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基于机器学习和logistic回归分析模型分析2型糖尿病轻度认知功能障碍的影响因素

张红梅 张宁 孙玉娇 张洲

张红梅, 张宁, 孙玉娇, 张洲. 基于机器学习和logistic回归分析模型分析2型糖尿病轻度认知功能障碍的影响因素[J]. 中华疾病控制杂志, 2024, 28(3): 269-276. doi: 10.16462/j.cnki.zhjbkz.2024.03.004
引用本文: 张红梅, 张宁, 孙玉娇, 张洲. 基于机器学习和logistic回归分析模型分析2型糖尿病轻度认知功能障碍的影响因素[J]. 中华疾病控制杂志, 2024, 28(3): 269-276. doi: 10.16462/j.cnki.zhjbkz.2024.03.004
ZHANG Hongmei, ZHANG Ning, SUN Yujiao, ZHANG Zhou. Influential factors of mild cognitive impairment in type 2 diabetes mellitus based on machine learning and logistic regression[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(3): 269-276. doi: 10.16462/j.cnki.zhjbkz.2024.03.004
Citation: ZHANG Hongmei, ZHANG Ning, SUN Yujiao, ZHANG Zhou. Influential factors of mild cognitive impairment in type 2 diabetes mellitus based on machine learning and logistic regression[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(3): 269-276. doi: 10.16462/j.cnki.zhjbkz.2024.03.004

基于机器学习和logistic回归分析模型分析2型糖尿病轻度认知功能障碍的影响因素

doi: 10.16462/j.cnki.zhjbkz.2024.03.004
基金项目: 

国家自然科学基金 82000775

南京大学中国医院改革发展研究院课题 NDYG2022078

详细信息
    通讯作者:

    张宁,E-mail:zjz1906@qq.com

  • 中图分类号: R587.1

Influential factors of mild cognitive impairment in type 2 diabetes mellitus based on machine learning and logistic regression

Funds: 

National Natural Science Foundation Project 82000775

Project of Nanjing University China Hospital Reform and Development Research Institute NDYG2022078

More Information
  • 摘要:   目的  应用机器学习和logistic回归分析模型分析2型糖尿病(type 2 diabetes mellitus, T2DM)轻度认知功能障碍的影响因素,为相关干预措施提供依据。  方法  收集南京鼓楼医院内分泌科1 284例T2DM患者的资料,将数据分为训练集(70%)和测试集(30%),分别采用logistic回归分析、随机森林、XGBoost模型对数据建模并进行可解释性分析。  结果  随机森林模型的预测性能最高,训练集的AUC值为1.0,测试集的AUC值为0.783(95% CI:0.660~0.894),模型筛选出T2DM患者发生轻度认知功能障碍的19个重要变量,如受教育时间、年龄、胰岛素敏感指数、周围神经病变、糖化血红蛋白、骨代谢异常等。  结论  随机森林模型的预测性能最佳,可以协助医务人员准确识别T2DM患者发生轻度认知功能障碍的危险因素,有助于医务人员对患者进行早期预防。
  • 图  1  T2DM患者发生MCI的多因素logistic回归分析模型

    T2DM: 2型糖尿病; MCI: 轻度认知功能障碍; GLP-1:胰高糖素样肽-1。

    Figure  1.  Multivariate logistic regression analysis model of MCI in T2DM patients

    T2DM: type 2 diabetes mellitus; MCI: mild cognitive impairment; GLP-1:glucagon-like peptide-1.

    图  2  随机森林模型的变量重要性排序

    Figure  2.  The importance ranking of variables in random forests model

    图  3  逐步随机森林模型袋外数据分类错误率

    Figure  3.  Classification error rate chart of stepwise random forest model out-of-bag data

    图  4  特征重要性排序图

    Figure  4.  Sorting chart of feature importance

    图  5  变量的SHAP摘要图

    Figure  5.  SHAP summary chart of variables

    图  6  Logistic回归分析模型、随机森林模型、XGBoost模型的ROC曲线

    AUC:曲线下面积。

    Figure  6.  ROC curve of logistic regression, random forest model, and XGBoost model

    AUC: area under curve.

    表  1  两组患者疾病资料的比较

    Table  1.   Comparison of disease data between two groups of patients

    变量variable 正常对照组
    Control group
    (n=865)
    MCI组
    group
    (n= 419)
    t/Z/χ2
    值value
    P
    value
    病程/年Course of disease /years 9.56±8.01 10.81±8.28 -2.56 0.010
    年龄/年Age/years 55.34±11.70 61.91±8.60 -11.36 < 0.001
    性别Gender 16.60 < 0.001
      男Male 605(69.94) 245(58.47)
      女Female 260(30.06) 174(41.53)
    受教育时间/年Years of education/years 12.97±3.24 10.41±3.35 13.15 < 0.001
    糖尿病家族史Family history of diabetes 467(53.99) 191(45.58) 7.65 0.006
    合并高血压Concomitant history of hypertension 407(47.05) 244(58.23) 14.12 < 0.001
    合并高血脂Concomitant history of hyperlipidemia 357(41.27) 147(35.08) 4.28 0.039
    体质指数/(kg·m-2) Body mass index/(kg·m-2) 24.84(23.03, 27.12) 24.14(22.23, 26.46) -3.54 < 0.001
    周围神经病变Diabetes peripheral neuropathy 235(27.17) 180(42.96) 31.46 < 0.001
    颈动脉斑块Complications of carotid artery plaques 471(54.45) 296(70.64) 30.11 < 0.001
    糖尿病肾病Complications of diabetic nephropathy 83(9.59) 58(13.84) 4.78 0.029
    骨代谢异常Abnormal bone metabolism 303(35.03) 217(51.79) -20.94 < 0.001
    下肢大血管病变Lower extremity atherosclerosis 408(47.17) 260(62.05) 24.46 < 0.001
    脑血管病史History of cerebrovascular diseases 83(9.60) 74(17.66) 16.37 < 0.001
    心血管病史History of cardiovascular 107(12.37) 85(20.29) 13.30 < 0.001
    使用GLP-1药物 Using GLP-1 medication 23(2.66) 13(3.10) 0.65 0.786
    三酰甘油/(mmol·L-1) Triglyceride/(mmol·L-1) 1.35(0.95, 2.06) 1.30(0.90, 1.83) -1.78 0.075
    胆固醇/(mmol·L-1) Cholesterol/(mmol·L-1) 4.53(3.79, 5.34) 4.45(3.70, 5.27) -1.21 0.225
    低密度脂蛋白胆固醇/(mmol·L-1) Low density lipoprotein cholesterol/(mmol·L-1) 2.64(2.00, 3.30) 2.99(2.32, 3.66) -5.52 < 0.001
    肾小球滤过率/(mL·min·1.73 m-2) Glomeruar filtration rate/(mL·min·1.73 m-2) 117.65(106.20, 135.60) 117.65(98.00, 134.05) -1.68 0.094
    尿酸/(μmol·L-1) Uric acid/(μmol·L-1) 326.00(275.00, 378.00) 305.00(253.00, 363.00) 3.57 < 0.001
    血清骨钙素/(μg·L-1) Serum osteocalcin/(μg·L-1) 12.26(9.87, 15.85) 12.13(9.45, 15.75) 0.57 0.163
    糖化血红蛋白/% Glycosylated hemoglobin/% 8.40(7.00, 10.00) 9.10(7.40, 10.90) -3.99 < 0.001
    空腹血糖/(mmol·L-1) Fasting plasma glucose/(mmol·L-1) 7.76(6.39, 9.85) 8.10(6.78, 10.27) -2.85 0.004
    餐后2 h血糖/(mmol·L-1) 2-hour postprandial blood glucose/(mmol·L-1) 15.50(12.90, 18.40) 16.30(13.15, 19.65) -2.79 0.005
    空腹胰岛素/(μIU·mL-1) Fasting insulin/(μIU·mL-1) 6.69(4.90, 9.91) 6.69(3.66, 8.34) -4.16 < 0.001
    餐后2 h胰岛素/(μIU·mL-1) 2-hour post meal insulin/(μIU·mL-1) 26.22(15.00, 47.20) 24.10(12.05, 40.70) -2.02 < 0.001
    胰岛素抵抗指数Homeostasis model assessment insulin resistance 2.51(1.60, 3.86) 2.21(1.34, 3.52) -2.59 0.010
    胰岛素敏感指数Homeostasis model assessment insulin sensitivity 32.35(18.16, 58.85) 25.00(13.10, 45.35) -4.62 < 0.001
    C-反应蛋白/(mg·L-1) C-reactive protein/(mg·L-1) 2.50(1.70, 3.50) 2.50(1.70, 3.60) -0.27 0.784
    注:GLP-1,胰高糖素样肽-1; MCI, 轻度认知功能障碍。
    ①使用GLP-1药物指使用利拉鲁肽注射液、度拉糖肽注射液; ②以M(P25P75)或人数(占比/%)或x±s表示。
    Note: GLP-1, glucagon-like peptide-1; MCI, mild cognitive impairment.
    ① The use of GLP-1 drug refers to the use of liraglutide injection and dulaglutide injection; ② M(P25P75) or number of people (proportion/%) or x±s.
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  • 收稿日期:  2023-05-27
  • 修回日期:  2023-09-04
  • 网络出版日期:  2024-04-08
  • 刊出日期:  2024-03-10

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