Association between Dichlorodiphenyldichloroethylene in the Serum and Adipose Tissue with Type 2 Diabetes: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
The number of diabetics is growing every day. In addition to the main risk factors of type 2 diabetes (obesity, age and etc.) other environmental risk factors such as persistent organic pollutants are also considered. Dichlorodiphenyldichloroethylene is one of the persistent organic pollutants which are produced by the pesticide DDT metabolized and its effects of type 2 diabetes are taken into consideration of many investigators. Inconsistency in the results caused to try to achieve a detailed assessment of the effect of dichlorodiphenyldichloroethylene on type 2 diabetes by systematic review and meta-analysis. In this review study after a systematic review, finally 6 prospective and 7 cross-sectional studies were obtained. After approving the heterogeneity of the studies based on random effect model, it was calculate the mean of odds ratio. Meta-analysis of the pooled random effects of Dichlorodiphenyldichloroethylene was done with type 2 diabetes in prospective and cross-sectional studies. In prospective studies, the highest and lowest odds ratio was 6.1(95%CI: 1.36-27.27) and 1.01(95%CI: 0.59-1.70) and cross-sectional studies was 5.5(95%CI: 1.2-25.10) and 0.61(95%CI: 0.32-1.15), respectively. In the all studies (sum of prospective and cross-sectional), in the random effect model odds ratio was 1.52 (95%CI: 1.26-1.84) and heterogeneity was 52.1% (ρheterogeneity=0.009). Increasing concentration of Dichlorodiphenyldichloroethylene in the serum and adipose tissue increased the risk of type 2 diabetes significantly (P<0.001). The results of this review study support the role of dichlorodiphenyldichloroethylene as an environmental risk factor for type 2 diabetes.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.033 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".