Browsing by Author "Eadon, Michael"

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    Analytical validity of a genotyping assay for use with personalized antihypertensive and chronic kidney disease therapy
    (Wolters Kluwer, 2019-01) Collins, Kimberly; Pratt, Victoria; Stansberry, Wesley; Medeiros, Elizabeth; Kannegolla, Karthik; Swart, Marelize; Skaar, Todd C.; Chapman, Arlene; Decker, Brian; Moorthi, Ranjani; Eadon, Michael; Medicine, School of Medicine
    Hypertension and chronic kidney disease are inextricably linked. Hypertension is a well-recognized contributor to chronic kidney disease progression and, in turn, renal disease potentiates hypertension. A generalized approach to drug selection and dosage has not proven effective in managing these conditions, in part, because patients with heterogeneous kidney disease and hypertension etiologies are frequently grouped according to functional or severity classifications. Genetic testing may serve as an important tool in the armamentarium of clinicians who embrace precision medicine. Increasing scientific evidence has supported the utilization of genomic information to select efficacious antihypertensive therapy and understand hereditary contributors to chronic kidney disease progression. Given the wide array of antihypertensive agents available and diversity of genetic renal disease predictors, a panel-based approach to genotyping may be an efficient and economic means of establishing an individualized blood pressure response profile for patients with various forms of chronic kidney disease and hypertension. In this manuscript, we discuss the validation process of a Clinical Laboratory Improvement Amendments (CLIA)-approved genetic test to relay information on 72 genetic variants associated with kidney disease progression and hypertension therapy. These genomic-based interventions, in addition to routine clinical data, may help inform physicians to provide personalized therapy.
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    DEPOT: graph learning delineates the roles of cancers in the progression trajectories of chronic kidney disease using electronic medical records
    (medRxiv, 2023-08-16) Song, Qianqian; Liu, Xiang; Li, Zuotian; Zhang, Pengyue; Eadon, Michael; Su, Jing; Biostatistics and Health Data Science, School of Medicine
    Chronic kidney disease (CKD) is a common, complex, and heterogeneous disease impacting aging populations. Determining the landscape of disease progression trajectories from midlife to senior age in a real-world context allows us to better understand the progression of CKD, the heterogeneity of progression patterns among the risk population, and the interactions with other clinical conditions like cancers. In this study, we use electronic health records (EHRs) to outline the CKD progression trajectory roadmap for the Wake Forest Baptist Medical Center (WFBMC) patient population. We establish an EHR cohort (n = 79,434) with patients' health status identified by 18 Essential Clinical Indices across 508,732 clinical encounters. We develop the DisEase PrOgression Trajectory (DEPOT) approach to model CKD progression trajectories and individualize clinical decision support. The DEPOT is an evidence-driven, graph-based clinical informatics approach that addresses the unique challenges in longitudinal EHR data by systematically using the graph artificial intelligence (graph-AI) model for representation learning and reverse graph embedding for trajectory reconstruction. Moreover, DEPOT includes a prediction model to assign new patients along the progression trajectory. We successfully establish the EHR-based CKD progression trajectories with DEPOT in the WFUBMC cohort. We annotate the trajectories with clinical features, including kidney function, age, and other indices, including cancer. This CKD progression trajectory roadmap reveals diverse kidney failure pathways associated with different clinical conditions. Specifically, we have identified one high-risk trajectory and two low-risk trajectories. Switching pathways from low-risk trajectories to the high-risk one is associated with accelerated decline in kidney function. On this roadmap, high-risk patients are enriched in the skin and GU cancers, which differs from low-risk patients, suggesting fundamentally different disease progression mechanisms. Overall, the CKD progression trajectory roadmap reveals novel diverse renal failure pathways in type 2 diabetes mellitus and highlights disease progression patterns associated with cancer phenotypes.
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    Molecular profiling of kidney compartments from serial biopsies differentiate treatment responders from non-responders in lupus nephritis
    (Elsevier, 2022) Parikh, Samir V.; Malvar, Ana; Song, Huijuan; Shapiro, John; Mejia-Vilet, Juan Manuel; Ayoub, Isabelle; Almaani, Salem; Madhavan, Sethu; Alberton, Valeria; Besso, Celeste; Lococo, Bruno; Satoskar, Anjali; Zhang, Jianying; Yu, Lianbo; Fadda, Paolo; Eadon, Michael; Birmingham, Dan; Ganesan, Latha P.; Jarjour, Wael; Rovin, Brad H.; Medicine, School of Medicine
    The immune pathways that define treatment response and non-response in lupus nephritis (LN) are unknown. To characterize these intra-kidney pathways, transcriptomic analysis was done on protocol kidney biopsies obtained at flare (initial biopsy (Bx1)) and after treatment (second biopsy (Bx2)) in 58 patients with LN. Glomeruli and tubulointerstitial compartments were isolated using laser microdissection. RNA was extracted and analyzed by nanostring technology with transcript expression from clinically complete responders, partial responders and non-responders compared at Bx1 and Bx2 and to the healthy controls. Top transcripts that differentiate clinically complete responders from non-responders were validated at the protein level by confocal microscopy and urine ELISA. At Bx1, cluster analysis determined that glomerular integrin, neutrophil, chemokines/cytokines and tubulointerstitial chemokines, T cell and leukocyte adhesion genes were able to differentiate non-responders from clinically complete responders. At Bx2, glomerular monocyte, extracellular matrix, and interferon, and tubulointerstitial interferon, complement, and T cell transcripts differentiated non-responders from clinically complete responders. Protein analysis identified several protein products of overexpressed glomerular and tubulointerstitial transcripts at LN flare, recapitulating top transcript findings. Urine complement component 5a and fibronectin-1 protein levels reflected complement and fibronectin expression at flare and after treatment. Thus, transcript analysis of serial LN kidney biopsies demonstrated how gene expression in the kidney changes with clinically successful and unsuccessful therapy. Hence, these insights into the molecular landscape of response and non-response may help align LN management with the pathogenesis of kidney injury.