Association of Exposure to Di-2-Ethylhexylphthalate Replacements With Increased Insulin Resistance in Adolescents From NHANES 2009–2012
Bibliographic record
Abstract
CONTEXT: Di-isononyl phthalate (DINP) and di-isodecyl phthalate (DIDP) are environmental chemicals increasingly used to replace di-2-ethylhexylphthalate (DEHP) and commonly found in processed foods. Phthalate exposures, in particular DEHP, have been associated with insulin resistance in adolescents, but there are no data regarding the two substitutes, DINP and DIDP. OBJECTIVE: This study aimed to examine associations of DINP, DIDP, and DEHP with insulin resistance outcomes. DESIGN, SETTING, AND PARTICIPANTS: This was a cross-sectional analysis of 2009-2012 National Health and Nutrition Examination Surveys (NHANES) composed of 356 fasting 12-19-year-olds. MAIN OUTCOME MEASURES: Insulin resistance as a categorical outcome expressed as homeostatic model assessment of insulin resistance (HOMA-IR), using a cut point of 4.39 to define insulin resistance. We also examined continuous HOMA-IR as an outcome in secondary analyses. RESULTS: Controlling for demographic and behavioral factors, diet, age, body mass index, and urinary creatinine, for each log increase in DINP metabolite, a 0.08 (P = .001) increase in HOMA-IR was identified. Compared with the first tertile of DINP (23.4% adjusted prevalence), the third tertile was associated with a 34.4% prevalence (95% confidence interval [CI], 27.3-41.6%; P = .033) of insulin resistance. Similarly, compared with the first tertile of DEHP (20.5% adjusted prevalence), the third tertile had 37.7% prevalence (95% CI 29.8-45.6%; P = .003). CONCLUSIONS: Urinary DINP concentrations were associated with increased insulin resistance in this cross-sectional study of adolescents. The previously identified association of DEHP with insulin resistance was also confirmed. Further, longitudinal studies are needed to confirm these associations, with the possibility to assess opportunities for intervention.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".