Proteomic analysis of cardiorespiratory fitness for prediction of mortality and multisystem disease risks.

Despite the wide effects of cardiorespiratory fitness (CRF) on metabolic, cardiovascular, pulmonary and neurological health, challenges in the feasibility and reproducibility of CRF measurements have impeded its use for clinical decision-making. Here we link proteomic profiles to CRF in 14,145 individuals across four international cohorts with diverse CRF ascertainment methods to establish, validate and characterize a proteomic CRF score. In a cohort of around 22,000 individuals in the UK Biobank, a proteomic CRF score was associated with a reduced risk of all-cause mortality (unadjusted hazard ratio 0.50 (95% confidence interval 0.48-0.52) per 1 s.d. increase). The proteomic CRF score was also associated with multisystem disease risk and provided risk reclassification and discrimination beyond clinical risk factors, as well as modulating high polygenic risk of certain diseases. Finally, we observed dynamicity of the proteomic CRF score in individuals who undertook a 20-week exercise training program and an association of the score with the degree of the effect of training on CRF, suggesting potential use of the score for personalization of exercise recommendations. These results indicate that population-based proteomics provides biologically relevant molecular readouts of CRF that are additive to genetic risk, potentially modifiable and clinically translatable.

Overcome the Limitation of Phenome-Wide Association Studies (PheWAS): Extension of PheWAS to Efficient and Robust Large-Scale ICD Codes Analysis.

The Phenome-wide association studies (PheWAS) have become widely used for efficient, high-throughput evaluation of relationship between a genetic factor and a large number of disease phenotypes, typically extracted from a DNA biobank linked with electronic medical records (EMR). Phecodes, billing code-derived disease case-control status, are usually used as outcome variables in PheWAS and logistic regression has been the standard choice of analysis method. Since the clinical diagnoses in EMR are often inaccurate with errors which can lead to biases in the odds ratio estimates, much effort has been put to accurately define the cases and controls to ensure an accurate analysis. Specifically in order to correctly classify controls in the population, an exclusion criteria list for each Phecode was manually compiled to obtain unbiased odds ratios. However, the accuracy of the list cannot be guaranteed without extensive data curation process. The costly curation process limits the efficiency of large-scale analyses that take full advantage of all structured phenotypic information available in EMR. Here, we proposed to estimate relative risks (RR) instead. We first demonstrated the desired nature of RR that overcomes the inaccuracy in the controls via theoretical formula. With simulation and real data application, we further confirmed that RR is unbiased without compiling exclusion criteria lists. With RR as estimates, we are able to efficiently extend PheWAS to a larger-scale, phenome construction agnostic analysis of phenotypes, using ICD 9/10 codes, which preserve much more disease-related clinical information than Phecodes.

Impact of Insurance Status and Region on Angiotensin Receptor-Neprilysin Inhibitor Prescription During Heart Failure Hospitalizations.

BACKGROUND: An angiotensin receptor-neprilysin inhibitor (ARNI) is the preferred renin-angiotensin system (RAS) inhibitor for heart failure with reduced ejection fraction (HFrEF). Among eligible patients, insurance status and prescriber concern regarding out-of-pocket costs may constrain early initiation of ARNI and other new therapies. OBJECTIVES: In this study, the authors sought to evaluate the association of insurance and other social determinants of health with ARNI initiation at discharge from HFrEF hospitalization. METHODS: The authors analyzed ARNI initiation from January 2017 to June 2020 among patients with HFrEF eligible to receive RAS inhibitor at discharge from hospitals in the Get With The Guidelines-Heart Failure registry. The primary outcome was the proportion of ARNI prescription at discharge among those prescribed RAS inhibitor who were not on ARNI on admission. A logistic regression model was used to determine the association of insurance status, U.S. region, and their interaction, as well as self-reported race, with ARNI initiation at discharge. RESULTS: From 42,766 admissions, 24,904 were excluded for absolute or relative contraindications to RAS inhibitors. RAS inhibitors were prescribed for 16,817 (94.2%) of remaining discharges, for which ARNI was prescribed in 1,640 (9.8%). Self-reported Black patients were less likely to be initiated on ARNI compared to self-reported White patients (OR: 0.64; 95% CI: 0.50-0.81). Compared to Medicare beneficiaries, patients with third-party insurance, Medicaid, or no insurance were less likely to be initiated on ARNI (OR: 0.47 [95% CI: 0.31-0.72], OR: 0.41 [95% CI: 0.25-0.67], and OR: 0.20 [95% CI: 0.08-0.47], respectively). ARNI therapy varied by hospital region, with lowest utilization in the Mountain region. An interaction was demonstrated between the impact of insurance disparities and hospital region. CONCLUSIONS: Among patients hospitalized between 2017 and 2020 for HFrEF who were prescribed RAS inhibitor therapy at discharge, insurance status, geographic region, and self-reported race were associated with ARNI initiation.

Development and Implementation of an Integrated Preclinical Atherosclerosis Database.

BACKGROUND: Basic scientists have used preclinical animal models to explore mechanisms driving human diseases for decades, resulting in thousands of publications, each supporting causative inferences. Despite substantial advances in the mechanistic construct of disease, there has been limited translation from individual studies to advances in clinical care. An integrated approach to these individual studies has the potential to improve translational success. METHODS: Using atherosclerosis as a test case, we extracted data from the 2 most common mouse models of atherosclerosis (ApoE [apolipoprotein E]-knockout and LDLR [low-density lipoprotein receptor]-knockout). We restricted analyses to manuscripts published in 2 well-established journals, Arteriosclerosis, Thrombosis, and Vascular Biology and Circulation, as of query in 2021. Predefined variables including experimental conditions, intervention, and outcomes were extracted from each publication to produce a preclinical atherosclerosis database. RESULTS: Extracted data include animal sex, diet, intervention type, and distinct plaque pathologies (size, inflammation, and lipid content). Procedures are provided to standardize data extraction, attribute interventions to specific genes, and transform the database for use with available transcriptomics software. The database integrates hundreds of genes, each directly tested in vivo for causation in a murine atherosclerosis model. The database is provided to allow the research community to perform integrated analyses that reflect the global impact of decades of atherosclerosis investigation. CONCLUSIONS: This database is provided as a resource for future interrogation of sub-data sets associated with distinct plaque pathologies, cell type, or sex. We also provide the methods and software needed to expand this data set and apply this approach to the extensive repository of peer-reviewed data utilizing preclinical models to interrogate mechanisms of diverse human diseases.

Interoperability of phenome-wide multimorbidity patterns: a comparative study of two large-scale EHR systems.

Background: Electronic health records (EHR) are increasingly used for studying multimorbidities. However, concerns about accuracy, completeness, and EHRs being primarily designed for billing and administrative purposes raise questions about the consistency and reproducibility of EHR-based multimorbidity research. Methods: Utilizing phecodes to represent the disease phenome, we analyzed pairwise comorbidity strengths using a dual logistic regression approach and constructed multimorbidity as an undirected weighted graph. We assessed the consistency of the multimorbidity networks within and between two major EHR systems at local (nodes and edges), meso (neighboring patterns), and global (network statistics) scales. We present case studies to identify disease clusters and uncover clinically interpretable disease relationships. We provide an interactive web tool and a knowledge base combining data from multiple sources for online multimorbidity analysis. Findings: Analyzing data from 500,000 patients across Vanderbilt University Medical Center and Mass General Brigham health systems, we observed a strong correlation in disease frequencies ( Kendall's τ = 0.643) and comorbidity strengths (Pearson ρ = 0.79). Consistent network statistics across EHRs suggest similar structures of multimorbidity networks at various scales. Comorbidity strengths and similarities of multimorbidity connection patterns align with the disease genetic correlations. Graph-theoretic analyses revealed a consistent core-periphery structure, implying efficient network clustering through threshold graph construction. Using hydronephrosis as a case study, we demonstrated the network's ability to uncover clinically relevant disease relationships and provide novel insights. Interpretation: Our findings demonstrate the robustness of large-scale EHR data for studying phenome-wide multimorbidities. The alignment of multimorbidity patterns with genetic data suggests the potential utility for uncovering shared biology of diseases. The consistent core-periphery structure offers analytical insights to discover complex disease interactions. This work also sets the stage for advanced disease modeling, with implications for precision medicine. Funding: VUMC Biostatistics Development Award, the National Institutes of Health, and the VA CSRD.

Recurrence After Atrial Fibrillation Ablation and Investigational Biomarkers of Cardiac Remodeling.

BACKGROUND: Recurrence after atrial fibrillation (AF) ablation remains common. We evaluated the association between recurrence and levels of biomarkers of cardiac remodeling, and their ability to improve recurrence prediction when added to a clinical prediction model. METHODS AND RESULTS: Blood samples collected before de novo catheter ablation were analyzed. Levels of bone morphogenetic protein-10, angiopoietin-2, fibroblast growth factor-23, insulin-like growth factor-binding protein-7, myosin-binding protein C3, growth differentiation factor-15, interleukin-6, N-terminal pro-brain natriuretic peptide, and high-sensitivity troponin T were measured. Recurrence was defined as ≥30 seconds of an atrial arrhythmia 3 to 12 months postablation. Multivariable logistic regression was performed using biomarker levels along with clinical covariates: APPLE score (Age >65 years, Persistent AF, imPaired eGFR [<60 ml/min/1.73m2], LA diameter ≥43 mm, EF <50%; which includes age, left atrial diameter, left ventricular ejection fraction, persistent atrial fibrillation, and estimated glomerular filtration rate), preablation rhythm, sex, height, body mass index, presence of an implanted continuous monitor, year of ablation, and additional linear ablation. A total of 1873 participants were included. A multivariable logistic regression showed an association between recurrence and levels of angiopoietin-2 (odds ratio, 1.08 [95% CI, 1.02-1.15], P=0.007) and interleukin-6 (odds ratio, 1.02 [95% CI, 1.003-1.03]; P=0.02). The area under the receiver operating characteristic curve of a model that only contained clinical predictors was 0.711. The addition of any of the 9 studied biomarkers to the predictive model did not result in a statistically significant improvement in the area under the receiver operating characteristic curve. CONCLUSIONS: Higher angiopoietin-2 and interleukin-6 levels were associated with recurrence after atrial fibrillation ablation in multivariable modeling. However, the addition of biomarkers to a clinical prediction model did not significantly improve recurrence prediction.

Volumetric brain MRI signatures of heart failure with preserved ejection fraction in the setting of dementia.

Heart failure with preserved ejection fraction (HFpEF) is an important, emerging risk factor for dementia, but it is not clear whether HFpEF contributes to a specific pattern of neuroanatomical changes in dementia. A major challenge to studying this is the relative paucity of datasets of patients with dementia, with/without HFpEF, and relevant neuroimaging. We sought to demonstrate the feasibility of using modern data mining tools to create and analyze clinical imaging datasets and identify the neuroanatomical signature of HFpEF-associated dementia. We leveraged the bioinformatics tools at Vanderbilt University Medical Center to identify patients with a diagnosis of dementia with and without comorbid HFpEF using the electronic health record. We identified high resolution, clinically-acquired neuroimaging data on 30 dementia patients with HFpEF (age 76.9 ± 8.12 years, 61% female) as well as 301 age- and sex-matched patients with dementia but without HFpEF to serve as comparators (age 76.2 ± 8.52 years, 60% female). We used automated image processing pipelines to parcellate the brain into 132 structures and quantify their volume. We found six regions with significant atrophy associated with HFpEF: accumbens area, amygdala, posterior insula, anterior orbital gyrus, angular gyrus, and cerebellar white matter. There were no regions with atrophy inversely associated with HFpEF. Patients with dementia and HFpEF have a distinct neuroimaging signature compared to patients with dementia only. Five of the six regions identified in are in the temporo-parietal region of the brain. Future studies should investigate mechanisms of injury associated with cerebrovascular disease leading to subsequent brain atrophy.

Feasibility of precision smoking treatment in a low-income community setting: results of a pilot randomized controlled trial in The Southern Community Cohort Study.

BACKGROUND: The feasibility of precision smoking treatment in socioeconomically disadvantaged communities has not been studied. METHODS: Participants in the Southern Community Cohort Study who smoked daily were invited to join a pilot randomized controlled trial of three smoking cessation interventions: guideline-based care (GBC), GBC plus nicotine metabolism-informed care (MIC), and GBC plus counseling guided by a polygenic risk score (PRS) for lung cancer. Feasibility was assessed by rates of study enrollment, engagement, and retention, targeting > 70% for each. Using logistic regression, we also assessed whether feasibility varied by age, sex, race, income, education, and attitudes toward precision smoking treatment. RESULTS: Of 92 eligible individuals (79.3% Black; 68.2% with household income < $15,000), 67 (72.8%; 95% CI 63.0-80.9%) enrolled and were randomized. Of these, 58 (86.6%; 95% CI 76.4-92.8%) engaged with the intervention, and of these engaged participants, 43 (74.1%; 95% CI 61.6-83.7%) were retained at 6-month follow-up. Conditional on enrollment, older age was associated with lower engagement (OR 0.83, 95% CI 0.73-0.95, p = 0.008). Conditional on engagement, retention was significantly lower in the PRS arm than in the GBC arm (OR 0.18, 95% CI 0.03-1.00, p = 0.050). No other selection effects were observed. CONCLUSIONS: Genetically informed precision smoking cessation interventions are feasible in socioeconomically disadvantaged communities, exhibiting high enrollment, engagement, and retention irrespective of race, sex, income, education, or attitudes toward precision smoking treatment. Future smoking cessation interventions in this population should take steps to engage older people and to sustain participation in interventions that include genetic risk counseling. TRIAL REGISTRATION: ClinicalTrials.gov No. NCT03521141, Registered 27 April 2018, https://www. CLINICALTRIALS: gov/study/NCT03521141.

A prognostic molecular signature of hepatic steatosis is spatially heterogeneous and dynamic in human liver.

Metabolic dysfunction-associated steatotic liver disease (MASLD) prevalence is increasing in parallel with an obesity pandemic, calling for novel strategies for prevention and treatment. We defined a circulating proteome of human MASLD across ≈7000 proteins in ≈5000 individuals from diverse, at-risk populations across the metabolic health spectrum, demonstrating reproducible diagnostic performance and specifying both known and novel metabolic pathways relevant to MASLD (central carbon and amino acid metabolism, hepatocyte regeneration, inflammation, fibrosis, insulin sensitivity). A parsimonious proteomic signature of MASLD was associated with a protection from MASLD and its related multi-system metabolic consequences in >26000 free-living individuals, with an additive effect to polygenic risk. The MASLD proteome was encoded by genes that demonstrated transcriptional enrichment in liver, with spatial transcriptional activity in areas of steatosis in human liver biopsy and dynamicity for select targets in human liver across stages of steatosis. We replicated several top relations from proteomics and spatial tissue transcriptomics in a humanized "liver-on-a-chip" model of MASLD, highlighting the power of a full translational approach to discovery in MASLD. Collectively, these results underscore utility of blood-based proteomics as a dynamic "liquid biopsy" of human liver relevant to clinical biomarker and mechanistic applications.

LNK/SH2B3 loss of function increases susceptibility to murine and human atrial fibrillation.

AIMS: The lymphocyte adaptor protein (LNK) is a negative regulator of cytokine and growth factor signaling. The rs3184504 variant in SH2B3 reduces LNK function and is linked to cardiovascular, inflammatory, and hematologic disorders including stroke. In mice, deletion of Lnk causes inflammation and oxidative stress. We hypothesized that Lnk-/- mice are susceptible to atrial fibrillation (AF) and that rs3184504 is associated with AF and AF-related stroke in humans. During inflammation, reactive lipid dicarbonyls are a major component of oxidative injury, and we further hypothesized that these mediators are critical drivers of the AF substrate in Lnk-/- mice. METHODS AND RESULTS: Lnk-/- or wild-type (WT) mice were treated with vehicle or 2-hydroxybenzylamine (2-HOBA), a dicarbonyl scavenger, for 3 months. Compared to WT, Lnk-/- mice displayed increased AF duration that was prevented by 2-HOBA. In the Lnk-/- atria, action potentials were prolonged with reduced transient outward K+ current, increased late Na+ current, and reduced peak Na+ current, proarrhythmic effects that were inhibited by 2-HOBA. Mitochondrial dysfunction, especially for complex I, was evident in Lnk-/- atria, while scavenging lipid dicarbonyls prevented this abnormality. Tumor necrosis factor-α (TNF-α) and interleukin-1ß (IL-1ß) were elevated in Lnk-/- plasma and atrial tissue, respectively, both of which caused electrical and bioenergetic remodeling in vitro. Inhibition of soluble TNF-α prevented electrical remodeling and AF susceptibility, while IL-1ß inhibition improved mitochondrial respiration but had no effect on AF susceptibility. In a large database of genotyped patients, rs3184504 was associated with AF, as well as AF-related stroke. CONCLUSIONS: These findings identify a novel role for LNK in the pathophysiology of AF in both experimental mice and in humans. Moreover, reactive lipid dicarbonyls are critical to the inflammatory AF substrate in Lnk-/- mice and mediate the proarrhythmic effects of pro-inflammatory cytokines, primarily through electrical remodeling.

Unbiased characterization of atrial fibrillation phenotypic architecture provides insight to genetic liability and clinically relevant outcomes.

Background: Atrial Fibrillation (AF) is a common and clinically heterogeneous arrythmia. Machine learning (ML) algorithms can define data-driven disease subtypes in an unbiased fashion, but whether the AF subgroups defined in this way align with underlying mechanisms, such as high polygenic liability to AF or inflammation, and associate with clinical outcomes is unclear. Methods: We identified individuals with AF in a large biobank linked to electronic health records (EHR) and genome-wide genotyping. The phenotypic architecture in the AF cohort was defined using principal component analysis of 35 expertly curated and uncorrelated clinical features. We applied an unsupervised co-clustering machine learning algorithm to the 35 features to identify distinct phenotypic AF clusters. The clinical inflammatory status of the clusters was defined using measured biomarkers (CRP, ESR, WBC, Neutrophil %, Platelet count, RDW) within 6 months of first AF mention in the EHR. Polygenic risk scores (PRS) for AF and cytokine levels were used to assess genetic liability of clusters to AF and inflammation, respectively. Clinical outcomes were collected from EHR up to the last medical contact. Results: The analysis included 23,271 subjects with AF, of which 6,023 had available genome-wide genotyping. The machine learning algorithm identified 3 phenotypic clusters that were distinguished by increasing prevalence of comorbidities, particularly renal dysfunction, and coronary artery disease. Polygenic liability to AF across clusters was highest in the low comorbidity cluster. Clinically measured inflammatory biomarkers were highest in the high comorbid cluster, while there was no difference between groups in genetically predicted levels of inflammatory biomarkers. Subgroup assignment was associated with multiple clinical outcomes including mortality, stroke, bleeding, and use of cardiac implantable electronic devices after AF diagnosis. Conclusion: Patient subgroups identified by unsupervised clustering were distinguished by comorbidity burden and associated with risk of clinically important outcomes. Polygenic liability to AF across clusters was greatest in the low comorbidity subgroup. Clinical inflammation, as reflected by measured biomarkers, was lowest in the subgroup with lowest comorbidities. However, there were no differences in genetically predicted levels of inflammatory biomarkers, suggesting associations between AF and inflammation is driven by acquired comorbidities rather than genetic predisposition.

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.

Next-generation phenotyping: introducing phecodeX for enhanced discovery research in medical phenomics.

MOTIVATION: Phecodes are widely used and easily adapted phenotypes based on International Classification of Diseases codes. The current version of phecodes (v1.2) was designed primarily to study common/complex diseases diagnosed in adults; however, there are numerous limitations in the codes and their structure. RESULTS: Here, we present phecodeX, an expanded version of phecodes with a revised structure and 1,761 new codes. PhecodeX adds granularity to phenotypes in key disease domains that are under-represented in the current phecode structure-including infectious disease, pregnancy, congenital anomalies, and neonatology-and is a more robust representation of the medical phenome for global use in discovery research. AVAILABILITY AND IMPLEMENTATION: phecodeX is available at https://github.com/PheWAS/phecodeX.

Identifying Patients With Peripheral Artery Disease Using the Electronic Health Record: A Pragmatic Approach.

BACKGROUND: Peripheral artery disease (PAD) is underdiagnosed due to poor patient and clinician awareness. Despite this, no widely accepted PAD screening is recommended. OBJECTIVES: The authors used machine learning to develop an automated risk stratification tool for identifying patients with a high likelihood of PAD. METHODS: Using data from the electronic health record (EHR), ankle-brachial indices (ABIs) were extracted for 3,298 patients. In addition to ABI, we extracted 60 other patient characteristics and used a random forest model to rank the features by association with ABI. The model identified several features independently correlated with PAD. We then built a logistic regression model to predict PAD status on a validation set of patients (n = 1,089), an external cohort of patients (n = 2,922), and a national database (n = 2,488). The model was compared to an age-based and random forest model. RESULTS: The model had an area under the curve (AUC) of 0.68 in the validation set. When evaluated on an external population using EHR data, it performed similarly with an AUC of 0.68. When evaluated on a national database, it had an AUC of 0.72. The model outperformed an age-based model (AUC: 0.62; P < 0.001). A random forest model with inclusion of all 60 features did not perform significantly better (AUC: 0.71; P = 0.31). CONCLUSIONS: Statistical techniques can be used to build models which identify individuals at high risk for PAD using information accessible from the EHR. Models such as this may allow large health care systems to efficiently identify patients that would benefit from aggressive preventive strategies or targeted-ABI screening.

Outcomes of Heart Transplant Donation After Circulatory Death.

BACKGROUND: Heart transplantation using donation after circulatory death (DCD) allografts is increasingly common, expanding the donor pool and reducing transplant wait times. However, data remain limited on clinical outcomes. OBJECTIVES: We sought to compare 6-month and 1-year clinical outcomes between recipients of DCD hearts, most of them recovered with the use of normothermic regional perfusion (NRP), and recipients of donation after brain death (DBD) hearts. METHODS: We conducted a single-center retrospective observational study of all adult heart-only transplants from January 2020 to January 2023. Recipient and donor data were abstracted from medical records and the United Network for Organ Sharing registry, respectively. Survival analysis and Cox regression were used to compare the groups. RESULTS: During the study period, 385 adults (median age 57.4 years [IQR: 48.0-63.7 years]) underwent heart-only transplantation, including 122 (32%) from DCD donors, 83% of which were recovered with the use of NRP. DCD donors were younger and had fewer comorbidities than DBD donors. DCD recipients were less often hospitalized before transplantation and less likely to require pretransplantation temporary mechanical circulatory support compared with DBD recipients. There were no significant differences between groups in 1-year survival, incidence of severe primary graft dysfunction, treated rejection during the first year, or likelihood of cardiac allograft vasculopathy at 1 year after transplantation. CONCLUSIONS: In the largest single-center comparison of DCD and DBD heart transplantations to date, outcomes among DCD recipients are noninferior to those of DBD recipients. This study adds to the published data supporting DCD donors as a safe means to expand the heart donor pool.

Development and implementation of an integrated preclinical atherosclerosis database.

Background: Basic scientists have used preclinical animal models to explore mechanisms driving human diseases for decades, resulting in thousands of publications, each supporting causative inferences. Despite substantial advances in the mechanistic construct of disease, there has been limited translation from individual studies to advances in clinical care. An integrated approach to these individual studies has the potential to improve translational success. Methods: Using atherosclerosis as a test case, we extracted data from the two most common mouse models of atherosclerosis (ApoE and LDLR knockout). We restricted analyses to manuscripts published in two well-established journals, Arteriosclerosis, Thrombosis, and Vascular Biology and Circulation, as of query in 2021. Predefined variables including experimental conditions, intervention and outcomes were extracted from each publication to produce a preclinical atherosclerosis database. Results: Extracted data include animal sex, diet, intervention type and distinct plaque pathologies (size, inflammation, lipid content). Procedures are provided to standardize data extraction, attribute interventions to specific genes and transform the database for use with available transcriptomics software. The database integrates hundreds of genes, each directly tested in vivo for causation in a murine atherosclerosis model. The database is provided to allow the research community to perform integrated analyses that reflect the global impact of decades of atherosclerosis investigation. Conclusions: Future database uses include interrogation of sub-datasets associated with distinct plaque pathologies, cell-type or sex. We provide the methods and software needed to apply this approach to the extensive repository of peer-reviewed data utilizing preclinical models to interrogate mechanisms of diverse human diseases.

Genome-wide association analysis and Mendelian randomization proteomics identify drug targets for heart failure.

We conduct a large-scale meta-analysis of heart failure genome-wide association studies (GWAS) consisting of over 90,000 heart failure cases and more than 1 million control individuals of European ancestry to uncover novel genetic determinants for heart failure. Using the GWAS results and blood protein quantitative loci, we perform Mendelian randomization and colocalization analyses on human proteins to provide putative causal evidence for the role of druggable proteins in the genesis of heart failure. We identify 39 genome-wide significant heart failure risk variants, of which 18 are previously unreported. Using a combination of Mendelian randomization proteomics and genetic cis-only colocalization analyses, we identify 10 additional putatively causal genes for heart failure. Findings from GWAS and Mendelian randomization-proteomics identify seven (CAMK2D, PRKD1, PRKD3, MAPK3, TNFSF12, APOC3 and NAE1) proteins as potential targets for interventions to be used in primary prevention of heart failure.