Serum Concentrations of Organochlorine Pesticides and Growth among Russian Boys
Bibliographic record
Abstract
BACKGROUND: Limited human data suggest an association of organochlorine pesticides (OCPs) with adverse effects on children's growth. OBJECTIVE: We evaluated the associations of OCPs with longitudinally assessed growth among peripubertal boys from a Russian cohort with high environmental OCP levels. METHODS: A cohort of 499 boys enrolled in the Russian Children's Study between 2003 and 2005 at 8-9 years of age were followed prospectively for 4 years. At study entry, 350 boys had serum OCPs measured. Physical examinations were conducted at entry and annually. The longitudinal associations of serum OCPs with annual measurements of body mass index (BMI), height, and height velocity were examined by multivariate mixed-effects regression models for repeated measures, controlling for potential confounders. RESULTS: Among the 350 boys with OCP measurements, median serum hexachlorobenzene (HCB), β-hexachlorocyclohexane (βHCH), and p,p´-dichlorodiphenyldichloroethylene (p,p´-DDE) concentrations were 159 ng/g lipid, 168 ng/g lipid, and 287 ng/g lipid, respectively. Age-adjusted BMI and height z-scores generally fell within the normal range per World Health Organization standards at entry and during follow-up. However, in adjusted models, boys with higher serum HCB, βHCH, and p,p´-DDE had significantly lower mean [95% confidence interval (CI)] BMI z-scores, by -0.84 (-1.23, -0.46), -1.32 (-1.70, -0.95), and -1.37 (-1.75, -0.98), respectively, for the highest versus lowest quintile. In addition, the highest quintile of p,p´-DDE was associated with a significantly lower mean (95% CI) height z-score, by -0.69 (-1.00, -0.39) than that of the lowest quintile. CONCLUSIONS: Serum OCP concentrations measured at 8-9 years of age were associated with reduced growth, particularly reduced BMI, during the peripubertal period, which may affect attainment of optimal adult body mass and height.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".