Lean‐seafood intake decreases urinary markers of mitochondrial lipid and energy metabolism in healthy subjects: Metabolomics results from a randomized crossover intervention study
Bibliographic record
Abstract
SCOPE: Proteins constitute an important part of the human diet, but understanding of the effects of different dietary protein sources on human metabolism is sparse. We aimed to elucidate diet-induced metabolic changes through untargeted urinary metabolomics after four weeks of intervention with lean-seafood or nonseafood diets. It is shown that lean-seafood intake reduces urinary excretion of metabolites involved in mitochondrial lipid and energy metabolism possibly facilitating a higher lipid catabolism in healthy subjects. METHODS: In a randomized controlled trial with crossover design, 20 healthy subjects consumed two balanced diets that varied in main protein sources for 4 weeks. Morning spot urine samples were collected before and after each intervention period. Untargeted metabolomics based on (1) H NMR spectroscopy and LC-MS analyses were applied to characterize the urinary metabolic response to the interventions. RESULTS: The lean-seafood diet period reduced the urinary level of l-carnitine, 2,6-dimethylheptanoylcarnitine, and N-methyl-2-pyridone-5-carboxamide, relative to the nonseafood period. The dietary analysis revealed that the higher urinary level of trimethylamine-N-oxide after the lean-seafood diet period and guanidinoacetate and 3-methylhistidine after the nonseafood diet period was related to the endogenous content of these compounds in the diets. CONCLUSIONS: Our data reveal that 4 weeks of lean-seafood intake reduces urinary excretion of metabolites involved in mitochondrial lipid and energy metabolism possibly facilitating a higher lipid catabolism in healthy subjects after the lean-seafood intake.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".