Obesity Metabolic Phenotypes and Unplanned Readmission Risk in Diabetic Kidney Disease: An Observational Study from the Nationwide Readmission Database

Zhang, Wei; Du, Jing; Dong, Hang; Cheng, Yiping; Zhong, Fang; Yuan, Zinuo; Dong, Yingchun; Wang, Rong; Mu, Shumin; Zhao, Jiajun; Han, Wenxia; Fan, Xiude

doi:10.1016/j.arcmed.2023.102840

Archives of Medical Research

2023

DOI: 10.1016/j.arcmed.2023.102840

|View full text |Cite

Obesity Metabolic Phenotypes and Unplanned Readmission Risk in Diabetic Kidney Disease: An Observational Study from the Nationwide Readmission Database

Wei Zhang,

Jing Du,

Hang Dong

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

(1 citation statement)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[22][23][24] In the pathological mechanism, higher BMI may promote the occurrence of DKD through promoting HOMA-IR, chronic inflammatory response, endothelial dysfunction, fibrosis, and thrombosis. [23][24][25][26] HOMA-IR, which has a complex relationship with DKD, usually promotes the occurrence of DKD in the early stage and is generally present in endstage DKD through various mechanisms. 27 There are many high-affinity insulin receptors in renal tubule cells and podocytes.…”

mentioning

confidence: 99%

Construction of a Nomogram-Based Prediction Model for the Risk of Diabetic Kidney Disease in T2DM

Wang,

Liu,

Zhao

et al. 2024

DMSO

View full text Add to dashboard Cite

Introduction To investigate the predictors of diabetic kidney disease (DKD) in type 2 diabetes mellitus (T2DM) patients and establish a nomogram model for predicting the risk of DKD. Methods The clinical data of T2DM patients, admitted to the Endocrinology Department of Chengde Central Hospital from October 2019 to September 2020 and divided into a case group or a control group based on whether they had DKD, were collected. The predictive factors of DKD were screened by univariate and multivariate analysis, and a nomogram prediction model was constructed for the risk of DKD in T2DM. Bootstrapping was used for model validation, receiver operating characteristic (ROC) curve and GiViTI calibration curve were used for evaluating the discrimination and calibration of prediction model, and decision analysis curve (DCA) was used for evaluating the practicality of model. Results Predictors for DKD are diabetic retinopathy (DR), hypertension, history of gout, smoking history, using insulin, elevation of body mass index (BMI), triglyceride (TG), cystatin C (Cys-C), and reduction of 25 (OH) D. The nomogram prediction model based on the above nine predictors had good representativeness (Bootstrap method: precision: 0.866, Kappa: 0.334), differentiation [the area under curve (AUC) value: 0.868], and accuracy (GiViTI-corrected curved bands, P = 0.836); the DAC curve analysis showed that the prediction model, whose threshold probability was in the range of 0.10 to 0.70, had clinical practical value. Conclusion The risk of DKD in T2DM could be predicted accurately by DR, hypertension, history of gout, smoking history, using insulin, elevation of BMI, TG, Cys-C, and reduction of 25 (OH) D.

show abstract

mentioning

confidence: 99%

Construction of a Nomogram-Based Prediction Model for the Risk of Diabetic Kidney Disease in T2DM

Wang,

Liu,

Zhao

et al. 2024

DMSO

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Obesity Metabolic Phenotypes and Unplanned Readmission Risk in Diabetic Kidney Disease: An Observational Study from the Nationwide Readmission Database

Cited by 1 publication

References 46 publications

Construction of a Nomogram-Based Prediction Model for the Risk of Diabetic Kidney Disease in T2DM

Construction of a Nomogram-Based Prediction Model for the Risk of Diabetic Kidney Disease in T2DM

Contact Info

Product

Resources

About