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.

Session Introduction: Drug-repurposing and discovery in the era of "big" real-world data: how the incorporation of observational data, genetics, and other -omic technologies can move us forward.

This PSB 2024 session discusses the many broad biological, computational, and statistical approaches currently being used for therapeutic drug target identification and repurposing of existing treatments. Drug repurposing efforts have the potential to dramatically improve the treatment landscape by more rapidly identifying drug targets and alternative strategies for untreated or poorly managed diseases. The overarching theme for this session is the use and integration of real-world data to identify drug-disease pairs with potential therapeutic use. These drug-disease pairs may be identified through genomic, proteomic, biomarkers, protein interaction analyses, electronic health records, and chemical profiling. Taken together, this session combines novel applications of methods and innovative modeling strategies with diverse real-world data to suggest new pharmaceutical treatments for human diseases.

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.

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.

Genetic examination of hematological parameters in SARS-CoV-2 infection and COVID-19.

People hospitalized with COVID-19 often exhibit altered hematological traits associated with disease prognosis (e.g., lower lymphocyte and platelet counts). We investigated whether inter-individual variability in baseline hematological traits influences risk of acute SARS-CoV-2 infection or progression to severe COVID-19. We report inconsistent associations between blood cell traits with incident SARS-CoV-2 infection and severe COVID-19 in UK Biobank and the Vanderbilt University Medical Center Synthetic Derivative (VUMC SD). Since genetically determined blood cell measures better represent cell abundance across the lifecourse, we also assessed the shared genetic architecture of baseline blood cell traits on COVID-19 related outcomes by Mendelian randomization (MR) analyses. We found significant relationships between COVID-19 severity and mean sphered cell volume after adjusting for multiple testing. However, MR results differed significantly across different freezes of COVID-19 summary statistics and genetic correlation between these traits was modest (0.1), decreasing our confidence in these results. We observed overlapping genetic association signals between other hematological and COVID-19 traits at specific loci such as MAPT and TYK2. In conclusion, we did not find convincing evidence of relationships between the genetic architecture of blood cell traits and either SARS-CoV-2 infection or COVID-19 hospitalization, though we do see evidence of shared signals at specific loci.

A genetically supported drug repurposing pipeline for diabetes treatment using electronic health records.

BACKGROUND: The identification of new uses for existing drug therapies has the potential to identify treatments for comorbid conditions that have the added benefit of glycemic control while also providing a rapid, low-cost approach to drug (re)discovery. METHODS: We developed and tested a genetically-informed drug-repurposing pipeline for diabetes management. This approach mapped genetically-predicted gene expression signals from the largest genome-wide association study for type 2 diabetes mellitus to drug targets using publicly available databases to identify drug-gene pairs. These drug-gene pairs were then validated using a two-step approach: 1) a self-controlled case-series (SCCS) using electronic health records from a discovery and replication population, and 2) Mendelian randomization (MR). FINDINGS: After filtering on sample size, 20 candidate drug-gene pairs were validated and various medications demonstrated evidence of glycemic regulation including two anti-hypertensive classes: angiotensin-converting enzyme inhibitors as well as calcium channel blockers (CCBs). The CCBs demonstrated the strongest evidence of glycemic reduction in both validation approaches (SCCS HbA1c and glucose reduction: -0.11%, p = 0.01 and -0.85 mg/dL, p = 0.02, respectively; MR: OR = 0.84, 95% CI = 0.81, 0.87, p = 5.0 x 10-25). INTERPRETATION: Our results support CCBs as a strong candidate medication for blood glucose reduction in addition to cardiovascular disease reduction. Further, these results support the adaptation of this approach for use in future drug-repurposing efforts for other conditions. FUNDING: National Institutes of Health, Medical Research Council Integrative Epidemiology Unit at the University of Bristol, UK Medical Research Council, American Heart Association, and Department of Veterans Affairs (VA) Informatics and Computing Infrastructure and VA Cooperative Studies Program.

Patient-Reported Functional Impairment Due to Hearing Loss and Tinnitus After Cisplatin-Based Chemotherapy.

PURPOSE: Cisplatin is widely used and highly ototoxic, but patient-reported functional impairment because of cisplatin-related hearing loss (HL) and tinnitus has not been comprehensively evaluated. PATIENTS AND METHODS: Testicular cancer survivors (TCS) given first-line cisplatin-based chemotherapy completed validated questionnaires, including the Hearing Handicap Inventory for Adults (HHIA) and Tinnitus Primary Function Questionnaire (TPFQ), each of which quantifies toxicity-specific functional impairment. Spearman correlations evaluated associations between HL and tinnitus severity and level of functional handicap quantified with the HHIA and TPFQ, respectively. Associations between HL or tinnitus and five prespecified adverse health outcomes (cognitive dysfunction, fatigue, depression, anxiety, and overall health) were evaluated. RESULTS: HL and tinnitus affected 137 (56.4%) and 147 (60.5%) of 243 TCS, respectively. Hearing aids were used by 10% TCS (14/137). Of TCS with HL, 35.8% reported clinically significant functional impairment. Severe HHIA-assessed functional impairment was associated with cognitive dysfunction (odds ratio [OR], 10.62; P < .001), fatigue (OR, 5.48; P = .003), and worse overall health (OR, 0.19; P = .012). Significant relationships existed between HL severity and HHIA score, and tinnitus severity and TPFQ score (P < .0001 each). TCS with either greater hearing difficulty or more severe tinnitus were more likely to report cognitive dysfunction (OR, 5.52; P = .002; and OR, 2.56; P = .05), fatigue (OR, 6.18; P < .001; and OR, 4.04; P < .001), depression (OR, 3.93; P < .01; and OR, 3.83; P < .01), and lower overall health (OR, 0.39; P = .03; and OR, 0.46; P = .02, respectively). CONCLUSION: One in three TCS with HL report clinically significant functional impairment. Follow-up of cisplatin-treated survivors should include routine assessment for HL and tinnitus. Use of the HHIA and TPFQ permit risk stratification and referral to audiologists as needed, since HL adversely affects functional status and is the single largest modifiable risk factor for cognitive decline and dementia in the general population.

Comprehensive association analysis of speech recognition thresholds after cisplatin-based chemotherapy in survivors of adult-onset cancer.

PURPOSE: Deficits in speech understanding constitute one of the most severe consequences of hearing loss. Here we investigate the clinical and genetic risk factors for symmetric deterioration of speech recognition thresholds (SRT) among cancer survivors treated with cisplatin. METHODS: SRT was measured using spondaic words and calculating the mean of measurements for both ears with symmetric SRT values. For clinical associations, SRT-based hearing disability (SHD) was defined as SRT≥15 dB hearing loss and clinical variables were derived from the study dataset. Genotyped blood samples were used for GWAS with rank-based inverse normal transformed SRT values as the response variable. Age was used as a covariate in association analyses. RESULTS: SHD was inversely associated with self-reported health (p = 0.004). Current smoking (p = 0.002), years of smoking (p = 0.02), BMI (p < 0.001), and peripheral motor neuropathy (p = 0.003) were positively associated with SHD, while physical activity was inversely associated with SHD (p = 0.005). In contrast, cumulative cisplatin dose, peripheral sensory neuropathy, hypertension, and hypercholesterolemia were not associated with SHD. Although no genetic variants had an association p value < 5 × 10-8 , 22 genetic variants were suggestively associated (p < 10-5 ) with SRT deterioration. Three of the top variants in 10 respective linkage disequilibrium regions were either positioned within the coding sequence or were eQTLs for genes involved in neuronal development (ATE1, ENAH, and ZFHX3). CONCLUSION: Current results improve our understanding of risk factors for SRT deterioration in cancer survivors. Higher BMI, lower physical activity, and smoking are associated with SHD. Larger samples would allow for expansion of the current findings on the genetic architecture of SRT.

Whole genome sequence analysis of apparent treatment resistant hypertension status in participants from the Trans-Omics for Precision Medicine program.

Introduction: Apparent treatment-resistant hypertension (aTRH) is characterized by the use of four or more antihypertensive (AHT) classes to achieve blood pressure (BP) control. In the current study, we conducted single-variant and gene-based analyses of aTRH among individuals from 12 Trans-Omics for Precision Medicine cohorts with whole-genome sequencing data. Methods: Cases were defined as individuals treated for hypertension (HTN) taking three different AHT classes, with average systolic BP ≥ 140 or diastolic BP ≥ 90 mmHg, or four or more medications regardless of BP (n = 1,705). A normotensive control group was defined as individuals with BP < 140/90 mmHg (n = 22,079), not on AHT medication. A second control group comprised individuals who were treatment responsive on one AHT medication with BP < 140/ 90 mmHg (n = 5,424). Logistic regression with kinship adjustment using the Scalable and Accurate Implementation of Generalized mixed models (SAIGE) was performed, adjusting for age, sex, and genetic ancestry. We assessed variants using SKAT-O in rare-variant analyses. Single-variant and gene-based tests were conducted in a pooled multi-ethnicity stratum, as well as self-reported ethnic/racial strata (European and African American). Results: One variant in the known HTN locus, KCNK3, was a top finding in the multi-ethnic analysis (p = 8.23E-07) for the normotensive control group [rs12476527, odds ratio (95% confidence interval) = 0.80 (0.74-0.88)]. This variant was replicated in the Vanderbilt University Medical Center's DNA repository data. Aggregate gene-based signals included the genes AGTPBP, MYL4, PDCD4, BBS9, ERG, and IER3. Discussion: Additional work validating these loci in larger, more diverse populations, is warranted to determine whether these regions influence the pathobiology of aTRH.

Improving the computation efficiency of polygenic risk score modeling: faster in Julia.

To enable large-scale application of polygenic risk scores (PRSs) in a computationally efficient manner, we translate a widely used PRS construction method, PRS-continuous shrinkage, to the Julia programming language, PRS.jl. On nine different traits with varying genetic architectures, we demonstrate that PRS.jl maintains accuracy of prediction while decreasing the average runtime by 5.5×. Additional programmatic modifications improve usability and robustness. This freely available software substantially improves work flow and democratizes usage of PRSs by lowering the computational burden of the PRS-continuous shrinkage method.

Using Mendelian randomisation to identify opportunities for type 2 diabetes prevention by repurposing medications used for lipid management.

BACKGROUND: Maintaining a healthy lifestyle to reduce type 2 diabetes (T2D) risk is challenging and additional strategies for T2D prevention are needed. We evaluated several lipid control medications as potential therapeutic options for T2D prevention using tissue-specific predicted gene expression summary statistics in a two-sample Mendelian randomisation (MR) design. METHODS: Large-scale European genome-wide summary statistics for lipids and T2D were leveraged in our multi-stage analysis to estimate changes in either lipid levels or T2D risk driven by tissue-specific predicted gene expression. We incorporated tissue-specific predicted gene expression summary statistics to proxy therapeutic effects of three lipid control medications [i.e., statins, icosapent ethyl (IPE), and proprotein convertase subtilisin/kexin type-9 inhibitors (PCSK-9i)] on T2D susceptibility using two-sample Mendelian randomisation (MR). FINDINGS: IPE, as proxied via increased FADS1 expression, was predicted to lower triglycerides and was associated with a 53% reduced risk of T2D. Statins and PCSK-9i, as proxied by reduced HMGCR and PCSK9 expression, respectively, were predicted to lower LDL-C levels but were not associated with T2D susceptibility. INTERPRETATION: Triglyceride lowering via IPE may reduce the risk of developing T2D in populations of European ancestry. However, experimental validation using animal models is needed to substantiate our results and to motivate randomized control trials (RCTs) for IPE as putative treatment for T2D prevention. FUNDING: Only summary statistics were used in this analysis. Funding information is detailed under Acknowledgments.

Pharmacogenomics of cisplatin-induced neurotoxicities: Hearing loss, tinnitus, and peripheral sensory neuropathy.

PURPOSE: Cisplatin is a critical component of first-line chemotherapy for several cancers, but causes peripheral sensory neuropathy, hearing loss, and tinnitus. We aimed to identify comorbidities for cisplatin-induced neurotoxicities among large numbers of similarly treated patients without the confounding effect of cranial radiotherapy. METHODS: Utilizing linear and logistic regression analyses on 1680 well-characterized cisplatin-treated testicular cancer survivors, we analyzed associations of hearing loss, tinnitus, and peripheral neuropathy with nongenetic comorbidities. Genome-wide association studies and gene-based analyses were performed on each phenotype. RESULTS: Hearing loss, tinnitus, and peripheral neuropathy, accounting for age and cisplatin dose, were interdependent. Survivors with these neurotoxicities experienced more hypertension and poorer self-reported health. In addition, hearing loss was positively associated with BMIs at clinical evaluation and nonwork-related noise exposure (>5 h/week). Tinnitus was positively associated with tobacco use, hypercholesterolemia, and noise exposure. We observed positive associations between peripheral neuropathy and persistent vertigo, tobacco use, and excess alcohol consumption. Hearing loss and TXNRD1, which plays a key role in redox regulation, showed borderline significance (p = 4.2 × 10-6 ) in gene-based analysis. rs62283056 in WFS1 previously found to be significantly associated with hearing loss (n = 511), was marginally significant in an independent replication cohort (p = 0.06; n = 606). Gene-based analyses identified significant associations between tinnitus and WNT8A (p = 2.5 × 10-6 ), encoding a signaling protein important in germ cell tumors. CONCLUSIONS: Genetics variants in TXNRD1 and WNT8A are notable risk factors for hearing loss and tinnitus, respectively. Future studies should investigate these genes and if replicated, identify their potential impact on preventive strategies.

Genetic Examination of Hematological Parameters in SARS-CoV-2 Infection and COVID-19.

Background: People hospitalized with COVID-19 often exhibit hematological alterations, such as lower lymphocyte and platelet counts, which have been reported to associate with disease prognosis. It is unclear whether inter-individual variability in baseline hematological parameters prior to acute infection influences risk of SARS-CoV-2 infection and progression to severe COVID-19. Methods: We assessed the association of blood cell counts and indices with incident SARS-CoV-2 infection and severe COVID-19 in UK Biobank and the Vanderbilt University Medical Center Synthetic Derivative (VUMC SD). Since genetically determined blood cell measures better represent cell abundance across the lifecourse, we used summary statistics from genome-wide association studies to assess the shared genetic architecture of baseline blood cell counts and indices on COVID-19 outcomes. Results: We observed inconsistent associations between measured blood cell indices and both SARS-CoV-2 infection and COVID-19 hospitalization in UK Biobank and VUMC SD. In Mendelian randomization analyses using genetic summary statistics, no putative causal relationships were identified between COVID-19 related outcomes and hematological indices after adjusting for multiple testing. We observed overlapping genetic association signals between hematological parameters and COVID-19 traits. For example, we observed overlap between infection susceptibility-associated variants at PPP1R15A and red blood cell parameters, and between disease severity-associated variants at TYK2 and lymphocyte and platelet phenotypes. Conclusions: We did not find convincing evidence of a relationship between baseline hematological parameters and susceptibility to SARS-CoV-2 infection or COVID-19 severity, though this relationship should be re-examined as larger and better-powered genetic analyses of SARS-CoV-2 infection and severe COVID-19 become available.

Exploration of an alternative to body mass index to characterize the relationship between height and weight for prediction of metabolic phenotypes and cardiovascular outcomes.

OBJECTIVE: Body mass index (BMI) is the most commonly used predictor of weight-related comorbidities and outcomes. However, the presumed relationship between height and weight intrinsic to BMI may introduce bias with respect to prediction of clinical outcomes. A series of analyses comparing the performance of models representing weight and height as separate interacting variables to models using BMI were performed using Vanderbilt University Medical Center's deidentified electronic health records and landmark methodology. METHODS: Use of BMI or height-weight interaction in prediction models for established weight-related cardiometabolic traits and metabolic syndrome was evaluated. Specifically, prediction models for hypertension, diabetes mellitus, low high-density lipoprotein, and elevated triglycerides, atrial fibrillation, coronary artery disease, heart failure, and peripheral artery disease were developed. Model performance was evaluated using likelihood ratio, R 2, and Somers' Dxy rank correlation. Differences in model predictions were visualized using heat maps. RESULTS: Compared to BMI, the maximally flexible height-weight interaction model demonstrated improved prediction, higher likelihood ratio, R 2, and Somers' Dxy rank correlation, for event-free probability for all outcomes. The degree of improvement to the prediction model differed based on the outcome and across the height and weight range. CONCLUSIONS: Because alternative measures of body composition such as waist-to-hip ratio are not routinely collected in the clinic clinical risk models quantifying risk based on height and weight measurements alone are essential to improve practice. Compared to BMI, modeling height and weight as independent, interacting variables results in less bias and improved predictive accuracy for all tested traits. Considering an individual's height and weight opposed to BMI is a better method for quantifying individual disease risk.

Systems Approach to Integrating Preclinical Apolipoprotein E-Knockout Investigations Reveals Novel Etiologic Pathways and Master Atherosclerosis Network in Humans.

OBJECTIVE: Animal models of atherosclerosis are used extensively to interrogate molecular mechanisms in serial fashion. We tested whether a novel systems biology approach to integration of preclinical data identifies novel pathways and regulators in human disease. Approach and Results: Of 716 articles published in ATVB from 1995 to 2019 using the apolipoprotein E knockout mouse to study atherosclerosis, data were extracted from 360 unique studies in which a gene was experimentally perturbed to impact plaque size or composition and analyzed using Ingenuity Pathway Analysis software. TREM1 (triggering receptor expressed on myeloid cells) signaling and LXR/RXR (liver X receptor/retinoid X receptor) activation were identified as the top atherosclerosis-associated pathways in mice (both P<1.93×10-4, TREM1 implicated early and LXR/RXR in late atherogenesis). The top upstream regulatory network in mice (sc-58125, a COX2 inhibitor) linked 64.0% of the genes into a single network. The pathways and networks identified in mice were interrogated by testing for associations between the genetically predicted gene expression of each mouse pathway-identified human homolog with clinical atherosclerosis in a cohort of 88 660 human subjects. Homologous human pathways and networks were significantly enriched for gene-atherosclerosis associations (empirical P<0.01 for TREM1 and LXR/RXR pathways and COX2 network). This included 12(60.0%) TREM1 pathway genes, 15(53.6%) LXR/RXR pathway genes, and 67(49.3%) COX2 network genes. Mouse analyses predicted, and human study validated, the strong association of COX2 expression (PTGS2) with increased likelihood of atherosclerosis (odds ratio, 1.68 per SD of genetically predicted gene expression; P=1.07×10-6). CONCLUSIONS: PRESCIANT (Preclinical Science Integration and Translation) leverages published preclinical investigations to identify high-confidence pathways, networks, and regulators of human disease.

Association of Apparent Treatment-Resistant Hypertension With Differential Risk of End-Stage Kidney Disease Across Racial Groups in the Million Veteran Program.

[Figure: see text].

Relationship Between Blood Pressure and Incident Cardiovascular Disease: Linear and Nonlinear Mendelian Randomization Analyses.

[Figure: see text].

Association of a glucagon-like peptide-1 receptor gene variant with glucose response to a mixed meal.

Dipeptidyl peptidase-4 (DPP-4) inhibitors increase endogenous glucagon-like peptide-1 (GLP-1). We hypothesized that genetic variation in the gene encoding the GLP-1 receptor (GLP1R) could affect the metabolic response to DPP-4 inhibition. To evaluate the relationship between the GLP1R rs6923761 variant (G-to-A nucleic acid substitution) and metabolic responses, we performed mixed meal studies in individuals with type 2 diabetes mellitus and hypertension after 7-day treatment with placebo and the DPP-4 inhibitor sitagliptin. This analysis is a substudy of NCT02130687. The genotype frequency was 13:12:7 GG:GA:AA among individuals of European ancestry. Postprandial glucose excursion was significantly decreased in individuals carrying the rs6923761 variant (GA or AA) as compared with GG individuals during both placebo (P = 0.001) and sitagliptin treatment (P = 0.045), while intact GLP-1 levels were similar among the genotype groups. In contrast, sitagliptin lowered postprandial glucose to a greater degree in GG as compared with GA/AA individuals (P = 0.035). The relationship between GLP1R rs6923761 genotype and therapies that modulate GLP-1 signalling merits study in large populations.