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Deng Z, Wawro N, Freuer D, Peters A, Heier M, Meisinger C, Breuninger TA, Linseisen J. Differential association of dietary scores with the risk of type 2 diabetes by metabotype. Eur J Nutr 2024:10.1007/s00394-024-03411-0. [PMID: 38714546 DOI: 10.1007/s00394-024-03411-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 04/22/2024] [Indexed: 05/10/2024]
Abstract
PURPOSE We aimed to examine the association between dietary patterns and type 2 diabetes mellitus (T2DM) while considering the potential effect modification by metabolic phenotypes (metabotypes). Additionally, we aimed to explore the association between dietary scores and prediabetes. METHODS A total of 1460 participants (11.8% with T2DM) from the cross-sectional population-based KORA FF4 study were included. Participants, classified into three metabotype subgroups, had both their FSAm-NPS dietary index (underpinning the Nutri-Score) and ultra-processed foods (UPF) intake (using NOVA classification) calculated. Glucose tolerance status was assessed via oral glucose tolerance tests (OGTT) in non-diabetic participants and was classified according to the American Diabetes Association criteria. Logistic regression models were used for both the overall and metabotype-stratified analyses of dietary scores' association with T2DM, and multinomial probit models for their association with prediabetes. RESULTS Participants who had a diet with a higher FSAm-NPS dietary index (i.e., a lower diet quality) or a greater percentage of UPF consumption showed a positive association with T2DM. Stratified analyses demonstrated a strengthened association between UPF consumption and T2DM specifically in the metabolically most unfavorable metabotype (Odds Ratio, OR 1.92; 95% Confidence Interval, CI 1.35, 2.73). A diet with a higher FSAm-NPS dietary index was also positively associated with prediabetes (OR 1.19; 95% CI 1.04, 1.35). CONCLUSION Our study suggests different associations between poorer diet quality and T2DM across individuals exhibiting diverse metabotypes, pointing to the option for stratified dietary interventions in diabetes prevention.
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Affiliation(s)
- Zhongyi Deng
- Institute for Medical Information Processing, Biometry, and Epidemiology - IBE, Ludwig- Maximilians University of Munich, Marchioninistr. 15, 81377, Munich, Germany
- Pettenkofer School of Public Health, Ludwig-Maximilians University of Munich, Pettenkoferstr. 9A, 80336, Munich, Germany
- Chair of Epidemiology, Medical Faculty, University of Augsburg, University Hospital of Augsburg, Stenglinstr. 2, 86156, Augsburg, Germany
| | - Nina Wawro
- Chair of Epidemiology, Medical Faculty, University of Augsburg, University Hospital of Augsburg, Stenglinstr. 2, 86156, Augsburg, Germany
- Institute of Epidemiology, Helmholtz Munich (GmbH) - German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
| | - Dennis Freuer
- Chair of Epidemiology, Medical Faculty, University of Augsburg, University Hospital of Augsburg, Stenglinstr. 2, 86156, Augsburg, Germany
| | - Annette Peters
- Institute for Medical Information Processing, Biometry, and Epidemiology - IBE, Ludwig- Maximilians University of Munich, Marchioninistr. 15, 81377, Munich, Germany
- Institute of Epidemiology, Helmholtz Munich (GmbH) - German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Munich-Neuherberg, Germany
| | - Margit Heier
- Institute of Epidemiology, Helmholtz Munich (GmbH) - German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
- KORA Study Centre, University Hospital Augsburg, Beim Glaspalast 1, 86153, Augsburg, Germany
| | - Christine Meisinger
- Chair of Epidemiology, Medical Faculty, University of Augsburg, University Hospital of Augsburg, Stenglinstr. 2, 86156, Augsburg, Germany
| | - Taylor A Breuninger
- Chair of Epidemiology, Medical Faculty, University of Augsburg, University Hospital of Augsburg, Stenglinstr. 2, 86156, Augsburg, Germany
| | - Jakob Linseisen
- Institute for Medical Information Processing, Biometry, and Epidemiology - IBE, Ludwig- Maximilians University of Munich, Marchioninistr. 15, 81377, Munich, Germany.
- Pettenkofer School of Public Health, Ludwig-Maximilians University of Munich, Pettenkoferstr. 9A, 80336, Munich, Germany.
- Chair of Epidemiology, Medical Faculty, University of Augsburg, University Hospital of Augsburg, Stenglinstr. 2, 86156, Augsburg, Germany.
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Schepp M, Freuer D, Wawro N, Peters A, Heier M, Teupser D, Meisinger C, Linseisen J. Association of the habitual dietary intake with the fatty liver index and effect modification by metabotypes in the population-based KORA-Fit study. Lipids Health Dis 2024; 23:99. [PMID: 38575962 PMCID: PMC10993479 DOI: 10.1186/s12944-024-02094-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Accepted: 03/26/2024] [Indexed: 04/06/2024] Open
Abstract
BACKGROUND Non-alcoholic fatty liver disease (NAFLD) is an emerging threat for public health with diet being a major risk factor in disease development and progression. However, the effects of habitual food consumption on fatty liver are still inconclusive as well as the proposed role of the individuals' metabolic profiles. Therefore, the aim of our study is to examine the associations between diet and NAFLD with an emphasis on the influence of specific metabotypes in the general population. METHODS A total of 689 participants (304 men and 385 women) of the KORA-Fit (S4) survey, a follow-up study of the population-based KORA cohort study running in the Region of Augsburg, Germany, were included in this analysis. Dietary information was derived from repeated 24-h food lists and a food frequency questionnaire. The intake of energy and energy-providing nutrients were calculated using the national food composition database. The presence of fatty liver was quantified by the fatty liver index (FLI), and metabotypes were calculated using K-means clustering. Multivariable linear regression models were used for the analysis of habitual food groups and FLI; for the evaluation of macronutrients, energy substitution models were applied. RESULTS A higher consumption of nuts and whole grains, and a better diet quality (according to Alternate Healthy Eating Index and Mediterranean Diet Score) were associated with lower FLI values, while the intake of soft drinks, meat, fish and eggs were associated with a higher FLI. The isocaloric substitution of carbohydrates with polyunsaturated fatty acids was associated with a decreased FLI, while substitution with monounsaturated fatty acids and protein showed increased FLI. Statistically significant interactions with the metabotype were observed for most food groups. CONCLUSION The consumption of plant-based food groups, including nuts and whole grains, and diet quality, were associated with lower FLI values, whereas the intake of soft drinks and products of animal origin (meat, fish, eggs) were associated with a higher FLI. The observed statistically significant interactions with the metabotype for most food groups could help to develop targeted prevention strategies on a population-based level if confirmed in independent prospective studies.
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Affiliation(s)
- M Schepp
- University of Augsburg, University Hospital Augsburg, EpidemiologyAugsburg, Germany.
| | - D Freuer
- University of Augsburg, University Hospital Augsburg, EpidemiologyAugsburg, Germany
| | - N Wawro
- University of Augsburg, University Hospital Augsburg, EpidemiologyAugsburg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | - A Peters
- Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health (GmbH), Neuherberg, Germany
- Chair of Epidemiology, Institute for Medical Information Processing, Biometry and Epidemiology, Medical Faculty, Ludwig-Maximilians-Universität München, Munich, Germany
- German Center for Diabetes Research (DZD), Munich-Neuherberg, Germany
| | - M Heier
- Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health (GmbH), Neuherberg, Germany
- KORA Study Centre, University Hospital Augsburg, Augsburg, Germany
| | - D Teupser
- Institute of Laboratory Medicine, University Hospital, LMU Munich, Munich, Germany
| | - C Meisinger
- University of Augsburg, University Hospital Augsburg, EpidemiologyAugsburg, Germany
| | - J Linseisen
- University of Augsburg, University Hospital Augsburg, EpidemiologyAugsburg, Germany
- Institute for Medical Information Processing, Biometry and Epidemiology, Medical Faculty, Ludwig-Maximilians-Universität München, Munich, Germany
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Masango B, Goedecke JH, Ramsay M, Storbeck KH, Micklesfield LK, Chikowore T. Postprandial glucose variability and clusters of sex hormones, liver enzymes, and cardiometabolic factors in a South African cohort of African ancestry. BMJ Open Diabetes Res Care 2024; 12:e003927. [PMID: 38453238 PMCID: PMC10921533 DOI: 10.1136/bmjdrc-2023-003927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 02/16/2024] [Indexed: 03/09/2024] Open
Abstract
INTRODUCTION This study aimed to, first, determine the clusters of sex hormones, liver enzymes, and cardiometabolic factors associated with postprandial glucose (PPG) and, second to evaluate the variation these clusters account for jointly and independently with polygenic risk scores (PRSs) in South Africans of African ancestry men and women. RESEARCH DESIGN AND METHODS PPG was calculated as the integrated area under the curve for glucose during the oral glucose tolerance test (OGTT) using the trapezoidal rule in 794 participants from the Middle-aged Soweto Cohort. Principal component analysis was used to cluster sex hormones, liver enzymes, and cardiometabolic factors, stratified by sex. Multivariable linear regression was used to assess the proportion of variance in PPG accounted for by principal components (PCs) and type 2 diabetes (T2D) PRS while adjusting for selected covariates in men and women. RESULTS The T2D PRS did not contribute to the PPG variability in both men and women. In men, the PCs' cluster of sex hormones, liver enzymes, and cardiometabolic explained 10.6% of the variance in PPG, with PC1 (peripheral fat), PC2 (liver enzymes and steroid hormones), and PC3 (lipids and peripheral fat) contributing significantly to PPG. In women, PC factors of sex hormones, cardiometabolic factors, and liver enzymes explained a similar amount of the variance in PPG (10.8%), with PC1 (central fat) and PC2 (lipids and liver enzymes) contributing significantly to PPG. CONCLUSIONS We demonstrated that inter-individual differences in PPG responses to an OGTT may be differentially explained by body fat distribution, serum lipids, liver enzymes, and steroid hormones in men and women.
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Affiliation(s)
- Bontle Masango
- Division of Human Genetics, National Health Laboratory Service (NHLS), School of Pathology, University of the Witwatersrand, Faculty of Health Sciences, Johannesburg, South Africa
- South African Medical Research Council/University of the Witwatersrand, Developmental Pathways for Health Research Unit (DPHRU), University of the Witwatersrand, Faculty of Health Sciences, Johannesburg, South Africa
- Biomedical Research and Innovation Platform, South African Medical Research Council, Cape Town, South Africa
| | - Julia H Goedecke
- South African Medical Research Council/University of the Witwatersrand, Developmental Pathways for Health Research Unit (DPHRU), University of the Witwatersrand, Faculty of Health Sciences, Johannesburg, South Africa
- Biomedical Research and Innovation Platform, South African Medical Research Council, Cape Town, South Africa
| | - Michèle Ramsay
- Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Faculty of Health Sciences, Johannesburg, South Africa
| | - Karl-Heinz Storbeck
- Department of Biochemistry, Stellenbosch University, Stellenbosch, South Africa
| | - Lisa K Micklesfield
- South African Medical Research Council/University of the Witwatersrand, Developmental Pathways for Health Research Unit (DPHRU), University of the Witwatersrand, Faculty of Health Sciences, Johannesburg, South Africa
| | - Tinashe Chikowore
- South African Medical Research Council/University of the Witwatersrand, Developmental Pathways for Health Research Unit (DPHRU), University of the Witwatersrand, Faculty of Health Sciences, Johannesburg, South Africa
- Harvard Medical School, Boston, Massachusetts, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
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Hillesheim E, Brennan L. Distinct patterns of personalised dietary advice delivered by a metabotype framework similarly improve dietary quality and metabolic health parameters: secondary analysis of a randomised controlled trial. Front Nutr 2023; 10:1282741. [PMID: 38035361 PMCID: PMC10684740 DOI: 10.3389/fnut.2023.1282741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 10/31/2023] [Indexed: 12/02/2023] Open
Abstract
Background In a 12-week randomised controlled trial, personalised nutrition delivered using a metabotype framework improved dietary intake, metabolic health parameters and the metabolomic profile compared to population-level dietary advice. The objective of the present work was to investigate the patterns of dietary advice delivered during the intervention and the alterations in dietary intake and metabolic and metabolomic profiles to obtain further insights into the effectiveness of the metabotype framework. Methods Forty-nine individuals were randomised into the intervention group and subsequently classified into metabotypes using four biomarkers (triacylglycerol, HDL-C, total cholesterol, glucose). These individuals received personalised dietary advice from decision tree algorithms containing metabotypes and individual characteristics. In a secondary analysis of the data, patterns of dietary advice were identified by clustering individuals according to the dietary messages received and clusters were compared for changes in dietary intake and metabolic health parameters. Correlations between changes in blood clinical chemistry and changes in metabolite levels were investigated. Results Two clusters of individuals with distinct patterns of dietary advice were identified. Cluster 1 had the highest percentage of messages delivered to increase the intake of beans and pulses and milk and dairy products. Cluster 2 had the highest percentage of messages delivered to limit the intake of foods high in added sugar, high-fat foods and alcohol. Following the intervention, both patterns improved dietary quality assessed by the Alternate Mediterranean Diet Score and the Alternative Healthy Eating Index, nutrient intakes, blood pressure, triacylglycerol and LDL-C (p ≤ 0.05). Several correlations were identified between changes in total cholesterol, LDL-C, triacylglycerol, insulin and HOMA-IR and changes in metabolites levels, including mostly lipids (sphingomyelins, lysophosphatidylcholines, glycerophosphocholines and fatty acid carnitines). Conclusion The findings indicate that the metabotype framework effectively personalises and delivers dietary advice to improve dietary quality and metabolic health. Clinical trial registration isrctn.com, identifier ISRCTN15305840.
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Affiliation(s)
- Elaine Hillesheim
- UCD School of Agriculture and Food Science, Institute of Food and Health, University College Dublin, Dublin, Ireland
- UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland
| | - Lorraine Brennan
- UCD School of Agriculture and Food Science, Institute of Food and Health, University College Dublin, Dublin, Ireland
- UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland
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Rundblad A, Christensen JJ, Hustad KS, Bastani NE, Ottestad I, Holven KB, Ulven SM. Associations between dietary intake and glucose tolerance in clinical and metabolomics-based metabotypes. GENES & NUTRITION 2023; 18:3. [PMID: 36899329 PMCID: PMC10007735 DOI: 10.1186/s12263-023-00721-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Accepted: 01/23/2023] [Indexed: 03/12/2023]
Abstract
BACKGROUND Metabotyping is a novel concept to group metabolically similar individuals. Different metabotypes may respond differently to dietary interventions; hence, metabotyping may become an important future tool in precision nutrition strategies. However, it is not known if metabotyping based on comprehensive omic data provides more useful identification of metabotypes compared to metabotyping based on only a few clinically relevant metabolites. AIM This study aimed to investigate if associations between habitual dietary intake and glucose tolerance depend on metabotypes identified from standard clinical variables or comprehensive nuclear magnetic resonance (NMR) metabolomics. METHODS We used cross-sectional data from participants recruited through advertisements aimed at people at risk of type 2 diabetes mellitus (n = 203). Glucose tolerance was assessed with a 2-h oral glucose tolerance test (OGTT), and habitual dietary intake was recorded with a food frequency questionnaire. Lipoprotein subclasses and various metabolites were quantified with NMR spectroscopy, and plasma carotenoids were quantified using high-performance liquid chromatography. We divided participants into favorable and unfavorable clinical metabotypes based on established cutoffs for HbA1c and fasting and 2-h OGTT glucose. Favorable and unfavorable NMR metabotypes were created using k-means clustering of NMR metabolites. RESULTS While the clinical metabotypes were separated by glycemic variables, the NMR metabotypes were mainly separated by variables related to lipoproteins. A high intake of vegetables was associated with a better glucose tolerance in the unfavorable, but not the favorable clinical metabotype (interaction, p = 0.01). This interaction was confirmed using plasma concentrations of lutein and zeaxanthin, objective biomarkers of vegetable intake. Although non-significantly, the association between glucose tolerance and fiber intake depended on the clinical metabotypes, while the association between glucose tolerance and intake of saturated fatty acids and dietary fat sources depended on the NMR metabotypes. CONCLUSION Metabotyping may be a useful tool to tailor dietary interventions that will benefit specific groups of individuals. The variables that are used to create metabotypes will affect the association between dietary intake and disease risk.
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Affiliation(s)
- Amanda Rundblad
- Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, P.O. Box 1046 Blindern, 0317, Oslo, Norway.
| | - Jacob J Christensen
- Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, P.O. Box 1046 Blindern, 0317, Oslo, Norway
| | - Kristin S Hustad
- Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, P.O. Box 1046 Blindern, 0317, Oslo, Norway
| | - Nasser E Bastani
- Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, P.O. Box 1046 Blindern, 0317, Oslo, Norway
| | - Inger Ottestad
- Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, P.O. Box 1046 Blindern, 0317, Oslo, Norway
| | - Kirsten B Holven
- Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, P.O. Box 1046 Blindern, 0317, Oslo, Norway.,National Advisory Unit on Familial Hypercholesterolemia, Department of Endocrinology, Morbid Obesity and Preventive Medicine, Oslo University Hospital, Oslo, Norway
| | - Stine M Ulven
- Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, P.O. Box 1046 Blindern, 0317, Oslo, Norway
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Lappa D, Meijnikman AS, Krautkramer KA, Olsson LM, Aydin Ö, Van Rijswijk AS, Acherman YIZ, De Brauw ML, Tremaroli V, Olofsson LE, Lundqvist A, Hjorth SA, Ji B, Gerdes VEA, Groen AK, Schwartz TW, Nieuwdorp M, Bäckhed F, Nielsen J. Self-organized metabotyping of obese individuals identifies clusters responding differently to bariatric surgery. PLoS One 2023; 18:e0279335. [PMID: 36862673 PMCID: PMC9980823 DOI: 10.1371/journal.pone.0279335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 12/05/2022] [Indexed: 03/03/2023] Open
Abstract
Weight loss through bariatric surgery is efficient for treatment or prevention of obesity related diseases such as type 2 diabetes and cardiovascular disease. Long term weight loss response does, however, vary among patients undergoing surgery. Thus, it is difficult to identify predictive markers while most obese individuals have one or more comorbidities. To overcome such challenges, an in-depth multiple omics analyses including fasting peripheral plasma metabolome, fecal metagenome as well as liver, jejunum, and adipose tissue transcriptome were performed for 106 individuals undergoing bariatric surgery. Machine leaning was applied to explore the metabolic differences in individuals and evaluate if metabolism-based patients' stratification is related to their weight loss responses to bariatric surgery. Using Self-Organizing Maps (SOMs) to analyze the plasma metabolome, we identified five distinct metabotypes, which were differentially enriched for KEGG pathways related to immune functions, fatty acid metabolism, protein-signaling, and obesity pathogenesis. The gut metagenome of the most heavily medicated metabotypes, treated simultaneously for multiple cardiometabolic comorbidities, was significantly enriched in Prevotella and Lactobacillus species. This unbiased stratification into SOM-defined metabotypes identified signatures for each metabolic phenotype and we found that the different metabotypes respond differently to bariatric surgery in terms of weight loss after 12 months. An integrative framework that utilizes SOMs and omics integration was developed for stratifying a heterogeneous bariatric surgery cohort. The multiple omics datasets described in this study reveal that the metabotypes are characterized by a concrete metabolic status and different responses in weight loss and adipose tissue reduction over time. Our study thus opens a path to enable patient stratification and hereby allow for improved clinical treatments.
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Affiliation(s)
- Dimitra Lappa
- Department of Biology and Biological Engineering, Systems and Synthetic Biology, Chalmers University of Technology, Gothenburg, Sweden
- * E-mail: (DL); (JN)
| | - Abraham S. Meijnikman
- Department of Internal and Vascular Medicine, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
- Department of Internal Medicine, Spaarne Gasthuis, Hoofddorp, The Netherlands
| | - Kimberly A. Krautkramer
- Department of Molecular and Clinical Medicine, Wallenberg Laboratory, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Lisa M. Olsson
- Department of Molecular and Clinical Medicine, Wallenberg Laboratory, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Ömrüm Aydin
- Department of Internal and Vascular Medicine, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
- Department of Internal Medicine, Spaarne Gasthuis, Hoofddorp, The Netherlands
| | | | | | | | - Valentina Tremaroli
- Department of Molecular and Clinical Medicine, Wallenberg Laboratory, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Louise E. Olofsson
- Department of Molecular and Clinical Medicine, Wallenberg Laboratory, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Annika Lundqvist
- Department of Molecular and Clinical Medicine, Wallenberg Laboratory, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Siv A. Hjorth
- Faculty of Health Sciences, Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Boyang Ji
- Department of Biology and Biological Engineering, Systems and Synthetic Biology, Chalmers University of Technology, Gothenburg, Sweden
| | - Victor E. A. Gerdes
- Department of Internal and Vascular Medicine, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
- Department of Internal Medicine, Spaarne Gasthuis, Hoofddorp, The Netherlands
| | - Albert K. Groen
- Department of Internal and Vascular Medicine, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
- Department of Pediatrics, Laboratory of Metabolic Diseases, University of Groningen, UMCG, Groningen, The Netherlands
| | - Thue W. Schwartz
- Faculty of Health Sciences, Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Max Nieuwdorp
- Department of Internal and Vascular Medicine, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
- Department of Molecular and Clinical Medicine, Wallenberg Laboratory, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Fredrik Bäckhed
- Department of Molecular and Clinical Medicine, Wallenberg Laboratory, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Faculty of Health Sciences, Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Physiology, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Systems and Synthetic Biology, Chalmers University of Technology, Gothenburg, Sweden
- BioInnovation Institute, Copenhagen N, Denmark
- * E-mail: (DL); (JN)
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Metabotyping: a tool for identifying subgroups for tailored nutrition advice. Proc Nutr Soc 2023:1-12. [PMID: 36727494 DOI: 10.1017/s0029665123000058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Diet-related diseases are the leading cause of death globally and strategies to tailor effective nutrition advice are required. Personalised nutrition advice is increasingly recognised as more effective than population-level advice to improve dietary intake and health outcomes. A potential tool to deliver personalised nutrition advice is metabotyping which groups individuals into homogeneous subgroups (metabotypes) using metabolic profiles. In summary, metabotyping has been successfully employed in human nutrition research to identify subgroups of individuals with differential responses to dietary challenges and interventions and diet–disease associations. The suitability of metabotyping to identify clinically relevant subgroups is corroborated by other fields such as diabetes research where metabolic profiling has been intensely used to identify subgroups of patients that display patterns of disease progression and complications. However, there is a paucity of studies examining the efficacy of the approach to improve dietary intake and health parameters. While the application of metabotypes to tailor and deliver nutrition advice is very promising, further evidence from randomised controlled trials is necessary for further development and acceptance of the approach.
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Optimized Metabotype Definition Based on a Limited Number of Standard Clinical Parameters in the Population-Based KORA Study. Life (Basel) 2022; 12:life12101460. [PMID: 36294895 PMCID: PMC9604647 DOI: 10.3390/life12101460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 09/07/2022] [Accepted: 09/16/2022] [Indexed: 11/23/2022] Open
Abstract
The aim of metabotyping is to categorize individuals into metabolically similar groups. Earlier studies that explored metabotyping used numerous parameters, which made it less transferable to apply. Therefore, this study aimed to identify metabotypes based on a set of standard laboratory parameters that are regularly determined in clinical practice. K-means cluster analysis was used to group 3001 adults from the KORA F4 cohort into three clusters. We identified the clustering parameters through variable importance methods, without including any specific disease endpoint. Several unique combinations of selected parameters were used to create different metabotype models. Metabotype models were then described and evaluated, based on various metabolic parameters and on the incidence of cardiometabolic diseases. As a result, two optimal models were identified: a model composed of five parameters, which were fasting glucose, HDLc, non-HDLc, uric acid, and BMI (the metabolic disease model) for clustering; and a model that included four parameters, which were fasting glucose, HDLc, non-HDLc, and triglycerides (the cardiovascular disease model). These identified metabotypes are based on a few common parameters that are measured in everyday clinical practice. These metabotypes are cost-effective, and can be easily applied on a large scale in order to identify specific risk groups that can benefit most from measures to prevent cardiometabolic diseases, such as dietary recommendations and lifestyle interventions.
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Xiong Y, Jiang L, Li T. Aberrant branched-chain amino acid catabolism in cardiovascular diseases. Front Cardiovasc Med 2022; 9:965899. [PMID: 35911554 PMCID: PMC9334649 DOI: 10.3389/fcvm.2022.965899] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 06/29/2022] [Indexed: 01/04/2023] Open
Abstract
Globally, cardiovascular diseases are the leading cause of death. Research has focused on the metabolism of carbohydrates, fatty acids, and amino acids to improve the prognosis of cardiovascular diseases. There are three types of branched-chain amino acids (BCAAs; valine, leucine, and isoleucine) required for protein homeostasis, energy balance, and signaling pathways. Increasing evidence has implicated BCAAs in the pathogenesis of multiple cardiovascular diseases. This review summarizes the biological origin, signal transduction pathways and function of BCAAs as well as their significance in cardiovascular diseases, including myocardial hypertrophy, heart failure, coronary artery disease, diabetic cardiomyopathy, dilated cardiomyopathy, arrhythmia and hypertension.
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Affiliation(s)
- Yixiao Xiong
- Department of Anesthesiology, National-Local Joint Engineering Research Centre of Translational Medicine of Anesthesiology, West China Hospital of Sichuan University, Chengdu, China
- Laboratory of Mitochondria and Metabolism, West China Hospital of Sichuan University, Chengdu, China
| | - Ling Jiang
- Department of Anesthesiology, National-Local Joint Engineering Research Centre of Translational Medicine of Anesthesiology, West China Hospital of Sichuan University, Chengdu, China
- Laboratory of Mitochondria and Metabolism, West China Hospital of Sichuan University, Chengdu, China
| | - Tao Li
- Department of Anesthesiology, National-Local Joint Engineering Research Centre of Translational Medicine of Anesthesiology, West China Hospital of Sichuan University, Chengdu, China
- Laboratory of Mitochondria and Metabolism, West China Hospital of Sichuan University, Chengdu, China
- *Correspondence: Tao Li,
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Orozco-Ruiz X, Anesi A, Mattivi F, Breteler MMB. Branched-Chain and Aromatic Amino Acids Related to Visceral Adipose Tissue Impact Metabolic Health Risk Markers. J Clin Endocrinol Metab 2022; 107:e2896-e2905. [PMID: 35325166 DOI: 10.1210/clinem/dgac160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Indexed: 11/19/2022]
Abstract
CONTEXT Visceral (VAT) and subcutaneous adipose tissue (SAT) function as endocrine organs capable of influencing metabolic health across adiposity levels. OBJECTIVE We aimed to investigate whether metabolites associated with VAT and SAT impact metabolic health through metabolite concentrations. METHODS Analyses are based on 1790 participants from the population-based Rhineland Study. We assessed plasma levels of methionine (Met), branched-chain amino acids (BCAA), aromatic amino acids (AAA), and their metabolic downstream metabolites with liquid chromatography-mass spectrometry. VAT and SAT volumes were assessed by magnetic resonance imaging (MRI). Metabolically healthy and unhealthy phenotypes were defined using Wildman criteria. RESULTS Metabolically unhealthy participants had higher concentrations of BCAA than metabolically healthy participants (P < 0.001). In metabolically unhealthy participants, VAT volumes were significantly associated with levels of L-isoleucine, L-leucine, indole-3-lactic acid, and indole-3-propionic acid (in log SD units: β = 0.16, P = 0.003; β = 0.12, P = 0.038; β = 0.11, P = 0.035 and β = -0.16, P = 0.010, respectively). Higher concentrations of certain BCAA and AAA-downstream metabolites significantly increased the odds of cardiometabolic risk markers. The relation between VAT volume and cardiometabolic risk markers was mediated by BCAA (indirect effects 3.7%-11%, P = 0.02 to < 0.0001), while the effect of VAT on systemic inflammation was mediated through higher kynurenine concentrations (indirect effect 6.4%, P < 0.0001). CONCLUSION Larger volumes of VAT in metabolically unhealthy individuals are associated with altered concentrations of circulating BCAA and AAA-downstream metabolites, increasing the odds of cardiometabolic risk markers. This suggests that these metabolites are involved in the mechanisms that underlie the relationship of abdominal VAT with metabolic health.
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Affiliation(s)
- Ximena Orozco-Ruiz
- Population Health Sciences, German Center for Neurodegenerative diseases (DZNE), 53127 Bonn, Germany
| | - Andrea Anesi
- Department of Food Quality and Nutrition, Research and Innovation Centre, Fondazione Edmund Mach (FEM), 38010 San Michele all'Adige, Italy
| | - Fulvio Mattivi
- Department of Food Quality and Nutrition, Research and Innovation Centre, Fondazione Edmund Mach (FEM), 38010 San Michele all'Adige, Italy
- University of Trento, Department of Cellular, Computational and Integrative Biology (CIBIO), 38123 Povo, Italy
| | - Monique M B Breteler
- Population Health Sciences, German Center for Neurodegenerative diseases (DZNE), 53127 Bonn, Germany
- Institute for Medical Biometry, Informatics and Epidemiology (IMBIE), Faculty of Medicine, University of Bonn, 53127 Bonn, Germany
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11
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Portero V, Nicol T, Podliesna S, Marchal GA, Baartscheer A, Casini S, Tadros R, Treur JL, Tanck MWT, Cox IJ, Probert F, Hough TA, Falcone S, Beekman L, Müller-Nurasyid M, Kastenmüller G, Gieger C, Peters A, Kääb S, Sinner MF, Blease A, Verkerk AO, Bezzina CR, Potter PK, Remme CA. Chronically elevated branched chain amino acid levels are pro-arrhythmic. Cardiovasc Res 2021; 118:1742-1757. [PMID: 34142125 PMCID: PMC9215196 DOI: 10.1093/cvr/cvab207] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 06/16/2021] [Indexed: 01/03/2023] Open
Abstract
Aims Cardiac arrhythmias comprise a major health and economic burden and are associated with significant morbidity and mortality, including cardiac failure, stroke, and sudden cardiac death (SCD). Development of efficient preventive and therapeutic strategies is hampered by incomplete knowledge of disease mechanisms and pathways. Our aim is to identify novel mechanisms underlying cardiac arrhythmia and SCD using an unbiased approach. Methods and results We employed a phenotype-driven N-ethyl-N-nitrosourea mutagenesis screen and identified a mouse line with a high incidence of sudden death at young age (6–9 weeks) in the absence of prior symptoms. Affected mice were found to be homozygous for the nonsense mutation Bcat2p.Q300*/p.Q300* in the Bcat2 gene encoding branched chain amino acid transaminase 2. At the age of 4–5 weeks, Bcat2p.Q300*/p.Q300* mice displayed drastic increase of plasma levels of branch chain amino acids (BCAAs—leucine, isoleucine, valine) due to the incomplete catabolism of BCAAs, in addition to inducible arrhythmias ex vivo as well as cardiac conduction and repolarization disturbances. In line with these findings, plasma BCAA levels were positively correlated to electrocardiogram indices of conduction and repolarization in the German community-based KORA F4 Study. Isolated cardiomyocytes from Bcat2p.Q300*/p.Q300* mice revealed action potential (AP) prolongation, pro-arrhythmic events (early and late afterdepolarizations, triggered APs), and dysregulated calcium homeostasis. Incubation of human pluripotent stem cell-derived cardiomyocytes with elevated concentration of BCAAs induced similar calcium dysregulation and pro-arrhythmic events which were prevented by rapamycin, demonstrating the crucial involvement of mTOR pathway activation. Conclusions Our findings identify for the first time a causative link between elevated BCAAs and arrhythmia, which has implications for arrhythmogenesis in conditions associated with BCAA metabolism dysregulation such as diabetes, metabolic syndrome, and heart failure.
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Affiliation(s)
- Vincent Portero
- Heart Center, Department of Clinical and Experimental Cardiology, Amsterdam UMC, location AMC, Amsterdam, The Netherlands
| | - Thomas Nicol
- Mammalian genetics Unit, MRC Harwell Institute, Harwell, Oxfordshire, United Kingdom
| | - Svitlana Podliesna
- Heart Center, Department of Clinical and Experimental Cardiology, Amsterdam UMC, location AMC, Amsterdam, The Netherlands
| | - Gerard A Marchal
- Heart Center, Department of Clinical and Experimental Cardiology, Amsterdam UMC, location AMC, Amsterdam, The Netherlands
| | - Antonius Baartscheer
- Heart Center, Department of Clinical and Experimental Cardiology, Amsterdam UMC, location AMC, Amsterdam, The Netherlands
| | - Simona Casini
- Heart Center, Department of Clinical and Experimental Cardiology, Amsterdam UMC, location AMC, Amsterdam, The Netherlands
| | - Rafik Tadros
- Cardiovascular Genetics Center, Montreal Heart Institute and Faculty of Medicine, Université de Montréal, Montreal, Canada
| | - Jorien L Treur
- Department of Psychiatry, Amsterdam UMC, Location AMC, Amsterdam, The Netherlands
| | - Michael W T Tanck
- Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Amsterdam UMC, Location AMC, Amsterdam, The Netherlands
| | - I Jane Cox
- Institute for Hepatology London, Foundation for Liver Research, London, UK.,Faculty of Life Sciences & Medicine, Kings College, London, UK
| | - Fay Probert
- Department of Chemistry, University of Oxford, Oxford UK
| | - Tertius A Hough
- Mammalian genetics Unit, MRC Harwell Institute, Harwell, Oxfordshire, United Kingdom
| | - Sara Falcone
- Mammalian genetics Unit, MRC Harwell Institute, Harwell, Oxfordshire, United Kingdom
| | - Leander Beekman
- Heart Center, Department of Clinical and Experimental Cardiology, Amsterdam UMC, location AMC, Amsterdam, The Netherlands
| | - Martina Müller-Nurasyid
- Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.,IBE, Faculty of Medicine, Ludwig Maximilian's University (LMU) Munich, Germany.,Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center, Johannes Gutenberg University, Mainz, Germany
| | - Gabi Kastenmüller
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.,German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Christian Gieger
- Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Annette Peters
- Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.,German Centre for Cardiovascular Research (DZHK), partner site: Munich Heart Alliance, Munich, Germany
| | - Stefan Kääb
- Department of Internal Medicine I (Cardiology), Hospital of the Ludwig-Maximilian's University (LMU) Munich, Germany.,German Centre for Cardiovascular Research (DZHK), partner site: Munich Heart Alliance, Munich, Germany
| | - Moritz F Sinner
- Department of Internal Medicine I (Cardiology), Hospital of the Ludwig-Maximilian's University (LMU) Munich, Germany.,German Centre for Cardiovascular Research (DZHK), partner site: Munich Heart Alliance, Munich, Germany
| | - Andrew Blease
- Mammalian genetics Unit, MRC Harwell Institute, Harwell, Oxfordshire, United Kingdom
| | - Arie O Verkerk
- Heart Center, Department of Clinical and Experimental Cardiology, Amsterdam UMC, location AMC, Amsterdam, The Netherlands
| | - Connie R Bezzina
- Heart Center, Department of Clinical and Experimental Cardiology, Amsterdam UMC, location AMC, Amsterdam, The Netherlands
| | - Paul K Potter
- Mammalian genetics Unit, MRC Harwell Institute, Harwell, Oxfordshire, United Kingdom.,Department of Biological and Medical Sciences, Faculty of Health and Life Sciences, Oxford Brookes University, Oxford, UK
| | - Carol Ann Remme
- Heart Center, Department of Clinical and Experimental Cardiology, Amsterdam UMC, location AMC, Amsterdam, The Netherlands
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12
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Hillesheim E, Ryan MF, Gibney E, Roche HM, Brennan L. Optimisation of a metabotype approach to deliver targeted dietary advice. Nutr Metab (Lond) 2020; 17:82. [PMID: 33005208 PMCID: PMC7523294 DOI: 10.1186/s12986-020-00499-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 09/08/2020] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Targeted nutrition is defined as dietary advice tailored at a group level. Groups known as metabotypes can be identified based on individual metabolic profiles. Metabotypes have been associated with differential responses to diet, which support their use to deliver dietary advice. We aimed to optimise a metabotype approach to deliver targeted dietary advice by encompassing more specific recommendations on nutrient and food intakes and dietary behaviours. METHODS Participants (n = 207) were classified into three metabotypes based on four biomarkers (triacylglycerol, total cholesterol, HDL-cholesterol and glucose) and using a k-means cluster model. Participants in metabotype-1 had the highest average HDL-cholesterol, in metabotype-2 the lowest triacylglycerol and total cholesterol, and in metabotype-3 the highest triacylglycerol and total cholesterol. For each participant, dietary advice was assigned using decision trees for both metabotype (group level) and personalised (individual level) approaches. Agreement between methods was compared at the message level and the metabotype approach was optimised to incorporate messages exclusively assigned by the personalised approach and current dietary guidelines. The optimised metabotype approach was subsequently compared with individualised advice manually compiled. RESULTS The metabotype approach comprised advice for improving the intake of saturated fat (69% of participants), fibre (66%) and salt (18%), while the personalised approach assigned advice for improving the intake of folate (63%), fibre (63%), saturated fat (61%), calcium (34%), monounsaturated fat (24%) and salt (14%). Following the optimisation of the metabotype approach, the most frequent messages assigned to address intake of key nutrients were to increase the intake of fruit and vegetables, beans and pulses, dark green vegetables, and oily fish, to limit processed meats and high-fat food products and to choose fibre-rich carbohydrates, low-fat dairy and lean meats (60-69%). An average agreement of 82.8% between metabotype and manual approaches was revealed, with excellent agreements in metabotype-1 (94.4%) and metabotype-3 (92.3%). CONCLUSIONS The optimised metabotype approach proved capable of delivering targeted dietary advice for healthy adults, being highly comparable with individualised advice. The next step is to ascertain whether the optimised metabotype approach is effective in changing diet quality.
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Affiliation(s)
- Elaine Hillesheim
- UCD Institute of Food and Health, UCD School of Agriculture and Food Science, UCD, Dublin 4, Belfield Ireland
- UCD Conway Institute of Biomolecular and Biomedical Research, UCD, Dublin 4, Belfield Ireland
| | - Miriam F. Ryan
- UCD Institute of Food and Health, UCD School of Agriculture and Food Science, UCD, Dublin 4, Belfield Ireland
| | - Eileen Gibney
- UCD Institute of Food and Health, UCD School of Agriculture and Food Science, UCD, Dublin 4, Belfield Ireland
| | - Helen M. Roche
- UCD Conway Institute of Biomolecular and Biomedical Research, UCD, Dublin 4, Belfield Ireland
- Nutrigenomics Research Group, School of Public Health, Physiotherapy and Sports Science & Diabetes Complications Research Centre, UCD, Dublin 4, Belfield Ireland
| | - Lorraine Brennan
- UCD Institute of Food and Health, UCD School of Agriculture and Food Science, UCD, Dublin 4, Belfield Ireland
- UCD Conway Institute of Biomolecular and Biomedical Research, UCD, Dublin 4, Belfield Ireland
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13
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Wawro N, Pestoni G, Riedl A, Breuninger TA, Peters A, Rathmann W, Koenig W, Huth C, Meisinger C, Rohrmann S, Linseisen J. Association of Dietary Patterns and Type-2 Diabetes Mellitus in Metabolically Homogeneous Subgroups in the KORA FF4 Study. Nutrients 2020; 12:nu12061684. [PMID: 32516903 PMCID: PMC7352280 DOI: 10.3390/nu12061684] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 06/02/2020] [Accepted: 06/03/2020] [Indexed: 12/16/2022] Open
Abstract
There is evidence that a change in lifestyle, especially physical activity and diet, can reduce the risk of developing type-2 diabetes mellitus (T2DM). However, the response to dietary changes varies among individuals due to differences in metabolic characteristics. Therefore, we investigated the association between dietary patterns and T2DM while taking into account these differences. For 1287 participants of the population-based KORA FF4 study (Cooperative Health Research in the Region of Augsburg), we identified three metabolically-homogenous subgroups (metabotypes) using 16 clinical markers. Based on usual dietary intake data, two diet quality scores, the Mediterranean Diet Score (MDS) and the Alternate Healthy Eating Index (AHEI), were calculated. We explored the associations between T2DM and diet quality scores. Multi-variable adjusted models, including metabotype subgroup, were fitted. In addition, analyses stratified by metabotype were carried out. We found significant interaction effects between metabotype and both diet quality scores (p < 0.05). In the analysis stratified by metabotype, significant negative associations between T2DM and both diet quality scores were detected only in the metabolically-unfavorable homogenous subgroup (Odds Ratio (OR) = 0.62, 95% confidence interval (CI) = 0.39-0.90 for AHEI and OR = 0.60, 95% CI = 0.40-0.96 for MDS). Prospective studies taking metabotype into account are needed to confirm our results, which allow for the tailoring of dietary recommendations in the prevention of T2DM.
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Affiliation(s)
- Nina Wawro
- Independent Research Group Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany; (G.P.); (A.R.); (T.A.B.); (C.M.); (J.L.)
- Chair of Epidemiology, Ludwig-Maximilians-Universität München at UNIKA-T (Universitäres Zentrum für Gesundheitswissenschaften am Klinikum Augsburg), Neusässer Str. 47, 86156 Augsburg, Germany
- Correspondence:
| | - Giulia Pestoni
- Independent Research Group Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany; (G.P.); (A.R.); (T.A.B.); (C.M.); (J.L.)
- Division of Chronic Disease Epidemiology, Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Hirschengraben 84, CH-8001 Zurich, Switzerland;
| | - Anna Riedl
- Independent Research Group Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany; (G.P.); (A.R.); (T.A.B.); (C.M.); (J.L.)
| | - Taylor A. Breuninger
- Independent Research Group Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany; (G.P.); (A.R.); (T.A.B.); (C.M.); (J.L.)
- Chair of Epidemiology, Ludwig-Maximilians-Universität München at UNIKA-T (Universitäres Zentrum für Gesundheitswissenschaften am Klinikum Augsburg), Neusässer Str. 47, 86156 Augsburg, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany; (A.P.); (C.H.)
- German Center for Diabetes Research (DZD e.V.), Ingolstädter Landstr. 1, 85764 München-Neuherberg, Germany;
| | - Wolfgang Rathmann
- German Center for Diabetes Research (DZD e.V.), Ingolstädter Landstr. 1, 85764 München-Neuherberg, Germany;
- Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Auf’m Hennekamp 65, 40225 Düsseldorf, Germany
| | - Wolfgang Koenig
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Pettenkoferstr. 8a & 9, 80336 Munich, Germany;
- Deutsches Herzzentrum München, Technische Universität München, Lazarettstr. 36, 80636 Munich
- Institute of Epidemiology and Medical Biometry, University of Ulm, Helmholtzstr. 22, 89081 Ulm, Germany
| | - Cornelia Huth
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany; (A.P.); (C.H.)
- German Center for Diabetes Research (DZD e.V.), Ingolstädter Landstr. 1, 85764 München-Neuherberg, Germany;
| | - Christa Meisinger
- Independent Research Group Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany; (G.P.); (A.R.); (T.A.B.); (C.M.); (J.L.)
- Chair of Epidemiology, Ludwig-Maximilians-Universität München at UNIKA-T (Universitäres Zentrum für Gesundheitswissenschaften am Klinikum Augsburg), Neusässer Str. 47, 86156 Augsburg, Germany
| | - Sabine Rohrmann
- Division of Chronic Disease Epidemiology, Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Hirschengraben 84, CH-8001 Zurich, Switzerland;
| | - Jakob Linseisen
- Independent Research Group Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany; (G.P.); (A.R.); (T.A.B.); (C.M.); (J.L.)
- Chair of Epidemiology, Ludwig-Maximilians-Universität München at UNIKA-T (Universitäres Zentrum für Gesundheitswissenschaften am Klinikum Augsburg), Neusässer Str. 47, 86156 Augsburg, Germany
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14
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Palmnäs M, Brunius C, Shi L, Rostgaard-Hansen A, Torres NE, González-Domínguez R, Zamora-Ros R, Ye YL, Halkjær J, Tjønneland A, Riccardi G, Giacco R, Costabile G, Vetrani C, Nielsen J, Andres-Lacueva C, Landberg R. Perspective: Metabotyping-A Potential Personalized Nutrition Strategy for Precision Prevention of Cardiometabolic Disease. Adv Nutr 2020; 11:524-532. [PMID: 31782487 PMCID: PMC7231594 DOI: 10.1093/advances/nmz121] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2019] [Revised: 09/26/2019] [Accepted: 10/14/2019] [Indexed: 12/22/2022] Open
Abstract
Diet is an important, modifiable lifestyle factor of cardiometabolic disease risk, and an improved diet can delay or even prevent the onset of disease. Recent evidence suggests that individuals could benefit from diets adapted to their genotype and phenotype: that is, personalized nutrition. A novel strategy is to tailor diets for groups of individuals according to their metabolic phenotypes (metabotypes). Randomized controlled trials evaluating metabotype-specific responses and nonresponses are urgently needed to bridge the current gap of knowledge with regard to the efficacy of personalized strategies in nutrition. In this Perspective, we discuss the concept of metabotyping, review the current literature on metabotyping in the context of cardiometabolic disease prevention, and suggest potential strategies for metabotype-based nutritional advice for future work. We also discuss potential determinants of metabotypes, including gut microbiota, and highlight the use of metabolomics to define effective markers for cardiometabolic disease-related metabotypes. Moreover, we hypothesize that people at high risk for cardiometabolic diseases have distinct metabotypes and that individuals grouped into specific metabotypes may respond differently to the same diet, which is being tested in a project of the Joint Programming Initiative: A Healthy Diet for a Healthy Life.
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Affiliation(s)
- Marie Palmnäs
- Department of Biology and Biological Engineering, Division of Food and Nutrition Science, Chalmers University of Technology, Gothenburg, Sweden
| | - Carl Brunius
- Department of Biology and Biological Engineering, Division of Food and Nutrition Science, Chalmers University of Technology, Gothenburg, Sweden
| | - Lin Shi
- Department of Biology and Biological Engineering, Division of Food and Nutrition Science, Chalmers University of Technology, Gothenburg, Sweden
- School of Food Engineering and Nutritional Science, Shaanxi Normal University, Xi'an, China
| | - Agneta Rostgaard-Hansen
- Department of Biology and Biological Engineering, Division of Food and Nutrition Science, Chalmers University of Technology, Gothenburg, Sweden
- Diet, Genes, and Environment, Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Núria Estanyol Torres
- Biomarkers and Nutrimetabolomics Laboratory, Department of Nutrition, Food Sciences, and Gastronomy, Institute for Research on Nutrition and Food Safety, Faculty of Pharmacy and Food Sciences, University of Barcelona, Barcelona, Spain
- Centro de Investigacion Biomedica en Red (CIBER) of Frailty and Healthy Aging (CIBERFES), Instituto de Salud Carlos III, Barcelona, Spain
| | - Raúl González-Domínguez
- Biomarkers and Nutrimetabolomics Laboratory, Department of Nutrition, Food Sciences, and Gastronomy, Institute for Research on Nutrition and Food Safety, Faculty of Pharmacy and Food Sciences, University of Barcelona, Barcelona, Spain
- Centro de Investigacion Biomedica en Red (CIBER) of Frailty and Healthy Aging (CIBERFES), Instituto de Salud Carlos III, Barcelona, Spain
| | - Raul Zamora-Ros
- Biomarkers and Nutrimetabolomics Laboratory, Department of Nutrition, Food Sciences, and Gastronomy, Institute for Research on Nutrition and Food Safety, Faculty of Pharmacy and Food Sciences, University of Barcelona, Barcelona, Spain
- Unit of Nutrition and Cancer, Cancer Epidemiology Research Prgramme, Catalan Institute of Oncology (ICO), Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de LLobregat, Barcelona, Spain
| | - Ye Lingqun Ye
- Department of Biology and Biological Engineering, Division of Systems and Synthetic Biology, Chalmers University of Technology, Gothenburg, Sweden
| | - Jytte Halkjær
- Diet, Genes, and Environment, Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Anne Tjønneland
- Diet, Genes, and Environment, Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Gabriele Riccardi
- Department of Clinical Medicine and Surgery, University of Naples Federico II, Naples, Italy
| | - Rosalba Giacco
- Institute of Food Science, Italian National Research Council, Avellino, Italy
| | - Giuseppina Costabile
- Department of Clinical Medicine and Surgery, University of Naples Federico II, Naples, Italy
| | - Claudia Vetrani
- Department of Clinical Medicine and Surgery, University of Naples Federico II, Naples, Italy
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Division of Systems and Synthetic Biology, Chalmers University of Technology, Gothenburg, Sweden
| | - Cristina Andres-Lacueva
- Biomarkers and Nutrimetabolomics Laboratory, Department of Nutrition, Food Sciences, and Gastronomy, Institute for Research on Nutrition and Food Safety, Faculty of Pharmacy and Food Sciences, University of Barcelona, Barcelona, Spain
- Centro de Investigacion Biomedica en Red (CIBER) of Frailty and Healthy Aging (CIBERFES), Instituto de Salud Carlos III, Barcelona, Spain
| | - Rikard Landberg
- Department of Biology and Biological Engineering, Division of Food and Nutrition Science, Chalmers University of Technology, Gothenburg, Sweden
- Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
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15
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Riedl A, Hillesheim E, Wawro N, Meisinger C, Peters A, Roden M, Kronenberg F, Herder C, Rathmann W, Völzke H, Reincke M, Koenig W, Wallaschofski H, Daniel H, Hauner H, Brennan L, Linseisen J. Evaluation of the Metabotype Concept Identified in an Irish Population in the German KORA Cohort Study. Mol Nutr Food Res 2020; 64:e1900918. [PMID: 32048458 DOI: 10.1002/mnfr.201900918] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Revised: 01/13/2020] [Indexed: 11/11/2022]
Abstract
SCOPE Previous work identified three metabolically homogeneous subgroups of individuals ("metabotypes") using k-means cluster analysis based on fasting serum levels of triacylglycerol, total cholesterol, HDL cholesterol, and glucose. The aim is to reproduce these findings and describe metabotype groups by dietary habits and by incident disease occurrence. METHODS AND RESULTS 1744 participants from the KORA F4 study and 2221 participants from the KORA FF4 study are assigned to the three metabotype clusters previously identified by minimizing the Euclidean distances. In both KORA studies, the assignment of participants results in three metabolically distinct clusters, with cluster 3 representing the group of participants with the most unfavorable metabolic characteristics. Individuals of cluster 3 are further characterized by the highest incident disease occurrence during follow-up; they also reveal the most unfavorable diet with significantly lowest intakes of vegetables, dairy products, and fibers, and highest intakes of total, red, and processed meat. CONCLUSION The three metabotypes originally identified in an Irish population are successfully reproduced. In addition to this validation approach, the observed differences in disease incidence across metabotypes represent an important new finding that strongly supports the metabotyping approach as a tool for risk stratification.
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Affiliation(s)
- Anna Riedl
- Independent Research Group Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.,Chair of Epidemiology, Ludwig-Maximilians-Universität München, at UNIKA-T, Neusässer Str. 47, 86156, Augsburg, Germany
| | - Elaine Hillesheim
- Institute of Food and Health, UCD School of Agriculture and Food Science, UCD, Stillorgan Rd, Belfield, Dublin, 4, Ireland
| | - Nina Wawro
- Independent Research Group Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.,Chair of Epidemiology, Ludwig-Maximilians-Universität München, at UNIKA-T, Neusässer Str. 47, 86156, Augsburg, Germany
| | - Christa Meisinger
- Independent Research Group Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.,Chair of Epidemiology, Ludwig-Maximilians-Universität München, at UNIKA-T, Neusässer Str. 47, 86156, Augsburg, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.,German Center for Diabetes Research (DZD e.V.), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
| | - Michael Roden
- German Center for Diabetes Research (DZD e.V.), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.,Division of Endocrinology and Diabetology, Medical Faculty, Heinrich Heine University Düsseldorf, Auf'm Hennekamp 65, 40225, Düsseldorf, Germany.,Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Auf'm Hennekamp 65, 40225, Düsseldorf, Germany
| | - Florian Kronenberg
- Institute of Genetic Epidemiology, Department of Genetics and Pharmacology, Medical University of Innsbruck, Schöpfstr. 41, 6020, Innsbruck, Austria
| | - Christian Herder
- German Center for Diabetes Research (DZD e.V.), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.,Division of Endocrinology and Diabetology, Medical Faculty, Heinrich Heine University Düsseldorf, Auf'm Hennekamp 65, 40225, Düsseldorf, Germany.,Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Auf'm Hennekamp 65, 40225, Düsseldorf, Germany
| | - Wolfgang Rathmann
- German Center for Diabetes Research (DZD e.V.), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.,Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Auf'm Hennekamp 65, 40225, Düsseldorf, Germany
| | - Henry Völzke
- German Center for Diabetes Research (DZD e.V.), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.,DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Pettenkoferstr. 8a & 9, 80336, Munich, Germany.,Institute for Community Medicine, University Medicine Greifswald, Walther-Rathenau-Str. 48, 17475, Greifswald, Germany
| | - Martin Reincke
- Medizinische Klinik und Poliklinik IV, Klinikum der Universität München, Ludwig-Maximilians-Universität München, Ziemssenstr. 1, 80336, Munich, Germany
| | - Wolfgang Koenig
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Pettenkoferstr. 8a & 9, 80336, Munich, Germany.,Deutsches Herzzentrum München, Technische Universität München, Lazarettstr. 36, 80636, Munich, Germany.,Institute of Epidemiology and Medical Biometry, University of Ulm, Helmholtzstr. 22, 89081, Ulm, Germany
| | - Henri Wallaschofski
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Ferdinand-Sauerbruch-Str., 17489, Greifswald, Germany
| | - Hannelore Daniel
- Chair of Nutritional Physiology, Technical University of Munich, Gregor-Mendel-Str. 2, 85354, Freising-Weihenstephan, Germany
| | - Hans Hauner
- Else Kröner-Fresenius Centre for Nutritional Medicine, Technical University of Munich, Gregor-Mendel-Str. 2, 85354, Freising-Weihenstephan, Germany.,ZIEL - Institute for Food and Health, Technical University of Munich, Weihenstephaner Berg 1, 85354, Freising, Germany.,Institute of Nutritional Medicine, Klinikum rechts der Isar, Technical University of Munich, Georg-Brauchle-Ring 62, 80992, Munich, Germany
| | - Lorraine Brennan
- Institute of Food and Health, UCD School of Agriculture and Food Science, UCD, Stillorgan Rd, Belfield, Dublin, 4, Ireland
| | - Jakob Linseisen
- Independent Research Group Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.,Chair of Epidemiology, Ludwig-Maximilians-Universität München, at UNIKA-T, Neusässer Str. 47, 86156, Augsburg, Germany.,ZIEL - Institute for Food and Health, Technical University of Munich, Weihenstephaner Berg 1, 85354, Freising, Germany
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Abstract
AbstractPersonalised nutrition is at its simplest form the delivery of dietary advice at an individual level. Incorporating response to different diets has resulted in the concept of precision nutrition. Harnessing the metabolic phenotype to identify subgroups of individuals that respond differentially to dietary interventions is becoming a reality. More specifically, the classification of individuals in subgroups according to their metabolic profile is defined as metabotyping and this approach has been employed to successfully identify differential response to dietary interventions. Furthermore, the approach has been expanded to develop a framework for the delivery of targeted nutrition. The present review examines the application of the metabotype approach in nutrition research with a focus on developing personalised nutrition. Application of metabotyping in longitudinal studies demonstrates that metabotypes can be associated with cardiometabolic risk factors and diet-related diseases while application in interventions can identify metabotypes with differential responses. In general, there is strong evidence that metabolic phenotyping is a promising strategy to identify groups at risk and to potentially improve health promotion at a population level. Future work should verify if targeted nutrition can change behaviours and have an impact on health outcomes.
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Modifying effect of metabotype on diet-diabetes associations. Eur J Nutr 2019; 59:1357-1369. [PMID: 31089867 PMCID: PMC7230059 DOI: 10.1007/s00394-019-01988-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2018] [Accepted: 05/05/2019] [Indexed: 12/18/2022]
Abstract
Purpose Inter-individual metabolic differences may be a reason for previously inconsistent results in diet–diabetes associations. We aimed to investigate associations between dietary intake and diabetes for metabolically homogeneous subgroups (‘metabotypes’) in a large cross-sectional study. Methods We used data of 1517 adults aged 38–87 years from the German population-based KORA FF4 study (2013/2014). Dietary intake was estimated based on the combination of a food frequency questionnaire and multiple 24-h food lists. Glucose tolerance status was classified based on an oral glucose tolerance test in participants without a previous diabetes diagnosis using American Diabetes Association criteria. Logistic regression was applied to examine the associations between dietary intake and diabetes for two distinct metabotypes, which were identified based on 16 biochemical and anthropometric parameters. Results A low intake of fruits and a high intake of total meat, processed meat and sugar-sweetened beverages (SSB) were significantly associated with diabetes in the total study population. Stratified by metabotype, associations with diabetes remained significant for intake of total meat (OR 1.67, 95% CI 1.04–2.67) and processed meat (OR 2.23, 95% CI 1.24–4.04) in the metabotypes with rather favorable metabolic characteristics, and for intake of fruits (OR 0.83, 95% CI 0.68–0.99) and SSB (OR:1.21, 95% CI 1.09–1.35) in the more unfavorable metabotype. However, only the association between SSB intake and diabetes differed significantly by metabotype (p value for interaction = 0.01). Conclusions Our findings suggest an influence of metabolic characteristics on diet–diabetes associations, which may help to explain inconsistent previous results. The causality of the observed associations needs to be confirmed in prospective and intervention studies. Electronic supplementary material The online version of this article (10.1007/s00394-019-01988-5) contains supplementary material, which is available to authorized users.
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