Identification of Serum Metabolites Associated with Development of Type 2 Diabetes by Non-targeted Metabolomics Approach

• Main Authors: Pei-Chen Chu, 褚霈貞
• Other Authors: Wen-Harn Pan 潘文涵
• Format: Others
• Language: en_US
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/36385303611818931611

碩士 === 國立臺灣大學 === 流行病學與預防醫學研究所 === 103 === Background: Traditional risk assessment to estimate of type 2 diabetes (T2D) risk for individual often include predictors, such as age, gender, family history, fasting plasma glucose, glycated hemoglobin (HbA1C) and metabolic syndrome components. However, it is not altogether satisfactory in term of predictability. With the development of metabolomics technology, it is likely to discover low-molecular-weight (<1000 Da) metabolites associated with disease etiology. Therefore, we aimed to identify novel metabolites for T2D with non-targeted metabolomics approach and with the goal to improve T2D risk prediction panel. Materials and Methods: We designed a nested case-control study, taking advantage of the data from CVDFACTS (the CardioVascular Disease risk FACtor Two-township Study), a community-based longitudinal cohort study designed to study risk factors and evaluation of cardio-metabolic diseases in Taiwan. We selected 50 new-onset T2D and 50 age and gender matched controls who were chosen from those who did not develop T2D. Their stored baseline fasting serums were used for metabolomics study. Univariate logistic regression with covariates adjustment (age, sex, BMI and serum glucose) was used to screen potential determinants. Multivariate logistic regression was used to generate risk assessment model for predicting T2D risk. Results: A total of 39 peaks were initially screened out as potential metabolites. Then by the forward stepwise selection, 6 candidate peaks in combination with 2 traditional risk factors (BMI and serum glucose) were selected into the T2D risk prediction panel. With the comparison to traditional risk factors model (AUCs=0.89 (95% CI = 0.82-0.96), our model performed significantly better in terms of ROC result (AUCs= 0.99, 95%CI=0.98-1.00) for T2D prediction (p-value =0.0018). Conclusion: After the identification on metabolomics database, we have identified 5 metabolites: hippuric acid in positive mode, hippuric acid in negative mode, C16H27O6P, alkane derivative, plant alkaloid and plant flavonoid together with 2 traditional risk factors (BMI and serum glucose). This diabetes risk prediction panel can distinguish future T2D cases from healthy controls with an AUC of 0.99 (95%CI=0.98-1.00). A significant improvement is achieved compared to the traditional risk factor panel (p-value =0.0018).