Pelvic body composition measurements by quantitative computed tomography: Association with recent hip fracture

Abstract

Introduction Loss of subcutaneous fat, decreased muscle cross-sectional area (CSA) and increased muscle adiposity are related to declining physical function and disability in the elderly, but there is little information about the relationship of these tissue changes to hip fracture. Thus we have compared body composition measures in women with hip fractures to age-matched controls, using quantitative computed tomography (QCT) imaging of the hip to characterize total adiposity, muscle CSA and muscle attenuation coefficient, a measure of adiposity. Materials and methods 45 Chinese women (mean age 74.71 ± 5.94) with hip fractures were compared to 66 healthy control subjects (mean age 70.70 ± 4.66). Hip QCT scans were analyzed to compute total adipose CSA as well as CSA and attenuation values of muscle groups in the CT scan field of view, including hip extensors, abductors, adductors and flexors. The total femur areal BMD (aBMD) was estimated from the QCT images. Logistic regression was employed to compare body composition measures between fracture subjects and controls after adjustment for age, height, BMI and aBMD. Receiver–operator curve (ROC) analyses determined whether combinations of aBMD and body composition had higher area under curve (AUC) than aBMD alone. Results and conclusions Fracture subjects had lower fat CSA ( p < 0.0001) than controls but had higher muscle adiposity as indicated by lower attenuation in the adductor, abductor and flexor groups (0.00001 < p < 0.02). Fracture subjects also had lower extensor and adductor CSA values ( p < 0.0001). After age and BMI adjustment, the total fat CSA, the extensor and adductor CSA values, and the adductor attenuation values remained significantly lower in the fracture subjects (0.001 < p < 0.05). In ROC analyses, models combining aBMD with soft tissue measures had higher AUC than models containing only BMD (0.001 < p < 0.05). Combining body composition with skeletal measures may improve fracture prediction compared to bone measures alone.