Evaluating vitamin C-related gene-environment and metabolite-environment interaction effects on intraocular pressure in the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
High intraocular pressure (IOP) is an important risk factor for glaucoma, which is influenced by genetic and environmental factors. However, the etiology of high IOP remains uncertain. Metabolites are compounds involved in metabolism which provide a link between the internal (genetic) and external environments. O-methylascorbate has been reported to be associated with IOP. In addition, researchers have identified several genetic variants which are associated with metabolite concentrations, including O-methylascorbate and another vitamin C related metabolite, ascorbic acid 2-sulfate. We aimed to understand how O-methylascorbate and ascorbic acid 2-sulfate, or genetic variants associated with these metabolites, modify the associations between dietary environmental variables and IOP. We used data from 8060 participants of the Canadian Longitudinal Study on Aging. Using linear models adjusted for relevant covariates, we tested for interactions between six genetic variants previously found to be associated with O-methylascorbate and ascorbic acid 2-sulfate and four environmental variables related to diet (alcohol consumption frequency, smoking status, fruit consumption, and vegetable consumption). We also tested for interactions between serum concentrations of O-methylascorbate and ascorbic acid 2-sulfate and these environmental factors. We used a False Discovery Rate approach to correct for the 32 interaction tests performed. One interaction was suggestively significant after multiple testing correction (adjusted P-value < 0.1): rs8050812 and alcohol consumption frequency. Understanding how genetic variants and metabolites interact with the environment could shed light on biological pathways controlling IOP and lead to improved prevention and treatment of glaucoma.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".