Background Airway and gut dysbiosis have been reported in Chronic Obstructive Pulmonary Disease (COPD); however, their relationship and association with clinical features remain poorly understood. We aimed to characterise the lung and gut microbiome in patients with stable COPD and controls. Methods Prospective, multicentre, longitudinal and controlled study of n = 60 stable patients with COPD and n = 30 controls. In them, we analysed 16S rRNA-seq in oropharyngeal (OP) swabs, sputum, bronchoalveolar lavage fluid (BALF) and stool. Weighted gene co-expression network analysis (WGCNA) was employed in each sample type to identify modules of co-abundant bacteria associated with clinical traits. Findings We found that the microbiome in airway and stool samples was highly dissimilar both in patients and controls, with 0.37% of this diversity associated to COPD. The microbiome taxa associated with COPD in OP swabs and sputum were highly similar, but different from BALF, suggesting that OP swabs can be a surrogate sample of sputum. Finally, using WGCNA, we identified: (a) 5 modules in OP swabs and 3 in sputum associated with FEV1, but some of them were also associated with exacerbations, dyspnoea and inhaled steroid (ICS) use; (b) In BALF 4 modules associated with FEV1 and dyspnoea, and 2 modules with ICS; and, finally, (c) in stool, 1 module related to FEV1, 1 to exacerbations and 3 with ICS. Interpretation The gut and lung microbiomes in patients with COPD are distinct, but both clinically relevant as both present bacterial associations with airflow limitation, exacerbation history, and ICS use. Funding ISC-III PI24/00476. FRPA.2014, ICREA-2024.

Metabolomic studies in COPD reveal systemic metabolic perturbations, yet sex is often treated as a covariate rather than a biological driver. We aimed to identify plasma metabolites differentiating COPD from controls and to define sex-specific metabolic signatures in both groups. Methods: In this controlled observational study (BIOMEPOC cohort), untargeted plasma metabolomics was performed by LC-MS/MS. Differential abundance was tested across four contrasts (COPD vs. controls; men vs. women within controls; men vs. women within COPD; sex-by-disease interaction) with a false discovery rate (FDR) correction. Because smoking history differed between COPD and controls, a post hoc ever-smokers analysis was conducted. Results: COPD differed from controls in nine metabolites (all decreased): DL-stachydrine, 3-methyl-L-histidine, fructose, pipecolinic and nipecotic acids, 5-nitro-o-toluidine, conjugated linoleic acid, aminoadipate, and creatinine. This pattern is compatible with metabolic depletion, remodeling, and/or altered flux across multiple compartments rather than simple substrate deficiency, spanning muscle-related pools, amino acid handling, carbohydrate-associated metabolism, and exposome-linked inputs. In ever-smokers, results were directionally consistent, with five metabolites remaining nominally significant. Among controls, five metabolites were higher in men after FDR correction (PABA, cis-4-hydroxy-D-proline, N-acetylasparagine, deoxycarnitine, and creatinine), consistent with physiological sex dimorphism in energy pathways, connective-tissue remodeling, and diet/microbiome-related metabolism. Within COPD, six metabolites differed by sex after FDR correction, defining three axes: creatine energy buffering (men: higher GAA/creatinine, lower creatine), purine/urate handling (men: higher urate), and conjugated bile acids (men: higher GCDCA), implicating muscle bioenergetics, redox/inflammatory tone, and gut-liver crosstalk. Conclusions: Plasma metabolomics identifies a pattern compatible with systemic remodeling in COPD and sex-associated divergences in creatine, purine/urate, and bile-acid pathways, supporting a sex-influenced view of systemic COPD heterogeneity and highlighting targets for mechanistic validation.

INTRODUCTION:Exacerbations, impaired health-related quality of life (HRQoL) and reduced exercise capacity increase the risk of hospitalisations and death in chronic obstructive pulmonary disease (COPD). However, their monitoring relies on in-person assessments, potentially delaying early care. While smart sensing technologies can enable remote monitoring, their use in predicting disease worsening remains limited. The TOLIFE Clinical Study A (CSA) aims to develop an artificial intelligence (AI) model integrating clinical data with smart sensing devices data to predict exacerbation onset and changes in HRQoL, dyspnoea and exercise capacity in people with COPD. METHODS AND ANALYSES:TOLIFE CSA is a longitudinal observational study that will recruit 150 clinically stable people with COPD from three clinical sites in Spain, Italy and Germany. Over 1 year, participants will attend quarterly in-person visits to collect clinical data, while being continuously monitored using six unobtrusive smart sensing devices collecting daily metrics calculated from triaxial acceleration, angular velocity, photoplethysmogram, sound intensity, changes in latitude and longitude, ambient light intensity, biomechanical pressure and respiratory airflow parameters. Clinical outcomes are exacerbation onset through medical records; 3-month changes in HRQoL through the COPD Assessment Test and the Clinical COPD Questionnaire; 3-month changes in dyspnoea severity through the modified Medical Research Council Dyspnoea Scale; and 6-month changes in functional exercise capacity through the 6-minute walk test. We will train, internally validate and test AI-based models (Random Forests, XGBoost, multilayer perceptrons, cumulative link model and standard classification model) to predict clinical outcomes. ETHICS AND DISSEMINATION:Ethical approval was issued for all sites by the Ethical Commission (EC) of the Medical Association of Schleswig-Holstein (Bad Segeberg; vote 074/23 ff), EC of the Tuscany Region-North West Area (Pisa; vote CET10/2023) and EC of Parc de Salut Mar (Barcelona; vote 2023/11230). All participants will sign a written informed consent. TRIAL REGISTRATION NUMBER:NCT06172712.

Background: Lung cancer and chronic obstructive pulmonary disease (COPD) are morbid and mortal conditions arising from noxious endothelial stress. Soluble Major Histocompatibility Complex I Chain Related A (sMICA) is an activating ligand for the NKG2C receptor, and the soluble form indicates endothelial stress and is a mechanism for evading immune surveillance in lung cancer. We provide independent associations between sMICA*008 levels and the prevalence of lung cancer, lung cancer histologies, COPD, and risk factors for both diseases. Methods: We describe statistical associations between sMICA and demographic and clinical variables. Multivariate linear regression determined the independent associations between sMICA levels and lung cancer histology, between those with and without primary lung cancer, and prevalent COPD in participants without lung cancer. Point estimates and 95% confidence intervals are reported; p < 0.05 is considered statistically significant. Results: The cohort (n = 586 patients) included 24% female and 48% current or former smokers. Mean sMICA were 5.20 pg/mL ×102, and FEV1%-predicted of 62. sMICA levels were higher in those who smoked vs. those who did not. In Multivariate regression, non-small cell lung cancer (NSCLC) was associated with 14.2 pg/mL ×102 (95% CI 3.57 to 24.9 pg/mL ×102) higher sMICA levels compared to those without cancer. No other histology was independently associated with higher sMICA. Primary lung cancer [12.5 pg/mL ×102 (2.85 to 22.2 pg/mL ×102)] and COPD in those without cancer [4.38 pg/mL ×102 (0.38 to 8.39 pg/mL ×102)] were associated with higher sMICA. Conclusions: sMICA*008 is independently associated with NSCLC, primary lung cancer, and COPD, respectively, in a cohort of current, former, and never smokers with and without lung cancer. sMICA levels were also higher in smokers. This study provides a foundation for future studies on sMICA activity in lung cancer and COPD, and assessment of sMICA as a biomarker for lung cancer cell type and risk of lung function loss in COPD.

Introduction: COPD treatment is often guided by the GOLD E phenotype (≥1 hospitalization or >1 exacerbation/prior year) versus GOLD AB. Alternatively, frequent exacerbators (FE) are defined as ≥3 exacerbations/year, regardless of hospitalization. Their biological basis requires exploration by omics approaches. Methods: Plasma from 136 COPD patients (103 FE, 33 NFE; 58 GOLD E, 78 GOLD AB) and 34 controls were analyzed using LC-MS/MS proteomics (with partial depletion of high-abundance proteins) and untargeted metabolomics. Differentially abundant proteins (DAPs) and metabolites (DAMs) were identified by comparisons with controls and between phenotypes. Functional annotation was performed. Predictive models were trained on a single-center cohort (n = 58) and then externally validated in a multicenter Spanish cohort (n = 80). Results: Compared with controls, GOLD E showed 47 DAMs (14 xenobiotics) and 92 DAPs, while FE showed 54 DAMs (18 xenobiotics) and 69 DAPs. GOLD AB and NFE revealed 44 and 60 DAPs, respectively. Direct COPD comparisons detected no differences between GOLD E and AB, but 45 DAMs (17 xenobiotics) between FE and NFE. When control-based differences were incorporated, there were 16 DAMs and 2 DAPs (HRG, FCR3B), and 60 DAMs, respectively. Their best models were biomics without xenobiotics (83% accuracy; 30% coverage) and metabolomics with xenobiotics (75% accuracy; 87.5% coverage). Proteomics highlighted COPD hemostasis and immunity, while metabolomics suggested alterations in catabolism and nitric oxide metabolism, among others. Conclusions: Omics-derived biomarkers may aid in the profiling of exacerbators. GOLD E and FE share molecular features, but FE exhibits greater biological homogeneity. Metabolomics outperformed proteomics in classification.

The aim of the study was to assess the microbial composition of bronchial secretions in chronic obstructive pulmonary disease (COPD), focusing on the impact of the exacerbation patterns on the common components of the respiratory flora and their relationship with inflammatory proteins. A total of 72 clinically stable COPD patients provided sputum and blood samples for 16S rRNA gene amplification and peripheral biomarkers. Beta-diversity analyses of the bronchial microbiome showed significant differences between infrequent and frequent (≥2) exacerbators (p = 0.001). Haemophilus was underrepresented in frequent exacerbators (relative abundance [RA] 0.07 [0.003-0.31] vs. 0.24 [0.06-2.36], p = 0.02) while the presence of Pseudomonas was increased (7.70 [0.66-11.68] vs. 1.11 [0.37-2.88], p = 0.01). Eight common taxa, Prevotella, Moryella, Atopobium, Megasphaera, Parvimonas, Veillonella, Bulleidia and Selenomonas, showed significant decreases in their RAs when exacerbations required hospitalization. RAs of Haemophilus and eight common taxa were positively correlated (p < 0.01). Among them, Porphyromonas, Leptotrichia and Selenomonas showed a negative correlation with blood interleukin-8 (IL-8) (p < 0.01) and an equivalent correlation was found for Haemophilus parainfluenzae. Frequent exacerbations cause a decrease in the RA of Haemophilus and have a more extensive impact when hospitalization is required. The RAs of common bronchial bacteria were closely related and some of them were inversely associated with blood IL-8 levels.

Patients surviving coronavirus disease of 2019 (COVID-19) often complain of skeletal muscle weakness that may be very limiting and long-lasting. There are almost no studies on the skeletal muscle of these patients, and electron microscopic data are scarce. We assessed the ultrastructural changes in the quadriceps of eight patients with COVID-19 and found a combination of features different from those reported in corticosteroid myopathy and acute relaxant-steroid myopathy. The most remarkable and constant changes involved the endothelial cells and consisted of massive amounts of pinocytotic vesicles, degenerative changes, platelet aggregates and, most characteristic of all, an increase in the external lamina thickness that seems to stem from reduplication due to successive bouts of endothelial cell damage and subsequent regeneration. Viral particles were not found in any of the cases. This distinct and quite common set of alterations defines the myopathy associated with infection by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This association seems to be the result of an inflammatory process that would arise in infected cells but could damage non-infected endomysial blood vessels, thus resulting in persistent changes of the microvasculature that would be related to long-standing myopathic clinical features.

The analysis of blood metabolites may help identify individuals at risk of having COPD and offer insights into its underlying pathophysiology. This study aimed to identify COPD-related metabolic alterations and generate a biological signature potentially useful for screening purposes. Plasma metabolomic profiles from 91 COPD patients and 91 controls were obtained using complementary semi-targeted and untargeted LC-MS approaches. Univariate analysis identified metabolites with significant differences between groups, and enrichment analysis highlighted the most affected metabolic pathways. Multivariate analysis, including ROC curve assessment and machine learning algorithms, was applied to assess the discriminatory capacity of selected metabolites. After adjustment for major potential confounders, 56 metabolites showed significant differences between COPD patients and controls. The enrichment analysis revealed that COPD-associated metabolic alterations primarily involved lipid metabolism (especially fatty acids and acylcarnitines), followed by amino acid pathways and xenobiotics. A panel of 10 metabolites, mostly related to lipid metabolism, demonstrated high discriminatory performance for COPD (ROC-AUC: 0.916; 90.1% sensitivity and 89% specificity). These findings may contribute to improving screening strategies and a better understanding of COPD-related metabolic changes. However, our findings remain exploratory and should be interpreted with caution, needing further validation and mechanistic studies.

Chronic Obstructive Pulmonary Disease (COPD) is a complex condition with high mortality. Early identification of patients at increased risk of death remains a major clinical challenge. This pilot study aimed to explore whether plasma metabolomic profiling could aid in the prediction of long-term (7-year) mortality and provide insight into potential underlying mechanisms. Plasma samples from 54 randomly selected stable COPD patients were analyzed using both untargeted and semi-targeted LC-MS approaches. After excluding patients with unclear death data, non-COPD-related deaths and metabolomic outliers, 41 individuals were included in the final analysis. During follow-up, 13 patients (32%) died, and 28 survived. Univariate analysis identified 12 metabolites—mainly amino acids—that differed significantly between the two groups. Functional analysis suggested a significant disruption in energy production pathways. Predictive models developed using machine learning algorithms, consisting of either ten metabolites alone or nine metabolites plus FEV1, achieved high accuracy for 7-year mortality prediction, with the latter model performing slightly better. Internal validation was conducted using five-fold cross-validation. While exploratory, these findings support the hypothesis that early metabolic alterations, particularly in energy pathways, may contribute to long-term mortality risk in stable COPD patients, and could complement traditional prognostic markers such as FEV1.

In the original publication, the authors identified a mistake in Table 4 as published [...]

Introduction: Currently, Chronic Obstructive Pulmonary Disease (COPD) has high levels of underdiagnosis (>60% in the United States and Europe) despite being the third leading cause of death worldwide. The analysis of blood metabolite composition can improve diagnostic accuracy and offer deeper insights into the molecular pathophysiology of COPD. Sadly, most of the studies lack including important emerging groups of patients (young COPD and women). In this study, plasma metabolomic profiling was conducted to identify disease-related metabolic biomarkers associated with COPD. Methods: 182 subjects from BIOMEPOC and BIOEARLY multicenter studies were included, including 91 COPD patients and 91 healthy controls (∼40-70yo, ∼50% [female]). Their plasma samples were metabolomic profiling using full untargeted and semi-targeted liquid chromatography-mass spectrometry. MetaboAnalyst was used to identify metabolites with significant differences between disease and control groups, and pathway enrichment analysis was performed on these metabolites to determine the most affected pathways. Random Forest was used to develop a diagnostic 10-metabolite panel model. The model was internally cross-validated. Results: Compared to controls, levels for 56 metabolite concentrations differed in COPD patients when adjusted for multiple comparisons (FDR<0.1) and for the confounding variables age, sex, BMI, and smoking status (multiple linear regression). Enrichment and pathway analysis revealed that metabolic dysregulations primarily affected lipid homeostasis (fatty acids and acylcarnitines), amino acid metabolism, and xenobiotics. Multivariate analysis detected a panel of 10 metabolites (mainly involved in lipid metabolism) that could be biomarkers for COPD discrimination. The model performance was excellent, with a sensitivity, sensibility, and accuracy of ∼90% in the internal cross-validation. Conclusion: We identified significantly altered metabolite profiles in COPD patients compared to healthy controls, highlighting specific metabolites that exhibited potential as discriminative biomarkers and providing novel insights into COPD pathogenesis. Future validation and mechanistic studies are still needed.

Skeletal muscle dysfunction is a major systemic manifestation of COPD that shapes symptoms, exercise tolerance and mortality. Current evidence can be integrated within a Damage–Regeneration–Remodeling framework linking mechanics and biology to clinical phenotypes. Pulmonary hyperinflation and chest wall geometry chronically load the diaphragm and other respiratory muscles in COPD, whereas inactivity and exacerbation-related disuse underload locomotor muscles. Across muscle compartments, oxidative/nitrosative stress, activation of proteolytic pathways, mitochondrial and endoplasmic reticulum stress, microvascular limitations, neuromuscular junction instability, and myosteatosis degrade muscle quality. The diaphragm adapts with a fast-to-slow fiber shift, greater oxidative capacity, and sarcomere foreshortening, improving endurance, whereas limb muscles show atrophy, a glycolytic shift, reduced oxidative enzymes, extracellular matrix accrual, and fat infiltration. Translational levers that address these mechanisms include: (I) Reduce damage: bronchodilation, lung-volume reduction, oxygen, non-invasive ventilation, early mobilization, pulmonary rehabilitation, neuromuscular stimulation, and corticosteroid stewardship; (II) Enable regeneration: progressive resistance plus high-intensity/heavy-load endurance training; adequate protein and vitamin-D intake, and endocrine correction; and (III) Steer remodeling: increase physical activity (with/without coaching/telecoaching), functional assessment and CT or MRI monitoring, inspiratory-muscle training, and phenotype-guided adjuncts in selected cases. This framework clarifies why lung deflation strategies benefit inspiratory mechanics, whereas limb recovery requires behavioral and metabolic interventions layered onto systemic optimization.

Although Chronic Obstructive Pulmonary Disease (COPD) is highly prevalent, it is often underdiagnosed. One of the main characteristics of this heterogeneous disease is the presence of periods of acute clinical impairment (exacerbations). Obtaining blood biomarkers for either COPD as a chronic entity or its exacerbations (AECOPD) will be particularly useful for the clinical management of patients. However, most of the earlier studies have been characterized by potential biases derived from pre-existing hypotheses in one or more of their analysis steps: some studies have only targeted molecules already suggested by pre-existing knowledge, and others had initially carried out a blind search but later compared the detected biomarkers among well-predefined clinical groups. We hypothesized that a clinically blind cluster analysis on the results of a non-hypothesis-driven wide proteomic search would determine an unbiased grouping of patients, potentially reflecting their endotypes and/or clinical characteristics. To check this hypothesis, we included the plasma samples from 24 clinically stable COPD patients, 10 additional patients with AECOPD, and 10 healthy controls. The samples were analyzed through label-free liquid chromatography/tandem mass spectrometry. Subsequently, the Scikit-learn machine learning module and K-means were used for clustering the individuals based solely on their proteomic profiles. The obtained clusters were confronted with clinical groups only at the end of the entire procedure. Although our clusters were unable to differentiate stable COPD patients from healthy individuals, they segregated those patients with AECOPD from the patients in stable conditions (sensitivity 80%, specificity 79%, and global accuracy, 79.4%). Moreover, the proteins involved in the blind grouping process to identify AECOPD were associated with five biological processes: inflammation, humoral immune response, blood coagulation, modulation of lipid metabolism, and complement system pathways. Even though the present results merit an external validation, our results suggest that the present blinded approach may be useful to segregate AECOPD from stability in both the clinical setting and trials, favoring more personalized medicine and clinical research.

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.

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.

La medicina familiar y comunitaria reúne buena parte de los elementos y valores que caracterizan la profesión de médico. Esto hace que pueda aportar unos fundamentos sólidos, útiles y duraderos al estudiante de Medicina sea cual sea la especialidad que elija en un futuro. Es necesario formar a profesionales capaces de ver al paciente desde un punto de vista holístico en su contexto personal y social, así como comprender los factores determinantes de la salud individual y comunitaria. Que comprendan al paciente y su sufrimiento, y que empaticen, para acompañarle durante su proceso de enfermedad. También se nos plantea el reto de situar a la atención primaria en el centro del sistema sanitario y hacer frente a una demanda creciente de médicos especialistas en Medicina Familiar y Comunitaria.Para la consecución de estos objetivos se hace estrictamente necesaria la integración de la Atención Primaria en los planes de estudios de las universidades, con intervención de médicos de familia en diferentes asignaturas, aportando una visión más transversal y holística en el abordaje de las distintas enfermedades y con más presencia en las actividades docentes y en las prácticas.El modelo propuesto por la Universidad Pompeu Fabra se inspira en la metodología de la mentoría en la que al estudiante se le asignará un médico de familia como mentor durante toda su formación de grado, y este le acompañará en sus estancias en el centro de atención primaria y en actividades relacionadas con diferentes asignaturas y velará por el cumplimiento de los objetivos de formación. También participará en su evaluación al finalizar cada curso. Este modelo pretende mejorar la calidad de las actividades vinculadas a la asistencia clínica y de las prácticas, y facilitará el conocimiento de la atención primaria y de la profesión de médico a través del contacto precoz y mantenido con la atención primaria.

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.