Clinical associations with a polygenic predisposition to benign lower white blood cell counts.

Polygenic variation unrelated to disease contributes to interindividual variation in baseline white blood cell (WBC) counts, but its clinical significance is uncharacterized. We investigated the clinical consequences of a genetic predisposition toward lower WBC counts among 89,559 biobank participants from tertiary care centers using a polygenic score for WBC count (PGSWBC) comprising single nucleotide polymorphisms not associated with disease. A predisposition to lower WBC counts was associated with a decreased risk of identifying pathology on a bone marrow biopsy performed for a low WBC count (odds-ratio = 0.55 per standard deviation increase in PGSWBC [95%CI, 0.30-0.94], p = 0.04), an increased risk of leukopenia (a low WBC count) when treated with a chemotherapeutic (n = 1724, hazard ratio [HR] = 0.78 [0.69-0.88], p = 4.0 × 10-5) or immunosuppressant (n = 354, HR = 0.61 [0.38-0.99], p = 0.04). A predisposition to benign lower WBC counts was associated with an increased risk of discontinuing azathioprine treatment (n = 1,466, HR = 0.62 [0.44-0.87], p = 0.006). Collectively, these findings suggest that there are genetically predisposed individuals who are susceptible to escalations or alterations in clinical care that may be harmful or of little benefit.

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.

Large language models facilitate the generation of electronic health record phenotyping algorithms.

OBJECTIVES: Phenotyping is a core task in observational health research utilizing electronic health records (EHRs). Developing an accurate algorithm demands substantial input from domain experts, involving extensive literature review and evidence synthesis. This burdensome process limits scalability and delays knowledge discovery. We investigate the potential for leveraging large language models (LLMs) to enhance the efficiency of EHR phenotyping by generating high-quality algorithm drafts. MATERIALS AND METHODS: We prompted four LLMs-GPT-4 and GPT-3.5 of ChatGPT, Claude 2, and Bard-in October 2023, asking them to generate executable phenotyping algorithms in the form of SQL queries adhering to a common data model (CDM) for three phenotypes (ie, type 2 diabetes mellitus, dementia, and hypothyroidism). Three phenotyping experts evaluated the returned algorithms across several critical metrics. We further implemented the top-rated algorithms and compared them against clinician-validated phenotyping algorithms from the Electronic Medical Records and Genomics (eMERGE) network. RESULTS: GPT-4 and GPT-3.5 exhibited significantly higher overall expert evaluation scores in instruction following, algorithmic logic, and SQL executability, when compared to Claude 2 and Bard. Although GPT-4 and GPT-3.5 effectively identified relevant clinical concepts, they exhibited immature capability in organizing phenotyping criteria with the proper logic, leading to phenotyping algorithms that were either excessively restrictive (with low recall) or overly broad (with low positive predictive values). CONCLUSION: GPT versions 3.5 and 4 are capable of drafting phenotyping algorithms by identifying relevant clinical criteria aligned with a CDM. However, expertise in informatics and clinical experience is still required to assess and further refine generated algorithms.

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 administration 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 combing 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 a similar structure 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 complex disease interactions. The alignment of multimorbidity patterns with genetic data suggests the potential utility for uncovering shared etiology of diseases. The consistent core-periphery network structure offers a strategic approach to analyze disease clusters. This work also sets the stage for advanced disease modeling, with implications for precision medicine. Funding: VUMC Biostatistics Development Award, UL1 TR002243, R21DK127075, R01HL140074, P50GM115305, R01CA227481.

Machine Learning Prediction of Treatment Response to Inhaled Corticosteroids in Asthma.

BACKGROUND: Although inhaled corticosteroids (ICS) are the first-line therapy for patients with persistent asthma, many patients continue to have exacerbations. We developed machine learning models to predict the ICS response in patients with asthma. METHODS: The subjects included asthma patients of European ancestry (n = 1371; 448 children; 916 adults). A genome-wide association study was performed to identify the SNPs associated with ICS response. Using the SNPs identified, two machine learning models were developed to predict ICS response: (1) least absolute shrinkage and selection operator (LASSO) regression and (2) random forest. RESULTS: The LASSO regression model achieved an AUC of 0.71 (95% CI 0.67-0.76; sensitivity: 0.57; specificity: 0.75) in an independent test cohort, and the random forest model achieved an AUC of 0.74 (95% CI 0.70-0.78; sensitivity: 0.70; specificity: 0.68). The genes contributing to the prediction of ICS response included those associated with ICS responses in asthma (TPSAB1, FBXL16), asthma symptoms and severity (ABCA7, CNN2, PTRN3, and BSG/CD147), airway remodeling (ELANE, FSTL3), mucin production (GAL3ST), leukotriene synthesis (GPX4), allergic asthma (ZFPM1, SBNO2), and others. CONCLUSIONS: An accurate risk prediction of ICS response can be obtained using machine learning methods, with the potential to inform personalized treatment decisions. Further studies are needed to examine if the integration of richer phenotype data could improve risk prediction.

Physical Activity and Incident Obesity Across the Spectrum of Genetic Risk for Obesity.

Importance: Despite consistent public health recommendations, obesity rates in the US continue to increase. Physical activity recommendations do not account for individual genetic variability, increasing risk of obesity. Objective: To use activity, clinical, and genetic data from the All of Us Research Program (AoURP) to explore the association of genetic risk of higher body mass index (BMI) with the level of physical activity needed to reduce incident obesity. Design, Setting, and Participants: In this US population-based retrospective cohort study, participants were enrolled in the AoURP between May 1, 2018, and July 1, 2022. Enrollees in the AoURP who were of European ancestry, owned a personal activity tracking device, and did not have obesity up to 6 months into activity tracking were included in the analysis. Exposure: Physical activity expressed as daily step counts and a polygenic risk score (PRS) for BMI, calculated as weight in kilograms divided by height in meters squared. Main Outcome and Measures: Incident obesity (BMI ≥30). Results: A total of 3124 participants met inclusion criteria. Among 3051 participants with available data, 2216 (73%) were women, and the median age was 52.7 (IQR, 36.4-62.8) years. The total cohort of 3124 participants walked a median of 8326 (IQR, 6499-10 389) steps/d over a median of 5.4 (IQR, 3.4-7.0) years of personal activity tracking. The incidence of obesity over the study period increased from 13% (101 of 781) to 43% (335 of 781) in the lowest and highest PRS quartiles, respectively (P = 1.0 × 10-20). The BMI PRS demonstrated an 81% increase in obesity risk (P = 3.57 × 10-20) while mean step count demonstrated a 43% reduction (P = 5.30 × 10-12) when comparing the 75th and 25th percentiles, respectively. Individuals with a PRS in the 75th percentile would need to walk a mean of 2280 (95% CI, 1680-3310) more steps per day (11 020 total) than those at the 50th percentile to have a comparable risk of obesity. To have a comparable risk of obesity to individuals at the 25th percentile of PRS, those at the 75th percentile with a baseline BMI of 22 would need to walk an additional 3460 steps/d; with a baseline BMI of 24, an additional 4430 steps/d; with a baseline BMI of 26, an additional 5380 steps/d; and with a baseline BMI of 28, an additional 6350 steps/d. Conclusions and Relevance: In this cohort study, the association between daily step count and obesity risk across genetic background and baseline BMI were quantified. Population-based recommendations may underestimate physical activity needed to prevent obesity among those at high genetic risk.

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.

The electrophysiologic effects of KCNQ1 extend beyond expression of IKs: evidence from genetic and pharmacologic block.

AIMS: While variants in KCNQ1 are the commonest cause of the congenital long QT syndrome, we and others find only a small IKs in cardiomyocytes from human induced pluripotent stem cells (iPSC-CMs) or human ventricular myocytes. METHODS AND RESULTS: We studied population control iPSC-CMs and iPSC-CMs from a patient with Jervell and Lange-Nielsen (JLN) syndrome due to compound heterozygous loss of function KCNQ1 variants. We compared the effects of pharmacologic IKs block to those of genetic KCNQ1 ablation, using JLN cells, cells homozygous for the KCNQ1 loss of function allele G643S, or siRNAs reducing KCNQ1 expression. We also studied the effects of two blockers of IKr, the other major cardiac repolarizing current, in the setting of pharmacologic or genetic ablation of KCNQ1: moxifloxacin, associated with a very low risk of drug-induced long QT, and dofetilide, a high-risk drug.In control cells, a small IKs was readily recorded but pharmacologic IKs block produced no change in action potential duration at 90% repolarization (APD90). By contrast, in cells with genetic ablation of KCNQ1 (JLN), baseline APD90 was markedly prolonged compared with control cells (469 ± 20 vs. 310 ± 16 ms). JLN cells displayed increased sensitivity to acute IKr block: the concentration (µM) of moxifloxacin required to prolong APD90 100 msec was 237.4 (median, IQR 100.6-391.6, n = 7) in population cells versus 23.7 (17.3-28.7, n = 11) in JLN cells. In control cells, chronic moxifloxacin exposure (300µM) mildly prolonged APD90 (10%) and increased IKs, while chronic exposure to dofetilide (5 nM) produced greater prolongation (67%) and no increase in IKs. However, in the siRNA-treated cells, moxifloxacin did not increase IKs, and markedly prolonged APD90. CONCLUSION: Our data strongly suggest that KCNQ1 expression modulates baseline cardiac repolarization, and the response to IKr block, through mechanisms beyond simply generating IKs. TRANSLATIONAL PERSPECTIVE: Mutations in KCNQ1 - whose expression generates IKs - are the major cause of long QT syndrome. We report here that while pharmacologic IKs block in human cardiomyocytes generates minimal change in repolarization, suppressing KCNQ1 expression markedly increases both baseline repolarization duration and sensitivity to some (but not all) specific IKr blockers. Thus, beyond simply generating IKs, KCNQ1 subserves critical additional role(s) in repolarization control at baseline and in response to IKr block. Our findings imply that assessment of arrhythmic risk in individual patients and by drugs requires a framework that extends beyond a simple one gene-one ion current paradigm.

Leveraging generative AI to prioritize drug repurposing candidates for Alzheimer's disease with real-world clinical validation.

Drug repurposing represents an attractive alternative to the costly and time-consuming process of new drug development, particularly for serious, widespread conditions with limited effective treatments, such as Alzheimer's disease (AD). Emerging generative artificial intelligence (GAI) technologies like ChatGPT offer the promise of expediting the review and summary of scientific knowledge. To examine the feasibility of using GAI for identifying drug repurposing candidates, we iteratively tasked ChatGPT with proposing the twenty most promising drugs for repurposing in AD, and tested the top ten for risk of incident AD in exposed and unexposed individuals over age 65 in two large clinical datasets: (1) Vanderbilt University Medical Center and (2) the All of Us Research Program. Among the candidates suggested by ChatGPT, metformin, simvastatin, and losartan were associated with lower AD risk in meta-analysis. These findings suggest GAI technologies can assimilate scientific insights from an extensive Internet-based search space, helping to prioritize drug repurposing candidates and facilitate the treatment of diseases.

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.

Multifocal Ectopic Purkinje Premature Contractions due to neutralization of an SCN5A negative charge: structural insights into the gating pore hypothesis.

Background: We identified a novel SCN5A variant, E171Q, in a neonate with very frequent ectopy and reduced ejection fraction which normalized after arrhythmia suppression by flecainide. This clinical picture is consistent with multifocal ectopic Purkinje-related premature contractions (MEPPC). Most previous reports of MEPPC have implicated SCN5A variants such as R222Q that neutralize positive charges in the S4 voltage sensor helix of the channel protein NaV1.5 and generate a gating pore current. Methods and Results: E171 is a highly conserved negatively-charged residue located in the S2 transmembrane helix of NaV1.5 domain I. E171 is a key component of the Gating Charge Transfer Center, a region thought to be critical for normal movement of the S4 voltage sensor helix. We used heterologous expression, CRISPR-edited induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs), and molecular dynamics simulations to demonstrate that E171Q generates a gating pore current, which was suppressed by a low concentration of flecainide (IC50 = 0.71±0.07 µM). R222Q shifts voltage dependence of activation and inactivation in a negative direction but we observed positive shifts with E171Q. E171Q iPSC-CMs demonstrated abnormal spontaneous activity and prolonged action potentials. Molecular dynamics simulations revealed that both R222Q and E171Q proteins generate a water-filled permeation pathway that underlies generation of the gating pore current. Conclusion: Previously identified MEPPC-associated variants that create gating pore currents are located in positively-charged residues in the S4 voltage sensor and generate negative shifts in the voltage dependence of activation and inactivation. We demonstrate that neutralizing a negatively charged S2 helix residue in the Gating Charge Transfer Center generates positive shifts but also create a gating pore pathway. These findings implicate the gating pore pathway as the primary functional and structural determinant of MEPPC and widen the spectrum of variants that are associated with gating pore-related disease in voltage-gated ion channels.

Prognostic Value of Multiplexed Assays of Variant Effect and Automated Patch-clamping for KCNH2-LQTS Risk Stratification.

Background: Long QT syndrome (LQTS) is a lethal arrhythmia condition, frequently caused by rare loss-of-function variants in the cardiac potassium channel encoded by KCNH2. Variant-based risk stratification is complicated by heterogenous clinical data, incomplete penetrance, and low-throughput functional data. Objective: To test the utility of variant-specific features, including high-throughput functional data, to predict cardiac events among KCNH2 variant heterozygotes. Methods: We quantified cell-surface trafficking of 18,323 variants in KCNH2 and recorded potassium current densities for 506 KCNH2 variants. Next, we deeply phenotyped 1150 KCNH2 missense variant patients, including ECG features, cardiac event history (528 total cardiac events), and mortality. We then assessed variant functional, in silico, structural, and LQTS penetrance data to stratify event-free survival for cardiac events in the study cohort. Results: Variant-specific current density (HR 0.28 [0.13-0.60]) and estimates of LQTS penetrance incorporating MAVE data (HR 3.16 [1.59-6.27]) were independently predictive of severe cardiac events when controlling for patient-specific features. Risk prediction models incorporating these data significantly improved prediction of 20 year cardiac events (AUC 0.79 [0.75-0.82]) over patient-only covariates (QTc and sex) (AUC 0.73 [0.70-0.77]). Conclusion: We show that high-throughput functional data, and other variant-specific features, meaningfully contribute to both diagnosis and prognosis of a clinically actionable monogenic disease.

Antipsychotics and the QTc Interval During Delirium in the Intensive Care Unit: A Secondary Analysis of a Randomized Clinical Trial.

Importance: Antipsychotic medications, often prescribed for delirium in intensive care units (ICUs), may contribute to QTc interval prolongation. Objective: To determine whether antipsychotics increase the QTc interval in patients with delirium in the ICU. Design, Setting, and Participants: An a priori analysis of a randomized clinical trial in medical/surgical ICUs within 16 centers across the US was conducted. Participants included adults with delirium in the ICU with baseline QTc interval less than 550 ms. The study was conducted from December 2011 to August 2017. Data analysis was performed from April 25 to August 18, 2021. Interventions: Patients were randomized 1:1:1 to intravenous haloperidol, ziprasidone, or saline placebo administered twice daily until resolution of delirium, ICU discharge, or 14 days. Main Outcomes and Measures: Twelve-lead electrocardiograms were used to measure baseline QTc before study drug initiation and telemetry was used to measure QTc before each subsequent dose of study drug. Unadjusted day-to-day changes in QTc were calculated and multivariable proportional odds regression was used to estimate the effects of antipsychotics vs placebo on next-day maximum QTc interval, adjusting for prespecified baseline covariates and potential interactions with sex. Safety end points, including the occurrence of torsade de pointes, were evaluated. All analyses were conducted based on the intention to treat principle. Results: A total of 566 patients were randomized to haloperidol (n = 192), ziprasidone (n = 190), or placebo (n = 184). Median age was 60.1 (IQR, 51.4-68.7) years; 323 were men (57%). Baseline median QTc intervals across the groups were similar: haloperidol, 458.0 (IQR, 432.0-479.0) ms; ziprasidone, 451.0 (IQR, 424.0-472.0) ms; and placebo, 452.0 (IQR, 432.0-472.0) ms. From day 1 to day 2, median QTc changed minimally: haloperidol, -1.0 (IQR, -28.0 to 15.0) ms; ziprasidone, 0 (IQR, -23.0 to 20.0) ms; and placebo, -3.5 (IQR, -24.8 to 17.0) ms. Compared with placebo, neither haloperidol (odds ratio [OR], 0.95; 95% CI, 0.66-1.37; P = .78) nor ziprasidone (OR, 1.09; 95% CI, 0.75-1.57; P = .78) was associated with next-day QTc intervals. Effects were not significantly modified by sex (P = .41 for interaction). There were 2 occurrences of nonfatal torsade de pointes, both in the haloperidol group. Neither was associated with study drug administration. Conclusions and Relevance: The findings of this trial suggest that daily QTc interval monitoring during antipsychotic use may have limited value in patients in the ICU with normal baseline QTc and few risk factors for QTc prolongation. Trial Registration: ClinicalTrials.gov Identifier: NCT01211522.

The impact of damaging epilepsy and cardiac genetic variant burden in sudden death in the young.

BACKGROUND: Sudden unexpected death in children is a tragic event. Understanding the genetics of sudden death in the young (SDY) enables family counseling and cascade screening. The objective of this study was to characterize genetic variation in an SDY cohort using whole genome sequencing. METHODS: The SDY Case Registry is a National Institutes of Health/Centers for Disease Control and Prevention surveillance effort to discern the prevalence, causes, and risk factors for SDY. The SDY Case Registry prospectively collected clinical data and DNA biospecimens from SDY cases < 20 years of age. SDY cases were collected from medical examiner and coroner offices spanning 13 US jurisdictions from 2015 to 2019. The cohort included 211 children (median age 0.33 year; range 0-20 years), determined to have died suddenly and unexpectedly and from whom DNA biospecimens for DNA extractions and next-of-kin consent were ascertained. A control cohort consisted of 211 randomly sampled, sex- and ancestry-matched individuals from the 1000 Genomes Project. Genetic variation was evaluated in epilepsy, cardiomyopathy, and arrhythmia genes in the SDY and control cohorts. American College of Medical Genetics/Genomics guidelines were used to classify variants as pathogenic or likely pathogenic. Additionally, pathogenic and likely pathogenic genetic variation was identified using a Bayesian-based artificial intelligence (AI) tool. RESULTS: The SDY cohort was 43% European, 29% African, 3% Asian, 16% Hispanic, and 9% with mixed ancestries and 39% female. Six percent of the cohort was found to harbor a pathogenic or likely pathogenic genetic variant in an epilepsy, cardiomyopathy, or arrhythmia gene. The genomes of SDY cases, but not controls, were enriched for rare, potentially damaging variants in epilepsy, cardiomyopathy, and arrhythmia-related genes. A greater number of rare epilepsy genetic variants correlated with younger age at death. CONCLUSIONS: While damaging cardiomyopathy and arrhythmia genes are recognized contributors to SDY, we also observed an enrichment in epilepsy-related genes in the SDY cohort and a correlation between rare epilepsy variation and younger age at death. These findings emphasize the importance of considering epilepsy genes when evaluating SDY.

Large Language Models Facilitate the Generation of Electronic Health Record Phenotyping Algorithms.

Objectives: Phenotyping is a core task in observational health research utilizing electronic health records (EHRs). Developing an accurate algorithm demands substantial input from domain experts, involving extensive literature review and evidence synthesis. This burdensome process limits scalability and delays knowledge discovery. We investigate the potential for leveraging large language models (LLMs) to enhance the efficiency of EHR phenotyping by generating high-quality algorithm drafts. Materials and Methods: We prompted four LLMs-GPT-4 and GPT-3.5 of ChatGPT, Claude 2, and Bard-in October 2023, asking them to generate executable phenotyping algorithms in the form of SQL queries adhering to a common data model (CDM) for three phenotypes (i.e., type 2 diabetes mellitus, dementia, and hypothyroidism). Three phenotyping experts evaluated the returned algorithms across several critical metrics. We further implemented the top-rated algorithms and compared them against clinician-validated phenotyping algorithms from the Electronic Medical Records and Genomics (eMERGE) network. Results: GPT-4 and GPT-3.5 exhibited significantly higher overall expert evaluation scores in instruction following, algorithmic logic, and SQL executability, when compared to Claude 2 and Bard. Although GPT-4 and GPT-3.5 effectively identified relevant clinical concepts, they exhibited immature capability in organizing phenotyping criteria with the proper logic, leading to phenotyping algorithms that were either excessively restrictive (with low recall) or overly broad (with low positive predictive values). Conclusion: GPT versions 3.5 and 4 are capable of drafting phenotyping algorithms by identifying relevant clinical criteria aligned with a CDM. However, expertise in informatics and clinical experience is still required to assess and further refine generated algorithms.

Multi-site validation of a functional assay to adjudicate SCN5A Brugada Syndrome-associated variants.

Brugada Syndrome (BrS) is an inheritable arrhythmia condition that is associated with rare, loss-of-function variants in the cardiac sodium channel gene, SCN5A. Interpreting the pathogenicity of SCN5A missense variants is challenging and ~79% of SCN5A missense variants in ClinVar are currently classified as Variants of Uncertain Significance (VUS). An in vitro SCN5A-BrS automated patch clamp assay was generated for high-throughput functional studies of NaV1.5. The assay was independently studied at two separate research sites - Vanderbilt University Medical Center and Victor Chang Cardiac Research Institute - revealing strong correlations, including peak INa density (R2=0.86). The assay was calibrated according to ClinGen Sequence Variant Interpretation recommendations using high-confidence variant controls (n=49). Normal and abnormal ranges of function were established based on the distribution of benign variant assay results. The assay accurately distinguished benign controls (24/25) from pathogenic controls (23/24). Odds of Pathogenicity values derived from the experimental results yielded 0.042 for normal function (BS3 criterion) and 24.0 for abnormal function (PS3 criterion), resulting in up to strong evidence for both ACMG criteria. The calibrated assay was then used to study SCN5A VUS observed in four families with BrS and other arrhythmia phenotypes associated with SCN5A loss-of-function. The assay revealed loss-of-function for three of four variants, enabling reclassification to likely pathogenic. This validated APC assay provides clinical-grade functional evidence for the reclassification of current VUS and will aid future SCN5A-BrS variant classification.

Evidence of recent and ongoing admixture in the U.S. and influences on health and disparities.

Many researchers in genetics and social science incorporate information about race in their work. However, migrations (historical and forced) and social mobility have brought formerly separated populations of humans together, creating younger generations of individuals who have more complex and diverse ancestry and race profiles than older age groups. Here, we sought to better understand how temporal changes in genetic admixture influence levels of heterozygosity and impact health outcomes. We evaluated variation in genetic ancestry over 100 birth years in a cohort of 35,842 individuals with electronic health record (EHR) information in the Southeastern United States. Using the software STRUCTURE, we analyzed 2,678 ancestrally informative markers relative to three ancestral clusters (African, East Asian, and European) and observed rising levels of admixture for all clinically-defined race groups since 1990. Most race groups also exhibited increases in heterozygosity and long-range linkage disequilibrium over time, further supporting the finding of increasing admixture in young individuals in our cohort. These data are consistent with United States Census information from broader geographic areas and highlight the changing demography of the population. This increased diversity challenges classic approaches to studies of genotype-phenotype relationships which motivated us to explore the relationship between heterozygosity and disease diagnosis. Using a phenome-wide association study approach, we explored the relationship between admixture and disease risk and found that increased admixture resulted in protective associations with female reproductive disorders and increased risk for diseases with links to autoimmune dysfunction. These data suggest that tendencies in the United States population are increasing ancestral complexity over time. Further, these observations imply that, because both prevalence and severity of many diseases vary by race groups, complexity of ancestral origins influences health and disparities.

Clinical Management of Brugada Syndrome: Commentary From the Experts.

Although there is consensus on the management of patients with Brugada Syndrome with high risk for sudden cardiac arrest, asymptomatic or intermediate-risk patients present clinical management challenges. This document explores the management opinions of experts throughout the world for patients with Brugada Syndrome who do not fit guideline recommendations. Four real-world clinical scenarios were presented with commentary from small expert groups for each case. All authors voted on case-specific questions to evaluate the level of consensus among the entire group in nuanced diagnostic and management decisions relevant to each case. Points of agreement, points of controversy, and gaps in knowledge are highlighted.