Environmental contaminants and the disproportionate prevalence of type-2 diabetes mellitus among Indigenous Cree women in James Bay Quebec, Canada
Bibliographic record
Abstract
Indigenous populations are disproportionately affected by type 2 diabetes (T2DM) compared to non-Indigenous people. Of importance, the prevalence of T2DM is greater amongst females than males in First Nations communities, in contrast to higher male prevalence reported in non-Indigenous Canadians. Therefore, in this study we extend our previously published work with respect to females, and the potential association between environmental exposures to organochlorine pesticides, such as dichlorodiphenyltrichloroethane (DDT), and dichlorodiphenyldichloroethylene (DDE) to explain the greater prevalence of T2DM among Indigenous females compared to males. Using data from the Multi-Community Environment-and-Health Study, Principal Component Analysis (PCA), examined 9-polychlorinated biphenyl congeners, 7-organic pesticides, and 4-metal/metalloids. Modified Poisson regression with robust error variance estimated adjusted prevalence ratios (PR) and corresponding 95% confidence intervals (95% CI), regressing prevalent T2DM on the newly derived principal components (PC), adjusting for a priori covariates, including parity. We further examined the relationship between high detection concentrations of DDT and tertials of categorized DDE exposures on T2DM among Indigenous Cree women. Among 419 female participants, 23% (n = 95) had physician-diagnosed T2DM. PCA analysis show that DDT and Lead (Pb) loaded highly on the second axis (PC-2), although in opposite directions, indicating the different exposure sources. As previously published, T2DM was significantly associated with PC-2 across adjusted models, however, after further adjusting for parity in this analysis, T2DM was no longer significantly associated with increasing PC-2 scores (PR = 0.88, 95% 0.76, 1.03). Furthermore, we found that the highest detectable levels of DDT, and tertiles of DDE were significantly associated with prevalent T2DM in the fully adjusted model (PR = 1.93, 1.17, 3.19), and (PR = 3.58, 1.10, 11.70), respectively. This cross-sectional analysis suggests organochlorines, specifically, detectable high exposure concentrations of DDT and DDE are associated with prevalent type 2 diabetes, signifying a possible important link between parity and environmental organochlorines pesticides among Indigenous Cree women.
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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 | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".