Temporal and Geographic Variability of International Urinary Phthalates in Humans: A Systematic Review and Meta-Analysis of Biomonitoring Data
Bibliographic record
Abstract
Abstract Phthalates are endocrine-disrupting chemicals (EDCs) that alter hormone functions throughout the lifespan. Growing awareness of the adverse health effects of phthalate exposure has led to regulating certain phthalates in the United States, Canada, and Europe. However, international comparisons of urinary phthalate metabolite concentrations as biomarkers of exposure are sparse, and few studies have controlled for cohort-specific variables like pregnancy. We aimed to examine trends in urinary phthalate monoester metabolite concentrations in non-occupationally exposed populations globally, excluding locations where representative data are already available at the country level. We systematically reviewed studies published between 2000 and 2023 that reported urinary phthalate monoester concentrations. We examined changes in metabolite concentrations across time, controlling for region, age, and pregnancy status, using mixed-effects meta-regression models with and without a quadratic term for time. We identified heterogeneity using Cochran’s Q-statistic and I 2 index, adjusting for it with the trim-and-fill method. The final analytic sample consisted of 216 studies. Significant differences in phthalate metabolite concentrations were observed across regions, age groups, and between pregnant and non-pregnant cohorts. Our meta-regression identified a significant non-linear trend with time for Mono-n-butyl phthalate and Mono-isononyl phthalate concentration internationally and in Eastern and Pacific Asia (EPA). We also observed significant non-linear associations between time and Mono(2-ethyl-5-hydroxyhexyl) phthalate, Mono(2-carboxymethylhexyl) phthalate, and Mono(3-carboxypropyl) phthalate concentration internationally and/or in EPA, along with Mono(2-ethylhexyl) phthalate, Mono-carboxy-isononyl phthalate, and Mono-ethyl phthalate. Additionally, Mono-ethyl phthalate concentration showed a significant negative linear association with time in Latin America and Africa. Heterogeneity was high, indicating potential bias in our results. Our findings indicate the need for increased awareness of phthalate exposure. Further analysis of the attributable disease burden and cost at regional and international levels, especially in low- and middle-income countries, is essential to understanding these and other EDCs impact on population health and the economy. Highlights Some phthalate levels significantly differed by region, age, and pregnancy status. Many phthalates had non-linear associations internationally from 2000 to 2023. MnBP and MiNP levels increased, driven by Eastern and Pacific Asia results. Most phthalate metabolites’ levels declined overall and region-specific over time. There was insufficient data on phthalate metabolite levels for many regions.
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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.031 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".