Brown C D, Davis H T, Ediger M N, Fleming C M, Hull E L, Rohrscheib M
Diabetes Technol Ther. 2005 Jun;7(3):456-66. doi: 10.1089/dia.2005.7.456.
Current diabetes screening techniques comprise the fasting plasma glucose (FPG) and oral glucose tolerance tests. Both tests demand patient compliance, and neither test has ideal performance. Near-infrared (NIR) spectroscopy is a noninvasive means of interrogating characteristics of a sample and is evaluated as a novel screening method for type 2 diabetes.
One hundred fifty-four patients with and without type 2 diabetes were recruited. Their forearm skin was measured with the NIR spectroscopic system, and a capillary blood glucose measurement was also taken. Sixty-six patients returned for a second visit at a later date. A multivariate model, generated from a separate training study, was employed to produce a quantitative risk marker of disease for each NIR spectrum. Sensitivity and specificity (the probabilities that the NIR method will correctly identify a subject as having diabetes or as not having diabetes, respectively) were calculated. As the NIR method produces a continuous rather than categorical classification, various thresholds were evaluated to give several sensitivity and specificity pairs. Test reproducibility was also determined.
At a false-positive rate of 70%, the NIR test had a sensitivity of 77.7%, which is comparable to the 77.3% sensitivity for the FPG test as reported for the Third National Health and Nutrition Examination Survey (NHANES III) study. The reproducibility of the NIR test was also similar to the FPG test (inter-day agreement rates of 84.2% and 79.2%, respectively).
A noninvasive NIR spectroscopic measurement of the volar forearm was shown to have comparable performance characteristics with the FPG test. The source of the spectroscopic signal is still uncertain and is the subject of ongoing research.
目前的糖尿病筛查技术包括空腹血糖(FPG)检测和口服葡萄糖耐量试验。这两种检测都要求患者配合,且都没有理想的性能表现。近红外(NIR)光谱法是一种用于探究样本特征的非侵入性方法,被评估为一种用于2型糖尿病的新型筛查方法。
招募了154名患有和未患有2型糖尿病的患者。使用NIR光谱系统测量他们的前臂皮肤,并同时进行毛细血管血糖测量。66名患者在之后返回进行第二次检查。采用从另一项单独的训练研究中生成的多变量模型,为每个NIR光谱生成疾病的定量风险标志物。计算敏感性和特异性(NIR方法分别正确识别受试者患有糖尿病或未患有糖尿病的概率)。由于NIR方法产生的是连续而非分类的分类结果,因此评估了各种阈值以给出几组敏感性和特异性配对。还确定了测试的可重复性。
在假阳性率为70%时,NIR检测的敏感性为77.7%,这与第三次全国健康和营养检查调查(NHANES III)研究报告的FPG检测77.3%的敏感性相当。NIR检测的可重复性也与FPG检测相似(日间一致性率分别为84.2%和79.2%)。
对掌侧前臂进行非侵入性NIR光谱测量显示出与FPG检测具有相当的性能特征。光谱信号的来源仍不确定,是正在进行的研究主题。