Assessment of individual metabolic response to a low‐calorie smoothie challenge using targeted metabolomics
Bibliographic record
Abstract
Assessing metabolic responsiveness to a standardized meal using metabolomics offers promise for identifying new interventions in metabolic diseases. However, previously tested meals appear to be too high in calories to be applicable to young patients with mitochondrial diseases, who often display a low nutrient handling capacity. This study assessed the metabolic response to a smoothie providing only 9% of daily calories (42% carbohydrate; 46% lipid; 12% protein). Plasma was collected from 4 healthy young adults before and 90 min after the smoothie and 111 metabolites reflecting normal (15 fatty acids; 12 amino acids) or dysregulated (58 organic acids, 25 acylcarnitines) nutrient metabolism were analyzed by mass spectrometry. Of all metabolites assessed, fatty acids displayed the greatest changes, but overall the average coefficient of variation (CV) for fold‐changes (26%) was smaller than inter‐individual CVs for absolute values assessed before and after the meal (40% for both). Principal component analysis revealed the consistency of the postprandial response within individuals, but clearly separated them from each other. This study shows the feasibility of measuring the metabolic response to a low calorie meal despite large inter‐individual variations. For future studies, it appears warranted to include additional metabolite classes, particularly lipids. Supp.: FRSQ & CIHR Emerging Team Grant.
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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 | 0.000 |
| 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) | 0.002 | 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".