Accelerating Biomarker Discovery Through Electronic Health Records, Automated Biobanking, and Proteomics

Clinical-transcriptional prioritization of the circulating proteome in human heart failure

Given expanding studies in epidemiology and disease-oriented human studies offering hundreds of associations between the human "ome" and disease, prioritizing molecules relevant to disease mechanisms among this growing breadth is important. Here, we link the circulating proteome to human heart failure (HF) propensity (via echocardiographic phenotyping and clinical outcomes) across the lifespan, demonstrating key pathways of fibrosis, inflammation, metabolism, and hypertrophy. We observe a broad array of genes encoding proteins linked to HF phenotypes and outcomes in clinical populations dynamically expressed at a transcriptional level in human myocardium during HF and cardiac recovery (several in a cell-specific fashion). Many identified targets do not have wide precedent in large-scale genomic discovery or human studies, highlighting the complementary roles for proteomic and tissue transcriptomic discovery to focus epidemiological targets to those relevant in human myocardium for further interrogation.

Revolutionary Point-of-Care Wearable Diagnostics for Early Disease Detection and Biomarker Discovery through Intelligent Technologies

Early-stage disease detection, particularly in Point-Of-Care (POC) wearable formats, assumes pivotal role in advancing healthcare services and precision-medicine. Public benefits of early detection extend beyond cost-effectively promoting healthcare outcomes, to also include reducing the risk of comorbid diseases. Technological advancements enabling POC biomarker recognition empower discovery of new markers for various health conditions. Integration of POC wearables for biomarker detection with intelligent frameworks represents ground-breaking innovations enabling automation of operations, conducting advanced large-scale data analysis, generating predictive models, and facilitating remote and guided clinical decision-making. These advancements substantially alleviate socioeconomic burdens, creating a paradigm shift in diagnostics, and revolutionizing medical assessments and technology development. This review explores critical topics and recent progress in development of 1) POC systems and wearable solutions for early disease detection and physiological monitoring, as well as 2) discussing current trends in adoption of smart technologies within clinical settings and in developing biological assays, and ultimately 3) exploring utilities of POC systems and smart platforms for biomarker discovery. Additionally, the review explores technology translation from research labs to broader applications. It also addresses associated risks, biases, and challenges of widespread Artificial Intelligence (AI) integration in diagnostics systems, while systematically outlining potential prospects, current challenges, and opportunities.

Using electronic health records for clinical pharmacology research: Challenges and considerations

Electronic health records (EHRs) contain a vast array of phenotypic data on large numbers of individuals, often collected over decades. Due to the wealth of information, EHR data have emerged as a powerful resource to make first discoveries and identify disparities in our healthcare system. While the number of EHR-based studies has exploded in recent years, most of these studies are directed at associations with disease rather than pharmacotherapeutic outcomes, such as drug response or adverse drug reactions. This is largely due to challenges specific to deriving drug-related phenotypes from the EHR. There is great potential for EHR-based discovery in clinical pharmacology research, and there is a critical need to address specific challenges related to accurate and reproducible derivation of drug-related phenotypes from the EHR. This review provides a detailed evaluation of challenges and considerations for deriving drug-related data from EHRs. We provide an examination of EHR-based computable phenotypes and discuss cutting-edge approaches to map medication information for clinical pharmacology research, including medication-based computable phenotypes and natural language processing. We also discuss additional considerations such as data structure, heterogeneity and missing data, rare phenotypes, and diversity within the EHR. By further understanding the complexities associated with conducting clinical pharmacology research using EHR-based data, investigators will be better equipped to design thoughtful studies with more reproducible results. Progress in utilizing EHRs for clinical pharmacology research should lead to significant advances in our ability to understand differential drug response and predict adverse drug reactions.

Proteomic Signature of HIV-Associated Subclinical Left Atrial Remodeling and Incident Heart Failure

Background

People living with HIV (PLWH) are at higher risk of heart failure (HF) and preceding subclinical cardiac abnormalities, including left atrial dilation, compared to people without HIV (PWOH). Hypothesized mechanisms include premature aging linked to chronic immune activation. We leveraged plasma proteomics to identify potential novel contributors to HIV-associated differences in indexed left atrial volume (LAVi) among PLWH and PWOH and externally validated identified proteomic signatures with incident HF among a cohort of older PWOH.

Methods

We performed proteomics (Olink Explore 3072) on plasma obtained concurrently with cardiac magnetic resonance imaging among PLWH and PWOH in the United States. Proteins were analyzed individually and as agnostically defined clusters. Cross-sectional associations with HIV and LAVi were estimated using multivariable regression with robust variance. Among an independent general population cohort, we estimated associations between identified signatures and LAVi using linear regression and incident HF using Cox regression.

Results

Among 352 participants (age 55±6 years; 25% female), 61% were PLWH (88% on ART; 73% with undetectable HIV RNA) and mean LAVi was 29±9 mL/m 2 . Of 2594 analyzed proteins, 439 were associated with HIV serostatus, independent of demographics, hepatitis C virus infection, renal function, and substance use (FDR<0.05). We identified 73 of these proteins as candidate contributors to the independent association between positive HIV serostatus and higher LAVi, enriched in tumor necrosis factor (TNF) signaling and immune checkpoint proteins regulating T cell, B cell, and NK cell activation. We identified one protein cluster associated with LAVi and HIV regardless of HIV viral suppression status, which comprised 42 proteins enriched in TNF signaling, ephrin signaling, and extracellular matrix (ECM) organization. This protein cluster and 30 of 73 individual proteins were associated with incident HF among 2273 older PWOH (age 68±9 years; 52% female; 8.5±1.4 years of follow-up).

Conclusion

Proteomic signatures that may contribute to HIV-associated LA remodeling were enriched in immune checkpoint proteins, cytokine signaling, and ECM organization. These signatures were also associated with incident HF among older PWOH, suggesting specific markers of chronic immune activation, systemic inflammation, and fibrosis may identify shared pathways in HIV and aging that contribute to risk of HF.

Identification of Clinical Drivers of Left Atrial Enlargement Through Genomics of Left Atrial Size

BACKGROUND

Greater left atrial size is associated with a higher incidence of cardiovascular disease and mortality, but the full spectrum of diagnoses associated with left atrial enlargement in sex-stratified clinical populations is not well known. Our study sought to identify genetic risk mechanisms affecting left atrial diameter (LAD) in a clinical cohort.

METHODS

Using Vanderbilt deidentified electronic health record, we studied 6163 females and 5993 males of European ancestry who had at least 1 LAD measure and available genotyping. A sex-stratified polygenic score was constructed for LAD variation and tested for association against 1680 International Classification of Diseases code-based phenotypes. Two-sample univariable and multivariable Mendelian randomization approaches were used to assess etiologic relationships between candidate associations and LAD.

RESULTS

A phenome-wide association study identified 25 International Classification of Diseases code-based diagnoses in females and 11 in males associated with a polygenic score of LAD (false discovery rate q<0.01), 5 of which were further evaluated by Mendelian randomization (waist circumference [WC], atrial fibrillation, heart failure, systolic blood pressure, and coronary artery disease). Sex-stratified differences in the genetic associations between risk factors and a polygenic score for LAD were observed (WC for females; heart failure, systolic blood pressure, atrial fibrillation, and WC for males). By multivariable Mendelian randomization, higher WC remained significantly associated with larger LAD in females, whereas coronary artery disease, WC, and atrial fibrillation remained significantly associated with larger LAD in males.

CONCLUSIONS

In a clinical population, we identified, by genomic approaches, potential etiologic risk factors for larger LAD. Further studies are needed to confirm the extent to which these risk factors may be modified to prevent or reverse adverse left atrial remodeling and the extent to which sex modifies these risk factors.

Novel Prediction Equations for Absolute Risk Assessment of Total Cardiovascular Disease Incorporating Cardiovascular-Kidney-Metabolic Health: A Scientific Statement From the American Heart Association

Cardiovascular-kidney-metabolic (CKM) syndrome is a novel construct recently defined by the American Heart Association in response to the high prevalence of metabolic and kidney disease. Epidemiological data demonstrate higher absolute risk of both atherosclerotic cardiovascular disease (CVD) and heart failure as an individual progresses from CKM stage 0 to stage 3, but optimal strategies for risk assessment need to be refined. Absolute risk assessment with the goal to match type and intensity of interventions with predicted risk and expected treatment benefit remains the cornerstone of primary prevention. Given the growing number of therapies in our armamentarium that simultaneously address all 3 CKM axes, novel risk prediction equations are needed that incorporate predictors and outcomes relevant to the CKM context. This should also include social determinants of health, which are key upstream drivers of CVD, to more equitably estimate and address risk. This scientific statement summarizes the background, rationale, and clinical implications for the newly developed sex-specific, race-free risk equations: PREVENT (AHA Predicting Risk of CVD Events). The PREVENT equations enable 10- and 30-year risk estimates for total CVD (composite of atherosclerotic CVD and heart failure), include estimated glomerular filtration rate as a predictor, and adjust for competing risk of non-CVD death among adults 30 to 79 years of age. Additional models accommodate enhanced predictive utility with the addition of CKM factors when clinically indicated for measurement (urine albumin-to-creatinine ratio and hemoglobin A1c) or social determinants of health (social deprivation index) when available. Approaches to implement risk-based prevention using PREVENT across various settings are discussed.

Opportunities and challenges for biomarker discovery using electronic health record data

Electronic health records (EHRs) have become increasingly relied upon as a source for biomedical research. One important research application of EHRs is the identification of biomarkers associated with specific patient states, especially within complex conditions. However, using EHRs for biomarker identification can be challenging because the EHR was not designed with research as the primary focus. Despite this challenge, the EHR offers huge potential for biomarker discovery research to transform our understanding of disease etiology and treatment and generate biological insights informing precision medicine initiatives. This review paper provides an in-depth analysis of how EHR data is currently used for phenotyping and identifying molecular biomarkers, current challenges and limitations, and strategies we can take to mitigate challenges going forward.

Genetics of Pulmonary Pressure and Right Ventricle Stress Identify Diabetes as a Causal Risk Factor

Background Epidemiologic studies have identified risk factors associated with pulmonary hypertension and right heart failure, but causative drivers of pulmonary hypertension and right heart adaptation are not well known. We sought to leverage unbiased genetic approaches to determine clinical conditions that share genetic architecture with pulmonary pressure and right ventricular dysfunction. Methods and Results We leveraged Vanderbilt University's deidentified electronic health records and DNA biobank to identify 14 861 subjects of European ancestry who underwent at least 1 echocardiogram with available estimates of pulmonary pressure and right ventricular function. Analyses of the study were performed between 2020 and 2022. The final analytical sample included 14 861 participants (mean [SD] age, 63 [15] years and mean [SD] body mass index, 29 [7] kg/m2). An unbiased phenome-wide association study identified diabetes as the most statistically significant clinical International Classifications of Diseases, Ninth Revision (ICD-9) code associated with polygenic risk for increased pulmonary pressure. We validated this finding further by finding significant associations between genetic risk for diabetes and a related condition, obesity, with pulmonary pressure estimate. We then used 2-sample univariable Mendelian randomization and multivariable Mendelian randomization to show that diabetes, but not obesity, was independently associated with genetic risk for increased pulmonary pressure and decreased right ventricle load stress. Conclusions Our findings show that genetic risk for diabetes is the only significant independent causative driver of genetic risk for increased pulmonary pressure and decreased right ventricle load stress. These findings suggest that therapies targeting genetic risk for diabetes may also potentially be beneficial in treating pulmonary hypertension and right heart dysfunction.

The application of Aptamer in biomarker discovery

Biomarkers are detectable molecules that can reflect specific physiological states of cells, organs, and organisms and therefore be regarded as indicators for specific diseases. And the discovery of biomarkers plays an essential role in cancer management from the initial diagnosis to the final treatment regime. Practically, reliable clinical biomarkers are still limited, restricted by the suboptimal methods in biomarker discovery. Nucleic acid aptamers nowadays could be used as a powerful tool in the discovery of protein biomarkers. Nucleic acid aptamers are single-strand oligonucleotides that can specifically bind to various targets with high affinity. As artificial ssDNA or RNA, aptamers possess unique advantages compared to conventional antibodies. They can be flexible in design, low immunogenicity, relative chemical/thermos stability, as well as modifying convenience. Several SELEX (Systematic Evolution of Ligands by Exponential Enrichment) based methods have been generated recently to construct aptamers for discovering new biomarkers in different cell locations. Secretome SELEX-based aptamers selection can facilitate the identification of secreted protein biomarkers. The aptamers developed by cell-SELEX can be used to unveil those biomarkers presented on the cell surface. The aptamers from tissue-SELEX could target intracellular biomarkers. And as a multiplexed protein biomarker detection technology, aptamer-based SOMAScan can analyze thousands of proteins in a single run. In this review, we will introduce the principle and workflow of variations of SELEX-based methods, including secretome SELEX, ADAPT, Cell-SELEX and tissue SELEX. Another powerful proteome analyzing tool, SOMAScan, will also be covered. In the second half of this review, how these methods accelerate biomarker discovery in various diseases, including cardiovascular diseases, cancer and neurodegenerative diseases, will be discussed.

Plasma angiopoietin-2 and its association with heart failure in patients with atrial fibrillation

AIMS

Several biomarkers are associated with clinical outcomes in patients with atrial fibrillation (AF), but a causal relationship has not been established. This study aimed to evaluate angiopoietin-2, a novel candidate biomarker of endothelial inflammation and vascular remodelling, in patients with AF.

METHODS AND RESULTS

Angiopoietin-2 was measured in plasma obtained from patients with AF treated with aspirin monotherapy (exploration cohort, n = 2987) or with oral anticoagulation (validation cohort, n = 13 079). Regression models were built to assess the associations between angiopoietin-2, clinical characteristics, and outcomes. In both cohorts, plasma angiopoietin-2 was independently associated with AF on the baseline electrocardiogram and persistent/permanent AF, age, history of heart failure, female sex, tobacco use/smoking, body mass index, renal dysfunction, diabetes, and N-terminal pro-B-type natriuretic peptide (NT-proBNP). Angiopoietin-2 was independently associated with subsequent hospitalization for heart failure after adjusting for age, creatinine, and clinical characteristics in the exploration cohort [c-index 0.79, 95% confidence interval (CI) 0.75-0.82; third vs. first quartile, hazard ratio (HR) 1.74, 95% CI 1.26-2.41] and in the validation cohort (c-index 0.76, 95% CI 0.74-0.78; HR 1.58, 95% CI 1.37-1.82). In both cohorts, the association persisted when also adjusting for NT-proBNP (P ≤ 0.001). In full multivariable models also adjusted for NT-proBNP, angiopoietin-2 did not show statistically significant associations with ischaemic stroke, cardiovascular and all-cause death, or major bleeding that were consistent across the two cohorts.

CONCLUSIONS

In patients with AF, plasma levels of angiopoietin-2 were independently associated with subsequent hospitalization for heart failure and provided incremental prognostic value to clinical risk factors and NT-proBNP.

Banking on a new understanding: translational opportunities from veterinary biobanks

Current advances in geroscience are due in part to the discovery of biomarkers with high predictive ability in short-lived laboratory animals such as flies and mice. These model species, however, do not always adequately reflect human physiology and disease, highlighting the need for a more comprehensive and relevant model of human aging. Domestic dogs offer a solution to this obstacle, as they share many aspects not only of the physiological and pathological trajectories of their human counterpart, but also of their environment. Furthermore, they age at a considerably faster rate. Studying aging in the companion dog provides an opportunity to better understand the biological and environmental determinants of healthy lifespan in our pets, and to translate those findings to human aging. Biobanking, the systematic collection, processing, storage, and distribution of biological material and associated data has contributed to basic, clinical, and translational research by streamlining the management of high-quality biospecimens for biomarker discovery and validation. In this review, we discuss how veterinary biobanks can support research on aging, particularly when integrated into large-scale longitudinal studies. As an example of this concept, we introduce the Dog Aging Project Biobank.

The effect of rivaroxaban on biomarkers in blood and plasma: a review of preclinical and clinical evidence

Rivaroxaban is a direct, oral factor Xa inhibitor that is used for the prevention and treatment of various thromboembolic disorders. Several preclinical and clinical studies have utilized specific molecules as biomarkers to investigate the potential role of rivaroxaban beyond its anticoagulant activity and across a range of biological processes. The aim of this review is to summarize the existing evidence regarding the use of blood-based biomarkers to characterize the effects of rivaroxaban on coagulation and other pathways, including platelet activation, inflammation and endothelial effects. After a literature search using PubMed, almost 100 preclinical and clinical studies were identified that investigated the effects of rivaroxaban using molecular biomarkers. In agreement with the preclinical data, clinical studies reported a trend for reduction in the blood concentrations of D-dimers, thrombin-antithrombin complex and prothrombin fragment 1 + 2 following treatment with rivaroxaban in both healthy individuals and those with various chronic conditions. Preclinical and also some clinical studies have also reported a potential impact of rivaroxaban on the concentrations of platelet activation biomarkers (von Willebrand factor, P-selectin and thrombomodulin), endothelial activation biomarkers (matrix metalloproteinase-9, intercellular adhesion molecule-1 and vascular cell adhesion molecule-1) and inflammation biomarkers (interleukin-6, tumor necrosis factor-α and monocyte chemoattractant protein-1). Based on the results of biomarker studies, molecular biomarkers can be used in addition to traditional coagulation assays to increase the understanding of the anticoagulation effects of rivaroxaban. Moreover, there is preliminary evidence to suggest that rivaroxaban may have an impact on the biological pathways of platelet activation, endothelial activation and inflammation; however, owing to paucity of clinical data to investigate the trends reported in preclinical studies, further investigation is required to clarify these observations.

Prospective associations of circulating thrombospondin-2 level with heart failure hospitalization, left ventricular remodeling and diastolic function in type 2 diabetes

Background

Circulating thrombospondin-2 (TSP2) levels were associated with the development of heart failure (HF) in recent studies. However, these studies included only a minority of patients with type 2 diabetes, which is associated with an increased HF risk. As hyperglycemia induces TSP2 expression and its tissue expression increases in type 2 diabetes, we investigated the prospective association of circulating TSP2 with incident HF hospitalization (HHF), and its associations with longitudinal changes of echocardiographic parameters in type 2 diabetes.

Methods

Baseline serum TSP2 levels were measured in 4949 patients with type 2 diabetes to determine its association with incident HHF using multivariable Cox regression analysis. In the echocardiographic study, baseline serum TSP2 levels were measured in another 146 patients with type 2 diabetes but without cardiovascular diseases who underwent detailed transthoracic echocardiography at baseline and after 1 year.

Results

Over a median follow-up of 7.8 years, 330 of 4949 patients (6.7%) developed incident HHF. Baseline serum TSP2 levels were independently associated with the development of HHF (HR 1.31, 95%CI 1.06–1.62, p = 0.014) after adjustments for baseline conventional cardiovascular risk factors, atrial fibrillation, estimated glomerular filtration rate, albuminuria and high-sensitivity C-reactive protein level, use of angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, loop-diuretics, aspirin, insulin, metformin and sodium-glucose co-transporter 2 inhibitors. Moreover, baseline serum TSP2 levels were independently associated with increase in average E/e’ and left atrial volume index (p = 0.04 and < 0.01, respectively).

Conclusion

Serum TSP2 levels were independently associated with both incident HHF and deterioration in diastolic function in type 2 diabetes.

Trial registration

Not Applicable

Supplementary information

The online version contains supplementary material available at 10.1186/s12933-022-01646-x.

Mining the Plasma Proteome for Insights into the Molecular Pathology of Pulmonary Arterial Hypertension

Rationale: Pulmonary arterial hypertension (PAH) is characterized by structural remodeling of pulmonary arteries and arterioles. Underlying biological processes are likely reflected in a perturbation of circulating proteins. Objectives: To quantify and analyze the plasma proteome of patients with PAH using inherited genetic variation to inform on underlying molecular drivers. Methods: An aptamer-based assay was used to measure plasma proteins in 357 patients with idiopathic or heritable PAH, 103 healthy volunteers, and 23 relatives of patients with PAH. In discovery and replication subgroups, the plasma proteomes of PAH and healthy individuals were compared, and the relationship to transplantation-free survival in PAH was determined. To examine causal relationships to PAH, protein quantitative trait loci (pQTL) that influenced protein levels in the patient population were used as instruments for Mendelian randomization (MR) analysis. Measurements and Main Results: From 4,152 annotated plasma proteins, levels of 208 differed between patients with PAH and healthy subjects, and 49 predicted long-term survival. MR based on cis-pQTL located in proximity to the encoding gene for proteins that were prognostic and distinguished PAH from health estimated an adverse effect for higher levels of netrin-4 (odds ratio [OR], 1.55; 95% confidence interval [CI], 1.16-2.08) and a protective effect for higher levels of thrombospondin-2 (OR, 0.83; 95% CI, 0.74-0.94) on PAH. Both proteins tracked the development of PAH in previously healthy relatives and changes in thrombospondin-2 associated with pulmonary arterial pressure at disease onset. Conclusions: Integrated analysis of the plasma proteome and genome implicates two secreted matrix-binding proteins, netrin-4 and thrombospondin-2, in the pathobiology of PAH.

The Translation and Commercialisation of Biomarkers for Cardiovascular Disease—A Review

As a leading cause of mortality and morbidity worldwide, cardiovascular disease and its diagnosis, quantification, and stratification remain significant health issues. Increasingly, patients present with cardiovascular disease in the absence of known risk factors, suggesting the presence of yet unrecognized pathological processes and disease predispositions. Fortunately, a host of emerging cardiovascular biomarkers characterizing and quantifying ischaemic heart disease have shown great promise in both laboratory settings and clinical trials. These have demonstrated improved predictive value additional to widely accepted biomarkers as well as providing insight into molecular phenotypes beneath the broad umbrella of cardiovascular disease that may allow for further personalized treatment regimens. However, the process of translation into clinical practice – particularly navigating the legal and commercial landscape – poses a number of challenges. Practical and legal barriers to the biomarker translational pipeline must be further considered to develop strategies to bring novel biomarkers into the clinical sphere and apply these advances at the patient bedside. Here we review the progress of emerging biomarkers in the cardiovascular space, with particular focus on those relevant to the unmet needs in ischaemic heart disease.

Cardiac remodelling - Part 1: From cells and tissues to circulating biomarkers. A review from the Study Group on Biomarkers of the Heart Failure Association of the European Society of Cardiology

Cardiac remodelling refers to changes in left ventricular structure and function over time, with a progressive deterioration that may lead to heart failure (HF) development (adverse remodelling) or vice versa a recovery (reverse remodelling) in response to HF treatment. Adverse remodelling predicts a worse outcome, whilst reverse remodelling predicts a better prognosis. The geometry, systolic and diastolic function and electric activity of the left ventricle are affected, as well as the left atrium and on the long term even right heart chambers. At a cellular and molecular level, remodelling involves all components of cardiac tissue: cardiomyocytes, fibroblasts, endothelial cells and leucocytes. The molecular, cellular and histological signatures of remodelling may differ according to the cause and severity of cardiac damage, and clearly to the global trend toward worsening or recovery. These processes cannot be routinely evaluated through endomyocardial biopsies, but may be reflected by circulating levels of several biomarkers. Different classes of biomarkers (e.g. proteins, non-coding RNAs, metabolites and/or epigenetic modifications) and several biomarkers of each class might inform on some aspects on HF development, progression and long-term outcomes, but most have failed to enter clinical practice. This may be due to the biological complexity of remodelling, so that no single biomarker could provide great insight on remodelling when assessed alone. Another possible reason is a still incomplete understanding of the role of biomarkers in the pathophysiology of cardiac remodelling. Such role will be investigated in the first part of this review paper on biomarkers of cardiac remodelling.

Using the Plasma Proteome for Risk Stratifying Patients with Pulmonary Arterial Hypertension

Rationale: NT-proBNP (N-terminal pro-brain natriuretic peptide), a biomarker of cardiac origin, is used to risk stratify patients with pulmonary arterial hypertension (PAH). Its limitations include poor sensitivity to early vascular pathology. Other biomarkers of vascular or systemic origin may also be useful in the management of PAH. Objectives: Identify prognostic proteins in PAH that complement NT-proBNP and clinical risk scores. Methods: An aptamer-based assay (SomaScan version 4) targeting 4,152 proteins was used to measure plasma proteins in patients with idiopathic, heritable, or drug-induced PAH from the UK National Cohort of PAH (n = 357) and the French EFORT (Evaluation of Prognostic Factors and Therapeutic Targets in PAH) study (n = 79). Prognostic proteins were identified in discovery-replication analyses of UK samples. Proteins independent of 6-minute-walk distance and NT-proBNP entered least absolute shrinkage and selection operator modeling, and the best combination in a single score was evaluated against clinical targets in EFORT. Measurements and Main Results: Thirty-one proteins robustly informed prognosis independent of NT-proBNP and 6-minute-walk distance in the UK cohort. A weighted combination score of six proteins was validated at baseline (5-yr mortality; area under the curve [AUC], 0.73; 95% confidence interval [CI], 0.63-0.85) and follow-up in EFORT (AUC, 0.84; 95% CI, 0.75-0.94; P = 9.96 × 10^-6). The protein score risk stratified patients independent of established clinical targets and risk equations. The addition of the six-protein model score to NT-proBNP improved prediction of 5-year outcomes from AUC 0.762 (0.702-0.821) to 0.818 (0.767-0.869) by receiver operating characteristic analysis (P = 0.00426 for difference in AUC) in the UK replication and French samples combined. Conclusions: The plasma proteome informs prognosis beyond established factors in PAH and may provide a more sensitive measure of therapeutic response.

Combating Childhood Infections in LMICs: evaluating the contribution of Big Data Big data, biomarkers and proteomics: informing childhood diarrhoeal disease management in Low- and Middle-Income Countries

Despite efforts to reduce the global burden of childhood diarrhoea, 50% of all cases globally occur in children under five years in Low–Income and Middle- Income Countries (LMICs) and knowledge gaps remain regarding the aetiological diagnosis, introduction of diarrhoeal vaccines, and the role of environmental enteric dysfunction and severe acute malnutrition. Biomarkers may assist in understanding disease processes, from diagnostics, to management of childhood diarrhoea and the sequelae to vaccine development. Proteomics has the potential to assist in the identification of new biomarkers to understand the processes in the development of childhood diarrhoea and to aid in developing new vaccines. Centralised repositories that enable mining of large data sets to better characterise risk factors, the proteome of both the patient and the different diarrhoeal pathogens, and the environment, could inform patient management and vaccine development, providing a systems biological approach to address the burden of childhood diarrhoea in LMICs.

Towards precision medicine in heart failure

The number of therapies for heart failure (HF) with reduced ejection fraction has nearly doubled in the past decade. In addition, new therapies for HF caused by hypertrophic and infiltrative disease are emerging rapidly. Indeed, we are on the verge of a new era in HF in which insights into the biology of myocardial disease can be matched to an understanding of the genetic predisposition in an individual patient to inform precision approaches to therapy. In this Review, we summarize the biology of HF, emphasizing the causal relationships between genetic contributors and traditional structure-based remodelling outcomes, and highlight the mechanisms of action of traditional and novel therapeutics. We discuss the latest advances in our understanding of both the Mendelian genetics of cardiomyopathy and the complex genetics of the clinical syndrome presenting as HF. In the phenotypic domain, we discuss applications of machine learning for the subcategorization of HF in ways that might inform rational prescribing of medications. We aim to bridge the gap between the biology of the failing heart, its diverse clinical presentations and the range of medications that we can now use to treat it. We present a roadmap for the future of precision medicine in HF.

Integration of Biobanks in National eHealth Ecosystems Facilitating Long-Term Longitudinal Clinical-Omics Studies and Citizens' Engagement in Research Through eHealthBioR

Biobanks have long existed to support research activities with BBMRI-ERIC formed as a European research infrastructure supporting the coordination for biobanking with 20 country members and one international organization. Although the benefits of biobanks to the research community are well-established, the direct benefit to citizens is limited to the generic benefit of promoting future research. Furthermore, the advent of General Data Protection Regulation (GDPR) legislation raised a series of challenges for scientific research especially related to biobanking associate activities and longitudinal research studies. Electronic health record (EHR) registries have long existed in healthcare providers. In some countries, even at the national level, these record the state of the health of citizens through time for the purposes of healthcare and data portability between different providers. The potential of EHRs in research is great and has been demonstrated in many projects that have transformed EHR data into retrospective medical history information on participating subjects directly from their physician's collected records; many key challenges, however, remain. In this paper, we present a citizen-centric framework called eHealthBioR, which would enable biobanks to link to EHR systems, thus enabling not just retrospective but also lifelong prospective longitudinal studies of participating citizens. It will also ensure strict adherence to legal and ethical requirements, enabling greater control that encourages participation. Citizens would benefit from the real and direct control of their data and samples, utilizing technology, to empower them to make informed decisions about providing consent and practicing their rights related to the use of their data, as well as by having access to knowledge and data generated from samples they provided to biobanks. This is expected to motivate patient engagement in future research and even leads to participatory design methodologies with citizen/patient-centric designed studies. The development of platforms based on the eHealthBioR framework would need to overcome significant challenges. However, it would shift the burden of addressing these to experts in the field while providing solutions enabling in the long term the lower monetary and time cost of longitudinal studies coupled with the option of lifelong monitoring through EHRs.

Multiomic Profiling in Black and White Populations Reveals Novel Candidate Pathways in Left Ventricular Hypertrophy and Incident Heart Failure Specific to Black Adults

BACKGROUND

Increased left ventricular (LV) mass is associated with adverse cardiovascular events including heart failure (HF). Both increased LV mass and HF disproportionately affect Black individuals. To understand the underlying mechanisms, we undertook a proteomic screen in a Black cohort and compared the findings to results from a White cohort.

METHODS

We measured 1305 plasma proteins using the SomaScan platform in 1772 Black participants (mean age, 56 years; 62% women) in JHS (Jackson Heart Study) with LV mass assessed by 2-dimensional echocardiography. Incident HF was assessed in 1600 participants. We then compared protein associations in JHS to those observed in White participants from FHS (Framingham Heart Study; mean age, 54 years; 56% women).

RESULTS

In JHS, there were 110 proteins associated with LV mass and 13 proteins associated with incident HF hospitalization with false discovery rate <5% after multivariable adjustment. Several proteins showed expected associations with both LV mass and HF, including NT-proBNP (N-terminal pro-B-type natriuretic peptide; β=0.04; P=2×10-8; hazard ratio, 1.48; P=0.0001). The strongest association with LV mass was novel: LKHA4 (leukotriene-A4 hydrolase; β=0.05; P=5×10-15). This association was confirmed on an alternate proteomics platform and further supported by related metabolomic data. Fractalkine/CX3CL1 (C-X3-C Motif Chemokine Ligand 1) showed a novel association with incident HF (hazard ratio, 1.32; P=0.0002). While established biomarkers such as cystatin C and NT-proBNP showed consistent associations in Black and White individuals, LKHA4 and fractalkine were significantly different between the two groups.

CONCLUSIONS

We identified several novel biological pathways specific to Black adults hypothesized to contribute to the pathophysiologic cascade of LV hypertrophy and incident HF including LKHA4 and fractalkine.

Advances in Aptamer-Based Biomarker Discovery

The discovery and identification of biomarkers promote the rational and fast development of medical diagnosis and therapeutics. Clinically, the application of ideal biomarkers still is limited due to the suboptimal technology in biomarker discovery. Aptamers are single-stranded deoxyribonucleic acid or ribonucleic acid molecules and can selectively bind to varied targets with high affinity and specificity. Compared with antibody, aptamers have desirable advantages, such as flexible design, easy synthesis and convenient modification with different functional groups. Currently, different aptamer-based technologies have been developed to facilitate biomarker discovery, especially CELL-SELEX and SOMAScan technology. CELL-SELEX technology is mainly used to identify cell membrane surface biomarkers of various cells. SOMAScan technology is an unbiased biomarker detection method that can analyze numerous and even thousands of proteins in complex biological samples at the same time. It has now become a large-scale multi-protein biomarker discovery platform. In this review, we introduce the aptamer-based biomarker discovery technologies, and summarize and highlight the discovered emerging biomarkers recently in several diseases.

New Approaches to Identify Sepsis Biomarkers: The Importance of Model and Sample Source for Mass Spectrometry

Septic shock is a systemic inflammatory response syndrome associated with circulatory failure leading to organ failure with a 40% mortality rate. Early diagnosis and prognosis of septic shock are necessary for specific and timely treatment. However, no predictive biomarker is available. In recent years, improvements in proteomics-based mass spectrometry have improved the detection of such biomarkers. This approach can be performed on different samples such as tissue or biological fluids. Working directly from human samples is complicated owing to interindividual variability. Indeed, patients are admitted at different stages of disease development and with signs of varying severity from one patient to another. All of these elements interfere with the identification of early, sensitive, and specific septic shock biomarkers. For these reasons, animal models of sepsis, although imperfect, are used to control the kinetics of the development of the pathology and to standardise experimentation, facilitating the identification of potential biomarkers. These elements underline the importance of the choice of animal model used and the sample to be studied during preclinical studies. The aim of this review is to discuss the relevance of different approaches to enable the identification of biomarkers that could indirectly be relevant to the clinical setting.

Insight into the Role of Angiopoietins in Ageing-Associated Diseases

Angiopoietin (Ang) and its receptor, TIE signaling, contribute to the development and maturation of embryonic vasculature as well as vascular remodeling and permeability in adult tissues. Targeting both this signaling pathway and the major pathway with vascular endothelial growth factor (VEGF) is expected to permit clinical applications, especially in antiangiogenic therapies against tumors. Several drugs targeting the Ang-TIE signaling pathway in cancer patients are under clinical development. Similar to how cancer increases with age, unsuitable angiogenesis or endothelial dysfunction is often seen in other ageing-associated diseases (AADs) such as atherosclerosis, Alzheimer's disease, type 2 diabetes, chronic kidney disease and cardiovascular diseases. Thus, the Ang-TIE pathway is a possible molecular target for AAD therapy. In this review, we focus on the potential role of the Ang-TIE signaling pathway in AADs, especially non-cancer-related AADs. We also suggest translational insights and future clinical applications of this pathway in those AADs.

Proteomic Signatures of Heart Failure in Relation to Left Ventricular Ejection Fraction

BACKGROUND

There is a growing recognition of the inherent limitations of the use of the left ventricular ejection fraction (LVEF) to accurately phenotype patients with heart failure (HF).

OBJECTIVES

The authors sought to identify unique proteomic signatures for patients with HF with reduced ejection fraction (HFrEF), HF with a midrange LVEF (HFmrEF), and HF with preserved ejection fraction (HFpEF), as well as to identify molecular differences between patients with ischemic and nonischemic HF.

METHODS

We used high-content aptamer-based proteomics technology (SOMAscan) to interrogate the blood proteome of age- and sex-matched patients with HF within different LVEF groups.

RESULTS

Within the Washington University Heart Failure Registry, we identified age/sex-matched patients within 3 LVEF categories: HFrEF (LVEF <40%), HFmrEF (LVEF 40% to 50%), and HFpEF (LVEF >50%). We found that patients with HFrEF, HFmrEF, and HFpEF had unique variations in circulating proteins that reflected distinct biological pathophysiologies. Bioinformatics analysis revealed that there were biological themes that were unique to patients with HFrEF, HFpEF, or HFmrEF. Comparative analyses of patients with HFmrEF with improved LVEF and patients with HFmrEF with unchanged LVEF revealed marked differences between these 2 patient populations and indicated that patients with recovered LVEF are more similar to patients with HFpEF than to patients with HFrEF. Moreover, there were marked differences in the proteomic signatures of patients with ischemic and nonischemic HF.

CONCLUSIONS

Viewed together, these findings suggest that it may be possible to use high-content multiplexed proteomics assays in combination with the clinical assessment of LVEF to more accurately identify clinical phenotypes of patients with HF.

Prioritizing Candidates of Post-Myocardial Infarction Heart Failure Using Plasma Proteomics and Single-Cell Transcriptomics

Supplemental Digital Content is available in the text.

Background:

Heart failure (HF) is the most common long-term complication of acute myocardial infarction (MI). Understanding plasma proteins associated with post-MI HF and their gene expression may identify new candidates for biomarker and drug target discovery.

Methods:

We used aptamer-based affinity-capture plasma proteomics to measure 1305 plasma proteins at 1 month post-MI in a New Zealand cohort (CDCS [Coronary Disease Cohort Study]) including 181 patients post-MI who were subsequently hospitalized for HF in comparison with 250 patients post-MI who remained event free over a median follow-up of 4.9 years. We then correlated plasma proteins with left ventricular ejection fraction measured at 4 months post-MI and identified proteins potentially coregulated in post-MI HF using weighted gene co-expression network analysis. A Singapore cohort (IMMACULATE [Improving Outcomes in Myocardial Infarction through Reversal of Cardiac Remodelling]) of 223 patients post-MI, of which 33 patients were hospitalized for HF (median follow-up, 2.0 years), was used for further candidate enrichment of plasma proteins by using Fisher meta-analysis, resampling-based statistical testing, and machine learning. We then cross-referenced differentially expressed proteins with their differentially expressed genes from single-cell transcriptomes of nonmyocyte cardiac cells isolated from a murine MI model, and single-cell and single-nucleus transcriptomes of cardiac myocytes from murine HF models and human patients with HF.

Results:

In the CDCS cohort, 212 differentially expressed plasma proteins were significantly associated with subsequent HF events. Of these, 96 correlated with left ventricular ejection fraction measured at 4 months post-MI. Weighted gene co-expression network analysis prioritized 63 of the 212 proteins that demonstrated significantly higher correlations among patients who developed post-MI HF in comparison with event-free controls (data set 1). Cross-cohort meta-analysis of the IMMACULATE cohort identified 36 plasma proteins associated with post-MI HF (data set 2), whereas single-cell transcriptomes identified 15 gene-protein candidates (data set 3). The majority of prioritized proteins were of matricellular origin. The 6 most highly enriched proteins that were common to all 3 data sets included well-established biomarkers of post-MI HF: N-terminal B-type natriuretic peptide and troponin T, and newly emergent biomarkers, angiopoietin-2, thrombospondin-2, latent transforming growth factor-β binding protein-4, and follistatin-related protein-3, as well.

Conclusions:

Large-scale human plasma proteomics, cross-referenced to unbiased cardiac transcriptomics at single-cell resolution, prioritized protein candidates associated with post-MI HF for further mechanistic and clinical validation.

How Will Genetics Inform the Clinical Care of Atrial Fibrillation?

Susceptibility to atrial fibrillation (AF) is determined by well-recognized risk factors such as diabetes mellitus or hypertension, emerging risk factors such as sleep apnea or inflammation, and increasingly well-defined genetic variants. As discussed in detail in a companion article in this series, studies in families and in large populations have identified multiple genetic loci, specific genes, and specific variants increasing susceptibility to AF. Since it is becoming increasingly inexpensive to obtain genotype data and indeed whole genome sequence data, the question then becomes to define whether using emerging new genetics knowledge can improve care for patients both before and after development of AF. Examples of improvements in care could include identifying patients at increased risk for AF (and thus deploying increased surveillance or even low-risk preventive therapies should these be available), identifying patient subsets in whom specific therapies are likely to be effective or ineffective or in whom the driving biology could motivate the development of new mechanism-based therapies or identifying an underlying susceptibility to comorbid cardiovascular disease. While current guidelines for the care of patients with AF do not recommend routine genetic testing, this rapidly increasing knowledge base suggests that testing may now or soon have a place in the management of select patients. The opportunity is to generate, validate, and deploy clinical predictors (including family history) of AF risk, to assess the utility of incorporating genomic variants into those predictors, and to identify and validate interventions such as wearable or implantable device-based monitoring ultimately to intervene in patients with AF before they present with catastrophic complications like heart failure or stroke.

Enabling Technologies for Personalized and Precision Medicine

Individualizing patient treatment is a core objective of the medical field. Reaching this objective has been elusive owing to the complex set of factors contributing to both disease and health; many factors, from genes to proteins, remain unknown in their role in human physiology. Accurately diagnosing, monitoring, and treating disorders requires advances in biomarker discovery, the subsequent development of accurate signatures that correspond with dynamic disease states, as well as therapeutic interventions that can be continuously optimized and modulated for dose and drug selection. This work highlights key breakthroughs in the development of enabling technologies that further the goal of personalized and precision medicine, and remaining challenges that, when addressed, may forge unprecedented capabilities in realizing truly individualized patient care.

Aptamer-Based Proteomic Platform Identifies Novel Protein Predictors of Incident Heart Failure and Echocardiographic Traits

BACKGROUND

We used a large-scale, high-throughput DNA aptamer-based discovery proteomic platform to identify circulating biomarkers of cardiac remodeling and incident heart failure (HF) in community-dwelling individuals.

METHODS

We evaluated 1895 FHS (Framingham Heart Study) participants (age 55±10 years, 54% women) who underwent proteomic profiling and echocardiography. Plasma levels of 1305 proteins were related to echocardiographic traits and to incident HF using multivariable regression. Statistically significant protein-HF associations were replicated in the HUNT (Nord-Trøndelag Health) study (n=2497, age 63±10 years, 43% women), and results were meta-analyzed. Genetic variants associated with circulating protein levels (pQTLs) were related to echocardiographic traits in the EchoGen (n=30 201) and to incident HF in the CHARGE (n=20 926) consortia.

RESULTS

Seventeen proteins associated with echocardiographic traits in cross-sectional analyses (false discovery rate <0.10), and 8 of these proteins had pQTLs associated with echocardiographic traits in EchoGen (P<0.0007). In Cox models adjusted for clinical risk factors, 29 proteins demonstrated associations with incident HF in FHS (174 HF events, mean follow-up 19 [limits, 0.2-23.7] years). In meta-analyses of FHS and HUNT, 6 of these proteins were associated with incident HF (P<3.8×10-5; 3 with higher risk: NT-proBNP [N-terminal proB-type natriuretic peptide], TSP2 [thrombospondin-2], MBL [mannose-binding lectin]; and 3 with lower risk: ErbB1 [epidermal growth factor receptor], GDF-11/8 [growth differentiation factor-11/8], and RGMC [hemojuvelin]). For 5 of the 6 proteins, pQTLs were associated with echocardiographic traits (P<0.0006) in EchoGen, and for RGMC, a protein quantitative trait loci was associated with incident HF (P=0.001).

CONCLUSIONS

A large-scale proteomics approach identified new predictors of cardiac remodeling and incident HF. Future studies are warranted to elucidate how biological pathways represented by these proteins may mediate cardiac remodeling and HF risk and to assess if these proteins can improve HF risk prediction.

Is proteomics of value in cardiovascular risk assessment?

PURPOSE OF REVIEW

To briefly summarize recently published evidence in the field of cardiovascular proteomics, focusing on its ability to improve cardiovascular risk stratification and critically discussing still open and burning issues and future perspectives of proteomics research.

RECENT FINDINGS

Several epidemiological studies have demonstrated an improvement in cardiovascular risk prediction beyond traditional risk factors by adding novel biomarkers, identified by both discovery and targeted proteomics. However, only a moderate improvement in risk discrimination over clinical variables was observed. Moreover, despite different outcomes there was also a strong overlap of identified candidates, with several of them being already well established cardiovascular risk markers such as growth differentiation factor 15, natriuretic peptides, C-reactive protein, interleukins, and metalloproteases.

SUMMARY

Although proteomics plays a crucial role in biomarker discovery, the modest discriminative ability of this technique raises the possibility that there are still hidden mechanisms in protein regulatory networks, which urgently need to be evaluated to improve a cardiovascular risk assessment to a clinically significant extent.

Cardiac Biomarkers in Advanced Heart Failure: How Can They Impact Our Pre-transplant or Pre-LVAD Decision-making

PURPOSE OF REVIEW

Decision-making in advanced heart failure (HF) is a complex process that involves careful consideration of competing tradeoffs of risks and benefits in regard to heart transplantation (HT) or left ventricular assist device (LVAD) placement. The purpose of this review is to discuss how biomarkers may affect decision-making for HT or LVAD implantation.

RECENT FINDINGS

N-Terminal probrain natriuretic peptide, soluble suppression of tumorigenicity-2, galectin-3, copeptin, and troponin T levels are associated with HF survival and can help identify the appropriate timing for advanced HF therapies. Patients at risk of right ventricular failure after LVAD implantation can be identified with preimplant biomarkers of extracellular matrix turnover, neurohormonal activation, and inflammation. There is limited data on the adoption of biomarker measurement for decision-making in the allocation of advanced HF therapies. Nonetheless, biomarkers can improve risk stratification and prognostication thereby optimizing patient selection for HT and LVAD implantation.