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arXiv 2020年

作者： Scheibner, James Raisaro, Jean Louis Troncoso-Pastoriza, Juan Ramón Ienca, Marcello Fellay, Jacques Vayena, Effy Hubaux, Jean-Pierre Health Ethics and Policy Laboratory Department of Health Sciences and Technology ETH Zürich Zürich Switzerland Precision Medicine Unit Lausanne University Hospital Lausanne Switzerland Data Science Group Lausanne University Hospital Lausanne Switzerland Laboratory for Data Security School of Computer and Communication Sciences EPFL Lausanne Switzerland School of Life Sciences EPFL Lausanne Switzerland Host-Pathogen Genomics Laboratory Swiss Institute of Bioinformatics Lausanne Switzerland

Multisite medical data sharing is critical in modern clinical practice and medical research. The challenge is to conduct data sharing that preserves individual privacy and data usability. The shortcomings of traditional privacy-enhancing technologies mean that institutions rely on bespoke data sharing contracts. These contracts increase the inefficiency of data sharing and may disincentivize important clinical treatment and medical research. This paper provides a synthesis between two novel advanced privacy-enhancing technologies (PETs): Homomorphic Encryption and Secure Multiparty Computation (defined together as Multiparty Homomorphic Encryption or MHE). These PETs provide a mathematical guarantee of privacy, with MHE providing a performance advantage over separately using HE or SMC. We argue MHE fulfills legal requirements for medical data sharing under the General data Protection Regulation (GDPR) which has set a global benchmark for data protection. Specifically, the data processed and shared using MHE can be considered anonymized data. We explain how MHE can reduce the reliance on customized contractual measures between institutions. The proposed approach can accelerate the pace of medical research whilst offering additional incentives for healthcare and research institutes to employ common data interoperability standards. © 2020, CC-BY.

关键词： data privacy

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Epitopedia: identifying molecular mimicry between pathogens and known immune epitopes

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ImmunoInformatics 2023年 9卷

作者： Christian A Balbin Janelle Nunez-Castilla Vitalii Stebliankin Prabin Baral Masrur Sobhan Trevor Cickovski Ananda Mohan Mondal Giri Narasimhan Prem Chapagain Kalai Mathee Jessica Siltberg-Liberles Department of Biological Sciences College of Arts Science and Education Florida International University Miami United States Bioinformatics Research Group (BioRG) Knight Foundation School of Computing and Information Sciences Florida International University Miami United States Department of Physics College of Arts Science and Education Florida International University Miami United States Machine Learning and Data Analytics Group (MLDAG) Knight Foundation School of Computing and Information Sciences Florida International University Miami United States Biomolecular Sciences Institute Florida International University Miami United States Department of Human and Molecular Genetics Herbert Wertheim College of Medicine Florida International University Miami United States

Upon infection, foreign antigenic proteins stimulate the host's immune system to produce antibodies targeting the pathogen. These antibodies bind to regions on the antigen called epitopes. Structural similarity (molecular mimicry) of epitopes between an infecting pathogen and host proteins or other pathogenic proteins the host has previously encountered can impact the host immune response to the pathogen and may lead to cross-reactive antibodies. The ability to identify potential regions of molecular mimicry in a pathogen can illuminate immune effects which are especially important to pathogen treatment and vaccine design. Here we present Epitopedia, a software pipeline that facilitates the identification of regions that may exhibit potential three-dimensional molecular mimicry between an antigenic pathogen protein and known immune epitopes as catalogued by the Immune Epitope database (IEDB). Epitopedia is open-source software released under the MIT license and is freely available on GitHub, including a Docker container with all other software dependencies preinstalled. We performed an analysis describing how various secondary structure states, identity between pentapeptide pairs, and identity between the parent sequences of pentapeptide pairs affects RMSD. We found that pentapeptides pairs in a helical conformation had considerably lower RMSD values than those in extended or coil conformations. We also found that RMSD is significantly increased when pentapeptide pairs are from non-homologous sequences.

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A simple linear-time algorithm for the common refinement of rooted phylogenetic trees on a common leaf set

arXiv

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arXiv 2021年

作者： Schaller, David Hellmuth, Marc Stadler, Peter F. Bioinformatics Group Department of Computer Science Interdisciplinary Center for Bioinformatics Universität Leipzig Härtelstrasse 16-18 LeipzigD-04107 Germany Department of Mathematics Faculty of Science Stockholm University StockholmSE-10691 Sweden Competence Center for Scalable Data Services and Solutions Dresden/Leipzig Germany Leipzig Research Center for Civilization Diseases Universität Leipzig Augustusplatz 12 LeipzigD-04107 Germany Max Planck Institute for Mathematics in the Sciences Inselstraße 22 LeipzigD-04103 Germany Department of Theoretical Chemistry University of Vienna Währinger Straße 17 ViennaA-1090 Austria Facultad de Ciencias Universidad Nacional de Colombia Sede Bogotá Ciudad Universitaria D.C. BogotáCOL-111321 Colombia Santa Fe Institute 1399 Hyde Park Rd. Santa FeNM87501 United States

Background The supertree problem, i.e., the task of finding a common refinement of a set of rooted trees is an important topic in mathematical phylogenetics. The special case of a common leaf set L is known to be solvable in linear time. Existing approaches refine one input tree using information of the others and then test whether the results are isomorphic. Results A linear-time algorithm, LinCR, for constructing the common refinement T of k input trees with a common leaf set is proposed that explicitly computes the parent function of T in a bottom-up approach. Conclusion LinCR is simpler to implement than other asymptotically optimal algorithms for the problem and outperforms the alternatives in empirical comparisons. Availability An implementation of LinCR in Python is freely available at https://***/david-schaller/tralda. Copyright © 2021, The Authors. All rights reserved.

关键词： Phylogenetic

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From modular decomposition trees to rooted median graphs

arXiv

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arXiv 2021年

作者： Bruckmann, Carmen Stadler, Peter F. Hellmuth, Marc Helmholtz-Centre for Environmental Research Permoserstraße 15 LeipzigD-04318 Germany Bioinformatics Group Department of Computer Science Interdisciplinary Center for Bioinformatics Universität Leipzig Härtelstraße 16–18 LeipzigD-04107 Germany Halle-Jena-Leipzig Competence Center for Scalable Data Services and Solutions Dresden-Leipzig Leipzig Research Center for Civilization Diseases Centre for Biotechnology and Biomedicine at Leipzig University Universität Leipzig Germany Max Planck Institute for Mathematics in the Sciences Inselstraße 22 LeipzigD-04103 Germany Institute for Theoretical Chemistry University of Vienna Währingerstrasse 17 WienA-1090 Austria Facultad de Ciencias Universidad National de Colombia Sede Bogotá Colombia Santa Fe Insitute 1399 Hyde Park Rd. Santa FeNM87501 United States Department of Mathematics Faculty of Science Stockholm University StockholmSE-10691 Sweden

The modular decomposition of a symmetric map δ: X ×X → ϒ (or, equivalently, a set of symmetric binary relations, a 2-structure, or an edge-colored undirected graph) is a natural construction to capture key features of δ in labeled trees. A map δ is explained by a vertex-labeled rooted tree (T,t) if the label δ(x,y) coincides with the label of the last common ancestor of x and y in T, i.e., if δ(x,y) = t(lca(x,y)). Only maps whose modular decomposition does not contain prime nodes, i.e., the symbolic ultrametrics, can be exaplained in this manner. Here we consider rooted median graphs as a generalization to (modular decomposition) trees to explain symmetric maps. We first show that every symmetric map can be explained by "extended" hypercubes and half-grids. We then derive a a linear-time algorithm that stepwisely resolves prime vertices in the modular decomposition tree to obtain a rooted and labeled median graph that explains a given symmetric map δ. We argue that the resulting "tree-like" median graphs may be of use in phylogenetics as a model of evolutionary relationships. © 2021, CC BY.

关键词： Undirected graphs

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Author Correction: Large-scale benchmarking of circRNA detection tools reveals large differences in sensitivity but not in precision

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Nature methods 2025年第2期22卷 448页

作者： Marieke Vromman Jasper Anckaert Stefania Bortoluzzi Alessia Buratin Chia-Ying Chen Qinjie Chu Trees-Juen Chuang Roozbeh Dehghannasiri Christoph Dieterich Xin Dong Paul Flicek Enrico Gaffo Wanjun Gu Chunjiang He Steve Hoffmann Osagie Izuogu Michael S Jackson Tobias Jakobi Eric C Lai Justine Nuytens Julia Salzman Mauro Santibanez-Koref Peter Stadler Olivier Thas Eveline Vanden Eynde Kimberly Verniers Guoxia Wen Jakub Westholm Li Yang Chu-Yu Ye Nurten Yigit Guo-Hua Yuan Jinyang Zhang Fangqing Zhao Jo Vandesompele Pieter-Jan Volders OncoRNALab Cancer Research Institute Ghent (CRIG) Department of Biomolecular Medicine Ghent University Ghent Belgium. Department of Molecular Medicine University of Padova Padova Italy. Genomics Research Center Academia Sinica Taipei City Taiwan. Institute of Crop Science and Institute of Bioinformatics Zhejiang University Zhejiang China. Department of Biomedical Data Science and of Biochemistry Stanford University Stanford CA USA. Klaus Tschira Institute for Integrative Computational Cardiology Department of Internal Medicine III University Hospital Heidelberg German Center for Cardiovascular Research (DZHK) Heidelberg Germany. School of Basic Medical Science Department of Medical Genetics Wuhan University Wuhan China. EMBL-EBI Hinxton UK. Collaborative Innovation Center of Jiangsu Province of Cancer Prevention and Treatment of Chinese Medicine School of Artificial Intelligence and Information Technology Nanjing University of Chinese Medicine Nanjing China. Computational Biology Group Leibniz Institute on Aging - Fritz Lipmann Institute (FLI) Jena Germany. Biosciences Institute Faculty of Medical Sciences Newcastle University Newcastle UK. Translational Cardiovascular Research Center University of Arizona - College of Medicine Phoenix Phoenix AZ USA. Sloan Kettering Institute New York NY USA. Bioinformatics Group Department of Computer Science and Interdisciplinary Center for Bioinformatics Universität Leipzig Leipzig Germany. Data Science Institute I-Biostat Hasselt University Hasselt Belgium. State Key Laboratory of Bioelectronics School of Biological Sciences and Medical Engineering Southeast University Nanjing China. Department of Biochemistry and Biophysics National Bioinformatics Infrastructure Sweden Science for Life Laboratory Stockholm University Stockholm Sweden. Center for Molecular Medicine Children's Hospital Fudan University and Shanghai Key Laboratory of Medical Epigenetics International Laboratory of Medical Epigenetics and Metab

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Planar median graphs and cubesquare-graphs

arXiv

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arXiv 2021年

作者： Seemann, Carsten R. Moulton, Vincent Stadler, Peter F. Hellmuth, Marc Max Planck Institute for Mathematics in the Sciences Inselstraße 22 LeipzigD-04103 Germany Bioinformatics Group Department of Computer Science Interdisciplinary Center for Bioinformatics University of Leipzig Härtelstraße 16-18 LeipzigD-04107 Germany School of Computing Sciences University of East Anglia NorwichNR4 7TJ United Kingdom Halle-Jena-Leipzig Competence Center for Scalable Data Services and Solutions Dresden-Leipzig Leipzig Germany Research Center for Civilization Diseases Centre for Biotechnology and Biomedicine Leipzig University Härtelstraße 16-18 LeipzigD-04107 Germany Institute for Theoretical Chemistry University of Vienna Währingerstraße 17 WienA-1090 Austria Facultad de Ciencias Universidad Nacional de Colombia Sede Bogotá Colombia The Santa Fe Institute 1399 Hyde Park Rd. Santa FeNM87501 United States Department of Mathematics Faculty of Science Stockholm University StockholmSE - 106 91 Sweden

Median graphs are connected graphs in which for all three vertices there is a unique vertex that belongs to shortest paths between each pair of these three vertices. In this paper we provide several novel characterizations of planar median graphs. More specifically, we characterize when a planar graph G is a median graph in terms of forbidden subgraphs and the structure of isometric cycles in G, and also in terms of subgraphs of G that are contained inside and outside of 4-cycles with respect to an arbitrary planar embedding of G. These results lead us to a new characterization of planar median graphs in terms of cubesquare-graphs that is, graphs that can be obtained by starting with cubes and square graphs, and iteratively replacing 4-cycle boundaries (relative to some embedding) by cubes or square-graphs. As a corollary we also show that a graph is planar median if and only if it can be obtained from cubes and square-graphs by a sequence of "square-boundary" amalgamations. These considerations also lead to an O(n log n)-time recognition algorithm to compute a decomposition of a planar median graph with n vertices into cubes and square-graphs. Copyright © 2021, The Authors. All rights reserved.

关键词： Graphic methods

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FAIR Principles:Interpretations and Implementation Considerations

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data Intelligence 2020年第1期2卷 10-29,293-302,322页

作者： Annika Jacobsen Ricardo de Miranda Azevedo Nick Juty Dominique Batista Simon Coles Ronald Cornet Melanie Courtot Merce Crosas Michel Dumontier Chris T.Evelo Carole Goble Giancarlo Guizzardi Karsten Kryger Hansen Ali Hasnain Kristina Hettne Jaap Heringa Rob W.W.Hooft Melanie Imming Keith G.Jeffery Rajaram Kaliyaperumal Martijn GKersloot Christine R.Kirkpatrick Tobias Kuhn Ignasi Labastida Barbara Magagna PeterMcQuilton Natalie Meyers Annalisa Montesanti Mirjam van Reisen Philippe Rocca-Serra Robert Pergl Susanna-Assunta Sansone Luiz Olavo Bonino da Silva Santos Juliane Schneider George Strawn Mark Thompson Andra Waagmeester Tobias Weigel Mark D.Wilkinson Egon L.Willighagen Peter Wittenburg Marco Roos Barend Mons Erik Schultes Leiden University Medical Center Leiden2333 ZAThe Netherlands Institute of Data Science Maastricht UniversityUniversiteitssingel 60Maastricht 6229 ERThe Netherlands Department of Computer Science The University of ManchesterOxford RoadManchester M139PLUK Oxford e-Research Centre Department of Engineering SciencesUniversity of OxfordOxford OX13PJUK School of Chemistry Faculty of Engineering and Physical SciencesUniversity of SouthamptonSO171BJUK Amsterdam UMC University of AmsterdamAmsterdam 1000 GGThe Netherlands European Bioinformatics Institute(EMBL-EBI) HinxtonCambridgeCB101SDUK Harvard University CambridgeMassachusetts 02138USA Department of Bioinformatics–BiGCaT NUTRIMMaastricht UniversityMaastricht 6229 ERThe Netherlands Conceptual and Cognitive Modeling Research Group(CORE) Free University of Bozen-BolzanoBolzano 39100Italy Aalborg University Aalborg DK-9220Denmark Insight Centre for Data Analytics National University of Ireland GalwayH91 TK33Ireland Centre for Digital Scholarship Leiden University LibrariesLeiden2333 ZAThe Netherlands Department of Computer Science Vrije Universiteit AmsterdamDe Boelelaan 11051081 HV AmsterdamThe Netherlands Dutch Techcentre for Life Sciences(DTL) UtrechtThe Netherlands SURF Utrecht 3511 EPThe Netherlands Keith G Jeffery Consultants FaringdonUK Castor EDC Paasheuvelweg 25Wing 5D1105 BPAmsterdamThe Netherlands San Diego Supercomputer Center University of California San DiegoLa JollaCalifornia 92093USA Learning and Research Resources Centre(CRAI) Universitat de Barcelona08007 BarcelonaSpain Environment Agency Austria A-1090 ViennaAustria University of Notre Dame 75004 ParisFrance Health Research Board(HRB) Dublin 2DO2 H638Ireland Liacs Institute of Advanced Computer Science Leiden University2311 GJ LeidenThe Netherlands Czech Technical University in Prague Faculty of Information Technology(FIT CTU)16000 Prague 6Czech Republic GO FAIR International Support&Coordination Office(GFISCO) LeidenThe Netherlands Harvard Catalys

The FAIR principles have been widely cited,endorsed and adopted by a broad range of stakeholders since their publication in *** intention,the 15 FAIR guiding principles do not dictate specific technological implementations,but provide guidance for improving Findability,Accessibility,Interoperability and Reusability of digital *** has likely contributed to the broad adoption of the FAIR principles,because individual stakeholder communities can implement their own FAIR ***,it has also resulted in inconsistent interpretations that carry the risk of leading to incompatible ***,while the FAIR principles are formulated on a high level and may be interpreted and implemented in different ways,for true interoperability we need to support convergence in implementation choices that are widely accessible and(re)-*** introduce the concept of FAIR implementation considerations to assist accelerated global participation and convergence towards accessible,robust,widespread and consistent FAIR *** self-identified stakeholder community may either choose to reuse solutions from existing implementations,or when they spot a gap,accept the challenge to create the needed solution,which,ideally,can be used again by other communities in the ***,we provide interpretations and implementation considerations(choices and challenges)for each FAIR principle.

关键词： FAIR guiding principles FAIR implementation FAIR convergence FAIR communities choices and challenges

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The 2022 Report on the Human Proteome from the HUPO Human Proteome Project

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Journal of proteome research 2023年第4期22卷 1024-1042页

作者： Gilbert S Omenn Lydie Lane Christopher M Overall Charles Pineau Nicolle H Packer Ileana M Cristea Cecilia Lindskog Susan T Weintraub Sandra Orchard Michael H A Roehrl Edouard Nice Siqi Liu Nuno Bandeira Yu-Ju Chen Tiannan Guo Ruedi Aebersold Robert L Moritz Eric W Deutsch University of Michigan Ann Arbor Michigan48109 United States. Institute for Systems Biology Seattle Washington98109 United States. CALIPHO Group SIB Swiss Institute of Bioinformatics and University of Geneva 1015Lausanne Switzerland. University of British Columbia Vancouver British ColumbiaV6T 1Z4 Canada. French Institute of Health and Medical Research 35042RENNESCedexFrance. Macquarie University Sydney New South Wales2109 Australia. Griffith University's Institute for Glycomics Sydney New South Wales2109 Australia. Princeton University Princeton New Jersey08544 United States. Uppsala University 752 36Uppsala Sweden. University of Texas Health Science Center-San Antonio San Antonio Texas78229-3900 United States. EMBL-EBI Hinxton CambridgeshireCB10 1SD United Kingdom. Memorial Sloan Kettering Cancer Center New York New York10065 United States. Monash University ClaytonVictoria3800 Australia. BGI Group Shenzhen518083 P. R. China. University of California San Diego La Jolla California92093 United States. National Taiwan University Academia Sinica Nankang Taipei11529 Taiwan. Westlake University Guomics Laboratory of Big Proteomic Data Hangzhou310024 ZhejiangProvinceP. R. China. Institute of Molecular Systems Biology in ETH Zurich 8092Zurich Switzerland.

The 2022 Metrics of the Human Proteome from the HUPO Human Proteome Project (HPP) show that protein expression has now been credibly detected (neXtProt PE1 level) for 18 407 (93.2%) of the 19 750 predicted proteins coded in the human genome, a net gain of 50 since 2021 from data sets generated around the world and reanalyzed by the HPP. Conversely, the number of neXtProt PE2, PE3, and PE4 missing proteins has been reduced by 78 from 1421 to 1343. This represents continuing experimental progress on the human proteome parts list across all the chromosomes, as well as significant reclassifications. Meanwhile, applying proteomics in a vast array of biological and clinical studies continues to yield significant findings and growing integration with other omics platforms. We present highlights from the Chromosome-Centric HPP, Biology and Disease-driven HPP, and HPP Resource Pillars, compare features of mass spectrometry and Olink and Somalogic platforms, note the emergence of translation products from ribosome profiling of small open reading frames, and discuss the launch of the initial HPP Grand Challenge Project, "A Function for Each Protein".

关键词： Biology and Disease-HPP (B/D-HPP) Grand Challenge Project Human Protein Atlas Human Proteome Project (HPP) Mass Spectrometry Interactive Virtual Environment (MassIVE) PeptideAtlas Ribo-Seq chromosome-centric HPP (C-HPP) missing proteins (MP) neXtProt protein existence (PE metrics) non-MS PE1 proteins small open reading frames (smORFs) uncharacterized protein existence 1 (uPE1)

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Transfer learning identifies bacterial signatures for cross-regional diagnosis of type 2 diabetes and enable stage-sensitive dietary fiber intervention

iMetaOmics

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iMetaOmics 2025年第2期2卷

作者： Qunye Zhang Nan Wang Fanghua Zhang Bin Chen Yihui Wang Zhongchao Wang Changying Zhao Chuandi Jin Dashuang Sheng Kaile Yue Daifeng Jiang Liaomei Gao Haohong Zhang Zixin Kang Mingyue Cheng Xiaoli Ma Haiyan Wang Dongming Hu Jun Wang Yuantao Liu Chenhong Zhou Minxiu Yao Guoping Zhao Yangang Wang Zhe Wang Kang Ning Lei Zhang Microbiome-X School of Public Health Department of Cardiology Qilu Hospital Cheeloo College of Medicine Shandong University Jinan China Key Laboratory of Molecular Biophysics of the Ministry of Education Hubei Key Laboratory of Bioinformatics and Molecular-Imaging Center of AI Biology Department of Bioinformatics and Systems Biology College of Life Science and Technology Huazhong University of Science and Technology Wuhan China Department of Endocrinology Qingdao Central Hospital University of Health and Rehabilitation Sciences (Qingdao Central Hospital) Qingdao China Qingdao University Medical Group: Department of Endocrinology and Metabolism The Affiliated Hospital of Qingdao University Department of Endocrinology and Metabolism The Third People's Hospital of Qingdao Qingdao China Shandong Academy of Sciences Science and Technology Services Platform (Shandong Academy of Sciences Foreign Students Pioneer Park) Jinan China Shandong Provincial Maternal and Child Health Care Hospital Affiliated to Qingdao University Jinan China Department of Endocrinology Qingdao Municipal Hospital (Group) Qingdao China Department of Endocrinology The Eighth People's Hospital of Qingdao Qingdao China Department of Endocrinology Qilu Hospital of Shandong University (Qingdao) Qingdao China State Key Laboratory of Microbial Technology Shandong University Qingdao China CAS Key Laboratory of Computational Biology Bio-Med Big Data Center Shanghai Institute of Nutrition and Health University of Chinese Academy of Sciences Chinese Academy of Sciences Shanghai China Department of Endocrinology & Geriatrics Shandong Provincial Hospital Affiliated to Shandong First Medical University Jinan China

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Understanding Social Inequalities in Childhood Asthma: Quantifying the Mediating Role of Modifiable Early-Life Risk Factors in Seven European Birth Cohorts

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Journal of Allergy and Clinical Immunology: In Practice 2025年第6期13卷 1385-1396页

作者： Pinot de Moira, Angela Aurup, Anne V. Avraam, Demetris Zugna, Daniela Jensen, Aksel Karl Georg Welten, Marieke Cadman, Timothy de Lauzon-Guillain, Blandine Duijts, Liesbeth Elhakeem, Ahmed Esplugues, Ana Garcia-Aymerich, Judith García-Baquero, Gonzalo González Safont, Llúcia Harris, Jennifer R. Iñiguez, Carmen Jaddoe, Vincent W.V. McEachan, Rosemary R.C. Nader, Johanna L.T. Santa Marina, Loreto Swertz, Morris A. Tafflet, Muriel Vrijheid, Martine Wright, John Yang, Tiffany C. Taylor-Robinson, David Richiardi, Lorenzo Nybo Andersen, Anne-Marie School of Public Health Imperial College London London United Kingdom Department of Public Health University of Copenhagen Copenhagen Denmark Department of Nutrition Exercise and Sports University of Copenhagen Copenhagen Denmark Department of Public Health Policy and Systems University of Liverpool Liverpool United Kingdom Cancer Epidemiology Unit Department of Medical Sciences University of Turin Turin Italy Department of Pediatrics Erasmus MC University Medical Center Rotterdam Rotterdam Netherlands Generation R Study Group Erasmus MC University Medical Center Rotterdam Rotterdam Netherlands Genomics Coordination Center University Medical Center Groningen University of Groningen Groningen Netherlands Department of Genetics University Medical Center Groningen University of Groningen Groningen Netherlands Université Paris Cité and Université Sorbonne Paris Nord INSERM INRAE Centre for Research in Epidemiology and Statistics Paris France Population Health Science Bristol Medical School Bristol United Kingdom MRC Integrative Epidemiology Unit at the University of Bristol Bristol United Kingdom Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP) Madrid Spain Department of Nursing University of Valencia Valencia Spain Epidemiology and Environmental Health Joint Research Unit the Foundation for the Promotion of Health and Biomedical Research of Valencia Region Universitat Jaume I-Universitat de València Valencia Spain Barcelona Institute for Global Health (ISGlobal) Barcelona Spain Universitat Pompeu Fabra (UPF) Barcelona Spain CEADIR Faculty of Biology University of Salamanca Campus Miguel de Unamuno Avda Licenciado Méndez Nieto s/n Salamanca Spain Biodonostia Environmental Epidemiology and Child Development Group San Sebastian Spain Centre for Fertility and Health Norwegian Institute of Public Health Oslo Norway Department of Statistics and Operational Research Universitat de València València Spain Bradford Institute f

Background: Children growing up in disadvantaged socioeconomic circumstances (SECs) have an increased risk of asthma. Objective: To increase our understanding of the pathways to inequalities in asthma and potential targets for intervention by (1) examining how the social patterning of asthma and its early-life risk factors varies across countries and (2) quantifying the mediation of observed inequalities by early-life risk factors. Methods: We used data for 107,884 mother-child dyads from 7 European birth cohorts across 6 countries. Maternal education was the primary exposure measure of early-life SECs. The outcome was current asthma in childhood (3-12 years). Inequalities were examined using multivariable regression and random effects meta-analysis. The mediating effects of early-life risk factors (maternal smoking during pregnancy, adverse birth outcomes, and breastfeeding duration) were examined using counterfactual mediation analysis. Results: In meta-analysis, children of mothers with low/medium versus high education had a 17% (95% confidence interval: 8%-27%, I² = 21.6%) increased risk of asthma. Cohort-specific risk ratios ranged between 1.07 (0.97-1.18, Danish National Birth Cohort, Denmark) and 1.61 (1.08-2.40, study on the pre- & early postnatal determinants of child health & development, France). The early-life risk factors were similarly socially patterned, but with greater heterogeneity across cohorts (I² range = 66.2%-95.3%). The mediation analysis suggested that these factors play a relevant role in mediating observed inequalities (proportion mediated range: 0.08-0.72). Conclusions: There was a consistent tendency for children from disadvantaged SECs to be at greater risk of asthma in the European cohorts examined. Our results suggest that early-life risk factors partially mediate these disparities and, therefore, that public health interventions in the perinatal period may help to address inequalities in asthma. © 2025 The Autho

关键词： Birth cohort Childhood asthma Cross-cohort Inequalities Meta-analysis Socioeconomic

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