Key genes and pathways common to both OA and KBD

Abstract

Purpose: Age, sex, and BMI can help to predict knee osteoarthritis (OA) patients from healthy individuals (HV). The metabolome, the comprehensive output of metabolic processes occurring within an individual, and the levels of individual metabolites can also be used to help with disease diagnosis. However, metabolite selection methods and modeling algorithms that best identify metabolites capable of predicting OA have not been well established. We sought to determine a method that was capable of effectively identifying metabolite signatures that were predictive of OA in demographically-stratified populations. Methods: Phosphatidylcholine (lysoPC) and lyso(PC) analogues from plasma of 152 OA patients undergoing total knee replacement and 194 HV (346 total individuals) were measured by metabolomics. Cohorts were stratified by age, sex and BMI. Analogue signatures were determined by generating univariate area under the receiver operator curve (UAUC) values from 1000 bootstrapped training and test sets. Metabolites with UAUC > 0.5 at the 2.5% quantile of the empirical distribution were selected as capable of predicting OA from HV within strata. Three multivariate classification algorithms were tested using each signature. The most consistent algorithm was determined by the minimum difference between training and test set AUC values, derived from 1000 resamplings. The effect of diabetes mellitus on signature elements was also determined by identifying metabolites that were significantly changed in diabetic patients and removing those signature elements from multivariate analyses. Results: The metabolite signature from males age > 50 years old encompassed the majority of identified metabolites in other strata, suggesting lysoPCs and PCs were dominant indicators of OA in older males. Principal component regression with logistic regression (PCR) was the most consistent classification algorithm tested. Using this algorithm, the males age > 50 years old signature had fair power to differentiate OA patients from HV. In individuals with diabetes mellitus compared to those without, 3 metabolites within HV individuals > age 50 and one metabolite in HV males age > 50 years were significantly different and corresponded to metabolites within identified signatures from each stratum used for multivariate prediction modeling. Removing these metabolites had a minimal effect on the empirical AUCs generated using PCR modeling compared to using the entire determined signatures. Conclusions: Individual levels of lysoPC and PC analogues may be indicative of individuals with OA in older male populations. In this cohort, diabetes had a minimal effect on our ability to classify individuals with OA. Our metabolite signature modeling method is likely to increase classification power in validation cohorts. IRISIN LEVELS ARE ASSOCIATED WITH EXERCISE LEVEL, PAIN AND FUNCTION IN PATIENTS WITH KNEE OSTEOARTHRITIS

Identification of N-Glycoproteins of Knee Cartilage from Adult Osteoarthritis and Kashin-Beck Disease Based on Quantitative Glycoproteomics, Compared with Normal Control Cartilage

“…Meanwhile, some pathologic similarities of cartilage damage between them have been found, such as degradation of extracellular matrix (ECM) and loss of proteoglycan and collagen. Furthermore, genomic and proteomic studies of KBD and OA have effectively confirmed that they share key genes, proteins, and pathways primarily associated with metabolism, focal adhesion, and signal transduction in ECM [ 3 , 4 , 5 ]. Additionally, they have different abundances in key proteins, including collagens, integrin, thrombospondin 2 (THBS2), thrombospondin 4 (THBS4), etc.…”