Genetic variants and type 2 diabetes in India: a systematic review and meta-analysis of associated polymorphisms in case-control studies
Bibliographic record
Abstract
Background India, with the largest population and second-highest type 2 diabetes mellitus (T2DM) prevalence, presents a unique genetic landscape. This study explores the genetic profiling of T2DM, aiming to bridge gaps in existing research and provide insights for further explorations. Methods We conducted a systematic review and meta-analysis of literature published up to September 2024 using databases like PubMed, Web of Science, Scopus, and Google Scholar to identify SNPs associated with T2DM in case–control studies within the Indian population. Data extraction followed a rigorously designed checklist independently verified by two reviewers. The quality of the studies assessed by utilizing Newcastle Ottawa scale, and heterogeneity through Cochran's Q, τ 2 , H 2 and I 2 statistics. Fixed effect and random effect model was employed for meta-analysis based on heterogeneity, and publication bias was assessed by funnel plot analysis, Egger's and Begg's statistical test. In SNPs with adequate studies meta-regression was used to assess source of heterogeneity. Statistical analyses were performed using Stata 18.0 software. Findings Our search identified 1309 articles, with 67 included in the systematic review and 35 in the meta-analysis. These 67 case–control studies, involving 33,407 cases and 30,762 controls, analyzed 167 SNPs across 61 genes. Of these, 89 SNPs mapped to 46 genes showed significant associations with T2DM risk ( P < 0.05), including 67 linked to increased risk and 16 with protective effects. Geographical analysis highlighted inter- and intra-regional variations. Meta-analysis of 25 SNPs revealed 12 SNPs with high T2DM risk compatibility. TCF7L2 gene exhibited a strong compatibility with an overall OR of 1.44 (95% CI 1.36–1.52) and S-value 112.41, while TCF7L2 variants rs7903146 and rs12255372, with OR 1.56 (95% CI 1.43–1.66) and S-value 89.036, OR of 1.36 (95% CI 1.17–1.35) with an S - value of 15.45 respectively. Interpretation Our study highlights the importance of considering the diverse ethnic groups of India for development of targeted and effective T2DM management strategies. Funding Department of Biotechnology (DBT) and Indian Council of Medical Research (ICMR), Government of India.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".