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Updated: Jan 8, 2026

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Basic Science and Pathogenesis.

Maryam Samieinasab1

  • 1Baylor College of Medicine, Houston, TX, USA.

Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association
|December 24, 2025
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Summary
This summary is machine-generated.

This study used machine learning and proteomic data to identify 35 proteins that distinguish Alzheimer's disease (AD) cases from controls, revealing potential new diagnostic and therapeutic targets for AD.

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Area of Science:

  • Neuroscience
  • Genetics
  • Proteomics

Background:

  • Alzheimer's disease (AD) is a highly heritable neurodegenerative disorder, but genome-wide association studies (GWAS) explain limited heritability.
  • Protein expression, influenced by regulatory effects, may account for unexplained heritability and offer insights into AD biology.

Purpose of the Study:

  • To investigate proteomic data for differences between AD cases and healthy controls.
  • To identify candidate genes and assess their predictive value for AD diagnosis by integrating genetic and proteomic data.

Main Methods:

  • Utilized UK Biobank (UKB) proteomic data (OLINK) for 2,848 proteins from 537 AD cases and matched controls.
  • Applied an ensemble machine learning (ML) approach with nine ML classifiers to rank proteins by their ability to distinguish AD cases from controls.
  • Integrated whole exome sequencing (WES) variants with evolutionary action (EA) scores and protein expression data as training features.

Main Results:

  • Identified 35 proteins, including APOE, NEFL, and GFAP, capable of separating AD cases from controls based on expression alone.
  • Found significant clustering and enrichment of these 35 proteins in AD-related pathways and strong connectivity to established AD gold-standard (GS) genes (AUC=0.94).
  • Identified 153 candidate genes via WES, which showed significant clustering, replicated known AD GWAS hits, and demonstrated connectivity to AD GS genes (AUC=0.78).

Conclusions:

  • Uncovered a potential proteomic architecture underlying Alzheimer's disease.
  • Demonstrated the interplay between genetic variants and protein expression in AD mechanisms and pathways.
  • Suggests identified proteins and genes may influence, regulate, or interact with key AD-related biological processes, advancing discovery of therapeutic targets.