Bettina Heidecker

Transcriptomic Biomarkers for Individual Risk Assessment in New-Onset Heart Failure

Background— Prediction of prognosis remains a major unmet need in new-onset heart failure (HF). Although several clinical tests are in use, none accurately distinguish between patients with poor versus excellent survival. We hypothesized that a transcriptomic signature, generated from a single endomyocardial biopsy, could serve as a novel prognostic biomarker in HF. Methods and Results— Endomyocardial biopsy samples and clinical data were collected from all patients presenting with new-onset HF from 1997 to 2006. Among a total of 350 endomyocardial biopsy samples, 180 were identified as idiopathic dilated cardiomyopathy. Patients with phenotypic extremes in survival were selected: good prognosis (event-free survival for at least 5 years; n=25) and poor prognosis (events [death, requirement for left ventricular assist device, or cardiac transplant] within the first 2 years of presentation with HF symptoms; n=18). We used human U133 Plus 2.0 microarrays (Affymetrix) and analyzed the data with significance analysis of microarrays and prediction analysis of microarrays. We identified 46 overexpressed genes in patients with good versus poor prognosis, of which 45 genes were selected by prediction analysis of microarrays for prediction of prognosis in a train set (n=29) with subsequent validation in test sets (n=14 each). The biomarker performed with 74% sensitivity (95% CI 69% to 79%) and 90% specificity (95% CI 87% to 93%) after 50 random partitions. Conclusions— These findings suggest the potential of transcriptomic biomarkers to predict prognosis in patients with new-onset HF from a single endomyocardial biopsy sample. In addition, our findings offer potential novel therapeutic targets for HF and cardiomyopathy.

The gene expression profile of patients with new-onset heart failure reveals important gender-specific differences

AIMS We sought to test the hypothesis that inherent biological factors contribute to gender differences in disease pathophysiology of new-onset heart failure (HF), which can be detected from the transcriptome of a single endomyocardial biopsy (EMB). METHODS AND RESULTS We analysed samples from male (n = 29) and female patients (n = 14) with idiopathic dilated cardiomyopathy (IDCM) and new-onset HF with U133 Plus 2.0 microarrays (Affymetrix) and significance analysis of microarrays (SAM). There were 35 overexpressed and 16 downregulated transcripts in men vs. women [q < 5%, fold change (FC) > 1.2]. In addition to overexpression of Y-chromosome-related transcripts (n = 18), such as USP9Y (FC > 13.1), DDX3Y (FC > 11.3), RPS4Y1 (FC > 9.9), and EIF1AY (FC > 11.8) in males, there was overexpression of CD24 (FC > 5.6) and KCNK1 (FC > 1.5). In females, XIST was highly overexpressed (FC > 28.9), together with X-linked zinc finger proteins (FC > 1.9) and autosomal genes GATAD1 (FC > 1.6), SLC2A12 (FC > 2.9), and PDE6B (FC > 1.5). Analysis of a public data set of end-stage IDCM (n = 15) resulted in approximately 85% overlap with our findings. CONCLUSION This is the first study that identified gender-specific transcriptomic differences in new-onset HF. Our findings may offer novel insights into fundamental biological differences in the pathophysiology of HF between sexes and provide a platform for personalized medicine.

Transcriptomic Biomarkers for the Accurate Diagnosis of Myocarditis

Background— Lymphocytic myocarditis is a clinically important condition that is difficult to diagnose and distinguish. We hypothesized that the transcriptome obtained from an endomyocardial biopsy would yield clinically relevant and accurate molecular signatures. Methods and Results— Microarray analysis was performed on samples from patients with histologically proven lymphocytic myocarditis (n=16) and idiopathic dilated cardiomyopathy (n=32) to develop accurate diagnostic transcriptome-based biomarkers using multiple classification algorithms. We identified 9878 differentially expressed genes in lymphocytic myocarditis versus idiopathic dilated cardiomyopathy (fold change >1.2; false discovery rate <5%) from which a transcriptome-based biomarker containing 62 genes was identified that distinguished myocarditis with 100% sensitivity (95% confidence interval, 46 to 100) and 100% specificity (95% confidence interval, 66 to 100) and was generalizable to a broad range of secondary cardiomyopathies associated with inflammation (n=27), ischemic cardiomyopathy (n=8), and the normal heart (n=11). Multiple classification algorithms and quantitative real-time reverse-transcription polymerase chain reaction analysis further reduced this subset to a highly robust molecular signature of 13 genes, which still performed with 100% accuracy. Conclusions— Together, these findings demonstrate that transcriptomic biomarkers from a single endomyocardial biopsy can improve the clinical detection of patients with inflammatory diseases of the heart. This approach advances the clinical management and treatment of cardiac disorders with highly variable outcome.

The use of transcriptomic biomarkers for personalized medicine

Microarrays are a high throughput technology that allows the quantification of tens of thousands of RNA transcripts in a single reaction. This new technology offers the promise of comprehensive study of disease at a genomic level, potentially identifying novel molecular abnormalities, developing novel clinical biomarkers, and investigating drug efficacy. The ability to develop a molecular profile corresponding to a therapeutic effect is the basis for the concept of drug repositioning. With regard to prediction of clinical events, microarray technology has the potential to contribute to the development of sophisticated new biomarkers useful as predictors of disease etiology, outcome, and responsiveness to therapy—so-called personalized medicine. Currently progress in the field is hampered by a degree of skepticism about the reliability of microarray data and its relevance for clinical applications. Here we discuss possible pitfalls of transcriptomic analysis, review current developments in the cardiovascular area and address the use of transcriptomics for clinical applications.

Association of growth differentiation factor 11/8, putative anti-ageing factor, with cardiovascular outcomes and overall mortality in humans: analysis of the Heart and Soul and HUNT3 cohorts

AIMS Growth differentiation factor 11 and/or its homologue growth differentiation factor 8 (GDF11/8) reverses age-related cardiac hypertrophy and vascular ageing in mice. We investigated whether GDF11/8 associates with cardiovascular outcomes, left ventricular hypertrophy (LVH), or age in humans. METHODS AND RESULTS We measured plasma GDF11/8 levels in 928 participants with stable ischaemic heart disease in the Heart and Soul study. We adjudicated heart failure hospitalization, stroke, myocardial infarction, death, and their composite endpoint. Left ventricular hypertrophy was evaluated by echocardiography. We used multivariable Cox proportional hazards models to compare rates of cardiovascular events and death across GDF11/8 quartiles and logistic regression models to evaluate the association between GDF11/8 and LVH. Four hundred and fifty participants (48.5%) experienced a cardiovascular event or death during 8.9 years of follow-up. The adjusted risk of the composite endpoint was lower in the highest compared with the lowest GDF11/8 quartile [hazard ratio (HR), 0.45; 95% confidence interval (CI), 0.33-0.60; P < 0.001]. We replicated this relationship of GDF11/8 to adverse events in 971 participants in the HUNT3 cohort (adjusted HR, 0.34; 95% CI, 0.23-0.51; P < 0.001). Left ventricular hypertrophy was present in 368 participants (39.7%) at baseline. Participants in the highest quartile of GDF11/8 were less likely to have LVH than those in the lowest quartile (adjusted OR, 0.55; 95% CI, 0.35-0.86; P = 0.009). GDF11/8 levels were lower in older individuals (P < 0.001). CONCLUSION In patients with stable ischaemic heart disease, higher GDF11/8 levels are associated with lower risk of cardiovascular events and death. Our findings suggest that GDF11/8 has similar cardioprotective properties in humans to those demonstrated in mice.

Sex-specific impact of aldosterone receptor antagonism on ventricular remodeling and gene expression after myocardial infarction.

Aldosterone receptor antagonism reduces mortality and improves post‐myocardial infarction (Ml) remodeling. Because aldosterone and estrogen signaling pathways interact, we hypothesized that aldosterone blockade is sex‐specific. Therefore, we investigated the mpact of eplerenone on left ventricular (LV) remodeling and gene expression of male infarcted rats versus female infarcted rats. Ml and Sham animals were randomized to receive eplerenone (100 mg/kg/day) or placebo 3 days post‐surgery for 4 weeks and assessed by echocardiography. In the Ml placebo group, left ventricular end‐diastolic dimension (LVEDD) increased from 7.3 ± 0.4 mm to 10.2 ± 1.0 mm (p < 0.05) and ejection fraction (EF) decreased from 82.3 + 4% to 45.5 + 11% (p < 0.05) in both sexes (p= NS between groups). Eplerenone attenuated LVEDD enlargement more effectively in females (8.8 ± 0.2 mm, p < 0.05 vs. placebo) than in males (9.7 ± 0.2 mm, p= NS vs. placebo) and improved EF in females (56.7 ± 3%, p < 0.05 vs. placebo) but not in males (50.6 + 3%, p= NS vs. placebo). Transcriptomic analysis using Rat_230–2.0 microarrays (Affymetrix) revealed that in females 19% of downregu‐lated genes and 44% of upregulated genes post‐MI were restored to normal by eplerenone. In contrast, eplerenone only restored 4% of overexpressed genes in males. Together, these data suggest that aldosterone blockade reduces Ml‐induced cardiac remodeling and phenotypic alterations of gene expression preferentially in females than in males. The use of transcriptomic signatures to detect greater benefit of eplerenone in females has potential implications for personalized medicine.

Biochemistry and Biology of GDF11 and Myostatin: Similarities, Differences, and Questions for Future Investigation.

Growth differentiation factor 11 (GDF11) and myostatin (or GDF8) are closely related members of the transforming growth factor β superfamily and are often perceived to serve similar or overlapping roles. Yet, despite commonalities in protein sequence, receptor utilization and signaling, accumulating evidence suggests that these 2 ligands can have distinct functions in many situations. GDF11 is essential for mammalian development and has been suggested to regulate aging of multiple tissues, whereas myostatin is a well-described negative regulator of postnatal skeletal and cardiac muscle mass and modulates metabolic processes. In this review, we discuss the biochemical regulation of GDF11 and myostatin and their functions in the heart, skeletal muscle, and brain. We also highlight recent clinical findings with respect to a potential role for GDF11 and/or myostatin in humans with heart disease. Finally, we address key outstanding questions related to GDF11 and myostatin dynamics and signaling during development, growth, and aging.Growth differentiation factor 11 (GDF11) and myostatin (or GDF8) are closely related members of the transforming growth factor β superfamily and are often perceived to serve similar or overlapping roles. Yet, despite commonalities in protein sequence, receptor utilization and signaling, accumulating evidence suggests that these 2 ligands can have distinct functions in many situations. GDF11 is essential for mammalian development and has been suggested to regulate aging of multiple tissues, whereas myostatin is a well-described negative regulator of postnatal skeletal and cardiac muscle mass and modulates metabolic processes. In this review, we discuss the biochemical regulation of GDF11 and myostatin and their functions in the heart, skeletal muscle, and brain. We also highlight recent clinical findings with respect to a potential role for GDF11 and/or myostatin in humans with heart disease. Finally, we address key outstanding questions related to GDF11 and myostatin dynamics and signaling during development, growth, and aging. # Response to Walker et al {#article-title-171}

Biochemistry and Biology of GDF11 and Myostatin

Growth differentiation factor 11 (GDF11) and myostatin (or GDF8) are closely related members of the transforming growth factor β superfamily and are often perceived to serve similar or overlapping roles. Yet, despite commonalities in protein sequence, receptor utilization and signaling, accumulating evidence suggests that these 2 ligands can have distinct functions in many situations. GDF11 is essential for mammalian development and has been suggested to regulate aging of multiple tissues, whereas myostatin is a well-described negative regulator of postnatal skeletal and cardiac muscle mass and modulates metabolic processes. In this review, we discuss the biochemical regulation of GDF11 and myostatin and their functions in the heart, skeletal muscle, and brain. We also highlight recent clinical findings with respect to a potential role for GDF11 and/or myostatin in humans with heart disease. Finally, we address key outstanding questions related to GDF11 and myostatin dynamics and signaling during development, growth, and aging.Growth differentiation factor 11 (GDF11) and myostatin (or GDF8) are closely related members of the transforming growth factor β superfamily and are often perceived to serve similar or overlapping roles. Yet, despite commonalities in protein sequence, receptor utilization and signaling, accumulating evidence suggests that these 2 ligands can have distinct functions in many situations. GDF11 is essential for mammalian development and has been suggested to regulate aging of multiple tissues, whereas myostatin is a well-described negative regulator of postnatal skeletal and cardiac muscle mass and modulates metabolic processes. In this review, we discuss the biochemical regulation of GDF11 and myostatin and their functions in the heart, skeletal muscle, and brain. We also highlight recent clinical findings with respect to a potential role for GDF11 and/or myostatin in humans with heart disease. Finally, we address key outstanding questions related to GDF11 and myostatin dynamics and signaling during development, growth, and aging. # Response to Walker et al {#article-title-171}

Behavioral Cardiovascular Risk Factors – Effect of Physical Activity and Cardiorespiratory Fitness on Cardiovascular Outcomes –

Primary and primordial prevention of cardiovascular disease (CVD) requires not only identification of risk factors, but also appropriate and timely therapy. In order to prevent the expected increase in prevalence of CVD, it is essential that clinicians are aware of behavioral cardiovascular risk factors. A basic review is critical to clarify the difference between physical activity and fitness, as well as to discuss the role each plays in cardiovascular outcomes. We discuss observational epidemiological studies and randomized control trials that have examined the effect of physical activity and cardiorespiratory fitness on CVD.

Cardiovascular Genetic Medicine: Genomic Assessment of Prognosis and Diagnosis in Patients with Cardiomyopathy and Heart Failure

In the last half century, epidemiologic studies and basic science investigations revealed that hypertension (Kannel et al., Ann Intern Med 55:33–50, 1961), hyperlipidemia (Dawber et al., Am J Public Health Nations Health 49:1349–1356, 1959), diabetes (Kannel et al., Am J Cardiol 34(1):29–34, 1974), smoking (Dawber et al., Am J Public Health Nations Health 49:1349–1356, 1959), and inflammation (Rossmann et al., Exp Gerontol 43(3):229–237, 2008) posed increased risk for cardiovascular disease. These associations served both as risk factors and offered insight into disease pathophysiology. Currently, it is increasingly appreciated that polygenic factors may also play a role as etiologic or risk factors (Chakravarti and Little, Nature 421(6921):412–414, 2003; Dorn and Molkentin, Circulation 109(2):150–158, 2004). Recent technologic advances in genomic screening make the search for these factors possible, and robust technologies are now available for both entire genome screening for expression or single nucleotide polymorphisms. In this paper, we review the basic principles of gene expression and molecular signature analysis in the context of potential clinical applications of transcriptomics.