Guo Vivian Yawei, Chan Juliana Chung Ngor, Chung Harriet, Ozaki Risa, So Wingyee, Luk Andrea, Lam Augustine, Lee Jack, Zee Benny Chung-Ying
Division of Biostatistics, The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China.
Department of Family Medicine and Primary Care, The University of Hong Kong, Hong Kong, China.
Sci Rep. 2016 Jan 12;6:19053. doi: 10.1038/srep19053.
To evaluate the association between a series of retinal information and cardiovascular disease (CVD) and to evaluate whether this association is independent of traditional CVD risk factors in type 2 diabetes patients, we undertook an age-sex matched case-control study with 79 CVD cases and 150 non-CVD controls. All the participants underwent standardized physical examinations and retinal imaging. Retinal information was extracted from the retinal images using a semi-automatic computer program. Three stepwise logistic regression models were evaluated: model 1 with cardiovascular risk factors only; model 2 with retinal information only and model 3 with both cardiovascular risk factors and retinal information. The areas under the receiver operating characteristic curves (AUCs) were used to compare the performances of different models. Results showed that the AUCs were 0.692 (95%CI: 0.622-0.761) and 0.661 (95%CI: 0.588-0.735) for model 1 and model 2, respectively. In addition, model 3 had an AUC of 0.775 (95%CI: 0.716-0.834). Compared to the previous two models, the AUC of model 3 increased significantly (p < 0.05 in both comparisons). In conclusion, retinal information is independently associated with CVD in type 2 diabetes. Further work is needed to validate the translational value of applying retinal imaging analysis into clinical practice.
为了评估一系列视网膜信息与心血管疾病(CVD)之间的关联,并评估这种关联在2型糖尿病患者中是否独立于传统的CVD危险因素,我们进行了一项年龄和性别匹配的病例对照研究,其中包括79例CVD病例和150例非CVD对照。所有参与者均接受了标准化体格检查和视网膜成像。使用半自动计算机程序从视网膜图像中提取视网膜信息。评估了三个逐步逻辑回归模型:模型1仅包含心血管危险因素;模型2仅包含视网膜信息;模型3同时包含心血管危险因素和视网膜信息。使用受试者工作特征曲线(AUC)下的面积来比较不同模型的性能。结果显示,模型1和模型2的AUC分别为0.692(95%CI:0.622 - 0.761)和0.661(95%CI:0.588 - 0.735)。此外,模型3的AUC为0.775(95%CI:0.716 - 0.834)。与前两个模型相比,模型3的AUC显著增加(两次比较的p均<0.05)。总之,视网膜信息与2型糖尿病患者的CVD独立相关。需要进一步开展工作以验证将视网膜成像分析应用于临床实践的转化价值。