Karaglani Makrina, Panagopoulou Maria, Cheimonidi Christina, Tsamardinos Ioannis, Maltezos Efstratios, Papanas Nikolaos, Papazoglou Dimitrios, Mastorakos George, Chatzaki Ekaterini
Laboratory of Pharmacology, Department of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece.
JADBio Gnosis DA, Science and Technology Park of Crete, 71500 Heraklion, Greece.
J Clin Med. 2022 Feb 17;11(4):1045. doi: 10.3390/jcm11041045.
The need for minimally invasive biomarkers for the early diagnosis of type 2 diabetes (T2DM) prior to the clinical onset and monitoring of β-pancreatic cell loss is emerging. Here, we focused on studying circulating cell-free DNA (ccfDNA) as a liquid biopsy biomaterial for accurate diagnosis/monitoring of T2DM.
ccfDNA levels were directly quantified in sera from 96 T2DM patients and 71 healthy individuals via fluorometry, and then fragment DNA size profiling was performed by capillary electrophoresis. Following this, ccfDNA methylation levels of five β-cell-related genes were measured via qPCR. Data were analyzed by automated machine learning to build classifying predictive models.
ccfDNA levels were found to be similar between groups but indicative of apoptosis in T2DM. (Insulin), (Islet Amyloid Polypeptide-Amylin), (Glucokinase), and (Potassium Inwardly Rectifying Channel Subfamily J member 11) levels differed significantly between groups. AutoML analysis delivered biosignatures including , and methylation, with the highest ever reported discriminating performance of T2DM from healthy individuals (AUC 0.927).
Our data unravel the value of ccfDNA as a minimally invasive biomaterial carrying important clinical information for T2DM. Upon prospective clinical evaluation, the built biosignature can be disruptive for T2DM clinical management.
对于在2型糖尿病（T2DM）临床发病前进行早期诊断以及监测β胰腺细胞丢失的微创生物标志物的需求正在出现。在此，我们专注于研究循环游离DNA（ccfDNA）作为一种液体活检生物材料用于T2DM的准确诊断/监测。
通过荧光测定法直接定量96例T2DM患者和71名健康个体血清中的ccfDNA水平，然后通过毛细管电泳进行片段DNA大小分析。在此之后，通过qPCR测量五个β细胞相关基因的ccfDNA甲基化水平。通过自动化机器学习分析数据以建立分类预测模型。
发现两组之间的ccfDNA水平相似，但表明T2DM中存在细胞凋亡。（胰岛素）、（胰岛淀粉样多肽 - 胰淀素）、（葡萄糖激酶）和（内向整流钾通道亚家族J成员11）水平在两组之间存在显著差异。自动机器学习分析得出包括、和甲基化的生物标志物，其区分T2DM与健康个体的性能是有史以来报道的最高水平（AUC 0.927）。
我们的数据揭示了ccfDNA作为一种携带T2DM重要临床信息的微创生物材料的价值。经过前瞻性临床评估，所建立的生物标志物可能会对T2DM的临床管理产生颠覆性影响。
