Urdea Mickey, Kolberg Janice, Wilber Judith, Gerwien Robert, Moler Edward, Rowe Michael, Jorgensen Paul, Hansen Torben, Pedersen Oluf, Jørgensen Torben, Borch-Johnsen Knut
Tethys Bioscience, Emeryville, California 94608, USA.
J Diabetes Sci Technol. 2009 Jul 1;3(4):748-55. doi: 10.1177/193229680900300422.
Improved identification of subjects at high risk for development of type 2 diabetes would allow preventive interventions to be targeted toward individuals most likely to benefit. In previous research, predictive biomarkers were identified and used to develop multivariate models to assess an individual's risk of developing diabetes. Here we describe the training and validation of the PreDx Diabetes Risk Score (DRS) model in a clinical laboratory setting using baseline serum samples from subjects in the Inter99 cohort, a population-based primary prevention study of cardiovascular disease.
Among 6784 subjects free of diabetes at baseline, 215 subjects progressed to diabetes (converters) during five years of follow-up. A nested case-control study was performed using serum samples from 202 converters and 597 randomly selected nonconverters. Samples were randomly assigned to equally sized training and validation sets. Seven biomarkers were measured using assays developed for use in a clinical reference laboratory.
The PreDx DRS model performed better on the training set (area under the curve [AUC] = 0.837) than fasting plasma glucose alone (AUC = 0.779). When applied to the sequestered validation set, the PreDx DRS showed the same performance (AUC = 0.838), thus validating the model. This model had a better AUC than any other single measure from a fasting sample. Moreover, the model provided further risk stratification among high-risk subpopulations with impaired fasting glucose or metabolic syndrome.
The PreDx DRS provides the absolute risk of diabetes conversion in five years for subjects identified to be "at risk" using the clinical factors.
更好地识别2型糖尿病发病高危人群，将有助于针对最可能受益的个体进行预防性干预。在以往研究中，已确定了预测生物标志物，并用于建立多变量模型以评估个体患糖尿病的风险。在此，我们描述了PreDx糖尿病风险评分（DRS）模型在临床实验室环境中的训练和验证情况，该模型使用了来自Inter99队列研究对象的基线血清样本，这是一项基于人群的心血管疾病一级预防研究。
在6784名基线时无糖尿病的研究对象中，有215名在五年随访期间进展为糖尿病（转变者）。采用巢式病例对照研究，使用了202名转变者和597名随机选择的未转变者的血清样本。样本被随机分配到大小相等的训练集和验证集。使用为临床参考实验室开发的检测方法测量了七种生物标志物。
PreDx DRS模型在训练集上的表现（曲线下面积[AUC]=0.837）优于单独的空腹血糖（AUC=0.779）。当应用于隔离的验证集时，PreDx DRS表现出相同的性能（AUC=0.838），从而验证了该模型。该模型的AUC比空腹样本中的任何其他单一指标都要好。此外，该模型在空腹血糖受损或患有代谢综合征的高危亚人群中提供了进一步的风险分层。
PreDx DRS为使用临床因素确定为“有风险”的研究对象提供了五年内糖尿病转变的绝对风险。
