutations in PSEN1, PSEN2, or APP exhibit variability in clinical presentation. Genetic studies of ADAD have shaped our understanding of the disease, and the discovery of genetic modifiers can inform therapeutic interventions and improve patient outcomes. We aimed to discover new genetic modifiers in individuals with mutations in the three ADAD genes. METHODS: In this genome-wide association study, we analysed data from participants in three study cohorts (the Knight Alzheimer Disease Research Center [Knight-ADRC], the Dominantly Inherited Alzheimer Network [DIAN] observational study, and the Alzheimer Disease Sequencing Project [ADSP] R4). We did whole-genome sequencing on 101 unrelated, non-Hispanic, White, symptomatic participants with ADAD mutations and 5050 asymptomatic, unrelated control participants. Sensitivity analyses included related participants (148 cases and 5813 controls). We assessed the molecular mechanisms associated with each risk variant, including cis-regulatory effects, plasma protein levels (Knight-ADRC, 2338 participants), CSF concentrations of Alzheimer's disease biomarkers (DIAN, 64 participants), and neuroimaging data (MRI and PET; DIAN, 64 participants). We evaluated the association of risk variants with age at onset in ADAD and in 6177 participants with sporadic Alzheimer's disease (ADSP R5). FINDINGS: Three genome-wide loci with significant risk were associated with ADAD risk, irrespective of the specific ADAD gene mutation. The CNIH4 locus association was driven by a missense variant (is caused by Gly54Ser, p<0·0001, odds ratio [OR] 11·99 [5·39-26·64]). The CCNG1 locus risk allele increased the risk of Alzheimer's disease (p<0·0001, OR 9·56 [4·29-21·24]) and reduced the age at dementia onset (p=0·0068, β=-10·15 [95% CI -17·31 to -2·77]). This allele was also positively associated with Tar DNA binding protein 43 (TDP-43) plasma protein levels and a larger gap between chronological age and structural MRI predicted brain age. The RHOJ risk allele (p<0·0001, OR 5·96 [3·42-10·36]) was associated with increased the risk of Alzheimer's disease, higher CSF total tau (p=0·0056, β=358·37) and phosphorated tau 181 (pTau181; p=0·0006, β=81·28), and lower Aβ42/Aβ40 ratio (p=0·016, β=-0·11) in DIAN ADAD participants, comparing those carrying the risk allele with those not carrying it. INTERPRETATION: Our findings provide potential insights into disease biology, emphasising the role of Aβ, tau, TDP-43, astrocytes, and angiogenesis in Alzheimer's disease aetiology. This study offers invaluable insight for family genetic counselling and future clinical trial designs. FUNDING: National Institute of Health, National Institute on Aging, Alzheimer's Association, Hope Center Pilot 2025 Award, NGI Pilot Grant 2025 Award, BrightFocus Foundation, UK Dementia Research Institute at University College London, UK National Institutes for Health and Care Research University College London Hospitals Biomedical Research Centre, Dominantly Inherited Alzheimer Network, Freedom Together Foundation.

Neurodegenerative diseases (including Alzheimer's disease, Parkinson's disease, Frontotemporal dementia, and Dementia with Lewy bodies) pose diagnostic challenges due to overlapping pathology and clinical heterogeneity. We leveraged proteomic data from more than 21,000 cerebrospinal fluid and plasma samples to develop and validate explainable, boosting-based multi-disease AI classifiers. The models achieved weighted AUCs in the testing datasets of 0.97 for CSF and 0.88 for plasma, equivalent to traditional biomarkers. The model was validated with neuropathological and clinical data, confirming robust generalizability without any retraining. Using zero-shot learning, we classified disease subtypes including autosomal dominant AD and prodromal PD and clarified disease states for those with conflicting clinical information. The model also showed the ability to prioritize cognitively normal individuals at disease risk. This framework enabled the identification and quantification of continuous, individual-level disease probabilities that allow for the quantification of overlap across diseases and co-pathologies within an individual. Through this work, we establish a benchmark computational framework for enhancing diagnostic precision in NDs.

INTRODUCTION:Most genetic studies for Alzheimer's disease (AD) have been focused on late-onset AD (LOAD). There are no large genetic studies on early-onset AD (EOAD). METHODS:We performed a multi-ancestry (non-Hispanic European, African, and East Asian) genome-wide association study (GWAS) including a total of 7,349 cases and 17,887 control. Cases with age at onset younger than 70 years were included. Sensitivity analysis including cases with onset <65 was performed. Only controls older than 70 were included to decrease the risk of developing LOAD. RESULTS:We identified eight novel significant loci: six in the ancestry-specific analyses and two in the trans-ancestry analysis. By integrating gene-based analysis, expression quantitative trait loci (eQTL), protein quantitative trait loci (pQTL), and functional annotations, we nominate eight novel genes that are involved in microglia activation, glutamate production, and signaling pathways. DISCUSSION:EOAD, although sharing genes with LOAD, harbors unique genes and pathways that could be used to create better prediction models or target identification. HIGHLIGHTS:We performed the largest and first multi-ethnic genetic screening for early-onset Alzheimer's disease (AD). We identified eight novel significant loci: six in the ancestry-specific analyses and two in the trans-ancestry analysis. The novel genes are implicated microglia activation, glutamate production, and signaling pathways. EOAD, although sharing many genes with LOAD, harbors unique genes and pathways that could be used to create better prediction models or target identification for this type of AD.

Cerebrospinal fluid (CSF) amyloid beta (Aβ42), total tau (t-tau), and phosphorylated tau (p-tau181) are well accepted markers of Alzheimer's disease. We performed a GWAS meta-analysis including 18,948 individuals of European and 416 non-European ancestry. We identified 12 genome-wide significant loci across all three biomarkers, eight of them novel. We replicated the association of CSF biomarkers with APOE , CR1 , GMNC/CCDC50 and C16orf95/MAP1LC3B . Novel loci included BIN1 for Aβ42 and GNA12, MS4A6A, SLCO1A2 with both t-tau and p-tau181, as well as additional loci on chr. 8, near ANGPT1 and chr. 9 near SMARCA2 . We also demonstrated that these variants were not only associated with CSF level of the three biomarkers but also showed significant association with AD risk, disease progression and/or brain amyloidosis. The associated genes are implicated in lipid metabolism independent APOE , as well as autophagy and brain volume regulation driven by t-tau and p-tau181 dysregulation.

Importance:Age, sex, and apolipoprotein E (APOE) are the strongest risk factors for late-onset Alzheimer disease (AD). The role of APOE in AD varies with sex and ancestry. While the association of APOE with AD biomarkers also varies across sex and ancestry, no study has systematically investigated both sex-specific and ancestry differences of APOE on cerebrospinal fluid (CSF) biomarkers together, resulting in limited insights and generalizability. Objective:To systematically investigate the association of sex and APOE-ε4 with 3 core CSF biomarkers across ancestries. Design, Setting, and Participants:This cohort study examined 3 CSF biomarkers (amyloid β1-42 [Aβ42], phosphorylated tau 181 [p-tau], and total tau, in participants from 20 cohorts from July 1, 1985, to March 31, 2020. These individuals were grouped into African, Asian, and European ancestries based on genetic data. Data analyses were conducted from June 1, 2023, to November 10, 2024. Exposure:Sex (male or female) and APOE-ε4. Main Outcomes and Measures:The associations of sex and APOE-ε4 with biomarker levels were assessed within each ancestry group, adjusting for age. Meta-analyses were performed to identify these associations across ancestries. Sensitivity analyses were conducted to exclude the potential influence of the APOE-ε2 allele. Results:This cohort study included 4592 individuals (mean [SD] age, 70.8 [10.2] years; 2425 [52.8%] female; 119 [2.6%] African, 52 [1.1%] Asian, and 4421 [96.3%] European). Higher APOE-ε4 dosage scores were associated with lower Aβ42 values (β [SE], -0.58 [0.02], P < .001), indicating more severe pathology; these associations were seen in men and women separately and jointly. The association with APOE-ε4 was statistically greater in men (β [SE], -0.63 [0.03]; P < .001) vs women (β [SE], -0.52 [0.03]; P < .001) of European ancestry (P = .01 for interaction). Women had higher levels of p-tau, indicating more severe neurofibrillary pathology. The association between APOE-ε4 dosage and p-tau was in the expected direction (higher APOE-ε4 dosage for higher p-tau values) in both sexes, but the difference between sexes was significant only in those of African ancestry (β [SE], 0.10 [0.18]; P = .57 for men; β [SE], 0.66 [0.17]; P < .001 for women; P = .03 for interaction). Women also had higher levels of total tau, indicating more neuronal damage. The association between APOE-ε4 dosage and total tau was stronger in women than in men in the African cohort (β [SE], 0.20 [0.22]; P = .36 for men and β [SE], 0.65 [0.22], P = .004 for women [P = .16 for interaction]) and European cohort (β [SE], 0.36 [0.03]; P < .001 in women and β [SE], 0.27 [0.03], P < .001 in men [P = .053 for interaction]); no significant associations were found in the Asian cohort. Sensitivity analysis excluding APOE-ε2 carriers yielded similar results. Conclusions and Relevance:In this cohort study, the association of the APOE-ε4 risk allele with tau accumulation was higher in women than in men. These findings underscore the importance of considering sex differences in APOE-ε4's association with AD biomarkers and tau pathology mechanisms in AD. Although this study provides robust evidence of complex interplay between sex and APOE-ε4 for European ancestry, further research is needed to fully understand other ancestry differences.

We leveraged transcriptomic data from 4,343 participants from four independent datasets to robustly identify and annotate circulating PD-associated transcripts. We identified 296 differentially expressed transcripts, 28 of which were transcribed from known PD-associated loci. Further, we found a significant overlap between our findings and transcripts dysregulated in brain, as well as proteins differentially accumulated in CSF. Expression of the identified transcripts was affected by genetic background including ancestry and PD-related mutations, and nearly half of the identified transcripts were dysregulated before symptom onset. The differentially expressed transcripts were utilized to develop three predictive models that distinguished between PD and healthy controls with a ROC AUC of 0.727-0.733. The predictive models were capable of detecting PD transcriptomic signatures even before symptom onset. One transcript, DLD, showed particular promise as an early stage, minimally invasive PD biomarker that was differentially expressed in whole blood, brain and CSF. This transcript significantly related to PD in the eQTL analyses and in two of the three predictive models.

Sex-specific genetic regulation of cerebrospinal fluid (CSF) protein levels may contribute to differential vulnerability to neurodegenerative diseases. To systematically identify sex differences in the genetic regulation of CSF proteome and their link to neurodegeneration, we performed sex-stratified pQTL analysis of 6,361 proteins in 1,713 males and 1,640 females, separately. We identified 1,729 pQTLs significant in either sex. They included 407 sex-specific pQTLs (genetic regulation in only one sex) and 159 sex-biased pQTLs (regulation in both sexes, but with different magnitudes of regulation between sexes). The HLA locus on chromosome 6 and the APOE locus on chromosome 19, two known pleiotropic regions, regulated several proteins in a sex-dependent way. Pathway enrichment revealed several biological processes that were shared and distinctive of sex. Using proteome-wide association study (PWAS) and colocalization, we identified 22 proteins associated and colocalized with AD risk loci. TMEM106B and ACE proteins were identified in only one sex. Four proteins were associated and colocalized with PD risk loci. These findings provide insights into dissecting the underlying mechanisms contributing to sex differences in neurodegeneration.

Age and APOE ε4 are major risk factors for Alzheimer’s disease (AD), while sex differences exist in disease prevalence and progression. Cerebrospinal fluid (CSF) proteomics can provide additional insights into brain aging and AD. To examine proteomic changes due to age, sex and apolipoprotein E (APOE) ε4 along with amyloid status before clinical AD occurs, we profiled 6,175 proteins in the CSF from 994 cognitively normal individuals aged 43–91 years. We identified and replicated 2,172 age-associated, 711 sex-associated, 193 APOE ε4-associated and 1,807 amyloid-associated proteins, with extensive overlap suggesting their interplay. These CSF-specific signatures were distinct from those in plasma. Network analysis revealed two proteomic modules—M2 (age-associated, sex-associated and amyloid-associated) and M6 (age-associated and sex-associated)—which were linked to neuropsychiatric and aging-related diseases. Together, our study provides proteomic changes during the early phase of AD, which may help identify new therapeutic targets of AD. Seo et al. present a cerebrospinal fluid (CSF) proteomic profiling of cognitively normal individuals, identifying age-associated, sex-associated, APOE ε4-associated and amyloid-associated changes. They unveil early Alzheimer’s disease CSF-specific proteomic signatures and potential therapeutic targets.