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Use of proteomics to identify biomarkers associated with chronic kidney disease and long-term outcomes in patients with myocardial infarction

Edfors, R. ; Lindhagen, L. ; Spaak, J. ; Evans, M. ; Andell, P. LU ; Baron, T. ; Mörtberg, J. ; Rezeli, M. LU ; Salzinger, B. and Lundman, P. , et al. (2020) In Journal of Internal Medicine
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... (More)

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.

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epub
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keywords
acute coronary syndrome and myocardial infarction, biomarkers, chronic kidney disease, proteomics, renal dysfunction, renal failure
in
Journal of Internal Medicine
publisher
Wiley-Blackwell Publishing Ltd
external identifiers
  • pmid:32638487
  • scopus:85087643252
ISSN
0954-6820
DOI
10.1111/joim.13116
language
English
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yes
id
48dc35e6-db78-4b76-ba70-2a01e249c2d0
date added to LUP
2020-07-23 12:40:19
date last changed
2020-07-29 06:16:58
@article{48dc35e6-db78-4b76-ba70-2a01e249c2d0,
  abstract     = {<p>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<sup>−1</sup>/1.73 m<sup>2</sup> 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.</p>},
  author       = {Edfors, R. and Lindhagen, L. and Spaak, J. and Evans, M. and Andell, P. and Baron, T. and Mörtberg, J. and Rezeli, M. and Salzinger, B. and Lundman, P. and Szummer, K. and Tornvall, P. and Wallén, H. N. and Jacobson, S. H. and Kahan, T. and Marko-Varga, G. and Erlinge, D. and James, S. and Lindahl, B. and Jernberg, T.},
  issn         = {0954-6820},
  language     = {eng},
  month        = {07},
  publisher    = {Wiley-Blackwell Publishing Ltd},
  series       = {Journal of Internal Medicine},
  title        = {Use of proteomics to identify biomarkers associated with chronic kidney disease and long-term outcomes in patients with myocardial infarction},
  url          = {http://dx.doi.org/10.1111/joim.13116},
  doi          = {10.1111/joim.13116},
  year         = {2020},
}