Glycemic Outcomes of Oral Hypoglycemics Versus GLP-1 Receptor Agonists in Type 2 Diabetes: A Comparative Clinical Study
Bibliographic record
Abstract
Background: Type 2 Diabetes Mellitus (T2DM) is one of the most significant health concerns around the world, especially in South Asian communities, where the escalating burden of obesity and sedentary lifestyles has intensified the disease. Although oral hypoglycemic agents (OHAs) remain first-line treatment, glucagon-like peptide-1 (GLP-1) receptor agonists have become an effective alternative since they have a glucose-dependent insulinotropic effect, reduce weight, and have fewer adverse effects, particularly hypoglycemia. There is limited comparison of clinical evidence of local settings, however. Objectives: To evaluate the efficacy of OHAs and GLP-1 receptor agonists in enhancing glycemic regulation in adults with T2DM. Methods: A comparative clinical study was carried out in Shaikh Zayed Hospital, Lahore, between January 2024 and March 2025. Eighty adult participants with T2DM were recruited through consecutive sampling and separated into 2 equal groups: Group A was provided with regular OHAs and Group B was prescribed GLP-1 receptor agonists. Fasting blood sugar (FBS), random blood sugar (RBS), and HbA1c were measured at baseline and 6 months. Statistical software SPSS 26 version was utilized and p less than 0.05 was regarded to be significant. Findings: Both groups experienced an improvement in glycemic parameters and the GLP-1 group had significantly more decreases in the HbA1c (1.8 vs. 1.0), FBS and RBS (p < 0.01). Also, the GLP-1 group obtained significant losses (3.4 kg) in weight, but the OHA did not. Hypoglycemia was more prevalent among users of OHA whereas gastrointestinal symptoms were a little bit more prevalent among users of GLP-1. Conclusion: GLP-1 receptor agonists offer a benefit in glycemic control and weight loss over traditional OHAs and reduced hypoglycemic events. These agents are a good therapeutic alternative in the management of T2DM, particularly when the patient needs a better metabolism.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".