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High-Risk Obesity Phenotypes: Target for Multimorbidity Prevention at the ROFEMI Study
Background: Describe the profile of patients with obesity in internal medicine to determine the role of adiposity and related inflammation on the metabolic risk profile and, identify various “high-risk obesity” phenotypes by means of a cluster analysis. This study aimed to identify different profile...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9410284/ https://www.ncbi.nlm.nih.gov/pubmed/36012889 http://dx.doi.org/10.3390/jcm11164644 |
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author | Carretero-Gómez, Juana Pérez-Martínez, Pablo Seguí-Ripoll, José Miguel Carrasco-Sánchez, Francisco Javier Lois Martínez, Nagore Fernández Pérez, Esther Pérez Hernández, Onán García Ordoñez, Miguel Ángel Martín González, Candelaria Vigueras-Pérez, Juan Francisco Puchades, Francesc Blasco Avaria, María Cristina Pérez Soto, María Isabel Ena, Javier Arévalo-Lorido, José Carlos |
author_facet | Carretero-Gómez, Juana Pérez-Martínez, Pablo Seguí-Ripoll, José Miguel Carrasco-Sánchez, Francisco Javier Lois Martínez, Nagore Fernández Pérez, Esther Pérez Hernández, Onán García Ordoñez, Miguel Ángel Martín González, Candelaria Vigueras-Pérez, Juan Francisco Puchades, Francesc Blasco Avaria, María Cristina Pérez Soto, María Isabel Ena, Javier Arévalo-Lorido, José Carlos |
author_sort | Carretero-Gómez, Juana |
collection | PubMed |
description | Background: Describe the profile of patients with obesity in internal medicine to determine the role of adiposity and related inflammation on the metabolic risk profile and, identify various “high-risk obesity” phenotypes by means of a cluster analysis. This study aimed to identify different profiles of patients with high-risk obesity based on a cluster analysis. Methods: Cross-sectional, multicenter project that included outpatients attended to in internal medicine. A total of 536 patients were studied. The mean age was 62 years, 51% were women. Patients were recruited from internal medicine departments over two weeks in November and December 2021 and classified into four risk groups according to body mass index (BMI) and waist circumference (WC). High-risk obesity was defined as BMI > 35 Kg/m(2) or BMI 30–34.9 Kg/m(2) and a high WC (>102 cm for men and >88 cm for women). Hierarchical and partitioning clustering approaches were performed to identify profiles. Results: A total of 462 (86%) subjects were classified into the high-risk obesity group. After excluding 19 patients missing critical data, two profiles emerged: cluster 1 (n = 396) and cluster 2 (n = 47). Compared to cluster 1, cluster 2 had a worse profile, characterized by older age (77 ± 16 vs. 61 ± 21 years, p < 0.01), a Charlson Comorbidity Index > 3 (53% vs. 5%, p < 0.001), depression (36% vs. 19%, p = 0.008), severe disability (64% vs. 3%, p < 0.001), and a sarcopenia score ≥ 4 (79% vs. 16%, p < 0.01). In addition, cluster 2 had greater inflammation than cluster 1 (hsCRP: 5.8 ± 4.1 vs. 2.1 ± 4.5 mg/dL, p = 0.008). Conclusions: Two profiles of subjects with high-risk obesity were identified. Based on that, older subjects with obesity require measures that target sarcopenia, disability, psychological health, and significant comorbidities to prevent further health deterioration. Longitudinal studies should be performed to identify potential risk factors of subjects who progress from cluster 1 to cluster 2. |
format | Online Article Text |
id | pubmed-9410284 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-94102842022-08-26 High-Risk Obesity Phenotypes: Target for Multimorbidity Prevention at the ROFEMI Study Carretero-Gómez, Juana Pérez-Martínez, Pablo Seguí-Ripoll, José Miguel Carrasco-Sánchez, Francisco Javier Lois Martínez, Nagore Fernández Pérez, Esther Pérez Hernández, Onán García Ordoñez, Miguel Ángel Martín González, Candelaria Vigueras-Pérez, Juan Francisco Puchades, Francesc Blasco Avaria, María Cristina Pérez Soto, María Isabel Ena, Javier Arévalo-Lorido, José Carlos J Clin Med Article Background: Describe the profile of patients with obesity in internal medicine to determine the role of adiposity and related inflammation on the metabolic risk profile and, identify various “high-risk obesity” phenotypes by means of a cluster analysis. This study aimed to identify different profiles of patients with high-risk obesity based on a cluster analysis. Methods: Cross-sectional, multicenter project that included outpatients attended to in internal medicine. A total of 536 patients were studied. The mean age was 62 years, 51% were women. Patients were recruited from internal medicine departments over two weeks in November and December 2021 and classified into four risk groups according to body mass index (BMI) and waist circumference (WC). High-risk obesity was defined as BMI > 35 Kg/m(2) or BMI 30–34.9 Kg/m(2) and a high WC (>102 cm for men and >88 cm for women). Hierarchical and partitioning clustering approaches were performed to identify profiles. Results: A total of 462 (86%) subjects were classified into the high-risk obesity group. After excluding 19 patients missing critical data, two profiles emerged: cluster 1 (n = 396) and cluster 2 (n = 47). Compared to cluster 1, cluster 2 had a worse profile, characterized by older age (77 ± 16 vs. 61 ± 21 years, p < 0.01), a Charlson Comorbidity Index > 3 (53% vs. 5%, p < 0.001), depression (36% vs. 19%, p = 0.008), severe disability (64% vs. 3%, p < 0.001), and a sarcopenia score ≥ 4 (79% vs. 16%, p < 0.01). In addition, cluster 2 had greater inflammation than cluster 1 (hsCRP: 5.8 ± 4.1 vs. 2.1 ± 4.5 mg/dL, p = 0.008). Conclusions: Two profiles of subjects with high-risk obesity were identified. Based on that, older subjects with obesity require measures that target sarcopenia, disability, psychological health, and significant comorbidities to prevent further health deterioration. Longitudinal studies should be performed to identify potential risk factors of subjects who progress from cluster 1 to cluster 2. MDPI 2022-08-09 /pmc/articles/PMC9410284/ /pubmed/36012889 http://dx.doi.org/10.3390/jcm11164644 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Carretero-Gómez, Juana Pérez-Martínez, Pablo Seguí-Ripoll, José Miguel Carrasco-Sánchez, Francisco Javier Lois Martínez, Nagore Fernández Pérez, Esther Pérez Hernández, Onán García Ordoñez, Miguel Ángel Martín González, Candelaria Vigueras-Pérez, Juan Francisco Puchades, Francesc Blasco Avaria, María Cristina Pérez Soto, María Isabel Ena, Javier Arévalo-Lorido, José Carlos High-Risk Obesity Phenotypes: Target for Multimorbidity Prevention at the ROFEMI Study |
title | High-Risk Obesity Phenotypes: Target for Multimorbidity Prevention at the ROFEMI Study |
title_full | High-Risk Obesity Phenotypes: Target for Multimorbidity Prevention at the ROFEMI Study |
title_fullStr | High-Risk Obesity Phenotypes: Target for Multimorbidity Prevention at the ROFEMI Study |
title_full_unstemmed | High-Risk Obesity Phenotypes: Target for Multimorbidity Prevention at the ROFEMI Study |
title_short | High-Risk Obesity Phenotypes: Target for Multimorbidity Prevention at the ROFEMI Study |
title_sort | high-risk obesity phenotypes: target for multimorbidity prevention at the rofemi study |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9410284/ https://www.ncbi.nlm.nih.gov/pubmed/36012889 http://dx.doi.org/10.3390/jcm11164644 |
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