Genomic and proteomic signatures highlight diverse pathways between obesity and type-2 diabetes

preprint OA: closed
Full text JSON View at publisher
AI-generated summary by claude@2026-07, 2026-07-14

This study used multi-omics to identify four clusters of obesity-associated genetic variants with differential T2D risk and found that eight proteins are causally linked to T2D through these clusters.

One-sentence paraphrase of the abstract; not a substitute for reading it. No clinical advice. How this works

AI-generated deep summary by claude@2026-07, 2026-07-14 · read from full text

The preprint investigated causal genomic and proteomic pathways that could explain heterogeneity in which obese individuals develop type 2 diabetes (T2D), using a multi-omics Mendelian randomization framework. The authors meta-analyzed genome-wide association study data from FinnGen and GIANT (N=699,431) to identify 513 obesity-associated independent SNPs, then clustered Mendelian randomization estimates using T2D summary data from DIAGRAM (74,124 cases and 824,006 controls), finding four BMI-increasing SNP clusters with effects on T2D risk ranging from harmful to protective. Cluster-specific analyses in UK Biobank data (N=54,219) identified 212 protein measurements causally affected by the clusters and, in downstream analyses, eight proteins associated with T2D, including SNAP25, PAM, and FSTL3, suggesting pathways tied to insulin synthesis and secretion. A major caveat is that the work is presented as an unreviewed preprint and includes multiple integrative, inference-based steps (e.g., clustering and MR with protein panels) rather than direct experimental validation. This paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

Read from the paper's body, not the abstract. Not a substitute for reading the paper. No clinical advice. How this works

Abstract

Abstract Obesity is a significant risk factor for Type 2 diabetes (T2D), a disease that affects about 10% of the global population. Obesity is also a heterogeneous condition, and the molecular mechanisms linking it to T2D are not yet fully understood. The aim of this study was to elucidate causal pathways between obesity and T2D and to characterize their molecular signatures using an innovative multi-omics approach, thereby highlighting why some, but not all, obese individuals develop T2D. We identified 513 independent obesity-associated SNPs by meta-analysing genome-wide association study data from FinnGen and GIANT (N=699,431). Clustering of Mendelian randomization (MR) estimates, computed using T2D data from DIAGRAM (74,124 cases and 824,006 controls), identified four clusters of SNPs. These clusters, all associated with increased body mass index (BMI), showed differential effects on T2D risk, ranging from harmful to protective. Cluster-specific MR analyses identified 212, out of 2922 protein measurements from the UK Biobank (N=54,219), to be causally affected by any of the clusters. Among these, eight proteins were significantly associated with T2D in downstream MR analyses, representing potential pathways responsible for the heterogeneous link between obesity and T2D. These proteins include, for example, SNAP25, PAM, and FSTL3, suggesting that one of the underlying molecular pathways is tightly linked to insulin synthesis and secretion.
Full text 12,478 characters · extracted from preprint-html · click to expand
Genomic and proteomic signatures highlight diverse pathways between obesity and type-2 diabetes | 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 and proteomic signatures highlight diverse pathways between obesity and type-2 diabetes Åsa Johansson, Pascal Mutie, Torgny Karlsson This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6079296/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Obesity is a significant risk factor for Type 2 diabetes (T2D), a disease that affects about 10% of the global population. Obesity is also a heterogeneous condition, and the molecular mechanisms linking it to T2D are not yet fully understood. The aim of this study was to elucidate causal pathways between obesity and T2D and to characterize their molecular signatures using an innovative multi-omics approach, thereby highlighting why some, but not all, obese individuals develop T2D. We identified 513 independent obesity-associated SNPs by meta-analysing genome-wide association study data from FinnGen and GIANT (N=699,431). Clustering of Mendelian randomization (MR) estimates, computed using T2D data from DIAGRAM (74,124 cases and 824,006 controls), identified four clusters of SNPs. These clusters, all associated with increased body mass index (BMI), showed differential effects on T2D risk, ranging from harmful to protective. Cluster-specific MR analyses identified 212, out of 2922 protein measurements from the UK Biobank (N=54,219), to be causally affected by any of the clusters. Among these, eight proteins were significantly associated with T2D in downstream MR analyses, representing potential pathways responsible for the heterogeneous link between obesity and T2D. These proteins include, for example, SNAP25, PAM, and FSTL3, suggesting that one of the underlying molecular pathways is tightly linked to insulin synthesis and secretion. Health sciences/Medical research/Genetics research Biological sciences/Computational biology and bioinformatics/Functional clustering Health sciences/Endocrinology/Endocrine system and metabolic diseases/Obesity Biological sciences/Genetics/Genomics/Personalized medicine Biological sciences/Drug discovery/Biomarkers/Predictive markers Full Text Additional Declarations There is NO Competing Interest. Supplementary Files NatMetT2DMRClustSupplementaryMaterials250221.pdf Supplementary Materials NatMedBMIT2DMRCLUSTSUPPLEMENTARYFILES250221.xlsx Supplementary Files Cite Share Download PDF Status: Under Review 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-6079296","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":422382061,"identity":"e4a32162-0a4a-41b3-9c91-e751abcf0090","order_by":0,"name":"Åsa Johansson","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+0lEQVRIiWNgGAWjYLACCYYDUFaFBMlazhCrhQGmhbGNCLX8/YcfMFhU3JE35z/+8HHhPAu77Qw8ZhIMf2xwu+lGmgGDxJlnhjtn5Bgbz9wmkbyzAaiFsS0NtzU3GAwYJNsOM264wcMmzQvUYnAApKXhME4d8uePfwBpsd9w/vgzad45UC0Mf/7j1GJwIAdsS+KGAwlm0rwNEnYQLWwHcGoxvJFTcEDizOHkDTeAfuE5JpFg2cxWbJHYloxTi9z54xsfS1QctgU67OFjnpo6e3P25o03Pvyxw+19IDiMHH+JG5iBZAJeDcAI/IDEsTcgoHoUjIJRMApGHgAAY75SfvnVQ9MAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-2915-4498","institution":"Uppsala University","correspondingAuthor":true,"prefix":"","firstName":"Åsa","middleName":"","lastName":"Johansson","suffix":""},{"id":422382062,"identity":"7a32c944-a9af-4127-9b3a-5a18c820dbb9","order_by":1,"name":"Pascal Mutie","email":"","orcid":"https://orcid.org/0000-0003-2626-1703","institution":"Uppsala University","correspondingAuthor":false,"prefix":"","firstName":"Pascal","middleName":"","lastName":"Mutie","suffix":""},{"id":422382063,"identity":"05fb7b55-beed-4b14-a144-39443eebd63b","order_by":2,"name":"Torgny Karlsson","email":"","orcid":"https://orcid.org/0000-0001-8095-6149","institution":"Uppsala University","correspondingAuthor":false,"prefix":"","firstName":"Torgny","middleName":"","lastName":"Karlsson","suffix":""}],"badges":[],"createdAt":"2025-02-21 12:01:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6079296/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6079296/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78492987,"identity":"c2eb11d0-81ef-442a-a142-f1dd98e1e3b1","added_by":"auto","created_at":"2025-03-14 03:13:31","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1350571,"visible":true,"origin":"","legend":"Article File","description":"","filename":"NatMetManuscriptT2Dmrclust250221.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6079296/v1_covered_cd44681f-cc4e-460b-85bb-9a7fadd31d3d.pdf"},{"id":78492504,"identity":"a5b73c86-2221-4195-a75a-015a86cde150","added_by":"auto","created_at":"2025-03-14 03:05:29","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":325875,"visible":true,"origin":"","legend":"Supplementary Materials","description":"","filename":"NatMetT2DMRClustSupplementaryMaterials250221.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6079296/v1/6108b47a16d8de5c0f0b6067.pdf"},{"id":78492507,"identity":"3bbc004d-dd89-40e1-9190-b53360456b11","added_by":"auto","created_at":"2025-03-14 03:05:29","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":3383313,"visible":true,"origin":"","legend":"Supplementary Files","description":"","filename":"NatMedBMIT2DMRCLUSTSUPPLEMENTARYFILES250221.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6079296/v1/e24636a7f5a7dbc5174da441.xlsx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Genomic and proteomic signatures highlight diverse pathways between obesity and type-2 diabetes","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"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":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6079296/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6079296/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Obesity is a significant risk factor for Type 2 diabetes (T2D), a disease that affects about 10% of the global population. Obesity is also a heterogeneous condition, and the molecular mechanisms linking it to T2D are not yet fully understood. The aim of this study was to elucidate causal pathways between obesity and T2D and to characterize their molecular signatures using an innovative multi-omics approach, thereby highlighting why some, but not all, obese individuals develop T2D. We identified 513 independent obesity-associated SNPs by meta-analysing genome-wide association study data from FinnGen and GIANT (N=699,431). Clustering of Mendelian randomization (MR) estimates, computed using T2D data from DIAGRAM (74,124 cases and 824,006 controls), identified four clusters of SNPs. These clusters, all associated with increased body mass index (BMI), showed differential effects on T2D risk, ranging from harmful to protective. Cluster-specific MR analyses identified 212, out of 2922 protein measurements from the UK Biobank (N=54,219), to be causally affected by any of the clusters. Among these, eight proteins were significantly associated with T2D in downstream MR analyses, representing potential pathways responsible for the heterogeneous link between obesity and T2D. These proteins include, for example, SNAP25, PAM, and FSTL3, suggesting that one of the underlying molecular pathways is tightly linked to insulin synthesis and secretion.","manuscriptTitle":"Genomic and proteomic signatures highlight diverse pathways between obesity and type-2 diabetes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-14 03:05:24","doi":"10.21203/rs.3.rs-6079296/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"296c58bf-b8e4-4038-9315-e379f17222d6","owner":[],"postedDate":"March 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":45024802,"name":"Health sciences/Medical research/Genetics research"},{"id":45024803,"name":"Biological sciences/Computational biology and bioinformatics/Functional clustering"},{"id":45024804,"name":"Health sciences/Endocrinology/Endocrine system and metabolic diseases/Obesity"},{"id":45024805,"name":"Biological sciences/Genetics/Genomics/Personalized medicine"},{"id":45024806,"name":"Biological sciences/Drug discovery/Biomarkers/Predictive markers"}],"tags":[],"updatedAt":"2025-03-14T03:05:24+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-14 03:05:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6079296","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6079296","identity":"rs-6079296","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}

Text is read by the "Ask this paper" AI Q&A widget below. Extraction quality varies by source — PMC NXML preserves structure cleanly, OA-HTML may include some navigation residue, and OA-PDF can have broken hyphenation. The publisher copy (via DOI) is the canonical version.

My notes (saved in your browser only)

Ask this paper AI returns verbatim quotes from the full text · source: preprint-html

Answers must be backed by verbatim quotes from this paper's full text. Hallucinated quotes are dropped automatically; if no verbatim passage answers the question, we say so. How this works

Citation neighborhood (no data yet)

We don't have any in-corpus citations linked to this paper yet. This is a recent paper (2025) — citers typically take a year or two to land, and the OpenAlex reference graph may still be filling in.

Source provenance

europepmc
last seen: 2026-05-20T01:45:00.602351+00:00