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J Thorac Imaging. 2019 Mar;34(2):116-125. doi: 10.1097/RTI.0000000000000391.

Noncontrast Chest Computed Tomographic Imaging of Obesity and the Metabolic Syndrome: Part I Cardiovascular Findings.

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Department of Diagnostic and Interventional Radiology, University Hospital of Heidelberg.
Translational Lung Research Center (TLRC) Heidelberg, Member of the German Center for Lung Research (DZL), Heidelberg.
Department of Diagnostic and Interventional Radiology, Medical Center, University of Freiburg.
Department of Radiology, Graduate School of Medical Science, University of the Ryukyus, Okinawa, Japan.
Department of Radiology, University of Wisconsin-Madison, Madison, WI.
Department of Radiology, University Hospital Hannover, Hannover, Germany.
Department of Radiology, Mayo Clinic, Rochester, MN.
Departments of Radiology.
Pulmonary Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA.
Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung.
Faculty of Medicine, School of Medicine, National Yang-Ming University, Taipei, Taiwan.
Edinburgh Imaging, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK.


There are physiological consequences of overeating that can lead to increased morbidity and mortality. The purpose of this review article is to acquaint the reader with the current state of the art in the non-cardiac-gated, noncontrast chest computed tomographic (NCCT) imaging biomarkers of the metabolic syndrome and their prognostic significance found in the lower neck and chest. NCCT imaging biomarkers associated with metabolic syndrome in the chest include premature coronary artery calcification, acceleration of large vessel arterial and valvular calcifications associated with atherosclerosis, and pulmonary arterial enlargement from pulmonary hypertension associated with sleep apnea. These easily identified imaging biomarkers have prognostic implications for major adverse cardiac events (MACE). These NCCT chest-imaging biomarkers are likely targets for artificial intelligence algorithms to harvest for longitudinal assessment of their individual and multifactorial contributions to chronic disease, MACE, and mortality. Early recognition and treatment of these common disorders may help improve patient outcomes and quality of life while decreasing medical costs.

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