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©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
Risk factors and a predictive model of diabetic foot in hospitalized patients with type 2 diabetes
Ming-Zhuo Li, Fang Tang, Ya-Fei Liu, Jia-Hui Lao, Yang Yang, Jia Cao, Ru Song, Peng Wu, Yi-Bing Wang
Ming-Zhuo Li, Ru Song, Peng Wu, Yi-Bing Wang, Department of Plastic Surgery, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan 250012, Shandong Province, China
Ming-Zhuo Li, Ru Song, Peng Wu, Jinan Clinical Research Center for Tissue Engineering Skin Regeneration and Wound Repair, Jinan 250012, Shandong Province, China
Ming-Zhuo Li, Fang Tang, Jia-Hui Lao, Yang Yang, Jia Cao, Center for Big Data Research in Health and Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan 250012, Shandong Province, China
Ming-Zhuo Li, Fang Tang, Jia-Hui Lao, Yang Yang, Jia Cao, Shandong Data Open Innovative Application Laboratory, Jinan 250012, Shandong Province, China
Ya-Fei Liu, Shandong Mental Health Center, Shandong University, Jinan 250012, Shandong Province, China
Co-first authors: Ming-Zhuo Li and Fang Tang.
Author contributions: Li MZ, Tang F, and Wang YB conceived the idea of the study; Li MZ and Tang F performed the analyses and prepared the manuscript; Liu YF, Lao JH, Yang Y, and Cao J helped manage the data; Song R and Wu P helped interpret the results; Li MZ, Tang F, and Wang YB supervised the study and contributed to the critical revision; All authors reviewed the manuscript and gave final approval of the version to be published.
Supported by National Natural Science Foundation of China, No. 81972947; and Academic Promotion Programme of Shandong First Medical University, No. 2019LJ005.
Institutional review board statement: The study was approved by the Ethics Committee of the First Affiliated Hospital of Shandong First Medical University, Approval No. YXLL-KY-2022(063).
Informed consent statement: The need for written informed consent for each patient was waived by due to retrospective nature of the study and encrypted personal information of the data.
Conflict-of-interest statement: The authors have no conflicts of interest to declare.
STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-checklist of items.
Data sharing statement: The datasets generated during and/or analyzed in the current study are not available because of privacy policy.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See:
https://creativecommons.org/Licenses/by-nc/4.0/ Corresponding author: Yi-Bing Wang, PhD, Department of Plastic Surgery, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, No. 16766 Jingshi Road, Jinan 250012, Shandong Province, China.
ybwang@sdfmu.edu.cn
Received: April 15, 2024
Revised: November 20, 2024
Accepted: December 19, 2024
Published online: March 15, 2025
Processing time: 281 Days and 1.2 Hours
BACKGROUND
The risk factors and prediction models for diabetic foot (DF) remain incompletely understood, with several potential factors still requiring in-depth investigations.
AIM
To identify risk factors for new-onset DF and develop a robust prediction model for hospitalized patients with type 2 diabetes.
METHODS
We included 6301 hospitalized patients with type 2 diabetes from January 2016 to December 2021. A univariate Cox model and least absolute shrinkage and selection operator analyses were applied to select the appropriate predictors. Nonlinear associations between continuous variables and the risk of DF were explored using restricted cubic spline functions. The Cox model was further employed to evaluate the impact of risk factors on DF. The area under the curve (AUC) was measured to evaluate the accuracy of the prediction model.
RESULTS
Seventy-five diabetic inpatients experienced DF. The incidence density of DF was 4.5/1000 person-years. A long duration of diabetes, lower extremity arterial disease, lower serum albumin, fasting plasma glucose (FPG), and diabetic nephropathy were independently associated with DF. Among these risk factors, the serum albumin concentration was inversely associated with DF, with a hazard ratio (HR) and 95% confidence interval (CI) of 0.91 (0.88-0.95) (P < 0.001). Additionally, a U-shaped nonlinear relationship was observed between the FPG level and DF. After adjusting for other variables, the HRs and 95%CI for FPG < 4.4 mmol/L and ≥ 7.0 mmol/L were 3.99 (1.55-10.25) (P = 0.004) and 3.12 (1.66-5.87) (P < 0.001), respectively, which was greater than the mid-range level (4.4-6.9 mmol/L). The AUC for predicting DF over 3 years was 0.797.
CONCLUSION
FPG demonstrated a U-shaped relationship with DF. Serum albumin levels were negatively associated with DF. The prediction nomogram model of DF showed good discrimination ability using diabetes duration, lower extremity arterial disease, serum albumin, FPG, and diabetic nephropathy (Clinicaltrial.gov NCT05519163).
Core Tip: The risk factors and prediction models for diabetic foot (DF) remain inconclusive, and various predictors may contribute to its development as type 2 diabetes progresses. In this study, we analyzed 6301 inpatients with type 2 diabetes in China from January 2016 to December 2021. Our findings revealed that fasting plasma glucose was positively associated with DF, both at low and high levels, while albumin showed a negative association. Additionally, prolonged duration of diabetes, lower extremity arterial disease, and diabetic nephropathy were related to DF. Based on these insights, targeted prevention strategies are needed for predictors with varying implications.
