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

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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.

Key words： Vascular dementia Learning and memory ability Characteristic index LASSO Multiple linear regression

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	WANG Yifan XU Hui LEI Guiyue HUANG Xin TIAN Yinkui MENG Nana ZHU Kunjie

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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#[J]. 中国医药导报, 2022, 19(1): 13-17,26.

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https://www.yiyaodaobao.com.cn/EN/ OR https://www.yiyaodaobao.com.cn/EN/Y2022/V19/I1/13

[1] 王彦超，于艳辉.缺血性脑卒中血管性认知障碍的危险因素分析[J].中国实用神经疾病杂志，2017，20（4）： 30-33.
[2] 郑远长.关于我国失智老年人研究工作综述及相关数据的比较分析[J].社会福利：理论版，2021（2）：3-15.
[3] 王立存，张玉莲，冯新义，等.血管性痴呆研究进展[J].疑难病杂志，2005，34（6）：373-378.
[4] Gupta M，Dasgupta A，Khwaja GA，et al. Behavioural and psychological symptoms in poststroke vascular cognitive impairment [J]. Behav Neurol，2014，2014：430128.
[5] 黄文革，郭芬芬，刘慰华，等.血管性痴呆动物模型制作方法的改良[J].中国比较医学杂志，2011，21（5）： 49-52.
[6] 周娇娇，阙建宇，于雯雯，等.Morris水迷宫检测动物学习记忆水平的方法学[J].中国老年学杂志，2017，37（24）：6274-6277.
[7] 闫盼盼，闫国立，詹向红，等.Morris水迷宫检实验设计的统计学方法探析[J].中华中医药学刊，2013，31（2）： 264-266.
[8] 卢杰，李雪，王璐，等.不同强度游泳训练对大鼠空间学习记忆和小脑食欲素的影响[J].中国组织工程研究，2021， 25（23）：3697-3703.
[9] Zhao FY，Fu QQ，Zheng Z，et al. Verum- versus sham-acupuncture on Alzheimer’s disease （AD） in animal models： A preclinical systematic review and meta-analysis [J]. Biomed Res Int，2020，2020（3）：1-21.
[10] Vatcheva KP，Lee M，McCormick JB，et al. Multicollinearity in Regression Analyses Conducted in Epidemiologic Studies [J]. Epidemiology，2016，6（2）：227.
[11] McEligot AJ，Poynor V，Sharma R，et al. Logistic LASSO Regression for Dietary Intakes and Breast Cancer [J]. Nutrients，2020，12（9）：2652.
[12] Llorens F，Hermann P，Anna VP，et al. Cerebrospinal fluid lipocalin 2 as a novel biomarker for the differential diagnosis of vascular dementia [J]. Nat Commun，2020， 11（1）：619.
[13] Venkat P，Chopp M，Chen J. Models and Mechanisms of Vascular Dementia [J]. Exp Neurol，2015，272：97-108.
[14] 陈罗西，郭玲玲，李亮.Morris圆形水迷宫的应用及其相关检测指标分析[J].辽宁中医药大学学报，2008，10（8）：55-57.
[15] 王晓燕，侯爱生，宋玉祥，等.小鼠学习记忆能力及海马区突触功能相关蛋白BDNF、PSD95、GluA1的增龄性变化[J].解放军医学院学报，2020，41（8）：808-812，837.
[16] 蒋宁，张宏超，刘港，等.快速老化小鼠脑内能量代谢及学习记忆能力的研究[J].中国药理学与毒理学杂志，2019， 33（6）：463.
[17] 王绪平，呙登俊，陈宇，等.Morris水迷宫实验评价血管性痴呆大鼠模型的学习记忆能力[J].中华中医药学刊，2013，31（1）：99-100.
[18] 宋博策，刘剑刚，李浩.构建血管性痴呆中西医结合临床预测模型的基本因素和策略构想[J].世界科学技术-中医药现代化，2020，22（5）：1654-1661.
[19] 刘晶靓.低频电刺激术治疗脑卒中后吞咽功能障碍对患者血清ALB、TP水平的影响[J].现代医学与健康研究电子杂志，2020，4（22）：27-29.
[20] 华键，周其达.急性脑梗死早期神经功能恶化与高密度脂蛋白胆固醇及脂蛋白（a）的关系[J].中华老年心脑血管病杂志，2018，20（4）：405-407.
[21] 刘恋，李梦，罗鹏，等.高密度脂蛋白与痴呆症研究进展[J].中国老年学杂志，2020，40（9）：1995-1999.
[22] 张丽，李富慧，许鹏飞.老年急性缺血性脑卒中患者脂蛋白、总胆红素、尿酸与其他因素的相关性分析[J].临床医学研究与实践，2020，5（31）：86-87，90.
[23] 刘亚军，徐艳.高密度脂蛋白胆固醇与老年心脑血管疾病的关系[J].中国医药科学，2013，3（2）：203-204.
[24] 王名南，万泽民，娄勍，等.血清非高密度脂蛋白胆固醇与脑梗死急性期神经功能损害的相关性分析[J].中国当代医药，2021，28（18）：4-7.
[25] 侍洪斌.成人血清免疫球蛋白M与甘油三酯相关性研究[D].天津：天津医科大学，2015.
[26] Zou J，Chen ZG，Cai QL，et al. Trefoil Factor 3，Cholinesterase and Homocysteine：Potential Predictors for Parkinson’s Disease Dementia and Vascular Parkinsonism Dementia in Advanced Stage [J]. Aging Dis，2018，9（1）：51-65.