Romero-Saldaña Manuel, Fuentes-Jiménez Francisco J, Vaquero-Abellán Manuel, Álvarez-Fernández Carlos, Molina-Recio Guillermo, López-Miranda José
Department of Occupational Safety and Health, Córdoba City Hall, Spain
IMIBIC, Reina Sofía University Hospital, University of Córdoba, Córdoba, Spain.
Eur J Cardiovasc Nurs. 2016 Dec;15(7):549-558. doi: 10.1177/1474515115626622. Epub 2016 Jan 7.
We propose a new method for the early detection of metabolic syndrome in the working population, which was free of biomarkers (non-invasive) and based on anthropometric variables, and to validate it in a new working population.
Prevalence studies and diagnostic test accuracy to determine the anthropometric variables associated with metabolic syndrome, as well as the screening validity of the new method proposed, were carried out between 2013 and 2015 on 636 and 550 workers, respectively. The anthropometric variables analysed were: blood pressure, body mass index, waist circumference, waist-height ratio, body fat percentage and waist-hip ratio. We performed a multivariate logistic regression analysis and obtained receiver operating curves to determine the predictive ability of the variables. The new method for the early detection of metabolic syndrome we present is based on a decision tree using chi-squared automatic interaction detection methodology.
The overall prevalence of metabolic syndrome was 14.9%. The area under the curve for waist-height ratio and waist circumference was 0.91 and 0.90, respectively. The anthropometric variables associated with metabolic syndrome in the adjusted model were waist-height ratio, body mass index, blood pressure and body fat percentage. The decision tree was configured from the waist-height ratio (⩾0.55) and hypertension (blood pressure ⩾128/85 mmHg), with a sensitivity of 91.6% and a specificity of 95.7% obtained.
The early detection of metabolic syndrome in a healthy population is possible through non-invasive methods, based on anthropometric indicators such as waist-height ratio and blood pressure. This method has a high degree of predictive validity and its use can be recommended in any healthcare context.
我们提出了一种针对在职人群早期检测代谢综合征的新方法，该方法无需生物标志物（非侵入性），基于人体测量学变量，并在新的在职人群中对其进行验证。
分别于2013年至2015年对636名和550名工人进行了患病率研究和诊断试验准确性研究，以确定与代谢综合征相关的人体测量学变量，以及所提出新方法的筛查有效性。分析的人体测量学变量包括：血压、体重指数、腰围、腰高比、体脂百分比和腰臀比。我们进行了多变量逻辑回归分析，并获得了受试者工作特征曲线以确定变量的预测能力。我们提出的代谢综合征早期检测新方法基于使用卡方自动交互检测方法的决策树。
代谢综合征的总体患病率为14.9%。腰高比和腰围的曲线下面积分别为0.91和0.90。在调整模型中与代谢综合征相关的人体测量学变量为腰高比、体重指数、血压和体脂百分比。决策树由腰高比（⩾0.55）和高血压（血压⩾128/85 mmHg）构建而成，获得的灵敏度为91.6%，特异度为95.7%。
通过基于腰高比和血压等人体测量指标的非侵入性方法，在健康人群中早期检测代谢综合征是可行的。该方法具有高度的预测有效性，可推荐在任何医疗环境中使用。
