Diabetic Retinopathy Environment-Wide Association Study (EWAS) in NHANES 2005-2008.
Diabetic Retinopathy Environment-Wide Association Study (EWAS) in NHANES 2005-2008.
复制标题
DOI：
10.3390/jcm9113643
复制
发表时间：
2020-11-12
影响因子：
3.9
通讯作者：
Sivaprasad S
中科院分区：
文献类型：
--
作者：
Blighe K;Gurudas S;Lee Y;Sivaprasad S
据报道，几种循环生物标志物与糖尿病视网膜病变（DR）相关。然而，与已知的风险因素（如高血脂、高血压和高血脂）相比，它们对DR的相对贡献仍不清楚。在这项数据驱动的研究中，我们使用新的模型来评估400多个实验室参数与DR的相关性，并与已确定的风险因素进行比较。研究方法：我们对2007 - 2008年国家健康和营养检查调查（NHANES）中可用的实验室参数进行了一项全环境关联研究（EWAS），以糖尿病患者为研究对象，以DR作为结果（测试集）。我们采用了自变量（特征）选择方法，包括并行单变量回归建模、主成分分析（PCA）、惩罚回归和RandomForest ™。这些模式在2005 - 2006年国家卫生和家庭健康调查中得到了复制（复制集）。我们的测试和复制集分别由1025和637名具有DR状态和实验室数据的个体组成。糖化血红蛋白（HbA1c）是DR最强的危险因素。我们基于PCA的方法产生了一个模型，该模型包含18个主成分（PC），曲线下面积（AUC）为0.796（95% CI 0.761 - 0.832），而惩罚回归确定了具有78.51%准确度和AUC 0.74（95% CI 0.72 - 0.77）的9特征模型。RandomForest ™识别了31个特征模型，准确率为78.4%，AUC为0.71（95% CI 0.65 - 0.77）。在我们的RandomForest ™中对所选变量进行分组时，仅高血糖症达到AUC 0.72（95% CI 0.68 - 0.76）。当模型还包括高血压、高胆固醇血症、红细胞压积、肾功能和肝功能检查时，AUC增加至0.84（95% CI 0.78 - 0.9）。
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.
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影响因子：
16.2
作者：
Chen YH;Chen HS;Tarng DC
通讯作者：
Tarng DC
影响因子：
16.2
作者：
Raymond NT;Varadhan L;Reynold DR;Bush K;Sankaranarayanan S;Bellary S;Barnett AH;Kumar S;O'Hare JP;UK Asian Diabetes Study Retinopathy Study Group
通讯作者：
UK Asian Diabetes Study Retinopathy Study Group
影响因子：
3.1
作者：
Ganesh S;Gupta A;Sharma M;Bhuttan S
通讯作者：
Bhuttan S
影响因子：
4.2
作者：
Wong, TY;Klein, R;Shea, S
通讯作者：
Shea, S
影响因子：
168.9
作者：
Turner, RC;Holman, RR;Ward, JD
通讯作者：
Ward, JD