Identification of type 2 diabetes subgroups through topological analysis of patient similarity

Abstract

Type 2 diabetes (T2D) is a heterogeneous complex disease affecting more than 29 million Americans alone with a rising prevalence trending toward steady increases in the coming decades. Thus, there is a pressing clinical need to improve early prevention and clinical management of T2D and its complications. Clinicians have understood that patients who carry the T2D diagnosis have a variety of phenotypes and susceptibilities to diabetes-related complications. We used a precision medicine approach to characterize the complexity of T2D patient populations based on high-dimensional electronic medical records (EMRs) and genotype data from 11,210 individuals. We successfully identified three distinct subgroups of T2D from topology-based patient-patient networks. Subtype 1 was characterized by T2D complications diabetic nephropathy and diabetic retinopathy; subtype 2 was enriched for cancer malignancy and cardiovascular diseases; and subtype 3 was associated most strongly with cardiovascular diseases, neurological diseases, allergies, and HIV infections. We performed a genetic association analysis of the emergent T2D subtypes to identify subtype-specific genetic markers and identified 1279, 1227, and 1338 single-nucleotide polymorphisms (SNPs) that mapped to 425, 322, and 437 unique genes specific to subtypes 1, 2, and 3, respectively. By assessing the human disease–SNP association for each subtype, the enriched phenotypes and biological functions at the gene level for each subtype matched with the disease comorbidities and clinical differences that we identified through EMRs. Our approach demonstrates the utility of applying the precision medicine paradigm in T2D and the promise of extending the approach to the study of other complex, multi-factorial diseases.

Precision Medicine in Diabetes: Is It Time?

“…This has been recently attempted at Mount Sinai Hospital in New York, where 11,210 patients with type 2 diabetes were studied for similarities through a topological approach that created datadriven patient-patient networks. Three separate clusters were identified with specific characteristics and outcomes; genetic polymorphisms associated with each cluster also demonstrated associations with the corresponding disease entities at the gene level (26). Although this represents a significant first step, three key questions emerge: 1) Where and how can these analyses be replicated, since the ability to refute a hypothesis is a crucial tenet of the scientific method; 2) Can the information gained be interpreted for physiological insight; and 3) How do these results support clinical decision making?…”

A User’s Guide to Topological Data Analysis

“…Since these graphs are much easier to visualize than the possibly high dimensional data used to construct it, mapper is an excellent tool for investigation and visualization of the structure of a data set. It has been used extensively in data analysis, particularly in the biology and health domains (Nicolau, Levine, & Carlsson, 2011;Li et al, 2015;Torres et al, 2016;Nielson et al, 2015;Yao et al, 2009). …”

Defining Glomerular Disease in Mechanistic Terms: Implementing an Integrative Biology Approach in Nephrology

“…A detailed discussion of research using electronic health records, its opportunities, and its challenges is beyond the scope of this review. However, it is noted that simplifying the ability to easily extract, annotate, and understand unstructured clinical data from electronic health records through natural language processing has been developed and is starting to be deployed for renal diseases (24).…”