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A population-based phenome-wide association study of cardiac and aortic structure and function

Nature Medicine volume 26, pages 1654–1662 (2020)Cite this article

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Abstract

Differences in cardiac and aortic structure and function are associated with cardiovascular diseases and a wide range of other types of disease. Here we analyzed cardiovascular magnetic resonance images from a population-based study, the UK Biobank, using an automated machine-learning-based analysis pipeline. We report a comprehensive range of structural and functional phenotypes for the heart and aorta across 26,893 participants, and explore how these phenotypes vary according to sex, age and major cardiovascular risk factors. We extended this analysis with a phenome-wide association study, in which we tested for correlations of a wide range of non-imaging phenotypes of the participants with imaging phenotypes. We further explored the associations of imaging phenotypes with early-life factors, mental health and cognitive function using both observational analysis and Mendelian randomization. Our study illustrates how population-based cardiac and aortic imaging phenotypes can be used to better define cardiovascular disease risks as well as heart–brain health interactions, highlighting new opportunities for studying disease mechanisms and developing image-based biomarkers.

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Fig. 1: Automated CMR image analysis pipeline.
Fig. 2: Associations of selected imaging phenotypes with sex and age.
Fig. 3: Regression coefficients for cardiac and aortic imaging phenotypes on demographics (blue), anthropometrics (green) and cardiovascular risk factors (red).
Fig. 4: Associations of cardiac and aortic imaging phenotypes with common diseases.
Fig. 5: Results from the PheWAS.

Data availability

The raw imaging data and non-imaging participant characteristics are available from UK Biobank via a standard application procedure at http://www.ukbiobank.ac.uk/register-apply. The image analysis code is available at https://github.com/baiwenjia/ukbb_cardiac. For PheWAS, category ID numbers and field ID numbers are defined in ukb_field_categories.py and included in perform_phenome_wide_association.py. The associations between imaging phenotypes and non-imaging phenotypes can be browsed at https://heartvis.doc.ic.ac.uk.

References

  1. Ponikowski, P. et al. 2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure. Eur. J. Heart Fail. 18, 891–975 (2016).

    Article  PubMed  Google Scholar 

  2. Erbel, R. et al. 2014 ESC Guidelines on the diagnosis and treatment of aortic diseases. Eur. Heart J. 35, 2873–2926 (2014).

    Article  PubMed  Google Scholar 

  3. Watz, H. et al. Decreasing cardiac chamber sizes and associated heart dysfunction in COPD. Chest 138, 32–38 (2010).

    Article  PubMed  Google Scholar 

  4. Alonso-Gonzalez, R. et al. Abnormal lung function in adults with congenital heart disease: prevalence, relation to cardiac anatomy, and association with survival. Circulation 127, 882–890 (2013).

    Article  PubMed  Google Scholar 

  5. Gansevoort, R. T. et al. Chronic kidney disease and cardiovascular risk: epidemiology, mechanisms, and prevention. Lancet 382, 339–352 (2013).

    Article  PubMed  Google Scholar 

  6. de Bruijn, R. F. & Ikram, M. A. Cardiovascular risk factors and future risk of Alzheimer’s disease. BMC Med. 12, 130 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Armstrong, A. C. et al. LV mass assessed by echocardiography and CMR, cardiovascular outcomes, and medical practice. JACC Cardiovasc. Imaging 5, 837–848 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Jefferson, A. L. et al. Relation of left ventricular ejection fraction to cognitive aging (from the Framingham Heart Study). Am. J. Cardiol. 108, 1346–1351 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Bild, D. E. Multi-ethnic study of atherosclerosis: objectives and design. Am. J. Epidemiol. 156, 871–881 (2002).

    Article  PubMed  Google Scholar 

  10. Kadish, A. H. et al. Rationale and design for the defibrillators to reduce risk by magnetic resonance imaging evaluation (DETERMINE) trial. J. Cardiovascular Electrophysiol. 20, 982–987 (2009).

    Article  Google Scholar 

  11. Victor, R. G. et al. The Dallas Heart Study: a population-based probability sample for the multidisciplinary study of ethnic differences in cardiovascular health. Am. J. Cardiol. 93, 1473–1480 (2004).

    Article  PubMed  Google Scholar 

  12. Bello, G. A. et al. Deep-learning cardiac motion analysis for human survival prediction. Nat. Mach. Intell. 1, 95–104 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Collins, R. What makes UK Biobank special? Lancet 379, 1173–1174 (2012).

    Article  PubMed  Google Scholar 

  14. Sudlow, C. et al. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12, e1001779 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Manolio, T. A. UK Biobank debuts as a powerful resource for genomic research. Nat. Med. 24, 1792–1794 (2018).

    Article  CAS  PubMed  Google Scholar 

  16. Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Alfaro-Almagro, F. et al. Image processing and quality control for the first 10,000 brain imaging datasets from UK Biobank. NeuroImage 166, 400–424 (2018).

    Article  PubMed  Google Scholar 

  18. Miller, K. L. et al. Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat. Neurosci. 19, 1523–1536 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Elliott, L. T. et al. Genome-wide association studies of brain imaging phenotypes in UK Biobank. Nature 562, 210–216 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Bai, W. et al. Automated cardiovascular magnetic resonance image analysis with fully convolutional networks. J. Cardiovascular Magn. Reson. 20, 65 (2018).

    Article  Google Scholar 

  21. Bai, W. et al. Recurrent neural networks for aortic image sequence segmentation with sparse annotations. In International Conference on Medical Image Computing and Computer-Assisted Intervention 586–594 (2018).

  22. Heckbert, S. R. et al. Traditional cardiovascular risk factors in relation to left ventricular mass, volume, and systolic function by cardiac magnetic resonance imaging. J. Am. Coll. Cardiol. 48, 2285–2292 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Geelhoed, J. J. M. & Jaddoe, V. W. V. Early influences on cardiovascular and renal development. Eur. J. Epidemiol. 25, 677–692 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Hardy, R., Ghosh, A. K., Deanfield, J., Kuh, D. & Hughes, A. D. Birthweight, childhood growth and left ventricular structure at age 60–64 years in a British birth cohort study. Int. J. Epidemiol. 45, 1091–1102 (2016).

  25. Chaddha, A., Robinson, E. A., Kline-Rogers, E., Alexandris-Souphis, T. & Rubenfire, M. Mental health and cardiovascular disease. Am. J. Med. 129, 1145–1148 (2016).

    Article  PubMed  Google Scholar 

  26. Sabayan, B. et al. Cardiac hemodynamics are linked with structural and functional features of brain aging: the age, gene/environment susceptibility (AGES)‐Reykjavik Study. J. Am. Heart Assoc. 4, e001294 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Friedrich, M. G. Interplay of cardiac and cognitive function: how much do we really understand? J. Am. Heart Assoc. 4, e001685 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Bowden, J. et al. Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression. Int. J. Epidemiol. 47, 1264–1278 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Redheuil, A. et al. Reduced ascending aortic strain and distensibility: earliest manifestations of vascular aging in humans. Hypertension 55, 319–326 (2010).

    Article  CAS  PubMed  Google Scholar 

  30. Nethononda, R. M. et al. Gender specific patterns of age-related decline in aortic stiffness: a cardiovascular magnetic resonance study including normal ranges. J. Cardiovascular Magn. Reson. 17, 20 (2015).

    Article  Google Scholar 

  31. Gibson, L. M. et al. Factors associated with potentially serious incidental findings and with serious final diagnoses on multi-modal imaging in the UK Biobank Imaging Study: a prospective cohort study. PLoS ONE 14, e0218267 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Fry, A. et al. Comparison of sociodemographic and health-related characteristics of UK Biobank participants with those of the general population. Am. J. Epidemiol. 186, 1026–1034 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Nadruz, W. et al. Smoking and cardiac structure and function in the elderly. Circ. Cardiovasc. Imaging 9, e004950 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Levy, D., Garrison, R. J., Savage, D. D., Kannel, W. B. & Castelli, W. P. Prognostic implications of echocardiographically determined left ventricular mass in the Framingham Heart Study. N. Engl. J. Med. 322, 1561–1566 (1990).

    Article  CAS  PubMed  Google Scholar 

  35. Steingrub, J. S., Tidswell, M. & Higgins, T. L. Hemodynamic consequences of heart–lung interactions. J. Intensive Care Med. 18, 92–99 (2003).

    Article  PubMed  Google Scholar 

  36. Repessé, X., Charron, C. & Vieillard-Baron, A. Acute cor pulmonale in ARDS: rationale for protecting the right ventricle. Chest 147, 259–265 (2015).

    Article  PubMed  Google Scholar 

  37. Alastruey, J., Xiao, N., Fok, H., Schaeffter, T. & Figueroa, C. A. On the impact of modelling assumptions in multi-scale, subject-specific models of aortic haemodynamics. J. R. Soc. Interface 13, 20160073 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Jiang, B., Godfrey, K. M., Martyn, C. N. & Gale, C. R. Birth weight and cardiac structure in children. Pediatrics 117, e257–e261 (2006).

    Article  PubMed  Google Scholar 

  39. Kamimura, D. et al. Increased proximal aortic diameter is associated with risk of cardiovascular events and all‐cause mortality in blacks the Jackson Heart Study. J. Am. Heart Assoc. 6, e005005 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  40. de Haan, L., Egberts, A. & Heerdink, E. The relation between risk-taking behavior and alcohol use in young adults is different for men and women. Drug Alcohol Depend. 155, 222–227 (2015).

    Article  PubMed  Google Scholar 

  41. Kreek, M. J., Nielsen, D. A., Butelman, E. R. & LaForge, K. S. Genetic influences on impulsivity, risk taking, stress responsivity and vulnerability to drug abuse and addiction. Nat. Neurosci. 8, 1450–1457 (2005).

    Article  CAS  PubMed  Google Scholar 

  42. Ambrose, J. A. & Barua, R. S. The pathophysiology of cigarette smoking and cardiovascular disease. J. Am. Coll. Cardiol. 43, 1731–1737 (2004).

    Article  CAS  PubMed  Google Scholar 

  43. Ronksley, P. E., Brien, S. E., Turner, B. J., Mukamal, K. J. & Ghali, W. A. Association of alcohol consumption with selected cardiovascular disease outcomes: a systematic review and meta-analysis. BMJ 342, d671–d671 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Rozanski, A., Blumenthal, J. A. & Kaplan, J. Impact of psychological factors on the pathogenesis of cardiovascular disease and implications for therapy. Circulation 99, 2192–2217 (1999).

    Article  CAS  PubMed  Google Scholar 

  45. Strawbridge, R. J. et al. Genetics of self-reported risk-taking behaviour, trans-ethnic consistency and relevance to brain gene expression. Transl. Psychiatry 8, 178 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Qiu, C. & Fratiglioni, L. A major role for cardiovascular burden in age-related cognitive decline. Nat. Rev. Cardiol. 12, 267–277 (2015).

    Article  PubMed  Google Scholar 

  47. Gorelick, P. B. et al. Vascular contributions to cognitive impairment and dementia. Stroke 42, 2672–2713 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  48. van Buchem, M. A. et al. The heart-brain connection: a multidisciplinary approach targeting a missing link in the pathophysiology of vascular cognitive impairment. J. Alzheimers Dis. 42, S443–S451 (2014).

    Article  PubMed  Google Scholar 

  49. Royle, N. A. et al. Estimated maximal and current brain volume predict cognitive ability in old age. Neurobiol. Aging 34, 2726–2733 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Arnott, C. et al. Subtle increases in heart size persist into adulthood in growth restricted babies: the cardiovascular risk in Young Finns Study. Open Heart 2, e000265 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Simpson, H. J. et al. Left ventricular hypertrophy: reduction of blood pressure already in the normal range further regresses left ventricular mass. Heart 96, 148–152 (2010).

    Article  CAS  PubMed  Google Scholar 

  52. Upadhya, B. et al. Effect of intensive blood pressure reduction on left ventricular mass, structure, function, and fibrosis in the SPRINT-HEART. Hypertension 74, 276–284 (2019).

    Article  CAS  Google Scholar 

  53. Cruickshank, K. et al. Aortic pulse-wave velocity and its relationship to mortality in diabetes and glucose intolerance. Circulation 106, 2085–2090 (2002).

    Article  PubMed  Google Scholar 

  54. Bhuva, A. N. et al. A multicenter, scan-rescan, human and machine learning CMR study to test generalizability and precision in imaging biomarker analysis. Circ. Cardiovasc. Imaging 12, e009214 (2019).

    Article  PubMed  Google Scholar 

  55. Marcus, G. Deep learning: a critical appraisal. Preprint at https://arxiv.org/abs/1801.00631 (2018).

  56. Davies, N. M., Holmes, M. V. & Davey Smith, G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ 362, k601 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Burgess, S., Davies, N. M. & Thompson, S. G. Bias due to participant overlap in two-sample Mendelian randomization. Genet. Epidemiol. 40, 597–608 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Petersen, S. E. et al. UK Biobank’s cardiovascular magnetic resonance protocol. J. Cardiovascular Magn. Reson. 18, 8 (2015).

    Article  Google Scholar 

  59. Cerqueira, M. D. et al. Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart. Circulation 105, 539–542 (2002).

    Article  PubMed  Google Scholar 

  60. Rueckert, D. et al. Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans. Med. Imaging 18, 712–721 (1999).

    Article  CAS  PubMed  Google Scholar 

  61. Tobon-Gomez, C. et al. Benchmarking framework for myocardial tracking and deformation algorithms: An open access database. Med. Image Anal. 17, 632–648 (2013).

    Article  CAS  PubMed  Google Scholar 

  62. Taylor, R. J. et al. Myocardial strain measurement with feature-tracking cardiovascular magnetic resonance: normal values. Eur. Heart J. Cardiovasc. Imaging 16, 871–881 (2015).

    Article  PubMed  Google Scholar 

  63. Schuster, A. et al. Cardiovascular magnetic resonance feature-tracking assessment of myocardial mechanics: intervendor agreement and considerations regarding reproducibility. Clin. Radiol. 70, 989–998 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Puyol-Anton, E. et al. Fully automated myocardial strain estimation from cine MRI using convolutional neural networks. In IEEE International Symposium on Biomedical Imaging 1139–1143 (2018).

  65. Tsang, T. S. et al. Prediction of cardiovascular outcomes with left atrial size. J. Am. Coll. Cardiol. 47, 1018–1023 (2006).

    Article  PubMed  Google Scholar 

  66. Maceira, A. M., Cosín-Sales, J., Roughton, M., Prasad, S. K. & Pennell, D. J. Reference left atrial dimensions and volumes by steady state free precession cardiovascular magnetic resonance. J. Cardiovascular Magn. Reson. 12, 65 (2010).

    Article  Google Scholar 

  67. Evangelou, E. et al. New alcohol-related genes suggest shared genetic mechanisms with neuropsychiatric disorders. Nat. Hum. Behav. 3, 950–961 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  68. Baron, R. M. & Kenny, D. A. The moderator–mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J. Personal. Soc. Psychol. 51, 1173–1182 (1986).

    Article  CAS  Google Scholar 

  69. Evangelou, E. et al. Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits. Nat. Genet. 50, 1412–1425 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Morris, A. et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat. Genet. 44, 981–990 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Warrington, N. M. et al. Maternal and fetal genetic effects on birth weight and their relevance to cardio-metabolic risk factors. Nat. Genet. 51, 804–814 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Linnér, R. K. et al. Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences. Nat. Genet. 51, 245–257 (2019).

    Article  PubMed Central  CAS  Google Scholar 

  73. Davies, G. et al. Genome-wide association studies establish that human intelligence is highly heritable and polygenic. Mol. Psychiatry 16, 996–1005 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Bowden, J., Davey Smith, G., Haycock, P. C. & Burgess, S. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet. Epidemiol. 40, 304–314 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  75. Burgess, S. & Thompson, S. G. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur. J. Epidemiol. 32, 377–389 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  76. Verbanck, M., Chen, C.-Y., Neale, B. & Do, R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat. Genet. 50, 693–698 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Hemani, G. et al. The MR-Base platform supports systematic causal inference across the human phenome. eLife 7, e34408 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We would like to thank H. Gao, D. Schneider-Luftman, T. J. W. Dawes and A. Kolbeinsson for fruitful discussion. This research was conducted using the UK Biobank Resource under Application Number 18545, using methods developed under Application Number 18545 or 2964. Images were reproduced with kind permission of UK Biobank. We wish to thank all UK Biobank participants and staff. This work is supported by the SmartHeart EPSRC Programme Grant (EP/P001009/1) and the Imperial BHF Centre of Excellence Grant (RE/18/4/34215). H.S. is supported by a research fellowship from the Uehara Memorial Foundation and the Grants-in-Aid program from the Japan Society for the Promotion of Science (20K07776). S.E.P. acknowledges support from the NIHR Barts Biomedical Research Centre and S.E.P. has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 825903 (euCanSHare project). S.E.P., S.N. and S.K.P. acknowledge the British Heart Foundation for funding the manual analysis to create a cardiovascular magnetic resonance imaging reference standard for the UK Biobank imaging resource in 5000 CMR scans (PG/14/89/31194). A.D. is funded by the Wellcome Trust seed award (206046/Z/17/Z). D.P.O. is supported by the Medical Research Council (MC-A651-53301) and British Heart Foundation (NH/17/1/32725, RG/19/6/34387, RE/18/4/34215). P.M.M. gratefully acknowledges support from the Edmond J. Safra Foundation and Lily Safra, the Imperial College Healthcare Trust Biomedical Research Centre, the EPSRC Centre for Mathematics in Precision Healthcare and the UK Dementia Research Institute.

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Author notes

  1. These authors jointly supervised this work: Paul M. Matthews, Daniel Rueckert.

Authors and Affiliations

  1. Data Science Institute, Imperial College London, London, UK

    Wenjia Bai, Shuo Wang, Florian Guitton & Yike Guo

  2. Department of Brain Sciences, Imperial College London, London, UK

    Wenjia Bai, Hideaki Suzuki & Paul M. Matthews

  3. Department of Cardiovascular Medicine, Tohoku University Hospital, Sendai, Japan

    Hideaki Suzuki

  4. Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan

    Hideaki Suzuki

  5. MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK

    Jian Huang, Evangelos Evangelou & Abbas Dehghan

  6. UK Dementia Research Institute, Imperial College London, London, UK

    Jian Huang, Abbas Dehghan & Paul M. Matthews

  7. National Heart and Lung Institute, Imperial College London, London, UK

    Catherine Francis & Martin R. Wilkins

  8. Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, UK

    Giacomo Tarroni & Daniel Rueckert

  9. CitAI Research Centre, Department of Computer Science, City University of London, London, UK

    Giacomo Tarroni

  10. NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, UK

    Nay Aung, Kenneth Fung & Steffen E. Petersen

  11. NIHR Oxford Biomedical Research Centre, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK

    Stefan K. Piechnik & Stefan Neubauer

  12. Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece

    Evangelos Evangelou

  13. MRC London Institute of Medical Sciences, Imperial College London, London, UK

    Declan P. O’Regan

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  1. Wenjia Bai
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Contributions

W.B. developed the analysis pipeline and wrote the manuscript; H.S. performed manual quality control of image segmentations and provided advice on clinical research; J.H. performed Mendelian randomization; C.F. provided support on genetic data analysis; S.W. developed the data visualization website; G.T. and Y.G. provided advice and support on the computational methodology; F.G. provided support on data management; H.S., N.A., K.F., S.E.P., S.K.P., S.N., E.E., A.D., D.P.O. and M.R.W. provided advice and support on clinical research; W.B., P.M.M. and D.R. conceived and designed the study. All authors read, contributed to revision and approved the manuscript.

Corresponding author

Correspondence to Wenjia Bai.

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Competing interests

S.E.P. acknowledges consultancy fees from Circle Cardiovascular Imaging. D.R. acknowledges consultancy fees from Circle Cardiovascular Imaging, Heartflow and IXICO. P.M.M. acknowledges consultancy fees from Roche, Adelphi Communications, Celgene and Biogen. He has received honoraria or speakers’ honoraria from Novartis, Biogen and Roche and has received research or educational funds from Biogen, Novartis, GlaxoSmithKline and Nodthera. He is a member of the scientific advisory board to the board of Ipsen Pharmaceuticals. The remaining authors declare no competing interests.

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Peer review information Michael Basson was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 The conditional plots of imaging phenotypes against birth weight.

The dark line denotes the conditional plot of an imaging phenotype against birth weight, with other variables (sex, age, sex * age, weight, height, SBP, DBP, current smoking status, alcohol intake, vigorous PA frequency, high cholesterol and diabetes) set to their mean. The grey area denotes the 95% confidence interval. n = 12,169 subjects were analysed with available birth weight information. The p-values were calculated from two-sided t-tests.

Extended Data Fig. 2 Mediation model for LVM, brain volume and fluid intelligence score.

The relationship between LVM and fluid intelligence score (path c) is 26% (difference between c and c’) mediated by total brain volume. n = 18,369 subjects were analysed with available fluid intelligence information. The values are depicted as regression coefficient (two-sided t-test p-value) for standardised imaging phenotypes.

Supplementary information

Supplementary Information

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Reporting Summary

Supplementary Tables

Supplementary Tables 19–26.

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Bai, W., Suzuki, H., Huang, J. et al. A population-based phenome-wide association study of cardiac and aortic structure and function. Nat Med 26, 1654–1662 (2020). https://doi.org/10.1038/s41591-020-1009-y

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