Urinary Biomarkers of Polycyclic Aromatic Hydrocarbons Are Associated with Cardiometabolic Health Risk
Bibliographic record
Abstract
BACKGROUND: Polycyclic aromatic hydrocarbons (PAH) are both man-made and naturally occurring environmental pollutants that may be related to cardiometabolic health risk. OBJECTIVE: To determine whether PAH is associated with obesity in the adult population and to examine whether urinary concentrations of PAH metabolites are associated with differences in how obesity relates to 3 or more risk factors for the metabolic syndrome (3RFMetS), type 2 diabetes (T2D), hypertension, and dyslipidemia. METHODS: A total of 4765 adult participants from the 2001-2008 National Health and Nutrition Examination Survey were examined. The association between 8 urinary hydroxylated PAH metabolites, obesity, and health were examined using weighted logistic regressions adjusting for age, sex, ethnicity, PIR, smoking status, and urinary creatinine. RESULTS: There was a positive dose-dependent association between obesity and 2-phenanthrene quintiles (P trend <0.0001). Contrarily, higher quintiles of 1-naphthalene were associated with lower risk of obesity (P trend = 0.0004). For a given BMI, those in the highest quintile of 2-naphthalene, 2-fluorene, 3-fluorene and 2-phenanthrene had a 66-80% greater likelihood of 3RFMetS (P≤0.05) compared to low levels. Higher quintiles of 1-naphthalene, 2-naphthalene, 2-phenanthrene and 1-pyrene were associated with a 78-124% greater likelihood of T2D (P≤0.05) compared to low levels while high 1-naphthalene, 2-naphthalene, 2-fluorene, 3-fluorene and 2-phenanthrene were associated with a 38-68% greater likelihood of dyslipidemia (P≤0.05) compared to lower levels. Finally, 2-naphthalene and 2-phenanthrene were positively associated with hypertension (P trend = 0.008 and P trend = 0.02 respectively). CONCLUSIONS: PAH is related to obesity and the expression of a number of obesity-related cardiometabolic health risk factors. Future research is needed to bring to light the mechanistic pathways related to these findings.
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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 | 0.001 |
| Science and technology studies | 0.000 | 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".