Abstract 26: Plasma Metabolomic Profile of Adiposity in Childhood: Gen3G Study
Bibliographic record
Abstract
Objective: Childhood obesity is associated with long-term adverse outcomes including increased risk of cardiometabolic diseases. The integration of metabolomics in determining metabolites specific to excess adiposity could identify novel markers of adverse adiposity accumulation and insights into the pathogenesis of obesity-related outcomes, but investigations in childhood are limited. Thus, our aim was to identify metabolite networks associated with adiposity in childhood using gold-standard measurements. Methods: This study used cross-sectional data from 329 children at mid-childhood (age 5.3 ± 0.3 years) from the Gen3G prospective pre-birth cohort. We quantified 1,038 plasma metabolites including 798 annotated and 240 unannotated molecules. We measured adiposity using the gold-standard dual-energy X-ray absorptiometry (DXA), as well as skinfold, waist circumference and body mass index (BMI). We applied weighted-correlation network analysis to identify networks of highly correlated metabolites. Spearman’s partial correlations were applied to determine the associations of adiposity measures with metabolite networks and individual metabolites, adjusting for age and sex, with false discovery rate correction. Results: We identified a network of 23 metabolites (MEmagenta on Figure), primarily comprised of lipids (primary and secondary bile acids metabolism sub-pathways), that showed positive correlations with DXA total and truncal fat (ρ adjusted = 0.11 to 0.19), skinfold measures (ρ adjusted = 0.09 to 0.26), and BMI and waist circumference (ρ adjusted = 0.15 and 0.18, respectively). Within this network, glycol-alpha-muricholate and glycol-beta-muricholate metabolites, bile acid sub-species associated with glucose and lipid metabolism, appeared to be driving the associations. These correlations remained consistent when stratified by sex. Conclusions: A unique network of metabolites was associated with adiposity measures at 5 years of age.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".