Search

Additional file2 .

Additional file 1: Table S1. Reproducibility of strain parameters. Table S2. Diagnostic accuracy of predicting new onset AF. Table S3. Differences in age and rate of hypertension between those with and without sarcomeric variants. Figure S1. Flow chart showing inclusion of subjects. Figure S2. Survival curve for HCM patients.

Background: Heart failure (HF) is characterized by altered myocardial substrate metabolism which can lead to myocardial triglyceride accumulation (steatosis) and lipotoxicity. However its role in mild HF with preserved ejection fraction (HFpEF) is uncertain. We measured myocardial triglyceride content (MTG) in HFpEF and assessed its relationships with diastolic function and exercise capacity. Methods: 27 HFpEF (clinical features of HF, left ventricular EF >50%, evidence of mild diastolic dysfunction and evidence of exercise limitation as assessed by cardiopulmonary exercise test) and 14 controls underwent 1H-magnetic resonance spectroscopy (1H-MRS) to measure MTG (lipid/water, %), 31P-MRS to measure myocardial energetics (phosphocreatine-to-adenosine triphosphate - PCr/ATP) and feature-tracking magnetic resonance imaging for diastolic strain rate. Results: When compared to controls, HFpEF had 2.3 fold higher in MTG (1.45±0.25% vs. 0.64±0.16%, p=0.009) and reduced PCr/ATP (1.60±0.09 vs. 2.00±0.10, p=0.005). HFpEF had significantly reduced diastolic strain rate and maximal oxygen consumption (VO2 max), which both correlated significantly with elevated MTG and reduced PCr/ATP. On multivariate analyses, MTG was independently associated with diastolic strain rate while diastolic strain rate was independently associated with VO2 max. Conclusions: Myocardial steatosis is pronounced in mild HFpEF, and is independently associated with impaired diastolic strain rate which is itself related to exercise capacity. Steatosis may adversely affect exercise capacity by indirect effect occurring via impairment in diastolic function. As such, myocardial triglyceride may become a potential therapeutic target to treat the increasing number of patients with HFpEF., This work was supported by Chest Heart and Stroke Association, Scotland. MM acknowledges support from the National University of Malaysia and Ministry of Higher Education Malaysia. SN and OR acknowledge support from the Oxford NIHR Biomedical Research Centre and the Oxford British Heart Foundation Centre of Research Excellence. JES is a Senior BHF Basic Science Research Fellow (FS/11/50/29038). CTR is funded by a Sir Henry Dale Fellowship from the Wellcome Trust and the Royal Society [098436/Z/12/Z/B]. OR is a BHF Clinical Intermediate Research Fellow FS/16/70/32157.

Aims Myocardial fibrosis as detected by late gadolinium enhancement (LGE) on cardiac magnetic resonance (CMR) is a powerful prognostic marker in hypertrophic cardiomyopathy (HCM) and may be progressive. The precise mechanisms underlying fibrosis progression are unclear. We sought to assess the extent of LGE progression in HCM and explore potential causal mechanisms and clinical implications. Methods and results Seventy-two HCM patients had two CMR (CMR1-CMR2) at an interval of 5.7 ± 2.8 years with annual clinical follow-up for 6.3 ± 3.6 years from CMR1. A combined endpoint of heart failure progression, cardiac hospitalization, and new onset ventricular tachycardia was assessed. Cine and LGE imaging were performed to assess left ventricular (LV) mass, function, and fibrosis on serial CMR. Stress perfusion imaging and cardiac energetics were undertaken in 38 patients on baseline CMR (CMR1). LGE mass increased from median 4.98 g [interquartile range (IQR) 0.97–13.48 g] to 6.30 g (IQR 1.38–17.51 g) from CMR1 to CMR2. Substantial LGE progression (ΔLGE ≥ 4.75 g) occurred in 26% of patients. LGE increment was significantly higher in those with impaired myocardial perfusion reserve (≺MPRI 1.40) and energetics (phosphocreatine/adenosine triphosphate ≺1.44) on baseline CMR (P ≤ 0.01 for both). Substantial LGE progression was associated with LV thinning, increased cavity size and reduced systolic function, and conferred a five-fold increased risk of subsequent clinical events (hazard ratio 5.04, 95% confidence interval 1.85–13.79; P = 0.002). Conclusion Myocardial fibrosis is progressive in some HCM patients. Impaired energetics and perfusion abnormalities are possible mechanistic drivers of the fibrotic process. Fibrosis progression is associated with adverse cardiac remodelling and predicts an increased risk of subsequent clinical events in HCM.

Aims: Ventricular arrhythmia triggers sudden cardiac death (SCD) in hypertrophic cardiomyopathy (HCM), yet electrophysiological biomarkers are not used for risk stratification. Our aim was to identify distinct HCM phenotypes based on ECG computational analysis, and characterize differences in clinical risk factors and anatomical differences using cardiac magnetic resonance (CMR) imaging. Methods: High-fidelity 12-lead Holter ECGs from 85 HCM patients and 38 healthy volunteers were analyzed using mathematical modeling and computational clustering to identify phenotypic subgroups. Clinical features and the extent and distribution of hypertrophy assessed by CMR were evaluated in the subgroups. Results: QRS morphology alone was crucial to identify three HCM phenotypes with very distinct QRS patterns. Group 1 (n = 44) showed normal QRS morphology, Group 2 (n = 19) showed short R and deep S waves in V4, and Group 3 (n = 22) exhibited short R and long S waves in V4-6, and left QRS axis deviation. However, no differences in arrhythmic risk or distribution of hypertrophy were observed between these groups. Including T wave biomarkers in the clustering, four HCM phenotypes were identified: Group 1A (n = 20), with primary repolarization abnormalities showing normal QRS yet inverted T waves, Group 1B (n = 24), with normal QRS morphology and upright T waves, and Group 2 and Group 3 remaining as before, with upright T waves. Group 1A patients, with normal QRS and inverted T wave, showed increased HCM Risk-SCD scores (1A: 4.0%, 1B: 1.8%, 2: 2.1%, 3: 2.5%, p = 0.0001), and a predominance of coexisting septal and apical hypertrophy (p < 0.0001). HCM patients in Groups 2 and 3 exhibited predominantly septal hypertrophy (85 and 90%, respectively). Conclusion: HCM patients were classified in four subgroups with distinct ECG features. Patients with primary T wave inversion not secondary to QRS abnormalities had increased HCM Risk-SCD scores and coexisting septal and apical hypertrophy, suggesting that primary T wave inversion may increase SCD risk in HCM, rather than T wave inversion secondary to depolarization abnormalities. Computational ECG phenotyping provides insight into the underlying processes captured by the ECG and has the potential to be a novel and independent factor for risk stratification.

Both biomedical research and clinical practice rely on complex datasets for the physiological and genetic characterization of human hearts in health and disease. Given the complexity and variety of approaches and recordings, there is now growing recognition of the need to embed computational methods in cardiovascular medicine and science for analysis, integration and prediction. This paper describes a Workshop on Computational Cardiovascular Science that created an international, interdisciplinary and inter-sectorial forum to define the next steps for a human-based approach to disease supported by computational methodologies. The main ideas highlighted were (i) a shift towards human-based methodologies, spurred by advances in new in silico, in vivo, in vitro, and ex vivo techniques and the increasing acknowledgement of the limitations of animal models. (ii) Computational approaches complement, expand, bridge, and integrate in vitro, in vivo, and ex vivo experimental and clinical data and methods, and as such they are an integral part of human-based methodologies in pharmacology and medicine. (iii) The effective implementation of multi- and interdisciplinary approaches, teams, and training combining and integrating computational methods with experimental and clinical approaches across academia, industry, and healthcare settings is a priority. (iv) The human-based cross-disciplinary approach requires experts in specific methodologies and domains, who also have the capacity to communicate and collaborate across disciplines and cross-sector environments. (v) This new translational domain for human-based cardiology and pharmacology requires new partnerships supported financially and institutionally across sectors. Institutional, organizational, and social barriers must be identified, understood and overcome in each specific setting. Both biomedical research and clinical practice rely on complex datasets for the physiological and genetic characterization of human hearts in health and disease. Given the complexity and variety of approaches and recordings, there is now growing recognition of the need to embed computational methods in cardiovascular medicine and science for analysis, integration and prediction. This paper describes a Workshop on Computational Cardiovascular Science that created an international, interdisciplinary and inter-sectorial forum to define the next steps for a human-based approach to disease supported by computational methodologies. The main ideas highlighted were (i) a shift towards humanbased methodologies, spurred by advances in new in silico, in vivo, in vitro, and ex vivo techniques and the increasing acknowledgement of the limitations of animal models. (ii) Computational approaches complement, expand, bridge, and integrate in vitro, in vivo, and ex vivo experimental and clinical data and methods, and as such they are an integral part of human-based methodologies in pharmacology and medicine. (iii) The effective implementation of multi- and interdisciplinary approaches, teams, and training combining and integrating computational methods with experimental and clinical approaches across academia, industry, and healthcare settings is a priority. (iv) The human-based cross-disciplinary approach requires experts in specific methodologies and domains, who also have the capacity to communicate and collaborate across disciplines and cross-sector environments. (v) This new translational domain for human-based cardiology and pharmacology requires new partnerships supported financially and institutionally across sectors. Institutional, organizational, and social barriers must be identified, understood and overcome in each specific setting.

Both biomedical research and clinical practice rely on complex datasets for the physiological and genetic characterization of human hearts in health and disease. Given the complexity and variety of approaches and recordings, there is now growing recognition of the need to embed computational methods in cardiovascular medicine and science for analysis, integration and prediction. This paper describes a Workshop on Computational Cardiovascular Science that created an international, interdisciplinary and inter-sectorial forum to define the next steps for a human-based approach to disease supported by computational methodologies. The main ideas highlighted were (i) a shift towards human-based methodologies, spurred by advances in new in silico, in vivo, in vitro, and ex vivo techniques and the increasing acknowledgement of the limitations of animal models. (ii) Computational approaches complement, expand, bridge, and integrate in vitro, in vivo, and ex vivo experimental and clinical data and methods, and as such they are an integral part of human-based methodologies in pharmacology and medicine. (iii) The effective implementation of multi- and interdisciplinary approaches, teams, and training combining and integrating computational methods with experimental and clinical approaches across academia, industry, and healthcare settings is a priority. (iv) The human-based cross-disciplinary approach requires experts in specific methodologies and domains, who also have the capacity to communicate and collaborate across disciplines and cross-sector environments. (v) This new translational domain for human-based cardiology and pharmacology requires new partnerships supported financially and institutionally across sectors. Institutional, organizational, and social barriers must be identified, understood and overcome in each specific setting.