PCEN Research Overview

PI: Michelle Denburg, MD, MSCE

Urinary stone disease (USD) is common and increasingly recognized as a chronic systemic disorder with skeletal and vascular morbidity. The incidence of USD is increasing disproportionately among adolescents, making it critical to understand its impact on bone and vascular health in this population.

Identifying modifiable factors that compromise bone strength and vascular health will facilitate the development of strategies to reduce fracture rates and cardiovascular events across a person's life. The primary objectives of this study are to (1) evaluate the impact of USD on gains in bone density, structure, and strength in adolescents and identify modifiable predictors of changes in bone strength via urine metabolic profiling and dietary assessment; and (2) determine if USD is associated with subclinical vascular disease and if markers of vascular disease are associated with lower bone strength in adolescents with USD.

A second generation high-resolution peripheral quantitative computed tomography (HR-pQCT) device will be used to assess bone microarchitecture and micro-finite element analysis (μFEA) estimates of bone strength (failure load) that are highly correlated with ex vivo biomechanical testing. Vascular assessment will combine markers of arterial stiffness (pulse wave velocity/analysis), subclinical atherosclerosis (carotid intima-media thickness), and endothelial function (EndoPAT), all of which have been shown to independently predict cardiovascular events in adults. The results of this study will inform future multicenter clinical trials of interventions to promote bone accrual and vascular health in adolescents with USD.

PI: Gregory Tasian, MD, MSc, MSCE

There is a need for biomarkers that can identify children with Congenital Anomalies of the Kidneys and Urinary Tract (CAKUT) early in life who are at high risk of future chronic kidney disease progression. Early identification would help guide trials of therapies for those most likely to benefit from early treatment and spare those patients who are at a low risk of progression from potential treatment-associated harms. As most children with structural kidney disease have frequent ultrasounds of their kidneys and bladder, measurements of the healthy kidney tissue and the health of the bladder may predict risk of kidney function decline.

Assessment of the characteristics of the kidney with imaging could provide important information about this risk. This research will apply machine learning methods to automatically extract informative features from ultrasound images and then, using the Chronic Kidney Disease in Children (CKiD) study, will validate these features as biomarkers of chronic kidney disease progression. These biomarkers may predict chronic kidney disease progression prior to the appearance of later serum or urine biomarkers, such as nadir creatinine or proteinuria, and can be measured noninvasively immediately after birth on routine clinical imaging.

Related publications:

Yin S, Peng Q, Li H, Zhang Z, You X, Liu H, Fischer K, Furth SL, Tasian GE, Fan Y. Multi-instance deep learning with graph convolutional neural networks for diagnosis of kidney diseases using ultrasound imaging. Uncertain Safe Util Machine Learn Med Imaging Clinical Image Based Proced. 2019 Oct,11840:146-154. PMID: 31893285

Yin S, Zhang Z, Li H, Peng Q, You X, Furth SL, Tasian GE, Fan Y. Fully-automatic segmentation of kidneys in clinical ultrasound images using a boundary distance regression network. Proc IEEE Int Sympo Biomed Imaging. 2019 Apr; 2019:1741-1744. PMID: 31803348

Zheng Q, Furth SL, Tasian GE, Fan Y. Computer-aided diagnosis of congenital abnormalities of the kidney and urinary tract in children based on ultrasound imaging data by integrating texture image features and deep transfer learning image features. J Pediatr Urol. 2019 Feb;15(1):75.e1-75.e7. PMID: 30473474

Zheng Q, Tasian G, Fan Y. 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 Apr;2018:1487-1490. PMID: 30079128

Zheng Q, Warner S, Tasian G, Fan Y. A dynamic cuts method with integrated multiple feature maps for segmenting kidneys in 2D ultrasound images. Acad Radiol. 2018 Sept;25(9):1136-1145. PMID: 29449144