Evidence map›Paper›PMID 41799762›Full record

ArticleInternational journal of medical sciences2026

Sex-Specific Prediction Models of Alzheimer's Disease: A Gene Expression Analysis.

Xiaomeng Ma, Abdilahi Abdi Ibrahim, Lili Ma, Xueying Ma, Zhan Ma, Yingying Liu, Donghong Li, Jia Liu, Xiaofeng Xu, Huimin Dong and 2 more

Abstract read
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Article in International journal of medical sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

12 authors.

Xiaomeng MaDepartment of Neurology, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.
Abdilahi Abdi IbrahimDepartment of Neurology, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.
Lili MaDepartment of Neurology, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.
Xueying MaDepartment of Neurology, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.
Zhan MaDepartment of Neurology, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.
Yingying LiuDepartment of Neurology, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.
Donghong LiDepartment of Neurology, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.
Jia LiuDepartment of Neurology, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.
Xiaofeng XuDepartment of Neurology, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.
Huimin DongDepartment of Laboratory Medicine, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
Xiaohong ChenDepartment of Neurology, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.
Fuhua PengDepartment of Neurology, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Alzheimer's disease (AD) exhibits sex-specific molecular signatures that may improve diagnostic precision. We aimed to identify and validate male- and female-specific blood and brain gene expression biomarkers for AD prediction. We analyzed four GEO datasets (blood- and brain-derived) using limma and Fisher's meta-analysis to identify sex-specific differentially expressed genes, assessed age associations via linear regression, and constructed 10-fold cross-validated logistic regression models. After performing a meta-analysis, 74 differentially expressed genes were identified in the female cohort and 89 DEGs were screened in the male cohort. ERH and MRPS33 were identified as the most relevant genes in the male cohort, and NDUFA1 and NDUFS5 were screened in the female cohort. The identified genes were downregulated in AD samples compared to controls. Both male-specific and female-specific prediction models achieved an AUC of above 0.7 in two external validation blood-derived datasets as well entorhinal cortex dataset. Paradoxically, qPCR showed significant upregulation of all four genes in the AD group compared to the control group.

Indexed as

Alzheimer DiseaseAgedAged, 80 and overBiomarkersBrainFemaleGene Expression ProfilingHumansMalePrediction AlgorithmsSex FactorsBiomarkersAlzheimer's diseasebioinformaticsgene expressionsex-specific

Identifiers

PMID41799762
PMCPMC12964574

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.