A common test for the diagnosis of type 2 diabetes is the Oral Glucose Tolerance Test (OGTT). Recent developments in the study of OGTT tests have framed it as a Bayesian inverse problem. These data analysis advances promise great improvements in the descriptive power of OGTTs. OGTT tests are typically done with invasive, bothersome, and somewhat expensive venous blood tests. A natural question is whether improved data analysis techniques would allow for less invasive and cheaper glucometer measurements to be used. In this paper we explore this question. Using one dynamic model, we develop an error model for glucometer capillary blood sugar measurements and compare results of venous blood sugar tests for 65 patients, finding a match in over 90% of observed cases. Our conclusion suggests that this model (or one much like it) may permit capillary glucose to be used with reasonable accuracy in performing OGTTs.

翻译：2型糖尿病诊断的常用测试是口服葡萄糖耐量测试。口服葡萄糖耐量测试研究的最新进展将其转化为贝叶斯反问题框架。这些数据分析进步有望显著提升口服葡萄糖耐量测试的描述能力。目前口服葡萄糖耐量测试通常采用侵入性、繁琐且费用较高的静脉血检测。一个自然的问题是：改进的数据分析技术能否允许使用侵入性更小、成本更低的血糖仪测量？本文针对此问题展开研究。我们基于一种动态模型，为血糖仪毛细血管血糖测量建立了误差模型，并将65名患者的静脉血糖检测结果进行对比，发现超过90%的观测案例中两者吻合。研究结论表明，该模型（或类似模型）可能允许以合理精度使用毛细血管血糖进行口服葡萄糖耐量测试。