Metabolomics of exercise pulmonary hypertension are intermediate between controls and patients with pulmonary arterial hypertension

Metabolomics in Pulmonary Hypertension-A Useful Tool to Provide Insights into the Dark Side of a Tricky Pathology

Pulmonary hypertension (PH) is a multifaceted illness causing clinical manifestations like dyspnea, fatigue, and cyanosis. If left untreated, it often evolves into irreversible pulmonary arterial hypertension (PAH), leading to death. Metabolomics is a laboratory technique capable of providing insights into the metabolic pathways that are responsible for a number of physiologic or pathologic events through the analysis of a biological fluid (such as blood, urine, and sputum) using proton nuclear magnetic resonance spectroscopy or mass spectrometry. A systematic review was finalized according to the PRISMA scheme, with the goal of providing an overview of the research papers released up to now on the application of metabolomics to PH/PAH. So, eighty-five papers were identified, of which twenty-four concerning PH, and sixty-one regarding PAH. We found that, from a metabolic standpoint, the hallmarks of the disease onset and progression are an increase in glycolysis and impaired mitochondrial respiration. Oxidation is exacerbated as well. Specific metabolic fingerprints allow the characterization of some of the specific PH and PAH subtypes. Overall, metabolomics provides insights into the biological processes happening in the body of a subject suffering from PH/PAH. The disarranged metabolic pathways underpinning the disease may be the target of new therapeutic agents. Metabolomics will allow investigators to make a step forward towards personalized medicine.

Deriving Convergent and Divergent Metabolomic Correlates of Pulmonary Arterial Hypertension

High-dimensional metabolomics analyses may identify convergent and divergent markers, potentially representing aligned or orthogonal disease pathways that underly conditions such as pulmonary arterial hypertension (PAH). Using a comprehensive PAH metabolomics dataset, we applied six different conventional and statistical learning techniques to identify analytes associated with key outcomes and compared the results. We found that certain conventional techniques, such as Bonferroni/FDR correction, prioritized metabolites that tended to be highly intercorrelated. Statistical learning techniques generally agreed with conventional techniques on the top-ranked metabolites, but were also more inclusive of different metabolite groups. In particular, conventional methods prioritized sterol and oxylipin metabolites in relation to idiopathic versus non-idiopathic PAH, whereas statistical learning methods tended to prioritize eicosanoid, bile acid, fatty acid, and fatty acyl ester metabolites. Our findings demonstrate how conventional and statistical learning techniques can offer both concordant or discordant results. In the case of a rare yet morbid condition, such as PAH, convergent metabolites may reflect common pathways to shared disease outcomes whereas divergent metabolites could signal either distinct etiologic mechanisms, different sub-phenotypes, or varying stages of disease progression. Notwithstanding the need to investigate the mechanisms underlying the observed results, our main findings suggest that a multi-method approach to statistical analyses of high-dimensional human metabolomics datasets could effectively broaden the scientific yield from a given study design.

Metabolomic Profiles Differentiate Scleroderma-PAH From Idiopathic PAH and Correspond With Worsened Functional Capacity

BACKGROUND

The prognosis and therapeutic responses are worse for pulmonary arterial hypertension associated with systemic sclerosis (SSc-PAH) compared with idiopathic pulmonary arterial hypertension (IPAH). This discrepancy could be driven by divergence in underlying metabolic determinants of disease.

RESEARCH QUESTION

Are circulating bioactive metabolites differentially altered in SSc-PAH vs IPAH, and can this alteration explain clinical disparity between these PAH subgroups?

STUDY DESIGN AND METHODS

Plasma biosamples from 400 patients with SSc-PAH and 1,082 patients with IPAH were included in the study. Another cohort of 100 patients with scleroderma with no PH and 44 patients with scleroderma with PH was included for external validation. More than 700 bioactive lipid metabolites, representing a range of vasoactive and immune-inflammatory pathways, were assayed in plasma samples from independent discovery and validation cohorts using liquid chromatography/high-resolution mass spectrometry-based approaches. Regression analyses were used to identify metabolites that exhibited differential levels between SSc-PAH and IPAH and associated with disease severity.

RESULTS

From hundreds of circulating bioactive lipid molecules, five metabolites were found to distinguish between SSc-PAH and IPAH, as well as associate with markers of disease severity. Relative to IPAH, patients with SSc-PAH carried increased levels of fatty acid metabolites, including lignoceric acid and nervonic acid, as well as eicosanoids/oxylipins and sex hormone metabolites.

INTERPRETATION

Patients with SSc-PAH are characterized by an unfavorable bioactive metabolic profile that may explain the poor and limited response to therapy. These data provide important metabolic insights into the molecular heterogeneity underlying differences between subgroups of PAH.

Exercise metabolomics in pulmonary arterial hypertension: Where pulmonary vascular metabolism meets exercise physiology

Pulmonary arterial hypertension is an incurable disease marked by dysregulated metabolism, both at the cellular level in the pulmonary vasculature, and at the whole-body level characterized by impaired exercise oxygen consumption. Though both altered pulmonary vascular metabolism and abnormal exercise physiology are key markers of disease severity and pulmonary arterial remodeling, their precise interactions are relatively unknown. Herein we review normal pulmonary vascular physiology and the current understanding of pulmonary vascular cell metabolism and cardiopulmonary response to exercise in Pulmonary arterial hypertension. We additionally introduce a newly developed international collaborative effort aimed at quantifying exercise-induced changes in pulmonary vascular metabolism, which will inform about underlying pathophysiology and clinical management. We support our investigative approach by presenting preliminary data and discuss potential future applications of our research platform.

A Systematic Review with Meta-analysis of Biomarkers for detection of Pulmonary Arterial Hypertension

Rationale

The blood is a rich source of potential biomarkers for the diagnosis of idiopathic and hereditary pulmonary arterial hypertension (iPAH and hPAH, referred to as “PAH”). While a lot of biomarkers have been identified for PAH, the clinical utility of these biomarkers often remains unclear. Here, we performed an unbiased meta-analysis of published biomarkers to identify biomarkers with the highest performance for detection of PAH.

Methods

A literature search (in PubMed, Embase.com, Clarivate Analytics/Web of Science Core Collection and Wiley/Cochrane Library) was performed up to 28 January 2021. Primary end points were blood biomarker levels in PAH versus asymptomatic controls or patients suspected of pulmonary hypertension (PH) with proven normal haemodynamic profiles.

Results

149 articles were identified by the literature search. Meta-analysis of 26 biomarkers yielded 17 biomarkers that were differentially expressed in PAH and non-PH control subjects. Red cell distribution width, low density lipid-cholesterol, d-dimer, N-terminal prohormone of brain natriuretic protein (NT-proBNP), interleukin-6 (IL-6) and uric acid were biomarkers with the largest observed differences, largest sample sizes and a low risk of publication bias. Receiver operating characteristic curves and sensitivity/specificity analyses demonstrated that NT-proBNP had a high sensitivity, but low specificity for PAH. For the other biomarkers, insufficient data on diagnostic accuracy with receiver operating characteristic curves were available for meta-analysis.

Conclusion

This meta-analysis validates NT-proBNP as a biomarker with high sensitivity for PAH, albeit with low specificity. The majority of biomarkers evaluated in this meta-analysis lacked either external validation or data on diagnostic accuracy. Further validation studies are required as well as studies that test combinations of biomarkers to improve specificity.

Meta-analysis of 26 biomarkers yielded 17 differentially expressed biomarkers in PAH. NT-proBNP had the highest diagnostic accuracy but had a low specificity for PAH. Other markers, including IL-6, RDW, LDL-c, D-dimer and UA, lacked clinical validation.https://bit.ly/3J4YAyC

Metabolic Reprogramming of the Right Ventricle and Pulmonary Arteries in a Flow-Associated Pulmonary Arterial Hypertension Rat Model

Pulmonary arterial hypertension (PAH) is a complex devastating disease relevant to remarkable metabolic dysregulation. Although various research studies on PAH from a metabolic perspective have been emerging, pathogenesis of PAH varies in different categories. Research on metabolic reprogramming in flow-associated PAH remains insufficient. An untargeted metabolomic profiling platform was used to evaluate the metabolic profile of pulmonary arteries (PAs) as well as the right ventricle (RV) in a flow-associated PAH rat model in the present work. A total of 79 PAs and 128 RV metabolites were significantly altered in PAH rats, among which 39 metabolites were assessed as shared dysregulated metabolites in PAs and the RV. Pathway analysis elucidated that, in PAs of PAH rats, pathways of phenylalanine, tyrosine, and tryptophan biosynthesis and linoleic acid metabolism were significantly altered, while in the RV, arginine biosynthesis and linoleic acid metabolism were altered dramatically. Further integrated analysis of shared dysregulated PA and RV metabolites demonstrated that the linoleic acid metabolism and the arachidonic acid (AA) metabolism were the key pathways involved in the pathogenesis of flow-associated PAH. Results obtained from the present work indicate that the PAH pathogenesis could be mediated by widespread metabolic reprogramming. In particular, the dysregulation of AA metabolism may considerably contribute to the development of high blood flow-associated PAH.

Metabolomics in Exercise and Sports: A Systematic Review

BACKGROUND

Metabolomics is a field of omics science that involves the comprehensive measurement of small metabolites in biological samples. It is increasingly being used to study exercise physiology and exercise-associated metabolism. However, the field of exercise metabolomics has not been extensively reviewed or assessed.

OBJECTIVE

This review on exercise metabolomics has three aims: (1) to provide an introduction to the general workflow and the different metabolomics technologies used to conduct exercise metabolomics studies; (2) to provide a systematic overview of published exercise metabolomics studies and their findings; and (3) to discuss future perspectives in the field of exercise metabolomics.

METHODS

We searched electronic databases including Google Scholar, Science Direct, PubMed, Scopus, Web of Science, and the SpringerLink academic journal database between January 1st 2000 and September 30th 2020.

RESULTS

Based on our detailed analysis of the field, exercise metabolomics studies fall into five major categories: (1) exercise nutrition metabolism; (2) exercise metabolism; (3) sport metabolism; (4) clinical exercise metabolism; and (5) metabolome comparisons. Exercise metabolism is the most popular category. The most common biological samples used in exercise metabolomics studies are blood and urine. Only a small minority of exercise metabolomics studies employ targeted or quantitative techniques, while most studies used untargeted metabolomics techniques. In addition, mass spectrometry was the most commonly used platform in exercise metabolomics studies, identified in approximately 54% of all published studies. Our data indicate that biomarkers or biomarker panels were identified in 34% of published exercise metabolomics studies.

CONCLUSION

Overall, there is an increasing trend towards better designed, more clinical, mass spectrometry-based metabolomics studies involving larger numbers of participants/patients and larger numbers of metabolites being identified.

Influence of atorvastatin on metabolic pattern of rats with pulmonary hypertension

BACKGROUND

Metabonomics has been widely used to analyze the initiation, progress, and development of diseases. However, application of metabonomics to explore the mechanism of pulmonary arterial hypertension (PAH) are poorly reported. This study aimed to investigate the influence of atorvastatin (Ato) on metabolic pattern of rats with pulmonary hypertension.

METHODS

PAH animal model was established using monocrotaline (MCT). The mean pulmonary artery pressure (mPAP) and right ventricular hypertrophy index (RVHI) were measured. The microstructure of pulmonary arterioles was observed by HE staining. Nuclear magnetic resonance was used to detect and analyze the serum metabolites. The levels of glycogen synthase kinase-3β (GSK-3β), hexokinase 2 (HK-2), sterol regulatory element-binding protein 1c (SREBP-1c), and carnitine palmitoyltransferase I (CPT-1) in the lung tissues were measured.

RESULTS

Ato significantly improved lung function by decreasing mPAP, RVHI, wall thickness, and wall area. Differences in metabolic patterns were observed among normal, PAH, and Ato group. The levels of GSK-3β and SREBP-1c were decreased, but HK-2 and CPT-1 were increased in the group PAH. Ato treatment markedly reversed the influence of MCT.

CONCLUSION

Ato significantly improved the pulmonary vascular remodeling and pulmonary hypertension of PAH rats due to its inhibition on Warburg effect and fatty acid β oxidation.

Vital capacity and valvular dysfunction could serve as non-invasive predictors to screen for exercise pulmonary hypertension in the elderly based on a new diagnostic score

Introduction: Exercise pulmonary hypertension (exPH) has been defined as total pulmonary resistance (TPR) >3 mm Hg/L/min and mean pulmonary artery pressure (mPAP) >30 mm Hg, albeit with a considerable risk of false positives in elderly patients with lower cardiac output during exercise.

Methods: We retrospectively analysed patients with unclear dyspnea receiving right heart catheterisation at rest and exercise (n=244) between January 2015 and January 2020. Lung function testing, blood gas analysis, and echocardiography were performed. We elaborated a combinatorial score to advance the current definition of exPH in an elderly population (mean age 67.0 years±11.9). A stepwise regression model was calculated to non-invasively predict exPH.

Results: Analysis of variables across the achieved peak power allowed the creation of a model for defining exPH, where three out of four criteria needed to be fulfilled: Peak power ≤100 Watt, pulmonary capillary wedge pressure ≥18 mm Hg, pulmonary vascular resistance >3 Wood Units, and mPAP ≥35 mm Hg. The new scoring model resulted in a lower number of exPH diagnoses than the current suggestion (63.1% vs. 78.3%). We present a combinatorial model with vital capacity (VC_max) and valvular dysfunction to predict exPH (sensitivity 93.2%; specificity 44.2%, area under the curve 0.73) based on our suggested criteria. The odds of the presence of exPH were 2.1 for a 1 l loss in VC_max and 3.6 for having valvular dysfunction.

Conclusion: We advance a revised definition of exPH in elderly patients in order to overcome current limitations. We establish a new non-invasive approach to predict exPH by assessing VC_max and valvular dysfunction for early risk stratification in elderly patients.

Integrative Omics to Characterize and Classify Pulmonary Vascular Disease

Advances in high-throughput biotechnologies have facilitated omics profiling, a key component of precision phenotyping, in patients with pulmonary vascular disease. Omics provides comprehensive information pertaining to genes, transcripts, proteins, and metabolites. The resulting omics big datasets may be integrated for more robust results and are amenable to analysis using machine learning or newer analytical methodologies, such as network analysis. Results from fully integrated multi-omics datasets combined with clinical data are poised to provide novel insight into pulmonary vascular disease as well as diagnose the presence of disease and prognosticate outcomes.

Pulmonary Arterial Hypertension Induces a Distinct Signature of Circulating Metabolites

Pulmonary arterial hypertension (PAH) is an incurable, progressive disorder, and the early diagnosis and treatment of PAH are associated with increased survival [...].