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Noerman S, Johansson A, Shi L, Lehtonen M, Hanhineva K, Johansson I, Brunius C, Landberg R. Fasting plasma metabolites reflecting meat consumption and their associations with incident type 2 diabetes in two Swedish cohorts. Am J Clin Nutr 2024; 119:1280-1292. [PMID: 38403167 DOI: 10.1016/j.ajcnut.2024.02.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 02/02/2024] [Accepted: 02/20/2024] [Indexed: 02/27/2024] Open
Abstract
BACKGROUND Consumption of processed red meat has been associated with increased risk of developing type 2 diabetes (T2D), but challenges in dietary assessment call for objective intake biomarkers. OBJECTIVES This study aimed to investigate metabolite biomarkers of meat intake and their associations with T2D risk. METHODS Fasting plasma samples were collected from a case-control study nested within Västerbotten Intervention Program (VIP) (214 females and 189 males) who developed T2D after a median follow-up of 7 years. Panels of biomarker candidates reflecting the consumption of total, processed, and unprocessed red meat and poultry were selected from the untargeted metabolomics data collected on the controls. Observed associations were then replicated in Swedish Mammography clinical subcohort in Uppsala (SMCC) (n = 4457 females). Replicated metabolites were assessed for potential association with T2D risk using multivariable conditional logistic regression in the discovery and Cox regression in the replication cohorts. RESULTS In total, 15 metabolites were associated with ≥1 meat group in both cohorts. Acylcarnitines 8:1, 8:2, 10:3, reflecting higher processed meat intake [r > 0.22, false discovery rate (FDR) < 0.001 for VIP and r > 0.05; FDR < 0.001 for SMCC) were consistently associated with higher T2D risk in both data sets. Conversely, lysophosphatidylcholine 17:1 and phosphatidylcholine (PC) 15:0/18:2 were associated with lower processed meat intake (r < -0.12; FDR < 0.023, for VIP and r < -0.05; FDR < 0.001, for SMCC) and with lower T2D risk in both data sets, except for PC 15:0/18:2, which was significant only in the VIP cohort. All associations were attenuated after adjustment for BMI (kg/m2). CONCLUSIONS Consistent associations of biomarker candidates involved in lipid metabolism between higher processed red meat intake with higher T2D risk and between those reflecting lower intake with the lower risk may suggest a relationship between processed meat intake and higher T2D risk. However, attenuated associations after adjusting for BMI indicates that such a relationship may at least partly be mediated or confounded by BMI.
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Affiliation(s)
- Stefania Noerman
- Division of Food and Nutrition Science, Department of Life Sciences, Chalmers University of Technology, Gothenburg, Sweden.
| | - Anna Johansson
- Division of Food and Nutrition Science, Department of Life Sciences, Chalmers University of Technology, Gothenburg, Sweden
| | - Lin Shi
- Division of Food and Nutrition Science, Department of Life Sciences, Chalmers University of Technology, Gothenburg, Sweden; School of Food Engineering and Nutritional Science, Shaanxi Normal University, Xi'an, China
| | - Marko Lehtonen
- School of Pharmacy, University of Eastern Finland, Kuopio, Finland
| | - Kati Hanhineva
- Department of Life Technologies, Food Sciences Unit, University of Turku, Turku, Finland; Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland
| | - Ingegerd Johansson
- Department of Odontology, School of Dentistry, Cariology, Umeå University, Sweden
| | - Carl Brunius
- Division of Food and Nutrition Science, Department of Life Sciences, Chalmers University of Technology, Gothenburg, Sweden
| | - Rikard Landberg
- Division of Food and Nutrition Science, Department of Life Sciences, Chalmers University of Technology, Gothenburg, Sweden
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2
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Margara-Escudero HJ, Paz-Graniel I, García-Gavilán J, Ruiz-Canela M, Sun Q, Clish CB, Toledo E, Corella D, Estruch R, Ros E, Castañer O, Arós F, Fiol M, Guasch-Ferré M, Lapetra J, Razquin C, Dennis C, Deik A, Li J, Gómez-Gracia E, Babio N, Martínez-González MA, Hu FB, Salas-Salvadó J. Plasma metabolite profile of legume consumption and future risk of type 2 diabetes and cardiovascular disease. Cardiovasc Diabetol 2024; 23:38. [PMID: 38245716 PMCID: PMC10800064 DOI: 10.1186/s12933-023-02111-z] [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: 10/31/2023] [Accepted: 12/29/2023] [Indexed: 01/22/2024] Open
Abstract
BACKGROUND Legume consumption has been linked to a reduced risk of type 2 diabetes (T2D) and cardiovascular disease (CVD), while the potential association between plasma metabolites associated with legume consumption and the risk of cardiometabolic diseases has never been explored. Therefore, we aimed to identify a metabolite signature of legume consumption, and subsequently investigate its potential association with the incidence of T2D and CVD. METHODS The current cross-sectional and longitudinal analysis was conducted in 1833 PREDIMED study participants (mean age 67 years, 57.6% women) with available baseline metabolomic data. A subset of these participants with 1-year follow-up metabolomics data (n = 1522) was used for internal validation. Plasma metabolites were assessed through liquid chromatography-tandem mass spectrometry. Cross-sectional associations between 382 different known metabolites and legume consumption were performed using elastic net regression. Associations between the identified metabolite profile and incident T2D and CVD were estimated using multivariable Cox regression models. RESULTS Specific metabolic signatures of legume consumption were identified, these included amino acids, cortisol, and various classes of lipid metabolites including diacylglycerols, triacylglycerols, plasmalogens, sphingomyelins and other metabolites. Among these identified metabolites, 22 were negatively and 18 were positively associated with legume consumption. After adjustment for recognized risk factors and legume consumption, the identified legume metabolite profile was inversely associated with T2D incidence (hazard ratio (HR) per 1 SD: 0.75, 95% CI 0.61-0.94; p = 0.017), but not with CVD incidence risk (1.01, 95% CI 0.86-1.19; p = 0.817) over the follow-up period. CONCLUSIONS This study identified a set of 40 metabolites associated with legume consumption and with a reduced risk of T2D development in a Mediterranean population at high risk of cardiovascular disease. TRIAL REGISTRATION ISRCTN35739639.
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Affiliation(s)
- Hernando J Margara-Escudero
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Alimentació, Nutrició, Desenvolupament i Salut Mental ANUT-DSM, Reus, Spain
- Alimentació, Nutrició, Desenvolupament i Salut Mental, Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain
| | - Indira Paz-Graniel
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Alimentació, Nutrició, Desenvolupament i Salut Mental ANUT-DSM, Reus, Spain
- Alimentació, Nutrició, Desenvolupament i Salut Mental, Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain
- Centro de Investigación Biomédica en Red Fisiopatología de La Obesidad y La Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Jesús García-Gavilán
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Alimentació, Nutrició, Desenvolupament i Salut Mental ANUT-DSM, Reus, Spain.
- Alimentació, Nutrició, Desenvolupament i Salut Mental, Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain.
- Centro de Investigación Biomédica en Red Fisiopatología de La Obesidad y La Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain.
| | - Miguel Ruiz-Canela
- Centro de Investigación Biomédica en Red Fisiopatología de La Obesidad y La Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Department of Preventive Medicine and Public Health, University of Navarra, Instituto de Investigación Sanitario de Navarra (IdiSNA), Pamplona, Spain
| | - Qi Sun
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Clary B Clish
- The Broad Institute of Harvard and MIT, Boston, MA, 02142, USA
| | - Estefania Toledo
- Centro de Investigación Biomédica en Red Fisiopatología de La Obesidad y La Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Department of Preventive Medicine and Public Health, University of Navarra, Instituto de Investigación Sanitario de Navarra (IdiSNA), Pamplona, Spain
- Navarra Institute for Health Research, IdiSNA, Pamplona, Navarre, Spain
| | - Dolores Corella
- Centro de Investigación Biomédica en Red Fisiopatología de La Obesidad y La Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Department of Preventive Medicine, University of Valencia, Valencia, Spain
| | - Ramón Estruch
- Centro de Investigación Biomédica en Red Fisiopatología de La Obesidad y La Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Department of Internal Medicine, Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Hospital Clinic, University of Barcelona, Barcelona, Spain
| | - Emilio Ros
- Centro de Investigación Biomédica en Red Fisiopatología de La Obesidad y La Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Department of Endocrinology and Nutrition, Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Lipid Clinic, Hospital Clínic, Barcelona, Spain
| | - Olga Castañer
- Centro de Investigación Biomédica en Red (CIBERESP) de Epidemiología y Salud Pública, Instituto de Salud Carlos III, Madrid, Spain
- Cardiovascular Risk and Nutrition Research Group, Hospital del Mar Research Institute, Barcelona, Spain
| | - Fernando Arós
- Centro de Investigación Biomédica en Red Fisiopatología de La Obesidad y La Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Department of Cardiology, University Hospital of Alava, Vitoria, Spain
| | - Miquel Fiol
- Centro de Investigación Biomédica en Red Fisiopatología de La Obesidad y La Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Illes Balears Health Research Institute (IdISBa), Hospital Son Espases, Palma, Spain
| | - Marta Guasch-Ferré
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Public Health, Section of Epidemiology, University of Copenhagen, Copenhagen, Denmark
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - José Lapetra
- Centro de Investigación Biomédica en Red Fisiopatología de La Obesidad y La Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Department of Family Medicine, Research Unit, Distrito Sanitario Atención Primaria Sevilla, Seville, Spain
| | - Cristina Razquin
- Centro de Investigación Biomédica en Red Fisiopatología de La Obesidad y La Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Department of Preventive Medicine and Public Health, University of Navarra, Instituto de Investigación Sanitario de Navarra (IdiSNA), Pamplona, Spain
| | - Courtney Dennis
- The Broad Institute of Harvard and MIT, Boston, MA, 02142, USA
| | - Amy Deik
- The Broad Institute of Harvard and MIT, Boston, MA, 02142, USA
| | - Jun Li
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Enrique Gómez-Gracia
- Preventive Medicine and Public Health Department, School of Medicine, University of Málaga, 29010, Malaga, Spain
- Biomedical Research Institute of Malaga-IBIMA Plataforma BIONAND, 29010, Malaga, Spain
| | - Nancy Babio
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Alimentació, Nutrició, Desenvolupament i Salut Mental ANUT-DSM, Reus, Spain.
- Alimentació, Nutrició, Desenvolupament i Salut Mental, Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain.
- Centro de Investigación Biomédica en Red Fisiopatología de La Obesidad y La Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain.
| | - Miguel A Martínez-González
- Department of Preventive Medicine and Public Health, University of Navarra, Instituto de Investigación Sanitario de Navarra (IdiSNA), Pamplona, Spain
| | - Frank B Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Jordi Salas-Salvadó
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Alimentació, Nutrició, Desenvolupament i Salut Mental ANUT-DSM, Reus, Spain
- Alimentació, Nutrició, Desenvolupament i Salut Mental, Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain
- Centro de Investigación Biomédica en Red Fisiopatología de La Obesidad y La Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
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3
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Gu X, Drouin-Chartier JP, Sacks FM, Hu FB, Rosner B, Willett WC. Red meat intake and risk of type 2 diabetes in a prospective cohort study of United States females and males. Am J Clin Nutr 2023; 118:1153-1163. [PMID: 38044023 PMCID: PMC10739777 DOI: 10.1016/j.ajcnut.2023.08.021] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 08/24/2023] [Accepted: 08/30/2023] [Indexed: 12/05/2023] Open
Abstract
BACKGROUND Studies with methodological advancements are warranted to confirm the relation of red meat consumption to the incidence of type 2 diabetes (T2D). OBJECTIVE We aimed to assess the relationships of intakes of total, processed, and unprocessed red meat to risk of T2D and to estimate the effects of substituting different protein sources for red meats on T2D risk. METHODS Our study included 216,695 participants (81% females) from the Nurses' Health Study (NHS), NHS II, and Health Professionals Follow-up Study (HPFS). Red meat intakes were assessed with semiquantitative food frequency questionnaires (FFQs) every 2 to 4 y since the study baselines. We used multivariable-adjusted proportional hazards models to estimate the associations between red meats and T2D. RESULTS Over 5,483,981 person-years of follow-up, we documented 22,761 T2D cases. Intakes of total, processed, and unprocessed red meat were positively and approximately linearly associated with higher risks of T2D. Comparing the highest to the lowest quintiles, hazard ratios (HR) were 1.62 (95% confidence interval [CI]: 1.53, 1.71) for total red meat, 1.51 (95% CI: 1.44, 1.58) for processed red meat, and 1.40 (95% CI: 1.33, 1.47) for unprocessed red meat. The percentage lower risk of T2D associated with substituting 1 serving/d of nuts and legumes for total red meat was 30% (HR = 0.70, 95% CI: 0.66, 0.74), for processed red meat was 41% (HR = 0.59, 95% CI: 0.55, 0.64), and for unprocessed red meat was 29% (HR = 0.71, 95% CI: 0.67, 0.75); Substituting 1 serving/d of dairy for total, processed, or unprocessed red meat was also associated with significantly lower risk of T2D. The observed associations became stronger after we calibrated dietary intakes to intakes assessed by weighed diet records. CONCLUSIONS Our study supports current dietary recommendations for limiting consumption of red meat intake and emphasizes the importance of different alternative sources of protein for T2D prevention.
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Affiliation(s)
- Xiao Gu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Jean-Philippe Drouin-Chartier
- Centre Nutrition, Santé et Société (NUTRISS), Institut sur la Nutrition et les Aliments Fonctionnels (INAF), Université Laval, Québec, Canada; Faculté de Pharmacie, Université Laval, Québec, Canada
| | - Frank M Sacks
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Frank B Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Bernard Rosner
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
| | - Walter C Willett
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States.
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4
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Rios S, García-Gavilán JF, Babio N, Paz-Graniel I, Ruiz-Canela M, Liang L, Clish CB, Toledo E, Corella D, Estruch R, Ros E, Fitó M, Arós F, Fiol M, Guasch-Ferré M, Santos-Lozano JM, Li J, Razquin C, Martínez-González MÁ, Hu FB, Salas-Salvadó J. Plasma metabolite profiles associated with the World Cancer Research Fund/American Institute for Cancer Research lifestyle score and future risk of cardiovascular disease and type 2 diabetes. Cardiovasc Diabetol 2023; 22:252. [PMID: 37716984 PMCID: PMC10505328 DOI: 10.1186/s12933-023-01912-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 07/01/2023] [Indexed: 09/18/2023] Open
Abstract
BACKGROUND A healthy lifestyle (HL) has been inversely related to type 2 diabetes (T2D) and cardiovascular disease (CVD). However, few studies have identified a metabolite profile associated with HL. The present study aims to identify a metabolite profile of a HL score and assess its association with the incidence of T2D and CVD in individuals at high cardiovascular risk. METHODS In a subset of 1833 participants (age 55-80y) of the PREDIMED study, we estimated adherence to a HL using a composite score based on the 2018 Word Cancer Research Fund/American Institute for Cancer Research recommendations. Plasma metabolites were analyzed using LC-MS/MS methods at baseline (discovery sample) and 1-year of follow-up (validation sample). Cross-sectional associations between 385 known metabolites and the HL score were assessed using elastic net regression. A 10-cross-validation procedure was used, and correlation coefficients or AUC were assessed between the identified metabolite profiles and the self-reported HL score. We estimated the associations between the identified metabolite profiles and T2D and CVD using multivariable Cox regression models. RESULTS The metabolite profiles that identified HL as a dichotomous or continuous variable included 24 and 58 metabolites, respectively. These are amino acids or derivatives, lipids, and energy intermediates or xenobiotic compounds. After adjustment for potential confounders, baseline metabolite profiles were associated with a lower risk of T2D (hazard ratio [HR] and 95% confidence interval (CI): 0.54, 0.38-0.77 for dichotomous HL, and 0.22, 0.11-0.43 for continuous HL). Similar results were observed with CVD (HR, 95% CI: 0.59, 0.42-0.83 for dichotomous HF and HR, 95%CI: 0.58, 0.31-1.07 for continuous HL). The reduction in the risk of T2D and CVD was maintained or attenuated, respectively, for the 1-year metabolomic profile. CONCLUSIONS In an elderly population at high risk of CVD, a set of metabolites was selected as potential metabolites associated with the HL pattern predicting the risk of T2D and, to a lesser extent, CVD. These results support previous findings that some of these metabolites are inversely associated with the risk of T2D and CVD. TRIAL REGISTRATION The PREDIMED trial was registered at ISRCTN ( http://www.isrctn.com/ , ISRCTN35739639).
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Affiliation(s)
- Santiago Rios
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Alimentaciò, Nutrició Desenvolupament i Salut Mental ANUT-DSM, Reus, Spain
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain
| | - Jesús F García-Gavilán
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Alimentaciò, Nutrició Desenvolupament i Salut Mental ANUT-DSM, Reus, Spain.
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain.
- Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain.
| | - Nancy Babio
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Alimentaciò, Nutrició Desenvolupament i Salut Mental ANUT-DSM, Reus, Spain
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain
| | - Indira Paz-Graniel
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Alimentaciò, Nutrició Desenvolupament i Salut Mental ANUT-DSM, Reus, Spain
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain
| | - Miguel Ruiz-Canela
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Department of Preventive Medicine and Public Health, University of Navarra, IdiSNA, Pamplona, Spain
| | - Liming Liang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Clary B Clish
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Estefania Toledo
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Department of Preventive Medicine and Public Health, University of Navarra, IdiSNA, Pamplona, Spain
| | - Dolores Corella
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Department of Preventive Medicine, University of Valencia, Valencia, Spain
| | - Ramón Estruch
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Department of Internal Medicine, Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Hospital Clínic, Barcelona, Spain
| | - Emilio Ros
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Department of Endocrinology and Nutrition, Lipid Clinic, Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Hospital Clínic, Barcelona, Spain
| | - Montserrat Fitó
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Unit of Cardiovascular Risk and Nutrition, Institut Hospital del Mar de Investigaciones Médicas Municipal d'Investigació Médica (IMIM), Barcelona, Spain
| | - Fernando Arós
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Department of Cardiology, Hospital Universitario de Álava, Vitoria, Spain
| | - Miquel Fiol
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Health Research Institute of the Balearic Islands (IdISBa), Hospital Son Espases, Palma de Mallorca, Spain
| | - Marta Guasch-Ferré
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Public Health and Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - José M Santos-Lozano
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Research Unit, Department of Family Medicine, Distrito Sanitario Atención Primaria Sevilla, Sevilla, Spain
| | - Jun Li
- Department of Preventive Medicine and Public Health, University of Navarra, IdiSNA, Pamplona, Spain
- Health Research Institute of the Balearic Islands (IdISBa), Hospital Son Espases, Palma de Mallorca, Spain
| | - Cristina Razquin
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Department of Preventive Medicine and Public Health, University of Navarra, IdiSNA, Pamplona, Spain
| | - Miguel Ángel Martínez-González
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Department of Preventive Medicine and Public Health, University of Navarra, IdiSNA, Pamplona, Spain
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Frank B Hu
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jordi Salas-Salvadó
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Alimentaciò, Nutrició Desenvolupament i Salut Mental ANUT-DSM, Reus, Spain.
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain.
- Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain.
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Wang F, Tessier AJ, Liang L, Wittenbecher C, Haslam DE, Fernández-Duval G, Heather Eliassen A, Rexrode KM, Tobias DK, Li J, Zeleznik O, Grodstein F, Martínez-González MA, Salas-Salvadó J, Clish C, Lee KH, Sun Q, Stampfer MJ, Hu FB, Guasch-Ferré M. Plasma metabolomic profiles associated with mortality and longevity in a prospective analysis of 13,512 individuals. Nat Commun 2023; 14:5744. [PMID: 37717037 PMCID: PMC10505179 DOI: 10.1038/s41467-023-41515-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 09/01/2023] [Indexed: 09/18/2023] Open
Abstract
Experimental studies reported biochemical actions underpinning aging processes and mortality, but the relevant metabolic alterations in humans are not well understood. Here we examine the associations of 243 plasma metabolites with mortality and longevity (attaining age 85 years) in 11,634 US (median follow-up of 22.6 years, with 4288 deaths) and 1878 Spanish participants (median follow-up of 14.5 years, with 525 deaths). We find that, higher levels of N2,N2-dimethylguanosine, pseudouridine, N4-acetylcytidine, 4-acetamidobutanoic acid, N1-acetylspermidine, and lipids with fewer double bonds are associated with increased risk of all-cause mortality and reduced odds of longevity; whereas L-serine and lipids with more double bonds are associated with lower mortality risk and a higher likelihood of longevity. We further develop a multi-metabolite profile score that is associated with higher mortality risk. Our findings suggest that differences in levels of nucleosides, amino acids, and several lipid subclasses can predict mortality. The underlying mechanisms remain to be determined.
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Affiliation(s)
- Fenglei Wang
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Anne-Julie Tessier
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Liming Liang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Clemens Wittenbecher
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- SciLifeLab, Division of Food Science and Nutrition, Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Danielle E Haslam
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Gonzalo Fernández-Duval
- Department of Preventive Medicine and Public Health, Navarra Health Research Institute (IDISNA), University of Navarra, Pamplona, Spain
| | - A Heather Eliassen
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Kathryn M Rexrode
- Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Women's Health, Department of Medicine, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA, USA
| | - Deirdre K Tobias
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Jun Li
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Oana Zeleznik
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Francine Grodstein
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Miguel A Martínez-González
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Preventive Medicine and Public Health, Navarra Health Research Institute (IDISNA), University of Navarra, Pamplona, Spain
- Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Jordi Salas-Salvadó
- Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana, Reus, Spain
- Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain
| | - Clary Clish
- Metabolomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kyu Ha Lee
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Qi Sun
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Meir J Stampfer
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Frank B Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
| | - Marta Guasch-Ferré
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Department of Public Health, Section of Epidemiology, University of Copenhagen, Copenhagen, Denmark.
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark.
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