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Wang F, Glenn AJ, Tessier AJ, Mei Z, Haslam DE, Guasch-Ferré M, Tobias DK, Eliassen AH, Manson JE, Clish C, Lee KH, Rimm EB, Wang DD, Sun Q, Liang L, Willett WC, Hu FB. Integration of epidemiological and blood biomarker analysis links haem iron intake to increased type 2 diabetes risk. Nat Metab 2024:10.1038/s42255-024-01109-5. [PMID: 39138340 DOI: 10.1038/s42255-024-01109-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/23/2024] [Accepted: 07/12/2024] [Indexed: 08/15/2024]
Abstract
Dietary haem iron intake is linked to an increased risk of type 2 diabetes (T2D), but the underlying plasma biomarkers are not well understood. We analysed data from 204,615 participants (79% females) in three large US cohorts over up to 36 years, examining the associations between iron intake and T2D risk. We also assessed plasma metabolic biomarkers and metabolomic profiles in subsets of 37,544 (82% females) and 9,024 (84% females) participants, respectively. Here we show that haem iron intake but not non-haem iron is associated with a higher T2D risk, with a multivariable-adjusted hazard ratio of 1.26 (95% confidence interval 1.20-1.33; P for trend <0.001) comparing the highest to the lowest quintiles. Haem iron accounts for significant proportions of the T2D risk linked to unprocessed red meat and specific dietary patterns. Increased haem iron intake correlates with unfavourable plasma profiles of insulinaemia, lipids, inflammation and T2D-linked metabolites. We also identify metabolites, including L-valine and uric acid, potentially mediating the haem iron-T2D relationship, highlighting their pivotal role in T2D pathogenesis.
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Affiliation(s)
- Fenglei Wang
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Andrea J Glenn
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Nutritional Sciences, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Toronto 3D Knowledge Synthesis and Clinical Trials Unit, Clinical Nutrition and Risk Factor Modification Centre, St. Michael's Hospital, Toronto, Ontario, Canada
| | - Anne-Julie Tessier
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Zhendong Mei
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Danielle E Haslam
- 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 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
| | - 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
| | - A Heather Eliassen
- 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 and Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - JoAnn E Manson
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - 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
| | - Eric B Rimm
- 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 and Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Dong D Wang
- 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 and Harvard Medical School, Boston, MA, USA
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Qi Sun
- 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 and Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, 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
| | - Walter C Willett
- 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
| | - Frank B Hu
- 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 and Harvard Medical School, Boston, MA, USA.
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
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2
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Hillesheim E, Liu W, Yin X, Smith T, Brennan L. Association of plant-based diet indexes with the metabolomic profile. Sci Rep 2024; 14:17927. [PMID: 39095501 PMCID: PMC11297169 DOI: 10.1038/s41598-024-68522-4] [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: 04/25/2024] [Accepted: 07/24/2024] [Indexed: 08/04/2024] Open
Abstract
Plant-based diets have gained attention for their potential benefits on both human health and environmental sustainability. The objective of this study was to investigate the association of plant-based dietary patterns with the endogenous metabolites of healthy individuals and identify metabolites that may act as mediators of the associations between dietary intake and modifiable disease risk factors. Adherence to plant-based dietary patterns was assessed for 170 healthy adults using plant-based diet indexes (PDI). Individuals with higher healthful PDI had lower BMI and fasting glucose and higher HDL-C, while those with higher unhealthful PDI had higher BMI, triacylglycerol and fasting glucose and lower HDL-C. Unhealthful PDI was associated with higher levels of several amino acids and biogenic amines previously associated with cardiometabolic diseases and an opposite pattern was observed for healthful PDI. Furthermore, healthful PDI was associated with higher levels of glycerophosphocholines containing very long-chain fatty acids. Glutamate, isoleucine, proline, tyrosine, α-aminoadipate and kynurenine had a statistically significant mediation effect on the associations between PDI scores and LDL-C, HDL-C and fasting glucose. These findings contribute to the growing evidence supporting the role of plant-based diets in promoting metabolic health and shed light on the potential mechanisms explaining their beneficial health effects.
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Affiliation(s)
- Elaine Hillesheim
- UCD School of Agriculture and Food Science, Institute of Food and Health, University College Dublin, Dublin 4, Ireland
- UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin 4, Ireland
| | - Wenxuan Liu
- UCD School of Agriculture and Food Science, Institute of Food and Health, University College Dublin, Dublin 4, Ireland
- UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin 4, Ireland
| | - Xiaofei Yin
- UCD School of Agriculture and Food Science, Institute of Food and Health, University College Dublin, Dublin 4, Ireland
- UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin 4, Ireland
| | - Thomas Smith
- Department of Clinical Chemistry, St. Vincents University Hospital, Elm Park, Dublin 4, Ireland
| | - Lorraine Brennan
- UCD School of Agriculture and Food Science, Institute of Food and Health, University College Dublin, Dublin 4, Ireland.
- UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin 4, Ireland.
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3
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Eichelmann F, Prada M, Sellem L, Jackson KG, Salas Salvadó J, Razquin Burillo C, Estruch R, Friedén M, Rosqvist F, Risérus U, Rexrode KM, Guasch-Ferré M, Sun Q, Willett WC, Martinez-Gonzalez MA, Lovegrove JA, Hu FB, Schulze MB, Wittenbecher C. Lipidome changes due to improved dietary fat quality inform cardiometabolic risk reduction and precision nutrition. Nat Med 2024:10.1038/s41591-024-03124-1. [PMID: 38992128 DOI: 10.1038/s41591-024-03124-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 06/11/2024] [Indexed: 07/13/2024]
Abstract
Current cardiometabolic disease prevention guidelines recommend increasing dietary unsaturated fat intake while reducing saturated fats. Here we use lipidomics data from a randomized controlled dietary intervention trial to construct a multilipid score (MLS), summarizing the effects of replacing saturated fat with unsaturated fat on 45 lipid metabolite concentrations. In the EPIC-Potsdam cohort, a difference in the MLS, reflecting better dietary fat quality, was associated with a significant reduction in the incidence of cardiovascular disease (-32%; 95% confidence interval (95% CI): -21% to -42%) and type 2 diabetes (-26%; 95% CI: -15% to -35%). We built a closely correlated simplified score, reduced MLS (rMLS), and observed that beneficial rMLS changes, suggesting improved dietary fat quality over 10 years, were associated with lower diabetes risk (odds ratio per standard deviation of 0.76; 95% CI: 0.59 to 0.98) in the Nurses' Health Study. Furthermore, in the PREDIMED trial, an olive oil-rich Mediterranean diet intervention primarily reduced diabetes incidence among participants with unfavorable preintervention rMLS levels, suggestive of disturbed lipid metabolism before intervention. Our findings indicate that the effects of dietary fat quality on the lipidome can contribute to a more precise understanding and possible prediction of the health outcomes of specific dietary fat modifications.
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Affiliation(s)
- Fabian Eichelmann
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany.
- German Center for Diabetes Research (DZD), Neuherberg, Germany.
| | - Marcela Prada
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Laury Sellem
- Hugh Sinclair Unit of Human Nutrition, Institute for Cardiovascular and Metabolic Research and Institute for Food, Nutrition and Health, Reading, UK
| | - Kim G Jackson
- Hugh Sinclair Unit of Human Nutrition, Institute for Cardiovascular and Metabolic Research and Institute for Food, Nutrition and Health, Reading, UK
| | - Jordi Salas Salvadó
- Human Nutrition Unit, Department of Biochemistry and Biotechnology, Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, Reus, Spain
- Consorcio CIBER, M.P. Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Cristina Razquin Burillo
- Human Nutrition Unit, Department of Biochemistry and Biotechnology, Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, Reus, Spain
- Department of Preventive Medicine and Public Health, IdiSNA (Instituto de Investigación Sanitaria de Navarra), University of Navarra, Pamplona, Spain
| | - Ramon Estruch
- Human Nutrition Unit, Department of Biochemistry and Biotechnology, Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, Reus, Spain
- Department of Internal Medicine, Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Hospital Clinic, University of Barcelona, Barcelona, Spain
| | - Michael Friedén
- Department of Public Health and Caring Sciences, Clinical Nutrition and Metabolism, Uppsala University, Uppsala, Sweden
| | - Frederik Rosqvist
- Department of Public Health and Caring Sciences, Clinical Nutrition and Metabolism, Uppsala University, Uppsala, Sweden
| | - Ulf Risérus
- Department of Public Health and Caring Sciences, Clinical Nutrition and Metabolism, Uppsala University, Uppsala, Sweden
| | - Kathryn M Rexrode
- Division of Women's Health, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Marta Guasch-Ferré
- Department of Public Health and Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Nutrition, 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, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Walter C Willett
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Miguel Angel Martinez-Gonzalez
- Consorcio CIBER, M.P. Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- Department of Preventive Medicine and Public Health, IdiSNA (Instituto de Investigación Sanitaria de Navarra), University of Navarra, Pamplona, Spain
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Julie A Lovegrove
- Hugh Sinclair Unit of Human Nutrition, Institute for Cardiovascular and Metabolic Research and Institute for Food, Nutrition and Health, Reading, UK
| | - Frank B Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Matthias B Schulze
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute of Nutritional Science, University of Potsdam, Nuthetal, Germany
| | - Clemens Wittenbecher
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany.
- Department of Life Sciences, SciLifeLab, Chalmers University of Technology, Gothenburg, Sweden.
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Ferreira H, Duarte D, Carneiro TJ, Costa C, Barbosa JC, Rodrigues JE, Alves P, Vasconcelos M, Pinto E, Gomes A, Gil AM. Impact of a legumes diet on the human gut microbiome articulated with fecal and plasma metabolomes: A pilot study. Clin Nutr ESPEN 2024; 63:332-345. [PMID: 38964655 DOI: 10.1016/j.clnesp.2024.06.051] [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: 04/18/2024] [Revised: 06/12/2024] [Accepted: 06/27/2024] [Indexed: 07/06/2024]
Abstract
BACKGROUND & AIMS Legumes intake is known to be associated with several health benefits the origins of which is still a matter of debate. This paper addresses a pilot small cohort to probe for metabolic aspects of the interplay between legumes intake, human metabolism and gut microbiota. METHODS Untargeted nuclear magnetic resonance (NMR) metabolomics of blood plasma and fecal extracts was carried out, in tandem with qPCR analysis of feces, to assess the impact of an 8-week pilot legumes diet intervention on the fecal and plasma metabolomes and gut microbiota of 19 subjects. RESULTS While the high inter-individual variability hindered the detection of statistically significant changes in the gut microbiome, increased fecal glucose and decreased threonine levels were noted. Correlation analysis between the microbiome and fecal metabolome lead to putative hypotheses regarding the metabolic activities of prevalent bacteria groups (Clostridium leptum subgroup, Roseburia spp., and Faecalibacterium prausnitzii). These included elevated fecal glucose as a preferential energy source, the involvement of valerate/isovalerate and reduced protein degradation in gut microbiota. Plasma metabolomics advanced mannose and betaine as potential markers of legume intake and unveiled a decrease in formate and ketone bodies, the latter suggesting improved energy utilization through legume carbohydrates. Amino acid metabolism was also apparently affected, as suggested by lowered urea, histidine and threonine levels. CONCLUSIONS Despite the high inter-individual gut microbiome variability characterizing the small cohort addressed, combination of microbiological measurements and untargeted metabolomics unveiled several metabolic effects putatively related to legumes intake. If confirmed in larger cohorts, our findings will support the inclusion of legumes in diets and contribute valuable new insight into the origins of associated health benefits.
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Affiliation(s)
- Helena Ferreira
- Universidade Católica Portuguesa, CBQF - Centro de Biotecnologia e Química Fina - Laboratório Associado, Escola Superior de Biotecnologia, Porto, Portugal; Department of Chemistry and CICECO-Aveiro Institute of Materials, University of Aveiro, Aveiro, Portugal
| | - Daniela Duarte
- Department of Chemistry and CICECO-Aveiro Institute of Materials, University of Aveiro, Aveiro, Portugal
| | - Tatiana J Carneiro
- Department of Chemistry and CICECO-Aveiro Institute of Materials, University of Aveiro, Aveiro, Portugal
| | - Célia Costa
- Universidade Católica Portuguesa, CBQF - Centro de Biotecnologia e Química Fina - Laboratório Associado, Escola Superior de Biotecnologia, Porto, Portugal
| | - Joana C Barbosa
- Universidade Católica Portuguesa, CBQF - Centro de Biotecnologia e Química Fina - Laboratório Associado, Escola Superior de Biotecnologia, Porto, Portugal
| | - João E Rodrigues
- Department of Chemistry and CICECO-Aveiro Institute of Materials, University of Aveiro, Aveiro, Portugal
| | - Paulo Alves
- Universidade Católica Portuguesa, CIIS - Centro de Investigação Interdisciplinar em Saúde, Escola Enfermagem (Porto), Portugal
| | - Marta Vasconcelos
- Universidade Católica Portuguesa, CBQF - Centro de Biotecnologia e Química Fina - Laboratório Associado, Escola Superior de Biotecnologia, Porto, Portugal
| | - Elisabete Pinto
- Universidade Católica Portuguesa, CBQF - Centro de Biotecnologia e Química Fina - Laboratório Associado, Escola Superior de Biotecnologia, Porto, Portugal; EPIUnit - Instituto de Saúde Pública, Universidade do Porto, Porto, Portugal
| | - Ana Gomes
- Universidade Católica Portuguesa, CBQF - Centro de Biotecnologia e Química Fina - Laboratório Associado, Escola Superior de Biotecnologia, Porto, Portugal
| | - Ana M Gil
- Department of Chemistry and CICECO-Aveiro Institute of Materials, University of Aveiro, Aveiro, Portugal.
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5
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Yang SH, Kim YJ, Lee H, Seo WD, Kwon EY, Kim JK. Comparative metabolomic analysis of mouse plasma in response to different dietary conditions. Food Sci Biotechnol 2024; 33:2169-2178. [PMID: 39130654 PMCID: PMC11315845 DOI: 10.1007/s10068-023-01479-8] [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: 06/29/2023] [Revised: 10/19/2023] [Accepted: 11/01/2023] [Indexed: 08/13/2024] Open
Abstract
Plasma metabolites offer insights into aging processes and aging-related biomarkers. Here, the dietary effects of various functional foods on older adult mice were evaluated using metabolomic techniques. Fifty-week-old mice were divided into four groups (n = 4 each) and fed either a normal diet (AC) or the diets from Triticum aestivum sprout (TA), Schisandra chinensis (SZ), or Pisum sativum sprout (PS) extracts. Additionally, a group of 8-week-old mice fed a normal diet (YC; n = 5) was included for the comparison. The PS group had a significantly lower free fatty acid content and higher ornithine, proline, citric acid, and oxalic acid contents than the AC group. The PS group also showed reduced oxidative stress and muscle damage, suggesting the higher anti-aging efficacy of P. sativum sprouts than the other diets. These findings suggest plasma metabolite profiling is an effective tool to assess the anti-aging effects of functional foods. Graphical abstract Supplementary Information The online version contains supplementary material available at 10.1007/s10068-023-01479-8.
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Affiliation(s)
- So Hwi Yang
- Division of Life Sciences, College of Life Sciences, and Bioengineering, Incheon National University, Inchon, 22012 Republic of Korea
| | - Ye Jin Kim
- Division of Life Sciences, College of Life Sciences, and Bioengineering, Incheon National University, Inchon, 22012 Republic of Korea
| | - HanGyeol Lee
- Division of Crop Foundation, National Institute of Crop Science, Rural Development Administration, Wanju, Jeonbuk 55365 Republic of Korea
| | - Woo Duck Seo
- Division of Crop Foundation, National Institute of Crop Science, Rural Development Administration, Wanju, Jeonbuk 55365 Republic of Korea
| | - Eun Young Kwon
- Department of Food Science and Nutrition, Kyungpook National University, 1370 San-Kyuk Dong, Puk-Ku, Daegu, 702-701 Republic of Korea
| | - Jae Kwang Kim
- Division of Life Sciences, College of Life Sciences, and Bioengineering, Incheon National University, Inchon, 22012 Republic of Korea
- Convergence Research Center for Insect Vectors, Incheon National University, Inchon, 22012 Republic of Korea
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6
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Bever AM, Hang D, Lee DH, Tabung FK, Ugai T, Ogino S, Meyerhardt JA, Chan AT, Eliassen AH, Liang L, Stampfer MJ, Song M. Metabolomic signatures of inflammation and metabolic dysregulation in relation to colorectal cancer risk. J Natl Cancer Inst 2024; 116:1126-1136. [PMID: 38430005 PMCID: PMC11223797 DOI: 10.1093/jnci/djae047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 01/18/2024] [Accepted: 02/22/2024] [Indexed: 03/03/2024] Open
Abstract
BACKGROUND Inflammation and metabolic dysregulation are associated with increased risk of colorectal cancer (CRC); the underlying mechanisms are not fully understood. We characterized metabolomic signatures of inflammation and metabolic dysregulation and evaluated the association of the signatures and individual metabolites with CRC risk. METHODS Among 684 incident CRC cases and 684 age-matched controls in the Nurses' Health Study (n = 818 women) and Health Professionals Follow-up Study (n = 550 men), we applied reduced rank and elastic net regression to 277 metabolites for markers of inflammation (C-reactive protein, interleukin 6, tumor necrosis factor receptor superfamily member 1B, and growth differentiation factor 15) or metabolic dysregulation (body mass index, waist circumference, C-peptide, and adiponectin) to derive metabolomic signatures. We evaluated the association of the signatures and individual metabolites with CRC using multivariable conditional logistic regression. All statistical tests were 2-sided. RESULTS We derived a signature of 100 metabolites that explained 24% of variation in markers of inflammation and a signature of 73 metabolites that explained 27% of variation in markers of metabolic dysregulation. Among men, both signatures were associated with CRC (odds ratio [OR] = 1.34, 95% confidence interval [CI] = 1.07 to 1.68 per 1-standard deviation increase, inflammation; OR = 1.25, 95% CI = 1.00 to 1.55 metabolic dysregulation); neither signature was associated with CRC in women. A total of 11 metabolites were individually associated with CRC and biomarkers of inflammation or metabolic dysregulation among either men or women. CONCLUSION We derived metabolomic signatures and identified individual metabolites associated with inflammation, metabolic dysregulation, and CRC, highlighting several metabolites as promising candidates involved in the inflammatory and metabolic dysregulation pathways for CRC incidence.
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Affiliation(s)
- Alaina M Bever
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School, Boston, MA, USA
| | - Dong Hang
- Department of Epidemiology and Biostatistics, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, China
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Dong Hoon Lee
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Sport Industry Studies, Yonsei University, Seoul, Republic of Korea
| | - Fred K Tabung
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- The Ohio State University College of Medicine and Comprehensive Cancer Center, Columbus, OH, USA
| | - Tomotaka Ugai
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Shuji Ogino
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jeffrey A Meyerhardt
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
| | - Andrew T Chan
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - A Heather Eliassen
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, 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
| | - Meir J Stampfer
- 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 and Harvard Medical School, Boston, MA, USA
| | - Mingyang Song
- 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
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
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7
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Sawicki CM, Ren Y, Kanaya AM, Kandula N, Gadgil M, Liang L, Haslam DE, Bhupathiraju SN. Metabolite Profiles of Plant-Based Diets and Cardiometabolic Risk in the Mediators of Atherosclerosis in South Asians Living in America Study. J Nutr 2024:S0022-3166(24)00346-8. [PMID: 38901635 DOI: 10.1016/j.tjnut.2024.06.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Revised: 05/21/2024] [Accepted: 06/12/2024] [Indexed: 06/22/2024] Open
Abstract
BACKGROUND Healthy plant-based diets have been associated with lower risk of type 2 diabetes (T2D). Metabolomics can be leveraged to identify potential pathways through which diet influences disease risk. OBJECTIVES This study aimed to identify profiles of serum metabolites reflective of plant-based diets of varying quality and examine associations with cardiometabolic risk and T2D. METHODS We included data from 687 participants of the Mediators of Atherosclerosis in South Asians Living in America (MASALA) cohort. An overall plant-based diet index (PDI), healthy PDI (hPDI), and unhealthy PDI (uPDI) were estimated from food frequency questionnaires. Serum metabolites were assayed using ultraperformance liquid chromatography mass spectrometry. Elastic net regression was used to identify sets of metabolites predictive of each diet index, and metabolite profile scores were calculated as the weighted sum of the selected metabolites. Cross-sectional associations between metabolite profile scores and cardiometabolic measures and prospective associations with incident T2D were evaluated with multivariable-adjusted linear and logistic regressions. RESULTS Metabolite profiles for PDI, hPDI, and uPDI consisted of n = 51, 55, and 45 metabolites, respectively. Metabolites strongly positively correlated with diet indices included phosphatidylcholine (16:0/18:3) for PDI, phosphatidylethanolamine (20:1/20:4) and pantothenate for hPDI, and lysophosphatidylglycerol (18:2/0:0), proline, and lauric acid for uPDI. Higher metabolite profile scores for PDI and hPDI were associated with lower glycemia and lipids measures, whereas a higher uPDI metabolite score was associated with higher triglycerides and lower low density lipoprotein cholesterol and high density lipoprotein cholesterol. A higher metabolite score for hPDI was additionally associated with lower adiposity measures, higher liver fat attenuation, higher adiponectin, lower odds of overweight (odds ratio [OR]: 0.64; 95% confidence interval [CI]: 0.51, 0.81) and obesity (OR: 0.59; 95% CI: 0.48, 0.74), and lower odds of incident T2D (OR: 0.66; 95% CI: 0.45, 0.97). CONCLUSIONS Metabolite profiles of different plant-based diets were identified. Metabolite profiles of overall and healthy plant-based diets were associated with favorable cardiometabolic risk profiles.
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Affiliation(s)
- Caleigh M Sawicki
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Yin Ren
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Alka M Kanaya
- Division of General Internal Medicine, Department of Medicine, University of California, San Francisco, San Francisco, CA, United States
| | - Namratha Kandula
- Division of General Internal Medicine and Geriatrics, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | - Meghana Gadgil
- Division of General Internal Medicine, Department of Medicine, University of California, San Francisco, San Francisco, CA, United States
| | - Liming Liang
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Danielle E Haslam
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Shilpa N Bhupathiraju
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States.
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Nikparast A, Mirzaei P, Tadayoni ZS, Asghari G. The Association Between Overall, Healthy, and Unhealthy Plant-Based Diet Index and Risk of Prediabetes and Type 2 Diabetes Mellitus: A Systematic Review and Dose-Response Meta-Analysis of Prospective Studies. Nutr Rev 2024:nuae049. [PMID: 38796844 DOI: 10.1093/nutrit/nuae049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/29/2024] Open
Abstract
CONTEXT The global incidence of prediabetes and type 2 diabetes mellitus (T2DM) has increased substantially in recent years. Among the established, modifiable lifestyle factors associated with favorable prediabetes and T2DM risk, healthy dietary patterns have attracted considerable attention. OBJECTIVE The association between adherence to plant-based dietary pattern indices (PDIs), including the overall PDI (O-PDI), healthy PDI (H-PDI), and unhealthy PDI (U-PDI), and the risk of prediabetes and T2DM was investigated in this study. DATA SOURCES A literature search was conducted of the PubMed/Medline, Scopus, and Web of Sciences databases from their inception to February 2024. A systematic review and meta-analysis were conducted using random effects models and dose-response analyses. The Cochran Q test and the I2 statistic were used to evaluate heterogeneity between studies. DATA EXTRACTION A total of 16 publications, with data on a total of 721 012 participants, were identified for the meta-analysis. DATA ANALYSIS According to a pooled analysis, compared with the lowest category of O-PDI and H-PDI adherence, the highest category was associated with a 14% and 19% reduction in T2DM risk, respectively, for O-PDI (effect size [ES] = 0.86; 95%CI, 0.82-0.90; I2 = 57.7) and H-PDI (ES = 0.81; 95%CI, 0.75-0.88; I2 = 82.6). Greater adherence to U-PDI was significantly associated with an 10% increase in the risk of T2DM (ES = 1.10; 95%CI, 1.04-1.16). Consistent associations were found within the predetermined subgroups. As well, there was a nonlinear inverse association between O-PDI, H-PDI, and T2DM risk. No significant association was found between adherence to O-PDI (ES = 0.87; 95%CI, 0.75-1.01; I2 = 68%), H-PDI (ES = 0.99; 95%CI, 0.87-1.13; I2 = 0.0%), and U-PDI (ES = 1.09; 95%CI, 0.94-1.21; I2 = 22.9%) and risk of prediabetes. CONCLUSION These findings underscore the importance of dietary selections within the framework of a plant-based dietary pattern, particularly when incorporating healthful, plant-based foods, which may have potential benefits in reducing the T2DM risk. SYSTEMATIC REVIEW REGISTRATION PROSPERO registration no. CRD42023459851.
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Affiliation(s)
- Ali Nikparast
- Pediatric Gastroenterology and Hepatology Research Center, Pediatrics Centre of Excellence, Children's Medical Center, Tehran University of Medical Sciences, Tehran, Iran
- Faculty of Nutrition Science and Food Technology, Department of Clinical Nutrition & Dietetics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Parmis Mirzaei
- Faculty of Nutrition Science and Food Technology, Department of Clinical Nutrition & Dietetics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Zeinab S Tadayoni
- Faculty of Nutrition Science and Food Technology, Department of Clinical Nutrition & Dietetics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Golaleh Asghari
- Faculty of Nutrition Science and Food Technology, Department of Clinical Nutrition & Dietetics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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9
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Haslam DE, Liang L, Guo K, Martínez-Lozano M, Pérez CM, Lee CH, Morou-Bermudez E, Clish C, Wong DTW, Manson JE, Hu FB, Stampfer MJ, Joshipura K, Bhupathiraju SN. Discovery and validation of plasma, saliva and multi-fluid plasma-saliva metabolomic scores predicting insulin resistance and diabetes progression or regression among Puerto Rican adults. Diabetologia 2024:10.1007/s00125-024-06169-6. [PMID: 38772919 DOI: 10.1007/s00125-024-06169-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 03/21/2024] [Indexed: 05/23/2024]
Abstract
AIMS/HYPOTHESIS Many studies have examined the relationship between plasma metabolites and type 2 diabetes progression, but few have explored saliva and multi-fluid metabolites. METHODS We used LC/MS to measure plasma (n=1051) and saliva (n=635) metabolites among Puerto Rican adults from the San Juan Overweight Adults Longitudinal Study. We used elastic net regression to identify plasma, saliva and multi-fluid plasma-saliva metabolomic scores predicting baseline HOMA-IR in a training set (n=509) and validated these scores in a testing set (n=340). We used multivariable Cox proportional hazards models to estimate HRs for the association of baseline metabolomic scores predicting insulin resistance with incident type 2 diabetes (n=54) and prediabetes (characterised by impaired glucose tolerance, impaired fasting glucose and/or high HbA1c) (n=130) at 3 years, along with regression from prediabetes to normoglycaemia (n=122), adjusting for traditional diabetes-related risk factors. RESULTS Plasma, saliva and multi-fluid plasma-saliva metabolomic scores predicting insulin resistance included highly weighted metabolites from fructose, tyrosine, lipid and amino acid metabolism. Each SD increase in the plasma (HR 1.99 [95% CI 1.18, 3.38]; p=0.01) and multi-fluid (1.80 [1.06, 3.07]; p=0.03) metabolomic scores was associated with higher risk of type 2 diabetes. The saliva metabolomic score was associated with incident prediabetes (1.48 [1.17, 1.86]; p=0.001). All three metabolomic scores were significantly associated with lower likelihood of regressing from prediabetes to normoglycaemia in models adjusting for adiposity (HRs 0.72 for plasma, 0.78 for saliva and 0.72 for multi-fluid), but associations were attenuated when adjusting for lipid and glycaemic measures. CONCLUSIONS/INTERPRETATION The plasma metabolomic score predicting insulin resistance was more strongly associated with incident type 2 diabetes than the saliva metabolomic score. Only the saliva metabolomic score was associated with incident prediabetes.
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Affiliation(s)
- Danielle E Haslam
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- 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
| | - Kai Guo
- Center for Clinical Research and Health Promotion, Graduate School of Public Health, University of Puerto Rico Medical Sciences Campus, San Juan, Puerto Rico
| | - Marijulie Martínez-Lozano
- Center for Clinical Research and Health Promotion, Graduate School of Public Health, University of Puerto Rico Medical Sciences Campus, San Juan, Puerto Rico
| | - Cynthia M Pérez
- Department of Biostatistics and Epidemiology, Graduate School of Public Health, University of Puerto Rico Medical Sciences Campus, San Juan, Puerto Rico
| | - Chih-Hao Lee
- Department of Molecular Metabolism, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Evangelia Morou-Bermudez
- School of Dental Medicine, University of Puerto Rico Medical Sciences Campus, San Juan, Puerto Rico
| | - Clary Clish
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
| | - David T W Wong
- Center for Oral/Head and Neck Oncology Research, School of Dentistry, University of California Los Angeles, Los Angeles, CA, USA
| | - JoAnn E Manson
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, 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
| | - Frank B Hu
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- 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
| | - Meir J Stampfer
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- 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
| | - Kaumudi Joshipura
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Center for Clinical Research and Health Promotion, Graduate School of Public Health, University of Puerto Rico Medical Sciences Campus, San Juan, Puerto Rico
| | - Shilpa N Bhupathiraju
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
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10
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Tessier AJ, Wang F, Liang L, Wittenbecher C, Haslam DE, Eliassen AH, Tobias DK, Li J, Zeleznik OA, Ascherio A, Sun Q, Stampfer MJ, Grodstein F, Rexrode KM, Manson JE, Balasubramanian R, Clish CB, Martínez-González MA, Chavarro JE, Hu FB, Guasch-Ferré M. Plasma metabolites of a healthy lifestyle in relation to mortality and longevity: Four prospective US cohort studies. MED 2024; 5:224-238.e5. [PMID: 38366602 PMCID: PMC10940196 DOI: 10.1016/j.medj.2024.01.010] [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: 06/09/2023] [Revised: 11/09/2023] [Accepted: 01/18/2024] [Indexed: 02/18/2024]
Abstract
BACKGROUND A healthy lifestyle is associated with a lower premature mortality risk and with longer life expectancy. However, the metabolic pathways of a healthy lifestyle and how they relate to mortality and longevity are unclear. We aimed to identify and replicate a healthy lifestyle metabolomic signature and examine how it is related to total and cause-specific mortality risk and longevity. METHODS In four large cohorts with 13,056 individuals and 28-year follow-up, we assessed five healthy lifestyle factors, used liquid chromatography mass spectrometry to profile plasma metabolites, and ascertained deaths with death certificates. The unique healthy lifestyle metabolomic signature was identified using an elastic regression. Multivariable Cox regressions were used to assess associations of the signature with mortality and longevity. FINDINGS The identified healthy lifestyle metabolomic signature was reflective of lipid metabolism pathways. Shorter and more saturated triacylglycerol and diacylglycerol metabolite sets were inversely associated with the healthy lifestyle score, whereas cholesteryl ester and phosphatidylcholine plasmalogen sets were positively associated. Participants with a higher healthy lifestyle metabolomic signature had a 17% lower risk of all-cause mortality, 19% for cardiovascular disease mortality, and 17% for cancer mortality and were 25% more likely to reach longevity. The healthy lifestyle metabolomic signature explained 38% of the association between the self-reported healthy lifestyle score and total mortality risk and 49% of the association with longevity. CONCLUSIONS This study identifies a metabolomic signature that measures adherence to a healthy lifestyle and shows prediction of total and cause-specific mortality and longevity. FUNDING This work was funded by the NIH, CIHR, AHA, Novo Nordisk Foundation, and SciLifeLab.
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Affiliation(s)
- Anne-Julie Tessier
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Fenglei Wang
- 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
| | | | - Danielle E Haslam
- 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 and Harvard Medical School, Boston, MA, USA
| | - 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, 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 A Zeleznik
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Alberto Ascherio
- 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 and Harvard Medical School, Boston, MA, USA
| | - Qi Sun
- 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 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, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Francine Grodstein
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Kathryn M Rexrode
- Division of Women's Health, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - JoAnn E Manson
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Raji Balasubramanian
- Department of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, MA, USA
| | - Clary B Clish
- Broad Institute of MIT and Harvard, Cambridge, MA, 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, University of Navarra, Pamplona, Spain
| | - Jorge E Chavarro
- 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, Department of 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, Department of 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 and Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
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11
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Winkvist A, Johansson I, Ellegård L, Lindqvist HM. Towards objective measurements of habitual dietary intake patterns: comparing NMR metabolomics and food frequency questionnaire data in a population-based cohort. Nutr J 2024; 23:29. [PMID: 38429740 PMCID: PMC10908051 DOI: 10.1186/s12937-024-00929-1] [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: 10/19/2023] [Accepted: 02/23/2024] [Indexed: 03/03/2024] Open
Abstract
BACKGROUND Low-quality, non-diverse diet is a main risk factor for premature death. Accurate measurement of habitual diet is challenging and there is a need for validated objective methods. Blood metabolite patterns reflect direct or enzymatically diet-induced metabolites. Here, we aimed to evaluate associations between blood metabolite patterns and a priori and data-driven food intake patterns. METHODS 1, 895 participants in the Northern Sweden Health and Disease Study, a population-based prospective cohort study, were included. Fasting plasma samples were analyzed with 1H Nuclear Magnetic Resonance. Food intake data from a 64-item validated food frequency questionnaire were summarized into a priori Healthy Diet Score (HDS), relative Mediterranean Diet Score (rMDS) and a set of plant-based diet indices (PDI) as well as data driven clusters from latent class analyses (LCA). Orthogonal projections to latent structures (OPLS) were used to explore clustering patterns of metabolites and their relation to reported dietary intake patterns. RESULTS Age, sex, body mass index, education and year of study participation had significant influence on OPLS metabolite models. OPLS models for healthful PDI and LCA-clusters were not significant, whereas for HDS, rMDS, PDI and unhealthful PDI significant models were obtained (CV-ANOVA p < 0.001). Still, model statistics were weak and the ability of the models to correctly classify participants into highest and lowest quartiles of rMDS, PDI and unhealthful PDI was poor (50%/78%, 42%/75% and 59%/70%, respectively). CONCLUSION Associations between blood metabolite patterns and a priori as well as data-driven food intake patterns were poor. NMR metabolomics may not be sufficiently sensitive to small metabolites that distinguish between complex dietary intake patterns, like lipids.
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Affiliation(s)
- Anna Winkvist
- Department of Internal Medicine and Clinical Nutrition, the Sahlgrenska Academy, University of Gothenburg, Box 459, Gothenburg, SE-405 30, Sweden.
- Department of Public Health and Clinical Medicine, Sustainable Health, Umeå University, Umeå, Sweden.
| | | | - Lars Ellegård
- Department of Internal Medicine and Clinical Nutrition, the Sahlgrenska Academy, University of Gothenburg, Box 459, Gothenburg, SE-405 30, Sweden
- Clinical Nutrition Unit, Department of Gastroenterology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Helen M Lindqvist
- Department of Internal Medicine and Clinical Nutrition, the Sahlgrenska Academy, University of Gothenburg, Box 459, Gothenburg, SE-405 30, Sweden
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12
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Shen X, Tilves C, Kim H, Tanaka T, Spira AP, Chia CW, Talegawkar SA, Ferrucci L, Mueller NT. Plant-based diets and the gut microbiome: findings from the Baltimore Longitudinal Study of Aging. Am J Clin Nutr 2024; 119:628-638. [PMID: 38218318 PMCID: PMC10972708 DOI: 10.1016/j.ajcnut.2024.01.006] [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: 10/13/2023] [Revised: 01/04/2024] [Accepted: 01/08/2024] [Indexed: 01/15/2024] Open
Abstract
BACKGROUND Mounting evidence indicates that although some plant-based diets are healthful, others are not. Changes in the gut microbiome and microbiome-dependent metabolites, such as trimethylamine N-oxide (TMAO), may explain differential health effects of plant-based diets. However, human data are sparse on whether qualitatively distinct types of plant-based diets differentially affect gut microbiome diversity, composition, particularly at the species level, and/or metabolites. OBJECTIVES We aimed to examine cross-sectional associations of different plant-based indices with adult gut microbiome diversity, composition, and the metabolite TMAO. METHODS We studied 705 adults in the Baltimore Longitudinal Study of Aging with data for diet, fecal microbiome (shotgun metagenomic sequencing), and key covariates. We derived healthful plant-based diet index (hPDI) and unhealthful plant-based diet index (uPDI) using data from food frequency questionnaires. We examined plant-based diet indices with microbiome α-diversity (richness and evenness measures), β-diversity (Bray-Curtis and UniFrac measures), composition (species level), and plasma TMAO. We used regression models to determine associations before and after adjustment for age, sex, education, physical activity, smoking status, body mass index, and total energy intake. RESULTS The analytic sample (mean age, 71.0 years, SD = 12.8 years) comprised 55.6% female and 67.5% non-Hispanic White participants. hPDI was positively and uPDI negatively associated with microbiome α-diversity, driven by microbial evenness (Pielou P < 0.05). hPDI was also positively associated with relative abundance of 3 polysaccharide-degrading bacterial species (Faecalibacterium prausnitzii, Eubacterium eligens, and Bacteroides thetaiotaomicron) and inversely associated with 6 species (Blautia hydrogenotrophica, Doreasp CAG 317, Eisenbergiella massiliensis, Sellimonas intestinalis, Blautia wexlerae, and Alistipes shahii). Furthermore, hPDI was inversely associated with TMAO. Associations did not differ by age, sex, or race. CONCLUSIONS Greater adherence to a healthful plant-based diet is associated with microbiome features that have been linked to positive health; adherence to an unhealthful plant-based diet has opposing or null associations with these features.
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Affiliation(s)
- Xinyi Shen
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, United States; Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD, United States; Tufts University School of Medicine, Tufts University, Boston, MA, United States
| | - Curtis Tilves
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, United States; Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD, United States; Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Hyunju Kim
- Department of Epidemiology, University of Washington School of Public Health, Seattle, WA, United States
| | - Toshiko Tanaka
- Intramural Research Program, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States
| | - Adam P Spira
- Department of Mental Health, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, United States; Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, United States; Center on Aging and Health, Johns Hopkins University, Baltimore, MD, United States
| | - Chee W Chia
- Intramural Research Program, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States
| | - Sameera A Talegawkar
- Departments of Exercise and Nutrition Sciences and Epidemiology, Milken Institute School of Public Health at the George Washington University, Washington, DC, United States
| | - Luigi Ferrucci
- Intramural Research Program, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States
| | - Noel T Mueller
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, United States; Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD, United States; Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University of Colorado Anschutz Medical Campus, Aurora, CO, United States; Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO, United States.
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13
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Kahleova H, Brennan H, Znayenko-Miller T, Holubkov R, Barnard ND. Does diet quality matter? A secondary analysis of a randomized clinical trial. Eur J Clin Nutr 2024; 78:270-273. [PMID: 38012413 DOI: 10.1038/s41430-023-01371-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 10/31/2023] [Accepted: 11/09/2023] [Indexed: 11/29/2023]
Abstract
This secondary analysis assessed the association of a plant-based index (PDI), healthful (hPDI), and unhealthful (uPDI), with weight loss in overweight adults. Participants (n = 244) were randomly assigned to a vegan (n = 122) or control group (n = 122) for 16 weeks. Three-day dietary records were analyzed and PDI indices were calculated. A repeated measure ANOVA was used for statistical analysis. All three scores increased in the vegan group; the effect sizes were: PDI +10.6 (95% CI +8.6 to +12.6; p < 0.001); hPDI +10.9 (95% CI +8.4 to +13.4; p < 0.001); and uPDI +5.4 (95% CI +3.4 to +7.4; p < 0.001). The change in all three scores significantly correlated with change in body weight: PDI (r = -0.40; p < 0.001); hPDI (r = -0.37; p < 0.001); and uPDI (r = -0.21; p = 0.002). These findings suggest that minimizing the consumption of animal products and oil may be an effective weight loss strategy in overweight adults. ClinicalTrials.gov number, NCT02939638.
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Affiliation(s)
- Hana Kahleova
- Physicians Committee for Responsible Medicine, Washington, DC, USA.
| | - Haley Brennan
- Physicians Committee for Responsible Medicine, Washington, DC, USA
| | | | | | - Neal D Barnard
- Physicians Committee for Responsible Medicine, Washington, DC, USA
- Adjunct faculty, George Washington University School of Medicine and Health Sciences, Washington, DC, USA
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Lützhøft DO, Bækgård C, Wimborne E, Straarup EM, Pedersen KM, Swann JR, Pedersen HD, Kristensen K, Morgills L, Nielsen DS, Hansen AK, Bracken MK, Cirera S, Christoffersen BØ. High fat diet is associated with gut microbiota dysbiosis and decreased gut microbial derived metabolites related to metabolic health in young Göttingen Minipigs. PLoS One 2024; 19:e0298602. [PMID: 38427692 PMCID: PMC10906878 DOI: 10.1371/journal.pone.0298602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 01/26/2024] [Indexed: 03/03/2024] Open
Abstract
The objectives were 1) to characterize a Göttingen Minipig model of metabolic syndrome regarding its colon microbiota and circulating microbial products, and 2) to assess whether ovariectomized female and castrated male minipigs show similar phenotypes. Twenty-four nine-week-old Göttingen Minipigs were allocated to four groups based on sex and diet: ovariectomized females and castrated males fed either chow or high-fat diet (HFD) for 12 weeks. At study end, body composition and plasma biomarkers were measured, and a mixed meal tolerance test (MMT) and an intravenous glucose tolerance test (IVGTT) were performed. The HFD groups had significantly higher weight gain, fat percentage, fasting plasma insulin and glucagon compared to the chow groups. Homeostatic model assessment of insulin resistance index (HOMA-IR) was increased and glucose effectiveness derived from the IVGTT and Matsuda´s insulin sensitivity index from the MMT were decreased in the HFD groups. The HFD groups displayed dyslipidemia, with significantly increased total-, LDL- and HDL-cholesterol, and decreased HDL/non-HDL cholesterol ratio. The colon microbiota of HFD minipigs clearly differed from the lean controls (GuniFrac distance matrix). The main bacteria families driving this separation were Clostridiaceae, Fibrobacteraceae, Flavobacteriaceae and Porphyromonadaceae. Moreover, the species richness was significantly decreased by HFD. In addition, HFD decreased the circulating level of short chain fatty acids and beneficial microbial metabolites hippuric acid, xanthine and trigonelline, while increasing the level of branched chain amino acids. Six and nine metabolically relevant genes were differentially expressed between chow-fed and HFD-fed animals in liver and omental adipose tissue, respectively. The HFD-fed pigs presented with metabolic syndrome, gut microbial dysbiosis and a marked decrease in healthy gut microbial products and thus displayed marked parallels to human obesity and insulin resistance. HFD-fed Göttingen Minipig therefore represents a relevant animal model for studying host-microbiota interactions. No significant differences between the castrated and ovariectomized minipigs were observed.
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Affiliation(s)
- Ditte Olsen Lützhøft
- Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Frederiksberg C, Denmark
| | - Cecilie Bækgård
- Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Frederiksberg C, Denmark
| | - Elizabeth Wimborne
- School of Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
| | | | | | - Jonathan R. Swann
- School of Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
| | | | | | | | - Dennis Sandris Nielsen
- Department of Food Science, Faculty of Science, University of Copenhagen, Frederiksberg C, Denmark
| | - Axel Kornerup Hansen
- Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Frederiksberg C, Denmark
| | | | - Susanna Cirera
- Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Frederiksberg C, Denmark
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15
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Cheng W, Lin X, Wang T, Zhang X, Xu F, Wang L, Wang M, Zhang M, Xia T, Zhang D, Qian S, Yang W, Hu A, Tang M, Hu X, Wang Y, Zhao Q. Healthy plant-based diet might be inversely associated with gastric precancerous lesions: new evidence from a case-control study based on dietary pattern and fecal metabolic profiling. Int J Food Sci Nutr 2024; 75:102-118. [PMID: 37941094 DOI: 10.1080/09637486.2023.2279916] [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: 07/03/2023] [Accepted: 11/01/2023] [Indexed: 11/10/2023]
Abstract
Preventing the progression of gastric precancerous lesions (GPLs) can reduce the morbidity and mortality of gastric cancer (GC). The preventive effect of a plant-based diet on cancers has been widely recognised. In this case-control study, 1,130 subjects were included using 1:1 propensity score matching for age and sex. Dietary habits, anthropometry and sample collection were conducted using standard and effective methods. Plant-based diet indices (PDIs) were calculated using a previously reported method. Faecal samples were analysed by untargeted metabolomics. Our study found that adherence to a healthy plant-based diet was inversely associated with the occurrence of GPLs. Metabolomic analysis identified six different metabolites correlated with GPLs, among which luteolin-related metabolites may be used as biomarkers of the association between PDIs and GPLs. In addition, the difference in N-acyl amides found in PDIs needs further verification. Our findings suggest that a healthy plant-based diet may have a protective effect against GPLs.
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Affiliation(s)
- Wenli Cheng
- Department of Nutrition and Food Hygiene, School of Public Health, Anhui Medical University, Hefei, China
| | - Xiao Lin
- Department of Nutrition and Food Hygiene, School of Public Health, Anhui Medical University, Hefei, China
| | - Tingting Wang
- Department of Nutrition and Food Hygiene, School of Public Health, Anhui Medical University, Hefei, China
| | - Xiaohui Zhang
- Department of Nutrition and Food Hygiene, School of Public Health, Anhui Medical University, Hefei, China
| | - Fang Xu
- Department of Nutrition and Food Hygiene, School of Public Health, Anhui Medical University, Hefei, China
| | - Li Wang
- Department of Nutrition and Food Hygiene, School of Public Health, Anhui Medical University, Hefei, China
| | - Min Wang
- Department of Nutrition and Food Hygiene, School of Public Health, Anhui Medical University, Hefei, China
| | - Meng Zhang
- Department of Nutrition and Food Hygiene, School of Public Health, Anhui Medical University, Hefei, China
| | - Tao Xia
- Department of Gastroenterology, Lujiang County People's Hospital, Hefei, China
| | - Daoming Zhang
- Department of Gastroenterology, Lujiang County People's Hospital, Hefei, China
| | - Shiqing Qian
- Department of Pathology, Lujiang County People's Hospital, Hefei, China
| | - Wanshui Yang
- Department of Nutrition and Food Hygiene, School of Public Health, Anhui Medical University, Hefei, China
| | - Anla Hu
- Department of Nutrition and Food Hygiene, School of Public Health, Anhui Medical University, Hefei, China
| | - Min Tang
- Department of Gastroenterology and Hepatology, the Fourth Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Xiangpeng Hu
- Department of Gastroenterology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yalei Wang
- Department of Gastroenterology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Qihong Zhao
- Department of Nutrition and Food Hygiene, School of Public Health, Anhui Medical University, Hefei, China
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16
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Playdon MC, Tinker LF, Prentice RL, Loftfield E, Hayden KM, Van Horn L, Sampson JN, Stolzenberg-Solomon R, Lampe JW, Neuhouser ML, Moore SC. Measuring diet by metabolomics: a 14-d controlled feeding study of weighed food intake. Am J Clin Nutr 2024; 119:511-526. [PMID: 38212160 PMCID: PMC10884612 DOI: 10.1016/j.ajcnut.2023.10.016] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 09/12/2023] [Accepted: 10/11/2023] [Indexed: 01/13/2024] Open
Abstract
BACKGROUND Metabolomics has the potential to enhance dietary assessment by revealing objective measures of many aspects of human food intake. Although metabolomics studies indicate that hundreds of metabolites are associated with dietary intake, correlations have been modest (e.g., r < 0.50), and few have been evaluated in controlled feeding studies. OBJECTIVES The aim of this study was to evaluate associations between metabolites and weighed food and beverage intake in a controlled feeding study of habitual diet. METHODS Healthy postmenopausal females from the Women's Health Initiative (N = 153) were provided with a customized 2-wk controlled diet designed to emulate their usual diet. Metabolites were measured by liquid chromatography tandem mass spectrometry in end-of-study 24-h urine and fasting serum samples (1293 urine metabolites; 1113 serum metabolites). We calculated partial Pearson correlations between these metabolites and intake of 65 food groups, beverages, and supplements during the feeding study. The threshold for significance was Bonferroni-adjusted to account for multiple testing (5.94 × 10-07 for urine metabolites; 6.91 × 10-07 for serum metabolites). RESULTS Significant diet-metabolite correlations were identified for 23 distinct foods, beverages, and supplements (171 distinct metabolites). Among foods, strong metabolite correlations (r ≥ 0.60) were evident for citrus (highest r = 0.80), dairy (r = 0.65), and broccoli (r = 0.63). Among beverages and supplements, strong correlations were evident for coffee (r = 0.86), alcohol (r = 0.69), multivitamins (r = 0.69), and vitamin E supplements (r = 0.65). Moderate correlations (r = 0.50-0.60) were also observed for avocado, fish, garlic, grains, onion, poultry, and black tea. Correlations were specific; each metabolite correlated with one food, beverage, or supplement, except for metabolites correlated with juice or multivitamins. CONCLUSIONS Metabolite levels had moderate to strong correlations with weighed intake of habitually consumed foods, beverages, and supplements. These findings exceed in magnitude those previously observed in population studies and exemplify the strong potential of metabolomics to contribute to nutrition research. The Women's Health Initiative is registered at clinicaltrials.gov as NCT00000611.
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Affiliation(s)
- Mary C Playdon
- Department of Nutrition and Integrative Physiology, University of Utah, Salt Lake City, UT; Department of Population Health Sciences, University of Utah, Salt Lake City, UT; Cancer Control and Population Sciences Division, Huntsman Cancer Institute, Salt Lake City, UT; Division of Cancer Epidemiology and Genetics, National Cancer institute, Rockville, MD
| | - Lesley F Tinker
- Cancer Prevention Program, Division of Public Health Sciences, Fred Hutchinson Cancer Center and University of Washington, Seattle, WA
| | - Ross L Prentice
- Cancer Prevention Program, Division of Public Health Sciences, Fred Hutchinson Cancer Center and University of Washington, Seattle, WA
| | - Erikka Loftfield
- Division of Cancer Epidemiology and Genetics, National Cancer institute, Rockville, MD
| | - Kathleen M Hayden
- School of Medicine, Wake Forest Baptist Medical Center, Winston-Salem, NC
| | - Linda Van Horn
- Feinberg School of Medicine, Northwestern University, Chicago IL
| | - Joshua N Sampson
- Division of Cancer Epidemiology and Genetics, National Cancer institute, Rockville, MD
| | | | - Johanna W Lampe
- Cancer Prevention Program, Division of Public Health Sciences, Fred Hutchinson Cancer Center and University of Washington, Seattle, WA
| | - Marian L Neuhouser
- Cancer Prevention Program, Division of Public Health Sciences, Fred Hutchinson Cancer Center and University of Washington, Seattle, WA
| | - Steven C Moore
- Division of Cancer Epidemiology and Genetics, National Cancer institute, Rockville, MD.
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17
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Zheng Y, Gao N, Li Y, Fan M, Tian W, Jiang Y, Wang Y, Cui M, Suo C, Zhang T, Jin L, Xu K, Chen X. Unraveling the role of serum metabolites in the relationship between plant-based diets and bone health in community-dwelling older adults. Curr Res Food Sci 2024; 8:100687. [PMID: 38318314 PMCID: PMC10839558 DOI: 10.1016/j.crfs.2024.100687] [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: 12/20/2023] [Revised: 01/20/2024] [Accepted: 01/22/2024] [Indexed: 02/07/2024] Open
Abstract
The potential adverse effects of the plant-based dietary pattern on bone health have received widespread attention. However, the biological mechanisms underlying the adverse effects of plant-based diets on bone health remain incompletely understood. The objective of this study was to identify potential biomarkers between plant-based diets and bone loss utilizing metabolomic techniques in the Taizhou Imaging Study (TIS) (N = 788). Plant-based diet indexes (overall plant-based diet index (PDI), healthy plant-based diet index (hPDI), and unhealthy plant-based diet index (uPDI)) were calculated using the food frequency questionnaire, and bone mineral density (BMD) was measured using dual-energy X-ray absorptiometry. A multinomial logistic regression was used to explore the associations of plant-based diet indexes with bone loss. Furthermore, mediation analysis and exploratory factor analysis (EFA) were performed to explore the mediated effects of metabolites on the association of plant-based diets with BMD T-score. Our results showed that higher hPDI and uPDI were positively associated with bone loss. Moreover, nineteen metabolites were significantly associated with BMD T-score, among them, seven metabolites were associated with uPDI. Except for cholesterol esters in VLDL-1, the remaining six metabolites significantly mediated the negative association between uPDI and BMD T-score. Interestingly, we observed that the same six metabolites mediated the positive association between fresh fruit and BMD T-score. Collectively, our results support the deleterious effects of plant-based diets on bone health and discover the potential mediation effect of metabolites on the association of plant-based diets with bone loss. The findings offer valuable insights that could optimize dietary recommendations and interventions, contributing to alleviate the potential adverse effects associated with plant-based diets.
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Affiliation(s)
- Yi Zheng
- State Key Laboratory of Genetic Engineering, Zhangjiang Fudan International Innovation Center, School of Life Sciences, Human Phenome Institute, Fudan University, Shanghai, China
| | - Ningxin Gao
- Department of Biostatistics, School of Public Health, The Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai, China
| | - Yucan Li
- State Key Laboratory of Genetic Engineering, Zhangjiang Fudan International Innovation Center, School of Life Sciences, Human Phenome Institute, Fudan University, Shanghai, China
| | - Min Fan
- Taixing Disease Control and Prevention Center, Taizhou, Jiangsu, China
| | - Weizhong Tian
- Taizhou People's Hospital Affiliated to Nantong University, Taizhou, Jiangsu, China
| | - Yanfeng Jiang
- State Key Laboratory of Genetic Engineering, Zhangjiang Fudan International Innovation Center, School of Life Sciences, Human Phenome Institute, Fudan University, Shanghai, China
- Fudan University Taizhou Institute of Health Sciences, Taizhou, Jiangsu, China
| | - Yingzhe Wang
- State Key Laboratory of Genetic Engineering, Zhangjiang Fudan International Innovation Center, School of Life Sciences, Human Phenome Institute, Fudan University, Shanghai, China
- Fudan University Taizhou Institute of Health Sciences, Taizhou, Jiangsu, China
| | - Mei Cui
- Fudan University Taizhou Institute of Health Sciences, Taizhou, Jiangsu, China
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - Chen Suo
- Department of Biostatistics, School of Public Health, The Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai, China
- Fudan University Taizhou Institute of Health Sciences, Taizhou, Jiangsu, China
| | - Tiejun Zhang
- Department of Biostatistics, School of Public Health, The Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai, China
- Fudan University Taizhou Institute of Health Sciences, Taizhou, Jiangsu, China
| | - Li Jin
- State Key Laboratory of Genetic Engineering, Zhangjiang Fudan International Innovation Center, School of Life Sciences, Human Phenome Institute, Fudan University, Shanghai, China
- Fudan University Taizhou Institute of Health Sciences, Taizhou, Jiangsu, China
| | - Kelin Xu
- Department of Biostatistics, School of Public Health, The Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai, China
- Fudan University Taizhou Institute of Health Sciences, Taizhou, Jiangsu, China
| | - Xingdong Chen
- State Key Laboratory of Genetic Engineering, Zhangjiang Fudan International Innovation Center, School of Life Sciences, Human Phenome Institute, Fudan University, Shanghai, China
- Fudan University Taizhou Institute of Health Sciences, Taizhou, Jiangsu, China
- National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
- Yiwu Research Institute of Fudan University, Yiwu, Zhejiang, China
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18
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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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19
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Lee DH, Jin Q, Shi N, Wang F, Bever AM, Liang L, Hu FB, Song M, Zeleznik OA, Zhang X, Joshi A, Wu K, Jeon JY, Meyerhardt JA, Chan AT, Eliassen AH, Clish C, Clinton SK, Giovannucci EL, Li J, Tabung FK. The metabolic potential of inflammatory and insulinaemic dietary patterns and risk of type 2 diabetes. Diabetologia 2024; 67:88-101. [PMID: 37816982 DOI: 10.1007/s00125-023-06021-3] [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: 04/08/2023] [Accepted: 08/31/2023] [Indexed: 10/12/2023]
Abstract
AIMS/HYPOTHESIS Diets with higher inflammatory and insulinaemic potential have been associated with an increased risk of type 2 diabetes. However, it remains unknown whether plasma metabolomic profiles related to proinflammatory/hyperinsulinaemic diets and to inflammatory/insulin biomarkers are associated with type 2 diabetes risk. METHODS We analysed 6840 participants from the Nurses' Health Study and Health Professionals Follow-up Study to identify the plasma metabolome related to empirical dietary inflammatory pattern (EDIP), empirical dietary index for hyperinsulinemia (EDIH), four circulating inflammatory biomarkers and C-peptide. Dietary intakes were assessed using validated food frequency questionnaires. Plasma metabolomic profiling was conducted by LC-MS/MS. Metabolomic signatures were derived using elastic net regression. Multivariable Cox regression was used to examine associations of the metabolomic profiles with type 2 diabetes risk. RESULTS We identified 27 metabolites commonly associated with both EDIP and inflammatory biomarker z score and 21 commonly associated with both EDIH and C-peptide. Higher metabolomic dietary inflammatory potential (MDIP), reflecting higher metabolic potential of both an inflammatory dietary pattern and circulating inflammatory biomarkers, was associated with higher type 2 diabetes risk. The HR comparing highest vs lowest quartiles of MDIP was 3.26 (95% CI 2.39, 4.44). We observed a strong positive association with type 2 diabetes risk for the metabolomic signature associated with EDIP-only (HR 3.75; 95% CI 2.71, 5.17) or inflammatory biomarkers-only (HR 4.07; 95% CI 2.91, 5.69). In addition, higher metabolomic dietary index for hyperinsulinaemia (MDIH), reflecting higher metabolic potential of both an insulinaemic dietary pattern and circulating C-peptide, was associated with greater type 2 diabetes risk (HR 3.00; 95% CI 2.22, 4.06); further associations with type 2 diabetes were HR 2.79 (95% CI 2.07, 3.76) for EDIH-only signature and HR 3.89 (95% CI 2.82, 5.35) for C-peptide-only signature. The diet scores were significantly associated with risk, although adjustment for the corresponding metabolomic signature scores attenuated the associations with type 2 diabetes, these remained significant. CONCLUSIONS/INTERPRETATION The metabolomic signatures reflecting proinflammatory or hyperinsulinaemic diets and related biomarkers were positively associated with type 2 diabetes risk, supporting that these dietary patterns may influence type 2 diabetes risk via the regulation of metabolism.
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Affiliation(s)
- Dong Hoon Lee
- Department of Sport Industry Studies, Yonsei University, Seoul, Republic of Korea
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Qi Jin
- Department of Exercise and Nutrition Sciences, Moyes College of Education, Weber State University, Ogden, UT, USA
- Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA
- Interdisciplinary Ph.D. Program in Nutrition, The Ohio State University, Columbus, OH, USA
| | - Ni Shi
- Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA
- Division of Medical Oncology, Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus, OH, USA
| | - Fenglei Wang
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Alaina M Bever
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School, 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
| | - 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
| | - Mingyang Song
- 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
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Oana A Zeleznik
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Xuehong Zhang
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Amit Joshi
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Kana Wu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Justin Y Jeon
- Department of Sport Industry Studies, Yonsei University, Seoul, Republic of Korea
- Cancer Prevention Center, Yonsei Cancer Center, Seoul, Republic of Korea
| | - Jeffrey A Meyerhardt
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
| | - Andrew T Chan
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - 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
| | - Clary Clish
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Steven K Clinton
- Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA
- Interdisciplinary Ph.D. Program in Nutrition, The Ohio State University, Columbus, OH, USA
- Division of Medical Oncology, Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus, OH, USA
| | - Edward L Giovannucci
- 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
| | - 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
| | - Fred K Tabung
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA.
- Interdisciplinary Ph.D. Program in Nutrition, The Ohio State University, Columbus, OH, USA.
- Division of Medical Oncology, Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus, OH, USA.
- Division of Epidemiology, College of Public Health, The Ohio State University, Columbus, OH, USA.
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20
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Thompson AS, Candussi CJ, Tresserra-Rimbau A, Jennings A, Bondonno NP, Hill C, Sowah SA, Cassidy A, Kühn T. A healthful plant-based diet is associated with lower type 2 diabetes risk via improved metabolic state and organ function: A prospective cohort study. DIABETES & METABOLISM 2024; 50:101499. [PMID: 38036055 DOI: 10.1016/j.diabet.2023.101499] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 11/15/2023] [Accepted: 11/21/2023] [Indexed: 12/02/2023]
Abstract
BACKGROUND Plant-based diets are becoming increasingly popular due to favourable environmental footprints and have been associated with lower risk of type 2 diabetes mellitus (T2DM). Here, we investigated the potential mechanisms to explain the lower T2DM risk observed among individuals following plant-based diets. METHODS Prospective data from the UK Biobank, a cohort study of participants aged 40 to 69 years at baseline, was evaluated. Associations between healthful and unhealthful plant-based indices (hPDI and uPDI) and T2DM risk were analysed by multivariable Cox regression models, followed by causal mediation analyses to investigate which cardiometabolic risk factors explained the observed associations. RESULTS Of 113,097 study participants 2,628 developed T2DM over 12 years of follow-up. Participants with the highest hPDI scores (Quartile 4) had a 24 % lower T2DM risk compared to those with the lowest scores (Quartile 1) [Hazard Ratio (HR): 0.76, 95 % Confidence Interval (CI): 0.68-0.85]. This association was mediated by a lower BMI (proportion mediated: 28 %), lower waist circumference (28 %), and lower concentrations of HBA1c (11 %), triglycerides (9 %), alanine aminotransferase (5 %), gamma glutamyl transferase (4 %), C-reactive protein (4 %), insulin-like growth factor 1 (4 %), cystatin C (4 %) and urate (4 %). Higher uPDI scores were associated with a 37 % higher T2DM risk [HR: 1.37, 95 % CI:1.22- 1.53], with higher waist circumference (proportion mediated: 17 %), BMI (7 %), and higher concentrations of triglycerides (13 %) potentially playing mediating roles. CONCLUSION Healthful plant-based diets may protect against T2DM via lower body fatness, but also via normoglycaemia, lower basal inflammation as well as improved kidney and liver function.
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Affiliation(s)
- Alysha S Thompson
- The Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Northern Ireland, United Kingdom
| | - Catharina J Candussi
- Department of Nutritional Sciences, University of Vienna, Vienna, Austria; Center for Public Health, Medical University of Vienna, Vienna, Austria
| | - Anna Tresserra-Rimbau
- The Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Northern Ireland, United Kingdom; Department of Nutrition, Food Science and Gastronomy, XIA, School of Pharmacy and Food Sciences, INSA, University of Barcelona, 08921 Barcelona, Spain; Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Institute of Health Carlos III, 28029 Madrid, Spain
| | - Amy Jennings
- The Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Northern Ireland, United Kingdom
| | - Nicola P Bondonno
- The Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Northern Ireland, United Kingdom; Danish Cancer Institute, Copenhagen, Denmark; Nutrition and Health Innovation Research Institute, School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia
| | - Claire Hill
- Centre for Public Health, Queen's University Belfast, Belfast, United Kingdom
| | - Solomon A Sowah
- Medical Research Council Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Aedín Cassidy
- The Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Northern Ireland, United Kingdom.
| | - Tilman Kühn
- The Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Northern Ireland, United Kingdom; Department of Nutritional Sciences, University of Vienna, Vienna, Austria; Center for Public Health, Medical University of Vienna, Vienna, Austria.
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21
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Li M, Yang K, De Vivo I, Eliassen AH, Qureshi AA, Nan H, Han J. Association between plasma L-carnitine levels and mitochondrial DNA copy number. BMC Mol Cell Biol 2023; 24:35. [PMID: 38082229 PMCID: PMC10712069 DOI: 10.1186/s12860-023-00496-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Accepted: 12/06/2023] [Indexed: 12/18/2023] Open
Abstract
Mitochondria are key cytoplasmic organelles in eukaryotic cells that generate adenosine triphosphate (ATP) through the electron transport chain and oxidative phosphorylation. Mitochondrial DNA (mtDNA) copy number (mtDNAcn) is considered a biomarker for both mitochondrial quantity and function as well as cellular oxidative stress level. Previous epidemiologic findings revealed that weight gain, higher body mass index (BMI), smoking, and high insulinemic potential of lifestyle were associated with lower leukocyte mtDNAcn. Carnitines are a group of compounds that play a critical role in energy production. We quantified the associations of plasma L-carnitine levels with leukocyte mtDNAcn. We then examined the association between mtDNAcn and L-carnitine (HMDB0000062) in 538 U.S. men without cancers, diabetes, or cardiovascular disease at blood collection from the Health Professionals Follow-Up Study (HPFS). We found a significant inverse association between L-carnitine and mtDNAcn (ρ = -0.1, P = 0.02). This implies that the carnitine metabolic pathway may be associated with mitochondrial function and oxidative stress.
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Affiliation(s)
- Mingyue Li
- Department of Epidemiology, Richard M. Fairbanks School of Public Health, Indiana University, 1050 Wishard Boulevard, RG 6124, Indianapolis, IN, 46202-2872, USA
| | - Keming Yang
- Department of Medicine, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
- Clinical and Translational Epidemiology Unit, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Immaculata De Vivo
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA
- Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, MA, USA
| | - A Heather Eliassen
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA
- Department of Nutrition, Harvard School of Public Health, Boston, MA, USA
| | - Abrar A Qureshi
- Warren Alpert Medical School, Brown University, Providence, RI, USA
| | - Hongmei Nan
- Department of Epidemiology, Richard M. Fairbanks School of Public Health, Indiana University, 1050 Wishard Boulevard, RG 6124, Indianapolis, IN, 46202-2872, USA
- Department of Global Health, Richard M. Fairbanks School of Public Health, Indiana University, Indianapolis, IN, USA
| | - Jiali Han
- Department of Epidemiology, Richard M. Fairbanks School of Public Health, Indiana University, 1050 Wishard Boulevard, RG 6124, Indianapolis, IN, 46202-2872, USA.
- Department of Global Health, Richard M. Fairbanks School of Public Health, Indiana University, Indianapolis, IN, USA.
- Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indianapolis, IN, USA.
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22
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Kortesniemi M, Noerman S, Kårlund A, Raita J, Meuronen T, Koistinen V, Landberg R, Hanhineva K. Nutritional metabolomics: Recent developments and future needs. Curr Opin Chem Biol 2023; 77:102400. [PMID: 37804582 DOI: 10.1016/j.cbpa.2023.102400] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 08/21/2023] [Accepted: 09/07/2023] [Indexed: 10/09/2023]
Abstract
Metabolomics has rapidly been adopted as one of the key methods in nutrition research. This review focuses on the recent developments and updates in the field, including the analytical methodologies that encompass improved instrument sensitivity, sampling techniques and data integration (multiomics). Metabolomics has advanced the discovery and validation of dietary biomarkers and their implementation in health research. Metabolomics has come to play an important role in the understanding of the role of small molecules resulting from the diet-microbiota interactions when gut microbiota research has shifted towards improving the understanding of the activity and functionality of gut microbiota rather than composition alone. Currently, metabolomics plays an emerging role in precision nutrition and the recent developments therein are discussed.
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Affiliation(s)
- Maaria Kortesniemi
- Food Sciences Unit, Department of Life Technologies, University of Turku, FI-20014 Turun yliopisto, Finland.
| | - Stefania Noerman
- Division of Food and Nutrition Science, Department of Life Sciences, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
| | - Anna Kårlund
- Food Sciences Unit, Department of Life Technologies, University of Turku, FI-20014 Turun yliopisto, Finland
| | - Jasmin Raita
- Food Sciences Unit, Department of Life Technologies, University of Turku, FI-20014 Turun yliopisto, Finland
| | - Topi Meuronen
- Food Sciences Unit, Department of Life Technologies, University of Turku, FI-20014 Turun yliopisto, Finland
| | - Ville Koistinen
- Food Sciences Unit, Department of Life Technologies, University of Turku, FI-20014 Turun yliopisto, Finland; Institute of Public Health and Clinical Nutrition, School of Medicine, University of Eastern Finland, FI-70211 Kuopio, Finland
| | - Rikard Landberg
- Division of Food and Nutrition Science, Department of Life Sciences, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
| | - Kati Hanhineva
- Food Sciences Unit, Department of Life Technologies, University of Turku, FI-20014 Turun yliopisto, Finland; Institute of Public Health and Clinical Nutrition, School of Medicine, University of Eastern Finland, FI-70211 Kuopio, Finland
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23
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Deng K, Gupta DK, Shu XO, Lipworth L, Zheng W, Thomas VE, Cai H, Cai Q, Wang TJ, Yu D. Metabolite Signature of Life's Essential 8 and Risk of Coronary Heart Disease Among Low-Income Black and White Americans. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2023; 16:e004230. [PMID: 38014580 PMCID: PMC10843634 DOI: 10.1161/circgen.123.004230] [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: 05/15/2023] [Accepted: 09/26/2023] [Indexed: 11/29/2023]
Abstract
BACKGROUND Life's essential 8 (LE8) is a comprehensive construct of cardiovascular health. Yet, little is known about the LE8 score, its metabolic correlates, and their predictive implications among Black Americans and low-income individuals. METHODS In a nested case-control study of coronary heart disease (CHD) among 299 pairs of Black and 298 pairs of White low-income Americans from the Southern Community Cohort Study, we estimated LE8 score and applied untargeted plasma metabolomics and elastic net with leave-one-out cross-validation to identify metabolite signature (MetaSig) of LE8. Associations of LE8 score and MetaSig with incident CHD were examined using conditional logistic regression. The mediation effect of MetaSig on the LE8-CHD association was also examined. The external validity of MetaSig was evaluated in another nested CHD case-control study among 299 pairs of Chinese adults. RESULTS Higher LE8 score was associated with lower CHD risk (standardized odds ratio, 0.61 [95% CI, 0.53-0.69]). The MetaSig, consisting of 133 metabolites, showed significant correlation with LE8 score (r=0.61) and inverse association with CHD (odds ratio, 0.57 [0.49-0.65]), robust to adjustment for LE8 score and across participants with different sociodemographic and health status ([odds ratios, 0.42-0.69]; all P<0.05). MetaSig mediated a large portion of the LE8-CHD association: 53% (32%-80%). Significant associations of MetaSig with LE8 score and CHD risk were found in validation cohort (r=0.49; odds ratio, 0.57 [0.46-0.69]). CONCLUSIONS Higher LE8 score and its MetaSig were associated with lower CHD risk among low-income Black and White Americans. Metabolomics may offer an objective measure of LE8 and its metabolic phenotype relevant to CHD prevention among diverse populations.
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Affiliation(s)
- Kui Deng
- Vanderbilt Epidemiology Center and Division of Epidemiology, Dept of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Deepak K. Gupta
- Vanderbilt Translational & Clinical Cardiovascular Research Center & Division of Cardiovascular Medicine, Dept of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Xiao-Ou Shu
- Vanderbilt Epidemiology Center and Division of Epidemiology, Dept of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Loren Lipworth
- Vanderbilt Epidemiology Center and Division of Epidemiology, Dept of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Wei Zheng
- Vanderbilt Epidemiology Center and Division of Epidemiology, Dept of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Victoria E. Thomas
- Vanderbilt Translational & Clinical Cardiovascular Research Center & Division of Cardiovascular Medicine, Dept of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Hui Cai
- Vanderbilt Epidemiology Center and Division of Epidemiology, Dept of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Qiuyin Cai
- Vanderbilt Epidemiology Center and Division of Epidemiology, Dept of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Thomas J. Wang
- Dept of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX
| | - Danxia Yu
- Vanderbilt Epidemiology Center and Division of Epidemiology, Dept of Medicine, Vanderbilt University Medical Center, Nashville, TN
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Acosta PFC, Landon OA, Ribau ZJ, Haines J, Ma DWL, Duncan AM. Plant-Based Dietary Indices in Relation to Nutrient and Food Group Intakes in Preschool-Aged Children. Nutrients 2023; 15:4617. [PMID: 37960273 PMCID: PMC10647719 DOI: 10.3390/nu15214617] [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: 10/02/2023] [Revised: 10/23/2023] [Accepted: 10/24/2023] [Indexed: 11/15/2023] Open
Abstract
Dietary guidance promotes plant-based foods, yet minimal research has examined intake in children. This study examined plant-based food intake in preschool-aged children using plant-based dietary index (PDI) metrics and related these metrics to nutrient and food group intakes. Dietary data were collected from preschool-aged children (n = 283, 3.45 ± 1.22 years) from the Guelph Family Health Study at baseline using the Automated Self-Administered 24-Hour Dietary Assessment Tool. Food intake servings were assigned to 16 food groups for calculation of overall PDI (oPDI), healthful PDI (hPDI), and less healthful (lhPDI) scores and summarized into tertiles for energy-adjusted comparisons. For oPDI, participants in the highest vs. lowest tertile had higher intakes of nutrients and food groups to encourage (e.g., dietary fiber, fruits) as well as lower intakes of nutrients to encourage (e.g., calcium, vitamin D). For hPDI, participants in the highest vs. lowest tertile had higher intakes of nutrients and food groups to encourage and lower intakes of those to limit (e.g., saturated fat, sweets and desserts). For lhPDI, participants in the highest vs. lowest tertile had higher intakes of nutrients and food groups to limit and lower intakes of those to encourage. These results can inform dietetic practice for dietary guidance that promotes plant-based foods in children.
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Affiliation(s)
- Patricia F. C. Acosta
- Department of Human Health and Nutritional Sciences, University of Guelph, Guelph, ON N1G 2W1, Canada; (P.F.C.A.)
| | - Olivia A. Landon
- Department of Human Health and Nutritional Sciences, University of Guelph, Guelph, ON N1G 2W1, Canada; (P.F.C.A.)
| | - Zachary J. Ribau
- Department of Human Health and Nutritional Sciences, University of Guelph, Guelph, ON N1G 2W1, Canada; (P.F.C.A.)
| | - Jess Haines
- Department of Family Relations and Applied Nutrition, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - David W. L. Ma
- Department of Human Health and Nutritional Sciences, University of Guelph, Guelph, ON N1G 2W1, Canada; (P.F.C.A.)
| | - Alison M. Duncan
- Department of Human Health and Nutritional Sciences, University of Guelph, Guelph, ON N1G 2W1, Canada; (P.F.C.A.)
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25
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Wang Y, Liu B, Han H, Hu Y, Zhu L, Rimm EB, Hu FB, Sun Q. Associations between plant-based dietary patterns and risks of type 2 diabetes, cardiovascular disease, cancer, and mortality - a systematic review and meta-analysis. Nutr J 2023; 22:46. [PMID: 37789346 PMCID: PMC10548756 DOI: 10.1186/s12937-023-00877-2] [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: 05/24/2023] [Accepted: 09/21/2023] [Indexed: 10/05/2023] Open
Abstract
BACKGROUND Plant-based dietary patterns are gaining more attention due to their potential in reducing the risk of developing major chronic diseases, including type 2 diabetes (T2D), cardiovascular disease (CVD), cancer, and mortality, while an up-to-date comprehensive quantitative review is lacking. This study aimed to summarize the existing prospective observational evidence on associations between adherence to plant-based dietary patterns and chronic disease outcomes. METHODS We conducted a systematic review and meta-analysis of evidence across prospective observational studies. The data sources used were PubMed and MEDLINE, Embase, Web of Science, and screening of references. We included all prospective observational studies that evaluated the association between adherence to plant-based dietary patterns and incidence of T2D, CVD, cancer, and mortality among adults (≥ 18 years). RESULTS A total of 76 publications were identified, including 2,230,443 participants with 60,718 cases of incident T2D, 157,335 CVD cases, 57,759 cancer cases, and 174,435 deaths. An inverse association was observed between higher adherence to a plant-based dietary pattern and risks of T2D (RR, 0.82 [95% CI: 0.77-0.86]), CVD (0.90 [0.85-0.94]), cancer (0.91 [0.87-0.96]), and all-cause mortality (0.84 [0.78-0.92]) with moderate to high heterogeneity across studies (I2 ranged: 47.8-95.4%). The inverse associations with T2D, CVD and cancer were strengthened when healthy plant-based foods, such as vegetables, fruits, whole grains, and legumes, were emphasized in the definition of plant-based dietary patterns (T2D: 0.79 [0.72-0.87]; CVD: 0.85 [0.80-0.92]; cancer: 0.86 [0.80-0.92]; I2 ranged: 53.1-84.1%). Association for mortality was largely similar when the analyses were restricted to healthy plant-based diets (0.86 [0.80-0.92], I2 = 91.9%). In contrast, unhealthy plant-based diets were positively associated with these disease outcomes. Among four studies that examined changes in dietary patterns, increased adherence to plant-based dietary patterns was associated with a significantly reduced risk of T2D (0.83 [0.71-0.96]; I2 = 71.5%) and a marginally lower risk of mortality (0.95 [0.91-1.00]; I2 = 0%). CONCLUSIONS Better adherence to plant-based dietary patterns, especially those emphasizing healthy plant-based foods, is beneficial for lowering the risks of major chronic conditions, including T2D, CVD, cancer, as well as premature deaths. REGISTRATION OF REVIEW PROTOCOL This review was registered at the PROSPERO International Prospective Register of Systematic Reviews ( https://www.crd.york.ac.uk/PROSPERO/ ) with the registration number CRD42022290202.
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Affiliation(s)
- Yeli Wang
- Department of Nutrition, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA
| | - Binkai Liu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA
| | - Han Han
- Department of Nutrition, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA
- CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Yang Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA
| | - Lu Zhu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA
| | - Eric B Rimm
- Department of Nutrition, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA
- Department of Epidemiology, 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
| | - Frank B Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA
- Department of Epidemiology, 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
| | - Qi Sun
- Department of Nutrition, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA.
- Department of Epidemiology, 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.
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Lanuza F, Meroño T, Zamora-Ros R, Bondonno NP, Rostgaard-Hansen AL, Sánchez-Pla A, Miro B, Carmona-Pontaque F, Riccardi G, Tjønneland A, Landberg R, Halkjær J, Andres-Lacueva C. Plasma metabolomic profiles of plant-based dietary indices reveal potential pathways for metabolic syndrome associations. Atherosclerosis 2023; 382:117285. [PMID: 37778133 DOI: 10.1016/j.atherosclerosis.2023.117285] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 08/24/2023] [Accepted: 09/06/2023] [Indexed: 10/03/2023]
Abstract
BACKGROUND AND AIMS Plant-based dietary patterns have been associated with improved health outcomes. This study aims to describe the metabolomic fingerprints of plant-based diet indices (PDI) and examine their association with metabolic syndrome (MetS) and its components in a Danish population. METHODS The MAX study comprised 676 participants (55% women, aged 18-67 y) from Copenhagen. Sociodemographic and dietary data were collected using questionnaires and three 24-h dietary recalls over one year (at baseline, and at 6 and 12 months). Mean dietary intakes were computed, as well as overall PDI, healthful (hPDI) and unhealthful (uPDI) scores, according to food groups for each plant-based index. Clinical variables were also collected at the same time points in a health examination that included complete blood tests. MetS was defined according to the International Diabetes Federation criteria. Plasma metabolites were measured using a targeted metabolomics approach. Metabolites associated with PDI were selected using random forest models and their relationships with PDIs and MetS were analyzed using generalized linear mixed models. RESULTS The mean prevalence of MetS was 10.8%. High, compared to low, hPDI and uPDI scores were associated with a lower and higher odd of MetS, respectively [odds ratio (95%CI); hPDI: 0.56 (0.43-0.74); uPDI: 1.61 (1.26-2.05)]. Out of 411 quantified plasma metabolites, machine-learning metabolomics fingerprinting revealed 13 metabolites, including food and food-related microbial metabolites, like hypaphorine, indolepropionic acid and lignan-derived enterolactones. These metabolites were associated with all PDIs and were inversely correlated with MetS components (p < 0.05). Furthermore, they had an explainable contribution of 12% and 14% for the association between hPDI or uPDI, respectively, and MetS only among participants with overweight/obesity. CONCLUSIONS Metabolites associated with PDIs were inversely associated with MetS and its components, and may partially explain the effects of plant-based diets on cardiometabolic risk factors.
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Affiliation(s)
- Fabian Lanuza
- Biomarkers and Nutrimetabolomics Laboratory, Department de Nutrició, Ciències de L'Alimentació i Gastronomia, Institut de Recerca en Nutrició i Seguretat Alimentària (INSA-UB), Facultat de Farmàcia i Ciències de L'Alimentació, Universitat de Barcelona (UB), 08028, Barcelona, Spain; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, 28029, Spain
| | - Tomas Meroño
- Biomarkers and Nutrimetabolomics Laboratory, Department de Nutrició, Ciències de L'Alimentació i Gastronomia, Institut de Recerca en Nutrició i Seguretat Alimentària (INSA-UB), Facultat de Farmàcia i Ciències de L'Alimentació, Universitat de Barcelona (UB), 08028, Barcelona, Spain; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, 28029, Spain.
| | - Raul Zamora-Ros
- Biomarkers and Nutrimetabolomics Laboratory, Department de Nutrició, Ciències de L'Alimentació i Gastronomia, Institut de Recerca en Nutrició i Seguretat Alimentària (INSA-UB), Facultat de Farmàcia i Ciències de L'Alimentació, Universitat de Barcelona (UB), 08028, Barcelona, Spain; Unit of Nutrition and Cancer, Cancer Epidemiology Research Program, Catalan Institute of Oncology (ICO), Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain.
| | - Nicola P Bondonno
- Danish Cancer Society Research Center, Strandboulevarden 49, DK 2100, Copenhagen, Denmark; Nutrition and Health Innovation Research Institute, School of Medical and Health Sciences, Edith Cowan University, Perth, Australia
| | | | - Alex Sánchez-Pla
- Statistics and Bioinformatics Research Group, Department of Genetics, Microbiology and Statistics, University of Barcelona, Barcelona, Spain
| | - Berta Miro
- Biomarkers and Nutrimetabolomics Laboratory, Department de Nutrició, Ciències de L'Alimentació i Gastronomia, Institut de Recerca en Nutrició i Seguretat Alimentària (INSA-UB), Facultat de Farmàcia i Ciències de L'Alimentació, Universitat de Barcelona (UB), 08028, Barcelona, Spain; Statistics and Bioinformatics Research Group, Department of Genetics, Microbiology and Statistics, University of Barcelona, Barcelona, Spain
| | - Francesc Carmona-Pontaque
- Statistics and Bioinformatics Research Group, Department of Genetics, Microbiology and Statistics, University of Barcelona, Barcelona, Spain
| | - Gabriele Riccardi
- Department of Clinical Medicine and Surgery, University of Naples Federico II, Naples, Italy
| | - Anne Tjønneland
- Danish Cancer Society Research Center, Strandboulevarden 49, DK 2100, Copenhagen, Denmark
| | - Rikard Landberg
- Department of Biology and Biological Engineering, Division of Food and Nutrition Science, Chalmers University of Technology, Gothenburg, Sweden
| | - Jytte Halkjær
- Danish Cancer Society Research Center, Strandboulevarden 49, DK 2100, Copenhagen, Denmark
| | - Cristina Andres-Lacueva
- Biomarkers and Nutrimetabolomics Laboratory, Department de Nutrició, Ciències de L'Alimentació i Gastronomia, Institut de Recerca en Nutrició i Seguretat Alimentària (INSA-UB), Facultat de Farmàcia i Ciències de L'Alimentació, Universitat de Barcelona (UB), 08028, Barcelona, Spain; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, 28029, Spain
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27
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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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28
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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: 6] [Impact Index Per Article: 6.0] [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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29
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Shi C, Wan Y, He A, Wu X, Shen X, Zhu X, Yang J, Zhou Y. Urinary metabolites associate with the presence of diabetic kidney disease in type 2 diabetes and mediate the effect of inflammation on kidney complication. Acta Diabetol 2023; 60:1199-1207. [PMID: 37184672 PMCID: PMC10359369 DOI: 10.1007/s00592-023-02094-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: 01/05/2023] [Accepted: 04/10/2023] [Indexed: 05/16/2023]
Abstract
AIMS Diabetic kidney disease (DKD) is the one of the leading causes of end-stage kidney disease. Unraveling novel biomarker signatures capable to identify patients with DKD is favorable for tackle the burden. Here, we investigated the possible association between urinary metabolites and the presence of DKD in type 2 diabetes (T2D), and further, whether the associated metabolites improve discrimination of DKD and mediate the effect of inflammation on kidney involvement was evaluated. METHODS Two independent cohorts comprising 192 individuals (92 DKD) were analyzed. Urinary metabolites were analyzed by targeted metabolome profiling and inflammatory cytokine IL-18 were measured by ELISA. Differentially expressed metabolites were selected and mediation analysis was carried out. RESULTS Seven potential metabolite biomarkers (i.e., S-Adenosyl-L-homocysteine, propionic acid, oxoadipic acid, leucine, isovaleric acid, isobutyric acid, and indole-3-carboxylic acid) were identified using the discovery and validation design. In the pooled analysis, propionic acid, oxoadipic acid, leucine, isovaleric acid, isobutyric acid, and indole-3-carboxylic acid were markedly and independently associated with DKD. The composite index of 7 potential metabolite biomarkers (CMI) mediated 32.99% of the significant association between the inflammatory IL-18 and DKD. Adding the metabolite biomarkers improved the discrimination of DKD. CONCLUSIONS In T2D, several associated urinary metabolites were identified to improve the prediction of DKD. Whether interventions aimed at reducing CMI also reduce the risk of DKD especially in patients with high IL-18 needs further investigations.
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Affiliation(s)
- Caifeng Shi
- Center for Kidney Disease, Second Affiliated Hospital of Nanjing Medical University, No. 262 N Zhongshan Road, Nanjing, 210003, Jiangsu, China
| | - Yemeng Wan
- Center for Kidney Disease, Second Affiliated Hospital of Nanjing Medical University, No. 262 N Zhongshan Road, Nanjing, 210003, Jiangsu, China
| | - Aiqin He
- Center for Kidney Disease, Second Affiliated Hospital of Nanjing Medical University, No. 262 N Zhongshan Road, Nanjing, 210003, Jiangsu, China
| | - Xiaomei Wu
- Center for Kidney Disease, Second Affiliated Hospital of Nanjing Medical University, No. 262 N Zhongshan Road, Nanjing, 210003, Jiangsu, China
| | - Xinjia Shen
- Center for Kidney Disease, Second Affiliated Hospital of Nanjing Medical University, No. 262 N Zhongshan Road, Nanjing, 210003, Jiangsu, China
| | - Xueting Zhu
- Center for Kidney Disease, Second Affiliated Hospital of Nanjing Medical University, No. 262 N Zhongshan Road, Nanjing, 210003, Jiangsu, China
| | - Junwei Yang
- Center for Kidney Disease, Second Affiliated Hospital of Nanjing Medical University, No. 262 N Zhongshan Road, Nanjing, 210003, Jiangsu, China.
| | - Yang Zhou
- Center for Kidney Disease, Second Affiliated Hospital of Nanjing Medical University, No. 262 N Zhongshan Road, Nanjing, 210003, Jiangsu, China.
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30
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Sawicki C, Haslam D, Bhupathiraju S. Utilising the precision nutrition toolkit in the path towards precision medicine. Proc Nutr Soc 2023; 82:359-369. [PMID: 37475596 DOI: 10.1017/s0029665123003038] [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] [Indexed: 07/22/2023]
Abstract
The overall aim of precision nutrition is to replace the 'one size fits all' approach to dietary advice with recommendations that are more specific to the individual in order to improve the prevention or management of chronic disease. Interest in precision nutrition has grown with advancements in technologies such as genomics, proteomics, metabolomics and measurement of the gut microbiome. Precision nutrition initiatives have three major applications in precision medicine. First, they aim to provide more 'precision' dietary assessments through artificial intelligence, wearable devices or by employing omic technologies to characterise diet more precisely. Secondly, precision nutrition allows us to understand the underlying mechanisms of how diet influences disease risk and identify individuals who are more susceptible to disease due to gene-diet or microbiota-diet interactions. Third, precision nutrition can be used for 'personalised nutrition' advice where machine-learning algorithms can integrate data from omic profiles with other personal and clinical measures to improve disease risk. Proteomics and metabolomics especially provide the ability to discover new biomarkers of food or nutrient intake, proteomic or metabolomic signatures of diet and disease, and discover potential mechanisms of diet-disease interactions. Although there are several challenges that must be overcome to improve the reproducibility, cost-effectiveness and efficacy of these approaches, precision nutrition methodologies have great potential for nutrition research and clinical application.
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Affiliation(s)
- Caleigh Sawicki
- 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, Boston, MA, USA
| | - Danielle Haslam
- 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, Boston, MA, USA
| | - Shilpa Bhupathiraju
- 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, Boston, MA, USA
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31
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Rosenfeld RM, Juszczak HM, Wong MA. Scoping review of the association of plant-based diet quality with health outcomes. Front Nutr 2023; 10:1211535. [PMID: 37637943 PMCID: PMC10447911 DOI: 10.3389/fnut.2023.1211535] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 07/28/2023] [Indexed: 08/29/2023] Open
Abstract
Introduction The association of plant-based dietary patterns with health outcomes has traditionally been assessed without considering nutritional value. The plant-based dietary index (PDI), first published in 2016, overcomes this limitation with both a healthful PDI (hPDI) and an unhealthful PDI (uPDI), based on the quality of plant foods consumed plus the frequency of animal foods. We sought to summarize the breadth of research using the hPDI and uPDI to gain insight into how the quality of plant-based dietary patterns might be associated with health outcomes. Methods Scoping review of studies that used the PDI, hPDI, or uPDI to report associations with health outcomes. Multiple databases were searched from 2010 through April 2023 with 2 authors independently assessing eligibility and extracting data. In addition to assessing the association of the indices to health outcomes, we determined the frequency of concordant or discordant findings for hPDI versus PDI and for hPDI versus uPDI. Results We included 95 articles (54% longitudinal, 37% cross-sectional, and 9% case-control) with a median sample size of 3,646. Higher hPDI levels were associated with favorable health outcomes in 36% of comparisons (most often for obesity, mortality, diabetes, cardiovascular disease, and psychiatric disorders), compared to 25% for the PDI and only 2% for the uPDI. Conversely, higher levels of the uPDI were associated with unfavorable health outcomes in 33% of comparisons, in contrast to under 1% for the hPDI and 2% for the PDI. When the hPDI association to an outcome was discordant with the uPDI or PDI, the significance and directionality always favored the hPDI over the uPDI, and nearly always favored the hPDI over the PDI. Discussion Dietary indices that account for the quality of plant foods can show health benefits that might be missed by a generic plant-based index. A greater focus on the quality of plant foods could improve nutrition guidelines, raise awareness about the benefits of adding unrefined plant foods to the diet, and empower consumers to make incremental additions of such foods to displace unhealthy foods. We anticipate increasing use of indices that address food quality in future research.
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Affiliation(s)
- Richard M. Rosenfeld
- Department of Otolaryngology, SUNY Downstate Health Sciences University, Brooklyn, NY, United States
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Tore EC, Eussen SJPM, Bastani NE, Dagnelie PC, Elshorbagy AK, Grootswagers P, Kožich V, Olsen T, Refsum H, Retterstøl K, Stehouwer CDA, Stolt ETK, Vinknes KJ, van Greevenbroek MMJ. The Associations of Habitual Intake of Sulfur Amino Acids, Proteins and Diet Quality with Plasma Sulfur Amino Acid Concentrations: The Maastricht Study. J Nutr 2023; 153:2027-2040. [PMID: 37164267 DOI: 10.1016/j.tjnut.2023.05.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 04/12/2023] [Accepted: 05/04/2023] [Indexed: 05/12/2023] Open
Abstract
BACKGROUND Plasma sulfur amino acids (SAAs), i.e., methionine, total cysteine (tCys), total homocysteine (tHcy), cystathionine, total glutathione (tGSH), and taurine, are potential risk factors for obesity and cardiometabolic disorders. However, except for plasma tHcy, little is known about how dietary intake modifies plasma SAA concentrations. OBJECTIVE To investigate whether the intake of SAAs and proteins or diet quality is associated with plasma SAAs. METHODS Data from a cross-sectional subset of The Maastricht Study (n = 1145, 50.5% men, 61 interquartile range: [55, 66] y, 22.5% with prediabetes and 34.3% with type 2 diabetes) were investigated. Dietary intake was assessed using a validated food frequency questionnaire. The intake of SAAs (total, methionine, and cysteine) and proteins (total, animal, and plant) was estimated from the Dutch and Danish food composition tables. Diet quality was assessed using the Dutch Healthy Diet Index, the Mediterranean Diet Score, and the Dietary Approaches to Stop Hypertension score. Fasting plasma SAAs were measured by liquid chromatography (LC) tandem mass spectrometry (MS) (LC/MS-MS). Associations were investigated with multiple linear regressions with tertiles of dietary intake measures (main exposures) and z-standardized plasma SAAs (outcomes). RESULTS Intake of total SAAs and total proteins was positively associated with plasma tCys and cystathionine. Associations were stronger in women and in those with normal body weight. Higher intake of cysteine and plant proteins was associated with lower plasma tHcy and higher cystathionine. Higher methionine intake was associated with lower plasma tGSH, whereas cysteine intake was positively associated with tGSH. Higher intake of methionine and animal proteins was associated with higher plasma taurine. Better diet quality was consistently related to lower plasma tHcy concentrations, but it was not associated with the other SAAs. CONCLUSION Targeted dietary modifications might be effective in modifying plasma concentrations of tCys, tHcy, and cystathionine, which have been associated with obesity and cardiometabolic disorders.
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Affiliation(s)
- Elena C Tore
- Department of Internal Medicine, Maastricht University, Maastricht, the Netherlands; CARIM School for Cardiovascular Disease, Maastricht University, Maastricht, the Netherlands.
| | - Simone J P M Eussen
- CARIM School for Cardiovascular Disease, Maastricht University, Maastricht, the Netherlands; Department of Epidemiology, Maastricht University, Maastricht, the Netherlands; CAPHRI Care and Public Health Research Institute, Maastricht University, Maastricht, the Netherlands
| | - Nasser E Bastani
- Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Pieter C Dagnelie
- Department of Internal Medicine, Maastricht University, Maastricht, the Netherlands; CARIM School for Cardiovascular Disease, Maastricht University, Maastricht, the Netherlands
| | - Amany K Elshorbagy
- Department of Pharmacology, University of Oxford, Oxford, United Kingdom; Department of Physiology, Faculty of Medicine, University of Alexandria, Alexandria, Egypt
| | - Pol Grootswagers
- Division of Human Nutrition and Health, Wageningen University, Wageningen, the Netherlands
| | - Viktor Kožich
- Department of Pediatrics and Inherited Metabolic Disorders, Charles University-First Faculty of Medicine, and General University Hospital in Prague, Czech Republic
| | - Thomas Olsen
- Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Helga Refsum
- Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway; Department of Pharmacology, University of Oxford, Oxford, United Kingdom
| | - Kjetil Retterstøl
- Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Coen DA Stehouwer
- Department of Internal Medicine, Maastricht University, Maastricht, the Netherlands; CARIM School for Cardiovascular Disease, Maastricht University, Maastricht, the Netherlands
| | - Emma T K Stolt
- Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Kathrine J Vinknes
- Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Marleen M J van Greevenbroek
- Department of Internal Medicine, Maastricht University, Maastricht, the Netherlands; CARIM School for Cardiovascular Disease, Maastricht University, Maastricht, the Netherlands
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Lee DH, Jin Q, Shi N, Wang F, Bever AM, Li J, Liang L, Hu FB, Song M, Zeleznik OA, Zhang X, Joshi A, Wu K, Jeon JY, Meyerhardt JA, Chan AT, Eliassen AH, Clish CB, Clinton SK, Giovannucci EL, Tabung FK. Dietary Inflammatory and Insulinemic Potentials, Plasma Metabolome and Risk of Colorectal Cancer. Metabolites 2023; 13:744. [PMID: 37367904 PMCID: PMC10304271 DOI: 10.3390/metabo13060744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 06/02/2023] [Accepted: 06/05/2023] [Indexed: 06/28/2023] Open
Abstract
The inflammatory and insulinemic potentials of diets have been associated with colorectal cancer risk. However, it is unknown whether the plasma metabolite profiles related to inflammatory diets, or to insulinemic diets, underlie this association. The aim of this study was to evaluate the association between metabolomic profile scores related to the food-based empirical dietary inflammatory patterns (EDIP), the empirical dietary index for hyperinsulinemia (EDIH), and plasma inflammation (CRP, IL-6, TNFα-R2, adiponectin) and insulin (C-peptide) biomarkers, and colorectal cancer risk. Elastic net regression was used to derive three metabolomic profile scores for each dietary pattern among 6840 participants from the Nurses' Health Study and Health Professionals Follow-up Study, and associations with CRC risk were examined using multivariable-adjusted logistic regression, in a case-control study of 524 matched pairs nested in both cohorts. Among 186 known metabolites, 27 were significantly associated with both the EDIP and inflammatory biomarkers, and 21 were significantly associated with both the EDIH and C-peptide. In men, odds ratios (ORs) of colorectal cancer, per 1 standard deviation (SD) increment in metabolomic score, were 1.91 (1.31-2.78) for the common EDIP and inflammatory-biomarker metabolome, 1.12 (0.78-1.60) for EDIP-only metabolome, and 1.65 (1.16-2.36) for the inflammatory-biomarkers-only metabolome. However, no association was found for EDIH-only, C-peptide-only, and the common metabolomic signatures in men. Moreover, the metabolomic signatures were not associated with colorectal cancer risk among women. Metabolomic profiles reflecting pro-inflammatory diets and inflammation biomarkers were associated with colorectal cancer risk in men, while no association was found in women. Larger studies are needed to confirm our findings.
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Affiliation(s)
- Dong Hoon Lee
- Department of Sport Industry Studies, Yonsei University, Seoul 03722, Republic of Korea; (D.H.L.)
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Qi Jin
- Department of Exercise and Nutrition Sciences, Moyes College of Education, Weber State University, Ogden, UT 84408, USA
- Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA
- Interdisciplinary Ph.D. Program in Nutrition, The Ohio State University, Columbus, OH 43210, USA
| | - Ni Shi
- Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA
- Division of Medical Oncology, Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Fenglei Wang
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Alaina M. Bever
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School, Boston, MA 02115, USA
| | - Jun Li
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Liming Liang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Frank B. Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Mingyang Song
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115, USA
- Division of Gastroenterology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Oana A. Zeleznik
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Xuehong Zhang
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Amit Joshi
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115, USA
- Division of Gastroenterology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Kana Wu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Justin Y. Jeon
- Department of Sport Industry Studies, Yonsei University, Seoul 03722, Republic of Korea; (D.H.L.)
- Cancer Prevention Center, Yonsei Cancer Center, Seoul 03722, Republic of Korea
| | - Jeffrey A. Meyerhardt
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Andrew T. Chan
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115, USA
- Division of Gastroenterology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - A. Heather Eliassen
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Clary B. Clish
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Steven K. Clinton
- Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA
- Interdisciplinary Ph.D. Program in Nutrition, The Ohio State University, Columbus, OH 43210, USA
- Division of Medical Oncology, Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Edward L. Giovannucci
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Fred K. Tabung
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA
- Interdisciplinary Ph.D. Program in Nutrition, The Ohio State University, Columbus, OH 43210, USA
- Division of Medical Oncology, Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
- Division of Epidemiology, College of Public Health, The Ohio State University, Columbus, OH 43220, USA
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Deng K, Gupta DK, Shu XO, Lipworth L, Zheng W, Thomas VE, Cai H, Cai Q, Wang TJ, Yu D. Metabolite Signature of Life's Essential 8 and Risk of Coronary Heart Disease among Low-Income Black and White Americans. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.04.24.23289055. [PMID: 37163035 PMCID: PMC10168489 DOI: 10.1101/2023.04.24.23289055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Background and Aims Life's Essential 8 (LE8) is a comprehensive construct of cardiovascular health. Yet, little is known about LE8 score, its metabolic correlates, and their predictive implications among Black Americans and low-income individuals. Methods In a nested case-control study of coronary heart disease (CHD) among 598 Black and 596 White low-income Americans, we estimated LE8 score, conducted untargeted plasma metabolites profiling, and used elastic net with leave-one-out cross-validation to identify metabolite signature (MetaSig) of LE8. Associations of LE8 score and MetaSig with incident CHD were examined using conditional logistic regression. Mediation effect of MetaSig on the LE8-CHD association was also examined. The external validity of MetaSig was evaluated in another nested CHD case-control study among 598 Chinese adults. Results Higher LE8 score was associated with lower CHD risk [standardized OR (95% CI)=0.61 (0.53-0.69)]. The identified MetaSig, consisting of 133 metabolites, showed strong correlation with LE8 score ( r =0.61) and inverse association with CHD risk [OR (95% CI)=0.57 (0.49-0.65)], robust to adjustment for LE8 score and across participants with different sociodemographic and health status (ORs: 0.42-0.69; all P <0.05). MetaSig mediated a large portion of the LE8-CHD association: 53% (32%-80%) ( P <0.001). Significant associations of MetaSig with LE8 score and CHD risk were found in validation cohort [ r =0.49; OR (95% CI)=0.57 (0.46-0.69)]. Conclusions Higher LE8 score and its MetaSig were associated with lower CHD risk among low-income Black and White Americans. Metabolomics may offer an objective and comprehensive measure of LE8 score and its metabolic phenotype relevant to CHD prevention among diverse populations.
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Zhao Y, Song P, Yin S, Fan T, Li F, Ge X, Liu T, Xu W, Xu S, Chen L. Onchidium struma polysaccharides exhibit hypoglycemic activity and modulate the gut microbiota in mice with type 2 diabetes mellitus. Food Funct 2023; 14:1937-1951. [PMID: 36691957 DOI: 10.1039/d2fo02450k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Onchidium struma polysaccharides (OsPs) are natural biologically active compounds, and our previous work showed that they can inhibit the activity of α-glucosidase in vitro, showing potential hypoglycemic activity. However, the effects of OsPs on type 2 diabetes mellitus (T2DM) in vivo remain unknown. Thus, the anti-diabetic activity of OsPs was evaluated in the present study in diabetic mice. The results showed that OsPs can significantly ameliorate the features of T2DM in mice by improving the levels of fasting blood glucose (FBG), oral glucose tolerance test (OGTT), and pro-inflammatory factors, and ameliorating insulin resistance. Furthermore, OsPs can significantly improve biochemical indicators, decrease the contents of total cholesterol (TC) and triglyceride (TG), and reduce lipid accumulation in the liver. The possible mechanism of the prevention and treatment of T2DM by OsPs may involve the phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT-1) signaling pathway. OsPs can regulate the dysbiosis of gut microbiota and reverse the abundance of Lactobacillus in mice with T2DM. Moreover, OsPs significantly increased the concentration of short-chain fatty acids (SCFAs) in mice with T2DM. Our results indicate that OsPs can be used as a novel food supplement for the prevention and treatment of T2DM.
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Affiliation(s)
- Yunfeng Zhao
- School of Marine and Bioengineering, Yancheng Institute of Technology, Yancheng 224051, China.
| | - Peilin Song
- School of Marine and Bioengineering, Yancheng Institute of Technology, Yancheng 224051, China. .,Public Analysis Department, Pharmaceutical Research Institute of Jumpcan Pharmaceutical Group Co., Ltd, Taizhou, Jiangsu 225300, China
| | - Shuai Yin
- School of Marine and Bioengineering, Yancheng Institute of Technology, Yancheng 224051, China.
| | - Tianyong Fan
- School of Marine and Bioengineering, Yancheng Institute of Technology, Yancheng 224051, China.
| | - Fengwei Li
- School of Marine and Bioengineering, Yancheng Institute of Technology, Yancheng 224051, China.
| | - Xiaodong Ge
- School of Marine and Bioengineering, Yancheng Institute of Technology, Yancheng 224051, China.
| | - Tingting Liu
- Clinical Pharmacy Department, Yancheng Second People's Hospital, Yancheng 224051, China
| | - Wei Xu
- School of Marine and Bioengineering, Yancheng Institute of Technology, Yancheng 224051, China. .,Jiangsu Key Laboratory of Biochemistry and Biotechnology of Marine Wetland, Yancheng Institute of Technology, Yancheng 224051, China
| | - Su Xu
- Department of Anorectal Surgery, Yancheng Hospital of Traditional Chinese Medicine Affiliated to Nanjing University of Chinese Medicine, Yancheng, Jiangsu 224001, China.
| | - Ligen Chen
- School of Marine and Bioengineering, Yancheng Institute of Technology, Yancheng 224051, China. .,Jiangsu Key Laboratory of Biochemistry and Biotechnology of Marine Wetland, Yancheng Institute of Technology, Yancheng 224051, China
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Wang L, Li Y, Liu Y, Zhang H, Qiao T, Chu L, Luo T, Zhang Z, Dai J. Association between Different Types of Plant-Based Diets and Dyslipidemia in Middle-Aged and Elderly Chinese Participants. Nutrients 2023; 15:nu15010230. [PMID: 36615887 PMCID: PMC9823762 DOI: 10.3390/nu15010230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 12/28/2022] [Accepted: 12/30/2022] [Indexed: 01/03/2023] Open
Abstract
Plant-based dietary patterns may reduce the risk of dyslipidemia. However, not all plant-based foods are beneficial, and limited data exist for the Chinese population. We investigated the association between different plant-based dietary indices and the risk of dyslipidemia in a Chinese middle-aged and elderly population. The study participants (n = 4096) consisted of adults between 35 and 74 years of age from Xinjiang, China. Dietary consumption of the study participants was evaluated using a semi-quantitative food-frequency questionnaire (FFQ). Three different plant-based dietary indices were calculated using data from dietary surveys, including overall plant-based diet index (PDI), healthy plant-based diet index (hPDI), and unhealthy plant-based diet index (uPDI). Based on these indices, we created an adjusted plant-based diet index (aPDI) based on the Xinjiang population actual dietary behavior and health effects of food. We measured the levels of total cholesterol, triglyceride, LDL-C, and HDL-C in the blood of the study participants. We used multivariable logistic regression and restricted cubic spline to analyze the relationship between plant-based diets and dyslipidemia. The findings showed that 36.6% of the participants had dyslipidemia. Higher PDI adherence was related to lower odds of dyslipidemia (Q3 vs. Q1, OR: 0.780, 95% CI: 0.641-0.949; Q4 vs. Q1, OR: 0.799, 95% CI: 0.659-0.970). High aPDI was related to lower odds of dyslipidemia (Q4 vs. Q1, OR: 0.770, 95% CI: 0.628-0.945; Q5 vs. Q1, OR: 0.748, 95% CI: 0.607-0.921). High scores for PDI, hPDI, and aPDI were all related to a reduced risk of low HDL-C (OR: 0.638, 95% CI: 0.491-0.823; OR: 0.661, 95% CI: 0.502-0.870; OR: 0.580, 95% CI: 0.443-0.758). Conversely, a high uPDI score was associated with an increased risk of low HDL-C (OR: 1.349, 95% CI: 1.046-1.740). There was no non-linear relationship between PDI, hPDI, uPDI, and aPDI and the risk of different types of dyslipidemia. Plant-based dietary indices are related to specific types of dyslipidemia risk. Appropriately increasing the consumption of plant-based foods while improving the quality of plant-based dietary patterns is critical for the prevention of dyslipidemia, especially low HDL-C, in the population.
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Affiliation(s)
- Lu Wang
- School of Public Health, Xinjiang Medical University, 393 Xin Medical Road, Urumqi 830011, China
| | - Yuanyuan Li
- School of Public Health, Xinjiang Medical University, 393 Xin Medical Road, Urumqi 830011, China
| | - Yan Liu
- Department of Nutrition, School of Public Health, Sun Yat-sen University, Guangzhou 510000, China
| | - Huanwen Zhang
- School of Public Health, Xinjiang Medical University, 393 Xin Medical Road, Urumqi 830011, China
| | - Tingting Qiao
- School of Public Health, Xinjiang Medical University, 393 Xin Medical Road, Urumqi 830011, China
| | - Lei Chu
- School of Public Health, Xinjiang Medical University, 393 Xin Medical Road, Urumqi 830011, China
| | - Tao Luo
- School of Public Health, Xinjiang Medical University, 393 Xin Medical Road, Urumqi 830011, China
| | - Zewen Zhang
- School of Public Health, Xinjiang Medical University, 393 Xin Medical Road, Urumqi 830011, China
| | - Jianghong Dai
- School of Public Health, Xinjiang Medical University, 393 Xin Medical Road, Urumqi 830011, China
- Correspondence:
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Affiliation(s)
- Sabrina Schlesinger
- Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research, Heinrich Heine University, Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research (DZD), Partner Düsseldorf, Munich-Neuherberg, Germany
- Corresponding author: Sabrina Schlesinger,
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Dietary regulation in health and disease. Signal Transduct Target Ther 2022; 7:252. [PMID: 35871218 PMCID: PMC9308782 DOI: 10.1038/s41392-022-01104-w] [Citation(s) in RCA: 44] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 06/21/2022] [Accepted: 07/04/2022] [Indexed: 02/08/2023] Open
Abstract
Nutriments have been deemed to impact all physiopathologic processes. Recent evidences in molecular medicine and clinical trials have demonstrated that adequate nutrition treatments are the golden criterion for extending healthspan and delaying ageing in various species such as yeast, drosophila, rodent, primate and human. It emerges to develop the precision-nutrition therapeutics to slow age-related biological processes and treat diverse diseases. However, the nutritive advantages frequently diversify among individuals as well as organs and tissues, which brings challenges in this field. In this review, we summarize the different forms of dietary interventions extensively prescribed for healthspan improvement and disease treatment in pre-clinical or clinical. We discuss the nutrient-mediated mechanisms including metabolic regulators, nutritive metabolism pathways, epigenetic mechanisms and circadian clocks. Comparably, we describe diet-responsive effectors by which dietary interventions influence the endocrinic, immunological, microbial and neural states responsible for improving health and preventing multiple diseases in humans. Furthermore, we expatiate diverse patterns of dietotheroapies, including different fasting, calorie-restricted diet, ketogenic diet, high-fibre diet, plants-based diet, protein restriction diet or diet with specific reduction in amino acids or microelements, potentially affecting the health and morbid states. Altogether, we emphasize the profound nutritional therapy, and highlight the crosstalk among explored mechanisms and critical factors to develop individualized therapeutic approaches and predictors.
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Wang F, Baden MY, Hu FB. Branding foods as 'healthy' or 'unhealthy' based on marginal data calls findings into question. Reply to Kanter M [letter]. Diabetologia 2022; 65:1753-1754. [PMID: 35962794 DOI: 10.1007/s00125-022-05766-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 06/08/2022] [Indexed: 11/03/2022]
Affiliation(s)
- Fenglei Wang
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Megu Y Baden
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Lifestyle Medicine, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Frank B Hu
- 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.
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
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Kanter M. Branding foods as 'healthy' or 'unhealthy' based on marginal data calls findings into question. Diabetologia 2022; 65:1751-1752. [PMID: 35970949 PMCID: PMC9477931 DOI: 10.1007/s00125-022-05767-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 05/17/2022] [Indexed: 01/18/2023]
Affiliation(s)
- Mitch Kanter
- Alliance for Potato Research and Education, Chicago, IL, USA.
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