Metabolic Signatures in Coronary Artery Disease: Results from the Bioheart-CT Study
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cells Article Metabolic Signatures in Coronary Artery Disease: Results from the BioHEART-CT Study Stephen T. Vernon 1,2,3, Owen Tang 1,3,4, Taiyun Kim 4,5,6 , Adam S. Chan 4,5, Katharine A. Kott 1,2,3 , John Park 1, Thomas Hansen 1,3, Yen C. Koay 4,7,8, Stuart M. Grieve 9,10, John F. O’Sullivan 4,7,8,†, Jean Y. Yang 4,5,† and Gemma A. Figtree 1,2,3,4,*,† 1 Cardiothoracic and Vascular Health, Kolling Institute, Northern Sydney Local Health District, Sydney, NSW 2065, Australia; [email protected] (S.T.V.); [email protected] (O.T.); [email protected] (K.A.K.); [email protected] (J.P.); [email protected] (T.H.) 2 Department of Cardiology, Royal North Shore Hospital, Sydney, NSW 2065, Australia 3 Northern Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW 2006, Australia 4 Charles Perkins Centre, University of Sydney, Sydney, NSW 2006, Australia; [email protected] (T.K.); [email protected] (A.S.C.); [email protected] (Y.C.K.); [email protected] (J.F.O.); [email protected] (J.Y.Y.) 5 School of Mathematics and Statistics, University of Sydney, Sydney, NSW 2006, Australia 6 Computational Systems Biology Group, Children’s Medical Research Institute, Westmead, NSW 2145, Australia 7 Central Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW 2006, Australia 8 Heart Research Institute, The University of Sydney, Sydney, NSW 2042, Australia 9 Imaging and Phenotyping Laboratory, Charles Perkins Centre, Faculty of Medicine and Health, University of Sydney, Sydney, NSW 2006, Australia; [email protected] 10 Citation: Vernon, S.T.; Tang, O.; Kim, Department of Radiology, Royal Prince Alfred Hospital, Sydney, NSW 2050, Australia * Correspondence: gemma.fi[email protected] T.; Chan, A.S.; Kott, K.A.; Park, J.; † Contributed equally. Hansen, T.; Koay, Y.C.; Grieve, S.M.; O’Sullivan, J.F.; et al. Metabolic Abstract: Signatures in Coronary Artery Despite effective prevention programs targeting cardiovascular risk factors, coronary Disease: Results from the artery disease (CAD) remains the leading cause of death. Novel biomarkers are needed for im- BioHEART-CT Study. Cells 2021, 10, proved risk stratification and primary prevention. To assess for independent associations between 980. https://doi.org/10.3390/ plasma metabolites and specific CAD plaque phenotypes we performed liquid chromatography cells10050980 mass-spectrometry on plasma from 1002 patients in the BioHEART-CT study. Four metabolites were examined as candidate biomarkers. Dimethylguanidino valerate (DMGV) was associated with Academic Editor: János G. Filep presence and amount of CAD (OR) 1.41 (95% Confidence Interval [CI] 1.12–1.79, p = 0.004), calcified plaque, and obstructive CAD (p < 0.05 for both). The association with amount of plaque remained Received: 14 March 2021 after adjustment for traditional risk factors, ß-coefficient 0.17 (95% CI 0.02–0.32, p = 0.026). Glutamate Accepted: 20 April 2021 was associated with the presence of non-calcified plaque, OR 1.48 (95% CI 1.09–2.01, p = 0.011). Pheny- Published: 22 April 2021 lalanine was associated with amount of CAD, ß-coefficient 0.33 (95% CI 0.04–0.62, p = 0.025), amount of calcified plaque, (ß-coefficient 0.88, 95% CI 0.23–1.53, p = 0.008), and obstructive CAD, OR 1.84 Publisher’s Note: MDPI stays neutral (95% CI 1.01–3.31, p = 0.046). Trimethylamine N-oxide was negatively associated non-calcified plaque with regard to jurisdictional claims in p published maps and institutional affil- OR 0.72 (95% CI 0.53–0.97, = 0.029) and the association remained when adjusted for traditional risk iations. factors. In targeted metabolomic analyses including 53 known metabolites and controlling for a 5% false discovery rate, DMGV was strongly associated with the presence of calcified plaque, OR 1.59 (95% CI 1.26–2.01, p = 0.006), obstructive CAD, OR 2.33 (95% CI 1.59–3.43, p = 0.0009), and amount of CAD, ß-coefficient 0.3 (95% CI 0.14–0.45, p = 0.014). In multivariate analyses the lipid and nucleotide Copyright: © 2021 by the authors. metabolic pathways were both associated with the presence of CAD, after adjustment for traditional Licensee MDPI, Basel, Switzerland. risk factors. We report novel associations between CAD plaque phenotypes and four metabolites This article is an open access article previously associated with CAD. We also identified two metabolic pathways strongly associated distributed under the terms and with CAD, independent of traditional risk factors. These pathways warrant further investigation at conditions of the Creative Commons both a biomarker and mechanistic level. Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Cells 2021, 10, 980. https://doi.org/10.3390/cells10050980 https://www.mdpi.com/journal/cells Cells 2021, 10, 980 2 of 16 Keywords: coronary artery disease; metabolites; risk factors; atherosclerosis; metabolomics 1. Introduction Large longitudinal epidemiological studies have identified a number of modifiable risk factors and disease processes associated with cardiovascular disease (CVD), including hypertension, hyperlipidaemia, cigarette smoking, and diabetes mellitus [1]. Public health and primary prevention programs targeting these modifiable risk factors have substantially reduced the burden of disease at the community level [2]. Despite this, coronary artery disease (CAD) remains the leading cause of death globally and at an individual level it is not uncommon for people to suffer life threating cardiovascular events such as ST elevation myocardial infarction in the absence of these well described risk factors [3–7]. Clinically utilised biomarkers, including standard lipid profiles, blood glucose levels, and HbA1c, reflect established traditional risk factors or CAD, however we lack biomarkers that reflect the residual risk not captured by traditional risk factors. Computed tomography coronary angiography (CTCA) can non-invasively assess and characterise coronary artery plaque, as well as clearly identify normal coronary arteries (the absence of plaque). This provides a powerful research opportunity for CAD biomarker discovery studies, overcoming a challenge faced by many prior studies that relied on cardiovascular events to define CAD, where many of the healthy control participants actually have substantial subclinical atherosclerosis [8–10] BioHEART-CT, a multicentre, prospective cohort study of patients with suspected CAD, utilises detailed demographic, clinical, and CTCA data to accurately quantify the disease burden in its participants, and stored blood samples for molecular phenotyping. It has been designed to identify novel biomarkers associated with CAD in order to improved risk stratification and to identify new mechanistic pathways that could be targeted to reduce the burden of CAD [11]. Metabolites are influenced by environmental factors, genetic factors, and interactions between the two [12,13]. Metabolomics, the systematic identification and quantification of small molecules (up to 1.5 kDa) in biological fluids, thus reflects the metabolic state of the biological system at a point in time [14]. The metabolome can be assessed at a cellular, tissue, organ, or whole-organismal level. A recent systematic review of metabolomics studies attempting to identify metabolic signatures of incident cardiovascular disease identified 5 metabolites acylcarnitine, dicarboxylacylcarnitine, trimethylamine N-oxide (TMAO), phenylalanine, and glutamate [15]. Another recent study identified an indepen- dent association between dimethylguanidino valerate (DMGV) and incident CAD [16]. We hypothesised that there are circulating metabolites and shifts in particular metabolic pathways associated with the presence of CAD, and specific CAD phenotypes, including calcified plaque and non-calcified “soft” plaque. We utilised a large, well phenotyped cohort of stable patients with suspected CAD undergoing CTCA, and considered the role of traditional CAD risk factors. We proceeded to test these hypotheses in the BioHEART CT study, utilising liquid chromatography mass spectrometry. 2. Methods 2.1. BioHEART-CT Study BioHEART-CT is a multicentre, longitudinal, prospective cohort study of patients un- dergoing a CTCA for investigation of suspected CAD. BioHEART is a registered Australian and New Zealand Clinical Trial (ACTRN12618001322224). The study protocol and design has been approved by the Northern Sydney Local Health District Human Research Ethics Committee (HREC/17/HAWKE/343) and described in detail previously [11]. Informed written consent was obtained. Patient level information was collected using a structured data collection form at the time of recruitment and entered into a secure, encrypted database. Baseline data collected includes demographics, anthropometric measures, past medical diagnoses (cardiovascular diagnoses and risk factors), current medication use, and social Cells 2021, 10, 980 3 of 16 history. Standard Modifiable cardiovascular Risk Factors (SMuRFs) were recorded based on the self-reported history or use of pharmacotherapy for any of hypertension, hyperc- holesterolaemia, diabetes mellitus, or a significant smoking history. Current smoking was defined as having regularly smoked within the past 12 months, and significant smoking history was considered to be 10 packet years or greater. A significant family history of premature ischaemic heart disease in a first degree relative under the age of 60 years was recorded. Blood samples were taken at the time of recruitment