Childhood Malnutrition and Association of Lean Mass with Metabolome and Hormone Profile in Later Life
Bibliographic record
Abstract
This study aimed to determine the associations of targeted metabolomics and hormone profiles data with lean mass index (LMI), which were estimated using bioelectrical impedance, in survivors of child severe malnutrition (SM) (n = 69) and controls (n = 77) in Malawi 7 years after being treated. Linear associations between individual metabolite or hormone and LMI were determined, including their interaction with nutrition status 7 years prior. Path analysis was performed to determine structural associations. Lastly, predictive models for LMI were developed using the metabolome and hormone profile by elastic net regularized regression (EN). Metabolites including several lipids, amino acids, and hormones were individually associated (p < 0.05 after false discovery rate correction) with LMI. However, plasma FGF21 (Control: β = −0.02, p = 0.59; Case: β = −0.14, p < 0.001) and tryptophan (Control: β = 0.15, p = 0.26; Case: β = 0.70, p < 0.001) were associated with LMI among cases but not among controls (both interaction p-values < 0.01). Moreover, path analysis revealed that tryptophan mediates the association between child SM and LMI. EN revealed that most predictors of LMI differed between groups, further indicating altered metabolic mechanisms driving lean mass accretion among SM survivors later in life.
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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".