Distribution of Traditional Chinese Medicine Syndrome Elements in Different Risk Populations of Heart Failure Complicated with Type 2 Diabetes： A Retrospective Study Based on Nomogram Model and Factor Analysis
10.13288/j.11-2166/r.2025.11.010
- VernacularTitle:2型糖尿病并发心力衰竭不同风险人群中医证素的分布
- Author:
Tingting LI
1
;
Zhipeng YAN
2
;
Yajie FAN
2
;
Wenxiu LI
2
;
Wenyu SHANG
2
;
Yongchun LIANG
2
;
Yiming ZUO
2
;
Yuxin KANG
2
;
Boyu ZHU
2
;
Junping ZHANG
1
Author Information
1. First Teaching Hospital of Tianjin University of Traditional Chinese Medicine/National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion，Tianjin，300381
2. Graduate School of Tianjin University of Traditional Chinese Medicine
- Publication Type:Journal Article
- Keywords:
type 2 diabetes;
heart failure;
risk stratification;
nomogram;
syndrome elements;
factor analysis
- From:
Journal of Traditional Chinese Medicine
2025;66(11):1140-1146
- CountryChina
- Language:Chinese
-
Abstract:
ObjectiveTo analyze the distribution characteristics of traditional Chinese medicine （TCM） syndrome elements in different risk populations of heart failure complicated with type 2 diabetes. MethodsClinical data of 675 type 2 diabetes patients were retrospectively collected. Lasso-multivariate Logistic regression was used to construct a clinical prediction nomogram model. Based on this， 441 non-heart failure patients were divided into a low-risk group （325 cases） and a high-risk group （116 cases） according to the median risk score of heart failure complicated with type 2 diabetes. TCM diagnostic information （four diagnostic methods） was collected for both groups， and factor analysis was applied to summarize the distribution of TCM syndrome elements in different risk populations. ResultsLasso-multivariate Logistic regression analysis identified age， disease duration， coronary heart disease， old myocardial infarction， arrhythmia， absolute neutrophil count， activated partial thromboplastin time， and α-hydroxybutyrate dehydrogenase as independent risk factors for heart failure complicated with type 2 diabetes. These were used as final predictive factors to construct the nomogram model. Model validation results showed that the area under the curve （AUC） of the receiver operating characteristic （ROC） curve for the modeling group and validation group were 0.934 and 0.935， respectively. The Hosmer-Lemeshow test （modeling group P = 0.996， validation group P = 0.121） indicated good model discrimination. Decision curve analysis showed that the curves for All and None crossed in the upper right corner， indicating high clinical utility. The low-risk and high-risk groups each obtained 14 common factors. Preliminary analysis revealed that the main disease elements in the low-risk group were qi deficiency （175 cases， 53.85%）， dampness （118 cases， 36.31%）， and heat （118 cases， 36.31%）， with the primary locations in the spleen （125 cases， 38.46%） and lungs （99 cases， 30.46%）. In the high-risk group， the main disease elements were yang deficiency （73 cases， 62.93%）， blood stasis （68 cases， 58.62%）， and heat （49 cases， 42.24%）， with the primary locations in the kidney （84 cases， 72.41%） and heart （70 cases， 60.34%）. ConclusionThe overall disease characteristics in different risk populations of type 2 diabetes patients with heart failure are a combination of deficiency and excess， with deficiency being predominant. Deficiency and heat are present throughout. The low-risk population mainly shows qi deficiency with dampness and heat， related to the spleen and lungs. The high-risk population shows yang deficiency with blood stasis and heat， related to the kidneys and heart.
