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; Cornelia Prehn; 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 (Lead / Corresponding author)
, Isabel Garcia Perez
, Joram M. Posma
, Mark Haid
, Sapna Sharma
, Cornelia Prehn
, Louise E. Thomas
, Robert W. Koivula
, Roberto Bizzotto
, Cornelia Prehn
, 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 (Lead / Corresponding author)

MED Population Health and Genomics

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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 (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.

Original language	English
Article number	102932
Number of pages	9
Journal	EBioMedicine
Volume	58
Early online date	4 Aug 2020
DOIs	https://doi.org/10.1016/j.ebiom.2020.102932
Publication status	Published - Aug 2020

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Metabolic profiling
Dietary patterns
Type 2 diabetes
Cardiometabolic health

ASJC Scopus subject areas

General Biochemistry,Genetics and Molecular Biology

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10.1016/j.ebiom.2020.102932Licence: CC BY-NC-ND

Final Published VersionFinal published version, 428 KBLicence: CC BY-NC-ND

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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., Prehn, C., Mari, A., Giordano, G. N., Pavo, I., Schwenk, J. M., De Masi, F., Tsirigos, K. D., Brunak, S., Viñuela, A., Mahajan, A., ... 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

@article{e7ece779a01c451891ba4f2722bb256a,

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.",

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

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 Cornelia Prehn 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",

note = "Funding for this research was provided by: National Institute for Health Research (NIHR-CDF-2017{\textendash}10{\textendash}032) Novo Nordisk European Research Council Novo Nordisk Fonden (NNF17OC0027594, NNF14CC0001) Rutherford Fund Fellowship at Health Data Research (HDR) UK (MR/S004033/1) Wellcome Investigator (090532, 2122590, 098381, 203141, 106130) NIHR Senior Investigator (U01-DK105535) Crown Copyright {\textcopyright} 2020. Published by Elsevier B.V. All rights reserved.",

year = "2020",

month = aug,

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

language = "English",

volume = "58",

journal = "EBioMedicine",

issn = "2352-3964",

publisher = "Elsevier",

}

Eriksen, R, Perez, IG, Posma, JM, Haid, M, Sharma, S, Prehn, C, Thomas, LE, Koivula, RW, Bizzotto, R, Prehn, C, 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

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 - Prehn, Cornelia

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

N1 - Funding for this research was provided by: National Institute for Health Research (NIHR-CDF-2017–10–032) Novo Nordisk European Research Council Novo Nordisk Fonden (NNF17OC0027594, NNF14CC0001) Rutherford Fund Fellowship at Health Data Research (HDR) UK (MR/S004033/1) Wellcome Investigator (090532, 2122590, 098381, 203141, 106130) NIHR Senior Investigator (U01-DK105535) Crown Copyright © 2020. Published by Elsevier B.V. All rights reserved.

PY - 2020/8

Y1 - 2020/8

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.

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.

KW - Metabolic profiling

KW - Dietary patterns

KW - Type 2 diabetes

KW - Cardiometabolic health

UR - https://www.scopus.com/pages/publications/85088933440

U2 - 10.1016/j.ebiom.2020.102932

DO - 10.1016/j.ebiom.2020.102932

M3 - Article

C2 - 32763829

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

Abstract

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

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Stratified Medicine in Type 2 Diabetes: Insights from the Study of Drug Response (New Investigator Award)

Cite this

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

Abstract

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Projects

Stratified Medicine in Type 2 Diabetes: Insights from the Study of Drug Response (New Investigator Award)

Cite this