Organic Pollutants and Risk of Type 2 Diabetes: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Objective: To evaluate the associations between organic pollutants (OPs) and risk of type 2 diabetes (T2D). Patients and Methods: We searched Medline, Embase, Scopus, Web of Science, and Cochrane Central from inception through March 18, 2024. We included studies reporting the adjusted or unadjusted association between serum concentration of OPs and risk of T2D. We excluded studies on type 1 diabetes, self-reported exposure, and if fewer than 100 T2D cases. We classified OPs using 2 classification methods and reported pooled risk estimates using a random-effects model (odds ratio [95% CI]) and assessed risk of bias at the levels of OPs and their classes. We conducted sex- and concentration-stratified analyses. Results: From 20,531 articles, we included 44 (0.2%) studies of 83 individual and 38 combination OPs in 54,967 participants. All but 1 study had low risk of bias. Ten of 12 OP classes were associated with risk of T2D, polychlorinated dibenzo-p-furans had the highest association (OR, 2.54; 95% CI, 1.94-3.33). Polychlorinated dibenzo-p-dioxins showed a significant association in men (OR, 3.21; 95% CI, 1.81-5.71). Polychlorinated biphenyls (OR, 1.72; 95% CI, 1.55-1.92) and dichlorodiphenyltrichloroethane (DDT) and DDT-like compounds (OR, 1.14; 95% CI, 1.01-1.29) showed a significant association in women. Moreover, 28 (33.7%) individual and 21 (55.3%) combination OPs had a significant association. Polychlorinated biphenyl 157 (OR, 1.93; 95% CI, 1.27-2.92) and organochlorine pesticides (OR, 4.35; 95% CI, 1.90-9.98) had the highest risk of T2D. Conclusion: Several OPs were associated with higher risk of T2D. Future work should evaluate the concentration threshold at which OPs increase risk to inform both T2D screening and OP advisories and regulation.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.033 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".