Comparative efficacy and safety of antidiabetic drugs for obese patients with knee osteoarthritis: a network meta-analysis of randomized controlled trials
Bibliographic record
Abstract
BACKGROUND: Obesity-related knee osteoarthritis (KOA) is a significant public health concern, affecting quality of life. Recent evidence suggests some antidiabetic drugs may help manage KOA in obese patients due to their anti-inflammatory and weight-reducing effects. OBJECTIVE: This study aimed to compare the efficacy and safety of antidiabetic drugs for managing pain and adverse events in obese KOA patients through a network meta-analysis of randomized controlled trials (RCTs). METHODS: A systematic search of multiple databases identified relevant RCTs on antidiabetic drugs for KOA. Treatment efficacy was assessed using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain score improvement, and safety was evaluated based on the incidence of serious adverse events. The Surface Under the Cumulative Ranking Curve (SUCRA) scores were used to rank the treatments, and effect sizes were reported as mean differences (MD) with 95% confidence intervals (CI). RESULTS: A total of nine RCTs were included in the analysis. For pain relief, metformin demonstrated the largest effect size with a mean difference of - 1.13 (95% CI - 1.48, - 0.78) compared to usual care, followed by Metformin-Phosphatidylcholine (MFPH) (- 0.92, 95% CI - 1.70, - 0.13) and semaglutide (- 0.90, 95% CI - 1.48, - 0.32). In terms of safety, usual care exhibited the lowest risk of adverse events, with liraglutide (0.09, 95% CI - 0.74, 0.92) and semaglutide (0.21, 95% CI - 0.46, 0.88) also showing favorable safety profiles. The SUCRA rankings further supported these findings, with metformin ranking highest for efficacy (SUCRA: 86.8%) and usual care ranking highest for safety (SUCRA: 75.7%). However, these rankings should be interpreted alongside the effect sizes and clinical context to fully assess the trade-offs between efficacy and safety across interventions. CONCLUSIONS: Metformin and MFPH are promising for managing KOA pain in obese patients. Semaglutide offers a balanced efficacy and safety profile, while liraglutide may be a safe option for selected patients. Further research is needed to confirm these findings and assess long-term outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.037 | 0.072 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.050 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".