Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China.
PLoS One. 2012;7(8):e43834. doi: 10.1371/journal.pone.0043834. Epub 2012 Aug 28.
Dyslipidemia is an extremely prevalent but preventable risk factor for cardiovascular disease. However, many dyslipidemia patients remain undetected in resource limited settings. The study was performed to develop and evaluate a simple and effective prediction approach without biochemical parameters to identify those at high risk of dyslipidemia in rural adult population.
Demographic, dietary and lifestyle, and anthropometric data were collected by a cross-sectional survey from 8,914 participants living in rural areas aged 35-78 years. There were 6,686 participants randomly selected into a training group for constructing the artificial neural network (ANN) and logistic regression (LR) prediction models. The remaining 2,228 participants were assigned to a validation group for performance comparisons of ANN and LR models. The predictors of dyslipidemia risk were identified from the training group using multivariate logistic regression analysis. Predictive performance was evaluated by receiver operating characteristic (ROC) curve.
Some risk factors were significantly associated with dyslipidemia, including age, gender, educational level, smoking, high-fat diet, vegetable and fruit intake, family history, physical activity, and central obesity. For the ANN model, the sensitivity, specificity, positive and negative likelihood ratio, positive and negative predictive values were 90.41%, 76.66%, 3.87, 0.13, 76.33%, and 90.58%, respectively, while LR model were only 57.37%, 70.91%, 1.97, 0.60, 62.09%, and 66.73%, respectively. The area under the ROC cure (AUC) value of the ANN model was 0.86±0.01, showing more accurate overall performance than traditional LR model (AUC = 0.68±0.01, P<0.001).
The ANN model is a simple and effective prediction approach to identify those at high risk of dyslipidemia, and it can be used to screen undiagnosed dyslipidemia patients in rural adult population. Further work is planned to confirm these results by incorporating multi-center and longer follow-up data.
血脂异常是心血管疾病的一个极其普遍但可预防的危险因素。然而，在资源有限的环境下，许多血脂异常患者仍未被发现。本研究旨在开发和评估一种简单有效的预测方法，无需生化参数，即可识别农村成年人群中血脂异常高危人群。
通过横断面调查，从 35-78 岁居住在农村地区的 8914 名参与者中收集人口统计学、饮食和生活方式以及人体测量数据。其中 6686 名参与者被随机抽取到训练组，用于构建人工神经网络（ANN）和逻辑回归（LR）预测模型。其余 2228 名参与者被分配到验证组，用于比较 ANN 和 LR 模型的性能。使用多变量逻辑回归分析从训练组中确定血脂异常风险的预测因素。通过接收者操作特征（ROC）曲线评估预测性能。
一些危险因素与血脂异常显著相关，包括年龄、性别、教育程度、吸烟、高脂肪饮食、蔬菜和水果摄入、家族史、身体活动和中心性肥胖。对于 ANN 模型，敏感性、特异性、阳性和阴性似然比、阳性和阴性预测值分别为 90.41%、76.66%、3.87、0.13、76.33%和 90.58%，而 LR 模型仅为 57.37%、70.91%、1.97、0.60、62.09%和 66.73%。ANN 模型的 ROC 曲线下面积（AUC）值为 0.86±0.01，表明整体性能优于传统 LR 模型（AUC=0.68±0.01，P<0.001）。
ANN 模型是一种简单有效的血脂异常高危人群预测方法，可用于筛查农村成年人群中未确诊的血脂异常患者。计划进一步开展工作，通过纳入多中心和更长随访数据来验证这些结果。
