{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T02:46:13Z","timestamp":1791773173336,"version":"4.3.4"},"reference-count":49,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T00:00:00Z","timestamp":1774483200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T00:00:00Z","timestamp":1774483200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100019540","name":"Science Fund for Distinguished Young Scholars of Zhejiang Province","doi-asserted-by":"publisher","award":["LR22H260001"],"award-info":[{"award-number":["LR22H260001"]}],"id":[{"id":"10.13039\/501100019540","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82204019"],"award-info":[{"award-number":["82204019"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dom-pubs.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Diabetes Obesity Metabolism"],"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Aims<\/jats:title>\n                    <jats:p>The burden of chronic liver disease (CLD) is increasing. This study aims to identify protein markers for CLD and its progression, and develop a protein\u2010based risk prediction model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>We used proteome\u2010wide Mendelian randomization (MR), Bayesian colocalization and summary\u2010data\u2010based MR with proteomic data from deCODE Genetics to identify CLD\u2010related proteins. Multivariable MR was used to assess independent protein effects. Protein\u2013protein interaction, druggability and mediation analyses were conducted to prioritise therapeutic targets. We further constructed a protein risk score to predict CLD and composite hepatic event outcomes, comparing its performance with existing clinical predictors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Genetically predicted levels of 16, 5 and 4 plasma proteins were associated with metabolic dysfunction\u2010associated steatotic liver disease (MASLD), alcoholic liver disease (ALD) and cirrhosis, respectively. IGSF3, FTCD, DCXR, ADH1B and ACY1 were associated with CLD progression. Genetically predicted five modifiable factors (body mass index, waist\u2010hip ratio, glycated haemoglobin, type 2 diabetes, leisure television watching) were associated with CLD\u2010related proteins. Proteomic\u2010based models showed high predictive performance for ALD (C\u2010index\u2009=\u20090.89), liver cancer (C\u2010index\u2009=\u20090.84) and liver failure (C\u2010index\u2009=\u20090.84) in a healthy population without liver diseases at baseline.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This study identified key circulating protein markers for CLD. Protein\u2010based profiling demonstrated strong predictive potential for CLD and related outcomes.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/dom.70696","type":"journal-article","created":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T06:26:02Z","timestamp":1774592762000},"page":"5029-5042","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Proteomic Profiling of Plasma to Uncover Novel Intervention Targets and Prognostic Biomarkers for Chronic Liver Diseases"],"prefix":"10.1111","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8922-661X","authenticated-orcid":false,"given":"Xinxuan","family":"Li","sequence":"first","affiliation":[{"name":"State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, the First Affiliated 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Second Affiliated Hospital, School of Public Health Zhejiang University School of Medicine  Hangzhou Zhejiang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Center of Clinical Big Data and Analytics of the Second Affiliated Hospital, School of Public Health Zhejiang University School of Medicine  Hangzhou Zhejiang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology National Clinical Research Center for Digestive Diseases, Changhai Hospital; National Key Laboratory of Immunity and Inflammation, Naval Medical University  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuai","family":"Yuan","sequence":"additional","affiliation":[{"name":"Unit of Cardiovascular and Nutritional Epidemiology Institute of Environmental Medicine, Karolinska Institutet  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