Urinary <sup>1</sup>H Nuclear Magnetic Resonance Metabolomic Fingerprinting Reveals Biomarkers of Pulse Consumption Related to Energy-Metabolism Modulation in a Subcohort from the PREDIMED study
Bibliographic record
Abstract
Little is known about the metabolome fingerprint of pulse consumption. The study of robust and accurate biomarkers for pulse dietary assessment has great value for nutritional epidemiology regarding health benefits and their mechanisms. To characterize the fingerprinting of dietary pulses (chickpeas, lentils, and beans), spot urine samples from a subcohort from the PREDIMED study were stratified using a validated food frequency questionnaire. Urine samples of nonpulse consumers (≤4 g/day of pulse intake) and habitual pulse consumers (≥25 g/day of pulse intake) were analyzed using a 1 H nuclear magnetic resonance (NMR) metabolomics approach combined with multi- and univariate data analysis. Pulse consumption showed differences through 16 metabolites coming from (i) choline metabolism, (ii) protein-related compounds, and (iii) energy metabolism (including lower urinary glucose). Stepwise logistic regression analysis was applied to design a combined model of pulse exposure, which resulted in glutamine, dimethylamine, and 3-methylhistidine. This model was evaluated by a receiver operating characteristic curve (AUC > 90% in both training and validation sets). The application of NMR-based metabolomics to reported pulse exposure highlighted new candidates for biomarkers of pulse consumption and the impact on energy metabolism, generating new hypotheses on energy modulation. Further intervention studies will confirm these findings.
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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) | 0.000 | 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".