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.

中文翻译：

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建模血脂异常和血脂指数的危险因素：Ravansar队列研究。

脂质紊乱是慢性疾病最重要的危险因素之一。确定影响脂质疾病发展的因素有助于减少慢性病，尤其是慢性心脏病（CHD）。这项研究的目的是模拟血脂异常和血脂指数的危险因素。这项研究是基于Ravansar队列研究初始阶段（2014-16年）收集的数据进行的。最初，通过感知器人工神经网络（ANN）数据挖掘模型在灵敏度分析的33个阶段中检查了所有453个可用变量。在每个阶段，确定在血脂异常诊断中更重要的变量。使用逐步回归研究变量之间的关系。在SPSS软件版本25（0）中分析获得的数据。05级的意义。40％的受试者被诊断出患有脂质异常。ANN确定了与营养和身体状况相关的血脂异常的12个预测变量。碱性磷酸酶，无脂肪质量（FFM）指数和血红蛋白（HGB）与所有七个血脂标记物都有显着关系。腰臀比是最有效的变量，与胆固醇和低密度脂质（LDL）的相关性更强。FFM指数对甘油三酸酯，高密度脂质（HDL），胆固醇/ HDL，甘油三酸酯/ HDL和LDL / HDL影响最大。最大的测定系数涉及具有九个变量的甘油三酸酯/ HDL（0.203）和胆固醇/ HDL（0.188）模型以及具有八个变量的LDL / HDL（0.180）模型。根据结果​​，碱性磷酸酶，FFM指数，HGB和HGB是所有血脂指标的三个常见预测变量。专家应集中精力控制这些因素，以便更好地控制血脂指标。