Whey Protein Components - Lactalbumin and Lactoferrin - Improve Energy Balance and Metabolism
Bibliographic record
Abstract
Whey protein promotes weight loss and improves diabetic control, however, less is known of its bioactive components that produce such benefits. We compared the effects of normal protein (control) diet with high protein diets containing whey, or its fractions lactalbumin and lactoferrin, on energy balance and metabolism. Diet-induced obese rats were randomized to isocaloric diets: Control, Whey, Lactalbumin, Lactoferrin, or pair-fed to lactoferrin. Whey and lactalbumin produced transient hypophagia, whereas lactoferrin caused prolonged hypophagia; the hypophagia was likely due to decreased preference. Lactalbumin decreased weight and fat gain. Notably, lactoferrin produced sustained weight and fat loss, and attenuated the reduction in energy expenditure associated with calorie restriction. Lactalbumin and lactoferrin decreased plasma leptin and insulin, and lactalbumin increased peptide YY. Whey, lactalbumin and lactoferrin improved glucose clearance partly through differential upregulation of glucoregulatory transcripts in the liver and skeletal muscle. Interestingly, lactalbumin and lactoferrin decreased hepatic lipidosis partly through downregulation of lipogenic and/or upregulation of β-oxidation transcripts, and differentially modulated cecal bacterial populations. Our findings demonstrate that protein quantity and quality are important for improving energy balance. Dietary lactalbumin and lactoferrin improved energy balance and metabolism, and decreased adiposity, with the effects of lactoferrin being partly independent of caloric 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 | 0.000 | 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 | 0.001 |
| 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".