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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] [What about the content of this article? (0)] [Affiliation(s)] [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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