Combination of a Glucagon-Like Peptide 1 Analog and a Sodium-Glucose Cotransporter 2 Inhibitor Improves Lipid Metabolism Compared to the Monotherapies in Experimental Metabolic Syndrome
Bibliographic record
Abstract
Background: Obesity is a risk factor for insulin resistance, dyslipidemia, fatty liver disease, and all disorders associated with metabolic syndrome. Here we evaluated the association of the glucagon-like peptide 1 (GLP-1) analog, liraglutide, and the sodium-glucose cotransporter-2 (SGLT-2) inhibitor, canagliflozin, on the improvement of metabolic syndrome symptoms in a high-fat diet (HFD)-induced obesity rat model. Methods: Male Wistar rats received either a control diet or HFD ad libitum for 5 months. After 4 months of diet, HFD rats were randomly divided into four experimental groups (HFD, HFD + liraglutide, HFD + canagliflozin, and HFD + liraglutide + canagliflozin). Treatment groups received liraglutide (100 µg/kg) and/or canagliflozin (10 mg/kg) once daily for one month. Body mass and food intake were monitored throughout the experiment. An oral glucose tolerance test, biochemical parameters, epididymal and liver fat, and adipocyte morphology were assessed after the treatment period. Results: Rats on the HFD developed obesity, glucose intolerance, dyslipidemia, and fatty liver. Liraglutide reduced food intake and body weight, normalized the lipid profile, and reduced abdominal and liver fat. Canagliflozin slightly reduced body mass and improved glucose tolerance and dyslipidemia. The combination therapy was more effective than the monotherapies in normalizing the lipid profile. Conclusions: The combination of liraglutide and canagliflozin was more effective than the monotherapies in improving dyslipidemia and liver fat. These results indicate that the combination of GLP-1 receptor agonists and SGLT-2 inhibitors is a promising therapeutic strategy to treat dyslipidemia, and possibly prevent fatty liver disease in metabolic syndrome and obese patients. J Endocrinol Metab. 2022;12(6):168-177 doi: https://doi.org/10.14740/jem843
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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) | 0.002 | 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".