Sgarro Giacinto Angelo, Grilli Luca, Valenzano Anna Antonia, Moscatelli Fiorenzo, Monacis Domenico, Toto Giusi, De Maria Antonella, Messina Giovanni, Polito Rita
Department of Economics, Management and Territory (DEMeT) and Grant Office, University of Foggia, 71121 Foggia, Italy.
Department of Clinical and Experimental Medicine, University of Foggia, 71122 Foggia, Italy.
Diagnostics (Basel). 2023 Jul 6;13(13):2292. doi: 10.3390/diagnostics13132292.
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.
骨质疏松症是老年人常见的肌肉骨骼疾病,是一种慢性病,与许多其他慢性病一样,需要长期的临床管理。它由多种因素引起,包括生活方式和肥胖。生物电阻抗分析(BIA)是一种基于通过身体的微弱电流来估计身体成分的方法。测量的电压用于计算身体生物电阻抗,分为电阻和电抗,可用于估计身体参数,如总体水(TBW)、去脂体重(FFM)、脂肪量(FM)和肌肉量(MM)。本研究旨在发现肥胖受试者骨质疏松症的趋势,提出一种基于层次聚类的方法,该方法使用BIA参数,可以对具有同质特征的患者进行分组。将相似患者分组为聚类在医学领域有助于识别疾病、病理状况,或更一般地说,识别具有重要意义的特征。聚类过程的另一个附加值是可以定义聚类原型,即代表“状态”模型的虚拟患者,可与聚类结果一起用于在分类背景下识别具有相似特征的受试者。结果表明,层次聚类是一种可用于提供状态检测的方法,因此可以提供更个性化的医疗方法。此外,该方法使我们能够选择BIA作为骨质疏松症风险发展的潜在预后和诊断工具。