Professor of Biostatistics, Biostatistics Department, Social Development and Health Promotion Research Center, Kermanshah University of medical sciences, Kermanshah, Iran.
Master of Biostatistics, Student's research committee, Faculty of Health, Kermanshah University of medical sciences, Kermanshah, Iran.
Lipids Health Dis. 2020 Jul 28;19(1):176. doi: 10.1186/s12944-020-01354-z.
Lipid disorder is one of the most important risk factors for chronic diseases. Identifying the factors affecting the development of lipid disorders helps reduce chronic diseases, especially Chronic Heart Disease (CHD). The aim of this study was to model the risk factors for dyslipidemia and blood lipid indices.
This study was conducted based on the data collected in the initial phase of Ravansar cohort study (2014-16). At the beginning, all the 453 available variables were examined in 33 stages of sensitivity analysis by perceptron Artificial Neural Network (ANN) data mining model. In each stage, the variables that were more important in the diagnosis of dyslipidemia were identified. The relationship among the variables was investigated using stepwise regression. The data obtained were analyzed in SPSS software version 25, at 0.05 level of significance.
Forty percent of the subjects were diagnosed with lipid disorder. ANN identified 12 predictor variables for dyslipidemia related to nutrition and physical status. Alkaline phosphatase, Fat Free Mass (FFM) index, and Hemoglobin (HGB) had a significant relationship with all the seven blood lipid markers. The Waist Hip Ratio was the most effective variable that showed a stronger correlation with cholesterol and Low-Density Lipid (LDL). The FFM index had the greatest effect on triglyceride, High-Density Lipid (HDL), cholesterol/HDL, triglyceride/HDL, and LDL/HDL. The greatest coefficients of determination pertained to the triglyceride/HDL (0.203) and cholesterol/HDL (0.188) model with nine variables and the LDL/HDL (0.180) model with eight variables.
According to the results, alkaline phosphatase, FFM index, and HGB were three common predictor variables for all the blood lipid markers. Specialists should focus on controlling these factors in order to gain greater control over blood lipid markers.
脂质紊乱是慢性疾病最重要的危险因素之一。确定影响脂质紊乱发展的因素有助于减少慢性疾病，尤其是慢性心脏病（CHD）。本研究旨在建立血脂紊乱和血脂指标的危险因素模型。
本研究基于 Ravansar 队列研究（2014-16 年）初始阶段收集的数据进行。首先，通过感知器人工神经网络（ANN）数据挖掘模型对 33 个阶段的敏感性分析中可用的 453 个变量进行检查。在每个阶段，确定对血脂紊乱诊断更重要的变量。使用逐步回归研究变量之间的关系。使用 SPSS 软件版本 25 对获得的数据进行分析，显著性水平为 0.05。
40%的研究对象被诊断为血脂异常。ANN 确定了与营养和身体状况有关的 12 个血脂紊乱预测变量。碱性磷酸酶、无脂肪质量（FFM）指数和血红蛋白（HGB）与所有七种血脂标志物均有显著关系。腰臀比是与胆固醇和低密度脂蛋白（LDL）相关性最强的最有效变量。FFM 指数对甘油三酯、高密度脂蛋白（HDL）、胆固醇/HDL、甘油三酯/HDL 和 LDL/HDL 的影响最大。决定系数最高的是包含九个变量的甘油三酯/HDL（0.203）和胆固醇/HDL（0.188）模型，以及包含八个变量的 LDL/HDL（0.180）模型。
根据结果，碱性磷酸酶、FFM 指数和 HGB 是所有血脂标志物的三个共同预测变量。专家应重点控制这些因素，以便更好地控制血脂标志物。
