Human Biological Monitoring of Diisononyl Phthalate and Diisodecyl Phthalate: A Review
Bibliographic record
Abstract
High molecular-weight phthalates, such as diisononyl phthalate (DINP), and diisodecyl phthalate (DIDP), are widely used as plasticizers in the manufacturing of polymers and consumer products. Human biological monitoring studies have employed the metabolites of DINP and DIDP as biomarkers to assess human exposure. In this review, we summarize and analyze publicly available scientific data on chemistry, metabolism, and excretion kinetics, of DINP and DIDP, to identify specific and sensitive metabolites. Human biological monitoring data on DINP and DIDP are scrutinised to assess the suitability of these metabolites as biomarkers of exposure. Results from studies carried out in animals and humans indicate that phthalates are metabolised rapidly and do not bioaccmulate. During Phase-I metabolism, ester hydrolysis of DINP and DIDP leads to the formation of hydrolytic monoesters. These primary metabolites undergo further oxidation reactions to produce secondary metabolites. Hence, the levels of secondary metabolites of DINP and DIDP in urine are found to be always higher than the primary metabolites. Results from human biological monitoring studies have shown that the secondary metabolites of DINP and DIDP in urine were detected in almost all tested samples, while the primary metabolites were detected in only about 10% of the samples. This indicates that the secondary metabolites are very sensitive biomarkers of DINP/DIDP exposure while primary metabolites are not. The NHANES data indicate that the median concentrations of MCIOP and MCINP (secondary metabolites of DINP and DIDP, resp.) at a population level are about 5.1 μg/L and 2.7 μg/L, respectively. Moreover, the available biological monitoring data suggest that infants/children are exposed to higher levels of phthalates than adults.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".