The Effect of Lean-Seafood and Non-Seafood Diets on Fasting and Postprandial Serum Metabolites and Lipid Species: Results from a Randomized Crossover Intervention Study in Healthy Adults
Bibliographic record
Abstract
The metabolic effects associated with intake of different dietary protein sources are not well characterized. We aimed to elucidate how two diets that varied in main protein sources affected the fasting and postprandial serum metabolites and lipid species. In a randomized controlled trial with crossover design, healthy adults (n = 20) underwent a 4-week intervention with two balanced diets that varied mainly in protein source (lean-seafood versus non-seafood proteins). Nuclear magnetic resonance spectroscopy and liquid chromatography-mass spectrometry analyses were applied to examine the effects of the two diets on serum metabolites. In the fasting state, the lean-seafood diet period, as opposed to the non-seafood diet period, significantly decreased the serum levels of isoleucine and valine, and during the postprandial state, a decreased level of lactate and increased levels of citrate and trimethylamine N-oxide were observed. The non-seafood diet significantly increased the fasting level of 26 lipid species including ceramides 18:1/14:0 and 18:1/23:0 and lysophosphatidylcholines 20:4 and 22:5, as compared to the lean-seafood diet. Thus, the lean-seafood diet decreased circulating isoleucine and valine levels, whereas the non-seafood diet elevated the levels of certain ceramides, metabolites that are associated with insulin-resistance.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".