Genetic markers for knee osteoarthritis presence are not associated with disease progression - data from the IMI-APPROACH cohort

Abstract

[Abstract]

Objective: Knee osteoarthritis (OA) is a heterogeneous disease with different endotypes and phenotypes, resulting in patients' varying clinical and structural progression. Several genomic markers have been associated with knee OA presence. This study aimed to find new associations of these genetic markers with knee OA progression and to investigate the risk of knee OA progression using a polygenic risk score (PRS).

Methods: Data from knee OA patients (n = 297) from the IMI-APPROACH cohort with detailed measurements on disease progression were used. Knee OA progression definitions were based on the decrease in minimum joint space width in mm (minJSW; primary outcome), increase in pain on the Knee injury and Osteoarthritis Outcome Score (KOOS), and presence of radiographic OA (based on the Kellgren-Lawrence score) over 24 months. 30 previously reported single nucleotide polymorphisms (SNPs) associated with presence of OA irrespective of affected joints or knee OA specifically were investigated. We performed a SNP based genome-wide association analysis using the disease progression definitions. Furthermore, a PRS was created using the 30 presence SNPs to predict knee OA progression.

Results: Existing genetic markers for knee OA presence were not found to be associated with knee OA progression. The PRS of the SNPs for knee OA presence did also not show significant predictive value for knee OA progression. Unexpectedly, nineteen different variants were associated significantly (P < 5 × 10-8) with minJSW decrease. Ten SNPs are located near protein coding genes PLCL2, CDYL2, and NTNG1, and several SNPs are located in or near long non-coding RNAs (lncRNA).

Conclusions: The 30 OA risk SNPs individually and combined in a PRS are not associated with progression of knee OA in the IMI-APPROACH cohort. 19 different SNPs were associated with minJSW decrease. We demonstrated how to employ multiple bioinformatics tools to, despite a limited dataset, still prioritise potential biomarkers for associations to knee OA progression.