Metabolomics in early life and the association with body composition at age 2 years
Bibliographic record
Abstract
Summary Background and Objectives Early life is a critical window for adiposity programming. Metabolic‐profile in early life may reflect this programming and correlate with later life adiposity. We investigated if metabolic‐profile at 3 months of age is predictive for body composition at 2 years and if there are differences between boys and girls and between infant feeding types. Methods In 318 healthy term‐born infants, we determined body composition with skinfold measurements and abdominal ultrasound at 3 months and 2 years of age. High‐throughput‐metabolic‐profiling was performed on 3‐month‐blood‐samples. Using random‐forest‐machine‐learning‐models, we studied if the metabolic‐profile at 3 months can predict body composition outcomes at 2 years of age. Results Plasma metabolite‐profile at 3 months was found to predict body composition at 2 years, based on truncal: peripheral‐fat‐skinfold‐ratio (T:P‐ratio), with a predictive value of 75.8%, sensitivity of 100% and specificity of 50%. Predictive value was higher in boys (Q2 = 0.322) than girls (Q2 = 0.117). Of the 15 metabolite variables most strongly associated with T:P‐ratio, 11 were also associated with visceral fat at 2 years of age. Conclusion Several plasma metabolites (LysoPC(22:2), dimethylarginine and others) at 3 months associate with body composition outcome at 2 years. These results highlight the importance of the first months of life for adiposity programming.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".