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

Author:

Sgarro Giacinto Angelo1,Grilli Luca1ORCID,Valenzano Anna Antonia2,Moscatelli Fiorenzo2ORCID,Monacis Domenico3ORCID,Toto Giusi3ORCID,De Maria Antonella4,Messina Giovanni2ORCID,Polito Rita2

Affiliation:

1. Department of Economics, Management and Territory (DEMeT) and Grant Office, University of Foggia, 71121 Foggia, Italy

2. Department of Clinical and Experimental Medicine, University of Foggia, 71122 Foggia, Italy

3. Department of Humanities, Letters, Cultural Heritage, Educational Sciences, University of Foggia, 71100 Foggia, Italy

4. Section of Human Physiology and Unit of Dietetics and Sports Medicine, Department of Experimental Medicine, University of Campania “Luigi Vanvitelli”, 80131 Naples, Italy

Abstract

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.

Funder

Project MiSE-DGPIIPMI—Artificial Intelligence to support the digitalization and industrial engineering process—2021

Publisher

MDPI AG

Subject

Clinical Biochemistry

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