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Li S, Chen Y, Zhang L, Li R, Kang N, Hou J, Wang J, Bao Y, Jiang F, Zhu R, Wang C, Zhang L. An environment-wide association study for the identification of non-invasive factors for type 2 diabetes mellitus: Analysis based on the Henan Rural Cohort study. Diabetes Res Clin Pract 2023; 204:110917. [PMID: 37748711 DOI: 10.1016/j.diabres.2023.110917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 09/16/2023] [Accepted: 09/21/2023] [Indexed: 09/27/2023]
Abstract
AIM To explore the influencing factors of Type 2 diabetes mellitus (T2DM) in the rural population of Henan Province and evaluate the predictive ability of non-invasive factors to T2DM. METHODS A total of 30,020 participants from the Henan Rural Cohort Study in China were included in this study. The dataset was randomly divided into a training set and a testing set with a 50:50 split for validation purposes. We used logistic regression analysis to investigate the association between 56 factors and T2DM in the training set (false discovery rate < 5 %) and significant factors were further validated in the testing set (P < 0.05). Gradient Boosting Machine (GBM) model was used to determine the ability of the non-invasive variables to classify T2DM individuals accurately and the importance ranking of these variables. RESULTS The overall population prevalence of T2DM was 9.10 %. After adjusting for age, sex, educational level, marital status, and body measure index (BMI), we identified 13 non-invasive variables and 6 blood biochemical indexes associated with T2DM in the training and testing dataset. The top three factors according to the GBM importance ranking were pulse pressure (PP), urine glucose (UGLU), and waist-to-hip ratio (WHR). The GBM model achieved a receiver operating characteristic (AUC) curve of 0.837 with non-invasive variables and 0.847 for the full model. CONCLUSIONS Our findings demonstrate that non-invasive variables that can be easily measured and quickly obtained may be used to predict T2DM risk in rural populations in Henan Province.
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Affiliation(s)
- Shuoyi Li
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan 450001, PR China
| | - Ying Chen
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan 450001, PR China
| | - Liying Zhang
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan 450001, PR China
| | - Ruiying Li
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan 450001, PR China
| | - Ning Kang
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan 450001, PR China
| | - Jian Hou
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan 450001, PR China
| | - Jing Wang
- China-Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi 710061, PR China
| | - Yining Bao
- China-Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi 710061, PR China
| | - Feng Jiang
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan 450001, PR China
| | - Ruifang Zhu
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan 450001, PR China
| | - Chongjian Wang
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan 450001, PR China.
| | - Lei Zhang
- China-Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi 710061, PR China; Artificial Intelligence and Modelling in Epidemiology Program, Melbourne Sexual Health Centre, Alfred Health, Melbourne, Australia; Central Clinical School, Faculty of Medicine, Monash University, Melbourne, Australia.
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Xu S, Coleman RL, Wan Q, Gu Y, Meng G, Song K, Shi Z, Xie Q, Tuomilehto J, Holman RR, Niu K, Tong N. Risk prediction models for incident type 2 diabetes in Chinese people with intermediate hyperglycemia: a systematic literature review and external validation study. Cardiovasc Diabetol 2022; 21:182. [PMID: 36100925 PMCID: PMC9472437 DOI: 10.1186/s12933-022-01622-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 09/07/2022] [Indexed: 11/23/2022] Open
Abstract
Background People with intermediate hyperglycemia (IH), including impaired fasting glucose and/or impaired glucose tolerance, are at higher risk of developing type 2 diabetes (T2D) than those with normoglycemia. We aimed to evaluate the performance of published T2D risk prediction models in Chinese people with IH to inform them about the choice of primary diabetes prevention measures. Methods A systematic literature search was conducted to identify Asian-derived T2D risk prediction models, which were eligible if they were built on a prospective cohort of Asian adults without diabetes at baseline and utilized routinely-available variables to predict future risk of T2D. These Asian-derived and five prespecified non-Asian derived T2D risk prediction models were divided into BASIC (clinical variables only) and EXTENDED (plus laboratory variables) versions, with validation performed on them in three prospective Chinese IH cohorts: ACE (n = 3241), Luzhou (n = 1333), and TCLSIH (n = 1702). Model performance was assessed in terms of discrimination (C-statistic) and calibration (Hosmer–Lemeshow test). Results Forty-four Asian and five non-Asian studies comprising 21 BASIC and 46 EXTENDED T2D risk prediction models for validation were identified. The majority were at high (n = 43, 87.8%) or unclear (n = 3, 6.1%) risk of bias, while only three studies (6.1%) were scored at low risk of bias. BASIC models showed poor-to-moderate discrimination with C-statistics 0.52–0.60, 0.50–0.59, and 0.50–0.64 in the ACE, Luzhou, and TCLSIH cohorts respectively. EXTENDED models showed poor-to-acceptable discrimination with C-statistics 0.54–0.73, 0.52–0.67, and 0.59–0.78 respectively. Fifteen BASIC and 40 EXTENDED models showed poor calibration (P < 0.05), overpredicting or underestimating the observed diabetes risk. Most recalibrated models showed improved calibration but modestly-to-severely overestimated diabetes risk in the three cohorts. The NAVIGATOR model showed the best discrimination in the three cohorts but had poor calibration (P < 0.05). Conclusions In Chinese people with IH, previously published BASIC models to predict T2D did not exhibit good discrimination or calibration. Several EXTENDED models performed better, but a robust Chinese T2D risk prediction tool in people with IH remains a major unmet need. Supplementary Information The online version contains supplementary material available at 10.1186/s12933-022-01622-5.
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Affiliation(s)
- Shishi Xu
- Division of Endocrinology and Metabolism, Center for Diabetes and Metabolism Research, Laboratory of Diabetes and Islet Transplantation Research, West China Medical School, West China Hospital, Sichuan University, Guo Xue Lane 37, Chengdu, China.,Diabetes Trials Unit, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Ruth L Coleman
- Diabetes Trials Unit, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Qin Wan
- Department of Endocrine and Metabolic Diseases, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Yeqing Gu
- Nutrition and Radiation Epidemiology Research Center, Institute of Radiation Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Ge Meng
- Nutritional Epidemiology Institute and School of Public Health, Tianjin Medical University, Tianjin, China
| | - Kun Song
- Health Management Centre, Tianjin Medical University General Hospital, Tianjin, China
| | - Zumin Shi
- Human Nutrition Department, College of Health Sciences, QU Health, Qatar University, Doha, Qatar
| | - Qian Xie
- Department of General Practice, People's Hospital of LeShan, LeShan, China
| | - Jaakko Tuomilehto
- Department of Public Health, University of Helsinki, Helsinki, Finland.,Population Health Unit, Finnish Institute for Health and Welfare, Helsinki, Finland.,Saudi Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Rury R Holman
- Diabetes Trials Unit, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Kaijun Niu
- Nutrition and Radiation Epidemiology Research Center, Institute of Radiation Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China. .,Nutritional Epidemiology Institute and School of Public Health, Tianjin Medical University, Tianjin, China.
| | - Nanwei Tong
- Division of Endocrinology and Metabolism, Center for Diabetes and Metabolism Research, Laboratory of Diabetes and Islet Transplantation Research, West China Medical School, West China Hospital, Sichuan University, Guo Xue Lane 37, Chengdu, China.
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Jia X, Wang A, Yang L, Cheng Y, Wang Y, Ba J, Dou J, Mu Y, Zhao D, Lyu Z. Clinical Significance of Lifetime Maximum Body Mass Index in Predicting the Development of T2DM: A Prospective Study in Beijing. Front Endocrinol (Lausanne) 2022; 13:839195. [PMID: 35721732 PMCID: PMC9201965 DOI: 10.3389/fendo.2022.839195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Accepted: 04/12/2022] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND Overweight and obesity are well-known risk factors for type 2 diabetes mellitus (T2DM). The effect of the maximum body mass index (BMImax), which indicates the highest body weight before the diagnosis of T2DM, is not fully understood. This study aimed to explore the predictive value of BMImax in the progression of diabetes. METHODS This prospective study recruited 2018 subjects with normal glucose tolerance in Beijing, China. The subjects were followed up for eight years, and the association between BMImax and glucose outcomes was evaluated. RESULTS Ninety-seven of the 2,018 participants developed diabetes by the end of the study. Compared to individuals with normal glucose tolerance, those who developed diabetes were characterized by higher levels of fasting plasma glucose (FPG), 2 h postload glucose (PBG), glycosylated hemoglobin (HbA1c), systolic blood pressure (SBP), and low-density lipoprotein cholesterol (LDL-c), a higher prevalence of a familial history of diabetes and a lower level of high-density lipoprotein cholesterol (HDL-c). Multivariate regression analysis of sex-stratified groups suggested that FPG, HbA1c, SBP and familial history of diabetes were independent risk factors for diabetes, but that BMImax was a unique indicator for female patients. CONCLUSIONS BMImax might be an independent predictor of T2DM in females, but it does not seem to be associated with the risk of diabetes in males. BMImax could be regarded as an indicator in the prevention and management of diabetes.
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Affiliation(s)
- Xiaomeng Jia
- Department of Endocrinology, The First Medical Center of Chinese PLA General Hospital, Beijing, China
- Center for Endocrine Metabolism and Immune Disease, Beijing Luhe Hospital, Capital Medical University, Beijing, China
| | - Anping Wang
- Department of Endocrinology, The First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Longyan Yang
- Center for Endocrine Metabolism and Immune Disease, Beijing Luhe Hospital, Capital Medical University, Beijing, China
| | - Yu Cheng
- Department of Endocrinology, The First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Yajing Wang
- Department of Endocrinology, The First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Jianming Ba
- Department of Endocrinology, The First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Jingtao Dou
- Department of Endocrinology, The First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Yiming Mu
- Department of Endocrinology, The First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Dong Zhao
- Center for Endocrine Metabolism and Immune Disease, Beijing Luhe Hospital, Capital Medical University, Beijing, China
- *Correspondence: Dong Zhao, ; Zhaohui Lyu,
| | - Zhaohui Lyu
- Department of Endocrinology, The First Medical Center of Chinese PLA General Hospital, Beijing, China
- *Correspondence: Dong Zhao, ; Zhaohui Lyu,
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Liang K, Guo X, Wang C, Yan F, Wang L, Liu J, Hou X, Li W, Chen L. Nomogram Predicting the Risk of Progression from Prediabetes to Diabetes After a 3-Year Follow-Up in Chinese Adults. Diabetes Metab Syndr Obes 2021; 14:2641-2649. [PMID: 34163192 PMCID: PMC8214014 DOI: 10.2147/dmso.s307456] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Accepted: 05/11/2021] [Indexed: 12/24/2022] Open
Abstract
PURPOSE To develop a nomogram for predicting the risk of progression from prediabetes to diabetes and provide a quantitative predictive tool for early clinical screening of high-risk populations of diabetes. MATERIALS AND METHODS This study was a retrospective cohort study and part of the investigation conducted for the Risk Evaluation of cAncers in Chinese diabeTic Individuals: a lONgitudinal (REACTION) study. A total of 1857 prediabetic participants at baseline underwent oral glucose tolerance test and hemoglobin A1c (HbA1c) testing after 3 years. The areas under the receiver operating characteristic curves (AUCs) were adopted to measure the predictive value of progression to diabetes, using baseline fasting plasma glucose (FPG), 2-hr postprandial plasma glucose (2hPG), HbA1c or combined models. Decision curve analysis determined the model with the best discriminative ability. A nomogram was formulated and internally validated, providing an individualized predictive tool by calculating total scores. RESULTS After 3 years, 145 participants developed diabetes, and the annual incidence was estimated to be 2.60%. Among the three single indicators and four combined models, model 4 combined of FPG, 2hPG, and HbA1c showed the best performance in risk predication, with an AUC of 0.742. The nomogram constructed via model 4 was validated and demonstrated good prediction for the risk of diabetes. The nomogram score/predicted probability was a numeric value representing the prediction model score of individual patients. Notably, all nomogram scores showed relatively high negative predictive values. CONCLUSION The nomogram constructed in this study effectively predicts and quantifies the risk of progression from prediabetes to diabetes after a 3-year follow-up and could be adopted to identify Chinese patients at high risk for diabetes in order to provide timely interventions.
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Affiliation(s)
- Kai Liang
- Department of Endocrinology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People’s Republic of China
- Institute of Endocrine and Metabolic Diseases of Shandong University, Jinan, 250012, People’s Republic of China
- Key Laboratory of Endocrine and Metabolic Diseases, Shandong Province Medicine & Health, Jinan, 250012, People’s Republic of China
- Jinan Clinical Research Center for Endocrine and Metabolic Diseases, Jinan, 250012, People’s Republic of China
| | - Xinghong Guo
- Department of Endocrinology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People’s Republic of China
- Institute of Endocrine and Metabolic Diseases of Shandong University, Jinan, 250012, People’s Republic of China
- Key Laboratory of Endocrine and Metabolic Diseases, Shandong Province Medicine & Health, Jinan, 250012, People’s Republic of China
- Jinan Clinical Research Center for Endocrine and Metabolic Diseases, Jinan, 250012, People’s Republic of China
| | - Chuan Wang
- Department of Endocrinology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People’s Republic of China
- Institute of Endocrine and Metabolic Diseases of Shandong University, Jinan, 250012, People’s Republic of China
- Key Laboratory of Endocrine and Metabolic Diseases, Shandong Province Medicine & Health, Jinan, 250012, People’s Republic of China
- Jinan Clinical Research Center for Endocrine and Metabolic Diseases, Jinan, 250012, People’s Republic of China
| | - Fei Yan
- Department of Endocrinology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People’s Republic of China
- Institute of Endocrine and Metabolic Diseases of Shandong University, Jinan, 250012, People’s Republic of China
- Key Laboratory of Endocrine and Metabolic Diseases, Shandong Province Medicine & Health, Jinan, 250012, People’s Republic of China
- Jinan Clinical Research Center for Endocrine and Metabolic Diseases, Jinan, 250012, People’s Republic of China
| | - Lingshu Wang
- Department of Endocrinology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People’s Republic of China
- Institute of Endocrine and Metabolic Diseases of Shandong University, Jinan, 250012, People’s Republic of China
- Key Laboratory of Endocrine and Metabolic Diseases, Shandong Province Medicine & Health, Jinan, 250012, People’s Republic of China
- Jinan Clinical Research Center for Endocrine and Metabolic Diseases, Jinan, 250012, People’s Republic of China
| | - Jinbo Liu
- Department of Endocrinology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People’s Republic of China
- Institute of Endocrine and Metabolic Diseases of Shandong University, Jinan, 250012, People’s Republic of China
- Key Laboratory of Endocrine and Metabolic Diseases, Shandong Province Medicine & Health, Jinan, 250012, People’s Republic of China
- Jinan Clinical Research Center for Endocrine and Metabolic Diseases, Jinan, 250012, People’s Republic of China
| | - Xinguo Hou
- Department of Endocrinology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People’s Republic of China
- Institute of Endocrine and Metabolic Diseases of Shandong University, Jinan, 250012, People’s Republic of China
- Key Laboratory of Endocrine and Metabolic Diseases, Shandong Province Medicine & Health, Jinan, 250012, People’s Republic of China
- Jinan Clinical Research Center for Endocrine and Metabolic Diseases, Jinan, 250012, People’s Republic of China
| | - Wenjuan Li
- Department of Endocrinology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People’s Republic of China
- Institute of Endocrine and Metabolic Diseases of Shandong University, Jinan, 250012, People’s Republic of China
- Key Laboratory of Endocrine and Metabolic Diseases, Shandong Province Medicine & Health, Jinan, 250012, People’s Republic of China
- Jinan Clinical Research Center for Endocrine and Metabolic Diseases, Jinan, 250012, People’s Republic of China
| | - Li Chen
- Department of Endocrinology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People’s Republic of China
- Institute of Endocrine and Metabolic Diseases of Shandong University, Jinan, 250012, People’s Republic of China
- Key Laboratory of Endocrine and Metabolic Diseases, Shandong Province Medicine & Health, Jinan, 250012, People’s Republic of China
- Jinan Clinical Research Center for Endocrine and Metabolic Diseases, Jinan, 250012, People’s Republic of China
- Correspondence: Li Chen; Wenjuan Li Department Of Endocrinology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People’s Republic of China, Tel +86 18560083989; +86 18560080331Fax +860531-82169323 Email ;
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Cai XT, Ji LW, Liu SS, Wang MR, Heizhati M, Li NF. Derivation and Validation of a Prediction Model for Predicting the 5-Year Incidence of Type 2 Diabetes in Non-Obese Adults: A Population-Based Cohort Study. Diabetes Metab Syndr Obes 2021; 14:2087-2101. [PMID: 34007195 PMCID: PMC8123981 DOI: 10.2147/dmso.s304994] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 04/28/2021] [Indexed: 02/05/2023] Open
Abstract
PURPOSE The aim of this study was to derivate and validate a nomogram based on independent predictors to better evaluate the 5-year risk of T2D in non-obese adults. PATIENTS AND METHODS This is a historical cohort study from a collection of databases that included 12,940 non-obese participants without diabetes at baseline. All participants were randomised to a derivation cohort (n = 9651) and a validation cohort (n = 3289). In the derivation cohort, the least absolute shrinkage and selection operator (LASSO) regression model was used to determine the optimal risk factors for T2D. Multivariate Cox regression analysis was used to establish the nomogram of T2D prediction. The receiver operating characteristic (ROC) curve, C-index, calibration curve, and decision curve analysis were performed by 1000 bootstrap resamplings to evaluate the discrimination ability, calibration, and clinical practicability of the nomogram. RESULTS After LASSO regression analysis of the derivation cohort, it was found that age, fatty liver, γ-glutamyltranspeptidase, triglycerides, glycosylated hemoglobin A1c and fasting plasma glucose were risk predictors, which were integrated into the nomogram. The C-index of derivation cohort and validation cohort were 0.906 [95% confidence interval (CI), 0.878-0.934] and 0.837 (95% CI, 0.760-0.914), respectively. The AUC of 5-year T2D risk in the derivation cohort and validation cohort was 0.916 (95% CI, 0.889-0.943) and 0.829 (95% CI, 0.753-0.905), respectively. The calibration curve indicated that the predicted probability of nomogram is in good agreement with the actual probability. The decision curve analysis demonstrated that the predicted nomogram was clinically useful. CONCLUSION Our nomogram can be used as a reasonable, affordable, simple, and widely implemented tool to predict the 5-year risk of T2D in non-obese adults. With this model, early identification of high-risk individuals is helpful to timely intervene and reduce the risk of T2D in non-obese adults.
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Affiliation(s)
- Xin-Tian Cai
- Hypertension Center of People’s Hospital of Xinjiang Uygur Autonomous Region, Xinjiang Hypertension Institute, National Health Committee Key Laboratory of Hypertension Clinical Research, Urumqi, People’s Republic of China
| | - Li-Wei Ji
- Laboratory of Mitochondrial and Metabolism, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, People’s Republic of China
| | - Sha-Sha Liu
- Hypertension Center of People’s Hospital of Xinjiang Uygur Autonomous Region, Xinjiang Hypertension Institute, National Health Committee Key Laboratory of Hypertension Clinical Research, Urumqi, People’s Republic of China
| | - Meng-Ru Wang
- Hypertension Center of People’s Hospital of Xinjiang Uygur Autonomous Region, Xinjiang Hypertension Institute, National Health Committee Key Laboratory of Hypertension Clinical Research, Urumqi, People’s Republic of China
| | - Mulalibieke Heizhati
- Hypertension Center of People’s Hospital of Xinjiang Uygur Autonomous Region, Xinjiang Hypertension Institute, National Health Committee Key Laboratory of Hypertension Clinical Research, Urumqi, People’s Republic of China
| | - Nan-Fang Li
- Hypertension Center of People’s Hospital of Xinjiang Uygur Autonomous Region, Xinjiang Hypertension Institute, National Health Committee Key Laboratory of Hypertension Clinical Research, Urumqi, People’s Republic of China
- Correspondence: Nan-Fang Li Hypertension Center of People’s Hospital of Xinjiang Uygur Autonomous Region, Xinjiang Hypertension Institute, National Health Committee Key Laboratory of Hypertension Clinical Research, Urumqi, Xinjiang, People’s Republic of ChinaTel +86 991 8564818 Email
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