Abstract：Medical University, Heilongjiang Province, Qiqihar 161000, China
[Abstract] Objective To explore a method to predict the learning and memory ability of rats with vascular dementia (VD) by constructing a multiple linear regression model based on LASSO. Methods Forty male clean grade SD rats aged three to five months, weighing (175±25)g, were divided into model group and sham operation group by random number table method with 20 rats in each group. Random number table method was used to select five rats from each group for prediction model validation, and 15 rats from each group for prediction model establishment. The model group was prepared by bilateral common carotid artery occlusion in stages. The common carotid artery was not ligated after isolated in the sham operation group. Morris water maze was used to detect the learning and memory ability (latency) of rats. After 12 weeks of modeling, 42 blood biochemical indexes such as total protein, total bile acid and total cholesterol were detected. The multiple linear regression model between latency and blood biochemical indexes was initially established and the model was tested for multicollinearity. LASSO regression was used to screen the predictors of blood biochemical indexes to further improve the model, and the error rate was calculated to verify the feasibility of the model. Results Compared with sham operation group, latency of model group was significantly prolonged (P ＜ 0.05). Variance inflation factor values of total protein, total bile acid, total cholesterol, and other 26 indexes were more than 10, indicating multicollinearity of the model. LASSO regression screened ten indexes such as total bilirubin, triglyceride, and cholinesterase, and established multiple linear regression model of latency: W = 51.887-0.384x1-0.104x2-0.154x3-4.988x4-1157.079x5-7.308x6+7.639x7+0.183x8-0.025x9+34.528x10 (x1: total protein; x2: total bile acid; x3: total bilirubin; x4: total cholesterol; x5: apolipoprotein A; x6: high density lipoprotein cholesterol; x7: triglyceride; x8: carbon dioxide; x9: cholinesterase; x10: immunoglobulin M) Model correlation coefficient R = 0.852, goodness of fit R2 = 0.725, regression model statistical value F = 5.016, significance P = 0.001, error rate was less than 5%. Conclusion The mathematical prediction model of ten blood biochemical characteristics and latency period established in this study has an obvious linear relationship and a high degree of fitting, which can not only assist in judging the success of replication of experimental VD animal model, but also provide a convenient and fast detection, which is expected to provide a reference for early prediction and intervention of clinical VD patients.
王一帆 许慧 雷贵月 黄歆 田寅魁 孟娜娜 朱坤杰. 基于LASSO的多重线性回归在预测血管性痴呆大鼠学习记忆能力中的应用[J]. 中国医药导报, 2022, 19(1): 13-17,26.
WANG Yifan XU Hui LEI Guiyue HUANG Xin TIAN Yinkui MENG Nana ZHU Kunjie. Application of multiple linear regression based on LASSO in the prediction model of learning and memory ability in vascular dementia rats#br#. 中国医药导报, 2022, 19(1): 13-17,26.
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