A hierarchical Bayesian framework to infer the progression level to diabetes based on deficient clinical data.
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 β-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 (HbA1c) levels.
Center of Medical Information Science, Kochi Medical School, Kochi University, Kohasu, Oko-cho, Nankoku, Kochi 783-8505, Japan. Electronic address: twatabe-mi@umin.ac.jp.
Teruaki
T
Watabe
Center of Medical Information Science, Kochi Medical School, Kochi University, Kohasu, Oko-cho, Nankoku, Kochi 783-8505, Japan; Center for Innovative and Translational Medicine, Kochi Medical School, Kochi University, Kohasu, Oko-cho, Nankoku, Kochi 783-8505, Japan.
Yoshiyasu
Y
Okuhara
Center for Innovative and Translational Medicine, Kochi Medical School, Kochi University, Kohasu, Oko-cho, Nankoku, Kochi 783-8505, Japan.
Yusuke
Y
Sagara
D000465:Algorithms; D001499:Bayes Theorem; D001786:Blood Glucose / Q000032:analysis; D003920:Diabetes Mellitus / Q000175:diagnosis* / Q000473:pathology*; D018450:Disease Progression; D005260:Female; D018149:Glucose Intolerance / Q000473:pathology; D005951:Glucose Tolerance Test; D006442:Glycated Hemoglobin / Q000032:analysis; D006801:Humans; D007328:Insulin / Q000032:analysis; D007333:Insulin Resistance; D008019:Life Style; D008297:Male; D008962:Models, Theoretical; D015999:Multivariate Analysis; D015203:Reproducibility of Results; D012984:Software; D013997:Time Factors
D001786:Blood Glucose; D006442:Glycated Hemoglobin; D007328:Insulin; C517652:hemoglobin A1c protein, human
Clinical data; Diabetes; Hierarchical Bayesian framework; Mathematical model; Metabolism