ArticlePloS one2016
Detection of Independent Associations of Plasma Lipidomic Parameters with Insulin Sensitivity Indices Using Data Mining Methodology.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Plasma metabolites predict both insulin resistance and incident type 2 diabetes: a metabolomics approach within the Prevención con Dieta Mediterránea (PREDIMED) study.The American journal of clinical nutrition · 2019Trial
- A lipid-immune network signature defines susceptibility to asparaginase-associated pancreatitis.JCI insight · 2026Article
- Promotion of Cardiovascular Homeostasis by the Perivascular Adipose Tissue Secretome.International journal of molecular sciences · 2025Review
- Fecal Dysosmobacter spp. concentration is linked to plasma lipidome in insulin-resistant individuals with overweight, obesity and metabolic syndrome.Lipids in health and disease · 2025Article
- Lipidomic signatures in Colombian adults with metabolic syndrome.Journal of diabetes and metabolic disorders · 2024Article
- Change in fatty acid composition of plasma triglyceride caused by a 2 week comprehensive risk management for diabetes: A prospective observational study of type 2 diabetes patients with supercritical fluid chromatography/mass spectrometry-based semi-target lipidomic analysis.Journal of diabetes investigation · 2023Observational
- Metabolic signatures of insulin resistance in non-diabetic individuals.BMC endocrine disorders · 2022Article
- Longitudinal Plasma Lipidome and Risk of Type 2 Diabetes in a Large Sample of American Indians With Normal Fasting Glucose: The Strong Heart Family Study.Diabetes care · 2021Article
- The Combination of Whole Cell Lipidomics Analysis and Single Cell Confocal Imaging of Fluidity and Micropolarity Provides Insight into Stress-Induced Lipid Turnover in Subcellular Organelles of Pancreatic Beta Cells.Molecules (Basel, Switzerland) · 2019Review
- An Updated Review of Lysophosphatidylcholine Metabolism in Human Diseases.International journal of molecular sciences · 2019Review
- Plasma Lipidomic Profiling and Risk of Type 2 Diabetes in the PREDIMED Trial.Diabetes care · 2018Article
- Form(ul)ation of adipocytes by lipids.Adipocyte · 2017Review
- Lipidomic profiling reveals distinct differences in plasma lipid composition in healthy, prediabetic, and type 2 diabetic individuals.GigaScience · 2017Article
- Lipidomics in the Study of Hypertension in Metabolic Syndrome.Current hypertension reports · 2017Review
Corrections and comments
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Authors and funding
10 authors.
Funding
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Abstract
objectiveGlucolipotoxicity 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.
methodsThe 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.
resultsAfter 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).
conclusionAlthough 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.
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