Evidence map›Paper›PMID 42787601›Full record

ArticleJournal of medical biochemistry2026

Correlation of bone metabolism with serum pain mediators and inflammatory cytokines in knee osteoarthritis and their role in predicting poor rehabilitation outcomes: A prospective cohort study.

Yan Tao, Nan Cai, Juxia Zhang, Yaoping Zhao, Yan Zhou, Pengfei Liu

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Article in Journal of medical biochemistry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Yan TaoBeijing Jishuitan Hospital, Capital Medical University, Department of Pain Management, Beijing, China.
Nan CaiBeijing Jishuitan Hospital, Capital Medical University, Department of Anesthesiology, Beijing, China.
Juxia ZhangBeijing Jishuitan Hospital, Capital Medical University, Department of Anesthesiology, Beijing, China.
Yaoping ZhaoBeijing Jishuitan Hospital, Capital Medical University, Department of Anesthesiology, Beijing, China.
Yan ZhouBeijing Jishuitan Hospital, Capital Medical University, Department of Pain Management, Beijing, China.
Pengfei LiuBeijing Shijitan Hospital, Capital Medical University, Department of Anesthesiology, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The present study was designed to investigate how bone metabolism markers - specifically Procollagen Type I N-Terminal Propeptide (PINP) and C-Telopeptide of Type I Collagen (CTX) - correlate with serum pain mediators (Prostaglandin E2 [PG E2], Norepinephrine [NE], Substance P [SP]) and inflammatory cytokines (Interleukin-1p [IL-ip], Interleukin-6 [IL-6], Tumor Necrosis Factor-a [TNF-a]) in individuals with knee osteoarthritis (KOA). A further aim was to develop a multi-biomarker predictive model for identifying patients at risk of suboptimal rehabilitation outcomes to guide targeted clinical management. Methods: In this prospective cohort study, 184 KOA patients and an 180 healthy controls were enrolled between January 2023 and May 2024. Using baseline serum, biomarker levels were assessed; Enzyme-Linked Immunosorbent Assay (ELISA) was employed for all analytes except SP, which was determined by radioimmunoassay. The primary endpoint, suboptimal rehabilitation outcome, was determined as either a <30% enhancement in the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score or a <2-point decrease on the Visual Analogue Scale (VAS). Model construction utilized multivariate logistic regression, with its performance evaluated through receiver operating characteristic (ROC) curve analysis, obtaining the area under the curve (AUC), sensitivity, and specificity. Results: KOA patients exhibited significantly lower serum PINP levels but elevated levels of CTX, PGE2, NE, SP, IL-1p, IL-6, and TNF-a compared to controls (P< 0.05). PINP demonstrated significant inverse correlations with the pain and inflammatory biomarkers, while CTX showed strong positive correlations with them (P< 0.05). The integrative logistic regression model, integrating PINP, CTX, inflammatory cytokines, and pain mediators, demonstrated excellent predictive value for poor outcomes, with an AUC of 0.862, 83.58% sensitivity, and 78.63% specificity (P< 0.05). Conclusions: Dysregulated bone metabolism (characterized by low PINP and high CTX) in KOA is significantly linked to heightened expression of pain-associated and inflammatory markers. A combined panel of these biomarkers serves as an effective tool for predicting individuals likely to experience suboptimal rehabilitation results.

Indexed as

bone metabolismdiagnostic modelsinflammatory factorsknee osteoarthritispain mediators

Identifiers

PMID42787601
PMCPMC13601834

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.