Diet quality in relation to serum perfluoroalkyl substance concentrations in Canadian preadolescents
Bibliographic record
Abstract
BACKGROUND: Exposure to perfluoroalkyl substances (PFAS) is concerning because some of these chemicals are associated with health effects. Preadolescents eat more food per body weight and may be more affected by substances in food compared to adults. OBJECTIVE: Estimate how diet quality and nutrients are associated with concurrently measured PFAS concentrations in Canadian preadolescents. METHODS: Using data from 157 participants (7-11 years) in a Maternal-Infant Research on Environmental Chemicals follow-up study (2018-2021), we analyzed serum concentrations of 9 PFAS, and derived 2019-Healthy Eating Food Index (HEFI-2019) scores and nutrient intakes from 24-h diet recalls. We used multivariable linear regression to estimate cross-sectional associations between diet and serum PFAS concentrations. RESULTS: Saturated fat consumption was associated with higher serum perfluorooctanesulfonic acid (PFOS) concentrations. The fruits and vegetables score was associated with higher perfluorodecanoic acid (PFDA), and perfluorononanoic acid (PFNA); similar associations appeared with the HEFI-2019 total score. Percentage of energy intake (%E) from protein was associated with lower perfluorooctanoic acid (PFOA), PFOS, and perfluorohexanesulphonic acid (PFHxS) while %E from fat was associated with higher PFDA and PFOS. Fiber and iron intakes were associated with higher PFHxS. Vitamin D intake was associated with lower PFNA. CONCLUSIONS: We observed some associations between diet and PFAS. Our findings may be partially explained by toxicokinetics and PFAS presence in Canadian food systems. However, our interpretation is hindered by lack of temporality and potential confounding. Additional investigations which integrate food systems information paired with PFAS concentrations from food and biomonitoring are required.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".