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中华妇幼临床医学杂志(电子版) ›› 2024, Vol. 20 ›› Issue (01) : 105 -113. doi: 10.3877/cma.j.issn.1673-5250.2024.01.014

论著

妊娠期糖尿病早孕期相关影响因素及基于早孕期孕妇糖脂相关生化指标与人口学资料的4种机器学习算法构建妊娠期糖尿病预测模型的临床价值
李莉1, 马梅2, 黄欣欣3, 杨丹林1, 潘勉1,†()   
  1. 1. 福建省妇幼保健院·福建医科大学妇儿临床医学院产科，福州　350001
    2. 福建省妇幼保健院·福建医科大学妇儿临床医学院检验科，福州　350001
    3. 福建省妇幼保健院·福建医科大学妇儿临床医学院保健部，福州　350001
  • 收稿日期:2023-11-11 修回日期:2024-01-06 出版日期:2024-02-01
  • 通信作者: 潘勉

Analysis of early pregnancy-related influencing factors of gestational diabetes mellitus, and clinical value of building gestational diabetes mellitus prediction model based on four machine learning algorithms of glycolipid-related biochemical indexes and demographic information of pregnant women in early pregnancy

Li Li1, Mei Ma2, Xinxin Huang3, Danlin Yang1, Mian Pan1,†()   

  1. 1. Department of Obstetrics, Fujian Maternity and Child Health Hospital, College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, Fuzhou 350001, Fujian Province, China
    2. Department of Laboratory Medicine, Fujian Maternity and Child Health Hospital, College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, Fuzhou 350001, Fujian Province, China
    3. Department of Healthcare, Fujian Maternity and Child Health Hospital, College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, Fuzhou 350001, Fujian Province, China
  • Received:2023-11-11 Revised:2024-01-06 Published:2024-02-01
  • Corresponding author: Mian Pan
  • Supported by:
    Natural Science Foundation of Department of Science and Technology in Fujian Province(2021J01406)
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引用本文：

李莉, 马梅, 黄欣欣, 杨丹林, 潘勉. 妊娠期糖尿病早孕期相关影响因素及基于早孕期孕妇糖脂相关生化指标与人口学资料的4种机器学习算法构建妊娠期糖尿病预测模型的临床价值[J]. 中华妇幼临床医学杂志(电子版), 2024, 20(01): 105-113.

Li Li, Mei Ma, Xinxin Huang, Danlin Yang, Mian Pan. Analysis of early pregnancy-related influencing factors of gestational diabetes mellitus, and clinical value of building gestational diabetes mellitus prediction model based on four machine learning algorithms of glycolipid-related biochemical indexes and demographic information of pregnant women in early pregnancy[J]. Chinese Journal of Obstetrics & Gynecology and Pediatrics(Electronic Edition), 2024, 20(01): 105-113.

目的

探讨妊娠期糖尿病(GDM)的早孕期相关影响因素，以及基于早孕期孕妇糖脂相关生化指标及人口学资料，采用4种机器学习算法构建GDM预测模型的临床价值。

方法

选择2021年12月至2022年12月在福建省妇幼保健院首次进行产前检查的6 257例孕龄为10～13＋6孕周孕妇为研究对象。采取回顾性分析法，根据孕妇24～27＋6孕周(中孕期)时是否被诊断为GDM，将其分为GDM组(n＝1 499，GDM孕妇)和非GDM组(n＝4 758，非GDM孕妇)。采用多因素非条件logistic回归分析法，对孕妇发生GDM的早孕期相关影响因素进行分析。基于早孕期孕妇糖脂相关生化指标和人口学资料(8个变量)，采用决策树(DT)、逻辑回归(LR)、随机森林(RF)及极致梯度提升(XGB) 4种机器学习算法构建GDM预测模型，并且采用十折交叉验证，评估4种模型的GDM预测性能；并对4种算法构建GDM预测模型的受试者工作特征(ROC)曲线的曲线下面积(AUC)进行比较。本研究经福建省妇幼保健院伦理委员会批准(审批文号：2021KRD018)。所有孕妇签署临床研究知情同意书。

结果

①多因素非条件logistic回归分析结果显示，孕妇高龄(分娩年龄≥35岁)(OR＝1.95，95%CI：1.70～2.24，P<0.001)，孕前人体质量指数(BMI)≥18.5～24.0 kg/m2(OR＝1.32，95%CI：1.11～1.58，P＝0.002)，孕前BMI≥24.0～28.0 kg/m2(OR＝2.17，95%CI：1.73～2.73，P<0.001)，孕前BMI≥28.0 kg/m2(OR＝2.53，95%CI：1.70～3.78，P<0.001)，早孕期血清载脂蛋白(Apo)B水平升高(OR＝3.06，95%CI：2.14～4.37，P<0.001)，早孕期血清空腹血糖(FPG)浓度增加(OR＝2.08，95%CI：1.79～2.41，P<0.001)，均为孕妇发生GDM的早孕期相关独立危险因素。②根据4种分类器的分类结果中特征值大小，采用孕妇年龄、受教育程度及孕前BMI，早孕期血清总胆固醇(TC)、甘油三酯(TG)、ApoA1、ApoB及FPG水平8个变量进行GDM预测模型构建的结果显示：DT、LR、RF、XGB 4种算法建立的GDM预测模型的AUC分别为0.645(95%CI：0.591～0.698)、0.699(95%CI：0.641～0.749)、0.672(95%CI：0.621～0.772)、0.597(95%CI：0.553～0.663)，LR算法的AUC大于XGB算法，并且差异有统计学意义(Z＝2.38、P＝0.017)，其余各算法的AUC分别两两比较，差异均无统计学意义(P>0.05)。③十折交叉验证结果显示，DT、LR、RF、XGB 4种算法构建GDM预测模型的平均AUC分别为0.586±0.025、0.661±0.020、0.632±0.023、0.576±0.019。

结论

基于早孕期糖脂相关生化指标及人口学资料，采用LR和RF算法构建GDM预测模型，对GDM具有一定预测价值，有助于GDM高危人群早期筛查，必要时对其进行临床干预，可降低母儿GDM相关不良妊娠结局。

关键词: 妊娠初期, 糖尿病，妊娠, 血糖, 血脂, 机器学习, 算法, Logistic模型, 决策树, 随机森林
Objective

To investigate the early pregnancy-related influencing factors of gestational diabetes mellitus (GDM), as well as the clinical value of building GDM prediction model based on the glycolipids-related biochemical indexes in early pregnancy and demographic information using four machine learning algorithms.

Methods

A total of 6 257 pregnant women with gestational age of 10 to 13+ 6 gestational weeks who had their first prenatal examinations in Fujian Maternity and Child Health Hospital from December 2021 to December 2022 were selected for the study. The pregnant women were categorized into the GDM group (n=1 499, GDM pregnant women) and the non-GDM group (n=4 758, non-GDM pregnant women) according to whether or not they were diagnosed with GDM at 24 to 27+ 6 gestational weeks by retrospective analysis. Early pregnancy-related influencing factors on the development of GDM in pregnant women were analyzed using multivariate unconditional logistic regression analysis. Based on the biochemical indexes related to glycolipids in early pregnancy and demographic information in pregnant women (8 variables), four machine learning algorithms, namely, decision tree (DT), logistic regression (LR), random forest (RF), and extreme gradient boosting (XGB) were used to build GDM prediction models, and ten-fold cross-validation was used to assess the performance of each model, and area under curve (AUC) of the receiver operating characteristic (ROC) curve among the GDM prediction models constructed by the four algorithms were compared. The study was approved by the Ethics Committee of Fujian Maternity and Child Health Hospital (Approval No. 2021KRD018). All pregnant women had signed the informed consent forms for clinical research.

Results

①The results of multivariate unconditional logistic regression analysis showed that pregnant women with advanced age (delivery age≥35 years) (OR=1.95, 95%CI: 1.70-2.24, P<0.001), with pre-pregnancy body mass index (BMI) ≥ 18.5-24.0 kg/m2 (OR=1.32, 95%CI: 1.11-1.58, P=0.002), pre-pregnancy BMI ≥ 24.0-28.0 kg/m2 (OR=2.17, 95%CI: 1.73-2.73, P<0.001), pre-pregnancy BMI ≥28.0 kg/m2 (OR=2.53, 95%CI: 1.70-3.78, P<0.001), elevated serum apolipoprotein (Apo) B levels during early pregnancy (OR=3.06, 95%CI: 2.14-4.37, P<0.001), and increased serum fasting glucose (FPG) concentration in early pregnancy (OR=2.08, 95%CI: 1.79-2.41, P<0.001) were all independent early pregnancy-related risk factors for the development of GDM in pregnant women. ②According to the magnitude of the eigenvalue in the classification results of the 4 classifiers, the results of GDM prediction model constructed using 8 variables of maternal age, degree of education and pre-pregnancy BMI, serum levels of total cholesterol (TC), triglyceride (TG), ApoA1, ApoB, and FPG during early pregnancy showed that the AUC of the GDM prediction models built by the 4 algorithms, namely, DT, LR, RF, and XGB, were 0.645 (95%CI: 0.591-0.698), 0.699 (95%CI: 0.641-0.749), 0.672 (95%CI: 0.621-0.772), and 0.597 (95%CI: 0.553-0.663), respectively, and the AUC of the LR algorithm was greater than that of the XGB algorithm, and the difference was statistically significant (Z=2.38, P=0.017), and there was no significant difference in AUC of pairwise comparison of the rest of the algorithms (P>0.05). ③The ten-fold cross-validation results showed that the average AUC of the GDM prediction models constructed by the four algorithms, DT, LR, RF, and XGB, were 0.586±0.025, 0.661±0.020, 0.632±0.023, and 0.576±0.019, respectively.

Conclusions

Based on the biochemical indexes related to glycolipids in early pregnancy and demographic data, GDM prediction models constructed with LR and RF algorithms, which has a certain predictive value for GDM, and helps to screen the high-risk group of GDM at early stage, and to provide clinical interventions when necessary to reduce GDM-related adverse pregnancy outcome of mother and fetus.

Key words: Pregnancy trimester, first, Diabetes, gestational, Blood glucose, Blood lipids, Machine learning, Algorithms, Logistic models, Decision trees, Random forest
表1 2组早孕期孕妇一般临床资料及糖脂相关生化指标比较
组别 例数 年龄[例数(%)] 受教育程度[例数(%)]
≥35岁 <35岁 初中/中专及以下 高中/大专 大学本科 研究生及以上
GDM组 1 499 543(36.2) 956(63.8) 273(18.3) 484(32.4) 637(42.6) 102(6.8)
非GDM组 4 758 943(19.8) 3 815(80.2) 752(15.8) 1 486(31.3) 2 130(44.9) 378(8.0)
统计量   χ2＝168.49 χ2＝7.69
P值   <0.001 0.053
组别 例数 孕前BMI[例数(%)] 血清TC浓度[例数(%)]
<18.5 kg/m2 ≥18.5～24.0 kg/m2 ≥24.0～28.0 kg/m2 ≥28.0 kg/m2 <5.18 mmol/L ≥5.18 mmol/L
GDM组 1 499 182(12.1) 990(66.0) 272(18.2) 55(3.7) 1 252(83.5) 247(16.5)
非GDM组 4 758 954(20.1) 3 278(68.9) 453(9.5) 73(1.5) 4 134(86.9) 624(13.1)
统计量   χ2＝139.20 χ2＝10.48
P值   <0.001 0.001
组别 例数 血清TG浓度[例数(%)] 血清HDL-C浓度[例数(%)] 血清LDL-C浓度[例数(%)]
<1.7 mmol/L ≥1.7 mmol/L <1.04 mmol/L ≥1.04 mmol/L <3.37 mmol/L ≥3.37 mmol/L
GDM组 1 499 1 255(83.7) 244(16.3) 10(0.7) 1 489(99.3) 1 460(97.4) 39(2.6)
非GDM组 4 758 4 052(85.2) 706(14.8) 15(0.3) 4 743(99.7) 4 671(98.2) 87(1.8)
统计量   χ2＝1.72 χ2＝2.72 χ2＝3.07
P值   0.189 0.099 0.080
组别 例数 血清ApoA1水平(g/L，±s) 血清ApoB水平(g/L，±s) 血清FPG浓度(mmol/L，±s) ApoB/A1[M(Q1，Q3)]
GDM组 1 499 1.57±0.23 0.76±0.16 4.7±0.5 0.481(0.411，0.561)
非GDM组 4 758 1.55±0.23 0.72±0.17 4.6±0.4 0.458(0.393，0.535)
统计量   t＝2.79 t＝8.50 t＝12.65 Z＝6.46
P值   0.005 <0.001 <0.001 <0.010
表2 影响孕妇发生GDM的多因素非条件logistic回归分析
影响因素 B SE Wald值 P值 OR值 OR值95%CI
孕妇年龄≥35岁(vs <35岁) 0.67 0.07 97.81 <0.001 1.95 1.70～2.24
孕前BMI(vs <18.5 kg/m2)            
≥18.5～24.0 kg/m2 0.28 0.09 9.49 0.002 1.32 1.11～1.58
≥24.0～28.0 kg/m2 0.78 0.12 44.57 <0.001 2.17 1.73～2.73
≥28.0 kg/m2 0.93 0.20 20.63 <0.001 2.53 1.70～3.78
早孕期血清ApoB(连续变量) 1.12 0.18 37.50 <0.001 3.06 2.14～4.37
早孕期血清FPG(连续变量) 0.73 0.08 94.13 <0.001 2.08 1.79～2.41
常数项 －5.20 0.39 176.48 <0.001 0.01 —
图1 4种机器学习算法构建GDM预测模型的ROC-AUC比较注：GDM为妊娠期糖尿病，ROC曲线为受试者工作特征曲线，AUC为曲线下面积。DT为决策树，LR为逻辑回归，RF为随机森林，XGB为极致梯度提升
表3 4种机器学习算法构建GDM预测模型的4个性能评估指标比较
机器学习算法 准确率(%) 准确率95%CI(%) 精确率(%) 精确率95%CI(%)
DT 78.8 78.1～79.6 65.5 64.4～66.6
LR 77.2 76.9～77.6 76.5 76.1～77.0
RF 77.3 77.1～77.5 76.6 76.3～76.9
XGB 78.0 77.5～78.5 72.6 71.7～73.4
机器学习算法 召回率(%) 召回率95%CI(%) AUC AUC 95%CI
DT 74.7 73.2～76.2 0.645 0.591～0.698
LR 98.0 97.7～98.4 0.699 0.641～0.749
RF 98.0 97.6～98.4 0.672 0.621～0.772
XGB 89.1 88.0～90.2 0.597 0.553～0.663
图2 在训练集中预测GDM患者的4种机器学习算法的十折交叉验证AUC结果折线图注：GDM为妊娠期糖尿病，AUC为曲线下面积。DT为决策树，LR为逻辑回归，RF为随机森林，XGB为极致梯度提升
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