An Unbiased Lipid Phenotyping Approach To Study the Genetic Determinants of Lipids and Their Association with Coronary Heart Disease Risk Factors

Direct infusion high-resolution mass spectrometry (DIHRMS) is a novel, high-throughput approach to rapidly and accurately profile hundreds of lipids in human serum without prior chromatography, facilitating in-depth lipid phenotyping for large epidemiological studies to reveal the detailed associations of individual lipids with coronary heart disease (CHD) risk factors. Intact lipid profiling by DIHRMS was performed on 5662 serum samples from healthy participants in the Pakistan Risk of Myocardial Infarction Study (PROMIS). We developed a novel semi-targeted peak-picking algorithm to detect mass-to-charge ratios in positive and negative ionization modes. We analyzed lipid partial correlations, assessed the association of lipid principal components with established CHD risk factors and genetic variants, and examined differences between lipids for a common genetic polymorphism. The DIHRMS method provided information on 360 lipids (including fatty acyls, glycerolipids, glycerophospholipids, sphingolipids, and sterol lipids), with a median coefficient of variation of 11.6% (range: 5.4–51.9). The lipids were highly correlated and exhibited a range of associations with clinical chemistry biomarkers and lifestyle factors. This platform can provide many novel insights into the effects of physiology and lifestyle on lipid metabolism, genetic determinants of lipids, and the relationship between individual lipids and CHD risk factors.

[1]  Raquel S. Sevilla,et al.  Exome-wide association study of plasma lipids in >300,000 individuals , 2017, Nature Genetics.

[2]  R. Krauss,et al.  Odd Chain Fatty Acids; New Insights of the Relationship Between the Gut Microbiota, Dietary Intake, Biosynthesis and Glucose Intolerance , 2017, Scientific Reports.

[3]  M. Mayr,et al.  Potential and Caveats of Lipidomics for Cardiovascular Disease , 2016, Circulation.

[4]  D. van der A,et al.  Association of Plasma Phospholipid n-3 and n-6 Polyunsaturated Fatty Acids with Type 2 Diabetes: The EPIC-InterAct Case-Cohort Study , 2016, PLoS medicine.

[5]  B. Nordestgaard.  Triglyceride-Rich Lipoproteins and Atherosclerotic Cardiovascular Disease: New Insights From Epidemiology, Genetics, and Biology. , 2016, Circulation research.

[6]  J. Griffin,et al.  Adipose tissue fatty acid chain length and mono-unsaturation increases with obesity and insulin resistance , 2015, Scientific Reports.

[7]  C. Gieger,et al.  Genetics of human metabolism: an update , 2015, Human molecular genetics.

[8]  S. O’Rahilly,et al.  Mechanistic insights revealed by lipid profiling in monogenic insulin resistance syndromes , 2015, Genome Medicine.

[9]  Christian Gieger,et al.  Genome-wide association study identifies novel genetic variants contributing to variation in blood metabolite levels , 2015, Nature Communications.

[10]  S. Baker,et al.  Increased apolipoprotein A5 expression in human and rat non-alcoholic fatty livers , 2015, Pathology.

[11]  Albert Koulman,et al.  Differences in the prospective association between individual plasma phospholipid saturated fatty acids and incident type 2 diabetes: the EPIC-InterAct case-cohort study , 2014, The lancet. Diabetes & endocrinology.

[12]  Julio Saez-Rodriguez,et al.  Fast randomization of large genomic datasets while preserving alteration counts , 2014, Bioinform..

[13]  Anette Varbo,et al.  Triglycerides and cardiovascular disease , 2014, The Lancet.

[14]  P. Meikle,et al.  Lipidomics: potential role in risk prediction and therapeutic monitoring for diabetes and cardiovascular disease. , 2014, Pharmacology & therapeutics.

[15]  John P. Overington,et al.  An atlas of genetic influences on human blood metabolites , 2014, Nature Genetics.

[16]  T. Spector,et al.  Lipidomics Profiling and Risk of Cardiovascular Disease in the Prospective Population-Based Bruneck Study , 2014, Circulation.

[17]  Tanya M. Teslovich,et al.  Discovery and refinement of loci associated with lipid levels , 2013, Nature Genetics.

[18]  J. Shaw,et al.  Plasma lipid profiling in a large population-based cohort[S] , 2013, Journal of Lipid Research.

[19]  Jeroen J. Bax,et al.  2007 ESH-ESC Guidelines for the management of arterial hypertension: the task force for the management of arterial hypertension of the European Society of Hypertension (ESH) and of the European Society of Cardiology (ESC). , 2007, Blood pressure.

[20]  J. Danesh,et al.  Large-scale association analysis identifies new risk loci for coronary artery disease , 2013 .

[21]  William E. Kraus,et al.  Metabolomic Profiling for the Identification of Novel Biomarkers and Mechanisms Related to Common Cardiovascular Diseases: Form and Function , 2012, Circulation.

[22]  Natalie I. Tasman,et al.  A Cross-platform Toolkit for Mass Spectrometry and Proteomics , 2012, Nature Biotechnology.

[23]  S. Neumann,et al.  CAMERA: an integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets. , 2012, Analytical chemistry.

[24]  Luigi Atzori,et al.  Metabolomics as a tool for cardiac research , 2011, Nature Reviews Cardiology.

[25]  C. Gieger,et al.  Human metabolic individuality in biomedical and pharmaceutical research , 2011, Nature.

[26]  Fabian J. Theis,et al.  Gaussian graphical modeling reconstructs pathway reactions from high-throughput metabolomics data , 2011, BMC Systems Biology.

[27]  Eoin Fahy,et al.  Lipidomics reveals a remarkable diversity of lipids in human plasma1[S] , 2010, Journal of Lipid Research.

[28]  A. Shevchenko,et al.  Lipidomics: coming to grips with lipid diversity , 2010, Nature Reviews Molecular Cell Biology.

[29]  V. Salomaa,et al.  Excess of rare variants in genes identified by genome-wide association study of hypertriglyceridemia , 2010, Nature Genetics.

[30]  Simon C. Potter,et al.  Genetic Determinants of Major Blood Lipids in Pakistanis Compared With Europeans , 2010, Circulation. Cardiovascular genetics.

[31]  Yun Li,et al.  METAL: fast and efficient meta-analysis of genomewide association scans , 2010, Bioinform..

[32]  J. Marchini,et al.  Genotype imputation for genome-wide association studies , 2010, Nature Reviews Genetics.

[33]  Christian Gieger,et al.  A genome-wide perspective of genetic variation in human metabolism , 2010, Nature Genetics.

[34]  Stefan R. Bornstein,et al.  Top-Down Lipidomics Reveals Ether Lipid Deficiency in Blood Plasma of Hypertensive Patients , 2009, PloS one.

[35]  Usman Ahmad,et al.  The Pakistan Risk of Myocardial Infarction Study: a resource for the study of genetic, lifestyle and other determinants of myocardial infarction in South Asia , 2009, European Journal of Epidemiology.

[36]  R. Collins,et al.  Newly identified loci that influence lipid concentrations and risk of coronary artery disease , 2008, Nature Genetics.

[37]  Taghreed Adam,et al.  The burden and costs of chronic diseases in low-income and middle-income countries , 2007, The Lancet.

[38]  Korbinian Strimmer,et al.  From correlation to causation networks: a simple approximate learning algorithm and its application to high-dimensional plant gene expression data , 2007, BMC Systems Biology.

[39]  Bieke Dejaegher,et al.  Ruggedness and robustness testing. , 2007, Journal of chromatography. A.

[40]  David W. Russell,et al.  LMSD: LIPID MAPS structure database , 2006, Nucleic Acids Res..

[41]  R. Abagyan,et al.  XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. , 2006, Analytical chemistry.

[42]  Xianlin Han,et al.  Global analyses of cellular lipidomes directly from crude extracts of biological samples by ESI mass spectrometry: a bridge to lipidomics. , 2003, Journal of lipid research.

[43]  M. Olivier,et al.  Relative contribution of variation within the APOC3/A4/A5 gene cluster in determining plasma triglycerides. , 2002, Human molecular genetics.

[44]  Jonathan C. Cohen,et al.  An Apolipoprotein Influencing Triglycerides in Humans and Mice Revealed by Comparative Sequencing , 2001, Science.

[45]  J. Griffin,et al.  Towards metabolic biomarkers of insulin resistance and type 2 diabetes: progress from the metabolome. , 2014, The lancet. Diabetes & endocrinology.

[46]  Helmut Baumgartner,et al.  The 2013 ESH/ESC guidelines for the management of arterial hypertension , 2013 .

[47]  D. Wishart.  Applications of Metabolomics in Drug Discovery and Development , 2008, Drugs in R&D.