Júnior Antonio Costa, França Ana Karina, Santos Elisângela Dos, Silveira Victor, Santos Alcione Dos
Coordenação do Curso de Medicina, Centro de Ciências de Pinheiro, Universidade Federal do Maranhão, São Luís 65200-000, Brazil.
Programa de Pós-Graduação em Saúde Coletiva, Departamento de Saúde Pública, Universidade Federal do Maranhão, São Luís 65020-070, Brazil.
J Clin Med. 2024 Oct 3;13(19):5914. doi: 10.3390/jcm13195914.
The prevalence of metabolic syndrome (MetS) is increasing worldwide, and an increasing number of cases are diagnosed in younger age groups. This study aimed to propose predictive models based on demographic, anthropometric, and non-invasive clinical variables to predict MetS in adolescents. : A total of 2064 adolescents aged 18-19 from São Luís-Maranhão, Brazil were enrolled. Demographic, anthropometric, and clinical variables were considered, and three criteria for diagnosing MetS were employed: Cook et al., De Ferranti et al. and the International Diabetes Federation (IDF). A feed-forward artificial neural network (ANN) was trained to predict MetS. Accuracy, sensitivity, and specificity were calculated to assess the ANN's performance. The ROC curve was constructed, and the area under the curve was analyzed to assess the discriminatory power of the networks. : The prevalence of MetS in adolescents ranged from 5.7% to 12.3%. The ANN that used the Cook et al. criterion performed best in predicting MetS. ANN 5, which included age, sex, waist circumference, weight, and systolic and diastolic blood pressure, showed the best performance and discriminatory power (sensitivity, 89.8%; accuracy, 86.8%). ANN 3 considered the same variables, except for weight, and exhibited good sensitivity (89.0%) and accuracy (87.0%). : Using non-invasive measures allows for predicting MetS in adolescents, thereby guiding the flow of care in primary healthcare and optimizing the management of public resources.
代谢综合征（MetS）在全球范围内的患病率正在上升，且越来越多的病例在较年轻的年龄组中被诊断出来。本研究旨在基于人口统计学、人体测量学和非侵入性临床变量提出预测模型，以预测青少年的代谢综合征。：共纳入了来自巴西圣路易斯 - 马拉尼昂的2064名18 - 19岁的青少年。考虑了人口统计学、人体测量学和临床变量，并采用了三种代谢综合征诊断标准：库克等人的标准、德费兰蒂等人的标准以及国际糖尿病联盟（IDF）的标准。训练了一个前馈人工神经网络（ANN）来预测代谢综合征。计算准确率、敏感性和特异性以评估人工神经网络的性能。构建了ROC曲线，并分析曲线下面积以评估网络的判别能力。：青少年代谢综合征的患病率在5.7%至12.3%之间。使用库克等人标准的人工神经网络在预测代谢综合征方面表现最佳。ANN 5纳入了年龄、性别、腰围、体重以及收缩压和舒张压，表现出最佳性能和判别能力（敏感性为89.8%；准确率为86.8%）。ANN 3考虑了相同的变量，但不包括体重，具有良好的敏感性（89.0%）和准确率（87.0%）。：使用非侵入性测量方法能够预测青少年的代谢综合征，从而指导初级医疗保健中的护理流程并优化公共资源的管理。
