The relationship between dietary exposure to persistent organic pollutants from fish consumption and type 2 diabetes among First Nations in Canada
Bibliographic record
Abstract
OBJECTIVE: We previously examined the associations between dietary dichlorodiphenyldichloroethylene (DDE) and polychlorinated biphenyls (PCBs) intake from fish consumption and type 2 diabetes (T2D) prevalence in Ontario and Manitoba. This study aims to further explore the relationship in a regionally representative sample of First Nations adults living on-reserve across Canada. METHODS: Dietary, health and lifestyle data collected by the cross-sectional First Nations Food, Nutrition and Environment Study (2008-2018) were analyzed. This participatory study included 6091 First Nations adult participants who answered questions on T2D. The consumption of locally caught fish was estimated with a food frequency questionnaire. A total of 551 samples from 96 fish species were collected and analyzed for the presence of DDE and PCBs. The associations between fish and dietary DDE/PCBs intake with self-reported T2D were investigated using multiple logistic regression models adjusted for confounders. RESULTS: Dietary exposure to DDE (>2.11 ng/kg/bw) and PCBs (>1.47 ng/kg/bw) vs no exposure was positively associated with T2D with ORs of 2.33 (95% CI: 1.24-4.35) for DDE and 1.43 (95% CI: 1.01-3.59) for PCBs. The associations were stronger among females (DDE OR = 3.11 (1.41-6.88); PCBs OR = 1.76 (1.10-3.65)) and older individuals (DDE OR = 2.64 (1.12-6.20); PCBs OR = 1.44 (1.01-3.91)) as compared with males and younger participants. Also, significant dose-response relationships were found for fish consumption in females only. CONCLUSION: This study confirms our previous findings that dietary DDE/PCBs exposure may increase the risk of T2D. The effect of DDE/PCBs from fish consumption is driven by geographical differences in DDE/PCBs concentrations in fish and by the amount of fish consumed, and is more prominent in females than in males.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".