H-NMR metabolomics identifies three distinct metabolic profiles differentially associated with cardiometabolic risk in patients with obesity in the Di@bet.es cohort.

Autor: Ozcariz E; Center for Health and Bioresources, Molecular Diagnostics, AIT Austrian Institute of Technology GmbH, Giefinggasse 4, Vienna, 1210, Austria., Guardiola M; CIBER de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, Madrid, Spain.; Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain.; Departament de Medicina i Cirurgia, Universitat Rovira i Virgili, Unitat de Recerca en Lípids i Arteriosclerosi, Reus, Spain., Amigó N; Biosfer Teslab, Plaça del Prim 10, 2on 5a, Reus, 43201, Spain.; CIBER de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, Madrid, Spain.; Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain.; Departament de Ciències Mèdiques Bàsiques, Universitat Rovira i Virgili, Reus, Spain.; Universitat Rovira i Virgili, Metabolomics Platform, Reus, Spain., Valdés S; CIBER de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, Madrid, Spain.; UGC Endocrinología y Nutrición. Hospital Regional Universitario de Málaga, Málaga, Spain.; Instituto de Investigación Biomédica de Málaga y Plataforma en Nanomedicina-IBIMA Plataforma BIONAND, Málaga, Spain., Oualla-Bachiri W; CIBER de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, Madrid, Spain.; UGC Endocrinología y Nutrición. Hospital Regional Universitario de Málaga, Málaga, Spain.; Instituto de Investigación Biomédica de Málaga y Plataforma en Nanomedicina-IBIMA Plataforma BIONAND, Málaga, Spain.; Universidad de Málaga, Málaga, Spain., Rehues P; CIBER de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, Madrid, Spain.; Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain.; Departament de Medicina i Cirurgia, Universitat Rovira i Virgili, Unitat de Recerca en Lípids i Arteriosclerosi, Reus, Spain., Rojo-Martinez G; CIBER de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, Madrid, Spain. gemma.rojo.m@gmail.com.; UGC Endocrinología y Nutrición. Hospital Regional Universitario de Málaga, Málaga, Spain. gemma.rojo.m@gmail.com.; Instituto de Investigación Biomédica de Málaga y Plataforma en Nanomedicina-IBIMA Plataforma BIONAND, Málaga, Spain. gemma.rojo.m@gmail.com., Ribalta J; CIBER de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, Madrid, Spain.; Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain.; Departament de Medicina i Cirurgia, Universitat Rovira i Virgili, Unitat de Recerca en Lípids i Arteriosclerosi, Reus, Spain.
Jazyk: angličtina
Zdroj: Cardiovascular diabetology [Cardiovasc Diabetol] 2024 Nov 07; Vol. 23 (1), pp. 402. Date of Electronic Publication: 2024 Nov 07.
DOI: 10.1186/s12933-024-02488-5
Abstrakt: Background: Obesity is a complex, diverse and multifactorial disease that has become a major public health concern in the last decades. The current classification systems relies on anthropometric measurements, such as BMI, that are unable to capture the physiopathological diversity of this disease. The aim of this study was to redefine the classification of obesity based on the different H-NMR metabolomics profiles found in individuals with obesity to better assess the risk of future development of cardiometabolic disease.
Materials and Methods: Serum samples of a subset of the Di@bet.es cohort consisting of 1387 individuals with obesity were analyzed by H-NMR. A K-means algorithm was deployed to define different H-NMR metabolomics-based clusters. Then, the association of these clusters with future development of cardiometabolic disease was evaluated using different univariate and multivariate statistical approaches. Moreover, machine learning-based models were built to predict the development of future cardiometabolic disease using BMI and waist-to-hip circumference ratio measures in combination with H-NMR metabolomics.
Results: Three clusters with no differences in BMI nor in waist-to-hip circumference ratio but with very different metabolomics profiles were obtained. The first cluster showed a metabolically healthy profile, whereas atherogenic dyslipidemia and hypercholesterolemia were predominant in the second and third clusters, respectively. Individuals within the cluster of atherogenic dyslipidemia were found to be at a higher risk of developing type 2 DM in a 8 years follow-up. On the other hand, individuals within the cluster of hypercholesterolemia showed a higher risk of suffering a cardiovascular event in the follow-up. The individuals with a metabolically healthy profile displayed a lower association with future cardiometabolic disease, even though some association with future development of type 2 DM was still observed. In addition, H-NMR metabolomics improved the prediction of future cardiometabolic disease in comparison with models relying on just anthropometric measures.
Conclusions: This study demonstrated the benefits of using precision techniques like H-NMR to better assess the risk of obesity-derived cardiometabolic disease.
(© 2024. The Author(s).)
Databáze: MEDLINE
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