Dapagliflozin-Saxagliptin Combination - The Quest for Optimal Glycemic Control With Cardio-Renal Protection in Type 2 Diabetes Mellitus: An Expert Consensus in Indian Settings
Bibliographic record
Abstract
The combination of dapagliflozin (DAPA; a sodium-glucose cotransporter-2 inhibitor (SGLT2i)) and saxagliptin (SAXA; a dipeptidyl peptidase-4 inhibitor (DPP4i)) added on to metformin targets multiple pathophysiological pathways and provides a synergistic effect on glycemic control. Notably, both DAPA and SAXA have demonstrated cardiovascular safety and shown to slow the progression of declining renal function in patients with type 2 diabetes mellitus (T2DM) having comorbid cardiovascular or renal diseases. Together, DAPA + SAXA has an acceptable tolerability profile, comparable with the individual agents and with a low propensity for hypoglycemia. The addition of DAPA + SAXA to metformin has been associated with low frequency of urinary tract and genital infections, attributed to the complementary effects of combining an SGLT2i and a DPP4i. This review compiles insights from a group of leading experts from India, summarizing concise clinical practice recommendations for the use of a fixed-dose combination of DAPA (10 mg) + SAXA (5 mg) in Indian patients with T2DM. The review encompasses available evidence and clinical experiences, highlighting the benefits of this combination for comprehensive glycemic control and enhanced cardio-renal protection in the management of T2DM. J Endocrinol Metab. 2024;14(3):128-148 doi: https://doi.org/10.14740/jem946
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".