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Genetics/Genomes/Proteomics/Metabolomics

Metformin Effect on Nontargeted Metabolite Profiles in Patients With Type 2 Diabetes and in Multiple Murine Tissues

  1. Jonathan Adam1,2,
  2. Stefan Brandmaier1,2,
  3. Jörn Leonhardt3,
  4. Markus F. Scheerer4,5,
  5. Robert P. Mohney6,
  6. Tao Xu1,2,
  7. Jie Bi7,
  8. Markus Rotter1,2,
  9. Martina Troll1,2,
  10. Shen Chi1,2,
  11. Margit Heier2,
  12. Christian Herder5,8,
  13. Wolfgang Rathmann5,9,
  14. Guido Giani9,
  15. Jerzy Adamski4,5,10,11,
  16. Thomas Illig12,13,
  17. Konstantin Strauch14,15,
  18. Yixue Li7,
  19. Christian Gieger1,2,5,
  20. Annette Peters1,2,5,16,
  21. Karsten Suhre3,17,18,
  22. Donna Ankerst19,
  23. Thomas Meitinger20,21,
  24. Martin Hrabĕ de Angelis4,5,11,
  25. Michael Roden5,22,23,
  26. Susanne Neschen4,5,
  27. Gabi Kastenmüller3 and
  28. Rui Wang-Sattler1,2,5⇑
  1. 1Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany
  2. 2Institute of Epidemiology II, Helmholtz Zentrum München, Neuherberg, Germany
  3. 3Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany
  4. 4Institute of Experimental Genetics, Helmholtz Zentrum München, Neuherberg, Germany
  5. 5German Center for Diabetes Research (DZD), Neuherberg, Germany
  6. 6Metabolon, Inc., Durham, NC
  7. 7Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
  8. 8Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, Düsseldorf, Germany
  9. 9Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, Düsseldorf, Germany
  10. 10Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, Neuherberg, Germany
  11. 11Institute of Experimental Genetics, Center of Life and Food Sciences Weihenstephan, Technische Universität München, Freising, Germany
  12. 12Hannover Unified Biobank, Hannover Medical School, Hannover, Germany
  13. 13Institute for Human Genetics, Hannover Medical School, Hannover, Germany
  14. 14Institute of Genetic Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany
  15. 15Genetic Epidemiology, Institute of Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians-Universität München, München, Germany
  16. 16Department of Environmental Health, Harvard School of Public Health, Boston, MA
  17. 17Faculty of Biology, Ludwig-Maximilians-Universität, Planegg-Martinsried, Germany
  18. 18Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar (WCMC-Q), Education City–Qatar Foundation, Doha, Qatar
  19. 19Lehrstuhl für Mathematische Modelle Biologischer Systeme, Technische Universität München, Garching, Germany
  20. 20Institute of Human Genetics, Helmholtz Zentrum München, Neuherberg, Germany
  21. 21Institute of Human Genetics, Technische Universität München, München, Germany
  22. 22ShanghaiTech University, Shanghai, China
  23. 23Department of Endocrinology and Diabetology, Medical Faculty, Düsseldorf, Düsseldorf, Germany
  1. Corresponding author: Rui Wang-Sattler, rui.wang-sattler{at}helmholtz-muenchen.de.
  1. G.K. and R.W.-S. contributed equally to this study.

Diabetes 2016 Dec; 65(12): 3776-3785. https://doi.org/10.2337/db16-0512
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    Figure 1

    Differences in relative metabolite concentrations in a human study, in a mouse study, and in organ-specific pathways. A: Mean relative residuals of the concentrations (with SEM) of two metabolites for the NGT, IGT, ndt-T2D, and mt-T2D groups derived in cross-sectional analysis of KORA F4. Residuals were calculated from a linear regression model with full adjustments. B: Mean relative concentrations (with SEM) of two metabolites in four different mouse tissues (plasma, liver, skeletal muscle, and epididymal adipose tissue). C: The connections indicated by liver, muscle, and blood (plasma and serum) show organ specificity between metabolites, pathway-related proteins, metformin targets, and metformin. The metabolites (ellipses) were connected to metformin treatment (straight-sided hexagons), proteins (hexagons), and metformin targets (rectangles). The activation/stimulation is indicated with arrows. For further information, see Tables 3–5 and Supplementary Tables 2 and 3.

Tables

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  • Table 1

    Characteristics of the KORA F4 cross-sectional study samples (n = 1,604)

    Clinical parametersNGTIGTndt-T2Dmt-T2D
    n1,14327211574
    Age, years58.9 (8.5)63.8 (8.1)65.1 (7.1)66.2 (7.5)
    Male, %45506158
    BMI, kg/m227.0 (4.3)29.8 (4.7)31.0 (4.8)32.0 (5.6)
    Waist, cm92 (12.9)100.2 (14.3)104.9 (12.1)106.7 (13.0)
    Physical activity, % >1 h per week63544836
    High alcohol intake, %*20182519
    Smoker, %1881215
    Systolic BP, mmHg121.6 (17.7)129.0 (19.0)134.3 (19.2)130.7 (18.2)
    HDL-C, mg/dL58.9 (14.8)54.4 (14.2)49.6 (11.5)50.4 (9.6)
    LDL-C, mg/dL140.7 (34.3)144.5 (36.2)136.6 (36.0)124.1 (28.3)
    Total cholesterol, mg/dL223.4 (37.7)226.4 (41.5)214.1 (37.0)203.8 (37.8)
    Triglycerides, mg/dL118.5 (84.6)150.7 (89.0)172.5 (128.7)177.5 (140.5)
    HbA1c, %5.4 (0.3)5.6 (0.3)6.3 (0.9)6.8 (1.1)
    HbA1c, mmol/mol35.7 (3.2)38.1 (3.9)44.91 (9.9)51.2 (11.6)
    Fasting glucose, mg/dL93.2 (7.5)101.2 (10.6)126.6 (30.5)142.0 (36.0)
    2-h postglucose load, mg/dL100.8 (20.6)162.6 (17.5)216.0 (50.7)†
    Time since diagnosis, years1.4 (2.6)‡7.4 (6.6)
    Insulin, µIU/mL7.1 (26.1)10.1 (10.9)14.3 (14.3)11.6 (11.0)
    Leptin, ng/mL17.2 (19.2)24.2 (21.3)26.8 (21.2)27.8 (25.0)
    Statin usage, %12152836
    β-Blocker usage, %16314338
    ACE inhibitor usage, %10213245
    ARB usage, %8101714
    Insulin therapy, %0000
    Metformin usage, %000100
    Parental T2D, %28303749
    • KORA F4 study characteristics (including solely subjects with available Metabolon measurements). Percentages of individuals or means (SD) are shown for each variable and each group (NGT, IGT, ndt-T2D, and mt-T2D).

    • *≥20 g/day for women; ≥40 g/day for men.

    • †n = 81.

    • ‡For newly diagnosed T2D patients (n = 74), years since T2D diagnosis was defined as 0.

  • Table 2

    Characteristics of the KORA S4 → F4 prospective study samples (n = 683)

    Clinical parametersKORA S4 w/o metformin§ → KORA F4 w/o metformin§KORA S4 w/o metformin§ → KORA F4 w/ metformin
    S4F4P valueS4F4P value
    n6466463737
    Age, years61.4 (4.2)68.5 (4.2)63.4 (3.8)70.4 (3.9)
    Male, %51515454
    BMI, kg/m227.9 (3.9)28.2 (4.2)0.232.8 (4.3)32 (4.5)0.49‖
    Waist, cm93.9 (11.0)96.8 (11.9)1.72E-05106.3 (11.3)106.7 (12.3)0.87‖
    Physical activity, % >1 h per week47575.47E-0432430.47
    High alcohol intake, %*20190.6527160.4
    Smoker, %1389.00E-031480.71
    Systolic BP, mmHg132.1 (18.7)128.2 (19.6)3.12E-04144.9 (18.1)131.6 (18.3)2.43E-03‖
    HDL-C, mg/dL59.1 (16.3)56.7 (14.2)0.0253.4 (11.8)52.4 (7.9)0.69‖
    LDL-C, mg/dL154.5 (40.8)142.5 (36.9)1.86E-08143.5 (37.6)123.5 (23.6)0.02
    Total cholesterol, mg/dL245.5 (42.0)225.2 (40.7)2.2E-16234.1 (41.0)202.1 (33.6)5.20E-04
    Triglycerides, mg/dL129.9 (76.4)132.7 (83.4)0.56170.6 (169)154.5 (159.2)0.79
    HbA1c, %5.6 (0.3)5.6 (0.5)0.536.4 (0.9)6.6 (0.7)0.14
    HbA1c, mmol/mol37.6 (3.7)38.0 (5.7)0.5346.8 (10.3)48.3 (7.8)0.14
    Fasting glucose, mg/dL99.7 (10.9)100.7 (17.6)0.53130.7 (30.1)129.6 (28.3)0.9
    2-h postglucose load, mg/dL115.1 (37.1)126.9 (40.8)8.34E-09205.5 (76.8)
    Statin usage, %10228.05E-108290.04
    β-Blocker usage, %17315.16E-0916290.27
    ACE inhibitor usage, %8212.72E-1116513.18E-03
    ARB usage, %3123.67E-103140.2
    Insulin therapy, %0000
    Metformin usage, %000100
    Parental T2D, %25254747
    • Percentages of individuals or means (SD) of participants (with available Metabolon measurements for KORA S4 and F4) are shown for each variable and each group. w/o, without; w/, with.

    • *>40 g/day in men; >20 g/day in women.

    • §Includes participants with NGT, isolated IFG, IGT, and ntd-T2D.

    • ‖Normally distributed (every other distribution is not normally distributed).

  • Table 3

    Two human serum metabolites significantly associated with metformin treatment in a cross-sectional analyses (KORA F4)

    Crude linear model
mt-T2D (n = 74) vs. ndt-T2D (n = 115)Full linear model
mt-T2D (n = 70)¶ vs. ndt-T2D (n = 114)¶
    Metaboliteβ (95% CI) per SDP valueFDRβ (95% CI) per SDP valueFDR
    Citrulline−0.75 (−1.09, −0.41)2.31E-052.83E-04−0.79 (−1.15, −0.43)2.54E-052.83E-04
    X-213650.67 (0.38, 0.96)7.54E-061.42E-040.65 (0.34, 0.97)5.20E-051.42E-04
    • Estimates (β) and P values for the comparison of 189 participants (74 mt-T2D and 115 ndt-T2D) were calculated using linear regression analysis with the crude and full adjustments. Because of missing confounding information, the models with full adjustment were based on fewer participants. Significant metabolites are highlighted in boldface type with respect to Bonferroni correction (P < 0.05/353 = 1.42E-04) or the FDR.

    • ¶After exclusion of individuals because of missing confounding information.

  • Table 4

    Citrulline remains significantly associated with metformin treatment in human serum in a longitudinal analysis (KORA S4 → F4)

    Crude GEE model
mt-T2D (n = 37) vs. nonmetformin-treated (n = 646) participants§Full GEE model
mt-T2D (n = 33)¶ vs. nonmetformin-treated (n = 629)¶ participants§
    Metaboliteβ (95% CI) per SDP valueFDRβ (95% CI) per SDP valueFDR
    Citrulline−0.67 (−0.98, −0.36)2.03E-051.76E-03−0.61 (−0.94, −0.28)2.96E-043.21E-04
    X-213650.41 (0.12, 0.69)5.62E-030.0110.14 (−0.17, 0.45)0.3740.024
    • GEE model with crude and full adjustment was used to assess the associations between metformin treatment and metabolite serum values in the longitudinal study of 683 participants with no antidiabetes medical treatment at KORA S4. Of these participants, 37 started metformin treatment after KORA S4. Because of missing confounding information, the models with full adjustment were based on fewer participants. Significant metabolites are highlighted in boldface type with respect to Bonferroni correction (P < 0.05/2 = 0.025) and the FDR.

    • §Includes participants with NGT, isolated IFG, IGT, and ntd-T2D.

    • ¶After exclusion of individuals because of missing confounding information.

  • Table 5

    Metabolites significantly associated with metformin treatment in mouse models

    MetaboliteIn plasmaIn liverIn skeletal muscleIn adipose tissue
    β (95% CI) per SDP valueβ (95% CI) per SDP valueβ (95% CI) per SDP valueβ (95% CI) per SDP value
    Citrulline−0.39 (−0.49, −0.28)2.56E-07−0.02 (−0.06, 0.02)0.258−0.35 (−0.41, −0.28)1.79E-09−0.26 (−0.33, −0.19)4.52E-07
    X-21365−0.08 (−0.24, 0.08)0.311−0.12 (−0.34, 0.11)0.295−0.12 (−0.27, 0.04)0.126−0.23 (−0.46, 0.01)0.063
    • Estimates (β) and P values for the comparison between metformin-treated (n = 10, in skeletal muscle [n = 9]) and nontreated mice (n = 10) sacrificed at 4 h after the last treatment. Significant metabolites are highlighted in boldface type (P < 0.05).

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Metformin Effect on Nontargeted Metabolite Profiles in Patients With Type 2 Diabetes and in Multiple Murine Tissues
Jonathan Adam, Stefan Brandmaier, Jörn Leonhardt, Markus F. Scheerer, Robert P. Mohney, Tao Xu, Jie Bi, Markus Rotter, Martina Troll, Shen Chi, Margit Heier, Christian Herder, Wolfgang Rathmann, Guido Giani, Jerzy Adamski, Thomas Illig, Konstantin Strauch, Yixue Li, Christian Gieger, Annette Peters, Karsten Suhre, Donna Ankerst, Thomas Meitinger, Martin Hrabĕ de Angelis, Michael Roden, Susanne Neschen, Gabi Kastenmüller, Rui Wang-Sattler
Diabetes Dec 2016, 65 (12) 3776-3785; DOI: 10.2337/db16-0512

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Metformin Effect on Nontargeted Metabolite Profiles in Patients With Type 2 Diabetes and in Multiple Murine Tissues
Jonathan Adam, Stefan Brandmaier, Jörn Leonhardt, Markus F. Scheerer, Robert P. Mohney, Tao Xu, Jie Bi, Markus Rotter, Martina Troll, Shen Chi, Margit Heier, Christian Herder, Wolfgang Rathmann, Guido Giani, Jerzy Adamski, Thomas Illig, Konstantin Strauch, Yixue Li, Christian Gieger, Annette Peters, Karsten Suhre, Donna Ankerst, Thomas Meitinger, Martin Hrabĕ de Angelis, Michael Roden, Susanne Neschen, Gabi Kastenmüller, Rui Wang-Sattler
Diabetes Dec 2016, 65 (12) 3776-3785; DOI: 10.2337/db16-0512
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