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Record W4406638606 · doi:10.1212/wnl.0000000000210250

Association of Polygenic Risk Score for 5 Diseases With Alzheimer Disease Progression, Biomarkers, and Amyloid Deposition

2025· article· en· W4406638606 on OpenAlexfundno aff
Sadiya Hussainy, Sara Michelle Lee, Gang Wu, Natalie Bautista, Mao Ding, Heming Wang, Bonnie LaFleur, George Perry, Xinglong Wang

Bibliographic record

VenueNeurology · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchNational Institutes of HealthGenentechIXICOH. Lundbeck A/SServierEisaiNorthern California Institute for Research and EducationPfizerNovartis Pharmaceuticals CorporationBiogenEli Lilly and CompanyBristol-Myers SquibbBioClinicaAlzheimer's Disease Neuroimaging InitiativeMeso Scale DiagnosticsNational Institute on AgingAlzheimer's AssociationFoundation for the National Institutes of Health
KeywordsDiseaseMedicineAssociation (psychology)Polygenic risk scoreAmyloid (mycology)Alzheimer's diseaseInternal medicineOncologyPathologyPsychologyBiologyGenotypeGeneticsSingle-nucleotide polymorphismGene

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: Alzheimer disease (AD) is a heterogeneous neurodegenerative disorder influenced by genetic and environmental factors. Conditions such as type 2 diabetes (T2D), cardiovascular disease, obesity, depression, and obstructive sleep apnea (OSA) increase AD risk and progression. This study aimed to examine the genetic predisposition to these conditions and their effect on AD pathophysiology, risk, and progression. METHODS: A retrospective analysis was conducted using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), a North American prospective cohort. Polygenic risk scores (PRSs) for OSA, T2D, coronary artery disease (CAD), major depression, and body mass index (BMI) were generated for 752 non-Hispanic White participants with whole-genome sequencing data. Logistic regression was used to evaluate associations between PRSs and progression from mild cognitive impairment (MCI) to AD. Time to progression across PRS quartiles was analyzed using Cox proportional hazards models. PET amyloid and tau deposition rates, regional neocortical atrophy, and cognitive composite score declines were compared across OSA PRS quartiles using analysis of variance (ANOVA). RESULTS: Among 463 ADNI participants with baseline MCI (mean age 72.6 ± 7.3 years, 43.4% female), the OSA PRS, adjusted for BMI, was significantly associated with MCI-to-AD progression. The highest OSA PRS quartile had an odds ratio of 1.86 (95% CI 1.03-3.37) at 3 years and 2.02 (95% CI 1.16-3.51) at 5 years, compared with the lowest quartile. PRSs for T2D, CAD, major depression, and BMI were not associated with MCI-to-AD progression. Participants in the highest OSA PRS quartile had higher PET amyloid deposition and greater cognitive decline. In 752 participants (mean age 74.1 ± 7.3 years, 43.6% female), OSA PRS was significantly associated with baseline levels of PET amyloid, CSF amyloid-β 42, phosphorylated tau (p-tau), visinin-like protein 1, tumor necrosis factor receptor 1, and plasma neurofilament light after multiple testing adjustments. DISCUSSION: Individuals with high polygenic susceptibility to OSA exhibited an increased risk of MCI-to-AD progression and a higher amyloid deposition rate, suggesting potential modifier effects of OSA or OSA-associated genes on AD progression and pathophysiology. However, the small sample size and lack of objective OSA diagnosis limit interpretation of these genetic effects.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.257
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

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