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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Vydáno v:Diagnostics (Basel) Ročník 13; číslo 13; s. 2292
Hlavní autoři: Sgarro, Giacinto Angelo, Grilli, Luca, Valenzano, Anna Antonia, Moscatelli, Fiorenzo, Monacis, Domenico, Toto, Giusi, De Maria, Antonella, Messina, Giovanni, Polito, Rita
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI AG 01.07.2023
MDPI
Témata:
Aged
Aging
bioelectrical impedance analysis (BIA)
Body composition
body composition (BC)
Body fat
Body mass index
Bone density
Chronic illnesses
classification
Cluster analysis
Clustering
Development and progression
Ethics
Exercise
Fractures
Lifestyles
Metabolism
Nutrition research
Obesity
Osteoporosis
Physical fitness
Physiological aspects
Risk factors
body composition (BC)
bioelectrical impedance analysis (BIA)
clustering
classification
osteoporosis
ISSN:2075-4418, 2075-4418
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Shrnutí: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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These authors contributed equally to this work.
ISSN:2075-4418
2075-4418
DOI:10.3390/diagnostics13132292