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Li X, Zeng J, Chen B, Yan Q, Cui Y, Xu W, Zhang X, Xu S. Daily higher tea consumption is associated with a reduced risk of type 2 diabetes: A cohort study and updated systematic review and meta-analysis. Nutr Res 2023; 118:116-127. [PMID: 37647847 DOI: 10.1016/j.nutres.2023.08.002] [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: 04/11/2023] [Revised: 07/31/2023] [Accepted: 08/06/2023] [Indexed: 09/01/2023]
Abstract
Tea is abundant in phytochemicals (such as polyphenols and theaflavins), which have a hypoglycemic effect. Previous studies investigating the relationship between tea consumption and the risk of type 2 diabetes mellitus (T2DM) have yielded inconsistent results. We hypothesized that tea consumption would be associated with a reduced risk of T2DM. This cohort study used data from the China Health and Nutrition Survey, involving a total of 5199 participants initially recruited in 1997 and subsequently followed until 2009. Consumption of any variety of tea was tracked using structured questionnaires, and T2DM was diagnosed according to the American Diabetes Association's criteria. We also performed a systematic literature search of PubMed, Web of Science, and EMBASE for publications through September 2021, including 19 cohort studies comprising 1,076,311 participants. In our cohort study, the logistic regression model showed a relative risk (RR) of T2DM among tea drinkers of 1.02 (95% confidence interval [CI], 0.82-1.28) compared with non-tea drinkers. Although our updated meta-analysis showed no significant association between tea consumption and T2DM on the whole (pooled RR of 0.96 [0.91-1.00]), compared with the non-tea-drinking group, participants consuming 4 or more cups of tea per day had a 17% reduced risk of T2DM, with an RR of 0.83 (95% CI, 0.76-0.90). These data support our hypothesis that tea consumption at higher doses (e.g., ≥4 cups/day) is associated with a reduced risk of T2DM.
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Affiliation(s)
- Xiaying Li
- College of Medicine, Wuhan University of Science and Technology, Wuhan, Hubei, China
| | - Jingjing Zeng
- Center for Clinical Evidence-Based and Translational Medicine, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China
| | - Bo Chen
- Center for Clinical Evidence-Based and Translational Medicine, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China
| | - Qiongjie Yan
- Center for Clinical Evidence-Based and Translational Medicine, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China
| | - Yuze Cui
- Center for Clinical Evidence-Based and Translational Medicine, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China
| | - Wenlei Xu
- Center for Clinical Evidence-Based and Translational Medicine, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China
| | - Xiaotong Zhang
- Department of Nephrology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China
| | - Shaoyong Xu
- Center for Clinical Evidence-Based and Translational Medicine, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China; Department of Endocrinology, Xiangyang Central Hospital Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China.
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Asgari S, Khalili D, Azizi F, Hadaegh F. External validation of the American prediction model for incident type 2 diabetes in the Iranian population. BMC Med Res Methodol 2023; 23:77. [PMID: 36991336 PMCID: PMC10053951 DOI: 10.1186/s12874-023-01891-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Accepted: 03/15/2023] [Indexed: 03/30/2023] Open
Abstract
Abstract
Background
The primary aim of the present study was to validate the REasons for Geographic and Racial Differences in Stroke (REGARDS) model for incident Type 2 diabetes (T2DM) in Iran.
Methods
Present study was a prospective cohort study on 1835 population aged ≥ 45 years from Tehran lipids and glucose study (TLGS).The predictors of REGARDS model based on Bayesian hierarchical techniques included age, sex, race, body mass index, systolic and diastolic blood pressures, triglycerides, high-density lipoprotein cholesterol, and fasting plasma glucose. For external validation, the area under the curve (AUC), sensitivity, specificity, Youden’s index, and positive and negative predictive values (PPV and NPV) were assessed.
Results
During the 10-year follow-up 15.3% experienced T2DM. The model showed acceptable discrimination (AUC (95%CI): 0.79 (0.76–0.82)), and good calibration. Based on the highest Youden’s index the suggested cut-point for the REGARDS probability would be ≥ 13% which yielded a sensitivity of 77.2%, specificity 66.8%, NPV 94.2%, and PPV 29.6%.
Conclusions
Our findings do support that the REGARDS model is a valid tool for incident T2DM in the Iranian population. Moreover, the probability value higher than the 13% cut-off point is stated to be significant for identifying those with incident T2DM.
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Tong C, Han Y, Zhang S, Li Q, Zhang J, Guo X, Tao L, Zheng D, Yang X. Establishment of dynamic nomogram and risk score models for T2DM: a retrospective cohort study in Beijing. BMC Public Health 2022; 22:2306. [PMID: 36494707 PMCID: PMC9733342 DOI: 10.1186/s12889-022-14782-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Accepted: 11/30/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Health interventions can delay or prevent the occurrence and development of diabetes. Dynamic nomogram and risk score (RS) models were developed to predict the probability of developing type 2 diabetes mellitus (T2DM) and identify high-risk groups. METHODS Participants (n = 44,852) from the Beijing Physical Examination Center were followed up for 11 years (2006-2017); the mean follow-up time was 4.06 ± 2.09 years. Multivariable Cox regression was conducted in the training cohort to identify risk factors associated with T2DM and develop dynamic nomogram and RS models using weighted estimators corresponding to each covariate derived from the fitted Cox regression coefficients and variance estimates, and then undergone internal validation and sensitivity analysis. The concordance index (C-index) was used to assess the accuracy and reliability of the model. RESULTS Of the 44,852 individuals at baseline, 2,912 were diagnosed with T2DM during the follow-up period, and the incidence density rate per 1,000 person-years was 16.00. Multivariate analysis indicated that male sex (P < 0.001), older age (P < 0.001), high body mass index (BMI, P < 0.05), high fasting plasma glucose (FPG, P < 0.001), hypertension (P = 0.015), dyslipidaemia (P < 0.001), and low serum creatinine (sCr, P < 0.05) at presentation were risk factors for T2DM. The dynamic nomogram achieved a high C-index of 0.909 in the training set and 0.905 in the validation set. A tenfold cross-validation estimated the area under the curve of the nomogram at 0.909 (95% confidence interval 0.897-0.920). Moreover, the dynamic nomogram and RS model exhibited acceptable discrimination and clinical usefulness in subgroup and sensitivity analyses. CONCLUSIONS The T2DM dynamic nomogram and RS models offer clinicians and others who conduct physical examinations, respectively, simple-to-use tools to assess the risk of developing T2DM in the urban Chinese current or retired employees.
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Affiliation(s)
- Chao Tong
- grid.24696.3f0000 0004 0369 153XSchool of Public Health, Capital Medical University, NO.10 Xitoutiao, Youanmen, Beijing, 100069 China
| | - Yumei Han
- Beijing Physical Examination Center, No. 59, Beiwei Road, Xicheng District, Beijing, China
| | - Shan Zhang
- grid.24696.3f0000 0004 0369 153XSchool of Public Health, Capital Medical University, NO.10 Xitoutiao, Youanmen, Beijing, 100069 China
| | - Qiang Li
- Beijing Physical Examination Center, No. 59, Beiwei Road, Xicheng District, Beijing, China
| | - Jingbo Zhang
- Beijing Physical Examination Center, No. 59, Beiwei Road, Xicheng District, Beijing, China
| | - Xiuhua Guo
- grid.24696.3f0000 0004 0369 153XSchool of Public Health, Capital Medical University, NO.10 Xitoutiao, Youanmen, Beijing, 100069 China
| | - Lixin Tao
- grid.24696.3f0000 0004 0369 153XSchool of Public Health, Capital Medical University, NO.10 Xitoutiao, Youanmen, Beijing, 100069 China
| | - Deqiang Zheng
- grid.24696.3f0000 0004 0369 153XSchool of Public Health, Capital Medical University, NO.10 Xitoutiao, Youanmen, Beijing, 100069 China
| | - Xinghua Yang
- grid.24696.3f0000 0004 0369 153XSchool of Public Health, Capital Medical University, NO.10 Xitoutiao, Youanmen, Beijing, 100069 China
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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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A systematic review of diabetes risk assessment tools in sub-Saharan Africa. Int J Diabetes Dev Ctries 2022. [DOI: 10.1007/s13410-022-01045-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/19/2022] Open
Abstract
Abstract
Objectives
To systematically review all current studies on diabetes risk assessment tools used in SSA to diagnose diabetes in symptomatic and asymptomatic patients.
Methods
Tools were identified through a systematic search of PubMed, Ovid, Google Scholar, and the Cochrane Library for articles published from January 2010 to January 2020. The search included articles reporting the use of diabetes risk assessment tool to detect individuals with type 2 diabetes in SSA. A standardized protocol was used for data extraction (registry #177726).
Results
Of the 825 articles identified, 39 articles met the inclusion criteria, and three articles reported tools used in SSA population but developed for the Western population. None was validated in SSA population. All but three articles were observational studies (136 and 58,657 study participants aged between the ages of 15 and 85 years). The Finnish Medical Association risk tool, World Health Organization (WHO) STEPS instrument, General Practice Physical Activity Questionnaire (GPPAQ), Rapid Eating and Activity Assessment for Patients (REAP), and an anthropometric tool were the most frequently used non-invasive tools in SSA. The accuracy of the tools was measured using sensitivity, specificity, or area under the receiver operating curve. The anthropometric predictor variables identified included age, body mass index, waist circumference, positive family of diabetes, and activity levels.
Conclusions
This systematic review demonstrated a paucity of validated diabetes risk assessment tools for SSA. There remains a need for the development and validation of a tool for the rapid identification of diabetes for targeted interventions.
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Zhang M, Zhao Y, Sun L, Xi Y, Zhang W, Lu J, Hu F, Shi X, Hu D. Cohort Profile: The Rural Chinese Cohort Study. Int J Epidemiol 2021; 50:723-724l. [PMID: 33367613 DOI: 10.1093/ije/dyaa204] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/21/2020] [Indexed: 12/17/2022] Open
Affiliation(s)
- Ming Zhang
- Department of Biostatistics and Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, China
| | - Yang Zhao
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, China
| | - Liang Sun
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, China
| | - Yuanlin Xi
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, China
| | - Weidong Zhang
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, China
| | - Jie Lu
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, China
| | - Fulan Hu
- Department of Biostatistics and Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, China
| | - Xuezhong Shi
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, China
| | - Dongsheng Hu
- Department of Biostatistics and Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, China
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Asgari S, Khalili D, Hosseinpanah F, Hadaegh F. Prediction Models for Type 2 Diabetes Risk in the General Population: A Systematic Review of Observational Studies. Int J Endocrinol Metab 2021; 19:e109206. [PMID: 34567135 PMCID: PMC8453657 DOI: 10.5812/ijem.109206] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Revised: 02/07/2021] [Accepted: 02/13/2021] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVES This study aimed to provide an overview of prediction models of undiagnosed type 2 diabetes mellitus (U-T2DM) or the incident T2DM (I-T2DM) using the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) checklist and the prediction model risk of the bias assessment tool (PROBAST). DATA SOURCES Both PUBMED and EMBASE databases were searched to guarantee adequate and efficient coverage. STUDY SELECTION Articles published between December 2011 and October 2019 were considered. DATA EXTRACTION For each article, information on model development requirements, discrimination measures, calibration, overall performance, clinical usefulness, overfitting, and risk of bias (ROB) was reported. RESULTS The median (interquartile range; IQR) number of the 46 study populations for model development was 5711 (1971 - 27426) and 2457 (2060 - 6995) individuals for I-T2DM and U-T2DM, respectively. The most common reported predictors were age and body mass index, and only the Qrisk-2017 study included social factors (e.g., Townsend score). Univariable analysis was reported in 46% of the studies, and the variable selection procedure was not clear in 17.4% of them. Moreover, internal and external validation was reported in 43% the studies, while over 63% of them reported calibration. The median (IQR) of AUC for I-T2DM models was 0.78 (0.74 - 0.82); the corresponding value for studies derived before October 2011 was 0.80 (0.77 - 0.83). The highest discrimination index was reported for Qrisk-2017 with C-statistics of 0.89 for women and 0.87 for men. Low ROB for I-T2DM and U-T2DM was assessed at 18% and 41%, respectively. CONCLUSIONS Among prediction models, an intermediate to poor quality was reassessed in several aspects of model development and validation. Generally, despite its new risk factors or new methodological aspects, the newly developed model did not increase our capability in screening/predicting T2DM, mainly in the analysis part. It was due to the lack of external validation of the prediction models.
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Affiliation(s)
- Samaneh Asgari
- Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Davood Khalili
- Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Farhad Hosseinpanah
- Obesity Research Center, Research Institute for Endocrine Sciences, Shaheed Beheshti University of Medical Sciences, Tehran, Iran
| | - Farzad Hadaegh
- Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- Corresponding Author: Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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Yao Q, Zhang J, Yan K, Zheng Q, Li Y, Zhang L, Wu C, Yang Y, Zhou M, Zhu C. Development and validation of a 2-year new-onset stroke risk prediction model for people over age 45 in China. Medicine (Baltimore) 2020; 99:e22680. [PMID: 33031337 PMCID: PMC7544427 DOI: 10.1097/md.0000000000022680] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
Multiple factors, including increasing incidence, poor knowledge of stroke and lack of effective, noninvasive and convenient stroke risk prediction tools, make it more difficult for precautions against stroke in China. Effective prediction models for stroke may assist to establish better risk awareness and management, healthier lifestyle, and lower stroke incidence for people.The China Health and Retirement Longitudinal Survey was the development cohort. Logistic regression was applied to model's development, in which the candidate variables with statistically significant coefficient were included in the prediction model. The area under receiver operating characteristic curve (AUC) and 10-times cross-validation were used for internal validation. Cutoff point of high-risk group was measured by Youden index. The China Health and Nutrition Survey was the validation cohort.The development cohort and the validation cohort included 16557 and 5065 participants, and the incidence density was 358.207/100,000 person-year and 350.701/100,000 person-year, respectively. The model for 2-year new-onset stroke risk prediction included age, hypertension, diabetes, heart disease, and smoking. The AUC and cross-validation AUC were 0.707 (95% confidence interval[CI]: 0.664, 0.750) and the 0.710 (95% CI: 0.650, 0.736). The sensitivity, specificity and accuracy of the cutoff point were 0.774, 0.545, and 0.319. The AUC and cross-validation AUC were 0.800 (95% CI: 0.744, 0.856) and 0.811(95% CI:0.714, 0.847), and the sensitivity, specificity and accuracy of cutoff point being 0.857,0.569, and 0.426 in external validation.A simple prediction tool using 5 noninvasive and easily accessible factors can assist in 2-year new-onset stroke risk prediction in Chinese people over 45 years old, which is believed to be applicable in identifying high-risk individuals and health management in China.
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Affiliation(s)
- Qiang Yao
- Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University
| | - Jing Zhang
- Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University
| | - Ke Yan
- Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University
| | - Qianwen Zheng
- Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University
| | - Yawen Li
- Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University
| | - Lu Zhang
- Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University
| | - Chenyao Wu
- Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University
| | - Yanling Yang
- Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University
| | - Muke Zhou
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Cairong Zhu
- Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University
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Kotzé-Hörstmann LM, Sadie-Van Gijsen H. Modulation of Glucose Metabolism by Leaf Tea Constituents: A Systematic Review of Recent Clinical and Pre-clinical Findings. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2020; 68:2973-3005. [PMID: 32105058 DOI: 10.1021/acs.jafc.9b07852] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Leaf teas are widely used as a purported treatment for dysregulated glucose homeostasis. The objective of this study was to systematically evaluate the clinical and cellular-metabolic evidence, published between January 2013 and May 2019, and indexed on PubMed, ScienceDirect, and Web of Science, supporting the use of leaf teas for this purpose. Fourteen randomized controlled trials (RCTs) (13 on Camellia sinensis teas) were included, with mixed results, and providing scant mechanistic information. In contrast, 74 animal and cell culture studies focusing on the pancreas, liver, muscle, and adipose tissue yielded mostly positive results and highlighted enhanced insulin signaling as a recurring target associated with the effects of teas on glucose metabolism. We conclude that more studies, including RCTs and pre-clinical studies examining teas from a wider variety of species beyond C. sinensis, are required to establish a stronger evidence base on the use of leaf teas to normalize glucose metabolism.
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Affiliation(s)
- Liske M Kotzé-Hörstmann
- Centre for Cardio-metabolic Research in Africa (CARMA), Division of Medical Physiology, Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Stellenbosch University Tygerberg Campus, Parow 7505, South Africa
| | - Hanél Sadie-Van Gijsen
- Centre for Cardio-metabolic Research in Africa (CARMA), Division of Medical Physiology, Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Stellenbosch University Tygerberg Campus, Parow 7505, South Africa
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Ibrahim M, Tuomilehto J, Aschner P, Beseler L, Cahn A, Eckel RH, Fischl AH, Guthrie G, Hill JO, Kumwenda M, Leslie RD, Olson DE, Pozzilli P, Weber SL, Umpierrez GE. Global status of diabetes prevention and prospects for action: A consensus statement. Diabetes Metab Res Rev 2018; 34:e3021. [PMID: 29757486 DOI: 10.1002/dmrr.3021] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2018] [Revised: 04/20/2018] [Accepted: 04/24/2018] [Indexed: 12/15/2022]
Abstract
Primary prevention of type 2 diabetes (T2D) should be achievable through the implementation of early and sustainable measures. Several randomized control studies that found success in preventing the progression to T2D in high-risk populations have identified early and intensive intervention based on an individualized prevention model as the key factor for participant benefit. The global prevalence of both overweight and obesity has now been widely recognized as the major epidemic of the 21st century. Obesity is a major risk factor for the progression from normal glucose tolerance to prediabetes and then to T2D. However, not all obese individuals will develop prediabetes or progress to diabetes. Intensive, multicomponent behavioural interventions for overweight and obese adults can lead to weight loss. Diabetes medications, including metformin, GLP-1 agonists, glitazones, and acarbose, can be considered for selected high-risk patients with prediabetes when lifestyle-based programmes are proven unsuccessful. Nutrition education is the cornerstone of a healthy lifestyle. Also, physical activity is an integral part of the prediabetes management plan and one of the main pillars in the prevention of diabetes. Mobile phones, used extensively worldwide, can facilitate communication between health professionals and the general population, and have been shown to be helpful in the prevention of T2D. Universal screening is needed. Noninvasive risk scores should be used in all countries, but they should be locally validated in all ethnic populations focusing on cultural differences around the world. Lifestyle interventions reduce the progression to prediabetes and diabetes. Nevertheless, many questions still need to be answered.
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Affiliation(s)
| | - Jaakko Tuomilehto
- Dasman Diabetes Institute, Kuwait, Chronic Disease Prevention Unit, National Institute for Health and Welfare, Helsinki, Finland, and Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Pablo Aschner
- Javeriana University School of Medicine, San Ignacio University Hospital, Bogota, Colombia
| | - Lucille Beseler
- Family Nutrition Center of South Florida, Coconut Creek, FL, USA
| | - Avivit Cahn
- Hadassah Hebrew University Hospital, The Diabetes Unit & Endocrinology and Metabolism Unit, Hadassah Hebrew University Hospital, Jerusalem, Israel
| | - Robert H Eckel
- University of Colorado Denver Anschutz Medical Campus, University of Colorado Hospital, Denver, CO, USA
| | - Amy Hess Fischl
- University of Chicago Kovler Diabetes Center, Chicago, IL, USA
| | - George Guthrie
- Florida Hospital Graduate Medical Education, Orlando, FL, USA
| | - James O Hill
- Colorado Nutrition Obesity Research Center (NORC), University of Colorado School of Medicine, Aurora, CO, USA
| | | | - R David Leslie
- Blizard Institute, Queen Mary, University of London, London, UK
| | - Darin E Olson
- Division of Endocrinology, Metabolism and Lipids, Emory University School of Medicine, Atlanta, GA, USA
| | - Paolo Pozzilli
- Unit of Endocrinology and Diabetes, University Campus Bio-Medico, Rome, Italy
- Centre of Immunobiology, Barts and the London School of Medicine, Queen Mary, University of London, London, UK
| | - Sandra L Weber
- Greenville Health System, University of South Carolina School of Medicine-Greenville, Greenville, SC, USA
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