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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...

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Flow Cytometry Analysis of Immune Cell Subsets within the Murine Spleen, Bone Marrow, Lymph Nodes and Synovial Tissue in an Osteoarthritis Model
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Plasma Proteomic Profiles Predict Individual Future Osteoarthritis Risk.

Zijian Kang1, Jianzheng Zhang2, Wenxin Liu1

  • 1Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Arthritis & Rheumatology (Hoboken, N.J.)
|February 24, 2025
PubMed
Summary

Researchers identified key plasma proteins, including COL9A1 and CRTAC1, that predict future osteoarthritis (OA) risk. These biomarkers show potential for early OA detection and improved management strategies.

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Area of Science:

  • Biochemistry and Molecular Biology
  • Genetics and Genomics
  • Biomarker Discovery

Background:

  • Osteoarthritis (OA) poses a significant socioeconomic burden, yet early diagnosis remains challenging due to a lack of sensitive biomarkers.
  • Current diagnostic limitations hinder timely intervention and effective management of OA.

Purpose of the Study:

  • To identify plasma proteins associated with the future risk of developing OA.
  • To develop a predictive model for OA risk using proteomic and clinical data.

Main Methods:

  • Large-scale proteomic analysis of 45,307 UK Biobank participants using Olink Explore Proximity Extension Assay (1,463 proteins).
  • Construction of a predictive model with LightGBM machine learning and SHapley Additive exPlanations (SHAP) for variable importance.
  • Integration of plasma protein data with clinical variables and electronic health records.

Main Results:

  • Identified a panel of proteins significantly associated with incident OA risk.
  • COL9A1 and CRTAC1 emerged as the strongest predictors (HR 1.54 and 1.65, respectively).
  • Proteins COL9A1 and CRTAC1 levels deviated over a decade before OA onset, indicating early detection potential. Predictive model achieved AUCs of 0.72-0.82 for OA risk prediction at various time points and joint-specific predictions.

Conclusions:

  • Plasma proteomics can predict future OA risk, enabling preemptive strategies.
  • An integrated model of proteomic biomarkers and clinical data offers a promising tool for OA risk assessment.
  • This approach may optimize OA management and enhance prevention efforts.