Detection of Independent Associations of Plasma Lipidomic Parameters with Insulin Sensitivity Indices Using Data Mining Methodology
| Autor: | Peter Schwarz, Steffi Kopprasch, Juergen Graessler, Andrej Shevchenko, Kai Schuhmann, Klaus-Martin Schulte, Charmaine J. Simeonovic, Stefan R. Bornstein, Aimin Xu, Srirangan Dheban |
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| Přispěvatelé: | University of Zurich |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
0301 basic medicine
Male Support Vector Machine Physiology 10265 Clinic for Endocrinology and Diabetology lcsh:Medicine computer.software_genre Biochemistry Impaired glucose tolerance Endocrinology Lasso (statistics) Medicine and Health Sciences Macromolecular Structure Analysis Insulin Data Mining lcsh:Science Glucose tolerance test Multidisciplinary Lipid Analysis medicine.diagnostic_test Hematology Middle Aged Lipids Body Fluids Blood Cholesterol Data mining Anatomy Research Article medicine.medical_specialty Endocrine Disorders 610 Medicine & health 1100 General Agricultural and Biological Sciences Biology Blood Plasma 03 medical and health sciences Insulin resistance 1300 General Biochemistry Genetics and Molecular Biology Diabetes mellitus Internal medicine Linear regression Glucose Intolerance medicine Diabetes Mellitus Humans Molecular Biology Aged Diabetic Endocrinology 1000 Multidisciplinary Endocrine Physiology lcsh:R Biology and Life Sciences Shotgun lipidomics Glucose Tolerance Test medicine.disease Lipid Metabolism Hormones 030104 developmental biology Metabolism Cross-Sectional Studies Diabetes Mellitus Type 2 Metabolic Disorders lcsh:Q Insulin Resistance Lipid profile computer |
| Zdroj: | PLoS ONE, Vol 11, Iss 10, p e0164173 (2016) PLoS ONE |
| ISSN: | 1932-6203 |
| Popis: | 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). Conclusion 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. |
| Databáze: | OpenAIRE |
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