The Implementation Science For Genomic Health Translation (insight) Study in Epilepsy: Protocol For a Learning Health Care System

BACKGROUND: Genomic medicine is poised to improve care for common complex diseases such as epilepsy, but additional clinical informatics and implementation science research is needed for it to become a part of the standard of care. Epilepsy is an exemplary complex neurological disorder for which DNA diagnostics have shown to be advantageous for patient care. OBJECTIVE: We designed the Implementation Science for Genomic Health Translation (INSIGHT) study to leverage the fact that both the clinic and testing laboratory control the development and customization of their respective electronic health records and clinical reporting platforms. Through INSIGHT, we can rapidly prototype and benchmark novel approaches to incorporating clinical genomics into patient care. Of particular interest are clinical decision support tools that take advantage of domain knowledge from clinical genomics and can be rapidly adjusted based on feedback from clinicians. METHODS: Building on previously developed evidence and infrastructure components, our model includes the following: establishment of an intervention-ready genomic knowledge base for patient care, creation of a health informatics platform and linking it to a clinical genomics reporting system, and scaling and evaluation of INSIGHT following established implementation science principles. RESULTS: INSIGHT was approved by the Institutional Review Board at the University of Texas Health Science Center at Houston on May 15, 2020, and is designed as a 2-year proof-of-concept study beginning in December 2021. By design, 120 patients from the Texas Comprehensive Epilepsy Program are to be enrolled to test the INSIGHT workflow. Initial results are expected in the first half of 2023. CONCLUSIONS: INSIGHT\u27s domain-specific, practical but generalizable approach may help catalyze a pathway to accelerate translation of genomic knowledge into impactful interventions in patient care. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/25576

A large-scale candidate gene association study of blood pressure change in children and adolescents

Hypertension, which affects about one-third of adults worldwide, is a major risk factor for the morbidity and mortality associated with cardiovascular diseases (CVDs) and end-stage renal damage. Some of the genetic factors that contribute to high blood pressure (BP) have been identified. However, all genetic variants found through linkage and genome-wide association studies (GWAS) to date can explain only 1% to 2% of the population variation in blood pressure and hypertension in adults. To our knowledge no large-scale candidate gene studies have been carried out in children or adolescents. To increase our understanding of the etiology of the disease, the study aimed to identify SNPs associated with the change in blood pressure in children and adolescents. Data on systolic and diastolic blood pressure from 2 longitudinal cohort studies [the Bogalusa Heart Study (BHS) and Project HeartBeat! (PHB)] was used. Genopyping was performed with the MetaboChip (Illumina iSelect array) and the HumanCVD BeadChip (Illumina’s Infinium II Assay). Multilevel models of change were built, which included dependent variables (SBP, DBP), and explanatory variables (sex, centered age, centered age 2, centered age3, race, SNP, centered BMI). Three ways of combining data in analyses were used and compared to one another. First, analyses were run separately for BHS Non-Hispanic Whites, BHS African-Americans, PHB Non-Hispanic Whites and PHB African-Americans, and then united by meta-analysis. Second, separate models were created for the two ethnicities (BHS/PHB African-Americans and BHS/PHB Non-Hispanic Whites), and combined by meta-analysis. Finally, models containing all 4 cohorts, including terms for race and study, were analyzed without meta-analysis. The first analytic method (meta-analysis of 4 cohorts) was considered to be the most reliable. It identified several SNPs with statistically significant effects on SBP and DBP – rs2378597 (in the AIDA gene), rs61824282 (in MIA3), rs6130608 (in HNF4A), rs7572476 (near the BOK gene), and rs7831963 (in VPS13B), all of which are biologically plausible candidates that could affect blood pressure through various mechanisms, including apoptosis of smooth muscle cells, arterial remodeling, Ca2+ signaling, oxidative stress, and secondary involvement in the renin-angiotensin system. The remaining 2 analytic methods (meta-analysis of 2 groups and combined analysis) also identified several other biologically plausible candidate genes (CPPED1, NPR3, XKR6, STX1A, CDK8 and SLC18A1)