{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T09:00:48Z","timestamp":1790413248565,"version":"4.1.0"},"reference-count":33,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T00:00:00Z","timestamp":1779235200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T00:00:00Z","timestamp":1779235200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["analyticalsciencejournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Proteomics"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:label\/>\n                    <jats:p>Plasma extracellular vesicles (EVs) are considered excellent sources for biomarker discovery since they carry signatures of their cellular origin and disease processes. In this paper, we evaluate the potential of plasma EV proteomics analysis for identifying predictive biomarkers of developing type 1 diabetes (T1D), which results from autoimmune destruction of insulin\u2010producing \u03b2 cells in the islet. We used strong anion exchange beads (Mag\u2010Net) to capture plasma EVs from 19 donors with islet autoimmunity (diagnosed by circulating autoantibodies against islet proteins\u2014AAB+) versus 17 control individuals and analyzed their protein cargo by mass spectrometry. The analysis identified and quantified 5,480 proteins, a 3.2\u2010fold increase in proteome coverage compared to our previous T1D biomarker proteomics study that used whole plasma depleted of the 14 most abundant proteins. The Mag\u2010Net approach also detected 1306 out of the 1717 proteins (76%) that we previously verified as EV proteins. Statistical tests revealed 448 proteins to be differentially abundant in AAB+ versus control volunteers, including 69 previously verified EV proteins. A functional\u2010enrichment analysis resulted in overrepresentation of 25 pathways among the differentially abundant proteins, including pathways related to autoimmune response and lipid metabolism. The capacity of this data to predict AAB+ was tested with a machine learning analysis using a random forest model, resulting in a receiver operating characteristic\u2010area under the curve of 0.81. Overall, our study indicates that plasma EV proteomics analysis can be an exciting approach for studying biomarkers for developing T1D.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Significance of the Study<\/jats:title>\n                    <jats:p>Type 1 diabetes (T1D) is a disease characterized by the body's inability to produce insulin and consequently, to control blood glucose levels. Despite the initial trigger being unclear, the disease development process involves an autoimmune response to the islets of Langerhans, resulting in the death of insulin\u2010producing \u03b2 cells. There is no cure for the disease, and treatment relies on exogenous administration of insulin. Therefore, preventive therapies that block the autoimmune process are attractive for treating T1D. In fact, anti\u2010CD3 antibody (Teplizumab) delays the onset of T1D by 2 years by targeting T cells. Predictive biomarkers for developing T1D are needed to aid the development and implementation of new therapies and to identify the initial trigger and mechanisms of the islet autoimmune process. In this paper, we assess the potential of plasma extracellular vesicle (EV) proteomics analysis for identifying predictive biomarkers of T1D. Our results show excellent potential of the approach, opening opportunities to perform broader studies to identify biomarkers for developing T1D.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/pmic.70148","type":"journal-article","created":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T05:43:35Z","timestamp":1779342215000},"page":"24-30","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Assessing Extracellular Vesicle Proteins as Predictive Biomarkers for Developing Type 1 Diabetes"],"prefix":"10.1002","volume":"26","author":[{"given":"Panshak P.","family":"Dakup","sequence":"first","affiliation":[{"name":"Biological Sciences Division Pacific Northwest National Laboratory  Richland Washington USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lisa M.","family":"Bramer","sequence":"additional","affiliation":[{"name":"Biological Sciences Division Pacific Northwest National Laboratory  Richland Washington USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Athena","family":"Schepmoes","sequence":"additional","affiliation":[{"name":"Biological Sciences Division Pacific Northwest National Laboratory  Richland Washington USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ivo","family":"Diaz Ludovico","sequence":"additional","affiliation":[{"name":"Biological Sciences Division Pacific Northwest National Laboratory  Richland Washington USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Javier","family":"Flores","sequence":"additional","affiliation":[{"name":"Biological Sciences Division Pacific Northwest National Laboratory  Richland Washington USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raghavendra G.","family":"Mirmira","sequence":"additional","affiliation":[{"name":"Department of Medicine and the Kovler Diabetes Center The University of Chicago  Chicago Illinois USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bobbie\u2010Jo M.","family":"Webb\u2010Robertson","sequence":"additional","affiliation":[{"name":"Biological Sciences Division Pacific Northwest National Laboratory  Richland Washington USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas O.","family":"Metz","sequence":"additional","affiliation":[{"name":"Biological Sciences Division Pacific Northwest National Laboratory  Richland Washington USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emily K.","family":"Sims","sequence":"additional","affiliation":[{"name":"Department of Pediatrics Indiana University School of Medicine  Indianapolis Indiana USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ernesto S.","family":"Nakayasu","sequence":"additional","affiliation":[{"name":"Biological Sciences Division Pacific Northwest National Laboratory  Richland Washington 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