Serum protein signature of coronary artery disease in type 2 diabetes mellitus

• DOI: 10.1186/s12967-018-1755-5
• Publication Date: 2019-01-10T12:43:52Z
• Authors: Ramu Adela; Podduturu Naveen ...; Tarini Shankar Ghosh; Suruchi Aggarwal; Amit Kumar Yadav; Bhabatosh Das; Sanjay K. Banerjee
• Keywords: Resistin

Coronary artery disease (CAD) is the leading cause of morbidity and mortality in patients with type 2 diabetes mellitus (T2DM). The purpose present study was to discriminate Indian CAD or without T2DM by using multiple pathophysiological biomarkers.Using sensitive multiplex protein assays, we assessed 46 markers including cytokines/chemokines, metabolic hormones, adipokines apolipoproteins for evaluating different conditions control, T2DM, (T2DM_CAD). Network analysis performed create protein-protein interaction networks significantly (p < 0.05) altered each STRING 10.5 database. We used two supervised methods i.e., between class (BCA) principal component (PCA) reveals distinct biomarkers profiles. Further, random forest classification (RF) classify diseases panel markers.Our BCA PCA revealed a biomarker profiles high degree variability marker T2DM_CAD CAD. Thereafter, identified potential differentiate CAD, based on their relative abundance serum. RF classified patterns nine IL-1β, GM-CSF, glucagon, PAI-I, rantes, IP-10, resistin, GIP Apo-B; 14 PDGF-BB, PAI-1, lipocalin-2, leptin, IL-13, eotaxin, Apo-E, ghrelin, adipsin, GIP, Apo-CII IP-10; _CAD 12 insulin, adiponectin, Apo-AII, IL-6 ghrelin respect control subjects. Using network analysis, have several cellular proteins like PTPN1, AKT1, INSR, LEPR, IRS1, IRS2, IL1R2, IL6R, PCSK9 MYD88, which are responsible regulating inflammation, insulin resistance, atherosclerosis.We three sets serum diabetes, associated nonparametric-based machine learning approach. These classifiers may be useful monitoring progression from healthy person T2DM_CAD. However, these findings need further confirmed future studies large number samples.