Serum Proteomic Profiles in Inflammatory and Non-Inflammatory Cardiomyopathies: A Novel Approach for Diagnostic Biomarker Discovery

The aim of this project is to develop a noninvasive serum test that predicts histologic forms of myocarditis (inflammatory) and dilated (non-inflammatory) cardiomyopathy using proteomic techniques to analyze serum proteins. Idiopathic dilated cardiomyopathy (DCM) and myocarditis (myocardial inflammation) represent a spectrum of heart muscle disease of various etiologies that usually present with progressive heart failure. Together, they constitute the leading cause of heart transplantation in the United States. Currently, the gold standard of diagnosis of myocarditis is by endomyocardial biopsy (EMB) and histopathological classification according to the Dallas Criteria ; however this diagnostic technique is severely limited by its invasiveness, a lack of sensitivity and an attendant sampling error, yielding diagnostic information in only 10-20% of the cases . As such, the development of a non-invasive highly specific test for myocarditis is of great value and importance particularly in the diagnosis of giant cell myocarditis, a rare but very fulminant form of autoimmune myocarditis where timely institution of appropriate immunosuppressive therapy significantly increases transplant-free survival. We proposed, using an observational case-control study, to undertake a proteomic analysis to compare serum proteomic profiles - determined by mass spectroscopy and isotope tagging- with histologic findings on endomyocardial biopsy. Our hypothesis is that different forms of myocarditis and dilated cardiomyopathy have distinct serum protein profiles and that these unique profiles which correlate with specific histologic types, will allow for noninvasive diagnosis of major forms of myocarditis and DCM

Socioeconomic Status Correlates with the Prevalence of Advanced Coronary Artery Disease in the United States

Background: Increasingly studies have identified socioeconomic factors adversely affecting healthcare outcomes for a multitude of diseases. To date, however, there has not been a study correlating socioeconomic details from nationwide databases on the prevalence of advanced coronary artery disease. We seek to identify whether socioeconomic factors contribute to advanced coronary artery disease prevalence in the United States. Methods and Findings: State specific prevalence data was queried form the United States Nationwide Inpatient Sample for 2009. Patients undergoing percutaneous coronary angioplasty and coronary artery bypass graft were identified as principal procedures. Non-cardiac related procedures, lung lobectomy and hip replacement (partial and total) were identified and used as control groups. Information regarding prevalence was then merged with data from the Behavioral Risk Factor Surveillance System, the largest, on-going telephone health survey system tracking health conditions and risk behaviors in the United States. Pearson's correlation coefficient was calculated for individual socioeconomic variables including employment status, level of education, and household income. Household income and education level were inversely correlated with the prevalence of percutaneous coronary angioplasty (−0.717; −0.787) and coronary artery bypass graft surgery (−0.541; −0.618). This phenomenon was not seen in the non-cardiac procedure control groups. In multiple linear regression analysis, socioeconomic factors were significant predictors of coronary artery bypass graft and percutaneous transluminal coronary angioplasty (p<0.001 and p = 0.005, respectively). Conclusions: Socioeconomic status is related to the prevalence of advanced coronary artery disease as measured by the prevalence of percutaneous coronary angioplasty and coronary artery bypass graft surgery

World Health Organization cardiovascular disease risk charts: revised models to estimate risk in 21 global regions

BACKGROUND: To help adapt cardiovascular disease risk prediction approaches to low-income and middle-income countries, WHO has convened an effort to develop, evaluate, and illustrate revised risk models. Here, we report the derivation, validation, and illustration of the revised WHO cardiovascular disease risk prediction charts that have been adapted to the circumstances of 21 global regions. METHODS: In this model revision initiative, we derived 10-year risk prediction models for fatal and non-fatal cardiovascular disease (ie, myocardial infarction and stroke) using individual participant data from the Emerging Risk Factors Collaboration. Models included information on age, smoking status, systolic blood pressure, history of diabetes, and total cholesterol. For derivation, we included participants aged 40-80 years without a known baseline history of cardiovascular disease, who were followed up until the first myocardial infarction, fatal coronary heart disease, or stroke event. We recalibrated models using age-specific and sex-specific incidences and risk factor values available from 21 global regions. For external validation, we analysed individual participant data from studies distinct from those used in model derivation. We illustrated models by analysing data on a further 123 743 individuals from surveys in 79 countries collected with the WHO STEPwise Approach to Surveillance. FINDINGS: Our risk model derivation involved 376 177 individuals from 85 cohorts, and 19 333 incident cardiovascular events recorded during 10 years of follow-up. The derived risk prediction models discriminated well in external validation cohorts (19 cohorts, 1 096 061 individuals, 25 950 cardiovascular disease events), with Harrell's C indices ranging from 0·685 (95% CI 0·629-0·741) to 0·833 (0·783-0·882). For a given risk factor profile, we found substantial variation across global regions in the estimated 10-year predicted risk. For example, estimated cardiovascular disease risk for a 60-year-old male smoker without diabetes and with systolic blood pressure of 140 mm Hg and total cholesterol of 5 mmol/L ranged from 11% in Andean Latin America to 30% in central Asia. When applied to data from 79 countries (mostly low-income and middle-income countries), the proportion of individuals aged 40-64 years estimated to be at greater than 20% risk ranged from less than 1% in Uganda to more than 16% in Egypt. INTERPRETATION: We have derived, calibrated, and validated new WHO risk prediction models to estimate cardiovascular disease risk in 21 Global Burden of Disease regions. The widespread use of these models could enhance the accuracy, practicability, and sustainability of efforts to reduce the burden of cardiovascular disease worldwide. FUNDING: World Health Organization, British Heart Foundation (BHF), BHF Cambridge Centre for Research Excellence, UK Medical Research Council, and National Institute for Health Research

Equalization of four cardiovascular risk algorithms after systematic recalibration: individual-participant meta-analysis of 86 prospective studies

Aims: There is debate about the optimum algorithm for cardiovascular disease (CVD) risk estimation. We conducted head-to-head comparisons of four algorithms recommended by primary prevention guidelines, before and after ‘recalibration’, a method that adapts risk algorithms to take account of differences in the risk characteristics of the populations being studied. Methods & Results: Using individual-participant data on 360737 participants without CVD at baseline in 86 prospective studies from 22 countries, we compared the Framingham risk score (FRS), Systematic COronary Risk Evaluation (SCORE), pooled cohort equations (PCE), and Reynolds risk score (RRS). We calculated measures of risk discrimination and calibration, and modelled clinical implications of initiating statin therapy in people judged to be at ‘high’ 10 year CVD risk. Original risk algorithms were recalibrated using the risk factor profile and CVD incidence of target populations. The four algorithms had similar risk discrimination. Before recalibration, FRS, SCORE, and PCE overpredicted CVD risk on average by 10%, 52%, and 41%, respectively, whereas RRS under-predicted by 10%. Original versions of algorithms classified 29–39% of individuals aged \u3e_40years as high risk. By contrast, recalibration reduced this proportion to 22–24% for every algorithm. We estimated that to prevent one CVD event, it would be necessary to initiate statin therapy in 44–51 such individuals using original algorithms, in contrast to 37–39 individuals with recalibrated algorithms. Conclusions: Before recalibration, the clinical performance of four widely used CVD risk algorithms varied substantially. By contrast, simple recalibration nearly equalized their performance and improved modelled targeting of preventive action to clinical need