Evaluation of Effectiveness and Tolerability of Saroglitazar in Metabolic Disease Patients of India: A Retrospective, Observational, Electronic Medical Record-Based Real-World Evidence Study
Bibliographic record
Abstract
Background Metabolic disorders, including diabetes mellitus (DM), diabetic dyslipidemia (DD), and metabolic-dysfunction-associated steatotic liver disease (MASLD), are significant health challenges in India. This study aims to evaluate the real-world effectiveness and tolerability of saroglitazar (4mg) in Indian adults with type 2 diabetes mellitus (T2DM), DD, and MASLD, focusing on changes in glycemic, lipid, and hepatic biomarkers. Methods This retrospective study included adult patients with metabolic diseases (≥ 18 years) who were prescribed saroglitazar (4 mg) at baseline and continued therapy at least till the next follow-up visit after 90 days. The patients with at least one follow-up visit after 90 days from baseline with values for glycemic parameters, lipid parameters, aspartate transaminase (AST), and alanine transaminase (ALT) available at both visits were included. Changes in glycemic parameters, lipid profile, and liver enzymes were assessed from baseline to the follow-up visit. Disease conditions, concomitant medications at baseline, and adverse events at the follow-up visit were also evaluated. Results A total of 553 patients were included in this study. The most common conditions at baseline were DM, dyslipidemia, and hypertension. Saroglitazar significantly improved glycemic control, reducing glycosylated hemoglobin (HbA1c) by -0.71% from baseline to the follow-up visit. Fasting blood glucose (FBG) and postprandial blood glucose (PPBG) in the overall patient population decreased by -21.24 mg/dL (n =410) and -24.28 mg/dL (n = 178), respectively. In patients with baseline FBG >100 mg/dL (n = 358), the FBG reduction was -26.46 mg/dL, while in patients with PPBG >140 mg/dL (n = 151), the PPBG reduction was -31.73 mg/dL. There was a substantial improvement in the lipid profile, including a significant reduction in serum triglycerides (TG) (-55.41 mg/dL) and LDL (-6.95 mg/dL). Hepatic parameters improved, with AST and ALT decreasing by -2.62 IU/L and -7.95 IU/L, respectively. No significant adverse events and renal impairment were observed. Conclusion Saroglitazar demonstrated significant improvements in glycemic control, lipid profile, and liver enzymes with a favorable safety profile in Indian patients with metabolic diseases.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".