Metabolomics through the lens of precision cardiovascular medicine

Blood-Derived Lipid and Metabolite Biomarkers in Cardiovascular Research from Clinical Studies: A Recent Update

The primary prevention, early detection, and treatment of cardiovascular disease (CVD) have been long-standing scientific research goals worldwide. In the past decades, traditional blood lipid profiles have been routinely used in clinical practice to estimate the risk of CVDs such as atherosclerotic cardiovascular disease (ASCVD) and as treatment targets for the primary prevention of adverse cardiac events. These blood lipid panel tests often fail to fully predict all CVD risks and thus need to be improved. A comprehensive analysis of molecular species of lipids and metabolites (defined as lipidomics and metabolomics, respectively) can provide molecular insights into the pathophysiology of the disease and could serve as diagnostic and prognostic indicators of disease. Mass spectrometry (MS) and nuclear magnetic resonance (NMR)-based lipidomics and metabolomics analysis have been increasingly used to study the metabolic changes that occur during CVD pathogenesis. In this review, we provide an overview of various MS-based platforms and approaches that are commonly used in lipidomics and metabolomics workflows. This review summarizes the lipids and metabolites in human plasma/serum that have recently (from 2018 to December 2022) been identified as promising CVD biomarkers. In addition, this review describes the potential pathophysiological mechanisms associated with candidate CVD biomarkers. Future studies focused on these potential biomarkers and pathways will provide mechanistic clues of CVD pathogenesis and thus help with the risk assessment, diagnosis, and treatment of CVD.

Challenges in Metabolomics-Based Tests, Biomarkers Revealed by Metabolomic Analysis, and the Promise of the Application of Metabolomics in Precision Medicine

Metabolomics helps identify metabolites to characterize/refine perturbations of biological pathways in living organisms. Pre-analytical, analytical, and post-analytical limitations that have hampered a wide implementation of metabolomics have been addressed. Several potential biomarkers originating from current targeted metabolomics-based approaches have been discovered. Precision medicine argues for algorithms to classify individuals based on susceptibility to disease, and/or by response to specific treatments. It also argues for a prevention-based health system. Because of its ability to explore gene-environment interactions, metabolomics is expected to be critical to personalize diagnosis and treatment. Stringent guidelines have been applied from the very beginning to design studies to acquire the information currently employed in precision medicine and precision prevention approaches. Large, prospective, expensive and time-consuming studies are now mandatory to validate old, and discover new, metabolomics-based biomarkers with high chances of translation into precision medicine. Metabolites from studies on saliva, sweat, breath, semen, feces, amniotic, cerebrospinal, and broncho-alveolar fluid are predicted to be needed to refine information from plasma and serum metabolome. In addition, a multi-omics data analysis system is predicted to be needed for omics-based precision medicine approaches. Omics-based approaches for the progress of precision medicine and prevention are expected to raise ethical issues.

Multi-omics strategies for personalized and predictive medicine: past, current, and future translational opportunities

Precision medicine is driven by the paradigm shift of empowering clinicians to predict the most appropriate course of action for patients with complex diseases and improve routine medical and public health practice. It promotes integrating collective and individualized clinical data with patient specific multi-omics data to develop therapeutic strategies, and knowledgebase for predictive and personalized medicine in diverse populations. This study is based on the hypothesis that understanding patient's metabolomics and genetic make-up in conjunction with clinical data will significantly lead to determining predisposition, diagnostic, prognostic and predictive biomarkers and optimal paths providing personalized care for diverse and targeted chronic, acute, and infectious diseases. This study briefs emerging significant, and recently reported multi-omics and translational approaches aimed to facilitate implementation of precision medicine. Furthermore, it discusses current grand challenges, and the future need of Findable, Accessible, Intelligent, and Reproducible (FAIR) approach to accelerate diagnostic and preventive care delivery strategies beyond traditional symptom-driven, disease-causal medical practice.

Multi-Omic Approaches to Identify Genetic Factors in Metabolic Syndrome

Metabolic syndrome (MetS) is a highly heritable disease and a major public health burden worldwide. MetS diagnosis criteria are met by the simultaneous presence of any three of the following: high triglycerides, low HDL/high LDL cholesterol, insulin resistance, hypertension, and central obesity. These diseases act synergistically in people suffering from MetS and dramatically increase risk of morbidity and mortality due to stroke and cardiovascular disease, as well as certain cancers. Each of these component features is itself a complex disease, as is MetS. As a genetically complex disease, genetic risk factors for MetS are numerous, but not very powerful individually, often requiring specific environmental stressors for the disease to manifest. When taken together, all sequence variants that contribute to MetS disease risk explain only a fraction of the heritable variance, suggesting additional, novel loci have yet to be discovered. In this article, we will give a brief overview on the genetic concepts needed to interpret genome-wide association studies (GWAS) and quantitative trait locus (QTL) data, summarize the state of the field of MetS physiological genomics, and to introduce tools and resources that can be used by the physiologist to integrate genomics into their own research on MetS and any of its component features. There is a wealth of phenotypic and molecular data in animal models and humans that can be leveraged as outlined in this article. Integrating these multi-omic QTL data for complex diseases such as MetS provides a means to unravel the pathways and mechanisms leading to complex disease and promise for novel treatments. © 2022 American Physiological Society. Compr Physiol 12:1-40, 2022.

Association of IL1R1 Coding Variant With Plasma-Level Soluble ST2 and Risk of Aortic Dissection

Objective: Aortic dissection (AD) is characterized by an acute onset, rapid progress, and high mortality. Levels of soluble ST2 (sST2) on presentation are elevated in patients with acute AD, which can be used to discriminate AD patients from patients with chest pain. sST2 concentrations were found to be highly heritable in the general population. The aim of this study was to investigate the associations of variations in ST2-related gene expression with sST2 concentrations and AD risk. Methods: This case-control study involving a total of 2,277 participants were conducted, including 435 AD patients and age- and sex-matched 435 controls in the discovery stage, and 464 patients and 943 controls in the validation stage. Eight ST2-related genes were selected by systematic review. Tag single-nucleotide polymorphisms (SNPs) were screened out from the Chinese population of the 1,000 Genomes Database. Twenty-one ST2-related SNPs were genotyped, and plasma sST2 concentrations were measured. Results: In the discovery stage, rs13019803 located in IL1R1 was significantly associated with AD after Bonferroni correction (p = 0.0009) and was correlated with circulating sST2 levels in patients with type A AD(AAD) [log-sST2 per C allele increased by 0.180 (95%) CI: 0.002 - 0.357] but not in type B. Combining the two stages together, rs13019803C was associated with plasma sST2 level in AAD patients [log-sST2 increased by 0.141 (95% CI: 0.055-0.227) for per C allele]. Odds ratio of rs13019803 on the risk of AAD is 1.67 (95% CI: 1.33-2.09). Conclusions: The IL1R1 SNP rs13019803C is associated with higher sST2 levels and increased risk of AAD.

Lipidomic Analysis to Assess Oxidative Stress in Acute Coronary Syndrome and Acute Stroke Patients

Alterations in lipid metabolism mediated by oxidative stress play a key role in the process of atherosclerosis and superimposed thrombosis; these can lead to acute coronary syndrome (ACS) and acute ischemic stroke (AIS). Multiple studies have shown that the formation of atheromatous lesions is initiated by oxidation of low-density lipoproteins incorporated into the intima of the vessel wall. Here, we studied lipids in plasma samples from three cohorts: 61 patients with ACS (group A), 49 patients with AIS (group D), and 82 controls (group K). Untargeted lipidomics based on high-performance liquid chromatography coupled to mass spectrometry (UHPLC-HRMS) was employed to obtain comprehensive information on whether relationships exist between these patient categories based on lipid patterns. In addition, malondialdehyde (MDA) as a standard marker of oxidative stress was monitored. The most characteristic lipids in group K were fatty acyls of hydroxyfatty acids (FAHFAs). As expected, MDA concentrations were the lowest in group K. Our findings can better explain ongoing pathologies, both acute and chronic, with the potential for future diagnosis and treatment.

Serum Metabolomic Analysis of Coronary Heart Disease Patients with Stable Angina Pectoris Subtyped by Traditional Chinese Medicine Diagnostics Reveals Biomarkers Relevant to Personalized Treatments

To improve the treatment of patients with coronary heart disease (CHD), personalized treatments based on potential biomarkers could make a difference. To investigate if such potential biomarkers could be found for CHD inhomogeneous, we combined traditional Chinese medicine based diagnosis with untargeted and targeted metabolomics analyses. Shi and Xu patient subtype groups of CHD with angina pectoris were identified. Different metabolites including lipids, fatty acids and amino acids were further analyzed with targeted metabolomics and mapped to disease-related pathways. The long-chain unsaturated lipids ceramides metabolism, bile acid metabolism were differentially affected in the Xu subtype groups. While, Shi-subtype patients seemed to show inflammation, anomalous levels of bioactive phospholipids and antioxidant molecules. Furthermore, variations in the endothelial damage response and energy metabolism found based on ELISA analysis are the key divergence points between different CHD subtypes. The results showed Xu subtype patients might benefit from long-chain unsaturated lipids ceramides as therapeutic targets. Shi subtype patients might benefit more from levels of polyunsaturated fatty acid consumption and treatments that help in restoring energy balance. Metabolic differences can be essential for treatment protocols. Thus, patient group specific differences can serve as important information to refine current treatment approaches in a personalized manner.

Association of cardiac autonomic dysfunction with higher levels of plasma lipid metabolites in recent-onset type 2 diabetes

AIMS/HYPOTHESIS

Emerging evidence suggests that in addition to hyperglycaemia, dyslipidaemia could represent a contributing pathogenetic factor to diabetic neuropathy, while obesity and insulin resistance play a role in the development of diabetic cardiac autonomic neuropathy (CAN) characterised by reduced heart rate variability (HRV), particularly in type 2 diabetes. We hypothesised that distinct lipid metabolites are associated with diminished HRV in recent-onset type 2 diabetes rather than type 1 diabetes.

METHODS

We analysed 127 plasma lipid metabolites (11 acylcarnitines, 39 NEFA, 12 sphingomyelins (SMs), 56 phosphatidylcholines and nine lysophosphatidylcholines) using MS in participants from the German Diabetes Study baseline cohort recently diagnosed with type 1 (n = 100) and type 2 diabetes (n = 206). Four time-domain HRV indices (number of normal-to-normal (NN) intervals >50 ms divided by the number of all NN intervals [pNN50]; root mean square of successive differences [RMSSD]; SD of NN intervals [SDNN]; and SD of differences between adjacent NN intervals) and three frequency-domain HRV indices (very-low-frequency [VLF], low-frequency [LF] and high-frequency [HF] power spectrum) were computed from NN intervals recorded during a 3 h hyperinsulinaemic-euglycaemic clamp at baseline and in subsets of participants with type 1 (n = 60) and type 2 diabetes (n = 95) after 5 years.

RESULTS

In participants with type 2 diabetes, after Bonferroni correction and rigorous adjustment, SDNN was inversely associated with higher levels of diacyl-phosphatidylcholine (PCaa) C32:0, PCaa C34:1, acyl-alkyl-phosphatidylcholine (PCae) C36:0, SM C16:0 and SM C16:1. SD of differences between NN intervals was inversely associated with PCaa C32:0, PCaa C34:1, PCaa C34:2, PCae C36:0 and SM C16:1, and RMSSD with PCae C36:0. For VLF power, inverse associations were found with PCaa C30:0, PCaa C32:0, PCaa C32:1, PCaa C34:2 and SM C16:1, and for LF power inverse associations were found with PCaa C32:0 and SM C16:1 (r = -0.242 to r = -0.349; p ≤ 0.0005 for all correlations). In contrast, no associations of lipid metabolites with measures of cardiac autonomic function were noted in participants recently diagnosed with type 1 diabetes. After 5 years, HRV declined due to ageing rather than diabetes, whereby prediction analyses for lipid metabolites were hampered.

CONCLUSIONS/INTERPRETATION

Higher plasma levels of specific lipid metabolites are closely linked to cardiac autonomic dysfunction in recent-onset type 2 diabetes but not type 1 diabetes, suggesting a role for perturbed lipid metabolism in the early development of CAN in type 2 diabetes. Graphical abstract.

Integration of lipidomics and metabolomics for in-depth understanding of cellular mechanism and disease progression

Mass spectrometry (MS)-based omics technologies are now widely used to profile small molecules in multiple matrices to confer comprehensive snapshots of cellular metabolic phenotypes. The metabolomes of cells, tissues, and organisms comprise a variety of molecules including lipids, amino acids, sugars, organic acids, and so on. Metabolomics mainly focus on the hydrophilic classes, while lipidomics has emerged as an independent omics owing to the complexities of the organismal lipidomes. The potential roles of lipids and small metabolites in disease pathogenesis have been widely investigated in various human diseases, but system-level understanding is largely lacking, which could be partly attributed to the insufficiency in terms of metabolite coverage and quantitation accuracy in current analytical technologies. While scientists are continuously striving to develop high-coverage omics approaches, integration of metabolomics and lipidomics is becoming an emerging approach to mechanistic investigation. Integration of metabolome and lipidome offers a complete atlas of the metabolic landscape, enabling comprehensive network analysis to identify critical metabolic drivers in disease pathology, facilitating the study of interconnection between lipids and other metabolites in disease progression. In this review, we summarize omics-based findings on the roles of lipids and metabolites in the pathogenesis of selected major diseases threatening public health. We also discuss the advantages of integrating lipidomics and metabolomics for in-depth understanding of molecular mechanism in disease pathogenesis.

Metabolomics technology and bioinformatics for precision medicine

Precision medicine is rapidly emerging as a strategy to tailor medical treatment to a small group or even individual patients based on their genetics, environment and lifestyle. Precision medicine relies heavily on developments in systems biology and omics disciplines, including metabolomics. Combination of metabolomics with sophisticated bioinformatics analysis and mathematical modeling has an extreme power to provide a metabolic snapshot of the patient over the course of disease and treatment or classifying patients into subpopulations and subgroups requiring individual medical treatment. Although a powerful approach, metabolomics have certain limitations in technology and bioinformatics. We will review various aspects of metabolomics technology and bioinformatics, from data generation, bioinformatics analysis, data fusion and mathematical modeling to data management, in the context of precision medicine.

High-Coverage Targeted Lipidomics Reveals Novel Serum Lipid Predictors and Lipid Pathway Dysregulation Antecedent to Type 2 Diabetes Onset in Normoglycemic Chinese Adults

OBJECTIVE

Comprehensive assessment of serum lipidomic aberrations before type 2 diabetes mellitus (T2DM) onset has remained lacking in Han Chinese. We evaluated changes in lipid coregulation antecedent to T2DM and identified novel lipid predictors for T2DM in individuals with normal glucose regulation (NGR).

RESEARCH DESIGN AND METHODS

In the discovery study, we tested 667 baseline serum lipids in subjects with incident diabetes and propensity score-matched control subjects (n = 200) from a prospective cohort comprising 3,821 Chinese adults with NGR. In the validation study, we tested 250 lipids in subjects with incident diabetes and matched control subjects (n = 724) from a pooled validation cohort of 14,651 individuals with NGR covering five geographical regions across China. Differential correlation network analyses revealed perturbed lipid coregulation antecedent to diabetes. The predictive value of a serum lipid panel independent of serum triglycerides and 2-h postload glucose was also evaluated.

RESULTS

At the level of false-discovery rate <0.05, 38 lipids, including triacylglycerols (TAGs), lyso-phosphatidylinositols, phosphatidylcholines, polyunsaturated fatty acid (PUFA)-plasmalogen phosphatidylethanolamines (PUFA-PEps), and cholesteryl esters, were significantly associated with T2DM risk in the discovery and validation cohorts. A preliminary study found most of the lipid predictors were also significantly associated with the risk of prediabetes. Differential correlation network analysis revealed that perturbations in intraclass (i.e., non-PUFA-TAG and PUFA-TAGs) and interclass (i.e., TAGs and PUFA-PEps) lipid coregulation preexisted before diabetes onset. Our lipid panel further improved prediction of incident diabetes over conventional clinical indices.

CONCLUSIONS

These findings revealed novel changes in lipid coregulation existing before diabetes onset and expanded the current panel of serum lipid predictors for T2DM in normoglycemic Chinese individuals.

Plasma lipidomic profiling in murine mutants of Hermansky-Pudlak syndrome reveals differential changes in pro- and anti-atherosclerotic lipids

Atherosclerosis is characterized by the accumulation of lipid-rich plaques in the arterial wall. Its pathogenesis is very complicated and has not yet been fully elucidated. It is known that dyslipidemia is a major factor in atherosclerosis. Several different Hermansky-Pudlak syndrome (HPS) mutant mice have been shown either anti-atherosclerotic or atherogenic phenotypes, which may be mainly attributed to corresponding lipid perturbation. To explore the effects of different HPS proteins on lipid metabolism and plasma lipid composition, we analyzed the plasma lipid profiles of three HPS mutant mice, pa (Hps9 ^-/-), ru (Hps6 ^-/-), ep (Hps1 ^-/-), and wild-type (WT) mice. In pa and ru mice, some pro-atherosclerotic lipids, e.g. ceramide (Cer) and diacylglycerol (DAG), were down-regulated whereas triacylglycerol (TAG) containing docosahexaenoic acid (DHA) (22:6) fatty acyl was up-regulated when compared with WT mice. Several pro-atherosclerotic lipids including phosphatidic acid (PA), lysophosphatidylserine (LPS), sphingomyelin (SM), and cholesterol (Cho) were up-regulated in ep mice compared with WT mice. The lipid droplets in hepatocytes showed corresponding changes in these mutants. Our data suggest that the pa mutant resembles the ru mutant in its anti-atherosclerotic effects, but the ep mutant has an atherogenic effect. Our findings may provide clues to explain why different HPS mutant mice exhibit distinct anti-atherosclerotic or atherogenic effects after being exposed to high-cholesterol diets.

Metabolic reprogramming of the urea cycle pathway in experimental pulmonary arterial hypertension rats induced by monocrotaline

Background

Pulmonary arterial hypertension (PAH) is a rare systemic disorder associated with considerable metabolic dysfunction. Although enormous metabolomic studies on PAH have been emerging, research remains lacking on metabolic reprogramming in experimental PAH models. We aim to evaluate the metabolic changes in PAH and provide new insight into endogenous metabolic disorders of PAH.

Method

A single subcutaneous injection of monocrotaline (MCT) (60 mg kg^− 1) was used for rats to establish PAH model. Hemodynamics and right ventricular hypertrophy were adopted to evaluate the successful establishment of PAH model. Plasma samples were assessed through targeted metabolomic profiling platform to quantify 126 endogenous metabolites. Orthogonal partial least squares discriminant analysis (OPLS-DA) was used to discriminate between MCT-treated model and control groups. Metabolite Set Enrichment Analysis was adapted to exploit the most disturbed metabolic pathways.

Results

Endogenous metabolites of MCT treated PAH model and control group were well profiled using this platform. A total of 13 plasma metabolites were significantly altered between the two groups. Metabolite Set Enrichment Analysis highlighted that a disruption in the urea cycle pathway may contribute to PAH onset. Moreover, five novel potential biomarkers in the urea cycle, adenosine monophosphate, urea, 4-hydroxy-proline, ornithine, N-acetylornithine, and two candidate biomarkers, namely, O-acetylcarnitine and betaine, were found to be highly correlated with PAH.

Conclusion

The present study suggests a new role of urea cycle disruption in the pathogenesis of PAH. We also found five urea cycle related biomarkers and another two candidate biomarkers to facilitate early diagnosis of PAH in metabolomic profile.

An integrated method for direct interrogation of sphingolipid homeostasis in the heart and brain tissues of mice through postnatal development up to reproductive senescence

Development of rapid metabolomic methods poised for pathway discovery is expected to facilitate the identification of therapeutic candidates in the metabolomic approach to translational medicine. Using sphingolipid homeostasis as a prototype, we present herein an integrated method to facilitate a fast interrogation of altered sphingolipid (and phospholipid) metabolism associated with perturbed endolysosomal functions in mammalian systems. Constructed upon high performance liquid chromatography coupled to mass spectrometry, this method allows semi-quantitative measurements of more than 300 individual species within 20 min. The method was applied to investigate cardiac- and neural-specific developmental changes in sphingolipid regulation from the postnatal stage to reproductive senescence in mice, revealing that endogenous lysobisphosphatidic acids and specific complex glycosphingolipids are tightly co-regulated to foster concerted reductions in sphingolipid levels at distinct stages of postnatal development. Our lipidomic data suggest that such changing regulatory patterns in sphingolipid homeostasis is attributed to differential endolysosomal degradation of complex sphingolipids, which may be critical in ensuring efficient sphingolipid catabolism and organismal health at each stage of postnatal development.

Sequestration of polyunsaturated fatty acids in membrane phospholipids of Caenorhabditis elegans dauer larva attenuates eicosanoid biosynthesis for prolonged survival

Mechanistic basis governing the extreme longevity and developmental quiescence of dauer juvenile, a "non-ageing" developmental variant of Caenorhabditis elegans, has remained largely obscure. Using a lipidomic approach comprising multiple reaction monitoring transitions specific to distinct fatty acyl moieties, we demonstrated that in comparison to other developmental stages, the membrane phospholipids of dauer larva contain a unique enrichment of polyunsaturated fatty acids (PUFAs). Esterified PUFAs in phospholipids exhibited temporal accumulation throughout the course of dauer endurance, followed by sharp reductions prior to termination of diapause. Reductions in esterified PUFAs were accompanied by concomitant increases in unbound PUFAs, as well as their corresponding downstream oxidized derivatives (i.e. eicosanoids). Global phospholipidomics has unveiled that PUFA sequestration in membrane phospholipids denotes an essential aspect of dauer dormancy, principally via suppression of eicosanoid production; and a failure to upkeep membrane lipid homeostasis is associated with termination of dauer endurance.