Serum periostin level is not sufficient to serve as a clinically applicable biomarker of osteoarthritis
Bibliographic record
Abstract
BACKGROUND: Emerging knowledge has highlighted the role of periostin (POSTN) in osteoarthritis (OA) process; however, whether POSTN is suitable as a biomarker of OA remains unclear. This study aimed to investigate the potential value of POSTN as a biomarker of OA. METHODS: Ten 6-month-old female Sprague-Dawley (SD) rats were used in this study. Five rats underwent ovariectomy (OVX) operation and the others were carried out sham operation. Thirty-two patients with OA and eighteen patients who had meniscus injuries or ligament injuries but with intact articular cartilages were recruited in this study from January to July 2019 at the Peking University International Hospital. We first detected the expression of POSTN in the cartilage of OVX induced OA rats and different compartments of the knee joint in patients with OA using immunohistochemistry. Besides, serum POSTN levels in patients with or without OA were examined using enzyme-linked immunosorbent assay (ELISA). The associations among serum POSTN levels, clinical symptoms, and radiological severity were assessed according to the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores and, Kellgren-Lawrence (KL) grading, respectively. Finally, multivariable cumulative link models were established to evaluate the validity of serum POSTN level as a predictor of knee OA. RESULTS: The significantly higher POSTN expression was found in OVX-OA rats than Sham rats, while, the expression of POSTN was significantly higher in the torn cartilage of patients with OA. However, the serum POSTN level did not differ significantly between patients with and without OA. Additionally, we found no remarkable associations between serum POSTN level and WOMAC scores and KL grading. Subsequent analysis revealed that serum POSTN was not a significant predictor of OA. CONCLUSION: Thus, although POSTN may be involved OA process and local POSTN is valuable in disease diagnosis and distinguishing of the severity of disease, its serum level is not sufficient to serve as a candidate biomarker of OA given the current analysis technology.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".