Effect modification by sex of genetic associations of vitamin C related metabolites in the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
Abstract Vitamin C is an essential dietary factor. There have been observed sex differences in serum vitamin C concentrations but the reason for this is not fully known. To understand how environmental factors like vitamin C intake interact with molecular processes, levels of metabolites can be used. Two metabolites associated with vitamin C are O-methylascorbate and ascorbic acid 2 sulfate. Past research has found there are genetic factors that influence these metabolite levels. Here, we aimed to investigate if there is effect modification by sex of these gene-metabolite associations and characterize the biological function of these interactions. We included individuals of European descent from the Canadian Longitudinal Study on Aging with available genetic and metabolic data (n= 9004). We conducted a genome wide association study with and without a sex interaction using mixed linear models. We also investigated the biological function of the important gene-sex interactions found for each metabolite. Two genome-wide statistically significant (p-value < 5×10 -8 ) interaction effects and several suggestive (p-value < 10 -5 ) interaction effects were found. The suggestive interaction associations were mapped to several genes including HSD11B2 , which is associated with sex hormones and AGRP that helps initiate hunger drive. By understanding the genetic factors that impact metabolites associated with vitamin C, we better understand its function in disease risk. In addition, highlighting genetic markers and genes whose effects are modified by sex can help to understand the mechanisms behind sex differences in vitamin C levels and guide further research.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".