Quantifying knee-adjacent subcutaneous fat in the entire OAI baseline dataset – Associations with cartilage MRI T2, thickness and pain, independent of BMI

Abstract

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 T₂ 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 T₂ 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, T₂, 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 T₂ 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 T₂ 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.