Effect of Add-On Therapy of Sodium-Glucose Cotransporter 2 Inhibitors and Dipeptidyl Peptidase 4 Inhibitors on Adipokines in Type 2 Diabetes Mellitus
Bibliographic record
Abstract
Background: Excess adiposity is associated with an increased risk of cardiovascular disease due to metabolic changes in the body. Visceral obesity increases the risk of diabetes mellitus through adipocytokines and hence the effective targeting therapies are essential to control obesity in high-risk individuals. The study’s main objective was to evaluate the effect of add-on therapy of sodium-glucose cotransporter 2 (SGLT2) inhibitors and dipeptidyl peptidase 4 (DPP4) inhibitors on visceral fat-associated serum adipokines. Methods: The study included 90 subjects diagnosed with type 2 diabetes mellitus. 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. Serum adipokines were analyzed with enzyme-linked immunosorbent assay (ELISA). Hemoglobin A1c (HbA1c) level was estimated with high-performance liquid chromatography (HPLC). The biochemical variables were measured using Cobas ® 6000 analyzer. Results: The mean adiponectin level was significantly elevated with add-on therapy using SGLT2 inhibitors and DPP4 inhibitors (P < 0.001). The mean retinol binding protein 4 (RBP4), fatty acid binding protein 4 (FABP4) and visfatin levels were reduced considerably (P < 0.001). The SGLT2 inhibitors are more effective on serum FABP4 in patients with type 2 diabetes (P = 0.038). The mean fasting plasma glucose (FPG), postprandial blood glucose (PPBG) and HbA1c levels were reduced significantly with add-on therapy (P < 0.001). Lipid profile was also altered significantly with this add-on therapy (P < 0.001). Conclusions: The results indicate that add-on therapy exerts a beneficial effect in type 2 diabetic patients insufficiently controlled with metformin only by altering the visceral fat-associated adipokine levels and controlling the metabolic activities. J Clin Med Res. 2021;13(6):355-362 doi: https://doi.org/10.14740/jocmr4510
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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".