CLINICAL PHENOTYPING CORE ABSTRACT Chronic kidney disease (CKD) is associated with progressive disturbances to growth and maturation, bone accrual and quality, nutritional status, and cardiovascular health. There is a critical need to develop validated strategies to prevent and treat these complications, to optimize growth and development, and to improve long- term outcomes for this high-risk population. Growing children are particularly vulnerable to the complications of kidney disease. Given the unique physiology of growing children, existing pediatric nephrology clinical practice guidelines ? which are largely opinion-based and extrapolated from data in adults ? need a stronger evidence- base. The proposed Pediatric Center of Excellence in Nephrology (PCEN) is designed to focus on barriers to implementing clinical trials in children. Since it would be impracticable to conduct trials with outcomes such as fractures or cardiovascular events, developing sensitive and valid clinical biomarkers of early musculoskeletal and vascular disease is essential to conducting interventional studies. This Clinical Phenotyping Core will leverage the resources of the Children?s Hospital of Philadelphia Clinical and Translational Research Center and state-of-the-art methods for the assessment of bone quality, body composition, bionutrition, and vascular health. Musculoskeletal imaging includes DXA measures of areal bone mineral density (BMD) and bone mineral content at multiple anatomic sites and peripheral quantitative computed tomography (pQCT) measures of volumetric BMD. The 2nd generation high-resolution pQCT (XCTII) device provides measures of bone microarchitecture and micro-finite element analysis estimates of bone strength (failure load) that are highly correlated with ex vivo biomechanical testing. In addition to ambulatory blood pressure monitoring and echocardiography, vascular phenotyping includes markers of arterial stiffness (pulse wave velocity/analysis), sub-clinical atherosclerosis (carotid intima-media thickness), and endothelial function (EndoPAT), all of which have been shown to independently predict cardiovascular events in adults, but need to be further evaluated in children.
The specific aims of the Clinical Phenotyping Core are: 1) To provide expert consultation to center investigators on clinical phenotyping protocols for observational studies and clinical trials in childhood kidney diseases; 2) To facilitate implementation of clinical research by center investigators through access to state-of-the-art and comprehensive core resources for assessment of growth and nutrition, body composition, bone quality, and cardiovascular health; and 3) To contribute to the expansion and generation of robust sources of normative longitudinal data for the clinical phenotyping measures. Core Investigators have a track record of implementing studies of bone health, mineral metabolism, body composition, nutrition, hypertension, and vascular disease in kidney disease, other childhood chronic diseases, and healthy children and adolescents. Through integration with other PCEN Cores and collaboration across the Research Base, the Clinical Phenotyping Core will decrease the barriers to implementing clinical trials to promote bone accrual and vascular health in childhood kidney disease.

Agency
National Institute of Health (NIH)
Institute
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Type
Specialized Center (P50)
Project #
5P50DK114786-02
Application #
9565980
Study Section
Special Emphasis Panel (ZDK1)
Project Start
Project End
Budget Start
2018-09-01
Budget End
2019-08-31
Support Year
2
Fiscal Year
2018
Total Cost
Indirect Cost
Name
Children's Hospital of Philadelphia
Department
Type
DUNS #
073757627
City
Philadelphia
State
PA
Country
United States
Zip Code
19104
Zheng, Qiang; Tasian, Gregory; Fan, Yong (2018) TRANSFER LEARNING FOR DIAGNOSIS OF CONGENITAL ABNORMALITIES OF THE KIDNEY AND URINARY TRACT IN CHILDREN BASED ON ULTRASOUND IMAGING DATA. Proc IEEE Int Symp Biomed Imaging 2018:1487-1490
Zheng, Qiang; Warner, Steven; Tasian, Gregory et al. (2018) A Dynamic Graph Cuts Method with Integrated Multiple Feature Maps for Segmenting Kidneys in 2D Ultrasound Images. Acad Radiol 25:1136-1145
Li, Hongming; Galperin-Aizenberg, Maya; Pryma, Daniel et al. (2018) Unsupervised machine learning of radiomic features for predicting treatment response and overall survival of early stage non-small cell lung cancer patients treated with stereotactic body radiation therapy. Radiother Oncol 129:218-226
Li, Hongming; Zhu, Xiaofeng; Fan, Yong (2018) Identification of Multi-scale Hierarchical Brain Functional Networks Using Deep Matrix Factorization. Med Image Comput Comput Assist Interv 11072:223-231
Li, Hongming; Fan, Yong (2018) Brain Decoding from Functional MRI Using Long Short-Term Memory Recurrent Neural Networks. Med Image Comput Comput Assist Interv 11072:320-328
Li, Hongming; Fan, Yong (2018) Identification of Temporal Transition of Functional States Using Recurrent Neural Networks from Functional MRI. Med Image Comput Comput Assist Interv 11072:232-239
Li, Hongming; Fan, Yong (2018) NON-RIGID IMAGE REGISTRATION USING SELF-SUPERVISED FULLY CONVOLUTIONAL NETWORKS WITHOUT TRAINING DATA. Proc IEEE Int Symp Biomed Imaging 2018:1075-1078
Li, Hongming; Satterthwaite, Theodore D; Fan, Yong (2018) BRAIN AGE PREDICTION BASED ON RESTING-STATE FUNCTIONAL CONNECTIVITY PATTERNS USING CONVOLUTIONAL NEURAL NETWORKS. Proc IEEE Int Symp Biomed Imaging 2018:101-104
Zhu, Xiaofeng; Zhang, Weihong; Fan, Yong et al. (2018) A Robust Reduced Rank Graph Regression Method for Neuroimaging Genetic Analysis. Neuroinformatics 16:351-361