Association of Lead Exposure and Untargeted Metabolomics with BMI and Hormones in Adolescence
Bibliographic record
Abstract
Lead, an endocrine disrupting chemical (EDC), has been related to delays in weight and pubertal onset, but limited data account for metabolic mechanisms. We examined the additional variance in Body Mass Index (BMI), estradiol and testosterone among n=40 children aged 7–15 yr predicted by metabolomics, using a multi‐index model with variable selection to construct 2 indices for % methylation of CpG sites in utero and peripuberty of 4 candidate genes associated with physical growth and 2 indices each built from 317 metabolites with known functions and 440 so called “known‐unknown” compounds. After adjusting for child sex, age, leptin, blood lead at 4 yr and % methylation, the R 2 for BMI increased from 0.81 to 0.95, when metabolites were included in the model. For estradiol and total testosterone, the R 2 increased from 0.51 and 0.33, without metabolites, to 0.95 and 0.94 with metabolites, respectively. Using Metscape 2.0 to visualize the pathways associated with lead exposure, we found bile acid, amino acid and nucleotide metabolism were enriched. Results suggest our capacity to predict BMI, and hormonal markers of sexual maturation related to EDCs is markedly improved with inclusion of metabolites. Research support: NIEHS/EPA P20 ES01817101/RD834800; NIEHS R01 ES007821 ; P30 ES017885 ; P30 DK 089503.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".