{"doi":"10.1101/2025.10.02.25337067","title":"Sex and insulin resistance biomarker modelling in a new large-scale Alzheimer’s disease transcriptomic resource","abstract":"Abstract Background The incidence of Alzheimer’s disease (AD) increases with age, is associated with insulin resistance (IR), and has a greater prevalence in women. Genome-wide technologies can yield novel biomarkers and may help identify aspects of AD pathophysiology. Individual AD blood transcriptomic studies are small, preventing the study of sex, while meta-analyses are technically challenging. Relationships between AD biomarkers and IR are also underexplored, largely due to a lack of metabolic phenotyping in AD cohorts. Methods We generated 1,021 new whole-blood transcriptomic profiles from the AddNeuroMed cohort (including 317 technical replicates) and 410 whole-blood transcript profiles (metabolic cohort). We aligned this data, and our large AD RNA-seq whole-blood transcriptome study, to a common genomic and transcriptomic reference and modelled blood cell composition. Bias was assessed using randomly sampled gene-sets and cross-validated classifiers. Further, a 62-gene IR signature was generated using our metabolic cohort studies, providing a surrogate IR RNA score to retrospectively phenotype the AD cohort. Machine learning was used to develop AD classifiers from the data and evaluate multimodal integration with magnetic resonance imaging. Sex-stratified differential expression and pathway analysis were used to explore sex differences in AD. Results The new data was more robust than the original AddNeuroMed data, with a lower ‘sampling at random’ score (AUC=0.61 vs 0.73-0.79). A novel AD classification signature was cross- and externally evaluated, achieving higher performance in women. Identification of AD-associated disease pathways, in whole-blood, was influenced by variation in blood cell composition. Notably, B-cell pathways were modified in AD (including genes BLNK and MS4A1 ), and this was relatively consistent across sexes and ethnicity. Previous reports that mitochondrial-DNA-encoded RNAs were upregulated, and nuclear-encoded mitochondrial transcripts were consistently downregulated, were not substantiated, with only women showing modest evidence for loss of nuclear-encoded mitochondrial transcripts. Conclusions We provide a large-scale, technically robust blood AD transcriptomic dataset that enhances legacy AD resources. Analysis revealed robust immune signatures and the statistical transfer of a classification signature across technologies and ethnicities. We add to the evidence for a role of altered B-cell biology in AD, while delivering an updateable transcriptomic resource for future machine learning and genomic studies.","journal":"medRxiv","year":2025,"id":576762,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9344,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1485599,"name":"Martin Miller","orcid":"0009-0004-6909-9894","position":1,"is_corresponding":false},{"id":1485600,"name":"Hannah Crossland","orcid":"0000-0002-9009-3049","position":2,"is_corresponding":false},{"id":1250501,"name":"Jalil-Ahmad Sharif","orcid":null,"position":3,"is_corresponding":false},{"id":279165,"name":"Claes Wahlestedt","orcid":"0000-0003-4471-5916","position":5,"is_corresponding":false},{"id":1485601,"name":"Kirill Shkura","orcid":"0000-0002-1722-0910","position":6,"is_corresponding":false},{"id":733967,"name":"Claude‐Henry Volmar","orcid":"0000-0001-9437-051X","position":7,"is_corresponding":false},{"id":674878,"name":"James A. Timmons","orcid":"0000-0002-2255-1220","position":9,"is_corresponding":false},{"id":906792,"name":"J. Paul Chapple","orcid":"0000-0003-1876-1505","position":10,"is_corresponding":false},{"id":1486018,"name":"William E Kraus","orcid":null,"position":11,"is_corresponding":false},{"id":1463652,"name":"Anthony J Griswold","orcid":null,"position":13,"is_corresponding":false},{"id":1168455,"name":"Greg Slabaugh","orcid":"0000-0003-4060-5226","position":15,"is_corresponding":false},{"id":1485598,"name":"N Ismail","orcid":"0009-0008-6711-5159","position":0,"is_corresponding":true}],"reference_count":88,"raw_metadata":null,"created_at":"2026-07-19T02:58:00.620755Z","pmid":"41256143","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}