期刊
DIABETOLOGIA
卷 60, 期 9, 页码 1740-1750出版社
SPRINGER
DOI: 10.1007/s00125-017-4325-0
关键词
Biomarkers; Early prediction; Kallikrein-kinin system; Machine learning; Metabolomics; Multivariate models; Prevention; Risk classification
资金
- Academy of Finland [265966, 269862, 272437, 295504, 263401, 267882]
- European Research Council [GA 269045]
- Sigrid Juselius Foundation
- Folkhalsan Research Foundation
- Nordic Center of Excellence in Disease Genetics
- European Union Framework Programme (EU FP6) project EXGENESIS
- Finnish Diabetes Research Foundation
- Foundation for Life and Health in Finland
- Finnish Medical Society
- Helsinki University Central Hospital Research Foundation
- Perklen Foundation
- Ollqvist Foundation
- Narpes Health Care Foundation
- Municipal Health Care Center and Hospital in Jakobstad
- Qatar Foundation
- European Research Council GEPIDIAB [294785]
- Health Care Center in Vaasa
- Health Care Center in Narpes
- Health Care Center in Korsholm
- [ANR-10-LABX-46]
- [ANR-10-EQPX-07-01]
- European Research Council (ERC) [294785] Funding Source: European Research Council (ERC)
Aims/hypothesis The aims of this study were to evaluate systematically the predictive power of comprehensive metabolomics profiles in predicting the future risk of type 2 diabetes, and to identify a panel of the most predictive metabolic markers. Methods We applied an unbiased systems medicine approach to mine metabolite combinations that provide added value in predicting the future incidence of type 2 diabetes beyond known risk factors. We performed mass spectrometry-based targeted, as well as global untargeted, metabolomics, measuring a total of 568 metabolites, in a Finnish cohort of 543 nondiabetic individuals from the Botnia Prospective Study, which included 146 individuals who progressed to type 2 diabetes by the end of a 10 year follow-up period. Multivariate logistic regression was used to assess statistical associations, and regularised least-squares modelling was used to perform machine learning-based risk classification and marker selection. The predictive performance of the machine learning models and marker panels was evaluated using repeated nested cross-validation, and replicated in an independent French cohort of 1044 individuals including 231 participants who progressed to type 2 diabetes during a 9 year follow-up period in the DESIR (Data from an Epidemiological Study on the Insulin Resistance Syndrome) study. Results Nine metabolites were negatively associated (potentially protective) and 25 were positively associated with progression to type 2 diabetes. Machine learning models based on the entire metabolome predicted progression to type 2 diabetes (area under the receiver operating characteristic curve, AUC = 0.77) significantly better than the reference model based on clinical risk factors alone (AUC = 0.68; DeLong's p = 0.0009). The panel of metabolic markers selected by the machine learning-based feature selection also significantly improved the predictive performance over the reference model (AUC = 0.78; p = 0.00019; integrated discrimination improvement, IDI = 66.7%). This approach identified novel predictive biomarkers, such as alpha-tocopherol, bradykinin hydroxyproline, X-12063 and X-13435, which showed added value in predicting progression to type 2 diabetes when combined with known biomarkers such as glucose, mannose and alpha-hydroxybutyrate and routinely used clinical risk factors. Conclusions/interpretation This study provides a panel of novel metabolic markers for future efforts aimed at the prevention of type 2 diabetes.
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