Jens Oellgaard1, Peter Gæde2, Frederik Persson3, Peter Rossing4, Hans-Henrik Parving5, Oluf Pedersen6. 1. Slagelse Hospital, Slagelse, Denmark; University of Southern Denmark, Odense, Denmark; Steno Diabetes Center, Gentofte, Denmark. Electronic address: joel0012@regionh.dk. 2. Slagelse Hospital, Slagelse, Denmark; University of Southern Denmark, Odense, Denmark. Electronic address: phgo@regionsjaelland.dk. 3. Steno Diabetes Center, Gentofte, Denmark. Electronic address: frederik.ivar.persson@regionh.dk. 4. Steno Diabetes Center, Gentofte, Denmark; University of Copenhagen, Denmark; Aarhus University, Aarhus, Denmark. Electronic address: peter.rossing@regionh.dk. 5. University of Copenhagen, Denmark; Department of Medical Endocrinology, Rigshospitalet, Denmark. Electronic address: d028916@dadlnet.dk. 6. Novo Nordisk Foundation Center for Basic Metabolic Research, Copenhagen, Denmark. Electronic address: oluf@sund.ku.dk.
Abstract
BACKGROUND: Analyses of the urinary proteome have been proposed as a novel approach for early assessment of increased risk of renal- or cardiovascular disease. Here we investigate the potentials of various classifiers derived from urinary proteomics for prediction of renal and cardiovascular comorbidities in patients with type 2-diabetes. METHODS: The study was a post hoc analysis of the randomized controlled Steno-2 trial comparing intensified multifactorial intervention to conventional treatment of type 2-diabetes and microalbuminuria. 151 diabetic patients with persistent microalbuminuria were included in year 1995 and followed for up to 19 years. For renal outcomes, two classifiers (CKD273 and a novel, GFR-based classifier) and for cardiovascular outcomes, three classifiers (CAD238, ACSP and ACSP75) were applied. Renal endpoints were progression to macroalbuminuria, impaired renal function (GFR < 45 ml/min/1.73 m2) or progression to end stage renal disease (ESRD) or death. Cardiovascular endpoints were coronary artery disease and a composite endpoint of incident death of cardiovascular disease, myocardial infarction or revascularization, stroke, amputation or peripheral revascularization. RESULTS:CKD273 was not consistently associated with renal outcomes. The GFR-based classifier was associated with impaired renal function, but lost significance in extensively adjusted models. Both the ACSP75 and ACSP-scores, but not the CAD238-score were inversely associated (opposing the hypothesis) with cardiovascular endpoints. None of the classifiers improved prediction of any outcome on top of standard risk factors. CONCLUSIONS: Risk-scores based upon urinary proteomics did not improve prediction of renal and cardiovascular endpoints on top of standard risk factors such as age and GFR during long-term (19 years) follow up in patients with type 2-diabetes and microalbuminuria.
RCT Entities:
BACKGROUND: Analyses of the urinary proteome have been proposed as a novel approach for early assessment of increased risk of renal- or cardiovascular disease. Here we investigate the potentials of various classifiers derived from urinary proteomics for prediction of renal and cardiovascular comorbidities in patients with type 2-diabetes. METHODS: The study was a post hoc analysis of the randomized controlled Steno-2 trial comparing intensified multifactorial intervention to conventional treatment of type 2-diabetes and microalbuminuria. 151 diabeticpatients with persistent microalbuminuria were included in year 1995 and followed for up to 19 years. For renal outcomes, two classifiers (CKD273 and a novel, GFR-based classifier) and for cardiovascular outcomes, three classifiers (CAD238, ACSP and ACSP75) were applied. Renal endpoints were progression to macroalbuminuria, impaired renal function (GFR < 45 ml/min/1.73 m2) or progression to end stage renal disease (ESRD) or death. Cardiovascular endpoints were coronary artery disease and a composite endpoint of incident death of cardiovascular disease, myocardial infarction or revascularization, stroke, amputation or peripheral revascularization. RESULTS: CKD273 was not consistently associated with renal outcomes. The GFR-based classifier was associated with impaired renal function, but lost significance in extensively adjusted models. Both the ACSP75 and ACSP-scores, but not the CAD238-score were inversely associated (opposing the hypothesis) with cardiovascular endpoints. None of the classifiers improved prediction of any outcome on top of standard risk factors. CONCLUSIONS: Risk-scores based upon urinary proteomics did not improve prediction of renal and cardiovascular endpoints on top of standard risk factors such as age and GFR during long-term (19 years) follow up in patients with type 2-diabetes and microalbuminuria.
Authors: Tarunveer S Ahluwalia; Tuomas O Kilpeläinen; Sandeep Singh; Peter Rossing Journal: Front Endocrinol (Lausanne) Date: 2019-09-27 Impact factor: 5.555