The association between gene polymorphism of TCF7L2 and type 2 diabetes in Chinese Han population: a meta-analysis. PloS One
Bibliographic record
Abstract
In recent years, it has been widely accepted that transcription factor 7-like 2 (TCF7L2) is associated with type 2 diabetes mellitus (T2DM) in multiple ethnic groups, especially its single nucleotide polymorphisms of rs7903146C/T, rs12255372G/T and rs290487T/C. However, the results previously obtained in Chinese Han population are often inconsistent. For clearing this issue, herein we performed meta-analysis based on the reports that can be found to assess the association. In the meta-analysis, Odds ratio (OR) and 95 % confidence interval (95 % CI) were calculated with random-effect model or fixed-effect model based on the heterogeneity analysis. The quality of included studies was evaluated by using the Newcastle-Ottawa Scale. The sensitivity analysis was used to confirm the reliability and stability of the meta-analysis. In total, 20 case-control studies with 9122 cases of T2DM and 8017 controls were included. Among these case-control studies, we selected 13 ones on rs7903146 C/T, 5 ones on rs12255372 G/T, 8 ones on rs290487 T/C. The results indicated that rs7903146C/T polymorphism was significantly associated with T2DM (T vs. C, OR = 1.73, 95 % CI = 1.39–2.16). There was no evidence that rs12255372G/T and rs290487T/C polymorphisms increased T2DM risk (T vs. G, OR = 1.77, 95 % CI = 0.88–3.56; C vs. T, OR = 1.08, 95 % CI = 0.93–1.25). Subgroup analysis of different regions proved the relationship between rs7903146C/T polymorphism and T2DM risk in both the northern and the southern China. The association of rs290487 with T2DM was affected by body mass index, whereas the association of rs7903146 and rs290487 with T2DM was influenced neither by age
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.044 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".