Determination of Pericardial Adipose Tissue Increases the Prognostic Accuracy of Coronary Artery Calcification for Future Cardiovascular Events

Objectives: Pericardial adipose tissue (PAT) is associated with coronary artery plaque accumulation and the incidence of coronary heart disease. We evaluated the possible incremental prognostic value of PAT for future cardiovascular events. Methods: 145 patients (94 males, age 60 10 years) with stable coronary artery disease underwent coronary artery calcification (CAC) scanning in a multislice CT scanner, and the volume of pericardial fat was measured. Mean observation time was 5.4 years. Results: 34 patients experienced a severe cardiac event. They had a significantly higher CAC score (1,708 +/- 2,269 vs. 538 +/- 1,150, p 400, 3.5 (1.9-5.4; p = 0.007) for scores > 800 and 5.9 (3.7-7.8; p = 0.005) for scores > 1,600. When additionally a PAT volume > 200 cm(3) was determined, there was a significant increase in the event rate and relative risk. We calculated a relative risk of 2.9 (1.9-4.2; p = 0.01) for scores > 400, 4.0 (2.1-5.0; p = 0.006) for scores > 800 and 7.1 (4.1-10.2; p = 0.005) for scores > 1,600. Conclusions:The additional determination of PAT increases the predictive power of CAC for future cardiovascular events. PAT might therefore be used as a further parameter for risk stratification. Copyright (C) 2012 S. Karger AG, Base

Evaluation of pulse wave analysis to assess coronary artery disease

Conventional risk factors for cardiovascular disease, such as age, gender, hyperlipidaemia and hypertension are useful clinical markers of coronary artery disease (CAD) in asymptomatic patients or those without a prior history of atherosclerosis. In patients referred for a cardiology opinion, modification of risk factors by lifestyle changes and cardiac medications as well as confounding co-morbidities limit the value of these markers. Patients are often referred for diagnostic coronary angiography to determine the presence and severity of CAD, stratify the risk of future events and determine appropriate management. Despite the use of a variety of tests to best identify those requiring angiography, up to half of all patients referred do not have significant disease. Pulse wave analysis (PWA) is a novel method to derive indices of central (aortic) blood pressure and arterial stiffness. Pressure waveforms are obtained non-invasively from the radial artery using a simple tonometry method and have been shown to correlate with clinical outcomes and cardiovascular events in selected populations. This thesis will explore, for the first time, the clinical potential for PWA as a non-invasive marker of CAD in an unselected contemporary cohort of patients referred for elective coronary angiography. The main hypotheses tested are first that PWA is a suitable tool for clinical use, including those with cardiac and non-cardiac co-morbidities and second that abnormalities of PWA are independent predictors of the presence and severity of CAD. Data have been derived from a prospective, protocol-driven, multi-centre cohort of 550 patients recruited from 2006-8. Results suggest that PWA has a useful clinical role in stratifying the risk of coronary disease. PWA variables were independent of conventional blood pressure measurement and superior to baseline risk factors, biomarkers and other non-invasive tests

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

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

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

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