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Complications
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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
  • 7,392 View
  • 146 Download
  • 7 Web of Science
  • 8 Crossref
Graphical AbstractGraphical Abstract AbstractAbstract PDFSupplementary MaterialPubReader   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  
  • 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
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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
  • 6,408 View
  • 107 Download
  • 4 Web of Science
  • 5 Crossref
AbstractAbstract PDFSupplementary MaterialPubReader   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  
  • 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
  • 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

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