INSR as a Potential Target of Bisphenol F-Induced Type 2 Diabetes: Causal Inference and Mechanistic Elucidation via Mendelian Randomization, Multi-Omics Integration, Single-Cell Transcriptomics, and Molecular Docking

INSR as a Potential Target of Bisphenol F-Induced Type 2 Diabetes: Causal Inference and Mechanistic Elucidation via Mendelian Randomization, Multi-Omics Integration, Single-Cell Transcriptomics, and Molecular Docking | 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 INSR as a Potential Target of Bisphenol F-Induced Type 2 Diabetes: Causal Inference and Mechanistic Elucidation via Mendelian Randomization, Multi-Omics Integration, Single-Cell Transcriptomics, and Molecular Docking Yunpeng You¹, Yidan Fan¹, Zhenzhong Zhang¹, Wengang Deng¹, Shipeng Huang¹, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9201479/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 Background Bisphenol F (BPF), a widely adopted substitute for bisphenol A, is a suspected endocrine-disrupting chemical; however, its causal relationship with type 2 diabetes mellitus (T2D) and the underlying molecular mechanisms remain inadequately characterized. Methods Two-sample Mendelian randomization (MR) using European GWAS data evaluated the causal effect of BPF exposure on T2D and eight common diabetic complications. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were applied to GSE76894 to identify T2D-associated genes. Three machine learning algorithms—Random Forest, XGBoost, and LASSO regression—were integrated to identify the core gene, with diagnostic performance validated in GSE64998. Single-cell RNA sequencing data (GSE255566) were analyzed using Seurat for immune cell annotation and CellChat for intercellular communication profiling. Molecular docking and 100 ns molecular dynamics (MD) simulation were conducted to characterize BPF–INSR interaction. Results MR analysis demonstrated a significant positive causal association between BPF exposure and T2D (OR = 1.006, 95% CI: 1.000–1.013, P = 0.040), with sensitivity analyses confirming robustness. All three machine learning algorithms converged on INSR (insulin receptor) as the sole core gene; INSR was significantly upregulated in T2D and achieved an AUC of 0.92 in the training set and 0.82 in the independent validation set. ScRNA-seq analysis identified five peripheral blood immune cell types, revealing a marked reduction in neutrophil proportion alongside monocyte expansion in T2D; INSR exhibited the highest expression in monocytes. CellChat analysis identified the IGF1–INSR ligand–receptor pair as a central intercellular communication axis, with monocytes functioning as the primary signaling hub and monocyte–neutrophil communication showing the strongest interaction intensity. Molecular docking confirmed stable BPF binding within the INSR ligand-binding pocket (ΔG = − 6.7 kcal/mol) at key residues Tyr232, Met56, and Ala227, with MD simulation corroborating complex stability over 100 ns. Conclusion This integrative multi-omics study establishes a genetic causal link between BPF exposure and T2D and identifies INSR as the core molecular target mediating BPF-induced metabolic and immune dysregulation, providing a mechanistic framework and a candidate biomarker for environmentally driven insulin resistance. Health sciences/Biomarkers Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Health sciences/Endocrinology Biological sciences/Genetics Biological sciences/Immunology Bisphenol F Type 2 diabetes INSR Mendelian randomization Single-cell sequencing Machine learning Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformation.docx 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9201479","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":611475354,"identity":"46ff1f62-5bce-48bd-8ecf-e9c32a0ef8fc","order_by":0,"name":"Yunpeng You¹","email":"","orcid":"","institution":"First Affiliated Hospital of Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Yunpeng","middleName":"","lastName":"You¹","suffix":""},{"id":611475355,"identity":"17575d47-d307-49e0-aa8e-99f328a427f3","order_by":1,"name":"Yidan Fan¹","email":"","orcid":"","institution":"First Affiliated Hospital of Nanchang 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Docking","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 Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Bisphenol F, Type 2 diabetes, INSR, Mendelian randomization, Single-cell sequencing, Machine learning","lastPublishedDoi":"10.21203/rs.3.rs-9201479/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9201479/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eBisphenol F (BPF), a widely adopted substitute for bisphenol A, is a suspected endocrine-disrupting chemical; however, its causal relationship with type 2 diabetes mellitus (T2D) and the underlying molecular mechanisms remain inadequately characterized.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eTwo-sample Mendelian randomization (MR) using European GWAS data evaluated the causal effect of BPF exposure on T2D and eight common diabetic complications. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were applied to GSE76894 to identify T2D-associated genes. Three machine learning algorithms\u0026mdash;Random Forest, XGBoost, and LASSO regression\u0026mdash;were integrated to identify the core gene, with diagnostic performance validated in GSE64998. Single-cell RNA sequencing data (GSE255566) were analyzed using Seurat for immune cell annotation and CellChat for intercellular communication profiling. Molecular docking and 100 ns molecular dynamics (MD) simulation were conducted to characterize BPF\u0026ndash;INSR interaction.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eMR analysis demonstrated a significant positive causal association between BPF exposure and T2D (OR\u0026thinsp;=\u0026thinsp;1.006, 95% CI: 1.000\u0026ndash;1.013, P\u0026thinsp;=\u0026thinsp;0.040), with sensitivity analyses confirming robustness. All three machine learning algorithms converged on INSR (insulin receptor) as the sole core gene; INSR was significantly upregulated in T2D and achieved an AUC of 0.92 in the training set and 0.82 in the independent validation set. ScRNA-seq analysis identified five peripheral blood immune cell types, revealing a marked reduction in neutrophil proportion alongside monocyte expansion in T2D; INSR exhibited the highest expression in monocytes. CellChat analysis identified the IGF1\u0026ndash;INSR ligand\u0026ndash;receptor pair as a central intercellular communication axis, with monocytes functioning as the primary signaling hub and monocyte\u0026ndash;neutrophil communication showing the strongest interaction intensity. Molecular docking confirmed stable BPF binding within the INSR ligand-binding pocket (ΔG\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;6.7 kcal/mol) at key residues Tyr232, Met56, and Ala227, with MD simulation corroborating complex stability over 100 ns.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis integrative multi-omics study establishes a genetic causal link between BPF exposure and T2D and identifies INSR as the core molecular target mediating BPF-induced metabolic and immune dysregulation, providing a mechanistic framework and a candidate biomarker for environmentally driven insulin resistance.\u003c/p\u003e","manuscriptTitle":"INSR as a Potential Target of Bisphenol F-Induced Type 2 Diabetes: Causal Inference and Mechanistic Elucidation via Mendelian Randomization, Multi-Omics Integration, Single-Cell Transcriptomics, and Molecular Docking","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-25 08:57:32","doi":"10.21203/rs.3.rs-9201479/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":"36b28d1f-2133-4035-8617-f14d888cb5b5","owner":[],"postedDate":"March 25th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":65062502,"name":"Health sciences/Biomarkers"},{"id":65062503,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":65062504,"name":"Health sciences/Diseases"},{"id":65062505,"name":"Health sciences/Endocrinology"},{"id":65062506,"name":"Biological sciences/Genetics"},{"id":65062507,"name":"Biological sciences/Immunology"}],"tags":[],"updatedAt":"2026-03-25T08:57:55+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-25 08:57:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9201479","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9201479","identity":"rs-9201479","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}