12 research outputs found
Spark solutions for discovering fuzzy association rules in Big Data
The research reported in this paper was partially supported the COPKIT project from the 8th Programme Framework (H2020) research and innovation programme (grant agreement No 786687) and from the BIGDATAMED projects with references B-TIC-145-UGR18 and P18-RT-2947.The high computational impact when mining fuzzy association rules grows significantly when managing very large data sets, triggering in many cases a memory overflow error and leading to the experiment failure without its conclusion. It is in these cases when the application of Big Data techniques can help to achieve the experiment completion. Therefore, in this paper several Spark algorithms are proposed to handle with massive fuzzy data and discover interesting association rules. For that, we based on a decomposition of interestingness measures in terms of α-cuts, and we experimentally demonstrate that it is sufficient to consider only 10equidistributed α-cuts in order to mine all significant fuzzy association rules. Additionally, all the proposals are compared and analysed in terms of efficiency and speed up, in several datasets, including a real dataset comprised of sensor measurements from an office building.COPKIT project from the 8th Programme Framework (H2020) research and innovation programme 786687BIGDATAMED projects B-TIC-145-UGR18
P18-RT-294
New Spark solutions for distributed frequent itemset and association rule mining algorithms
Funding for open access publishing: Universidad de Gran-
ada/CBUA. The research reported in this paper was partially sup-
ported by the BIGDATAMED project, which has received funding
from the Andalusian Government (Junta de Andalucı Ìa) under grant
agreement No P18-RT-1765, by Grants PID2021-123960OB-I00 and
Grant TED2021-129402B-C21 funded by Ministerio de Ciencia e
Innovacio Ìn and, by ERDF A way of making Europe and by the
European Union NextGenerationEU. In addition, this work has been
partially supported by the Ministry of Universities through the EU-
funded Margarita Salas programme NextGenerationEU. Funding for
open access charge: Universidad de Granada/CBUAThe large amount of data generated every day makes necessary the re-implementation of new methods capable of handle with
massive data efficiently. This is the case of Association Rules, an unsupervised data mining tool capable of extracting information
in the form of IF-THEN patterns. Although several methods have been proposed for the extraction of frequent itemsets (previous
phase before mining association rules) in very large databases, the high computational cost and lack of memory remains a major
problem to be solved when processing large data. Therefore, the aim of this paper is three fold: (1) to review existent algorithms for
frequent itemset and association rule mining, (2)to develop new efficient frequent itemset Big Data algorithms using distributive
computation, as well as a new association rule mining algorithm in Spark, and (3) to compare the proposed algorithms with the
existent proposals varying the number of transactions and the number of items. To this purpose, we have used the Spark platform
which has been demonstrated to outperform existing distributive algorithmic implementations.Universidad de Granada/CBUAJunta de Andalucia
P18-RT-1765Ministry of Science and Innovation, Spain (MICINN)
Instituto de Salud Carlos III
Spanish Government
PID2021-123960OB-I00,
TED2021-129402B-C21ERDF A way of making EuropeEuropean Union NextGenerationEUMinistry of Universities through the E
A fuzzy-based medical system for pattern mining in a distributed environment: Application to diagnostic and co-morbidity
In this paper we have addressed the extraction of hidden knowledge from medical records using
data mining techniques such as association rules in conjunction with fuzzy logic in a distributed
environment. A significant challenge in this domain is that although there are a lot of studies devoted
to analysing health data, very few focus on the understanding and interpretability of the data and
the hidden patterns present within the data. A major challenge in this area is that many health data
analysis studies have focussed on classification, prediction or knowledge extraction and end users find
little interpretability or understanding of the results. This is due to the use of black-box algorithms or
because the nature of the data is not represented correctly. This is why it is necessary to focus the
analysis not only on knowledge extraction but also on the transformation and processing of the data
to improve the modelling of the nature of the data. Techniques such as association rule mining and
fuzzy logic help to improve the interpretability of the data and treat it with the inherent uncertainty
of real-world data. To this end, we propose a system that automatically: a) pre-processes the database
by transforming and adapting the data for the data mining process and enriching the data to generate
more interesting patterns, b) performs the fuzzification of the medical database to represent and
analyse real-world medical data with its inherent uncertainty, c) discovers interrelations and patterns
amongst different features (diagnostic, hospital discharge, etc.), and d) visualizes the obtained results
efficiently to facilitate the analysis and improve the interpretability of the information extracted. Our
proposed system yields a significant increase in the compression and interpretability of medical data
for end-users, allowing them to analyse the data correctly and make the right decisions. We present
one practical case using two health-related datasets to demonstrate the feasibility of our proposal for
real data.Junta de Andalucia P18-RT-1765Ministry of Universities through the E
A Probabilistic Algorithm for Predictive Control With Full-Complexity Models in Non-Residential Buildings
Despite the increasing capabilities of information technologies for data acquisition and processing,
building energy management systems still require manual configuration and supervision to achieve
optimal performance. Model predictive control (MPC) aims to leverage equipment control-particularly
heating, ventilation, and air conditioning (HVAC)-by using a model of the building to capture its dynamic
characteristics and to predict its response to alternative control scenarios. Usually, MPC approaches are based
on simplified linear models, which support faster computation but also present some limitations regarding
interpretability, solution diversification, and longer-term optimization. In this paper, we propose a novel
MPC algorithm that uses a full-complexity grey-box simulation model to optimize HVAC operation in
non-residential buildings. Our system generates hundreds of candidate operation plans, typically for the next
day, and evaluates them in terms of consumption and comfort by means of a parallel simulator configured
according to the expected building conditions (weather and occupancy). The system has been implemented
and tested in an office building in Helsinki, both in a simulated environment and in the real building, yielding
energy savings around 35% during the intermediate winter season and 20% in the whole winter season with
respect to the current operation of the heating equipment.This work was supported in part by the Universidad de Granada under Grant P9-2014-ING, in part by the Spanish Ministry of Science,
Innovation and Universities under Grant TIN2017-91223-EXP, in part by the Spanish Ministry of Economy and Competitiveness under
Grant TIN2015-64776-C3-1-R, and in part by the European Union (Energy IN TIME EeB.NMP.2013-4), under Grant 608981
Detailed stratified GWAS analysis for severe COVID-19 in four European populations
Given the highly variable clinical phenotype of Coronavirus disease 2019 (COVID-19), a deeper analysis of the host genetic contribution to severe COVID-19 is important to improve our understanding of underlying disease mechanisms. Here, we describe an extended genome-wide association meta-analysis of a well-characterized cohort of 3255 COVID-19 patients with respiratory failure and 12â488 population controls from Italy, Spain, Norway and Germany/Austria, including stratified analyses based on age, sex and disease severity, as well as targeted analyses of chromosome Y haplotypes, the human leukocyte antigen region and the SARS-CoV-2 peptidome. By inversion imputation, we traced a reported association at 17q21.31 to a ~0.9-Mb inversion polymorphism that creates two highly differentiated haplotypes and characterized the potential effects of the inversion in detail. Our data, together with the 5th release of summary statistics from the COVID-19 Host Genetics Initiative including non-Caucasian individuals, also identified a new locus at 19q13.33, including NAPSA, a gene which is expressed primarily in alveolar cells responsible for gas exchange in the lung.S.E.H. and C.A.S. partially supported genotyping through a philanthropic donation. A.F. and D.E. were supported by a grant from the German Federal Ministry of Education and COVID-19 grant Research (BMBF; ID:01KI20197); A.F., D.E. and F.D. were supported by the Deutsche Forschungsgemeinschaft Cluster of Excellence âPrecision Medicine in Chronic Inflammationâ (EXC2167). D.E. was supported by the German Federal Ministry of Education and Research (BMBF) within the framework of the Computational Life Sciences funding concept (CompLS grant 031L0165). D.E., K.B. and S.B. acknowledge the Novo Nordisk Foundation (NNF14CC0001 and NNF17OC0027594). T.L.L., A.T. and O.Ă. were funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), project numbers 279645989; 433116033; 437857095. M.W. and H.E. are supported by the German Research Foundation (DFG) through the Research Training Group 1743, âGenes, Environment and Inflammationâ. L.V. received funding from: Ricerca Finalizzata Ministero della Salute (RF-2016-02364358), Italian Ministry of Health âCV PREVITALââstrategie di prevenzione primaria cardiovascolare primaria nella popolazione italiana; The European Union (EU) Programme Horizon 2020 (under grant agreement No. 777377) for the project LITMUS- and for the project âREVEALâ; Fondazione IRCCS Caâ Granda âRicerca correnteâ, Fondazione Sviluppo Caâ Granda âLiver-BIBLEâ (PR-0391), Fondazione IRCCS Caâ Granda â5permilleâ âCOVID-19 Biobankâ (RC100017A). A.B. was supported by a grant from Fondazione Cariplo to Fondazione Tettamanti: âBio-banking of Covid-19 patient samples to support national and international research (Covid-Bank). This research was partly funded by an MIUR grant to the Department of Medical Sciences, under the program âDipartimenti di Eccellenza 2018â2022â. This study makes use of data generated by the GCAT-Genomes for Life. Cohort study of the Genomes of Catalonia, FundaciĂł IGTP (The Institute for Health Science Research Germans Trias i Pujol) IGTP is part of the CERCA Program/Generalitat de Catalunya. GCAT is supported by AcciĂłn de DinamizaciĂłn del ISCIII-MINECO and the Ministry of Health of the Generalitat of Catalunya (ADE 10/00026); the AgĂšncia de GestiĂł dâAjuts Universitaris i de Recerca (AGAUR) (2017-SGR 529). M.M. received research funding from grant PI19/00335 AcciĂłn EstratĂ©gica en Salud, integrated in the Spanish National RDI Plan and financed by ISCIII-SubdirecciĂłn General de EvaluaciĂłn and the Fondo Europeo de Desarrollo Regional (European Regional Development Fund (FEDER)-Una manera de hacer Europaâ). B.C. is supported by national grants PI18/01512. X.F. is supported by the VEIS project (001-P-001647) (co-funded by the European Regional Development Fund (ERDF), âA way to build Europeâ). Additional data included in this study were obtained in part by the COVICAT Study Group (Cohort Covid de Catalunya) supported by IsGlobal and IGTP, European Institute of Innovation & Technology (EIT), a body of the European Union, COVID-19 Rapid Response activity 73A and SR20-01024 La Caixa Foundation. A.J. and S.M. were supported by the Spanish Ministry of Economy and Competitiveness (grant numbers: PSE-010000-2006-6 and IPT-010000-2010-36). A.J. was also supported by national grant PI17/00019 from the AcciĂłn EstratĂ©gica en Salud (ISCIII) and the European Regional Development Fund (FEDER). The Basque Biobank, a hospital-related platform that also involves all Osakidetza health centres, the Basque governmentâs Department of Health and Onkologikoa, is operated by the Basque Foundation for Health Innovation and Research-BIOEF. M.C. received Grants BFU2016-77244-R and PID2019-107836RB-I00 funded by the Agencia Estatal de InvestigaciĂłn (AEI, Spain) and the European Regional Development Fund (FEDER, EU). M.R.G., J.A.H., R.G.D. and D.M.M. are supported by the âSpanish Ministry of Economy, Innovation and Competition, the Instituto de Salud Carlos IIIâ (PI19/01404, PI16/01842, PI19/00589, PI17/00535 and GLD19/00100) and by the Andalussian government (Proyectos EstratĂ©gicos-Fondos Feder PE-0451-2018, COVID-Premed, COVID GWAs). The position held by Itziar de Rojas Salarich is funded by grant FI20/00215, PFIS Contratos Predoctorales de FormaciĂłn en InvestigaciĂłn en Salud. Enrique CalderĂłnâs team is supported by CIBER of Epidemiology and Public Health (CIBERESP), âInstituto de Salud Carlos IIIâ. J.C.H. reports grants from Research Council of Norway grant no 312780 during the conduct of the study. E.S. reports grants from Research Council of Norway grant no. 312769. The BioMaterialBank Nord is supported by the German Center for Lung Research (DZL), Airway Research Center North (ARCN). The BioMaterialBank Nord is member of popgen 2.0 network (P2N). P.K. Bergisch Gladbach, Germany and the Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases, University of Cologne, Cologne, Germany. He is supported by the German Federal Ministry of Education and Research (BMBF). O.A.C. is supported by the German Federal Ministry of Research and Education and is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germanyâs Excellence StrategyâCECAD, EXC 2030â390661388. The COMRI cohort is funded by Technical University of Munich, Munich, Germany. This work was supported by grants of the Rolf M. Schwiete Stiftung, the Saarland University, BMBF and The States of Saarland and Lower Saxony. K.U.L. is supported by the German Research Foundation (DFG, LU-1944/3-1). Genotyping for the BoSCO study is funded by the Institute of Human Genetics, University Hospital Bonn. F.H. was supported by the Bavarian State Ministry for Science and Arts. Part of the genotyping was supported by a grant to A.R. from the German Federal Ministry of Education and Research (BMBF, grant: 01ED1619A, European Alzheimer DNA BioBank, EADB) within the context of the EU Joint ProgrammeâNeurodegenerative Disease Research (JPND). Additional funding was derived from the German Research Foundation (DFG) grant: RA 1971/6-1 to A.R. P.R. is supported by the DFG (CCGA Sequencing Centre and DFG ExC2167 PMI and by SH state funds for COVID19 research). F.T. is supported by the Clinician Scientist Program of the Deutsche Forschungsgemeinschaft Cluster of Excellence âPrecision Medicine in Chronic Inflammationâ (EXC2167). C.L. and J.H. are supported by the German Center for Infection Research (DZIF). T.B., M.M.B., O.W. und A.H. are supported by the Stiftung UniversitĂ€tsmedizin Essen. M.A.-H. was supported by Juan de la Cierva Incorporacion program, grant IJC2018-035131-I funded by MCIN/AEI/10.13039/501100011033. E.C.S. is supported by the Deutsche Forschungsgemeinschaft (DFG; SCHU 2419/2-1).Peer reviewe
Detailed stratified GWAS analysis for severe COVID-19 in four European populations
Given the highly variable clinical phenotype of Coronavirus disease 2019 (COVID-19), a deeper analysis of the host genetic contribution to severe COVID-19 is important to improve our understanding of underlying disease mechanisms. Here, we describe an extended GWAS meta-analysis of a well-characterized cohort of 3,260 COVID-19 patients with respiratory failure and 12,483 population controls from Italy, Spain, Norway and Germany/Austria, including stratified analyses based on age, sex and disease severity, as well as targeted analyses of chromosome Y haplotypes, the human leukocyte antigen (HLA) region and the SARS-CoV-2 peptidome. By inversion imputation, we traced a reported association at 17q21.31 to a highly pleiotropic âŒ0.9-Mb inversion polymorphism and characterized the potential effects of the inversion in detail. Our data, together with the 5th release of summary statistics from the COVID-19 Host Genetics Initiative, also identified a new locus at 19q13.33, including NAPSA, a gene which is expressed primarily in alveolar cells responsible for gas exchange in the lung.Andre Franke and David Ellinghaus were supported by a grant from the German
Federal Ministry of Education and Research (01KI20197), Andre Franke, David
Ellinghaus and Frauke Degenhardt were supported by the Deutsche
Forschungsgemeinschaft Cluster of Excellence âPrecision Medicine in Chronic
Inflammationâ (EXC2167). David Ellinghaus was supported by the German Federal
Ministry of Education and Research (BMBF) within the framework of the
Computational Life Sciences funding concept (CompLS grant 031L0165). David
Ellinghaus, Karina Banasik and SĂžren Brunak acknowledge the Novo Nordisk
Foundation (grant NNF14CC0001 and NNF17OC0027594). Tobias L. Lenz, Ana
Teles and Onur Ăzer were funded by the Deutsche Forschungsgemeinschaft (DFG,
German Research Foundation), project numbers 279645989; 433116033; 437857095. Mareike Wendorff and Hesham ElAbd are supported by the German
Research Foundation (DFG) through the Research Training Group 1743, "Genes,
Environment and Inflammation". This project was supported by a Covid-19 grant from
the German Federal Ministry of Education and Research (BMBF; ID: 01KI20197).
Luca Valenti received funding from: Ricerca Finalizzata Ministero della Salute RF2016-02364358, Italian Ministry of Health ""CV PREVITAL â strategie di prevenzione
primaria cardiovascolare primaria nella popolazione italiana; The European Union
(EU) Programme Horizon 2020 (under grant agreement No. 777377) for the project
LITMUS- and for the project ""REVEAL""; Fondazione IRCCS Ca' Granda ""Ricerca
corrente"", Fondazione Sviluppo Ca' Granda ""Liver-BIBLE"" (PR-0391), Fondazione
IRCCS Ca' Granda ""5permille"" ""COVID-19 Biobank"" (RC100017A). Andrea Biondi
was supported by the grant from Fondazione Cariplo to Fondazione Tettamanti: "Biobanking of Covid-19 patient samples to support national and international research
(Covid-Bank). This research was partly funded by a MIUR grant to the Department of
Medical Sciences, under the program "Dipartimenti di Eccellenza 2018â2022". This
study makes use of data generated by the GCAT-Genomes for Life. Cohort study of
the Genomes of Catalonia, FundaciĂł IGTP. IGTP is part of the CERCA Program /
Generalitat de Catalunya. GCAT is supported by AcciĂłn de DinamizaciĂłn del ISCIIIMINECO and the Ministry of Health of the Generalitat of Catalunya (ADE 10/00026);
the AgĂšncia de GestiĂł dâAjuts Universitaris i de Recerca (AGAUR) (2017-SGR 529).
Marta Marquié received research funding from ant PI19/00335 Acción Estratégica en
Salud, integrated in the Spanish National RDI Plan and financed by ISCIIISubdirecciĂłn General de EvaluaciĂłn and the Fondo Europeo de Desarrollo Regional
(FEDER-Una manera de hacer Europa").Beatriz Cortes is supported by national
grants PI18/01512. Xavier Farre is supported by VEIS project (001-P-001647) (cofunded by European Regional Development Fund (ERDF), âA way to build Europeâ).
Additional data included in this study was obtained in part by the COVICAT Study
Group (Cohort Covid de Catalunya) supported by IsGlobal and IGTP, EIT COVID-19
Rapid Response activity 73A and SR20-01024 La Caixa Foundation. Antonio JuliĂ
and Sara Marsal were supported by the Spanish Ministry of Economy and
Competitiveness (grant numbers: PSE-010000-2006-6 and IPT-010000-2010-36).
Antonio JuliĂ was also supported the by national grant PI17/00019 from the AcciĂłn
Estratégica en Salud (ISCIII) and the FEDER. The Basque Biobank is a hospitalrelated platform that also involves all Osakidetza health centres, the Basque government's Department of Health and Onkologikoa, is operated by the Basque
Foundation for Health Innovation and Research-BIOEF. Mario CĂĄceres received
Grants BFU2016-77244-R and PID2019-107836RB-I00 funded by the Agencia Estatal
de InvestigaciĂłn (AEI, Spain) and the European Regional Development Fund
(FEDER, EU). Manuel Romero GĂłmez, Javier Ampuero Herrojo, RocĂo Gallego DurĂĄn
and Douglas Maya Miles are supported by the âSpanish Ministry of Economy,
Innovation and Competition, the Instituto de Salud Carlos IIIâ (PI19/01404,
PI16/01842, PI19/00589, PI17/00535 and GLD19/00100), and by the Andalussian
government (Proyectos Estratégicos-Fondos Feder PE-0451-2018, COVID-Premed,
COVID GWAs). The position held by Itziar de Rojas Salarich is funded by grant
FI20/00215, PFIS Contratos Predoctorales de FormaciĂłn en InvestigaciĂłn en Salud.
Enrique CalderĂłn's team is supported by CIBER of Epidemiology and Public Health
(CIBERESP), "Instituto de Salud Carlos III". Jan Cato Holter reports grants from
Research Council of Norway grant no 312780 during the conduct of the study. Dr.
SolligÄrd: reports grants from Research Council of Norway grant no 312769. The
BioMaterialBank Nord is supported by the German Center for Lung Research (DZL),
Airway Research Center North (ARCN). The BioMaterialBank Nord is member of
popgen 2.0 network (P2N). Philipp Koehler has received non-financial scientific grants
from Miltenyi Biotec GmbH, Bergisch Gladbach, Germany, and the Cologne
Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases,
University of Cologne, Cologne, Germany. He is supported by the German Federal
Ministry of Education and Research (BMBF).Oliver A. Cornely is supported by the
German Federal Ministry of Research and Education and is funded by the Deutsche
Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's
Excellence Strategy â CECAD, EXC 2030 â 390661388. The COMRI cohort is funded
by Technical University of Munich, Munich, Germany. Genotyping was performed by
the Genotyping laboratory of Institute for Molecular Medicine Finland FIMM
Technology Centre, University of Helsinki. This work was supported by grants of the
Rolf M. Schwiete Stiftung, the Saarland University, BMBF and The States of Saarland
and Lower Saxony. Kerstin U. Ludwig is supported by the German Research
Foundation (DFG, LU-1944/3-1). Genotyping for the BoSCO study is funded by the
Institute of Human Genetics, University Hospital Bonn. Frank Hanses was supported
by the Bavarian State Ministry for Science and Arts. Part of the genotyping was
supported by a grant to Alfredo Ramirez from the German Federal Ministry of Education and Research (BMBF, grant: 01ED1619A, European Alzheimer DNA
BioBank, EADB) within the context of the EU Joint Programme â Neurodegenerative
Disease Research (JPND). Additional funding was derived from the German Research
Foundation (DFG) grant: RA 1971/6-1 to Alfredo Ramirez. Philip Rosenstiel is
supported by the DFG (CCGA Sequencing Centre and DFG ExC2167 PMI and by SH
state funds for COVID19 research). Florian Tran is supported by the Clinician Scientist
Program of the Deutsche Forschungsgemeinschaft Cluster of Excellence âPrecision
Medicine in Chronic Inflammationâ (EXC2167). Christoph Lange and Jan Heyckendorf
are supported by the German Center for Infection Research (DZIF). Thorsen Brenner,
Marc M Berger, Oliver Witzke und Anke Hinney are supported by the Stiftung
UniversitÀtsmedizin Essen. Marialbert Acosta-Herrera was supported by Juan de la
Cierva Incorporacion program, grant IJC2018-035131-I funded by
MCIN/AEI/10.13039/501100011033. Eva C Schulte is supported by the Deutsche
Forschungsgemeinschaft (DFG; SCHU 2419/2-1).N
Fuzzy association rules in Big Data
This paper has reviewed the field of Data Science and how Data Science techniques can be applied
to building energy management. Specifically, we have focused on building operation, energy load
prediction, and identification of consumption patterns. Our experiments show that Big Data
technologies can solve the computational problems that appear when processing of large amounts of
data, which are likely to have an increasing relevance with the advent of the Internet of Things âwith
smart meters and appliances fully connected to the Internet. However, the applications to real-world
scenarios are still scarce. In our experience, one of the most important aspects to improve is
achieving a greater involvement of the building managers in the data analysis process. To do this,
future research work should explore two complementary directions, namely, showing the potential of
Data Science to building managers, and developing more user-friendly algorithms and tools. In this
way, we expect that new approaches will be less opaque, easier to use, more customizable, and
above all other features, more engaging.Tesis Univ. Granada.Spanish Ministries of Science, Innovation and Universities (TIN2017-91223-EXP)Economy and Competitiveness (TIN2015-64776-C3-1-R)Energy IN TIME project from the EU 7o Framework Programme (grant agreement No. 608981)COPKIT project from the EU 8 Framework Programme (H2020) researchInnovation programme (grant agreement No 786687)University of Granada (Programa de Proyectos para la incorporaciĂłn de jĂłvenes doctores a nuevas lĂneas de investigaciĂłn)Spanish Government (TIN2012-30939 project
Significado clĂnico del realce tardĂo de gadolinio con resonancia magnĂ©tica en pacientes con miocardiopatĂa hipertrĂłfica
The risk of COVID-19 death is much greater and age dependent with type I IFN autoantibodies
International audienceSignificance There is growing evidence that preexisting autoantibodies neutralizing type I interferons (IFNs) are strong determinants of life-threatening COVID-19 pneumonia. It is important to estimate their quantitative impact on COVID-19 mortality upon SARS-CoV-2 infection, by age and sex, as both the prevalence of these autoantibodies and the risk of COVID-19 death increase with age and are higher in men. Using an unvaccinated sample of 1,261 deceased patients and 34,159 individuals from the general population, we found that autoantibodies against type I IFNs strongly increased the SARS-CoV-2 infection fatality rate at all ages, in both men and women. Autoantibodies against type I IFNs are strong and common predictors of life-threatening COVID-19. Testing for these autoantibodies should be considered in the general population
The risk of COVID-19 death is much greater and age dependent with type I IFN autoantibodies
International audienceSignificance There is growing evidence that preexisting autoantibodies neutralizing type I interferons (IFNs) are strong determinants of life-threatening COVID-19 pneumonia. It is important to estimate their quantitative impact on COVID-19 mortality upon SARS-CoV-2 infection, by age and sex, as both the prevalence of these autoantibodies and the risk of COVID-19 death increase with age and are higher in men. Using an unvaccinated sample of 1,261 deceased patients and 34,159 individuals from the general population, we found that autoantibodies against type I IFNs strongly increased the SARS-CoV-2 infection fatality rate at all ages, in both men and women. Autoantibodies against type I IFNs are strong and common predictors of life-threatening COVID-19. Testing for these autoantibodies should be considered in the general population