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Seah JYH, Yao J, Hong Y, Lim CGY, Sabanayagam C, Nusinovici S, Gardner DSL, Loh M, Müller-Riemenschneider F, Tan CS, Yeo KK, Wong TY, Cheng CY, Ma S, Tai ES, Chambers JC, van Dam RM, Sim X. Risk prediction models for type 2 diabetes using either fasting plasma glucose or HbA1c in Chinese, Malay, and Indians: Results from three multi-ethnic Singapore cohorts. Diabetes Res Clin Pract 2023; 203:110878. [PMID: 37591346 DOI: 10.1016/j.diabres.2023.110878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Revised: 08/07/2023] [Accepted: 08/14/2023] [Indexed: 08/19/2023]
Abstract
AIMS To assess three well-established type 2 diabetes (T2D) risk prediction models based on fasting plasma glucose (FPG) in Chinese, Malays, and Indians, and to develop simplified risk models based on either FPG or HbA1c. METHODS We used a prospective multiethnic Singapore cohort to evaluate the established models and develop simplified models. 6,217 participants without T2D at baseline were included, with an average follow-up duration of 8.3 years. The simplified risk models were validated in two independent multiethnic Singapore cohorts (N = 12,720). RESULTS The established risk models had moderate-to-good discrimination (area under the receiver operating characteristic curves, AUCs 0.762 - 0.828) but a lack of fit (P-values < 0.05). Simplified risk models that included fewer predictors (age, BMI, systolic blood pressure, triglycerides, and HbA1c or FPG) showed good discrimination in all cohorts (AUCs ≥ 0.810), and sufficiently captured differences between the ethnic groups. While recalibration improved fit the simplified models in validation cohorts, there remained evidence of miscalibration in Chinese (p ≤ 0.012). CONCLUSIONS Simplified risk models including HbA1c or FPG had good discrimination in predicting incidence of T2D in three major Asian ethnic groups. Risk functions with HbA1c performed as well as those with FPG.
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Affiliation(s)
- Jowy Yi Hong Seah
- Centre for Population Health Research and Implementation, SingHealth, Singapore 150167, Singapore; Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 117549, Singapore
| | - Jiali Yao
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 117549, Singapore
| | - Yueheng Hong
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 117549, Singapore
| | - Charlie Guan Yi Lim
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 117549, Singapore
| | - Charumathi Sabanayagam
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 169856, Singapore; Ophthalmology & Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore 169857, Singapore
| | - Simon Nusinovici
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 169856, Singapore
| | - Daphne Su-Lyn Gardner
- Department of Endocrinology, Singapore General Hospital, Singapore 169608, Singapore
| | - Marie Loh
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 308232, Singapore; Research Division, National Skin Centre, Singapore 308205, Singapore
| | - Falk Müller-Riemenschneider
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 117549, Singapore
| | - Chuen Seng Tan
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 117549, Singapore
| | - Khung Keong Yeo
- Department of Cardiology, National Heart Centre, Singapore 169609, Singapore; Duke-NUS Medical School, Singapore
| | - Tien Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 169856, Singapore; Tsinghua Medicine, Tsinghua University, Beijing, China; Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore 117597, Singapore
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 169856, Singapore; Ophthalmology & Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore 169857, Singapore; Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore 117597, Singapore; Center for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore 117597, Singapore
| | - Stefan Ma
- Epidemiology & Disease Control Division, Ministry of Health, Singapore 169854, Singapore
| | - E Shyong Tai
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 117549, Singapore; Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore 117597, Singapore
| | - John C Chambers
- Department of Endocrinology, Singapore General Hospital, Singapore 169608, Singapore; Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London W2 1PG, United Kingdom
| | - Rob M van Dam
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 117549, Singapore; Departments of Exercise and Nutrition Sciences and Epidemiology, Milken Institute School of Public Health, The George Washington University, Washington, DC 20052, United States.
| | - Xueling Sim
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 117549, Singapore.
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2
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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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3
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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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4
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Wang Y, Zhang WS, Hao YT, Jiang CQ, Jin YL, Cheng KK, Lam TH, Xu L. A Bayesian network model of new-onset diabetes in older Chinese: The Guangzhou biobank cohort study. Front Endocrinol (Lausanne) 2022; 13:916851. [PMID: 35992128 PMCID: PMC9382298 DOI: 10.3389/fendo.2022.916851] [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: 04/10/2022] [Accepted: 07/06/2022] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Existing diabetes risk prediction models based on regression were limited in dealing with collinearity and complex interactions. Bayesian network (BN) model that considers interactions may provide additional information to predict risk and infer causation. METHODS BN model was constructed for new-onset diabetes using prospective data of 15,934 participants without diabetes at baseline [73% women; mean (standard deviation) age = 61.0 (6.9) years]. Participants were randomly assigned to a training (n = 12,748) set and a validation (n = 3,186) set. Model performances were assessed using area under the receiver operating characteristic curve (AUC). RESULTS During an average follow-up of 4.1 (interquartile range = 3.3-4.5) years, 1,302 (8.17%) participants developed diabetes. The constructed BN model showed the associations (direct, indirect, or no) among 24 risk factors, and only hypertension, impaired fasting glucose (IFG; fasting glucose of 5.6-6.9 mmol/L), and greater waist circumference (WC) were directly associated with new-onset diabetes. The risk prediction model showed that the post-test probability of developing diabetes in participants with hypertension, IFG, and greater WC was 27.5%, with AUC of 0.746 [95% confidence interval CI) = 0.732-0.760], sensitivity of 0.727 (95% CI = 0.703-0.752), and specificity of 0.660 (95% CI = 0.652-0.667). This prediction model appeared to perform better than a logistic regression model using the same three predictors (AUC = 0.734, 95% CI = 0.703-0.764, sensitivity = 0.604, and specificity = 0.745). CONCLUSIONS We have first reported a BN model in predicting new-onset diabetes with the smallest number of factors among existing models in the literature. BN yielded a more comprehensive figure showing graphically the inter-relations for multiple factors with diabetes than existing regression models.
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Affiliation(s)
- Ying Wang
- School of Public Health, Sun Yat-Sen University, Guangzhou, China
| | - Wei Sen Zhang
- Molecular Epidemiology Research Centre, Guangzhou Twelfth People’s Hospital, Guangzhou, China
| | - Yuan Tao Hao
- School of Public Health, Sun Yat-Sen University, Guangzhou, China
| | - Chao Qiang Jiang
- Molecular Epidemiology Research Centre, Guangzhou Twelfth People’s Hospital, Guangzhou, China
| | - Ya Li Jin
- Molecular Epidemiology Research Centre, Guangzhou Twelfth People’s Hospital, Guangzhou, China
| | - Kar Keung Cheng
- Institute of Applied Health Research, University of Birmingham, Birmingham, United Kingdom
| | - Tai Hing Lam
- Molecular Epidemiology Research Centre, Guangzhou Twelfth People’s Hospital, Guangzhou, China
- School of Public Health, The University of Hong Kong, Hong Kong SAR, China
- *Correspondence: Tai Hing Lam, ; Lin Xu,
| | - Lin Xu
- School of Public Health, Sun Yat-Sen University, Guangzhou, China
- School of Public Health, The University of Hong Kong, Hong Kong SAR, China
- *Correspondence: Tai Hing Lam, ; Lin Xu,
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5
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Perry BI, Upthegrove R, Crawford O, Jang S, Lau E, McGill I, Carver E, Jones PB, Khandaker GM. Cardiometabolic risk prediction algorithms for young people with psychosis: a systematic review and exploratory analysis. Acta Psychiatr Scand 2020; 142:215-232. [PMID: 32654119 DOI: 10.1111/acps.13212] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Accepted: 07/06/2020] [Indexed: 12/25/2022]
Abstract
OBJECTIVE Cardiometabolic risk prediction algorithms are common in clinical practice. Young people with psychosis are at high risk for developing cardiometabolic disorders. We aimed to examine whether existing cardiometabolic risk prediction algorithms are suitable for young people with psychosis. METHODS We conducted a systematic review and narrative synthesis of studies reporting the development and validation of cardiometabolic risk prediction algorithms for general or psychiatric populations. Furthermore, we used data from 505 participants with or at risk of psychosis at age 18 years in the ALSPAC birth cohort, to explore the performance of three algorithms (QDiabetes, QRISK3 and PRIMROSE) highlighted as potentially suitable. We repeated analyses after artificially increasing participant age to the mean age of the original algorithm studies to examine the impact of age on predictive performance. RESULTS We screened 7820 results, including 110 studies. All algorithms were developed in relatively older participants, and most were at high risk of bias. Three studies (QDiabetes, QRISK3 and PRIMROSE) featured psychiatric predictors. Age was more strongly weighted than other risk factors in each algorithm. In our exploratory analysis, calibration plots for all three algorithms implied a consistent systematic underprediction of cardiometabolic risk in the younger sample. After increasing participant age, calibration plots were markedly improved. CONCLUSION Existing cardiometabolic risk prediction algorithms cannot be recommended for young people with or at risk of psychosis. Existing algorithms may underpredict risk in young people, even in the face of other high-risk features. Recalibration of existing algorithms or a new tailored algorithm for the population is required.
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Affiliation(s)
- B I Perry
- Department of Psychiatry, University of Cambridge, Cambridge, UK.,Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - R Upthegrove
- Institute for Mental Health, University of Birmingham, Birmingham, UK
| | - O Crawford
- University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - S Jang
- University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - E Lau
- University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - I McGill
- University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - E Carver
- University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - P B Jones
- Department of Psychiatry, University of Cambridge, Cambridge, UK.,Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - G M Khandaker
- Department of Psychiatry, University of Cambridge, Cambridge, UK.,Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
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6
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Agreement between Type 2 Diabetes Risk Scales in a Caucasian Population: A Systematic Review and Report. J Clin Med 2020; 9:jcm9051546. [PMID: 32443837 PMCID: PMC7290893 DOI: 10.3390/jcm9051546] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Revised: 05/05/2020] [Accepted: 05/18/2020] [Indexed: 02/07/2023] Open
Abstract
Early detection of people with undiagnosed type 2 diabetes (T2D) is an important public health concern. Several predictive equations for T2D have been proposed but most of them have not been externally validated and their performance could be compromised when clinical data is used. Clinical practice guidelines increasingly incorporate T2D risk prediction models as they support clinical decision making. The aims of this study were to systematically review prediction scores for T2D and to analyze the agreement between these risk scores in a large cross-sectional study of white western European workers. A systematic review of the PubMed, CINAHL, and EMBASE databases and a cross-sectional study in 59,042 Spanish workers was performed. Agreement between scores classifying participants as high risk was evaluated using the kappa statistic. The systematic review of 26 predictive models highlights a great heterogeneity in the risk predictors; there is a poor level of reporting, and most of them have not been externally validated. Regarding the agreement between risk scores, the DETECT-2 risk score scale classified 14.1% of subjects as high-risk, FINDRISC score 20.8%, Cambridge score 19.8%, the AUSDRISK score 26.4%, the EGAD study 30.3%, the Hisayama study 30.9%, the ARIC score 6.3%, and the ITD score 3.1%. The lowest agreement was observed between the ITD and the NUDS study derived score (κ = 0.067). Differences in diabetes incidence, prevalence, and weight of risk factors seem to account for the agreement differences between scores. A better agreement between the multi-ethnic derivate score (DETECT-2) and European derivate scores was observed. Risk models should be designed using more easily identifiable and reproducible health data in clinical practice.
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7
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Liu X, Li Z, Zhang J, Chen S, Tao L, Luo Y, Xu X, Fine JP, Li X, Guo X. A Novel Risk Score for Type 2 Diabetes Containing Sleep Duration: A 7-Year Prospective Cohort Study among Chinese Participants. J Diabetes Res 2020; 2020:2969105. [PMID: 31998805 PMCID: PMC6964717 DOI: 10.1155/2020/2969105] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Revised: 10/08/2019] [Accepted: 12/05/2019] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Sleep duration is associated with type 2 diabetes (T2D). However, few T2D risk scores include sleep duration. We aimed to develop T2D scores containing sleep duration and to estimate the additive value of sleep duration. METHODS We used data from 43,404 adults without T2D in the Beijing Health Management Cohort study. The participants were surveyed approximately every 2 years from 2007/2008 to 2014/2015. Sleep duration was calculated from the self-reported usual time of going to bed and waking up at baseline. Logistic regression was employed to construct the risk scores. Integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were used to estimate the additional value of sleep duration. RESULTS After a median follow-up of 6.8 years, we recorded 2623 (6.04%) new cases of T2D. Shorter (both 6-8 h/night and <6 h/night) sleep durations were associated with an increased risk of T2D (odds ratio (OR) = 1.43, 95% confidence interval (CI) = 1.30-1.59; OR = 1.98, 95%CI = 1.63-2.41, respectively) compared with a sleep duration of >8 h/night in the adjusted model. Seven variables, including age, education, waist-hip ratio, body mass index, parental history of diabetes, fasting plasma glucose, and sleep duration, were selected to form the comprehensive score; the C-index was 0.74 (95% CI: 0.71-0.76) for the test set. The IDI and NRI values for sleep duration were 0.017 (95% CI: 0.012-0.022) and 0.619 (95% CI: 0.518-0.695), respectively, suggesting good improvement in the predictive ability of the comprehensive nomogram. The decision curves showed that women and individuals older than 50 had more net benefit. CONCLUSIONS The performance of T2D risk scores developed in the study could be improved by containing the shorter estimated sleep duration, particularly in women and individuals older than 50.
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Affiliation(s)
- Xiangtong Liu
- School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Zhiwei Li
- School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Jingbo Zhang
- Beijing Physical Examination Center, Beijing 100077, China
| | - Shuo Chen
- Beijing Physical Examination Center, Beijing 100077, China
| | - Lixin Tao
- School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Yanxia Luo
- School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Xiaolin Xu
- The University of Queensland, Brisbane, Australia
| | | | - Xia Li
- La Trobe University, Melbourne, Australia
| | - Xiuhua Guo
- School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
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Mitchell AJ, Vancampfort D, Manu P, Correll CU, Wampers M, van Winkel R, Yu W, De Hert M. Which clinical and biochemical predictors should be used to screen for diabetes in patients with serious mental illness receiving antipsychotic medication? A large observational study. PLoS One 2019; 14:e0210674. [PMID: 31513598 PMCID: PMC6742458 DOI: 10.1371/journal.pone.0210674] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Accepted: 12/28/2018] [Indexed: 01/01/2023] Open
Abstract
OBJECTIVE We aimed to investigate which clinical and metabolic tests offer optimal accuracy and acceptability to help diagnose diabetes among a large sample of people with serious mental illness in receipt of antipsychotic medication. METHODS A prospective observational study design of biochemical and clinical factors was used. Biochemical measures were fasting glucose, insulin and lipids, oral glucose tolerance testing (OGTT), hemoglobin A1c, and insulin resistance assessed with the homeostatic model (HOMA-IR) were determined in a consecutive cohort of 798 adult psychiatric inpatients receiving antipsychotics. Clinical variables were gender, age, global assessment of functioning (GAF), mental health clinicians' global impression (CGI), duration of severe mental illness, height, weight, BMI and waist/hip ratio. In addition, we calculated the risk using combined clinical predictors using the Leicester Practice Risk Score (LPRS) and the Topics Diabetes Risk Score (TDRS). Diabetes was defined by older criteria (impaired fasting glucose (IFG) or OGTT) as well as2010 criteria (IFG or OGTT or Glycated haemoglobin (HBA1c)) at conventional cut-offs. RESULTS Using the older criteria, 7.8% had diabetes (men: 6.3%; women: 10.3%). Using the new criteria, 10.2% had diabetes (men: 8.2%, women: 13.2%), representing a 30.7% increase (p = 0.02) in the prevalence of diabetes. Regarding biochemical predictors, conventional OGTT, IFG, and HbA1c thresholds used to identify newly defined diabetes missed 25%, 50% and 75% of people with diabetes, respectively. The conventional HBA1c cut-point of ≥6.5% (48 mmol/mol) missed 7 of 10 newly defined cases of diabetes while a cut-point of ≥5.7% improved sensitivity from 44.4% to up to 85%. Specific algorithm approaches offered reasonable accuracy. Unfortunately no single clinical factor was able to accurately rule-in a diagnosis of diabetes. Three clinical factors were able to rule-out diabetes with good accuracy namely: BMI, waist/hip ratio and height. A BMI < 30 had a 92% negative predictive value in ruling-out diabetes. Of those not diabetic, 20% had a BMI ≥ 30. However, for complete diagnosis a specific biochemical protocol is still necessary. CONCLUSIONS Patients with SMI maintained on antipsychotic medication cannot be reliably screened for diabetes using clinical variables alone. Accurate assessment requires a two-step algorithm consisting of HBA1c ≥5.7% followed by both FG and OGTT which does not require all patients to have OGTT and FG.
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Affiliation(s)
| | - Davy Vancampfort
- University Psychiatric Center, Catholic University Leuven, Kortenberg, Belgium
| | - Peter Manu
- University Psychiatric Center, Kortenberg, Belgium
- School of Mental Health and Neuroscience (EURON), University Medical Center, Maastricht, The Netherlands
| | - Christoph U. Correll
- Zucker Hillside Hospital, Glen Oaks, New York, United States
- Hofstra North Shore–LIJ School of Medicine, Hempstead, New York, United States
| | - Martien Wampers
- University Psychiatric Center, Catholic University Leuven, Kortenberg, Belgium
| | - Ruud van Winkel
- University Psychiatric Center, Catholic University Leuven, Kortenberg, Belgium
| | - Weiping Yu
- University Psychiatric Center, Catholic University Leuven, Kortenberg, Belgium
| | - Marc De Hert
- University Psychiatric Center, Catholic University Leuven, Kortenberg, Belgium
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9
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Zhang H, Wang C, Ren Y, Wang B, Yang X, Zhao Y, Han C, Zhou J, Zhang L, Qi M, Zhai Y, Pang C, Yin L, Zhao J, Hu D, Zhang M. A risk-score model for predicting risk of type 2 diabetes mellitus in a rural Chinese adult population: A cohort study with a 6-year follow-up. Diabetes Metab Res Rev 2017; 33. [PMID: 28608942 DOI: 10.1002/dmrr.2911] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2016] [Revised: 04/20/2017] [Accepted: 05/22/2017] [Indexed: 01/19/2023]
Abstract
BACKGROUND Several prediction tools have been developed to identify people with type 2 diabetes mellitus (T2DM) and to quantify the probability of developing T2DM. However, most of the risk models were constructed based on cross-sectional studies and tea-drinking was not included. METHODS A total of 15 768 participants without known T2DM were followed up from 2007-2008 to 2013-2014; 12 654 were randomly assigned to the derivation dataset and 3114 to the validation dataset. We constructed a risk-score model for T2DM by using a Cox proportional-hazards model. Risk scores were calculated by multiplying β by 10 in the derivation cohort and were verified in the validation dataset. The model's accuracy was assessed by the area under the receiver operating characteristic curve (AUC). RESULTS Predictors for T2DM risk in the derivation dataset were drinking tea frequently, body mass index ≥28.0 kg/m2 , waist to height ratio ≥ 0.5, triglycerides level 1.70 to 2.25 and ≥2.26 mmol/L, and fasting plasma glucose 5.6 to 6.0 and ≥6.1 mmol/L. The corresponding scores were -2, 7, 7, 4, 6, 11, and 25, respectively. The sensitivity, specificity, and AUC (95% confidence interval) for this full model were 69.63%, 75.56%, and 0.791 (0.783-0.799), respectively. The ability of the non-invasive models to predict T2DM was not superior to that of the full model. With the validation dataset, the predictive performance was better for our full model than the Framingham risk-score model (AUC 0.731 vs 0.525, P < .001). CONCLUSIONS Our risk-score model has fair efficacy for predicting 6-year risk of T2DM in a rural adult Chinese population.
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Affiliation(s)
- Hongyan Zhang
- Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China
- The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Guangdong, People's Republic of China
| | - Chongjian Wang
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Yongcheng Ren
- Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China
| | - Bingyuan Wang
- Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China
- The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Guangdong, People's Republic of China
| | - Xiangyu Yang
- Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China
- The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Guangdong, People's Republic of China
| | - Yang Zhao
- Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China
- The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Guangdong, People's Republic of China
| | - Chengyi Han
- Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China
- The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Guangdong, People's Republic of China
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Junmei Zhou
- Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China
| | - Lu Zhang
- Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China
| | - Minjie Qi
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Yujia Zhai
- Department of Public Health Surveillance, Zhejiang Provincial Centre for Disease Control and Prevention, Hangzhou, Zhejiang, People's Republic of China
| | - Chao Pang
- Department of Prevention and Health Care, Military Hospital of Henan Province, Zhengzhou, Henan, People's Republic of China
| | - Lei Yin
- Department of Prevention and Health Care, Military Hospital of Henan Province, Zhengzhou, Henan, People's Republic of China
| | - Jingzhi Zhao
- Department of Prevention and Health Care, Military Hospital of Henan Province, Zhengzhou, Henan, People's Republic of China
| | - Dongsheng Hu
- Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China
- The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Guangdong, People's Republic of China
| | - Ming Zhang
- Department of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China
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10
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Hu PL, Koh YLE, Tan NC. The utility of diabetes risk score items as predictors of incident type 2 diabetes in Asian populations: An evidence-based review. Diabetes Res Clin Pract 2016; 122:179-189. [PMID: 27865165 DOI: 10.1016/j.diabres.2016.10.019] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2016] [Accepted: 10/27/2016] [Indexed: 02/08/2023]
Abstract
BACKGROUND The prevalence of type 2 diabetes mellitus is rising, with many Asian countries featured in the top 10 countries with the highest numbers of persons with diabetes. Reliable diabetes risk scores enable the identification of individuals at risk of developing diabetes for early intervention. OBJECTIVES This article aims to identify common risk factors in the risk scores with the highest discrimination; factors with the most influence on the risk score in Asian populations, and to propose a set of factors translatable to the multi-ethnic Singapore population. METHODS A systematic search of PubMed and EMBASE databases was conducted to identify studies published before August 2016 that developed risk prediction models for incident diabetes. RESULTS 12 studies were identified. Risk scores that included laboratory measurements had better discrimination. Coefficient analysis showed fasting glucose and HbA1c having the greatest impact on the risk score. CONCLUSION A proposed Asian risk score would include: family history of diabetes, age, gender, smoking status, body mass index, waist circumference, hypertension, fasting plasma glucose, HbA1c, HDL-cholesterol and triglycerides. Future research is required on the influence of ethnicity in Singapore. The risk score may potentially be used to stratify individuals for enrolment into diabetes prevention programmes.
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11
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Liu X, Chen Z, Fine JP, Liu L, Wang A, Guo J, Tao L, Mahara G, Yang K, Zhang J, Tian S, Li H, Liu K, Luo Y, Zhang F, Tang Z, Guo X. A competing-risk-based score for predicting twenty-year risk of incident diabetes: the Beijing Longitudinal Study of Ageing study. Sci Rep 2016; 6:37248. [PMID: 27849048 PMCID: PMC5110955 DOI: 10.1038/srep37248] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2016] [Accepted: 10/26/2016] [Indexed: 11/09/2022] Open
Abstract
Few risk tools have been proposed to quantify the long-term risk of diabetes among middle-aged and elderly individuals in China. The present study aimed to develop a risk tool to estimate the 20-year risk of developing diabetes while incorporating competing risks. A three-stage stratification random-clustering sampling procedure was conducted to ensure the representativeness of the Beijing elderly. We prospectively followed 1857 community residents aged 55 years and above who were free of diabetes at baseline examination. Sub-distribution hazards models were used to adjust for the competing risks of non-diabetes death. The cumulative incidence function of twenty-year diabetes event rates was 11.60% after adjusting for the competing risks of non-diabetes death. Age, body mass index, fasting plasma glucose, health status, and physical activity were selected to form the score. The area under the ROC curve (AUC) was 0.76 (95% Confidence Interval: 0.72-0.80), and the optimism-corrected AUC was 0.78 (95% Confidence Interval: 0.69-0.87) after internal validation by bootstrapping. The calibration plot showed that the actual diabetes risk was similar to the predicted risk. The cut-off value of the risk score was 19 points, marking mark the difference between low-risk and high-risk patients, which exhibited a sensitivity of 0.74 and specificity of 0.65.
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Affiliation(s)
- Xiangtong Liu
- School of Public Health, Capital Medical University, Beijing 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Zhenghong Chen
- Beijing Neurosurgical Institute, Capital Medical University, 6, Tiantanxili, Beijing, 100050, China
| | - Jason Peter Fine
- Department of Biostatistics, University of North Carolina, Chapel Hill, 46200, NC, U.S.A.,Department of Statistics &Operations Research, University of North Carolina, Chapel Hill, 319200, NC, U.S.A
| | - Long Liu
- School of Public Health, Capital Medical University, Beijing 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Anxin Wang
- School of Public Health, Capital Medical University, Beijing 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Jin Guo
- School of Public Health, Capital Medical University, Beijing 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Lixin Tao
- School of Public Health, Capital Medical University, Beijing 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Gehendra Mahara
- School of Public Health, Capital Medical University, Beijing 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Kun Yang
- School of Public Health, Capital Medical University, Beijing 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Jie Zhang
- School of Public Health, Capital Medical University, Beijing 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Sijia Tian
- School of Public Health, Capital Medical University, Beijing 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Haibin Li
- School of Public Health, Capital Medical University, Beijing 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Kuo Liu
- School of Public Health, Capital Medical University, Beijing 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Yanxia Luo
- School of Public Health, Capital Medical University, Beijing 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Feng Zhang
- School of Public Health, Capital Medical University, Beijing 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Zhe Tang
- Beijing Geriatric Clinical and Research Center, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
| | - Xiuhua Guo
- School of Public Health, Capital Medical University, Beijing 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
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12
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Risk scores for predicting incidence of type 2 diabetes in the Chinese population: the Kailuan prospective study. Sci Rep 2016; 6:26548. [PMID: 27221651 PMCID: PMC4879553 DOI: 10.1038/srep26548] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2015] [Accepted: 05/04/2016] [Indexed: 12/18/2022] Open
Abstract
Few risk scores have been specifically developed to identify individuals at high risk of type 2 diabetes in China. In the present study, we aimed to develop such risk scores, based on simple clinical variables. We studied a population-based cohort of 73,987 adults, aged 18 years and over. After 5.35 ± 1.59 years of follow-up, 4,726 participants (9.58%) in the exploration cohort developed type 2 diabetes and 2,327 participants (9.44%) in the validation cohort developed type 2 diabetes. Age, gender, body mass index, family history of diabetes, education, blood pressure, and resting heart rate were selected to form the concise score with an area under the receiver operating characteristic curve (AUC) of 0.67. The variables in the concise score combined with fasting plasma glucose (FPG), and triglyceride (TG) or use of lipid-lowering drugs constituted the accurate score with an AUC value of 0.77. The utility of the two scores was confirmed in the validation cohort with AUCs of 0.66 and 0.77, respectively. In summary, the concise score, based on non-laboratory variables, could be used to identify individuals at high risk of developing diabetes within Chinese population; the accurate score, which also uses FPG and TG data, is better at identifying such individuals.
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13
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Cichosz SL, Johansen MD, Hejlesen O. Toward Big Data Analytics: Review of Predictive Models in Management of Diabetes and Its Complications. J Diabetes Sci Technol 2015; 10:27-34. [PMID: 26468133 PMCID: PMC4738225 DOI: 10.1177/1932296815611680] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Diabetes is one of the top priorities in medical science and health care management, and an abundance of data and information is available on these patients. Whether data stem from statistical models or complex pattern recognition models, they may be fused into predictive models that combine patient information and prognostic outcome results. Such knowledge could be used in clinical decision support, disease surveillance, and public health management to improve patient care. Our aim was to review the literature and give an introduction to predictive models in screening for and the management of prevalent short- and long-term complications in diabetes. Predictive models have been developed for management of diabetes and its complications, and the number of publications on such models has been growing over the past decade. Often multiple logistic or a similar linear regression is used for prediction model development, possibly owing to its transparent functionality. Ultimately, for prediction models to prove useful, they must demonstrate impact, namely, their use must generate better patient outcomes. Although extensive effort has been put in to building these predictive models, there is a remarkable scarcity of impact studies.
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Affiliation(s)
- Simon Lebech Cichosz
- Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | | | - Ole Hejlesen
- Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
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14
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Mbanya V, Hussain A, Kengne AP. Application and applicability of non-invasive risk models for predicting undiagnosed prevalent diabetes in Africa: A systematic literature search. Prim Care Diabetes 2015; 9:317-329. [PMID: 25975760 DOI: 10.1016/j.pcd.2015.04.004] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/06/2014] [Revised: 04/01/2015] [Accepted: 04/02/2015] [Indexed: 11/28/2022]
Abstract
BACKGROUND AND PURPOSE Prediction algorithms are increasingly advocated in diabetes screening strategies, particularly in developing countries. We conducted a systematic review to assess the application and applicability of existing non-invasive prevalent diabetes risk models to populations within Africa. DESIGN systematic review data sources A systematic search of English literatures in Medline via PubMed from 1999 until June, 2014. Study selection Included studies had to report on the development, validation or implementation of a model that was primarily constructed to predict prevalent undiagnosed diabetes using non-laboratory based predictors. DATA EXTRACTION Data were extracted on the type of statistical model, type and range of predictors in the model, performance measures in both internal and external validation, and whether the model was developed from, validated or implemented in an African population. RESULTS Twenty-three studies reporting on non-invasive prevalent diabetes models were identified. Ten from Europe (some with multiethnic populations), nine models were developed among Asian population, two from the USA and two from the Middle-East. The c-statistics for these models ranged from 0.65 to 0.88 in the development studies, and from 0.63 to 0.80 in the validation studies. Twenty models were validated, and none in Africa. Among predictors commonly included in models, parental/family history of diabetes and personal history of hypertension appear to be more prone to measurement errors in the African context. CONCLUSION Existing prevalent diabetes prediction models have not been applied to African populations, and issues with the measurement of key predictors make their applicability likely inaccurate.
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Affiliation(s)
- Vivian Mbanya
- Department of Community Medicine, University of Oslo, Oslo, Norway; Health of Populations in Transition (HoPiT) Research Group, Faculty of Medicine and Biomedical Sciences, The University of Yaoundé 1, Yaoundé, Cameroon.
| | - Akhtar Hussain
- Department of Community Medicine, University of Oslo, Oslo, Norway
| | - Andre Pascal Kengne
- Non-Communicable Diseases Research Unit, South African Medical Research Council & Department of Medicine, University of Cape Town, Cape Town, South Africa
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15
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Wang A, Stronks K, Arah OA. Global educational disparities in the associations between body mass index and diabetes mellitus in 49 low-income and middle-income countries. J Epidemiol Community Health 2014; 68:705-11. [PMID: 24683177 DOI: 10.1136/jech-2013-203200] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
BACKGROUND Despite the well-established link between body mass index (BMI) and diabetes mellitus (DM), it remains unclear whether this association is more pronounced at certain levels of education. This study assessed the modifying effect of educational attainment on the associations between BMI and DM-as well as the joint associations of BMI and education with DM-in low-income countries (LICs) and middle-income countries (MICs). METHODS The authors used cross-sectional data from 160 381 participants among 49 LICs and MICs in the World Health Survey. Overweight and obesity levels were defined using WHO's classification. Educational attainment was classified in four categories: 'no formal education', 'some/completed primary school', 'secondary/high school completed' and 'college and beyond'. We used random-intercept multilevel logistic regressions to investigate the modifying influence of educational attainment on the associations of different BMI levels-as well as their joint associations-with DM. RESULTS We found positive associations between excessive BMI and DM at each education level in both LICs and MICs. We found that the joint associations of BMI and education with DM were larger than the product of their separate single associations among females in LICs. With joint increases in BMI and education, males and females in LICs had similar increased odds of DM, but males had higher such odds than females in MICs. CONCLUSIONS BMI and education are associated with the DM, but the associations seem to differ in complex ways between LICs and MICs and by gender.
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Affiliation(s)
- Aolin Wang
- Department of Epidemiology, The Fielding School of Public Health, University of California, Los Angeles (UCLA), Los Angeles, California, USA
| | - Karien Stronks
- Department of Public Health, Academic Medical Center, Amsterdam, The Netherlands
| | - Onyebuchi A Arah
- Department of Epidemiology, The Fielding School of Public Health, University of California, Los Angeles (UCLA), Los Angeles, California, USA Department of Public Health, Academic Medical Center, Amsterdam, The Netherlands UCLA Center for Health Policy Research, Los Angeles, California, USA
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16
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Zhou X, Qiao Q, Ji L, Ning F, Yang W, Weng J, Shan Z, Tian H, Ji Q, Lin L, Li Q, Xiao J, Gao W, Pang Z, Sun J. Nonlaboratory-based risk assessment algorithm for undiagnosed type 2 diabetes developed on a nation-wide diabetes survey. Diabetes Care 2013; 36:3944-52. [PMID: 24144651 PMCID: PMC3836161 DOI: 10.2337/dc13-0593] [Citation(s) in RCA: 71] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
OBJECTIVE To develop a New Chinese Diabetes Risk Score for screening undiagnosed type 2 diabetes in China. RESEARCH DESIGN AND METHODS Data from the China National Diabetes and Metabolic Disorders Study conducted from June 2007 to May 2008 comprising 16,525 men and 25,284 women aged 20-74 years were analyzed. Undiagnosed type 2 diabetes was detected based on fasting plasma glucose ≥7.0 mmol/L or 2-h plasma glucose ≥11.1 mmol/L in people without a prior history of diabetes. β-Coefficients derived from a multiple logistic regression model predicting the presence of undiagnosed type 2 diabetes were used to calculate the New Chinese Diabetes Risk Score. The performance of the New Chinese Diabetes Risk Score was externally validated in two studies in Qingdao: one is prospective with follow-up from 2006 to 2009 (validation 1) and another cross-sectional conducted in 2009 (validation 2). RESULTS The New Chinese Diabetes Risk Score includes age, sex, waist circumference, BMI, systolic blood pressure, and family history of diabetes. The score ranges from 0 to 51. The area under the receiver operating curve of the score for undiagnosed type 2 diabetes was 0.748 (0.739-0.756) in the exploratory population, 0.725 (0.683-0.767) in validation 1, and 0.702 (0.680-0.724) in validation 2. At the optimal cutoff value of 25, the sensitivity and specificity of the score for predicting undiagnosed type 2 diabetes were 92.3 and 35.5%, respectively, in validation 1 and 86.8 and 38.8% in validation 2. CONCLUSIONS The New Chinese Diabetes Risk Score based on nonlaboratory data appears to be a reliable screening tool to detect undiagnosed type 2 diabetes in Chinese population.
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17
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Heianza Y, Arase Y, Hsieh SD, Saito K, Tsuji H, Kodama S, Tanaka S, Ohashi Y, Shimano H, Yamada N, Hara S, Sone H. Development of a new scoring system for predicting the 5 year incidence of type 2 diabetes in Japan: the Toranomon Hospital Health Management Center Study 6 (TOPICS 6). Diabetologia 2012; 55:3213-23. [PMID: 22955996 DOI: 10.1007/s00125-012-2712-0] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2012] [Accepted: 08/09/2012] [Indexed: 01/11/2023]
Abstract
AIMS/HYPOTHESIS The aims of this study were to assess the clinical significance of introducing HbA(1c) into a risk score for diabetes and to develop a scoring system to predict the 5 year incidence of diabetes in Japanese individuals. METHODS The study included 7,654 non-diabetic individuals aged 40-75 years. Incident diabetes was defined as fasting plasma glucose (FPG) ≥7.0 mmol/l, HbA(1c) ≥6.5% (48 mmol/mol) or self-reported clinician-diagnosed diabetes. We constructed a risk score using non-laboratory assessments (NLA) and evaluated improvements in risk prediction by adding elevated FPG, elevated HbA(1c) or both to NLA. RESULTS The discriminative ability of the NLA score (age, sex, family history of diabetes, current smoking and BMI) was 0.708. The difference in discrimination between the NLA + FPG and NLA + HbA(1c) scores was non-significant (0.836 vs 0.837; p = 0.898). A risk score including family history of diabetes, smoking, obesity and both FPG and HbA(1c) had the highest discrimination (0.887, 95% CI 0.871, 0.903). At an optimal cut-off point, sensitivity and specificity were high at 83.7% and 79.0%, respectively. After initial screening using NLA scores, subsequent information on either FPG or HbA(1c) resulted in a net reclassification improvement of 42.7% or 52.3%, respectively (p < 0.0001). When both were available, net reclassification improvement and integrated discrimination improvement were further improved at 56.7% (95% CI 47.3%, 66.1%) and 10.9% (9.7%, 12.1%), respectively. CONCLUSIONS/INTERPRETATION Information on HbA(1c) or FPG levels after initial screening by NLA can precisely refine diabetes risk reclassification.
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Affiliation(s)
- Y Heianza
- Department of Internal Medicine, Niigata University Faculty of Medicine, Niigata, Japan
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Thoopputra T, Newby D, Schneider J, Li SC. Survey of diabetes risk assessment tools: concepts, structure and performance. Diabetes Metab Res Rev 2012; 28:485-98. [PMID: 22407958 DOI: 10.1002/dmrr.2296] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The objective of this study is to review the effectiveness and limitations of existing diabetes risk screening tools to assess the need for further developing of such tools. An electronic search of the EMBASE, MEDLINE, and Cochrane library supplemented by a manual search was performed from 1995-2010. The search retrieved a total of 2168 articles reporting diabetes risk assessment tools which, after culling, produced 41 tools developed in 22 countries, with the majority (n = 26) developed in North America and Europe. All are short questionnaires of 2-16 questions incorporating common variables including age, gender, waist circumference, BMI, family history of diabetes, history of hypertension or antihypertensive medications. While scoring format and cut-offs point are diverse between questionnaires, overall accuracy value range of 40-97%, 24-86% and 62-87% were reported for sensitivity, specificity and receiver operating characteristic curve respectively. In summary, there is a trend of increasing availability of diabetes prediction tools with the existing risk assessment tools being generally a short questionnaire aiming for ease of use in clinical practice. The overall performance of existing tools showed moderate to high accuracy in their predictive performance. However, further detailed comparison of existing questionnaires is needed to evaluate whether they can serve adequately as diabetes risk assessment tool in clinical practice.
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Affiliation(s)
- Thitaporn Thoopputra
- Discipline of Pharmacy and Experimental Pharmacology, School of Biomedical Sciences and Pharmacy, University of Newcastle, Newcastle, Australia
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19
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Validation of a screening tool for identifying Brazilians with impaired glucose tolerance. Int J Diabetes Dev Ctries 2012. [DOI: 10.1007/s13410-012-0074-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
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Abstract
OBJECTIVE To evaluate current risk models and scores for type 2 diabetes and inform selection and implementation of these in practice. DESIGN Systematic review using standard (quantitative) and realist (mainly qualitative) methodology. Inclusion criteria Papers in any language describing the development or external validation, or both, of models and scores to predict the risk of an adult developing type 2 diabetes. DATA SOURCES Medline, PreMedline, Embase, and Cochrane databases were searched. Included studies were citation tracked in Google Scholar to identify follow-on studies of usability or impact. DATA EXTRACTION Data were extracted on statistical properties of models, details of internal or external validation, and use of risk scores beyond the studies that developed them. Quantitative data were tabulated to compare model components and statistical properties. Qualitative data were analysed thematically to identify mechanisms by which use of the risk model or score might improve patient outcomes. RESULTS 8864 titles were scanned, 115 full text papers considered, and 43 papers included in the final sample. These described the prospective development or validation, or both, of 145 risk prediction models and scores, 94 of which were studied in detail here. They had been tested on 6.88 million participants followed for up to 28 years. Heterogeneity of primary studies precluded meta-analysis. Some but not all risk models or scores had robust statistical properties (for example, good discrimination and calibration) and had been externally validated on a different population. Genetic markers added nothing to models over clinical and sociodemographic factors. Most authors described their score as "simple" or "easily implemented," although few were specific about the intended users and under what circumstances. Ten mechanisms were identified by which measuring diabetes risk might improve outcomes. Follow-on studies that applied a risk score as part of an intervention aimed at reducing actual risk in people were sparse. CONCLUSION Much work has been done to develop diabetes risk models and scores, but most are rarely used because they require tests not routinely available or they were developed without a specific user or clear use in mind. Encouragingly, recent research has begun to tackle usability and the impact of diabetes risk scores. Two promising areas for further research are interventions that prompt lay people to check their own diabetes risk and use of risk scores on population datasets to identify high risk "hotspots" for targeted public health interventions.
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Affiliation(s)
- Douglas Noble
- Centre for Primary Care and Public Health, Barts and the London School of Medicine and Dentistry, London E1 2AT, UK.
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Collins GS, Mallett S, Omar O, Yu LM. Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting. BMC Med 2011; 9:103. [PMID: 21902820 PMCID: PMC3180398 DOI: 10.1186/1741-7015-9-103] [Citation(s) in RCA: 328] [Impact Index Per Article: 25.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/09/2011] [Accepted: 09/08/2011] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND The World Health Organisation estimates that by 2030 there will be approximately 350 million people with type 2 diabetes. Associated with renal complications, heart disease, stroke and peripheral vascular disease, early identification of patients with undiagnosed type 2 diabetes or those at an increased risk of developing type 2 diabetes is an important challenge. We sought to systematically review and critically assess the conduct and reporting of methods used to develop risk prediction models for predicting the risk of having undiagnosed (prevalent) or future risk of developing (incident) type 2 diabetes in adults. METHODS We conducted a systematic search of PubMed and EMBASE databases to identify studies published before May 2011 that describe the development of models combining two or more variables to predict the risk of prevalent or incident type 2 diabetes. We extracted key information that describes aspects of developing a prediction model including study design, sample size and number of events, outcome definition, risk predictor selection and coding, missing data, model-building strategies and aspects of performance. RESULTS Thirty-nine studies comprising 43 risk prediction models were included. Seventeen studies (44%) reported the development of models to predict incident type 2 diabetes, whilst 15 studies (38%) described the derivation of models to predict prevalent type 2 diabetes. In nine studies (23%), the number of events per variable was less than ten, whilst in fourteen studies there was insufficient information reported for this measure to be calculated. The number of candidate risk predictors ranged from four to sixty-four, and in seven studies it was unclear how many risk predictors were considered. A method, not recommended to select risk predictors for inclusion in the multivariate model, using statistical significance from univariate screening was carried out in eight studies (21%), whilst the selection procedure was unclear in ten studies (26%). Twenty-one risk prediction models (49%) were developed by categorising all continuous risk predictors. The treatment and handling of missing data were not reported in 16 studies (41%). CONCLUSIONS We found widespread use of poor methods that could jeopardise model development, including univariate pre-screening of variables, categorisation of continuous risk predictors and poor handling of missing data. The use of poor methods affects the reliability of the prediction model and ultimately compromises the accuracy of the probability estimates of having undiagnosed type 2 diabetes or the predicted risk of developing type 2 diabetes. In addition, many studies were characterised by a generally poor level of reporting, with many key details to objectively judge the usefulness of the models often omitted.
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Affiliation(s)
- Gary S Collins
- Centre for Statistics in Medicine, University of Oxford, Wolfson College Annexe, Oxford, OX2 6UD, UK.
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Buijsse B, Simmons RK, Griffin SJ, Schulze MB. Risk assessment tools for identifying individuals at risk of developing type 2 diabetes. Epidemiol Rev 2011; 33:46-62. [PMID: 21622851 PMCID: PMC3132807 DOI: 10.1093/epirev/mxq019] [Citation(s) in RCA: 191] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Trials have demonstrated the preventability of type 2 diabetes through lifestyle modifications or drugs in people with impaired glucose tolerance. However, alternative ways of identifying people at risk of developing diabetes are required. Multivariate risk scores have been developed for this purpose. This article examines the evidence for performance of diabetes risk scores in adults by 1) systematically reviewing the literature on available scores and 2) their validation in external populations; and 3) exploring methodological issues surrounding the development, validation, and comparison of risk scores. Risk scores show overall good discriminatory ability in populations for whom they were developed. However, discriminatory performance is more heterogeneous and generally weaker in external populations, which suggests that risk scores may need to be validated within the population in which they are intended to be used. Whether risk scores enable accurate estimation of absolute risk remains unknown; thus, care is needed when using scores to communicate absolute diabetes risk to individuals. Several risk scores predict diabetes risk based on routine noninvasive measures or on data from questionnaires. Biochemical measures, in particular fasting plasma glucose, can improve prediction of such models. On the other hand, usefulness of genetic profiling currently appears limited.
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Affiliation(s)
- Brian Buijsse
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Arthur-Scheunert-Allee 114-116, 14558 Nuthetal, Germany
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Abstract
BACKGROUND A small number of risk scores for the risk of developing diabetes have been produced but none has yet been widely used in clinical practice in the UK. The aim of this study is to independently evaluate the performance of QDSCORE(®) for predicting the 10-year risk of developing diagnosed Type 2 diabetes in a large independent UK cohort of patients from general practice. METHODS A prospective cohort study of 2.4 million patients (13.6 million person years) aged between 25 and 79 years from 364 practices from the UK contributing to The Health Improvement Network (THIN) database between 1 January 1993 and 20 June 2008. RESULTS QDSCORE(®) showed good performance data when evaluated on a large external data set. The score is well calibrated with reasonable agreement between observed and predicted outcomes. There is a slight underestimation of risk in both men and women aged 60 years and above, although the magnitude of underestimation is small. The ability of the score to differentiate between those who develop diabetes and those who do not is good, with values for the area under the receiver operating characteristic curve exceeding 0.8 for both men and women. Performance data in this external validation are consistent with those reported in the development and internal validation of the risk score. CONCLUSIONS QDSCORE(®) has shown to be a useful tool to predict the 10-year risk of developing Type 2 diabetes in the UK.
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Affiliation(s)
- G S Collins
- Centre for Statistics in Medicine, University of Oxford, Oxford UK.
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Chen L, Magliano DJ, Balkau B, Wolfe R, Brown L, Tonkin AM, Zimmet PZ, Shaw JE. Maximizing efficiency and cost-effectiveness of Type 2 diabetes screening: the AusDiab study. Diabet Med 2011; 28:414-23. [PMID: 21392062 DOI: 10.1111/j.1464-5491.2010.03188.x] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
AIMS To evaluate how to most efficiently screen populations to detect people at high risk of incident Type 2 diabetes and those with prevalent, but undiagnosed, Type 2 diabetes. METHODS Data from 5814 adults in the Australian Diabetes, Obesity and Lifestyle study were used to examine four different types of screening strategies. The strategies incorporated various combinations of cut-points of fasting plasma glucose, the non-invasive Australian Type 2 Diabetes Risk Assessment Tool (AUSDRISK1) and a modified version of the tool incorporating fasting plasma glucose (AUSDRISK2). Sensitivity, specificity, positive predictive value, screening costs per case of incident or prevalent undiagnosed diabetes identified and intervention costs per case of diabetes prevented or reverted were compared. RESULTS Of the four strategies that maximized sensitivity and specificity, use of the non-invasive AUSDRISK1, followed by AUSDRISK2 in those found to be at increased risk on AUSDRISK1, had the highest sensitivity (80.3%; 95% confidence interval 76.6-84.1%), specificity (78.1%; 95% confidence interval 76.9-79.2%) and positive predictive value (22.3%; 95% confidence interval 20.2-24.4%) for identifying people with either prevalent undiagnosed diabetes or future incident diabetes. It required the fewest people (24.1%; 95% confidence interval 23.0-25.2%) to enter lifestyle modification programmes, and also had the lowest intervention costs and combined costs of running screening and intervention programmes per case of diabetes prevented or reverted. CONCLUSIONS Using a self-assessed diabetes risk score as an initial screening step, followed by a second risk score incorporating fasting plasma glucose, would maximize efficiency of identifying people with undiagnosed Type 2 diabetes and those at high risk of future diabetes.
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Affiliation(s)
- L Chen
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
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Agardh E, Allebeck P, Hallqvist J, Moradi T, Sidorchuk A. Type 2 diabetes incidence and socio-economic position: a systematic review and meta-analysis. Int J Epidemiol 2011; 40:804-18. [PMID: 21335614 DOI: 10.1093/ije/dyr029] [Citation(s) in RCA: 593] [Impact Index Per Article: 45.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND We conducted a systematic review and meta-analysis, the first to our knowledge, summarizing and quantifying the published evidence on associations between type 2 diabetes incidence and socio-economic position (SEP) (measured by educational level, occupation and income) worldwide and when sub-divided into high-, middle- and low-income countries. METHODS Relevant case-control and cohort studies published between 1966 and January 2010 were searched in PubMed and EMBASE using the keywords: diabetes vs educational level, occupation or income. All identified citations were screened by one author, and two authors independently evaluated and extracted data from relevant publications. Risk estimates from individual studies were pooled using random-effects models quantifying the associations. RESULTS Out of 5120 citations, 23 studies, including 41 measures of association, were found to be relevant. Compared with high educational level, occupation and income, low levels of these determinants were associated with an overall increased risk of type 2 diabetes; [relative risk (RR) = 1.41, 95% confidence interval (CI): 1.28-1.51], (RR = 1.31, 95% CI: 1.09-1.57) and (RR = 1.40, 95% CI: 1.04-1.88), respectively. The increased risks were independent of the income levels of countries, although based on limited data in middle- and low-income countries. CONCLUSIONS The risk of getting type 2 diabetes was associated with low SEP in high-, middle- and low-income countries and overall. The strength of the associations was consistent in high-income countries, whereas there is a strong need for further investigation in middle- and low-income countries.
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Affiliation(s)
- Emilie Agardh
- Department of Public Health Sciences, Division of Social Medicine, Karolinska Institutet, Stockholm, Sweden.
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Current literature in diabetes. Diabetes Metab Res Rev 2010; 26:i-xi. [PMID: 20474064 DOI: 10.1002/dmrr.1019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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