Sex-specific associations between type 2 diabetes incidence and exposure to dioxin and dioxin-like pollutants: a meta-analysis
Bibliographic record
Abstract
Abstract The relationship between persistent organic pollutants (POPs), including dioxins and dioxin-like polychlorinated biphenyls (DL-PCBs), and diabetes incidence in adults has been extensively studied. However, significant variability exists in the reported associations both between and within studies. Emerging data from rodent studies suggest that dioxin exposure disrupts glucose homeostasis in a sex-specific manner. Thus, we performed a meta-analysis of relevant epidemiological studies to investigate whether there are sex-specific associations between dioxin or DL-PCB exposure and type 2 diabetes incidence. Articles were organized into the following subcategories: data stratified by sex (16%), unstratified data (56%), and data from only 1 sex (16% male, 12% female). We also considered whether exposure occurred either abruptly at high levels through a contamination event (“disaster exposure”) or chronically at background levels (“non-disaster exposure”). Only 8 studies 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, the summary odds ratio (OR) for increased diabetes risk was similar between females and males (1.78 and 1.95, respectively) when comparing exposed to reference populations, suggesting that this relationship is not sex-specific. However, when we considered disaster-exposed populations separately, the association differed substantially between sexes, with females showing a much higher OR than males (2.86 and 1.59, respectively). Moreover, the association between dioxin/DL-PCB exposure and diabetes was stronger for females than males in disaster-exposed populations. In contrast, both sexes had significantly increased ORs in non-disaster exposure populations and the OR for females was lower than males (1.40 and 2.02, respectively). Our review emphasizes the importance of considering sex differences, as well as the mode of pollutant exposure, when exploring the relationship between pollutant exposure and diabetes in epidemiological studies.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Meta-analysis of dioxin exposure and diabetes risk by sex; it notes how often studies stratify by sex, but the knowledge produced is about an exposure-disease association.
This meta-analysis answers a substantive epidemiological question about pollutants and diabetes rather than studying evidence synthesis methods.
Meta-analysis answering whether dioxin exposure associates with diabetes by sex; synthesis used for a health question.
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.057 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".