RT Journal Article
T1 High-Risk Obesity Phenotypes: Target for Multimorbidity Prevention at the ROFEMI Study.
A1 Carretero-Gomez, Juana
A1 Perez-Martinez, Pablo
A1 Segui-Ripoll, Jose Miguel
A1 Carrasco-Sanchez, Francisco Javier
A1 Lois Martinez, Nagore
A1 Fernandez Perez, Esther
A1 Perez Hernandez, Onan
A1 Garcia Ordoñez, Miguel Angel
A1 Martin Gonzalez, Candelaria
A1 Vigueras-Perez, Juan Francisco
A1 Puchades, Francesc
A1 Blasco Avaria, Maria Cristina
A1 Perez Soto, Maria Isabel
A1 Ena, Javier
A1 Arevalo-Lorido, Jose Carlos
K1 Adiposity
K1 Inflammation
K1 Obesity
K1 Phenotypes
K1 Waist circumference
K1 Área de Gestión Sanitaria Norte de Málaga
AB 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  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 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 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  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 obesityrequire 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.
PB MDPI
SN 2077-0383
YR 2022
FD 2022-08-02
LK http://hdl.handle.net/10668/21332
UL http://hdl.handle.net/10668/21332
LA en
NO Carretero-Gómez J, Pérez-Martínez P, Seguí-Ripoll JM, Carrasco-Sánchez FJ, Lois Martínez N, Fernández Pérez E, et al. High-Risk Obesity Phenotypes: Target for Multimorbidity Prevention at the ROFEMI Study. J Clin Med. 2022 Aug 9;11(16):4644
DS RISalud
RD Apr 6, 2025