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Eosinophil-related diseases represent a group of pathologic conditions with highly heterogeneous clinical presentation and symptoms ranging from mild to critical. Both systemic and localized forms of disease are typically treated with glucocorticoids. The approval of novel biologic therapies targeting the interleukin-5 pathway can help reduce the use of systemic glucocorticoids (SGC) in eosinophilic diseases and reduce the risk of SGC-related adverse effects (AEs). In this article, a panel of experts from different medical specialties reviewed current evidence on the use of SGC in two systemic eosinophilic diseases: Eosinophilic Granulomatosis with PolyAngiitis (EGPA) and HyperEosinophilic Syndrome (HES); and in two single-organ (respiratory) eosinophilic diseases: Chronic RhinoSinusitis with Nasal Polyps (CRSwNP) and Severe Asthma with Eosinophil Phenotype (SA-EP), and contrasted it with their experience in clinical practice. Using nominal group technique, they reached consensus on key aspects related to the dose and tapering of SGC as well as on the initiation of biologics as SGC-sparing agents. Early treatment with biologics could help prevent AEs associated with medium and long-term use of SGC.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection causes coronavirus disease 2019 (COVID-19) and can be associated with serious complications, including acute respiratory distress syndrome. This condition is accompanied by a massive release of cytokines, also denominated cytokine storm, development of systemic oxidative stress and a prothrombotic state. In this context, it has been proposed a role for acetylcysteine (NAC) in the management of patients with COVID-19. NAC is a molecule classically known for its mucolytic effect, but it also has direct and indirect antioxidant activity as a precursor of reduced glutathione. Other effects of NAC have also been described, such as modulating the immune and inflammatory response, counteracting the thrombotic state, and having an antiviral effect. The pharmacological activities of NAC and its effects on the mechanisms of disease progression make it a potential therapeutic agent for COVID-19. NAC is safe, tolerable, affordable, and easily available. Moreover, the antioxidant effects of the molecule may even prevent infection and play an important role as a complement to vaccination. Although the clinical efficacy and dosing regimens of NAC have been evaluated in the clinical setting with small series of patients, the results are promising. In this article, we review the pathogenesis of SARS-CoV-2 infection and the current knowledge of the mechanisms of action of NAC across disease stages. We also propose NAC posology strategies to manage COVID-19 patients in different clinical scenarios.

COVID-19 was the most significant infectious-agent-related cause of death in the 2020-2021 period. On average, over 60% of those admitted to ICU facilities with this disease died across the globe. In severe cases, COVID-19 leads to respiratory and systemic compromise, including pneumonia-like symptoms, acute respiratory distress syndrome, and multiorgan failure. While the upper respiratory tract and lungs are the principal sites of infection and injury, most studies on the metabolic signatures in COVID-19 patients have been carried out on serum and plasma samples. In this report we attempt to characterize the metabolome of lung parenchyma extracts from fatal COVID-19 cases and compare them with that from other respiratory diseases. Our findings indicate that the metabolomic profiles from fatal COVID-19 and non-COVID-19 cases are markedly different, with the former being the result of increased lactate and amino acid metabolism, altered energy pathways, oxidative stress, and inflammatory response. Overall, these findings provide additional insights into the pathophysiology of COVID-19 that could lead to the development of targeted therapies for the treatment of severe cases of the disease, and further highlight the potential of metabolomic approaches in COVID-19 research.

Abstract Tuberculosis (TB) is a major cause of morbidity and mortality in children, and early diagnosis and treatment are crucial to reduce long-term morbidity and mortality. In this study, we explore whether urine nuclear magnetic resonance (NMR)-based metabolomics could be used to identify differences in the metabolic response of children with different diagnostic certainty of TB. We included 62 children with signs and symptoms of TB and 55 apparently healthy children. Six of the children with presumptive TB had bacteriologically confirmed TB, 52 children with unconfirmed TB, and 4 children with unlikely TB. Urine metabolic fingerprints were identified using high- and low-field proton NMR platforms and assessed with pattern recognition techniques such as principal components analysis and partial least squares discriminant analysis. We observed differences in the metabolic fingerprint of children with bacteriologically confirmed and unconfirmed TB compared to children with unlikely TB (p = 0.041 and p = 0.013, respectively). Moreover, children with unconfirmed TB with X-rays compatible with TB showed differences in the metabolic fingerprint compared to children with non-pathological X-rays (p = 0.009). Differences in the metabolic fingerprint in children with different diagnostic certainty of TB could contribute to a more accurate characterisation of TB in the paediatric population. The use of metabolomics could be useful to improve the prediction of TB progression and diagnosis in children.

Background Patients with chronic obstructive pulmonary disease (COPD) often suffer episodes of exacerbation of symptoms (ECOPD) that may eventually require hospitalisation due to several, often overlapping, causes. We aimed to analyse the characteristics of patients hospitalised because of ECOPD in a real-life setting using a “big data” approach. Methods The study population included all patients over 40 years old with a diagnosis of COPD (n=69 359; prevalence 3.72%) registered from 1 January 2011 to 1 March 2020 in the database of the public healthcare service (SESCAM) of Castilla-La Mancha (Spain) (n=1 863 759 subjects). We used natural language processing (Savana Manager version 3.0) to identify those who were hospitalised during this period for any cause, including ECOPD. Results During the study 26 453 COPD patients (38.1%) were hospitalised (at least once). Main diagnoses at discharge were respiratory infection (51%), heart failure (38%) or pneumonia (19%). ECOPD was the main diagnosis at discharge (or hospital death) in 8331 patients (12.0% of the entire COPD population and 31.5% of those hospitalised). In-hospital ECOPD-related mortality rate was 3.11%. These patients were hospitalised 2.36 times per patient, with a mean hospital stay of 6.1 days. Heart failure was the most frequent comorbidity in patients hospitalised because of ECOPD (52.6%). Conclusions This analysis shows that, in a real-life setting, ECOPD hospitalisations are prevalent, complex (particularly in relation to heart failure), repetitive and associated with significant in-hospital mortality.

Abstract Despite efforts to improve tuberculosis (TB) detection, limitations in access, quality and timeliness of diagnostic services in low- and middle-income countries are challenging for current TB diagnostics. This study aimed to identify and characterise a metabolic profile of TB in urine by high-field nuclear magnetic resonance (NMR) spectrometry and assess whether the TB metabolic profile is also detected by a low-field benchtop NMR spectrometer. We included 189 patients with tuberculosis, 42 patients with pneumococcal pneumonia, 61 individuals infected with latent tuberculosis and 40 uninfected individuals. We acquired the urine spectra from high and low-field NMR. We characterised a TB metabolic fingerprint from the Principal Component Analysis. We developed a classification model from the Partial Least Squares-Discriminant Analysis and evaluated its performance. We identified a metabolic fingerprint of 31 chemical shift regions assigned to eight metabolites (aminoadipic acid, citrate, creatine, creatinine, glucose, mannitol, phenylalanine, and hippurate). The model developed using low-field NMR urine spectra correctly classified 87.32%, 85.21% and 100% of the TB patients compared to pneumococcal pneumonia patients, LTBI and uninfected individuals, respectively. The model validation correctly classified 84.10% of the TB patients. We have identified and characterised a metabolic profile of TB in urine from a high-field NMR spectrometer and have also detected it using a low-field benchtop NMR spectrometer. The models developed from the metabolic profile of TB identified by both NMR technologies were able to discriminate TB patients from the rest of the study groups and the results were not influenced by anti-TB treatment or TB location. This provides a new approach in the search for possible biomarkers for the diagnosis of TB.

Resumen: Uno de los elementos claves en la medicina de la segunda década del siglo XXI es el crecimiento exponencial de la información producida por los pacientes que se debe no solo a la transición hacia la digitalización de las historias clínicas, sino también a la aparición de nuevas fuentes de información y a la capacidad de análisis e interpretación de las ya existentes. Se estima que la cantidad de información médica se duplica cada 2 años, lo que supone que en 2020 existirá 50 veces más información que en 2011. En este contexto, el manejo adecuado de grandes cantidades de datos o big data debe traducirse en nuevas iniciativas que mejoren el diagnóstico, tratamiento y pronóstico de los pacientes en el camino hacia la medicina personalizada.El concepto de personalización o de medicina de precisión cobra especial interés en la patología respiratoria crónica. En los últimos años la investigación en entidades como el asma, la EPOC, el cáncer o el SAHS se ha centrado en la identificación de alteraciones genómicas, moleculares, metabólicas y proteicas (biomarcadores). Su análisis mediante herramientas de análisis de big data permite dejar atrás los modelos centrados en la respuesta media al tratamiento, que a su vez resultan subóptimos para la mayoría, para dar paso a la respuesta individualizada. En este camino se incorpora la medicina de sistemas, que, integrando también datos clínicos y poblacionales, aporta una visión multidimensional de la enfermedad y facilita el establecimiento de asociaciones causales, la mayoría de las cuales solo son apreciables mediante análisis de big data. Abstract: One of the key elements of medicine in the second decade of the 21st century is the exponential growth of patient-produced information, due not only to the transition to the digitization of medical records, but also to the emergence of new sources of information and the capacity for analysis and interpretation of existing ones. The amount of medical information is expected to double every 2 years, which means that there will be 50 times more information available in 2020 than in 2011. In this setting, these large amounts of data or «big data» must be properly managed to implement new initiatives that improve the diagnosis, treatment, and prognosis of patients on the path to personalized medicine.The concept of personalization or precision medicine is of special interest in chronic respiratory disease. In recent years, research in entities such as asthma, COPD, cancer, or SAHS has focused on the identification of genomic, molecular, metabolic, and protein changes (biomarkers). Big data analysis tools can be used to move on from models based on the mean response to treatment, which are suboptimal for most patients, to focus on the individualized response. Part of this journey involves systems medicine, which also integrates clinical and population data to provide a multidimensional view of the disease and help identify causal associations that are usually only evident on big data analysis.

La persistencia de errores graves en el proceso diagnóstico, una escasa variación en los tratamientos farmacológicos en una década marcada por cambios en las guías de práctica clínica (GPC) y una baja mortalidad intrahospitalaria, a pesar del elevado número de comorbilidades que presentan los pacientes, mortalidad que presenta importantes diferencias por especialidades y que es especialmente baja en Neumología. Este trabajo evidencia el escaso impacto asistencial de las GPC, tanto de GESEPOC como de las recomendaciones GOLD en la mejora de los problemas asistenciales de la EPOC. Esta información no puede ignorarse a la hora de elaborar GPC o proyectos asistenciales. Con la popularización de esta tecnología es factible identificar los problemas reales en una población, implementar proyectos que ayuden a mejorar la práctica clínica y, simultáneamente, una monitorización continua de los resultados.

José Luis Izquierdo, Arturo Martínez, Elizabet Guzmán, Pilar de Lucas, José Miguel RodríguezPulmonology Department, Hospital Universitario, Guadalajara, Spain; Pulmonology Department, Hospital Gregorio Marañón, Madrid, SpainAbstract: The aim of our study is to determine whether chronic obstructive pulmonary disease (COPD) is an independent risk factor for ischemic heart disease and whether this association is related with a greater prevalence of classical cardiovascular risk factors. Ours is a case-control cross-sectional study design. Cases were hospital patients with ischemic heart disease in stable phase, compared with control hospital patients. All patients underwent post-bronchodilator (PBD) spirometry, a standardized questionnaire, and blood analysis. COPD was defined as per GOLD PBD forced expiratory volume in the first second (FEV1)/forced vital capacity (FVC) < 0.70. In our series of patient cases (n = 204) and controls (n = 100), there were 169 men in the case group (83%) and 84 in the control group (84%). Ages were 67 and 64 years, respectively (P < 0.05). There were no significant differences by weight, body mass index (BMI), pack-years, leukocytes, or homocysteine. The abdominal perimeter was significantly greater in cases (mean 101 cm ± standard deviation [SD] 10 versus 96 cm ± 11; P < 0.000). Both groups also had significant differences by C-reactive protein (CRP), fibrinogen, and hemoglobin values. In univariate analysis, increased risks for cases to show with individual classical cardiovascular risk factors were seen, with odds ratio (OR) 1.86 and 95% confidence interval (CI) (1.04–3.33) for diabetes mellitus, dyslipidemia (OR 2.10, 95% CI: 1.29–3.42), arterial hypertension (OR 2.47, 95% CI: 1.51–4.05), and increased abdominal perimeter (OR 1.71, 95% CI: 1.06–2.78). Percent predicted PBD FEV1 was 97.6% ± 23% in the patient group and 104% ± 19% in the control group (P = 0.01), but the prevalence of COPD was 24.1% in cases and 21% in controls. Therefore, COPD was not associated with ischemic heart disease: at the crude level (OR 1.19, 95% CI: 0.67–2.13) or after adjustment (OR 1.14, 95% CI:0.57–2.29). In conclusion, COPD was not associated with ischemic heart disease. The greater prevalence of classical cardiovascular risk factors in COPD patients could explain the higher occurrence of ischemic heart disease in these patients.Keywords: chronic obstructive pulmonary disease, cardiovascular disease, systemic inflammation, comorbidity

José Luis Izquierdo1, Antonio Martín2, Pilar de Lucas3, José Miguel Rodríguez-González-Moro3, Carlos Almonacid1, Alexandra Paravisini21Pneumology Department, University Hospital of Guadalajara, 2Pfizer Medical Department, 3Pneumology Department, University Hospital Gregorio Marañón, Madrid, SpainAim: To analyze the accuracy of diagnosis in a population receiving inhaled therapies due to respiratory diseases in a primary care setting.Method: Noninterventional, multicenter, cross-sectional, observational epidemiologic study methodology.Results: A total of 9752 subjects were evaluated. Of these, 4188 (42.9%) patients were ¬diagnosed with asthma, 4175 (42.8%) with chronic obstructive pulmonary disease (COPD), and 1389 had a diagnosis of disease of unknown origin. Of those over the age of 40 years, 4079 (50.9%) had COPD and 2877 (35.9%) had asthma. Sixty percent of the subjects were men, and the proportion of men was higher in patients with COPD (83.2%) than in the group with asthma (39.8%, P < 0.0001). Of subjects with COPD, 17.3% had mild, 55.3% had moderate, 24.1% had severe, and 3.2% had very severe disease. With regard to the level of severity of asthma, 34.9% of subjects had intermittent, 34.6% had mild persistent, 27.1% had moderate persistent, and 3.5% had severe persistent disease. Only 13.9% of patients in the COPD group had all the characteristics of COPD based on the Global Initiative for Chronic Obstructive Lung Disease criteria and an absence of the characteristics of asthma.Conclusions: The majority of patients receiving inhaled therapy in primary care did not have an accurate diagnosis according to current international guidelines for COPD and asthma. More initiatives for improving diagnostic accuracy in respiratory diseases must be implemented in primary care.Keywords: respiratory disease, COPD, primary care, asthma, diagnosis

Diagnosis of pulmonary arterial hypertension (PAH) is difficult due to the lack of specific clinical symptoms and biomarkers, especially at early stages. We compared plasma metabolic fingerprints of PAH patients (n = 20) with matched healthy volunteers (n = 20) using, for the first time, untargeted multiplatform metabolomics approach consisting of high-performance liquid and gas chromatography coupled with mass spectrometry. Multivariate statistical analyses were performed to select metabolites that contribute most to groups' classification (21 from liquid in both ionization modes and 9 from gas chromatography-mass spectrometry). We found metabolites related to energy imbalance, such as glycolysis-derived metabolites, as well as metabolites involved in fatty acid, lipid and amino acid metabolism. We observed statistically significant changes in threitol and aminomalonic acid in PAH patients, which could provide new biochemical insights into the pathogenesis of the disease. The results were externally validated on independent case and control cohorts, confirming up to 16 metabolites as statistically significant in the validation study. Multiplatform metabolomics, followed by multivariate chemometric data analysis has a huge potential for explaining pathogenesis of PAH and for searching potential and new more specific and less invasive markers of the disease.

A new species of Sideritis (Sideritis carpetana) is described from the calcareous, high-mountain Spanish flora in the central part of the Iberian Peninsula. It is found in a Mediterranean climate at high-elevation, perennial, calcareous grasslands, as well as in marble screes of anthropogenic origin in the Sierra de Guadarrama, Central System (Spain), in a reserve area within the Sierra de Guadarrama National Park, at 1996 m asl. Taxonomic morphological measurements were performed on collected specimens from Sierra de Guadarrama as well as on geographically-adjacent Sideritis (i.e., S. glacialis, S. pungens, S. hyssopifolia). The relationships among them were then explored with multivariate analysis. Sideritis carpetana is a dwarf shrub with an erect or decumbent habit, growing up to 15 cm; non-woody twigs with long hairs of 3–4 cells, leaves are entire, linear-oblanceolate, sparsely covered with trichomes; inflorescence is spiciform or slightly verticillated, flowers are yellow and nutlets ovoid. A key is supplied to help distinguish it from other high-mountain Iberian species included in section Sideritis. The species is unique in its combination of morphological and autoecological characters. S. carpetana shares similarities with S. glacialis, a species from Sierra Nevada, and its northern Mediterranean variant, S. glacialis subsp. fontqueriana from Sierra de Gúdar. They share morphological characters that are absent in other high-mountain Sideritis, reinforcing their Mediterranean character, as opposed to a more temperate or submediterranean character, such as those of the hyssopifolia group.