Urinary proteomic diagnosis of coronary artery disease: identification and clinical validation in 623 individuals

Objectives We studied the urinary proteome in a total of 623 individuals with and without coronary artery disease (CAD) in order to characterize multiple biomarkers that enable prediction of the presence of CAD. Methods Urine samples were analyzed by capillary electrophoresis coupled online to micro time-of-flight mass spectrometry. Results We defined a pattern of 238 CAD-specific polypeptides from comparison of 586 spot urine samples from 408 individuals. This pattern identified patients with CAD in a blinded cohort of 138 urine samples (71 patients with CAD and 67 healthy individuals) with high sensitivity and specificity (area under the receiver operator characteristic curve 87%, 95% confidence interval 81-92) and was superior to previously developed 15-marker (area under the receiver operator characteristic curve 68%, P < 0.0001) and 17-marker panels (area under the receiver operator characteristic curve 77%, P < 0.0001). The sequences of the discriminatory polypeptides include fragments of alpha-1-antitrypsin, collagen types 1 and 3, granin-like neuroendocrine peptide precursor, membrane-associated progesterone receptor component 1, sodium/potassium-transporting ATPase gamma chain and fibrinogen-alpha chain. Several biomarkers changed significantly toward the healthy signature following 2-year treatment with irbesartan, whereas short-term treatment with irbesartan did not significantly affect the polypeptide pattern. Conclusion Urinary proteomics identifies CAD with high confidence and might also be useful for monitoring the effects of therapeutic interventions

Predicting major outcomes in type 1 diabetes: a model development and validation study

Aims/hypothesis Type 1 diabetes is associated with a higher risk of major vascular complications and death. A reliable method that predicted these outcomes early in the disease process would help in risk classification. We therefore developed such a prognostic model and quantified its performance in independent cohorts. Methods Data were analysed from 1,973 participants with type 1 diabetes followed for 7 years in the EURODIAB Prospective Complications Study. Strong prognostic factors for major outcomes were combined in a Weibull regression model. The performance of the model was tested in three different prospective cohorts: the Pittsburgh Epidemiology of Diabetes Complications study (EDC, n¿=¿554), the Finnish Diabetic Nephropathy study (FinnDiane, n¿=¿2,999) and the Coronary Artery Calcification in Type 1 Diabetes study (CACTI, n¿=¿580). Major outcomes included major CHD, stroke, end-stage renal failure, amputations, blindness and all-cause death. Results A total of 95 EURODIAB patients with type 1 diabetes developed major outcomes during follow-up. Prognostic factors were age, HbA1c, WHR, albumin/creatinine ratio and HDL-cholesterol level. The discriminative ability of the model was adequate, with a concordance statistic (C-statistic) of 0.74. Discrimination was similar or even better in the independent cohorts, the C-statistics being: EDC, 0.79; FinnDiane, 0.82; and CACTI, 0.73. Conclusions/interpretation Our prognostic model, which uses easily accessible clinical features can discriminate between type 1 diabetes patients who have a good or a poor prognosis. Such a prognostic model may be helpful in clinical practice and for risk stratification in clinical trials