Genetics and Age-Related Eye Disease Study Formulation Interaction in Neovascular Age-Related Macular Degeneration
Bibliographic record
Abstract
Purpose: This work aims to determine whether previously defined genotype risk groups interact with Age-Related Eye Disease Study formulation (AREDS-F) use in progression to neovascular age-related macular degeneration (nvAMD). Methods: We conducted a case-only study of 265 nvAMD patients. Patients were anonymously genotyped at the complement factor H and age-related maculopathy susceptibility 2 loci and segregated into genotype groups (GTGs) defined by specific combinations of risk alleles. Physicians, who were blind to patients’ GTGs, obtained patients’ AREDS-F use history. The facility performing genetic analysis was blind to the AREDS-F use history. We used logistic analysis to estimate the interaction coefficient between AREDS-F use and GTG 2 vs GTG 3 in a general-population model. Results: The odds ratio of numbers of patients reporting prior AREDS-F use to nonuse for GTG 2 vs GTG 3 was 4.18 ( P = .001). Logistic regression, correcting for nongenetic risk factors, gave an estimate of the β for interaction of AREDS-F with genotype of 1.57 ( P = .001). This estimates a corrected odds ratio associated with the effect of interaction of 4.81 between those in GTG 2 compared with those in GTG 3. Conclusions: Our data indicate an interaction between GTGs and AREDS-F use that is consistent in size and direction with previously published reports, which had found that using AREDS-F supplements significantly increases the risk of nvAMD for some users and significantly protects other users.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".