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

Abstract

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.

Keywords: coronary heart disease; genetics; lipidomics; mass spectrometry; protocol.

Figure 1

Schematic of the peak-picking process. (a) XCMS was used to average 50 spectra in positive and negative ionization modes, yielding (b) the average mass spectrum for that particular polarity, for which signals were obtained using a peak-picking algorithm that determined the (c) peak signal at the midpoint of a line drawn at half-height for peaks near signals that corresponded to known lipids. Signals and deviations from known lipids were then (d) combined in a database, and separated into individual files for (e) signals and (f) deviations for each lipid.

Figure 2

Heat map showing relationships between lipid subclasses and constituent fatty acid chains of lipids based on partial correlations derived using Gaussian Graphical Modeling. These heat maps show the relationships between (a) lipid subclasses and (b) constituent fatty acid chains based on the inferred Gaussian Graphical Model (GGM). The number in each cell shows the observed number of GGM edges connecting two lipids in subclasses or constituent fatty acid chains specified on the x- and y-axes. The cells are colored red or blue according to whether the observed number of GGM edges is more or less than expected due to chance alone, and a box is drawn around the cell if there is a significant difference between the numbers of observed versus expected GGM edges.

Figure 3

Cross-correlations of circulating biomarkers with the lipids within each overall lipid category most strongly associated with rs662799 in the APOA5–APOC3 locus. For the lipids within each overall lipid category that were most strongly associated with rs662799 (chr11:116663707) in the APOA5–APOC3 region, the correlations of these lipids with a range of circulating biomarkers are shown. Analyses were adjusted for age and sex.

Figure 4

Association of lipids with obesity, hypertension, and diabetes. All analyses were adjusted for age and sex. Out of the lipids that were associated with rs662799 in the APOA5–APOC3 locus, results are shown for (a) the top 20 lipids that were most significantly associated with obesity, (b) the top 20 lipids that were most significantly associated with hypertension, and (c) the top 20 lipids that were most significantly associated with diabetes. Definitions: Diabetes = HbA_1c ≥ 6.5%; Hypertension = SBP ≥ 140 mm Hg or DBP ≥ 90 mm Hg; Obese = BMI ≥ 30 kg/m². Abbreviations: BMI, body mass index; DBP, diastolic blood pressure; SBP, systolic blood pressure.

Figure 5

Association of the top 20 most significantly associated lipids with rs662799 in the APOA5–APOC3 locus. Note: *P < 0.001; **P < 5 × 10^–8; ***P < 8.9 × 10^–10.