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Dr Nay Aung

Nay

Senior Clinical Lecturer, Consultant Cardiologist, MRC Clinician Scientist

Centre: Advanced Cardiovascular Imaging

Email: n.aung@qmul.ac.uk
Twitter: @NayAungMD

Profile

ORCID iD: 0000-0001-5095-1611

Dr Nay Aung is a Senior Clinical Lecturer and Consultant Cardiologist at Barts Heart Centre, Barts Health NHS Trust. He specialises in heart failure, inherited cardiac conditions, and multi-modal cardiac imaging. He has advanced proficiency in data science, including artificial intelligence (AI) and machine learning, genomics, bioinformatics, and epidemiology. He was awarded the Young Investigator Award at EuroCMR 2019 and the Royal Society of Medicine President's Medal in 2020. Dr Aung has established collaborations with internationally renowned institutes including Oxford, Harvard, and the NIH. In March 2023, he was awarded a highly prestigious MRC Clinician Scientist Fellowship to investigate the genetic underpinnings of heart failure imaging signatures in large-scale, multi-centre, multi-ancestral genomic studies using AI approaches. He is a regular invited speaker for national and international conferences. He serves as a member of multiple committees in the European Society of Cardiology (ESC) and the European Association of Cardiovascular Imaging.

Research

Dr Aung's research investigates the intricate interplay of genetic and environmental determinants underlying adverse cardiac remodelling and heart failure. Through the strategic integration of cutting-edge AI methodologies and multi-omics techniques, his work delves into the core mechanisms of cardiomyopathy and heart failure. Dr Aung's research group plays a pioneering role in the creation and assessment of novel biomarkers derived from cardiovascular imaging and ECG data. Beyond their clinical applicability and prognostic roles, the genetic architectures of cardiac imaging phenotypes are systematically evaluated using in-silico bioinformatic approaches to discern potential therapeutic applications in large-scale, transnational datasets encompassing diverse populations and patients.

Publications

Peer-review publications

  1. Aung N, Wang Q, van Duijvenboden S, et al. Association of longer leukocyte telomere length with cardiac size, function and heart failure. JAMA Cardiology doi:10.1001/jamacardio.2023.2167.
  2. Aung N, Lopes LR, van Duijvenboden S, et al. Genome-wide analysis of left ventricular maximum wall thickness in the UK Biobank cohort reveals a shared genetic background with hypertrophic cardiomyopathy. Circulation: Genomic and Precision Medicine. 2023;16:e003716.
  3. Aung N, Vargas JD, Yang C, et al. Genome-wide association analysis reveals insights into the genetic architecture of right ventricular structure and function. Nature Genetics. 2022;54:783–791.
  4. Lopes LR^ , Aung N^ (^Joint first-author), van Duijvenboden S, Munroe PB, Elliott PM, Petersen SE. Prevalence of Hypertrophic Cardiomyopathy in the UK Biobank Population. JAMA Cardiology. 2021;6:852–854.
  5. Aung N, Sanghvi MM, Piechnik SK, Neubauer S, Munroe PB, Petersen SE. The Effect of Blood Lipids on the Left Ventricle: A Mendelian Randomization Study. Journal of the American College of Cardiology. 2020;76:2477–2488.
  6. Aung N, Khanji MY, Munroe PB, Petersen SE. Causal Inference for Genetic Obesity, Cardiometabolic Profile and COVID-19 Susceptibility: A Mendelian Randomization Study. Front Genet. 2020;11: doi:10.3389/fgene.2020.586308.
  7. Aung N, Vargas JD, Chaojie Y, Cabrera CP, Warren HR, Fung K, et al. Genome-wide association study of left ventricular image-derived phenotypes identifies fourteen loci implicated in cardiogenesis and heart failure development. Circulation. 2019;140:1318–1330.
  8. Shah RA, Asatryan B, Sharaf Dabbagh G, Aung N, et al. Frequency, penetrance, and variable expressivity of dilated cardiomyopathy–associated putative pathogenic gene variants in UK Biobank participants. Circulation. 2022;146:110–124.
  9. Aung N, Doimo S, Ricci F, Sanghvi MM, Pedrosa C, et al. Prognostic Significance of Left Ventricular Noncompaction: Systematic Review and Meta-Analysis of Observational Studies. Circulation: Cardiovascular Imaging. 2020;13:e009712.
  10. Xia Y, Chen X, Ravikumar N, Kelly C, Attar R, Aung N, et al. Automatic 3D+t four-chamber CMR quantification of the UK biobank: integrating imaging and non-imaging data priors at scale. Medical Image Analysis. 2022;80:102498.
  11. Ricci F^, Aung N^ (^Joint first-author), Thomson R, et al. Pulmonary blood volume index as a quantitative biomarker of haemodynamic congestion in hypertrophic cardiomyopathy. Eur Heart J Cardiovasc Imaging. 2019;20:1368–1376.
  12. Raisi-Estabragh Z, McCracken C, Condurache D, Aung N, et al. Left atrial structure and function are associated with cardiovascular outcomes independent of left ventricular measures: a UK Biobank CMR study. European Heart Journal - Cardiovascular Imaging. 2022;23:1191–1200.
  13. Ricci F, Aung N, Gallina S, Zemrak F, Fung K, et al. Cardiovascular magnetic resonance reference values of mitral and tricuspid annular dimensions: the UK Biobank cohort. J Cardiovasc Magn Reson; 2020;23:5.
  14. Simon J, Fung K, Kolossváry M, Sanghvi MM, Aung N, et al. Sex-specific associations between alcohol consumption, cardiac morphology and function as assessed by magnetic resonance imaging - Insights from the UK Biobank Population Study. Eur Heart J Cardiovasc Imaging; 2021;22:1009–1016.
  15. Aung N, Sanghvi MM, Zemrak F, Lee AM, Cooper JA, Paiva JM, et al. Association Between Ambient Air Pollution and Cardiac Morpho-Functional Phenotypes: Insights From the UK Biobank Population Imaging Study. Circulation. 2018;138:2175–2186.
  16. Aung N, Zemrak F, Mohiddin SA, Petersen SE. LV Noncompaction Cardiomyopathy or Just a Lot of Trabeculations? JACC: Cardiovascular Imaging. 2017;10:704–707.

Book Chapters

  1. Abdulkareem M, Aung N, Petersen SE. Chapter 28: Artificial Intelligence and Biobanks. In Artificial Intelligence in Cardiothoracic Imaging. Springer International Publishing; 2022.

Review articles and other publications

  1. Petersen SE, Aung N. Benefits of Machine Learning to Predict Survival Using Stress Perfusion CMR and Basic Clinical Information. JACC Cardiovasc Imaging. 2022;S1936-878X(22)00408–9 [Editorial]
  2. Khanji MY, Aung N, Chahal CAA, Petersen SE. COVID-19 and the UK Biobank—Opportunities and Challenges for Research and Collaboration With Other Large Population Studies. Front Cardiovasc Med. 2020;7:156. [Review]
  3. Ricci F, De Innocentiis C, Verrengia E, Ceriello L, Mantini C, Pietrangelo C, Irsuti F, Gabriele S, D’Alleva A, Khanji MY, Aung N, et al. The Role of Multimodality Cardiovascular Imaging in Peripartum Cardiomyopathy. Front Cardiovasc Med. 2020 (epub ahead of print) [Review]
  4. De Innocentiis C, Ricci F, Khanji MY, Aung N, et al. Athlete’s Heart: Diagnostic Challenges and Future Perspectives. Sports Med. 2018;48:2463–2477. [Review]
  5. Khanji MY, Jensen MT, Kenawy AA, Raisi-Estabragh Z, Paiva JM, Aung N, et al. Association Between Recreational Cannabis Use and Cardiac Structure and Function. J Am Coll Cardiol Img. 2020;13:886–888. [Letter]
  6. Hann E, Biasiolli L, Zhang Q, Popescu IA, Werys K, Lukaschuk E, Carapella V, Paiva JM, Aung N, et al. Quality Control-Driven Image Segmentation Towards Reliable Automatic Image Analysis in Large-Scale Cardiovascular Magnetic Resonance Aortic Cine Imaging. In: Shen D, Liu T, Peters TM, Staib LH, Essert C, Zhou S, Yap P-T, Khan A, editors. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. Cham: Springer International Publishing; 2019. p. 750–758. [Conference paper]
  7. Gilbert K, Suinesiaputra A, Neubauer S, Piechnik S, Aung N, et al. End-Diastolic and End-Systolic LV Morphology in the Presence of Cardiovascular Risk Factors: A UK Biobank Study. In: Coudière Y, Ozenne V, Vigmond E, Zemzemi N, editors. Functional Imaging and Modeling of the Heart. Cham: Springer International Publishing; 2019. p. 304–312. [Conference paper]
  8. Robinson R, Oktay O, Bai W, Valindria VV, Sanghvi MM, Aung N, et al. Real-Time Prediction of Segmentation Quality. In: Frangi AF, Schnabel JA, Davatzikos C, Alberola-López C, Fichtinger G, editors. Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. Cham: Springer International Publishing; 2018. p. 578–585. [Conference paper]
  9. Jiménez-Ruiz E, Carapella V, Lukaschuk E, Aung N, et al. Towards the Creation of the Cardiovascular Magnetic Resonance Quality Assessment Ontology (CMR-QA). In: SWAT4LS. 2016 [Conference paper]
  10. Aung N, Zemrak F, Petersen SE. Left Ventricular Noncompaction, or Is It? J Am Coll Cardiol. 2016;68:2182–2184. [Editorial]
  11. Carapella V, Jiménez-Ruiz E, Lukaschuk E, Aung N, et al. Towards the Semantic Enrichment of Free-Text Annotation of Image Quality Assessment for UK Biobank Cardiac Cine MRI Scans. In: Carneiro G, Mateus D, Peter L, Bradley A, Tavares JMRS, Belagiannis V, Papa JP, Nascimento JC, Loog M, Lu Z, Cardoso JS, Cornebise J, editors. Deep Learning and Data Labeling for Medical Applications. Cham: Springer International Publishing; 2016. p. 238–248. [Conference paper].

Sponsors

Collaborators

Internal

External

  • Dr Jose Vargas (VA Medical Centre, Washington DC)
  • Dr Luis Lopes (UCL)
  • Dr Anawar Chalal (U Penn)

Industry

  • Nvidia Corp

News

Disclosures

No disclosures.

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