Autologous Versus Allogeneic Adipose-Derived Mesenchymal Stem Cell Therapy for Knee Osteoarthritis: A Systematic Review, Pairwise and Network Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Knee osteoarthritis (OA) is a degenerative joint disorder with limited non-surgical treatment options. Adipose-derived mesenchymal stem cell (AD-MSC) therapy has emerged as a promising regenerative approach; however, the comparative efficacy and safety of autologous versus allogeneic AD-MSCs remain unclear. This systematic review and network meta-analysis (NMA) evaluated the effectiveness and safety of intra-articular AD-MSCs in adults with Kellgren-Lawrence Grade II-IV knee OA. A comprehensive search identified eight randomized controlled trials that compared high-dose autologous, high-dose allogeneic, and low-dose allogeneic AD-MSCs to placebo or standard care interventions, such as hyaluronic acid, corticosteroids, or physical therapy. The primary outcomes were pain relief, assessed by the Visual Analog Scale (VAS), and functional improvement, measured by the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), at three, six, and 12 months. Treatment rankings were determined using Surface Under the Cumulative Ranking (SUCRA) probabilities. High-dose autologous AD-MSCs ranked highest for pain relief at three, six, and 12 months (VAS SUCRA: 75.99%, 82.27%, 81.65%), while high-dose allogeneic AD-MSCs ranked highest for functional improvement at six and 12 months (WOMAC SUCRA: 74.6%, 71.71%). Low-dose allogeneic AD-MSCs consistently ranked lowest for both outcomes. Adverse event analysis indicated that low-dose allogeneic AD-MSCs had the highest risk of adverse effects (SUCRA: 22.24%), followed by high-dose allogeneic AD-MSCs (26.52%). In contrast, high-dose autologous AD-MSCs ranked safer (SUCRA: 54.08%). Serious adverse events were rare and unrelated to treatment, and consistency testing confirmed no significant inconsistencies in the NMA framework. Overall, high-dose autologous AD-MSCs provided sustained pain relief over 12 months, while high-dose allogeneic AD-MSCs demonstrated superior long-term functional improvement. These findings support a two-phase treatment model in which autologous AD-MSCs offer early and prolonged symptom relief, and allogeneic AD-MSCs assist in long-term joint recovery. Overall, AD-MSC therapy was well tolerated and may represent a viable, personalized, non-surgical knee OA management strategy.
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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 | 0.018 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.040 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".