Lipidomic analyses in epidemiology

Identification of pre-diagnostic lipid sets associated with liver cancer risk using untargeted lipidomics and chemical set analysis: A nested case-control study within the ATBC cohort

In pre-disposed individuals, a reprogramming of the hepatic lipid metabolism may support liver cancer initiation. We conducted a high-resolution mass spectrometry based untargeted lipidomics analysis of pre-diagnostic serum samples from a nested case-control study (219 liver cancer cases and 219 controls) within the Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study. Out of 462 annotated lipids, 158 (34.2%) were associated with liver cancer risk in a conditional logistic regression analysis at a false discovery rate (FDR) <0.05. A chemical set enrichment analysis (ChemRICH) and co-regulatory set analysis suggested that 22/28 lipid classes and 47/83 correlation modules were significantly associated with liver cancer risk (FDR <0.05). Strong positive associations were observed for monounsaturated fatty acids (MUFA), triacylglycerols (TAGs) and phosphatidylcholines (PCs) having MUFA acyl chains. Negative associations were observed for sphingolipids (ceramides and sphingomyelins), lysophosphatidylcholines, cholesterol esters and polyunsaturated fatty acids (PUFA) containing TAGs and PCs. Stearoyl-CoA desaturase enzyme 1 (SCD1), a rate limiting enzyme in fatty acid metabolism and ceramidases seems to be critical in this reprogramming. In conclusion, our study reports pre-diagnostic lipid changes that provide novel insights into hepatic lipid metabolism reprogramming may contribute to a pro-cell growth and anti-apoptotic tissue environment and, in turn, support liver cancer initiation.

Genetic Architecture of Untargeted Lipidomics in Cardiometabolic-Disease Patients Combines Strong Polygenic Control and Pleiotropy

Analysis of the genetic control of small metabolites provides powerful information on the regulation of the endpoints of genome expression. We carried out untargeted liquid chromatography−high-resolution mass spectrometry in 273 individuals characterized for pathophysiological elements of the cardiometabolic syndrome. We quantified 3013 serum lipidomic features, which we used in both genome-wide association studies (GWAS), using a panel of over 2.5 M imputed single-nucleotide polymorphisms (SNPs), and metabolome-wide association studies (MWAS) with phenotypes. Genetic analyses showed that 926 SNPs at 551 genetic loci significantly (q-value < 10−8) regulate the abundance of 74 lipidomic features in the group, with evidence of monogenic control for only 22 of these. In addition to this strong polygenic control of serum lipids, our results underscore instances of pleiotropy, when a single genetic locus controls the abundance of several distinct lipid features. Using the LIPID MAPS database, we assigned putative lipids, predominantly fatty acyls and sterol lipids, to 77% of the lipidome signals mapped to the genome. We identified significant correlations between lipids and clinical and biochemical phenotypes. These results demonstrate the power of untargeted lipidomic profiling for high-density quantitative molecular phenotyping in human-genetic studies and illustrate the complex genetic control of lipid metabolism.

A multi-omics study of circulating phospholipid markers of blood pressure

High-throughput techniques allow us to measure a wide-range of phospholipids which can provide insight into the mechanisms of hypertension. We aimed to conduct an in-depth multi-omics study of various phospholipids with systolic blood pressure (SBP) and diastolic blood pressure (DBP). The associations of blood pressure and 151 plasma phospholipids measured by electrospray ionization tandem mass spectrometry were performed by linear regression in five European cohorts (n = 2786 in discovery and n = 1185 in replication). We further explored the blood pressure-related phospholipids in Erasmus Rucphen Family (ERF) study by associating them with multiple cardiometabolic traits (linear regression) and predicting incident hypertension (Cox regression). Mendelian Randomization (MR) and phenome-wide association study (Phewas) were also explored to further investigate these association results. We identified six phosphatidylethanolamines (PE 38:3, PE 38:4, PE 38:6, PE 40:4, PE 40:5 and PE 40:6) and two phosphatidylcholines (PC 32:1 and PC 40:5) which together predicted incident hypertension with an area under the ROC curve (AUC) of 0.61. The identified eight phospholipids are strongly associated with triglycerides, obesity related traits (e.g. waist, waist-hip ratio, total fat percentage, body mass index, lipid-lowering medication, and leptin), diabetes related traits (e.g. glucose, insulin resistance and insulin) and prevalent type 2 diabetes. The genetic determinants of these phospholipids also associated with many lipoproteins, heart rate, pulse rate and blood cell counts. No significant association was identified by bi-directional MR approach. We identified eight blood pressure-related circulating phospholipids that have a predictive value for incident hypertension. Our cross-omics analyses show that phospholipid metabolites in the circulation may yield insight into blood pressure regulation and raise a number of testable hypothesis for future research.

Postprandial plasma lipidome responses to a high-fat meal among healthy women

Postprandial lipemia consists of changes in concentrations and composition of plasma lipids after food intake, commonly presented as increased levels of triglyceride-rich lipoproteins. Postprandial hypertriglyceridemia may also affect high-density lipoprotein (HDL) structure and function, resulting in a net decrease in HDL concentrations. Elevated triglycerides (TG) and reduced HDL levels have been positively associated with risk of cardiovascular diseases development. Here, we investigated the plasma lipidome composition of 12 clinically healthy, nonobese and young women in response to an acute high-caloric (1135 kcal) and high-fat (64 g) breakfast meal. For this purpose, we employed a detailed untargeted mass spectrometry-based lipidomic approach and data was obtained at four sampling points: fasting and 1, 3 and 5 h postprandial. Analysis of variance revealed 73 significantly altered lipid species between all sampling points. Nonetheless, two divergent subgroups have emerged at 5 h postprandial as a function of differential plasma lipidome responses, and were thereby designated slow and fast TG metabolizers. Late responses by slow TG metabolizers were associated with increased concentrations of several species of TG and phosphatidylinositol (PI). Lipidomic analysis of lipoprotein fractions at 5 h postprandial revealed higher TG and PI concentrations in HDL from slow relative to fast TG metabolizers, but not in apoB-containing fraction. These data indicate that modulations in HDL lipidome during prolonged postprandial lipemia may potentially impact HDL functions. A comprehensive characterization of plasma lipidome responses to acute metabolic challenges may contribute to a better understanding of diet/lifestyle regulation in the metabolism of lipid and glucose.

High Density Lipoproteins: Is There a Comeback as a Therapeutic Target?

Low plasma levels of High Density Lipoprotein (HDL) cholesterol (HDL-C) are associated with increased risks of atherosclerotic cardiovascular disease (ASCVD). In cell culture and animal models, HDL particles exert multiple potentially anti-atherogenic effects. However, drugs increasing HDL-C have failed to prevent cardiovascular endpoints. Mendelian Randomization studies neither found any genetic causality for the associations of HDL-C levels with differences in cardiovascular risk. Therefore, the causal role and, hence, utility as a therapeutic target of HDL has been questioned. However, the biomarker "HDL-C" as well as the interpretation of previous data has several important limitations: First, the inverse relationship of HDL-C with risk of ASCVD is neither linear nor continuous. Hence, neither the-higher-the-better strategies of previous drug developments nor previous linear cause-effect relationships assuming Mendelian randomization approaches appear appropriate. Second, most of the drugs previously tested do not target HDL metabolism specifically so that the futile trials question the clinical utility of the investigated drugs rather than the causal role of HDL in ASCVD. Third, the cholesterol of HDL measured as HDL-C neither exerts nor reports any HDL function. Comprehensive knowledge of structure-function-disease relationships of HDL particles and associated molecules will be a pre-requisite, to test them for their physiological and pathogenic relevance and exploit them for the diagnostic and therapeutic management of individuals at HDL-associated risk of ASCVD but also other diseases, for example diabetes, chronic kidney disease, infections, autoimmune and neurodegenerative diseases.

Insights into the prognosis of lipidomic dysregulation for death risk in patients with coronary artery disease

BACKGROUND

Dyslipidaemia contributes to the progression of coronary artery disease (CAD) toward adverse outcomes. Plasma lipidomic measure may improve the prognostic performances of clinical endpoints of CAD. Our research is designed to identify the correlations between plasma lipid species and the risks of death, major adverse cardiovascular event (MACE) and left ventricular (LV) remodeling in patients with CAD.

METHODS

A total of 1569 Chinese patients with CAD, 1011 single-centre patients as internal training cohort, and 558 multicentre patients as external validation cohort, were enrolled. The concentration of plasma lipids in both cohorts was determined through widely targeted lipidomic profiling. Least absolute shrinkage and selection operator Cox and multivariate Cox regressions were used to develop prognostic models for death and MACE, respectively.

RESULTS

Ten (Cer(d18:1/20:1), Cer(d18:1/24:1), PE(30:2), PE(32:0), PE(32:2), PC(O-38:2), PC(O-36:4), PC(16:1/22:2), LPC(18:2/0:0) and LPE(0:0/24:6)) and two (Cer(d18:1/20:1) and LPC(20:0/0:0)) lipid species were independently related to death and MACE, respectively. Cer(d18:1/20:1) and Cer(d18:1/24:1) were correlated with LV remodeling (P < .05). The lipidic panel incorporating 10 lipid species and two traditional biomarkers for predicting 5-year death risk represented a remarkable higher discrimination than traditional model with increased area under the curve from 76.56 to 83.65%, continuous NRI of 0.634 and IDI of 0.131. Furthermore, the panel was successfully used in differentiating multicentre patients with low, middle, or high risks (P < .0001). Further analysis indicated that the number of double bonds of phosphatidyl choline and the content of carbon atoms of phosphatidyl ethanolamines were negatively associated with death risk.

CONCLUSIONS

Improvement in the prediction of death confirms the effectiveness of plasma lipids as predictors to risk classification in patients with CAD. The association between the structural characteristics of long-chain polyunsaturated fatty acids and death risk highlights the need for mechanistic research that characterizes the role of individual lipid species in disease pathogenesis.

Plasma metabolites mediate the association of coarse grain intake with blood pressure in hypertension-free adults

BACKGROUND AND AIMS

Increased intake of whole/coarse grains was associated with improved blood pressure control, but concurrent metabolism alterations are less clear. We sought to identify metabolomic profiles of blood pressure, and to explore their mediation effects on the coarse grain intake-blood pressure association among young adults free of hypertension.

METHODS AND RESULTS

Plasma metabolome of 86 participants from the Carbohydrate Alternatives and Metabolic Phenotypes study was characterized by untargeted lipidomics and metabolomics using liquid chromatography-high-resolution mass spectrometry. We identified 24 and 117 metabolites associated with systolic blood pressure (SBP) and diastolic blood pressure (DBP), respectively, using random forest modeling and partial correlation analysis. Moreover, metabolite panels for highly specific prediction of blood pressure (8 metabolites for SBP and 11 metabolites for DBP) were determined using ten-fold cross-validated ridge regression (R² ≥ 0.70). We also observed an inverse association between metabolite panel of SBP (β ± SE = -0.02 ± 0.01, P = 0.04) or DBP (β ± SE = -0.03 ± 0.01, P = 0.02) and coarse grain intake. Furthermore, we observed significant mediating effects of metabolites, in particular, sphingolipid ceramides, on the association between coarse grain exposure and blood pressure using both bias-corrected bootstrap tests and high-dimensional mediation analysis adapted for large-scale and high-throughput omics data.

CONCLUSIONS

We identified metabolomic profiles specifically associated with blood pressure in young Chinese adults without diagnosed hypertension. The inverse association between coarse grain intake and blood pressure may be mediated by sphingolipid metabolites.

EpiMetal: an open-source graphical web browser tool for easy statistical analyses in epidemiology and metabolomics

MOTIVATION

An intuitive graphical interface that allows statistical analyses and visualizations of extensive data without any knowledge of dedicated statistical software or programming.

IMPLEMENTATION

EpiMetal is a single-page web application written in JavaScript, to be used via a modern desktop web browser.

GENERAL FEATURES

Standard epidemiological analyses and self-organizing maps for data-driven metabolic profiling are included. Multiple extensive datasets with an arbitrary number of continuous and category variables can be integrated with the software. Any snapshot of the analyses can be saved and shared with others via a www-link. We demonstrate the usage of EpiMetal using pilot data with over 500 quantitative molecular measures for each sample as well as in two large-scale epidemiological cohorts (N >10 000).

AVAILABILITY

The software usage exemplar and the pilot data are open access online at [http://EpiMetal.computationalmedicine.fi]. MIT licensed source code is available at the Github repository at [https://github.com/amergin/epimetal].

Heritability of 596 lipid species and genetic correlation with cardiovascular traits in the Busselton Family Heart Study

CVD is the leading cause of death worldwide, and genetic investigations into the human lipidome may provide insight into CVD risk. The aim of this study was to estimate the heritability of circulating lipid species and their genetic correlation with CVD traits. Targeted lipidomic profiling was performed on 4,492 participants from the Busselton Family Heart Study to quantify the major fatty acids of 596 lipid species from 33 classes. We estimated narrow-sense heritabilities of lipid species/classes and their genetic correlations with eight CVD traits: BMI, HDL-C, LDL-C, triglycerides, total cholesterol, waist-hip ratio, systolic blood pressure, and diastolic blood pressure. We report heritabilities and genetic correlations of new lipid species/subclasses, including acylcarnitine (AC), ubiquinone, sulfatide, and oxidized cholesteryl esters. Over 99% of lipid species were significantly heritable (h²: 0.06-0.50) and all lipid classes were significantly heritable (h²: 0.14-0.50). The monohexosylceramide and AC classes had the highest median heritabilities (h² = 0.43). The largest genetic correlation was between clinical triglycerides and total diacylglycerol (r_g = 0.88). We observed novel positive genetic correlations between clinical triglycerides and phosphatidylglycerol species (r_g: 0.64-0.82), and HDL-C and alkenylphosphatidylcholine species (r_g: 0.45-0.74). Overall, 51% of the 4,768 lipid species-CVD trait genetic correlations were statistically significant after correction for multiple comparisons. This is the largest lipidomic study to address the heritability of lipids and their genetic correlation with CVD traits. Future work includes identifying putative causal genetic variants for lipid species and CVD using genome-wide SNP and whole-genome sequencing data.

Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine

Despite the tremendous success, pitfalls have been observed in every step of a clinical metabolomics workflow, which impedes the internal validity of the study. Furthermore, the demand for logistics, instrumentations, and computational resources for metabolic phenotyping studies has far exceeded our expectations. In this conceptual review, we will cover inclusive barriers of a metabolomics-based clinical study and suggest potential solutions in the hope of enhancing study robustness, usability, and transferability. The importance of quality assurance and quality control procedures is discussed, followed by a practical rule containing five phases, including two additional "pre-pre-" and "post-post-" analytical steps. Besides, we will elucidate the potential involvement of machine learning and demonstrate that the need for automated data mining algorithms to improve the quality of future research is undeniable. Consequently, we propose a comprehensive metabolomics framework, along with an appropriate checklist refined from current guidelines and our previously published assessment, in the attempt to accurately translate achievements in metabolomics into clinical and epidemiological research. Furthermore, the integration of multifaceted multi-omics approaches with metabolomics as the pillar member is in urgent need. When combining with other social or nutritional factors, we can gather complete omics profiles for a particular disease. Our discussion reflects the current obstacles and potential solutions toward the progressing trend of utilizing metabolomics in clinical research to create the next-generation healthcare system.

Associations of Adiposity and Diet Quality with Serum Ceramides in Middle-Aged Adults with Cardiovascular Risk Factors

Rates of adverse cardiovascular events have increased among middle-aged adults. Elevated ceramides have been proposed as a risk factor for cardiovascular events. Diet quality and weight status are inversely associated with several traditional risk factors; however, the relationship to ceramides is less clear. This study aimed to determine associations of adiposity and diet quality with circulating ceramides in middle-aged adults (n = 96). Diet quality was estimated using the Healthy Eating Index 2015 (HEI-2015). Serum ceramide concentrations were determined by liquid chromatography-mass spectrometry. A ceramide risk score was determined based on ceramides C16:0, C18:0, and C24:1 and their ratios to C24:0. Participants who were classified as at 'moderate risk' compared to 'lower-risk' based on a ceramide risk score had significantly higher body mass index (BMI) values, as well as higher rates of elevated fibrinogen levels, metabolic syndrome, and former smoking status. BMI was positively associated with the ceramide C18:0 (R2 = 0.31, p < 0.0001), the ratio between C18:0/C24:0 ceramides (R2 = 0.30, p < 0.0001), and the ceramide risk score (R2 = 0.11, p < 0.009). Total HEI-2015 scores (R2 = 0.42, p = 0.02), higher intakes of vegetables (R2 = 0.44, p = 0.02) and whole grains (R2 = 0.43, p = 0.03), and lower intakes of saturated fats (R2 = 0.43, p = 0.04) and added sugar (R2 = 0.44, p = 0.01) were associated with lower C22:0 values. These findings suggest that circulating ceramides are more strongly related to adiposity than overall diet quality. Studies are needed to determine if improvements in weight status result in lower ceramides and ceramide risk scores.

From lipid locus to drug target through human genomics

In the last decade, over 175 genetic loci have robustly been associated to levels of major circulating blood lipids. Most loci are specific to one or two lipids, whereas some (SUGP1, ZPR1, TRIB1, HERPUD1, and FADS1) are associated to all. While exposing the polygenic architecture of circulating lipids and the underpinnings of dyslipidaemia, these genome-wide association studies (GWAS) have provided further evidence of the critical role that lipids play in coronary heart disease (CHD) risk, as indicated by the 2.7-fold enrichment for macrophage gene expression in atherosclerotic plaques and the association of 25 loci (such as PCSK9, APOB, ABCG5-G8, KCNK5, LPL, HMGCR, NPC1L1, CETP, TRIB1, ABO, PMAIP1-MC4R, and LDLR) with CHD. These GWAS also confirmed known and commonly used therapeutic targets, including HMGCR (statins), PCSK9 (antibodies), and NPC1L1 (ezetimibe). As we head into the post-GWAS era, we offer suggestions for how to move forward beyond genetic risk loci, towards refining the biology behind the associations and identifying causal genes and therapeutic targets. Deep phenotyping through lipidomics and metabolomics will refine and increase the resolution to find causal and druggable targets, and studies aimed at demonstrating gene transcriptional and regulatory effects of lipid associated loci will further aid in identifying these targets. Thus, we argue the need for deeply phenotyped, large genetic association studies to reduce costs and failures and increase the efficiency of the drug discovery pipeline. We conjecture that in the next decade a paradigm shift will tip the balance towards a data-driven approach to therapeutic target development and the application of precision medicine where human genomics takes centre stage.

Quantitative Serum Nuclear Magnetic Resonance Metabolomics in Large-Scale Epidemiology: A Primer on -Omic Technologies

Detailed metabolic profiling in large-scale epidemiologic studies has uncovered novel biomarkers for cardiometabolic diseases and clarified the molecular associations of established risk factors. A quantitative metabolomics platform based on nuclear magnetic resonance spectroscopy has found widespread use, already profiling over 400,000 blood samples. Over 200 metabolic measures are quantified per sample; in addition to many biomarkers routinely used in epidemiology, the method simultaneously provides fine-grained lipoprotein subclass profiling and quantification of circulating fatty acids, amino acids, gluconeogenesis-related metabolites, and many other molecules from multiple metabolic pathways. Here we focus on applications of magnetic resonance metabolomics for quantifying circulating biomarkers in large-scale epidemiology. We highlight the molecular characterization of risk factors, use of Mendelian randomization, and the key issues of study design and analyses of metabolic profiling for epidemiology. We also detail how integration of metabolic profiling data with genetics can enhance drug development. We discuss why quantitative metabolic profiling is becoming widespread in epidemiology and biobanking. Although large-scale applications of metabolic profiling are still novel, it seems likely that comprehensive biomarker data will contribute to etiologic understanding of various diseases and abilities to predict disease risks, with the potential to translate into multiple clinical settings.

Plasma lipidomic profiles and cardiovascular events in a randomized intervention trial with the Mediterranean diet

Background: Lipid metabolites may partially explain the inverse association between the Mediterranean diet (MedDiet) and cardiovascular disease (CVD).Objective: We evaluated the associations between 1) lipid species and the risk of CVD (myocardial infarction, stroke, or cardiovascular death); 2) a MedDiet intervention [supplemented with extra virgin olive oil (EVOO) or nuts] and 1-y changes in these molecules; and 3) 1-y changes in lipid species and subsequent CVD.Design: With the use of a case-cohort design, we profiled 202 lipid species at baseline and after 1 y of intervention in the PREDIMED (PREvención con DIeta MEDiterránea) trial in 983 participants [230 cases and a random subcohort of 790 participants (37 overlapping cases)].Results: Baseline concentrations of cholesterol esters (CEs) were inversely associated with CVD. A shorter chain length and higher saturation of some lipids were directly associated with CVD. After adjusting for multiple testing, direct associations remained significant for 20 lipids, and inverse associations remained significant for 6 lipids. When lipid species were weighted by the number of carbon atoms and double bonds, the strongest inverse association was found for CEs [HR: 0.39 (95% CI: 0.22, 0.68)] between extreme quintiles (P-trend = 0.002). Participants in the MedDiet + EVOO and MedDiet + nut groups experienced significant (P < 0.05) 1-y changes in 20 and 17 lipids, respectively, compared with the control group. Of these changes, only those in CE(20:3) in the MedDiet + nuts group remained significant after correcting for multiple testing. None of the 1-y changes was significantly associated with CVD risk after correcting for multiple comparisons.Conclusions: Although the MedDiet interventions induced some significant 1-y changes in the lipidome, they were not significantly associated with subsequent CVD risk. Lipid metabolites with a longer acyl chain and higher number of double bonds at baseline were significantly and inversely associated with the risk of CVD.

Mendelian randomization in cardiometabolic disease: challenges in evaluating causality

Mendelian randomization (MR) is a burgeoning field that involves the use of genetic variants to assess causal relationships between exposures and outcomes. MR studies can be straightforward; for example, genetic variants within or near the encoding locus that is associated with protein concentrations can help to assess their causal role in disease. However, a more complex relationship between the genetic variants and an exposure can make findings from MR more difficult to interpret. In this Review, we describe some of these challenges in interpreting MR analyses, including those from studies using genetic variants to assess causality of multiple traits (such as branched-chain amino acids and risk of diabetes mellitus); studies describing pleiotropic variants (for example, C-reactive protein and its contribution to coronary heart disease); and those investigating variants that disrupt normal function of an exposure (for example, HDL cholesterol or IL-6 and coronary heart disease). Furthermore, MR studies on variants that encode enzymes responsible for the metabolism of an exposure (such as alcohol) are discussed, in addition to those assessing the effects of variants on time-dependent exposures (extracellular superoxide dismutase), cumulative exposures (LDL cholesterol), and overlapping exposures (triglycerides and non-HDL cholesterol). We elaborate on the molecular features of each relationship, and provide explanations for the likely causal associations. In doing so, we hope to contribute towards more reliable evaluations of MR findings.