This thesis shows the potential utility of imaging biomarkers of the heart, kidneys, and visceral adiposity in the clinical management of cardiorenal syndrome and type 2 diabetes. We explored the... Show moreThis thesis shows the potential utility of imaging biomarkers of the heart, kidneys, and visceral adiposity in the clinical management of cardiorenal syndrome and type 2 diabetes. We explored the methods to quantify visceral fat, and demonstrated the potential clinical implications of a special compartment of visceral fat, renal sinus fat. We summarized the current role of imaging techniques in the clinical management of cardiorenal syndrome, and evaluated the preclinical MRI-derived imaging biomarkers in a group of patients with type 4 cardiorenal syndrome. Our findings could potentially benefit the clinical care for patients with metabolic disorders and/or cardiorenal syndrome. Continuous technical developments in quantitative imaging will increase the potential for applying imaging biomarkers in the management of reno-cardiometabolic diseases, and contribute to achieving the goals of personalized medicine. Show less
Background: There is a growing interest in fast and reliable assessment of abdominal visceral adipose tissue (VAT) volume for risk stratification of metabolic disorders. However, imaging based... Show moreBackground: There is a growing interest in fast and reliable assessment of abdominal visceral adipose tissue (VAT) volume for risk stratification of metabolic disorders. However, imaging based measurement of VAT is costly and limited by scanner availability. Therefore, we aimed to develop equations to estimate abdominal VAT volume from simple anthropometric parameters and to assess whether linear regression based equations differed in performance from artificial neural network (ANN) based equations.Methods: MRI-measured abdominal VAT volumes and anthropometric parameters of 5772 subjects (White ethnicity, age 45-76 years, 52.7% females) were obtained from the UK Biobank. Subjects were divided into the derivation sample (n = 5195) and the validation sample (n = 577). Basic models (age, sex, height, weight) and expanded models (basic model + waist circumference and hip circumference) were constructed from the derivation sample by linear regression and ANN respectively. Performance of the linear regression and ANN based equations in the validation sample were compared and estimating accuracies were evaluated by receiver-operating characteristic curves (ROC).Results: The basic and expanded equations based on linear regression and ANN demonstrated the adjusted coefficient of determination (R-2) ranging from 0.71 to 0.78, with bias ranging from less than 0.001 L-0.07 L in comparison with MRI-measured VAT. Both basic and expanded ANN based equations demonstrated slightly higher adjusted R-2 and lower error measurements than linear regression equations. However, no statistical difference was found between linear regression equations and their ANN based counterparts in ROC analysis. Both linear regression and ANN based expanded equations presented higher estimating accuracies (76.9%-90.1%) than the basic equations (74.5%-87.5%) in ROC analysis.Conclusions: We present equations based on linear regression and artificial neural networks to estimate abdominal VAT volume by simple anthropometric parameters for middle-aged and elderly White population. These equations can be used to estimate VAT volume in general practice as well as population-based studies. (C) 2020 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved. Show less
