Abstract
The aim of this study is to explore the effect of genetic variation on diabetic retinopathy (DR) risk in a Taiwanese population. The logistic regression model was used to evaluate the relationship between DR status and risk factors, including the conventional parameters and genetic risk score (GRS). Candidate single nucleotide polymorphisms (SNPs) in GRS were selected based on previous reports with a combined P < 10−4 (genome-wide association) and P < 0.05 (meta-analysis). In total, 58 SNPs in 44 susceptibility loci were selected, and four were used to calculate GRS. After adjustment for age, systolic blood pressure, diabetes duration, and HbA1c, the DR risk was 4.95 times higher for patients in the top GRS third tile than for those in the bottom third tile (95% CI = 2.99–8.18; P < 0.001). The addition of genetic information improved DR prediction, increasing the area under the curve (AUC) from 0.72 to 0.77 (P = 0.0024) and improving the sensitivity of the model such that 40 more subjects were reclassified into DR status. The developed multivariate logistic regression model combining conventional risk factors and the multilocus GRS can predict DR, thus enabling timely treatment to reduce blindness in T2D patients.
Highlights
Diabetic retinopathy (DR) is a common microvascular complication of diabetes and a leading cause of blindness in adults[1,2]
We investigated the diabetic retinopathy (DR) risk among the Taiwanese population according to genetic variants identified by genome-wide association studies (GWASs) and meta-analysis, and built a prediction model
A genetic risk score (GRS) based on the number of risk alleles from these four single nucleotide polymorphisms (SNPs) was calculated for each individual, and an independently cumulative genetic effect on the DR risk was observed in the multivariate models after adjusting for diabetes duration, HbA1c, and systolic blood pressure (SBP)
Summary
Diabetic retinopathy (DR) is a common microvascular complication of diabetes and a leading cause of blindness in adults[1,2]. As DR is a consequence of diabetes, patients should take general precautions to improve their blood sugar control in order to stop or slow the disease progression, but there are no reliable biomarkers for predicting NPDR and/or its development into PDR. It is important to identify risk factors for DR progression, which would enable implementation of timely and effective treatment to reduce blindness in T2D patients. It is established that heredity plays a key role in the pathogenesis of diabetes and its complications[12,13,14], and familial clustering of DR among T2D patients suggests strong contribution of genetic factors to the risk of developing the disease[13]. A comprehensive composite model that can estimate the combined effect of conventional risk factors and genetic background to predict the occurrence of DR in diabetic patients is limited. The aim of this study was to investigate the association between previously reported genetic variants and DR risk, and develop a multifactorial logistic regression model to predict DR in the Han Chinese population of Taiwan
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