Increased Levels of Urine Volatile Organic Compounds Are Associated With Diabetes Risk and Impaired Glucose Homeostasis
Bibliographic record
Abstract
CONTEXT: Volatile organic compounds (VOCs) are pervasive environmental pollutants that have been linked to various adverse health effects. However, the effect of ambient VOCs, whether individually or in mixtures, on diabetes remains uncertain and requires further investigation. OBJECTIVE: This study investigates the effects of ambient VOCs exposure, whether single or mixed, on diabetes mellitus and glucose homeostasis in the general population. METHODS: Urinary concentrations of VOC metabolites were obtained from the National Health and Nutrition Examination Survey. Survey-weighted logistic regression and generalized linear regression were used to explore the associations between individual VOC exposure and diabetes risk and glucose homeostasis indicators, respectively. Weighted quantile sum (WQS) regression models were applied to assess the combined effects of VOC mixtures. RESULTS: Out of 8468 participants, 1504 had diabetes mellitus. Eight VOC metabolites showed positive associations with diabetes mellitus (OR, 1.15-1.43; all P < .05), insulin resistance (IR) (OR, 1.02-1.06; P < .05), and other glucose homeostasis indicators (β, 0.04-2.32; all P < .05). Mixed VOC models revealed positive correlations between the WQS indices and diabetes risk (OR = 1.52; 95% CI, 1.29-1.81), IR (OR = 1.36; 95% CI, 1.14-1.62), and other glucose homeostasis indicators (β, 0.17-2.22; all P < .05). CONCLUSION: Urinary metabolites of ambient VOCs are significantly associated with an increased diabetes risk and impaired glucose homeostasis. Thus, primary prevention policies aimed at reducing ambient VOCs could attenuate diabetes burden.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".