Novel urinary protein panels for the non‐invasive diagnosis of non‐alcoholic fatty liver disease and fibrosis stages
Background & Aims There is an unmet clinical need for non‐invasive tests to diagnose non‐alcoholic fatty liver disease (NAFLD) and individual fibrosis stages. We aimed to test whether urine protein panels could be used to identify NAFLD, NAFLD with fibrosis (stage F ≥ 1) and NAFLD with significa...
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Veröffentlicht in: | Liver international 2023-06, Vol.43 (6), p.1234-1246 |
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creator | Feng, Gong Zhang, Xiaoxun Zhang, Liangjun Liu, Wen‐Yue Geng, Shi Yuan, Hai‐Yang Sha, Jun‐Cheng Wang, Xiao‐Dong Sun, Dan‐Qin Targher, Giovanni Byrne, Christopher D. Zheng, Tian‐Lei Ye, Feng Zheng, Ming‐Hua Chai, Jin |
description | Background & Aims
There is an unmet clinical need for non‐invasive tests to diagnose non‐alcoholic fatty liver disease (NAFLD) and individual fibrosis stages. We aimed to test whether urine protein panels could be used to identify NAFLD, NAFLD with fibrosis (stage F ≥ 1) and NAFLD with significant fibrosis (stage F ≥ 2).
Methods
We collected urine samples from 100 patients with biopsy‐confirmed NAFLD and 40 healthy volunteers, and proteomics and bioinformatics analyses were performed in this derivation cohort. Diagnostic models were developed for detecting NAFLD (UPNAFLD model), NAFLD with fibrosis (UPfibrosis model), or NAFLD with significant fibrosis (UPsignificant fibrosis model). Subsequently, the derivation cohort was divided into training and testing sets to evaluate the efficacy of these diagnostic models. Finally, in a separate independent validation cohort of 100 patients with biopsy‐confirmed NAFLD and 45 healthy controls, urinary enzyme‐linked immunosorbent assay analyses were undertaken to validate the accuracy of these new diagnostic models.
Results
The UPfibrosis model and the UPsignificant fibrosis model showed an AUROC of .863 (95% CI: .725–1.000) and 0.858 (95% CI: .712–1.000) in the training set; and .837 (95% CI: .711–.963) and .916 (95% CI: .825–1.000) in the testing set respectively. The UPNAFLD model showed an excellent diagnostic performance and the area under the receiver operator characteristic curve (AUROC) exceeded .90 in the derivation cohort. In the independent validation cohort, the AUROC for all three of the above diagnostic models exceeded .80.
Conclusions
Our newly developed models constructed from urine protein biomarkers have good accuracy for non‐invasively diagnosing liver fibrosis in NAFLD. |
doi_str_mv | 10.1111/liv.15565 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2791704153</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2811896440</sourcerecordid><originalsourceid>FETCH-LOGICAL-c3535-7c2d0ea9107853940981a2d3a26cc26b776781e7ec3479904b8c96b550098e5b3</originalsourceid><addsrcrecordid>eNp1kc9OHDEMxiPUCijtoS9QReqFHhbydzI5VqhQpFW5ANcok_FAUDbZJjNL98Yj8Iw8SQO7cKhUX2zJP3_yZyP0mZIjWuM4-NURlbKRO2ifCtXOOOP03VvN-B76UModIVRrSXfRHm80E4I3--jPr7SCgKfso81rvMxpBB_x0kYIBQ8p4_EWcEzx6eHRx5UtfgW49_YmpuILTsO2Z4NLtyl4hwc7jmtcV4JcwQK2ALaxx4Pv8stMGe0NlI_o_WBDgU_bfICuTn9cnvyczS_Ozk--z2eOSy5nyrGegNWUqFZyLYhuqWU9t6xxjjWdUo1qKShwXCitiehap5tOSlJJkB0_QIcb3Wrt9wRlNAtfHIRQHaapGKY0VURQySv69R_0Lk051u0MayltdSMEqdS3DeWqnZJhMMvsF_V4hhLz_A5TvZuXd1T2y1Zx6hbQv5Gv96_A8Qa49wHW_1cy8_PrjeRfmViWVA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2811896440</pqid></control><display><type>article</type><title>Novel urinary protein panels for the non‐invasive diagnosis of non‐alcoholic fatty liver disease and fibrosis stages</title><source>MEDLINE</source><source>Wiley Online Library Journals Frontfile Complete</source><creator>Feng, Gong ; Zhang, Xiaoxun ; Zhang, Liangjun ; Liu, Wen‐Yue ; Geng, Shi ; Yuan, Hai‐Yang ; Sha, Jun‐Cheng ; Wang, Xiao‐Dong ; Sun, Dan‐Qin ; Targher, Giovanni ; Byrne, Christopher D. ; Zheng, Tian‐Lei ; Ye, Feng ; Zheng, Ming‐Hua ; Chai, Jin</creator><creatorcontrib>Feng, Gong ; Zhang, Xiaoxun ; Zhang, Liangjun ; Liu, Wen‐Yue ; Geng, Shi ; Yuan, Hai‐Yang ; Sha, Jun‐Cheng ; Wang, Xiao‐Dong ; Sun, Dan‐Qin ; Targher, Giovanni ; Byrne, Christopher D. ; Zheng, Tian‐Lei ; Ye, Feng ; Zheng, Ming‐Hua ; Chai, Jin ; CHESS-MAFLD Consortium ; CHESS‐MAFLD Consortium</creatorcontrib><description>Background & Aims
There is an unmet clinical need for non‐invasive tests to diagnose non‐alcoholic fatty liver disease (NAFLD) and individual fibrosis stages. We aimed to test whether urine protein panels could be used to identify NAFLD, NAFLD with fibrosis (stage F ≥ 1) and NAFLD with significant fibrosis (stage F ≥ 2).
Methods
We collected urine samples from 100 patients with biopsy‐confirmed NAFLD and 40 healthy volunteers, and proteomics and bioinformatics analyses were performed in this derivation cohort. Diagnostic models were developed for detecting NAFLD (UPNAFLD model), NAFLD with fibrosis (UPfibrosis model), or NAFLD with significant fibrosis (UPsignificant fibrosis model). Subsequently, the derivation cohort was divided into training and testing sets to evaluate the efficacy of these diagnostic models. Finally, in a separate independent validation cohort of 100 patients with biopsy‐confirmed NAFLD and 45 healthy controls, urinary enzyme‐linked immunosorbent assay analyses were undertaken to validate the accuracy of these new diagnostic models.
Results
The UPfibrosis model and the UPsignificant fibrosis model showed an AUROC of .863 (95% CI: .725–1.000) and 0.858 (95% CI: .712–1.000) in the training set; and .837 (95% CI: .711–.963) and .916 (95% CI: .825–1.000) in the testing set respectively. The UPNAFLD model showed an excellent diagnostic performance and the area under the receiver operator characteristic curve (AUROC) exceeded .90 in the derivation cohort. In the independent validation cohort, the AUROC for all three of the above diagnostic models exceeded .80.
Conclusions
Our newly developed models constructed from urine protein biomarkers have good accuracy for non‐invasively diagnosing liver fibrosis in NAFLD.</description><identifier>ISSN: 1478-3223</identifier><identifier>EISSN: 1478-3231</identifier><identifier>DOI: 10.1111/liv.15565</identifier><identifier>PMID: 36924436</identifier><language>eng</language><publisher>United States: Wiley Subscription Services, Inc</publisher><subject>Accuracy ; Bioinformatics ; Biomarkers ; Biomarkers - metabolism ; Biopsy ; Derivation ; diagnosis ; Diagnostic systems ; Fatty liver ; Fibrosis ; Humans ; Liver ; Liver - pathology ; liver biopsy ; Liver Cirrhosis - pathology ; Liver diseases ; NAFLD ; Non-alcoholic Fatty Liver Disease - pathology ; Panels ; Proteins ; Proteomics ; Training ; urinary proteomics ; Urine</subject><ispartof>Liver international, 2023-06, Vol.43 (6), p.1234-1246</ispartof><rights>2023 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.</rights><rights>2023 John Wiley & Sons A/S</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3535-7c2d0ea9107853940981a2d3a26cc26b776781e7ec3479904b8c96b550098e5b3</citedby><cites>FETCH-LOGICAL-c3535-7c2d0ea9107853940981a2d3a26cc26b776781e7ec3479904b8c96b550098e5b3</cites><orcidid>0000-0003-4984-2631 ; 0000-0002-8543-4566 ; 0000-0002-3892-7464 ; 0000-0002-7053-8661 ; 0000-0002-4325-3900</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1111%2Fliv.15565$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2Fliv.15565$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,27901,27902,45550,45551</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36924436$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Feng, Gong</creatorcontrib><creatorcontrib>Zhang, Xiaoxun</creatorcontrib><creatorcontrib>Zhang, Liangjun</creatorcontrib><creatorcontrib>Liu, Wen‐Yue</creatorcontrib><creatorcontrib>Geng, Shi</creatorcontrib><creatorcontrib>Yuan, Hai‐Yang</creatorcontrib><creatorcontrib>Sha, Jun‐Cheng</creatorcontrib><creatorcontrib>Wang, Xiao‐Dong</creatorcontrib><creatorcontrib>Sun, Dan‐Qin</creatorcontrib><creatorcontrib>Targher, Giovanni</creatorcontrib><creatorcontrib>Byrne, Christopher D.</creatorcontrib><creatorcontrib>Zheng, Tian‐Lei</creatorcontrib><creatorcontrib>Ye, Feng</creatorcontrib><creatorcontrib>Zheng, Ming‐Hua</creatorcontrib><creatorcontrib>Chai, Jin</creatorcontrib><creatorcontrib>CHESS-MAFLD Consortium</creatorcontrib><creatorcontrib>CHESS‐MAFLD Consortium</creatorcontrib><title>Novel urinary protein panels for the non‐invasive diagnosis of non‐alcoholic fatty liver disease and fibrosis stages</title><title>Liver international</title><addtitle>Liver Int</addtitle><description>Background & Aims
There is an unmet clinical need for non‐invasive tests to diagnose non‐alcoholic fatty liver disease (NAFLD) and individual fibrosis stages. We aimed to test whether urine protein panels could be used to identify NAFLD, NAFLD with fibrosis (stage F ≥ 1) and NAFLD with significant fibrosis (stage F ≥ 2).
Methods
We collected urine samples from 100 patients with biopsy‐confirmed NAFLD and 40 healthy volunteers, and proteomics and bioinformatics analyses were performed in this derivation cohort. Diagnostic models were developed for detecting NAFLD (UPNAFLD model), NAFLD with fibrosis (UPfibrosis model), or NAFLD with significant fibrosis (UPsignificant fibrosis model). Subsequently, the derivation cohort was divided into training and testing sets to evaluate the efficacy of these diagnostic models. Finally, in a separate independent validation cohort of 100 patients with biopsy‐confirmed NAFLD and 45 healthy controls, urinary enzyme‐linked immunosorbent assay analyses were undertaken to validate the accuracy of these new diagnostic models.
Results
The UPfibrosis model and the UPsignificant fibrosis model showed an AUROC of .863 (95% CI: .725–1.000) and 0.858 (95% CI: .712–1.000) in the training set; and .837 (95% CI: .711–.963) and .916 (95% CI: .825–1.000) in the testing set respectively. The UPNAFLD model showed an excellent diagnostic performance and the area under the receiver operator characteristic curve (AUROC) exceeded .90 in the derivation cohort. In the independent validation cohort, the AUROC for all three of the above diagnostic models exceeded .80.
Conclusions
Our newly developed models constructed from urine protein biomarkers have good accuracy for non‐invasively diagnosing liver fibrosis in NAFLD.</description><subject>Accuracy</subject><subject>Bioinformatics</subject><subject>Biomarkers</subject><subject>Biomarkers - metabolism</subject><subject>Biopsy</subject><subject>Derivation</subject><subject>diagnosis</subject><subject>Diagnostic systems</subject><subject>Fatty liver</subject><subject>Fibrosis</subject><subject>Humans</subject><subject>Liver</subject><subject>Liver - pathology</subject><subject>liver biopsy</subject><subject>Liver Cirrhosis - pathology</subject><subject>Liver diseases</subject><subject>NAFLD</subject><subject>Non-alcoholic Fatty Liver Disease - pathology</subject><subject>Panels</subject><subject>Proteins</subject><subject>Proteomics</subject><subject>Training</subject><subject>urinary proteomics</subject><subject>Urine</subject><issn>1478-3223</issn><issn>1478-3231</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp1kc9OHDEMxiPUCijtoS9QReqFHhbydzI5VqhQpFW5ANcok_FAUDbZJjNL98Yj8Iw8SQO7cKhUX2zJP3_yZyP0mZIjWuM4-NURlbKRO2ifCtXOOOP03VvN-B76UModIVRrSXfRHm80E4I3--jPr7SCgKfso81rvMxpBB_x0kYIBQ8p4_EWcEzx6eHRx5UtfgW49_YmpuILTsO2Z4NLtyl4hwc7jmtcV4JcwQK2ALaxx4Pv8stMGe0NlI_o_WBDgU_bfICuTn9cnvyczS_Ozk--z2eOSy5nyrGegNWUqFZyLYhuqWU9t6xxjjWdUo1qKShwXCitiehap5tOSlJJkB0_QIcb3Wrt9wRlNAtfHIRQHaapGKY0VURQySv69R_0Lk051u0MayltdSMEqdS3DeWqnZJhMMvsF_V4hhLz_A5TvZuXd1T2y1Zx6hbQv5Gv96_A8Qa49wHW_1cy8_PrjeRfmViWVA</recordid><startdate>202306</startdate><enddate>202306</enddate><creator>Feng, Gong</creator><creator>Zhang, Xiaoxun</creator><creator>Zhang, Liangjun</creator><creator>Liu, Wen‐Yue</creator><creator>Geng, Shi</creator><creator>Yuan, Hai‐Yang</creator><creator>Sha, Jun‐Cheng</creator><creator>Wang, Xiao‐Dong</creator><creator>Sun, Dan‐Qin</creator><creator>Targher, Giovanni</creator><creator>Byrne, Christopher D.</creator><creator>Zheng, Tian‐Lei</creator><creator>Ye, Feng</creator><creator>Zheng, Ming‐Hua</creator><creator>Chai, Jin</creator><general>Wiley Subscription Services, Inc</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QO</scope><scope>7T5</scope><scope>7U9</scope><scope>8FD</scope><scope>FR3</scope><scope>H94</scope><scope>P64</scope><scope>RC3</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-4984-2631</orcidid><orcidid>https://orcid.org/0000-0002-8543-4566</orcidid><orcidid>https://orcid.org/0000-0002-3892-7464</orcidid><orcidid>https://orcid.org/0000-0002-7053-8661</orcidid><orcidid>https://orcid.org/0000-0002-4325-3900</orcidid></search><sort><creationdate>202306</creationdate><title>Novel urinary protein panels for the non‐invasive diagnosis of non‐alcoholic fatty liver disease and fibrosis stages</title><author>Feng, Gong ; Zhang, Xiaoxun ; Zhang, Liangjun ; Liu, Wen‐Yue ; Geng, Shi ; Yuan, Hai‐Yang ; Sha, Jun‐Cheng ; Wang, Xiao‐Dong ; Sun, Dan‐Qin ; Targher, Giovanni ; Byrne, Christopher D. ; Zheng, Tian‐Lei ; Ye, Feng ; Zheng, Ming‐Hua ; Chai, Jin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3535-7c2d0ea9107853940981a2d3a26cc26b776781e7ec3479904b8c96b550098e5b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Accuracy</topic><topic>Bioinformatics</topic><topic>Biomarkers</topic><topic>Biomarkers - metabolism</topic><topic>Biopsy</topic><topic>Derivation</topic><topic>diagnosis</topic><topic>Diagnostic systems</topic><topic>Fatty liver</topic><topic>Fibrosis</topic><topic>Humans</topic><topic>Liver</topic><topic>Liver - pathology</topic><topic>liver biopsy</topic><topic>Liver Cirrhosis - pathology</topic><topic>Liver diseases</topic><topic>NAFLD</topic><topic>Non-alcoholic Fatty Liver Disease - pathology</topic><topic>Panels</topic><topic>Proteins</topic><topic>Proteomics</topic><topic>Training</topic><topic>urinary proteomics</topic><topic>Urine</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Feng, Gong</creatorcontrib><creatorcontrib>Zhang, Xiaoxun</creatorcontrib><creatorcontrib>Zhang, Liangjun</creatorcontrib><creatorcontrib>Liu, Wen‐Yue</creatorcontrib><creatorcontrib>Geng, Shi</creatorcontrib><creatorcontrib>Yuan, Hai‐Yang</creatorcontrib><creatorcontrib>Sha, Jun‐Cheng</creatorcontrib><creatorcontrib>Wang, Xiao‐Dong</creatorcontrib><creatorcontrib>Sun, Dan‐Qin</creatorcontrib><creatorcontrib>Targher, Giovanni</creatorcontrib><creatorcontrib>Byrne, Christopher D.</creatorcontrib><creatorcontrib>Zheng, Tian‐Lei</creatorcontrib><creatorcontrib>Ye, Feng</creatorcontrib><creatorcontrib>Zheng, Ming‐Hua</creatorcontrib><creatorcontrib>Chai, Jin</creatorcontrib><creatorcontrib>CHESS-MAFLD Consortium</creatorcontrib><creatorcontrib>CHESS‐MAFLD Consortium</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Immunology Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Liver international</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Feng, Gong</au><au>Zhang, Xiaoxun</au><au>Zhang, Liangjun</au><au>Liu, Wen‐Yue</au><au>Geng, Shi</au><au>Yuan, Hai‐Yang</au><au>Sha, Jun‐Cheng</au><au>Wang, Xiao‐Dong</au><au>Sun, Dan‐Qin</au><au>Targher, Giovanni</au><au>Byrne, Christopher D.</au><au>Zheng, Tian‐Lei</au><au>Ye, Feng</au><au>Zheng, Ming‐Hua</au><au>Chai, Jin</au><aucorp>CHESS-MAFLD Consortium</aucorp><aucorp>CHESS‐MAFLD Consortium</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Novel urinary protein panels for the non‐invasive diagnosis of non‐alcoholic fatty liver disease and fibrosis stages</atitle><jtitle>Liver international</jtitle><addtitle>Liver Int</addtitle><date>2023-06</date><risdate>2023</risdate><volume>43</volume><issue>6</issue><spage>1234</spage><epage>1246</epage><pages>1234-1246</pages><issn>1478-3223</issn><eissn>1478-3231</eissn><abstract>Background & Aims
There is an unmet clinical need for non‐invasive tests to diagnose non‐alcoholic fatty liver disease (NAFLD) and individual fibrosis stages. We aimed to test whether urine protein panels could be used to identify NAFLD, NAFLD with fibrosis (stage F ≥ 1) and NAFLD with significant fibrosis (stage F ≥ 2).
Methods
We collected urine samples from 100 patients with biopsy‐confirmed NAFLD and 40 healthy volunteers, and proteomics and bioinformatics analyses were performed in this derivation cohort. Diagnostic models were developed for detecting NAFLD (UPNAFLD model), NAFLD with fibrosis (UPfibrosis model), or NAFLD with significant fibrosis (UPsignificant fibrosis model). Subsequently, the derivation cohort was divided into training and testing sets to evaluate the efficacy of these diagnostic models. Finally, in a separate independent validation cohort of 100 patients with biopsy‐confirmed NAFLD and 45 healthy controls, urinary enzyme‐linked immunosorbent assay analyses were undertaken to validate the accuracy of these new diagnostic models.
Results
The UPfibrosis model and the UPsignificant fibrosis model showed an AUROC of .863 (95% CI: .725–1.000) and 0.858 (95% CI: .712–1.000) in the training set; and .837 (95% CI: .711–.963) and .916 (95% CI: .825–1.000) in the testing set respectively. The UPNAFLD model showed an excellent diagnostic performance and the area under the receiver operator characteristic curve (AUROC) exceeded .90 in the derivation cohort. In the independent validation cohort, the AUROC for all three of the above diagnostic models exceeded .80.
Conclusions
Our newly developed models constructed from urine protein biomarkers have good accuracy for non‐invasively diagnosing liver fibrosis in NAFLD.</abstract><cop>United States</cop><pub>Wiley Subscription Services, Inc</pub><pmid>36924436</pmid><doi>10.1111/liv.15565</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0003-4984-2631</orcidid><orcidid>https://orcid.org/0000-0002-8543-4566</orcidid><orcidid>https://orcid.org/0000-0002-3892-7464</orcidid><orcidid>https://orcid.org/0000-0002-7053-8661</orcidid><orcidid>https://orcid.org/0000-0002-4325-3900</orcidid></addata></record> |
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subjects | Accuracy Bioinformatics Biomarkers Biomarkers - metabolism Biopsy Derivation diagnosis Diagnostic systems Fatty liver Fibrosis Humans Liver Liver - pathology liver biopsy Liver Cirrhosis - pathology Liver diseases NAFLD Non-alcoholic Fatty Liver Disease - pathology Panels Proteins Proteomics Training urinary proteomics Urine |
title | Novel urinary protein panels for the non‐invasive diagnosis of non‐alcoholic fatty liver disease and fibrosis stages |
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