H-NMR metabolomics identifies three distinct metabolic profiles differentially associated with cardiometabolic risk in patients with obesity in the Di@bet.es cohort
Abstract 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 d...
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| Format: | Article |
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BMC
2024-11-01
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| Series: | Cardiovascular Diabetology |
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| Online Access: | https://doi.org/10.1186/s12933-024-02488-5 |
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| author | Enrique Ozcariz Montse Guardiola Núria Amigó Sergio Valdés Wasima Oualla-Bachiri Pere Rehues Gemma Rojo-Martinez Josep Ribalta |
| author_facet | Enrique Ozcariz Montse Guardiola Núria Amigó Sergio Valdés Wasima Oualla-Bachiri Pere Rehues Gemma Rojo-Martinez Josep Ribalta |
| author_sort | Enrique Ozcariz |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-81df02f3e5e34cc29b4657a36d4b6f34 |
| institution | Kabale University |
| issn | 1475-2840 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | BMC |
| record_format | Article |
| series | Cardiovascular Diabetology |
| spelling | doaj-art-81df02f3e5e34cc29b4657a36d4b6f342024-11-10T12:05:22ZengBMCCardiovascular Diabetology1475-28402024-11-0123111310.1186/s12933-024-02488-5H-NMR metabolomics identifies three distinct metabolic profiles differentially associated with cardiometabolic risk in patients with obesity in the Di@bet.es cohortEnrique Ozcariz0Montse Guardiola1Núria Amigó2Sergio Valdés3Wasima Oualla-Bachiri4Pere Rehues5Gemma Rojo-Martinez6Josep Ribalta7Center for Health and Bioresources, Molecular Diagnostics, AIT Austrian Institute of Technology GmbHCIBER de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos IIIBiosfer TeslabCIBER de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos IIICIBER de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos IIICIBER de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos IIICIBER de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos IIICIBER de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos IIIAbstract 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.https://doi.org/10.1186/s12933-024-02488-5ObesityMetabolomicsType 2 diabetes mellitusCardiovascular diseaseAtherogenic dyslipidemiaHypercholesterolemia |
| spellingShingle | Enrique Ozcariz Montse Guardiola Núria Amigó Sergio Valdés Wasima Oualla-Bachiri Pere Rehues Gemma Rojo-Martinez Josep Ribalta H-NMR metabolomics identifies three distinct metabolic profiles differentially associated with cardiometabolic risk in patients with obesity in the Di@bet.es cohort Cardiovascular Diabetology Obesity Metabolomics Type 2 diabetes mellitus Cardiovascular disease Atherogenic dyslipidemia Hypercholesterolemia |
| title | H-NMR metabolomics identifies three distinct metabolic profiles differentially associated with cardiometabolic risk in patients with obesity in the Di@bet.es cohort |
| title_full | H-NMR metabolomics identifies three distinct metabolic profiles differentially associated with cardiometabolic risk in patients with obesity in the Di@bet.es cohort |
| title_fullStr | H-NMR metabolomics identifies three distinct metabolic profiles differentially associated with cardiometabolic risk in patients with obesity in the Di@bet.es cohort |
| title_full_unstemmed | H-NMR metabolomics identifies three distinct metabolic profiles differentially associated with cardiometabolic risk in patients with obesity in the Di@bet.es cohort |
| title_short | H-NMR metabolomics identifies three distinct metabolic profiles differentially associated with cardiometabolic risk in patients with obesity in the Di@bet.es cohort |
| title_sort | h nmr metabolomics identifies three distinct metabolic profiles differentially associated with cardiometabolic risk in patients with obesity in the di bet es cohort |
| topic | Obesity Metabolomics Type 2 diabetes mellitus Cardiovascular disease Atherogenic dyslipidemia Hypercholesterolemia |
| url | https://doi.org/10.1186/s12933-024-02488-5 |
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