糖尿病肾病合并心脏自主神经病变随机森林模型的构建及验证

doi:10.11958/20252563

天津医药 ›› 2026, Vol. 54 ›› Issue (1): 41-45.doi: 10.11958/20252563

糖尿病肾病合并心脏自主神经病变随机森林模型的构建及验证

李霖¹, 李丹阳¹, 崔岩²^,^△()

1 佳木斯大学附属第一医院肾内科（邮编154002）
2 大庆油田总医院血液净化中心

收稿日期:2025-07-22 修回日期:2025-09-22 出版日期:2026-01-15 发布日期:2026-01-19
通讯作者: ^△ E-mail：yancuicuicc@163.com
作者简介:李霖（1994），男，医师，主要从事糖尿病肾病方面研究。E-mail：13214526018@163.com
基金资助:
黑龙江省医药卫生科研课题(20230707020332)

Development and validation of a random forest model for diabetic nephropathy with cardiac autonomic neuropathy

LI Lin¹, LI Danyang¹, CUI Yan²^,^△()

1 Department of Nephrology, First Affiliated Hospital of Jiamusi University, Jiamusi 154002, China
2 Blood Purification Center of Daqing Oilfield General Hospital

Received:2025-07-22 Revised:2025-09-22 Published:2026-01-15 Online:2026-01-19
Contact: ^△ E-mail：yancuicuicc@163.com

李霖, 李丹阳, 崔岩. 糖尿病肾病合并心脏自主神经病变随机森林模型的构建及验证[J]. 天津医药, 2026, 54(1): 41-45.

LI Lin, LI Danyang, CUI Yan. Development and validation of a random forest model for diabetic nephropathy with cardiac autonomic neuropathy[J]. Tianjin Medical Journal, 2026, 54(1): 41-45.

摘要/Abstract

摘要：

目的基于糖脂代谢和心脏代谢指数（CMI）构建并验证预测2型糖尿病（T2DM）患者发生糖尿病肾病（DKD）合并心脏自主神经病变（CAN）风险的随机森林模型。方法采取回顾性单中心研究设计，连续纳入2023年2月—2025年2月收治的109例DKD合并CAN患者为合并症组，另根据病例组的基线资料，进行1∶1匹配，选取同期109例无DKD且无CAN的T2DM患者为无合并症组。比较2组基线资料；采用二元Logistic回归分析T2DM患者发生DKD合并CAN的影响因素，并应用R（R4.1.0）软件包构建随机森林模型，绘制受试者工作特征（ROC）曲线分析模型预测价值。结果二元Logistic回归显示，尿白蛋白/肌酐比值（UACR）>30 mg/g，空腹血糖（FPG）、CMI、糖化血红蛋白（HbA1c）、三酰甘油（TG）水平升高是T2DM患者发生DKD合并CAN的危险因素，估算肾小球滤过率（eGFR）、高密度脂蛋白胆固醇（HDL-C）水平升高是保护因素；采用变量重要性度量（%IncMse）打分并进行特征重要性排序，其中重要性前三者分别为CMI、HDL-C、FPG，%IncMse分别为26.700%、16.300%、13.400%；基于影响因素建立随机森林模型预测T2DM患者发生DKD合并CAN的曲线下面积为0.849（95%CI：0.738~0.933），敏感度为0.862，特异度为0.730，约登指数为0.592，具有较好的预测效能。结论 UACR>30 mg/g、FPG、CMI、HbA1c、TG水平升高，eGFR、HDL-C水平下降是T2DM患者发生DKD合并CAN的危险因素，其中CMI为关键驱动因素。

关键词: 糖尿病, 2型, 糖尿病肾病, 糖尿病神经病变, 心脏自主神经病变, 糖脂代谢, 心脏代谢指数

Abstract:

Objective To develop and validate the random forest model based on glycolipid metabolism and cardiometabolic index (CMI) and to predict the risk of diabetic kidney disease (DKD) combined with cardiac autonomic neuropathy (CAN) in patients with type 2 diabetes mellitus (T2DM). Methods A retrospective single-center study design was adopted. A total of 109 patients with DKD and CAN admitted between February 2023 and February 2025 were consecutively enrolled as the comorbidity group. Based on the baseline data of the case group, 109 T2DM patients without DKD or CAN during the same period were selected in a 1∶1 matching ratio as the non-comorbidity group. The baseline characteristics of the two groups were compared. Binary Logistic regression was used to analyze the influencing factors for the occurrence of DKD combined with CAN in T2DM patients. A random forest model was constructed using the R software package (version 4.1.0), and a receiver operating characteristic (ROC) curve was plotted to analyze the predictive value of the model. Results Binary Logistic regression revealed that the urinary albumin-to-creatinine ratio (UACR)>30 mg/g, elevated fasting plasma glucose (FPG), CMI, glycated hemoglobin (HbA1c) and triglyceride (TG) levels were risk factors for the development of DKD combined with CAN in T2DM patients, while estimated glomerular filtration rate (eGFR) and high-density lipoprotein cholesterol (HDL-C) were protective factors. Using the variable importance measure (%IncMSE) for scoring and feature importance ranking, the top three factors were CMI, HDL-C and FPG, with %IncMSE values of 26.700%, 16.300% and 13.400%, respectively. Based on these influencing factors, the random forest model established to predict the occurrence of DKD combined with CAN in T2DM patients achieved an AUC of 0.849, a sensitivity of 0.862, a specificity of 0.730 and a Youden index of 0.592, with a 95% confidence interval of 0.738-0.933, demonstrating good predictive performance. Conclusion UACR >30 mg/g，elevated levels of FPG, CMI, HbA1c and TG, along with decreased levels of eGFR and HDL-C are risk factors for the occurrence of DKD combined with CAN in patients with T2DM, among which CMI is the key driver factor.

Key words: diabetes mellitus, type 2, diabetic nephropathies, diabetic neuropathies, cardiac autonomic neuropathy, glucose-lipid metabolism, cardiac metabolic index

中图分类号:

R587.2

图/表 6

参考文献 17

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组别	BMI≥ 24 kg/m²		男性		高血压		吸烟史		饮酒史	UACR> 30 mg/g		FPG/ （mmol/L）		CMI		WHtR	eGFR/[mL/（min·1.73 m²）]			HbA1c/ %
A组	63（57.80）		61（55.96）		18（16.51）		35（32.11）		39（35.78）	34（31.19）		6.89±0.55		0.64±0.25		0.47±0.16	90.21±5.70			6.13±1.05
B组	61（55.96）		58（53.21）		20（18.35）		37（33.94）		42（38.53）	62（56.88）		7.31±0.53		0.95±0.38		0.49±0.15	88.31±6.60			6.85±1.03
χ²或t	0.075		0.167		0.127		0.083		0.177	14.593^＊＊		5.742^＊＊		7.162^＊＊		0.930	2.272^＊			5.105^＊＊
组别		TC/ （mmol/L）		TG/ （mmol/L）		HDL-C/ （mmol/L）		LDL-C/ （mmol/L）			年龄/岁		糖尿病病程/年		运动次数/（次/周）			SDNN/ ms	SDANN/ ms
A组		4.59±0.63		1.81±0.51		1.39±0.24		2.91±0.34			55.49±5.89		8.42±0.66		3.05±0.53			88.73±7.12	76.22±5.93
B组		4.73±0.61		2.19±0.54		1.20±0.19		2.98±0.37			55.56±5.97		8.56±0.62		3.01±0.51			86.91±6.98	74.63±6.14
t		1.784		5.391^＊＊		6.610^＊＊		1.474			0.091		1.597		0.524			1.913	1.941

组别	BMI≥ 24 kg/m²		男性		高血压		吸烟史		饮酒史	UACR> 30 mg/g		FPG/ （mmol/L）		CMI		WHtR	eGFR/[mL/（min·1.73 m²）]			HbA1c/ %
A组	63（57.80）		61（55.96）		18（16.51）		35（32.11）		39（35.78）	34（31.19）		6.89±0.55		0.64±0.25		0.47±0.16	90.21±5.70			6.13±1.05
B组	61（55.96）		58（53.21）		20（18.35）		37（33.94）		42（38.53）	62（56.88）		7.31±0.53		0.95±0.38		0.49±0.15	88.31±6.60			6.85±1.03
χ²或t	0.075		0.167		0.127		0.083		0.177	14.593^＊＊		5.742^＊＊		7.162^＊＊		0.930	2.272^＊			5.105^＊＊
组别		TC/ （mmol/L）		TG/ （mmol/L）		HDL-C/ （mmol/L）		LDL-C/ （mmol/L）			年龄/岁		糖尿病病程/年		运动次数/（次/周）			SDNN/ ms	SDANN/ ms
A组		4.59±0.63		1.81±0.51		1.39±0.24		2.91±0.34			55.49±5.89		8.42±0.66		3.05±0.53			88.73±7.12	76.22±5.93
B组		4.73±0.61		2.19±0.54		1.20±0.19		2.98±0.37			55.56±5.97		8.56±0.62		3.01±0.51			86.91±6.98	74.63±6.14
t		1.784		5.391^＊＊		6.610^＊＊		1.474			0.091		1.597		0.524			1.913	1.941

变量	β	SE	Wald χ²	P	OR	OR 95%CI	方差膨胀因子
UACR	0.844	0.407	4.312	0.038	2.326	1.048~5.160	1.073
FPG	1.453	0.394	13.618	<0.001	4.278	1.977~9.257	1.066
CMI	2.737	0.680	16.184	<0.001	15.466	4.070~58.615	1.161
eGFR	-0.104	0.034	9.470	0.002	0.901	0.843~0.963	1.027
HbA1c	0.954	0.213	20.063	<0.001	2.595	1.710~3.938	1.027
TG	1.490	0.434	11.766	0.001	4.436	1.894~10.393	1.073
HDL-C	-4.478	1.219	13.485	<0.001	0.011	0.001~0.124	1.210
常数项	-6.994	4.557	2.356	0.125	0.001

变量	β	SE	Wald χ²	P	OR	OR 95%CI	方差膨胀因子
UACR	0.844	0.407	4.312	0.038	2.326	1.048~5.160	1.073
FPG	1.453	0.394	13.618	<0.001	4.278	1.977~9.257	1.066
CMI	2.737	0.680	16.184	<0.001	15.466	4.070~58.615	1.161
eGFR	-0.104	0.034	9.470	0.002	0.901	0.843~0.963	1.027
HbA1c	0.954	0.213	20.063	<0.001	2.595	1.710~3.938	1.027
TG	1.490	0.434	11.766	0.001	4.436	1.894~10.393	1.073
HDL-C	-4.478	1.219	13.485	<0.001	0.011	0.001~0.124	1.210
常数项	-6.994	4.557	2.356	0.125	0.001

混淆矩阵		预测值		准确率/%
混淆矩阵		发生	未发生	准确率/%
真实值	发生	30	7	85.00
真实值	未发生	3	26	85.00

糖尿病肾病合并心脏自主神经病变随机森林模型的构建及验证

Development and validation of a random forest model for diabetic nephropathy with cardiac autonomic neuropathy

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