Dietary metabolite profiling brings new insight into the relationship between nutrition and metabolic risk: An IMI DIRECT study.
- Eriksen R Section for Nutrition Research, Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, United Kingdom. Electronic address: r.eriksen13@imperial.ac.uk.
- Perez IG Section for Nutrition Research, Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, United Kingdom.
- Posma JM Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College, London, United Kingdom; Health Data Research UK, London, United Kingdom.
- Haid M Research Unit Molecular Endocrinology And Metabolism, Helmholtz Zentrum Muenchen, German Research Center for Environemental Health (GmbH), Neuherberg, Germany.
- Sharma S German Center for Diabetes Research (DZD), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany; Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Bavaria, Germany.
- Prehn C Research Unit Molecular Endocrinology And Metabolism, Helmholtz Zentrum Muenchen, German Research Center for Environemental Health (GmbH), Neuherberg, Germany.
- Thomas LE Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom.
- Koivula RW Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden; Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Radcliffe Department of Medicine, Oxford, United Kingdom.
- Bizzotto R Institute of Neuroscience - National Research Council, Padova, Italy.
- Prehn C Research Unit Molecular Endocrinology And Metabolism, Helmholtz Zentrum Muenchen, German Research Center for Environemental Health (GmbH), Neuherberg, Germany.
- Mari A Institute of Neuroscience - National Research Council, Padova, Italy.
- Giordano GN Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden.
- Pavo I Eli Lilly Regional Operations GmbH, Vienna, Austria.
- Schwenk JM Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH - Royal Institute of Technology, Stockholm, Sweden.
- De Masi F Department of Health Technology, Technical University of Denmark, Kgs Lyngby and The Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark.
- Tsirigos KD Department of Health Technology, Technical University of Denmark, Kgs Lyngby and The Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark.
- Brunak S Department of Health Technology, Technical University of Denmark, Kgs Lyngby and The Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark; Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, DK-2200 Copenhagen, Denmark.
- Viñuela A Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland.
- Mahajan A Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom.
- McDonald TJ Medical School, Exeter, UK NIHR Exeter Clinical Research Facility, University of Exeter.
- Kokkola T Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland.
- Rutter F Department of Epidemiology and Biostatistics, Amsterdam Public Health Institute, Amsterdam UMC, locationVUMC, Amsterdam, Netherlands.
- Teare H Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom.
- Hansen TH The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark.
- Fernandez J Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom.
- Jones A Medical School, Exeter, UK NIHR Exeter Clinical Research Facility, University of Exeter.
- Jennison C Department of Mathematical Sciences, University of Bath, Bath, United Kingdom.
- Walker M Institute of Cellular Medicine (Diabetes), Newcastle University, Newcastle upon Tyne, United Kingdom.
- McCarthy MI Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Radcliffe Department of Medicine, Oxford, United Kingdom; Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom.
- Pedersen O The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark.
- Ruetten H Sanofi-Aventis Deutschland GmbH, R&D, Frankfurt am Main, Germany.
- Forgie I Population Health & Genomics, School of Medicine, University of Dundee, Dundee, United Kingdom.
- Bell JD Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom.
- Pearson ER Population Health & Genomics, School of Medicine, University of Dundee, Dundee, United Kingdom.
- Franks PW Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden.
- Adamski J Research Unit Molecular Endocrinology And Metabolism, Helmholtz Zentrum Muenchen, German Research Center for Environemental Health (GmbH), Neuherberg, Germany; Lehrstuhl für Experimentelle Genetik, Technische Universität München, 85350 Freising-Weihenstephan, Germany; Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore 117597, Singapore.
- Holmes E Section for Nutrition Research, Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, United Kingdom.
- Frost G Section for Nutrition Research, Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, United Kingdom; NIHR Oxford Biomedical Research Centre, Churchill Hospital, Oxford, United Kingdom. Electronic address: g.frost@imperial.ac.uk.
- 2020-08-09
Published in:
- EBioMedicine. - 2020
English
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.
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.
- Language
-
- English
- Open access status
- gold
- Identifiers
-
- DOI 10.1016/j.ebiom.2020.102932
- PMID 32763829
- Persistent URL
- https://folia.unifr.ch/global/documents/76439
Statistics
Document views: 36
File downloads:
- Full-text: 0