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1. A metabolic profile of all-cause mortality risk identified in an observational study of 44,168 individuals

Author

Deelen, J. (Joris), Kettunen, J. (Johannes), Fischer, K. (Krista), Spek, A. (Ashley) van der, Trompet, S. (Stella), Kastenmuller, G. (Gabi), Boyd, A. (Andy), Zierer, J. (Jonas), Akker, E.B. (Erik) van den, Ala-Korpela, M. (Mika), Amin, N. (Najaf), Demirkan, A. (Ayse), Ghanbari, M. (Mohsen), Heemst, D. (Diana) van, Ikram, M.A. (Arfan), Klinken, J.B. (Jan Bert) van, Mooijaart, S.P. (Simon), Peters, A. (Annette), Salomaa, V. (Veikko), Sattar, N. (Naveed), Spector, T.D. (Timothy), Tiemeier, H.W. (Henning), Verhoeven, A. (Aswin), Waldenberger, M. (Melanie), Würtz, P. (Peter), Davey Smith, G. (George), Metspalu, A. (Andres), Perola, M. (Markus), Menni, C. (Cristina), Geleijnse, J.M. (Johanna M.), Drenos, F. (Fotios), Beekman, M. (Marian), Jukema, J.W. (Jan Wouter), Duijn, C.M. (Cornelia) van, Slagboom, P.E. (Eline), Deelen, J. (Joris), Kettunen, J. (Johannes), Fischer, K. (Krista), Spek, A. (Ashley) van der, Trompet, S. (Stella), Kastenmuller, G. (Gabi), Boyd, A. (Andy), Zierer, J. (Jonas), Akker, E.B. (Erik) van den, Ala-Korpela, M. (Mika), Amin, N. (Najaf), Demirkan, A. (Ayse), Ghanbari, M. (Mohsen), Heemst, D. (Diana) van, Ikram, M.A. (Arfan), Klinken, J.B. (Jan Bert) van, Mooijaart, S.P. (Simon), Peters, A. (Annette), Salomaa, V. (Veikko), Sattar, N. (Naveed), Spector, T.D. (Timothy), Tiemeier, H.W. (Henning), Verhoeven, A. (Aswin), Waldenberger, M. (Melanie), Würtz, P. (Peter), Davey Smith, G. (George), Metspalu, A. (Andres), Perola, M. (Markus), Menni, C. (Cristina), Geleijnse, J.M. (Johanna M.), Drenos, F. (Fotios), Beekman, M. (Marian), Jukema, J.W. (Jan Wouter), Duijn, C.M. (Cornelia) van, and Slagboom, P.E. (Eline)

Abstract

Predicting longer-term mortality risk requires collection of clinical data, which is often cumbersome. Therefore, we use a well-standardized metabolomics platform to identify metabolic predictors of long-term mortality in the circulation of 44,168 individuals (age at baseline 18–109), of whom 5512 died during follow-up. We apply a stepwise (forward-backward) procedure based on meta-analysis results and identify 14 circulating biomarkers independently associating with all-cause mortality. Overall, these associations are similar in men and women and across different age strata. We subsequently show that the prediction accuracy of 5- and 10-year mortality based on a model containing the identified biomarkers and sex (C-statistic = 0.837 and 0.830, respectively) is better than that of a model containing conventional risk factors for mortality (C-statistic = 0.772 and 0.790, respectively). The use of the identified metabolic profile as a predictor of mortality or surrogate endpoint in clinical studies needs further investigation.

Published
2019
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2. Effect of sitagliptin on energy metabolism and brown adipose tissue in overweight individuals with prediabetes: a randomised placebo-controlled trial

Author

Nahon, K.J. (Kimberly J.), Doornink, F. (Fleur), Straat, M.E. (Maaike E.), Botani, K. (Kani), Martinez-Tellez, B. (Borja), Abreu-Vieira, G. (Gustavo), Klinken, J.B. (Jan Bert) van, Voortman, G.J. (Gardi), Friesema, E.C.H. (Edith), Ruiz, J.R. (Jonatan R.), Velden, F.H.P. (Floris) van, de Geus-Oei, L.-F. (Lioe-Fee), Smit, F. (F.), Pereira Arias-Bouda, L.M. (Lenka M.), Berbee, J.F.P. (Jimmy), Jazet, I.M. (Ingrid M.), Boon, M.R. (Mariette R.), Rensen, P.C.N. (Patrick), Nahon, K.J. (Kimberly J.), Doornink, F. (Fleur), Straat, M.E. (Maaike E.), Botani, K. (Kani), Martinez-Tellez, B. (Borja), Abreu-Vieira, G. (Gustavo), Klinken, J.B. (Jan Bert) van, Voortman, G.J. (Gardi), Friesema, E.C.H. (Edith), Ruiz, J.R. (Jonatan R.), Velden, F.H.P. (Floris) van, de Geus-Oei, L.-F. (Lioe-Fee), Smit, F. (F.), Pereira Arias-Bouda, L.M. (Lenka M.), Berbee, J.F.P. (Jimmy), Jazet, I.M. (Ingrid M.), Boon, M.R. (Mariette R.), and Rensen, P.C.N. (Patrick)

Abstract

Aims/hypothesis: The aim of this study was to evaluate the effect of sitagliptin on glucose tolerance, plasma lipids, energy expenditure and metabolism of brown adipose tissue (BAT), white adipose tissue (WAT) and skeletal muscle in overweight individuals with prediabetes (impaired glucose tolerance and/or impaired fasting glucose). Methods: We performed a randomised, double-blinded, placebo-controlled trial in 30 overweight, Europid men (age 45.9 ± 6.2 years; BMI 28.8 ± 2.3 kg/m2) with prediabetes in the Leiden University Medical Center and the Alrijne Hospital between March 2015 and September 2016. Participants were initially randomly allocated to receive sitagliptin (100 mg/day) (n = 15) or placebo (n = 15) for 12 weeks, using a randomisation list that was set up by an unblinded pharmacist. All people involved in the study as well as participants were blinded to group assignment. Two participants withdrew from the study prior to completion (both in the sitagliptin group) and were subsequently replaced with two new participants that were allocated to the same treatment. Before and after treatment, fasting venous blood samples and skeletal muscle biopsies were obtained, OGTT was performed and body composition, resting energy expenditure and [18F] fluorodeoxyglucose ([18F]FDG) uptake by metabolic tissues were assessed. The primary study endpoint was the effect of sitagliptin on BAT volume and activity. Results: One participant from the sitagliptin group was excluded from analysis, due to a distribution error, leaving 29 participants for further analysis. Sitagliptin, but not placebo, lowered glucose excursion (−40%; p < 0.003) during OGTT, accompanied by an improved insulinogenic index (+38%; p < 0.003) and oral disposition index (+44%; p < 0.003). In addition, sitagliptin lowered serum concentrations of triacylglycerol (−29%) and very large (−46%), large (−35%) and medium-sized (−24%) VLDL particles (all p < 0.05). Body weight, body composition and energy expenditu

Published
2018
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3. Metabolomics based markers predict type 2 diabetes in a 14-year follow-up study

Author

Liu, J. (Jun), Semiz, S. (Sabina), Lee, S.J. (Sven) van der, Spek, A. (Ashley) van der, Verhoeven, A. (Aswin), Klinken, J.B. (Jan Bert) van, Sijbrands, E.J.G. (Eric), Harms, A.C. (Amy), Hankemeier, T. (Thomas), Willems van Dijk, J.A.P. (Ko), Duijn, C.M. (Cornelia) van, Demirkan, A. (Ayşe), Liu, J. (Jun), Semiz, S. (Sabina), Lee, S.J. (Sven) van der, Spek, A. (Ashley) van der, Verhoeven, A. (Aswin), Klinken, J.B. (Jan Bert) van, Sijbrands, E.J.G. (Eric), Harms, A.C. (Amy), Hankemeier, T. (Thomas), Willems van Dijk, J.A.P. (Ko), Duijn, C.M. (Cornelia) van, and Demirkan, A. (Ayşe)

Abstract

Background: The growing field of metabolomics has opened up new opportunities for prediction of type 2 diabetes (T2D) going beyond the classical biochemistry assays. Objectives: We aimed to identify markers from different pathways which represent early metabolic changes and test their predictive performance for T2D, as compared to the performance of traditional risk factors (TRF). Methods: We analyzed 2776 participants from the Erasmus Rucphen Family study from which 1571 disease free individuals were followed up to 14-years. The targeted metabolomics measurements at baseline were performed by three different platforms using either nuclear magnetic resonance spectroscopy or mass spectrometry. We selected 24 T2D markers by using Least Absolute Shrinkage and Selection operator (LASSO) regression and tested their association to incidence of disease during follow-up. Results: The 24 markers i.e. high-density, low-density and very low-density lipoprotein sub-fractions, certain

Published
2017
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4. Meta-analysis of 49 549 individuals imputed with the 1000 Genomes Project reveals an exonic damaging variant in ANGPTL4 determining fasting TG levels

Author

Leeuwen, E.M. (Elisa) van, Sabo, A. (Aniko), Bis, J.C. (Joshua), Huffman, J.E. (Jennifer), Manichaikul, A. (Ani), Smith, A.V. (Davey), Feitosa, M.F. (Mary Furlan), Demissie, S. (Serkalem), Joshi, P.K. (Peter), Duan, Q. (Qing), Marten, J. (Jonathan), Klinken, J.B. (Jan Bert) van, Surakka, I. (Ida), Nolte, I.M. (Ilja), Zhang, W. (Weihua), Mbarek, H., Li-Gao, R. (Ruifang), Trompet, S. (Stella), Verweij, N. (Niek), Evangelou, E. (Evangelos), Lyytikäinen, L.-P. (Leo-Pekka), Tayo, B. (Bamidele), Deelen, J. (Joris), Most, P.J. (Peter) van der, Van Der Laan, S.W. (Sander W.), Arking, D.E. (Dan E.), Morrison, A. (Alanna), Dehghan, A. (Abbas), Franco, O.H. (Oscar), Hofman, A. (Albert), Rivadeneira Ramirez, F. (Fernando), Sijbrands, E.J.G. (Eric), Uitterlinden, A.G. (André), Mychaleckyj, J.C. (Josyf), Campbell, A. (Archie), Hocking, L.J. (Lynne), Padmanabhan, S. (Sandosh), Brody, J.A. (Jennifer A.), Rice, K.M. (Kenneth), White, C.C. (Charles), Harris, T.B. (Tamara), Isaacs, A.J. (Aaron), Campbell, H. (Harry), Lange, L.A. (Leslie), Rudan, I. (Igor), Kolcic, I. (Ivana), Navarro, P. (Pau), Zemunik, T. (Tatijana), Salomaa, V. (Veikko), Kooner, J.S. (Jaspal), Kooner, J.S. (Jaspal S.), Lehne, B. (Benjamin), Scott, W.R. (William R.), Tan, S.-T. (Sian-Tsung), Geus, E.J.C. (Eco) de, Milaneschi, Y. (Yuri), Penninx, B.W.J.H. (Brenda), Willemsen, G.A.H.M. (Gonneke), Mutsert, R. (Reneé) de, Ford, I. (Ian), Gansevoort, R.T. (Ron), Segura-Lepe, M.P. (Marcelo P.), Raitakari, O.T. (Olli T.), Viikari, J. (Jorma), Nikus, K. (Kjell), Forrester, T. (Terrence), McKenzie, C.A. (Colin), Craen, A.J. (Anton) de, de Ruijter, H.M. (Hester M.), Pasterkamp, G. (Gerard), Snieder, H. (Harold), Oldehinkel, A.J. (Albertine), Slagboom, P.E. (Eline), Cooper, R.S. (Richard S.), Kähönen, M. (Mika), Lehtimäki, T. (Terho), Elliott, P. (Paul), Harst, P. (Pim) van der, Jukema, J.W. (Jan Wouter), Mook-Kanamori, D.O. (Dennis), Boomsma, D.I. (Dorret), Chambers, J.C. (John C.), Swertz, M. (Morris), Ripatti, S. (Samuli), Willems van Dijk, J.A.P. (Ko), Vitart, V. (Veronique), Polasek, O. (Ozren), Hayward, C. (Caroline), Wilson, J.G. (James G.), Wilson, J.F. (James F), Gudnason, V. (Vilmundur), Rich, S.S. (Stephen), Psaty, B.M. (Bruce), Borecki, I.B. (Ingrid), Boerwinkle, E. (Eric), Rotter, J.I. (Jerome I.), Cupples, L.A. (Adrienne), Duijn, C.M. (Cornelia) van, Leeuwen, E.M. (Elisa) van, Sabo, A. (Aniko), Bis, J.C. (Joshua), Huffman, J.E. (Jennifer), Manichaikul, A. (Ani), Smith, A.V. (Davey), Feitosa, M.F. (Mary Furlan), Demissie, S. (Serkalem), Joshi, P.K. (Peter), Duan, Q. (Qing), Marten, J. (Jonathan), Klinken, J.B. (Jan Bert) van, Surakka, I. (Ida), Nolte, I.M. (Ilja), Zhang, W. (Weihua), Mbarek, H., Li-Gao, R. (Ruifang), Trompet, S. (Stella), Verweij, N. (Niek), Evangelou, E. (Evangelos), Lyytikäinen, L.-P. (Leo-Pekka), Tayo, B. (Bamidele), Deelen, J. (Joris), Most, P.J. (Peter) van der, Van Der Laan, S.W. (Sander W.), Arking, D.E. (Dan E.), Morrison, A. (Alanna), Dehghan, A. (Abbas), Franco, O.H. (Oscar), Hofman, A. (Albert), Rivadeneira Ramirez, F. (Fernando), Sijbrands, E.J.G. (Eric), Uitterlinden, A.G. (André), Mychaleckyj, J.C. (Josyf), Campbell, A. (Archie), Hocking, L.J. (Lynne), Padmanabhan, S. (Sandosh), Brody, J.A. (Jennifer A.), Rice, K.M. (Kenneth), White, C.C. (Charles), Harris, T.B. (Tamara), Isaacs, A.J. (Aaron), Campbell, H. (Harry), Lange, L.A. (Leslie), Rudan, I. (Igor), Kolcic, I. (Ivana), Navarro, P. (Pau), Zemunik, T. (Tatijana), Salomaa, V. (Veikko), Kooner, J.S. (Jaspal), Kooner, J.S. (Jaspal S.), Lehne, B. (Benjamin), Scott, W.R. (William R.), Tan, S.-T. (Sian-Tsung), Geus, E.J.C. (Eco) de, Milaneschi, Y. (Yuri), Penninx, B.W.J.H. (Brenda), Willemsen, G.A.H.M. (Gonneke), Mutsert, R. (Reneé) de, Ford, I. (Ian), Gansevoort, R.T. (Ron), Segura-Lepe, M.P. (Marcelo P.), Raitakari, O.T. (Olli T.), Viikari, J. (Jorma), Nikus, K. (Kjell), Forrester, T. (Terrence), McKenzie, C.A. (Colin), Craen, A.J. (Anton) de, de Ruijter, H.M. (Hester M.), Pasterkamp, G. (Gerard), Snieder, H. (Harold), Oldehinkel, A.J. (Albertine), Slagboom, P.E. (Eline), Cooper, R.S. (Richard S.), Kähönen, M. (Mika), Lehtimäki, T. (Terho), Elliott, P. (Paul), Harst, P. (Pim) van der, Jukema, J.W. (Jan Wouter), Mook-Kanamori, D.O. (Dennis), Boomsma, D.I. (Dorret), Chambers, J.C. (John C.), Swertz, M. (Morris), Ripatti, S. (Samuli), Willems van Dijk, J.A.P. (Ko), Vitart, V. (Veronique), Polasek, O. (Ozren), Hayward, C. (Caroline), Wilson, J.G. (James G.), Wilson, J.F. (James F), Gudnason, V. (Vilmundur), Rich, S.S. (Stephen), Psaty, B.M. (Bruce), Borecki, I.B. (Ingrid), Boerwinkle, E. (Eric), Rotter, J.I. (Jerome I.), Cupples, L.A. (Adrienne), and Duijn, C.M. (Cornelia) van

Abstract

Background So far, more than 170 loci have been associated with circulating lipid levels through genomewide association studies (GWAS). These associations are largely driven by common variants, their function is often not known, and many are likely to be markers for the causal variants. In this study we aimed to identify more new rare and low-frequency functional variants associated with circulating lipid levels. Methods We used the 1000 Genomes Project as a reference panel for the imputations of GWAS data from ~60 000 individuals in the discovery stage and ~90 000 samples in the replication stage. Results Our study resul

Published
2016
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5. Genome-wide association study identifies novel genetic variants contributing to variation in blood metabolite levels

Author

Draisma, G. (Gerrit), Pool, R. (Reńe), Kobl, M. (Michael), Jansen, R. (Rick), Petersen, A.K., Vaarhorst, A.A.M. (Anika), Yet, I. (Idil), Haller, T. (Toomas), Demirkan, A. (Ayşe), Esko, T. (Tõnu), Zhu, G. (Gu), Böhringer, S. (Stefan), Beekman, M. (Marian), Klinken, J.B. (Jan Bert) van, Römisch-Margl, W. (Werner), Prehn, C. (Cornelia), Adamski, J. (Jerzy), De Craen, A.J.M. (Anton J. M.), Leeuwen, E.M. (Elisa) van, Amin, N. (Najaf), Dharuri, H. (Harish), Westra, H.J. (Harm-Jan), Franke, L. (Lude), Geus, E.J.C. (Eco) de, Hottenga, J.J. (Jouke Jan), Willemsen, G.A.H.M. (Gonneke), Henders, A.K. (Anjali), Montgomery, G.W. (Grant), Dimas, A.S. (Antigone), Whitfield, J.B. (John B.), Penninx, B.W.J.H. (Brenda), Spector, T.D. (Timothy), Metspalu, A. (Andres), Slagboom, P.E. (Eline), Van Dijk, K.W. (Ko Willems), Hoen, P.A.C. (Peter) 't, Strauch, K. (Konstantin), Martin, N.G. (Nicholas), Ommen, G.J. (Gert) van, Illig, T. (Thomas), Bell, J.T. (Jordana), Mangino, M. (Massimo), Suhre, K. (Karsten), McCarthy, M.I. (Mark), Gieger, C. (Christian), Isaacs, A. (Aaron), Duijn, C.M. (Cornelia) van, Boomsma, D.I. (Dorret), Draisma, G. (Gerrit), Pool, R. (Reńe), Kobl, M. (Michael), Jansen, R. (Rick), Petersen, A.K., Vaarhorst, A.A.M. (Anika), Yet, I. (Idil), Haller, T. (Toomas), Demirkan, A. (Ayşe), Esko, T. (Tõnu), Zhu, G. (Gu), Böhringer, S. (Stefan), Beekman, M. (Marian), Klinken, J.B. (Jan Bert) van, Römisch-Margl, W. (Werner), Prehn, C. (Cornelia), Adamski, J. (Jerzy), De Craen, A.J.M. (Anton J. M.), Leeuwen, E.M. (Elisa) van, Amin, N. (Najaf), Dharuri, H. (Harish), Westra, H.J. (Harm-Jan), Franke, L. (Lude), Geus, E.J.C. (Eco) de, Hottenga, J.J. (Jouke Jan), Willemsen, G.A.H.M. (Gonneke), Henders, A.K. (Anjali), Montgomery, G.W. (Grant), Dimas, A.S. (Antigone), Whitfield, J.B. (John B.), Penninx, B.W.J.H. (Brenda), Spector, T.D. (Timothy), Metspalu, A. (Andres), Slagboom, P.E. (Eline), Van Dijk, K.W. (Ko Willems), Hoen, P.A.C. (Peter) 't, Strauch, K. (Konstantin), Martin, N.G. (Nicholas), Ommen, G.J. (Gert) van, Illig, T. (Thomas), Bell, J.T. (Jordana), Mangino, M. (Massimo), Suhre, K. (Karsten), McCarthy, M.I. (Mark), Gieger, C. (Christian), Isaacs, A. (Aaron), Duijn, C.M. (Cornelia) van, and Boomsma, D.I. (Dorret)

Abstract

Metabolites are small molecules involved in cellular metabolism, which can be detected in biological samples using metabolomic techniques. Here we present the results of genome-wide association and meta-analyses for variation in the blood serum levels of 129 metabolites as measured by the Biocrates metabolomic platfo

Published
2015
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6. Insight in Genome-Wide Association of Metabolite Quantitative Traits by Exome Sequence Analyses

Author

Demirkan, A. (Ayşe), Henneman, P. (Peter), Verhoeven, A. (Aswin), Dharuri, H. (Harish), Amin, N. (Najaf), Klinken, J.B. (Jan Bert) van, Karssen, L.C. (Lennart), Stam, A.H. (Anine), Meissner, A. (Axel), Goraler, S. (Sibel), Maagdenberg, A.M.J.M. (Arn), Deelder, A.M. (André), Hoen, P.A.C. (Peter) 't, Duijn, C.M. (Cornelia) van, Willems van Dijk, J.A.P. (Ko), Demirkan, A. (Ayşe), Henneman, P. (Peter), Verhoeven, A. (Aswin), Dharuri, H. (Harish), Amin, N. (Najaf), Klinken, J.B. (Jan Bert) van, Karssen, L.C. (Lennart), Stam, A.H. (Anine), Meissner, A. (Axel), Goraler, S. (Sibel), Maagdenberg, A.M.J.M. (Arn), Deelder, A.M. (André), Hoen, P.A.C. (Peter) 't, Duijn, C.M. (Cornelia) van, and Willems van Dijk, J.A.P. (Ko)

Abstract

Metabolite quantitative traits carry great promise for epidemiological studies, and their genetic background has been addressed using Genome-Wide Association Studies (GWAS). Thus far, the role of less common variants has not been exhaustively studied. Here, we set out a GWAS for metabolite quantitative traits in serum, followed by exome sequence analysis to zoom in on putative causal variants in the associated genes. 1H Nuclear Magnetic Resonance (1H-NMR) spectroscopy experiments yielded successful quantification of 42 unique metabolites in 2,482 individuals from The Erasmus Rucphen Family (ERF) study. Heritability of metabolites were estimated by SOLAR. GWAS was performed by linear mixed models, using HapMap imputations. Based on physical vicinity and pathway analyses, candidate genes were screened for coding region variation using exome sequence data. Heritability estimates for metabolites ranged between 10% and 52%. GWAS replicated three known loci in the metabolome wide significance: CPS1 with glycine (P-value = 1.27×10−32), PRODH with proline (P-value = 1.11×10−19), SLC16A9 with carnitine level (P-value = 4.81×10−14) and uncovered a novel association between DMGDH and dimethyl-glycine (P-value = 1.65×10−19) level. In addition, we found three novel, suggestively significant loci: TNP1 with pyruvate (P-value = 1.26×10−8), KCNJ16 with 3-hydroxybutyrate (P-value = 1.65×10−8) and 2p12 locus with valine (P-value = 3.49×10−8). Exome sequence analysis identified potentially causal coding and regulatory variants located in the genes CPS1, KCNJ2 and PRODH, and revealed allelic heterogeneity for CPS1 and PRODH. Combined GWAS and exome analyses of metabolites detected by high-resolution 1H-NMR is a robust approach to uncover metabolite quantitative trait loci (mQTL), and the likely causative variants in these loci. It is anticipated that insight in the genetics of intermediate phenotypes will provide additional insight into the genetics of complex traits.

Published
2015
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