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Article

Liquid Biopsy in Type 2 Diabetes Mellitus Management: Building Specific Biosignatures via Machine Learning

1
Laboratory of Pharmacology, Department of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece
2
JADBio Gnosis DA, Science and Technology Park of Crete, 71500 Heraklion, Greece
3
Diabetes Centre, 2nd Department of Internal Medicine, Democritus University of Thrace, University Hospital of Alexandroupolis, 68100 Alexandroupolis, Greece
4
Endocrine Unit, 2nd Department of Obstetrics and Gynecology, National and Kapodistrian University of Athens, “Aretaieion” University Hospital, 11528 Athens, Greece
5
Institute of Agri-Food and Life Sciences, Hellenic Mediterranean University Research Centre, 71003 Heraklion, Greece
*
Author to whom correspondence should be addressed.
Academic Editor: Marilena Durazzo
J. Clin. Med. 2022, 11(4), 1045; https://doi.org/10.3390/jcm11041045
Received: 18 January 2022 / Revised: 9 February 2022 / Accepted: 15 February 2022 / Published: 17 February 2022
(This article belongs to the Section Clinical Laboratory Medicine)
Background: 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. Methods: 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. Results: ccfDNA levels were found to be similar between groups but indicative of apoptosis in T2DM. INS (Insulin), IAPP (Islet Amyloid Polypeptide-Amylin), GCK (Glucokinase), and KCNJ11 (Potassium Inwardly Rectifying Channel Subfamily J member 11) levels differed significantly between groups. AutoML analysis delivered biosignatures including GCK, IAPP and KCNJ11 methylation, with the highest ever reported discriminating performance of T2DM from healthy individuals (AUC 0.927). Conclusions: 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. View Full-Text
Keywords: type 2 diabetes; circulating cell free DNA; DNA methylation; machine learning type 2 diabetes; circulating cell free DNA; DNA methylation; machine learning
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MDPI and ACS Style

Karaglani, M.; Panagopoulou, M.; Cheimonidi, C.; Tsamardinos, I.; Maltezos, E.; Papanas, N.; Papazoglou, D.; Mastorakos, G.; Chatzaki, E. Liquid Biopsy in Type 2 Diabetes Mellitus Management: Building Specific Biosignatures via Machine Learning. J. Clin. Med. 2022, 11, 1045. https://doi.org/10.3390/jcm11041045

AMA Style

Karaglani M, Panagopoulou M, Cheimonidi C, Tsamardinos I, Maltezos E, Papanas N, Papazoglou D, Mastorakos G, Chatzaki E. Liquid Biopsy in Type 2 Diabetes Mellitus Management: Building Specific Biosignatures via Machine Learning. Journal of Clinical Medicine. 2022; 11(4):1045. https://doi.org/10.3390/jcm11041045

Chicago/Turabian Style

Karaglani, Makrina, Maria Panagopoulou, Christina Cheimonidi, Ioannis Tsamardinos, Efstratios Maltezos, Nikolaos Papanas, Dimitrios Papazoglou, George Mastorakos, and Ekaterini Chatzaki. 2022. "Liquid Biopsy in Type 2 Diabetes Mellitus Management: Building Specific Biosignatures via Machine Learning" Journal of Clinical Medicine 11, no. 4: 1045. https://doi.org/10.3390/jcm11041045

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