1
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Martínez-González MA, Planes FJ, Ruiz-Canela M, Toledo E, Estruch R, Salas-Salvadó J, Valdés-Más R, Mena P, Castañer O, Fitó M, Clish C, Landberg R, Wittenbecher C, Liang L, Guasch-Ferré M, Lamuela-Raventós RM, Wang DD, Forouhi N, Razquin C, Hu FB. Recent advances in precision nutrition and cardiometabolic diseases. REVISTA ESPANOLA DE CARDIOLOGIA (ENGLISH ED.) 2025; 78:263-271. [PMID: 39357800 PMCID: PMC11875914 DOI: 10.1016/j.rec.2024.09.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2024] [Accepted: 09/17/2024] [Indexed: 10/04/2024]
Abstract
A growing body of research on nutrition omics has led to recent advances in cardiovascular disease epidemiology and prevention. Within the PREDIMED trial, significant associations between diet-related metabolites and cardiovascular disease were identified, which were subsequently replicated in independent cohorts. Some notable metabolites identified include plasma levels of ceramides, acyl-carnitines, branched-chain amino acids, tryptophan, urea cycle pathways, and the lipidome. These metabolites and their related pathways have been associated with incidence of both cardiovascular disease and type 2 diabetes. Future directions in precision nutrition research include: a) developing more robust multimetabolomic scores to predict long-term risk of cardiovascular disease and mortality; b) incorporating more diverse populations and a broader range of dietary patterns; and c) conducting more translational research to bridge the gap between precision nutrition studies and clinical applications.
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Affiliation(s)
- Miguel A Martínez-González
- Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Spain; Instituto de Investigación Sanitaria de Navarra (IdiSNA), Pamplona, Navarra, Spain; Universidad de Navarra, Departamento de Medicina Preventiva y Salud Pública, Pamplona, Navarra, Spain; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States.
| | - Francisco J Planes
- Tecnun Escuela de Ingeniería, Departamento de Ingeniería Biomédica y Ciencias, Universidad de Navarra, San Sebastián, Guipúzcoa, Spain
| | - Miguel Ruiz-Canela
- Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Spain; Instituto de Investigación Sanitaria de Navarra (IdiSNA), Pamplona, Navarra, Spain; Universidad de Navarra, Departamento de Medicina Preventiva y Salud Pública, Pamplona, Navarra, Spain
| | - Estefanía Toledo
- Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Spain; Instituto de Investigación Sanitaria de Navarra (IdiSNA), Pamplona, Navarra, Spain; Universidad de Navarra, Departamento de Medicina Preventiva y Salud Pública, Pamplona, Navarra, Spain
| | - Ramón Estruch
- Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Spain; Departamento de Medicina Interna, Instituto de Investigaciones Biomédicas August Pi Sunyer (IDIBAPS), Hospital Clínico, Universidad de Barcelona, Barcelona, Spain
| | - Jordi Salas-Salvadó
- Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Spain; Instituto de Investigación Sanitaria Pere i Virgili, Departamento de Bioquímica y Biotecnología, Unidad de Nutrición Humana Universidad Rovira i Virgili, Reus, Tarragona, Spain
| | - Rafael Valdés-Más
- Immunology Department, Weizmann Institute of Science, Rehovot, Israel
| | - Pedro Mena
- Dipartimento di Scienze degli Alimenti e del Farmaco, Universitá di Parma, Parma, Italy
| | - Olga Castañer
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Spain
| | - Montse Fitó
- Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Spain; Unidad de Riesgo Cardiovascular y Nutrición, Instituto Hospital del Mar de Investigaciones Médicas (IMIM), Barcelona, Spain
| | - Clary Clish
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States
| | - Rikard Landberg
- Division of Food and Nutrition Science, Department of Life Sciences, Chalmers University of Technology, Gothenburg, Sweden
| | - Clemens Wittenbecher
- Department of Life Sciences, SciLifeLab, Chalmers University of Technology, Gothenburg, Sweden
| | - Liming Liang
- Departments of Epidemiology and Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States
| | - Marta Guasch-Ferré
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States; Department of Public Health and Novo Nordisk Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Rosa M Lamuela-Raventós
- Grup de recerca antioxidants naturals: polifenols, Departament de Nutrició, Ciències de l'Alimentació i Gastronomia, Facultat de Farmàcia, Universitat de Barcelona, Barcelona, Spain; Institut de Nutrició i Seguretat Alimentària (INSA), Universitat de Barcelona (UB), Barcelona, Spain
| | - Dong D Wang
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States; Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, United States
| | - Nita Forouhi
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
| | - Cristina Razquin
- Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Spain; Instituto de Investigación Sanitaria de Navarra (IdiSNA), Pamplona, Navarra, Spain; Universidad de Navarra, Departamento de Medicina Preventiva y Salud Pública, Pamplona, Navarra, Spain
| | - Frank B Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, United States
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2
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Martínez-González MA, Planes FJ, Ruiz-Canela M, Toledo E, Estruch R, Salas-Salvadó J, Valdés-Más R, Mena P, Castañer O, Fitó M, Clish C, Landberg R, Wittenbecher C, Liang L, Guasch-Ferré M, Lamuela-Raventós RM, Wang DD, Forouhi N, Razquin C, Hu FB. Recent advances in precision nutrition and cardiometabolic diseases. Rev Esp Cardiol 2025; 78:263-271. [PMID: 39357800 DOI: 10.1016/j.recesp.2024.09.005] [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: 08/31/2024] [Accepted: 09/17/2024] [Indexed: 01/11/2025]
Abstract
A growing body of research on nutrition omics has led to recent advances in cardiovascular disease epidemiology and prevention. Within the PREDIMED trial, significant associations between diet-related metabolites and cardiovascular disease were identified, which were subsequently replicated in independent cohorts. Some notable metabolites identified include plasma levels of ceramides, acyl-carnitines, branched-chain amino acids, tryptophan, urea cycle pathways, and the lipidome. These metabolites and their related pathways have been associated with incidence of both cardiovascular disease and type 2 diabetes. Future directions in precision nutrition research include: a) developing more robust multimetabolomic scores to predict long-term risk of cardiovascular disease and mortality; b) incorporating more diverse populations and a broader range of dietary patterns; and c) conducting more translational research to bridge the gap between precision nutrition studies and clinical applications.
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Affiliation(s)
- Miguel A Martínez-González
- Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Spain; Instituto de Investigación Sanitaria de Navarra (IdiSNA), Pamplona, Navarra, Spain; Universidad de Navarra, Departamento de Medicina Preventiva y Salud Pública, Pamplona, Navarra, Spain; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States.
| | - Francisco J Planes
- Tecnun Escuela de Ingeniería, Departamento de Ingeniería Biomédica y Ciencias, Universidad de Navarra, San Sebastián, Guipúzcoa, Spain
| | - Miguel Ruiz-Canela
- Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Spain; Instituto de Investigación Sanitaria de Navarra (IdiSNA), Pamplona, Navarra, Spain; Universidad de Navarra, Departamento de Medicina Preventiva y Salud Pública, Pamplona, Navarra, Spain
| | - Estefanía Toledo
- Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Spain; Instituto de Investigación Sanitaria de Navarra (IdiSNA), Pamplona, Navarra, Spain; Universidad de Navarra, Departamento de Medicina Preventiva y Salud Pública, Pamplona, Navarra, Spain
| | - Ramón Estruch
- Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Spain; Departamento de Medicina Interna, Instituto de Investigaciones Biomédicas August Pi Sunyer (IDIBAPS), Hospital Clínico, Universidad de Barcelona, Barcelona, Spain
| | - Jordi Salas-Salvadó
- Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Spain; Instituto de Investigación Sanitaria Pere i Virgili, Departamento de Bioquímica y Biotecnología, Unidad de Nutrición Humana Universidad Rovira i Virgili, Reus, Tarragona, Spain
| | - Rafael Valdés-Más
- Immunology Department, Weizmann Institute of Science, Rehovot, Israel
| | - Pedro Mena
- Dipartimento di Scienze degli Alimenti e del Farmaco, Universitá di Parma, Parma, Italy
| | - Olga Castañer
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Spain
| | - Montse Fitó
- Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Spain; Unidad de Riesgo Cardiovascular y Nutrición, Instituto Hospital del Mar de Investigaciones Médicas (IMIM), Barcelona, Spain
| | - Clary Clish
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States
| | - Rikard Landberg
- Division of Food and Nutrition Science, Department of Life Sciences, Chalmers University of Technology, Gothenburg, Sweden
| | - Clemens Wittenbecher
- Department of Life Sciences, SciLifeLab, Chalmers University of Technology, Gothenburg, Sweden
| | - Liming Liang
- Departments of Epidemiology and Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States
| | - Marta Guasch-Ferré
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States; Department of Public Health and Novo Nordisk Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Rosa M Lamuela-Raventós
- Grup de recerca antioxidants naturals: polifenols, Departament de Nutrició, Ciències de l'Alimentació i Gastronomia, Facultat de Farmàcia, Universitat de Barcelona, Barcelona, Spain; Institut de Nutrició i Seguretat Alimentària (INSA), Universitat de Barcelona (UB), Barcelona, Spain
| | - Dong D Wang
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States; Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, United States
| | - Nita Forouhi
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
| | - Cristina Razquin
- Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Spain; Instituto de Investigación Sanitaria de Navarra (IdiSNA), Pamplona, Navarra, Spain; Universidad de Navarra, Departamento de Medicina Preventiva y Salud Pública, Pamplona, Navarra, Spain
| | - Frank B Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, United States
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3
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Wang J, Li T, Gu Y, Su B, Wang H, Lai C, Liu Y. The value of anxiety and depression in predicting physical function and major adverse cardiovascular events in patients with acute coronary syndrome. J Thorac Dis 2024; 16:6849-6862. [PMID: 39552880 PMCID: PMC11565334 DOI: 10.21037/jtd-24-576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Accepted: 08/16/2024] [Indexed: 11/19/2024]
Abstract
Background Psychological distress, including anxiety and depression, is not only prevalent in patients with coronary heart disease (CHD) but can actually predict adverse clinical events. Therefore, the necessity of addressing psychological problems among patients with CHD to improve their treatment results is increasingly acknowledged. This study's objective was to examine the prognostic impact of anxiety and depression on major adverse cardiovascular events (MACEs) and physical function among patients with acute coronary syndrome (ACS). Methods A total of 978 patients admitted to our hospital from September 2021 to September 2022 and diagnosed with severe vascular lesions using coronary angiography were enrolled. According to their scores on the Hospital Anxiety and Depression Scale (HADS) and the Center for Epidemiologic Studies Depression Scale at admission, they were divided into two groups and four subgroups: an anxiety group, a non-anxiety group, a depression group, and a non-depression group. The participants' baseline information, clinical characteristics, coronary angiography findings, MACEs, and changes in physical functionality were compared. Results There were statistically significant differences between the anxiety and depression groups in marital status, education level, history of diabetes, clinical diagnosis, cardiac troponin T (cTnI), B-type natriuretic peptide (BNP), coronary angiography, and synergy between percutaneous coronary intervention with taxus and cardiac surgery (SYNTAX) score. Logistic regression analysis showed that gender, education level, diabetes history, cTnI, and SYNTAX score were risk factors for anxiety, while education level, diabetes, and SYNTAX score were risk factors for depression. A Kaplan-Meier survival curve model was used to analyze survival rates in the anxiety and depression groups. Hierarchical regression analyses of anxiety and depression at baseline predicted significant declines in physical functionality. Conclusions Social support improved physical functionality and reduced the impact of psychological distress. Psychological state had the greatest long-term prognostic value in patients with CHD.
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Affiliation(s)
- Jianlong Wang
- The Third Central Clinical College of Tianjin Medical University, Tianjin, China
| | - Tianle Li
- The Third Central Clinical College of Tianjin Medical University, Tianjin, China
| | - Yan Gu
- The Third Central Clinical College of Tianjin Medical University, Tianjin, China
| | - Bin Su
- The Third Central Clinical College of Tianjin Medical University, Tianjin, China
| | - Hui Wang
- The Third Central Clinical College of Tianjin Medical University, Tianjin, China
| | - Chaohui Lai
- The Third Central Clinical College of Tianjin Medical University, Tianjin, China
| | - Yingwu Liu
- The Third Central Clinical College of Tianjin Medical University, Tianjin, China
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4
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Mäkinen VP, Ala-Korpela M. Influence of age and sex on longitudinal metabolic profiles and body weight trajectories in the UK Biobank. Int J Epidemiol 2024; 53:dyae055. [PMID: 38641429 PMCID: PMC11031410 DOI: 10.1093/ije/dyae055] [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: 12/01/2023] [Accepted: 04/04/2024] [Indexed: 04/21/2024] Open
Abstract
BACKGROUND Accurate characterization of how age influences body weight and metabolism at different stages of life is important for understanding ageing processes. Here, we explore observational longitudinal associations between metabolic health and weight from the fifth to the seventh decade of life, using carefully adjusted statistical designs. METHODS Body measures and biochemical data from blood and urine (220 measures) across two visits were available from 10 104 UK Biobank participants. Participants were divided into stable (within ±4% per decade), weight loss and weight gain categories. Final subgroups were metabolically matched at baseline (48% women, follow-up 4.3 years, ages 41-70; n = 3368 per subgroup) and further stratified by the median age of 59.3 years and sex. RESULTS Pulse pressure, haemoglobin A1c and cystatin-C tracked ageing consistently (P < 0.0001). In women under 59, age-associated increases in citrate, pyruvate, alkaline phosphatase and calcium were observed along with adverse changes across lipoprotein measures, fatty acid species and liver enzymes (P < 0.0001). Principal component analysis revealed a qualitative sex difference in the temporal relationship between body weight and metabolism: weight loss was not associated with systemic metabolic improvement in women, whereas both age strata converged consistently towards beneficial (weight loss) or adverse (weight gain) phenotypes in men. CONCLUSIONS We report longitudinal ageing trends for 220 metabolic measures in absolute concentrations, many of which have not been described for older individuals before. Our results also revealed a fundamental dynamic sex divergence that we speculate is caused by menopause-driven metabolic deterioration in women.
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Affiliation(s)
- Ville-Petteri Mäkinen
- Systems Epidemiology, Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
| | - Mika Ala-Korpela
- Systems Epidemiology, Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
- NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland
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5
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Gonzalez Izundegui D, Miller PE, Shah RV, Clish CB, Walker ME, Mitchell GF, Gerszten RE, Larson MG, Vasan RS, Nayor M. Response of circulating metabolites to an oral glucose challenge and risk of cardiovascular disease and mortality in the community. Cardiovasc Diabetol 2022; 21:213. [PMID: 36243866 PMCID: PMC9568897 DOI: 10.1186/s12933-022-01647-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 09/29/2022] [Indexed: 11/17/2022] Open
Abstract
Background New biomarkers to identify cardiovascular disease (CVD) risk earlier in its course are needed to enable targeted approaches for primordial prevention. We evaluated whether intraindividual changes in blood metabolites in response to an oral glucose tolerance test (OGTT) may provide incremental information regarding the risk of future CVD and mortality in the community. Methods An OGTT (75 g glucose) was administered to a subsample of Framingham Heart Study participants free from diabetes (n = 361). Profiling of 211 plasma metabolites was performed from blood samples drawn before and 2 h after OGTT. The log2(post/pre) metabolite levels (Δmetabolites) were related to incident CVD and mortality in Cox regression models adjusted for age, sex, baseline metabolite level, systolic blood pressure, hypertension treatment, body mass index, smoking, and total/high-density lipoprotein cholesterol. Select metabolites were related to subclinical cardiometabolic phenotypes using Spearman correlations adjusted for age, sex, and fasting metabolite level. Results Our sample included 42% women, with a mean age of 56 ± 9 years and a body mass index of 30.2 ± 5.3 kg/m2. The pre- to post-OGTT changes (Δmetabolite) were non-zero for 168 metabolites (at FDR ≤ 5%). A total of 132 CVD events and 144 deaths occurred during median follow-up of 24.9 years. In Cox models adjusted for clinical risk factors, four Δmetabolites were associated with incident CVD (higher glutamate and deoxycholate, lower inosine and lysophosphatidylcholine 18:2) and six Δmetabolites (higher hydroxyphenylacetate, triacylglycerol 56:5, alpha-ketogluturate, and lower phosphatidylcholine 32:0, glucuronate, N-monomethyl-arginine) were associated with death (P < 0.05). Notably, baseline metabolite levels were not associated with either outcome in models excluding Δmetabolites. The Δmetabolites exhibited varying cross-sectional correlation with subclinical risk factors such as visceral adiposity, insulin resistance, and vascular stiffness, but overall relations were modest. Significant Δmetabolites included those with established roles in cardiometabolic disease (e.g., glutamate, alpha-ketoglutarate) and metabolites with less defined roles (e.g., glucuronate, lipid species). Conclusions Dynamic changes in metabolite levels with an OGTT are associated with incident CVD and mortality and have potential relevance for identifying CVD risk earlier in its development and for discovering new potential therapeutic targets. Supplementary Information The online version contains supplementary material available at 10.1186/s12933-022-01647-w.
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Affiliation(s)
| | - Patricia E Miller
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Ravi V Shah
- Vanderbilt Translational and Clinical Research Center, Cardiology Division, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Clary B Clish
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Maura E Walker
- Section of Preventive Medicine and Epidemiology, Department of Medicine, Boston University School of Medicine, 72 E Concord Street, Suite L-516, Boston, MA, 02118, USA.,Department of Health Sciences, Program in Nutrition, Sargent College of Health and Rehabilitation Sciences, Boston University, Boston, MA, USA.,Boston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, USA
| | | | - Robert E Gerszten
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Martin G Larson
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.,Boston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, USA
| | - Ramachandran S Vasan
- Section of Preventive Medicine and Epidemiology, Department of Medicine, Boston University School of Medicine, 72 E Concord Street, Suite L-516, Boston, MA, 02118, USA.,Boston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, USA.,Section of Cardiovascular Medicine, Department of Medicine, Boston University School of Medicine, 72 E Concord Street, Suite L-516, Boston, MA, 02118, USA.,Department of Epidemiology, Boston University Schools of Medicine and Public Health, Center for Computing and Data Sciences, Boston University, Boston, MA, USA
| | - Matthew Nayor
- Section of Preventive Medicine and Epidemiology, Department of Medicine, Boston University School of Medicine, 72 E Concord Street, Suite L-516, Boston, MA, 02118, USA. .,Boston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, USA. .,Section of Cardiovascular Medicine, Department of Medicine, Boston University School of Medicine, 72 E Concord Street, Suite L-516, Boston, MA, 02118, USA.
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6
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Hu J, Yao J, Deng S, Balasubramanian R, Jiménez MC, Li J, Guo X, Cruz DE, Gao Y, Huang T, Zeleznik OA, Ngo D, Liu S, Rosal MC, Nassir R, Paynter NP, Albert CM, Tracy RP, Durda P, Liu Y, Taylor KD, Johnson WC, Sun Q, Rimm EB, Eliassen AH, Rich SS, Rotter JI, Gerszten RE, Clish CB, Rexrode KM. Differences in Metabolomic Profiles Between Black and White Women and Risk of Coronary Heart Disease: an Observational Study of Women From Four US Cohorts. Circ Res 2022; 131:601-615. [PMID: 36052690 PMCID: PMC9473718 DOI: 10.1161/circresaha.121.320134] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 08/13/2022] [Accepted: 08/21/2022] [Indexed: 01/26/2023]
Abstract
BACKGROUND Racial differences in metabolomic profiles may reflect underlying differences in social determinants of health by self-reported race and may be related to racial disparities in coronary heart disease (CHD) among women in the United States. However, the magnitude of differences in metabolomic profiles between Black and White women in the United States has not been well-described. It also remains unknown whether such differences are related to differences in CHD risk. METHODS Plasma metabolomic profiles were analyzed using liquid chromatography-tandem mass spectrometry in the WHI-OS (Women's Health Initiative-Observational Study; 138 Black and 696 White women), WHI-HT trials (WHI-Hormone Therapy; 156 Black and 1138 White women), MESA (Multi-Ethnic Study of Atherosclerosis; 114 Black and 219 White women), JHS (Jackson Heart Study; 1465 Black women with 107 incident CHD cases), and NHS (Nurses' Health Study; 2506 White women with 136 incident CHD cases). First, linear regression models were used to estimate associations between self-reported race and 472 metabolites in WHI-OS (discovery); findings were replicated in WHI-HT and validated in MESA. Second, we used elastic net regression to construct a racial difference metabolomic pattern (RDMP) representing differences in the metabolomic patterns between Black and White women in the WHI-OS; the RDMP was validated in the WHI-HT and MESA. Third, using conditional logistic regressions in the WHI (717 CHD cases and 719 matched controls), we examined associations of metabolites with large differences in levels by race and the RDMP with risk of CHD, and the results were replicated in Black women from the JHS and White women from the NHS. RESULTS Of the 472 tested metabolites, levels of 259 (54.9%) metabolites, mostly lipid metabolites and amino acids, significantly differed between Black and White women in both WHI-OS and WHI-HT after adjusting for baseline characteristics, socioeconomic status, lifestyle factors, baseline health conditions, and medication use (false discovery rate <0.05); similar trends were observed in MESA. The RDMP, composed of 152 metabolites, was identified in the WHI-OS and showed significantly different distributions between Black and White women in the WHI-HT and MESA. Higher RDMP quartiles were associated with an increased risk of incident CHD (odds ratio=1.51 [0.97-2.37] for the highest quartile comparing to the lowest; Ptrend=0.02), independent of self-reported race and known CHD risk factors. In race-stratified analyses, the RDMP-CHD associations were more pronounced in White women. Similar patterns were observed in Black women from the JHS and White women from the NHS. CONCLUSIONS Metabolomic profiles significantly and substantially differ between Black and White women and may be associated with CHD risk and racial disparities in US women.
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Affiliation(s)
- Jie Hu
- Division of Women’s Health (J.H., M.C.J., K.M.R.), Harvard Medical School, Boston, MA
- Department of Epidemiology (J.H., M.C.J., J.L., Q.S., E.B.R., A.H.E.), Harvard T.H. Chan School of Public Health, Boston, MA
| | - Jie Yao
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA (J.Y., X.G., K.D.T., J.I.R.)
| | - Shuliang Deng
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA (S.D., D.E.C., R.E.G.)
| | - Raji Balasubramanian
- Department of Biostatistics and Epidemiology, University of Massachusetts – Amherst (R.B.)
| | - Monik C. Jiménez
- Division of Women’s Health (J.H., M.C.J., K.M.R.), Harvard Medical School, Boston, MA
- Department of Epidemiology (J.H., M.C.J., J.L., Q.S., E.B.R., A.H.E.), Harvard T.H. Chan School of Public Health, Boston, MA
| | - Jun Li
- Division of Preventive Medicine (J.L., N.P.P.), Harvard Medical School, Boston, MA
- Department of Epidemiology (J.H., M.C.J., J.L., Q.S., E.B.R., A.H.E.), Harvard T.H. Chan School of Public Health, Boston, MA
- Department of Nutrition (J.L., Q.S., E.B.R., A.H.E.), Harvard T.H. Chan School of Public Health, Boston, MA
| | - Xiuqing Guo
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA (J.Y., X.G., K.D.T., J.I.R.)
| | - Daniel E. Cruz
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA (S.D., D.E.C., R.E.G.)
| | - Yan Gao
- Department of Medicine, University of Mississippi Medical Center, Jackson (Y.G.)
| | - Tianyi Huang
- Channing Division of Network Medicine, Department of Medicine (T.H., O.A.Z., Q.S., E.B.R., A.H.E.), Harvard Medical School, Boston, MA
| | - Oana A. Zeleznik
- Channing Division of Network Medicine, Department of Medicine (T.H., O.A.Z., Q.S., E.B.R., A.H.E.), Harvard Medical School, Boston, MA
| | - Debby Ngo
- Brigham and Women’s Hospital and Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Beth Israel Deaconess Medical Center (D.N.), Harvard Medical School, Boston, MA
| | - Simin Liu
- Department of Epidemiology, Brown University School of Public Health, Providence, RI (S.L.)
- Division of Endocrinology, Department of Medicine, Warren Alpert Medical School of Brown University, Providence, RI (S.L.)
| | - Milagros C. Rosal
- Division of Preventive and Behavioral Medicine, Department of Population and Quantitative Sciences, University of Massachusetts Medical School, Worcester (M.C.R.)
| | - Rami Nassir
- Department of Pathology, School of Medicine, Umm Al-Qura University, Saudi Arabia (R.N.)
| | - Nina P. Paynter
- Division of Preventive Medicine (J.L., N.P.P.), Harvard Medical School, Boston, MA
| | - Christine M. Albert
- Department of Cardiology, Smidt Heart Institute, Cedars Sinai Medical Center, Los Angeles, CA (C.M.A.)
| | - Russell P. Tracy
- Department of Pathology and Laboratory Medicine (R.P.T., P.D.), Larner College of Medicine, University of Vermont, Burlington
- Department of Biochemistry (R.P.T.), Larner College of Medicine, University of Vermont, Burlington
| | - Peter Durda
- Department of Pathology and Laboratory Medicine (R.P.T., P.D.), Larner College of Medicine, University of Vermont, Burlington
| | - Yongmei Liu
- Divisions of Cardiology and Neurology, Department of Medicine, Duke University Medical Center, Durham, NC (Y.L.)
| | - Kent D. Taylor
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA (J.Y., X.G., K.D.T., J.I.R.)
| | - W. Craig Johnson
- Department of Biostatistics, University of Washington, Seattle (W.C.J.)
| | - Qi Sun
- Channing Division of Network Medicine, Department of Medicine (T.H., O.A.Z., Q.S., E.B.R., A.H.E.), Harvard Medical School, Boston, MA
- Department of Epidemiology (J.H., M.C.J., J.L., Q.S., E.B.R., A.H.E.), Harvard T.H. Chan School of Public Health, Boston, MA
- Department of Nutrition (J.L., Q.S., E.B.R., A.H.E.), Harvard T.H. Chan School of Public Health, Boston, MA
| | - Eric B. Rimm
- Channing Division of Network Medicine, Department of Medicine (T.H., O.A.Z., Q.S., E.B.R., A.H.E.), Harvard Medical School, Boston, MA
- Department of Epidemiology (J.H., M.C.J., J.L., Q.S., E.B.R., A.H.E.), Harvard T.H. Chan School of Public Health, Boston, MA
- Department of Nutrition (J.L., Q.S., E.B.R., A.H.E.), Harvard T.H. Chan School of Public Health, Boston, MA
| | - A. Heather Eliassen
- Channing Division of Network Medicine, Department of Medicine (T.H., O.A.Z., Q.S., E.B.R., A.H.E.), Harvard Medical School, Boston, MA
- Department of Epidemiology (J.H., M.C.J., J.L., Q.S., E.B.R., A.H.E.), Harvard T.H. Chan School of Public Health, Boston, MA
- Department of Nutrition (J.L., Q.S., E.B.R., A.H.E.), Harvard T.H. Chan School of Public Health, Boston, MA
| | - Stephen S. Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville (S.S.R.)
| | - Jerome I. Rotter
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA (J.Y., X.G., K.D.T., J.I.R.)
| | - Robert E. Gerszten
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA (S.D., D.E.C., R.E.G.)
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge (R.E.G., C.B.C.)
| | - Clary B. Clish
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge (R.E.G., C.B.C.)
| | - Kathryn M. Rexrode
- Division of Women’s Health (J.H., M.C.J., K.M.R.), Harvard Medical School, Boston, MA
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7
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Costanzo M, Caterino M, Sotgiu G, Ruoppolo M, Franconi F, Campesi I. Sex differences in the human metabolome. Biol Sex Differ 2022; 13:30. [PMID: 35706042 PMCID: PMC9199320 DOI: 10.1186/s13293-022-00440-4] [Citation(s) in RCA: 71] [Impact Index Per Article: 23.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 06/02/2022] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND The sexual dimorphism represents one of the triggers of the metabolic disparities between the organisms, advising about wild implications in research or diagnostics contexts. Despite the mounting recognition of the importance of sex consideration in the biomedical fields, the identification of male- and female-specific metabolic signatures has not been achieved. MAIN BODY This review pointed the focus on the metabolic differences related to the sex, evidenced by metabolomics studies performed on healthy populations, with the leading aim of understanding how the sex influences the baseline metabolome. The main shared signatures and the apparent dissimilarities between males and females were extracted and highlighted from the metabolome of the most commonly analyzed biological fluids, such as serum, plasma, and urine. Furthermore, the influence of age and the significant interactions between sex and age have been taken into account. CONCLUSIONS The recognition of sex patterns in human metabolomics has been defined in diverse biofluids. The detection of sex- and age-related differences in the metabolome of healthy individuals are helpful for translational applications from the bench to the bedside to set targeted diagnostic and prevention approaches in the context of personalized medicine.
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Affiliation(s)
- Michele Costanzo
- Department of Molecular Medicine and Medical Biotechnology, School of Medicine, University of Naples Federico II, 80131 Naples, Italy
- CEINGE – Biotecnologie Avanzate s.c.ar.l., 80145 Naples, Italy
| | - Marianna Caterino
- Department of Molecular Medicine and Medical Biotechnology, School of Medicine, University of Naples Federico II, 80131 Naples, Italy
- CEINGE – Biotecnologie Avanzate s.c.ar.l., 80145 Naples, Italy
| | - Giovanni Sotgiu
- Clinical Epidemiology and Medical Statistics Unit, Department of Medical, Surgical and Experimental Sciences, University of Sassari, 07100 Sassari, Italy
| | - Margherita Ruoppolo
- Department of Molecular Medicine and Medical Biotechnology, School of Medicine, University of Naples Federico II, 80131 Naples, Italy
- CEINGE – Biotecnologie Avanzate s.c.ar.l., 80145 Naples, Italy
| | - Flavia Franconi
- Laboratory of Sex-Gender Medicine, National Institute of Biostructures and Biosystems, 07100 Sassari, Italy
| | - Ilaria Campesi
- Laboratory of Sex-Gender Medicine, National Institute of Biostructures and Biosystems, 07100 Sassari, Italy
- Department of Biomedical Sciences, University of Sassari, 07100 Sassari, Italy
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8
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Zhang M, Buckley JP, Liang L, Hong X, Wang G, Wang MC, Wills-Karp M, Wang X, Mueller NT. A metabolome-wide association study of in utero metal and trace element exposures with cord blood metabolome profile: Findings from the Boston Birth Cohort. ENVIRONMENT INTERNATIONAL 2022; 158:106976. [PMID: 34991243 PMCID: PMC8742133 DOI: 10.1016/j.envint.2021.106976] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 10/18/2021] [Accepted: 11/07/2021] [Indexed: 05/07/2023]
Abstract
BACKGROUND Exposure to metals lead (Pb), mercury (Hg), and cadmium (Cd) and trace elements selenium (Se) and manganese (Mn) has been linked to the developmental origins of cardiometabolic diseases, but the mechanisms are not well-understood. OBJECTIVE Conduct a metabolome-wide association study to understand how in utero exposure to Pb, Hg, Cd, Se, and Mn affects the metabolic programming of fetuses. METHODS We used data from the Boston Birth Cohort, which enrolled mother-child pairs from Boston, MA. We measured metals and trace elements in maternal red blood cells (RBCs) collected 24-72 h after delivery, and metabolites in cord blood collected at birth. We used multivariable linear regression to examine associations of metals and trace elements with metabolites and Bonferroni correction to account for multiple comparisons. We assessed non-linear associations of metals and trace elements with metabolites using restricted cubic spline plots. RESULTS This analysis included 670 mother-child pairs (57% non-Hispanic Black and 24% Hispanic). After Bonferroni correction, there were 25 cord metabolites associated with at least one of the metals or trace elements. Pb was negatively associated with the xenobiotic piperine, Cd was positively associated with xenobiotics cotinine and hydroxycotinine, and Hg was associated with 8 lipid metabolites (in both directions). Se and Mn shared associations with 6 metabolites (in both directions), which mostly included nucleotides and amino acids; Se was additionally associated with 7 metabolites (mostly amino acids, nucleotides, and carnitines) and Mn was additionally associated with C36:4 hydroxy phosphatidylcholine. Restricted cubic spline plots showed that most associations were linear. DISCUSSION Maternal RBC metal and trace element concentrations were associated in a dose-dependent fashion with cord blood metabolites. What remains to be determined is whether these metals- and trace elements-associated changes in cord metabolites can influence a child's risk of cardiometabolic diseases.
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Affiliation(s)
- Mingyu Zhang
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD, USA
| | - Jessie P Buckley
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 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
| | - Xiumei Hong
- Center on the Early Life Origins of Disease, Department of Population, Family and Reproductive Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Guoying Wang
- Center on the Early Life Origins of Disease, Department of Population, Family and Reproductive Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Mei-Cheng Wang
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Marsha Wills-Karp
- Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Xiaobin Wang
- Center on the Early Life Origins of Disease, Department of Population, Family and Reproductive Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; Department of Pediatrics, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Noel T Mueller
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD, USA.
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