Fibulin-3 serum and urine levels in the diagnosis and severity assessment of primary knee osteoarthritis

Serum fibulin-3 as a diagnostic and prognostic biomarker in patients with knee osteoarthritis

BACKGROUND

Osteoarthritis is a chronic degenerative disorder with rising prevalence. Early detection of structural damage is difficult. Consequently, there is a pressing demand for reliable biomarkers that enable an earlier diagnosis of osteoarthritis. The aim is to investigate the level of serum fibulin-3 in patients with primary knee osteoarthritis and its correlation with disease severity.

SUBJECTS AND METHODS

A case-control study was conducted at the Baqubah Teaching Hospital from November 2023 to January 2024. One hundred twenty persons participated in this study (eighty females with the diagnosis of knee osteoarthritis in its early and late stages, and forty age-matched, apparently healthy control. Serum fibulin-3, ESR, CRP, and calcium levels were measured for all participants. Ethical approval was obtained. SPSS was used for data analysis.

RESULTS

Patients with osteoarthritis had considerably higher serum levels of fibulin-3. In patients with late-stage knee osteoarthritis, this rise was greater than in earlier stages. Serum fibulin and ESR are positively correlated. Fibulin's area under the curve is 0.830 for diagnosis and 0.709 for differentiating between osteoarthritis's early and late stages.

CONCLUSION

Serum levels of fibulin-3 can act as diagnostic markers for OA and may be useful in determining the severity of knee osteoarthritis.

Novel genetic loci associated with osteoarthritis in multi-ancestry analyses in the Million Veteran Program and UK Biobank

Osteoarthritis is a common progressive joint disease. As no effective medical interventions are available, osteoarthritis often progresses to the end stage, in which only surgical options such as total joint replacement are available. A more thorough understanding of genetic influences of osteoarthritis is essential to develop targeted personalized approaches to treatment, ideally long before the end stage is reached. To date, there have been no large multiancestry genetic studies of osteoarthritis. Here, we leveraged the unique resources of 484,374 participants in the Million Veteran Program and UK Biobank to address this gap. Analyses included participants of European, African, Asian and Hispanic descent. We discovered osteoarthritis-associated genetic variation at 10 loci and replicated findings from previous osteoarthritis studies. We also present evidence that some osteoarthritis-associated regions are robust to population ancestry. Drug repurposing analyses revealed enrichment of targets of several medication classes and provide potential insight into the etiology of beneficial effects of antiepileptics on osteoarthritis pain.

Identification of Diagnostic Biomarkers of Osteoarthritis Based on Multi-Chip Integrated Analysis and Machine Learning

The pathogenesis of osteoarthritis (OA) is still unclear. It is therefore important to identify relevant diagnostic marker genes for OA. We performed an integrated analysis with multiple microarray data cohorts to identify potential transcriptome markers of OA development. Further, to identify OA diagnostic markers, we established gene regulatory networks based on the protein-protein interaction network involved in these differentially expressed genes (DEGs). Using support vector machine (SVM) pattern recognition, a diagnostic model for OA prediction and prevention was established. Kyoto Encyclopedia of Genes and Genomes pathway analysis revealed that 190 DEGs were mainly enriched in pathways like the tumor necrosis factor signaling pathway, interleukin-17 signaling pathway, mitogen-activated protein kinase signaling pathway, nuclear factor kappa-light-chain-enhancer of activated B cells signaling pathway, and osteoclast differentiation. Eight hub genes (POSTN, MMP2, CTSG, ELANE, COL3A1, MPO, COL1A1, and COL1A2) were considered potential diagnostic biomarkers for OA, the area under curve (AUC) was >0.95, which showed high accuracy. The sensitivity and specificity of the SVM model of OA based on these eight genes reached 100% in multiple external verification cohorts. Our research provides a theoretical basis for OA diagnosis for clinicians.