Characterization of systemic metabolic phenotypes associated with subclinical atherosclerosis

Identification of Metabolomics Biomarkers in Extracranial Carotid Artery Stenosis

The biochemical identification of carotid artery stenosis (CAS) is still a challenge. Hence, 349 male subjects (176 normal controls and 173 stroke patients with extracranial CAS ≥ 50% diameter stenosis) were recruited. Blood samples were collected 14 days after stroke onset with no acute illness. Carotid plaque score (≥2, ≥5 and ≥8) was used to define CAS severity. Serum metabolites were analyzed using a targeted Absolute IDQ®p180 kit. Results showed hypertension, diabetes, smoking, and alcohol consumption were more common, but levels of diastolic blood pressure, HDL-C, LDL-C, and cholesterol were lower in CAS patients than controls (p < 0.05), suggesting intensive medical treatment for CAS. PCA and PLS-DA did not demonstrate clear separation between controls and CAS patients. Decision tree and random forest showed that acylcarnitine species (C4, C14:1, C18), amino acids and biogenic amines (SDMA), and glycerophospholipids (PC aa C36:6, PC ae C34:3) contributed to the prediction of CAS. Metabolite panel analysis showed high specificity (0.923 ± 0.081, 0.906 ± 0.086 and 0.881 ± 0.109) but low sensitivity (0.230 ± 0.166, 0.240 ± 0.176 and 0.271 ± 0.169) in the detection of CAS (≥2, ≥5 and ≥8, respectively). The present study suggests that metabolomics profiles could help in differentiating between controls and CAS patients and in monitoring the progression of CAS.

Integrative analysis of metabolomic, genomic, and imaging-based phenotypes identify very-low-density lipoprotein as a potential risk factor for lumbar Modic changes

PURPOSE

Modic changes (MC) on magnetic resonance imaging (MRI) have been associated with the development and severity of low back pain (LBP). The etiology of MC remains elusive, but it has been suggested that altered metabolism may be a risk factor. As such, this study aimed to identify metabolomic biomarkers for MC phenotypes of the lumbar spine via a combined metabolomic-genomic approach.

METHODS

A population cohort of 3,584 southern Chinese underwent lumbar spine MRI. Blood samples were genotyped with single-nucleotide polymorphisms (SNP) arrays (n = 2,482) and serum metabolomics profiling using magnetic resonance spectroscopy (n = 757), covering 130 metabolites representing three molecular windows, were assessed. Genome-wide association studies (GWAS) were performed on each metabolite, to construct polygenic scores for predicting metabolite levels in subjects who had GWAS but not metabolomic data. Associations between predicted metabolite levels and MC phenotypes were assessed using linear/logistic regression and least absolute shrinkage and selection operator (LASSO). Two-sample Mendelian randomization analysis tested for causal relationships between metabolic biomarkers and MC.

RESULTS

20.4% had MC (10.6% type 1, 67.2% type 2, 22.2% mixed types). Significant MC metabolomic biomarkers were mean diameter of very-low-density lipoprotein (VLDL)/low-density lipoprotein (LDL) particles and cholesterol esters/phospholipids in large LDL. Mendelian randomization indicated that decreased VLDL mean diameter may lead to MC.

CONCLUSIONS

This large-scale study is the first to address metabolomics in subject with/without lumbar MC. Causality studies implicate VLDL related to MC, noting a metabolic etiology. Our study substantiates the field of "spino-metabolomics" and illustrates the power of integrating metabolomics-genomics-imaging phenotypes to discover biomarkers for spinal disorders, paving the way for more personalized spine care for patients.

Biomarker Discovery in Atherosclerotic Diseases Using Quantitative Nuclear Magnetic Resonance Metabolomics

Despite great progress in the management of atherosclerosis (AS), its subsequent cardiovascular disease (CVD) remains the leading cause of morbidity and mortality. This is probably due to insufficient risk detection using routine lipid testing; thus, there is a need for more effective approaches relying on new biomarkers. Quantitative nuclear magnetic resonance (qNMR) metabolomics is able to phenotype holistic metabolic changes, with a unique advantage in regard to quantifying lipid-protein complexes. The rapidly increasing literature has indicated that qNMR-based lipoprotein particle number, particle size, lipid components, and some molecular metabolites can provide deeper insight into atherogenic diseases and could serve as novel promising determinants. Therefore, this article aims to offer an updated review of the qNMR biomarkers of AS and CVD found in epidemiological studies, with a special emphasis on lipoprotein-related parameters. As more researches are performed, we can envision more qNMR metabolite biomarkers being successfully translated into daily clinical practice to enhance the prevention, detection and intervention of atherosclerotic diseases.

Serum Metabolomic Signatures Can Predict Subclinical Atherosclerosis in Patients With Systemic Lupus Erythematosus

[Figure: see text].

Establishing reference intervals for triglyceride-containing lipoprotein subfraction metabolites measured using nuclear magnetic resonance spectroscopy in a UK population

BACKGROUND

Nuclear magnetic resonance (NMR) spectroscopy allows triglycerides to be subclassified into 14 different classes based on particle size and lipid content. We recently showed that these subfractions have differential associations with cardiovascular disease events. Here we report the distributions and define reference interval ranges for 14 triglyceride-containing lipoprotein subfraction metabolites.

METHODS

Lipoprotein subfractions using the Nightingale NMR platform were measured in 9073 participants from four cohort studies contributing to the UCL-Edinburgh-Bristol consortium. The distribution of each metabolite was assessed, and reference interval ranges were calculated for a disease-free population, by sex and age group (<55, 55-65, >65 years), and in a subgroup population of participants with cardiovascular disease or type 2 diabetes. We also determined the distribution across body mass index and smoking status.

RESULTS

The largest reference interval range was observed in the medium very-low density lipoprotein subclass (2.5th 97.5th percentile; 0.08 to 0.68 mmol/L). The reference intervals were comparable among male and female participants, with the exception of triglyceride in high-density lipoprotein. Triglyceride subfraction concentrations in very-low density lipoprotein, intermediate-density lipoprotein, low-density lipoprotein and high-density lipoprotein subclasses increased with increasing age and increasing body mass index. Triglyceride subfraction concentrations were significantly higher in ever smokers compared to never smokers, among those with clinical chemistry measured total triglyceride greater than 1.7 mmol/L, and in those with cardiovascular disease, and type 2 diabetes as compared to disease-free subjects.

CONCLUSION

This is the first study to establish reference interval ranges for 14 triglyceride-containing lipoprotein subfractions in samples from the general population measured using the nuclear magnetic resonance platform. The utility of nuclear magnetic resonance lipid measures may lead to greater insights for the role of triglyceride in cardiovascular disease, emphasizing the importance of appropriate reference interval ranges for future clinical decision making.

Metabolomics evaluation of repeated administration of potassium iodide on adult male rats

The long-lasting consequence of a new iodine thyroid blocking strategy (ITB) to be used in case of nuclear accident is evaluated in male Wistar rats using a metabolomics approach applied 30 days after ITB completion. The design used 1 mg/kg/day of KI over 8 days. Thyroid hormones remained unchanged, but there was a metabolic shift measured mainly in thyroid then in plasma and urine. In the thyroid, tyrosine metabolism associated to catecholamine metabolism was more clearly impacted than thyroid hormones pathway. It was accompanied by a peripheral metabolic shift including metabolic regulators, branched-chain amino acids, oxidant stress and inflammation-associated response. Our results suggested that iodide intake can impact gut microbiota metabolism, which was related to host metabolic regulations including in the thyroid. As there were no clear clinical signs of dysfunction or toxicity, we concluded that the measured metabolomics response to the new ITB strategy, especially in thyroid, is unlikely to reveal a pathological condition but a shift towards a new adaptive homeostatic state, called 'allostatic regulation'. The question now is whether or not the shift is permanent and if so at what cost for long-term health. We anticipate our data as a start point for further regulatory toxicity studies.

A Cross-Cohort Study Examining the Associations of Metabolomic Profile and Subclinical Atherosclerosis in Children and Their Parents: The Child Health CheckPoint Study and Avon Longitudinal Study of Parents and Children

Background High-throughput nuclear magnetic resonance profiling of circulating metabolites is suggested as an adjunct for cardiovascular risk evaluation. The relationship between metabolites and subclinical atherosclerosis remains unclear, particularly among children. Therefore, we examined the associations of metabolites with carotid intima-media thickness ( cIMT ) and arterial pulse wave velocity ( PWV ). Methods and Results Data from two independent population-based studies was examined; (1) cross-sectional associations with cIMT and PWV in 1178 children (age 11-12 years, 51% female) and 1316 parents (mean age 45 years, 87% female) from the CheckPoint study (Australia); and (2) longitudinal associations in 4249 children (metabolites at 7-8 years, PWV at 10-11 years, 52% female), and cross-sectional associations in 4171 of their mothers (mean age 48 years, cIMT data) from ALSPAC (The Avon Longitudinal Study of Parents and Children; UK ). Metabolites were measured by the same nuclear magnetic resonance platform in both studies, comprising of 69 biomarkers. Biophysical assessments included body mass index, blood pressure, cIMT and PWV . In linear regression analyses adjusted for age, sex, body mass index, and blood pressure, there was no evidence of metabolite associations in either children or adults for cIMT at a 10% false discovery threshold. In CheckPoint adults, glucose was positively, and some high-density lipoprotein-cholesterol derived measures and amino acids (glutamine, histidine, tyrosine) inversely associated with PWV. Conclusions These data suggest that in children circulating metabolites have no consistent association with cIMT and PWV once adjusted for body mass index and blood pressure. In their middle-aged parents, some evidence of metabolite associations with PWV were identified that warrant further investigation.

Andrée Gruslin award lecture: Metabolomics as an important modality to better understand preeclampsia

Preeclampsia (PE) is a complex disorder that affects 3-5% of all pregnancies and is a leading cause of maternal and fetal morbidity and mortality. To date, the heterogeneity of clinical presentation, disease severity and outcomes have limited significant advances in early prediction, diagnosis, and therapeutic intervention of PE. The rapidly expanding field of metabolomics, which has the capacity to quantitatively detect low molecular weight compounds (metabolites) in tissue and biological fluids, shows tremendous promise in gaining a better understanding of PE. This review will discuss this emerging field and its contribution to recent advances in the understanding of PE pathophysiology, and identification of early predictive metabolic biomarkers for this complex disorder.

Effect of Theobromine Consumption on Serum Lipoprotein Profiles in Apparently Healthy Humans with Low HDL-Cholesterol Concentrations

Scope: Theobromine is a major active compound in cocoa with allegedly beneficial effect on high-density-lipoprotein-cholesterol (HDL-CH). We have investigated the effect of theobromine (TB) consumption on the concentrations of triglyceride (TG) and cholesterol (CH) in various lipoprotein (LP) subclasses.

Methods: In a randomized, double-blind, placebo-controlled, cross-over study, 44 apparently healthy women and men (age: 60 ± 6 years, BMI: 29 ± 3 kg/m²) with low baseline HDL-CH concentrations consumed a drink supplemented with 500 mg/d theobromine for 4 weeks. TG and CH concentrations in 15 LP subclasses were predicted from diffusion-edited ¹H NMR spectra of fasting serum.

Results: The LP phenotype of the subjects was characterized by low CH concentrations in the large HDL particles and high TG concentrations in large VLDL and chylomicron (CM) particles, which clearly differed from a LP phenotype of subjects with normal HDL-CH. TB only reduced CH concentrations in the LDL particles by 3.64 and 6.79%, but had no effect on TG and CH in any of the HDL, VLDL and CM subclasses.

Conclusion: TB was not effective on HDL-CH in subjects with a LP phenotype characterized by low HDL-CH and high TG in VLDL.

Metabolomics in nutrition research-a powerful window into nutritional metabolism

Metabolomics is the study of small molecules present in biological samples. In recent years it has become evident that such small molecules, called metabolites, play a key role in the development of disease states. Furthermore, metabolomic applications can reveal information about alterations in certain metabolic pathways under different conditions. Data acquisition in metabolomics is usually performed using nuclear magnetic resonance (NMR)-based approaches or mass spectrometry (MS)-based approaches with a more recent trend including the application of multiple platforms in order to maximise the coverage in terms of metabolites measured. The application of metabolomics is rapidly increasing and the present review will highlight applications in nutrition research.

Quantitative serum nuclear magnetic resonance metabolomics in cardiovascular epidemiology and genetics

Metabolomics is becoming common in epidemiology due to recent developments in quantitative profiling technologies and appealing results from their applications for understanding health and disease. Our team has developed an automated high-throughput serum NMR metabolomics platform that provides quantitative molecular data on 14 lipoprotein subclasses, their lipid concentrations and composition, apolipoprotein A-I and B, multiple cholesterol and triglyceride measures, albumin, various fatty acids as well as on numerous low-molecular-weight metabolites, including amino acids, glycolysis related measures and ketone bodies. The molar concentrations of these measures are obtained from a single serum sample with costs comparable to standard lipid measurements. We have analyzed almost 250 000 samples from around 100 epidemiological cohorts and biobanks and the new international set-up of multiple platforms will allow an annual throughput of more than 250 000 samples. The molecular data have been used to study type 1 and type 2 diabetes etiology as well as to characterize the molecular reflections of the metabolic syndrome, long-term physical activity, diet and lipoprotein metabolism. The results have revealed new biomarkers for early atherosclerosis, type 2 diabetes, diabetic nephropathy, cardiovascular disease and all-cause mortality. We have also combined genomics and metabolomics in diverse studies. We envision that quantitative high-throughput NMR metabolomics will be incorporated as a routine in large biobanks; this would make perfect sense both from the biological research and cost point of view - the standard output of over 200 molecular measures would vastly extend the relevance of the sample collections and make many separate clinical chemistry assays redundant.

Branched-chain and aromatic amino acids are predictors of insulin resistance in young adults

OBJECTIVE

Branched-chain and aromatic amino acids are associated with the risk for future type 2 diabetes; however, the underlying mechanisms remain elusive. We tested whether amino acids predict insulin resistance index in healthy young adults.

RESEARCH DESIGN AND METHODS

Circulating isoleucine, leucine, valine, phenylalanine, tyrosine, and six additional amino acids were quantified in 1,680 individuals from the population-based Cardiovascular Risk in Young Finns Study (baseline age 32 ± 5 years; 54% women). Insulin resistance was estimated by homeostasis model assessment (HOMA) at baseline and 6-year follow-up. Amino acid associations with HOMA of insulin resistance (HOMA-IR) and glucose were assessed using regression models adjusted for established risk factors. We further examined whether amino acid profiling could augment risk assessment of insulin resistance (defined as 6-year HOMA-IR >90th percentile) in early adulthood.

RESULTS

Isoleucine, leucine, valine, phenylalanine, and tyrosine were associated with HOMA-IR at baseline and for men at 6-year follow-up, while for women only leucine, valine, and phenylalanine predicted 6-year HOMA-IR (P < 0.05). None of the other amino acids were prospectively associated with HOMA-IR. The sum of branched-chain and aromatic amino acid concentrations was associated with 6-year insulin resistance for men (odds ratio 2.09 [95% CI 1.38-3.17]; P = 0.0005); however, including the amino acid score in prediction models did not improve risk discrimination.

CONCLUSIONS

Branched-chain and aromatic amino acids are markers of the development of insulin resistance in young, normoglycemic adults, with most pronounced associations for men. These findings suggest that the association of branched-chain and aromatic amino acids with the risk for future diabetes is at least partly mediated through insulin resistance.

A practical guide to metabolomic profiling as a discovery tool for human heart disease

Metabolomics has refreshed interest in metabolism across biology and medicine, particularly in the areas of functional genomics and biomarker discovery. In this review we will discuss the experimental techniques and challenges involved in metabolomic profiling and how these technologies have been applied to cardiovascular disease. Open profiling and targeted approaches to metabolomics are compared, focusing on high resolution NMR spectroscopy and mass spectrometry, as well as discussing how to analyse the large amounts of data generated using multivariate statistics. Finally, the current literature on metabolomic profiling in human cardiovascular disease is reviewed to illustrate the diversity of approaches, and discuss some of the key metabolites and pathways found to be perturbed in plasma, urine and tissue from patients with these diseases. This includes studies of coronary artery disease, myocardial infarction, and ischemic heart disease. These studies demonstrate the potential of the technology for biomarker discovery and elucidating metabolic mechanisms associated with given pathologies, but also in some cases provide a warning of the pitfalls of poor study design. This article is part of a Special Issue entitled "Focus on Cardiac Metabolism".

Metabolomics and ischaemic heart disease

Ischaemic heart disease accounts for nearly half of the global cardiovascular disease burden. Aetiologies relating to heart disease are complex, but dyslipidaemia, oxidative stress and inflammation are cardinal features. Despite preventative measures and advancements in treatment regimens with lipid-lowering agents, the high prevalence of heart disease and the residual risk of recurrent events continue to be a significant burden to the health sector and to the affected individuals and their families. The development of improved risk models for the early detection and prevention of cardiovascular events in addition to new therapeutic strategies to address this residual risk are required if we are to continue to make inroads into this most prevalent of diseases. Metabolomics and lipidomics are modern disciplines that characterize the metabolite and lipid complement respectively, of a given system. Their application to ischaemic heart disease has demonstrated utilities in population profiling, identification of multivariate biomarkers and in monitoring of therapeutic response, as well as in basic mechanistic studies. Although advances in magnetic resonance and mass spectrometry technologies have given rise to the fields of metabolomics and lipidomics, the plethora of data generated presents challenges requiring specific statistical and bioinformatics applications, together with appropriate study designs. Nonetheless, the predictive and re-classification capacity of individuals with various degrees of risk by the plasma lipidome has recently been demonstrated. In the present review, we summarize evidence derived exclusively by metabolomic and lipidomic studies in the context of ischaemic heart disease. We consider the potential role of plasma lipid profiling in assessing heart disease risk and therapeutic responses, and explore the potential mechanisms. Finally, we highlight where metabolomic studies together with complementary -omic disciplines may make further inroads into the understanding, detection and treatment of ischaemic heart disease.

Circulating metabolite predictors of glycemia in middle-aged men and women

OBJECTIVE

Metabolite predictors of deteriorating glucose tolerance may elucidate the pathogenesis of type 2 diabetes. We investigated associations of circulating metabolites from high-throughput profiling with fasting and postload glycemia cross-sectionally and prospectively on the population level.

RESEARCH DESIGN AND METHODS

Oral glucose tolerance was assessed in two Finnish, population-based studies consisting of 1,873 individuals (mean age 52 years, 58% women) and reexamined after 6.5 years for 618 individuals in one of the cohorts. Metabolites were quantified by nuclear magnetic resonance spectroscopy from fasting serum samples. Associations were studied by linear regression models adjusted for established risk factors.

RESULTS

Nineteen circulating metabolites, including amino acids, gluconeogenic substrates, and fatty acid measures, were cross-sectionally associated with fasting and/or postload glucose (P < 0.001). Among these metabolic intermediates, branched-chain amino acids, phenylalanine, and α1-acid glycoprotein were predictors of both fasting and 2-h glucose at 6.5-year follow-up (P < 0.05), whereas alanine, lactate, pyruvate, and tyrosine were uniquely associated with 6.5-year postload glucose (P = 0.003-0.04). None of the fatty acid measures were prospectively associated with glycemia. Changes in fatty acid concentrations were associated with changes in fasting and postload glycemia during follow-up; however, changes in branched-chain amino acids did not follow glucose dynamics, and gluconeogenic substrates only paralleled changes in fasting glucose.

CONCLUSIONS

Alterations in branched-chain and aromatic amino acid metabolism precede hyperglycemia in the general population. Further, alanine, lactate, and pyruvate were predictive of postchallenge glucose exclusively. These gluconeogenic precursors are potential markers of long-term impaired insulin sensitivity that may relate to attenuated glucose tolerance later in life.

High-throughput quantification of circulating metabolites improves prediction of subclinical atherosclerosis

AIMS

High-throughput metabolite quantification holds promise for cardiovascular risk assessment. Here, we evaluated whether metabolite quantification by nuclear magnetic resonance (NMR) improves prediction of subclinical atherosclerosis in comparison to conventional lipid testing.

METHODS AND RESULTS

Circulating lipids, lipoprotein subclasses, and small molecules were assayed by NMR for 1595 individuals aged 24-39 years from the population-based Cardiovascular Risk in Young Finns Study. Carotid intima-media thickness (IMT), a marker of subclinical atherosclerosis, was measured in 2001 and 2007. Baseline conventional risk factors and systemic metabolites were used to predict 6-year incidence of high IMT (≥ 90 th percentile) or plaque. The best prediction of high intima-media thickness was achieved when total and HDL cholesterol were replaced by NMR-determined LDL cholesterol and medium HDL, docosahexaenoic acid, and tyrosine in prediction models with risk factors from the Framingham risk score. The extended prediction model improved risk stratification beyond established risk factors alone; area under the receiver operating characteristic curve 0.764 vs. 0.737, P =0.02, and net reclassification index 17.6%, P =0.0008. Higher docosahexaenoic acid levels were associated with decreased risk for incident high IMT (odds ratio: 0.74; 95% confidence interval: 0.67-0.98; P = 0.007). Tyrosine (1.33; 1.10-1.60; P = 0.003) and glutamine (1.38; 1.13-1.68; P = 0.001) levels were associated with 6-year incident high IMT independent of lipid measures. Furthermore, these amino acids were cross-sectionally associated with carotid IMT and the presence of angiographically ascertained coronary artery disease in independent populations.

CONCLUSION

High-throughput metabolite quantification, with new systemic biomarkers, improved risk stratification for subclinical atherosclerosis in comparison to conventional lipids and could potentially be useful for early cardiovascular risk assessment.

Genome-wide association study identifies loci influencing concentrations of liver enzymes in plasma

Concentrations of liver enzymes in plasma are widely used as indicators of liver disease. We carried out a genome-wide association study in 61,089 individuals, identifying 42 loci associated with concentrations of liver enzymes in plasma, of which 32 are new associations (P = 10(-8) to P = 10(-190)). We used functional genomic approaches including metabonomic profiling and gene expression analyses to identify probable candidate genes at these regions. We identified 69 candidate genes, including genes involved in biliary transport (ATP8B1 and ABCB11), glucose, carbohydrate and lipid metabolism (FADS1, FADS2, GCKR, JMJD1C, HNF1A, MLXIPL, PNPLA3, PPP1R3B, SLC2A2 and TRIB1), glycoprotein biosynthesis and cell surface glycobiology (ABO, ASGR1, FUT2, GPLD1 and ST3GAL4), inflammation and immunity (CD276, CDH6, GCKR, HNF1A, HPR, ITGA1, RORA and STAT4) and glutathione metabolism (GSTT1, GSTT2 and GGT), as well as several genes of uncertain or unknown function (including ABHD12, EFHD1, EFNA1, EPHA2, MICAL3 and ZNF827). Our results provide new insight into genetic mechanisms and pathways influencing markers of liver function.

Metabolomics as a tool for cardiac research

Metabolomics represents a paradigm shift in metabolic research, away from approaches that focus on a limited number of enzymatic reactions or single pathways, to approaches that attempt to capture the complexity of metabolic networks. Additionally, the high-throughput nature of metabolomics makes it ideal to perform biomarker screens for diseases or follow drug efficacy. In this Review, we explore the role of metabolomics in gaining mechanistic insight into cardiac disease processes, and in the search for novel biomarkers. High-resolution NMR spectroscopy and mass spectrometry are both highly discriminatory for a range of pathological processes affecting the heart, including cardiac ischemia, myocardial infarction, and heart failure. We also discuss the position of metabolomics in the range of functional-genomic approaches, being complementary to proteomic and transcriptomic studies, and having subdivisions such as lipidomics (the study of intact lipid species). In addition to techniques that monitor changes in the total sizes of pools of metabolites in the heart and biofluids, the role of stable-isotope methods for monitoring fluxes through pathways is examined. The use of these novel functional-genomic tools to study metabolism provides a unique insight into cardiac disease progression.