@article{e9c24a8b83de4131a4b91b63c9b7735e,
title = "Predictive utilities of lipid traits, lipoprotein subfractions and other risk factors for incident diabetes: A machine learning approach in the Diabetes Prevention Program",
abstract = "Introduction Although various lipid and non-lipid analytes measured by nuclear magnetic resonance (NMR) spectroscopy have been associated with type 2 diabetes, a structured comparison of the ability of NMR-derived biomarkers and standard lipids to predict individual diabetes risk has not been undertaken in larger studies nor among individuals at high risk of diabetes. Research design and methods Cumulative discriminative utilities of various groups of biomarkers including NMR lipoproteins, related non-lipid biomarkers, standard lipids, and demographic and glycemic traits were compared for short-term (3.2 years) and long-term (15 years) diabetes development in the Diabetes Prevention Program, a multiethnic, placebo-controlled, randomized controlled trial of individuals with pre-diabetes in the USA (N=2590). Logistic regression, Cox proportional hazards model and six different hyperparameter-tuned machine learning algorithms were compared. The Matthews Correlation Coefficient (MCC) was used as the primary measure of discriminative utility. Results Models with baseline NMR analytes and their changes did not improve the discriminative utility of simpler models including standard lipids or demographic and glycemic traits. Across all algorithms, models with baseline 2-hour glucose performed the best (max MCC=0.36). Sophisticated machine learning algorithms performed similarly to logistic regression in this study. Conclusions NMR lipoproteins and related non-lipid biomarkers were associated but did not augment discrimination of diabetes risk beyond traditional diabetes risk factors except for 2-hour glucose. Machine learning algorithms provided no meaningful improvement for discrimination compared with logistic regression, which suggests a lack of influential latent interactions among the analytes assessed in this study. Trial registration number Diabetes Prevention Program: NCT00004992; Diabetes Prevention Program Outcomes Study: NCT00038727.",
keywords = "diabetes mellitus, lipids, lipoproteins, prediabetic state, type 2",
author = "Varga, {Tibor V.} and Jinxi Liu and Goldberg, {Ronald B.} and Guannan Chen and Samuel Dagogo-Jack and Carlos Lorenzo and Mather, {Kieren J.} and Xavier Pi-Sunyer and S{\o}ren Brunak and Marinella Temprosa",
note = "Funding Information: Funding TVV is supported by the Novo Nordisk Foundation (https:// novonordiskfonden.dk/en/) Postdoctoral Fellowship within Endocrinology/ Metabolism at International Elite Research Environments via NNF16OC0020698, the Swedish Research Council (Strategic Research Area Exodiab) (https://www. vr.se/english/) via Dnr 2009-1039, and the Swedish Foundation for Strategic Research (https://strategiska.se/en/) via Dnr IRC15-0067. SB is supported by the Novo Nordisk Foundation via NNF14CC0001 and NNF17OC0027594. The National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) of the National Institutes of Health provided funding to the clinical centers and the coordinating center for the design and conduct of the study, and collection, management, analysis, and interpretation of the data (U01 DK048489). The Southwestern American Indian Centers were supported directly by the NIDDK, including its Intramural Research Program, and the Indian Health Service. The General Clinical Research Center Program, National Center for Research Resources, and the Department of Veterans Affairs supported data collection at many of the clinical centers. Funding was also provided by the National Institute of Child Health and Human Development, the National Institute on Aging, the National Eye Institute, the National Heart, Lung, and Blood Institute, the National Cancer Institute, the Office of Research on Women{\textquoteright}s Health, the National Institute on Minority Health and Health Disparities, the Centers for Disease Control and Prevention, and the American Diabetes Association. Merck KGaA provides medication for DPPOS. DPP/DPPOS have also received donated materials from Bristol Myers Squibb, Parke-Davis, and LifeScan. LifeScan, Health O Meter, Hoechst Marion Roussel, Merck-Medco Managed Care, Merck and Co, Nike Sports Marketing, Slim Fast Foods, and Quaker Oats donated materials, equipment, or medicines for concomitant conditions. McKesson BioServices, Matthews Media Group, and the Henry M Jackson Foundation provided support services under subcontract with the coordinating center. The sponsor of this study was represented on the steering committee and played a part in study design, how the study was done, and publication. All authors in the writing group had access to all data. The opinions expressed are those of the study group and do not necessarily reflect the views of the funding agencies. A complete list of centers, investigators, and staff can be found in online supplemental file 1. Publisher Copyright: {\textcopyright} ",
year = "2021",
month = mar,
day = "31",
doi = "10.1136/bmjdrc-2020-001953",
language = "English (US)",
volume = "9",
journal = "BMJ Open Diabetes Research and Care",
issn = "2052-4897",
publisher = "BMJ Publishing Group",
number = "1",
}