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

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

SUBMITTER: Hathaway QA 

PROVIDER: S-EPMC6560734 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature