Exploring the Use of Diabetic Medications for Non-Diabetic Conditions: A Comprehensive Literature Review
Bibliographic record
Abstract
Purpose: Currently, a significant portion of the US population has been impacted by diabetes, and alongside it, heart failure, chronic kidney disease (CKD), obesity, and non-alcoholic fatty liver disease (NAFLD) rates are on the rise. Recent research has highlighted the potential of antidiabetic medications to extend beyond diabetes management. This literature review is aimed at exploring the evolving roles of GLP-1 receptor agonists, SGLT2 inhibitors, and thiazolidinediones. It specifically investigates their applications in cardiovascular disease, chronic kidney disease (CKD), obesity, polycystic ovarian syndrome (PCOS), and NAFLD. As a novel therapeutic approach, their application could reshape treatment strategies for interconnected metabolic disorders, offering a new horizon in patient care. Methodology: We conducted a comprehensive search of PubMed and Google Scholar for English-language studies published in the United States and Canada between January 1st, 2013, and August 31st, 2023 focusing on trials involving GLP-1RA, SGLT-2i, and thiazolidinediones therapy. Findings: In our study, GLP-1 receptor agonists emerged as multifaceted pharmaceutical agents, showcasing improvements in cardiovascular disease outcomes, nephroprotective effects, substantial efficacy in obesity management, and promising prospects in addressing non-alcoholic fatty liver disease (NAFLD) and polycystic ovary syndrome (PCOS). Additionally, thiazolidinediones displayed effectiveness in the management of non-alcoholic steatohepatitis (NASH), while metformin exhibited notable benefits in PCOS management. Furthermore, Sodium-glucose Cotransporter-2 (SGLT2) Inhibitors demonstrated remarkable advancements, particularly in terms of cardiovascular and renal benefits. Our findings emphasize the diverse and evolving applications of diabetic medications in addressing a wide range of chronic medical conditions, ultimately leading to enhanced patient care and improved outcomes. Recommendations: We recommend that healthcare practitioners carefully consider the expanded utility of these medications beyond their conventional diabetic indications and explore their integration into treatment strategies for the indicated medical conditions. This should be done while thoughtfully weighing the associated risks and benefits to ensure the most effective and safe patient care possible.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.014 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".