The purpose of this study is to demonstrate advantages of principal component analysis (PCA) as a standardized procedure in the evaluation of osteoarthritis (OA) in a skeletal series to: (1) compute aggregate scores for joint complexes that accurately capture pathological expression, (2) reveal which variables describe the most sample variation in OA, (3) enable inter- and intra-sample comparison of results, and (4) formulate predictive models from component-based arthritic scores.
The sample (144 males, 145 females) is drawn from a large skeletal cemetery collection of modern Europeans of known sex, age, and occupation. OA data was collected using standard ranked categorical scoring. PCA was conducted separately on lumbar spine, pelvis, and knee regions to generate composite OA scores from eigenequations of the first and second principal components (PC).
Results demonstrate that as severity in OA increases, so does the distribution of OA within the joint surface. In each region, PCA produced the same general pattern with eburnation scoring driving significant changes in composite OA scores, representing earlier to later stages of cartilage degeneration. The distribution of arthritic traits determined by PCA produced an OA score that quantifies the expression of joint changes in varied biological joint structures from most moveable to least mobile, the final stage being joint fusion. OA scores are most highly variable in the lumbar region for both males and females, as compared to the pelvis and knee.
PCA is a simple, non-parametric method of extracting relevant information from complex OA datasets and summarizes variation based on correlated multi-attributes to reveal a simplified structure of OA expression. Multivariate techniques like PCA should be used to describe discrete OA samples, and are useful to compute population-specific representative measurements for idiopathic joint OA in a skeletal sample.