Urinary proteomics for prediction of mortality in patients with type 2 diabetes and microalbuminuria

Gemma E. Currie; Bernt Johan von Scholten; Sheon Mary; Jose Luis Flores Guerrero; Morten Lindhardt; Henrik Reinhard; Peter K. Jacobsen; William Mullen; Hans Henrik Parving; Harald Mischak; Peter Rossing; Christian Delles

doi:10.1186/s12933-018-0697-9

Urinary proteomics for prediction of mortality in patients with type 2 diabetes and microalbuminuria

Gemma E. Currie^*, Bernt Johan von Scholten, Sheon Mary, Jose Luis Flores Guerrero, Morten Lindhardt, Henrik Reinhard, Peter K. Jacobsen, William Mullen, Hans Henrik Parving, Harald Mischak, Peter Rossing, Christian Delles

^*Corresponding author af dette arbejde

Institut for Klinisk Medicin

16 Citationer (Scopus)

41 Downloads (Pure)

Abstract

Background: The urinary proteomic classifier CKD273 has shown promise for prediction of progressive diabetic nephropathy (DN). Whether it is also a determinant of mortality and cardiovascular disease in patients with microalbuminuria (MA) is unknown. Methods: Urine samples were obtained from 155 patients with type 2 diabetes and confirmed microalbuminuria. Proteomic analysis was undertaken using capillary electrophoresis coupled to mass spectrometry to determine the CKD273 classifier score. A previously defined CKD273 threshold of 0.343 for identification of DN was used to categorise the cohort in Kaplan-Meier and Cox regression models with all-cause mortality as the primary endpoint. Outcomes were traced through national health registers after 6 years. Results: CKD273 correlated with urine albumin excretion rate (UAER) (r = 0.481, p = <0.001), age (r = 0.238, p = 0.003), coronary artery calcium (CAC) score (r = 0.236, p = 0.003), N-terminal pro-brain natriuretic peptide (NT-proBNP) (r = 0.190, p = 0.018) and estimated glomerular filtration rate (eGFR) (r = 0.265, p = 0.001). On multivariate analysis only UAER (β = 0.402, p < 0.001) and eGFR (β = - 0.184, p = 0.039) were statistically significant determinants of CKD273. Twenty participants died during follow-up. CKD273 was a determinant of mortality (log rank [Mantel-Cox] p = 0.004), and retained significance (p = 0.048) after adjustment for age, sex, blood pressure, NT-proBNP and CAC score in a Cox regression model. Conclusion: A multidimensional biomarker can provide information on outcomes associated with its primary diagnostic purpose. Here we demonstrate that the urinary proteomic classifier CKD273 is associated with mortality in individuals with type 2 diabetes and MA even when adjusted for other established cardiovascular and renal biomarkers.

Originalsprog	Engelsk
Artikelnummer	50
Tidsskrift	Cardiovascular Diabetology
Vol/bind	17
Antal sider	8
ISSN	1475-2840
DOI	https://doi.org/10.1186/s12933-018-0697-9
Status	Udgivet - 2018

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Currie, G. E., von Scholten, B. J., Mary, S., Flores Guerrero, J. L., Lindhardt, M., Reinhard, H., Jacobsen, P. K., Mullen, W., Parving, H. H., Mischak, H., Rossing, P., & Delles, C. (2018). Urinary proteomics for prediction of mortality in patients with type 2 diabetes and microalbuminuria. Cardiovascular Diabetology, 17, [50]. https://doi.org/10.1186/s12933-018-0697-9

@article{9f63fce419cd4ac699fd301f12696016,

title = "Urinary proteomics for prediction of mortality in patients with type 2 diabetes and microalbuminuria",

abstract = "Background: The urinary proteomic classifier CKD273 has shown promise for prediction of progressive diabetic nephropathy (DN). Whether it is also a determinant of mortality and cardiovascular disease in patients with microalbuminuria (MA) is unknown. Methods: Urine samples were obtained from 155 patients with type 2 diabetes and confirmed microalbuminuria. Proteomic analysis was undertaken using capillary electrophoresis coupled to mass spectrometry to determine the CKD273 classifier score. A previously defined CKD273 threshold of 0.343 for identification of DN was used to categorise the cohort in Kaplan-Meier and Cox regression models with all-cause mortality as the primary endpoint. Outcomes were traced through national health registers after 6 years. Results: CKD273 correlated with urine albumin excretion rate (UAER) (r = 0.481, p = <0.001), age (r = 0.238, p = 0.003), coronary artery calcium (CAC) score (r = 0.236, p = 0.003), N-terminal pro-brain natriuretic peptide (NT-proBNP) (r = 0.190, p = 0.018) and estimated glomerular filtration rate (eGFR) (r = 0.265, p = 0.001). On multivariate analysis only UAER (β = 0.402, p < 0.001) and eGFR (β = - 0.184, p = 0.039) were statistically significant determinants of CKD273. Twenty participants died during follow-up. CKD273 was a determinant of mortality (log rank [Mantel-Cox] p = 0.004), and retained significance (p = 0.048) after adjustment for age, sex, blood pressure, NT-proBNP and CAC score in a Cox regression model. Conclusion: A multidimensional biomarker can provide information on outcomes associated with its primary diagnostic purpose. Here we demonstrate that the urinary proteomic classifier CKD273 is associated with mortality in individuals with type 2 diabetes and MA even when adjusted for other established cardiovascular and renal biomarkers.",

keywords = "Biomarkers, Diabetes, Microalbuminuria, Mortality, Proteomics",

author = "Currie, {Gemma E.} and {von Scholten}, {Bernt Johan} and Sheon Mary and {Flores Guerrero}, {Jose Luis} and Morten Lindhardt and Henrik Reinhard and Jacobsen, {Peter K.} and William Mullen and Parving, {Hans Henrik} and Harald Mischak and Peter Rossing and Christian Delles",

year = "2018",

doi = "10.1186/s12933-018-0697-9",

language = "English",

volume = "17",

journal = "Cardiovascular Diabetology",

issn = "1475-2840",

publisher = "BioMed Central Ltd.",

}

TY - JOUR

T1 - Urinary proteomics for prediction of mortality in patients with type 2 diabetes and microalbuminuria

AU - Currie, Gemma E.

AU - von Scholten, Bernt Johan

AU - Mary, Sheon

AU - Flores Guerrero, Jose Luis

AU - Lindhardt, Morten

AU - Reinhard, Henrik

AU - Jacobsen, Peter K.

AU - Mullen, William

AU - Parving, Hans Henrik

AU - Mischak, Harald

AU - Rossing, Peter

AU - Delles, Christian

PY - 2018

Y1 - 2018

N2 - Background: The urinary proteomic classifier CKD273 has shown promise for prediction of progressive diabetic nephropathy (DN). Whether it is also a determinant of mortality and cardiovascular disease in patients with microalbuminuria (MA) is unknown. Methods: Urine samples were obtained from 155 patients with type 2 diabetes and confirmed microalbuminuria. Proteomic analysis was undertaken using capillary electrophoresis coupled to mass spectrometry to determine the CKD273 classifier score. A previously defined CKD273 threshold of 0.343 for identification of DN was used to categorise the cohort in Kaplan-Meier and Cox regression models with all-cause mortality as the primary endpoint. Outcomes were traced through national health registers after 6 years. Results: CKD273 correlated with urine albumin excretion rate (UAER) (r = 0.481, p = <0.001), age (r = 0.238, p = 0.003), coronary artery calcium (CAC) score (r = 0.236, p = 0.003), N-terminal pro-brain natriuretic peptide (NT-proBNP) (r = 0.190, p = 0.018) and estimated glomerular filtration rate (eGFR) (r = 0.265, p = 0.001). On multivariate analysis only UAER (β = 0.402, p < 0.001) and eGFR (β = - 0.184, p = 0.039) were statistically significant determinants of CKD273. Twenty participants died during follow-up. CKD273 was a determinant of mortality (log rank [Mantel-Cox] p = 0.004), and retained significance (p = 0.048) after adjustment for age, sex, blood pressure, NT-proBNP and CAC score in a Cox regression model. Conclusion: A multidimensional biomarker can provide information on outcomes associated with its primary diagnostic purpose. Here we demonstrate that the urinary proteomic classifier CKD273 is associated with mortality in individuals with type 2 diabetes and MA even when adjusted for other established cardiovascular and renal biomarkers.

AB - Background: The urinary proteomic classifier CKD273 has shown promise for prediction of progressive diabetic nephropathy (DN). Whether it is also a determinant of mortality and cardiovascular disease in patients with microalbuminuria (MA) is unknown. Methods: Urine samples were obtained from 155 patients with type 2 diabetes and confirmed microalbuminuria. Proteomic analysis was undertaken using capillary electrophoresis coupled to mass spectrometry to determine the CKD273 classifier score. A previously defined CKD273 threshold of 0.343 for identification of DN was used to categorise the cohort in Kaplan-Meier and Cox regression models with all-cause mortality as the primary endpoint. Outcomes were traced through national health registers after 6 years. Results: CKD273 correlated with urine albumin excretion rate (UAER) (r = 0.481, p = <0.001), age (r = 0.238, p = 0.003), coronary artery calcium (CAC) score (r = 0.236, p = 0.003), N-terminal pro-brain natriuretic peptide (NT-proBNP) (r = 0.190, p = 0.018) and estimated glomerular filtration rate (eGFR) (r = 0.265, p = 0.001). On multivariate analysis only UAER (β = 0.402, p < 0.001) and eGFR (β = - 0.184, p = 0.039) were statistically significant determinants of CKD273. Twenty participants died during follow-up. CKD273 was a determinant of mortality (log rank [Mantel-Cox] p = 0.004), and retained significance (p = 0.048) after adjustment for age, sex, blood pressure, NT-proBNP and CAC score in a Cox regression model. Conclusion: A multidimensional biomarker can provide information on outcomes associated with its primary diagnostic purpose. Here we demonstrate that the urinary proteomic classifier CKD273 is associated with mortality in individuals with type 2 diabetes and MA even when adjusted for other established cardiovascular and renal biomarkers.

KW - Biomarkers

KW - Diabetes

KW - Microalbuminuria

KW - Mortality

KW - Proteomics

U2 - 10.1186/s12933-018-0697-9

DO - 10.1186/s12933-018-0697-9

M3 - Journal article

C2 - 29625564

AN - SCOPUS:85045004269

SN - 1475-2840

VL - 17

JO - Cardiovascular Diabetology

JF - Cardiovascular Diabetology

M1 - 50

ER -

Urinary proteomics for prediction of mortality in patients with type 2 diabetes and microalbuminuria

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