Antioxidant and Anti-inflammatory Diagnostic Biomarkers in Multiple Sclerosis: A Machine Learning Study

Abstract

An imbalance of inflammatory/anti-inflammatory and oxidant/antioxidant molecules has been implicated in the demyelination and axonal damage in multiple sclerosis (MS). The current study aimed to evaluate the plasma levels of tumor necrosis factor (TNF)-α, soluble TNF receptor (sTNFR)1, sTNFR2, adiponectin, hydroperoxides, advanced oxidation protein products (AOPP), nitric oxide metabolites, total plasma antioxidant capacity using the total radical-trapping antioxidant parameter (TRAP), sulfhydryl (SH) groups, as well as serum levels of zinc in 174 MS patients and 182 controls. The results show that MS is characterized by lowered levels of zinc, adiponectin, TRAP, and SH groups and increased levels of AOPP. MS was best predicted by a combination of lowered levels of zinc, adiponectin, TRAP, and SH groups yielding an area under the receiver operating characteristic (AUC/ROC) curve of 0.986 (±0.005). The combination of these four antioxidants with sTNFR2 showed an AUC/ROC of 0.997 and TRAP, adiponectin, and zinc are the most important biomarkers for MS diagnosis followed at a distance by sTNFR2. Support vector machine with tenfold validation performed on the four antioxidants showed a training accuracy of 92.9% and a validation accuracy of 90.6%. The results indicate that lowered levels of those four antioxidants are associated with MS and that these antioxidants are more important biomarkers of MS than TNF-α signaling and nitro-oxidative biomarkers. Adiponectin, TRAP, SH groups, zinc, and sTNFR2 play a role in the pathophysiology of MS, and a combination of these biomarkers is useful for predicting MS with high sensitivity, specificity, and accuracy. Drugs that increase the antioxidant capacity may offer novel therapeutic opportunities for MS.

Keywords: Adiponectin; Machine learning study; Multiple sclerosis; Oxidative stress; TNF receptors; Zinc.