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Record W4320487388 · doi:10.1016/j.xops.2023.100287

No Strong Association between the Apolipoprotein E E4 Allele and Glaucoma

2023· article· en· W4320487388 on OpenAlexfundaboutno aff
Sean Mullany, Santiago Díaz‐Torres, Joshua M. Schmidt, Daniel Thomson, Ayub Qassim, Henry Marshall, Lachlan S.W. Knight, Ella C. Berry, Antonia Kolovos, David P. Dimasi, Stewart Lake, Richard Mills, John Landers, Paul Mitchell, Paul R. Healey, Toby Commerford, Sonja Klebe, Emmanuelle Souzeau, Mark Hassall, Stuart MacGregor, Puya Gharahkhani, Owen M. Siggs, Jamie E. Craig

Bibliographic record

VenueOphthalmology Science · 2023
Typearticle
Languageen
FieldMedicine
TopicGlaucoma and retinal disorders
Canadian institutionsnot available
FundersNational Health and Medical Research CouncilCanadian Institutes of Health ResearchMedical Research CouncilSnow MedicalFlinders FoundationMoorfields Eye CharityMoorfields Eye Hospital NHS Foundation TrustMacula SocietyNational Institute for Health and Care ResearchCanada Foundation for InnovationGovernment of CanadaNorthwest Regional Development AgencyInternational Glaucoma AssociationScottish GovernmentBritish Heart FoundationWellcome TrustHospital Research Foundation
KeywordsApolipoprotein EAlleleAssociation (psychology)GeneticsGlaucomaMedicineOphthalmologyBiologyInternal medicinePsychologyGeneDisease

Abstract

fetched live from OpenAlex

Purpose To elucidate a potential association between the apolipoprotein E ( APOE ) E4 allele and glaucoma prevalence in large cohorts. Design A cross-sectional analysis of baseline and prospectively collected cohort data. Participants UK Biobank (UKBB) participants of genetically determined European ancestry (n = 438 711). Replication analyses were performed using clinical and genotyping data collected from European participants recruited to the Canadian Longitudinal Study of Aging (CLSA; n = 18 199), the Australian and New Zealand Registry of Advanced Glaucoma (ANZRAG; n = 1970), and the Blue Mountains Eye Study (BMES; n = 2440). Methods Apolipoprotein E alleles and genotypes were determined, and their distributions were compared on the basis of glaucoma status. Similar analyses were performed using positive control outcomes associated with the APOE E4 allele (death, dementia, age-related macular degeneration) and negative control outcomes not associated with the APOE E4 allele (cataract, diabetic eye disease). Outcome phenotypes were also correlated with Alzheimer's dementia (AD), a clinical outcome highly associated with the APOE E4 allele. Main Outcome Measures Results of APOE E4 genotype-phenotype comparisons were reported as odds ratios (ORs) with 95% confidence intervals (CIs). Replication analyses investigated APOE E4 associations in 2 replication cohorts (CLSA and ANZRAG/BMES). Results The APOE E4 allele was inversely associated with glaucoma (OR, 0.96; 95% CI, 0.93–0.99; P = 0.016) and both negative controls (cataract: OR, 0.98; 95% CI, 0.96–0.99; P = 0.015; diabetic eye disease: OR, 0.92; 95% CI, 0.87–0.97; P = 0.003) in the UKBB cohort. A paradoxical positive association was observed between AD and both glaucoma (OR, 1.30; 95% CI, 1.08–1.54; P < 0.01) and cataract (OR, 1.15; 1.04–1.28; P = 0.018). No association between the APOE E4 allele and glaucoma was observed in either replication cohort (CLSA: OR, 1.03; 95% CI, 0.89–1.19; P = 0.66; ANZRAG/BMES: OR, 0.97; 95% CI, 0.84–1.12; P = 0.65). Conclusions A small negative association observed between APOE E4 and glaucoma within the UKBB was not evident in either replication cohort and may represent an artifact of glaucoma underdiagnosis in APOE E4 carriers. Financial Disclosure(s) The author(s) have no proprietary or commercial interest in any materials discussed in this article.

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)0.005
metaresearch head score (Gemma)0.013
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 score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.020
GPT teacher head0.302
Teacher spread0.281 · 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

Citations4
Published2023
Admission routes2
Has abstractyes

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