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: | , , , , , , , , , , , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Universidad Miguel Hernández de Elche |
| Repositorio: | REDIUMH. Depósito Digital de la UMH |
| OAI Identifier: | oai:dspace.umh.es:11000/37870 |
| Acceso en línea: | https://hdl.handle.net/11000/37870 |
| Access Level: | acceso abierto |
| Palabra clave: | adiposity inflammation obesity phenotypes waist circumference |
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High-Risk Obesity Phenotypes: Target for Multimorbidity Prevention at the ROFEMI StudyCarretero-Gómez, JuanaPérez Martínez, PabloSeguí-Ripoll, José MiguelCarrasco-Sánchez, Francisco JavierLois Martínez, NagoreFernández Pérez, EstherPérez Hernández, OnánGarcía Ordoñez, Miguel ÁngelMartín González, CandelariaVigueras-Pérez, Juan FranciscoPuchades, FrancescBlasco Avaria, María CristinaPérez Soto, María IsabelEna, JavierArévalo-Lorido, José Carlosadiposityinflammationobesityphenotypeswaist circumferenceBackground: 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/m2 or BMI 30−34.9 Kg/m2 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.MDPIDepartamentos de la UMH::Medicina Clínica202520252022info:eu-repo/semantics/articleapplication/pdf14application/pdfhttps://hdl.handle.net/11000/37870reponame:REDIUMH. Depósito Digital de la UMHinstname:Universidad Miguel Hernández de ElcheInglés10.3390/jcm11164644info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/oai:dspace.umh.es:11000/378702026-05-27T13:36:21Z |
| dc.title.none.fl_str_mv |
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 |
| spellingShingle |
High-Risk Obesity Phenotypes: Target for Multimorbidity Prevention at the ROFEMI Study Carretero-Gómez, Juana adiposity inflammation obesity phenotypes waist circumference |
| title_short |
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_sort |
High-Risk Obesity Phenotypes: Target for Multimorbidity Prevention at the ROFEMI Study |
| dc.creator.none.fl_str_mv |
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 |
Carretero-Gómez, Juana |
| 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_role |
author |
| author2 |
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 |
| author2_role |
author author author author author author author author author author author author author author |
| dc.contributor.none.fl_str_mv |
Departamentos de la UMH::Medicina Clínica |
| dc.subject.none.fl_str_mv |
adiposity inflammation obesity phenotypes waist circumference |
| topic |
adiposity inflammation obesity phenotypes waist circumference |
| 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/m2 or BMI 30−34.9 Kg/m2 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. |
| publishDate |
2022 |
| dc.date.none.fl_str_mv |
2022 2025 2025 |
| dc.type.none.fl_str_mv |
info:eu-repo/semantics/article |
| format |
article |
| dc.identifier.none.fl_str_mv |
https://hdl.handle.net/11000/37870 |
| url |
https://hdl.handle.net/11000/37870 |
| dc.language.none.fl_str_mv |
Inglés |
| language_invalid_str_mv |
Inglés |
| dc.relation.none.fl_str_mv |
10.3390/jcm11164644 |
| dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| eu_rights_str_mv |
openAccess |
| rights_invalid_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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application/pdf 14 application/pdf |
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MDPI |
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MDPI |
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reponame:REDIUMH. Depósito Digital de la UMH instname:Universidad Miguel Hernández de Elche |
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Universidad Miguel Hernández de Elche |
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REDIUMH. Depósito Digital de la UMH |
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REDIUMH. Depósito Digital de la UMH |
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