The antianginal ranolazine mitigates obesity-induced nonalcoholic fatty liver disease and increases hepatic pyruvate dehydrogenase activity
Bibliographic record
Abstract
Obese individuals are often at risk for nonalcoholic fatty liver disease (NAFLD), insulin resistance, type 2 diabetes (T2D), and cardiovascular diseases such as angina, thereby requiring combination therapies for their comorbidities. Ranolazine is a second-line antianginal agent that also improves glycemia, and our aim was to determine whether ranolazine modifies the progression of obesity-induced NAFLD. Twelve-week-old C57BL/6J male mice were fed a low-fat or high-fat diet for 10 weeks and then treated for 30 days with either vehicle control or ranolazine (50 mg/kg via daily s.c. injection). Glycemia was monitored via glucose/pyruvate/insulin tolerance testing, whereas in vivo metabolism was assessed via indirect calorimetry. Hepatic triacylglycerol content was quantified via the Bligh and Dyer method. Consistent with previous reports, ranolazine treatment reversed obesity-induced glucose intolerance, which was associated with reduced body weight and hepatic steatosis, as well as increased hepatic pyruvate dehydrogenase (PDH) activity. Ranolazine's actions on hepatic PDH activity may be directly mediated, as ranolazine treatment reduced PDH phosphorylation (indicative of increased PDH activity) in HepG2 cells. Therefore, in addition to mitigating angina, ranolazine also reverses NAFLD, which may contribute to its documented glucose-lowering actions, situating ranolazine as an ideal antianginal therapy for obese patients comorbid for NAFLD and T2D.
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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) | 0.000 | 0.000 |
| 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".