Association of PON2 Gene Polymorphisms (Ser311Cys and Ala148Gly) With the Risk of Developing Type 2 Diabetes Mellitus in the Chinese Population
Bibliographic record
Abstract
Background: The association between paraoxonase 2 (PON2) gene polymorphisms and type 2 diabetes mellitus (T2DM) has been extensively investigated in the Chinese population with conflicting results. In this study, we systemically evaluated the association between PON2 Ser311Cys and Ala148Gly polymorphisms and T2DM risk by pooling all relevant studies. Methods: We searched the PubMed, Embase, CNKI and Wanfang databases for studies. The strength of association was determined in the allelic, homozygous, heterozygous, recessive and dominant genetic models and measured as odds ratio (OR) and 95% confidence interval (CI), under fixed- or random-effect models. Results: There was no significant association between PON2 Ser311Cys polymorphism and T2DM under all genetic models: allelic (OR = 1.06, 95% CI = 0.77-1.45; P = 0.721), heterozygous (OR = 1.13, 95% CI = 0.87-1.45; P = 0.362), dominant (OR = 1.10, 95% CI = 0.80-1.51; p = 0.562), recessive (OR = 0.87, 95% CI = 0.48-1.58; P = 0.648), homozygous (OR = 0.94, 95% CI = 0.47-1.89; P = 0.865). Similarly, no significant association was found for PON2 Arg148Gly polymorphism in all models: allelic (OR = 1.17, 95% CI = 0.91-1.50; P = 0.218), heterozygous (OR = 1.28, 95% CI = 0.94-1.74; P = 0.117), dominant (OR = 1.25, 95% CI = 0.93-1.67; P = 0.142), recessive (OR = 0.99, 95% CI = 0.52-1.88; P = 0.973), homozygous (OR = 1.08, 95% CI = 0.57-2.07; P = 0.08). Conclusions: The PON2 Ser311Cys and Ala148Gly polymorphisms were not associated with the risk of T2DM in the Chinese population.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".