Drug Discovery Research Center, Translational Health Science and Technology Institute (THSTI), Faridabad, Haryana, 121001, India.
Department of Pharmacy Practice, National Institute of Pharmaceutical Education and Research, NIPER, Guwahati, Assam, India.
J Transl Med. 2019 Jan 24;17(1):17. doi: 10.1186/s12967-018-1755-5.
Coronary artery disease (CAD) is the leading cause of morbidity and mortality in patients with type 2 diabetes mellitus (T2DM). The purpose of the present study was to discriminate the Indian CAD patients with or without T2DM by using multiple pathophysiological biomarkers.
Using sensitive multiplex protein assays, we assessed 46 protein markers including cytokines/chemokines, metabolic hormones, adipokines and apolipoproteins for evaluating different pathophysiological conditions of control, T2DM, CAD and T2DM with CAD patients (T2DM_CAD). Network analysis was performed to create protein-protein interaction networks by using significantly (p < 0.05) altered protein markers in each disease using STRING 10.5 database. We used two supervised analysis methods i.e., between class analysis (BCA) and principal component analysis (PCA) to reveals distinct biomarkers profiles. Further, random forest classification (RF) was used to classify the diseases by the panel of markers.
Our two supervised analysis methods BCA and PCA revealed a distinct biomarker profiles and high degree of variability in the marker profiles for T2DM_CAD and CAD. Thereafter, the present study identified multiple potential biomarkers to differentiate T2DM, CAD, and T2DM_CAD patients based on their relative abundance in serum. RF classified T2DM based on the abundance patterns of nine markers i.e., IL-1β, GM-CSF, glucagon, PAI-I, rantes, IP-10, resistin, GIP and Apo-B; CAD by 14 markers i.e., resistin, PDGF-BB, PAI-1, lipocalin-2, leptin, IL-13, eotaxin, GM-CSF, Apo-E, ghrelin, adipsin, GIP, Apo-CII and IP-10; and T2DM _CAD by 12 markers i.e., insulin, resistin, PAI-1, adiponectin, lipocalin-2, GM-CSF, adipsin, leptin, Apo-AII, rantes, IL-6 and ghrelin with respect to the control subjects. Using network analysis, we have identified several cellular network proteins like PTPN1, AKT1, INSR, LEPR, IRS1, IRS2, IL1R2, IL6R, PCSK9 and MYD88, which are responsible for regulating inflammation, insulin resistance, and atherosclerosis.
We have identified three distinct sets of serum markers for diabetes, CAD and diabetes associated with CAD in Indian patients using nonparametric-based machine learning approach. These multiple marker classifiers may be useful for monitoring progression from a healthy person to T2DM and T2DM to T2DM_CAD. However, these findings need to be further confirmed in the future studies with large number of samples.
冠心病（CAD）是 2 型糖尿病（T2DM）患者发病率和死亡率的主要原因。本研究的目的是使用多种病理生理生物标志物来区分印度 CAD 患者是否合并 T2DM。
使用敏感的多重蛋白分析，我们评估了 46 种蛋白标志物，包括细胞因子/趋化因子、代谢激素、脂肪因子和载脂蛋白，以评估对照、T2DM、CAD 和 T2DM 合并 CAD（T2DM_CAD）患者的不同病理生理状况。使用 STRING 10.5 数据库中的显著改变的蛋白标志物（p<0.05），通过网络分析创建蛋白-蛋白相互作用网络。我们使用两种有监督分析方法，即分类间分析（BCA）和主成分分析（PCA），来揭示不同疾病的特征生物标志物图谱。此外，随机森林分类（RF）用于根据标志物组对疾病进行分类。
我们的两种有监督分析方法 BCA 和 PCA 揭示了 T2DM_CAD 和 CAD 患者的特征生物标志物图谱和高度可变的标志物图谱。此后，本研究基于血清中相对丰度确定了多种潜在的生物标志物，以区分 T2DM、CAD 和 T2DM_CAD 患者。RF 根据 9 种标志物的丰度模式对 T2DM 进行分类，即 IL-1β、GM-CSF、胰高血糖素、PAI-1、rantes、IP-10、抵抗素、GIP 和 Apo-B；CAD 由 14 种标志物，即抵抗素、PDGF-BB、PAI-1、脂联素、瘦素、IL-13、嗜酸性粒细胞趋化因子、GM-CSF、Apo-E、ghrelin、内脂素、GIP、Apo-CII 和 IP-10 进行分类；T2DM_CAD 由 12 种标志物，即胰岛素、抵抗素、PAI-1、脂联素、脂联素、GM-CSF、内脂素、瘦素、Apo-AII、rantes、IL-6 和 ghrelin 进行分类，与对照组相比。使用网络分析，我们确定了几个细胞网络蛋白，如 PTPN1、AKT1、INSR、LEPR、IRS1、IRS2、IL1R2、IL6R、PCSK9 和 MYD88，它们负责调节炎症、胰岛素抵抗和动脉粥样硬化。
我们使用基于非参数的机器学习方法，在印度患者中为糖尿病、CAD 和糖尿病合并 CAD 确定了三组不同的血清标志物。这些多标志物分类器可用于监测从健康人到 T2DM 和 T2DM 进展为 T2DM_CAD 的情况。然而，这些发现需要在未来的研究中进一步用大量样本进行验证。
