基于常规生物标志物的妊娠期糖尿病早期列线图预测模型构建与验证

doi:10.3969/j.issn.1004-583X.2026.04.007

摘要/Abstract

摘要：

目的基于常规产前检查指标,构建并验证妊娠期糖尿病(GDM)的列线图预测模型,为早期风险分层提供量化工具。方法采用回顾性队列研究设计,纳入2018年9月-2024年12月于东莞仁康医院接受产前检查的4 547例单胎孕妇,按7∶3比例随机分为建模队列(3 183例)与内部验证队列(1 364例)。采用LASSO回归筛选变量,并构建多因素logistic回归模型绘制列线图。通过受试者工作特征(ROC)曲线下面积(AUC)、校准曲线及决策曲线分析(DCA)评估模型的区分度、校准度与潜在临床效用。结果多因素logistic回归分析显示,年龄(OR=1.08,95%CI:1.06~1.09)、孕前体质指数(OR=1.05,95%CI:1.02~1.08)、尿酸(OR=1.00,95%CI:1.00~1.01)、糖化血红蛋白(OR=3.53,95%CI:2.79~4.45)及空腹血糖(OR=2.44,95%CI:1.99~3.00)是GDM的独立预测因素(孕前体质指数P=0.002,余均P<0.01)。该预测模型在建模队列与内部验证队列中AUC值分别为0.723(95%CI:0.703~0.742)和0.720(95%CI:0.690~0.750),显示中等区分度;校准曲线显示预测概率与实际风险吻合良好;DCA提示在0.10~0.80的阈值概率范围内,与全部筛查或全部不筛查策略相比,模型具有潜在的临床净获益。结论本研究构建的列线图模型基于孕早期常规指标,对GDM发生风险具有中等预测效能,可作为早期风险分层的初步工具。但模型效能仍有提升空间,其普适性及临床实用性需通过多中心外部验证和前瞻性干预试验进一步确认。

关键词: 糖尿病, 妊娠期, 列线图, 早期预测, 危险因素, 受试者工作特征, 决策曲线分析

Abstract:

Objective To develop and validate a nomogram prediction model for gestational diabetes mellitus (GDM) based on routine antenatal examination indicators, providing a quantitative tool for early risk stratification. Methods A retrospective cohort study was conducted. A total of 4, 547 singleton pregnant women who underwent antenatal examinations at Dongguan Renkang Hospital from September 2018 to December 2024 were included and randomly assigned in a 7∶3 ratio to a development cohort (3, 183 cases) and an internal validation cohort (1, 364 cases). Variables were selected using the least absolute shrinkage and selection operator (LASSO) regression, and a multivariate logistic regression model was constructed to generate the nomogram. Model discrimination, calibration, and potential clinical utility were evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), calibration curves, and decision curve analysis (DCA), respectively. Results Multivariate logistic regression analysis showed that age (OR=1.08, 95%CI: 1.06-1.09), pre-pregnancy body mass index (OR=1.05, 95%CI: 1.02-1.08), uric acid (OR=1.00, 95%CI: 1.00-1.01), glycated hemoglobin (OR=3.53, 95%CI: 2.79-4.45), and fasting plasma glucose (OR=2.44, 95%CI: 1.99-3.00) were independent predictors of GDM (pre-pregnancy body mass index, P=0.002; all others P<0.01). The AUC of the prediction model was 0.723 (95%CI: 0.703-0.742) in the development cohort and 0.720(95%CI: 0.690-0.750) in the internal validation cohort, indicating moderate discrimination. The calibration curves showed good agreement between predicted probability and observed risk. DCA indicated that within a threshold probability range of 0.10-0.80, the model provided potential net clinical benefit compared with strategies of screening all or screening none. Conclusion The nomogram model developed in this study, based on routine indicators in early pregnancy, showed moderate predictive performance for GDM risk and may serve as a preliminary tool for early risk stratification. However, further improvement is needed, and its generalizability and clinical utility should be confirmed through multicenter external validation and prospective intervention trials.

Key words: diabetes mellitus, gestational, nomogram, early prediction, risk factors, receiver operating characteristic, decision curve analysis

中图分类号:

R587.1

任加银, 陈春芹, 刘发生, 申辉. 基于常规生物标志物的妊娠期糖尿病早期列线图预测模型构建与验证[J]. 临床荟萃, 2026, 41(4): 328-334.

Ren Jiayin, Chen Chunqin, Liu Fasheng, Shen Hui. Development and validation of an early nomogram prediction model for gestational diabetes mellitus based on routine biomarkers[J]. Clinical Focus, 2026, 41(4): 328-334.

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链接本文: https://www.lchc.cn/CN/10.3969/j.issn.1004-583X.2026.04.007

https://www.lchc.cn/CN/Y2026/V41/I4/328

图/表 9

表1 建模队列与内部验证队列及建模队列亚组间基线特征均衡性检验和结局的关系分析

Tab.1 Balance test of baseline characteristics and association with outcomes between the development cohort, internal validation cohort, and subgroups within the development cohort

项目	建模队列 ( $n$ =3 183)	内部验证队列 ( $n$ =1 364)	$P$ 值(均衡性检验)	建模队列non-GDM ( $n$ =2 257)	建模队列GDM ( $n$ =926)	$P$ 值 (组间差异)
年龄(岁)	29.0(26.0, 33.0)	29.0(26.0, 32.0)	0.657^*	28.0(25.0, 32.0)	31.0(28.0, 34.0)	<0.01^*
Pre-BMI	20.8(19.1, 23.0)	20.8(19.0, 23.1)	0.788^*	20.7(18.8, 22.7)	21.5(19.8, 23.9)	<0.01^*
孕次	2.00(2.00, 3.00)	2.00(2.00, 3.00)	0.714^*	2.00(1.00, 3.00)	2.00(2.00, 4.00)	0.010
产次	2.00(1.00, 2.00)	2.00(1.00, 2.00)	0.846^*	2.00(1.00, 2.00)	2.00(2.00, 2.00)	<0.01^*
ALT(U/L)	11(8, 15)	11(8, 15)	0.791^*	11(8, 15)	11(8, 16)	0.013^*
AST(U/L)	16.0(14.0, 19.0)	16.0(14.0, 19.0)	0.687^*	16.0(14.0, 19.0)	16.0(14.0, 20.0)	0.556^*
TBIL(μmol/L)	8.5(6.9, 10.6)	8.6(6.8, 10.7)	0.366^*	8.5(6.8, 10.5)	8.6(7.0, 10.7)	0.465^*
DBIL(μmol/L)	2.40(1.70, 3.10)	2.30(1.70, 3.10)	0.464^*	2.30(1.70, 3.10)	2.30(1.70, 3.00)	<0.01^*
TBA(μmol/L)	1.90(1.30, 2.80)	1.90(1.30, 2.85)	0.722^*	1.90(1.30, 2.90)	1.80(1.20, 2.80)	0.015^*
TP(g/L)	66.0(63.0, 70.0)	66.0(63.0, 70.0)	0.844^*	66.0(63.0, 70.0)	66.0(63.0, 70.0)	0.028^*
ALB(g/L)	39.0(36.1, 42.0)	39.4(36.2, 42.0)	0.461^*	39.2(36.3, 42.2)	39.0(36.0, 42.0)	0.123^*
PA(mg/L)	232(211, 255)	232(211, 255)	0.677^*	231(211, 254)	236(212, 259)	0.006^*
UREA(mmol/L)	2.67(2.24, 3.16)	2.67(2.25, 3.18)	0.449^*	2.65(2.23, 3.13)	2.72(2.26, 3.23)	0.019^*
Cr(μmol/L)	44(40, 48)	44(40, 48)	0.577^*	44(40, 48)	44(39, 48)	0.302^*
UA(μmol/L)	250(214, 295)	250(215, 294)	0.862^*	245(211, 288)	264(223, 316)	<0.01^*
CYsC(mg/L)	0.64(0.55, 0.78)	0.65(0.55, 0.79)	0.943^*	0.64(0.55, 0.78)	0.66(0.56, 0.80)	0.014^*
FBG(mmol/L)	4.80(4.58, 5.04)	4.80(4.57, 5.04)	0.453^*	4.75(4.53, 4.98)	4.94(4.65, 5.20)	<0.01^*
HbA₁c(%)	4.90(4.70, 5.20)	4.90(4.70, 5.20)	0.177^*	4.90(4.60, 5.10)	5.10(4.80, 5.40)	<0.01^*
WBC(10⁹/L)	8.78(7.47, 10.46)	8.78(7.33, 10.34)	0.193^*	8.73(7.41, 10.36)	8.98(7.62, 10.76)	<0.01^*
NEUT#(10⁹/L)	6.42(5.32, 7.82)	6.37(5.24, 7.76)	0.147^*	6.34(5.26, 7.76)	6.59(5.45, 8.17)	<0.01^*
NLR	3.68(2.94, 4.58)	3.68(3.00, 4.48)	0.405^*	3.69(2.96, 4.56)	3.74(2.98, 4.66)	0.051^*
RBC(10¹²/L)	3.95(3.67, 4.22)	3.96(3.65, 4.22)	0.898^*	3.94(3.66, 4.21)	3.97(3.70, 4.25)	0.014^*
HGB(g/L)	119(112, 127)	119(112, 126)	0.351^*	119(112, 127)	120(112, 127)	0.056^*
RDW-CV(%)	13.0(12.5, 13.5)	13.0(12.6, 13.5)	0.339^*	12.9(12.5, 13.5)	13.0(12.6, 13.6)	0.004^*
TPOAb阳性[例(%)]	247(7.8)	109(8.0)	0.790^#	187(8.3)	60(6.5)	0.084^#
TPOAb阴性[例(%)]	2 936(92.2)	1 255(92.0)		2 070(91.7)	866(93.5)

表1 建模队列与内部验证队列及建模队列亚组间基线特征均衡性检验和结局的关系分析

Tab.1 Balance test of baseline characteristics and association with outcomes between the development cohort, internal validation cohort, and subgroups within the development cohort

项目	建模队列 ( $n$ =3 183)	内部验证队列 ( $n$ =1 364)	$P$ 值(均衡性检验)	建模队列non-GDM ( $n$ =2 257)	建模队列GDM ( $n$ =926)	$P$ 值 (组间差异)
年龄(岁)	29.0(26.0, 33.0)	29.0(26.0, 32.0)	0.657^*	28.0(25.0, 32.0)	31.0(28.0, 34.0)	<0.01^*
Pre-BMI	20.8(19.1, 23.0)	20.8(19.0, 23.1)	0.788^*	20.7(18.8, 22.7)	21.5(19.8, 23.9)	<0.01^*
孕次	2.00(2.00, 3.00)	2.00(2.00, 3.00)	0.714^*	2.00(1.00, 3.00)	2.00(2.00, 4.00)	0.010
产次	2.00(1.00, 2.00)	2.00(1.00, 2.00)	0.846^*	2.00(1.00, 2.00)	2.00(2.00, 2.00)	<0.01^*
ALT(U/L)	11(8, 15)	11(8, 15)	0.791^*	11(8, 15)	11(8, 16)	0.013^*
AST(U/L)	16.0(14.0, 19.0)	16.0(14.0, 19.0)	0.687^*	16.0(14.0, 19.0)	16.0(14.0, 20.0)	0.556^*
TBIL(μmol/L)	8.5(6.9, 10.6)	8.6(6.8, 10.7)	0.366^*	8.5(6.8, 10.5)	8.6(7.0, 10.7)	0.465^*
DBIL(μmol/L)	2.40(1.70, 3.10)	2.30(1.70, 3.10)	0.464^*	2.30(1.70, 3.10)	2.30(1.70, 3.00)	<0.01^*
TBA(μmol/L)	1.90(1.30, 2.80)	1.90(1.30, 2.85)	0.722^*	1.90(1.30, 2.90)	1.80(1.20, 2.80)	0.015^*
TP(g/L)	66.0(63.0, 70.0)	66.0(63.0, 70.0)	0.844^*	66.0(63.0, 70.0)	66.0(63.0, 70.0)	0.028^*
ALB(g/L)	39.0(36.1, 42.0)	39.4(36.2, 42.0)	0.461^*	39.2(36.3, 42.2)	39.0(36.0, 42.0)	0.123^*
PA(mg/L)	232(211, 255)	232(211, 255)	0.677^*	231(211, 254)	236(212, 259)	0.006^*
UREA(mmol/L)	2.67(2.24, 3.16)	2.67(2.25, 3.18)	0.449^*	2.65(2.23, 3.13)	2.72(2.26, 3.23)	0.019^*
Cr(μmol/L)	44(40, 48)	44(40, 48)	0.577^*	44(40, 48)	44(39, 48)	0.302^*
UA(μmol/L)	250(214, 295)	250(215, 294)	0.862^*	245(211, 288)	264(223, 316)	<0.01^*
CYsC(mg/L)	0.64(0.55, 0.78)	0.65(0.55, 0.79)	0.943^*	0.64(0.55, 0.78)	0.66(0.56, 0.80)	0.014^*
FBG(mmol/L)	4.80(4.58, 5.04)	4.80(4.57, 5.04)	0.453^*	4.75(4.53, 4.98)	4.94(4.65, 5.20)	<0.01^*
HbA₁c(%)	4.90(4.70, 5.20)	4.90(4.70, 5.20)	0.177^*	4.90(4.60, 5.10)	5.10(4.80, 5.40)	<0.01^*
WBC(10⁹/L)	8.78(7.47, 10.46)	8.78(7.33, 10.34)	0.193^*	8.73(7.41, 10.36)	8.98(7.62, 10.76)	<0.01^*
NEUT#(10⁹/L)	6.42(5.32, 7.82)	6.37(5.24, 7.76)	0.147^*	6.34(5.26, 7.76)	6.59(5.45, 8.17)	<0.01^*
NLR	3.68(2.94, 4.58)	3.68(3.00, 4.48)	0.405^*	3.69(2.96, 4.56)	3.74(2.98, 4.66)	0.051^*
RBC(10¹²/L)	3.95(3.67, 4.22)	3.96(3.65, 4.22)	0.898^*	3.94(3.66, 4.21)	3.97(3.70, 4.25)	0.014^*
HGB(g/L)	119(112, 127)	119(112, 126)	0.351^*	119(112, 127)	120(112, 127)	0.056^*
RDW-CV(%)	13.0(12.5, 13.5)	13.0(12.6, 13.5)	0.339^*	12.9(12.5, 13.5)	13.0(12.6, 13.6)	0.004^*
TPOAb阳性[例(%)]	247(7.8)	109(8.0)	0.790^#	187(8.3)	60(6.5)	0.084^#
TPOAb阴性[例(%)]	2 936(92.2)	1 255(92.0)		2 070(91.7)	866(93.5)

图1 LASSO回归模型用于GDM预测变量的筛选 a.LASSO回归变量选择路径图;b.LASSO回归的交叉验证图,λ.1se=0.02504

Fig. 1 LASSO regression model for selecting predictive variables for GDM a. LASSO variable selection path; b. LASSO cross-validation plot, λ.1se=0.02504

图2 经过Lasso选择的预测变量及其对应的系数的直方图

Fig. 2 Histogram of predictive variables selected by LASSO and their corresponding coefficients

表2 单变量logistic回归及单个自变量预测的AUC值

Tab.2 Univariate logistic regression and AUC values for individual predictors

变量	建模队列	GDM	OR(95% $C I$ )	$P$ 值	AUC(95% $C I$ )
年龄(岁)	3 183	926	1.10(1.08, 1.11)	<0.01	0.629(0.608-0.650)
pre-BMI	3 183	926	1.11(1.08, 1.15)	<0.01	0.587(0.566-0.607)
UA(μmol/L)	3 183	926	1.00(1.00, 1.01)	<0.01	0.574(0.552-0.595)
HbA₁c(%)	3 183	926	5.22(4.17, 6.53)	<0.01	0.664(0.643-0.685)
FBG(mmol/L)	3 183	926	3.18(2.62, 3.85)	<0.01	0.628(0.607-0.650)

表2 单变量logistic回归及单个自变量预测的AUC值

Tab.2 Univariate logistic regression and AUC values for individual predictors

变量	建模队列	GDM	OR(95% $C I$ )	$P$ 值	AUC(95% $C I$ )
年龄(岁)	3 183	926	1.10(1.08, 1.11)	<0.01	0.629(0.608-0.650)
pre-BMI	3 183	926	1.11(1.08, 1.15)	<0.01	0.587(0.566-0.607)
UA(μmol/L)	3 183	926	1.00(1.00, 1.01)	<0.01	0.574(0.552-0.595)
HbA₁c(%)	3 183	926	5.22(4.17, 6.53)	<0.01	0.664(0.643-0.685)
FBG(mmol/L)	3 183	926	3.18(2.62, 3.85)	<0.01	0.628(0.607-0.650)

表3 建模队列多变量logistic回归结果

Tab.3 Multivariate logistic regression results in the development cohort

变量	建模队列	GDM	OR(95% $C I$ )	$P$ 值
年龄(岁)	3 183	926	1.08(1.06, 1.09)	<0.01
pre-BMI	3 183	926	1.05(1.02, 1.08)	0.002
UA(μmol/L)	3 183	926	1.00(1.00, 1.01)	<0.01
HbA₁c(%)	3 183	926	3.53(2.79, 4.45)	<0.01
FBG(mmol/L)	3 183	926	2.44(1.99, 3.00)	<0.01

表3 建模队列多变量logistic回归结果

Tab.3 Multivariate logistic regression results in the development cohort

变量	建模队列	GDM	OR(95% $C I$ )	$P$ 值
年龄(岁)	3 183	926	1.08(1.06, 1.09)	<0.01
pre-BMI	3 183	926	1.05(1.02, 1.08)	0.002
UA(μmol/L)	3 183	926	1.00(1.00, 1.01)	<0.01
HbA₁c(%)	3 183	926	3.53(2.79, 4.45)	<0.01
FBG(mmol/L)	3 183	926	2.44(1.99, 3.00)	<0.01

图3 建模队列开发的列线图

Fig.3 Nomogram developed in the development cohort

图4 预测模型在队列中的ROC曲线

Fig.4 ROC curve of the prediction model in the cohort

图5 列线图预测模型的校准曲线 a.基于建模队列的校准曲线;b.基于内部验证队列的校准曲线

Fig.5 Calibration curves of the nomogram prediction model a. based on the development cohort; b. based on the internal validation cohort

图6 模型决策曲线分析 a.基于建模队列决策曲线分析;b.基于内部验证队列决策曲线分析

Fig.6 Decision curve analysis of the model a. based on the development cohort; b. based on the internal validation cohort

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