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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".