title: Detection of Independent Associations of Plasma Lipidomic Parameters with Insulin Sensitivity Indices Using Data Mining Methodology.
abstract: OBJECTIVE
  Glucolipotoxicity is a major pathophysiological mechanism in the development of insulin resistance and type 2 diabetes mellitus (T2D). We aimed to detect subtle changes in the circulating lipid profile by shotgun lipidomics analyses and to associate them with four different insulin sensitivity indices.
  
  
  METHODS
  The cross-sectional study comprised 90 men with a broad range of insulin sensitivity including normal glucose tolerance (NGT, n = 33), impaired glucose tolerance (IGT, n = 32) and newly detected T2D (n = 25). Prior to oral glucose challenge plasma was obtained and quantitatively analyzed for 198 lipid molecular species from 13 different lipid classes including triacylglycerls (TAGs), phosphatidylcholine plasmalogen/ether (PC O-s), sphingomyelins (SMs), and lysophosphatidylcholines (LPCs). To identify a lipidomic signature of individual insulin sensitivity we applied three data mining approaches, namely least absolute shrinkage and selection operator (LASSO), Support Vector Regression (SVR) and Random Forests (RF) for the following insulin sensitivity indices: homeostasis model of insulin resistance (HOMA-IR), glucose insulin sensitivity index (GSI), insulin sensitivity index (ISI), and disposition index (DI). The LASSO procedure offers a high prediction accuracy and and an easier interpretability than SVR and RF.
  
  
  RESULTS
  After LASSO selection, the plasma lipidome explained 3% (DI) to maximal 53% (HOMA-IR) variability of the sensitivity indexes. Among the lipid species with the highest positive LASSO regression coefficient were TAG 54:2 (HOMA-IR), PC O- 32:0 (GSI), and SM 40:3:1 (ISI). The highest negative regression coefficient was obtained for LPC 22:5 (HOMA-IR), TAG 51:1 (GSI), and TAG 58:6 (ISI).
  
  
  CONCLUSIONS
  Although a substantial part of lipid molecular species showed a significant correlation with insulin sensitivity indices we were able to identify a limited number of lipid metabolites of particular importance based on the LASSO approach. These few selected lipids with the closest connection to sensitivity indices may help to further improve disease risk prediction and disease and therapy monitoring.
authors.affiliation: Department of Internal Medicine 3, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.
authors.forename: Steffi
authors.identifier: http://orcid.org/0000-0003-2591-3650
authors.initials: S
authors.lastname: Kopprasch
authors.affiliation: Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.
authors.forename: Srirangan
authors.initials: S
authors.lastname: Dheban
authors.affiliation: Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany.
authors.forename: Kai
authors.initials: K
authors.lastname: Schuhmann
authors.affiliation: Department of Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.
authors.forename: Aimin
authors.initials: A
authors.lastname: Xu
authors.affiliation: Department of Endocrine Surgery, King's College Hospital, NHS Foundation Trust, London, United Kingdom.
authors.forename: Klaus-Martin
authors.initials: KM
authors.lastname: Schulte
authors.affiliation: Department of Immunology and Infectious Disease, The John Curtin School of Medical Research, The Australian National University, Canberra, Australia.
authors.forename: Charmaine J
authors.initials: CJ
authors.lastname: Simeonovic
authors.affiliation: Department of Internal Medicine 3, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.
authors.forename: Peter E H
authors.initials: PE
authors.lastname: Schwarz
authors.affiliation: Department of Internal Medicine 3, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.
authors.forename: Stefan R
authors.initials: SR
authors.lastname: Bornstein
authors.affiliation: Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany.
authors.forename: Andrej
authors.initials: A
authors.lastname: Shevchenko
authors.affiliation: Department of Internal Medicine 3, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.
authors.forename: Juergen
authors.initials: J
authors.lastname: Graessler
mesh_terms: D000368:Aged; D003430:Cross-Sectional Studies; D057225:Data Mining / Q000379:methods*; D003924:Diabetes Mellitus, Type 2 / Q000378:metabolism*; D018149:Glucose Intolerance / Q000378:metabolism*; D005951:Glucose Tolerance Test; D006801:Humans; D007333:Insulin Resistance; D008055:Lipids / Q000097:blood*; D008297:Male; D008875:Middle Aged; D060388:Support Vector Machine
chemical_list: D008055:Lipids