title: Prediction of Incident Diabetes in the Jackson Heart Study Using High-Dimensional Machine Learning.
abstract: Statistical models to predict incident diabetes are often based on limited variables. Here we pursued two main goals: 1) investigate the relative performance of a machine learning method such as Random Forests (RF) for detecting incident diabetes in a high-dimensional setting defined by a large set of observational data, and 2) uncover potential predictors of diabetes. The Jackson Heart Study collected data at baseline and in two follow-up visits from 5,301 African Americans. We excluded those with baseline diabetes and no follow-up, leaving 3,633 individuals for analyses. Over a mean 8-year follow-up, 584 participants developed diabetes. The full RF model evaluated 93 variables including demographic, anthropometric, blood biomarker, medical history, and echocardiogram data. We also used RF metrics of variable importance to rank variables according to their contribution to diabetes prediction. We implemented other models based on logistic regression and RF where features were preselected. The RF full model performance was similar (AUC = 0.82) to those more parsimonious models. The top-ranked variables according to RF included hemoglobin A1C, fasting plasma glucose, waist circumference, adiponectin, c-reactive protein, triglycerides, leptin, left ventricular mass, high-density lipoprotein cholesterol, and aldosterone. This work shows the potential of RF for incident diabetes prediction while dealing with high-dimensional data.
authors.affiliation: Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America.
authors.forename: Ramon
authors.initials: R
authors.lastname: Casanova
authors.affiliation: Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America.
authors.forename: Santiago
authors.initials: S
authors.lastname: Saldana
authors.affiliation: Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America.
authors.forename: Sean L
authors.initials: SL
authors.lastname: Simpson
authors.affiliation: Department of Epidemiology, Brown University School of Public Health, Providence, Rhode Island, United States of America.
authors.forename: Mary E
authors.initials: ME
authors.lastname: Lacy
authors.affiliation: Division of Endocrinology and Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi, United States of America.
authors.forename: Angela R
authors.initials: AR
authors.lastname: Subauste
authors.affiliation: Division of Endocrinology and Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi, United States of America.
authors.forename: Chad
authors.initials: C
authors.lastname: Blackshear
authors.affiliation: Department of Epidemiology and Prevention, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America.
authors.forename: Lynne
authors.initials: L
authors.lastname: Wagenknecht
authors.affiliation: Department of Epidemiology and Prevention, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America.
authors.forename: Alain G
authors.initials: AG
authors.lastname: Bertoni
mesh_terms: D052242:Adiponectin / Q000097:blood; D000328:Adult; D001741:Black or African American; D000368:Aged; D000886:Anthropometry; D015415:Biomarkers / Q000097:blood; D001786:Blood Glucose / Q000032:analysis; D002097:C-Reactive Protein / Q000032:analysis; D008076:Cholesterol, HDL / Q000097:blood; D003710:Demography; D003924:Diabetes Mellitus, Type 2 / Q000453:epidemiology*; D005260:Female; D005500:Follow-Up Studies; D006442:Glycated Hemoglobin / Q000032:analysis; D006801:Humans; D015994:Incidence; D020738:Leptin / Q000097:blood; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D014280:Triglycerides / Q000097:blood; D055105:Waist Circumference
chemical_list: D052242:Adiponectin; D015415:Biomarkers; D001786:Blood Glucose; D002097:C-Reactive Protein; D008076:Cholesterol, HDL; D006442:Glycated Hemoglobin; D020738:Leptin; D014280:Triglycerides