Gene Set Enrichment Analsyes Identify Pathways Involved in Genetic Risk for Diabetic Retinopathy

Development of Portable Electronic Health Record Based Algorithms to Identify Individuals with Diabetic Retinopathy

Objectives

To develop, validate and implement algorithms to identify diabetic retinopathy (DR) cases and controls from electronic health care records (EHR)s. Methods : We developed and validated EHR-based algorithms to identify DR cases and individuals with type I or II diabetes without DR (controls) in three independent EHR systems: Vanderbilt University Medical Center Synthetic Derivative (VUMC), the VA Northeast Ohio Healthcare System (VANEOHS), and Massachusetts General Brigham (MGB). Cases were required to meet one of three criteria: 1) two or more dates with any DR ICD-9/10 code documented in the EHR, or 2) at least one affirmative health-factor or EPIC code for DR along with an ICD9/10 code for DR on a different day, or 3) at least one ICD-9/10 code for any DR occurring within 24 hours of an ophthalmology exam. Criteria for controls included affirmative evidence for diabetes as well as an ophthalmology exam.

Results

The algorithms, developed and evaluated in VUMC through manual chart review, resulted in a positive predictive value (PPV) of 0.93 for cases and negative predictive value (NPV) of 0.97 for controls. Implementation of algorithms yielded similar metrics in VANEOHS (PPV=0.94; NPV=0.86) and lower in MGB (PPV=0.84; NPV=0.76). In comparison, use of DR definition as implemented in Phenome-wide association study (PheWAS) in VUMC, yielded similar PPV (0.92) but substantially reduced NPV (0.48). Implementation of the algorithms to the Million Veteran Program identified over 62,000 DR cases with genetic data including 14,549 African Americans and 6,209 Hispanics with DR.

Conclusions/Discussion

We demonstrate the robustness of the algorithms at three separate health-care centers, with a minimum PPV of 0.84 and substantially improved NPV than existing high-throughput methods. We strongly encourage independent validation and incorporation of features unique to each EHR to enhance algorithm performance for DR cases and controls.

Precision Medicine in Type 1 Diabetes

Type 1 diabetes is a complex, chronic disease in which the insulin-producing beta cells in the pancreas are sufficiently altered or impaired to result in requirement of exogenous insulin for survival. The development of type 1 diabetes is thought to be an autoimmune process, in which an environmental (unknown) trigger initiates a T cell-mediated immune response in genetically susceptible individuals. The presence of islet autoantibodies in the blood are signs of type 1 diabetes development, and risk of progressing to clinical type 1 diabetes is correlated with the presence of multiple islet autoantibodies. Currently, a "staging" model of type 1 diabetes proposes discrete components consisting of normal blood glucose but at least two islet autoantibodies (Stage 1), abnormal blood glucose with at least two islet autoantibodies (Stage 2), and clinical diagnosis (Stage 3). While these stages may, in fact, not be discrete and vary by individual, the format suggests important applications of precision medicine to diagnosis, prevention, prognosis, treatment and monitoring. In this paper, applications of precision medicine in type 1 diabetes are discussed, with both opportunities and barriers to global implementation highlighted. Several groups have implemented components of precision medicine, yet the integration of the necessary steps to achieve both short- and long-term solutions will need to involve researchers, patients, families, and healthcare providers to fully impact and reduce the burden of type 1 diabetes.

Genome-Wide Associations and Confirmatory Meta-Analyses in Diabetic Retinopathy

The present study aimed to summarize and validate the genomic association signals for diabetic retinopathy (DR), proliferative DR, and diabetic macular edema/diabetic maculopathy. A systematic search of the genome-wide association study (GWAS) catalog and PubMed/MELINE databases was conducted to curate a comprehensive list of significant GWAS discoveries. The top signals were then subjected to meta-analysis using established protocols. The results indicate the need for improved consensus among DR GWASs, highlighting the importance of validation efforts. A subsequent meta-analysis confirmed the association of two SNPs, rs4462262 (ZWINT-MRPS35P3) (odds ratio = 1.38, p = 0.001) and rs7903146 (TCF7L2) (odd ratio = 1.30, p < 0.001), with DR in independent populations, strengthening the evidence of their true association. We also compiled a list of candidate SNPs for further validation. This study highlights the importance of consistent validation and replication efforts in the field of DR genetics. The two identified gene loci warrant further functional investigation to understand their role in DR pathogenesis.

Circulatory Biomarkers and Diabetic Retinopathy in Racial and Ethnic Populations

Clinical staging systems for diagnosis and treatment of diabetic retinopathy (DR) must closely relate to endpoints that are both relevant for patients and feasible for physicians to implement. Current DR staging systems for clinical eye care and research provide detailed phenotypic characterization to predict patient outcomes in diabetes but have limitations. Biochemical biomarkers provide a rich pool of potential candidates for new DR staging systems that can be readily measured in accessible fluids. Circulating biomarkers that are specific to the retina and relate to angiogenesis and inflammation have been suggested as relevant for DR. Although there is a lack of multi-ethnic studies evaluating circulatory biomarkers in DR, variability in circulatory biomarkers have been reported in people from different ethnic and racial backgrounds. Therefore, there is a need for future studies to evaluate individual or combinations of biomarkers in diverse populations with DR from different ethnic and racial backgrounds.

Long Noncoding RNAs and Mitochondrial Homeostasis in the Development of Diabetic Retinopathy

Retinopathy is one of the most devastating complications of diabetes, which a patient fears the most. Hyperglycemic environment results in many structural, functional, molecular and biochemical abnormalities in the retina, and overproduction of mitochondrial superoxide, induced by hyperglycemic milieu, is considered to play a central role in the development of diabetic retinopathy. Expression of many genes associated with maintaining mitochondrial homeostasis is also altered. Recent research has shown that several long noncoding RNAs, RNAs with more than 200 nucleotides but without any reading frames, are aberrantly expressed in diabetes, and altered expression of these long noncoding RNAs is now being implicated in the development of diabetes and its complications including retinopathy. This review focuses the role of long noncoding RNAs in the development of diabetic retinopathy, with a special emphasis on the maintenance of mitochondrial homeostasis.