Abstract
Several circulating biomarkers are reported to be associated with diabetic retinopathy (DR). However, their relative contributions to DR compared to known risk factors, such as hyperglycaemia, hypertension, and hyperlipidaemia, remain unclear. In this data driven study, we used novel models to evaluate the associations of over 400 laboratory parameters with DR compared to the established risk factors. Methods: we performed an environment-wide association study (EWAS) of laboratory parameters available in National Health and Nutrition Examination Survey (NHANES) 2007–2008 in individuals with diabetes with DR as the outcome (test set). We employed independent variable (feature) selection approaches, including parallelised univariate regression modelling, Principal Component Analysis (PCA), penalised regression, and RandomForest™. These models were replicated in NHANES 2005–2006 (replication set). Our test and replication sets consisted of 1025 and 637 individuals with available DR status and laboratory data respectively. Glycohemoglobin (HbA1c) was the strongest risk factor for DR. Our PCA-based approach produced a model that incorporated 18 principal components (PCs) that had an Area under the Curve (AUC) 0.796 (95% CI 0.761–0.832), while penalised regression identified a 9-feature model with 78.51% accuracy and AUC 0.74 (95% CI 0.72–0.77). RandomForest™ identified a 31-feature model with 78.4% accuracy and AUC 0.71 (95% CI 0.65–0.77). On grouping the selected variables in our RandomForest™, hyperglycaemia alone achieved AUC 0.72 (95% CI 0.68–0.76). The AUC increased to 0.84 (95% CI 0.78–0.9) when the model also included hypertension, hypercholesterolemia, haematocrit, renal, and liver function tests.
Highlights
Diabetic retinopathy (DR), a chronic diabetes complication, is generally believed to be the most common cause of microvascular changes in the retina
We focused only on continuous laboratory variables for the following reasons: 1, in National Health and Nutrition Examination Survey (NHANES), the majority of categorical variables are derived from the continuous variables; 2, our Principal Component Analysis (PCA)-based approach can only work on continuous variables; 3, for RandomForestTM, having continuous variables increases the number of splitting points in the data, and metrics of importance such as Gini are known to exhibit less bias on such data [26]
Study Cohort In NHANES 2007–2008, retinal imaging data is available for 3863 individuals, demographics data is available for 10,149 individuals, and laboratory data is available for between 394 and 9307 individuals, depending on the individual laboratory dataset in NHANES
Summary
Diabetic retinopathy (DR), a chronic diabetes complication, is generally believed to be the most common cause of microvascular changes in the retina. The initial retinal lesions of diabetic retinopathy (DR) are microaneurysms, but they can occur in eyes with and without diabetes [1,2,3]. With increasing duration of diabetes, other lesions develop and co-exist in the retina, such as retinal haemorrhages, exudates, intraretinal microvascular abnormalities, and neovascularization of the retina or optic disc. Based on the presence of individual lesions or a constellation of them, DR severity level is graded from mild, moderate, and severe non-proliferative diabetic retinopathy (NPDR) to proliferative diabetic retinopathy (PDR) [4,5]. In population-based studies, approximately a third of people with diabetes have DR [6,7]
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