Cardiometabolic health and per and polyfluoroalkyl substances in an Inuit population
Bibliographic record
Abstract
INTRODUCTION: The cardiometabolic health status of Inuit in Nunavik has worsened in the last thirty years. The high concentrations of perfluoroalkyl acids (PFAAs) may be contributing to this since PFAAs have been linked with hypercholesterolemia, diabetes, and high blood pressure. The aim of this study was to examine the association between a PFAAs mixture and lipid profiles, Type II diabetes, prediabetes, and high blood pressure in this Inuit population. METHODS: We included 1212 participants of the Qanuilirpitaa? 2017 survey aged 16-80 years. Two mixture models (quantile g-computation and Bayesian Kernel Machine Regression (BKMR)) were used to investigate the associations between six PFAAs (PFHxS, PFOS, PFOA and three long-chain PFAAs (PFNA, PFDA and PFUnDA)) with five lipid profiles and three cardiometabolic outcomes. Non-linearity and interaction between PFAAs were further assessed. RESULTS: An IQR increase in all PFAAs congeners resulted in an increase in total cholesterol (β 0.15, 95% confidence interval (CI) 0.06, 0.24), low-density lipoprotein cholesterol (LDL) (β 0.08, 95% CI 0.01, 0.16), high-density lipoprotein cholesterol (HDL) (β 0.04, 95% CI 0.002, 0.08), apolipoprotein B-100 (β 0.03, 95% CI 0.004, 0.05), and prediabetes (OR 1.80, 95% CI 1.11, 2.91). There was no association between PFAAs and triglycerides, diabetes, or high blood pressure. Long-chain PFAAs congeners were the main contributors driving the associations. Associations were largely linear, and there was no evidence of interaction between the PFAAs congeners. CONCLUSIONS: Our study provides further evidence of increasing circulating lipids with increased exposure to PFAAs. The increased risk of prediabetes points to the influence of PFAAs on potential clinical outcomes. International regulation of PFAAs is essential to curb PFAAs exposure and related health effects in Arctic communities.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".