Journal Article

Machine-learning to stratify diabetic patients using novel cardiac biomarkers and integrative genomics

Giromini, Andrew P., Kunovac, Amina, Pinti, Mark V., Sprando, Daniel C., Cheuvront, Tristen B., Taylor, Andrew D., Roth, Skyler M., Cook, Chris C., Fink, Garrett K., Allen, Jessica L., Durr, Andrya J., Grossman, Jasmine H., Hollander, John M., Hathaway, Quincy A., Aljahli, Ghadah A.

2019

Overview

Background Diabetes mellitus is a chronic disease that impacts an increasing percentage of people each year. Among its comorbidities, diabetics are two to four times more likely to develop cardiovascular diseases. While HbA1c remains the primary diagnostic for diabetics, its ability to predict long-term, health outcomes across diverse demographics, ethnic groups, and at a personalized level are limited. The purpose of this study was to provide a model for precision medicine through the implementation of machine-learning algorithms using multiple cardiac biomarkers as a means for predicting diabetes mellitus development. Methods Right atrial appendages from 50 patients, 30 non-diabetic and 20 type 2 diabetic, were procured from the WVU Ruby Memorial Hospital. Machine-learning was applied to physiological, biochemical, and sequencing data for each patient. Supervised learning implementing SHapley Additive exPlanations (SHAP) allowed binary (no diabetes or type 2 diabetes) and multiple classification (no diabetes, prediabetes, and type 2 diabetes) of the patient cohort with and without the inclusion of HbA1c levels. Findings were validated through Logistic Regression (LR), Linear Discriminant Analysis (LDA), Gaussian Naïve Bayes (NB), Support Vector Machine (SVM), and Classification and Regression Tree (CART) models with tenfold cross validation. Results Total nuclear methylation and hydroxymethylation were highly correlated to diabetic status, with nuclear methylation and mitochondrial electron transport chain (ETC) activities achieving superior testing accuracies in the predictive model (~ 84% testing, binary). Mitochondrial DNA SNPs found in the D-Loop region (SNP-73G, -16126C, and -16362C) were highly associated with diabetes mellitus. The CpG island of transcription factor A, mitochondrial (TFAM) revealed CpG24 (chr10:58385262, P  = 0.003) and CpG29 (chr10:58385324, P  = 0.001) as markers correlating with diabetic progression. When combining the most predictive factors from each set, total nuclear methylation and CpG24 methylation were the best diagnostic measures in both binary and multiple classification sets. Conclusions Using machine-learning, we were able to identify novel as well as the most relevant biomarkers associated with type 2 diabetes mellitus by integrating physiological, biochemical, and sequencing datasets. Ultimately, this approach may be used as a guideline for future investigations into disease pathogenesis and novel biomarker discovery.

• Publisher: BioMed Central,Springer Nature B.V,BMC
• Subject: Accuracy / Algorithms / Angiology / Appendages / Artificial intelligence / Bayesian analysis / Biomarkers / Cardiology / Cardiovascular diseases / CART / Classification / CpG Islands / Deep learning / Demography / Diabetes / Diabetes mellitus / Diabetes mellitus (non-insulin dependent) / Diabetes Mellitus, Type 2 - blood / Diabetes Mellitus, Type 2 - complications / Diabetes Mellitus, Type 2 - genetics / Diabetic Cardiomyopathies - etiology / Diabetic Cardiomyopathies - genetics / Disease Progression / DNA Methylation / DNA, Mitochondrial - genetics / Electron transport chain / Epigenesis, Genetic / Epigenetics / Female / Genetic Markers / Genetic Predisposition to Disease / Genomics - methods / Glycated Hemoglobin - analysis / Heart / Heart surgery / Humans / International conferences / Learning / Learning algorithms / Machine learning / Male / Medical diagnosis / Medical imaging / Medicine / Medicine & Public Health / Metadata / Middle Aged / Minority & ethnic groups / Mitochondria / Mitochondria, Heart - genetics / Mitochondrial DNA / Models, Genetic / Original Investigation / Pathogenesis / Pathology / Patients / Physiology / Polymorphism, Single Nucleotide / Precision medicine / Prediction models / Prognosis / Regression analysis / Risk Assessment / Risk Factors / SHAP / Single-nucleotide polymorphism / Support Vector Machine / Systems Integration