Relationship between Respiratory Microbiome and Systemic Inflammatory Markers in COPD: A Pilot Study.

The respiratory microbiome may influence the development and progression of COPD by modulating local immune and inflammatory events. We aimed to investigate whether relative changes in respiratory bacterial abundance are also associated with systemic inflammation, and explore their relationship with the main clinical COPD phenotypes. Multiplex analysis of inflammatory markers and transcript eosinophil-related markers were analyzed on peripheral blood in a cohort of stable COPD patients (n = 72). Respiratory microbiome composition was analyzed by 16S rRNA microbial sequencing on spontaneous sputum. Spearman correlations were applied to test the relationship between the microbiome composition and systemic inflammation. The concentration of the plasma IL-8 showed an inverted correlation with the relative abundance of 17 bacterial genera in the whole COPD cohort. COPD patients categorized as eosinophilic showed positive relationships with blood eosinophil markers and inversely correlated with the degree of airway obstruction and the number of exacerbations during the previous year. COPD patients categorized as frequent exacerbators were enriched with the bacterial genera Pseudomonas which, in turn, was positively associated with the severity of airflow limitation and the prior year's exacerbation history. The associative relationships of the sputum microbiome with the severity of the disease emphasize the relevance of the interaction between the respiratory microbiota and systemic inflammation.

A Pilot Study on Proteomic Predictors of Mortality in Stable COPD.

Chronic Obstructive Pulmonary Disease (COPD) is the third leading cause of global mortality. Despite clinical predictors (age, severity, comorbidities, etc.) being established, proteomics offers comprehensive biological profiling to obtain deeper insights into COPD pathophysiology and survival prognoses. This pilot study aimed to identify proteomic footprints that could be potentially useful in predicting mortality in stable COPD patients. Plasma samples from 40 patients were subjected to both blind (liquid chromatography-mass spectrometry) and hypothesis-driven (multiplex immunoassays) proteomic analyses supported by artificial intelligence (AI) before a 4-year clinical follow-up. Among the 34 patients whose survival status was confirmed (mean age 69 ± 9 years, 29.5% women, FEV1 42 ± 15.3% ref.), 32% were dead in the fourth year. The analysis identified 363 proteins/peptides, with 31 showing significant differences between the survivors and non-survivors. These proteins predominantly belonged to different aspects of the immune response (12 proteins), hemostasis (9), and proinflammatory cytokines (5). The predictive modeling achieved excellent accuracy for mortality (90%) but a weaker performance for days of survival (Q2 0.18), improving mildly with AI-mediated blind selection of proteins (accuracy of 95%, Q2 of 0.52). Further stratification by protein groups highlighted the predictive value for mortality of either hemostasis or pro-inflammatory markers alone (accuracies of 95 and 89%, respectively). Therefore, stable COPD patients' proteomic footprints can effectively forecast 4-year mortality, emphasizing the role of inflammatory, immune, and cardiovascular events. Future applications may enhance the prognostic precision and guide preventive interventions.

Pathophysiology and genomics of bronchiectasis.

Bronchiectasis is a complex and heterogeneous inflammatory chronic respiratory disease with an unknown cause in around 30-40% of patients. The presence of airway infection together with chronic inflammation, airway mucociliary dysfunction and lung damage are key components of the vicious vortex model that better describes its pathophysiology. Although bronchiectasis research has significantly increased over the past years and different endotypes have been identified, there are still major gaps in the understanding of the pathophysiology. Genomic approaches may help to identify new endotypes, as has been shown in other chronic airway diseases, such as COPD.Different studies have started to work in this direction, and significant contributions to the understanding of the microbiome and proteome diversity have been made in bronchiectasis in recent years. However, the systematic application of omics approaches to identify new molecular insights into the pathophysiology of bronchiectasis (endotypes) is still limited compared with other respiratory diseases.Given the complexity and diversity of these technologies, this review describes the key components of the pathophysiology of bronchiectasis and how genomics can be applied to increase our knowledge, including the study of new techniques such as proteomics, metabolomics and epigenomics. Furthermore, we propose that the novel concept of trained innate immunity, which is driven by microbiome exposures leading to epigenetic modifications, can complement our current understanding of the vicious vortex. Finally, we discuss the challenges, opportunities and implications of genomics application in clinical practice for better patient stratification into new therapies.

Exploring the Interplay between Cellular Senescence, Immunity, and Fibrosing Interstitial Lung Diseases: Challenges and Opportunities.

Fibrosing interstitial lung diseases (ILDs) are characterized by the gradual and irreversible accumulation of scar tissue in the lung parenchyma. The role of the immune response in the pathogenesis of pulmonary fibrosis remains unclear. In recent years, substantial advancements have been made in our comprehension of the pathobiology driving fibrosing ILDs, particularly concerning various age-related cellular disturbances and immune mechanisms believed to contribute to an inadequate response to stress and increased susceptibility to lung fibrosis. Emerging studies emphasize cellular senescence as a key mechanism implicated in the pathobiology of age-related diseases, including pulmonary fibrosis. Cellular senescence, marked by antagonistic pleiotropy, and the complex interplay with immunity, are pivotal in comprehending many aspects of lung fibrosis. Here, we review progress in novel concepts in cellular senescence, its association with the dysregulation of the immune response, and the evidence underlining its detrimental role in fibrosing ILDs.

Treatable traits in pre-COPD: Time to extend the treatable traits paradigm beyond established disease.

To date, the treatable traits (TTs) approach has been applied in the context of managing diagnosed diseases. TTs are clinical characteristics and risk factors that can be identified clinically and/or biologically, and that merit treatment if present. There has been an exponential increase in the uptake of this approach by both researchers and clinicians. Realizing the potential of the TTs approach to pre-clinical disease, this expert review proposes that it is timely to consider acting on TTs present before a clinical diagnosis is made, which might help to prevent development of the full disease. Such an approach is ideal for diseases where there is a long pre-clinical phase, such as in chronic obstructive pulmonary disease (COPD). The term 'pre-COPD' has been recently proposed to identify patients with respiratory symptoms and/or structural or functional abnormalities without airflow limitation. They may eventually develop airflow limitation with time but patients with pre-COPD are likely to have traits that are already treatable. This review first outlines the contribution of recently generated knowledge into lifetime lung function trajectories and the conceptual framework of 'GETomics' to the field of pre-COPD. GETomics is a dynamic and cumulative model of interactions between genes and the environment throughout the lifetime that integrates information from multi-omics to understand aetiology and mechanisms of diseases. This review then discusses the current evidence on potential TTs in pre-COPD patients and makes recommendations for practice and future research. At a broader level, this review proposes that introducing the TTs in pre-COPD may help reenergize the preventive approaches to health and diseases.

Triple Therapy and Clinical Control in B+ COPD Patients: A Pragmatic, Prospective, Randomized Trial.

INTRODUCTION: Treatment with LABA/LAMA is recommended in GOLD B patients. We hypothesized that triple therapy (LABA/LAMA/ICS) will be superior to LABA/LAMA in achieving and maintaining clinical control (CC), a composite outcome that considers both impact and disease stability in a subgroup of GOLD B patients (here termed GOLD B+ patients) characterized by: (1) remaining symptomatic (CAT≥10) despite regular LABA/LAMA therapy; (2) having suffered one moderate exacerbation in the previous year; and (3) having blood eosinophil counts (BEC) ≥150cells/µL. METHODS: The ANTES B+ study is a prospective, multicenter, open label, randomized, pragmatic, controlled trial designed to test this hypothesis. It will randomize 1028 B+ patients to continue with their usual LABA/LAMA combination prescribed by their attending physician or to begin fluticasone furoate (FF) 92µg/umeclidinium (UMEC) 55µg/vilanterol (VI) 22µg in a single inhaler q.d. for 12 months. The primary efficacy outcome will be the level of CC achieved. Secondary outcomes include the clinical important deterioration index (CID), annual rate of exacerbations, and FEV1. Exploratory objectives include the interaction of BEC and smoking status, all-cause mortality and proportion of patients on LABA/LAMA arm that switch therapy arms. Safety analysis include adverse events and incidence of pneumonia. RESULTS: The first patient was recruited on February 29, 2024; results are expected in the first quarter of 2026. CONCLUSIONS: The ANTES B+ study is the first to: (1) explore the efficacy and safety of triple therapy in a population of B+ COPD patients and (2) use a composite index (CC) as the primary result of a COPD trial.

Lung Tissue Multi-Layer Network Analysis Uncovers the Molecular Heterogeneity of COPD.

BACKGROUND: Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous condition. We hypothesized that the unbiased integration of different COPD lung omics using a novel multi-layer approach may unravel mechanisms associated with clinical characteristics. METHODS: We profiled mRNA, miRNA and methylome in lung tissue samples from 135 former smokers with COPD. For each omic (layer) we built a patient network based on molecular similarity. The three networks were used to build a multi-layer network, and optimization of multiplex-modularity was employed to identify patient communities across the three distinct layers. Uncovered communities were related to clinical features. RESULTS: We identified five patient communities in the multi-layer network which were molecularly distinct and related to clinical characteristics, such as FEV1 and blood eosinophils. Two communities (C#3 and C#4) had both similarly low FEV1 values and emphysema, but were molecularly different: C#3, but not C#4, presented B and T cell signatures and a downregulation of secretory (SCGB1A1/SCGB3A1) and ciliated cells. A machine learning model was set up to discriminate C#3 and C#4 in our cohort, and to validate them in an independent cohort. Finally, using spatial transcriptomics we characterized the small airway differences between C#3 and C#4, identifying an upregulation of T/B cell homing chemokines, and bacterial response genes in C#3. CONCLUSIONS: A novel multi-layer network analysis is able to identify clinically relevant COPD patient communities. Patients with similarly low FEV1 and emphysema can have molecularly distinct small airways and immune response patterns, indicating that different endotypes can lead to similar clinical presentation.

COPD: systemic proteomic profiles in frequent and infrequent exacerbators.

Background: Some patients with COPD suffer frequent exacerbations (FE). We hypothesised that their systemic proteomic profile would be different from that of non-frequent exacerbators (NFE). The objective of the present study was to contrast the systemic proteomic profile in FE versus NFE. As a reference, we also determined the systemic proteomic profile of healthy controls (HC) and COPD patients during an actual episode of exacerbation (AE). Methods: In the analysis we included 40 clinically stable COPD patients (20 FE and 20 NFE), and 20 HC and 10 AE patients. Their plasma samples were analysed by combining two complementary proteomic approaches: label-free liquid chromatography-tandem mass spectrometry and multiplex immunoassays. Gene Ontology annotation, pathway enrichment and network analyses were used to investigate molecular pathways associated with differentially abundant proteins/peptides (DAPs). Results: Compared with HC, we identified 40 DAPs in FE, 10 in NFE and 63 in AE. Also compared to HC, pathway functional and protein-protein network analyses revealed dysregulation of inflammatory responses involving innate and antibody-mediated immunity in COPD, particularly in the FE group, as well as during an AE episode. Besides, we only identified alterations in the complement and coagulation cascades in AE. Conclusion: There are specific plasma proteome profiles associated with FE, which are partially shared with findings observed during AE, albeit others are uniquely present during the actual episode of AE.

Pulmonary vascular reactivity in growth restricted fetuses using computational modelling and machine learning analysis of fetal Doppler waveforms.

The aim of this study was to investigate the pulmonary vasculature in baseline conditions and after maternal hyperoxygenation in growth restricted fetuses (FGR). A prospective cohort study of singleton pregnancies including 97 FGR and 111 normally grown fetuses was carried out. Ultrasound Doppler of the pulmonary vessels was obtained at 24-37 weeks of gestation and data were acquired before and after oxygen administration. After, Machine Learning (ML) and a computational model were used on the Doppler waveforms to classify individuals and estimate pulmonary vascular resistance (PVR). Our results showed lower mean velocity time integral (VTI) in the main pulmonary and intrapulmonary arteries in baseline conditions in FGR individuals. Delta changes of the main pulmonary artery VTI and intrapulmonary artery pulsatility index before and after hyperoxygenation were significantly greater in FGR when compared with controls. Also, ML identified two clusters: A (including 66% controls and 34% FGR) with similar Doppler traces over time and B (including 33% controls and 67% FGR) with changes after hyperoxygenation. The computational model estimated the ratio of PVR before and after maternal hyperoxygenation which was closer to 1 in cluster A (cluster A 0.98 ± 0.33 vs cluster B 0.78 ± 0.28, p = 0.0156). Doppler ultrasound allows the detection of significant changes in pulmonary vasculature in most FGR at baseline, and distinct responses to hyperoxygenation. Future studies are warranted to assess its potential applicability in the clinical management of FGR.

Lung-function trajectories: relevance and implementation in clinical practice.

Lung development starts in utero and continues during childhood through to adolescence, reaching its peak in early adulthood. This growth is followed by gradual decline due to physiological lung ageing. Lung-function development can be altered by several host and environmental factors during the life course. As a result, a range of lung-function trajectories exist in the population. Below average trajectories are associated with respiratory, cardiovascular, metabolic, and mental health comorbidities, as well as with premature death. This Review presents progressive research into lung-function trajectories and assists the implementation of this knowledge in clinical practice as an innovative approach to detect poor lung health early, monitor respiratory disease progression, and promote lung health. Specifically, we propose that, similar to paediatric height and weight charts used globally to monitor children's growth, lung-function charts could be used for both children and adults to monitor lung health status across the life course. To achieve this proposal, we introduce our free online Lung Function Tracker tool. Finally, we discuss challenges and opportunities for effective implementation of the trajectory concept at population level and outline an agenda for crucial research needed to support such implementation.

Epigenome-Wide Association Studies of Chronic Obstructive Pulmonary Disease and Lung Function: A Systematic Review.

Rationale: Chronic obstructive pulmonary disease (COPD) results from gene-environment interactions over the lifetime. These interactions are captured by epigenetic changes, such as DNA methylation. Objectives: To systematically review the evidence form epigenome-wide association studies related to COPD and lung function. Methods: A systematic literature search performed on PubMed, Embase, and Cumulative Index to Nursing and Allied Health Literature (CINAHL) databases identified 1,947 articles that investigated epigenetic changes associated with COPD and/or lung function; 17 of them met our eligibility criteria, from which data were manually extracted. Differentially methylated positions (DMPs) and/or annotated genes were considered replicated if identified by two or more studies with a P < 1 × 10-4. Measurements and Main Results: Ten studies profiled DNA methylation changes in blood and seven in respiratory samples, including surgically resected lung tissue (n = 3), small airway epithelial brushings (n = 2), BAL (n = 1), and sputum (n = 1). Main results showed: 1) high variability in study design, covariates, and effect sizes, which prevented a formal meta-analysis; 2) in blood samples, 51 DMPs were replicated in relation to lung function and 12 related to COPD; 3) in respiratory samples, 42 DMPs were replicated in relation to COPD but none in relation to lung function; and 4) in COPD versus control studies, 123 genes (2.6% of total) were shared between one or more blood and one or more respiratory samples and associated with chronic inflammation, ion transport, and coagulation. Conclusions: There is high heterogeneity across published COPD and/or lung function epigenome-wide association studies. A few genes (n = 123; 2.6%) were replicated in blood and respiratory samples, suggesting that blood can recapitulate some changes in respiratory tissues. These findings have implications for future research. Systematic Review [protocol] registered with Open Science Framework (OSF).

Clinical relevance of lung function trajectory clusters in middle-aged and older adults.

Background: The determinants and health outcomes of lung function trajectories in adults among the general population are poorly understood. We aimed to identify and characterise clusters of lung function trajectories in adults aged ≥45 years. Methods: Gaussian finite-mixture modelling was applied to baseline and annualised change of forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC) and FEV1/FVC ratio z-scores in participants of the Rotterdam Study, a prospective population-based cohort study, with repeated spirometry (n=3884; mean±sd age 64.7±8.9 years). Longitudinal outcomes were all-cause mortality, respiratory outcomes (symptoms, COPD (FEV1/FVC <0.7 in absence of asthma), preserved ratio impaired spirometry (PRISm; FEV1/FVC ≥0.7 and FEV1 or FVC <80%)), smoking cessation and weight changes. Independent risk factors, including genetics, were identified by multiple logistic regression. Results: We identified eight trajectory clusters, with the reference group having persistently normal spirometry (prevalence 42.8%). Three clusters showed higher mortality, adjusted for confounders: 1) the persistently low FEV1 cluster (prevalence 6.8%, hazard ratio (HR) 1.71, 95% CI 1.37-2.13); 2) rapid FEV1 decliners (prevalence 4.6%, HR 1.48, 95% CI 1.10-1.99); and 3) FVC decliners (prevalence 3.7%, HR 1.49, 95% CI 1.09-2.03). In contrast, FVC improvers (prevalence 6.7%, HR 0.61, 95% CI 0.41-0.90) and persistently high FEV1 (prevalence 29.2%, HR 0.82, 95% CI 0.69-0.98) were protective trajectory clusters. Clusters were characterised by differences in genetic predisposition (polygenic scores of FEV1 and FEV1/FVC), demographics, cigarette smoking, respiratory symptoms (chronic cough, wheezing and dyspnoea), cardiovascular factors (body mass index, hypertension and heart failure) and serum C-reactive protein levels. Frailty, weight changes and the development of respiratory symptoms, COPD and PRISm were significantly associated with trajectory clusters. Conclusions: This study reveals clinically relevant lung function trajectory clusters in older adults of the general population.

From treatable traits to GETomics in airway disease: moving towards clinical practice.

The treatable traits approach represents a strategy for patient management. It is based on the identification of characteristics susceptible to treatments or predictive of treatment response in each individual patient. With the objective of accelerating progress in research and clinical practice relating to such a treatable traits approach, the Portraits event was convened in Barcelona, Spain, in November 2022. Here, while reporting the key concepts that emerged from the discussions during the meeting, we review the current state of the art related to treatable traits and chronic respiratory diseases management, and we describe the possible actions that clinicians can take in clinical practice to implement the treatable traits framework. Furthermore, we explore the new concept of GETomics and the new models of research in the field of COPD.

Iron accumulation drives fibrosis, senescence and the senescence-associated secretory phenotype.

Fibrogenesis is part of a normal protective response to tissue injury that can become irreversible and progressive, leading to fatal diseases. Senescent cells are a main driver of fibrotic diseases through their secretome, known as senescence-associated secretory phenotype (SASP). Here, we report that cellular senescence, and multiple types of fibrotic diseases in mice and humans are characterized by the accumulation of iron. We show that vascular and hemolytic injuries are efficient in triggering iron accumulation, which in turn can cause senescence and promote fibrosis. Notably, we find that senescent cells persistently accumulate iron, even when the surge of extracellular iron has subdued. Indeed, under normal conditions of extracellular iron, cells exposed to different types of senescence-inducing insults accumulate abundant ferritin-bound iron, mostly within lysosomes, and present high levels of labile iron, which fuels the generation of reactive oxygen species and the SASP. Finally, we demonstrate that detection of iron by magnetic resonance imaging might allow non-invasive assessment of fibrotic burden in the kidneys of mice and in patients with renal fibrosis. Our findings suggest that iron accumulation plays a central role in senescence and fibrosis, even when the initiating events may be independent of iron, and identify iron metabolism as a potential therapeutic target for senescence-associated diseases.

Similarity network fusion to identify phenotypes of small-for-gestational-age fetuses.

Fetal growth restriction (FGR) affects 5-10% of pregnancies, is the largest contributor to fetal death, and can have long-term consequences for the child. Implementation of a standard clinical classification system is hampered by the multiphenotypic spectrum of small fetuses with substantial differences in perinatal risks. Machine learning and multiomics data can potentially revolutionize clinical decision-making in FGR by identifying new phenotypes. Herein, we describe a cluster analysis of FGR based on an unbiased machine-learning method. Our results confirm the existence of two subtypes of human FGR with distinct molecular and clinical features based on multiomic analysis. In addition, we demonstrated that clusters generated by machine learning significantly outperform single data subtype analysis and biologically support the current clinical classification in predicting adverse maternal and neonatal outcomes. Our approach can aid in the refinement of clinical classification systems for FGR supported by molecular and clinical signatures.

Lung immune signatures define two groups of end-stage IPF patients.

BACKGROUND: The role of the immune system in the pathobiology of Idiopathic Pulmonary Fibrosis (IPF) is controversial. METHODS: To investigate it, we calculated immune signatures with Gene Set Variation Analysis (GSVA) and applied them to the lung transcriptome followed by unbiased cluster analysis of GSVA immune-enrichment scores, in 109 IPF patients from the Lung Tissue Research Consortium (LTRC). Results were validated experimentally using cell-based methods (flow cytometry) in lung tissue of IPF patients from the University of Pittsburgh (n = 26). Finally, differential gene expression and hypergeometric test were used to explore non-immune differences between clusters. RESULTS: We identified two clusters (C#1 and C#2) of IPF patients of similar size in the LTRC dataset. C#1 included 58 patients (53%) with enrichment in GSVA immune signatures, particularly cytotoxic and memory T cells signatures, whereas C#2 included 51 patients (47%) with an overall lower expression of GSVA immune signatures (results were validated by flow cytometry with similar unbiased clustering generation). Differential gene expression between clusters identified differences in cilium, epithelial and secretory cell genes, all of them showing an inverse correlation with the immune response signatures. Notably, both clusters showed distinct features despite clinical similarities. CONCLUSIONS: In end-stage IPF lung tissue, we identified two clusters of patients with very different levels of immune signatures and gene expression but with similar clinical characteristics. Weather these immune clusters differentiate diverse disease trajectories remains unexplored.