Construction and validation of a prediction model for developing type 2 diabetes mellitus in patients with chronic obstructive pulmonary disease
BackgroundType 2 diabetes mellitus (T2DM) is a common comorbidity of chronic obstructive pulmonary disease (COPD), which significantly increases the risk of rehospitalization and mortality in patients with COPD. Therefore, the purpose of this study was to identify the influencing factors of COPD com...
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Endocrinology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fendo.2025.1560631/full |
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| author | Xi Kang Tianye Li Qinyang Chen Hao Xu Yanqiu Jiang Hongjun Zhao Xuhong Chang |
| author_facet | Xi Kang Tianye Li Qinyang Chen Hao Xu Yanqiu Jiang Hongjun Zhao Xuhong Chang |
| author_sort | Xi Kang |
| collection | DOAJ |
| description | BackgroundType 2 diabetes mellitus (T2DM) is a common comorbidity of chronic obstructive pulmonary disease (COPD), which significantly increases the risk of rehospitalization and mortality in patients with COPD. Therefore, the purpose of this study was to identify the influencing factors of COPD complicated by T2DM and to construct a visualized disease prediction model.MethodWe included the medical records of 1,773 patients with COPD treated at Quzhou People’s Hospital from 2020 to 2023. Subjects were randomly divided into a training set (n = 1,241) and a test set (n = 532) in a 7:3 ratio. Variable selection was performed using the least absolute shrinkage and selection operator (LASSO), Pearson correlation, and multicollinearity diagnostics. Variables were then refined through backward stepwise selection based on the Akaike Information Criterion (AIC) to construct a nomogram. The accuracy of the nomogram was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and the Hosmer–Lemeshow test (H-L test). The clinical utility of the model was evaluated using decision analysis curves (DCA). Additionally, k-fold cross-validation (k = 10) was performed to rigorously assess model stability and mitigate the risk of overfitting. A sex-stratified subgroup analysis was also conducted to address potential sex-related bias.ResultsThe prevalence of T2DM in COPD patients was 27.13%. Seven independent predictors of COPD complicated by T2DM were identified: arterial partial pressure of carbon dioxide (PCO2) (OR = 1.04, 95%CI: 1.02–1.05), neutrophil number (NEUT) (OR = 1.15, 95%CI: 1.10–1.19), C-reactive protein (CRP) (OR = 1.01, 95%CI: 1.01–1.02), erythrocyte sedimentation rate (ESR) (OR = 1.03, 95%CI: 1.02–1.05), bilirubin (OR = 0.92, 95%CI: 0.88–0.96), triglyceride (TG) (OR = 1.33, 95%CI: 1.13–1.56), and body mass index (BMI) (OR = 1.16, 95%CI: 1.11–1.20). The model demonstrated good predictive performance, with a C-index of 0.78. The area under the curve (AUC) values were 0.79 (95%CI: 0.76–0.81) for the training set and 0.80 (95%CI: 0.76–0.84) for the test set, consistent with the k-fold cross-validation average AUC of 0.79 (95%CI: 0.76–0.81). Calibration curves and the H-L test (P >0.05) indicated good agreement between predicted and observed outcomes. DCA curves demonstrated clinical utility across threshold probabilities. Subgroup analysis showed robust performance in both male (0.82, 95%CI: 0.77–0.86) and female (0.71, 95%CI: 0.60–0.83) groups, with no significant difference in discriminatory ability (DeLong P = 0.101).ConclusionIn this study, we developed and internally validated a visualized prediction model for early identification of T2DM risk in patients with COPD. This tool may facilitate targeted prevention strategies by identifying high-risk populations. While the model demonstrated good performance, external validation is still required to confirm its generalizability. |
| format | Article |
| id | doaj-art-71e5a42d0d4e4b2aa0a129e11a3f3aa9 |
| institution | DOAJ |
| issn | 1664-2392 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Endocrinology |
| spelling | doaj-art-71e5a42d0d4e4b2aa0a129e11a3f3aa92025-08-20T03:05:44ZengFrontiers Media S.A.Frontiers in Endocrinology1664-23922025-08-011610.3389/fendo.2025.15606311560631Construction and validation of a prediction model for developing type 2 diabetes mellitus in patients with chronic obstructive pulmonary diseaseXi Kang0Tianye Li1Qinyang Chen2Hao Xu3Yanqiu Jiang4Hongjun Zhao5Xuhong Chang6School of Public Health, Lan Zhou University, Lanzhou, ChinaDepartment of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, ChinaSchool of Public Health, Lan Zhou University, Lanzhou, ChinaZhejiang Province Engineering Research Center for Endoscope Instruments and Technology Development, Clinical Research Center, Department of Pulmonary and Critical Care Medicine, Quzhou People’s Hospital, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou, ChinaZhejiang Province Engineering Research Center for Endoscope Instruments and Technology Development, Clinical Research Center, Department of Pulmonary and Critical Care Medicine, Quzhou People’s Hospital, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou, ChinaZhejiang Province Engineering Research Center for Endoscope Instruments and Technology Development, Clinical Research Center, Department of Pulmonary and Critical Care Medicine, Quzhou People’s Hospital, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou, ChinaSchool of Public Health, Lan Zhou University, Lanzhou, ChinaBackgroundType 2 diabetes mellitus (T2DM) is a common comorbidity of chronic obstructive pulmonary disease (COPD), which significantly increases the risk of rehospitalization and mortality in patients with COPD. Therefore, the purpose of this study was to identify the influencing factors of COPD complicated by T2DM and to construct a visualized disease prediction model.MethodWe included the medical records of 1,773 patients with COPD treated at Quzhou People’s Hospital from 2020 to 2023. Subjects were randomly divided into a training set (n = 1,241) and a test set (n = 532) in a 7:3 ratio. Variable selection was performed using the least absolute shrinkage and selection operator (LASSO), Pearson correlation, and multicollinearity diagnostics. Variables were then refined through backward stepwise selection based on the Akaike Information Criterion (AIC) to construct a nomogram. The accuracy of the nomogram was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and the Hosmer–Lemeshow test (H-L test). The clinical utility of the model was evaluated using decision analysis curves (DCA). Additionally, k-fold cross-validation (k = 10) was performed to rigorously assess model stability and mitigate the risk of overfitting. A sex-stratified subgroup analysis was also conducted to address potential sex-related bias.ResultsThe prevalence of T2DM in COPD patients was 27.13%. Seven independent predictors of COPD complicated by T2DM were identified: arterial partial pressure of carbon dioxide (PCO2) (OR = 1.04, 95%CI: 1.02–1.05), neutrophil number (NEUT) (OR = 1.15, 95%CI: 1.10–1.19), C-reactive protein (CRP) (OR = 1.01, 95%CI: 1.01–1.02), erythrocyte sedimentation rate (ESR) (OR = 1.03, 95%CI: 1.02–1.05), bilirubin (OR = 0.92, 95%CI: 0.88–0.96), triglyceride (TG) (OR = 1.33, 95%CI: 1.13–1.56), and body mass index (BMI) (OR = 1.16, 95%CI: 1.11–1.20). The model demonstrated good predictive performance, with a C-index of 0.78. The area under the curve (AUC) values were 0.79 (95%CI: 0.76–0.81) for the training set and 0.80 (95%CI: 0.76–0.84) for the test set, consistent with the k-fold cross-validation average AUC of 0.79 (95%CI: 0.76–0.81). Calibration curves and the H-L test (P >0.05) indicated good agreement between predicted and observed outcomes. DCA curves demonstrated clinical utility across threshold probabilities. Subgroup analysis showed robust performance in both male (0.82, 95%CI: 0.77–0.86) and female (0.71, 95%CI: 0.60–0.83) groups, with no significant difference in discriminatory ability (DeLong P = 0.101).ConclusionIn this study, we developed and internally validated a visualized prediction model for early identification of T2DM risk in patients with COPD. This tool may facilitate targeted prevention strategies by identifying high-risk populations. While the model demonstrated good performance, external validation is still required to confirm its generalizability.https://www.frontiersin.org/articles/10.3389/fendo.2025.1560631/fullchronic obstructive pulmonary diseasetype 2 diabetes mellituscomplicationpredictionnomogram |
| spellingShingle | Xi Kang Tianye Li Qinyang Chen Hao Xu Yanqiu Jiang Hongjun Zhao Xuhong Chang Construction and validation of a prediction model for developing type 2 diabetes mellitus in patients with chronic obstructive pulmonary disease Frontiers in Endocrinology chronic obstructive pulmonary disease type 2 diabetes mellitus complication prediction nomogram |
| title | Construction and validation of a prediction model for developing type 2 diabetes mellitus in patients with chronic obstructive pulmonary disease |
| title_full | Construction and validation of a prediction model for developing type 2 diabetes mellitus in patients with chronic obstructive pulmonary disease |
| title_fullStr | Construction and validation of a prediction model for developing type 2 diabetes mellitus in patients with chronic obstructive pulmonary disease |
| title_full_unstemmed | Construction and validation of a prediction model for developing type 2 diabetes mellitus in patients with chronic obstructive pulmonary disease |
| title_short | Construction and validation of a prediction model for developing type 2 diabetes mellitus in patients with chronic obstructive pulmonary disease |
| title_sort | construction and validation of a prediction model for developing type 2 diabetes mellitus in patients with chronic obstructive pulmonary disease |
| topic | chronic obstructive pulmonary disease type 2 diabetes mellitus complication prediction nomogram |
| url | https://www.frontiersin.org/articles/10.3389/fendo.2025.1560631/full |
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