Xu L, Jiang C Q, Schooling C M, Zhang W S, Cheng K K, Lam T H
School of Public Health, The University of Hong Kong, Hong Kong.
Guangzhou No.12 Hospital, Guangzhou 510620, China.
Prev Med. 2014 Dec;69:63-8. doi: 10.1016/j.ypmed.2014.09.004. Epub 2014 Sep 17.
To recalibrate and modify the Framingham diabetes mellitus (DM) function and establish a simple point score for predicting near-term incident diabetes in a large sample of Chinese.
A total of 16,043 participants aged 50years or above without diabetes at baseline from the Guangzhou Biobank Cohort Study (GBCS) were recruited from 2003 to 2008 and followed up until 31 December 2012, with an average follow-up period of 4.1years. A randomly selected sub-sample of 8000 participants was used to calculate the predictive model and the remaining sample including 8043 participants was used for validating the prediction model.
During follow-up, 5.2% (95% confidence interval (CI) 4.6-5.9) of men and 5.2% (95% CI 4.8-5.6) of women developed diabetes. A GBCS point score prediction model was constructed based on the Framingham DM function risk factors using the randomly selected sub-sample. Compared with the Framingham DM risk score (AUC 0.740, 95% CI 0.715-0.766), the GBCS point score prediction model predicted the development of diabetes well, with an AUC of 0.779 (95% CI 0.756-0.801, P for comparison <0.001). Validation analysis showed that the new GBCS function had satisfactory predictive ability for actual DM incidence and improved the calibration substantially. The original Framingham DM score underestimated diabetes incidence in the GBCS sample.
The constructed GBCS point score prediction model based on GBCS coefficients could be more useful for identifying high risk individuals in Chinese populations than the original Framingham DM score.
重新校准和修改弗雷明汉糖尿病(DM)功能,并建立一个简单的积分系统,用于预测大量中国人群近期发生糖尿病的风险。
2003年至2008年,从广州生物银行队列研究(GBCS)中招募了16043名年龄在50岁及以上、基线时无糖尿病的参与者,并随访至2012年12月31日,平均随访时间为4.1年。随机抽取8000名参与者的子样本用于计算预测模型,其余8043名参与者的样本用于验证预测模型。
随访期间,男性中有5.2%(95%置信区间[CI] 4.6 - 5.9)、女性中有5.2%(95% CI 4.8 - 5.6)发生糖尿病。使用随机抽取的子样本,基于弗雷明汉DM功能风险因素构建了GBCS积分预测模型。与弗雷明汉DM风险评分(AUC 0.740,95% CI 0.715 - 0.766)相比,GBCS积分预测模型对糖尿病发生的预测效果良好,AUC为0.779(95% CI 0.756 - 0.801,比较P <0.001)。验证分析表明,新的GBCS功能对实际DM发病率具有令人满意的预测能力,并显著改善了校准。原始的弗雷明汉DM评分低估了GBCS样本中的糖尿病发病率。
基于GBCS系数构建的GBCS积分预测模型,在识别中国人群中的高危个体方面,可能比原始的弗雷明汉DM评分更有用。