Environmental occurrence, human exposure, and endocrine disruption of di-iso-nonyl phthalate and di-iso-decyl phthalate: A systematic review
Bibliographic record
Abstract
Di-iso-nonyl phthalate (DINP) and di-iso-decyl phthalate (DIDP) have been employed increasingly as plasticizers to replace di-(2-ethylhexyl) phthalate (DEHP), a hormonal disruptor. Through this systematic review, we reviewed their (1) contamination levels in the environmental media, foods, and consumer products, (2) human exposure levels in national biomonitoring studies, and (3) associations with human sex and thyroid hormone disruption. PubMed, Scopus, and Web of Science were searched and eligible studies were identified. DINP and DIDP were found at higher concentrations in indoor environments, especially with high human activity and PVC use. In foods, contamination levels vary by production methods and tend to be higher in fatty foods. In children’s products, both plasticizers were more highly detected in samples measured before 2010. National biomonitoring data from several countries demonstrated that urinary levels of DINP and DIDP metabolites were relatively lower than those of DEHP. However, exposure to DINP has been associated with anti-androgenic potential in male offspring and adults and decreased thyroid hormones in mother–child pairs. In conclusion, existing literatures demonstrated widespread occurrence of DINP and DIDP in the indoor environment, diet, and children’s products, and in the human populations worldwide. At the current levels of exposure, DINP exhibited endocrine disruption potentials similar to those of DEHP, especially among males and pregnant women. Knowledge gaps in DIDP exposure among the human population were identified and should be considered for future studies.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".