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Rigamonti AE, Polledri E, Favero C, Caroli D, Bondesan A, Grugni G, Mai S, Cella SG, Fustinoni S, Sartorio A. Metabolomic profiling of Prader-Willi syndrome compared with essential obesity. Front Endocrinol (Lausanne) 2024; 15:1386265. [PMID: 38812813 PMCID: PMC11133515 DOI: 10.3389/fendo.2024.1386265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Accepted: 04/29/2024] [Indexed: 05/31/2024] Open
Abstract
Introduction Prader-Willi syndrome (PWS) is a rare disease, which shows a peculiar clinical phenotype, including obesity, which is different from essential obesity (EOB). Metabolomics might represent a valuable tool to reveal the biochemical mechanisms/pathways underlying clinical differences between PWS and EOB. The aim of the present (case-control, retrospective) study was to determine the metabolomic profile that characterizes PWS compared to EOB. Methods A validated liquid chromatography-tandem mass spectrometry (LC-MS/MS) targeted metabolomic approach was used to measure a total of 188 endogenous metabolites in plasma samples of 32 patients with PWS (F/M = 23/9; age: 31.6 ± 9.2 years; body mass index [BMI]: 42.1 ± 7.0 kg/m2), compared to a sex-, age- and BMI-matched group of patients with EOB (F/M = 23/9; age: 31.4 ± 6.9 years; BMI: 43.5 ± 3.5 kg/m2). Results Body composition in PWS was different when compared to EOB, with increased fat mass and decreased fat-free mass. Glycemia and HDL cholesterol were higher in patients with PWS than in those with EOB, while insulinemia was lower, as well as heart rate. Resting energy expenditure was lower in the group with PWS than in the one with EOB, a difference that was missed after fat-free mass correction. Carrying out a series of Tobit multivariable linear regressions, adjusted for sex, diastolic blood pressure, and C reactive protein, a total of 28 metabolites was found to be associated with PWS (vs. non-PWS, i.e., EOB), including 9 phosphatidylcholines (PCs) ae, 5 PCs aa, all PCs aa, 7 lysoPCs a, all lysoPCs, 4 acetylcarnitines, and 1 sphingomyelin, all of which were higher in PWS than EOB. Conclusions PWS exhibits a specific metabolomic profile when compared to EOB, suggesting a different regulation of some biochemical pathways, fundamentally related to lipid metabolism.
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Affiliation(s)
| | - Elisa Polledri
- Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy
| | - Chiara Favero
- Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy
| | - Diana Caroli
- Istituto Auxologico Italiano, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Experimental Laboratory for Auxo-endocrinological Research, Piancavallo-Verbania, Italy
| | - Adele Bondesan
- Istituto Auxologico Italiano, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Experimental Laboratory for Auxo-endocrinological Research, Piancavallo-Verbania, Italy
| | - Graziano Grugni
- Istituto Auxologico Italiano, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Experimental Laboratory for Auxo-endocrinological Research, Piancavallo-Verbania, Italy
| | - Stefania Mai
- Istituto Auxologico Italiano, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Laboratory of Metabolic Research, Piancavallo-Verbania, Italy
| | - Silvano G. Cella
- Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy
| | - Silvia Fustinoni
- Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy
- Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Alessandro Sartorio
- Istituto Auxologico Italiano, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Experimental Laboratory for Auxo-endocrinological Research, Piancavallo-Verbania, Italy
- Istituto Auxologico Italiano, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Experimental Laboratory for Auxo-endocrinological Research, Milan, Italy
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García-Gavilán JF, Atzeni A, Babio N, Liang L, Belzer C, Vioque J, Corella D, Fitó M, Vidal J, Moreno-Indias I, Torres-Collado L, Coltell O, Toledo E, Clish C, Hernando J, Yun H, Hernández-Cacho A, Jeanfavre S, Dennis C, Gómez-Pérez AM, Martínez MA, Ruiz-Canela M, Tinahones FJ, Hu FB, Salas-Salvadó J. Effect of 1-year lifestyle intervention with energy-reduced Mediterranean diet and physical activity promotion on the gut metabolome and microbiota: a randomized clinical trial. Am J Clin Nutr 2024; 119:1143-1154. [PMID: 38428742 DOI: 10.1016/j.ajcnut.2024.02.021] [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: 11/30/2023] [Revised: 02/23/2024] [Accepted: 02/26/2024] [Indexed: 03/03/2024] Open
Abstract
BACKGROUND The health benefits of the Mediterranean diet (MedDiet) have been linked to the presence of beneficial gut microbes and related metabolites. However, its impact on the fecal metabolome remains poorly understood. OBJECTIVES Our goal was to investigate the weight-loss effects of a 1-y lifestyle intervention based on an energy-reduced MedDiet coupled with physical activity (intervention group), compared with an ad libitum MedDiet (control group), on fecal metabolites, fecal microbiota, and their potential association with cardiovascular disease risk factors. METHODS A total of 400 participants (200 from each study group), aged 55-75 y, and at high cardiovascular disease risk, were included. Dietary and lifestyle information, anthropometric measurements, blood biochemical parameters, and stool samples were collected at baseline and after 1 y of follow-up. Liquid chromatography-tandem mass spectrometry was used to profile endogenous fecal metabolites, and 16S amplicon sequencing was employed to profile the fecal microbiota. RESULTS Compared with the control group, the intervention group exhibited greater weight loss and improvement in various cardiovascular disease risk factors. We identified intervention effects on 4 stool metabolites and subnetworks primarily composed of bile acids, ceramides, and sphingosines, fatty acids, carnitines, nucleotides, and metabolites of purine and the Krebs cycle. Some of these were associated with changes in several cardiovascular disease risk factors. In addition, we observed a reduction in the abundance of the genera Eubacterium hallii group and Dorea, and an increase in alpha diversity in the intervention group after 1 y of follow-up. Changes in the intervention-related microbiota profiles were also associated with alterations in different fecal metabolite subnetworks and some cardiovascular disease risk factors. CONCLUSIONS An intervention based on an energy-reduced MedDiet and physical activity promotion, compared with an ad libitum MedDiet, was associated with improvements in cardiometabolic risk factors, potentially through modulation of the fecal microbiota and metabolome. This trial was registered at https://www.isrctn.com/ as ISRCTN89898870 (https://doi.org/10.1186/ISRCTN89898870).
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Affiliation(s)
- Jesús F García-Gavilán
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain; Departament de Bioquímica i Biotecnologia, Alimentaciò, Nutrició, Desenvolupament i Salut Mental (ANUT-DSM), Universitat Rovira i Virgili, Reus, Spain; Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain
| | - Alessandro Atzeni
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain; Departament de Bioquímica i Biotecnologia, Alimentaciò, Nutrició, Desenvolupament i Salut Mental (ANUT-DSM), Universitat Rovira i Virgili, Reus, Spain; Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain.
| | - Nancy Babio
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain; Departament de Bioquímica i Biotecnologia, Alimentaciò, Nutrició, Desenvolupament i Salut Mental (ANUT-DSM), Universitat Rovira i Virgili, Reus, Spain; Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain
| | - Liming Liang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Clara Belzer
- Laboratory of Microbiology, Wageningen University, Wageningen, The Netherlands
| | - Jesús Vioque
- CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain; Instituto de Investigación Sanitaria y Biomédica de Alicante, Universidad Miguel Hernández (ISABIAL-UMH), Alicante, 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
| | - 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
| | - Josep Vidal
- CIBER Diabetes y Enfermedades Metabólicas (CIBERDEM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain; Department of Endocrinology, Institut d'Investigacions Biomédiques August Pi Sunyer (IDIBAPS), Hospital Clinic, University of Barcelona, Barcelona, Spain
| | - Isabel Moreno-Indias
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain; Department of Endocrinology and Nutrition, Instituto de Investigación Biomédica de Málaga (IBIMA), Hospital Universitario Virgen de la Victoria, Málaga, Spain
| | - Laura Torres-Collado
- CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain; Instituto de Investigación Sanitaria y Biomédica de Alicante, Universidad Miguel Hernández (ISABIAL-UMH), Alicante, Spain
| | - Oscar Coltell
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain; Department of Computer Languages and Systems, Jaume I University, Castellón, Spain
| | - Estefanía 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, Pamplona, Spain; Epidemiología y Salud Pública, Instituto de Investigación Sanitaria de Navarra (IdiSNA), Pamplona, Spain
| | - Clary Clish
- Metabolomics Platform, The Broad Institute of MIT and Harvard, Boston, MA, United States
| | - Javier Hernando
- 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
| | - Huan Yun
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Adrián Hernández-Cacho
- Departament de Bioquímica i Biotecnologia, Alimentaciò, Nutrició, Desenvolupament i Salut Mental (ANUT-DSM), Universitat Rovira i Virgili, Reus, Spain; Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain
| | - Sarah Jeanfavre
- Metabolomics Platform, The Broad Institute of MIT and Harvard, Boston, MA, United States
| | - Courtney Dennis
- Metabolomics Platform, The Broad Institute of MIT and Harvard, Boston, MA, United States
| | - Ana M Gómez-Pérez
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain; Department of Endocrinology and Nutrition, Instituto de Investigación Biomédica de Málaga (IBIMA), Hospital Universitario Virgen de la Victoria, Málaga, Spain
| | - Maria Angeles Martínez
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain; Departament de Bioquímica i Biotecnologia, Alimentaciò, Nutrició, Desenvolupament i Salut Mental (ANUT-DSM), Universitat Rovira i Virgili, Reus, 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, Pamplona, Spain; Epidemiología y Salud Pública, Instituto de Investigación Sanitaria de Navarra (IdiSNA), Pamplona, Spain
| | - Francisco J Tinahones
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain; Department of Endocrinology and Nutrition, Instituto de Investigación Biomédica de Málaga (IBIMA), Hospital Universitario Virgen de la Victoria, Málaga, Spain
| | - Frank B Hu
- Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, United States; Channing Division for Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
| | - Jordi Salas-Salvadó
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain; Departament de Bioquímica i Biotecnologia, Alimentaciò, Nutrició, Desenvolupament i Salut Mental (ANUT-DSM), Universitat Rovira i Virgili, Reus, Spain; Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain.
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