April 17, 2024

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Multi-organ imaging demonstrates the heart-brain-liver axis in UK Biobank participants

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Multi-organ imaging demonstrates the heart-brain-liver axis in UK Biobank participants
  • Deckers, K. et al. Coronary heart disease and risk for cognitive impairment or dementia: Systematic review and meta-analysis. PLoS ONE 12, e0184244 (2017).

    Article 

    Google Scholar
     

  • Stahl, E. P. et al. Nonalcoholic Fatty Liver Disease and the Heart: JACC State-of-the-Art Review. J. Am. Coll. Cardiol. 73, 948–963 (2019).

    Article 

    Google Scholar
     

  • Butterworth, R. F. The liver–brain axis in liver failure: neuroinflammation and encephalopathy. Nat. Rev. Gastroenterol. Hepatol. 10, 522–528 (2013).

    Article 
    CAS 

    Google Scholar
     

  • Anstee, Q. M., Targher, G. & Day, C. P. Progression of NAFLD to diabetes mellitus, cardiovascular disease or cirrhosis. Nat. Rev. Gastroenterol. Hepatol. 10, 330–344 (2013).

    Article 
    CAS 

    Google Scholar
     

  • Iadecola, C. & Gottesman, R. F. Neurovascular and Cognitive Dysfunction in Hypertension: Epidemiology, Pathobiology, and Treatment. Circ. Res. 124, 1025–1044 (2019).

    Article 
    CAS 

    Google Scholar
     

  • Lee, L., Pandey, A., Maron, B. & Loscalzo, J. Network medicine in Cardiovascular Research. Cardiovasc. Res. 117, 2186–2202 (2021).

    Article 
    CAS 

    Google Scholar
     

  • Targher, G., Tilg, H. & Byrne, C. D. Non-alcoholic fatty liver disease: a multisystem disease requiring a multidisciplinary and holistic approach. Lancet Gastroenterol. Hepatol. 6, 578–588 (2021).

    Article 

    Google Scholar
     

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

    Article 

    Google Scholar
     

  • Lombardi, R., Fargion, S. & Fracanzani, A. L. Brain involvement in non-alcoholic fatty liver disease (NAFLD): A systematic review. Dig. Liver Dis. 51, 1214–1222 (2019).

    Article 

    Google Scholar
     

  • Ismaiel, A. & Dumitraşcu, D. L. Cardiovascular Risk in Fatty Liver Disease: The Liver-Heart Axis—Literature Review. Front. Med. 6, 202 (2019).

    Article 

    Google Scholar
     

  • Jokinen, H. et al. Global Burden of Small Vessel Disease-Related Brain Changes on MRI Predicts Cognitive and Functional Decline. Stroke 51, 170–178 (2020).

    Article 

    Google Scholar
     

  • Nwabuo, C. C. et al. Left ventricular global function index predicts incident heart failure and cardiovascular disease in young adults: The coronary artery risk development in young adults (CARDIA) study. Eur. Heart J. Cardiovasc. Imaging 20, 533–540 (2019).

    Article 

    Google Scholar
     

  • Jayaswal, A. N. A. et al. Prognostic value of multiparametric magnetic resonance imaging, transient elastography and blood-based fibrosis markers in patients with chronic liver disease. Liver Int 40, 3071–3082 (2020).

    Article 
    CAS 

    Google Scholar
     

  • Mojtahed, A. et al. Reference range of liver corrected T1 values in a population at low risk for fatty liver disease—a UK Biobank sub-study, with an appendix of interesting cases. Abdom. Radiol. 44, 72–84 (2019).

    Article 
    CAS 

    Google Scholar
     

  • Beller, E. et al. Hepatic fat is superior to BMI, visceral and pancreatic fat as a potential risk biomarker for neurodegenerative disease. Eur. Radiol. 29, 6662–6670 (2019).

    Article 

    Google Scholar
     

  • Weinstein, G. et al. Association of nonalcoholic fatty liver disease with lower brain volume in healthy middle-aged adults in the Framingham Study. JAMA Neurol. 75, 97–104 (2018).

    Article 

    Google Scholar
     

  • Gurholt, T. P. et al. Population-based body–brain mapping links brain morphology with anthropometrics and body composition. Transl. Psychiatry 11, 295 (2021).

    Article 

    Google Scholar
     

  • VanWagner, L. B. et al. Nonalcoholic fatty liver disease and measures of early brain health in middle-aged adults: The CARDIA study. Obesity 25, 642–651 (2017).

    Article 
    CAS 

    Google Scholar
     

  • Rovira, A. et al. Decreased white matter lesion volume and improved cognitive function after liver transplantation. Hepatology 46, 1485–1490 (2007).

    Article 

    Google Scholar
     

  • Jang, H. et al. Non-alcoholic fatty liver disease and cerebral small vessel disease in Korean cognitively normal individuals. Sci. Rep. 9, 1814 (2019).

    Article 
    ADS 

    Google Scholar
     

  • Petta, S. et al. The Presence of White Matter Lesions Is Associated With the Fibrosis Severity of Nonalcoholic Fatty Liver Disease. Med. (Baltim.) 95, e3446 (2016).

    Article 
    CAS 

    Google Scholar
     

  • Parisinos, C. A. et al. Genome-wide and Mendelian randomisation studies of liver MRI yield insights into the pathogenesis of steatohepatitis. J. Hepatol. 73, 241–251 (2020).

    Article 
    CAS 

    Google Scholar
     

  • Dekkers, I. A., Jansen, P. R. & Lamb, H. J. Obesity, Brain Volume, and White Matter Microstructure at MRI: A Cross-sectional UK Biobank Study. Radiology 291, 763–771 (2019).

    Article 

    Google Scholar
     

  • Poitelon, Y., Kopec, A. M. & Belin, S. Myelin Fat Facts: An Overview of Lipids and Fatty Acid Metabolism. Cells 9, 812 (2020).

    Article 
    CAS 

    Google Scholar
     

  • Pase, M. P. et al. Association of Aortic Stiffness with Cognition and Brain Aging in Young and Middle-Aged Adults: The Framingham Third Generation Cohort Study. Hypertension 67, 513–519 (2016).

    Article 
    CAS 

    Google Scholar
     

  • Pase, M. P. et al. Vascular risk at younger ages most strongly associates with current and future brain volume. Neurology 91, e1479 (2018).

    Article 

    Google Scholar
     

  • Singer, J., Trollor, J. N., Baune, B. T., Sachdev, P. S. & Smith, E. Arterial stiffness, the brain and cognition: A systematic review. Ageing Research Reviews 15, 16–27 (2014).

  • G, D. et al. Heart and Brain: Complex Relationships for Left Ventricular Dysfunction. Curr. Cardiol. Rep. 22, 72 (2020).

    Article 

    Google Scholar
     

  • van Hout, M. J. P. et al. Associations between left ventricular function, vascular function and measures of cerebral small vessel disease: a cross-sectional magnetic resonance imaging study of the UK Biobank. Eur. Radiol. 31, 5068–5076 (2021).

    Article 

    Google Scholar
     

  • Moore, E. E. et al. Increased Left Ventricular Mass Index Is Associated With Compromised White Matter Microstructure Among Older Adults. J. Am. Heart Assoc. 7, e009041 (2018).

    Article 
    CAS 

    Google Scholar
     

  • Salerno, J. A. et al. Brain atrophy in hypertension: A volumetric magnetic resonance imaging study. Hypertension 20, 340–348 (1992).

    Article 
    CAS 

    Google Scholar
     

  • Dickie, D. A. et al. Vascular risk factors and progression of white matter hyperintensities in the Lothian Birth Cohort 1936. Neurobiol. Aging 42, 116–123 (2016).

    Article 

    Google Scholar
     

  • Suzuki, H. et al. Abnormal brain white matter microstructure is associated with both pre-hypertension and hypertension. PLoS ONE 12, e0187600 (2017).

    Article 

    Google Scholar
     

  • Wassenaar, T. M., Yaffe, K., van der Werf, Y. D. & Sexton, C. E. Associations between modifiable risk factors and white matter of the aging brain: insights from diffusion tensor imaging studies. Neurobiol. Aging 80, 56–70 (2019).

    Article 

    Google Scholar
     

  • Veldsman, M. et al. Cerebrovascular risk factors impact frontoparietal network integrity and executive function in healthy ageing. Nat. Commun. 11, 4340 (2020).

    Article 
    ADS 
    CAS 

    Google Scholar
     

  • Tsao, C. W. et al. Left ventricular structure and risk of cardiovascular events: A framingham heart study cardiac magnetic resonance study. J. Am. Heart Assoc. 4, e002188 (2015).

    Article 

    Google Scholar
     

  • Borges-Canha, M. et al. Association between nonalcoholic fatty liver disease and cardiac function and structure—a meta-analysis. Endocrine 66, 467–476 (2019).

    Article 
    CAS 

    Google Scholar
     

  • Raisi-Estabragh, Z. et al. Left atrial structure and function are associated with cardiovascular outcomes independent of left ventricular measures: a UK Biobank CMR study. Eur. Heart J. – Cardiovasc. Imaging jeac119, https://doi.org/10.1093/EHJCI/JEAB266 (2021).

  • Littlejohns, T. J. et al. The UK Biobank imaging enhancement of 100,000 participants: rationale, data collection, management and future directions. Nat. Commun. 11, 2624 (2020).

    Article 
    ADS 
    CAS 

    Google Scholar
     

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

    Article 
    CAS 

    Google Scholar
     

  • Petersen, S. E. et al. UK Biobank’s cardiovascular magnetic resonance protocol. J. Cardiovasc. Magn. Reson. 18, 8 (2016).

    Article 

    Google Scholar
     

  • Wilman, H. R. et al. Characterisation of liver fat in the UK Biobank cohort. PLoS ONE 12, e0172921 (2017).

    Article 

    Google Scholar
     

  • McKay, A. et al. Measurement of liver iron by magnetic resonance imaging in the UK Biobank population. PLOS ONE 13, e0209340 (2018).

    Article 

    Google Scholar
     

  • Bachtiar, V. et al. Repeatability and reproducibility of multiparametric magnetic resonance imaging of the liver. PloS One 14, e0214921 (2019).

    Article 
    CAS 

    Google Scholar
     

  • Harrison, S. A. et al. Prospective evaluation of the prevalence of non-alcoholic fatty liver disease and steatohepatitis in a large middle-aged US cohort. J. Hepatol. 75, 284–291 (2021).

    Article 

    Google Scholar
     

  • Smith, S. M., Alfaro-Almagro, F. & Miller, K. L. UK Biobank – Brain Imaging Documentation https://biobank.ctsu.ox.ac.uk/crystal/crystal/docs/brain_mri.pdf (Date accessed: 29 October 2021) (2020).

  • 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 

    Google Scholar
     

  • van den Heuvel, M. P. & Yeo, B. T. T. A Spotlight on Bridging Microscale and Macroscale Human Brain Architecture. Neuron 93, 1248–1251 (2017).

    Article 

    Google Scholar
     

  • Smith, S. M. et al. Accurate, robust, and automated longitudinal and cross-sectional brain change analysis. NeuroImage 17, 479–489 (2002).

    Article 

    Google Scholar
     

  • Prins, N. D. & Scheltens, P. White matter hyperintensities, cognitive impairment and dementia: an update. Nat. Rev. Neurol. 11, 157–165 (2015).

    Article 

    Google Scholar
     

  • Debette, S., Schilling, S., Duperron, M. G., Larsson, S. C. & Markus, H. S. Clinical Significance of Magnetic Resonance Imaging Markers of Vascular Brain Injury: A Systematic Review and Meta-analysis. JAMA Neurol. 76, 81–94 (2019).

    Article 

    Google Scholar
     

  • Zhang, H., Schneider, T., Wheeler-Kingshott, C. A. & Alexander, D. C. NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain. NeuroImage 61, 1000–1016 (2012).

    Article 

    Google Scholar
     

  • Taoka, T. et al. White matter microstructural changes in tuberous sclerosis: Evaluation by neurite orientation dispersion and density imaging (NODDI) and diffusion tensor images. Sci. Rep. 2020 101 10, 1–9 (2020).


    Google Scholar
     

  • Venkatesh, A., Stark, S. M., Stark, C. E. L. & Bennett, I. J. Age- and memory- related differences in hippocampal gray matter integrity are better captured by NODDI compared to single-tensor diffusion imaging. Neurobiol. Aging 96, 12–21 ne to (2020).

    Article 

    Google Scholar
     

  • Jones, D. K., Knösche, T. R. & Turner, R. White matter integrity, fiber count, and other fallacies: The do’s and don’ts of diffusion MRI. NeuroImage 73, 239–254 (2013).

    Article 

    Google Scholar
     

  • Peterson, R. A. & Cavanaugh, J. E. Ordered quantile normalization: a semiparametric transformation built for the cross-validation era. J. Appl. Stat. 47, 2312–2327 (2020).

    Article 
    MathSciNet 
    MATH 

    Google Scholar
     

  • Petersen, S. E. et al. Reference ranges for cardiac structure and function using cardiovascular magnetic resonance (CMR) in Caucasians from the UK Biobank population cohort. J. Cardiovasc. Magn. Reson. 19, 1–19 (2017).

    Article 

    Google Scholar
     

  • Bai, W. et al. Automated cardiovascular magnetic resonance image analysis with fully convolutional networks. J. Cardiovasc. Magn. Reson. 20, 1–12 (2018).

    Article 

    Google Scholar
     

  • Raisi-Estabragh, Z. et al. Associations of cognitive performance with cardiovascular magnetic resonance phenotypes in the UK Biobank. Eur. Heart J. – Cardiovasc. Imaging jeab075 (2021).

  • Desai, M. Y. et al. LV Global Function Index Provides Incremental Prognostic Value Over LGE and LV GLS in HCM. JACC Cardiovasc. Imaging 13, 2052–2054 (2020).

    Article 

    Google Scholar
     

  • Biasiolli, L. et al. Automated localization and quality control of the aorta in cine CMR can significantly accelerate processing of the UK Biobank population data. PLOS ONE 14, e0212272 (2019).

    Article 
    CAS 

    Google Scholar
     

  • Redheuil, A. et al. Proximal aortic distensibility is an independent predictor of all-cause mortality and incident CV events: The MESA study. J. Am. Coll. Cardiol. 64, 2619–2629 (2014).

    Article 

    Google Scholar
     

  • Fawns-Ritchie, C. & Deary, I. J. Reliability and validity of the UK Biobank cognitive tests. PLOS ONE 15, e0231627 (2020).

    Article 
    CAS 

    Google Scholar
     

  • Lyall, D. M. et al. Cognitive Test Scores in UK Biobank: Data Reduction in 480,416 Participants and Longitudinal Stability in 20,346 Participants. PLOS ONE 11, e0154222 (2016).

    Article 

    Google Scholar
     

  • Craig, C. L. et al. International Physical Activity Questionnaire: 12-Country Reliability and Validity. Med Sci. Sports Exerc 35, 1381–1395 (2003).

    Article 

    Google Scholar
     

  • The IPAQ group. Guidelines for Data Processing and Analysis of the International Physical Activity Questionnaire (IPAQ) – Short and Long Forms. https://biobank.ndph.ox.ac.uk/ukb/ukb/docs/ipaq_analysis.pdf (Date accessed: 21 October 2021) (2005).

  • Alfaro-Almagro, F. et al. Confound modelling in UK Biobank brain imaging. NeuroImage 224, 117002 (2021).

    Article 

    Google Scholar
     

  • R. Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. in R Version 4.0.3 Vienna, Austria. URL https://www.R-project.org/ (2019).

  • RStudio Team. RStudio: Integrated Development Environment for R. (RStudio, PBC, Boston, MA, 2020). http://www.rstudio.com/.

  • Rosseel, Y. Lavaan: An R package for structural equation modeling. J. Stat. Softw. 48, 1–36 (2012).

    Article 

    Google Scholar
     

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