Department of Occupational Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
Center of Planning, Budgeting and Performance Evaluation, Department of Environment, Tehran, Iran.
BMC Endocr Disord. 2020 Nov 12;20(1):169. doi: 10.1186/s12902-020-00645-x.
Metabolic syndrome (MetS) is a major public health concern due to its high prevalence and association with heart disease and diabetes. Artificial neural networks (ANN) are emerging as a reliable means of modelling relationships towards understanding complex illness situations such as MetS. Using ANN, this research sought to clarify predictors of metabolic syndrome (MetS) in a working age population.
Four hundred sixty-eight employees of an oil refinery in Iran consented to providing anthropometric and biochemical measurements, and survey data pertaining to lifestyle, work-related stressors and sleep variables. National Cholesterol Education Programme Adult Treatment Panel ІІI criteria was used for determining MetS status. The Management Standards Indicator Tool and STOP-BANG questionnaire were used to measure work-related stress and obstructive sleep apnoea respectively. With 17 input variables, multilayer perceptron was used to develop ANNs in 16 rounds of learning. ANNs were compared to logistic regression models using the mean squared error criterion for validation.
Sex, age, exercise habit, smoking, high risk of obstructive sleep apnoea, and work-related stressors, particularly Role, all significantly affected the odds of MetS, but shiftworking did not. Prediction accuracy for an ANN using two hidden layers and all available input variables was 89%, compared to 72% for the logistic regression model. Sensitivity was 82.5% for ANN compared to 67.5% for the logistic regression, while specificities were 92.2 and 74% respectively.
Our analyses indicate that ANN models which include psychosocial stressors and sleep variables as well as biomedical and clinical variables perform well in predicting MetS. The findings can be helpful in designing preventative strategies to reduce the cost of healthcare associated with MetS in the workplace.
代谢综合征（MetS）患病率高，与心脏病和糖尿病相关，因此成为一个主要的公共卫生关注点。人工神经网络（ANN）作为一种可靠的建模方法，正在兴起，可以帮助理解代谢综合征等复杂疾病情况。本研究使用 ANN 来明确工作年龄段人群代谢综合征（MetS）的预测因子。
伊朗一家炼油厂的 468 名员工同意提供人体测量和生化测量值以及与生活方式、工作相关压力源和睡眠变量相关的调查数据。采用美国国家胆固醇教育计划成人治疗专家组 III 标准确定 MetS 状态。使用管理标准指标工具和 STOP-BANG 问卷分别测量工作相关压力和阻塞性睡眠呼吸暂停。使用 17 个输入变量，通过 16 轮学习，使用多层感知器开发 ANN。使用均方误差标准比较 ANN 和逻辑回归模型，以验证模型。
性别、年龄、运动习惯、吸烟、患阻塞性睡眠呼吸暂停的高风险，以及工作相关压力源，特别是角色，均显著影响 MetS 的发生几率，但轮班工作并没有影响。使用两层隐藏层和所有可用输入变量的 ANN 的预测准确率为 89%，而逻辑回归模型为 72%。ANN 的灵敏度为 82.5%，逻辑回归模型为 67.5%，特异性分别为 92.2%和 74%。
我们的分析表明，ANN 模型包含社会心理压力源和睡眠变量以及生物医学和临床变量，可很好地预测 MetS。这些发现有助于设计预防策略，以减少与工作场所 MetS 相关的医疗保健成本。
