Prediction of acute coronary syndromes by urinary proteome analysis

Nay Min Htun, Dianna J. Magliano, Zhen-Yu Zhang, Jasmine G. Lyons, Thibault Petit, Esther Nkuipou-Kenfack, Adela Ramirez-Torres, Constantin Von Zur Muhlen, David Maahs, Joost P. Schanstra, Claudia Pontillo, Martin Pejchinovski, Janet K. Snell-Bergeon, Christian Delles, Harald Mischak, Jan A. Staessen, Jonathan E. Shaw, Thomas Koeck, Karlheinz Peter

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Abstract

Identification of individuals who are at risk of suffering from acute coronary syndromes (ACS) may allow to introduce preventative measures. We aimed to identify ACS-related urinary peptides, that combined as a pattern can be used as prognostic biomarker. Proteomic data of 252 individuals enrolled in four prospective studies from Australia, Europe and North America were analyzed. 126 of these had suffered from ACS within a period of up to 5 years post urine sampling (cases). Proteomic analysis of 84 cases and 84 matched controls resulted in the discovery of 75 ACS-related urinary peptides. Combining these to a peptide pattern, we established a prognostic biomarker named Acute Coronary Syndrome Predictor 75 (ACSP75). ACSP75 demonstrated reasonable prognostic discrimination (c-statistic = 0.664), which was similar to Framingham risk scoring (c-statistics = 0.644) in a validation cohort of 42 cases and 42 controls. However, generating by a composite algorithm named Acute Coronary Syndrome Composite Predictor (ACSCP), combining the biomarker pattern ACSP75 with the previously established urinary proteomic biomarker CAD238 characterizing coronary artery disease as the underlying aetiology, and age as a risk factor, further improved discrimination (c-statistic = 0.751) resulting in an added prognostic value over Framingham risk scoring expressed by an integrated discrimination improvement of 0.2730.048 (P <0.0001) and net reclassification improvement of 0.405 ± 0.113 (P = 0.0007). In conclusion, we demonstrate that urinary peptide biomarkers have the potential to predict future ACS events in asymptomatic patients. Further large scale studies are warranted to determine the role of urinary biomarkers in clinical practice.

Original languageEnglish
Article number0172036
Number of pages18
JournalPLoS ONE
Volume12
Issue number3
DOIs
Publication statusPublished - 8 Mar 2017

Cite this

Htun, Nay Min ; Magliano, Dianna J. ; Zhang, Zhen-Yu ; Lyons, Jasmine G. ; Petit, Thibault ; Nkuipou-Kenfack, Esther ; Ramirez-Torres, Adela ; Von Zur Muhlen, Constantin ; Maahs, David ; Schanstra, Joost P. ; Pontillo, Claudia ; Pejchinovski, Martin ; Snell-Bergeon, Janet K. ; Delles, Christian ; Mischak, Harald ; Staessen, Jan A. ; Shaw, Jonathan E. ; Koeck, Thomas ; Peter, Karlheinz. / Prediction of acute coronary syndromes by urinary proteome analysis. In: PLoS ONE. 2017 ; Vol. 12, No. 3.
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abstract = "Identification of individuals who are at risk of suffering from acute coronary syndromes (ACS) may allow to introduce preventative measures. We aimed to identify ACS-related urinary peptides, that combined as a pattern can be used as prognostic biomarker. Proteomic data of 252 individuals enrolled in four prospective studies from Australia, Europe and North America were analyzed. 126 of these had suffered from ACS within a period of up to 5 years post urine sampling (cases). Proteomic analysis of 84 cases and 84 matched controls resulted in the discovery of 75 ACS-related urinary peptides. Combining these to a peptide pattern, we established a prognostic biomarker named Acute Coronary Syndrome Predictor 75 (ACSP75). ACSP75 demonstrated reasonable prognostic discrimination (c-statistic = 0.664), which was similar to Framingham risk scoring (c-statistics = 0.644) in a validation cohort of 42 cases and 42 controls. However, generating by a composite algorithm named Acute Coronary Syndrome Composite Predictor (ACSCP), combining the biomarker pattern ACSP75 with the previously established urinary proteomic biomarker CAD238 characterizing coronary artery disease as the underlying aetiology, and age as a risk factor, further improved discrimination (c-statistic = 0.751) resulting in an added prognostic value over Framingham risk scoring expressed by an integrated discrimination improvement of 0.2730.048 (P <0.0001) and net reclassification improvement of 0.405 ± 0.113 (P = 0.0007). In conclusion, we demonstrate that urinary peptide biomarkers have the potential to predict future ACS events in asymptomatic patients. Further large scale studies are warranted to determine the role of urinary biomarkers in clinical practice.",
author = "Htun, {Nay Min} and Magliano, {Dianna J.} and Zhen-Yu Zhang and Lyons, {Jasmine G.} and Thibault Petit and Esther Nkuipou-Kenfack and Adela Ramirez-Torres and {Von Zur Muhlen}, Constantin and David Maahs and Schanstra, {Joost P.} and Claudia Pontillo and Martin Pejchinovski and Snell-Bergeon, {Janet K.} and Christian Delles and Harald Mischak and Staessen, {Jan A.} and Shaw, {Jonathan E.} and Thomas Koeck and Karlheinz Peter",
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Htun, NM, Magliano, DJ, Zhang, Z-Y, Lyons, JG, Petit, T, Nkuipou-Kenfack, E, Ramirez-Torres, A, Von Zur Muhlen, C, Maahs, D, Schanstra, JP, Pontillo, C, Pejchinovski, M, Snell-Bergeon, JK, Delles, C, Mischak, H, Staessen, JA, Shaw, JE, Koeck, T & Peter, K 2017, 'Prediction of acute coronary syndromes by urinary proteome analysis', PLoS ONE, vol. 12, no. 3, 0172036. https://doi.org/10.1371/journal.pone.0172036

Prediction of acute coronary syndromes by urinary proteome analysis. / Htun, Nay Min; Magliano, Dianna J.; Zhang, Zhen-Yu; Lyons, Jasmine G.; Petit, Thibault; Nkuipou-Kenfack, Esther; Ramirez-Torres, Adela; Von Zur Muhlen, Constantin; Maahs, David; Schanstra, Joost P.; Pontillo, Claudia; Pejchinovski, Martin; Snell-Bergeon, Janet K.; Delles, Christian; Mischak, Harald; Staessen, Jan A.; Shaw, Jonathan E.; Koeck, Thomas; Peter, Karlheinz.

In: PLoS ONE, Vol. 12, No. 3, 0172036, 08.03.2017.

Research output: Contribution to journalArticleResearchpeer-review

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T1 - Prediction of acute coronary syndromes by urinary proteome analysis

AU - Htun, Nay Min

AU - Magliano, Dianna J.

AU - Zhang, Zhen-Yu

AU - Lyons, Jasmine G.

AU - Petit, Thibault

AU - Nkuipou-Kenfack, Esther

AU - Ramirez-Torres, Adela

AU - Von Zur Muhlen, Constantin

AU - Maahs, David

AU - Schanstra, Joost P.

AU - Pontillo, Claudia

AU - Pejchinovski, Martin

AU - Snell-Bergeon, Janet K.

AU - Delles, Christian

AU - Mischak, Harald

AU - Staessen, Jan A.

AU - Shaw, Jonathan E.

AU - Koeck, Thomas

AU - Peter, Karlheinz

PY - 2017/3/8

Y1 - 2017/3/8

N2 - Identification of individuals who are at risk of suffering from acute coronary syndromes (ACS) may allow to introduce preventative measures. We aimed to identify ACS-related urinary peptides, that combined as a pattern can be used as prognostic biomarker. Proteomic data of 252 individuals enrolled in four prospective studies from Australia, Europe and North America were analyzed. 126 of these had suffered from ACS within a period of up to 5 years post urine sampling (cases). Proteomic analysis of 84 cases and 84 matched controls resulted in the discovery of 75 ACS-related urinary peptides. Combining these to a peptide pattern, we established a prognostic biomarker named Acute Coronary Syndrome Predictor 75 (ACSP75). ACSP75 demonstrated reasonable prognostic discrimination (c-statistic = 0.664), which was similar to Framingham risk scoring (c-statistics = 0.644) in a validation cohort of 42 cases and 42 controls. However, generating by a composite algorithm named Acute Coronary Syndrome Composite Predictor (ACSCP), combining the biomarker pattern ACSP75 with the previously established urinary proteomic biomarker CAD238 characterizing coronary artery disease as the underlying aetiology, and age as a risk factor, further improved discrimination (c-statistic = 0.751) resulting in an added prognostic value over Framingham risk scoring expressed by an integrated discrimination improvement of 0.2730.048 (P <0.0001) and net reclassification improvement of 0.405 ± 0.113 (P = 0.0007). In conclusion, we demonstrate that urinary peptide biomarkers have the potential to predict future ACS events in asymptomatic patients. Further large scale studies are warranted to determine the role of urinary biomarkers in clinical practice.

AB - Identification of individuals who are at risk of suffering from acute coronary syndromes (ACS) may allow to introduce preventative measures. We aimed to identify ACS-related urinary peptides, that combined as a pattern can be used as prognostic biomarker. Proteomic data of 252 individuals enrolled in four prospective studies from Australia, Europe and North America were analyzed. 126 of these had suffered from ACS within a period of up to 5 years post urine sampling (cases). Proteomic analysis of 84 cases and 84 matched controls resulted in the discovery of 75 ACS-related urinary peptides. Combining these to a peptide pattern, we established a prognostic biomarker named Acute Coronary Syndrome Predictor 75 (ACSP75). ACSP75 demonstrated reasonable prognostic discrimination (c-statistic = 0.664), which was similar to Framingham risk scoring (c-statistics = 0.644) in a validation cohort of 42 cases and 42 controls. However, generating by a composite algorithm named Acute Coronary Syndrome Composite Predictor (ACSCP), combining the biomarker pattern ACSP75 with the previously established urinary proteomic biomarker CAD238 characterizing coronary artery disease as the underlying aetiology, and age as a risk factor, further improved discrimination (c-statistic = 0.751) resulting in an added prognostic value over Framingham risk scoring expressed by an integrated discrimination improvement of 0.2730.048 (P <0.0001) and net reclassification improvement of 0.405 ± 0.113 (P = 0.0007). In conclusion, we demonstrate that urinary peptide biomarkers have the potential to predict future ACS events in asymptomatic patients. Further large scale studies are warranted to determine the role of urinary biomarkers in clinical practice.

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