Use of proteomics to identify biomarkers associated with chronic kidney disease and long‐term outcomes in patients with myocardial infarction
Background Patients with chronic kidney disease (CKD) have poor outcomes following myocardial infarction (MI). We performed an untargeted examination of 175 biomarkers to identify those with the strongest association with CKD and to examine the association of those biomarkers with long‐term outcomes...
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| Veröffentlicht in: | Journal of internal medicine 2020-11, Vol.288 (5), p.581-592 |
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| creator | Edfors, R. Lindhagen, L. Spaak, J. Evans, M. Andell, P. Baron, T. Mörtberg, J. Rezeli, M. Salzinger, B. Lundman, P. Szummer, K. Tornvall, P. Wallén, H. N. Jacobson, S. H. Kahan, T. Marko‐Varga, G. Erlinge, D. James, S. Lindahl, B. Jernberg, T. |
| description | Background
Patients with chronic kidney disease (CKD) have poor outcomes following myocardial infarction (MI). We performed an untargeted examination of 175 biomarkers to identify those with the strongest association with CKD and to examine the association of those biomarkers with long‐term outcomes.
Methods
A total of 175 different biomarkers from MI patients enrolled in the Swedish Web‐System for Enhancement and Development of Evidence‐Based Care in Heart Disease Evaluated According to Recommended Therapies (SWEDEHEART) registry were analysed either by a multiple reaction monitoring mass spectrometry assay or by a multiplex assay (proximity extension assay). Random forests statistical models were used to assess the predictor importance of biomarkers, CKD and outcomes.
Results
A total of 1098 MI patients with a median estimated glomerular filtration rate of 85 mL min−1/1.73 m2 were followed for a median of 3.2 years. The random forests analyses, without and with adjustment for differences in demography, comorbidities and severity of disease, identified six biomarkers (adrenomedullin, TNF receptor‐1, adipocyte fatty acid‐binding protein‐4, TNF‐related apoptosis‐inducing ligand receptor 2, growth differentiation factor‐15 and TNF receptor‐2) to be strongly associated with CKD. All six biomarkers were also amongst the 15 strongest predictors for death, and four of them were amongst the strongest predictors of subsequent MI and heart failure hospitalization.
Conclusion
In patients with MI, a proteomic approach could identify six biomarkers that best predicted CKD. These biomarkers were also amongst the most important predictors of long‐term outcomes. Thus, these biomarkers indicate underlying mechanisms that may contribute to the poor prognosis seen in patients with MI and CKD. |
| doi_str_mv | 10.1111/joim.13116 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_swepu</sourceid><recordid>TN_cdi_swepub_primary_oai_swepub_ki_se_469889</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2421459952</sourcerecordid><originalsourceid>FETCH-LOGICAL-c5876-fe86d946a00a6892ba5e64fcda7f5c1d5c91974f24430ff49dade6b7b44b25bb3</originalsourceid><addsrcrecordid>eNp9kstu1DAUQCMEoqWw4QOQJTYIkWI7thMvq_IqGtQNZWv5cdN6JokHO9FodvwBfCNfgqeZFglpsHRlyz73-HWL4jnBpyS3t8vg-1NSESIeFMekEryktRQPi2MsOStFQ_FR8SSlJcakwgI_Lo4qKqqGNfVx8fMqAQotWscwQui9TWgMyDsYRt9ukfGh13EFMSGdUrBej-DQxo83yN7EMHiLVt4NsEXOJ9DZpQeHujBc__7xa4TYozCNNvSQkB_QWo8-i9Ms6LfB6ui87vJaq6MdfRieFo9a3SV4tu9PiqsP77-efyoXlx8vzs8WpeVNLcoWGuEkExpjLRpJjeYgWGudrltuieNWElmzljJW4bZl0mkHwtSGMUO5MdVJUc7etIH1ZNQ6-nzRrQraq_3UKo9AMSGbRmZeHuTz27m_SXeJhDHJJa52uYuDud20zmFy3G7WOFvlmyhn6kYxUwtldI0V1ZgAZdJSh7PuzUHdO__tTIV4raZJMco4oxl_NeP5mN8nSKPqfbLQdXqAMCVFGSWMS8l36Mt_0GWY4pD_IVOc0qrObKZez5SNIaUI7f0JCFa7glS7glS3BZnhF3vlZHpw9-hdBWaAzMDGd7D9j0p9vrz4Mkv_AB2R8OA</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2452237421</pqid></control><display><type>article</type><title>Use of proteomics to identify biomarkers associated with chronic kidney disease and long‐term outcomes in patients with myocardial infarction</title><source>MEDLINE</source><source>Access via Wiley Online Library</source><source>EZB-FREE-00999 freely available EZB journals</source><source>Wiley Online Library (Open Access Collection)</source><creator>Edfors, R. ; Lindhagen, L. ; Spaak, J. ; Evans, M. ; Andell, P. ; Baron, T. ; Mörtberg, J. ; Rezeli, M. ; Salzinger, B. ; Lundman, P. ; Szummer, K. ; Tornvall, P. ; Wallén, H. N. ; Jacobson, S. H. ; Kahan, T. ; Marko‐Varga, G. ; Erlinge, D. ; James, S. ; Lindahl, B. ; Jernberg, T.</creator><creatorcontrib>Edfors, R. ; Lindhagen, L. ; Spaak, J. ; Evans, M. ; Andell, P. ; Baron, T. ; Mörtberg, J. ; Rezeli, M. ; Salzinger, B. ; Lundman, P. ; Szummer, K. ; Tornvall, P. ; Wallén, H. N. ; Jacobson, S. H. ; Kahan, T. ; Marko‐Varga, G. ; Erlinge, D. ; James, S. ; Lindahl, B. ; Jernberg, T.</creatorcontrib><description>Background
Patients with chronic kidney disease (CKD) have poor outcomes following myocardial infarction (MI). We performed an untargeted examination of 175 biomarkers to identify those with the strongest association with CKD and to examine the association of those biomarkers with long‐term outcomes.
Methods
A total of 175 different biomarkers from MI patients enrolled in the Swedish Web‐System for Enhancement and Development of Evidence‐Based Care in Heart Disease Evaluated According to Recommended Therapies (SWEDEHEART) registry were analysed either by a multiple reaction monitoring mass spectrometry assay or by a multiplex assay (proximity extension assay). Random forests statistical models were used to assess the predictor importance of biomarkers, CKD and outcomes.
Results
A total of 1098 MI patients with a median estimated glomerular filtration rate of 85 mL min−1/1.73 m2 were followed for a median of 3.2 years. The random forests analyses, without and with adjustment for differences in demography, comorbidities and severity of disease, identified six biomarkers (adrenomedullin, TNF receptor‐1, adipocyte fatty acid‐binding protein‐4, TNF‐related apoptosis‐inducing ligand receptor 2, growth differentiation factor‐15 and TNF receptor‐2) to be strongly associated with CKD. All six biomarkers were also amongst the 15 strongest predictors for death, and four of them were amongst the strongest predictors of subsequent MI and heart failure hospitalization.
Conclusion
In patients with MI, a proteomic approach could identify six biomarkers that best predicted CKD. These biomarkers were also amongst the most important predictors of long‐term outcomes. Thus, these biomarkers indicate underlying mechanisms that may contribute to the poor prognosis seen in patients with MI and CKD.</description><identifier>ISSN: 0954-6820</identifier><identifier>ISSN: 1365-2796</identifier><identifier>EISSN: 1365-2796</identifier><identifier>DOI: 10.1111/joim.13116</identifier><identifier>PMID: 32638487</identifier><language>eng</language><publisher>England: Blackwell Publishing Ltd</publisher><subject>acute coronary syndrome and myocardial infarction ; Adrenomedullin ; Adrenomedullin - blood ; Aged ; Apoptosis ; Assaying ; Biomarkers ; Biomarkers - blood ; Cardiac and Cardiovascular Systems ; Cardiovascular diseases ; chronic kidney disease ; Clinical Medicine ; Congestive heart failure ; Coronary artery disease ; Demography ; Fatty acids ; Female ; Glomerular filtration rate ; Growth Differentiation Factor 15 - blood ; Heart attacks ; Heart diseases ; Humans ; Kardiologi ; Kidney diseases ; Kidneys ; Klinisk medicin ; Male ; Mass spectrometry ; Mass spectroscopy ; Mathematical models ; Median (statistics) ; Medical and Health Sciences ; Medicin och hälsovetenskap ; Middle Aged ; Myocardial infarction ; Myocardial Infarction - complications ; Perilipin-2 - blood ; Proteomics ; Receptors ; Receptors, TNF-Related Apoptosis-Inducing Ligand - blood ; Receptors, Tumor Necrosis Factor - blood ; renal dysfunction ; renal failure ; Renal Insufficiency, Chronic - complications ; Renal Insufficiency, Chronic - diagnosis ; Statistical analysis ; Statistical models ; Tumor necrosis factor receptors ; Urologi och njurmedicin ; Urology and Nephrology</subject><ispartof>Journal of internal medicine, 2020-11, Vol.288 (5), p.581-592</ispartof><rights>2020 The Association for the Publication of the Journal of Internal Medicine</rights><rights>2020 The Association for the Publication of the Journal of Internal Medicine.</rights><rights>Copyright © 2020 The Association for the Publication of the Journal of Internal Medicine</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c5876-fe86d946a00a6892ba5e64fcda7f5c1d5c91974f24430ff49dade6b7b44b25bb3</citedby><cites>FETCH-LOGICAL-c5876-fe86d946a00a6892ba5e64fcda7f5c1d5c91974f24430ff49dade6b7b44b25bb3</cites><orcidid>0000-0003-2377-436X</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%2Fjoim.13116$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2Fjoim.13116$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>230,314,780,784,885,1417,1433,27924,27925,45574,45575,46409,46833</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32638487$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-424542$$DView record from Swedish Publication Index$$Hfree_for_read</backlink><backlink>$$Uhttps://lup.lub.lu.se/record/48dc35e6-db78-4b76-ba70-2a01e249c2d0$$DView record from Swedish Publication Index$$Hfree_for_read</backlink><backlink>$$Uhttp://kipublications.ki.se/Default.aspx?queryparsed=id:144959039$$DView record from Swedish Publication Index$$Hfree_for_read</backlink></links><search><creatorcontrib>Edfors, R.</creatorcontrib><creatorcontrib>Lindhagen, L.</creatorcontrib><creatorcontrib>Spaak, J.</creatorcontrib><creatorcontrib>Evans, M.</creatorcontrib><creatorcontrib>Andell, P.</creatorcontrib><creatorcontrib>Baron, T.</creatorcontrib><creatorcontrib>Mörtberg, J.</creatorcontrib><creatorcontrib>Rezeli, M.</creatorcontrib><creatorcontrib>Salzinger, B.</creatorcontrib><creatorcontrib>Lundman, P.</creatorcontrib><creatorcontrib>Szummer, K.</creatorcontrib><creatorcontrib>Tornvall, P.</creatorcontrib><creatorcontrib>Wallén, H. N.</creatorcontrib><creatorcontrib>Jacobson, S. H.</creatorcontrib><creatorcontrib>Kahan, T.</creatorcontrib><creatorcontrib>Marko‐Varga, G.</creatorcontrib><creatorcontrib>Erlinge, D.</creatorcontrib><creatorcontrib>James, S.</creatorcontrib><creatorcontrib>Lindahl, B.</creatorcontrib><creatorcontrib>Jernberg, T.</creatorcontrib><title>Use of proteomics to identify biomarkers associated with chronic kidney disease and long‐term outcomes in patients with myocardial infarction</title><title>Journal of internal medicine</title><addtitle>J Intern Med</addtitle><description>Background
Patients with chronic kidney disease (CKD) have poor outcomes following myocardial infarction (MI). We performed an untargeted examination of 175 biomarkers to identify those with the strongest association with CKD and to examine the association of those biomarkers with long‐term outcomes.
Methods
A total of 175 different biomarkers from MI patients enrolled in the Swedish Web‐System for Enhancement and Development of Evidence‐Based Care in Heart Disease Evaluated According to Recommended Therapies (SWEDEHEART) registry were analysed either by a multiple reaction monitoring mass spectrometry assay or by a multiplex assay (proximity extension assay). Random forests statistical models were used to assess the predictor importance of biomarkers, CKD and outcomes.
Results
A total of 1098 MI patients with a median estimated glomerular filtration rate of 85 mL min−1/1.73 m2 were followed for a median of 3.2 years. The random forests analyses, without and with adjustment for differences in demography, comorbidities and severity of disease, identified six biomarkers (adrenomedullin, TNF receptor‐1, adipocyte fatty acid‐binding protein‐4, TNF‐related apoptosis‐inducing ligand receptor 2, growth differentiation factor‐15 and TNF receptor‐2) to be strongly associated with CKD. All six biomarkers were also amongst the 15 strongest predictors for death, and four of them were amongst the strongest predictors of subsequent MI and heart failure hospitalization.
Conclusion
In patients with MI, a proteomic approach could identify six biomarkers that best predicted CKD. These biomarkers were also amongst the most important predictors of long‐term outcomes. Thus, these biomarkers indicate underlying mechanisms that may contribute to the poor prognosis seen in patients with MI and CKD.</description><subject>acute coronary syndrome and myocardial infarction</subject><subject>Adrenomedullin</subject><subject>Adrenomedullin - blood</subject><subject>Aged</subject><subject>Apoptosis</subject><subject>Assaying</subject><subject>Biomarkers</subject><subject>Biomarkers - blood</subject><subject>Cardiac and Cardiovascular Systems</subject><subject>Cardiovascular diseases</subject><subject>chronic kidney disease</subject><subject>Clinical Medicine</subject><subject>Congestive heart failure</subject><subject>Coronary artery disease</subject><subject>Demography</subject><subject>Fatty acids</subject><subject>Female</subject><subject>Glomerular filtration rate</subject><subject>Growth Differentiation Factor 15 - blood</subject><subject>Heart attacks</subject><subject>Heart diseases</subject><subject>Humans</subject><subject>Kardiologi</subject><subject>Kidney diseases</subject><subject>Kidneys</subject><subject>Klinisk medicin</subject><subject>Male</subject><subject>Mass spectrometry</subject><subject>Mass spectroscopy</subject><subject>Mathematical models</subject><subject>Median (statistics)</subject><subject>Medical and Health Sciences</subject><subject>Medicin och hälsovetenskap</subject><subject>Middle Aged</subject><subject>Myocardial infarction</subject><subject>Myocardial Infarction - complications</subject><subject>Perilipin-2 - blood</subject><subject>Proteomics</subject><subject>Receptors</subject><subject>Receptors, TNF-Related Apoptosis-Inducing Ligand - blood</subject><subject>Receptors, Tumor Necrosis Factor - blood</subject><subject>renal dysfunction</subject><subject>renal failure</subject><subject>Renal Insufficiency, Chronic - complications</subject><subject>Renal Insufficiency, Chronic - diagnosis</subject><subject>Statistical analysis</subject><subject>Statistical models</subject><subject>Tumor necrosis factor receptors</subject><subject>Urologi och njurmedicin</subject><subject>Urology and Nephrology</subject><issn>0954-6820</issn><issn>1365-2796</issn><issn>1365-2796</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kstu1DAUQCMEoqWw4QOQJTYIkWI7thMvq_IqGtQNZWv5cdN6JokHO9FodvwBfCNfgqeZFglpsHRlyz73-HWL4jnBpyS3t8vg-1NSESIeFMekEryktRQPi2MsOStFQ_FR8SSlJcakwgI_Lo4qKqqGNfVx8fMqAQotWscwQui9TWgMyDsYRt9ukfGh13EFMSGdUrBej-DQxo83yN7EMHiLVt4NsEXOJ9DZpQeHujBc__7xa4TYozCNNvSQkB_QWo8-i9Ms6LfB6ui87vJaq6MdfRieFo9a3SV4tu9PiqsP77-efyoXlx8vzs8WpeVNLcoWGuEkExpjLRpJjeYgWGudrltuieNWElmzljJW4bZl0mkHwtSGMUO5MdVJUc7etIH1ZNQ6-nzRrQraq_3UKo9AMSGbRmZeHuTz27m_SXeJhDHJJa52uYuDud20zmFy3G7WOFvlmyhn6kYxUwtldI0V1ZgAZdJSh7PuzUHdO__tTIV4raZJMco4oxl_NeP5mN8nSKPqfbLQdXqAMCVFGSWMS8l36Mt_0GWY4pD_IVOc0qrObKZez5SNIaUI7f0JCFa7glS7glS3BZnhF3vlZHpw9-hdBWaAzMDGd7D9j0p9vrz4Mkv_AB2R8OA</recordid><startdate>202011</startdate><enddate>202011</enddate><creator>Edfors, R.</creator><creator>Lindhagen, L.</creator><creator>Spaak, J.</creator><creator>Evans, M.</creator><creator>Andell, P.</creator><creator>Baron, T.</creator><creator>Mörtberg, J.</creator><creator>Rezeli, M.</creator><creator>Salzinger, B.</creator><creator>Lundman, P.</creator><creator>Szummer, K.</creator><creator>Tornvall, P.</creator><creator>Wallén, H. N.</creator><creator>Jacobson, S. H.</creator><creator>Kahan, T.</creator><creator>Marko‐Varga, G.</creator><creator>Erlinge, D.</creator><creator>James, S.</creator><creator>Lindahl, B.</creator><creator>Jernberg, T.</creator><general>Blackwell Publishing Ltd</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>7QL</scope><scope>C1K</scope><scope>K9.</scope><scope>7X8</scope><scope>ADTPV</scope><scope>AOWAS</scope><scope>DF2</scope><scope>D95</scope><orcidid>https://orcid.org/0000-0003-2377-436X</orcidid></search><sort><creationdate>202011</creationdate><title>Use of proteomics to identify biomarkers associated with chronic kidney disease and long‐term outcomes in patients with myocardial infarction</title><author>Edfors, R. ; Lindhagen, L. ; Spaak, J. ; Evans, M. ; Andell, P. ; Baron, T. ; Mörtberg, J. ; Rezeli, M. ; Salzinger, B. ; Lundman, P. ; Szummer, K. ; Tornvall, P. ; Wallén, H. N. ; Jacobson, S. H. ; Kahan, T. ; Marko‐Varga, G. ; Erlinge, D. ; James, S. ; Lindahl, B. ; Jernberg, T.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c5876-fe86d946a00a6892ba5e64fcda7f5c1d5c91974f24430ff49dade6b7b44b25bb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>acute coronary syndrome and myocardial infarction</topic><topic>Adrenomedullin</topic><topic>Adrenomedullin - blood</topic><topic>Aged</topic><topic>Apoptosis</topic><topic>Assaying</topic><topic>Biomarkers</topic><topic>Biomarkers - blood</topic><topic>Cardiac and Cardiovascular Systems</topic><topic>Cardiovascular diseases</topic><topic>chronic kidney disease</topic><topic>Clinical Medicine</topic><topic>Congestive heart failure</topic><topic>Coronary artery disease</topic><topic>Demography</topic><topic>Fatty acids</topic><topic>Female</topic><topic>Glomerular filtration rate</topic><topic>Growth Differentiation Factor 15 - blood</topic><topic>Heart attacks</topic><topic>Heart diseases</topic><topic>Humans</topic><topic>Kardiologi</topic><topic>Kidney diseases</topic><topic>Kidneys</topic><topic>Klinisk medicin</topic><topic>Male</topic><topic>Mass spectrometry</topic><topic>Mass spectroscopy</topic><topic>Mathematical models</topic><topic>Median (statistics)</topic><topic>Medical and Health Sciences</topic><topic>Medicin och hälsovetenskap</topic><topic>Middle Aged</topic><topic>Myocardial infarction</topic><topic>Myocardial Infarction - complications</topic><topic>Perilipin-2 - blood</topic><topic>Proteomics</topic><topic>Receptors</topic><topic>Receptors, TNF-Related Apoptosis-Inducing Ligand - blood</topic><topic>Receptors, Tumor Necrosis Factor - blood</topic><topic>renal dysfunction</topic><topic>renal failure</topic><topic>Renal Insufficiency, Chronic - complications</topic><topic>Renal Insufficiency, Chronic - diagnosis</topic><topic>Statistical analysis</topic><topic>Statistical models</topic><topic>Tumor necrosis factor receptors</topic><topic>Urologi och njurmedicin</topic><topic>Urology and Nephrology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Edfors, R.</creatorcontrib><creatorcontrib>Lindhagen, L.</creatorcontrib><creatorcontrib>Spaak, J.</creatorcontrib><creatorcontrib>Evans, M.</creatorcontrib><creatorcontrib>Andell, P.</creatorcontrib><creatorcontrib>Baron, T.</creatorcontrib><creatorcontrib>Mörtberg, J.</creatorcontrib><creatorcontrib>Rezeli, M.</creatorcontrib><creatorcontrib>Salzinger, B.</creatorcontrib><creatorcontrib>Lundman, P.</creatorcontrib><creatorcontrib>Szummer, K.</creatorcontrib><creatorcontrib>Tornvall, P.</creatorcontrib><creatorcontrib>Wallén, H. N.</creatorcontrib><creatorcontrib>Jacobson, S. H.</creatorcontrib><creatorcontrib>Kahan, T.</creatorcontrib><creatorcontrib>Marko‐Varga, G.</creatorcontrib><creatorcontrib>Erlinge, D.</creatorcontrib><creatorcontrib>James, S.</creatorcontrib><creatorcontrib>Lindahl, B.</creatorcontrib><creatorcontrib>Jernberg, T.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>MEDLINE - Academic</collection><collection>SwePub</collection><collection>SwePub Articles</collection><collection>SWEPUB Uppsala universitet</collection><collection>SWEPUB Lunds universitet</collection><jtitle>Journal of internal medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Edfors, R.</au><au>Lindhagen, L.</au><au>Spaak, J.</au><au>Evans, M.</au><au>Andell, P.</au><au>Baron, T.</au><au>Mörtberg, J.</au><au>Rezeli, M.</au><au>Salzinger, B.</au><au>Lundman, P.</au><au>Szummer, K.</au><au>Tornvall, P.</au><au>Wallén, H. N.</au><au>Jacobson, S. H.</au><au>Kahan, T.</au><au>Marko‐Varga, G.</au><au>Erlinge, D.</au><au>James, S.</au><au>Lindahl, B.</au><au>Jernberg, T.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Use of proteomics to identify biomarkers associated with chronic kidney disease and long‐term outcomes in patients with myocardial infarction</atitle><jtitle>Journal of internal medicine</jtitle><addtitle>J Intern Med</addtitle><date>2020-11</date><risdate>2020</risdate><volume>288</volume><issue>5</issue><spage>581</spage><epage>592</epage><pages>581-592</pages><issn>0954-6820</issn><issn>1365-2796</issn><eissn>1365-2796</eissn><abstract>Background
Patients with chronic kidney disease (CKD) have poor outcomes following myocardial infarction (MI). We performed an untargeted examination of 175 biomarkers to identify those with the strongest association with CKD and to examine the association of those biomarkers with long‐term outcomes.
Methods
A total of 175 different biomarkers from MI patients enrolled in the Swedish Web‐System for Enhancement and Development of Evidence‐Based Care in Heart Disease Evaluated According to Recommended Therapies (SWEDEHEART) registry were analysed either by a multiple reaction monitoring mass spectrometry assay or by a multiplex assay (proximity extension assay). Random forests statistical models were used to assess the predictor importance of biomarkers, CKD and outcomes.
Results
A total of 1098 MI patients with a median estimated glomerular filtration rate of 85 mL min−1/1.73 m2 were followed for a median of 3.2 years. The random forests analyses, without and with adjustment for differences in demography, comorbidities and severity of disease, identified six biomarkers (adrenomedullin, TNF receptor‐1, adipocyte fatty acid‐binding protein‐4, TNF‐related apoptosis‐inducing ligand receptor 2, growth differentiation factor‐15 and TNF receptor‐2) to be strongly associated with CKD. All six biomarkers were also amongst the 15 strongest predictors for death, and four of them were amongst the strongest predictors of subsequent MI and heart failure hospitalization.
Conclusion
In patients with MI, a proteomic approach could identify six biomarkers that best predicted CKD. These biomarkers were also amongst the most important predictors of long‐term outcomes. Thus, these biomarkers indicate underlying mechanisms that may contribute to the poor prognosis seen in patients with MI and CKD.</abstract><cop>England</cop><pub>Blackwell Publishing Ltd</pub><pmid>32638487</pmid><doi>10.1111/joim.13116</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0003-2377-436X</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0954-6820 |
| ispartof | Journal of internal medicine, 2020-11, Vol.288 (5), p.581-592 |
| issn | 0954-6820 1365-2796 1365-2796 |
| language | eng |
| recordid | cdi_swepub_primary_oai_swepub_ki_se_469889 |
| source | MEDLINE; Access via Wiley Online Library; EZB-FREE-00999 freely available EZB journals; Wiley Online Library (Open Access Collection) |
| subjects | acute coronary syndrome and myocardial infarction Adrenomedullin Adrenomedullin - blood Aged Apoptosis Assaying Biomarkers Biomarkers - blood Cardiac and Cardiovascular Systems Cardiovascular diseases chronic kidney disease Clinical Medicine Congestive heart failure Coronary artery disease Demography Fatty acids Female Glomerular filtration rate Growth Differentiation Factor 15 - blood Heart attacks Heart diseases Humans Kardiologi Kidney diseases Kidneys Klinisk medicin Male Mass spectrometry Mass spectroscopy Mathematical models Median (statistics) Medical and Health Sciences Medicin och hälsovetenskap Middle Aged Myocardial infarction Myocardial Infarction - complications Perilipin-2 - blood Proteomics Receptors Receptors, TNF-Related Apoptosis-Inducing Ligand - blood Receptors, Tumor Necrosis Factor - blood renal dysfunction renal failure Renal Insufficiency, Chronic - complications Renal Insufficiency, Chronic - diagnosis Statistical analysis Statistical models Tumor necrosis factor receptors Urologi och njurmedicin Urology and Nephrology |
| title | Use of proteomics to identify biomarkers associated with chronic kidney disease and long‐term outcomes in patients with myocardial infarction |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-27T04%3A24%3A15IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_swepu&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Use%20of%20proteomics%20to%20identify%20biomarkers%20associated%20with%20chronic%20kidney%20disease%20and%20long%E2%80%90term%20outcomes%20in%20patients%20with%20myocardial%20infarction&rft.jtitle=Journal%20of%20internal%20medicine&rft.au=Edfors,%20R.&rft.date=2020-11&rft.volume=288&rft.issue=5&rft.spage=581&rft.epage=592&rft.pages=581-592&rft.issn=0954-6820&rft.eissn=1365-2796&rft_id=info:doi/10.1111/joim.13116&rft_dat=%3Cproquest_swepu%3E2421459952%3C/proquest_swepu%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2452237421&rft_id=info:pmid/32638487&rfr_iscdi=true |