A hierarchical Bayesian framework to infer the progression level to diabetes based on deficient clinical data
A hierarchical Bayesian framework to infer the progression level to diabetes based on deficient clinical data
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DOI：
10.1016/j.compbiomed.2014.04.017
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发表时间：
2014-07-01
影响因子：
7.7
通讯作者：
Sagara, Yusuke
中科院分区：
文献类型：
--
作者：
Watabe, Teruaki;Okuhara, Yoshiyasu;Sagara, Yusuke
心脏病、糖尿病和高血压等与生活方式有关的疾病的增加是一个必须解决的挑战性问题。长期以来，人们一直使用数学模型来研究人体的生理机制。特别是，为了研究葡萄糖代谢，已经开发了几种推断胰岛素敏感性和β细胞功能的模型。根据临床数据使用数学模型评估糖尿病进展可能有效预防糖尿病的发生。然而，为了评估进展水平，我们需要临床数据，包括口服葡萄糖耐量试验的数据，这些数据通常不会在葡萄糖耐量受损的患者中进行。为了解决这一缺点，我们开发了一个分层贝叶斯框架来推断葡萄糖耐受不良的进展的基础上缺乏数据。我们证明了该框架如何推断糖尿病的进展水平，并表明葡萄糖处理能力和胰岛素分泌功能取决于空腹葡萄糖和糖化血红蛋白（HbAlc）水平。(C)2014爱思唯尔有限公司版权所有。
The increase in lifestyle-related diseases such as heart disease, diabetes, and high blood pressure is a challenging problem that should be resolved. The physiological mechanisms of the human body have long been studied using mathematical models. In particular, to study glucose metabolism, several models that infer insulin sensitivity and beta-cell function have been developed. The use of mathematical models to assess progression to diabetes based on clinical data could be effective for preventing the onset of diabetes. However, to assess the progression level, we need clinical data including data from oral glucose tolerance tests, which are not typically performed on patients whose glucose tolerance may be impaired. To address this shortcoming, we developed a hierarchical Bayesian framework to infer the progression of glucose intolerance based on deficient data. We demonstrated how the framework infers the level of progression to diabetes and showed that glucose disposal capacity and insulin-secretory function depend on the fasting glucose and glycated hemoglobin (HbAlc) levels. (C) 2014 Elsevier Ltd. All rights reserved.