Dietary metabolite profiling brings new insight into the relationship between nutrition and metabolic risk: An IMI DIRECT study

Rebeca Eriksen; Isabel Garcia Perez; Joram M. Posma; Mark Haid; Sapna Sharma; Cornelia Prehn; Louise E. Thomas; Robert W. Koivula; Roberto Bizzotto; Andrea Mari; Giuseppe N. Giordano; Imre Pavo; Jochen M. Schwenk; Federico De Masi; Konstantinos D. Tsirigos; Søren Brunak; Ana Viñuela; Anubha Mahajan; Timothy J. McDonald; Tarja Kokkola; Femke Rutter; Harriet Teare; Tue H. Hansen; Juan Fernandez; Angus Jones; Chris Jennison; Mark Walker; Mark I. McCarthy; Oluf Pedersen; Hartmut Ruetten; Ian Forgie; Jimmy D. Bell; Ewan R. Pearson; Paul W. Franks; Jerzy Adamski; Elaine Holmes; Gary Frost

doi:10.1016/j.ebiom.2020.102932

Dietary metabolite profiling brings new insight into the relationship between nutrition and metabolic risk: An IMI DIRECT study

Rebeca Eriksen, Isabel Garcia Perez, Joram M. Posma, Mark Haid, Sapna Sharma, Cornelia Prehn, Louise E. Thomas, Robert W. Koivula, Roberto Bizzotto, Andrea Mari, Giuseppe N. Giordano, Imre Pavo, Jochen M. Schwenk, Federico De Masi, Konstantinos D. Tsirigos, Søren Brunak, Ana Viñuela, Anubha Mahajan, Timothy J. McDonald, Tarja KokkolaFemke Rutter, Harriet Teare, Tue H. Hansen, Juan Fernandez, Angus Jones, Chris Jennison, Mark Walker, Mark I. McCarthy, Oluf Pedersen, Hartmut Ruetten, Ian Forgie, Jimmy D. Bell, Ewan R. Pearson, Paul W. Franks, Jerzy Adamski, Elaine Holmes, Gary Frost

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Abstract

Background: Dietary advice remains the cornerstone of prevention and management of type 2 diabetes (T2D). However, understanding the efficacy of dietary interventions is confounded by the challenges inherent in assessing free living diet. Here we profiled dietary metabolites to investigate glycaemic deterioration and cardiometabolic risk in people at risk of or living with T2D. Methods: We analysed data from plasma collected at baseline and 18-month follow-up in individuals from the Innovative Medicines Initiative (IMI) Diabetes Research on Patient Stratification (DIRECT) cohort 1 n = 403 individuals with normal or impaired glucose regulation (prediabetic) and cohort 2 n = 458 individuals with new onset of T2D. A dietary metabolite profile model (T_pred) was constructed using multivariable regression of 113 plasma metabolites obtained from targeted metabolomics assays. The continuous T_pred score was used to explore the relationships between diet, glycaemic deterioration and cardio-metabolic risk via multiple linear regression models. Findings: A higher T_pred score was associated with healthier diets high in wholegrain (β=3.36 g, 95% CI 0.31, 6.40 and β=2.82 g, 95% CI 0.06, 5.57) and lower energy intake (β=-75.53 kcal, 95% CI -144.71, -2.35 and β=-122.51 kcal, 95% CI -186.56, -38.46), and saturated fat (β=-0.92 g, 95% CI -1.56, -0.28 and β=–0.98 g, 95% CI -1.53, -0.42 g), respectively for cohort 1 and 2. In both cohorts a higher T_pred score was also associated with lower total body adiposity and favourable lipid profiles HDL-cholesterol (β=0.07 mmol/L, 95% CI 0.03, 0.1), (β=0.08 mmol/L, 95% CI 0.04, 0.1), and triglycerides (β=-0.1 mmol/L, 95% CI -0.2, -0.03), (β=-0.2 mmol/L, 95% CI -0.3, -0.09), respectively for cohort 1 and 2. In cohort 2, the T_pred score was negatively associated with liver fat (β=-0.74%, 95% CI -0.67, -0.81), and lower fasting concentrations of HbA1c (β=-0.9 mmol/mol, 95% CI -1.5, -0.1), glucose (β=-0.2 mmol/L, 95% CI -0.4, -0.05) and insulin (β=-11.0 pmol/mol, 95% CI -19.5, -2.6). Longitudinal analysis showed at 18-month follow up a higher T_pred score was also associated lower total body adiposity in both cohorts and lower fasting glucose (β=-0.2 mmol/L, 95% CI -0.3, -0.01) and insulin (β=-9.2 pmol/mol, 95% CI -17.9, -0.4) concentrations in cohort 2. Interpretation: Plasma dietary metabolite profiling provides objective measures of diet intake, showing a relationship to glycaemic deterioration and cardiometabolic health. Funding: This work was supported by the Innovative Medicines Initiative Joint Undertaking under grant agreement no. 115,317 (DIRECT), resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007–2013) and EFPIA companies.

Original language	English
Article number	102932
Journal	EBioMedicine
Volume	58
DOIs	https://doi.org/10.1016/j.ebiom.2020.102932
Publication status	Published - 1 Aug 2020

Funding

This work was supported by the Innovative Medicines Initiative Joint Undertaking under grant agreement no. 115317 (DIRECT), resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007–2013) and EFPIA companies in kind contribution. G.F. funded by Imperial College NIHR BRC and is an NIHR senior investigator. I.G.-P. is supported by a National Institute for Health Research (NIHR) Fellowship ( NIHR-CDF-2017–10–032 ). J.M.P. is supported by a Rutherford Fund Fellowship at Health Data Research (HDR) UK ( MR/S004033/1 ). MMcC was a Wellcome Investigator ( 090532 , 098381 , 106130 , 203141 , 2122590 and an NIHR Senior Investigator (Niddk. U01-DK105535 ). RWK was funded by a STAR Award Novo Nordisk co-financed PhD fellowship. This work was supported in part by ERC -2015-CoG_NASCENT_681742 and the Swedish Research Council; strategic funding for Lund University Diabetes Centre, where some of the work described herein was performed, was provided by the Swedish Research Council, Strategic Research Area Exodiab, (Dnr 2009–1039), the Swedish Foundation for Strategic Research (IRC15–0067), the Swedish Research Council, Linnaeus grant (Dnr 349–2006–237). EP holds a Wellcome Trust Investigator award (grant reference 102820/Z/13/Z). Contributions to this work by SBru. were co-financed by the Novo Nordisk Foundation (grants NNF17OC0027594 and NNF14CC0001 ). The views expressed in this article are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health. MMcC has served on advisory panels for Pfizer, NovoNordisk and Zoe Global, has received honoraria from Merck, Pfizer, Novo Nordisk and Eli Lilly, and research funding from Abbvie, Astra Zeneca, Boehringer Ingelheim, Eli Lilly, Janssen, Merck, NovoNordisk, Pfizer, Roche, Sanofi Aventis, Servier, and Takeda. As of June 2019, MMcC and AM are employees of Genentech, and a holder of Roche stock.

Keywords

Cardiometabolic health
Dietary patterns
Metabolic profiling
Type 2 diabetes

ASJC Scopus subject areas

General Biochemistry,Genetics and Molecular Biology

UN SDGs

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

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Eriksen, R., Perez, I. G., Posma, J. M., Haid, M., Sharma, S., Prehn, C., Thomas, L. E., Koivula, R. W., Bizzotto, R., Mari, A., Giordano, G. N., Pavo, I., Schwenk, J. M., De Masi, F., Tsirigos, K. D., Brunak, S., Viñuela, A., Mahajan, A., McDonald, T. J., ... Frost, G. (2020). Dietary metabolite profiling brings new insight into the relationship between nutrition and metabolic risk: An IMI DIRECT study. EBioMedicine, 58, Article 102932. https://doi.org/10.1016/j.ebiom.2020.102932

Eriksen, R, Perez, IG, Posma, JM, Haid, M, Sharma, S, Prehn, C, Thomas, LE, Koivula, RW, Bizzotto, R, Mari, A, Giordano, GN, Pavo, I, Schwenk, JM, De Masi, F, Tsirigos, KD, Brunak, S, Viñuela, A, Mahajan, A, McDonald, TJ, Kokkola, T, Rutter, F, Teare, H, Hansen, TH, Fernandez, J, Jones, A, Jennison, C, Walker, M, McCarthy, MI, Pedersen, O, Ruetten, H, Forgie, I, Bell, JD, Pearson, ER, Franks, PW, Adamski, J, Holmes, E & Frost, G 2020, 'Dietary metabolite profiling brings new insight into the relationship between nutrition and metabolic risk: An IMI DIRECT study', EBioMedicine, vol. 58, 102932. https://doi.org/10.1016/j.ebiom.2020.102932

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title = "Dietary metabolite profiling brings new insight into the relationship between nutrition and metabolic risk: An IMI DIRECT study",

abstract = "Background: Dietary advice remains the cornerstone of prevention and management of type 2 diabetes (T2D). However, understanding the efficacy of dietary interventions is confounded by the challenges inherent in assessing free living diet. Here we profiled dietary metabolites to investigate glycaemic deterioration and cardiometabolic risk in people at risk of or living with T2D. Methods: We analysed data from plasma collected at baseline and 18-month follow-up in individuals from the Innovative Medicines Initiative (IMI) Diabetes Research on Patient Stratification (DIRECT) cohort 1 n = 403 individuals with normal or impaired glucose regulation (prediabetic) and cohort 2 n = 458 individuals with new onset of T2D. A dietary metabolite profile model (Tpred) was constructed using multivariable regression of 113 plasma metabolites obtained from targeted metabolomics assays. The continuous Tpred score was used to explore the relationships between diet, glycaemic deterioration and cardio-metabolic risk via multiple linear regression models. Findings: A higher Tpred score was associated with healthier diets high in wholegrain (β=3.36 g, 95% CI 0.31, 6.40 and β=2.82 g, 95% CI 0.06, 5.57) and lower energy intake (β=-75.53 kcal, 95% CI -144.71, -2.35 and β=-122.51 kcal, 95% CI -186.56, -38.46), and saturated fat (β=-0.92 g, 95% CI -1.56, -0.28 and β=–0.98 g, 95% CI -1.53, -0.42 g), respectively for cohort 1 and 2. In both cohorts a higher Tpred score was also associated with lower total body adiposity and favourable lipid profiles HDL-cholesterol (β=0.07 mmol/L, 95% CI 0.03, 0.1), (β=0.08 mmol/L, 95% CI 0.04, 0.1), and triglycerides (β=-0.1 mmol/L, 95% CI -0.2, -0.03), (β=-0.2 mmol/L, 95% CI -0.3, -0.09), respectively for cohort 1 and 2. In cohort 2, the Tpred score was negatively associated with liver fat (β=-0.74%, 95% CI -0.67, -0.81), and lower fasting concentrations of HbA1c (β=-0.9 mmol/mol, 95% CI -1.5, -0.1), glucose (β=-0.2 mmol/L, 95% CI -0.4, -0.05) and insulin (β=-11.0 pmol/mol, 95% CI -19.5, -2.6). Longitudinal analysis showed at 18-month follow up a higher Tpred score was also associated lower total body adiposity in both cohorts and lower fasting glucose (β=-0.2 mmol/L, 95% CI -0.3, -0.01) and insulin (β=-9.2 pmol/mol, 95% CI -17.9, -0.4) concentrations in cohort 2. Interpretation: Plasma dietary metabolite profiling provides objective measures of diet intake, showing a relationship to glycaemic deterioration and cardiometabolic health. Funding: This work was supported by the Innovative Medicines Initiative Joint Undertaking under grant agreement no. 115,317 (DIRECT), resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007–2013) and EFPIA companies.",

keywords = "Cardiometabolic health, Dietary patterns, Metabolic profiling, Type 2 diabetes",

author = "Rebeca Eriksen and Perez, {Isabel Garcia} and Posma, {Joram M.} and Mark Haid and Sapna Sharma and Cornelia Prehn and Thomas, {Louise E.} and Koivula, {Robert W.} and Roberto Bizzotto and Andrea Mari and Giordano, {Giuseppe N.} and Imre Pavo and Schwenk, {Jochen M.} and {De Masi}, Federico and Tsirigos, {Konstantinos D.} and S{\o}ren Brunak and Ana Vi{\~n}uela and Anubha Mahajan and McDonald, {Timothy J.} and Tarja Kokkola and Femke Rutter and Harriet Teare and Hansen, {Tue H.} and Juan Fernandez and Angus Jones and Chris Jennison and Mark Walker and McCarthy, {Mark I.} and Oluf Pedersen and Hartmut Ruetten and Ian Forgie and Bell, {Jimmy D.} and Pearson, {Ewan R.} and Franks, {Paul W.} and Jerzy Adamski and Elaine Holmes and Gary Frost",

year = "2020",

month = aug,

day = "1",

doi = "10.1016/j.ebiom.2020.102932",

language = "English",

volume = "58",

journal = "EBioMedicine",

issn = "2352-3964",

publisher = "Elsevier",

}

TY - JOUR

T1 - Dietary metabolite profiling brings new insight into the relationship between nutrition and metabolic risk

T2 - An IMI DIRECT study

AU - Eriksen, Rebeca

AU - Perez, Isabel Garcia

AU - Posma, Joram M.

AU - Haid, Mark

AU - Sharma, Sapna

AU - Prehn, Cornelia

AU - Thomas, Louise E.

AU - Koivula, Robert W.

AU - Bizzotto, Roberto

AU - Mari, Andrea

AU - Giordano, Giuseppe N.

AU - Pavo, Imre

AU - Schwenk, Jochen M.

AU - De Masi, Federico

AU - Tsirigos, Konstantinos D.

AU - Brunak, Søren

AU - Viñuela, Ana

AU - Mahajan, Anubha

AU - McDonald, Timothy J.

AU - Kokkola, Tarja

AU - Rutter, Femke

AU - Teare, Harriet

AU - Hansen, Tue H.

AU - Fernandez, Juan

AU - Jones, Angus

AU - Jennison, Chris

AU - Walker, Mark

AU - McCarthy, Mark I.

AU - Pedersen, Oluf

AU - Ruetten, Hartmut

AU - Forgie, Ian

AU - Bell, Jimmy D.

AU - Pearson, Ewan R.

AU - Franks, Paul W.

AU - Adamski, Jerzy

AU - Holmes, Elaine

AU - Frost, Gary

PY - 2020/8/1

Y1 - 2020/8/1

N2 - Background: Dietary advice remains the cornerstone of prevention and management of type 2 diabetes (T2D). However, understanding the efficacy of dietary interventions is confounded by the challenges inherent in assessing free living diet. Here we profiled dietary metabolites to investigate glycaemic deterioration and cardiometabolic risk in people at risk of or living with T2D. Methods: We analysed data from plasma collected at baseline and 18-month follow-up in individuals from the Innovative Medicines Initiative (IMI) Diabetes Research on Patient Stratification (DIRECT) cohort 1 n = 403 individuals with normal or impaired glucose regulation (prediabetic) and cohort 2 n = 458 individuals with new onset of T2D. A dietary metabolite profile model (Tpred) was constructed using multivariable regression of 113 plasma metabolites obtained from targeted metabolomics assays. The continuous Tpred score was used to explore the relationships between diet, glycaemic deterioration and cardio-metabolic risk via multiple linear regression models. Findings: A higher Tpred score was associated with healthier diets high in wholegrain (β=3.36 g, 95% CI 0.31, 6.40 and β=2.82 g, 95% CI 0.06, 5.57) and lower energy intake (β=-75.53 kcal, 95% CI -144.71, -2.35 and β=-122.51 kcal, 95% CI -186.56, -38.46), and saturated fat (β=-0.92 g, 95% CI -1.56, -0.28 and β=–0.98 g, 95% CI -1.53, -0.42 g), respectively for cohort 1 and 2. In both cohorts a higher Tpred score was also associated with lower total body adiposity and favourable lipid profiles HDL-cholesterol (β=0.07 mmol/L, 95% CI 0.03, 0.1), (β=0.08 mmol/L, 95% CI 0.04, 0.1), and triglycerides (β=-0.1 mmol/L, 95% CI -0.2, -0.03), (β=-0.2 mmol/L, 95% CI -0.3, -0.09), respectively for cohort 1 and 2. In cohort 2, the Tpred score was negatively associated with liver fat (β=-0.74%, 95% CI -0.67, -0.81), and lower fasting concentrations of HbA1c (β=-0.9 mmol/mol, 95% CI -1.5, -0.1), glucose (β=-0.2 mmol/L, 95% CI -0.4, -0.05) and insulin (β=-11.0 pmol/mol, 95% CI -19.5, -2.6). Longitudinal analysis showed at 18-month follow up a higher Tpred score was also associated lower total body adiposity in both cohorts and lower fasting glucose (β=-0.2 mmol/L, 95% CI -0.3, -0.01) and insulin (β=-9.2 pmol/mol, 95% CI -17.9, -0.4) concentrations in cohort 2. Interpretation: Plasma dietary metabolite profiling provides objective measures of diet intake, showing a relationship to glycaemic deterioration and cardiometabolic health. Funding: This work was supported by the Innovative Medicines Initiative Joint Undertaking under grant agreement no. 115,317 (DIRECT), resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007–2013) and EFPIA companies.

AB - Background: Dietary advice remains the cornerstone of prevention and management of type 2 diabetes (T2D). However, understanding the efficacy of dietary interventions is confounded by the challenges inherent in assessing free living diet. Here we profiled dietary metabolites to investigate glycaemic deterioration and cardiometabolic risk in people at risk of or living with T2D. Methods: We analysed data from plasma collected at baseline and 18-month follow-up in individuals from the Innovative Medicines Initiative (IMI) Diabetes Research on Patient Stratification (DIRECT) cohort 1 n = 403 individuals with normal or impaired glucose regulation (prediabetic) and cohort 2 n = 458 individuals with new onset of T2D. A dietary metabolite profile model (Tpred) was constructed using multivariable regression of 113 plasma metabolites obtained from targeted metabolomics assays. The continuous Tpred score was used to explore the relationships between diet, glycaemic deterioration and cardio-metabolic risk via multiple linear regression models. Findings: A higher Tpred score was associated with healthier diets high in wholegrain (β=3.36 g, 95% CI 0.31, 6.40 and β=2.82 g, 95% CI 0.06, 5.57) and lower energy intake (β=-75.53 kcal, 95% CI -144.71, -2.35 and β=-122.51 kcal, 95% CI -186.56, -38.46), and saturated fat (β=-0.92 g, 95% CI -1.56, -0.28 and β=–0.98 g, 95% CI -1.53, -0.42 g), respectively for cohort 1 and 2. In both cohorts a higher Tpred score was also associated with lower total body adiposity and favourable lipid profiles HDL-cholesterol (β=0.07 mmol/L, 95% CI 0.03, 0.1), (β=0.08 mmol/L, 95% CI 0.04, 0.1), and triglycerides (β=-0.1 mmol/L, 95% CI -0.2, -0.03), (β=-0.2 mmol/L, 95% CI -0.3, -0.09), respectively for cohort 1 and 2. In cohort 2, the Tpred score was negatively associated with liver fat (β=-0.74%, 95% CI -0.67, -0.81), and lower fasting concentrations of HbA1c (β=-0.9 mmol/mol, 95% CI -1.5, -0.1), glucose (β=-0.2 mmol/L, 95% CI -0.4, -0.05) and insulin (β=-11.0 pmol/mol, 95% CI -19.5, -2.6). Longitudinal analysis showed at 18-month follow up a higher Tpred score was also associated lower total body adiposity in both cohorts and lower fasting glucose (β=-0.2 mmol/L, 95% CI -0.3, -0.01) and insulin (β=-9.2 pmol/mol, 95% CI -17.9, -0.4) concentrations in cohort 2. Interpretation: Plasma dietary metabolite profiling provides objective measures of diet intake, showing a relationship to glycaemic deterioration and cardiometabolic health. Funding: This work was supported by the Innovative Medicines Initiative Joint Undertaking under grant agreement no. 115,317 (DIRECT), resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007–2013) and EFPIA companies.

KW - Cardiometabolic health

KW - Dietary patterns

KW - Metabolic profiling

KW - Type 2 diabetes

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U2 - 10.1016/j.ebiom.2020.102932

DO - 10.1016/j.ebiom.2020.102932

M3 - Article

AN - SCOPUS:85088933440

SN - 2352-3964

VL - 58

JO - EBioMedicine

JF - EBioMedicine

M1 - 102932

ER -

Dietary metabolite profiling brings new insight into the relationship between nutrition and metabolic risk: An IMI DIRECT study

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Funding

Keywords

ASJC Scopus subject areas

UN SDGs

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