Whey Peptides Stimulate Differentiation and Lipid Metabolism in Adipocytes and Ameliorate Lipotoxicity-Induced Insulin Resistance in Muscle Cells
Bibliographic record
Abstract
Deregulation of lipid metabolism and insulin function in muscle and adipose tissue are hallmarks of systemic insulin resistance, which can progress to type 2 diabetes. While previous studies suggested that milk proteins influence systemic glucose homeostasis and insulin function, it remains unclear whether bioactive peptides generated from whey alter lipid metabolism and its accumulation in muscle and adipose tissue. Therefore, we incubated murine 3T3-L1 preadipocytes and C2C12 myotubes with a whey peptide mixture produced through pepsin-pancreatin digestion, mimicking peptides generated in the gut from whey protein hydrolysis, and examined its effect on indicators of lipid metabolism and insulin sensitivity. Whey peptides, particularly those derived from bovine serum albumin (BSA), promoted 3T3-L1 adipocyte differentiation and triacylglycerol (TG) accumulation in accordance with peroxisome proliferator-activated receptor γ (PPARγ) upregulation. Whey/BSA peptides also increased lipolysis and mitochondrial fat oxidation in adipocytes, which was associated with the upregulation of peroxisome proliferator-activated receptor δ (PPARδ). In C2C12 myotubes, whey but not BSA peptides ameliorated palmitate-induced insulin resistance, which was associated with reduced inflammation and diacylglycerol accumulation, and increased sequestration of fatty acids in the TG pool. Taken together, our study suggests that whey peptides generated via pepsin-pancreatin digestion profoundly alter lipid metabolism and accumulation in adipocytes and skeletal myotubes.
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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".