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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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Chen L, Shao X, Yu P. Machine learning prediction models for diabetic kidney disease: systematic review and meta-analysis. Endocrine 2024; 84:890-902. [PMID: 38141061 DOI: 10.1007/s12020-023-03637-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 11/28/2023] [Indexed: 12/24/2023]
Abstract
BACKGROUND Machine learning is increasingly recognized as a viable approach for identifying risk factors associated with diabetic kidney disease (DKD). However, the current state of real-world research lacks a comprehensive systematic analysis of the predictive performance of machine learning (ML) models for DKD. OBJECTIVES The objectives of this study were to systematically summarize the predictive capabilities of various ML methods in forecasting the onset and the advancement of DKD, and to provide a basic outline for ML methods in DKD. METHODS We have searched mainstream databases, including PubMed, Web of Science, Embase, and MEDLINE databases to obtain the eligible studies. Subsequently, we categorized various ML techniques and analyzed the differences in their performance in predicting DKD. RESULTS Logistic regression (LR) was the prevailing ML method, yielding an overall pooled area under the receiver operating characteristic curve (AUROC) of 0.83. On the other hand, the non-LR models also performed well with an overall pooled AUROC of 0.80. Our t-tests showed no statistically significant difference in predicting ability between LR and non-LR models (t = 1.6767, p > 0.05). CONCLUSION All ML predicting models yielded relatively satisfied DKD predicting ability with their AUROCs greater than 0.7. However, we found no evidence that non-LR models outperformed the LR model. LR exhibits high performance or accuracy in practice, while it is known for algorithmic simplicity and computational efficiency compared to others. Thus, LR may be considered a cost-effective ML model in practice.
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Affiliation(s)
- Lianqin Chen
- NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, 300134, China
| | - Xian Shao
- NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, 300134, China
| | - Pei Yu
- NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, 300134, China.
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Sammut-Powell C, Sisk R, Silva-Tinoco R, de la Pena G, Almeda-Valdes P, Juarez Comboni SC, Goncalves S, Cameron R. External validation of a minimal-resource model to predict reduced estimated glomerular filtration rate in people with type 2 diabetes without diagnosis of chronic kidney disease in Mexico: a comparison between country-level and regional performance. Front Endocrinol (Lausanne) 2024; 15:1253492. [PMID: 38586458 PMCID: PMC10998449 DOI: 10.3389/fendo.2024.1253492] [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: 07/05/2023] [Accepted: 02/19/2024] [Indexed: 04/09/2024] Open
Abstract
Background Patients with type 2 diabetes are at an increased risk of chronic kidney disease (CKD) hence it is recommended that they receive annual CKD screening. The huge burden of diabetes in Mexico and limited screening resource mean that CKD screening is underperformed. Consequently, patients often have a late diagnosis of CKD. A regional minimal-resource model to support risk-tailored CKD screening in patients with type 2 diabetes has been developed and globally validated. However, population heath and care services between countries within a region are expected to differ. The aim of this study was to evaluate the performance of the model within Mexico and compare this with the performance demonstrated within the Americas in the global validation. Methods We performed a retrospective observational study with data from primary care (Clinic Specialized in Diabetes Management in Mexico City), tertiary care (Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán) and the Mexican national survey of health and nutrition (ENSANUT-MC 2016). We applied the minimal-resource model across the datasets and evaluated model performance metrics, with the primary interest in the sensitivity and increase in the positive predictive value (PPV) compared to a screen-everyone approach. Results The model was evaluated on 2510 patients from Mexico (primary care: 1358, tertiary care: 735, ENSANUT-MC: 417). Across the Mexico data, the sensitivity was 0.730 (95% CI: 0.689 - 0.779) and the relative increase in PPV was 61.0% (95% CI: 52.1% - 70.8%). These were not statistically different to the regional performance metrics for the Americas (sensitivity: p=0.964; relative improvement: p=0.132), however considerable variability was observed across the data sources. Conclusion The minimal-resource model performs consistently in a representative Mexican population sample compared with the Americas regional performance. In primary care settings where screening is underperformed and access to laboratory testing is limited, the model can act as a risk-tailored CKD screening solution, directing screening resources to patients who are at highest risk.
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Affiliation(s)
| | - Rose Sisk
- Gendius Ltd, Alderley Edge, United Kingdom
| | - Ruben Silva-Tinoco
- Clinic Specialized in the Diabetes Management of the Mexico City Government, Public Health Services of the Mexico City Government, Mexico, City, Mexico
| | - Gustavo de la Pena
- Department of Endocrinology and Metabolism, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán (INCMNSZ), Mexico City, Mexico
| | - Paloma Almeda-Valdes
- Department of Endocrinology and Metabolism, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán (INCMNSZ), Mexico City, Mexico
- Metabolic Diseases Research, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
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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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5
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Yan P, Yang Y, Zhang X, Zhang Y, Li J, Wu Z, Dan X, Wu X, Chen X, Li S, Xu Y, Wan Q. Association of systemic immune-inflammation index with diabetic kidney disease in patients with type 2 diabetes: a cross-sectional study in Chinese population. Front Endocrinol (Lausanne) 2024; 14:1307692. [PMID: 38239983 PMCID: PMC10795757 DOI: 10.3389/fendo.2023.1307692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Accepted: 12/11/2023] [Indexed: 01/22/2024] Open
Abstract
Objective Systemic immune-inflammation index (SII), a novel inflammatory marker, has been reported to be associated with diabetic kidney disease (DKD) in the U.S., however, such a close relationship with DKD in other countries, including China, has not been never determined. We aimed to explore the association between SII and DKD in Chinese population. Methods A total of 1922 hospitalized patients with type 2 diabetes mellitus (T2DM) included in this cross-sectional study were divided into three groups based on estimated glomerular filtration rate (eGFR) and urinary albumin-to-creatinine ratio (ACR): non-DKD group, DKD stages 1-2 Alb group, and DKD-non-Alb+DKD stage 3 Alb group. The possible association of SII with DKD was investigated by correlation and multivariate logistic regression analysis, and receiver-operating characteristic (ROC) curves analysis. Results Moving from the non-DKD group to the DKD-non-Alb+DKD stage 3 Alb group, SII level was gradually increased (P for trend <0.01). Partial correlation analysis revealed that SII was positively associated with urinary ACR and prevalence of DKD, and negatively with eGFR (all P<0.01). Multivariate logistic regression analysis showed that SII remained independently significantly associated with the presence of DKD after adjustment for all confounding factors [(odds ratio (OR), 2.735; 95% confidence interval (CI), 1.840-4.063; P < 0.01)]. Moreover, compared with subjects in the lowest quartile of SII (Q1), the fully adjusted OR for presence of DKD was 1.060 (95% CI 0.773-1.455) in Q2, 1.167 (95% CI 0.995-1.368) in Q3, 1.266 (95% CI 1.129-1.420) in the highest quartile (Q4) (P for trend <0.01). Similar results were observed in presence of DKD stages 1-2 Alb or presence of DKD-non- Alb+DKD stage 3 Alb among SII quartiles. Last, the analysis of ROC curves revealed that the best cutoff values for SII to predict DKD, Alb DKD stages 1- 2, and DKD-non-Alb+ DKD stage 3 Alb were 609.85 (sensitivity: 48.3%; specificity: 72.8%), 601.71 (sensitivity: 43.9%; specificity: 72.3%), and 589.27 (sensitivity: 61.1%; specificity: 71.1%), respectively. Conclusion Higher SII is independently associated with an increased risk of the presence and severity of DKD, and SII might be a promising biomarker for DKD and its distinct phenotypes in Chinese population.
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Affiliation(s)
- Pijun Yan
- Department of Endocrinology and Metabolism, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Metabolic Vascular Disease Key Laboratory of Sichuan Province, Luzhou, China
- Sichuan Clinical Research Center for Diabetes and Metabolism, Luzhou, China
- Sichuan Clinical Research Center for Nephropathy, Luzhou, China
- Cardiovascular and Metabolic Diseases Key Laboratory of Luzhou, Luzhou, China
| | - Yuxia Yang
- Department of Endocrinology and Metabolism, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Metabolic Vascular Disease Key Laboratory of Sichuan Province, Luzhou, China
- Sichuan Clinical Research Center for Diabetes and Metabolism, Luzhou, China
- Sichuan Clinical Research Center for Nephropathy, Luzhou, China
- Cardiovascular and Metabolic Diseases Key Laboratory of Luzhou, Luzhou, China
| | - Xing Zhang
- Department of Endocrinology and Metabolism, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Metabolic Vascular Disease Key Laboratory of Sichuan Province, Luzhou, China
- Sichuan Clinical Research Center for Diabetes and Metabolism, Luzhou, China
- Sichuan Clinical Research Center for Nephropathy, Luzhou, China
- Cardiovascular and Metabolic Diseases Key Laboratory of Luzhou, Luzhou, China
| | - Yi Zhang
- Department of Endocrinology and Metabolism, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Metabolic Vascular Disease Key Laboratory of Sichuan Province, Luzhou, China
- Sichuan Clinical Research Center for Diabetes and Metabolism, Luzhou, China
- Sichuan Clinical Research Center for Nephropathy, Luzhou, China
- Cardiovascular and Metabolic Diseases Key Laboratory of Luzhou, Luzhou, China
| | - Jia Li
- Department of Endocrinology and Metabolism, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Metabolic Vascular Disease Key Laboratory of Sichuan Province, Luzhou, China
- Sichuan Clinical Research Center for Diabetes and Metabolism, Luzhou, China
- Sichuan Clinical Research Center for Nephropathy, Luzhou, China
- Cardiovascular and Metabolic Diseases Key Laboratory of Luzhou, Luzhou, China
| | - Zujiao Wu
- Department of Clinical Nutrition, Chengdu Eighth People’s Hospital (Geriatric Hospital of Chengdu Medical College), Chengdu, China
| | - Xiaofang Dan
- Department of Endocrinology and Metabolism, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Metabolic Vascular Disease Key Laboratory of Sichuan Province, Luzhou, China
- Sichuan Clinical Research Center for Diabetes and Metabolism, Luzhou, China
- Sichuan Clinical Research Center for Nephropathy, Luzhou, China
- Cardiovascular and Metabolic Diseases Key Laboratory of Luzhou, Luzhou, China
| | - Xian Wu
- Department of Endocrinology and Metabolism, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Metabolic Vascular Disease Key Laboratory of Sichuan Province, Luzhou, China
- Sichuan Clinical Research Center for Diabetes and Metabolism, Luzhou, China
- Sichuan Clinical Research Center for Nephropathy, Luzhou, China
- Cardiovascular and Metabolic Diseases Key Laboratory of Luzhou, Luzhou, China
| | - Xiping Chen
- Clinical Medical College, Southwest Medical University, Luzhou, China
| | - Shengxi Li
- Clinical Medical College, Southwest Medical University, Luzhou, China
| | - Yong Xu
- Department of Endocrinology and Metabolism, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Metabolic Vascular Disease Key Laboratory of Sichuan Province, Luzhou, China
- Sichuan Clinical Research Center for Diabetes and Metabolism, Luzhou, China
- Sichuan Clinical Research Center for Nephropathy, Luzhou, China
- Cardiovascular and Metabolic Diseases Key Laboratory of Luzhou, Luzhou, China
| | - Qin Wan
- Department of Endocrinology and Metabolism, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Metabolic Vascular Disease Key Laboratory of Sichuan Province, Luzhou, China
- Sichuan Clinical Research Center for Diabetes and Metabolism, Luzhou, China
- Sichuan Clinical Research Center for Nephropathy, Luzhou, China
- Cardiovascular and Metabolic Diseases Key Laboratory of Luzhou, Luzhou, China
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