Effect of Add-On Therapy of Dapagliflozin and Empagliflozin on Adipokines in Type 2 Diabetes Mellitus
Bibliographic record
Abstract
Background: The alteration of adipokine secretion leads to the development of insulin resistance or impaired function of insulin in type 2 diabetes with obesity. The main objective of the study was to evaluate the effect of add-on therapy of dapagliflozin and empagliflozin on visceral fat-associated adipokines in inadequately controlled overweight and obese type 2 diabetic patients on metformin monotherapy. Methods: The study included 60 participants diagnosed with type 2 diabetes mellitus with overweight or obesity. The blood samples were taken before starting first-line therapy with metformin, 12 weeks after starting metformin therapy and 12 weeks after starting add-on therapy. The biochemical variables were analyzed using Cobas ® 6000 analyzer. Hemoglobin A1c (HbA1c) level was measured with high-performance liquid chromatography (HPLC). Serum adipokines were estimated with enzyme-linked immunosorbent assay (ELISA). Results: The mean adiponectin level was significantly elevated with add-on therapy using dapagliflozin and empagliflozin (P < 0.001). The mean fatty-acid binding protein 4 (FABP4), retinol-binding protein 4 (RBP4) and visfatin levels were reduced considerably (P < 0.001). The mean HbA1c, fasting plasma glucose (FPG) and postprandial blood glucose (PPBG) levels were reduced significantly with add-on therapy (P < 0.001). Lipid profile and creatinine were also altered significantly with the add-on therapy (P < 0.001). Conclusions: Add-on therapy of dapagliflozin and empagliflozin are beneficial to control the adipokines that regulate the visceral fat in overweight and obese type 2 diabetic patients. The effective therapeutic target to control adipokines with metabolic variables reduces body weight, obesity, cardiovascular risk and renal disease in type 2 diabetes. J Endocrinol Metab. 2021;11(3-4):83-90 doi: https://doi.org/10.14740/jem751
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".