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Journal articleMazzarotto F, Kalay O, Arslan E, et al., 2026,
Pangenomes aid accurate detection of large insertions and deletions from targeted sequencing: the case of cardiomyopathies
, Genome Medicine, Vol: 18, ISSN: 1756-994XBackgroundGene panels represent a widely used strategy for genetic testing in a vast range of Mendelian disorders. While this approach aids reliable bioinformatic detection of short coding variants, it often fails to detect many larger variants. Recent studies have recommended the adoption of pangenome references (as opposed to linear reference genomes like GRCh38) to augment detection of large variants from targeted sequencing, potentially providing diagnostic laboratories with the possibility to streamline diagnostic work-ups and reduce costs.MethodsHere, we analyze 1969 cardiomyopathy cases and 1805 controls sequenced with the Illumina Trusight Cardio panel using a pangenome-based workflow (GRAF) and five conventional orthogonal methodologies (GATK HaplotypeCaller, GATK-gCNV, ExomeDepth, Manta and Lumpy-SV) to detect variants ≥ 20 bp in size.ResultsFollowing lab-based variant validation by means of PCR and Sanger sequencing, we show that GRAF conjugates higher precision and recall (F1 score 0.86) compared with other methods (F1 0-0.57) in detecting potentially pathogenic variants ≥ 20 bp from short-read panel data. Results were complemented by a comparison of the tools’ performance in detecting ground truth variants on reference sample HG002 from Genome In A Bottle, which confirmed GRAF to outperform other tools also on exome sequencing (F1 0.97 vs. 0-0.94). Notably, in the HG002 benchmark dataset, GRAF also showed slightly improved performance compared to GATK HaplotypeCaller in the identification of small variants (1–19 bp; F1 0.975 vs. 0.968).ConclusionsOur results indicate that pangenome-based workflows aid improved detection of large variants from targeted sequencing data in the clinical context and suggest that they may contribute to more unified variant detection frameworks for all-size genetic variants in the future.
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Journal articleEllard S, Hanson H, Cassidy E-J, et al., 2026,
The British Society for Genetic Medicine guidance on managing incidental findings identified during rare disease genomic testing.
, J Med Genet, Vol: 63, Pages: 697-706 -
Journal articleTopriceanu C-C, Balakrishnan ID, Vissing CR, et al., 2026,
Natural History of Asymptomatic Phenotypically Mild HCM: Insights From the SHaRe Registry.
, J Am Coll Cardiol, Vol: 88, Pages: 1850-1870BACKGROUND: Patients with phenotypically mild hypertrophic cardiomyopathy (HCM) do not require symptom management, but may be at an earlier stage in the disease course, with potential to benefit from disease-modifying therapies. However, little is known about the natural history and predictors of major adverse cardiovascular events (MACE). OBJECTIVES: Using the Sarcomeric Human Cardiomyopathy Registry, we identified predictors of incident MACE and characterized disease progression in phenotypically mild HCM. METHODS: Phenotypically mild HCM was defined as: having shorter disease duration (<10 years since diagnosis or age ≤30 years), no previous MACE, being NYHA functional class I, and having a left ventricular (LV) maximal wall thickness (MWT) <25 mm. These individuals were followed prospectively for the development of symptoms or MACE: atrial fibrillation (AF), malignant ventricular arrhythmia (MVA) (sudden cardiac death, resuscitated arrest, or appropriate defibrillator therapy), heart failure (HF) (cardiac transplantation, LV assist device implantation, LV ejection fraction <35%, or NYHA functional class III or IV symptoms), stroke, or all-cause mortality. Cox regression identified MACE predictors. Linear and latent class mixed models characterized LV remodeling trajectories and risk clusters. RESULTS: Of 2,500 participants with phenotypically mild HCM (mean age 43 years, 31% women) followed for a mean duration of 7 ± 6 years, 534 (21%) developed MACE, including 289 with AF, 69 with MVA, and 193 with HF. Individuals who progressed from NYHA functional class I to ≥ II symptoms during follow-up (n = 585, 23%) were 2.79 times (95% CI: 2.30-3.39 times) more likely to experience MACE. Age at baseline (HR: 1.24; 95% CI: 1.17-1.32 per 10-year increase), body mass index (HR: 1.10; 95% CI: 1.01-1.21 per 5-kg/m2 increase), left atrial (LA) diameter (HR: 1.16; 95% CI: 1.09-1.25 per 5-mm increase), LV MWT (HR: 1.27; 95% CI: 1.10-1.46 per 5-mm increase)
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Journal articlePerotto M, Radesich C, Paldino A, et al., 2026,
Variant Site-Specific Natural History of Titin-Induced Cardiomyopathy: An International Multicenter Registry.
, Circ Genom Precis Med, Vol: 19BACKGROUND: Titin truncating variants (TTNtv) represent the most common genotype underlying dilated cardiomyopathy but are also detected in the general population, exhibiting incomplete penetrance and marked phenotypic variability. This heterogeneity complicates clinical interpretation and risk stratification. Emerging molecular evidence suggests that truncating location within the gene may influence disease mechanisms. We aimed to investigate whether TTNtv location also affects clinical phenotype and prognosis. METHODS: We established an international multicenter registry of phenotypically affected carriers of pathogenic or likely pathogenic TTNtv. Patients were classified into 3 groups: A-band, Z/I-band, and M-band. A case-control study assessed the enrichment of TTNtv across regions. The primary outcome was a composite of all-cause mortality and heart transplantation. Secondary outcomes included: (1) sudden cardiac death or major ventricular arrhythmias and (2) heart failure-related death/heart transplantation/left ventricular assist device implantation. RESULTS: The study included 467 patients (81% probands, 73% men, median age 47 years, 81% dilated cardiomyopathy phenotype). Most carried TTNtv in the A-band (80% versus 15% Z/I-band and 5% M-band). All groups showed enrichment compared with GnomAD, with greater Bayesian-estimated penetrance for A-band variants. Over a median follow-up of 83 months, the primary end point was similar across groups. However, the risk of sudden cardiac death/major ventricular arrhythmias was significantly higher in M-band carriers (45% M-band versus 23% Z/I-band versus 12% A-band; P=0.001), especially as the first disease manifestation. Band location independently predicted sudden cardiac death/major ventricular arrhythmias risk, whereas left ventricular ejection fraction was predictive only in A- and Z/I-band groups. CONCLUSIONS: TTNtv are differently enriched across the gene in patients with dilated cardiomyopathy/non-dilated left
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Journal articleArgirò A, Meisner JK, Adler ED, et al., 2026,
Clinical Characteristics and Outcomes of Hypertrophic Cardiomyopathy Associated With Thin Filament Variants: Insights From the SHaRe Registry.
, Circ Genom Precis Med, Vol: 19BACKGROUND: Conflicting evidence exists about whether sarcomeric hypertrophic cardiomyopathy (HCM) caused by genes encoding thin filament proteins (TNNT2, TNNI3, ACTC, TPM1, TNNC1; thin filament HCM) has a distinct clinical presentation and natural history from disease caused by the more commonly involved thick filament genes (MYH7, MYBPC3, MYL2, MYL3; thick filament HCM). METHODS: Retrospective analysis of the Sarcomeric Human Cardiomyopathy Registry. RESULTS: Four hundred eighty-eight patients with thin filament HCM were compared with 3494 patients with thick filament HCM. After controlling for age at diagnosis, sex, and proband status, thin filament HCM was associated with a higher risk of composite ventricular arrhythmias (hazard ratio, 1.48 [95% CI, 1.09-2.01]), cardiac arrest (hazard ratio, 2.02 [95% CI, 1.33-3.06]), and heart transplant or left ventricular assist device implantation (hazard ratio, 2.18 [95% CI, 1.3-3.65]). Younger age at diagnosis was associated with an increased risk for the ventricular arrhythmia composite outcome in thin and thick filament HCM (hazard ratio, 0.93 [95% CI, 0.91-0.94]; hazard ratio, 0.93 [95% CI, 0.93-0.94]; age reported as 5-year increment). Within specific thin filament genes, an increased risk of arrhythmic events or heart transplant/left ventricular assist device implantation was evident for variants in TNNT2 amino acids 92 to 94 (odds ratio versus other TNNT2 variants; odds ratio, 3.02 [95% CI, 1.17-7.49]) and TNNI3 amino acids 191 to 210 (odds ratio versus other TNNI3 variants; odds ratio, 3.5 [95% CI, 1.58-8.02]). CONCLUSIONS: Thin filament HCM seems to carry a higher risk for ventricular arrhythmias and advanced heart failure compared with thick filament HCM. Integrating gene-level and intragene-level information may improve risk stratification in patients with HCM.
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Journal articleXu W, Sa F, Song Y, et al., 2026,
Cardiac MRI-Derived Right Ventricular Strain for Risk Stratification in Pediatric and Adolescent Arrhythmogenic Cardiomyopathy: A Multicenter Cohort Study.
, JACC Cardiovasc Imaging, Vol: 19, Pages: 1222-1235BACKGROUND: Pediatric and adolescent arrhythmogenic cardiomyopathy (ACM) is a clinically severe phenotype that remains underrepresented in cardiac magnetic resonance (CMR) research. The prognostic value of CMR-derived right ventricular deformation analysis in this population is unclear. OBJECTIVES: This study sought to describe the burden of severe cardiac outcomes in pediatric and adolescent ACM, and to assess the prognostic and incremental value of CMR-derived right ventricular global longitudinal strain (RVGLS) for risk stratification when incorporated into conventional clinical and CMR markers. METHODS: This multicenter study included consecutive pediatric and adolescent patients (≤21 years of age) with ACM who underwent baseline CMR evaluation. We assessed clinical, electrocardiographic, and CMR parameters according to the 2010 Task Force Criteria, along with biventricular strain parameters. Severe cardiac outcomes were defined as a composite of malignant ventricular arrhythmias (MVAs), heart transplantation, and heart failure-related death. RESULTS: The cohort included 102 patients (16.3 ± 3.4 years; 72% male) and 95 controls (mean age: 15.6 ± 3.3 years; 70% male). During a median of 58-month follow-up, 51 (50%) patients experienced severe cardiac outcomes and 28 (27.5%) MVA outcomes. Reduced RVGLS was independently associated with severe cardiac outcomes (HR: 1.14; P = 0.004) and MVA outcomes (subdistribution HR: 1.15; P < 0.001) after adjustment for clinical and conventional CMR parameters. Incorporation of RVGLS significantly improved prediction of severe cardiac outcomes beyond both right ventricular-based and left ventricular-based multivariable models, with significant improvement in discrimination and reclassification. Kaplan-Meier analysis further showed that RVGLS ≥-12.2% identified patients at higher risk of severe cardiac outcomes and MVA outcomes (both log-rank P < 0.001). CONCLUSIONS: Pediatric and adolescent ACM manifest
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Journal articleRenwick C, Ogden M, Ramos M, et al., 2026,
Maximum heart rate prediction in children: are maximal heart rate prediction equations suitable for use in paediatric clinical practice?
, Cardiol Young, Pages: 1-6AIMS: Assessment of maximal heart rate is used in clinical practice to determine the degree of beta-blockade during exercise and periods of adrenergic stress. When direct measurement is not possible, indirect methods utilising heart rate prediction equations are commonly used, but their validity in children has been challenged. METHODS: Retrospective data on 109 healthy children (male 63, 58%; mean age 11.00 ± 3.09) undergoing routine exercise testing were analysed. Measured maximal heart rate was compared with estimates obtained from twenty-two prediction equations. RESULTS: All prediction equations overestimated maximal heart rate in males. In females, two equations underpredicted (Fairbarn and Heinzmann-Filho et al.), whilst the remaining equations overpredicted. The equations that predicted most accurately differed between males (Fairbarn) and females (Whyte (males). The prediction equation used commonly in clinical practice, Fox (220-age), overestimated significantly in males and females (p = < 0.001). A moderate positive correlation with age and maximal heart rate was seen in males only (r = 0.3195, p = 0.0107). CONCLUSIONS: This study shows that adult age regression equations do not accurately predict childhood maximal heart rate as determined by exercise testing. An inability to accurately predict maximal heart rate could have important implications for both childhood exercise prescription and the safe monitoring of pharmacological therapy that limits exertional heart rate response in children with inherited arrhythmic syndromes. In the absence of direct measurement of maximal heart rate, alternative paediatric-specific prediction methods should be developed to ensure appropriate estimation of childhood maximal heart rate.
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Journal articleRenberg A, Coppersmith S, Merritt O, et al., 2026,
Mapping and rewiring the MYBPC3 promoter for rescue of haploinsufficiency driven hypertrophic cardiomyopathy.
, bioRxivAutosomal dominant loss-of-function variants in the gene MYBPC3 are, collectively, the most common genetic cause of hypertrophic cardiomyopathy (HCM) and are a prototype of haploinsufficient human disease. Typical for haploinsufficiency-associated genes, hundreds of unique loss-of-function pathogenic variants have been reported for MYBPC3 - therapeutic gene editing to correct each of these variants poses major regulatory and logistical hurdles. Upregulating wild-type allele expression could offer a generalizable therapeutic strategy, but the capacity to modulate native MYBPC3 transcription is unknown. Here, we present a variant-agnostic approach to rescue haploinsufficiency by mapping and rationally redesigning the MYBPC3 promoter. Using massively parallel reporter assays (MPRAs) in human induced pluripotent stem cell-derived cardiomyocytes, we performed saturation mutagenesis of the MYBPC3 promoter at single base-pair resolution. This defined a new class of clinically relevant noncoding loss-of-function variants while revealing essential cis-regulatory grammar anchored by key transcription factor binding sites (TFBSs). Furthermore, by systematically screening thousands of variant combinations, modular promoter elements, and heterologous TFBS insertions, we identified synergistic sequence edits that drive robust increases in MYBPC3 expression. Together, our findings improve the clinical interpretation of noncoding variants and establish a scalable blueprint for promoter editing to treat MYBPC3-associated HCM and other haploinsufficient diseases.
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Journal articleAdami E, Kim Y, Zheng SL, et al., 2026,
The molecular landscape of hypertrophic cardiomyopathy across disease stages and genotypes.
, Sci Transl Med, Vol: 18Hypertrophic cardiomyopathy (HCM) is marked by asymmetric cardiac wall thickening, hypercontractility, diastolic dysfunction, and fibrosis. Pathogenic sarcomere gene variants cause HCM, but comparable abnormalities occur in patients with unexplained disease, albeit with fewer adverse events. To investigate stage- and genotype-specific disease mechanisms, we performed single-nucleus RNA sequencing of cardiac tissues from 47 patients with HCM, spanning obstructive HCM with preserved systolic function and end-stage HCM, and compared them with nonfailing donor and dilated cardiomyopathy hearts. We identified transcriptional programs associated with cardiomyocyte hypertrophy, fibrosis, and vascular remodeling. Pathogenic variant-positive early-stage HCM samples showed reduced cardiomyocyte abundance and expansion of a proarrhythmogenic cardiomyocyte state. We identified proline-rich 16 (PRR16) as a cardiomyocyte growth-associated gene in HCM and validated its increased expression by RNA in situ hybridization and in a human induced pluripotent stem cell-derived cardiomyocyte HCM model. In HCM samples, fibroblast compositional shifts were associated with profibrotic activation and adverse extracellular matrix remodeling, accompanied by reduced collagen IV (COL4A1/COL4A2) expression and ultrastructural basement membrane abnormalities. HCM samples also exhibited extensive vascular alterations, including shifts in endothelial cell subpopulations, reduced pericyte abundance suggestive of microvascular dysfunction, and increased lymphangiogenic vascular endothelial growth factor C signaling. Unsupervised and supervised machine learning approaches distinguished HCM from dilated cardiomyopathy and accurately predicted genotype status in early-stage HCM from cell type-resolved transcriptional profiles, revealing widespread genotype-driven remodeling. Together, our findings uncover multicellular, genotype-associated remodeling programs in HCM, providing insight into mechanisms unde
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Journal articleHunt SE, Lemos D, Pericherla SR, et al., 2026,
Gene2Phenotype: A Database of Structured Human Monogenic Diseases and Pathomechanisms.
, J Mol Biol, Vol: 438To facilitate both disease research and personalised medicine, there is an urgent need for accessible, structured data models describing the molecular basis of genetically determined disease. Gene2Phenotype is a database of expert-curated monogenic gene-disease associations, which was established in 2012 to enable efficient prioritisation of likely diagnostic genomic variants. Initially focused on developmental disorders, it has since been extended to support cardiac, eye, skeletal and skin disorders and germline cancer predisposition. We have redesigned and extended Gene2Phenotype, which now openly shares standardised, structured models of rare monogenic diseases, detailing genotype, molecular mechanism and associated phenotypes, curated from scientific literature. The updated platform, which includes a new API, enabling programmatic access, improves the findability, accessibility, interoperability and reusability of detailed rare monogenic disease association data. These data have the potential to accelerate disease research, clinical diagnosis, treatment selection and the development of novel therapies. Gene2Phenotype is available at https://www.ebi.ac.uk/gene2phenotype/.
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Journal articleBalakrishnan ID, Heymans ABM, Claggett B, et al., 2026,
Atrial Fibrillation in Genotyped Dilated Cardiomyopathy: Epidemiology, Risk Factors, and Outcomes: Insights From the SHaRe Registry.
, Circ Heart FailBACKGROUND: Atrial fibrillation (AF) is the most common arrhythmia in dilated cardiomyopathy (DCM) and is associated with adverse outcomes. However, the genetic determinants of AF risk and its prognostic significance across genotyped DCM subtypes remain unknown. METHODS: In this observational cohort study, we analyzed 3117 genotyped patients with DCM from the SHaRe (Sarcomeric Human Cardiomyopathy Registry). AF prevalence, incidence, and clinical characteristics were assessed by genotype. Patients were classified as genotype-positive [G(+)] if they had a pathogenic or likely pathogenic variant in a DCM-associated gene, and genotype-negative [G(-)] if genetic testing was negative. Factors associated with incident AF were evaluated using Cox regression. AF was modeled as a time-dependent variable to evaluate associations with clinical outcomes. RESULTS: Among 3117 genotyped patients with DCM (mean age, 48±15 years; 39% were female; and 35% were G[+]), 12.3% (n=384) had prevalent AF. Of 2491 patients without prevalent AF and with follow-up, 312 (12.5%) developed AF during a median of 4.5 years (interquartile range, 1.6-9.0). Among 2851 patients with follow-up, cumulative AF prevalence was 23.6%. LMNA had the highest AF incidence (7.6/100 patient-years) and cumulative AF prevalence (56.7%) and was the only genotype independently associated with incident AF compared with that in G(-) patients (hazard ratio, 5.52 [95% CI, 3.84-7.95]; P<0.001). TTN had an AF incidence of 2.1 per 100 patient-years and cumulative AF prevalence of 24.4%, comparable to that in G(-) patients. Older age, male sex, and prior heart failure hospitalization were also independently associated with incident AF. AF was independently associated with a higher risk of the composite clinical outcome (hazard ratio, 1.58 [95% CI, 1.29-1.94]; P<0.001), including heart failure, ventricular arrhythmias, and all-cause mortality. CONCLUSIONS: In this large genotyped DCM cohort, AF burden and incident
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Journal articleMazzarotto F, Ware J, 2026,
An updated evidence assessment of the genetic causes of dilated cardiomyopathy
, Circulation, ISSN: 0009-7322Background: Evidence of the diverse genetic architecture of dilated cardiomyopathy (DCM) continues to emerge and requires reassessment of the clinical relevance of implicated disease genes. Building on the 2019-2020 Clinical Genome Resource (ClinGen) evaluation, the DCM Gene Curation Expert Panel (GCEP) reconvened in 2024-2025 to conduct a reassessment of genes in DCM.Methods: The ClinGen semi-quantitative clinical validity classification framework was applied with specifications to DCM to classify genes into categories based upon strength of published evidence for a DCM phenotype. Previously curated genes were reassessed and newly reported gene-disease-mode of inheritance (MOI) relationships, termed “curations,” were evaluated.Results: Sixty-eight genes were evaluated, inclusive of 72 unique gene-disease-MOI relationships across 51 previously evaluated and 17 newly assessed genes. Thirty-five curations were classified as high evidence (16 Definitive, 10 Strong, 9 Moderate), increasing by 16 from the prior assessment. Nine newly assessed genes were classified as high evidence, including BAG5, FLII, LMOD2, MYLK3, MYZAP, NRAP, PPA2, PPP1R13L, and RPL3L. Twelve genes (11 newly appraised) were rated as high evidence with an autosomal recessive (AR) MOI. Five re-evaluated genes from 2019-2020 had clinically significant changes in classification. Except for JPH2, for which curation was modified to separate autosomal dominant (-AD) and -AR MOI curations, clinically significant changes involved upgrades from low to high evidence categories (PLEKHM2, PRDM16, TBX20, TNNI3K), demonstrating the robustness of the ClinGen gene curation process over time. An additional 29 gene-disease-MOI curations were classified as Limited, including six newly evaluated genes and one new MOI for a previously evaluated gene, MYBPC3-AR; four were classified as No Known Disease Relationship, and four remained Disputed. Four previously evaluated genes were curated for both AD and AR MOIs
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Journal articleLuo Y, Ferreira PF, Wen K, et al., 2026,
Optimized Reduced Field of View and Fat Suppression Methods for Interleaved Multislice In Vivo Cardiac Diffusion Tensor Imaging.
, Magn Reson Med, Vol: 96, Pages: 1097-1110PURPOSE: Slice interleaving, a limited phase encode (PE) field of view (FOV), and effective fat suppression are vital for efficient cardiac diffusion tensor imaging (cDTI) with minimal artifacts. This study aimed to optimize reduced FOV and fat suppression methods for interleaved multislice cDTI to improve signal-to-noise ratio (SNR) and minimize artifacts. METHODS: Two-slice motion compensated spin echo datasets from 20 healthy volunteers were acquired. Four reduced PE FOV sequences were evaluated: 2DRF pulse; applying either 180 ° or 90 ° pulses in PE direction; and the proposed flip-back sequence with a nonselective 180 ° pulse after readout to restore inverted magnetization. Four fat suppression techniques were implemented: no fat suppression (standard); fat saturation; binomial water excitation and spectral attenuated inversion recovery (SPAIR). RESULTS: The proposed flip-back sequence with SPAIR achieved the highest median SNR, and its SNR values are significantly higher ( p < 0.01 ) than 2DRF with SPAIR as current state-of-the-art. SPAIR and water excitation demonstrated comparable performance when combined with the flip-back sequence, and both yielded superior image quality than with no suppression or fat saturation. SPAIR showed robust fat suppression across most subjects, whilst water excitation exhibited advantages in some subjects with a high body mass index. CONCLUSION: The proposed flip-back sequence with SPAIR enables efficient interleaved multislice imaging with reduced PE FOV and effective fat suppression, facilitating clinical translation of in vivo cDTI.
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Journal articleRodero C, Curran L, Lanyon CW, et al., 2026,
Computational models show that functional remodeling, not anatomy, reshapes physiological determinants of cardiac performance in hypertrophic cardiomyopathy.
, J Mol Cell Cardiol, Vol: 218, Pages: 137-150Hypertrophic cardiomyopathy (HCM) is a condition characterized by variable patterns of myocardial hypertrophy. Progressive functional remodeling of material properties contributes to symptoms, disease progression, and variability in treatment response. However, it is not clear how these properties interact to determine cardiac performance and response to treatment. We quantified how cardiac anatomy variability, functional remodeling, and pharmacological treatment influence the relative importance of biophysical mechanisms that govern cardiac function. We built five four-chamber electromechanical heart models representing anatomical clusters. We performed global sensitivity analyses on 46 parameters across 32 outputs. We repeated this process 18 times to model different types of functional remodeling and two times to model mavacamten and aficamten. Across all representative HCM phenotypes, sensitivity profiles were preserved. Within the parameter ranges examined, ventricular afterload explained the largest share of variance in hemodynamic outputs across anatomies (up to 69%). Functional remodeling led to shifts in parameter importance, mostly when ventricular stiffness was increased. The modeling of pharmacological treatments led to modest but targeted changes, with mavacamten and aficamten producing comparable effects, particularly for outputs related to arterial pressures. Across major anatomical phenotypes of hypertrophic cardiomyopathy, functional remodeling, rather than anatomy alone, reshaped the relative importance of cellular, myocardial, and loading-related parameters, with the largest shifts under increased ventricular stiffness and under aficamten in obstructive physiology. These hypothesis-generating results suggest that the integration of biomarkers reflecting ventricular-arterial coupling, myocardial stiffness, and contractile state may help anticipate symptoms and treatment response in HCM.
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Journal articleSchiratti P-R, Kalaie S, Zheng J, et al., 2026,
Four-dimensional left ventricular motion clustering reveals cardiovascular phenotypes at population scale
, Scientific Reports, Vol: 16, ISSN: 2045-2322Characterisation of the motion dynamics of the left ventricle is key to understanding pathophysiological mechanisms and transitions from health to disease. Conventional volumetric assessments of the heart using imaging represent mainly aggregate global features of function that are poorly discriminating. Here we present a novel approach to quantify and visualise how the left ventricle is affected by cardiovascular risk factors through efficient representations of motion trajectories. We use computer vision to survey four-dimensional cardiac motion traits using densely sampled point clouds of the left ventricle in over 20,000 participants of UK Biobank. We developed a computational framework for dimensionalityreduction of spatiotemporal information to derive a human-interpretable signature summarising variation in complexpatterns of motion. We found six phenogroups representing a novel classification of heterogenous motion phenotypes with differential enrichment of cardiovascular outcomes and genetic risk. Low dimensional representations of motion are visualised as a simple spatial signature capturing deviation from an average state. Discovering compact cardiac motion signatures of health and disease from dynamic point clouds enables efficient classification of patient risk and predisposing polygenic factors.
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Journal articleRaman B, Prasad SK, 2026,
Late Gadolinium Enhancement in Nonischemic Dilated Cardiomyopathy.
, Circulation, Vol: 154, Pages: 512-516 -
Journal articlePastika L, Patlatzoglou K, Sieliwonczyk E, et al., 2026,
Artificial intelligence-enhanced electrocardiography for the prediction of future type 2 diabetes mellitus: a model-development and multicentre validation study.
, Eur Heart J Digit Health, Vol: 7AIMS: A significant proportion of type 2 diabetes cases remain undiagnosed despite screening advances, carrying substantial cardiometabolic risk. Artificial intelligence-enhanced electrocardiography (AI-ECG) detects subtle ECG changes in subclinical disease, potentially enabling opportunistic screening. METHODS AND RESULTS: We developed AI-ECG Risk Estimator for Diabetes Mellitus (AIRE-DM), a convolutional neural network with discrete-time survival loss, for diagnosis of prevalent and prediction of incident type 2 diabetes. It was trained on 1 163 401 ECGs from 189 537 individuals from Beth Israel Deaconess Medical Center (BIDMC) and externally validated in UK Biobank (UKB; n = 65 606) and ELSA-Brasil (n = 13 739). AI-ECG Risk Estimator for Diabetes Mellitus demonstrated moderate discrimination for prevalent type 2 diabetes (area under the receiver operating characteristic curve: BIDMC 0.724, UKB 0.733, ELSA-Brasil 0.706) and incident type 2 diabetes (C-index: BIDMC 0.667, UKB 0.688, ELSA-Brasil 0.625). The highest AIRE-DM risk quartile had elevated incident diabetes risk vs. the lowest (hazard ratio: BIDMC 4.75, UKB 7.52, ELSA-Brasil 3.96). AI-ECG Risk Estimator for Diabetes Mellitus was non-inferior to the American Diabetes Association Diabetes Risk Test in BIDMC, with improved predictive accuracy when combined. In normoglycaemic patients, AIRE-DM was superior to glycated haemoglobin (HbA1c) for predicting incident diabetes in BIDMC and non-inferior in ELSA-Brasil. The highest risk quartile reached 5% cumulative type 2 diabetes mellitus incidence 5.4 years (BIDMC) and 4.8 years (ELSA-Brasil) earlier than the lowest risk quartile, after adjusting for HbA1c, age, and sex. Phenome- and genome-wide association studies revealed biologically plausible associations with glucose regulation, cardiac morphology, diastolic dysfunction, arterial stiffness, and lipid metabolism. CONCLUSION: AI-ECG Risk Estimator for Diabetes Mellitus detects prevalent type 2 diabetes and predi
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Journal articleSen A, Jones RE, Morgan H, et al., 2026,
Improving arrhythmic risk prediction using cardiac magnetic resonance within deep learning in ischemic heart disease.
, NPJ Cardiovasc Health, Vol: 3Sudden arrhythmic death remains a major clinical risk in ischemic heart disease (IHD), underscoring the need for improved risk stratification. Late gadolinium enhancement cardiac magnetic resonance (LGE-CMR) provides measures of scar burden and heterogeneity, but its incremental prognostic value beyond conventional markers such as left ventricular ejection fraction remains uncertain. We analysed two independent IHD cohorts (Dataset 1: n = 399, 54 events; National Research Ethics Service approvals 07/H0708/83 and 09/H0504/104+5; Dataset 2: n = 424, 50 events; derived from the prospectively registered REVIVED-BCIS2 trial, ISRCTN45979711, registered 20 November 2012)using clinical and LGE-CMR-derived variables to evaluate the contribution of LGE-CMR features, and compare machine learning-based survival modelling approaches. A brute-force feature-selection strategy identified optimal predictor subsets for Cox proportional hazards models, Random Survival Forests, and DeepSurv, evaluated using cross-cohort and pooled validation strategies. Scar entropy consistently emerged as a strong predictor of major arrhythmic events. Non-linear approaches outperformed Cox regression, with DeepSurv demonstrating superior generalization across cohorts and Random Survival Forests showing robust performance in pooled analyses. These findings support scar heterogeneity as an important prognostic marker and suggest that machine-learning survival models may improve arrhythmic risk prediction in patients with IHD.
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Journal articleTänzer M, Lim EJ, Qiu HH, et al., 2026,
Simultaneous multi-slice Cardiac Diffusion Tensor Imaging with variable CAIPIRINHA shifts and artefact-aware AI.
, Med Image Anal, Vol: 112Cardiac Diffusion Tensor Imaging (cDTI) provides unique insights into myocardial microstructure in-vivo but requires averaging multiple repetitions for adequate signal quality, leading to prohibitively long acquisition times. Standard acceleration strategies, such as reducing repetitions and employing simultaneous multi-slice (SMS) imaging, are limited by low signal-to-noise ratio (SNR) and inter-slice leakage artefacts, respectively. We introduce ORCAS, a unified framework that synergistically combines a novel variable CAIPIRINHA acquisition with an artefact-aware AI reconstruction to overcome these challenges. The variable CAIPIRINHA scheme decoheres SMS artefacts across repetitions, while our dual-domain deep learning model simultaneously suppresses these artefacts and combats the low SNR from fewer repetitions. The model is guided by patient-specific single-band auxiliary data to preserve anatomical fidelity. Validated on ex-vivo hearts with and without anomalies, ORCAS achieves an over 18-fold acceleration by combining these strategies, reducing a whole-heart scan from over two hours to under 7 min. This is accomplished while reducing errors in key biomarkers, such as Fractional Anisotropy, by up to 64%. The framework preserves essential microstructural properties and the delineation of abnormalities, representing a significant step towards the clinical translation of whole-heart cDTI.
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Journal articleMcGurk K, Halliday B, O'Regan D, 2026,
Left ventricular non-compaction: time to retire a flawed diagnosis
, Journal of the American College of Cardiology, Vol: 87, ISSN: 0735-1097 -
Journal articleStroeks SLVM, Bart NK, Rossano J, et al., 2026,
Sex and age specific genetic risk across the dilated and arrhythmogenic cardiomyopathy spectrum: insights from the SHaRe registry
, Journal of the American College of Cardiology, Vol: 87, Pages: 3573-3588, ISSN: 0735-1097BackgroundDilated cardiomyopathy (DCM) and arrhythmogenic cardiomyopathy (ACM) are progressive cardiac muscle disorders with phenotypic and genetic overlap. Although a male predominance is noted in DCM/ACM, it remains unclear whether this extends to specific genetic subtypes or reflects variation in disease stage and whether sex influences age-dependent disease onset across pediatric and adult groups.ObjectivesThe aim of this study was to define sex-based differences in genetic architecture and age at diagnosis of DCM/ACM across pediatric and adult populations.MethodsGenetically tested adult and pediatric DCM/ACM patients and asymptomatic genotype-positive relatives enrolled in the multicenter SHaRe (Sarcomeric Human Cardiomyopathy Registry) were analyzed. Sex distribution across 27 DCM- and ACM-associated genes was evaluated using logistic regression. Age at diagnosis was compared across sex and genes using Kaplan-Meier cumulative incidence estimates.ResultsAmong 3,410 patients, a 61% male predominance was present across subgroups of genotype positive, genotype negative, and variants of uncertain significance (P = 0.008), with significant gene-specific variation. TTN truncating variants (TTNtv) were less common in females (OR: 0.42 [95% CI: 0.33-0.54]; P < 0.01), while DSP (OR: 3.3 [95% CI: 2.35-4.78]; P < 0.01) and grouped non-TTN sarcomeric variants (ACTC1, TNNT2, MYH7, TNNC1, TNNI3, and TPM1) were more common (OR: 1.68 [95% CI: 1.15-2.47]; P < 0.001) in females compared with males with DCM/ACM. Age at diagnosis was comparable between sexes, except in TTNtv carriers, among whom males exhibited earlier disease onset compared with females (median age 45 years [Q1-Q3: 33-55 years] vs 51 years [Q1-Q3: 38-60 years]; P = 0.003). Pediatric-onset (diagnosis at <18 years) cases (n = 174) also demonstrated a male predominance (60% male) but had distinct genetic characteristics, with non-TTN sarcomeric and PKP2 variants being more prevalent compared with adult-o
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Journal articlePrzybylski R, Norrish G, Claggett B, et al., 2026,
Correction to: The Natural History of Massive Left Ventricular Hypertrophy in Pediatric Hypertrophic Cardiomyopathy: A Multiregistry Analysis.
, Circulation, Vol: 153 -
Journal articleVerdonschot JAJ, van Spaendonck-Zwarts KY, Hellebrekers DMEI, et al., 2026,
Genetic counselling implementation in dilated cardiomyopathy.
, Eur Heart J, Vol: 47, Pages: 3040-3051Genetic testing has become an integral part of the diagnostic workup of patients with dilated cardiomyopathy (DCM). While the initial goal of genetic testing was to identify family members at risk, recent advances have now extended their relevance to clinical decision-making. Our knowledge of the genetic architecture of DCM has expanded significantly, promoting a shift from the monogenic dogma towards a broader polygenic spectrum. However, current genetic testing strategies still primarily rely on the model of monogenic inheritance with an incomplete penetrance. Large studies have shown a yield varying from 8% to 36% of genetic testing in patients with DCM, depending on aetiology or family history. Genetic testing is generally warranted for every patient with DCM where genetic results could have an impact on risk stratification, the prognosis or the treatment of the patient, or its family members with an opportunity for reassurance or early disease detection. There are various strategies for genetic testing including broad multigene panels, or more targeted panels limited to specific disease-associated genes. Identified variants are classified by genetic laboratories, where pathogenic or likely pathogenic variants often have actionable clinical implications. It is crucial to interpret these variants in the context of the individual patient considering the phenotype and other contributing factors. When the genetic results are consistent with the patients' broader phenotype, potential clinical implications may include decision for device therapy, recommendations for family screening, and reproductive options. A comprehensive approach to integrate genetic testing in the clinical care of patients with DCM is proposed.
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Journal articleMarwaha SK, Basu J, Bhatia R, et al., 2026,
Unravelling the grey zone: submaximal-to-peak stress echocardiography enhances diagnostic accuracy in differentiating early dilated cardiomyopathy from physiological adaptation.
, Echo Res Pract, Vol: 13, ISSN: 2055-0464BACKGROUND: Participation in regular endurance exercise may be associated with physiological left ventricular (LV) dilatation and concomitant low resting LV ejection fraction (LVEF), a phenotype indistinguishable from early dilated cardiomyopathy (DCM) on resting imaging alone and termed the "grey zone". Stress echocardiography has emerged as a potential arbiter and has been proposed to resolve this dilemma. OBJECTIVES: We evaluated the diagnostic accuracy of stress echocardiography in distinguishing physiological from pathological LV dilatation and assessed whether incorporating submaximal-to-peak contractile reserve improved discriminatory value in athletes in the grey zone. METHODS: Of the 182 athletic individuals, 62 control athletes with an enlarged LV and normal LVEF, 58 athletic DCM individuals, and 62 grey zone athletes underwent stress echocardiography using a semi-supine bicycle. In addition to the ability to augment LVEF ≥ 10% from rest to maximal exercise, we evaluated the ability to augment LVEF from submaximal exercise (80% of maximal heart rate) to peak exercise. RESULTS: Resting LV dimensions did not differ significantly amongst the groups. Control athletes had higher resting LVEF than grey zone athletes and DCM individuals (62.1% vs 52.1% and 53.1%; p=<0.001). Control and grey zone athletes showed greater ΔLVEF from rest to peak exercise than DCM individuals (21% and 19.2% vs 4.9%; p < 0.001). Most control (98.3%) and grey-zone athletes (90.3%) achieved a ΔLVEF ≥ 10% from rest to peak exercise compared with 20.6% of athletic DCM individuals. Control and grey zone athletes also revealed a mean increase in LVEF from submaximal to peak exercise of 7.5% and 3.8%, respectively, whereas DCM individuals showed a mean LVEF decline of -4.3% (p < 0.001). Although 20.6% of DCM individuals demonstrated a ΔLVEF ≥ 10% from rest to peak, only a singl
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Journal articleHCMR Investigators, Kramer CM, Kolm P, et al., 2026,
Predictors of Long-Term Outcomes in Hypertrophic Cardiomyopathy: The NHLBI HCM Registry.
, JAMA, Vol: 335, Pages: 1959-1969IMPORTANCE: Current risk prediction guidelines for hypertrophic cardiomyopathy predict only sudden cardiac death and are imperfect, leading to avoidable deaths and unnecessary implantable cardioverter defibrillators. OBJECTIVE: To combine prospectively collected clinical history, imaging, genetic, and biomarker data to improve risk prediction of adverse events in hypertrophic cardiomyopathy. DESIGN, SETTING, AND PARTICIPANTS: A total of 2750 patients with hypertrophic cardiomyopathy were prospectively enrolled in the registry-based study from 44 sites in North America and Europe with expertise in hypertrophic cardiomyopathy and cardiac magnetic resonance (CMR) imaging. Participants were enrolled from April 1, 2014, to April 7, 2017. EXPOSURES: Patients underwent a health history questionnaire, blood sampling for biomarkers and genotyping, and contrast-enhanced CMR. Patients were followed up yearly by telephone and through records review regarding event documentation. MAIN OUTCOMES AND MEASURES: The predefined composite adjudicated primary end point was time to first event for hypertrophic cardiomyopathy-related deaths; nonfatal sustained ventricular arrhythmias (VAs) requiring cardioversion or defibrillation; and left ventricular (LV) assist device implant or heart transplant. A secondary end point was a composite of sudden cardiac death and nonfatal VA events. The elastic-net method identified the most important predictors. Cox proportional hazards regression assessed associations with time to the first end point. RESULTS: Of the 2750 prospectively enrolled patients, 2698 (98%) had analyzable data after 9 were excluded because they had hypertrophic cardiomyopathy phenocopies and 43 withdrew. Of these remaining patients, 1919 (71%) were male, mean age was 50 years (SD, 11 years), and 423 (16%) were from underrepresented racial and minority groups. The mean follow-up was 6.9 years (SD, 2.1 years). The primary event model in 104 patients included LV scar as a percenta
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