Jack W. O'SUllivan

BACKGROUND: Variants in the ion channel gene KCNQ1 are the leading cause of congenital long QT syndrome (LQTS), a disorder characterized by QT prolongation and risk of fatal arrhythmias. The expanding use of genetic testing has led to the identification of numerous variants of uncertain significance, complicating clinical interpretation and risk stratification. Functional characterization of KCNQ1 variants is essential to define their role in long QT syndrome and advance precision medicine in cardiovascular care. METHODS: We generated a nearly comprehensive variant library of 13 403 protein-coding variants in KCNQ1 . Using 4 pooled assays followed by high-throughput sequencing, we characterized the variants’ effects on cell surface abundance and channel function in both the variant-only and heterozygous states. Case–control analyses and data from large-scale biobanks were used to assess the clinical impact of loss-of-function variants on QT prolongation and long QT syndrome. Heterozygous assay results were used to determine dominant-negative effects, which were also evaluated for association with arrhythmia risk. Protein structural clustering of distinct classes of deleterious variants was analyzed. RESULTS: Across the 4 assays, we generated 49 305 functional measurements of KCNQ1 variants. Of the 13 403 KCNQ1 studied variants, 1757 demonstrated strong pathogenic evidence, and 6660 showed moderate benign evidence. Loss-of-function variants were significantly associated with QT prolongation and long QT syndrome in case–control and biobank analyses. Deleterious variants clustered within distinct structural domains based on their functional classification. A majority of pathogenic variants exhibited dominant-negative effects; these variants were linked to a higher risk of arrhythmia diagnoses than were haploinsufficient variants. CONCLUSIONS: This comprehensive KCNQ1 functional atlas provides novel functional evidence for most KCNQ1 variants of uncertain significance. It also reveals mechanistic principles linking genetic variation to ion channel biology and arrhythmia susceptibility, advancing cardiovascular precision medicine.

BACKGROUND: The 2016 American Society of Echocardiography (ASE) guidelines for left ventricular diastolic dysfunction (LVDD) classification resulted in a significant proportion of indeterminate classifications and grades. To address these limitations and incorporate new evidence, the ASE updated its recommendations in 2025. The impact of these revisions in community cohorts remains unclear. METHODS: We studied 1953 Project Baseline Health Study participants who underwent comprehensive transthoracic echocardiography. LVDD was classified using the 2016 and 2025 ASE recommendations. For the 2025 recommendations, fixed and age-specific thresholds were evaluated separately. We compared LVDD prevalence, reclassification patterns, associations with cardiovascular risk factors, and prognostic value for major adverse cardiovascular events over a median follow-up of 4.3 years. RESULTS: Median patient age was 50.6 years (Q1–Q3: 36.3–64.2); 56.3% of patients were female, 35.3% had hypertension, and 14.2% had diabetes. The prevalence of LVDD was higher with the 2025 recommendations than with the 2016 algorithm: fixed criteria 308 (15.8%), age-specific criteria 220 (11.3%) versus ASE 2016 154 (8.0%). Among 119 (6.1%) participants classified as indeterminate by the 2016 algorithm, the 2025 recommendations reclassified 51.2% as no LVDD and 31.8% as Grade 2 LVDD. Participants reclassified as no LVDD had event-free survival that was not statistically different from those without LVDD ( P =0.26), whereas those reclassified as Grade 2 had higher event rates (12.5% versus 3.8%; P =0.02). Major adverse cardiovascular events occurred in 98 (5.0%) participants over the follow-up period. LVDD by all classification approaches was independently associated with major adverse cardiovascular events after adjustment for baseline risk factors. CONCLUSIONS: The 2025 ASE recommendations identified more participants with LVDD than the 2016 algorithm without indeterminate classification or grading. LVDD by the 2025 classification was significantly associated with major adverse cardiovascular events, supporting the clinical relevance of the revised framework.

Background: Clinical trials are essential for therapeutic development but increasingly face challenges due to imprecise inclusion criteria, leading to low event rates and the need for large sample sizes. This inefficiency makes modern trials costly and time-consuming. Despite the availability of extensive clinical, genomic, and biological data, current trial enrollment strategies do not fully leverage this information. Incorporating genomic information into trial design could enable risk-based participant enrichment by preferentially enrolling individuals with higher disease risk, thereby increasing event rates and improving trial efficiency. Methods: In this study, we developed an in silico framework for evaluating prognostic enrichment guided by polygenic risk scores (PRS) in clinical trial design using genomic and electronic health record data from large-scale biobanks. Naturally occurring protective genetic variants were used as analogs of therapeutic interventions, with variant carriers treated as ’treatment’ arms and non-carriers as ’control’ arms. We compared unenriched designs, in which carriers and non-carriers were drawn from the full population, against PRS-enriched designs in which both arms were restricted to participants in the upper 75%, 50%, or 25% of the PRS distribution, respectively. Across these four designs, we quantified disease prevalence, statistical power, sample size requirements, and time-to-event accrual. Results: We applied this approach to the UK Biobank using three model gene–disease pairs: the protective variant p.Arg46Leu in PCSK9 for coronary artery disease (CAD), p.Gln175His in ANGPTL7 for glaucoma, and p.Arg381Gln in IL23R for inflammatory bowel disease (IBD). Across all three disease contexts, PRS-enriched designs increased disease prevalence, improved empirical power, and accelerated event accrual relative to unenriched cohorts. At 80% power, restricting enrollment to the upper 25% of the PRS distribution reduced required per-arm sample sizes by approximately 60% for CAD-PCSK9 and 78% for IBD-IL23R. Consistent reductions in time-to-event were also observed across enriched strata, suggesting that PRS-enriched trials could achieve target event counts with both smaller sample sizes and shorter follow-up. However, for glaucoma-ANGPTL7, the most restrictive threshold did not yield additional gains over moderate enrichment, as reduced sample size attenuated the detectable difference between arms. These results highlight the need to balance enrichment for higher-risk participants against retaining a sufficient eligible population, and underscore that optimal PRS thresholds are disease-context dependent. Conclusions: These findings establish a generalizable, data-driven framework for prospectively evaluating PRS-guided prognostic enrichment prior to trial initiation. In general, PRS-guided study designs lead to improved empirical power, lower required sample sizes, and faster trials. As population-scale genomic data become increasingly available within healthcare systems and biobanks, this framework provides a scalable foundation for integrating genetic risk information into clinical trial design.

The scarcity of subspecialist medical expertise poses a considerable challenge for healthcare delivery. This issue is particularly acute in cardiology, where timely, accurate management determines outcomes. We explored the potential of Articulate Medical Intelligence Explorer (AMIE), a large language model-based experimental medical artificial intelligence system, to augment clinical decision-making in this challenging context. We conducted a randomized controlled trial comparing large language model-assisted care with the usual care of complex patients suspected of having a genetic cardiomyopathy, and we curated a real-world dataset of complex cases from a subspecialist cardiology practice. Nine participating general cardiologists were provided with access to both clinical text reports and raw diagnostic data—including electrocardiograms, echocardiograms, cardiac magnetic resonance imaging scans and cardiopulmonary exercise testing—and were randomized to manage these cases, either with or without assistance from AMIE. We developed a ten-domain evaluation rubric used by three blinded subspecialists to evaluate the quality of triage, diagnosis and management. In our randomized controlled trial with retrospective patient data, subspecialists favored large language model-assisted responses overall, and for the management plan and diagnostic testing domains, with the remaining domains considered a tie. Overall, subspecialists preferred AMIE-assisted cardiology assessments 46.7% of the time, compared with 32.7% for cardiologists alone ( P = 0.02), with 20.6% rated as a tie. Subspecialists also quantified errors, extra and missing content, reasoning and potential bias. Cardiologists alone had more clinically significant errors (24.3% versus 13.1%, P = 0.033) and more missing content (37.4% versus 17.8%, P = 0.0021) than cardiologists assisted by AMIE. Lastly, cardiologists who used AMIE reported that AMIE helped their assessment more than half the time (57.0%) and saved time in 50.5% of cases.

Polygenic risk scores (PRS) have demonstrated predictive validity across a range of cohorts and diseases, but quantifying their clinical utility remains a challenge. As PRS can be derived from a single biological sample and remains stable throughout life, we explore the potential of PRS to optimize existing screening programs. Via an integrated modelling approach, we quantify the potential clinical benefits arising from a knowledge of PRS across seven diseases with existing screening programs (abdominal aortic aneurysm, breast cancer, colorectal cancer, coronary artery disease, hypertension, prostate cancer, and type 2 diabetes). We identify individuals at high genetic risk (PRS OR>2) and very high genetic risk (PRS OR>3) and estimate the optimal screening ages for these genetically high-risk individuals, based on the equivalent risk to population-level risk at recommended screening ages. We then leverage published data on differential mortality and other outcomes, with and without screening-based interventions, to assess the potential benefits of tailoring screening age based on genetic risk. Very high risk individuals reach the risk level associated with usual starting screening age on average 10.8 years earlier, high risk individuals 8.9 years earlier and reduced risk individuals (OR<0.5) 16.8 years later. During this time, case enrichment (the ratio of the percentage of cases in the high PRS risk group and in the total population) in the high risk group is between 1.7 and 3.0, depending on the disease. Across all seven diseases, appropriate interventions following PRS-guided screening could reduce premature deaths in high-risk individuals by 23.3%. Knowledge of genetic risk, measured using PRS, has the potential to deliver substantial public health benefits when aggregated across conditions, and could reduce premature mortality by tailoring existing screening programs.

Cardiac fibrosis is a central pathological process in heart failure, yet the molecular mechanisms governing its spatial organization remain poorly defined. We developed an artificial intelligence (AI)-based phenotyping approach to decode the spatial organization of cardiac fibrosis from routine cardiac magnetic resonance imaging (MRI). We applied convolutional variational autoencoders (VAEs) and distributional metrics to native T1 maps from 50,239 UK Biobank participants. VAE-derived features predicted mortality with greater precision than standard T1 measures (C-index 0.614 vs. 0.547; likelihood ratio p = 2.9×10 ^-3 ), identifying spatial fibrosis patterns as independent prognostic indicators. Through genome-wide association studies, we identified genetic loci underlying T1 distribution metrics, implicating oxidative stress pathways (SOD2, GSS) and calcium signaling (CAMK2D, CALU). Pathway enrichment revealed distinct biological processes: T1 distributions reflected metabolic and coagulation activity, while spatial VAE dimensions reflected extracellular matrix organization and complement regulation. Mendelian randomization identified cathepsin S (CTSS) and extracellular matrix protein 1 (ECM1) as causal mediators with near-certain colocalization evidence (PP.H4>0.88), validated in an independent Icelandic cohort (n=35,559). FKBPL demonstrated causal effects on both T1 distributional and spatial features. Published preclinical studies show CTSS inhibition reduces collagen deposition and ventricular stiffening, ECM1 stabilizes extracellular matrix and prevents fibrosis, and FKBPL peptides attenuate fibroblast activation. These findings highlight tractable pathways for therapeutic modulation of myocardial fibrosis.

Background Polygenic risk scores (PRS) have demonstrated predictive validity across a range of cohorts and diseases, but quantifying their clinical utility remains a challenge. As PRS can be derived from a single biological sample and remain stable throughout life, we explore the potential of PRS to optimize existing screening programs. Methods Using UK Biobank (n = 444,949), we quantified the potential clinical benefits arising from a knowledge of PRS across seven diseases with existing screening programs globally (breast cancer, prostate cancer, colorectal cancer, abdominal aortic aneurysm, type 2 diabetes, hypertension, and coronary artery disease). We identified individuals at high genetic risk (PRS OR>2) and very high genetic risk (PRS OR>3) and estimated the optimal screening ages for these genetically high risk individuals, based on the equivalent risk to population-level risk at recommended screening ages. We then leveraged published mortality estimates, with and without screening-based interventions, to assess the potential benefits of tailoring screening age based on genetic risk. We also estimated the case enrichment ratio, which is a ratio of the percentage of cases in the high PRS risk group and in the total population. Findings Very high risk individuals reach the risk level associated with usual starting screening age on average 10.8 years earlier, high risk individuals 8.9 years earlier and reduced risk individuals (ORConclusion Knowledge of genetic risk, measured using PRS, has the potential to deliver substantial public health benefits when aggregated across conditions, and could reduce premature mortality by tailoring existing screening programs.

Cardiac blood flow is a critical determinant of human health. However, the definition of its genetic architecture is limited by the technical challenge of capturing dynamic flow volumes from cardiac imaging at scale. We present DeepFlow, a deep-learning system to extract cardiac flow and volumes from phase-contrast cardiac magnetic resonance imaging. A mixed-linear model applied to 37,653 individuals from the UK Biobank reveals genome-wide significant associations across cardiac dynamic flow volumes spanning from aortic forward velocity to aortic regurgitation fraction. Mendelian randomization reveals a causal role for aortic root size in aortic valve regurgitation. Among the most significant contributing variants, localizing genes (near ELN, PRDM6 and ADAMTS7) are implicated in connective tissue and blood pressure pathways. Here we show that DeepFlow cardiac flow phenotyping at scale, combined with genotyping data, reinforces the contribution of connective tissue genes, blood pressure and root size to aortic valve function.

We describe real-world use of mavacamten in 50 patients with oHCM. Consistent with EXPLORER-HCM and VALOR-HCM, we report significant improvement in wall thickness, mitral regurgitation, left ventricular outflow tract obstruction and NYHA class. Moreover, in our center’s experience, neither arrhythmia burden, nor contractility have worsened in the vast majority of patients: We note a clinically insignificant mean decrease in left ventricular ejection fraction (LVEF), with only two patients requiring temporary mavacamten discontinuance for LVEF < 50%. Adverse events were rare, unrelated to mavacamten itself, and seen solely in patients with disease too advanced to have been represented in clinical trials. Our multidisciplinary pathway enabled us to provide a large number of patients with a novel closely-monitored therapeutic within just a few months of commercial availability. These data lead us to conclude that mavacamten, as a first-in-class cardiac myosin inhibitor, is safe and efficacious in real-world settings.

As the heritability of abdominal aortic aneurysm (AAA) is high and AAA partially shares genetic architecture with other cardiovascular diseases, there is interest in whether genetic information could inform AAA screening strategies. Exploiting pleiotropy and meta-analysing summary data from large AAA studies, we constructed a polygenic risk score (PRS) for AAA. Compared with the low PRS tertile, the intermediate and high PRS tertiles had hazard ratios for AAA of 2.13 (95%CI 1.61, 2.82) and 3.70 (95%CI 2.86, 4.80) respectively, after adjusting for known clinical risk factors. Using simulation modelling, we compared PRS- and smoking-stratified screening with inviting men at age 65 and not inviting women (current UK strategy). In a futuristic scenario where genomic information is available on the population, our modelling suggests inviting male current smokers with high PRS earlier than 65 and targeting screening in female smokers with high/intermediate PRS at 65 and 70 respectively, may improve cost-effectiveness.