1
|
Hathaway QA, Roth SM, Pinti MV, Sprando DC, Kunovac A, Durr AJ, Cook CC, Fink GK, Cheuvront TB, Grossman JH, Aljahli GA, Taylor AD, Giromini AP, Allen JL, Hollander JM. Machine-learning to stratify diabetic patients using novel cardiac biomarkers and integrative genomics. Cardiovasc Diabetol 2019; 18:78. [PMID: 31185988 PMCID: PMC6560734 DOI: 10.1186/s12933-019-0879-0] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Accepted: 05/29/2019] [Indexed: 12/22/2022] Open
Abstract
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.
Collapse
Affiliation(s)
- Quincy A Hathaway
- Division of Exercise Physiology, West Virginia University School of Medicine, PO Box 9227, 1 Medical Center Drive, Morgantown, WV, 26505, USA
- Mitochondria, Metabolism & Bioenergetics Working Group, West Virginia University School of Medicine, Morgantown, WV, 26505, USA
| | - Skyler M Roth
- Department of Chemical and Biomedical Engineering, West Virginia University, Morgantown, WV, 26505, USA
| | - Mark V Pinti
- West Virginia University School of Pharmacy, Morgantown, WV, 26505, USA
| | - Daniel C Sprando
- West Virginia University School of Medicine, Morgantown, WV, 26505, USA
| | - Amina Kunovac
- Division of Exercise Physiology, West Virginia University School of Medicine, PO Box 9227, 1 Medical Center Drive, Morgantown, WV, 26505, USA
- Mitochondria, Metabolism & Bioenergetics Working Group, West Virginia University School of Medicine, Morgantown, WV, 26505, USA
| | - Andrya J Durr
- Division of Exercise Physiology, West Virginia University School of Medicine, PO Box 9227, 1 Medical Center Drive, Morgantown, WV, 26505, USA
- Mitochondria, Metabolism & Bioenergetics Working Group, West Virginia University School of Medicine, Morgantown, WV, 26505, USA
| | - Chris C Cook
- Cardiovascular and Thoracic Surgery, West Virginia University School of Medicine, Morgantown, WV, 26505, USA
| | - Garrett K Fink
- Division of Exercise Physiology, West Virginia University School of Medicine, PO Box 9227, 1 Medical Center Drive, Morgantown, WV, 26505, USA
| | - Tristen B Cheuvront
- Department of Chemical and Biomedical Engineering, West Virginia University, Morgantown, WV, 26505, USA
| | - Jasmine H Grossman
- Department of Chemical and Biomedical Engineering, West Virginia University, Morgantown, WV, 26505, USA
| | - Ghadah A Aljahli
- Department of Chemical and Biomedical Engineering, West Virginia University, Morgantown, WV, 26505, USA
| | - Andrew D Taylor
- Division of Exercise Physiology, West Virginia University School of Medicine, PO Box 9227, 1 Medical Center Drive, Morgantown, WV, 26505, USA
- Mitochondria, Metabolism & Bioenergetics Working Group, West Virginia University School of Medicine, Morgantown, WV, 26505, USA
| | - Andrew P Giromini
- West Virginia University School of Medicine, Morgantown, WV, 26505, USA
| | - Jessica L Allen
- Department of Chemical and Biomedical Engineering, West Virginia University, Morgantown, WV, 26505, USA
| | - John M Hollander
- Division of Exercise Physiology, West Virginia University School of Medicine, PO Box 9227, 1 Medical Center Drive, Morgantown, WV, 26505, USA.
- Mitochondria, Metabolism & Bioenergetics Working Group, West Virginia University School of Medicine, Morgantown, WV, 26505, USA.
| |
Collapse
|