| 1. |
- Andersen, Gregers Stig Tig, et al.
(författare)
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The DEXLIFE study methods : identifying novel candidate biomarkers that predict progression to type 2 diabetes in high risk individuals
- 2014
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Ingår i: Diabetes Research and Clinical Practice. - : Elsevier. - 0168-8227 .- 1872-8227. ; 106:2, s. 383-389
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Tidskriftsartikel (refereegranskat)abstract
- The incidence of type 2 diabetes (T2D) is rapidly increasing worldwide and T2D is likely to affect 592 million people in 2035 if the current rate of progression is continued. Today, patients are diagnosed with T2D based on elevated blood glucose, either directly or indirectly (HbA1c). However, the information on disease progression is limited. Therefore, there is a need to identify novel early markers of glucose intolerance that reflect the underlying biology and the overall physiological, metabolic and clinical characteristics of progression towards diabetes. In the DEXLIFE study, several clinical cohorts provide the basis for a series of clinical, physiological and mechanistic investigations in combination with a range of--omic technologies to construct a detailed metabolic profile of high-risk individuals across multiple cohorts. In addition, an exercise and dietary intervention study is conducted, that will assess the impact on both plasma biomarkers and specific functional tissue-based markers. The DEXLIFE study will provide novel diagnostic and predictive biomarkers which may not only effectively detect the progression towards diabetes in high risk individuals but also predict responsiveness to lifestyle interventions known to be effective in the prevention of diabetes.
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| 2. |
- Cobb, Jeff, et al.
(författare)
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α-Hydroxybutyric acid is a selective metabolite biomarker of impaired glucose tolerance
- 2016
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Ingår i: Diabetes Care. - : American Diabetes Association. - 0149-5992 .- 1935-5548. ; 39:6, s. 988-995
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Tidskriftsartikel (refereegranskat)abstract
- OBJECTIVE Plasma metabolites that distinguish isolated impaired glucose tolerance (iIGT) from isolated impaired fasting glucose (iIFG) may be useful biomarkers to predict IGT, a high-risk state for the development of type 2 diabetes. RESEARCH DESIGN AND METHODS Targeted metabolomics with 23 metabolites previously associated with dysglycemia was performed with fasting plasma samples from subjects without diabetes at time 0 of an oral glucose tolerance test (OGTT) in two observational cohorts: RISC (Relationship Between Insulin Sensitivity and Cardiovascular Disease) and DMVhi (Diabetes Mellitus and Vascular Health Initiative). Odds ratios (ORs) for a one-SD change in the metabolite level were calculated using multiple logistic regression models controlling for age, sex, and BMI to test for associations with iIGT or iIFG versus normal. Selective biomarkers of iIGT were further validated in the Botnia study. RESULTS α-Hydroxybutyric acid (α-HB) was most strongly associated with iIGT in RISC (OR 2.54 [95% CI 1.86-3.48], P value 5E-9) and DMVhi (2.75 [1.81-4.19], 4E-5) while having no significant association with iIFG. In Botnia, a-HB was selectively associated with iIGT (2.03 [1.65-2.49], 3E-11) and had no significant association with iIFG. Linoleoyl-glycerophosphocholine (L-GPC) and oleic acid were also found to be selective biomarkers of iIGT. In multivariate IGT prediction models, addition of α-HB, L-GPC, and oleic acid to age, sex, BMI, and fasting glucose significantly improved area under the curve in all three cohorts. CONCLUSIONS α-HB, L-GPC, and oleic acid were shown to be selective biomarkers of iIGT, independent of age, sex, BMI, and fasting glucose, in 4,053 subjects without diabetes from three European cohorts. These biomarkers can be used in predictive models to identify subjects with IGT without performing an OGTT.
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| 3. |
- Peddinti, Gopal, et al.
(författare)
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Early metabolic markers identify potential targets for the prevention of type 2 diabetes
- 2017
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Ingår i: Diabetologia. - : Springer Science and Business Media LLC. - 0012-186X .- 1432-0428. ; , s. 1-11
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Tidskriftsartikel (refereegranskat)abstract
- 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 non-diabetic 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 α-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 α-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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