Impacts of Non- and Whole-fat Milk Supplementation on Metabolism-Associated Fatty Liver Disease (MAFLD) in C57BL/6 Mice
Bibliographic record
Abstract
Background: Metabolism-associated fatty liver disease (MAFLD) is a widespread metabolic disease affecting 25% of the global adult population following Westernized lifestyles. Lifestyle modifications, specifically adherence to healthy dietary patterns, are pivotal in preventing and mitigating this disease. Consuming milk and dairy products is considered a cornerstone of a healthy dietary pattern, gathering significant attention due to compelling evidence from observational studies. These studies suggest that a higher intake of milk and certain dairy products is associated with a reduced risk of MAFLD. However, the relative health benefits of high- compared to lower-fat milk is a current topic of interest, with ongoing debates about their equivalency in promoting metabolic health. Aims & Hypothesis: In a MAFLD mouse model, we aim to determine the dependence of milk’s fat content on hepatic steatosis and lipid metabolic pathways by comparing whole- (WFM) to non-fat (NFM) milk supplementation in high-fat diet-fed mice (HFD). We hypothesize that both WFM and NFM will significantly improve HFD-induced hepatic lipid accumulation but via differential molecular mechanisms. Methods: Two cohorts of n=24 and n=36 6-week-old male C57BL/6 mice were acclimatized for one week, then randomly assigned to a HFD (45 kcal% fat) or a low-fat diet (LFD; 10 kcal% fat) control group. The HFD mice were randomized to either a WFM or NFM treatment of 0.425 mL milk for 8 weeks. At the end of the study, mice underwent an insulin tolerance and pyruvate tolerance test and body composition was measured. The animals were then euthanized, tissues were collected, and hepatic lipid metabolism was compared using western blot, histology, hepatic lipid assays, gas chromatography, and qPCR. Statistics were then completed using GraphPad Prism software 7.0, utilizing T-test, one-way ANOVA, and Tukey’s post-hoc tests with a p-value ≤ 0.05 considered as statistically significant. Results: Body weight gain was significantly reduced in NFM groups compared to HFD and WFM, accounted for by a significant reduction in fat mass. ITT was unaltered when corrected to baseline, but NFM groups had significantly higher PTT values when corrected to baseline. Overnight-fasted blood glucose was significantly reduced in NFM animals compared to WFM animals, suggesting an altered metabolic state between the two treatment groups. Histological results indicate a significant reduction in lipid accumulation by the NFM group compared to the HFD group (p<0.05), with WFM trending towards a similar reduction (p=0.051). Hepatic triglyceride concentrations were significantly reduced in NFM animals compared to WFM and HFD animals, suggesting a greater effect of NFM in reducing hepatic steatosis. Hepatic molecular results display an enhancement of enzymes involved in fatty acid oxidation and export pathways in the NFM group. In contrast, WFM displays an enhancement of de novo lipogenesis, fatty acid uptake, and fatty acid oxidation. Conclusion: The findings suggest that NFM may be more effective at reducing hepatic steatosis through enhanced fatty acid oxidation and export. WFM may reduce steatosis less effectively because of counteractive effects on fatty acid uptake and de novo lipogenesis versus beta-oxidation. These results provide mechanistic evidence of the protective effects of milk on MAFLD. The funding sources for this project include the Alberta Diabetes Institute Studentship Grant, CIHR Canadian Graduate Studentship, and Dairy Farmers of Canada. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".