The Influence of Urinary Concentrations of Organophosphate Metabolites on the Relationship between BMI and Cardiometabolic Health Risk
Bibliographic record
Abstract
The objective was to determine whether detectable levels of OP metabolites influence the relationship between BMI and cardiometabolic health. This cross-sectional study was conducted using 2227 adults from the 1999-2008 NHANES datasets. Urinary concentrations of six dialkyl phosphate metabolites were dichotomized to above and below the detection limit. Weighted multiple regression analysis was performed adjusting for confounding variables. Independent of BMI, individuals with detectable metabolites had higher diastolic blood pressure (for dimethylphosphate, diethylphosphate, and diethyldithiophosphate; P < 0.05), lower HDL (for diethyldithiophosphate; P = 0.02), and higher triglyceride (for dimethyldithiophosphate; P = 0.05) than those below detection. Contrarily, those with detectable dimethylthiophosphate had better LDL, HDL, and total cholesterol, independent of BMI. Individuals at a higher BMI range who had detectable diethylphosphate (interaction: P = 0.03) and diethylthiophosphate (interaction: P = 0.02) exhibited lower HDL, while little difference existed between OP metabolite detection statuses at lower BMIs. Similarly, individuals with high BMIs and detectable diethylphosphate had higher triglyceride than those without detectable levels, while minimal differences between diethylphosphate detection statuses were observed at lower BMIs (interaction: P = 0.02). Thus, cardiometabolic health outcome differs depending on the specific OP metabolite being examined, with higher BMIs amplifying health risk.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".