Phenotyping of Chronic Obstructive Pulmonary Disease Based on the Integration of Metabolomes and Clinical Characteristics

An integrated metabo-lipidomics profile of induced sputum for the identification of novel biomarkers in the differential diagnosis of asthma and COPD

BACKGROUND

Due to their complexity and to the presence of common clinical features, differentiation between asthma and chronic obstructive pulmonary disease (COPD) can be a challenging task, complicated in such cases also by asthma-COPD overlap syndrome. The distinct immune/inflammatory and structural substrates of COPD and asthma are responsible for significant differences in the responses to standard pharmacologic treatments. Therefore, an accurate diagnosis is of central relevance to assure the appropriate therapeutic intervention in order to achieve safe and effective patient care. Induced sputum (IS) accurately mirrors inflammation in the airways, providing a more direct picture of lung cell metabolism in comparison to those specimen that reflect analytes in the systemic circulation.

METHODS

An integrated untargeted metabolomics and lipidomics analysis was performed in IS of asthmatic (n = 15) and COPD (n = 22) patients based on Ultra-High-Pressure Liquid Chromatography-Mass Spectrometry (UHPLC-MS) and UHPLC-tandem MS (UHPLC-MS/MS). Partial Least Squares-Discriminant Analysis (PLS-DA) was applied to resulting dataset. The analysis of main enriched metabolic pathways and the association of the preliminary metabolites/lipids pattern identified to clinical parameters of asthma/COPD differentiation were explored. Multivariate ROC analysis was performed in order to determine the discriminatory power and the reliability of the putative biomarkers for diagnosis between COPD and asthma.

RESULTS

PLS-DA indicated a clear separation between COPD and asthmatic patients. Among the 15 selected candidate biomarkers based on Variable Importance in Projection scores, putrescine showed the highest score. A differential IS bio-signature of 22 metabolites and lipids was found, which showed statistically significant variations between asthma and COPD. Of these 22 compounds, 18 were decreased and 4 increased in COPD compared to asthmatic patients. The IS levels of Phosphatidylethanolamine (PE) (34:1), Phosphatidylglycerol (PG) (18:1;18:2) and spermine were significantly higher in asthmatic subjects compared to COPD.

CONCLUSIONS

This is the first pilot study to analyse the IS metabolomics/lipidomics signatures relevant in discriminating asthma vs COPD. The role of polyamines, of 6-Hydroxykynurenic acid and of D-rhamnose as well as of other important players related to the alteration of glycerophospholipid, aminoacid/biotin and energy metabolism provided the construction of a diagnostic model that, if validated on a larger prospective cohort, might be used to rapidly and accurately discriminate asthma from COPD.

Lipid mediators of inhalation exposure-induced pulmonary toxicity and inflammation

Many inhalation exposures induce pulmonary inflammation contributing to disease progression. Inflammatory processes are actively regulated via mediators including bioactive lipids. Bioactive lipids are potent signaling molecules involved in both pro-inflammatory and resolution processes through receptor interactions. The formation and clearance of lipid signaling mediators are controlled by multiple metabolic enzymes. An imbalance of these lipids can result in exacerbated and sustained inflammatory processes which may result in pulmonary damage and disease. Dysregulation of pulmonary bioactive lipids contribute to inflammation and pulmonary toxicity following exposures. For example, inhalation of cigarette smoke induces activation of pro-inflammatory bioactive lipids such as sphingolipids, and ceramides contributing to chronic obstructive pulmonary disease. Additionally, exposure to silver nanoparticles causes dysregulation of inflammatory resolution lipids. As inflammation is a common consequence resulting from inhaled exposures and a component of numerous diseases it represents a broadly applicable target for therapeutic intervention. With new appreciation for bioactive lipids, technological advances to reliably identify and quantify lipids have occurred. In this review, we will summarize, integrate, and discuss findings from recent studies investigating the impact of inhaled exposures on pro-inflammatory and resolution lipids within the lung and their contribution to disease. Throughout the review current knowledge gaps in our understanding of bioactive lipids and their contribution to pulmonary effects of inhaled exposures will be presented. New methods being employed to detect and quantify disruption of pulmonary lipid levels following inhalation exposures will be highlighted. Lastly, we will describe how lipid dysregulation could potentially be addressed by therapeutic strategies to address inflammation.

Integration of metabolomics and network pharmacology to reveal the protective mechanism underlying Qibai Pingfei capsule on chronic obstructive pulmonary disease

In this study, we have employed metabolomics technology in combination with network pharmacology to ascertain the key metabolites and hub genes. The objective was to explore the pathway of Qibai Pingfei Capsule (QBPF) in treating COPD through metabolomics. We identified 96 differential metabolites in the lung tissues of rats belonging to control and model groups, out of which 47 were observed to be critical (VIP >2, p < 0.05). Furthermore, 16 important differential metabolites were reversed after QBPF treatment. Using network pharmacology, we identified 176 core targets of 81 drug-active ingredients. Our comprehensive analysis of network pharmacology and metabolomics enabled us to identify a core target, prostaglandin-endoperoxide synthase 2 (PTGS2), and a core metabolic pathway for glutathione metabolism. Finally, the result of molecular docking showed that PTGS2 had strong binding activity to 18 compounds including Fumarine and Kaempferol, etc.. PTGS2 is a marker of ferroptosis, so we wanted to explore whether QBPF could inhibit ferroptosis in COPD. The results showed that ferroptosis was involved in the pathogenesis of COPD, and QBPF could inhibit the occurrence of ferroptosis. In conclusion, the mechanism of QBPF for treating COPD may be related to PTGS2 expression, glutathione metabolism and ferroptosis.

Update on metabolomic findings in COPD patients

COPD is a heterogeneous disorder that shows diverse clinical presentations (phenotypes and "treatable traits") and biological mechanisms (endotypes). This heterogeneity implies that to carry out a more personalised clinical management, it is necessary to classify each patient accurately. With this objective, and in addition to clinical features, it would be very useful to have well-defined biological markers. The search for these markers may either be done through more conventional laboratory and hypothesis-driven techniques or relatively blind high-throughput methods, with the omics approaches being suitable for the latter. Metabolomics is the science that studies biological processes through their metabolites, using various techniques such as gas and liquid chromatography, mass spectrometry and nuclear magnetic resonance. The most relevant metabolomics studies carried out in COPD highlight the importance of metabolites involved in pathways directly related to proteins (peptides and amino acids), nucleic acids (nitrogenous bases and nucleosides), and lipids and their derivatives (especially fatty acids, phospholipids, ceramides and eicosanoids). These findings indicate the relevance of inflammatory-immune processes, oxidative stress, increased catabolism and alterations in the energy production. However, some specific findings have also been reported for different COPD phenotypes, demographic characteristics of the patients, disease progression profiles, exacerbations, systemic manifestations and even diverse treatments. Unfortunately, the studies carried out to date have some limitations and shortcomings and there is still a need to define clear metabolomic profiles with clinical utility for the management of COPD and its implicit heterogeneity.

Metabolomic Analysis of Respiratory Epithelial Lining Fluid in Patients with Chronic Obstructive Pulmonary Disease—A Systematic Review

Chronic obstructive pulmonary disease (COPD), as the third leading cause of death among adults, is a significant public health problem around the world. However, about 75% of smokers do not develop the disease despite the severe smoking burden. COPD is a heterogeneous disease, and several phenotypes, with differences in their clinical picture and response to treatment, have been distinguished. Metabolomic studies provide information on metabolic pathways, and therefore are a promising tool for understanding disease etiopathogenesis and the development of effective causal treatment. The aim of this systematic review was to analyze the metabolome of the respiratory epithelial lining fluid of patients with COPD, compared to healthy volunteers, refractory smokers, and subjects with other lung diseases. We included observational human studies. Sphingolipids, phosphatidylethanolamines, and sphingomyelins distinguished COPD from non-smokers; volatile organic compounds, lipids, and amino acids distinguished COPD from smokers without the disease. Five volatile organic compounds were correlated with eosinophilia and four were associated with a phenotype with frequent exacerbations. Fatty acids and ornithine metabolism were correlated with the severity of COPD. Metabolomics, by searching for biomarkers and distinguishing metabolic pathways, can allow us to understand the pathophysiology of COPD and the development of its phenotypes.

Metabolomics of asthma, COPD, and asthma-COPD overlap: an overview

The two common progressive lung diseases, asthma and chronic obstructive pulmonary disease (COPD), are the leading causes of morbidity and mortality worldwide. Asthma-COPD overlap, referred to as ACO, is another complex pulmonary disease that manifests itself with features of both asthma and COPD. The disease has no clear diagnostic or therapeutic guidelines, thereby making both diagnosis and treatment challenging. Though a number of studies on ACO have been documented, gaps in knowledge regarding the pathophysiologic mechanism of this disorder exist. Addressing this issue is an urgent need for improved diagnostic and therapeutic management of the disease. Metabolomics, an increasingly popular technique, reveals the pathogenesis of complex diseases and holds promise in biomarker discovery. This comprehensive narrative review, comprising 99 original research articles in the last five years (2017-2022), summarizes the scientific advances in terms of metabolic alterations in patients with asthma, COPD, and ACO. The analytical tools, nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC-MS), and liquid chromatography-mass spectrometry (LC-MS), commonly used to study the expression of the metabolome, are discussed. Challenges frequently encountered during metabolite identification and quality assessment are highlighted. Bridging the gap between phenotype and metabotype is envisioned in the future.

Metabolomics in COPD

The clinical presentation of chronic obstructive pulmonary disease (COPD) is highly heterogeneous. Attempts have been made to define subpopulations of patients who share clinical characteristics (phenotypes and treatable traits) and/or biological characteristics (endotypes), in order to offer more personalized care. Assigning a patient to any of these groups requires the identification of both clinical and biological markers. Ideally, biological markers should be easily obtained from blood or urine, but these may lack specificity. Biomarkers can be identified initially using conventional or more sophisticated techniques. However, the more sophisticated techniques should be simplified in the future if they are to have clinical utility. The -omics approach offers a methodology that can assist in the investigation and identification of useful markers in both targeted and blind searches. Specifically, metabolomics is the science that studies biological processes involving metabolites, which can be intermediate or final products. The metabolites associated with COPD and their specific phenotypic and endotypic features have been studied using various techniques. Several compounds of particular interest have emerged, namely, several types of lipids and derivatives (mainly phospholipids, but also ceramides, fatty acids and eicosanoids), amino acids, coagulation factors, and nucleic acid components, likely to be involved in their function, protein catabolism, energy production, oxidative stress, immune-inflammatory response and coagulation disorders. However, clear metabolomic profiles of the disease and its various manifestations that may already be applicable in clinical practice still need to be defined.

Metabolomics of Respiratory Diseases

Metabolomics is an expanding field of systems biology that is gaining significant attention in respiratory research. As a unique approach to understanding and diagnosing diseases, metabolomics provides a snapshot of all metabolites present in biological samples such as exhaled breath condensate, bronchoalveolar lavage, plasma, serum, urine, and other specimens that may be obtained from patients with respiratory diseases. In this article, we review the rapidly expanding field of metabolomics in its application to respiratory diseases, including asthma, chronic obstructive pulmonary disease (COPD), pneumonia, and acute lung injury, along with its more severe form, adult respiratory disease syndrome. We also discuss the potential applications of metabolomics for monitoring exposure to aerosolized occupational and environmental materials. With the latest advances in our understanding of the microbiome, we discuss microbiome-derived metabolites that arise from the gut and lung in asthma and COPD that have mechanistic implications for these diseases. Recent literature has suggested that metabolomics analysis using nuclear magnetic resonance (NMR) and mass spectrometry (MS) approaches may provide clinicians with the opportunity to identify new biomarkers that may predict progression to more severe diseases which may be fatal for many patients each year.

Pharmacological effects of Bufei Jianpi granule on chronic obstructive pulmonary disease and its metabolism in rats

This work was performed to determine the pharmacological effects of Bufei Jianpi granules on chronic obstructive pulmonary disease and its metabolism in rats. Chronic obstructive pulmonary disease (COPD), ranked as the third leading cause of death worldwide, is seriously endangering human health. At present, the pathogenesis of COPD is complex and unclear, and the drug treatment mainly aims to alleviate and improve symptoms; however, they cannot achieve the purpose of eradicating the disease. Bufei Jianpi granule (BJG) is a Chinese medicine developed by the First Affiliated Hospital of Henan University of Traditional Chinese Medicine for treating COPD. This study focuses on the pharmacological effects of BJG on COPD and its metabolism in rats, aiming to provide a scientific basis for developing BJG against COPD. A total of 72 Sprague-Dawley (SD) rats were divided into the blank group, model group, positive control group, and BJG groups (2.36, 1.18, and 0.59 g/kg). Except for the blank group, rats in other groups were administered lipopolysaccharide (LPS) combined with smoking for 6 weeks to establish the COPD model. After another 6 weeks of treatment, the therapeutic effect of BJG on COPD rats was evaluated. In the BJG (2.36 g/kg) group, the cough condition of rats was significantly relieved and the body weight was close to that of the blank group. Compared with the mortality of 16.7% in the model group, no deaths occurred in the BJG (2.36 g/kg) and (1.18 g/kg) groups. The lung tissue damage in the BJG groups was less than that in the COPD group. Compared with the model group, MV, PIF, PEF, and EF50 in the BJG groups were observably increased in a dose-dependent manner, while sRaw, Raw, and FRC were obviously decreased. Also, the contents of IL-6, IL-8, TNF-α, PGE2, MMP-9, and NO in the serum and BALF were lowered dramatically in all BJG groups. All indicators present an obvious dose-effect relationship. On this basis, the UPLC-QTOF-MS/MS technology was used to analyze characteristic metabolites in rats under physiological and pathological conditions. A total of 17 prototype and 7 metabolite components were detected, and the concentration of most components was increased in the COPD pathologic state. It is suggested that BJG has a pharmacological effect in the treatment of COPD and the absorption and metabolism of chemical components of BJG in rats exhibited significant differences under physiological and pathological conditions.

Metabolome Features of COPD: A Scoping Review

Chronic obstructive pulmonary disease (COPD) is a complex heterogeneous disease state with multiple phenotypic presentations that include chronic bronchitis and emphysema. Although COPD is a lung disease, it has systemic manifestations that are associated with a dysregulated metabolome in extrapulmonary compartments (e.g., blood and urine). In this scoping review of the COPD metabolomics literature, we identified 37 publications with a primary metabolomics investigation of COPD phenotypes in human subjects through Google Scholar, PubMed, and Web of Science databases. These studies consistently identified a dysregulation of the TCA cycle, carnitines, sphingolipids, and branched-chain amino acids. Many of the COPD metabolome pathways are confounded by age and sex. The effects of COPD in young versus old and male versus female need further focused investigations. There are also few studies of the metabolome's association with COPD progression, and it is unclear whether the markers of disease and disease severity are also important predictors of disease progression.

A Metabolomic Severity Score for Airflow Obstruction and Emphysema

Chronic obstructive pulmonary disease (COPD) is a disease with marked metabolic disturbance. Previous studies have shown the association between single metabolites and lung function for COPD, but whether a combination of metabolites could predict phenotype is unknown. We developed metabolomic severity scores using plasma metabolomics from the Metabolon platform from two US cohorts of ever-smokers: the Subpopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS) (n = 648; training/testing cohort; 72% non-Hispanic, white; average age 63 years) and the COPDGene Study (n = 1120; validation cohort; 92% non-Hispanic, white; average age 67 years). Separate adaptive LASSO (adaLASSO) models were used to model forced expiratory volume at one second (FEV1) and MESA-adjusted lung density using 762 metabolites common between studies. Metabolite coefficients selected by the adaLASSO procedure were used to create a metabolomic severity score (metSS) for each outcome. A total of 132 metabolites were selected to create a metSS for FEV1. The metSS-only models explained 64.8% and 31.7% of the variability in FEV1 in the training and validation cohorts, respectively. For MESA-adjusted lung density, 129 metabolites were selected, and metSS-only models explained 59.0% of the variability in the training cohort and 17.4% in the validation cohort. Regression models including both clinical covariates and the metSS explained more variability than either the clinical covariate or metSS-only models (53.4% vs. 46.4% and 31.6%) in the validation dataset. The metabolomic pathways for arginine biosynthesis; aminoacyl-tRNA biosynthesis; and glycine, serine, and threonine pathway were enriched by adaLASSO metabolites for FEV1. This is the first demonstration of a respiratory metabolomic severity score, which shows how a metSS can add explanation of variance to clinical predictors of FEV1 and MESA-adjusted lung density. The advantage of a comprehensive metSS is that it explains more disease than individual metabolites and can account for substantial collinearity among classes of metabolites. Future studies should be performed to determine whether metSSs are similar in younger, and more racially and ethnically diverse populations as well as whether a metabolomic severity score can predict disease development in individuals who do not yet have COPD.

Detection of the Disorders of Glycerophospholipids and Amino Acids Metabolism in Lung Tissue From Male COPD Patients

Background: At present, few studies have reported the metabolic profiles of lung tissue in patients with COPD. Our study attempted to analyze the lung metabolome in male COPD patients and to screen the overlapping biomarkers of the lung and plasma metabolomes.

Methods: We performed untargeted metabolomic analysis of normal lung tissue from two independent sets (the discovery set: 20 male COPD patients and 20 controls and the replication set: 47 male COPD patients and 27 controls) and of plasma samples from 80 male subjects containing 40 COPD patients and 40 controls.

Results: We found glycerophospholipids (GPs) and Amino acids were the primary classes of differential metabolites between male COPD patients and controls. The disorders of GPs metabolism and the valine, leucine and isoleucine biosynthesis metabolism pathways were identified in lung discovery set and then also validated in the lung replication set. Combining lung tissue and plasma metabolome, Phytosphingosine and l-tryptophan were two overlapping metabolites biomarkers. Binary logistic regression suggested that phytosphingosine together with l-tryptophan was closely associated with male COPD and showed strong diagnostic power with an AUC of 0.911 (95% CI: 0.8460-0.9765).

Conclusion: Our study revealed the metabolic perturbations of lung tissues from male COPD patients. The detected disorders of GPs and amino acids may provide an insight into the pathological mechanism of COPD. Phytosphingosine and l-tryptophan were two novel metabolic biomarkers for differentiating COPD patients and controls.

Inflammation-associated pulmonary microbiome and metabolome changes in broilers exposed to particulate matter in broiler houses

The particulate matter (PM) in livestock houses, one of the primary sources of atmospheric PM, is not only detrimental to the respiratory health of animals and farmworkers but also poses a threat to the public environment and public health and warrants increased attention. In this study, we investigated the variation in the pulmonary microbiome and metabolome in broiler chickens exposed to PM collected from a broiler house. We examined the pulmonary microbiome and metabolome in broilers, observing that PM induced a visible change in α and β diversity. A total of 66 differential genera, including unclassified_f_Ruminococcaceae and Campylobacter, were associated with pulmonary inflammation. Untargeted metabolomics was utilised to identify 63 differential metabolites induced by PM and correlated with differential bacteria. We observed that PM resulted in injury of the broiler lung and disruption of the microbial community, as well as causing changes in the observed metabolites. These results imply that perturbations to the microbiome and metabolome may play pivotal roles in the mechanism underlying PM-induced broiler lung damage.

Metabolomic differences in lung function metrics: evidence from two cohorts

RATIONALE

The biochemical mechanisms underlying lung function are incompletely understood.

OBJECTIVES

To identify and validate the plasma metabolome of lung function using two independent adult cohorts: discovery-the European Prospective Investigation into Cancer-Norfolk (EPIC-Norfolk, n=10 460) and validation-the VA Normative Aging Study (NAS) metabolomic cohort (n=437).

METHODS

We ran linear regression models for 693 metabolites to identify associations with forced expiratory volume in one second (FEV₁) and the ratio of FEV₁ to forced vital capacity (FEV₁/FVC), in EPIC-Norfolk then validated significant findings in NAS. Significance in EPIC-Norfolk was denoted using an effective number of tests threshold of 95%; a metabolite was considered validated in NAS if the direction of effect was consistent and p<0.05.

MEASUREMENTS AND MAIN RESULTS

Of 156 metabolites that associated with FEV₁ in EPIC-Norfolk after adjustment for age, sex, body mass index, height, smoking and asthma status, 34 (21.8%) validated in NAS, including several metabolites involved in oxidative stress. When restricting the discovery sample to men only, a similar percentage, 18 of 79 significant metabolites (22.8%) were validated. A smaller number of metabolites were validated for FEV₁/FVC, 6 of 65 (9.2%) when including all EPIC-Norfolk as the discovery population, and 2 of 34 (5.9%) when restricting to men. These metabolites were characterised by involvement in respiratory track secretants. Interestingly, no metabolites were validated for both FEV₁ and FEV₁/FVC.

CONCLUSIONS

The validation of metabolites associated with respiratory function can help to better understand mechanisms of lung health and may assist the development of biomarkers.

Identification of novel metabolic signatures potentially involved in the pathogenesis of COPD associated pulmonary hypertension

INTRODUCTION

Chronic obstructive pulmonary disease (COPD) associated pulmonary hypertension (COPD-PH), one of the most prevalent forms of PH, is a major burden on the healthcare system. Although PH in COPD is usually of mild-to-moderate severity, its presence is associated with shorter survival, more frequent exacerbations and worse clinical outcomes. The pathophysiologic mechanisms responsible for PH development in COPD patients remain unclear. It is envisioned that a better understanding of the underlying mechanism will help in diagnosis and future treatment strategies.

OBJECTIVES

The present study aims to determine metabolomic alterations in COPD-PH patients as compared to healthy controls. Additionally, to ensure that the dysregulated metabolites arise due to the presence of PH per se, an independent COPD cohort is included for comparison purposes.

METHODS

Paired serum and exhaled breath condensate (EBC) samples were collected from male patients with COPD-PH (n = 60) in accordance with the 2015 European Society of Cardiology (ESC)/European Respiratory Society (ERS) guidelines. Age, sex and BMI matched healthy controls (n = 57) and COPD patients (n = 59) were recruited for comparison purposes. All samples were characterized using ¹H nuclear magnetic resonance (NMR) spectroscopy.

RESULTS

Fifteen serum and 9 EBC metabolites were found to be significantly altered in COPD-PH patients as compared to healthy controls. Lactate and pyruvate were dysregulated in both the biofluids and were further correlated with echocardiographic systolic pulmonary artery pressure (sPAP). Multivariate analysis showed distinct class separation between COPD-PH and COPD.

CONCLUSIONS

The findings of this study indicate an increased energy demand in patients with COPD-PH. Furthermore, both lactate and pyruvate correlate with sPAP, indicating their importance in the clinical course of the disease.

Comparison of Kit-Based Metabolomics with Other Methodologies in a Large Cohort, towards Establishing Reference Values

Metabolic profiling is an omics approach that can be used to observe phenotypic changes, making it particularly attractive for biomarker discovery. Although several candidate metabolites biomarkers for disease expression have been identified in recent clinical studies, the reference values of healthy subjects have not been established. In particular, the accuracy of concentrations measured by mass spectrometry (MS) is unclear. Therefore, comprehensive metabolic profiling in large-scale cohorts by MS to create a database with reference ranges is essential for evaluating the quality of the discovered biomarkers. In this study, we tested 8700 plasma samples by commercial kit-based metabolomics and separated them into two groups of 6159 and 2541 analyses based on the different ultra-high-performance tandem mass spectrometry (UHPLC-MS/MS) systems. We evaluated the quality of the quantified values of the detected metabolites from the reference materials in the group of 2541 compared with the quantified values from other platforms, such as nuclear magnetic resonance (NMR), supercritical fluid chromatography tandem mass spectrometry (SFC-MS/MS) and UHPLC-Fourier transform mass spectrometry (FTMS). The values of the amino acids were highly correlated with the NMR results, and lipid species such as phosphatidylcholines and ceramides showed good correlation, while the values of triglycerides and cholesterol esters correlated less to the lipidomics analyses performed using SFC-MS/MS and UHPLC-FTMS. The evaluation of the quantified values by MS-based techniques is essential for metabolic profiling in a large-scale cohort.

Global metabolome profiling of exhaled breath condensates in male smokers with asthma COPD overlap and prediction of the disease

Asthma-chronic obstructive pulmonary disease (COPD) overlap, termed as ACO, is a complex heterogeneous disease characterised by persistent airflow limitation, which manifests features of both asthma and COPD. These patients have a worse prognosis, in terms of more frequent and severe exacerbations, more frequent symptoms, worse quality of life, increased comorbidities and a faster lung function decline. In absence of clear diagnostic or therapeutic guidelines, ACO presents as a challenge to clinicians. The present study aims to investigate whether ACO patients have a distinct exhaled breath condensate (EBC) metabolic profile in comparison to asthma and COPD. A total of 132 age and BMI matched male smokers were recruited in the exploratory phase which consisted of (i) controls = 33 (ii) asthma = 34 (iii) COPD = 30 and (iv) ACO = 35. Using nuclear magnetic resonance (NMR) metabolomics, 8 metabolites (fatty acid, propionate, isopropanol, lactate, acetone, valine, methanol and formate) were identified to be significantly dysregulated in ACO subjects when compared to both, asthma and COPD. The expression of these dysregulated metabolites were further validated in a fresh patient cohort consisting of (i) asthma = 32 (ii) COPD = 32 and (iii) ACO = 40, which exhibited a similar expression pattern. Multivariate receiver operating characteristic (ROC) curves generated using these metabolites provided a robust ACO classification model. The findings were also integrated with previously identified serum metabolites and inflammatory markers to develop a robust predictive model for differentiation of ACO. Our findings suggest that NMR metabolomics of EBC holds potential as a platform to identify robust, non-invasive biomarkers for differentiating ACO from asthma and COPD.

Serum Glycerophospholipid Profile in Acute Exacerbation of Chronic Obstructive Pulmonary Disease

Studies have shown that glycerophospholipids are involved in the pathogenesis of chronic obstructive pulmonary disease (COPD). This study adopted targeted metabolomic analysis to investigate the changes in serum glycerophospholipids in acute exacerbation of chronic obstructive pulmonary disease (AECOPD) and their differential expression in patients with different inflammatory subtypes. Patients with AECOPD admitted between January 2015 and December 2017 were enrolled, and their clinical data were collected. The patients' gender, age, body mass index, and lung function were recorded. Routine blood and induced sputum tests were performed. Liquid chromatography-mass spectrometry was used to detect the serum glycerophospholipid metabolic profiles and to analyze the metabolic profile changes between the acute exacerbation and recovery stages as well as the differences between different inflammatory subtypes. A total of 58 patients were hospitalized for AECOPD, including 49 male patients with a mean age of 74.8 ± 10.0 years. In the metabolic profiles, the expression of lysophosphatidylcholine (LPC) 18:3, lysophosphatidylethanolamine (LPE) 16:1, and phosphatidylinositol (PI) 32:1 was significantly reduced in the acute exacerbation stage compared to the recovery stage (P < 0.05). The three glycerophospholipids were used to plot the receiver operating characteristic curves to predict the acute exacerbation/recovery stage, and the areas under the curves were all above 70%. There were no differential metabolites between the two groups of patients with blood eosinophil percentage (EOS%) ≥2% and <2% at exacerbation. The expression of LPC 18:3, LPE 16:1, and PI 32:1 was significantly reduced in the acute exacerbation stage compared to the recovery stage in the inflammatory subtype with blood EOS <2% (P < 0.05). Abnormalities in the metabolism of glycerophospholipids may be involved in the onset of AECOPD, especially in the non-eosinophilic subtype.

Plasma Metabolomic Signatures of Chronic Obstructive Pulmonary Disease and the Impact of Genetic Variants on Phenotype-Driven Modules

Background: Small studies have recently suggested that there are specific plasma metabolic signatures in chronic obstructive pulmonary disease (COPD), but there have been no large comprehensive study of metabolomic signatures in COPD that also integrate genetic variants. Materials and Methods: Fresh frozen plasma from 957 non-Hispanic white subjects in COPDGene was used to quantify 995 metabolites with Metabolon's global metabolomics platform. Metabolite associations with five COPD phenotypes (chronic bronchitis, exacerbation frequency, percent emphysema, post-bronchodilator forced expiratory volume at one second [FEV1]/forced vital capacity [FVC], and FEV1 percent predicted) were assessed. A metabolome-wide association study was performed to find genetic associations with metabolite levels. Significantly associated single-nucleotide polymorphisms were tested for replication with independent metabolomic platforms and independent cohorts. COPD phenotype-driven modules were identified in network analysis integrated with genetic associations to assess gene-metabolite-phenotype interactions. Results: Of metabolites tested, 147 (14.8%) were significantly associated with at least 1 COPD phenotype. Associations with airflow obstruction were enriched for diacylglycerols and branched chain amino acids. Genetic associations were observed with 109 (11%) metabolites, 72 (66%) of which replicated in an independent cohort. For 20 metabolites, more than 20% of variance was explained by genetics. A sparse network of COPD phenotype-driven modules was identified, often containing metabolites missed in previous testing. Of the 26 COPD phenotype-driven modules, 6 contained metabolites with significant met-QTLs, although little module variance was explained by genetics. Conclusion: A dysregulation of systemic metabolism was predominantly found in COPD phenotypes characterized by airflow obstruction, where we identified robust heritable effects on individual metabolite abundances. However, network analysis, which increased the statistical power to detect associations missed previously in classic regression analyses, revealed that the genetic influence on COPD phenotype-driven metabolomic modules was modest when compared with clinical and environmental factors.

Metabolomic analysis of lung cancer patients with chronic obstructive pulmonary disease using gas chromatography-mass spectrometry

Chronic obstructive pulmonary disease (COPD), characterized by intermittent exacerbations and clinical subphenotypes like emphysema and chronic bronchitis, poses a significant risk of lung cancer (LC) development. Metabolomic studies of COPD are scarce, and those of LC patients with COPD subphenotypes have not been investigated. To study metabolite profile alteration in LC patients with different COPD subphenotypes, lung paracancer tissue from 10 LC (CON) patients, 10 LC patients with emphysema (E), and 9 LC patients with chronic bronchitis (CB) were analyzed using gas chromatography-mass spectrometry. Multivariate analysis indicated a distinct separation between LC patients with COPD subphenotypes and LC patients. Overall, 60, 55, 33 and 63 differential metabolites (DM) were identified in comparisons between CB vs CON, E vs CON, CB vs E, and CB + E vs CON, respectively, and of these, 8 DM were shared in all comparisons. Among the high altered metabolites, E samples showed higher 'acetol' than CON samples, and lower 'azelaic acid', '3-methylglutaric acid' and 'allose'. CB samples showed higher 'turanose' and 'o-phosphoserine' and lower 'anandamide' than CON and E samples. In CB and E samples, 'galactonic acid', '2-mercaptoethanesulfonic acid', 'D-alanyl-D-alanine' '3-methylglutaric acid', 'glycine', 'L-4-Hydroxyphenylglycine' and 'O-phosphonothreonine' had common alteration trends compared with those of CON samples. 'Glycine', 'L-4-Hydroxyphenylglycine' and 'O-phosphonothreonine' were significantly enriched in glycine, serine and threonine metabolism pathways. The total differential metabolites detected were remarkably altered in pyrimidine, beta-alanine and purine metabolism. Our study provided altered DM patterns of lung paracancer tissue, the key metabolites and their enriched metabolic pathways in LC patients with different COPD subphenotypes.

COPD phenotypes and machine learning cluster analysis: A systematic review and future research agenda

Chronic Obstructive Pulmonary Disease (COPD) is a highly heterogeneous condition projected to become the third leading cause of death worldwide by 2030. To better characterize this condition, clinicians have classified patients sharing certain symptomatic characteristics, such as symptom intensity and history of exacerbations, into distinct phenotypes. In recent years, the growing use of machine learning algorithms, and cluster analysis in particular, has promised to advance this classification through the integration of additional patient characteristics, including comorbidities, biomarkers, and genomic information. This combination would allow researchers to more reliably identify new COPD phenotypes, as well as better characterize existing ones, with the aim of improving diagnosis and developing novel treatments. Here, we systematically review the last decade of research progress, which uses cluster analysis to identify COPD phenotypes. Collectively, we provide a systematized account of the extant evidence, describe the strengths and weaknesses of the main methods used, identify gaps in the literature, and suggest recommendations for future research.

Metabolic profiling of organic and fatty acids in chronic and autoimmune diseases

Metabolomics is a powerful tool of omics that permits the simultaneous identification of metabolic perturbations in several autoimmune and chronic diseases. Several parameters can affect a metabolic profile, from the population characteristics to the selection of the analytical method. In the current chapter, we summarize the main analytical methods and results of the metabolic profiling of fatty and organic acids performed in human metabolomic studies for asthma, COPD, psoriasis and Hashimoto's thyroiditis. We discuss the most significant metabolic alterations associated with these diseases, after comparison of either a single patient's group with healthy controls or several patient's subgroups of different disease severity and phenotype with healthy controls or of a patient's group before and after treatment. Finally, we present critical metabolic patterns that are associated with each disease and their potency for the unraveling of disease pathogenesis, prediction, diagnosis, patient stratification and treatment selection.

Metabolomics of Exhaled Breath Condensate by Nuclear Magnetic Resonance Spectroscopy and Mass Spectrometry: A Methodological Approach

:

Respiratory diseases present a very high prevalence in the general population, with an increase in morbidity, mortality and health-care expenses worldwide. They are complex and heterogeneous pathologies that may present different pathological facets in different subjects, often with personal evolution. Therefore, there is a need to identify patients with similar characteristics, prognosis or treatment, defining the so-called phenotype, but also to mark specific differences within each phenotype, defining the endotypes.

:

Biomarkers are very useful to study respiratory phenotypes and endotypes. Metabolomics, one of the recently introduced “omics”, is becoming a leading technique for biomarker discovery. For the airways, metabolomics appears to be well suited as the respiratory tract offers a natural matrix, the Exhaled Breath Condensate (EBC), in which several biomarkers can be measured. In this review, we will discuss the main methodological issues related to the application of Nuclear Magnetic Resonance (NMR) spectroscopy and Mass Spectrometry (MS) to EBC metabolomics for investigating respiratory diseases.

Plasma Metabolomics and Lipidomics Reveal Perturbed Metabolites in Different Disease Stages of Chronic Obstructive Pulmonary Disease

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is a common disease characterized by persistent respiratory symptoms and airflow restriction. It is usually manifested as airway and/or alveolar abnormalities caused by significant exposure to harmful particulates or gases.

OBJECTIVE

We aim to explore plasma metabolomic changes in the acute exacerbation stage of COPD (AECOPD) and stable stage of COPD (Stable COPD) to identify potential biomarkers for diagnosis or prognosis in clinical practice.

METHODS

Untargeted metabolomics and lipidomics analyses were performed to investigate dysregulated molecules in blood plasma of AECOPD patients (n=48) and Stable COPD (n=48), and a cohort of healthy people were included as a control group (n=48). Statistical analysis and bioinformatics analysis were performed to reveal dysregulated metabolites and perturbed metabolic pathways. SVM-based multivariate ROC analysis was used for candidate biomarker screening.

RESULTS

A total of 142 metabolites and 688 lipids were dysregulated in COPD patients. Pathway enrichment analysis showed that several metabolic pathways were perturbed after COPD onset. Several biomarker panels were proposed for diagnosis of COPD vs healthy control and AECOPD vs Stable COPD with AUC greater than 0.9.

CONCLUSION

Numerous plasma metabolites and several metabolic pathways were detected relevant to COPD disease onset or progression. These metabolites may be considered as candidate biomarkers for diagnosis or prognosis of COPD. The perturbed pathways involved in COPD provide clues for further pathological mechanism studies of COPD.

Metabolic Fingerprint of Chronic Obstructive Lung Diseases: A New Diagnostic Perspective

Chronic obstructive lung disease (COLD) is a group of airway diseases, previously known as emphysema and chronic bronchitis. The heterogeneity of COLD does not allow early diagnosis and leads to increased morbidity and mortality. The increasing number of COLD incidences stresses the need for precision medicine approaches that are specific to the patient. Metabolomics is an emerging technology that allows for the discrimination of metabolic changes in the cell as a result of environmental factors and specific genetic background. Thus, quantification of metabolites in human biofluids can provide insights into the metabolic state of the individual in real time and unravel the presence of, or predisposition to, a disease. In this article, the advantages of and potential barriers to putting metabolomics into clinical practice for COLD are discussed. Today, metabolomics is mostly lab-based, and research studies with novel COLD-specific biomarkers are continuously being published. Several obstacles in the research and the market field hamper the translation of these data into clinical practice. However, technological and computational advances will facilitate the clinical interpretation of data and provide healthcare professionals with the tools to prevent, diagnose, and treat COLD with precision in the coming decades.

Bronchoalveolar Lavage Fluid from COPD Patients Reveals More Compounds Associated with Disease than Matched Plasma

Smoking causes chronic obstructive pulmonary disease (COPD). Though recent studies identified a COPD metabolomic signature in blood, no large studies examine the metabolome in bronchoalveolar lavage (BAL) fluid, a more direct representation of lung cell metabolism. We performed untargeted liquid chromatography-mass spectrometry (LC-MS) on BAL and matched plasma from 115 subjects from the SPIROMICS cohort. Regression was performed with COPD phenotypes as the outcome and metabolites as the predictor, adjusted for clinical covariates and false discovery rate. Weighted gene co-expression network analysis (WGCNA) grouped metabolites into modules which were then associated with phenotypes. K-means clustering grouped similar subjects. We detected 7939 and 10,561 compounds in BAL and paired plasma samples, respectively. FEV₁/FVC (Forced Expiratory Volume in One Second/Forced Vital Capacity) ratio, emphysema, FEV₁ % predicted, and COPD exacerbations associated with 1230, 792, eight, and one BAL compounds, respectively. Only two plasma compounds associated with a COPD phenotype (emphysema). Three BAL co-expression modules associated with FEV₁/FVC and emphysema. K-means BAL metabolomic signature clustering identified two groups, one with more airway obstruction (34% of subjects, median FEV₁/FVC 0.67), one with less (66% of subjects, median FEV₁/FVC 0.77; p < 2 × 10^-4). Associations between metabolites and COPD phenotypes are more robustly represented in BAL compared to plasma.

An Updated Overview of Metabolomic Profile Changes in Chronic Obstructive Pulmonary Disease

Chronic obstructive pulmonary disease (COPD), a common and heterogeneous respiratory disease, is characterized by persistent and incompletely reversible airflow limitation. Metabolomics is applied to analyze the difference of metabolic profile based on the low-molecular-weight metabolites (<1 kDa). Emerging metabolomic analysis may provide insights into the pathogenesis and diagnosis of COPD. This review aims to summarize the alteration of metabolites in blood/serum/plasma, urine, exhaled breath condensate, lung tissue samples, etc. from COPD individuals, thereby uncovering the potential pathogenesis of COPD according to the perturbed metabolic pathways. Metabolomic researches have indicated that the dysfunctions of amino acid metabolism, lipid metabolism, energy production pathways, and the imbalance of oxidations and antioxidations might lead to local and systematic inflammation by activating the Nuclear factor kappa-light-chain-enhancer of activated B cells signaling pathway and releasing inflammatory cytokines, like interleutin-6 (IL-6), tumor necrosis factor-α, and IL-8. In addition, they might cause protein malnutrition and oxidative stress and contribute to the development and exacerbation of COPD.

Lipid metabolism in chronic obstructive pulmonary disease

Dysregulated lipid metabolism plays crucial roles in various diseases, including diabetes mellitus, cancer, and neurodegeneration. Recent studies suggest that alterations in major lipid metabolic pathways contribute to pathogenesis of lung diseases, including chronic obstructive pulmonary disease (COPD). These changes allow lung tissue to meet the energy needs and trigger anabolic pathways that initiate the synthesis of active molecules directly involved in the inflammation. In this review, we summarize the changes of catabolism and anabolism of lipids, lipid molecules including lipid mediators, lipid synthesis transcription factors, cholesterol, and phospholipids, and how those lipid molecules participate in the initiation and resolution of inflammation in COPD.

Metabolomics Identifies Novel Blood Biomarkers of Pulmonary Function and COPD in the General Population

Determination of metabolomic signatures of pulmonary function and chronic obstructive pulmonary disease (COPD) in the general population could aid in identification and understanding of early disease processes. Metabolome measurements were performed on serum from 4742 individuals (2354 African-Americans and 1529 European-Americans from the Atherosclerosis Risk in Communities study and 859 Europeans from the Cooperative Health Research in the Region of Augsburg study). We examined 368 metabolites in relation to cross-sectional measures of forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC), their ratio (FEV1/FVC) and COPD using multivariable regression followed by meta-analysis. At a false discovery rate of 0.05, 95 metabolites were associated with FEV1 and 100 with FVC (73 overlapping), including inverse associations with branched-chain amino acids and positive associations with glutamine. Ten metabolites were associated with FEV1/FVC and seventeen with COPD (393 cases). Enriched pathways of amino acid metabolism were identified. Associations with FEV1 and FVC were not driven by individuals with COPD. We identified novel metabolic signatures of pulmonary function and COPD in African and European ancestry populations. These may allow development of biomarkers in the general population of early disease pathogenesis, before pulmonary function has decreased to levels diagnostic for COPD.

Metabolomics in chronic lung diseases

Chronic lung diseases represent a significant global burden. Their increasing incidence and complexity render a comprehensive, multidisciplinary and personalized approach to each patient, critically important. Most recently, unique biochemical pathways and disease markers have been identified through large-scale metabolomic studies. Metabolomics is the study of metabolic pathways and the measurement of unique biomolecules in a living system. Analysing samples from different compartments such as bronchoalveolar lavage fluid (BALF) and plasma has proven useful for the characterization of a number of pathological conditions and offers promise as a clinical tool. For example, several studies using mass spectrometry (MS) have shown alterations in the sphingolipid metabolism of chronic obstructive pulmonary disease (COPD) sufferers. In this article, we present a practical review of the application of metabolomics to the study of chronic lung diseases (CLD): COPD, idiopathic pulmonary fibrosis (IPF) and asthma. The insights, which the analytical strategies employed in metabolomics, have provided to the dissection of the biochemistry of CLD and future clinical biomarkers are explored.

The modification of indoor PM2.5 exposure to chronic obstructive pulmonary disease in Chinese elderly people: A meet-in-metabolite analysis

BACKGROUND

Exposure to airborne fine particulate matter (PM_2.5) has been associated with a variety of adverse health outcomes including chronic obstructive pulmonary disease (COPD). However, the linkages between PM_2.5 exposure, PM_2.5-related biomarkers, COPD-related biomarkers and COPD remain poorly elucidated.

OBJECTIVES

To investigate the linkages between PM_2.5 exposure and COPD outcome by using the meet-in-middle strategy based on urinary metabolic biomarkers.

METHODS

A cross-sectional study was designed to illustrate the mentioned quadripartite linkages. Indoor PM_2.5 and its element components were assessed in 41 Chinese elderly participants including COPD patients and their healthy spouses. Metabolic biomarkers involved in PM_2.5 exposure and COPD were identified by using urinary metabolomics. The associations between PM_2.5- and COPD-related biomarkers were investigated by statistics and metabolic pathway analysis.

RESULTS

Seven metabolites were screened and identified with significant correlations to PM_2.5 exposure, which were majorly involved in purine and amino acid metabolism as well as glycolysis. Ten COPD-related metabolic biomarkers were identified, which suggested that amino acid metabolism, lipid and fatty acid metabolism, and glucose metabolism were disturbed in the patients. Also, PM_2.5 and its many elemental components were significantly associated with COPD-related biomarkers. We observed that the two kinds of biomarkers (PM_2.5- and COPD-related) integrated in a locally connected network and the alterations of these metabolic biomarkers can biologically link PM_2.5 exposure to COPD outcome.

CONCLUSIONS

Our study indicated the modification of PM_2.5 to COPD via both modes of action of lowering participants' antioxidation capacity and decreasing their lung energy generation; this information would be valuable for the prevention strategy of COPD.

Study of the metabolomic relationship between lung cancer and chronic obstructive pulmonary disease based on direct infusion mass spectrometry

The high prevalence of lung cancer (LC) has triggered the search of biomarkers for early diagnosis of this disease. For this purpose the study of metabolic changes related to the development of lung cancer could provide interesting information about its early diagnosis. In this sense, chronic obstructive pulmonary disease (COPD), a disease associated with tumor development, is a comorbidity that increases the risk of onset and progression of lung neoplasia and has also to be considered in the study of pathology related to lung cancer. This work develop a metabolomic approach based on direct infusion mass spectrometry using a hybrid triple quadrupole-time of flight mass spectrometer (DI-ESI-QqQ-TOF-MS) in order to identify altered metabolites from serum of LC and COPD patients and evaluate its relationship and implication in the progression of LC. This methodology has been applied to 30 serum samples from LC, 30 healthy patients used as controls (HC) and 30 serum samples from COPD to found altered metabolites from both LC and COPD diseases. In addition, some metabolic differences and similarities were found in Pulmonary Emphysema and Chronic Bronchitis patients. On the other hand, altered metabolites were studied in different stages of LC (II, III and IV) to evaluate the perturbation of them throughout the progression of disease. The sample treatment consisted of the extraction of polar and non-polar metabolites from serum that was later infused into the mass spectrometer using an electrospray ionization source in positive and negative mode. Partial least squares discriminant analysis (PLS-DA) allowed a classification between LC, HC and COPD groups in all acquisition modes. A total of 35 altered and common metabolites between LC and COPD, including amino acids, fatty acids, lysophospholipids, phospholipids and triacylglycerides were identified, being alanine, aspartate and glutamate metabolism the most altered. Finally, ROC curves were applied to the dataset and metabolites with AUC value higher than 0.70 were considered as relevant in the progression of LC.

1H-NMR-based metabolic profiling of healthy individuals and high-resolution CT-classified phenotypes of COPD with treatment of tiotropium bromide

Background

Heterogeneity of COPD results in different therapeutic effects for different patients receiving the same treatment. COPD patients need to be individually treated according to their own characteristics. The purpose of this study was to explore the differences in different CT phenotypic COPD by molecular metabolites through the use of metabolomics.

Methods

According to the characteristics of CT imaging, 42 COPD patients were grouped into phenotype E (n=20) or phenotype M (n=24). Each COPD patient received tiotropium bromide powder for inhalation for a therapeutic period of 3 months. All subjects were assigned into phenotype E in pre-therapy (EB, n=20), phenotype E in post-therapy (EA, n=20), phenotype M in pre-therapy (MB, n=22), phenotype M in post-therapy (MA, n=22), or normal control (N, n=24). The method of metabolomics based on ¹H nuclear magnetic resonance (¹H-NMR) was used to compare the changes in serum metabolites between COPD patients and normal controls and between different phenotypes of COPD patients in pre- and post-therapy.

Results

Patients with COPD phenotype E responded better to tiotropium bromide than patients with COPD phenotype M in terms of pulmonary function and COPD assessment test scores. There were differences in metabolites in COPD patients vs normal control people. Differences were also observed between different COPD phenotypic patients receiving the treatment in comparison with those who did not receive treatment. The changes of metabolites involved lactate, phenylalanine, fructose, glycine, asparagine, citric acid, pyruvic acid, proline, acetone, ornithine, lipid, pyridoxine, maltose, betaine, lipoprotein, and so on. These identified metabolites covered the metabolic pathways of amino acids, carbohydrates, lipids, genetic materials, and vitamin.

Conclusion

The efficacy of tiotropium bromide on COPD phenotype E is better than that of phenotype M. Metabolites detected by ¹H-NMR metabolomics have potentialities of differentiation of COPD and healthy people, discrimination of different COPD phenotypes, and giving insight into the individualized treatment of COPD.