Metabolic Stress Accelerates Dysregulated Synovial Macrophage-Fibroblast Communication and Htra1 Overproduction in Osteoarthritis
Bibliographic record
Abstract
Abstract Biomechanical and metabolic factors increase the risk for osteoarthritis (OA) by causing supraphysiological stresses on joint tissues. Chronic exposure to these stresses contributes to failure of the joint organ system, resulting in pain and loss of function for patients with OA. The synovium is vital for joint organ health but during OA, synovial inflammation and damage are associated with worse outcomes including pain. Unfortunately, the separate and combined effects of metabolic and biomechanical stresses on synovial tissues are not well understood. In this study, metabolic syndrome (MetS) was associated with worse knee pain in patients with early-stage knee OA, suggesting that metabolic stress may act on synovial tissues during early-stage OA, exacerbating outcomes. In a rat model of experimental knee OA, the combined effects of biomechanical and metabolic stresses induced worse knee pain, cartilage damage, and synovial inflammation than biomechanical stress alone. Further, single-cell RNA sequencing of synovial macrophages and fibroblasts identified earlier metabolic (glycolytic and respiratory) shifts, neurogenesis, dysregulated communication, and cell activation when metabolic and biomechanical stresses were combined. Lastly, using a direct contact co-culture system, we showed that metabolic stress alters macrophage-fibroblast communication leading to increased expression of Htra1, a pathogenic protease in OA. This study identifies novel mechanisms that may represent amenable therapeutic targets for patients experiencing MetS and OA. One-sentence summary: Metabolic stress may cause worse outcomes in OA through dysregulated synovial cell communication that activates synovial fibroblasts and increases Htra1 production.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".