Interactions between genetic polymorphisms of glucose metabolizing genes and smoking and alcohol consumption in the risk of type 2 diabetes mellitus
Bibliographic record
Abstract
The impact of gene-environment interaction on diabetes remains largely unknown. We aimed to investigate if interaction between glucose metabolizing genes and lifestyle factors is associated with type 2 diabetes mellitus (T2DM). Interactions between genotypes of 4 glucose metabolizing genes (MTNR1B, KCNQ1, KLF14, and GCKR) and lifestyle factors were estimated in 722 T2DM patients and 759 controls, using multiple logistic regression. No significant associations with T2DM were detected for the single nucleotide polymorphisms of MTNR1B, KLF14 and GCKR. However, rs151290 (KCNQ1) polymorphisms were found to be associated with risk of T2DM. Compared with AA, the odds ratios (ORs) of AC or CC genotypes for developing T2DM were 1.545 (P = 0.0489) and 1.603 (P = 0.0383), respectively. In stratified analyses, the associations were stronger in smokers with CC than smokers with AA (OR = 3.668, P = 0.013); drinkers with AC (OR = 5.518, P = 0.036), CC (OR = 8.691, P = 0.0095), and AC+CC (OR = 6.764, P = 0.016) than drinkers with AA. Compared with nondrinkers with AA, drinkers who carry AC and CC had 12.072-fold (P = 0.0007) and 8.147-fold (P = 0.0052) higher risk of developing T2DM. In conclusions, rs151290 (KCNQ1) polymorphisms are associated with increased risk of T2DM, alone and especially in interaction with smoking and alcohol.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".