Metabolite ratios as potential biomarkers for type 2 diabetes: a DIRECT study

Sophie Molnos; Simone Wahl; Mark Haid; E. Marelise W. Eekhoff; René Pool; Anna Floegel; Joris Deelen; Daniela Much; Cornelia Prehn; Michaela Breier; Harmen H. M. Draisma; Nienke  van Leeuwen; Annemarie M. C. Simonis-Bik; Anna Jonsson; Gonneke Willemsen; Wolfgang Bernigau; Rui Wang-Sattler; Karsten Suhre; Annette Peters; Barbara Thorand; Christian Herder; Wolfgang Rathmann; Michael Roden; Christian Gieger; Mark H. H. Kramer; Diana van Heemst; Helle K. Pedersen; Valborg Gudmundsdottir; Matthias B. Schulze; Tobias Pischon; Eco J. C. de Geus; Heiner Boeing; Dorret I. Boomsma; Anette G. Ziegler; P. Eline Slagboom; Sandra Hummel; Marian Beekman; Harald Grallert; Søren Brunak; Mark I. McCarthy; Ramneek Gupta; Ewan R. Pearson; Jerzy Adamski; Leen M. 't Hart

doi:10.1007/s00125-017-4436-7

Metabolite ratios as potential biomarkers for type 2 diabetes: a DIRECT study

Sophie Molnos, Simone Wahl, Mark Haid, E. Marelise W. Eekhoff, René Pool, Anna Floegel, Joris Deelen, Daniela Much, Cornelia Prehn, Michaela Breier, Harmen H. M. Draisma, Nienke van Leeuwen, Annemarie M. C. Simonis-Bik, Anna Jonsson, Gonneke Willemsen, Wolfgang Bernigau, Rui Wang-Sattler, Karsten Suhre, Annette Peters, Barbara ThorandChristian Herder, Wolfgang Rathmann, Michael Roden, Christian Gieger, Mark H. H. Kramer, Diana van Heemst, Helle K. Pedersen, Valborg Gudmundsdottir, Matthias B. Schulze, Tobias Pischon, Eco J. C. de Geus, Heiner Boeing, Dorret I. Boomsma, Anette G. Ziegler, P. Eline Slagboom, Sandra Hummel, Marian Beekman, Harald Grallert, Søren Brunak, Mark I. McCarthy, Ramneek Gupta, Ewan R. Pearson, Jerzy Adamski, Leen M. 't Hart (Lead / Corresponding author)

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Abstract

Aims/hypothesis: Circulating metabolites have been shown to reflect metabolic changes during the development of type 2 diabetes. In this study we examined the association of metabolite levels and pairwise metabolite ratios with insulin responses after glucose, glucagon-like peptide-1 (GLP-1) and arginine stimulation. We then investigated if the identified metabolite ratios were associated with measures of OGTT-derived beta cell function and with prevalent and incident type 2 diabetes.

Methods: We measured the levels of 188 metabolites in plasma samples from 130 healthy members of twin families (from the Netherlands Twin Register) at five time points during a modified 3 h hyperglycaemic clamp with glucose, GLP-1 and arginine stimulation. We validated our results in cohorts with OGTT data (n = 340) and epidemiological case-control studies of prevalent (n = 4925) and incident (n = 4277) diabetes. The data were analysed using regression models with adjustment for potential confounders.

Results: There were dynamic changes in metabolite levels in response to the different secretagogues. Furthermore, several fasting pairwise metabolite ratios were associated with one or multiple clamp-derived measures of insulin secretion (all p < 9.2 × 10(-7)). These associations were significantly stronger compared with the individual metabolite components. One of the ratios, valine to phosphatidylcholine acyl-alkyl C32:2 (PC ae C32:2), in addition showed a directionally consistent positive association with OGTT-derived measures of insulin secretion and resistance (p ≤ 5.4 × 10(-3)) and prevalent type 2 diabetes (ORVal_PC ae C32:2 2.64 [β 0.97 ± 0.09], p = 1.0 × 10(-27)). Furthermore, Val_PC ae C32:2 predicted incident diabetes independent of established risk factors in two epidemiological cohort studies (HRVal_PC ae C32:2 1.57 [β 0.45 ± 0.06]; p = 1.3 × 10(-15)), leading to modest improvements in the receiver operating characteristics when added to a model containing a set of established risk factors in both cohorts (increases from 0.780 to 0.801 and from 0.862 to 0.865 respectively, when added to the model containing traditional risk factors + glucose).

Conclusions/interpretation: In this study we have shown that the Val_PC ae C32:2 metabolite ratio is associated with an increased risk of type 2 diabetes and measures of insulin secretion and resistance. The observed effects were stronger than that of the individual metabolites and independent of known risk factors.

Original language	English
Pages (from-to)	117-129
Number of pages	13
Journal	Diabetologia
Volume	61
Issue number	1
Early online date	21 Sept 2017
DOIs	https://doi.org/10.1007/s00125-017-4436-7
Publication status	Published - Jan 2018

Keywords

Epidemiology
Insulin secretion
Metabolomics
Prediction of diabetes
Type 2 diabetes

ASJC Scopus subject areas

Internal Medicine
Endocrinology, Diabetes and Metabolism

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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© The Author(s) 2017. Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Final published version, 559 KBLicence: CC BY

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Molnos, S., Wahl, S., Haid, M., Eekhoff, E. M. W., Pool, R., Floegel, A., Deelen, J., Much, D., Prehn, C., Breier, M., Draisma, H. H. M., van Leeuwen, N., Simonis-Bik, A. M. C., Jonsson, A., Willemsen, G., Bernigau, W., Wang-Sattler, R., Suhre, K., Peters, A., ... 't Hart, L. M. (2018). Metabolite ratios as potential biomarkers for type 2 diabetes: a DIRECT study. Diabetologia, 61(1), 117-129. https://doi.org/10.1007/s00125-017-4436-7

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title = "Metabolite ratios as potential biomarkers for type 2 diabetes: a DIRECT study",

abstract = "Aims/hypothesis: Circulating metabolites have been shown to reflect metabolic changes during the development of type 2 diabetes. In this study we examined the association of metabolite levels and pairwise metabolite ratios with insulin responses after glucose, glucagon-like peptide-1 (GLP-1) and arginine stimulation. We then investigated if the identified metabolite ratios were associated with measures of OGTT-derived beta cell function and with prevalent and incident type 2 diabetes.Methods: We measured the levels of 188 metabolites in plasma samples from 130 healthy members of twin families (from the Netherlands Twin Register) at five time points during a modified 3 h hyperglycaemic clamp with glucose, GLP-1 and arginine stimulation. We validated our results in cohorts with OGTT data (n = 340) and epidemiological case-control studies of prevalent (n = 4925) and incident (n = 4277) diabetes. The data were analysed using regression models with adjustment for potential confounders.Results: There were dynamic changes in metabolite levels in response to the different secretagogues. Furthermore, several fasting pairwise metabolite ratios were associated with one or multiple clamp-derived measures of insulin secretion (all p < 9.2 × 10(-7)). These associations were significantly stronger compared with the individual metabolite components. One of the ratios, valine to phosphatidylcholine acyl-alkyl C32:2 (PC ae C32:2), in addition showed a directionally consistent positive association with OGTT-derived measures of insulin secretion and resistance (p ≤ 5.4 × 10(-3)) and prevalent type 2 diabetes (ORVal_PC ae C32:2 2.64 [β 0.97 ± 0.09], p = 1.0 × 10(-27)). Furthermore, Val_PC ae C32:2 predicted incident diabetes independent of established risk factors in two epidemiological cohort studies (HRVal_PC ae C32:2 1.57 [β 0.45 ± 0.06]; p = 1.3 × 10(-15)), leading to modest improvements in the receiver operating characteristics when added to a model containing a set of established risk factors in both cohorts (increases from 0.780 to 0.801 and from 0.862 to 0.865 respectively, when added to the model containing traditional risk factors + glucose).Conclusions/interpretation: In this study we have shown that the Val_PC ae C32:2 metabolite ratio is associated with an increased risk of type 2 diabetes and measures of insulin secretion and resistance. The observed effects were stronger than that of the individual metabolites and independent of known risk factors.",

keywords = "Epidemiology, Insulin secretion, Metabolomics, Prediction of diabetes, Type 2 diabetes",

author = "Sophie Molnos and Simone Wahl and Mark Haid and Eekhoff, {E. Marelise W.} and Ren{\'e} Pool and Anna Floegel and Joris Deelen and Daniela Much and Cornelia Prehn and Michaela Breier and Draisma, {Harmen H. M.} and {van Leeuwen}, Nienke and Simonis-Bik, {Annemarie M. C.} and Anna Jonsson and Gonneke Willemsen and Wolfgang Bernigau and Rui Wang-Sattler and Karsten Suhre and Annette Peters and Barbara Thorand and Christian Herder and Wolfgang Rathmann and Michael Roden and Christian Gieger and Kramer, {Mark H. H.} and {van Heemst}, Diana and Pedersen, {Helle K.} and Valborg Gudmundsdottir and Schulze, {Matthias B.} and Tobias Pischon and {de Geus}, {Eco J. C.} and Heiner Boeing and Boomsma, {Dorret I.} and Ziegler, {Anette G.} and Slagboom, {P. Eline} and Sandra Hummel and Marian Beekman and Harald Grallert and S{\o}ren Brunak and McCarthy, {Mark I.} and Ramneek Gupta and Pearson, {Ewan R.} and Jerzy Adamski and {'t Hart}, {Leen M.}",

note = "This work was partly funded by the Innovative Medicines Initiative Joint Undertaking under grant agreement no. 115317 (DIRECT), resources of which are composed of financial contributions from the European Union{\textquoteright}s Seventh Framework Programme (FP7/2007–2013) and EFPIA companies{\textquoteright} in kind contributions (http://www.direct-diabetes.org/); the Netherlands Organization for Health Research and Development (Priority Medicines Elderly Program 113102006); a grant from the German Federal Ministry of Education and Research (BMBF) to the German Centre Diabetes Research (DZD); a grant from the Helmholtz Initiative Personalized Medicine (iMED); grants from the German Diabetes Association and the Helmholtz International Research Group (Helmholtz HIRG-0018); the German Diabetes Centre is funded by the German Federal Ministry of Health (BMG) and the Ministry of Innovation, Science, Research and Technology (MIWF) of the State North Rhine-Westphalia. KS was supported by {\textquoteleft}Biomedical Research Program{\textquoteright} funds at Weill Cornell Medicine in Qatar, a programme funded by the Qatar Foundation. The NTR is supported by the European Research Council (grant 230374), by BBMRI-NL, a Research Infrastructure financed by the Dutch government (NWO 184.021.007) and the Netherlands Organization for Scientific Research (grant NWO 480-04-004, NWO/SPI 56-464-14192). The LLS was financially supported by the Innovation-Oriented Research Program on Genomics (SenterNovem IGE01014 and IGE05007), the Centre for Medical Systems Biology and the Netherlands Consortium for Healthy Ageing (grant 05040202 and 050-060-810), all in the framework of the Netherlands Genomics Initiative, Netherlands Organization for Scientific Research (NWO), by BBMRI-NL, a Research Infrastructure financed by the Dutch government (NWO 184.021.007), the European Union-funded Network of Excellence Lifespan (FP6 036894) and the European Union{\textquoteright}s Seventh Framework Programme (FP7/2007-2011) under grant agreement no. 259679.",

year = "2018",

month = jan,

doi = "10.1007/s00125-017-4436-7",

language = "English",

volume = "61",

pages = "117--129",

journal = "Diabetologia",

issn = "0012-186X",

publisher = "Springer Verlag",

number = "1",

}

Molnos, S, Wahl, S, Haid, M, Eekhoff, EMW, Pool, R, Floegel, A, Deelen, J, Much, D, Prehn, C, Breier, M, Draisma, HHM, van Leeuwen, N, Simonis-Bik, AMC, Jonsson, A, Willemsen, G, Bernigau, W, Wang-Sattler, R, Suhre, K, Peters, A, Thorand, B, Herder, C, Rathmann, W, Roden, M, Gieger, C, Kramer, MHH, van Heemst, D, Pedersen, HK, Gudmundsdottir, V, Schulze, MB, Pischon, T, de Geus, EJC, Boeing, H, Boomsma, DI, Ziegler, AG, Slagboom, PE, Hummel, S, Beekman, M, Grallert, H, Brunak, S, McCarthy, MI, Gupta, R, Pearson, ER, Adamski, J & 't Hart, LM 2018, 'Metabolite ratios as potential biomarkers for type 2 diabetes: a DIRECT study', Diabetologia, vol. 61, no. 1, pp. 117-129. https://doi.org/10.1007/s00125-017-4436-7

TY - JOUR

T1 - Metabolite ratios as potential biomarkers for type 2 diabetes

T2 - a DIRECT study

AU - Molnos, Sophie

AU - Wahl, Simone

AU - Haid, Mark

AU - Eekhoff, E. Marelise W.

AU - Pool, René

AU - Floegel, Anna

AU - Deelen, Joris

AU - Much, Daniela

AU - Prehn, Cornelia

AU - Breier, Michaela

AU - Draisma, Harmen H. M.

AU - van Leeuwen, Nienke

AU - Simonis-Bik, Annemarie M. C.

AU - Jonsson, Anna

AU - Willemsen, Gonneke

AU - Bernigau, Wolfgang

AU - Wang-Sattler, Rui

AU - Suhre, Karsten

AU - Peters, Annette

AU - Thorand, Barbara

AU - Herder, Christian

AU - Rathmann, Wolfgang

AU - Roden, Michael

AU - Gieger, Christian

AU - Kramer, Mark H. H.

AU - van Heemst, Diana

AU - Pedersen, Helle K.

AU - Gudmundsdottir, Valborg

AU - Schulze, Matthias B.

AU - Pischon, Tobias

AU - de Geus, Eco J. C.

AU - Boeing, Heiner

AU - Boomsma, Dorret I.

AU - Ziegler, Anette G.

AU - Slagboom, P. Eline

AU - Hummel, Sandra

AU - Beekman, Marian

AU - Grallert, Harald

AU - Brunak, Søren

AU - McCarthy, Mark I.

AU - Gupta, Ramneek

AU - Pearson, Ewan R.

AU - Adamski, Jerzy

AU - 't Hart, Leen M.

N1 - This work was partly funded by the Innovative Medicines Initiative Joint Undertaking under grant agreement no. 115317 (DIRECT), resources of which are composed of financial contributions from the European Union’s Seventh Framework Programme (FP7/2007–2013) and EFPIA companies’ in kind contributions (http://www.direct-diabetes.org/); the Netherlands Organization for Health Research and Development (Priority Medicines Elderly Program 113102006); a grant from the German Federal Ministry of Education and Research (BMBF) to the German Centre Diabetes Research (DZD); a grant from the Helmholtz Initiative Personalized Medicine (iMED); grants from the German Diabetes Association and the Helmholtz International Research Group (Helmholtz HIRG-0018); the German Diabetes Centre is funded by the German Federal Ministry of Health (BMG) and the Ministry of Innovation, Science, Research and Technology (MIWF) of the State North Rhine-Westphalia. KS was supported by ‘Biomedical Research Program’ funds at Weill Cornell Medicine in Qatar, a programme funded by the Qatar Foundation. The NTR is supported by the European Research Council (grant 230374), by BBMRI-NL, a Research Infrastructure financed by the Dutch government (NWO 184.021.007) and the Netherlands Organization for Scientific Research (grant NWO 480-04-004, NWO/SPI 56-464-14192). The LLS was financially supported by the Innovation-Oriented Research Program on Genomics (SenterNovem IGE01014 and IGE05007), the Centre for Medical Systems Biology and the Netherlands Consortium for Healthy Ageing (grant 05040202 and 050-060-810), all in the framework of the Netherlands Genomics Initiative, Netherlands Organization for Scientific Research (NWO), by BBMRI-NL, a Research Infrastructure financed by the Dutch government (NWO 184.021.007), the European Union-funded Network of Excellence Lifespan (FP6 036894) and the European Union’s Seventh Framework Programme (FP7/2007-2011) under grant agreement no. 259679.

PY - 2018/1

Y1 - 2018/1

N2 - Aims/hypothesis: Circulating metabolites have been shown to reflect metabolic changes during the development of type 2 diabetes. In this study we examined the association of metabolite levels and pairwise metabolite ratios with insulin responses after glucose, glucagon-like peptide-1 (GLP-1) and arginine stimulation. We then investigated if the identified metabolite ratios were associated with measures of OGTT-derived beta cell function and with prevalent and incident type 2 diabetes.Methods: We measured the levels of 188 metabolites in plasma samples from 130 healthy members of twin families (from the Netherlands Twin Register) at five time points during a modified 3 h hyperglycaemic clamp with glucose, GLP-1 and arginine stimulation. We validated our results in cohorts with OGTT data (n = 340) and epidemiological case-control studies of prevalent (n = 4925) and incident (n = 4277) diabetes. The data were analysed using regression models with adjustment for potential confounders.Results: There were dynamic changes in metabolite levels in response to the different secretagogues. Furthermore, several fasting pairwise metabolite ratios were associated with one or multiple clamp-derived measures of insulin secretion (all p < 9.2 × 10(-7)). These associations were significantly stronger compared with the individual metabolite components. One of the ratios, valine to phosphatidylcholine acyl-alkyl C32:2 (PC ae C32:2), in addition showed a directionally consistent positive association with OGTT-derived measures of insulin secretion and resistance (p ≤ 5.4 × 10(-3)) and prevalent type 2 diabetes (ORVal_PC ae C32:2 2.64 [β 0.97 ± 0.09], p = 1.0 × 10(-27)). Furthermore, Val_PC ae C32:2 predicted incident diabetes independent of established risk factors in two epidemiological cohort studies (HRVal_PC ae C32:2 1.57 [β 0.45 ± 0.06]; p = 1.3 × 10(-15)), leading to modest improvements in the receiver operating characteristics when added to a model containing a set of established risk factors in both cohorts (increases from 0.780 to 0.801 and from 0.862 to 0.865 respectively, when added to the model containing traditional risk factors + glucose).Conclusions/interpretation: In this study we have shown that the Val_PC ae C32:2 metabolite ratio is associated with an increased risk of type 2 diabetes and measures of insulin secretion and resistance. The observed effects were stronger than that of the individual metabolites and independent of known risk factors.

AB - Aims/hypothesis: Circulating metabolites have been shown to reflect metabolic changes during the development of type 2 diabetes. In this study we examined the association of metabolite levels and pairwise metabolite ratios with insulin responses after glucose, glucagon-like peptide-1 (GLP-1) and arginine stimulation. We then investigated if the identified metabolite ratios were associated with measures of OGTT-derived beta cell function and with prevalent and incident type 2 diabetes.Methods: We measured the levels of 188 metabolites in plasma samples from 130 healthy members of twin families (from the Netherlands Twin Register) at five time points during a modified 3 h hyperglycaemic clamp with glucose, GLP-1 and arginine stimulation. We validated our results in cohorts with OGTT data (n = 340) and epidemiological case-control studies of prevalent (n = 4925) and incident (n = 4277) diabetes. The data were analysed using regression models with adjustment for potential confounders.Results: There were dynamic changes in metabolite levels in response to the different secretagogues. Furthermore, several fasting pairwise metabolite ratios were associated with one or multiple clamp-derived measures of insulin secretion (all p < 9.2 × 10(-7)). These associations were significantly stronger compared with the individual metabolite components. One of the ratios, valine to phosphatidylcholine acyl-alkyl C32:2 (PC ae C32:2), in addition showed a directionally consistent positive association with OGTT-derived measures of insulin secretion and resistance (p ≤ 5.4 × 10(-3)) and prevalent type 2 diabetes (ORVal_PC ae C32:2 2.64 [β 0.97 ± 0.09], p = 1.0 × 10(-27)). Furthermore, Val_PC ae C32:2 predicted incident diabetes independent of established risk factors in two epidemiological cohort studies (HRVal_PC ae C32:2 1.57 [β 0.45 ± 0.06]; p = 1.3 × 10(-15)), leading to modest improvements in the receiver operating characteristics when added to a model containing a set of established risk factors in both cohorts (increases from 0.780 to 0.801 and from 0.862 to 0.865 respectively, when added to the model containing traditional risk factors + glucose).Conclusions/interpretation: In this study we have shown that the Val_PC ae C32:2 metabolite ratio is associated with an increased risk of type 2 diabetes and measures of insulin secretion and resistance. The observed effects were stronger than that of the individual metabolites and independent of known risk factors.

KW - Epidemiology

KW - Insulin secretion

KW - Metabolomics

KW - Prediction of diabetes

KW - Type 2 diabetes

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U2 - 10.1007/s00125-017-4436-7

DO - 10.1007/s00125-017-4436-7

M3 - Article

C2 - 28936587

SN - 0012-186X

VL - 61

SP - 117

EP - 129

JO - Diabetologia

JF - Diabetologia

IS - 1

ER -

Metabolite ratios as potential biomarkers for type 2 diabetes: a DIRECT study

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

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DIRECT: Diabetes Research on Patient Stratification (joint with 25 other partners)

Cite this

Metabolite ratios as potential biomarkers for type 2 diabetes: a DIRECT study

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

Other files and links

Fingerprint

Projects

DIRECT: Diabetes Research on Patient Stratification (joint with 25 other partners)

Cite this