A Comparison of the Effects of Milk, Yogurt, and Cheese on Insulin Sensitivity, Hepatic Steatosis, and Gut Microbiota in Diet-Induced Obese Male Mice
Bibliographic record
Abstract
The effects of low-fat dairy products on insulin resistance (IR), hepatic steatosis, and gut microbiota composition in high-fat diet (HFD)-fed obese mice were examined. C57BL/6 male mice (n = 16/group) were fed a high-fat diet (HFD, 45% fat) or HFD supplemented with either fat-free milk (MILK), fat-free yogurt (YOG), or reduced-fat (19% milk fat) cheddar cheese (CHE) at 10% of the total energy intake for 8 weeks. Body weight, fat mass, liver lipids, and metabolic enzymes were evaluated. Compared with HFD, MILK reduced homeostatic assessment of insulin resistance along with increased hepatic insulin signaling and decreased hepatic gluconeogenic enzymes. YOG and MILK decreased hepatic triacylglycerol content and lipid droplet size, while CHE had no effect. In the liver, MILK and YOG downregulated de novo lipogenesis enzymes. In MILK, fat oxidation capacity was elevated. Compared with HFD, liver lipidomic analysis in MILK and YOG revealed unique profiles of decreased proinflammatory lipid species, including ceramides. Dairy feeding elicited an increase in beneficial bacteria, such as Streptococcus in YOG and Anaero-tignum in MILK, as shown by 16S rRNA sequencing of gut microbiota. In conclusion, the ability of milk and yogurt to reduce hepatic steatosis in HFD mice may be explained, at least in part, by the regulation of the gut microbiome and liver lipidome.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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".