Quantifying knee-adjacent subcutaneous fat in the entire OAI baseline dataset - Associations with cartilage MRI T, thickness and pain, independent of BMI.

Objective: Knee-adjacent subcutaneous fat (kaSCF) has emerged as a potential biomarker and risk factor for osteoarthritis (OA) progression. This study aims to develop an artificial intelligence-based tool for the automatic segmentation of kaSCF thickness and evaluate the cross-sectional associations between kaSCF, cartilage thickness, magnetic resonance imaging-based cartilage T relaxation time, knee pain, and muscle strength independent of body mass index (BMI).

Design: Baseline 3.0T MR images of the right knee from the entire Osteoarthritis Initiative cohort (n=4796) were used to quantify average values of kaSCF, cartilage thickness, and T using deep learning algorithms. Regression models (adjusted for age, gender, BMI, and race) were used to evaluate the associations between standardized kaSCF and outcomes of cartilage thickness, T, pain, and knee extension strength.

Results: Model prediction CVs for kaSCF thickness ranged from 3.57% to 9.87% across femoral and tibial regions. Greater average kaSCF was associated with thinner cartilage in men (std. β= -0.029, 95% CI: -0.050 to -0.007, p=0.010) and higher T in women (std. β=0.169, 95% CI: 0.072 to 0.265, p=0.001). Greater kaSCF was also associated with lower knee extension force (std. β= -15.36, 95% CI: -20.39 to -10.33, p<0.001) and higher odds of frequent knee pain (std. odds ratio=1.156, 95% CI: 1.046 to 1.278, p=0.005) across all participants.

Conclusions: Greater kaSCF was associated with thinner cartilage in men, higher T in women, reduced knee strength, and greater knee pain, independent of BMI. These findings suggest a potential role of kaSCF as a predictor for knee osteoarthrits-related structural, functional, and clinical outcomes independent of the effects of BMI.