Sex-specific Associations Between Type 2 Diabetes Incidence and Exposure to Dioxin and Dioxin-like Pollutants: A Meta-analysis
Bibliographic record
Abstract
The potential for persistent organic pollutants (POPs), including dioxins and dioxin-like polychlorinated biphenyls (DL-PCBs), to increase the risk of incident diabetes in adults has been extensively studied. However, there is substantial variability in the reported associations both between and within studies. Emerging data from rodent studies suggest that dioxin disrupts glucose homeostasis in a sex-specific manner. Thus, we performed a review and meta-analysis of relevant epidemiological studies to investigate sex differences in associations between dioxin or DL-PCB exposure and type 2 diabetes incidence. Articles that met our selection criteria (n = 81) were organized into the following subcategories: data stratified by sex (n = 13), unstratified data (n = 45), and data from only 1 sex (n = 13 male, n = 10 female). We also considered whether exposure occurred either abruptly at high concentrations through a contamination event ("disaster exposure") or chronically at low concentrations ("non-disaster exposure"). There were 8 studies that compared associations between dioxin/DL-PCB exposure and diabetes risk in males versus females within the same population. When all sex-stratified or single-sex studies were considered in the meta-analysis (n = 18), the summary relative risk (RR) for incident diabetes among those exposed relative to reference populations was 1.78 (95% CI = 1.37-2.31) and 1.95 (95% CI = 1.56-2.43) for female and males, respectively. However, when we restricted the meta-analysis to disaster-exposed populations, the RR was higher in females than males (2.86 versus 1.59, respectively). In contrast, in non-disaster exposed populations the RR for females was lower than males (1.40 and 2.02, respectively). Our meta-analysis suggests that there are sex differences in the associations between dioxin/DL-PCBs exposure and incident diabetes, and that the mode of exposure modifies these differences.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.066 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".