Myocardial gene expression profiles and cardiodepressant autoantibodies predict response of patients with dilated cardiomyopathy to immunoadsorption therapy

Exploring the predictive values of SERP4 and FRZB in dilated cardiomyopathy based on an integrated analysis

BACKGROUND AND OBJECTIVE

The aim of this study was to investigate potential hub genes for dilated cardiomyopathy (DCM).

METHODS

Five DCM-related microarray datasets were downloaded from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were used for identification. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, disease ontology, gene ontology annotation and protein-protein interaction (PPI) network analysis were then performed, while a random forest was constructed to explore central genes. Artificial neural networks were used to compare with known genes and to develop new diagnostic models. 240 population blood samples were collected and expression of hub genes was verified in these samples using RT-PCR and demonstrated by Nomogram.

RESULTS

After differential analysis, 33 genes were statistically significant (adjusted P < 0.05). Functional enrichment of these differential genes resulted in 85 Gene Ontology (GO) functions identified and 6 pathways enriched for the KEGG pathway. PPI networks and molecular complex assays identified 10 hub genes (adjusted P < 0.05). Random forest identified SMOC2 and SFRP4 as the most important, followed by FCER1G and FRZB. NeuraHF models (SMOC2, SFRP4, FCER1G and FRZB) were selected by artificial neural network model and had better diagnostic efficacy for the onset of DCM, compared with the traditional KG-DCM models (MYH7, ACTC1, TTN and LMNA). Finally, SFRP4 and FRZB were expressed higher in DCM verified by RT-PCR and as a factor for DCM identified by Nomogram.

CONCLUSIONS

We performed an integrated analysis and identified SFRP4 and FRZB as a new factor for DCM. But the exact mechanism still needs further experimental verification.

Multicentre, randomized, double-blind, prospective study on the effects of ImmunoAdSorptiOn on cardiac function in patients with Dilated CardioMyopathy (IASO-DCM): Rationale and design

AIMS

Pilot studies indicate that immunoadsorption with subsequent IgG substitution (IA/IgG) induces beneficial effects in patients with dilated cardiomyopathy (DCM) and heart failure. This placebo-controlled study investigates whether IA/IgG treatment enhances left ventricular (LV) systolic function as compared to a control group receiving pseudo-treatment.

METHODS

This multicentre, randomized, double-blind, parallel-group trial aims to include 200 patients with heart failure due to DCM (LV ejection fraction [LVEF] <40%) on optimized guideline-directed heart failure medication. Participants are randomly assigned in a 1:1 ratio to IA/IgG using protein-A columns, or to pseudo-immunoadsorption followed by an intravenous infusion without IgG. Follow-up visits take place by telephone after 1 and 3 months and at the study centres after 6, 12 and 24 months. The primary efficacy endpoint is the change in LVEF from baseline to 6 months determined by contrast echocardiography, analysed at a core lab. In addition, LV end-diastolic and end-systolic volumes will be analysed as secondary endpoints over the entire study period to assess whether IA/IgG affects LV remodelling. As main secondary outcome, a composite of all-cause death, cardiac resuscitation, hospitalization for heart failure, and need for cardiac surgery to improve myocardial pump function will be evaluated after 24 months. In addition, exploratory outcomes as well as safety endpoints related to the treatment will be assessed throughout the whole study period.

CONCLUSION

IASO-DCM is a randomized study which will provide comprehensive insights into the effects of immunoadsorption with subsequent IgG substitution in patients with DCM.

Identification of dilated cardiomyopathy-linked key genes by bioinformatics methods and evaluating the impact of tannic acid and monosodium glutamate in rats

Dilated cardiomyopathy (DCM) is the most common type of myocardial dysfunction, affecting mostly young adults, but its therapeutic diagnosis and biomarkers for prognosis are lacking. This study aimed to investigate the possible effect of the common food additive monosodium glutamate (MSG) and tannic acid (TA), a phenolic compound, on the key molecular actors responsible for DCM. DCM-related publicly available microarray datasets (GSE120895, GSE17800, and GSE19303) were downloaded from the comprehensive Gene Expression Omnibus (GEO) database, and analyzed to identify differentially expressed genes (DEGs). By integrating DEGs and gene-disease validity curation results, overlapping genes were screened and identified as hub genes. Protein-protein interaction (PPI) network and ontology analysis were performed to make sense of the identified biological data. Finally, mRNA expression changes of identified hub genes in the heart tissues of rats treated with MSG and TA were measured by the qPCR method. Six upregulated (IGF1, TTN, ACTB, LMNA, EDN1, and NPPB) DEGs were identified between the DCM and healthy control samples as the hub genes. qPCR results revealed that the mRNA levels of these genes involved in DCM development increased significantly in rat heart tissues exposed to MSG. In contrast, this increase was remarkably alleviated by TA treatment. Our results provide new insights into critical molecular mechanisms that should be focused on in future DCM studies. Moreover, MSG may play a critical role in DCM formation, and TA may be used as a promising therapeutic agent in DCM.

Integrative Analyses of Circulating Proteins and Metabolites Reveal Sex Differences in the Associations with Cardiac Function among DCM Patients

Dilated cardiomyopathy (DCM) is characterized by reduced left ventricular ejection fraction (LVEF) and left or biventricular dilatation. We evaluated sex-specific associations of circulating proteins and metabolites with structural and functional heart parameters in DCM. Plasma samples (297 men, 71 women) were analyzed for proteins using Olink assays (targeted analysis) or LC-MS/MS (untargeted analysis), and for metabolites using LC MS/MS (Biocrates AbsoluteIDQ p180 Kit). Associations of proteins (n = 571) or metabolites (n = 163) with LVEF, measured left ventricular end diastolic diameter (LVEDDmeasured), and the dilation percentage of LVEDD from the norm (LVEDDacc. to HENRY) were examined in combined and sex-specific regression models. To disclose protein-metabolite relations, correlation analyses were performed. Associations between proteins, metabolites and LVEF were restricted to men, while associations with LVEDD were absent in both sexes. Significant metabolites were validated in a second independent DCM cohort (93 men). Integrative analyses demonstrated close relations between altered proteins and metabolites involved in lipid metabolism, inflammation, and endothelial dysfunction with declining LVEF, with kynurenine as the most prominent finding. In DCM, the loss of cardiac function was reflected by circulating proteins and metabolites with sex-specific differences. Our integrative approach demonstrated that concurrently assessing specific proteins and metabolites might help us to gain insights into the alterations associated with DCM.

Identification of the Key Genes of Immune Infiltration in Dilated Cardiomyopathy

Dilated cardiomyopathy (DCM) is a common cause of heart failure. In this study, we screened the immune infiltration-related genes associated with DCM to explore the potential molecular mechanisms and provide a basis for the early diagnosis and development of new immunotherapeutic targets. A dataset related to DCM was downloaded from the Gene Expression Omnibus (GEO) database. R software was applied to the genetic differential analysis of patients with DCM and healthy individuals, and the obtained differential expressed genes (DEGs) were screened for differentially expressed immune-related genes (DEIRGs) after comparison with the immune microsatellite database. Gene functional analysis established a protein interaction network (PPI). The immune infiltration in patients with DCM versus normal controls was assessed using the CIBERSORT algorithm, the hub genes were screened using the MOCDE app, and the hubs were validated in multiple datasets. A total of 246 DEGs were screened (adj. P < 0.05 and |logFC| > 0.3), and a total of 170 DEIRGs were compared. Gene Ontology analysis showed significant (adj. P < 0.05) Biological Process entries of 591, Cellular Component of 10, and Molecular Function of 39; Kyoto Encyclopedia of Genes and Genomes showed 20 significant entries, mainly focused on cytokines involved in immune-related response, etc. A protein interaction network comprising 28 hub DEGs was constructed in combination with the PPI network interactions. DEIRG was mainly distributed in the T-cell receptor pathway by immune infiltration detection analysis, and significant changes in central memory T-cells were found by analyzing T-cell-related subpathways, where INSR, HLA-B, IFITM1, and HBEGF were significantly differentially expressed. We selected 632 hospitalized patients for validation and found that INSR and HLA-B expression were associated with DCM development by Nomogram. The expression of HLA-B in peripheral blood T-cells was higher in DCM patients than in the normal group, as verified by qRT-PCR. However, the detailed mechanism needs to be further explored.

The Molecular Role of Immune Cells in Dilated Cardiomyopathy

Dilated cardiomyopathy (DCM) is a rare and severe condition characterized by chamber dilation and impaired contraction of the left ventricle. It constitutes a fundamental etiology for profound heart failure and abrupt cardiac demise, rendering it a prominent clinical indication for heart transplantation (HTx) among both adult and pediatric populations. DCM arises from various etiologies, including genetic variants, epigenetic disorders, infectious insults, autoimmune diseases, and cardiac conduction abnormalities. The maintenance of cardiac function involves two distinct types of immune cells: resident immune cells and recruited immune cells. Resident immune cells play a crucial role in establishing a harmonious microenvironment within the cardiac tissue. Nevertheless, in response to injury, cardiomyocytes initiate a cytokine cascade that attracts peripheral immune cells, thus perturbing this intricate equilibrium and actively participating in the initiation and pathological remodeling of dilated cardiomyopathy (DCM), particularly during the progression of myocardial fibrosis. Additionally, immune cells assume a pivotal role in orchestrating the inflammatory processes, which are intimately linked to the prognosis of DCM. Consequently, understanding the molecular role of various immune cells and their regulation mechanisms would provide an emerging era for managing DCM. In this review, we provide a summary of the most recent advancements in our understanding of the molecular mechanisms of immune cells in DCM. Additionally, we evaluate the effectiveness and limitations of immunotherapy approaches for the treatment of DCM, with the aim of optimizing future immunotherapeutic strategies for this condition.

Integrated genomic analysis defines molecular subgroups in dilated cardiomyopathy and identifies novel biomarkers based on machine learning methods

Aim: As the most common cardiomyopathy, dilated cardiomyopathy (DCM) often leads to progressive heart failure and sudden cardiac death. This study was designed to investigate the molecular subgroups of DCM. Methods: Three datasets of DCM were downloaded from GEO database (GSE17800, GSE79962 and GSE3585). After log2-transformation and background correction with "limma" package in R software, the three datasets were merged into a metadata cohort. The consensus clustering was conducted by the "Consensus Cluster Plus" package to uncover the molecular subgroups of DCM. Moreover, clinical characteristics of different molecular subgroups were compared in detail. We also adopted Weighted gene co-expression network analysis (WGCNA) analysis based on subgroup-specific signatures of gene expression profiles to further explore the specific gene modules of each molecular subgroup and its biological function. Two machine learning methods of LASSO regression algorithm and SVM-RFE algorithm was used to screen out the genetic biomarkers, of which the discriminative ability of molecular subgroups was evaluated by receiver operating characteristic (ROC) curve. Results: Based on the gene expression profiles, heart tissue samples from patients with DCM were clustered into three molecular subgroups. No statistical difference was found in age, body mass index (BMI) and left ventricular internal diameter at end-diastole (LVIDD) among three molecular subgroups. However, the results of left ventricular ejection fraction (LVEF) statistics showed that patients from subgroup 2 had a worse condition than the other group. We found that some of the gene modules (pink, black and grey) in WGCNA analysis were significantly related to cardiac function, and each molecular subgroup had its specific gene modules functions in modulating occurrence and progression of DCM. LASSO regression algorithm and SVM-RFE algorithm was used to further screen out genetic biomarkers of molecular subgroup 2, including TCEAL4, ISG15, RWDD1, ALG5, MRPL20, JTB and LITAF. The results of ROC curves showed that all of the genetic biomarkers had favorable discriminative effectiveness. Conclusion: Patients from different molecular subgroups have their unique gene expression patterns and different clinical characteristics. More personalized treatment under the guidance of gene expression patterns should be realized.

Integrated analysis of WGCNA and machine learning identified diagnostic biomarkers in dilated cardiomyopathy with heart failure

The etiologies and pathogenesis of dilated cardiomyopathy (DCM) with heart failure (HF) remain to be defined. Thus, exploring specific diagnosis biomarkers and mechanisms is urgently needed to improve this situation. In this study, three gene expression profiling datasets (GSE29819, GSE21610, GSE17800) and one single-cell RNA sequencing dataset (GSE95140) were obtained from the Gene Expression Omnibus (GEO) database. GSE29819 and GSE21610 were combined into the training group, while GSE17800 was the test group. We used the weighted gene co-expression network analysis (WGCNA) and identified fifteen driver genes highly associated with DCM with HF in the module. We performed the least absolute shrinkage and selection operator (LASSO) on the driver genes and then constructed five machine learning classifiers (random forest, gradient boosting machine, neural network, eXtreme gradient boosting, and support vector machine). Random forest was the best-performing classifier established on five Lasso-selected genes, which was utilized to select out NPPA, OMD, and PRELP for diagnosing DCM with HF. Moreover, we observed the up-regulation mRNA levels and robust diagnostic accuracies of NPPA, OMD, and PRELP in the training group and test group. Single-cell RNA-seq analysis further demonstrated their stable up-regulation expression patterns in various cardiomyocytes of DCM patients. Besides, through gene set enrichment analysis (GSEA), we found TGF-β signaling pathway, correlated with NPPA, OMD, and PRELP, was the underlying mechanism of DCM with HF. Overall, our study revealed NPPA, OMD, and PRELP serving as diagnostic biomarkers for DCM with HF, deepening the understanding of its pathogenesis.

Bioinformatics Analysis of Common Differential Genes of Viral Myocarditis and Dilated Cardiomyopathy: Screening for Potential Pharmacological Compounds

(1) Background: The mechanism of viral myocarditis (VMC) progression to dilated cardiomyopathy (DCM) remains unclear. The aim of this study was to identify key genes in the progression of VMC to DCM, so as to find potential therapeutic drugs and provide insights for future research. (2) Methods: Differential expression analysis of GSE4172 and GSE17800 from the Gene Expression Omnibus (GEO) database was performed using GEO2R, which contained genome-wide analysis of myocardial biopsies from VMC and DCM, respectively. We used the Venn diagram analysis to screen the common differentially expressed genes (DEGs). GO functional enrichment analysis and KEGG pathway analysis were also performed. Then we conducted protein-protein interaction (PPI) networks using STRING and identified hub genes using Cytoscape. Finally, we used cMAP to screen out candidate compounds targeting these hub genes; (3) Results: In total, 2143 DEGs for VMC and 1365 DEGs for DCM were found. Then a total of 191 common DEGs were identified. Biological processes and pathway involved in these genes mainly include GABA-gated chloride ion channel activity and Rap1 signaling pathway. A total of 14 hub genes were identified. PPI network showed these hubs mainly enriched in regulation of WNT signaling pathway and GABA-gated chloride ion channel activity. Subgroup analysis of Severe VMC cohort revealed 10 hub genes which mainly clustered in GABA channel activity, extracellular matrix remodeling and sarcomere dysfunction. Using cMAP, we obtained top 10 potential medications, but only amlodipine is currently viable; (4) Conclusions: Our study finds the hub genes and reveals the important role of GABA-gated chloride ion channel, Rap1 signaling pathway, WNT signaling pathway, extracellular matrix remodeling and sarcomere dysfunction in the progression from VMC to DCM. Amlodipine is a potential viable drug in preventing the progression of VMC to DCM.

The Variation of Transcriptomic Perturbations is Associated with the Development and Progression of Various Diseases

Background

Although transcriptomic data have been widely applied to explore various diseases, few studies have investigated the association between transcriptomic perturbations and disease development in a wide variety of diseases.

Methods

Based on a previously developed algorithm for quantifying intratumor heterogeneity at the transcriptomic level, we defined the variation of transcriptomic perturbations (VTP) of a disease relative to the health status. Based on publicly available transcriptome datasets, we compared VTP values between the disease and health status and analyzed correlations between VTP values and disease progression or severity in various diseases, including neurological disorders, infectious diseases, cardiovascular diseases, respiratory diseases, liver diseases, kidney diseases, digestive diseases, and endocrine diseases. We also identified the genes and pathways whose expression perturbations correlated positively with VTP across diverse diseases.

Results

VTP values were upregulated in various diseases relative to their normal controls. VTP values were significantly greater in define than in possible or probable Alzheimer's disease. VTP values were significantly larger in intensive care unit (ICU) COVID-19 patients than in non-ICU patients, and in COVID-19 patients requiring mechanical ventilatory support (MVS) than in those not requiring MVS. VTP correlated positively with viral loads in acquired immune deficiency syndrome (AIDS) patients. Moreover, the AIDS patients treated with abacavir or zidovudine had lower VTP values than those without such therapies. In pulmonary tuberculosis (TB) patients, VTP values followed the pattern: active TB > latent TB > normal controls. VTP values were greater in clinically apparent than in presymptomatic malaria. VTP correlated negatively with the cardiac index of left ventricular ejection fraction (LVEF). In chronic obstructive pulmonary disease (COPD), VTP showed a negative correlation with forced expiratory volume in the first second (FEV1). VTP values increased with H. pylori infection and were upregulated in atrophic gastritis caused by H. pylori infection. The genes and pathways whose expression perturbations correlated positively with VTP scores across diseases were mainly involved in the regulation of immune, metabolic, and cellular activities.

Conclusions

VTP is upregulated in the disease versus health status, and its upregulation is associated with disease progression and severity in various diseases. Thus, VTP has potential clinical implications for disease diagnosis and prognosis.

Construction of Immune-Related ceRNA Network in Dilated Cardiomyopathy: Based on Sex Differences

Background: Immune targeted therapy has become an attractive therapeutic approach for patients with dilated cardiomyopathy (DCM) recently. Genetic predisposition and gender play a critical role in immune-related responses of DCM. This study aimed to perform a bioinformatics analysis of molecular differences between male and female samples and identify immune-related ceRNA network in DCM.

Methods: The gene expression microarray and clinical features dataset of GSE19303 was downloaded from the GEO. The raw data were preprocessed, followed by identification of differentially expressed genes (DEGs) between male and female DCM samples. Crucial functions and pathway enrichment analysis of DEGs were investigated through GO analysis and KEGG pathway analysis, respectively. A lncRNA–miRNA–mRNA network was constructed and a central module was extracted from the ceRNA network.

Results: Compared with the female group, the male group benefits more from IA/IgG immunotherapy. Male patients of DCM had a significant positive correlation with the abundance of inflammatory cells (B cells, memory B cells, CD8⁺ Tem cells, and NK cells). Sex difference DEGs had a widespread impact on the signaling transduction, transcriptional regulation, and metabolism in DCM. Subsequently, we constructed an immune-related ceRNA network based on sex differences in DCM, including five lncRNAs, six miRNAs, and 29 mRNAs. Furthermore, we extracted a central module from the ceRNA network, including two lncRNAs (XIST and LINC00632), three miRNAs (miR-1-3p, miR-17-5p, and miR-22-3p), and six mRNAs (CBL, CXCL12, ESR1, IGF1R, IL6ST, and STC1). Among these DEGs, CBL, CXCL12, and IL6ST expression was considered to be associated with inflammatory cell infiltration in DCM.

Conclusions: The identified ceRNA network and their enriched pathways may provide genetic insights into the phenotypic diversity of female and male patients with DCM and may provide a basis for development of sex-related individualization of immunotherapy.

Increasing reproducibility, robustness, and generalizability of biomarker selection from meta-analysis using Bayesian methodology

A major limitation of gene expression biomarker studies is that they are not reproducible as they simply do not generalize to larger, real-world, heterogeneous populations. Frequentist multi-cohort gene expression meta-analysis has been frequently used as a solution to this problem to identify biomarkers that are truly differentially expressed. However, the frequentist meta-analysis framework has its limitations-it needs at least 4-5 datasets with hundreds of samples, is prone to confounding from outliers and relies on multiple-hypothesis corrected p-values. To address these shortcomings, we have created a Bayesian meta-analysis framework for the analysis of gene expression data. Using real-world data from three different diseases, we show that the Bayesian method is more robust to outliers, creates more informative estimates of between-study heterogeneity, reduces the number of false positive and false negative biomarkers and selects more generalizable biomarkers with less data. We have compared the Bayesian framework to a previously published frequentist framework and have developed a publicly available R package for use.

Identification of Immune Markers in Dilated Cardiomyopathies with Heart Failure by Integrated Weighted Gene Coexpression Network Analysis

Dilated cardiomyopathy (DCM), a heterogeneous cardiomyopathy, is a major cause of heart failure and heart transplant. Currently, immunotherapy is believed to be an effective treatment method for DCM. However, individual differences are so obvious that the clinical effect is not satisfactory. In order to find immune-related biomarkers of DCM to guide treatment and improve clinical efficacy, we downloaded a GSE120895 dataset from the Gene Expression Omnibus (GEO) database using CIBERSORT and WGCNA algorithms in RStudio and visualizing the protein–protein interaction (PPI) network for key modules by Cytoscape, and finally obtained six hub genes. A GSE17800 dataset was downloaded from the GEO dataset to verify the diagnostic values of hub genes, MYG1, FLOT1, and ATG13, which were excellent. Our study revealed unpublished potential immune mechanisms, biomarkers, and therapeutic targets of DCM.

An Robust Rank Aggregation and Least Absolute Shrinkage and Selection Operator Analysis of Novel Gene Signatures in Dilated Cardiomyopathy

Objective: Dilated cardiomyopathy (DCM) is a heart disease with high mortality characterized by progressive cardiac dilation and myocardial contractility reduction. The molecular signature of dilated cardiomyopathy remains to be defined. Hence, seeking potential biomarkers and therapeutic of DCM is urgent and necessary.

Methods: In this study, we utilized the Robust Rank Aggregation (RRA) method to integrate four eligible DCM microarray datasets from the GEO and identified a set of significant differentially expressed genes (DEGs) between dilated cardiomyopathy and non-heart failure. Moreover, LASSO analysis was carried out to clarify the diagnostic and DCM clinical features of these genes and identify dilated cardiomyopathy derived diagnostic signatures (DCMDDS).

Results: A total of 117 DEGs were identified across the four microarrays. Furthermore, GO analysis demonstrated that these DEGs were mainly enriched in the regulation of inflammatory response, the humoral immune response, the regulation of blood pressure and collagen–containing extracellular matrix. In addition, KEGG analysis revealed that DEGs were mainly enriched in diverse infected signaling pathways. Moreover, Gene set enrichment analysis revealed that immune and inflammatory biological processes such as adaptive immune response, cellular response to interferon and cardiac muscle contraction, dilated cardiomyopathy are significantly enriched in DCM. Moreover, Least absolute shrinkage and selection operator (LASSO) analyses of the 18 DCM-related genes developed a 7-gene signature predictive of DCM. This signature included ANKRD1, COL1A1, MYH6, PERELP, PRKACA, CDKN1A, and OMD. Interestingly, five of these seven genes have a correlation with left ventricular ejection fraction (LVEF) in DCM patients.

Conclusion: Our present study demonstrated that the signatures could be robust tools for predicting DCM in clinical practice. And may also be potential treatment targets for clinical implication in the future.

The Role of AI in Characterizing the DCM Phenotype

Dilated Cardiomyopathy is conventionally defined by left ventricular dilatation and dysfunction in the absence of coronary disease. Emerging evidence suggests many patients remain vulnerable to major adverse outcomes despite clear therapeutic success of modern evidence-based heart failure therapy. In this era of personalized medical care, the conventional assessment of left ventricular ejection fraction falls short in fully predicting evolution and risk of outcomes in this heterogenous group of heart muscle disease, as such, a more refined means of phenotyping this disease appears essential. Cardiac MRI (CMR) is well-placed in this respect, not only for its diagnostic utility, but the wealth of information captured in global and regional function assessment with the addition of unique tissue characterization across different disease states and patient cohorts. Advanced tools are needed to leverage these sensitive metrics and integrate with clinical, genetic and biochemical information for personalized, and more clinically useful characterization of the dilated cardiomyopathy phenotype. Recent advances in artificial intelligence offers the unique opportunity to impact clinical decision making through enhanced precision image-analysis tasks, multi-source extraction of relevant features and seamless integration to enhance understanding, improve diagnosis, and subsequently clinical outcomes. Focusing particularly on deep learning, a subfield of artificial intelligence, that has garnered significant interest in the imaging community, this paper reviews the main developments that could offer more robust disease characterization and risk stratification in the Dilated Cardiomyopathy phenotype. Given its promising utility in the non-invasive assessment of cardiac diseases, we firstly highlight the key applications in CMR, set to enable comprehensive quantitative measures of function beyond the standard of care assessment. Concurrently, we revisit the added value of tissue characterization techniques for risk stratification, showcasing the deep learning platforms that overcome limitations in current clinical workflows and discuss how they could be utilized to better differentiate at-risk subgroups of this phenotype. The final section of this paper is dedicated to the allied clinical applications to imaging, that incorporate artificial intelligence and have harnessed the comprehensive abundance of data from genetics and relevant clinical variables to facilitate better classification and enable enhanced risk prediction for relevant outcomes.

Decreased circulating follicular regulatory T cells in patients with dilated cardiomyopathy

Follicular regulatory T cells (Tfr) have critical functions in inflammatory and autoimmune disorders. The main purpose of the current work was to assess Tfr cell frequency in patients with dilated cardiomyopathy (DCM). Flow cytometry showed that, compared with normal controls, DCM cases showed markedly reduced Tfr cell rates and Tfr/Tfh ratios, but significantly increased follicular helper T cell (Tfh) rates. Correlation analysis showed that the Tfr rate in DCM patients was positively correlated with left ventricular ejection fraction (LVEF), and negatively correlated with N-terminal brain natriuretic peptide (NT-proBNP) levels. Lower Foxp3 and higher Bcl-6, ICOS, and PD-1 mRNA expression levels were found in patients with DCM. In addition, plasma interleukin (IL)-6, tumor necrosis factor (TNF)-α, and IL-21 levels were significantly increased in DCM cases. Moreover, IgG and IgG3 levels were also elevated in individuals with DCM. Correlation analysis showed that the Tfr rate in DCM patients was negatively correlated with IgG and IgG3, while the Tfh rate was positively correlated with IgG and IgG3. Changes in circulating Tfr levels may have a critical immunomodulatory function in DCM and may become a new therapeutic target for DCM.

From combined heart-kidney to kidney transplantation program: what nephrologists should know about dilated cardiomyopathy

A 42-year-old hemodialysis (HD) patient was investigated in our department for symptomatic heart failure (HF) despite daily home dialysis. He had a history of living donor kidney transplantation at the age of 18 that lasted 7 years. Home dialysis was then started. At the age of 40, he developed acute heart failure symptoms. Echocardiography revealed severe dilated cardiomyopathy (DCM). Coronarography and myocardial perfusion scintigraphy showed no abnormal findings. Betablockers were administrated, and RAAS inhibitor dosing was optimized. Dyspnea persisted, and patient was referred to our department. At admission, blood pressure was 116/82 mmHg, and pulse 68 beats/min. No peripheral edema was observed. Dry weight was 62.5 kg. Patient was anuric. Hemoglobin level was 9.8 g/dl, highly sensitive troponin level was 62 ng/ml, and BNP level was 1527 ng/ml. The liver enzyme levels were normal. C-reactive protein was 4.2 mg/ml. Vitamin level, zinc levels, and thyroid function were normal.

Immunoadsorption treatment for dilated cardiomyopathy: A PRISMA-compliant systematic review and meta-analysis

BACKGROUND

As one of the leading causes of heart failure, dilated cardiomyopathy (DCM) is characterized by dysfunctional muscle contraction and enlarged ventricular chamber. Patients with DCM have been shown to respond well to immunoadsorption (IA) therapies. However, the efficacy and safety of IA treatment for DCM patients remained to be evaluated.

METHODS

This study was designed in accordance with the Preferred Reporting Items for Systematic Review and Meta-analysis. We searched the databases such as Cochrane library, Cochrane Central Register of Controlled Trials, Embase, OVID, and Web of Science from January 1990 to March 20, 2020, and performed meta-analysis using Stata MP Version 13.0.

RESULTS

We performed meta-analysis on 12 studies that included a total of 395 patients with DCM. Overall, IA treatment significantly improved the left ventricular ejection fraction (6.01, 95% confidence interval [CI] [4.84-7.19]), reduced the left ventricular end diastolic diameter (-3.62, 95% CI [-4.06 to -3.19]), reduced severity of symptoms according to the New York Heart Association (NYHA) functional classification (-1.37, 95% CI [-1.73 to -1.02]) as compared with the controls, but had no effect on values for safety parameters (1.13, 95% CI [0.58-2.19]).

CONCLUSIONS

Results of this meta-analysis indicated that the IA treatment can improve the left ventricular ejection fraction, reduce left ventricular end diastolic diameter, and thus improve clinical outcome in DCM patients. However, further evidence are required to validate the relative safety of IA treatment. Multi-center, double blind studies should be conducted to elucidate the precise effect of IA treatment in DCM patients.

Identification of a Novel Six-Long Noncoding RNA Signature for Molecular Diagnosis of Dilated Cardiomyopathy

Long noncoding RNAs (lncRNAs) may serve as potential molecular diagnostic markers to improve the capacity of earlier and more accurate diagnosis of dilated cardiomyopathy (DCM). We integrated five independent transcriptomic datasets (n = 504) from Gene Expression Omnibus for systematic identification of lncRNA-based diagnostic biomarkers in DCM. The multivariate logistic regression model based on the six lncRNAs (AC016722.3, AL589986.2, AC006007.1, AC092687.3, GS1-124K5.4, and AC007126.1) in the ceRNA networks showed high sensitivity and specificity (area under curves >0.8, p < 0.0001) of DCM diagnosis in the training and validation datasets. Functional analysis revealed that the autophagy, protein acetyltransferase, and DNA polymerase activity were associated with high levels of the six-lncRNA signature, while the collagen trimer, extracellular matrix structural constituent, and MHC protein complex were associated with low levels of the signature. Pathway analysis showed that high levels of the six-lncRNA signature were associated with upregulated selective autophagy, interleukin 17 signalings, and extracellular matrix interactions, while were associated with downregulated extracellular matrix organization and collagen formation. The identified six-lncRNA signature, with high performance in molecular diagnosis of DCM, might be applied in future clinical practices combined with traditional markers.

Analysis of DCM associated protein alterations of human right and left ventricles

Dilated cardiomyopathy (DCM) is characterized by ventricular chamber enlargement and impaired myocardial function. Endomyocardial biopsies (EMB) enable immunohistochemical and molecular characterization of this disease. However, knowledge about specific molecular patterns and their relation to cardiac function in both ventricles is rare. Therefore, we performed a mass spectrometric analysis of 28 paired EMBs of left (LV) and right ventricles (RV) of patients with DCM or suspected myocarditis allowing quantitative profiling of 743 proteins. We analysed associations between protein abundance of LV and RV as well as the echocardiographic parameters LVEF, TAPSE, LVEDDI, and RVEDDI by linear regression models. Overall, more LV than RV proteins were associated with LV parameters or with RVEDDI. Most LV and RV proteins increasing in level with impairing of LVEF were annotated to structural components of cardiac tissue. Additionally, a high proportion of LV proteins with metabolic functions decreased in level with decreasing LVEF. Results were validated with LV heart sections of a genetic murine heart failure model. The study shows, that remodelling and systolic dysfunction in DCM is mirrored by distinct alterations in protein composition of both ventricles. Loss of LV systolic function is reflected predominantly by alterations in proteins assigned to metabolic functions in the LV whereas structural remodelling was more obvious in the RV. Alterations related to intermediate filaments were seen in both ventricles and highlight such proteins as early indicators of LV loss of function. SIGNIFICANCE: The present study report protein sets in the RV and the LV being associated with ventricular function and remodelling in DCM. Protein abundances in the LV and the RV emphasize and expand current knowledge on pathophysiological changes in heart failure and DCM. While RV and LV EMBs do not differ concerning diagnostic assessment of inflammatory status and virus persistence, additional information reflecting disease severity associated protein alterations can be gained by EMB protein profiling. RV and LV protein data provided complementary information. The protein pattern of the LV reflects metabolic changes and an impaired energy production, which is associated with the degree of LV systolic dysfunction and remodelling and may yield important information about the disease status in DCM. On the other hand, at this disease stage of DCM with still preserved RV function, RV alterations in structural proteins may reflect myocardial compensatory protective mechanisms for maintenance of structure and cellular function. The study highlight particular proteins being of interest as heart failure biomarkers in both ventricles which seem to reflect the severity of the disease. Further comparative studies between different HF aetiologies have to evaluate those proteins as markers specific for DCM.

Merging microarray studies to identify a common gene expression signature to several structural heart diseases

BACKGROUND

Heart disease is the leading cause of death worldwide. Knowing a gene expression signature in heart disease can lead to the development of more efficient diagnosis and treatments that may prevent premature deaths. A large amount of microarray data is available in public repositories and can be used to identify differentially expressed genes. However, most of the microarray datasets are composed of a reduced number of samples and to obtain more reliable results, several datasets have to be merged, which is a challenging task. The identification of differentially expressed genes is commonly done using statistical methods. Nonetheless, these methods are based on the definition of an arbitrary threshold to select the differentially expressed genes and there is no consensus on the values that should be used.

RESULTS

Nine publicly available microarray datasets from studies of different heart diseases were merged to form a dataset composed of 689 samples and 8354 features. Subsequently, the adjusted p-value and fold change were determined and by combining a set of adjusted p-values cutoffs with a list of different fold change thresholds, 12 sets of differentially expressed genes were obtained. To select the set of differentially expressed genes that has the best accuracy in classifying samples from patients with heart diseases and samples from patients with no heart condition, the random forest algorithm was used. A set of 62 differentially expressed genes having a classification accuracy of approximately 95% was identified.

CONCLUSIONS

We identified a gene expression signature common to different cardiac diseases and supported our findings by showing their involvement in the pathophysiology of the heart. The approach used in this study is suitable for the identification of gene expression signatures, and can be extended to different diseases.

Artificial Intelligence for Cardiac Imaging-Genetics Research

Cardiovascular conditions remain the leading cause of mortality and morbidity worldwide, with genotype being a significant influence on disease risk. Cardiac imaging-genetics aims to identify and characterize the genetic variants that influence functional, physiological, and anatomical phenotypes derived from cardiovascular imaging. High-throughput DNA sequencing and genotyping have greatly accelerated genetic discovery, making variant interpretation one of the key challenges in contemporary clinical genetics. Heterogeneous, low-fidelity phenotyping and difficulties integrating and then analyzing large-scale genetic, imaging and clinical datasets using traditional statistical approaches have impeded process. Artificial intelligence (AI) methods, such as deep learning, are particularly suited to tackle the challenges of scalability and high dimensionality of data and show promise in the field of cardiac imaging-genetics. Here we review the current state of AI as applied to imaging-genetics research and discuss outstanding methodological challenges, as the field moves from pilot studies to mainstream applications, from one dimensional global descriptors to high-resolution models of whole-organ shape and function, from univariate to multivariate analysis and from candidate gene to genome-wide approaches. Finally, we consider the future directions and prospects of AI imaging-genetics for ultimately helping understand the genetic and environmental underpinnings of cardiovascular health and disease.

Correlation of gene expression and clinical parameters identifies a set of genes reflecting LV systolic dysfunction and morphological alterations

To gain new insights into the complex pathophysiology of dilated cardiomyopathy (DCM) we performed a quantitative approach to identify genes with expression patterns that linearly correlate with parameters of cardiac morphology (left ventricular end-diastolic diameter indexed by body surface are (LVEDDI), systolic function [LV ejection fraction (LVEF)], and serum levels of cardiac peptide hormone NH2-terminal probrain natriuretic peptide (NT-proBNP) in human endomyocardial biopsies of 47 DCM patients and eight individuals with normal LVEF. A set of genes was identified as common heart failure markers characterized by correlation of their expression with cardiac morphology, systolic function, and NT-proBNP. Among them are already known genes encoding e.g., the natriuretic peptide hormones NPPA and NPPB and its converting enzyme corin, but also potential new heart failure markers like EP300 antisense RNA1 and dimethylarginine dimethylaminohydrolase 1 (DDAH1) along with other genes with so far unknown relation to heart function. In contrast, the expression of other genes including the Ca2+ flux regulating genes phospholamban (PLN), sarcoplasmic/endoplasmic reticulum calcium ATPase 2 (SERCA), and extracellular matrix proteins showed significant correlation with LVEF and LVEDDI only. Those genes seem to reflect more specifically pathological alterations of systolic function and morphology in DCM hearts.

Add-on Immunoadsorption Shortly-after Optimal Medical Treatment Further Significantly and Persistently Improves Cardiac Function and Symptoms in Recent-Onset Heart Failure-A Single Center Experience

Background: Immunoadsorption and intravenous immunoglobulin (IVIG) administration may have beneficial effects in patients with dilated cardiomyopathy with end-stage heart failure. We investigated the effect of immunoadsorption with subsequent IVIG administration on cardiac function and symptoms in patients on optimal medical treatment (OMT) for heart failure (HF) with recent-onset cardiomyopathy during long-term follow-up. Methods: Thirty-five patients with recent-onset of HF symptoms received intensive guideline-recommended medical HF therapy for 5.2 months. Subsequently, all patients received a single cycle of immunoadsorption for five days followed by IVIG administration. During the 29-month follow-up period, New York Heart Association (NYHA) functional class, left ventricular ejection fraction (LVEF) and N-terminal pro brain natriuretic peptide (NT-proBNP) were evaluated. Changes in quality of life (QoL) were assessed using the Minnesota Living with HF Questionnaire. Results: Three months after immunoadsorption, NYHA functional class improved from 2.0 to 1.5 (p < 0.005) and LVEF significantly increased from 27.0% to 39.0% (p < 0.0001). Long-term follow-up of 29 months showed stable NYHA functional class and a further moderate increase in LVEF from 39.0% to 42.0% (p < 0.0001) accompanied by a significant improvement in NT-proBNP and QoL scores. Conclusion: Immunoadsorption followed by IVIG administration further enhances LVEF, HF symptoms, QoL and biomarkers in patients with recent-onset HF on OMT.

Guidelines on the Use of Therapeutic Apheresis in Clinical Practice-Evidence-Based Approach from the Writing Committee of the American Society for Apheresis: The Seventh Special Issue

The American Society for Apheresis (ASFA) Journal of Clinical Apheresis (JCA) Special Issue Writing Committee is charged with reviewing, updating, and categorizing indications for the evidence-based use of therapeutic apheresis in human disease. Since the 2007 JCA Special Issue (Fourth Edition), the Committee has incorporated systematic review and evidence-based approaches in the grading and categorization of apheresis indications. This Seventh Edition of the JCA Special Issue continues to maintain this methodology and rigor to make recommendations on the use of apheresis in a wide variety of diseases/conditions. The JCA Seventh Edition, like its predecessor, has consistently applied the category and grading system definitions in the fact sheets. The general layout and concept of a fact sheet that was used since the fourth edition has largely been maintained in this edition. Each fact sheet succinctly summarizes the evidence for the use of therapeutic apheresis in a specific disease entity. The Seventh Edition discusses 87 fact sheets (14 new fact sheets since the Sixth Edition) for therapeutic apheresis diseases and medical conditions, with 179 indications, which are separately graded and categorized within the listed fact sheets. Several diseases that are Category IV which have been described in detail in previous editions and do not have significant new evidence since the last publication are summarized in a separate table. The Seventh Edition of the JCA Special Issue serves as a key resource that guides the utilization of therapeutic apheresis in the treatment of human disease. J. Clin. Apheresis 31:149-162, 2016. © 2016 Wiley Periodicals, Inc.

The Quest for New Approaches in Myocarditis and Inflammatory Cardiomyopathy

Myocarditis is a diverse group of heart-specific immune processes classified by clinical and histopathological manifestations. Up to 40% of dilated cardiomyopathy is associated with inflammation or viral infection. Recent experimental studies revealed complex regulatory roles for several microribonucleic acids and T-cell and macrophage subtypes. Although the prevalence of myocarditis remained stable between 1990 and 2013 at about 22 per 100,000 people, overall mortality from cardiomyopathy and myocarditis has decreased since 2005. The diagnostic and prognostic value of cardiac magnetic resonance has increased with new, higher-sensitivity sequences. Positron emission tomography has emerged as a useful tool for diagnosis of cardiac sarcoidosis. The sensitivity of endomyocardial biopsy may be increased, especially in suspected sarcoidosis, by the use of electrogram guidance to target regions of abnormal signal. Investigational treatments on the basis of mechanistic advances are entering clinical trials. Revised management recommendations regarding athletic participation after acute myocarditis have heightened the importance of early diagnosis.

Methods to increase reproducibility in differential gene expression via meta-analysis

Findings from clinical and biological studies are often not reproducible when tested in independent cohorts. Due to the testing of a large number of hypotheses and relatively small sample sizes, results from whole-genome expression studies in particular are often not reproducible. Compared to single-study analysis, gene expression meta-analysis can improve reproducibility by integrating data from multiple studies. However, there are multiple choices in designing and carrying out a meta-analysis. Yet, clear guidelines on best practices are scarce. Here, we hypothesized that studying subsets of very large meta-analyses would allow for systematic identification of best practices to improve reproducibility. We therefore constructed three very large gene expression meta-analyses from clinical samples, and then examined meta-analyses of subsets of the datasets (all combinations of datasets with up to N/2 samples and K/2 datasets) compared to a ‘silver standard’ of differentially expressed genes found in the entire cohort. We tested three random-effects meta-analysis models using this procedure. We showed relatively greater reproducibility with more-stringent effect size thresholds with relaxed significance thresholds; relatively lower reproducibility when imposing extraneous constraints on residual heterogeneity; and an underestimation of actual false positive rate by Benjamini–Hochberg correction. In addition, multivariate regression showed that the accuracy of a meta-analysis increased significantly with more included datasets even when controlling for sample size.

Changes of myocardial gene expression and protein composition in patients with dilated cardiomyopathy after immunoadsorption with subsequent immunoglobulin substitution

Immunoadsorption with subsequent immunoglobulin substitution (IA/IgG) represents a therapeutic approach for patients with dilated cardiomyopathy (DCM). Here, we studied which molecular cardiac alterations are initiated after this treatment. Transcription profiling of endomyocardial biopsies with Affymetrix whole genome arrays was performed on 33 paired samples of DCM patients collected before and 6 months after IA/IgG. Therapy-related effects on myocardial protein levels were analysed by label-free proteome profiling for a subset of 23 DCM patients. Data were analysed regarding therapy-associated differences in gene expression and protein levels by comparing responders (defined by improvement of left ventricular ejection fraction ≥20 % relative and ≥5 % absolute) and non-responders. Responders to IA/IgG showed a decrease in serum N-terminal proBNP levels in comparison with baseline which was accompanied by a decreased expression of heart failure markers, such as angiotensin converting enzyme 2 or periostin. However, despite clinical improvement even in responders, IA/IgG did not trigger general inversion of DCM-associated molecular alterations in myocardial tissue. Transcriptome profiling revealed reduced gene expression for connective tissue growth factor, fibronectin, and collagen type I in responders. In contrast, in non-responders after IA/IgG, fibrosis-associated genes and proteins showed elevated levels, whereas values were reduced or maintained in responders. Thus, improvement of LV function after IA/IgG seems to be related to a reduced gene expression of heart failure markers and pro-fibrotic molecules as well as reduced fibrosis progression.

Emerging pharmacologic targets and treatments for myocarditis

Myocarditis is a heterogeneous group of disorders defined by inflammation of the heart muscle. The primary clinical manifestations of myocarditis are heart failure and sudden death in children and young adults. Numerous interventions have been investigated for the treatment of myocarditis, including broad spectrum alteration of the immune response and antiviral treatments; however, success has been limited. Since the myocarditis treatment trials in the 1990s there has been an improved understanding of disease progression and new facets of the immune response have been discovered. This new information provides fresh opportunities to develop therapeutics to treat myocarditis. This review analyzes previous pharmacologic approaches including immunosuppression, high dose intravenous immunoglobulin treatment, immunoadsorption and antiviral treatments, and looks forward toward recently identified immune factors that can be exploited as targets for new treatments. Such strategies include bolstering beneficial regulatory T cells or mitigating the detrimental Th17 T cells which can drive autoimmunity in the heart. The surging interest of the application of humanized monoclonal antibodies makes targeting deleterious arms of the immune response like Th17 cells a tangible goal in the near future. Promising constituents of herbal remedies have also been identified that may hold potential as new pharmacological treatments for myocarditis, however, significant work remains to elucidate the pharmacokinetics and side-effects of these compounds. Finally, advances in our understanding of the function of Matrix Metalloproteinases yield another target for altering disease progression given their role in the development of fibrosis during Dilated Cardiomyopathy. In bringing to light the various new targets and treatments available since the last myocarditis treatment trials, the aim of this review is to explore the new treatments that are possible in new myocarditis treatment trials.

Immunoadsorption therapy for dilated cardiomyopathy using tryptophan column-A prospective, multicenter, randomized, within-patient and parallel-group comparative study to evaluate efficacy and safety

Over the past few decades, several cardiac autoantibodies have been reported in sera from patients with dilated cardiomyopathy (DCM). Immunoadsorption (IA) therapy is one of the therapeutic tools to remove such autoantibodies. The objective of this study was to investigate functional effects of IA therapy using a tryptophan column in severe DCM patients. Of 49 patients enrolled, 44 were randomized from 10 sites in Japan. IA therapy was conducted in 40 patients with DCM (refractory to standard therapy for heart failure, New York Heart Association [NYHA] class III/IV, left ventricular ejection fraction [LVEF] <30%). Mean echocardiographic LVEF was significantly improved (23.8 ± 1.3% to 25.9 ± 1.3%, P = 0.0015). However, mean radionuclide LVEF over 3 months of IA therapy was not significantly improved (20.8 ± 1.1% to 21.9 ± 1%, P = 0.0605). The cardiothoracic ratio was also significantly decreased (P = 0.0010). NYHA functional class (P < 0.0001), subjective symptoms assessed by a quality of life questionnaire (P = 0.0022), maximum oxygen consumption (P = 0.0074), and 6‐minute walk distance (P = 0.0050) were improved after IA therapy. Subgroup analysis revealed improvement of echocardiographic LVEF in patients with higher baseline autoantibody scores but not in those with lower scores. IA therapy improved subjective symptoms and exercise capacity in patients with refractory heart failure resulting from DCM. Favorable effect on cardiac function was noted in patients with higher autoantibody scores. J. Clin. Apheresis 31:535–544, 2016. © 2015 Wiley Periodicals, Inc.

Defective circulating CD4+LAP+ regulatory T cells in patients with dilated cardiomyopathy

There has been increasing evidence that chronic immune activation plays critical roles in the pathogenesis of DCM. CD4(+) LAP(+) Tregs are a newly identified T cell subset with suppressive function on the immune response. This study was designed to investigate whether the circulating frequency and function of CD4(+)LAP(+) Tregs would be impaired in patients with DCM. The results demonstrated that DCM patients had a significantly lower frequency of circulating CD4(+)LAP(+) Tregs compared with control donors. CD4(+)LAP(+) Tregs from DCM patients showed compromised function to suppress proliferation of CD4(+) LAP(-)CD25(int/low) T cells and proliferation and IgG production of B cells. Moreover, B cell proliferation and IgG subset production could be directly suppressed by CD4(+) LAP(+) Tregs. TGF-β and contact-dependent mechanisms were involved in CD4(+)LAP(+) Treg-mediated suppression. Correlation analysis suggested that CD4(+)LAP(+) Treg frequency was positively correlated with LVEF and negatively correlated with serum IgG3 and NT-proBNP concentration in patients with DCM. Our results are the first to demonstrate that the frequencies of CD4(+)LAP(+) Tregs in patients with DCM are reduced and that their suppressive function is compromised. Defective CD4(+) LAP(+) Tregs may be an underlying mechanism of immune activation in DCM patients.

Immunoadsorption therapy in dilated cardiomyopathy

Dilated cardiomyopathy is a common myocardial disease characterized by ventricular chamber enlargement and systolic dysfunction that result in heart failure. In addition to genetic predisposition, viral infection and myocardial inflammation play a causal role in the disease process of dilated cardiomyopathy. Experimental and clinical studies suggest that activation of the humoral immune system, with production of circulating cardiac autoantibodies, plays an important functional role in the development and progression of cardiac dysfunction in patients with dilated cardiomyopathy. Small open-controlled studies showed that removal of circulating antibodies by immunoadsorption results in improvement of cardiac function and decrease in myocardial inflammation. At present, immunoadsorption is an experimental treatment option for improvement of cardiac function - therapy that calls for confirmation by a placebo-controlled multicenter study.

The effect of a repeated immunoadsorption in patients with dilated cardiomyopathy after recurrence of severe heart failure symptoms

BACKGROUND

In patients suffering from dilated cardiomyopathy (DCM), immunoadsorption with subsequent IgG substitution (IA/IgG) leads to an acute and prolonged improvement of hemodynamics and heart failure symptoms. However, some patients receiving IA/IgG experience recurrence of heart failure after an initial benefit. The aim of this study was to investigate whether a second IA/IgG treatment episode improves left ventricular systolic function and further mitigates heart failure symptoms in these patients.

METHODS

We retrospectively analyzed 15 DCM patients who experienced a significant improvement of LVEF (≥ 5% absolute or ≥ 20% relative) and heart failure symptoms (≥ 1 NYHA functional class) but a subsequent deterioration (decline in LVEF ≥ 5% absolute or ≥ 20% relative and NYHA worsening ≥1 class) after the first IA/IgG. These patients underwent a second IA/IgG treatment 41.7 ± 27.4 months after the first cycle. Follow up data were acquired 3-6 months after both IA/IgG treatments.

RESULTS

The first IA/IgG induced an improvement of LVEF from 33 ± 6.4% to 43.2 ± 7.9% (P < 0.001) and of mean NYHA functional class from 2.9 ± 0.26 to 1.8 ± 0.56 (P < 0.001). The second treatment was associated with a significant improvement in LVEF (from 29.7 ± 4.6% to 34.9 ± 8.3%, P = 0.013) and NYHA functional class (2.87 ± 0.64 to 2.33 ± 0.72; P = 0.02). This improvement was less pronounced compared to the first treatment with respect to both, LVEF (P = 0.09) and NYHA improvement (P = 0.04).

CONCLUSION

In DCM patients, who experience a significant improvement of LVEF and heart failure symptoms after IA/IgG but a subsequent relapse during follow up, repeated IA/IgG may be considered.

Cohort profile: Greifswald approach to individualized medicine (GANI_MED)

BACKGROUND

Individualized Medicine aims at providing optimal treatment for an individual patient at a given time based on his specific genetic and molecular characteristics. This requires excellent clinical stratification of patients as well as the availability of genomic data and biomarkers as prerequisites for the development of novel diagnostic tools and therapeutic strategies. The University Medicine Greifswald, Germany, has launched the "Greifswald Approach to Individualized Medicine" (GANI_MED) project to address major challenges of Individualized Medicine. Herein, we describe the implementation of the scientific and clinical infrastructure that allows future translation of findings relevant to Individualized Medicine into clinical practice.

METHODS/DESIGN

Clinical patient cohorts (N > 5,000) with an emphasis on metabolic and cardiovascular diseases are being established following a standardized protocol for the assessment of medical history, laboratory biomarkers, and the collection of various biosamples for bio-banking purposes. A multi-omics based biomarker assessment including genome-wide genotyping, transcriptome, metabolome, and proteome analyses complements the multi-level approach of GANI_MED. Comparisons with the general background population as characterized by our Study of Health in Pomerania (SHIP) are performed. A central data management structure has been implemented to capture and integrate all relevant clinical data for research purposes. Ethical research projects on informed consent procedures, reporting of incidental findings, and economic evaluations were launched in parallel.

How can we cure a heart "in flame"? A translational view on inflammation in heart failure

The prevalence of chronic heart failure is still increasing making it a major health issue in the 21st century. Tremendous evidence has emerged over the past decades that heart failure is associated with a wide array of mechanisms subsumed under the term “inflammation”. Based on the great success of immuno-suppressive treatments in auto-immunity and transplantation, clinical trials were launched targeting inflammatory mediators in patients with chronic heart failure. However, they widely lacked positive outcomes. The failure of the initial study program directed against tumor necrosis factor-α led to the search for alternative therapeutic targets involving a broader spectrum of mechanisms besides cytokines. We here provide an overview of the current knowledge on immune activation in chronic heart failure of different etiologies, summarize clinical studies in the field, address unresolved key questions, and highlight some promising novel therapeutic targets for clinical trials from a translational basic science and clinical perspective.