Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore.
Epidemiology & Disease Control Division, Ministry of Health, Singapore, Singapore.
Sci Rep. 2017 Oct 20;7(1):13721. doi: 10.1038/s41598-017-14172-8.
Historically, diabetes is diagnosed by measuring fasting (FPG) and two-hour post oral glucose load (OGTT) plasma concentration and interpreting it against recommended clinical thresholds of the patient. More recently, glycated haemoglobin A1c (HbA1c) has been included as a diagnostic criterion. Within-individual biological variation (CVi), analytical variation (CVa) and analytical bias of a test can impact on the accuracy and reproducibility of the classification of a disease. A test with large biological and analytical variation increases the likelihood of erroneous classification of the underlying disease state of a patient. Through numerical simulations based on the laboratory results generated from a large population health survey, we examined the impact of CVi, CVa and bias on the classification of diabetes using fasting plasma glucose (FPG), oral glucose tolerance test (OGTT) and HbA1c. From the results of the simulations, HbA1c has comparable performance to FPG and is better than OGTT in classifying subjects with diabetes, particularly when laboratory methods with smaller CVa are used. The use of the average of the results of the repeat laboratory tests has the effect of ameliorating the combined (analytical and biological) variation. The averaged result improves the consistency of the disease classification.
从历史上看，糖尿病的诊断是通过测量空腹（FPG）和口服葡萄糖负荷后两小时（OGTT）的血浆浓度，并根据患者的推荐临床阈值进行解释。最近，糖化血红蛋白 A1c（HbA1c）也被纳入诊断标准。测试的个体内生物学变异（CVi）、分析变异（CVa）和分析偏倚会影响疾病分类的准确性和可重复性。具有较大生物学和分析变异的测试会增加错误分类患者潜在疾病状态的可能性。通过基于大型人群健康调查生成的实验室结果进行数值模拟，我们研究了 CVi、CVa 和偏倚对使用空腹血浆葡萄糖（FPG）、口服葡萄糖耐量试验（OGTT）和 HbA1c 诊断糖尿病的影响。从模拟结果来看，HbA1c 在诊断糖尿病患者方面与 FPG 的性能相当，并且优于 OGTT，尤其是当使用 CVa 较小的实验室方法时。重复实验室测试结果的平均值的使用具有改善综合（分析和生物学）变异的效果。平均结果提高了疾病分类的一致性。
