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Novel biomarkers for pre-diabetes identified by metabolomics.

Wang-Sattler R, Yu Z, Herder C, Messias AC, Floegel A, He Y, Heim K, Campillos M, Holzapfel C, Thorand B, Grallert H, Xu T, Bader E, Huth C, Mittelstrass K, Döring A, Meisinger C, Gieger C, Prehn C, Roemisch-Margl W, Carstensen M, Xie L, Yamanaka-Okumura H, Xing G, Ceglarek U, Thiery J, Giani G, Lickert H, Lin X, Li Y, Boeing H, Joost HG, de Angelis MH, Rathmann W, Suhre K, Prokisch H, Peters A, Meitinger T, Roden M, Wichmann HE, Pischon T, Adamski J, Illig T - Mol. Syst. Biol. (2012)

Bottom Line: We identified three metabolites (glycine, lysophosphatidylcholine (LPC) (18:2) and acetylcarnitine) that had significantly altered levels in IGT individuals as compared to those with normal glucose tolerance, with P-values ranging from 2.4×10(-4) to 2.1×10(-13).Lower levels of glycine and LPC were found to be predictors not only for IGT but also for T2D, and were independently confirmed in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam cohort.Our results may help developing novel strategies to prevent T2D.

View Article: PubMed Central - PubMed

Affiliation: Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany. rui.wang-sattler@helmholtz-muenchen.de

ABSTRACT
Type 2 diabetes (T2D) can be prevented in pre-diabetic individuals with impaired glucose tolerance (IGT). Here, we have used a metabolomics approach to identify candidate biomarkers of pre-diabetes. We quantified 140 metabolites for 4297 fasting serum samples in the population-based Cooperative Health Research in the Region of Augsburg (KORA) cohort. Our study revealed significant metabolic variation in pre-diabetic individuals that are distinct from known diabetes risk indicators, such as glycosylated hemoglobin levels, fasting glucose and insulin. We identified three metabolites (glycine, lysophosphatidylcholine (LPC) (18:2) and acetylcarnitine) that had significantly altered levels in IGT individuals as compared to those with normal glucose tolerance, with P-values ranging from 2.4×10(-4) to 2.1×10(-13). Lower levels of glycine and LPC were found to be predictors not only for IGT but also for T2D, and were independently confirmed in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam cohort. Using metabolite-protein network analysis, we identified seven T2D-related genes that are associated with these three IGT-specific metabolites by multiple interactions with four enzymes. The expression levels of these enzymes correlate with changes in the metabolite concentrations linked to diabetes. Our results may help developing novel strategies to prevent T2D.

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Differences in metabolite concentrations from cross-sectional analysis of KORA S4. Plots (A, B) show the names of metabolites with significantly different concentrations in multivariate logistic regression analyses (after the Bonferroni correction for multiple testing with P<3.6 × 10−4) in the five pairwise comparisons of model 1 and model 2. Plot (C) shows the average residues of the concentrations with standard errors of the three metabolites (glycine, LPC (18:2) and acetylcarnitine C2) for the NGT, IGT and dT2D groups. Plot (A) shows the results with adjustment for model 1 (age, sex, BMI, physical activity, alcohol intake, smoking, systolic BP and HDL cholesterol), whereas plots (B, C) have additional adjustments for HbA1c, fasting glucose and fasting insulin (model 2). Residuals were calculated from linear regression model (formula: T2D status∼metabolite concentration+model 2). For further information, see Supplementary Table S4.
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f2: Differences in metabolite concentrations from cross-sectional analysis of KORA S4. Plots (A, B) show the names of metabolites with significantly different concentrations in multivariate logistic regression analyses (after the Bonferroni correction for multiple testing with P<3.6 × 10−4) in the five pairwise comparisons of model 1 and model 2. Plot (C) shows the average residues of the concentrations with standard errors of the three metabolites (glycine, LPC (18:2) and acetylcarnitine C2) for the NGT, IGT and dT2D groups. Plot (A) shows the results with adjustment for model 1 (age, sex, BMI, physical activity, alcohol intake, smoking, systolic BP and HDL cholesterol), whereas plots (B, C) have additional adjustments for HbA1c, fasting glucose and fasting insulin (model 2). Residuals were calculated from linear regression model (formula: T2D status∼metabolite concentration+model 2). For further information, see Supplementary Table S4.

Mentions: To identify metabolites with altered concentrations between the individuals with NGT, i-IFG, IGT and dT2D, we first examined five pairwise comparisons (i-IFG, IGT and dT2D versus NGT, as well as dT2D versus either i-IFG or IGT) in the cross-sectional KORA S4. Based on multivariate logistic regression analysis, 26 metabolite concentrations differed significantly (P-values<3.6 × 10−4) between two groups in at least one of the five comparisons (Figure 2A; odds ratios (ORs) and P-values are shown in Table III). These associations were independent of age, sex, body mass index (BMI), physical activity, alcohol intake, smoking, systolic blood pressure (BP) and HDL cholesterol (model 1). As expected, the level of total hexose H1, which is mainly represented by glucose (Pearson's correlation coefficient value r between H1 and fasting glucose reached 0.85; Supplementary Table S3), was significantly different in all five comparisons. The significantly changed metabolite panel differed from NGT to i-IFG or to IGT. Most of the significantly altered metabolite concentrations were found between individuals with dT2D and IGT as compared with NGT (Supplementary Table S4A).


Novel biomarkers for pre-diabetes identified by metabolomics.

Wang-Sattler R, Yu Z, Herder C, Messias AC, Floegel A, He Y, Heim K, Campillos M, Holzapfel C, Thorand B, Grallert H, Xu T, Bader E, Huth C, Mittelstrass K, Döring A, Meisinger C, Gieger C, Prehn C, Roemisch-Margl W, Carstensen M, Xie L, Yamanaka-Okumura H, Xing G, Ceglarek U, Thiery J, Giani G, Lickert H, Lin X, Li Y, Boeing H, Joost HG, de Angelis MH, Rathmann W, Suhre K, Prokisch H, Peters A, Meitinger T, Roden M, Wichmann HE, Pischon T, Adamski J, Illig T - Mol. Syst. Biol. (2012)

Differences in metabolite concentrations from cross-sectional analysis of KORA S4. Plots (A, B) show the names of metabolites with significantly different concentrations in multivariate logistic regression analyses (after the Bonferroni correction for multiple testing with P<3.6 × 10−4) in the five pairwise comparisons of model 1 and model 2. Plot (C) shows the average residues of the concentrations with standard errors of the three metabolites (glycine, LPC (18:2) and acetylcarnitine C2) for the NGT, IGT and dT2D groups. Plot (A) shows the results with adjustment for model 1 (age, sex, BMI, physical activity, alcohol intake, smoking, systolic BP and HDL cholesterol), whereas plots (B, C) have additional adjustments for HbA1c, fasting glucose and fasting insulin (model 2). Residuals were calculated from linear regression model (formula: T2D status∼metabolite concentration+model 2). For further information, see Supplementary Table S4.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3472689&req=5

f2: Differences in metabolite concentrations from cross-sectional analysis of KORA S4. Plots (A, B) show the names of metabolites with significantly different concentrations in multivariate logistic regression analyses (after the Bonferroni correction for multiple testing with P<3.6 × 10−4) in the five pairwise comparisons of model 1 and model 2. Plot (C) shows the average residues of the concentrations with standard errors of the three metabolites (glycine, LPC (18:2) and acetylcarnitine C2) for the NGT, IGT and dT2D groups. Plot (A) shows the results with adjustment for model 1 (age, sex, BMI, physical activity, alcohol intake, smoking, systolic BP and HDL cholesterol), whereas plots (B, C) have additional adjustments for HbA1c, fasting glucose and fasting insulin (model 2). Residuals were calculated from linear regression model (formula: T2D status∼metabolite concentration+model 2). For further information, see Supplementary Table S4.
Mentions: To identify metabolites with altered concentrations between the individuals with NGT, i-IFG, IGT and dT2D, we first examined five pairwise comparisons (i-IFG, IGT and dT2D versus NGT, as well as dT2D versus either i-IFG or IGT) in the cross-sectional KORA S4. Based on multivariate logistic regression analysis, 26 metabolite concentrations differed significantly (P-values<3.6 × 10−4) between two groups in at least one of the five comparisons (Figure 2A; odds ratios (ORs) and P-values are shown in Table III). These associations were independent of age, sex, body mass index (BMI), physical activity, alcohol intake, smoking, systolic blood pressure (BP) and HDL cholesterol (model 1). As expected, the level of total hexose H1, which is mainly represented by glucose (Pearson's correlation coefficient value r between H1 and fasting glucose reached 0.85; Supplementary Table S3), was significantly different in all five comparisons. The significantly changed metabolite panel differed from NGT to i-IFG or to IGT. Most of the significantly altered metabolite concentrations were found between individuals with dT2D and IGT as compared with NGT (Supplementary Table S4A).

Bottom Line: We identified three metabolites (glycine, lysophosphatidylcholine (LPC) (18:2) and acetylcarnitine) that had significantly altered levels in IGT individuals as compared to those with normal glucose tolerance, with P-values ranging from 2.4×10(-4) to 2.1×10(-13).Lower levels of glycine and LPC were found to be predictors not only for IGT but also for T2D, and were independently confirmed in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam cohort.Our results may help developing novel strategies to prevent T2D.

View Article: PubMed Central - PubMed

Affiliation: Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany. rui.wang-sattler@helmholtz-muenchen.de

ABSTRACT
Type 2 diabetes (T2D) can be prevented in pre-diabetic individuals with impaired glucose tolerance (IGT). Here, we have used a metabolomics approach to identify candidate biomarkers of pre-diabetes. We quantified 140 metabolites for 4297 fasting serum samples in the population-based Cooperative Health Research in the Region of Augsburg (KORA) cohort. Our study revealed significant metabolic variation in pre-diabetic individuals that are distinct from known diabetes risk indicators, such as glycosylated hemoglobin levels, fasting glucose and insulin. We identified three metabolites (glycine, lysophosphatidylcholine (LPC) (18:2) and acetylcarnitine) that had significantly altered levels in IGT individuals as compared to those with normal glucose tolerance, with P-values ranging from 2.4×10(-4) to 2.1×10(-13). Lower levels of glycine and LPC were found to be predictors not only for IGT but also for T2D, and were independently confirmed in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam cohort. Using metabolite-protein network analysis, we identified seven T2D-related genes that are associated with these three IGT-specific metabolites by multiple interactions with four enzymes. The expression levels of these enzymes correlate with changes in the metabolite concentrations linked to diabetes. Our results may help developing novel strategies to prevent T2D.

Show MeSH
Related in: MedlinePlus