A Metabolomic Severity Score for Airflow Obstruction and Emphysema

Metabolomic and genomic prediction of common diseases in 700,217 participants in three national biobanks

Identifying individuals at high risk of chronic diseases via easily measured biomarkers could enhance efforts to prevent avoidable illness and death. Using 'omic data can stratify risk for many diseases simultaneously from a single measurement that captures multiple molecular predictors of risk. Here we present nuclear magnetic resonance metabolomics in blood samples from 700,217 participants in three national biobanks. We built metabolomic scores that identify high-risk groups for diseases that cause the most morbidity in high-income countries and show consistent cross-biobank replication of the relative risk of disease for these groups. We show that these metabolomic scores are more strongly associated with disease onset than polygenic scores for most of these diseases. In a subset of 18,709 individuals with metabolomic biomarkers measured at two time points we show that people whose scores change have different risk of disease, suggesting that repeat measurements capture changes both to health status and disease risk possibly due to treatment, lifestyle changes or other factors. Lastly, we assessed the incremental predictive value of metabolomic scores over existing clinical risk scores for multiple diseases and found modest improvements in discrimination for several diseases whose clinical utility, while promising, remains to be determined.

Metabolic Aging as an Increased Risk for Chronic Obstructive Pulmonary Disease

BACKGROUND/OBJECTIVES

Both aging and chronic obstructive pulmonary disease (COPD) are strongly associated with changes in the metabolome; however, it is unknown whether there are common aging/COPD metabolomic signatures and if accelerated aging is associated with COPD.

METHODS

Plasma from 5704 subjects from the Genetic Epidemiology of COPD study (COPDGene) and 2449 subjects from Subpopulations and intermediate outcome measures in COPD study (SPIROMICS) were profiled using the Metabolon global metabolomics platform (1013 annotated metabolites). Post-bronchodilator spirometry measures of airflow obstruction (forced expiratory volume at one second (FEV1)/forced vital capacity (FVC) < 0.7) were used to define COPD. Elastic net regression was trained on never and former smokers with normal spirometry and no emphysema to create a metabolomic age score which was validated in SPIROMICS subjects.

RESULTS

Our metabolic age score was strongly associated with chronic age in the validation cohort (correlation coefficient = 0.8). COPD subjects with accelerated aging (>7 years difference between metabolic and actual age) had more severe disease compared with those who had decelerated aging (<-7 years difference between metabolic and actual age). COPD and aging metabolites were shared more than expected (p < 0.001), with amino acid and glutathione metabolism among pathways overrepresented.

CONCLUSIONS

These findings suggest a common mechanism between aging and COPD and that COPD is associated with accelerated metabolic aging.

Limitations of Clustering with PCA and Correlated Noise

It is now common to have a modest to large number of features on individuals with complex diseases. Unsupervised analyses, such as clustering with and without preprocessing by Principle Component Analysis (PCA), is widely used in practice to uncover subgroups in a sample. However, in many modern studies features are often highly correlated and noisy (e.g. SNP's, -omics, quantitative imaging markers, and electronic health record data). The practical performance of clustering approaches in these settings remains unclear. Through extensive simulations and empirical examples applying Gaussian Mixture Models and related clustering methods, we show these approaches (including variants of kmeans, VarSelLCM, HDClassifier, and Fisher-EM) can have very poor performance in many settings. We also show the poor performance is often driven by either an explicit or implicit assumption by the clustering algorithm that high variance features are relevant while lower variance features are irrelevant, called the variance as relevance assumption. We develop practical pre-processing approaches that improve analysis performance in some cases. This work offers practical guidance on the strengths and limitations of unsupervised clustering approaches in modern data analysis applications.

Metabolic pathways in immune senescence and inflammaging: Novel therapeutic strategy for chronic inflammatory lung diseases. An EAACI position paper from the Task Force for Immunopharmacology

The accumulation of senescent cells drives inflammaging and increases morbidity of chronic inflammatory lung diseases. Immune responses are built upon dynamic changes in cell metabolism that supply energy and substrates for cell proliferation, differentiation, and activation. Metabolic changes imposed by environmental stress and inflammation on immune cells and tissue microenvironment are thus chiefly involved in the pathophysiology of allergic and other immune-driven diseases. Altered cell metabolism is also a hallmark of cell senescence, a condition characterized by loss of proliferative activity in cells that remain metabolically active. Accelerated senescence can be triggered by acute or chronic stress and inflammatory responses. In contrast, replicative senescence occurs as part of the physiological aging process and has protective roles in cancer surveillance and wound healing. Importantly, cell senescence can also change or hamper response to diverse therapeutic treatments. Understanding the metabolic pathways of senescence in immune and structural cells is therefore critical to detect, prevent, or revert detrimental aspects of senescence-related immunopathology, by developing specific diagnostics and targeted therapies. In this paper, we review the main changes and metabolic alterations occurring in senescent immune cells (macrophages, B cells, T cells). Subsequently, we present the metabolic footprints described in translational studies in patients with chronic asthma and chronic obstructive pulmonary disease (COPD), and review the ongoing preclinical studies and clinical trials of therapeutic approaches aiming at targeting metabolic pathways to antagonize pathological senescence. Because this is a recently emerging field in allergy and clinical immunology, a better understanding of the metabolic profile of the complex landscape of cell senescence is needed. The progress achieved so far is already providing opportunities for new therapies, as well as for strategies aimed at disease prevention and supporting healthy aging.

Plasmonic Alloys Enhanced Metabolic Fingerprints for the Diagnosis of COPD and Exacerbations

Accurate diagnosis of chronic obstructive pulmonary disease (COPD) and exacerbations by metabolic biomarkers enables individualized treatment. Advanced metabolic detection platforms rely on designed materials. Here, we design mesoporous PdPt alloys to characterize metabolic fingerprints for diagnosing COPD and exacerbations. As a result, the optimized PdPt alloys enable the acquisition of metabolic fingerprints within seconds, requiring only 0.5 μL of native plasma by laser desorption/ionization mass spectrometry owing to the enhanced electric field, photothermal conversion, and photocurrent response. Machine learning decodes metabolic profiles acquired from 431 individuals, achieving a precise diagnosis of COPD with an area under the curve (AUC) of 0.904 and an accurate distinction between stable COPD and acute exacerbations of COPD (AECOPD) with an AUC of 0.951. Notably, eight metabolic biomarkers identified accurately discriminate AECOPD from stable COPD while providing valuable information on disease progress. Our platform will offer an advanced nanoplatform for the management of COPD, complementing standard clinical techniques.

Suppressing inflammatory signals and apoptosis-linked sphingolipid metabolism underlies therapeutic potential of Qing-Jin-Hua-Tan decoction against chronic obstructive pulmonary disease

Background

Qing-Jin-Hua-Tan decoction (QJHTD) is a classic traditional Chinese medicine (TCM) prescription that first appeared in the ancient book Yi-Xue-Tong-Zhi. QJHTD has shown effectiveness for treating chronic obstructive pulmonary disease (COPD), although its mechanisms of action are still perplexing. The molecular mechanisms underlying the curative effects of QJHTD on COPD is worth exploring.

Methods

In vitro antiapoptotic and antiinflammatory activities of QJHTD were evaluated using cell viability, proliferation, apoptosis rate, and expression of IL-1β and TNF-α in BEAS-2B and RAW264.7 cells challenged with cigarette smoke (CS) extract (CSE) and lipopolysaccharide (LPS). In vivo therapeutic activities of QJHTD were evaluated using respiratory parameters (peak inspiratory flow (PIFb) and peak expiratory flow (PEFb) values), histopathology (mean linear intercept, MLI), and proinflammatory cytokine (IL-1β and TNF-α) and cleaved caspase-3 (c-Casp3) levels in the lung tissue of CS-LPS-exposed BALB/c mice. Network pharmacology-based prediction, transcriptomic analysis, and metabolic profiling were employed to investigate the signaling molecules and metabolites pertinent to the anti-COPD action of QJHTD.

Results

Increased cell viability and proliferation with decreased apoptosis rate and proinflammatory cytokine expression were noted after QJHTD intervention. QJHTD administration elevated PEFb and PIFb values, reduced MLI, and inhibited IL-1β, TNF-α, and c-Casp3 expression in vivo. Integrated network pharmacology-transcriptomics revealed that suppressing inflammatory signals (IL-1β, IL-6, TNF, IκB-NF-κB, TLR, and MAPK) and apoptosis contributed to the anti-COPD property of QJHTD. Metabolomic profiling unveiled prominent roles for the suppression of apoptosis and sphingolipid (SL) metabolism and the promotion of choline (Ch) metabolism in the anti-COPD effect of QJHTD. Integrative transcriptomics-metabolomics unraveled the correlation between SL metabolism and apoptosis. In silico molecular docking revealed that acacetin, as an active compound in QJHTD, could bind with high affinity to MEK1, MEK2, ERK1, ERK2, Bcl2, NF-κB, and alCDase target proteins.

Conclusion

The therapeutic effect of QJHTD on COPD is dependent on regulating inflammatory signals and apoptosis-directed SL metabolism. These findings provide deeper insights into the molecular mechanism of action of QJHTD against COPD and justify its theoretical promise in novel pharmacotherapy for this multifactorial disease.

Proteomics and Metabolomics in Biomedicine

The technological advances of recent years have significantly enhanced medical discoveries [...].

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.

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.