Aims Chronic kidney disease (CKD) is widely prevalent and independently increases cardiovascular risk. Cardiovascular risk prediction tools derived in the general population perform poorly in CKD. Through large-scale proteomics discovery, this study aimed to create more accurate cardiovascular risk models. Methods and results Elastic net regression was used to derive a proteomic risk model for incident cardiovascular risk in 2182 participants from the Chronic Renal Insufficiency Cohort. The model was then validated in 485 participants from the Atherosclerosis Risk in Communities cohort. All participants had CKD and no history of cardiovascular disease at study baseline when ∼5000 proteins were measured. The proteomic risk model, which consisted of 32 proteins, was superior to both the 2013 ACC/AHA Pooled Cohort Equation and a modified Pooled Cohort Equation that included estimated glomerular filtrate rate. The Chronic Renal Insufficiency Cohort internal validation set demonstrated annualized receiver operating characteristic area under the curve values from 1 to 10 years ranging between 0.84 and 0.89 for the protein and 0.70 and 0.73 for the clinical models. Similar findings were observed in the Atherosclerosis Risk in Communities validation cohort. For nearly half of the individual proteins independently associated with cardiovascular risk, Mendelian randomization suggested a causal link to cardiovascular events or risk factors. Pathway analyses revealed enrichment of proteins involved in immunologic function, vascular and neuronal development, and hepatic fibrosis. Conclusion In two sizeable populations with CKD, a proteomic risk model for incident cardiovascular disease surpassed clinical risk models recommended in clinical practice, even after including estimated glomerular filtration rate. New biological insights may prioritize the development of therapeutic strategies for cardiovascular risk reduction in the CKD population.

Introduction: Unsupervised machine learning (UML) applied to high dimensional data has been used to discover cardiovascular disease subtypes; however, the reproducibility of subtypes identified by different algorithms has not been explored. We compared the ability of several promising UML and clustering algorithms to identify heart failure (HF) subtypes using high dimensional electronic health record (EHR) data. Methods: Using the Penn Medicine EHR, we identified all patients who had > 2 instances of ICD-10-CM HF diagnosis. We extracted 1272 EHR-based features (vital signs, demographics, echocardiographic measurements, laboratories, comorbidities) from time of HF diagnosis and limited the cohort based on data completeness (n=8569). We selected the following methods based on prior success in simulation studies and used them to identify HF subtypes: Similarity Network Fusion (SNF), Locally Linear Embedding (LLE), Modified LLE, Uniform Manifold Approximation and Projection (UMAP), and Principal Component Analysis (PCA) followed by several clustering algorithms including K-means, Density-based spatial clustering of applications with noise (DBSCAN), and Spectral Clustering. K groups 2-12 were evaluated. Clustering performance was assessed by silhouette score and visual separation. Results: Model visualizations are shown in the Figure. Highest silhouette score achieved for each model varied widely from 0.02-0.62; optimal cluster number ranged from 2-4 across models. Normalization and standardization of continuous data did not significantly alter silhouette scores or optimal cluster number. Conclusions: HF subtypes identified through UML applied to EHR data may vary substantially depending on the algorithms used. Benchmarking strategies to evaluate reproducibility of UML in the EHR are needed to ensure valid HF patient stratification and phenotypic refinement.

ABSTRACT IMPACT: Measuring and analyzing qualitative and quantitative traits using phenomics approaches will yield previously unrecognized heart failure subphenotypes and has the potential to improve our knowledge of heart failure pathophysiology, identify novel biomarkers of disease, and guide the development of targeted therapeutics for heart failure. OBJECTIVES/GOALS: Current classification schemes fail to capture the broader pathophysiologic heterogeneity in heart failure. Phenomics offers a newer unbiased approach to identify subtypes of complex disease syndromes, like heart failure. The goal of this research is to use data-driven associations to redefine the classification of the heart failure syndrome. METHODS/STUDY POPULATION: We will identify < 10 subphenotypes of patients with heart failure using unsupervised machine learning approaches for dense multidimensional quantitative (i.e. demographics, comorbid conditions, physiologic measurements, clinical laboratory, imaging, and medication variables; disease diagnosis, procedure, and billing codes) and qualitative data extracted from an integrated health system electronic health record. The heart failure subphenotypes we identify from the integrated health system electronic health record will be replicated in other heart failure population datasets using unsupervised learning approaches. We will explore the potential to establish associations between identified subphenotypes and clinical outcomes (e.g. all-cause mortality, cardiovascular mortality). RESULTS/ANTICIPATED RESULTS: We expect to identify < 10 mutually exclusive phenogroups of patients with heart failure that have differential risk profiles and clinical trajectories. DISCUSSION/SIGNIFICANCE OF FINDINGS: We will attempt to derive and validate a data-driven unbiased approach to the categorization of novel phenogroups in heart failure. This has the potential to improve our knowledge of heart failure pathophysiology, identify novel biomarkers of disease, and guide the development of targeted therapeutics for heart failure.

Background Chronic kidney disease ( CKD ) confers increased cardiovascular risk, not fully explained by traditional factors. Proteins regulate biological processes and inform the risk of diseases. Thus, in 938 patients with stable coronary heart disease from the Heart and Soul cohort, we quantified 1054 plasma proteins using modified aptamers ( SOMA scan) to: (1) discern how reduced glomerular filtration influences the circulating proteome, (2) learn of the importance of kidney function to the prognostic information contained in recently identified protein cardiovascular risk biomarkers, and (3) identify novel and even unique cardiovascular risk biomarkers among individuals with CKD . Methods and Results Plasma protein levels were correlated to estimated glomerular filtration rate (eGFR) using Spearman‐rank correlation coefficients. Cox proportional hazard models were used to estimate the association between individual protein levels and the risk of the cardiovascular outcome (first among myocardial infarction, stroke, heart failure hospitalization, or mortality). Seven hundred and nine (67.3%) plasma proteins correlated with eGFR at P <0.05 (ρ 0.06–0.74); 218 (20.7%) proteins correlated with eGFR moderately or strongly (ρ 0.2–0.74). Among the previously identified 196 protein cardiovascular biomarkers, just 87 remained prognostic after correction for eGFR . Among patients with CKD ( eGFR <60 mL/min per 1.73 m 2 ), we identified 21 protein cardiovascular risk biomarkers of which 8 are unique to CKD. Conclusions CKD broadly alters the composition of the circulating proteome. We describe protein biomarkers capable of predicting cardiovascular risk independently of glomerular filtration, and those that are prognostic of cardiovascular risk specifically in patients with CKD and even unique to patients with CKD .

Background: Early detection of adverse effects of novel therapies and understanding of their mechanisms could improve the safety and efficiency of drug development. We have retrospectively applied large-scale proteomics to blood samples from ILLUMINATE (Investigation of Lipid Level Management to Understand its Impact in Atherosclerotic Events), a trial of torcetrapib (a cholesterol ester transfer protein inhibitor), that involved 15 067 participants at high cardiovascular risk. ILLUMINATE was terminated at a median of 550 days because of significant absolute increases of 1.2% in cardiovascular events and 0.4% in mortality with torcetrapib. The aims of our analysis were to determine whether a proteomic analysis might reveal biological mechanisms responsible for these harmful effects and whether harmful effects of torcetrapib could have been detected early in the ILLUMINATE trial with proteomics. Methods: A nested case-control analysis of paired plasma samples at baseline and at 3 months was performed in 249 participants assigned to torcetrapib plus atorvastatin and 223 participants assigned to atorvastatin only. Within each treatment arm, cases with events were matched to controls 1:1. Main outcomes were a survey of 1129 proteins for discovery of biological pathways altered by torcetrapib and a 9-protein risk score validated to predict myocardial infarction, stroke, heart failure, or death. Results: Plasma concentrations of 200 proteins changed significantly with torcetrapib. Their pathway analysis revealed unexpected and widespread changes in immune and inflammatory functions, as well as changes in endocrine systems, including in aldosterone function and glycemic control. At baseline, 9-protein risk scores were similar in the 2 treatment arms and higher in participants with subsequent events. At 3 months, the absolute 9-protein derived risk increased in the torcetrapib plus atorvastatin arm compared with the atorvastatin-only arm by 1.08% (P=0.0004). Thirty-seven proteins changed in the direction of increased risk of 49 proteins previously associated with cardiovascular and mortality risk. Conclusions: Heretofore unknown effects of torcetrapib were revealed in immune and inflammatory functions. A protein-based risk score predicted harm from torcetrapib within just 3 months. A protein-based risk assessment embedded within a large proteomic survey may prove to be useful in the evaluation of therapies to prevent harm to patients. Clinical Trial Registration: URL: https://www.clinicaltrials.gov. Unique identifier: NCT00134264.

Steinhauser, Luis M. Alvarez, Joseph Gannon, Calum A. Macrae, Linda G. Griffith and Richard Steven M. Jay, Ashwin C. Murthy, Jessica F. Hawkins, Joshua R. Wortzel, Matthew L. With Reduced Proneoplastic Potential An Engineered Bivalent Neuregulin Protects Against Doxorubicin-Induced Cardiotoxicity Print ISSN: 0009-7322. Online ISSN: 1524-4539 Copyright © 2013 American Heart Association, Inc. All rights reserved. is published by the American Heart Association, 7272 Greenville Avenue, Dallas, TX 75231 Circulation doi: 10.1161/CIRCULATIONAHA.113.002203 2013;128:152-161; originally published online June 11, 2013; Circulation. http://circ.ahajournals.org/content/128/2/152 World Wide Web at: The online version of this article, along with updated information and services, is located on the http://circ.ahajournals.org/content/suppl/2013/06/11/CIRCULATIONAHA.113.002203.DC1.html Data Supplement (unedited) at:

Background— Doxorubicin (DOXO) is an effective anthracycline chemotherapeutic, but its use is limited by cumulative dose-dependent cardiotoxicity. Neuregulin-1β is an ErbB receptor family ligand that is effective against DOXO-induced cardiomyopathy in experimental models but is also proneoplastic. We previously showed that an engineered bivalent neuregulin-1β (NN) has reduced proneoplastic potential in comparison with the epidermal growth factor–like domain of neuregulin-1β (NRG), an effect mediated by receptor biasing toward ErbB3 homotypic interactions uncommonly formed by native neuregulin-1β. Here, we hypothesized that a newly formulated, covalent NN would be cardioprotective with reduced proneoplastic effects in comparison with NRG. Methods and Results— NN was expressed as a maltose-binding protein fusion in Escherichia coli . As established previously, NN stimulated antineoplastic or cytostatic signaling and phenotype in cancer cells, whereas NRG stimulated proneoplastic signaling and phenotype. In neonatal rat cardiomyocytes, NN and NRG induced similar downstream signaling. NN, like NRG, attenuated the double-stranded DNA breaks associated with DOXO exposure in neonatal rat cardiomyocytes and human cardiomyocytes derived from induced pluripotent stem cells. NN treatment significantly attenuated DOXO-induced decrease in fractional shortening as measured by blinded echocardiography in mice in a chronic cardiomyopathy model (57.7±0.6% versus 50.9±2.6%, P =0.004), whereas native NRG had no significant effect (49.4±3.7% versus 50.9±2.6%, P =0.813). Conclusions— NN is a cardioprotective agent that promotes cardiomyocyte survival and improves cardiac function in DOXO-induced cardiotoxicity. Given the reduced proneoplastic potential of NN versus NRG, NN has translational potential for cardioprotection in patients with cancer receiving anthracyclines.