Correlation between leptin receptor gene polymorphism and type 2 diabetes in Chinese population: a meta-analysis
Bibliographic record
Abstract
Objective To evaluate the correlation between receptor (LEPR) polymorphism and 2 (T2DM) in Chinese population. Methods The literature concerning the correlation between LEPR polymorphism and in Chinese population were searched from Chinese databases (CNKI, VIP, WanFang, CBM) with leptin receptor gene and type 2 diabetes as keywords, and from English databases (PubMed, Web of Knowledge, EBSCO) with leptin receptor gene, LEPR, OBR, OB-R, type 2 diabetes and T2DM as keywords. The relevant articles were searched up to September 20, 2014. Then, meta-analysis was performed using RevMan 5.1 and Stata 11.0 software. The Newcastle-Ottawa Scale was applied to assess methodological quality of included articles from 3 aspects, namely, selection of participants, comparability and outcome assessment. Results Seventeen case-control studies involving 12 533 cases of and 3348 controls were included in Meta-analysis. A significant correlation was found between rs1137100 polymorphism in LEPR and (for recessive genetic model: OR=0.67, 95%CI 0.52-0.88, P=0.00; for allele contrast genetic model: OR=1.46, 95%CI 1.15-1.85, P=0.00). A strong correlation was also found between rs1137101 polymorphism and (for additive genetic model: OR=1.54, 95%CI 1.20-1.98, P=0.00; for allele contrast genetic model: OR=1.15, 95%CI 1.01-1.30, P=0.00). In addition, rs1805096 polymorphism was closely correlated with (for dominant genetic model: OR=1.32, 95%CI 1.07-1.62, P=0.00; for recessive genetic model: OR=1.30, 95%CI 1.09-1.54, P=0.00; for allele contrast genetic model: OR=0.67, 95%CI 0.59-0.75, P=0.00). Conclusions There is a significant correlation between rs1137100, rs1805096 of LEPR and in Chinese population under allele contrast genetic model as well as in recessive genetic model. Rs1137101 of LEPR is closely correlated with in Chinese population under additive genetic model. For dominant genetic model, rs1805096 of LEPR is correlated significantly with in Chinese population. The allele A carriers in Chinese population are at a high risk of 2 diabetes. DOI: 10.11855/j.issn.0577-7402.2015.10.08
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.041 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 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".