ArticleAlzheimer's & dementia : the journal of the Alzheimer's Association2025
Novel set of plasma proteins classifies Alzheimer's dementia in African American individuals with high accuracy.
Article in Alzheimer's & dementia : the journal of the Alzheimer's Association, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Deep Plasma Proteomics-Based Diagnostic Panel for Early Detection of Amnestic Mild Cognitive Impairment.Journal of proteome research · 2026Article
- Article
- A diagnostic plasma omics-biomarker for Alzheimer's disease informed by microglial single-cell transcriptomics: A pilot study.bioRxiv : the preprint server for biology · 2026Article
- Novel set of plasma proteins classifies Alzheimer's dementia in African American individuals with high accuracy.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- A diagnostic plasma omics-biomarker for Alzheimer's disease informed by microglial single-cell transcriptomics: A pilot study.Alzheimer's & dementia (New York, N. Y.)Article
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Authors and funding
22 authors.
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Abstract
introductionAfrican American (AA) individuals are underrepresented in biomarker studies for Alzheimer's disease (AD). Biomarkers that reflect the heterogeneity of AD and achieve the greatest accuracy across populations are sorely needed.
methodsUntargeted proteome measurements were obtained using the SomaScan 7k platform to identify novel plasma biomarkers for AD in AA participants with clinical diagnoses of AD dementia (n = 181) and cognitively unimpaired (CU, n = 142). Machine learning was used to identify a set of plasma proteins that yielded the best classification accuracy.
resultsA set of 36 proteins achieved an area under the curve (AUC) of 0.94 to classify AD dementia versus CU, a 16% improvement over age, sex, and apolipoprotein E (APOE). This finding was replicated in multiple plasma and brain datasets (AUCs 0.73-0.97). Our findings underscore the importance of matrisome and cerebrovascular dysfunction in AD pathophysiology. DISCUSSION: This study demonstrates the potential of biomarker discovery through untargeted plasma proteomics and machine learning. HIGHLIGHTS: Conducted large-scale plasma proteomics in Alzheimer's disease (AD) versus cognitively unimpaired controls. Machine learning biomarker discovery was replicated in an independent cohort. Novel set of proteins distinguishes AD versus controls with high accuracy (area under the curve [AUC] = 0.94). Achieved reproducibility across multiple replication cohorts (AUC = 0.73-0.97). Network analyses implicates matrisome biology and cerebrovascular dysfunction.
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