Prediction of sarcopenia using a combination of multiple serum biomarkers

Abstract

Sarcopenia is a gradual loss of skeletal muscle mass and function with aging. Given that sarcopenia has been recognized as a disease entity, effective molecular biomarkers for early diagnosis are required. We recruited 46 normal subjects and 50 patients with moderate sarcopenia aged 60 years and older. Sarcopenia was clinically identified on the basis of the appendicular skeletal muscle index by applying cutoff values derived from the Asian Working Group for Sarcopenia. The serum levels of 21 potential biomarkers were analyzed and statistically examined. Interleukin 6, secreted protein acidic and rich in cysteine, macrophage migration inhibitory factor, and insulin-like growth factor 1 levels differed significantly between the normal and sarcopenia groups. However, in each case, the area under the receiver operating characteristics curve (AUC) was <0.7. Subsequent combination of the measurements of these biomarkers into a single risk score based on logistic regression coefficients enhanced the accuracy of diagnosis, yielding an AUC value of 0.763. The best cutoff value of 1.529 had 70.0% sensitivity and 78.3% specificity (95% CI = 2.80-21.69, p < 0.0001). Combined use of the selected biomarkers provides higher diagnostic accuracy than individual biomarkers, and may be effectively utilized for early diagnosis and prognosis of sarcopenia