Multivariate functional principal component analysis identifies waveform features of gait biomechanics related to early-to-moderate hip osteoarthritis.

Clinicians often examine movement patterns to design hip osteoarthritis (OA) interventions, yet traditional biomechanical analyses only report a single timepoint. Multivariate principal component analysis (MFPCA) analyzes the entire waveform (i.e., movement pattern), which clinicians observe to direct treatment. This study investigated hip OA indicators, by (1) employing MFPCA to characterize variance across the hip, knee, and ankle angles in healthy and early-to-moderate hip OA participants; and (2) investigating relationships between these waveform features and hip cartilage health. Bilateral hip magnetic resonance images from 72 participants with Kellgren-Lawrence grades ranging from 0 to 3 were used to calculate mean T and T relaxation times in the femoral and acetabular cartilage. MFPCA was performed on lower-limb gait biomechanics and used to identify primary modes of variation, which were related to T and T relaxation times. Here, a MFPC = mode of variation = waveform feature. In the femoral cartilage, transverse plane MFPCs 3 and 5 and body mass index (BMI) was related to T , while MFPC 2 and BMI were related to T relaxation times. In the acetabular cartilage, sagittal plane MFPC 1 and BMI were related to T , while BMI was related to T relaxation times. Greater internal rotation was related to increased T and T relaxation times in the femoral cartilage, while the greater extension was related to increased T relaxation times in the acetabular cartilage. This study established a data-driven framework to assess relationships between multi-joint biomechanics and quantitative assessments of cartilage health and identified waveform features that could be evaluated in future hip OA intervention studies.