Human plasma lipidome is pleiotropically associated with cardiovascular risk factors and death

Abstract

Background: Cardiovascular disease (CVD) is the most common cause of death in the United States and is associated with a high economic burden. Prevention of CVD focuses on controlling or improving the lipid profile of patients at risk. The human lipidome is made up of thousands of ubiquitous lipid species. By studying biologically simple canonical lipid species, we investigated whether the lipidome is genetically redundant and whether its genetic influences can provide clinically relevant clues of CVD risk.

Methods and results: We performed a genetic study of the human lipidome in 1212 individuals from 42 extended Mexican American families. High-throughput mass spectrometry enabled rapid capture of precise lipidomic profiles, providing 319 unique species. Using variance component-based heritability analyses and bivariate trait analyses, we detected significant genetic influences on each lipid assayed. Median heritability of the plasma lipid species was 0.37. Hierarchical clustering based on complex genetic correlation patterns identified 12 genetic clusters that characterized the plasma lipidome. These genetic clusters were differentially but consistently associated with risk factors of CVD, including central obesity, obesity, type 2 diabetes mellitus, raised serum triglycerides, and metabolic syndrome. Also, these clusters consistently predicted occurrence of cardiovascular deaths during follow-up.

Conclusions: The human plasma lipidome is heritable. Shared genetic influences reduce the dimensionality of the human lipidome into clusters that are associated with risk factors of CVD.

Keywords: cardiovascular diseases; genetics; lipids.

Figure 1

Heritability of the human plasma lipidome. (A) Histogram showing distribution of the estimated heritability of plasma lipid species. (B) Box plot summarizing the heritability distribution. (C) Box plot showing the distribution of heritability within each lipid class. Lipid classes are abbreviated as shown in Table 2.

Figure 2

Heatmap depicting genetic correlation coefficient between each pair of lipid species. Lipid classes are separated by dark colored boxes. Lipid classes are abbreviated as shown in Table 2.

Figure 3

Hierarchical clustering of lipid species based on the pair-wise genetic correlations. (A) Results of hierarchical clustering. Twelve genetic clusters with deep separation are identified as color-coded branches. The numbers on the right correspond to the reordered lipid species identifier (see Supplementary Table 3). (B and C) Efficiency of the clustering algorithm as examined using average inter- and intra-cluster genetic correlation. Panel B compares the overall average genetic correlation between (box labeled as B) and within (box labeled as W) the genetic clusters. z and p refer to the results from Mann-Whitney U test. Panel C shows the average (± standard deviation) genetic correlation within each genetic cluster. (D) Agreement between chemically defined lipid classes and genetically derived clusters. The columns are color-coded in accordance with the colors shown in Panels A and C. Grey background cells indicate statistically significant over-representation of a lipid class within a cluster. Results of Fisher's exact tests used to assess significant over-representation are shown in Supplementary Table 4.

Figure 4

Association of genetic clusters with clinical conditions. For each indicated trait, the results are from a single polygenic regression model that was adjusted for age, age, sex, age × sex interaction, age × sex interaction and receipt of anti-hypertensive and lipid-lowering drugs. Average inverse-normalized scores for lipid species belonging to each genetic cluster were then included as covariates. The plots report the polygenic regression coefficients. The squares and error bars represent the point and 95% confidence intervals. The squares are color-coded as follows: red – significantly increased liability of the trait, gray – statistically not significant, and blue – significantly decreased liability of the trait. The significance values are indicated at the top of each plot.