Alpha-1-antitrypsin-deficiency is associated with lower cardiovascular risk: an approach based on federated learning.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is an inflammatory multisystemic disease caused by environmental exposures and/or genetic factors. Inherited alpha-1-antitrypsin deficiency (AATD) is one of the best recognized genetic factors increasing the risk for an early onset COPD with emphysema. The aim of this study was to gain a better understanding of the associations between comorbidities and specific biomarkers in COPD patients with and without AATD to enable future investigations aimed, for example, at identifying risk factors or improving care. METHODS: We focused on cardiovascular comorbidities, blood high sensitivity troponin (hs-troponin) and lipid profiles in COPD patients with and without AATD. We used clinical data from six German University Medical Centres of the MIRACUM (Medical Informatics Initiative in Research and Medicine) consortium. The codes for the international classification of diseases (ICD) were used for COPD as a main diagnosis and for comorbidities and blood laboratory data were obtained. Data analyses were based on the DataSHIELD framework. RESULTS: Out of 112,852 visits complete information was available for 43,057 COPD patients. According to our findings, 746 patients with AATD (1.73%) showed significantly lower total blood cholesterol levels and less cardiovascular comorbidities than non-AATD COPD patients. Moreover, after adjusting for the confounder factors, such as age, gender, and nicotine abuse, we confirmed that hs-troponin is a suitable predictor of overall mortality in COPD patients. The comorbidities associated with AATD in the current study differ from other studies, which may reflect geographic and population-based differences as well as the heterogeneous characteristics of AATD. CONCLUSION: The concept of MIRACUM is suitable for the analysis of a large healthcare database. This study provided evidence that COPD patients with AATD have a lower cardiovascular risk and revealed that hs-troponin is a predictor for hospital mortality in individuals with COPD.

Comorbidity defines asthmatic patients' risk of COVID-19 hospitalization: A global perspective.

BACKGROUND: The global epidemiology of asthma among patients with coronavirus disease 2019 (COVID-19) presents striking geographic differences, defining prevalence zones of high and low co-occurrence of asthma and COVID-19. OBJECTIVE: We aimed to compare asthma prevalence among hospitalized patients with COVID-19 in major global hubs across the world by applying common inclusion criteria and definitions. METHODS: We built a network of 6 academic hospitals in Stanford (Stanford University)/the United States; Frankfurt (Goethe University), Giessen (Justus Liebig University), and Marburg (Philipps University)/Germany; and Moscow (Clinical Hospital 52 in collaboration with Sechenov University)/Russia. We collected clinical and laboratory data for patients hospitalized due to COVID-19. RESULTS: Asthmatic individuals were overrepresented among hospitalized patients with COVID-19 in Stanford and underrepresented in Moscow and Germany as compared with their prevalence among adults in the local community. Asthma prevalence was similar among patients hospitalized in an intensive care unit and patients hospitalized in other than an intensive care unit, which implied that the risk for development of severe COVID-19 was not higher among asthmatic patients. The numbers of males and comorbidities were higher among patients with COVID-19 in the Stanford cohort, and the most frequent comorbidities among these patients with asthma were other chronic inflammatory airway disorders such as chronic obstructive pulmonary disease. CONCLUSION: The observed disparity in COVID-19-associated risk among asthmatic patients across countries and continents is connected to the varying prevalence of underlying comorbidities, particularly chronic obstructive pulmonary disease.

DistSNE: Distributed computing and online visualization of DNA methylation-based central nervous system tumor classification.

The current state-of-the-art analysis of central nervous system (CNS) tumors through DNA methylation profiling relies on the tumor classifier developed by Capper and colleagues, which centrally harnesses DNA methylation data provided by users. Here, we present a distributed-computing-based approach for CNS tumor classification that achieves a comparable performance to centralized systems while safeguarding privacy. We utilize the t-distributed neighborhood embedding (t-SNE) model for dimensionality reduction and visualization of tumor classification results in two-dimensional graphs in a distributed approach across multiple sites (DistSNE). DistSNE provides an intuitive web interface (https://gin-tsne.med.uni-giessen.de) for user-friendly local data management and federated methylome-based tumor classification calculations for multiple collaborators in a DataSHIELD environment. The freely accessible web interface supports convenient data upload, result review, and summary report generation. Importantly, increasing sample size as achieved through distributed access to additional datasets allows DistSNE to improve cluster analysis and enhance predictive power. Collectively, DistSNE enables a simple and fast classification of CNS tumors using large-scale methylation data from distributed sources, while maintaining the privacy and allowing easy and flexible network expansion to other institutes. This approach holds great potential for advancing human brain tumor classification and fostering collaborative precision medicine in neuro-oncology.