Genomic traits and social determinants of health drive bacterial antimicrobial resistance: current trends and projections to 2050

Genomic traits and social determinants of health drive bacterial antimicrobial resistance: current trends and projections to 2050 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Genomic traits and social determinants of health drive bacterial antimicrobial resistance: current trends and projections to 2050 Tania Dottorini, Alexandre Maciel-Guerra, Michelle Baker, Ruoqi Wang, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6129213/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Antimicrobial resistance (AMR) is projected to increase globally, yet the genomic traits and social determinants of health driving its trajectory are poorly understood. Here, we present the first global-scale AMR forecasting analysis, integrating machine learning, Monte Carlo simulations and forecasting modelling to identify clinically relevant AMR traits projected to increase, and the key determinants driving their increase over the next 25 years. We analysed 45,645 bacterial genomes (including ESKAPE and World Health Organisation high/critical priority pathogens), 298,288 resistance profiles, spanning 84 antibiotics across 27 antimicrobial classes and 1112 determinants of health across 125 countries. We identified 78 clinically relevant AMR feature-pathogen pairs projected to rise globally by 2050. Among these, 94.9% were multidrug resistant (MDR), 89.7% globally widespread, 80.8% persisting over time, and 48.7% multi-host. Our analysis reveals mortality indicators (68.79%) are the strongest predictors, followed by socioeconomic factors (15.29%), and antibiotic consumption (4.46%). However, at the molecular level, socioeconomic disparities are the primary drivers of AMR, strongly associated to the fourteen most critical features (aadA, aadA2, APH(3')-Ia, APH(3'')-Ib, APH(6)-Id, ermB, mphA, qacEdelta1, rep_cluster_1118, sul1, sul2, TEM-34, tet(A), and tet(M)). These features emerged as primary health threats, clinically relevant, MDR, human-enriched, inter-species spreaders, plasmid-associated, and present across up to 10 hosts in at least four World Bank regions. By understanding the interplay of biological and social factors in shaping AMR trends to 2050, we provide a roadmap for targeted AMR mitigation. Biological sciences/Microbiology/Bacteria/Bacterial genomics Biological sciences/Computational biology and bioinformatics/Data mining Biological sciences/Genetics/Microbial genetics/Bacterial genes Biological sciences/Microbiology/Antimicrobials/Antimicrobial resistance Biological sciences/Computational biology and bioinformatics/Computational models Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryData4.csv Supplementary Data 4 SupplementaryData3.csv Supplementary Data 3 SupplementaryData12.xlsx Supplementary Data 12 SupplementaryData5.csv Supplementary Data 5 SupplementaryData8.csv Supplementary Data 8 SupplementaryData6.csv Supplementary Data 6 SupplementaryData11.csv Supplementary Data 11 SupplementaryData10.csv Supplementary Data 10 SupplementaryData9.csv Supplementary Data 9 SupplementaryData7.csv Supplementary Data 7 SupplementaryData2.xlsb Supplementary Data 2 SupplementaryMaterial.pdf Supplementary Material SupplementaryData1.csv Supplementary Data 1 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6129213","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":424588753,"identity":"7afe4da3-ef93-4752-9837-17e82604ea58","order_by":0,"name":"Tania 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2050","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research 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Here, we present the first global-scale AMR forecasting analysis, integrating machine learning, Monte Carlo simulations and forecasting modelling to identify clinically relevant AMR traits projected to increase, and the key determinants driving their increase over the next 25 years. \r\nWe analysed 45,645 bacterial genomes (including ESKAPE and World Health Organisation high/critical priority pathogens), 298,288 resistance profiles, spanning 84 antibiotics across 27 antimicrobial classes and 1112 determinants of health across 125 countries.\r\nWe identified 78 clinically relevant AMR feature-pathogen pairs projected to rise globally by 2050. Among these, 94.9% were multidrug resistant (MDR), 89.7% globally widespread, 80.8% persisting over time, and 48.7% multi-host. Our analysis reveals mortality indicators (68.79%) are the strongest predictors, followed by socioeconomic factors (15.29%), and antibiotic consumption (4.46%). However, at the molecular level, socioeconomic disparities are the primary drivers of AMR, strongly associated to the fourteen most critical features (aadA, aadA2, APH(3')-Ia, APH(3'')-Ib, APH(6)-Id, ermB, mphA, qacEdelta1, rep_cluster_1118, sul1, sul2, TEM-34, tet(A), and tet(M)). These features emerged as primary health threats, clinically relevant, MDR, human-enriched, inter-species spreaders, plasmid-associated, and present across up to 10 hosts in at least four World Bank regions.\r\nBy understanding the interplay of biological and social factors in shaping AMR trends to 2050, we provide a roadmap for targeted AMR mitigation.","manuscriptTitle":"Genomic traits and social determinants of health drive bacterial antimicrobial resistance: current trends and projections to 2050","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-07 03:16:38","doi":"10.21203/rs.3.rs-6129213/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f1ff59c0-1137-498b-955d-a69c0f6a2476","owner":[],"postedDate":"March 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":45246870,"name":"Biological sciences/Microbiology/Bacteria/Bacterial genomics"},{"id":45246871,"name":"Biological sciences/Computational biology and bioinformatics/Data mining"},{"id":45246872,"name":"Biological sciences/Genetics/Microbial genetics/Bacterial genes"},{"id":45246873,"name":"Biological sciences/Microbiology/Antimicrobials/Antimicrobial resistance"},{"id":45246874,"name":"Biological sciences/Computational biology and bioinformatics/Computational models"}],"tags":[],"updatedAt":"2025-07-18T15:25:36+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-07 03:16:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6129213","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6129213","identity":"rs-6129213","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}