ArticleAlzheimer's & dementia : the journal of the Alzheimer's Association2026
Integrating polygenic and transcriptional risk scores for detecting Alzheimer's disease.
Article in Alzheimer's & dementia : the journal of the Alzheimer's Association, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Artificial Intelligence and Genomic Data Analysis: New Frontiers in Precision Medicine.International journal of molecular sciences · 2026Review
- Integrating polygenic and transcriptional risk scores for detecting Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
introductionEarly detection of Alzheimer's disease (AD) is essential, yet existing biomarkers are invasive or costly. Polygenic risk scores (PRS) and transcriptional risk scores (TRS) may offer accessible alternatives, but their combined predictive performance remains understudied.
methodsWe calculated PRS and TRS using genome-wide genotype and blood transcriptome data from two ancestrally distinct cohorts: Alzheimer's Disease Neuroimaging Initiative (ADNI, N = 313) and Seoul National University Bundang Hospital (SNUBH, N = 173). Logistic regression and machine learning models assessed associations of PRS and TRS with AD and AD classification performance.
resultsIndividuals with high PRS and TRS values showed larger odds ratios for AD, 2.5-fold in ADNI and 3.4-fold in SNUBH, compared to those with low PRS and TRS values. The integrated PRS-TRS model achieved better classification performance (area under the curve [AUC] 0.705) than the PRS model (AUC 0.635). DISCUSSION: Integrating static genetic and dynamic transcriptomic information from blood improves early detection of AD across diverse populations.
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Registered trials
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 Socratic graph.