Diabetes & Metabolism Journal

Original Articles

Complications
Screening Tools Based on Nomogram for Diabetic Kidney Diseases in Chinese Type 2 Diabetes Mellitus Patients: Ganyi Wang, Biyao Wang, Gaoxing Qiao, Hao Lou, Fei Xu, Zhan Chen, Shiwei Chen; Diabetes Metab J. 2021;45(5):708-718. Published online April 13, 2021; DOI: https://doi.org/10.4093/dmj.2020.0117

8,988 View
158 Download
11 Web of Science
11 Crossref

Graphical Abstract Abstract PDF Supplementary Material PubReader ePub

Background
The influencing factors of diabetic kidney disease (DKD) in Chinese patients with type 2 diabetes mellitus (T2DM) were explored to develop and validate a DKD diagnostic tool based on nomogram approach for patients with T2DM.
Methods
A total of 2,163 in-hospital patients with diabetes diagnosed from March 2015 to March 2017 were enrolled. Specified logistic regression models were used to screen the factors and establish four different diagnostic tools based on nomogram according to the final included variables. Discrimination and calibration were used to assess the performance of screening tools.
Results
Among the 2,163 participants with diabetes (1,227 men and 949 women), 313 patients (194 men and 120 women) were diagnosed with DKD. Four different screening equations (full model, laboratory-based model 1 [LBM1], laboratory-based model 2 [LBM2], and simplified model) showed good discriminations and calibrations. The C-indexes were 0.8450 (95% confidence interval [CI], 0.8202 to 0.8690) for full model, 0.8149 (95% CI, 0.7892 to 0.8405) for LBM1, 0.8171 (95% CI, 0.7912 to 0.8430) for LBM2, and 0.8083 (95% CI, 0.7824 to 0.8342) for simplified model. According to Hosmer-Lemeshow goodness-of-fit test, good agreement between the predicted and observed DKD events in patients with diabetes was observed for full model (χ²=3.2756, P=0.9159), LBM1 (χ²=7.749, P=0.4584), LBM2 (χ²=10.023, P=0.2634), and simplified model (χ²=12.294, P=0.1387).
Conclusion
LBM1, LBM2, and simplified model exhibited excellent predictive performance and availability and could be recommended for screening DKD cases among Chinese patients with diabetes.

Citations

Citations to this article as recorded by

Machine learning-based risk predictive models for diabetic kidney disease in type 2 diabetes mellitus patients: a systematic review and meta-analysis
Yihan Li, Nan Jin, Qiuzhong Zhan, Yue Huang, Aochuan Sun, Fen Yin, Zhuangzhuang Li, Jiayu Hu, Zhengtang Liu
Frontiers in Endocrinology.2025;[Epub] CrossRef
Deep learning for early detection of chronic kidney disease stages in diabetes patients: A TabNet approach
Md Nakib Hayat Chowdhury, Mamun Bin Ibne Reaz, Sawal Hamid Md Ali, María Liz Crespo, Shamim Ahmad, Ghassan Maan Salim, Fahmida Haque, Luis Guillermo García Ordóñez, Md. Johirul Islam, Taher Muhammad Mahdee, Kh Shahriya Zaman, Md Shahriar Khan Hemel, Moham
Artificial Intelligence in Medicine.2025; 166: 103153. CrossRef
The accuracy of Machine learning in the prediction and diagnosis of diabetic kidney Disease: A systematic review and Meta-Analysis
Changmao Dai, Xiaolan Sun, Jia Xu, Maojun Chen, Wei Chen, Xueping Li
International Journal of Medical Informatics.2025; 202: 105975. CrossRef
Developing screening tools to estimate the risk of diabetic kidney disease in patients with type 2 diabetes mellitus
Xu Cao, Xiaomei Pei
Technology and Health Care.2024; 32(3): 1807. CrossRef
Development of Serum Lactate Level-Based Nomograms for Predicting Diabetic Kidney Disease in Type 2 Diabetes Mellitus Patients
Chunxia Jiang, Xiumei Ma, Jiao Chen, Yan Zeng, Man Guo, Xiaozhen Tan, Yuping Wang, Peng Wang, Pijun Yan, Yi Lei, Yang Long, Betty Yuen Kwan Law, Yong Xu
Diabetes, Metabolic Syndrome and Obesity.2024; Volume 17: 1051. CrossRef
Two-Dimensional Ultrasound-Based Radiomics Nomogram for Diabetic Kidney Disease: A Pilot Study
Xingyue Huang, Yugang Hu, Yao Zhang, Qing Zhou
International Journal of General Medicine.2024; Volume 17: 1877. CrossRef
Risk prediction models for diabetic nephropathy among type 2 diabetes patients in China: a systematic review and meta-analysis
Wenbin Xu, Yanfei Zhou, Qian Jiang, Yiqian Fang, Qian Yang
Frontiers in Endocrinology.2024;[Epub] CrossRef
Changes in urinary exosomal protein CALM1 may serve as an early noninvasive biomarker for diagnosing diabetic kidney disease
Tao Li, Tian ci Liu, Na Liu, Man Zhang
Clinica Chimica Acta.2023; 547: 117466. CrossRef
Development and validation of a novel nomogram to predict diabetic kidney disease in patients with type 2 diabetic mellitus and proteinuric kidney disease
Hui Zhuan Tan, Jason Chon Jun Choo, Stephanie Fook-Chong, Yok Mooi Chin, Choong Meng Chan, Chieh Suai Tan, Keng Thye Woo, Jia Liang Kwek
International Urology and Nephrology.2022; 55(1): 191. CrossRef
Nomogram-Based Chronic Kidney Disease Prediction Model for Type 1 Diabetes Mellitus Patients Using Routine Pathological Data
Nakib Hayat Chowdhury, Mamun Bin Ibne Reaz, Sawal Hamid Md Ali, Shamim Ahmad, María Liz Crespo, Andrés Cicuttin, Fahmida Haque, Ahmad Ashrif A. Bakar, Mohammad Arif Sobhan Bhuiyan
Journal of Personalized Medicine.2022; 12(9): 1507. CrossRef
Development and assessment of diabetic nephropathy prediction model using hub genes identified by weighted correlation network analysis
Xuelian Zhang, Yao Wang, Zhaojun Yang, Xiaoping Chen, Jinping Zhang, Xin Wang, Xian Jin, Lili Wu, Xiaoyan Xing, Wenying Yang, Bo Zhang
Aging.2022; 14(19): 8095. CrossRef

Genetics
Enhancer-Gene Interaction Analyses Identified the Epidermal Growth Factor Receptor as a Susceptibility Gene for Type 2 Diabetes Mellitus: Yang Yang, Shi Yao, Jing-Miao Ding, Wei Chen, Yan Guo; Diabetes Metab J. 2021;45(2):241-250. Published online June 10, 2020; DOI: https://doi.org/10.4093/dmj.2019.0204

7,748 View
118 Download
8 Web of Science
9 Crossref

Abstract PDF Supplementary Material PubReader ePub

Background

Genetic interactions are known to play an important role in the missing heritability problem for type 2 diabetes mellitus (T2DM). Interactions between enhancers and their target genes play important roles in gene regulation and disease pathogenesis. In the present study, we aimed to identify genetic interactions between enhancers and their target genes associated with T2DM.

Methods

We performed genetic interaction analyses of enhancers and protein-coding genes for T2DM in 2,696 T2DM patients and 3,548 controls of European ancestry. A linear regression model was used to identify single nucleotide polymorphism (SNP) pairs that could affect the expression of the protein-coding genes. Differential expression analyses were used to identify differentially expressed susceptibility genes in diabetic and nondiabetic subjects.

Results

We identified one SNP pair, rs4947941×rs7785013, significantly associated with T2DM (combined P=4.84×10⁻¹⁰). The SNP rs4947941 was annotated as an enhancer, and rs7785013 was located in the epidermal growth factor receptor (EGFR) gene. This SNP pair was significantly associated with EGFR expression in the pancreas (P=0.033), and the minor allele “A” of rs7785013 decreased EGFR gene expression and the risk of T2DM with an increase in the dosage of “T” of rs4947941. EGFR expression was significantly upregulated in T2DM patients, which was consistent with the effect of rs4947941×rs7785013 on T2DM and EGFR expression. A functional validation study using the Mouse Genome Informatics (MGI) database showed that EGFR was associated with diabetes-relevant phenotypes.

Conclusion

Genetic interaction analyses of enhancers and protein-coding genes suggested that EGFR may be a novel susceptibility gene for T2DM.

Citations

Citations to this article as recorded by

Genetic Nurture Effects on Type 2 Diabetes Among Chinese Han Adults: A Family-Based Design
Xiaoyi Li, Zechen Zhou, Yujia Ma, Kexin Ding, Han Xiao, Tao Wu, Dafang Chen, Yiqun Wu
Biomedicines.2025; 13(1): 120. CrossRef
Diabesity: New Candidate Genes and Structural and Functional Effects of Non-Synonymous Single Nucleotide Polymorphisms Identified by Computational Biology
Naveenn Kumar, Karthiga Selvaraj, Lakshmiganesh Kadumbur Gopalshami, Riitvek Baddireddi, Kothai Thiruvengadam, Baddireddi Subhadra Lakshmi
OMICS: A Journal of Integrative Biology.2025; 29(3): 96. CrossRef
Therapeutic role of Crateva religiosa in diabetic nephropathy: Insights into key signaling pathways
Muhammad Ali, Hafiz M. Irfan, Alamgeer, Aman Ullah, Magda H. Abdellattif, Mahmoud Elodemi, Mohammad Zubair, Ajmal Khan, Ahmed Al-Harrasi, Ahmed E. Abdel Moneim
PLOS One.2025; 20(5): e0324028. CrossRef
Hypoglycemic Activity of Rice Resistant-Starch Metabolites: A Mechanistic Network Pharmacology and In Vitro Approach
Jianing Ren, Jing Dai, Yue Chen, Zhenzhen Wang, Ruyi Sha, Jianwei Mao, Yangchen Mao
Metabolites.2024; 14(4): 224. CrossRef
Association of Uterine Leiomyoma with Type 2 Diabetes Mellitus in Young Women: A Population-Based Cohort Study
Ji-Hee Sung, Kyung-Soo Kim, Kyungdo Han, Cheol-Young Park
Diabetes & Metabolism Journal.2024; 48(6): 1105. CrossRef
Genome-Wide Epistasis Study of Cerebrospinal Fluid Hyperphosphorylated Tau in ADNI Cohort
Dandan Chen, Jin Li, Hongwei Liu, Xiaolong Liu, Chenghao Zhang, Haoran Luo, Yiming Wei, Yang Xi, Hong Liang, Qiushi Zhang
Genes.2023; 14(7): 1322. CrossRef
Investigation of the mechanism of Shen Qi Wan prescription in the treatment of T2DM via network pharmacology and molecular docking
Piaopiao Zhao, Xiaoxiao Zhang, Yuning Gong, Weihua Li, Zengrui Wu, Yun Tang, Guixia Liu
In Silico Pharmacology.2022;[Epub] CrossRef
The Role of the Epidermal Growth Factor Receptor in Diabetic Kidney Disease
Raymond C. Harris
Cells.2022; 11(21): 3416. CrossRef
Co-expression Network Revealed Roles of RNA m6A Methylation in Human β-Cell of Type 2 Diabetes Mellitus
Cong Chen, Qing Xiang, Weilin Liu, Shengxiang Liang, Minguang Yang, Jing Tao
Frontiers in Cell and Developmental Biology.2021;[Epub] CrossRef

First
Prev
Page of 1
Next
Last

Search