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Xu W, Zhou Y, Jiang Q, Fang Y, Yang Q. Risk prediction models for diabetic nephropathy among type 2 diabetes patients in China: a systematic review and meta-analysis. Front Endocrinol (Lausanne) 2024; 15:1407348. [PMID: 39022345 PMCID: PMC11251916 DOI: 10.3389/fendo.2024.1407348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Accepted: 06/07/2024] [Indexed: 07/20/2024] Open
Abstract
Objective This study systematically reviews and meta-analyzes existing risk prediction models for diabetic kidney disease (DKD) among patients with type 2 diabetes, aiming to provide references for scholars in China to develop higher-quality risk prediction models. Methods We searched databases including China National Knowledge Infrastructure (CNKI), Wanfang Data, VIP Chinese Science and Technology Journal Database, Chinese Biomedical Literature Database (CBM), PubMed, Web of Science, Embase, and the Cochrane Library for studies on the construction of DKD risk prediction models among type 2 diabetes patients, up until 28 December 2023. Two researchers independently screened the literature and extracted and evaluated information according to a data extraction form and bias risk assessment tool for prediction model studies. The area under the curve (AUC) values of the models were meta-analyzed using STATA 14.0 software. Results A total of 32 studies were included, with 31 performing internal validation and 22 reporting calibration. The incidence rate of DKD among patients with type 2 diabetes ranged from 6.0% to 62.3%. The AUC ranged from 0.713 to 0.949, indicating the prediction models have fair to excellent prediction accuracy. The overall applicability of the included studies was good; however, there was a high overall risk of bias, mainly due to the retrospective nature of most studies, unreasonable sample sizes, and studies conducted in a single center. Meta-analysis of the models yielded a combined AUC of 0.810 (95% CI: 0.780-0.840), indicating good predictive performance. Conclusion Research on DKD risk prediction models for patients with type 2 diabetes in China is still in its initial stages, with a high overall risk of bias and a lack of clinical application. Future efforts could focus on constructing high-performance, easy-to-use prediction models based on interpretable machine learning methods and applying them in clinical settings. Registration This systematic review and meta-analysis was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, a recognized guideline for such research. Systematic review registration https://www.crd.york.ac.uk/prospero/, identifier CRD42024498015.
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Affiliation(s)
| | | | | | | | - Qian Yang
- School of Nursing, Chengdu Medical College, Chengdu, Sichuan, China
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Huang X, Hu Y, Zhang Y, Zhou Q. Two-Dimensional Ultrasound-Based Radiomics Nomogram for Diabetic Kidney Disease: A Pilot Study. Int J Gen Med 2024; 17:1877-1885. [PMID: 38736665 PMCID: PMC11086428 DOI: 10.2147/ijgm.s462896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Accepted: 04/21/2024] [Indexed: 05/14/2024] Open
Abstract
Objective To establish a radiomics nomogram based on two-dimensional ultrasound for risk assessment of diabetic kidney disease (DKD) in patients with type 2 diabetes mellitus (T2DM). Methods This study retrospectively collected two-dimensional ultrasound images and clinical data from 52 patients with T2DM who underwent renal biopsy in our hospital from January 2023 to August 2023. Based on the pathological results, all patients were categorized into two groups: DKD (n=33) and non-DKD (n=19). The radiomic features of the segmented kidney in ultrasound pictures were retrieved and selected to calculate each patient's rad-score. A predictive nomogram based on rad-score and clinical features was then constructed and validated based on the calibration curve. Results The rad-score for all patients were computed based on five imaging characteristics extracted from the ultrasound images. The predictive nomogram was developed with the rad-score, diabetic retinopathy, duration of diabetes, and glycosylated hemoglobin. Moreover, This radiomics nomogram showed outstanding calibration capability, discrimination as well as therapeutic usefulness. Conclusion We constructed a nomogram based on two-dimensional ultrasound for DKD in T2DM patientsThe model has been proven to have good predictive performance, showing its potential in identifying DKD in T2DM patients and assisting in making appropriate early interventions.
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Affiliation(s)
- Xingyue Huang
- Department of Ultrasound Imaging, Renmin Hospital of Wuhan University, Wuhan, 430061, People’s Republic of China
| | - Yugang Hu
- Department of Ultrasound Imaging, Renmin Hospital of Wuhan University, Wuhan, 430061, People’s Republic of China
| | - Yao Zhang
- Department of Ultrasound Imaging, Renmin Hospital of Wuhan University, Wuhan, 430061, People’s Republic of China
| | - Qing Zhou
- Department of Ultrasound Imaging, Renmin Hospital of Wuhan University, Wuhan, 430061, People’s Republic of China
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Jiang C, Ma X, Chen J, Zeng Y, Guo M, Tan X, Wang Y, Wang P, Yan P, Lei Y, Long Y, Law BYK, Xu Y. Development of Serum Lactate Level-Based Nomograms for Predicting Diabetic Kidney Disease in Type 2 Diabetes Mellitus Patients. Diabetes Metab Syndr Obes 2024; 17:1051-1068. [PMID: 38445169 PMCID: PMC10913800 DOI: 10.2147/dmso.s453543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 02/19/2024] [Indexed: 03/07/2024] Open
Abstract
Purpose To establish nomograms integrating serum lactate levels and traditional risk factors for predicting diabetic kidney disease (DKD) in type 2 diabetes mellitus (T2DM) patients. Patients and methods A total of 570 T2DM patients and 100 healthy subjects were enrolled. T2DM patients were categorized into normal and high lactate groups. Univariate and multivariate logistic regression analyses were employed to identify independent predictors for DKD. Then, nomograms for predicting DKD were established, and the model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration, and decision curve analysis (DCA). Results T2DM patients exhibited higher lactate levels compared to those in healthy subjects. Glucose, platelet, uric acid, creatinine, and hypertension were independent factors for DKD in T2DM patients with normal lactate levels, while diabetes duration, creatinine, total cholesterol, and hypertension were indicators in high lactate levels group (P<0.05). The AUC values were 0.834 (95% CI, 0.776 to 0.891) and 0.741 (95% CI, 0.688 to 0.795) for nomograms in both normal lactate and high lactate groups, respectively. The calibration curve demonstrated excellent agreement of fit. Furthermore, the DCA revealed that the threshold probability and highest Net Yield were 17-99% and 0.36, and 24-99% and 0.24 for the models in normal lactate and high lactate groups, respectively. Conclusion The serum lactate level-based nomogram models, combined with traditional risk factors, offer an effective tool for predicting DKD probability in T2DM patients. This approach holds promise for early risk assessment and tailored intervention strategies.
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Affiliation(s)
- Chunxia Jiang
- Dr. Neher’s Biophysics Laboratory for Innovative Drug Discovery, State Key Laboratory of Quality Research in Chinese Medicine, Faculty of Chinese Medicine, Macau University of Science and Technology, Macao, People’s Republic of China
- Department of Endocrinology and Metabolism, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, People’s Republic of China
- Metabolic Vascular Disease Key Laboratory of Sichuan Province, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, People’s Republic of China
- Sichuan Clinical Research Center for Nephropathy, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, People’s Republic of China
| | - Xiumei Ma
- Dr. Neher’s Biophysics Laboratory for Innovative Drug Discovery, State Key Laboratory of Quality Research in Chinese Medicine, Faculty of Chinese Medicine, Macau University of Science and Technology, Macao, People’s Republic of China
- Department of Endocrinology and Metabolism, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, People’s Republic of China
- Metabolic Vascular Disease Key Laboratory of Sichuan Province, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, People’s Republic of China
- Sichuan Clinical Research Center for Nephropathy, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, People’s Republic of China
| | - Jiao Chen
- Department of Endocrinology and Metabolism, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, People’s Republic of China
- Metabolic Vascular Disease Key Laboratory of Sichuan Province, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, People’s Republic of China
- Sichuan Clinical Research Center for Nephropathy, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, People’s Republic of China
- Department of Endocrinology, The Third’s Hospital of Mianyang, Sichuan Mental Health Center, Mianyang, Sichuan, People’s Republic of China
| | - Yan Zeng
- Dr. Neher’s Biophysics Laboratory for Innovative Drug Discovery, State Key Laboratory of Quality Research in Chinese Medicine, Faculty of Chinese Medicine, Macau University of Science and Technology, Macao, People’s Republic of China
- Department of Endocrinology and Metabolism, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, People’s Republic of China
- Metabolic Vascular Disease Key Laboratory of Sichuan Province, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, People’s Republic of China
- Sichuan Clinical Research Center for Nephropathy, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, People’s Republic of China
| | - Man Guo
- Department of Endocrinology and Metabolism, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, People’s Republic of China
- Metabolic Vascular Disease Key Laboratory of Sichuan Province, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, People’s Republic of China
- Sichuan Clinical Research Center for Nephropathy, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, People’s Republic of China
| | - Xiaozhen Tan
- Department of Endocrinology and Metabolism, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, People’s Republic of China
- Metabolic Vascular Disease Key Laboratory of Sichuan Province, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, People’s Republic of China
- Sichuan Clinical Research Center for Nephropathy, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, People’s Republic of China
| | - Yuping Wang
- Dr. Neher’s Biophysics Laboratory for Innovative Drug Discovery, State Key Laboratory of Quality Research in Chinese Medicine, Faculty of Chinese Medicine, Macau University of Science and Technology, Macao, People’s Republic of China
- Department of Breast, Thyroid and Vascular Surgery, Traditional Chinese Medicine Hospital Affiliated to Southwest Medical University, Luzhou, Sichuan, People’s Republic of China
| | - Peng Wang
- Dr. Neher’s Biophysics Laboratory for Innovative Drug Discovery, State Key Laboratory of Quality Research in Chinese Medicine, Faculty of Chinese Medicine, Macau University of Science and Technology, Macao, People’s Republic of China
| | - Pijun Yan
- Department of Endocrinology and Metabolism, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, People’s Republic of China
- Metabolic Vascular Disease Key Laboratory of Sichuan Province, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, People’s Republic of China
- Sichuan Clinical Research Center for Nephropathy, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, People’s Republic of China
| | - Yi Lei
- Dr. Neher’s Biophysics Laboratory for Innovative Drug Discovery, State Key Laboratory of Quality Research in Chinese Medicine, Faculty of Chinese Medicine, Macau University of Science and Technology, Macao, People’s Republic of China
- Department of Endocrinology and Metabolism, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, People’s Republic of China
- Metabolic Vascular Disease Key Laboratory of Sichuan Province, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, People’s Republic of China
- Sichuan Clinical Research Center for Nephropathy, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, People’s Republic of China
| | - Yang Long
- Department of Endocrinology and Metabolism, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, People’s Republic of China
- Metabolic Vascular Disease Key Laboratory of Sichuan Province, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, People’s Republic of China
- Sichuan Clinical Research Center for Nephropathy, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, People’s Republic of China
| | - Betty Yuen Kwan Law
- Dr. Neher’s Biophysics Laboratory for Innovative Drug Discovery, State Key Laboratory of Quality Research in Chinese Medicine, Faculty of Chinese Medicine, Macau University of Science and Technology, Macao, People’s Republic of China
| | - Yong Xu
- Dr. Neher’s Biophysics Laboratory for Innovative Drug Discovery, State Key Laboratory of Quality Research in Chinese Medicine, Faculty of Chinese Medicine, Macau University of Science and Technology, Macao, People’s Republic of China
- Department of Endocrinology and Metabolism, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, People’s Republic of China
- Metabolic Vascular Disease Key Laboratory of Sichuan Province, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, People’s Republic of China
- Sichuan Clinical Research Center for Nephropathy, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, People’s Republic of China
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Li T, Ci Liu T, Liu N, Zhang M. Changes in urinary exosomal protein CALM1 may serve as an early noninvasive biomarker for diagnosing diabetic kidney disease. Clin Chim Acta 2023; 547:117466. [PMID: 37406751 DOI: 10.1016/j.cca.2023.117466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 06/28/2023] [Accepted: 06/30/2023] [Indexed: 07/07/2023]
Abstract
BACKGROUND The risk of the development and progression of diabetic kidney disease (DKD) was increased by abnormal calcium release. However, it is still unknown whether calcium signal pathway-related proteins are changed in urinary exosomes. This study aims to explore the changes in urinary exosomal proteins, which may provide novel biomarkers for diagnosing DKD. METHODS Urinary exosomes were isolated from 132 participants by size exclusion chromatography method and 72 participants were tested by LC-MS/MS (Discovery phase). Correlation and multivariate logistics analysis were applied to evaluate selected urinary proteins. Western blot and ELISA were used to validate the selected protein (Validation phase: n = 60). The diagnostic performance of the selected biomarker was evaluated by receiver operating characteristic curve analyses between the discovery and validation phases. RESULTS Sixteen calcium signal pathway-related proteins were identified, however, only Calmodulin-1(CALM1) was continuously increased. Different expression of CALM1 was found in patients with different level of estimated glomerular filtration rate (eGFR) in two cohorts. The level of CALM1 was correlated with eGFR and serum creatinine levels in two cohorts. Multivariate analysis revealed that serum albumin (ALB) levels and CALM1 were independent risk factors for DKD. A diagnostic model based on CALM1 and serum ALB levels that could significantly distinguish DKD was established and validated. CONCLUSIONS Significant changes in calcium signal pathway-related urinary exosomal proteins were observed. The CALM1 may serve as an early noninvasive biomarker for diagnosing DKD.
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Affiliation(s)
- Tao Li
- Clinical Laboratory Medicine, Beijing Shijitan Hospital, Capital Medical University, Beijing 100038, China; Beijing Key Laboratory of Urinary Cellular Molecular Diagnostics, Beijing 100038, China
| | - Tian Ci Liu
- Clinical Laboratory Medicine, Beijing Shijitan Hospital, Capital Medical University, Beijing 100038, China; Beijing Key Laboratory of Urinary Cellular Molecular Diagnostics, Beijing 100038, China
| | - Na Liu
- Clinical Laboratory Medicine, Beijing Shijitan Hospital, Capital Medical University, Beijing 100038, China; Beijing Key Laboratory of Urinary Cellular Molecular Diagnostics, Beijing 100038, China
| | - Man Zhang
- Clinical Laboratory Medicine, Beijing Shijitan Hospital, Capital Medical University, Beijing 100038, China; Beijing Key Laboratory of Urinary Cellular Molecular Diagnostics, Beijing 100038, China; Institute of Regenerative Medicine and Laboratory Technology Innovation, Qingdao University, Qingdao 266071, China.
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Zhang X, Wang Y, Yang Z, Chen X, Zhang J, Wang X, Jin X, Wu L, Xing X, Yang W, Zhang B. Development and assessment of diabetic nephropathy prediction model using hub genes identified by weighted correlation network analysis. Aging (Albany NY) 2022; 14:8095-8109. [PMID: 36242604 PMCID: PMC9596198 DOI: 10.18632/aging.204340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 09/23/2022] [Indexed: 11/25/2022]
Abstract
Diabetic nephropathy (DN) is one microvascular complication of diabetes. About 30% of diabetic patients can develop DN, which is closely related to the high incidence and mortality of heart diseases, and then develop end-stage renal diseases. Therefore, early detection and screening of high-risk patients with DN is important. Herein, we explored the differences of serum transcriptomics between DN and non-DN in type II diabetes mellitus (T2DM) patients. We obtained 110 target genes using weighted correlation network analysis. Gene Ontology enrichment analysis indicates these target genes are mainly related to membrane adhesion, alpha-amino acid biosynthesis, metabolism, and binding, terminus, inhibitory synapse, clathrinid-sculpted vesicle, kinase activity, hormone binding, receptor activity, and transporter activity. Kyoto Encyclopedia of Genes and Genomes analysis indicates the process of DN in diabetic patients can involve synaptic vesicle cycle, cysteine and methionine metabolism, N-Glycan biosynthesis, osteoclast differentiation, and cAMP signaling pathway. Next, we detected the expression levels of hub genes in a retrospective cohort. Then, we developed a risk score tool included in the prediction model for early DN in T2DM patients. The prediction model was well applied into clinical practice, as confirmed by internal validation and several other methods. A novel DN risk model with relatively high prediction accuracy was established based on clinical characteristics and hub genes of serum detection. The estimated risk score can help clinicians develop individualized intervention programs for DN in T2DM. External validation data are required before individualized intervention measures.
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Affiliation(s)
- Xuelian Zhang
- Department of Endocrinology, China-Japan Friendship Hospital, Beijing 100029, People's Republic of China
| | - Yao Wang
- Department of Endocrinology, China-Japan Friendship Hospital, Beijing 100029, People's Republic of China
| | - Zhaojun Yang
- Department of Endocrinology, China-Japan Friendship Hospital, Beijing 100029, People's Republic of China
| | - Xiaoping Chen
- Department of Endocrinology, China-Japan Friendship Hospital, Beijing 100029, People's Republic of China
| | - Jinping Zhang
- Department of Endocrinology, China-Japan Friendship Hospital, Beijing 100029, People's Republic of China
| | - Xin Wang
- Department of Endocrinology, China-Japan Friendship Hospital, Beijing 100029, People's Republic of China
| | - Xian Jin
- Department of Endocrinology, China-Japan Friendship Hospital, Beijing 100029, People's Republic of China
| | - Lili Wu
- Department of Endocrinology, China-Japan Friendship Hospital, Beijing 100029, People's Republic of China
| | - Xiaoyan Xing
- Department of Endocrinology, China-Japan Friendship Hospital, Beijing 100029, People's Republic of China
| | - Wenying Yang
- Department of Endocrinology, China-Japan Friendship Hospital, Beijing 100029, People's Republic of China
| | - Bo Zhang
- Department of Endocrinology, China-Japan Friendship Hospital, Beijing 100029, People's Republic of China
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Nomogram-Based Chronic Kidney Disease Prediction Model for Type 1 Diabetes Mellitus Patients Using Routine Pathological Data. J Pers Med 2022; 12:jpm12091507. [PMID: 36143293 PMCID: PMC9501949 DOI: 10.3390/jpm12091507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 09/12/2022] [Accepted: 09/13/2022] [Indexed: 11/16/2022] Open
Abstract
Type 1 diabetes mellitus (T1DM) patients are a significant threat to chronic kidney disease (CKD) development during their life. However, there is always a high chance of delay in CKD detection because CKD can be asymptomatic, and T1DM patients bypass traditional CKD tests during their routine checkups. This study aims to develop and validate a prediction model and nomogram of CKD in T1DM patients using readily available routine checkup data for early CKD detection. This research utilized 1375 T1DM patients’ sixteen years of longitudinal data from multi-center Epidemiology of Diabetes Interventions and Complications (EDIC) clinical trials conducted at 28 sites in the USA and Canada and considered 17 routinely available features. Three feature ranking algorithms, extreme gradient boosting (XGB), random forest (RF), and extremely randomized trees classifier (ERT), were applied to create three feature ranking lists, and logistic regression analyses were performed to develop CKD prediction models using these ranked feature lists to identify the best performing top-ranked features combination. Finally, the most significant features were selected to develop a multivariate logistic regression-based CKD prediction model for T1DM patients. This model was evaluated using sensitivity, specificity, accuracy, precision, and F1 score on train and test data. A nomogram of the final model was further generated for easy application in clinical practices. Hypertension, duration of diabetes, drinking habit, triglycerides, ACE inhibitors, low-density lipoprotein (LDL) cholesterol, age, and smoking habit were the top-8 features ranked by the XGB model and identified as the most important features for predicting CKD in T1DM patients. These eight features were selected to develop the final prediction model using multivariate logistic regression, which showed 90.04% and 88.59% accuracy in internal and test data validation. The proposed model showed excellent performance and can be used for CKD identification in T1DM patients during routine checkups.
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