Integrating lipidomics and genomics: emerging tools to understand cardiovascular diseases

Abstract

Cardiovascular diseases (CVDs) are the leading cause of mortality and morbidity worldwide leading to 31% of all global deaths. Early prediction and prevention could greatly reduce the enormous socio-economic burden posed by CVDs. Plasma lipids have been at the center stage of the prediction and prevention strategies for CVDs that have mostly relied on traditional lipids (total cholesterol, total triglycerides, HDL-C and LDL-C). The tremendous advancement in the field of lipidomics in last two decades has facilitated the research efforts to unravel the metabolic dysregulation in CVDs and their genetic determinants, enabling the understanding of pathophysiological mechanisms and identification of predictive biomarkers, beyond traditional lipids. This review presents an overview of the application of lipidomics in epidemiological and genetic studies and their contributions to the current understanding of the field. We review findings of these studies and discuss examples that demonstrates the potential of lipidomics in revealing new biology not captured by traditional lipids and lipoprotein measurements. The promising findings from these studies have raised new opportunities in the fields of personalized and predictive medicine for CVDs. The review further discusses prospects of integrating emerging genomics tools with the high-dimensional lipidome to move forward from the statistical associations towards biological understanding, therapeutic target development and risk prediction. We believe that integrating genomics with lipidome holds a great potential but further advancements in statistical and computational tools are needed to handle the high-dimensional and correlated lipidome.

Keywords: Biomarkers; Disease risk; Genome-wide association studies; Lipid species; Pathophysiology.

Fig. 1

Cardiovascular diseases and their risk factors. CVDs encompass a broad range of disorders affecting the heart, brain and blood vessels. The different manifestations of CVDs include myocardial infarction, stroke and peripheral artery disease. A number of modifiable and non-modifiable risk factors have been identified that predispose individuals to CVDs. Relationships between lifestyle factors and lipids are well known and have been the target for prevention strategies

Fig. 2

Human plasma lipidome. Six major lipid categories, of eight described by the LIPID MAPS classification system, are illustrated with their classes/subclasses and structure of representative of each lipid category. As shown, esterification of fatty acids with different backbone generates complex lipids including glycerolipids, glycerophospholipids and sphingolipds. Lipids in each lipid category are further divided into classes and subclasses based on the head group and type of linkages between the backbone and acyl chains [https://www.lipidmaps.org/]

Fig. 3

Role of sphingolipid associated loci in major sphingolipid metabolic pathways. Most of the sphingolipid associated loci contain genes that code for enzymes (highlighted in red font) involved in sphingolipid metabolic pathways. SGPP1 codes for a S1P phosphatase that catalyzes the degradation of sphingosine-1-P to sphingosine to facilitate ceramides synthesis catalyzed by ceramide synthases (CERS1-6) including CERS4 and CERS6. SPTLC3 gene encodes a subunit of the serine palmitoyltransferase complex which catalyzes the rate-limiting step of de novo pathway in sphingolipid biosynthesis. FADS1-2-3 locus encodes enzymes that regulate the desaturation of fatty acids and have important role in generation of unsaturated ceramides. GLTPD2 codes for glycolipid transfer protein domain-containing protein 2 and has putative role in transfer of ceramide-1-phosphate

Fig. 4

Genetic loci identified in GWAS with lipidome. Genetic loci identified for different lipid classes and their overlap are shown. Genes highlighted in red font have direct role in lipid metabolic pathways. SMs: Sphingomyelins; PLs: Phospholipids; CEs: Cholesteryl esters; TGs: Triacylglycerides

Fig. 5

Approaches to move beyond GWAS. New opportunities and prospects of application of genomics to translate findings from lipidomics to develop better predictive and preventive strategies are illustrated