High‐protein diet more effectively reduces hepatic fat than low‐protein diet despite lower autophagy and FGF21 levels
Bibliographic record
Abstract
BACKGROUND AND AIMS: Non-alcoholic fatty liver disease (NAFLD) is becoming increasingly prevalent and nutrition intervention remains the most important therapeutic approach for NAFLD. Our aim was to investigate whether low- (LP) or high-protein (HP) diets are more effective in reducing liver fat and reversing NAFLD and which mechanisms are involved. METHODS: 19 participants with morbid obesity undergoing bariatric surgery were randomized into two hypocaloric (1500-1600 kcal/day) diet groups, a low protein (10E% protein) and a high protein (30E% protein), for three weeks prior to surgery. Intrahepatic lipid levels (IHL) and serum fibroblast growth factor 21 (FGF21) were measured before and after the dietary intervention. Autophagy flux, histology, mitochondrial activity and gene expression analyses were performed in liver samples collected during surgery. RESULTS: IHL levels decreased by 42.6% in the HP group, but were not significantly changed in the LP group despite similar weight loss. Hepatic autophagy flux and serum FGF21 increased by 66.7% and 42.2%, respectively, after 3 weeks in the LP group only. Expression levels of fat uptake and lipid biosynthesis genes were lower in the HP group compared with those in the LP group. RNA-seq analysis revealed lower activity of inflammatory pathways upon HP diet. Hepatic mitochondrial activity and expression of β-oxidation genes did not increase in the HP group. CONCLUSIONS: HP diet more effectively reduces hepatic fat than LP diet despite of lower autophagy and FGF21. Our data suggest that liver fat reduction upon HP diets result primarily from suppression of fat uptake and lipid biosynthesis.
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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) | 0.001 | 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) | 0.002 | 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".