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The Role of BIA Analysis in Osteoporosis Risk Development: Hierarchical Clustering Approach

Osteoporosis is a common musculoskeletal disorder among the elderly and a chronic condition which, like many other chronic conditions, requires long-term clinical management. It is caused by many factors, including lifestyle and obesity. Bioelectrical impedance analysis (BIA) is a method to estimate...

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Autores principales: Sgarro, Giacinto Angelo, Grilli, Luca, Valenzano, Anna Antonia, Moscatelli, Fiorenzo, Monacis, Domenico, Toto, Giusi, De Maria, Antonella, Messina, Giovanni, Polito, Rita
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10340711/
https://www.ncbi.nlm.nih.gov/pubmed/37443685
http://dx.doi.org/10.3390/diagnostics13132292
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author Sgarro, Giacinto Angelo
Grilli, Luca
Valenzano, Anna Antonia
Moscatelli, Fiorenzo
Monacis, Domenico
Toto, Giusi
De Maria, Antonella
Messina, Giovanni
Polito, Rita
author_facet Sgarro, Giacinto Angelo
Grilli, Luca
Valenzano, Anna Antonia
Moscatelli, Fiorenzo
Monacis, Domenico
Toto, Giusi
De Maria, Antonella
Messina, Giovanni
Polito, Rita
author_sort Sgarro, Giacinto Angelo
collection PubMed
description Osteoporosis is a common musculoskeletal disorder among the elderly and a chronic condition which, like many other chronic conditions, requires long-term clinical management. It is caused by many factors, including lifestyle and obesity. Bioelectrical impedance analysis (BIA) is a method to estimate body composition based on a weak electric current flow through the body. The measured voltage is used to calculate body bioelectrical impedance, divided into resistance and reactance, which can be used to estimate body parameters such as total body water (TBW), fat-free mass (FFM), fat mass (FM), and muscle mass (MM). This study aims to find the tendency of osteoporosis in obese subjects, presenting a method based on hierarchical clustering, which, using BIA parameters, can group patients who show homogeneous characteristics. Grouping similar patients into clusters can be helpful in the field of medicine to identify disorders, pathologies, or more generally, characteristics of significant importance. Another added value of the clustering process is the possibility to define cluster prototypes, i.e., imaginary patients who represent models of “states”, which can be used together with clustering results to identify subjects with similar characteristics in a classification context. The results show that hierarchical clustering is a method that can be used to provide the detection of states and, consequently, supply a more personalized medicine approach. In addition, this method allowed us to elect BIA as a potential prognostic and diagnostic instrument in osteoporosis risk development.
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spelling pubmed-103407112023-07-14 The Role of BIA Analysis in Osteoporosis Risk Development: Hierarchical Clustering Approach Sgarro, Giacinto Angelo Grilli, Luca Valenzano, Anna Antonia Moscatelli, Fiorenzo Monacis, Domenico Toto, Giusi De Maria, Antonella Messina, Giovanni Polito, Rita Diagnostics (Basel) Article Osteoporosis is a common musculoskeletal disorder among the elderly and a chronic condition which, like many other chronic conditions, requires long-term clinical management. It is caused by many factors, including lifestyle and obesity. Bioelectrical impedance analysis (BIA) is a method to estimate body composition based on a weak electric current flow through the body. The measured voltage is used to calculate body bioelectrical impedance, divided into resistance and reactance, which can be used to estimate body parameters such as total body water (TBW), fat-free mass (FFM), fat mass (FM), and muscle mass (MM). This study aims to find the tendency of osteoporosis in obese subjects, presenting a method based on hierarchical clustering, which, using BIA parameters, can group patients who show homogeneous characteristics. Grouping similar patients into clusters can be helpful in the field of medicine to identify disorders, pathologies, or more generally, characteristics of significant importance. Another added value of the clustering process is the possibility to define cluster prototypes, i.e., imaginary patients who represent models of “states”, which can be used together with clustering results to identify subjects with similar characteristics in a classification context. The results show that hierarchical clustering is a method that can be used to provide the detection of states and, consequently, supply a more personalized medicine approach. In addition, this method allowed us to elect BIA as a potential prognostic and diagnostic instrument in osteoporosis risk development. MDPI 2023-07-06 /pmc/articles/PMC10340711/ /pubmed/37443685 http://dx.doi.org/10.3390/diagnostics13132292 Text en © 2023 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
Sgarro, Giacinto Angelo
Grilli, Luca
Valenzano, Anna Antonia
Moscatelli, Fiorenzo
Monacis, Domenico
Toto, Giusi
De Maria, Antonella
Messina, Giovanni
Polito, Rita
The Role of BIA Analysis in Osteoporosis Risk Development: Hierarchical Clustering Approach
title The Role of BIA Analysis in Osteoporosis Risk Development: Hierarchical Clustering Approach
title_full The Role of BIA Analysis in Osteoporosis Risk Development: Hierarchical Clustering Approach
title_fullStr The Role of BIA Analysis in Osteoporosis Risk Development: Hierarchical Clustering Approach
title_full_unstemmed The Role of BIA Analysis in Osteoporosis Risk Development: Hierarchical Clustering Approach
title_short The Role of BIA Analysis in Osteoporosis Risk Development: Hierarchical Clustering Approach
title_sort role of bia analysis in osteoporosis risk development: hierarchical clustering approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10340711/
https://www.ncbi.nlm.nih.gov/pubmed/37443685
http://dx.doi.org/10.3390/diagnostics13132292
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