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  • Review Article
  • Published:

Cardiovascular calcification: artificial intelligence and big data accelerate mechanistic discovery

Nature Reviews Cardiology volume 16, pages 261–274 (2019)Cite this article

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Abstract

Cardiovascular calcification is a health disorder with increasing prevalence and high morbidity and mortality. The only available therapeutic options for calcific vascular and valvular heart disease are invasive transcatheter procedures or surgeries that do not fully address the wide spectrum of these conditions; therefore, an urgent need exists for medical options. Cardiovascular calcification is an active process, which provides a potential opportunity for effective therapeutic targeting. Numerous biological processes are involved in calcific disease, including matrix remodelling, transcriptional regulation, mitochondrial dysfunction, oxidative stress, calcium and phosphate signalling, endoplasmic reticulum stress, lipid and mineral metabolism, autophagy, inflammation, apoptosis, loss of mineralization inhibition, impaired mineral resorption, cellular senescence and extracellular vesicles that act as precursors of microcalcification. Advances in molecular imaging and big data technology, including in multiomics and network medicine, and the integration of these approaches are helping to provide a more comprehensive map of human disease. In this Review, we discuss ectopic calcification processes in the cardiovascular system, with an emphasis on emerging mechanistic knowledge obtained through patient data and advances in imaging methods, experimental models and multiomics-generated big data. We also highlight the potential and challenges of artificial intelligence, machine learning and deep learning to integrate imaging and mechanistic data for drug discovery.

Key points

  • Cardiovascular calcification is an urgent worldwide health problem with no available treatments beyond surgery and invasive transcatheter valve replacement; therefore, an unmet need exists for medical options.

  • Current methods for cardiovascular calcification imaging are mostly limited to advanced calcification and miss clinically relevant early microcalcifications; implementation of advanced imaging tools and artificial intelligence might improve diagnostics and risk assessment.

  • Several biological processes contribute to cardiovascular calcification, including extracellular vesicle release, matrix remodelling, loss of mineral-deposition inhibition, impaired mineral resorption, oxidative stress, mitochondrial dysfunction, inflammation and stem and progenitor cell programmes.

  • Multiomics data integration reveals novel disease pathways and therapeutic targets, but its implementation in cardiovascular calcification research is failing to keep pace with other research fields, such as oncology.

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Fig. 1: Cardiovascular calcification discovery pipeline.
Fig. 2: Artificial intelligence: terminology, advantages and current challenges.
Fig. 3: STRING database networks for arterial and valvular calcification.
Fig. 4: Novel mechanistic pathways driving cardiovascular calcification.

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Acknowledgements

Reviewer information

Nature Reviews Cardiology thanks L. J. Schurgers, M. Y. Speer and the other, anonymous reviewer(s) for their contribution to the peer review of this work.

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  1. Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Maximillian A. Rogers & Elena Aikawa

  2. Center for Excellence in Vascular Biology, Cardiovascular Division, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Elena Aikawa

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M.A.R. researched data for the article and wrote the manuscript. E.A. contributed substantially to the concept and discussion and revised and edited the manuscript.

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Rogers, M.A., Aikawa, E. Cardiovascular calcification: artificial intelligence and big data accelerate mechanistic discovery. Nat Rev Cardiol 16, 261–274 (2019). https://doi.org/10.1038/s41569-018-0123-8

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