Multiomics Analysis Provides Novel Pathways Related to Progression of Heart Failure.

BACKGROUND: Despite major advances in pharmacological treatment for patients with heart failure, residual mortality remains high. This suggests that important pathways are not yet targeted by current heart failure therapies. OBJECTIVES: We sought integration of genetic, transcriptomic, and proteomic data in a large cohort of patients with heart failure to detect major pathways related to progression of heart failure leading to death. METHODS: We used machine learning methodology based on stacked generalization framework and gradient boosting algorithms, using 54 clinical phenotypes, 403 circulating plasma proteins, 36,046 transcript expression levels in whole blood, and 6 million genomic markers to model all-cause mortality in 2,516 patients with heart failure from the BIOSTAT-CHF (Systems BIOlogy Study to TAilored Treatment in Chronic Heart Failure) study. Results were validated in an independent cohort of 1,738 patients. RESULTS: The mean age of the patients was 70 years (Q1-Q3: 61-78 years), 27% were female, median N-terminal pro-B-type natriuretic peptide was 4,275 ng/L (Q1-Q3: 2,360-8,486 ng/L), and 7% had heart failure with preserved ejection fraction. During a median follow-up of 21 months, 657 (26%) of patients died. The 4 major pathways with a significant association to all-cause mortality were: 1) the PI3K/Akt pathway; 2) the MAPK pathway; 3) the Ras signaling pathway; and 4) epidermal growth factor receptor tyrosine kinase inhibitor resistance. Results were validated in an independent cohort of 1,738 patients. CONCLUSIONS: A systems biology approach integrating genomic, transcriptomic, and proteomic data identified 4 major pathways related to mortality. These pathways are related to decreased activation of the cardioprotective ERBB2 receptor, which can be modified by neuregulin.

Pathophysiological pathways related to high plasma growth differentiation factor 15 concentrations in patients with heart failure.

AIMS: Elevated concentrations of growth differentiation factor 15 (GDF-15) in patients with heart failure (HF) have been consistently associated with worse clinical outcomes, but what disease mechanisms high GDF-15 concentrations represent remains unclear. Here, we aim to identify activated pathophysiological pathways related to elevated GDF-15 expression in patients with HF. METHODS AND RESULTS: In 2279 patients with HF, we measured circulating levels of 363 biomarkers. Then, we performed a pathway over-representation analysis to identify key biological pathways between patients in the highest and lowest GDF-15 concentration quartiles. Data were validated in an independent cohort of 1705 patients with HF. In both cohorts, the strongest up-regulated biomarkers in those with high GDF-15 were fibroblast growth factor 23 (FGF-23), death receptor 5 (TRAIL-R2), WNT1-inducible signalling pathway protein 1 (WISP-1), tumour necrosis factor receptor superfamily member 11a (TNFRSF11A), leucocyte immunoglobulin-like receptor subfamily B member 4 (LILRB4), and trefoil factor 3 (TFF3). Pathway over-representation analysis revealed that high GDF-15 patients had increased activity of pathways related to inflammatory processes, notably positive regulation of chemokine production; response to interleukin-6; tumour necrosis factor and death receptor activity; and positive regulation of T-cell differentiation and inflammatory response. Furthermore, we found pathways involved in regulation of insulin-like growth factor (IGF) receptor signalling and regulatory pathways of tissue, bones, and branching structures. GDF-15 quartiles significantly predicted all-cause mortality and HF hospitalization. CONCLUSION: Patients with HF and high plasma concentrations of GDF-15 are characterized by increased activation of inflammatory pathways and pathways related to IGF-1 regulation and bone/tissue remodelling.

Machine learning based on biomarker profiles identifies distinct subgroups of heart failure with preserved ejection fraction.

AIMS: The lack of effective therapies for patients with heart failure with preserved ejection fraction (HFpEF) is often ascribed to the heterogeneity of patients with HFpEF. We aimed to identify distinct pathophysiologic clusters of HFpEF based on circulating biomarkers. METHODS AND RESULTS: We performed an unsupervised cluster analysis using 363 biomarkers from 429 patients with HFpEF. Relative differences in expression profiles of the biomarkers between clusters were assessed and used for pathway over-representation analyses. We identified four distinct patient subgroups based on their biomarker profiles: cluster 1 with the highest prevalence of diabetes mellitus and renal disease; cluster 2 with oldest age and frequent age-related comorbidities; cluster 3 with youngest age, largest body size, least symptoms and lowest N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels; and cluster 4 with highest prevalence of ischaemic aetiology, smoking and chronic lung disease, most symptoms, as well as highest NT-proBNP and troponin levels. Over a median follow-up of 21 months, the occurrence of death or heart failure hospitalization was highest in clusters 1 and 4 (62.1% and 62.8%, respectively) and lowest in cluster 3 (25.6%). Pathway over-representation analyses revealed that the biomarker profile of patients in cluster 1 was associated with activation of inflammatory pathways while the biomarker profile of patients in cluster 4 was specifically associated with pathways implicated in cell proliferation regulation and cell survival. CONCLUSION: Unsupervised cluster analysis based on biomarker profiles identified mutually exclusive subgroups of patients with HFpEF with distinct biomarker profiles, clinical characteristics and outcomes, suggesting different underlying pathophysiological pathways.

Associations of Circulating Protein Levels With Lipid Fractions in the General Population.

Objective- Revealing patterns of associations between circulating protein and lipid levels could improve biological understanding of cardiovascular disease (CVD). In this study, we investigated the associations between proteins related to CVD and triglyceride (TG), total cholesterol, LDL (low-density lipoprotein), and HDL (high-density lipoprotein) cholesterol levels in individuals from the general population. Approach and Results- We measured plasma protein levels using the Olink ProSeek CVD I or II+III arrays and analyzed 57 proteins available in 3 population-based cohorts: EpiHealth (n=2029; 52% women; median age, 61 years), PIVUS (Prospective Study of the Vasculature in Uppsala Seniors; n=790; 51% women; all aged 70 years), and ULSAM (Uppsala Longitudinal Study of Adult Men; n=551; all men aged 77 years). A discovery analysis was performed in EpiHealth in a regression framework (adjusted for sex, age, body mass index, smoking, glucose levels, systolic blood pressure, blood pressure medication, diabetes mellitus medication, and CVD history), and associations with false discovery rate <0.05 were further tested in PIVUS and ULSAM, where a P value of 0.05 was considered a successful replication (validation false discovery rate of 0.1%). We used summary statistics from a genome-wide association study on each protein biomarker (meta-analysis of EpiHealth, PIVUS, ULSAM, and IMPROVE [Carotid Intima-Media Thickness and IMT-Progression as Predictors of Vascular Events in a High-Risk European Population]) and publicly available data from Global Lipids Genetics Consortium to perform Mendelian randomization analyses to address possible causality of protein levels. Of 57 tested proteins, 42 demonstrated an association with at least 1 lipid fraction; 35 were associated with TG, 15 with total cholesterol, 9 with LDL cholesterol, and 24 with HDL cholesterol. Among these associations, we found KIM-1 (kidney injury molecule-1), TNFR (TNF [tumor necrosis factor] receptor) 1 and 2, TRAIL-R2 (TRAIL [TNF-related apoptosis-inducing ligand] receptor 2), and RETN (resistin) to be associated with all 4 lipid fractions. Further, 15 proteins were related to both TG and HDL cholesterol in a consistent and biologically expected manner, that is, higher TG and lower HDL cholesterol or vice versa. Another common pattern of associations was concomitantly higher TG, total cholesterol, and LDL cholesterol, which is associated with higher CVD risk. We did not find evidence of causal links for protein levels. Conclusions- Our comprehensive analysis of plasma proteins and lipid fractions of 3370 individuals from the general population provides new information about lipid metabolism.

ADAM33 gene polymorphisms and mortality. A prospective cohort study.

The ADAM33 gene is associated with the pathophysiology of Chronic Obstructive Pulmonary Disease (COPD) and atherosclerosis. In this study we investigated all-cause, COPD and cardiovascular mortality, in relation to single nucleotide polymorphisms (SNPs) in ADAM33 (Q_1, S_1, S_2, T_1 and T_2) that were genotyped in 1,390 subjects from the Vlagtwedde/Vlaardingen cohort. Participants were examined at entry in 1989/1990 and followed up till evaluation of the vital status on December 31(st), 2008. Using Cox proportional hazards regression we estimated the risk of the SNPs in relation to mortality, adjusting for gender, age, FEV1, height, place of residence and packyears of smoking. Additionally, we performed stratified analyses according to gender and smoking habits. After 18 years, 284 (20.4%) subjects had died (107 due to cardiovascular disease and 20 due to COPD). Individuals homozygous for the minor allele of SNP T_2 had an increased risk of all-cause and cardiovascular mortality compared to wild types: hazard ratio 3.6 (95% confidence interval 2.0 to 6.7) and 3.4 (1.2 to 9.5) respectively. Individuals homozygous for the minor allele of S_1, S_2, T_2 or Q_1 had a significantly increased risk of COPD mortality. In stratified analyses the risk of all-cause mortality associated with SNP T_2 did not change: females 3.5 (1.5 to 8.3), males 3.1 (1.2 to 7.6), never smokers 3.8 (0.9 to 16.3), ever smokers 3.6 (1.8 to 7.2). This study shows for the first time that ADAM33 is a pleiotropic gene that is associated with all-cause, COPD and cardiovascular mortality, independent of potential confounders.

Dyspnea severity, changes in dyspnea status and mortality in the general population: the Vlagtwedde/Vlaardingen study.

Dyspnea is a predictor of mortality. The effects of dyspnea severity and changes in dyspnea status on all-cause and cause-specific mortality remain unclear. The Vlagtwedde/Vlaardingen study started in 1965 and subjects were re-examined every 3 years until 1989/1990. Vital status of all 8,465 subjects on December 31st, 2008 was assessed. Associations between mortality and dyspnea severity and changes in dyspnea status were investigated using Cox regression adjusted for gender, age, FEV1% predicted, place of residence, smoking and BMI. After 43 years of follow-up, 2,883 (39%) of 7,360 subjects examined for dyspnea severity had died, 1,386 (19%) due to cardiovascular disease, 267 (4%) due to chronic obstructive pulmonary disease (COPD). Subjects with moderate and severe dyspnea had increased all-cause and cardiovascular mortality [moderate: HR=1.3 (95% CI 1.2-1.5) and 1.4 (1.1-1.6), severe: 1.5 (1.1-2.0) and 1.9 (1.3-2.6) respectively] compared to asymptomatics. Severe dyspnea was significantly associated with COPD mortality [3.3 (2.0-5.2)]. Subjects who lost dyspnea had hazard ratios for all-cause and cause-specific mortality comparable to asymptomatics. Persistent dyspnea and dyspnea development were risk factors for all-cause, cardiovascular and COPD mortality [persistent: 2.0 (1.4-2.8), 1.9 (1.2-3.3) and 3.3 (1.2-8.9), development: 1.5 (1.2-1.8), 2.0 (1.5-2.6) and 3.8 (2.3-6.3) respectively]. Additionally, dyspnea effects on mortality were more pronounced in overweight/obese and older subjects and in subjects with better lung function. These results show that dyspnea is associated with mortality in a severity-dependent manner. Furthermore this study is the first showing that dyspnea remission normalizes mortality risk. Having or developing dyspnea is a risk factor for mortality.