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Progression of chronic kidney disease in African American with type 2 diabetes mellitus using topology learning in electronic medical records

By Li Wang, Xufei Zheng, Lynn S. Huang, Jianzhao Xu, Fang-Chi Hsu, Shyh-Huei Chen, Maggie C.Y. Ng, Donald W Bowden, Barry I Freedman, Jing Su

Posted 04 Jul 2018
bioRxiv DOI: 10.1101/361956

Background: 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. Genetics also plays an important role. In previous work, we and others have demonstrated that African Americans with high-risk APOL1 genotypes are more likely to develop CKD, tend to develop CKD earlier, and the disease progresses faster. Diabetes, which is more common in African Americans, also significantly increases risk for CKD. Data and Method: Electronic medical records (EMRs) were used to outline the first CKD progression trajectory roadmap for an African American population with type 2 diabetes. By linking participants in 5 genome-wide association study (GWAS) to their clinical records at Wake Forest Baptist Medical Center (WFBMC), an EMR-GWAS cohort was established (n = 1,581). Patients' health status was described by 18 Essential Clinical Indices across 84,009 clinical encounters. A novel graph learning algorithm, Discriminative Dimensionality Reduction Tree (DDRTree) was implemented, to establish the trajectories of declines in health. Moreover, a prediction model for new patients was proposed along the learned graph structure. We annotated these trajectories with clinical and genomic features including kidney function, other major risk indices of CKD, APOL1 genotypes, and age. The prediction power of the learned disease progression trajectories was further examined using the k-nearest neighbor model. Results: The CKD progression trajectory roadmap revealed diverse kidney failure pathways associated with different clinical conditions. Specifically, we identified one high-risk trajectory and two low-risk trajectories. Switching pathways from low-risk trajectories to the high-risk one was associated with accelerated decline in kidney function. On this roadmap, patients with APOL1 high-risk genotypes were enriched in the high-risk trajectory, suggesting fundamentally different disease progression mechanisms from those without APOL1 risk genotypes. The k-nearest neighbor-based prediction showed effective prediction rate of 87%. Conclusion: The CKD progression trajectory roadmap revealed novel diverse renal failure pathways in African Americans with type 2 diabetes mellitus and highlights disease progression patterns that associate with APOL1 renal-risk genotypes.

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