Integration of metabolomics, lipidomics and clinical data using a machine learning method.

BACKGROUND: The recent pandemic of obesity and the metabolic syndrome (MetS) has led to the realisation that new drug targets are needed to either reduce obesity or the subsequent pathophysiological consequences associated with excess weight gain. Certain nuclear hormone receptors (NRs) play a pivotal role in lipid and carbohydrate metabolism and have been highlighted as potential treatments for obesity. This realisation started a search for NR agonists in order to understand and successfully treat MetS and associated conditions such as insulin resistance, dyslipidaemia, hypertension, hypertriglyceridemia, obesity and cardiovascular disease. The most studied NRs for treating metabolic diseases are the peroxisome proliferator-activated receptors (PPARs), PPAR-α, PPAR-γ, and PPAR-δ. However, prolonged PPAR treatment in animal models has led to adverse side effects including increased risk of a number of cancers, but how these receptors change metabolism long term in terms of pathology, despite many beneficial effects shorter term, is not fully understood. In the current study, changes in male Sprague Dawley rat liver caused by dietary treatment with a PPAR-pan (PPAR-α, -γ, and -δ) agonist were profiled by classical toxicology (clinical chemistry) and high throughput metabolomics and lipidomics approaches using mass spectrometry. RESULTS: In order to integrate an extensive set of nine different multivariate metabolic and lipidomics datasets with classical toxicological parameters we developed a hypotheses free, data driven machine learning approach. From the data analysis, we examined how the nine datasets were able to model dose and clinical chemistry results, with the different datasets having very different information content. CONCLUSIONS: We found lipidomics (Direct Infusion-Mass Spectrometry) data the most predictive for different dose responses. In addition, associations with the metabolic and lipidomic data with aspartate amino transaminase (AST), a hepatic leakage enzyme to assess organ damage, and albumin, indicative of altered liver synthetic function, were established. Furthermore, by establishing correlations and network connections between eicosanoids, phospholipids and triacylglycerols, we provide evidence that these lipids function as a key link between inflammatory processes and intermediary metabolism

Metabolic profiling of aortic stenosis and hypertrophic cardiomyopathy identifies mechanistic contrasts in substrate utilization

Aortic stenosis (AS) and hypertrophic cardiomyopathy (HCM) are distinct disorders leading to left ventricular hypertrophy (LVH), but whether cardiac metabolism substantially differs between these in humans remains to be elucidated. We undertook an invasive (aortic root, coronary sinus) metabolic profiling in patients with severe AS and HCM in comparison with non‐LVH controls to investigate cardiac fuel selection and metabolic remodeling. These patients were assessed under different physiological states (at rest, during stress induced by pacing). The identified changes in the metabolome were further validated by metabolomic and orthogonal transcriptomic analysis, in separately recruited patient cohorts. We identified a highly discriminant metabolomic signature in severe AS in all samples, regardless of sampling site, characterized by striking accumulation of long‐chain acylcarnitines, intermediates of fatty acid transport across the inner mitochondrial membrane, and validated this in a separate cohort. Mechanistically, we identify a downregulation in the PPAR‐α transcriptional network, including expression of genes regulating fatty acid oxidation (FAO). In silico modeling of β‐oxidation demonstrated that flux could be inhibited by both the accumulation of fatty acids as a substrate for mitochondria and the accumulation of medium‐chain carnitines which induce competitive inhibition of the acyl‐CoA dehydrogenases. We present a comprehensive analysis of changes in the metabolic pathways (transcriptome to metabolome) in severe AS, and its comparison to HCM. Our results demonstrate a progressive impairment of β‐oxidation from HCM to AS, particularly for FAO of long‐chain fatty acids, and that the PPAR‐α signaling network may be a specific metabolic therapeutic target in AS

Mechanisms of Vascular Dysfunction in COPD and Effects of a Novel Soluble Epoxide Hydrolase Inhibitor in Smokers.

BACKGROUND: Smoking and COPD are risk factors for cardiovascular disease, and the pathogenesis may involve endothelial dysfunction. We tested the hypothesis that endothelium-derived epoxyeicosatrienoic acid (EET)-mediated endothelial function is impaired in patients with COPD and that a novel soluble epoxide hydrolase inhibitor, GSK2256294, attenuates EET-mediated endothelial dysfunction in human resistance vessels both in vitro and in vivo. METHODS: Endogenous and stimulated endothelial release of EETs was assessed in 12 patients with COPD, 11 overweight smokers, and two matched control groups, using forearm plethysmography with intraarterial infusions of fluconazole, bradykinin, and the combination. The effects of GSK2256294 on EET-mediated vasodilation in human resistance arteries were assessed in vitro and in vivo in a phase I clinical trial in healthy overweight smokers. RESULTS: Compared with control groups, there was reduced vasodilation with bradykinin (P = .005), a blunted effect of fluconazole on bradykinin-induced vasodilation (P = .03), and a trend toward reduced basal EET/dihydroxyepoxyeicosatrienoic acid ratio in patients with COPD (P = .08). A similar pattern was observed in overweight smokers. In vitro, 10 μM GSK2256294 increased 11,12-EET-mediated vasodilation compared with vehicle (90% ± 4.2% vs 72.6% ± 6.2% maximal dilatation) and shifted the bradykinin half-maximal effective concentration (EC50) (-8.33 ± 0.172 logM vs -8.10 ± 0.118 logM; P = .001 for EC50). In vivo, 18 mg GSK2256294 improved the maximum bradykinin response from 338% ± 46% before a dose to 566% ± 110% after a single dose (P = .02) and to 503% ± 123% after a chronic dose (P = .003). CONCLUSIONS: GSK2256294 attenuates smoking-related EET-mediated endothelial dysfunction, suggesting potential therapeutic benefits in patients with COPD. TRIAL REGISTRY: ClinicalTrials.gov; No.: NCT01762774; URL: www.clinicaltrials.gov.This work was supported by GSK [SEH114068] and Innovate UK (ERICA Consortium 10037625), the Wellcome Trust grant numbers 100780/Z/12/Z, and WT103782AIA awarded to LY, and DEN respectively; the Raymond and Beverley Sackler fellowship awarded to LY; National Institute for Health Research funding awarded to IBW, and JC in the Cambridge Comprehensive Biomedical Research, and the British Heart Foundation grant numbers CH/0 9/002, and RG66885 RCZA/008 awarded to DEN, and IBW. JLG and ZA are funded by the Medical Research Council (Medical Research Council Lipid Profiling and Signalling, MC UP A90 1006 & Lipid Dynamics and Regulation, MC PC 130 30)

Metabolic basis to Sherpa altitude adaptation.

The Himalayan Sherpas, a human population of Tibetan descent, are highly adapted to life in the hypobaric hypoxia of high altitude. Mechanisms involving enhanced tissue oxygen delivery in comparison to Lowlander populations have been postulated to play a role in such adaptation. Whether differences in tissue oxygen utilization (i.e., metabolic adaptation) underpin this adaptation is not known, however. We sought to address this issue, applying parallel molecular, biochemical, physiological, and genetic approaches to the study of Sherpas and native Lowlanders, studied before and during exposure to hypobaric hypoxia on a gradual ascent to Mount Everest Base Camp (5,300 m). Compared with Lowlanders, Sherpas demonstrated a lower capacity for fatty acid oxidation in skeletal muscle biopsies, along with enhanced efficiency of oxygen utilization, improved muscle energetics, and protection against oxidative stress. This adaptation appeared to be related, in part, to a putatively advantageous allele for the peroxisome proliferator-activated receptor A (PPARA) gene, which was enriched in the Sherpas compared with the Lowlanders. Our findings suggest that metabolic adaptations underpin human evolution to life at high altitude, and could have an impact upon our understanding of human diseases in which hypoxia is a feature.The work was supported by PhD studentships from the BBSRC to JH (BB/F016581/1) and British Heart Foundation to AK (FS/09/050), an Academic Fellowship to AM from the Research Councils UK (EP/E500552/1), a Physiological Society grant and support from Oroboros Instruments. JG thanks the MRC (MC UP A90 1006) and AB Sciex. MF thanks the MRC and Faculty of Medicine, Southampton University. For full acknowledgements see SI

Uric Acid and Gluconic Acid as Predictors of Hyperglycemia and Cytotoxic Injury after Stroke.

Hyperglycemia is a feature of worse brain injury after acute ischemic stroke, but the underlying metabolic changes and the link to cytotoxic brain injury are not fully understood. In this observational study, we applied regression and machine learning classification analyses to identify metabolites associated with hyperglycemia and a neuroimaging proxy for cytotoxic brain injury. Metabolomics and lipidomics were carried out using liquid chromatography-tandem mass spectrometry in admission plasma samples from 381 patients presenting with an acute stroke. Glucose was measured by a central clinical laboratory, and a subgroup of patients (n = 201) had apparent diffusion coefficient (ADC) imaging quantified on magnetic resonance imaging (MRI) to estimate cytotoxic injury. Uric acid was the leading metabolite in univariate analysis of both hyperglycemia (OR 19.6, 95% CI 8.6-44.7, P = 1.44 × 10) and ADC (OR 5.3, 95% CI 2.2-13.0, P = 2.42 × 10). To further prioritize model features and account for non-linear correlation structure, a random forest machine learning algorithm was applied to separately model hyperglycemia and ADC. The statistical techniques used have identified uric acid and gluconic acids as leading candidate markers common to all models (R = 68%, P = 2.2 × 10 for uric acid; R = 15%, P = 8.09 × 10 for gluconic acid). Both uric acid and gluconic acid were associated with hyperglycemia and cytotoxic brain injury. Both metabolites are linked to oxidative stress, which highlights two candidate targets for limiting brain injury after stroke

Data from: Succinate links atrial dysfunction and cardioembolic stroke

Objective: To determine whether altered metabolic profiles represent a link between atrial dysfunction and cardioembolic (CE) stroke, and thus whether underlying dysfunctional atrial substrate may contribute to thromboembolism risk in CE stroke. Methods: One hundred forty-four metabolites were measured using liquid chromatography-tandem mass spectrometry in plasma samples collected within 9 hours of stroke onset in 367 acute stroke patients. Stroke subtype was assigned using the causative classification of stroke, and CE stroke (n=181) was compared to non-CE stroke (n=186). Markers of left atrial dysfunction included abnormal atrial function (P-wave terminal force in lead V1, PTFV1 >4000 µV•ms), left atrial enlargement on echocardiography, and frank atrial fibrillation on electrocardiograms. Stroke recurrence risk was assessed using CHADS2 and CHA2DS2-VASc scores. Associations between metabolites and CE stroke, atrial dysfunction, and stroke recurrence risk were evaluated using logistic regression models. Results: Three tricarboxylic acid (TCA) cycle acids, succinate (OR 1.71, 95% CI 1.36-2.15, P=1.37x10-6), alpha-ketoglutarate (OR 1.62, 95% CI 1.29-2.04, P=1.62x10-5), and malate (OR 1.58, 95% CI 1.26-1.97, P=2.57x10-5), were associated with CE stroke. Succinate (OR 1.36, 95% CI 1.31-1.98, P=1.22x10-6), alpha-ketoglutarate (OR 2.14, 95% CI 1.60-2.87, P=2.08x10-8), and malate (OR 2.02, 95% CI 1.53-2.66, P=1.60x10-7) were among metabolites also associated with subclinical atrial dysfunction. Of these, succinate was also associated with left atrial enlargement (OR 1.54, 95% CI 1.23-1.94, P=1.06x10-4) and stroke recurrence based on dichotomized CHADS2 (OR 2.63, 95% CI 1.68-4.13, P=3.00x10-6) and CHA2DS2-VASc (OR 2.43, 95% CI 1.60-3.68, P=4.25x10-6) scores. Conclusions: Metabolite profiling identified changes in succinate associated with CE stroke, atrial dysfunction, and stroke recurrence, revealing a putative underlying link between CE stroke and energy metabolism

From the Margins: A Literary Perspective

Box plot of selected variables using RF classification. (PPTX 104 kb

Additional file 3: of Integration of metabolomics, lipidomics and clinical data using a machine learning method

List of lipidomics, metabolomics and clinical chemistry data set. (XLSX 897 kb