Comparing serum responses to acute feedings of an extensively hydrolyzed whey protein concentrate versus a native whey protein concentrate in rats: a metabolomics approach
Bibliographic record
Abstract
We examined how gavage feeding extensively hydrolyzed whey protein (WPH) versus a native whey protein concentrate (WPC) transiently affected serum biochemical profiles in rodents. Male Wistar rats (250-300 g) were 8 h fasted and subsequently fed isonitrogenous amounts of WPH or WPC, or remained unfed (control). Animals were sacrificed 15 min, 30 min, and 60 min post-gavage for serum extraction, and serum was analyzed using untargeted global metabolic profiling via gas chromatography/mass spectrometry (MS) and liquid chromatography/MS/MS platforms. We detected 333 serum metabolites amongst the experimental and control groups. Both WPH and WPC generally increased amino acids (1.2-2.8-fold), branched-chain amino acids (1.2-1.7-fold), and serum di- and oligo-peptides (1.1-2.7-fold) over the 60 min time course compared with control (q < 0.05). However, WPH increased lysine (false discovery rate using a q-value <0.05) and tended to increase isoleucine and valine 15 min post-feeding (q < 0.10) as well as aspartylleucine 30 min post-feeding compared with WPC (q < 0.05). While both protein sources led to a dramatic increase in free fatty acids compared with control (up to 6-fold increases, q < 0.05), WPH also uniquely resulted in a 30 min post-feeding elevation in free fatty acids compared with WPC (q < 0.05), an effect which may be due to the robust 30 min postprandial increase in epinephrine in the WPH cohort. These data provide a unique postprandial time-course perspective on how WPH versus WPC feedings affect circulating biochemicals and will guide future research comparing these 2 protein sources.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".