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Lv K, Cui C, Fan R, Zha X, Wang P, Zhang J, Zhang L, Ke J, Zhao D, Cui Q, Yang L. Detection of diabetic patients in people with normal fasting glucose using machine learning. BMC Med 2023; 21:342. [PMID: 37674168 PMCID: PMC10483877 DOI: 10.1186/s12916-023-03045-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 08/23/2023] [Indexed: 09/08/2023] Open
Abstract
BACKGROUND Diabetes mellitus (DM) is a chronic metabolic disease that could produce severe complications threatening life. Its early detection is thus quite important for the timely prevention and treatment. Normally, fasting blood glucose (FBG) by physical examination is used for large-scale screening of DM; however, some people with normal fasting glucose (NFG) actually have suffered from diabetes but are missed by the examination. This study aimed to investigate whether common physical examination indexes for diabetes can be used to identify the diabetes individuals from the populations with NFG. METHODS The physical examination data from over 60,000 individuals with NFG in three Chinese cohorts were used. The diabetes patients were defined by HbA1c ≥ 48 mmol/mol (6.5%). We constructed the models using multiple machine learning methods, including logistic regression, random forest, deep neural network, and support vector machine, and selected the optimal one on the validation set. A framework using permutation feature importance algorithm was devised to discover the personalized risk factors. RESULTS The prediction model constructed by logistic regression achieved the best performance with an AUC, sensitivity, and specificity of 0.899, 85.0%, and 81.1% on the validation set and 0.872, 77.9%, and 81.0% on the test set, respectively. Following feature selection, the final classifier only requiring 13 features, named as DRING (diabetes risk of individuals with normal fasting glucose), exhibited reliable performance on two newly recruited independent datasets, with the AUC of 0.964 and 0.899, the balanced accuracy of 84.2% and 81.1%, the sensitivity of 100% and 76.2%, and the specificity of 68.3% and 86.0%, respectively. The feature importance ranking analysis revealed that BMI, age, sex, absolute lymphocyte count, and mean corpuscular volume are important factors for the risk stratification of diabetes. With a case, the framework for identifying personalized risk factors revealed FBG, age, and BMI as significant hazard factors that contribute to an increased incidence of diabetes. DRING webserver is available for ease of application ( http://www.cuilab.cn/dring ). CONCLUSIONS DRING was demonstrated to perform well on identifying the diabetes individuals among populations with NFG, which could aid in early diagnosis and interventions for those individuals who are most likely missed.
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Affiliation(s)
- Kun Lv
- Key Laboratory of Non-Coding RNA Transformation Research of Anhui Higher Education Institutes, Wuhu, China.
- Central Laboratory, First Affiliated Hospital of Wannan Medical College, Wuhu, People's Republic of China.
| | - Chunmei Cui
- Department of Biomedical Informatics, State Key Laboratory of Vascular Homeostasis and Remodeling, School of Basic Medical Sciences, Peking University, Beijing, People's Republic of China.
| | - Rui Fan
- Department of Biomedical Informatics, State Key Laboratory of Vascular Homeostasis and Remodeling, School of Basic Medical Sciences, Peking University, Beijing, People's Republic of China
| | - Xiaojuan Zha
- Laboratory Medicine, First Affiliated Hospital of Wannan Medical College, Wuhu, People's Republic of China
| | - Pengyu Wang
- Department of Pathophysiology, Harbin Medical University, Harbin, People's Republic of China
| | - Jun Zhang
- Medical College of Shihezi University, Shihezi, People's Republic of China
| | - Lina Zhang
- Department of Laboratory Diagnosis, Daqing Oil Field General Hospital, Daqing, People's Republic of China
| | - Jing Ke
- Beijing Key Laboratory of Diabetes Research and Care, Center for Endocrine Metabolism and Immune Diseases, Beijing Luhe Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Dong Zhao
- Beijing Key Laboratory of Diabetes Research and Care, Center for Endocrine Metabolism and Immune Diseases, Beijing Luhe Hospital, Capital Medical University, Beijing, People's Republic of China.
| | - Qinghua Cui
- Department of Biomedical Informatics, State Key Laboratory of Vascular Homeostasis and Remodeling, School of Basic Medical Sciences, Peking University, Beijing, People's Republic of China.
| | - Liming Yang
- Department of Pathophysiology, Harbin Medical University, Harbin, People's Republic of China.
- National Key Laboratory of Frigid Zone Cardiovascular Diseases (NKLFZCD), Harbin Medical University, Harbin, People's Republic of China.
- NHC Key Laboratory of Cell Transplantation, The First Affiliated Hospital of Harbin Medical University, Harbin, People's Republic of China.
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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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Liu Y, Feng W, Lou J, Qiu W, Shen J, Zhu Z, Hua Y, Zhang M, Billong LF. Performance of a prediabetes risk prediction model: A systematic review. Heliyon 2023; 9:e15529. [PMID: 37215820 PMCID: PMC10196520 DOI: 10.1016/j.heliyon.2023.e15529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 04/11/2023] [Accepted: 04/12/2023] [Indexed: 05/24/2023] Open
Abstract
Backgrounds The prediabetes population is large and easily overlooked because of the lack of obvious symptoms, which can progress to diabetes. Early screening and targeted interventions can substantially reduce the rate of conversion of prediabetes to diabetes. Therefore, this study systematically reviewed prediabetes risk prediction models, performed a summary and quality evaluation, and aimed to recommend the optimal model. Methods We systematically searched five databases (Cochrane, PubMed, Embase, Web Of Science, and CNKI) for published literature related to prediabetes risk prediction models and excluded preprints, duplicate publications, reviews, editorials, and other studies, with a search time frame of March 01, 2023. Data were categorized and summarized using a standardized data extraction form that extracted data including author; publication date; study design; country; demographic characteristics; assessment tool name; sample size; study type; and model-related indicators. The PROBAST tool was used to assess the risk of bias profile of included studies. Findings 14 studies with a total of 15 models were eventually included in the systematic review. We found that the most common predictors of models were age, family history of diabetes, gender, history of hypertension, and BMI. Most of the studies (83.3%) had a high risk of bias, mainly related to under-reporting of outcome information and poor methodological design during the development and validation of models. Due to the low quality of included studies, the evidence for predictive validity of the available models is unclear. Interpretation We should pay attention to the early screening of prediabetes patients and give timely pharmacological and lifestyle interventions. The predictive performance of the existing model is not satisfactory, and the model building process can be standardized and external validation can be added to improve the accuracy of the model in the future.
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Affiliation(s)
- Yujin Liu
- Schools of Nursing and Medicine, Huzhou University, Huzhou, 313000, China
| | - Wenming Feng
- Huzhou First People's Hospital, Huzhou, 313000, China
| | - Jianlin Lou
- Huzhou Key Laboratory of Precise Prevention and Control of Major Chronic Diseases, Huzhou University, Huzhou, 313000, China
| | - Wei Qiu
- Department of Endocrinology, Huzhou Central Hospital, Huzhou, 313000, China
| | - Jiantong Shen
- Schools of Nursing and Medicine, Huzhou University, Huzhou, 313000, China
- Huzhou Key Laboratory of Precise Prevention and Control of Major Chronic Diseases, Huzhou University, Huzhou, 313000, China
| | - Zhichao Zhu
- Schools of Nursing and Medicine, Huzhou University, Huzhou, 313000, China
- Internal Medicine General Ward, Jinhua Municipal Central Hospital Medical Group, Jinhua, 321200, China
| | - Yuting Hua
- Schools of Nursing and Medicine, Huzhou University, Huzhou, 313000, China
| | - Mei Zhang
- Schools of Nursing and Medicine, Huzhou University, Huzhou, 313000, China
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Doan L, Nguyen HT, Nguyen TTP, Phan TTL, Huy LD, Nguyen TTH, Doan TP. ModAsian FINDRISC as a Screening Tool for People with Undiagnosed Type 2 Diabetes Mellitus in Vietnam: A Community-Based Cross-Sectional Study. J Multidiscip Healthc 2023; 16:439-449. [PMID: 36814807 PMCID: PMC9940497 DOI: 10.2147/jmdh.s398455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Accepted: 02/02/2023] [Indexed: 02/18/2023] Open
Abstract
Purpose Our study aims to evaluate the risk of developing type 2 diabetes mellitus in the next 10 years using ModAsian FINDRISC and additionally explore associated factors among the Vietnam population. Participants and Methods A cross-sectional study was conducted on 2258 participants aged 25 years old or above in Thua Thien Hue Province, Vietnam. The sample size is calculated based on the estimated sensitivity, and participants were randomly selected from different geographical and socio-economic areas. All participants were thoroughly medically examined, taking blood lipid profile and fasting blood glucose, taking blood pressure, anthropometric indexes, 12-lead electrocardiogram, and behavioral factors were investigated using the Vietnamese version of the WHO STEPS toolkit. The risk of developing T2DM was made based on the ModAsian FINDRISC. Results The incidence of developing type 2 diabetes mellitus among the study population was 4.21%. The group with a high or very high risk of developing type 2 diabetes mellitus in the next 10 years accounted for 2.52%. Body mass index (AUC = 0.840, 95% CI: 0.792-0.888), waist circumference (AUC = 0.824, 95% CI: 0.777-0.871), family history of diabetes mellitus (AUC = 0.751, 95% CI = 0.668-0.833), and history of antihypertensive medication use regularly (AUC = 0.708, 95% CI: 0.632-0.784) are the most associated factors of the ModAsian FINDRISC. Residential location (OR = 5.62, 95% CI: 1.91-16.54) and occupational status (OR = 0.35, 95% CI: 0.20-0.62) were significant factors associated with a high and very high risk of developing type 2 diabetes mellitus in the next 10 year. Conclusion Screening for the risk of type 2 diabetes mellitus and implementing intervention programs focusing on controlling weight, waist circumference, and blood pressure are essential for reducing type 2 diabetes mellitus incidence and burden in Vietnam.
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Affiliation(s)
- Long Doan
- Internal Medicine Department, University of Medicine and Pharmacy, Hue University, Hue, Thua Thien Hue, Vietnam
| | - Huong T Nguyen
- Faculty of Public Health, University of Medicine and Pharmacy, Hue University, Hue, Thua Thien Hue, Vietnam
| | - Thao T P Nguyen
- Institute for Community Health Research, University of Medicine and Pharmacy, Hue University, Hue, Thua Thien Hue, Vietnam
| | - Thi Thuy Linh Phan
- Health Personnel Training Institute, University of Medicine and Pharmacy, Hue University, Hue, Thua Thien Hue, Vietnam
| | - Le Duc Huy
- Health Personnel Training Institute, University of Medicine and Pharmacy, Hue University, Hue, Thua Thien Hue, Vietnam
| | - Thi Thuy Hang Nguyen
- Health Personnel Training Institute, University of Medicine and Pharmacy, Hue University, Hue, Thua Thien Hue, Vietnam
| | - Thuoc Phuoc Doan
- Faculty of Public Health, University of Medicine and Pharmacy, Hue University, Hue, Thua Thien Hue, Vietnam,Correspondence: Thuoc Phuoc Doan, Faculty of Public Health, University of Medicine and Pharmacy, Hue University, Hue, Thua Thien Hue, 53000, Vietnam, Tel +84 914932577, Email
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5
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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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Three-stage intelligent support of clinical decision making for higher trust, validity, and explainability. J Biomed Inform 2022; 127:104013. [DOI: 10.1016/j.jbi.2022.104013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 01/03/2022] [Accepted: 02/02/2022] [Indexed: 01/02/2023]
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Li L, Wang Z, Zhang M, Ruan H, Zhou L, Wei X, Zhu Y, Wei J, He S. New risk score model for identifying individuals at risk for diabetes in southwest China. Prev Med Rep 2021; 24:101618. [PMID: 34976674 PMCID: PMC8684021 DOI: 10.1016/j.pmedr.2021.101618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Revised: 10/13/2021] [Accepted: 10/22/2021] [Indexed: 11/01/2022] Open
Abstract
The prevalence of diabetes is increasing rapidly and becoming a major public health issue worldwide. We aimed to develop a novel nomogram model for long-term diabetic risk prediction in a Chinese population. A prospective cohort study was performed on 687 nondiabetic individuals who underwent routine physical examination in 1992 and 2007. Using the least absolute shrinkage and selection operator model to optimize feature selection. Multiple Cox regression analysis was performed, and a simple nomogram was constructed. The area under receiver operating characteristic curve (AUC) and calibration plot were conducted to assess the predictive accuracy of the model. The model was subjected to bootstrap internal validation. Of the 687 participants without diabetes at baseline, 74 developed diabetes during the follow-up time. This simple nomogram model was constructed by family history of diabetes, height, waist circumference, triglycerides, fasting plasma glucose and white blood cell count. The AUCs were 0.812 (95% CI: 0.729-0.895) and 0.794 (95% CI: 0.734-0.854) for 10-year and 15-year diabetic risk. The bootstrap corrected c-index was 0.771 (95% CI: 0.721-0.821). The calibration plot also achieved good agreement between observational and actual diabetic incidence. The stratification into different risk groups by optimal cut-off value of 12.8 allowed significant distinction between cumulative diabetic incidence curves in the whole cohort and several subgroups. We established and internally validated a novel nomogram which can provide individual diabetic risk prediction for Chinese population and this practical screening model may help clinicians to identify individuals at high risk of diabetes.
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Affiliation(s)
- Liying Li
- Department of Cardiology, West China Hospital of Sichuan University, Chengdu, China
| | - Ziqiong Wang
- Department of Cardiology, West China Hospital of Sichuan University, Chengdu, China
| | - Muxin Zhang
- Department of Cardiology, West China Hospital of Sichuan University, Chengdu, China.,Department of Cardiology, First People's Hospital, Longquanyi District, Chengdu, China
| | - Haiyan Ruan
- Department of Cardiology, West China Hospital of Sichuan University, Chengdu, China.,Department of Cardiology, Traditional Chinese Medicine Hospital of Shuangliu District, Chengdu, China
| | - Linxia Zhou
- Department of Cardiology, West China Hospital of Sichuan University, Chengdu, China.,Department of Cardiology, Traditional Chinese Medicine Hospital of Shuangliu District, Chengdu, China
| | - Xin Wei
- Department of Cardiology, West China Hospital of Sichuan University, Chengdu, China.,Department of Cardiology and National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China
| | - Ye Zhu
- Department of Cardiology, West China Hospital of Sichuan University, Chengdu, China
| | - Jiafu Wei
- Department of Cardiology, West China Hospital of Sichuan University, Chengdu, China
| | - Sen He
- Department of Cardiology, West China Hospital of Sichuan University, Chengdu, China
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Baytaroglu C, Sevgili E. Association of Metabolic Syndrome Components and Overactive Bladder in Women. Cureus 2021; 13:e14765. [PMID: 34094730 PMCID: PMC8169015 DOI: 10.7759/cureus.14765] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Background To identify associations between metabolic syndrome (MS) components and overactive bladder (OAB) in women. Methodology The present study was conducted prospectively between February 2021 and April 2021 and included the assessment of women admitted to the cardiology outpatient clinic and their female relatives. Records were made of the demographic characteristics of patients and blood tests, including cholesterol, high-density lipoproteins (HDL), low-density lipoproteins (LDL), triglyceride, and fasting glucose levels (FG). In addition, the score on the Overactive Bladder Questionnaire-8-item (OAB-V8) form was noted. The study population was divided into two groups according to OAB-V8 score. The groups were compared in terms of participant demographic properties, OAB-V8 scores, metabolic component values, and blood test results. Results In total, 200 participants with a mean age of 49.8 years were enrolled in the study. Participants with OAB had significantly higher body mass index (BMI) (30.1 kg/m2 versus 27.1 kg/m2; p = 0.001) and longer waist circumference (97.8 cm versus 89.0 cm; p = 0.001). Similarly, the mean FG and LDL levels were significantly higher in participants with OAB (p = 0.001 and p = 0.001). Lastly, mean OAB-V8 score was 20.2 for participants with OAB and 4.8 for participants without OAB. Multivariate regression analysis showed that higher BMI and longer waist circumference were significantly associated with OAB (1.228-fold; p = 0.001 and 1.058-fold; p = 0.001, respectively). Additionally, multivariate regression analysis found that higher LDL level and FG were predictive factors for OAB (1.115-fold; p = 0.003 and 1.229-fold; p = 0.001, respectively). Conclusions The present study found that higher BMI, longer waist circumference, and higher LDL and FG levels were predictive factors for OAB development in women.
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Oh TJ, Moon JH, Choi SH, Cho YM, Park KS, Cho NH, Jang HC. Development of a clinical risk score for incident diabetes: A 10-year prospective cohort study. J Diabetes Investig 2020; 12:610-618. [PMID: 32750227 PMCID: PMC8015827 DOI: 10.1111/jdi.13382] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 07/28/2020] [Accepted: 07/29/2020] [Indexed: 01/07/2023] Open
Abstract
Aims/Introduction We developed a self‐assessable Korean Diabetes Risk score using the data of the Korean Genome and Epidemiology Study. Materials and Methods A total of 8,740 participants without diabetes at baseline were followed up biannually over a period of 10 years. We included variables that were significantly different between participants who developed diabetes mellitus and those who did not in the development cohort at baseline. We assigned a maximum score of 100 to the selected variable in each gender group. Next, the 10‐year probability of incident diabetes was calculated and validated in the validation cohort. Finally, we compared the predictive power of Korean Diabetes Risk score with models including fasting plasma glucose or glycated hemoglobin and other cohort models of Atherosclerosis Risk in Communities and Korea National Health and Nutrition Examination Survey. Results During a median follow‐up period of 9.7 years, 22.7% of the participants progressed to diabetes. The Korean Diabetes Risk score included age, living location (urban or rural area), waist circumference, hypertension, family history of diabetes and smoking history. The developed risk score yielded acceptable discrimination for incident diabetes (area under the curve 0.657) and the predictive power was improved when the model included fasting plasma glucose (area under the curve 0.690) or glycated hemoglobin (area under the curve 0.746). In addition, our model predicted incident diabetes more accurately than previous Western or Korean models. Conclusions This newly developed self‐assessable diabetes risk score is easily applicable to predict the future risk of diabetes even without the necessity for laboratory tests. This score is useful for the Korean diabetes prevention program, because high‐risk individuals can be easily screened.
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Affiliation(s)
- Tae Jung Oh
- Department of Internal Medicine, Seoul National University College of Medicine and Seoul National University Bundang Hospital, Seongnam, Korea
| | - Jae Hoon Moon
- Department of Internal Medicine, Seoul National University College of Medicine and Seoul National University Bundang Hospital, Seongnam, Korea
| | - Sung Hee Choi
- Department of Internal Medicine, Seoul National University College of Medicine and Seoul National University Bundang Hospital, Seongnam, Korea
| | - Young Min Cho
- Department of Internal Medicine, Seoul National University College of Medicine and Seoul National University Hospital, Seoul, Korea
| | - Kyong Soo Park
- Department of Internal Medicine, Seoul National University College of Medicine and Seoul National University Hospital, Seoul, Korea
| | - Nam H Cho
- Department of Preventive Medicine, Ajou University School of Medicine, Suwon, Korea
| | - Hak Chul Jang
- Department of Internal Medicine, Seoul National University College of Medicine and Seoul National University Bundang Hospital, Seongnam, Korea
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10
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Shao X, Wang Y, Huang S, Liu H, Zhou S, Zhang R, Yu P. Development and validation of a prediction model estimating the 10-year risk for type 2 diabetes in China. PLoS One 2020; 15:e0237936. [PMID: 32881911 PMCID: PMC7470416 DOI: 10.1371/journal.pone.0237936] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2020] [Accepted: 08/05/2020] [Indexed: 01/19/2023] Open
Abstract
PURPOSE To derive and validate a concise prediction model estimating the 10-year risk for type 2 diabetes (T2DM) in China. METHODS A total of 11494 subjects from the China Health and Nutrition Survey recorded from 2004 to 2015 were analyzed and only 6023 participants were enrolled in this study. Four logistic models were analyzed using the derivation cohort. Methods of calibration and discrimination were used for the validation cohort. RESULTS In the derivation cohort, 257 patients were identified from a total of 4498 cases. In the validation cohort, 92 patients were identified from a total of 1525 cases. Four models performed nicely for both calibration and discrimination. The AUC in the derivation cohort for models A, B, C and D were 0.788 (0.761-0.816), 0.807 (0.780-0.834), 0.905 (0.879-0.932) and 0.882 (0.853-0.912), respectively. The Youden index for models A, B, C and D were 1.46, 1.48, 1.67 and 1.65, respectively. Model C showed the highest sensitivity and model D showed the highest specificity. CONCLUSION Models A and B were non-invasive and can be used to identify high-risk patients for broad screening. Models C and D may be used to provide more accurate assessments of diabetes risk. Furthermore, model C showed the best performance for predicting T2DM risk and identifying individuals who are in need of interventions, current approach improvement and additional follow-up.
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Affiliation(s)
- Xian Shao
- NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, China
| | - Yao Wang
- NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, China
| | - Shuai Huang
- NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, China
| | - Hongyan Liu
- NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, China
| | - Saijun Zhou
- NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, China
| | - Rui Zhang
- NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, China
| | - Pei Yu
- NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, China
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11
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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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12
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Félix-Martínez GJ, Godínez-Fernández JR. Comparative analysis of screening models for undiagnosed diabetes in Mexico. ENDOCRINOL DIAB NUTR 2020; 67:333-341. [PMID: 31796340 DOI: 10.1016/j.endinu.2019.08.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Revised: 08/29/2019] [Accepted: 08/30/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND It is estimated that 37% of Mexican adults have undiagnosed diabetes, and are therefore at high risk of developing the severe and devastating complications associated to it. In recent years, a variety of screening tools based on the characteristics of the adult Mexican population have been proposed in order to reduce the negative effects of the disease. OBJECTIVES To assess the performance of screening models to diagnose diabetes in the Mexican adult population and to propose a screening model based on HbA1c measurements. MATERIALS AND METHODS Data from the 2016 Halfway National Health and Nutrition Survey (NHNS) were used to assess the screening models and to develop and validate the proposed 2016 NHNS model, built using a multivariate logistic regression model. Explanatory variables included in the 2016 NHNS 2016 model were selected through a stepwise backward procedure, using sensitivity and specificity as performance indicators. RESULTS Of the screening models assessed, only the model based on the 2006 NHNS survey showed a performance consistent with previous reports. The proposed 2016 NHNS model included age, waist circumference, and systolic blood pressure as explanatory variables and showed a sensitivity of 0.72 and a specificity of 0.80 in the validation data set. CONCLUSIONS Age, waist circumference, and systolic blood pressure are variables of special importance for early detection of undiagnosed diabetes in Mexican adults. Based on the consistent performance of the 2006 NHNS model in different data sets, its use as a screening tool for adults with undiagnosed diabetes in Mexico is recommended.
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Affiliation(s)
- Gerardo Jorge Félix-Martínez
- Cátedras CONACYT (Consejo Nacional de Ciencia y Tecnología, México), Mexico; Departamento de Ingeniería Eléctrica, Universidad Autónoma Metropolitana, Unidad Iztapalapa, Mexico.
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13
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Liu Y, Guo H, Wang Q, Lian D, Yang M, Huang K, Chen J, Xuan Y, Zhang J, Wei Q, Fang S, Xu J, Liu Y, Sun K, Sun Z, Wang B. Use of capillary glucose combined with other non-laboratory examinations to screen for diabetes and prediabetes. Diabet Med 2019; 36:1671-1678. [PMID: 31392737 DOI: 10.1111/dme.14101] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/05/2019] [Indexed: 01/19/2023]
Abstract
AIM To evaluate the value and feasibility of capillary glucose assessment, combined with other non-laboratory measures, in screening for diabetes and prediabetes in the community. METHODS In this cross-sectional study, we assessed fasting capillary glucose, fasting plasma glucose, and both capillary glucose and plasma glucose values after 2-h oral glucose tolerance tests in a total of 3736 samples. We determined the optimal threshold of capillary glucose using receiver-operating characteristic curve analysis. The effect of screening methods using capillary glucose combined with other variables, such as age, BMI and waist circumference, was assessed according to area under the receiver-operating characteristic curve. RESULTS There was a strong positive correlation between capillary glucose and venous plasma glucose. The area under the curve for the model using fasting capillary glucose to screen for impaired fasting glucose was 0.722, while that for the model using capillary glucose after a 2-h oral glucose tolerance test to screen for impaired glucose tolerance was 0.916. The area under the curve for the model using fasting capillary glucose to screen for diabetes was 0.835, while that for the model using 2-h oral glucose tolerance test capillary glucose was 0.912. The area under the curve for the model using fasting capillary glucose + 2-h oral glucose tolerance test capillary glucose to screen for diabetes was 0.945. The discriminatory capability of models using capillary glucose was somewhat improved by adding non-laboratory variables. CONCLUSIONS Capillary glucose could be an alternative for screening for diabetes and prediabetes, especially in low-resource areas.
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Affiliation(s)
- Yuxiang Liu
- Department of Epidemiology and Statistics, Key Laboratory of Environmental Medicine Engineering of Ministry of Education, School of Public Health, Southeast University, Nanjing, Jiangsu, China
| | - Haijian Guo
- Integrated Business Management Office, Jiangsu Provincial Centre for Disease Control and Prevention, Nanjing, Jiangsu, China
| | - Qing Wang
- Department of Epidemiology and Statistics, Key Laboratory of Environmental Medicine Engineering of Ministry of Education, School of Public Health, Southeast University, Nanjing, Jiangsu, China
| | - Dashuai Lian
- Department of Epidemiology and Statistics, Key Laboratory of Environmental Medicine Engineering of Ministry of Education, School of Public Health, Southeast University, Nanjing, Jiangsu, China
| | - Man Yang
- Department of Epidemiology and Statistics, Key Laboratory of Environmental Medicine Engineering of Ministry of Education, School of Public Health, Southeast University, Nanjing, Jiangsu, China
| | - Kaiping Huang
- Department of Epidemiology and Statistics, Key Laboratory of Environmental Medicine Engineering of Ministry of Education, School of Public Health, Southeast University, Nanjing, Jiangsu, China
| | - Jianshuang Chen
- Department of Epidemiology and Statistics, Key Laboratory of Environmental Medicine Engineering of Ministry of Education, School of Public Health, Southeast University, Nanjing, Jiangsu, China
| | - Yan Xuan
- Department of Epidemiology and Statistics, Key Laboratory of Environmental Medicine Engineering of Ministry of Education, School of Public Health, Southeast University, Nanjing, Jiangsu, China
| | - Jiarong Zhang
- Department of Epidemiology and Statistics, Key Laboratory of Environmental Medicine Engineering of Ministry of Education, School of Public Health, Southeast University, Nanjing, Jiangsu, China
| | - Qiankun Wei
- Department of Epidemiology and Statistics, Key Laboratory of Environmental Medicine Engineering of Ministry of Education, School of Public Health, Southeast University, Nanjing, Jiangsu, China
| | | | - Jinshui Xu
- Integrated Business Management Office, Jiangsu Provincial Centre for Disease Control and Prevention, Nanjing, Jiangsu, China
| | - Yu Liu
- Centre for Disease Control and Prevention, Jurong, Jiangsu, China
| | - Kaicheng Sun
- Centre for Disease Control and Prevention, Yandu, Jiangsu, China
| | - Zilin Sun
- Department of Endocrinology, Institute of Diabetes, Medical School, Southeast University, Nanjing, Jiangsu, China
| | - Bei Wang
- Department of Epidemiology and Statistics, Key Laboratory of Environmental Medicine Engineering of Ministry of Education, School of Public Health, Southeast University, Nanjing, Jiangsu, China
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Thakur JS, Jeet G, Nangia R, Singh D, Grover S, Lyngdoh T, Pal A, Verma R, Aggarwal R, Khan MH, Saran R, Jain S, Gupta KL, Kumar V. Non-communicable diseases risk factors and their determinants: A cross-sectional state-wide STEPS survey, Haryana, North India. PLoS One 2019; 14:e0208872. [PMID: 31774812 PMCID: PMC6881003 DOI: 10.1371/journal.pone.0208872] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2018] [Accepted: 10/21/2019] [Indexed: 12/28/2022] Open
Abstract
Background Recent studies have documented high variation in epidemiologic transition levels among Indian states with noncommunicable disease epidemic rising swiftly. However, the estimates suffer from non-availability of reliable data for NCDs from sub populations. In order to fill the knowledge gap, the distribution and determinants of NCD risk factors were studied along with awareness, treatment and control of NCDs among the adult population in Haryana, India. Methods NCD risk factors survey was conducted among 5078 residents, aged 18–69 years during 2016–17. Behavioural risk factors were assessed using STEPS instrument, administered through an android software (mSTEPS). This was followed by physical measurements using standard protocols. Finally, biological risk factors were determined through the analysis of serum and urine samples. Results Males were found to be consuming tobacco and alcohol at higher rates of 38.9% (95% CI: 35.3–42.4) and 18.8% (95% CI: 15.8–21.8). One- tenth (11%) (95% CI: 8.6–13.4) of the respondents did not meet the specified WHO recommendations for physical activity for health. Around 35.2% (95%CI: 32.6–37.7) were overweight or obese. Hypertension and diabetes were prevalent at 26.2% (95% CI: 24.6–27.8) and 15.5% (95% CI: 11.0–20.0). 91.3% (95% CI: 89.3–93.3) of the population had higher salt intake than recommended 5gms per day. Conclusion The documentation of strikingly high and uniform distribution of different NCDs and their risk factors in state warrants urgent need for evidence based interventions and advocacy of policy measures.
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Affiliation(s)
- JS Thakur
- Department of Community Medicine and School of Public Heath, Post Graduate Institute of Medical Education and Research, Chandigarh, India
- * E-mail:
| | - Gursimer Jeet
- Department of Community Medicine and School of Public Heath, Post Graduate Institute of Medical Education and Research, Chandigarh, India
| | - Ria Nangia
- Department of Community Medicine and School of Public Heath, Post Graduate Institute of Medical Education and Research, Chandigarh, India
| | - Divya Singh
- Department of Community Medicine and School of Public Heath, Post Graduate Institute of Medical Education and Research, Chandigarh, India
| | - Sandeep Grover
- Department of Psychiatry, Post Graduate of Medical Education and Research, Chandigarh, India
| | - Tanica Lyngdoh
- Indian Institute of Public Health Association, Public Health Foundation of India, Gurugram, India
| | - Arnab Pal
- Department of Biochemistry, Post Graduate Institute of Medical Education and Research, Chandigarh, India
| | - Ramesh Verma
- Department of Social and Preventive Medicine, Post Graduate Institute of Medical Sciences, Rohtak, India
| | - Ramnika Aggarwal
- Department of Community Medicine, Kalpana Chawla Medical College, Karnal, India
| | - Mohd. Haroon Khan
- Department of Community Medicine, Shaheed Hasan Khan Mewati Government Medical College, Mewat, India
| | - Rajiv Saran
- Department of Internal Medicine and Epidemiology, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Sanjay Jain
- Department of Internal Medicine, Post Graduate Institute of Medical Education and Research, Chandigarh, India
| | - K. L. Gupta
- Department of Nephrology, Post Graduate of Medical Education and Research, Chandigarh, India
| | - Vivek Kumar
- Department of Nephrology, Post Graduate of Medical Education and Research, Chandigarh, India
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Liu S, Gao Y, Shen Y, Zhang M, Li J, Sun P. Application of three statistical models for predicting the risk of diabetes. BMC Endocr Disord 2019; 19:126. [PMID: 31771577 PMCID: PMC6878628 DOI: 10.1186/s12902-019-0456-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Accepted: 11/13/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND At present, the proportion of undiagnosed diabetes in Chinese adults is as high as 15.5%. People with diabetes who are not treated and controlled in time may have various complications, such as cardiovascular and cerebrovascular diseases and diabetic foot disorders, which not only seriously affect the quality of life of people with diabetes but also impose a heavy burden on families and society. Therefore, prevention and control of type 2 diabetes is of great significance. METHODS We constructed a logistic regression model, a neural network model and a decision tree model to analyse the risk factors for type 2 diabetes and then compared the prediction accuracy of the different models by calculating the area under the relative operating characteristic (ROC) curve and back-inputting the data into the model. RESULTS The prevalence of type 2 diabetes in 4177 subjects who were not diagnosed with type 2 diabetes was 9.31%. The most influential factors associated with type 2 diabetes were triglyceride (TG) ≥ 1.17 mmol/L (odds ratio (OR) =2.233), age ≥ 70 years (OR = 1.734), hypertension (OR = 1.703), alcohol consumption (OR = 1.674), and total cholesterol≥5.2 mmol/L (TC) (OR = 1.463). The prediction accuracies of the three prediction models were 90.8, 91.2, and 90.7%, respectively, and the areas under curve (AUCs) were 0.711, 0.780, and 0.698, respectively. The differences in the AUCs after back propagation (BP) of the neural network model, logistic regression model and decision tree model were statistically significant (P < 0.05). CONCLUSION BP neural networks have a higher predictive power for identifying the associated risk factors of type 2 diabetes than the other two models, but it is necessary to select a suitable model for specific situations.
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Affiliation(s)
- Siyu Liu
- Epidemiology and Statistics, School of Public Health, Jilin University, Changchun, 130021 China
| | - Yue Gao
- Epidemiology and Statistics, School of Public Health, Jilin University, Changchun, 130021 China
| | - Yuhang Shen
- Epidemiology and Statistics, School of Public Health, Jilin University, Changchun, 130021 China
| | - Min Zhang
- Epidemiology and Statistics, School of Public Health, Jilin University, Changchun, 130021 China
| | - Jingjing Li
- Epidemiology and Statistics, School of Public Health, Jilin University, Changchun, 130021 China
| | - Pinghui Sun
- Epidemiology and Statistics, School of Public Health, Jilin University, Changchun, 130021 China
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16
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Kim SH, Lee ES, Yoo J, Kim Y. Predicting risk of type 2 diabetes mellitus in Korean adults aged 40-69 by integrating clinical and genetic factors. Prim Care Diabetes 2019; 13:3-10. [PMID: 30477970 DOI: 10.1016/j.pcd.2018.07.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2018] [Revised: 05/23/2018] [Accepted: 07/01/2018] [Indexed: 12/25/2022]
Abstract
AIMS The purpose of our investigation was to identify the genetic and clinical risk factors of type 2 diabetes mellitus (T2DM) and to predict the incidence of T2DM in Korean adults aged 40-69 at follow-up intervals of 5, 7, and 10years. METHODS Korean Genome and Epidemiology Study (KoGES) cohort data (n=10,030) were used to develop T2DM prediction models. Both clinical-only and integrated (clinical factors+genetic factors) models were derived using the Cox proportional hazards model. Internal validation was performed to evaluate the prediction capabilities of the clinical and integrated models. RESULTS The clinical model included 10 selected clinical risk factors. The selected SNPs for the integrated model were rs9311835 in PTPRG, rs10975266 in RIC1, rs11057302 in TMED2, rs17154562 in ADAM12, and rs8038172 in CGNL1. For the clinical model, validated c-indices with time points of 5, 7, and 10 years were 0.744, 0.732, and 0.732, respectively. Slightly higher validated c-indices were observed for the integrated model at 0.747, 0.736, and 0.738, respectively. The p-values of the survival net reclassification improvement (NRI) for the SNP point-based score were statistically significant. CONCLUSIONS Clinical and integrated models can be effectively used to predict the incidence of T2DM in Koreans.
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Affiliation(s)
- Soo-Hwan Kim
- Bio-Age Medical Research Institute, Bio-Age Inc., 644, Bongeunsa-ro, Gangnam-gu, Seoul, 06170, Republic of Korea.
| | - Eun-Sol Lee
- Bio-Age Medical Research Institute, Bio-Age Inc., 644, Bongeunsa-ro, Gangnam-gu, Seoul, 06170, Republic of Korea.
| | - Jinho Yoo
- YooJinBioSoft Inc., 24, Jeongbalsan-ro, Ilsandong-gu, Goyang-si, Gyeonggi-do, 10403, Republic of Korea.
| | - Yangseok Kim
- Bio-Age Medical Research Institute, Bio-Age Inc., 644, Bongeunsa-ro, Gangnam-gu, Seoul, 06170, Republic of Korea; College of Korean Medicine, Kyung Hee University, 26, Kyungheedae-ro, Dongdaemun-gu, Seoul, 02447, Republic of Korea.
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17
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Noninvasive screening tool to detect undiagnosed diabetes among young and middle-aged people in Chinese community. Int J Diabetes Dev Ctries 2018. [DOI: 10.1007/s13410-018-0698-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
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18
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Li W, Xie B, Qiu S, Huang X, Chen J, Wang X, Li H, Chen Q, Wang Q, Tu P, Zhang L, Yan S, Li K, Maimaitiming J, Nian X, Liang M, Wen Y, Liu J, Wang M, Zhang Y, Ma L, Wu H, Wang X, Wang X, Liu J, Cai M, Wang Z, Guo L, Chen F, Wang B, Monica S, Carlsson PO, Sun Z. Non-lab and semi-lab algorithms for screening undiagnosed diabetes: A cross-sectional study. EBioMedicine 2018; 35:307-316. [PMID: 30115607 PMCID: PMC6154869 DOI: 10.1016/j.ebiom.2018.08.009] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2018] [Revised: 08/06/2018] [Accepted: 08/06/2018] [Indexed: 12/29/2022] Open
Abstract
Background The terrifying undiagnosed rate and high prevalence of diabetes have become a public emergency. A high efficiency and cost-effective early recognition method is urgently needed. We aimed to generate innovative, user-friendly nomograms that can be applied for diabetes screening in different ethnic groups in China using the non-lab or noninvasive semi-lab data. Methods This multicenter, multi-ethnic, population-based, cross-sectional study was conducted in eight sites in China by enrolling subjects aged 20–70. Sociodemographic and anthropometric characteristics were collected. Blood and urine samples were obtained 2 h following a standard 75 g glucose solution. In the final analysis, 10,794 participants were included and randomized into model development (n = 8096) and model validation (n = 2698) group with a ratio of 3:1. Nomograms were developed by the stepwise binary logistic regression. The nomograms were validated internally by a bootstrap sampling method in the model development set and externally in the model validation set. The area under the receiver operating characteristic curve (AUC) was used to assess the screening performance of the nomograms. Decision curve analysis was applied to calculate the net benefit of the screening model. Results The overall prevalence of undiagnosed diabetes was 9.8% (1059/10794) according to ADA criteria. The non-lab model revealed that gender, age, body mass index, waist circumference, hypertension, ethnicities, vegetable daily consumption and family history of diabetes were independent risk factors for diabetes. By adding 2 h post meal glycosuria qualitative to the non-lab model, the semi-lab model showed an improved Akaike information criterion (AIC: 4506 to 3580). The AUC of the semi-lab model was statistically larger than the non-lab model (0.868 vs 0.763, P < 0.001). The optimal cutoff probability in semi-lab and non-lab nomograms were 0.088 and 0.098, respectively. The sensitivity and specificity were 76.3% and 81.6%, respectively in semi-lab nomogram, and 72.1% and 67.3% in non-lab nomogram at the optimal cut off point. The decision curve analysis also revealed a bigger decrease of avoidable OGTT test (52 per 100 subjects) in the semi-lab model compared to the non-lab model (36 per 100 subjects) and the existed New Chinese Diabetes Risk Score (NCDRS, 35 per 100 subjects). Conclusion The non-lab and semi-lab nomograms appear to be reliable tools for diabetes screening, especially in developing countries. However, the semi-lab model outperformed the non-lab model and NCDRS prediction systems and might be worth being adopted as decision support in diabetes screening in China.
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Affiliation(s)
- Wei Li
- Department of Endocrinology, Zhongda Hospital, Institute of Diabetes, School of Medicine, Southeast Uiversity, Nanjing, China
| | - Bo Xie
- Department of Endocrinology, Zhongda Hospital, Institute of Diabetes, School of Medicine, Southeast Uiversity, Nanjing, China
| | - Shanhu Qiu
- Department of Endocrinology, Zhongda Hospital, Institute of Diabetes, School of Medicine, Southeast Uiversity, Nanjing, China
| | - Xin Huang
- School of Public Health, Southeast University, Nanjing, China
| | - Juan Chen
- Department of Endocrinology, Zhongda Hospital, Institute of Diabetes, School of Medicine, Southeast Uiversity, Nanjing, China
| | - Xinling Wang
- Department of Endocrinology, People's Hospital of Xinjiang Uyghur Autonomous Region, Urumqi, China
| | - Hong Li
- Department of Endocrinology, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Qingyun Chen
- Department of Endocrinology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Qing Wang
- Department of Endocrinology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Ping Tu
- Department of Endocrinology, The Third Hospital of Nanchang, Nanchang, China
| | - Lihui Zhang
- Department of Endocrinology, The Second Hospital of Hebei Medical University, Shijiazhuang, China
| | - Sunjie Yan
- Department of Endocrinology, The First Affiliated Hospital of Fujian Medical University, Diabetes Research Institute of Fujian Province, Fuzhou, China
| | - Kaili Li
- Department of Endocrinology, Xinjiang Uygur Autonomous Region Hospital of traditional Chinese Medicine, Urumqi, China
| | - Jimilanmu Maimaitiming
- Department of Endocrinology, People's Hospital of Xinjiang Uyghur Autonomous Region, Urumqi, China
| | - Xin Nian
- Department of Endocrinology, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Min Liang
- Department of Endocrinology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Yan Wen
- Department of Endocrinology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Jiang Liu
- Department of Endocrinology, The Third Hospital of Nanchang, Nanchang, China
| | - Mian Wang
- Department of Endocrinology, The Second Hospital of Hebei Medical University, Shijiazhuang, China
| | - Yongze Zhang
- Department of Endocrinology, The First Affiliated Hospital of Fujian Medical University, Diabetes Research Institute of Fujian Province, Fuzhou, China
| | - Li Ma
- Department of Endocrinology, Xinjiang Uygur Autonomous Region Hospital of traditional Chinese Medicine, Urumqi, China
| | - Hang Wu
- Department of Endocrinology, Zhongda Hospital, Institute of Diabetes, School of Medicine, Southeast Uiversity, Nanjing, China
| | - Xuyi Wang
- Department of Endocrinology, Zhongda Hospital, Institute of Diabetes, School of Medicine, Southeast Uiversity, Nanjing, China
| | - Xiaohang Wang
- Department of Endocrinology, Zhongda Hospital, Institute of Diabetes, School of Medicine, Southeast Uiversity, Nanjing, China
| | - Jingbao Liu
- Department of Endocrinology, Zhongda Hospital, Institute of Diabetes, School of Medicine, Southeast Uiversity, Nanjing, China
| | - Min Cai
- Department of Endocrinology, Zhongda Hospital, Institute of Diabetes, School of Medicine, Southeast Uiversity, Nanjing, China
| | - Zhiyao Wang
- Suzhou MetroHealth Medical Technology, Co., LTD, Suzhou, China
| | - Lin Guo
- Suzhou MetroHealth Medical Technology, Co., LTD, Suzhou, China
| | - Fangqun Chen
- Suzhou MetroHealth Medical Technology, Co., LTD, Suzhou, China
| | - Bei Wang
- School of Public Health, Southeast University, Nanjing, China
| | - Sandberg Monica
- Department of Medical Cell Biology, Uppsala University, SE-75123 Uppsala, Sweden
| | - Per-Ola Carlsson
- Department of Medical Cell Biology, Uppsala University, SE-75123 Uppsala, Sweden.
| | - Zilin Sun
- Department of Endocrinology, Zhongda Hospital, Institute of Diabetes, School of Medicine, Southeast Uiversity, Nanjing, China.
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19
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Félix-Martínez GJ, Godínez-Fernández JR. Screening models for undiagnosed diabetes in Mexican adults using clinical and self-reported information. ACTA ACUST UNITED AC 2018; 65:603-610. [PMID: 29945768 DOI: 10.1016/j.endinu.2018.04.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2017] [Revised: 04/09/2018] [Accepted: 04/13/2018] [Indexed: 01/17/2023]
Abstract
BACKGROUND Prevalence of diabetes in Mexico has constantly increased since 1993. Since type 2 diabetes may remain undiagnosed for many years, identification of subjects at high risk of diabetes is very important to reduce its impact and to prevent its associated complications. OBJECTIVE To develop easily implementable screening models to identify subjects with undiagnosed diabetes based on the characteristics of Mexican adults. SUBJECTS AND METHODS Screening models were developed using datasets from the 2006 and 2012 National Health and Nutrition Surveys (NHNS). Variables used to develop the multivariate logistic regression models were selected using a backward stepwise procedure. Final models were validated using data from the 2000 National Health Survey (NHS). RESULTS The model based on the 2006 NHNS included age, waist circumference, and systolic blood pressure as explanatory variables, while the model based on the 2012 NHNS included age, waist circumference, height, and family history of diabetes. The sensitivity and specificity values obtained from the external validation procedure were 0.74 and 0.62 (2006 NHNS model) and 0.76 and 0.55 (2012 NHNS model) respectively. CONCLUSIONS Both models were equally capable of identifying subjects with undiagnosed diabetes (∼75%), and performed satisfactorily when compared to other models developed for other regions or countries.
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Affiliation(s)
- Gerardo J Félix-Martínez
- Department of Electrical Engineering, Universidad Autónoma Metropolitana, Iztapalapa, Ciudad de México, Mexico; Department of Applied Mathematics and Computer Sciences, Universidad de Cantabria, Santander, Cantabria, Spain.
| | - J Rafael Godínez-Fernández
- Department of Electrical Engineering, Universidad Autónoma Metropolitana, Iztapalapa, Ciudad de México, Mexico
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20
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Yatsuya H, Li Y, Hirakawa Y, Ota A, Matsunaga M, Haregot HE, Chiang C, Zhang Y, Tamakoshi K, Toyoshima H, Aoyama A. A Point System for Predicting 10-Year Risk of Developing Type 2 Diabetes Mellitus in Japanese Men: Aichi Workers' Cohort Study. J Epidemiol 2018; 28:347-352. [PMID: 29553059 PMCID: PMC6048299 DOI: 10.2188/jea.je20170048] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Background Relatively little evidence exists for type 2 diabetes mellitus (T2DM) prediction models from long-term follow-up studies in East Asians. This study aims to develop a point-based prediction model for 10-year risk of developing T2DM in middle-aged Japanese men. Methods We followed 3,540 male participants of Aichi Workers’ Cohort Study, who were aged 35–64 years and were free of diabetes in 2002, until March 31, 2015. Baseline age, body mass index (BMI), smoking status, alcohol consumption, regular exercise, medication for dyslipidemia, diabetes family history, and blood levels of triglycerides (TG), high density lipoprotein cholesterol (HDLC) and fasting blood glucose (FBG) were examined using Cox proportional hazard model. Variables significantly associated with T2DM in univariable models were simultaneously entered in a multivariable model for determination of the final model using backward variable selection. Performance of an existing T2DM model when applied to the current dataset was compared to that obtained in the present study’s model. Results During the median follow-up of 12.2 years, 342 incident T2DM cases were documented. The prediction system using points assigned to age, BMI, smoking status, diabetes family history, and TG and FBG showed reasonable discrimination (c-index: 0.77) and goodness-of-fit (Hosmer-Lemeshow test, P = 0.22). The present model outperformed the previous one in the present subjects. Conclusion The point system, once validated in the other populations, could be applied to middle-aged Japanese male workers to identify those at high risk of developing T2DM. In addition, further investigation is also required to examine whether the use of this system will reduce incidence.
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Affiliation(s)
- Hiroshi Yatsuya
- Department of Public Health, Fujita Health University School of Medicine.,Department of Public Health and Health Systems, Nagoya University Graduate School of Medicine
| | - Yuanying Li
- Department of Public Health, Fujita Health University School of Medicine
| | - Yoshihisa Hirakawa
- Department of Public Health and Health Systems, Nagoya University Graduate School of Medicine
| | - Atsuhiko Ota
- Department of Public Health, Fujita Health University School of Medicine
| | - Masaaki Matsunaga
- Department of Public Health, Fujita Health University School of Medicine
| | - Hilawe Esayas Haregot
- Department of Public Health and Health Systems, Nagoya University Graduate School of Medicine
| | - Chifa Chiang
- Department of Public Health and Health Systems, Nagoya University Graduate School of Medicine
| | - Yan Zhang
- Department of Public Health and Health Systems, Nagoya University Graduate School of Medicine
| | - Koji Tamakoshi
- Department of Nursing, Nagoya University School of Health Science
| | - Hideaki Toyoshima
- Education and Clinical Research Training Center, Anjo Kosei Hospital
| | - Atsuko Aoyama
- Department of Public Health and Health Systems, Nagoya University Graduate School of Medicine
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21
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Han X, Wang J, Li Y, Hu H, Li X, Yuan J, Yao P, Miao X, Wei S, Wang Y, Liang Y, Zhang X, Guo H, Pan A, Yang H, Wu T, He M. Development of a new scoring system to predict 5-year incident diabetes risk in middle-aged and older Chinese. Acta Diabetol 2018; 55:13-19. [PMID: 28918462 DOI: 10.1007/s00592-017-1047-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/29/2017] [Accepted: 09/02/2017] [Indexed: 01/19/2023]
Abstract
AIMS The aim of this study was to develop a new risk score system to predict 5-year incident diabetes risk among middle-aged and older Chinese population. METHODS This prospective study included 17,690 individuals derived from the Dongfeng-Tongji cohort. Participants were recruited in 2008 and were followed until October 2013. Incident diabetes was defined as self-reported clinician diagnosed diabetes, fasting glucose ≥7.0 mmol/l, or the use of insulin or oral hypoglycemic agent. A total of 1390 incident diabetic cases were diagnosed during the follow-up period. β-Coefficients were derived from Cox proportional hazard regression model and were used to calculate the risk score. RESULTS The diabetes risk score includes BMI, fasting glucose, hypertension, hyperlipidemia, current smoking status, and family history of diabetes. The β-coefficients of these variables ranged from 0.139 to 1.914, and the optimal cutoff value was 1.5. The diabetes risk score was calculated by multiplying the β-coefficients of the significant variables by 10 and rounding to the nearest integer. The score ranges from 0 to 36. The area under the receiver operating curve of the score was 0.751. At the optimal cutoff value of 15, the sensitivity and specificity were 65.6 and 72.9%, respectively. Based upon these risk factors, this model had the highest discrimination compared with several commonly used diabetes prediction models. CONCLUSIONS The newly established diabetes risk score with six parameters appears to be a reliable screening tool to predict 5-year risk of incident diabetes in a middle-aged and older Chinese population.
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Affiliation(s)
- Xu Han
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd., Wuhan, 430030, Hubei, China
| | - Jing Wang
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd., Wuhan, 430030, Hubei, China
| | - Yaru Li
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd., Wuhan, 430030, Hubei, China
| | - Hua Hu
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd., Wuhan, 430030, Hubei, China
| | - Xiulou Li
- Dongfeng Central Hospital, Dongfeng Motor Corporation and Hubei University of Medicine, Shiyan, Hubei, China
| | - Jing Yuan
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd., Wuhan, 430030, Hubei, China
| | - Ping Yao
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd., Wuhan, 430030, Hubei, China
| | - Xiaoping Miao
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd., Wuhan, 430030, Hubei, China
| | - Sheng Wei
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd., Wuhan, 430030, Hubei, China
| | - Youjie Wang
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd., Wuhan, 430030, Hubei, China
| | - Yuan Liang
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd., Wuhan, 430030, Hubei, China
| | - Xiaomin Zhang
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd., Wuhan, 430030, Hubei, China
| | - Huan Guo
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd., Wuhan, 430030, Hubei, China
| | - An Pan
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd., Wuhan, 430030, Hubei, China
| | - Handong Yang
- Dongfeng Central Hospital, Dongfeng Motor Corporation and Hubei University of Medicine, Shiyan, Hubei, China
| | - Tangchun Wu
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd., Wuhan, 430030, Hubei, China
| | - Meian He
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd., Wuhan, 430030, Hubei, China.
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22
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Xue Y, Lv Y, Tang Z, Dong J. Analysis of a Screening System for Diabetic Cardiovascular Autonomic Neuropathy in China. Med Sci Monit 2017; 23:5354-5362. [PMID: 29125834 PMCID: PMC5694192 DOI: 10.12659/msm.905240] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022] Open
Abstract
Background The aim of this study was to create a screening system for diabetic cardiovascular autonomic neuropathy (DCAN) in diabetic patients. Material/Methods A Chinese cohort of 455 diabetic participants was recruited between 2011 and 2013. Short-term heart rate variability testing was used to evaluate cardiovascular autonomic function. A simple model was developed using multiple variable regression to include only significant risk factors that were simple and easily assessed. A DCAN score was determined based on the coefficients of the multiple variable model. This score was tested on the entire cohort of 455 diabetic patients and another independent, external cohort of 115 diabetic patients. Results The screening system consisted of age, body mass index, duration of diabetes mellitus, and resting heart rate, and these factors were significantly (P<0.05) associated with DCAN. Receiver operating characteristic (ROC) curve analysis was done. The areas under the ROC curve were 0.798, 0.756, and 0.729 for the total sample, validation cohort, and external set, respectively. A cutoff DCAN score of 12 out of 25 produced optimal results for sensitivity (80.36%), specificity (58.27%), and percentage of patients that needed subsequent testing (43.55%) for the validation set. Conclusions The study concludes that a simple and practical DCAN screening can be applied for early intervention to delay or prevent the disease in the Chinese population.
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Affiliation(s)
- Ying Xue
- Department of Integrative Medicine, Huashan Hospital, Fudan University, Shanghai, China (mainland).,Department of Endocrinology and Metabolism, Shanghai Tongji Hospital, Tongji University School of Medicine, Shanghai, China (mainland)
| | - Yubao Lv
- Department of Integrative Medicine, Huashan Hospital of Fudan University, Shanghai, China (mainland).,Institutes of Integrative Medicine, Fudan University, Shanghai, China (mainland)
| | - Zihui Tang
- Department of Integrative Medicine, Huashan Hospital of Fudan University, Shanghai, China (mainland).,Institutes of Integrative Medicine, Fudan University, Shanghai, China (mainland)
| | - Jingcheng Dong
- Department of Integrative Medicine, Huashan Hospital of Fudan University, Shanghai, China (mainland).,Institutes of Integrative Medicine, Fudan University, Shanghai, China (mainland)
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23
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Adua E, Roberts P, Wang W. Incorporation of suboptimal health status as a potential risk assessment for type II diabetes mellitus: a case-control study in a Ghanaian population. EPMA J 2017; 8:345-355. [PMID: 29209438 DOI: 10.1007/s13167-017-0119-1] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2017] [Accepted: 09/28/2017] [Indexed: 02/07/2023]
Abstract
Due to a paradigm shift in lifestyles, there is growing concern that type 2 diabetes mellitus (T2DM) will reach epidemic proportions in Ghana. However, specific characteristics of the disease are under explored in this region. More challenging are those yet to be diagnosed or who complain of poor health in the absence of a diagnosed disease-suboptimal health status (SHS). We conducted a study to examine various factors that characterise SHS and T2DM. Using a cross-sectional design, we recruited 264 people as controls and 241 T2DM patients from January to June 2016. The controls were categorised into high and low SHS based on how they rated on an SHS questionnaire-25 (SHSQ-25). Anthropometric and biochemical parameters: body mass index (BMI); blood pressure (BP); fasting plasma glucose (FPG); glycated haemoglobin (HbA1c); serum lipids [(total cholesterol, triglycerides (TG), high- and low-density lipoprotein-cholesterol (HDL-c and LDL-c)] were measured. The male to female ratio for T2DM and controls were 99:142 and 98:166, respectively, whilst the mean ages were 55.89 and 51.52 years. Compared to controls, T2DM patients had higher FPG (8.96 ± 4.18 vs. 6.08 ± 1.79; p < 0.0001) and HbA1c (8.23 ± 2.09 vs. 5.45 ± 1.00; p < 0.0001). Primarily sedentary [adjusted odds ratio (aOR) = 2.97 (1.38-6.39); p = 0.034)], systolic blood pressure (SBP) (p = 0.001) and diastolic blood pressure (DBP) (p = 0.001) significantly correlated with high SHS. After adjusting for age and gender, central adiposity [aOR = 1.74 (1.06-2.83); p = 0.027)], underweight [aOR = 5.82 (1.23-27.52); p = 0.018)], high SBP [aOR = 1.86 (1.14-3.05); p = 0.012)], high DBP [aOR = 2.39 (1.40-4.07); p = 0.001)] and high TG [aOR = 2.17 (1.09-4.33); p = 0.029)] were found to be independent risk factors associated with high SHS. The management of T2DM in Ghana is suboptimal and undiagnosed risk factors remain prevalent. The SHSQ-25 can be translated and applied as a practical tool to screen at-risk individuals and hence prove useful for the purpose of predictive, preventive and personalised medicine.
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Affiliation(s)
- Eric Adua
- School of Medical and Health Sciences, Edith Cowan University, 270 Joondalup Drive, Perth, WA 6027 Australia
| | - Peter Roberts
- School of Medical and Health Sciences, Edith Cowan University, 270 Joondalup Drive, Perth, WA 6027 Australia
| | - Wei Wang
- School of Medical and Health Sciences, Edith Cowan University, 270 Joondalup Drive, Perth, WA 6027 Australia.,Beijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, Beijing, 100069 China.,School of Public Health, Taishan Medical University, Taian, Shandong 271000 China
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24
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Chen X, Wu Z, Chen Y, Wang X, Zhu J, Wang N, Jiang Q, Fu C. Risk score model of type 2 diabetes prediction for rural Chinese adults: the Rural Deqing Cohort Study. J Endocrinol Invest 2017; 40:1115-1123. [PMID: 28474301 DOI: 10.1007/s40618-017-0680-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2016] [Accepted: 04/26/2017] [Indexed: 01/05/2023]
Abstract
OBJECTIVE Risk score (RS) model is a cost-effective tool to identify adults who are at high risk for diabetes. This study was to develop an RS model of type 2 diabetes (T2DM) prediction specifically for rural Chinese adults. METHODS A prospective whole cohort study (n = 28,251) and a sub-cohort study (n = 3043) were conducted from 2006-2014 and 2006-2008 to 2015 in rural Deqing, China. All participants were free of T2DM at baseline. Incident T2DM cases were identified through electronic health records, self-reported and fasting plasma glucose testing for the sub-cohort, respectively. RS models were constructed with coefficients (β) of Cox regression. Receiver-operating characteristic curves were plotted and the area under the curve (AUC) reflected the discriminating accuracy of an RS model. RESULTS By 2015, the incidence of T2DM was 3.3 and 7.7 per 1000 person-years in the whole cohort and the sub-cohort, respectively. Based on data from the whole cohort, the non-invasive RS model included age (4 points), overweight (2 points), obesity (4 points), family history of T2DM (3 points), meat diet (3 points), and hypertension (2 points). The plus-fasting plasma glucose (FPG) model added impaired fasting glucose (4 points). The AUC was 0.705 with a positive predictive value of 2.5% for the non-invasive model, and for the plus-FPG model the AUC was 0.754 with a positive predictive value of 2.5%. These models performed better as compared with 12 existing RS models for the study population. CONCLUSIONS Our non-invasive RS model can be used to identify individuals who are at high risk of T2DM as a simple, fast, and cost-effective tool for rural Chinese adults.
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Affiliation(s)
- X Chen
- School of Public Health, Key Laboratory of Public Health Safety and Pudong Institute of Preventive Medicine, Fudan University, Shanghai, 200032, China
| | - Z Wu
- School of Public Health, Key Laboratory of Public Health Safety and Pudong Institute of Preventive Medicine, Fudan University, Shanghai, 200032, China
| | - Y Chen
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada
| | - X Wang
- Deqing County Center of Disease Prevention and Control, Huzhou, Zhejiang, China
| | - J Zhu
- Deqing County Center of Disease Prevention and Control, Huzhou, Zhejiang, China
| | - N Wang
- School of Public Health, Key Laboratory of Public Health Safety and Pudong Institute of Preventive Medicine, Fudan University, Shanghai, 200032, China
| | - Q Jiang
- School of Public Health, Key Laboratory of Public Health Safety and Pudong Institute of Preventive Medicine, Fudan University, Shanghai, 200032, China
| | - C Fu
- School of Public Health, Key Laboratory of Public Health Safety and Pudong Institute of Preventive Medicine, Fudan University, Shanghai, 200032, China.
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25
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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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26
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Zheng L, Wang Y, Hao S, Shin AY, Jin B, Ngo AD, Jackson-Browne MS, Feller DJ, Fu T, Zhang K, Zhou X, Zhu C, Dai D, Yu Y, Zheng G, Li YM, McElhinney DB, Culver DS, Alfreds ST, Stearns F, Sylvester KG, Widen E, Ling XB. Web-based Real-Time Case Finding for the Population Health Management of Patients With Diabetes Mellitus: A Prospective Validation of the Natural Language Processing-Based Algorithm With Statewide Electronic Medical Records. JMIR Med Inform 2016; 4:e37. [PMID: 27836816 PMCID: PMC5124114 DOI: 10.2196/medinform.6328] [Citation(s) in RCA: 44] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2016] [Revised: 10/01/2016] [Accepted: 10/12/2016] [Indexed: 02/06/2023] Open
Abstract
Background Diabetes case finding based on structured medical records does not fully identify diabetic patients whose medical histories related to diabetes are available in the form of free text. Manual chart reviews have been used but involve high labor costs and long latency. Objective This study developed and tested a Web-based diabetes case finding algorithm using both structured and unstructured electronic medical records (EMRs). Methods This study was based on the health information exchange (HIE) EMR database that covers almost all health facilities in the state of Maine, United States. Using narrative clinical notes, a Web-based natural language processing (NLP) case finding algorithm was retrospectively (July 1, 2012, to June 30, 2013) developed with a random subset of HIE-associated facilities, which was then blind tested with the remaining facilities. The NLP-based algorithm was subsequently integrated into the HIE database and validated prospectively (July 1, 2013, to June 30, 2014). Results Of the 935,891 patients in the prospective cohort, 64,168 diabetes cases were identified using diagnosis codes alone. Our NLP-based case finding algorithm prospectively found an additional 5756 uncodified cases (5756/64,168, 8.97% increase) with a positive predictive value of .90. Of the 21,720 diabetic patients identified by both methods, 6616 patients (6616/21,720, 30.46%) were identified by the NLP-based algorithm before a diabetes diagnosis was noted in the structured EMR (mean time difference = 48 days). Conclusions The online NLP algorithm was effective in identifying uncodified diabetes cases in real time, leading to a significant improvement in diabetes case finding. The successful integration of the NLP-based case finding algorithm into the Maine HIE database indicates a strong potential for application of this novel method to achieve a more complete ascertainment of diagnoses of diabetes mellitus.
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Affiliation(s)
- Le Zheng
- Tsinghua University, Beijing, China.,Stanford University, Stanford, CA, United States
| | - Yue Wang
- Stanford University, Stanford, CA, United States.,Zhejiang University, Hangzhou, China
| | - Shiying Hao
- Stanford University, Stanford, CA, United States
| | | | - Bo Jin
- HBI Solutions Inc, Palo Alto, CA, United States
| | - Anh D Ngo
- HBI Solutions Inc, Palo Alto, CA, United States
| | | | | | - Tianyun Fu
- HBI Solutions Inc, Palo Alto, CA, United States
| | - Karena Zhang
- Stanford University, Stanford, CA, United States
| | - Xin Zhou
- Tianjin Key Laboratory of Cardiovascular Remodeling and Target Organ Injury, Pingjin Hospital Heart Center, Tianjin, China
| | | | - Dorothy Dai
- HBI Solutions Inc, Palo Alto, CA, United States
| | - Yunxian Yu
- School of Medicine, Zhejiang University, Hangzhou, China
| | | | - Yu-Ming Li
- Tianjin Key Laboratory of Cardiovascular Remodeling and Target Organ Injury, Pingjin Hospital Heart Center, Tianjin, China
| | | | | | | | | | | | - Eric Widen
- HBI Solutions Inc, Palo Alto, CA, United States
| | - Xuefeng Bruce Ling
- Stanford University, Stanford, CA, United States.,School of Medicine, Zhejiang University, Hangzhou, China
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Fu S, Ping P, Luo L, Ye P. Deep analyses of the associations of a series of biomarkers with insulin resistance, metabolic syndrome, and diabetes risk in nondiabetic middle-aged and elderly individuals: results from a Chinese community-based study. Clin Interv Aging 2016; 11:1531-1538. [PMID: 27822025 PMCID: PMC5094606 DOI: 10.2147/cia.s109583] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
OBJECTIVE The current study was designed to perform deep analyses of the associations of biomarkers, including high-sensitivity C-reactive protein (hs-CRP), N-terminal prohormone of brain natriuretic peptide (NT-proBNP), and homocysteine (Hcy), with insulin resistance (IR), metabolic syndrome (MetS), and diabetes risk and evaluate the abilities of biomarkers to identify IR, MetS, and diabetes risk in Chinese community-dwelling middle-aged and elderly residents. PARTICIPANTS AND METHODS A total of 396 participants older than 45 years underwent physical examinations and laboratory analyses following standardized protocol. RESULTS Serum hs-CRP concentrations were able to identify MetS, Chinese diabetes risk score (CDRS) ≥4, high-density lipoprotein-cholesterol (HDL-c) <0.9/1.0 mmol/L, and HDL-c <1.0/1.3 mmol/L (P<0.05 for all). Serum NT-proBNP concentrations were able to identify homeostasis model assessment of IR >1.5, CDRS ≥4, overweight, and blood pressure (BP) ≥140/90 mmHg (P<0.05 for all). Serum Hcy concentrations were able to identify CDRS ≥4, general obesity, overweight, and BP ≥140/90 mmHg (P<0.05 for all). Serum hs-CRP concentrations were independently associated with MetS as well as HDL-c <1.0/1.3 mmol/L and HDL-c <0.9/1.0 mmol/L (P<0.05 for all). Serum NT-proBNP concentrations were independently associated with BP ≥140/90 mmHg (P<0.05). Serum Hcy concentrations were independently associated with CDRS ≥4 (P<0.05). CONCLUSION Serum HDL-c levels were the major determinant of the associations between serum hs-CRP levels and MetS and the key link between inflammation and MetS. There was no other association of these biomarkers with IR, MetS, and diabetes risk after full adjustment.
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Affiliation(s)
- Shihui Fu
- Department of Geriatric Cardiology; Department of Cardiology and Hainan Branch
| | - Ping Ping
- Department of Pharmaceutical Care, Chinese People's Liberation Army General Hospital, Beijing, People's Republic of China
| | | | - Ping Ye
- Department of Geriatric Cardiology
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Wong CKH, Siu SC, Wan EYF, Jiao FF, Yu EYT, Fung CSC, Wong KW, Leung AYM, Lam CLK. Simple non-laboratory- and laboratory-based risk assessment algorithms and nomogram for detecting undiagnosed diabetes mellitus. J Diabetes 2016; 8:414-21. [PMID: 25952330 DOI: 10.1111/1753-0407.12310] [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: 11/05/2014] [Revised: 03/26/2015] [Accepted: 05/05/2015] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND The aim of the present study was to develop a simple nomogram that can be used to predict the risk of diabetes mellitus (DM) in the asymptomatic non-diabetic subjects based on non-laboratory- and laboratory-based risk algorithms. METHODS Anthropometric data, plasma fasting glucose, full lipid profile, exercise habits, and family history of DM were collected from Chinese non-diabetic subjects aged 18-70 years. Logistic regression analysis was performed on a random sample of 2518 subjects to construct non-laboratory- and laboratory-based risk assessment algorithms for detection of undiagnosed DM; both algorithms were validated on data of the remaining sample (n = 839). The Hosmer-Lemeshow test and area under the receiver operating characteristic (ROC) curve (AUC) were used to assess the calibration and discrimination of the DM risk algorithms. RESULTS Of 3357 subjects recruited, 271 (8.1%) had undiagnosed DM defined by fasting glucose ≥7.0 mmol/L or 2-h post-load plasma glucose ≥11.1 mmol/L after an oral glucose tolerance test. The non-laboratory-based risk algorithm, with scores ranging from 0 to 33, included age, body mass index, family history of DM, regular exercise, and uncontrolled blood pressure; the laboratory-based risk algorithm, with scores ranging from 0 to 37, added triglyceride level to the risk factors. Both algorithms demonstrated acceptable calibration (Hosmer-Lemeshow test: P = 0.229 and P = 0.483) and discrimination (AUC 0.709 and 0.711) for detection of undiagnosed DM. CONCLUSION A simple-to-use nomogram for detecting undiagnosed DM has been developed using validated non-laboratory-based and laboratory-based risk algorithms.
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Affiliation(s)
- Carlos K H Wong
- Department of Family Medicine and Primary Care, The University of Hong Kong, Hong Kong
| | - Shing-Chung Siu
- Department of Medicine and Rehabilitation, Tung Wah Eastern Hospital, Hong Kong
| | - Eric Y F Wan
- Department of Family Medicine and Primary Care, The University of Hong Kong, Hong Kong
| | - Fang-Fang Jiao
- Department of Family Medicine and Primary Care, The University of Hong Kong, Hong Kong
| | - Esther Y T Yu
- Department of Family Medicine and Primary Care, The University of Hong Kong, Hong Kong
| | - Colman S C Fung
- Department of Family Medicine and Primary Care, The University of Hong Kong, Hong Kong
| | - Ka-Wai Wong
- Department of Medicine and Rehabilitation, Tung Wah Eastern Hospital, Hong Kong
| | | | - Cindy L K Lam
- Department of Family Medicine and Primary Care, The University of Hong Kong, Hong Kong
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Development of Risk Score for Predicting 3-Year Incidence of Type 2 Diabetes: Japan Epidemiology Collaboration on Occupational Health Study. PLoS One 2015; 10:e0142779. [PMID: 26558900 PMCID: PMC4641714 DOI: 10.1371/journal.pone.0142779] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2014] [Accepted: 10/27/2015] [Indexed: 11/18/2022] Open
Abstract
OBJECTIVE Risk models and scores have been developed to predict incidence of type 2 diabetes in Western populations, but their performance may differ when applied to non-Western populations. We developed and validated a risk score for predicting 3-year incidence of type 2 diabetes in a Japanese population. METHODS Participants were 37,416 men and women, aged 30 or older, who received periodic health checkup in 2008-2009 in eight companies. Diabetes was defined as fasting plasma glucose (FPG) ≥ 126 mg/dl, random plasma glucose ≥ 200 mg/dl, glycated hemoglobin (HbA1c) ≥ 6.5%, or receiving medical treatment for diabetes. Risk scores on non-invasive and invasive models including FPG and HbA1c were developed using logistic regression in a derivation cohort and validated in the remaining cohort. RESULTS The area under the curve (AUC) for the non-invasive model including age, sex, body mass index, waist circumference, hypertension, and smoking status was 0.717 (95% CI, 0.703-0.731). In the invasive model in which both FPG and HbA1c were added to the non-invasive model, AUC was increased to 0.893 (95% CI, 0.883-0.902). When the risk scores were applied to the validation cohort, AUCs (95% CI) for the non-invasive and invasive model were 0.734 (0.715-0.753) and 0.882 (0.868-0.895), respectively. Participants with a non-invasive score of ≥ 15 and invasive score of ≥ 19 were projected to have >20% and >50% risk, respectively, of developing type 2 diabetes within 3 years. CONCLUSIONS The simple risk score of the non-invasive model might be useful for predicting incident type 2 diabetes, and its predictive performance may be markedly improved by incorporating FPG and HbA1c.
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Masconi KL, Echouffo-Tcheugui JB, Matsha TE, Erasmus RT, Kengne AP. Predictive modeling for incident and prevalent diabetes risk evaluation. Expert Rev Endocrinol Metab 2015; 10:277-284. [PMID: 30298773 DOI: 10.1586/17446651.2015.1015989] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
With half of individuals with diabetes undiagnosed worldwide and a projected 55% increase of the population with diabetes by 2035, the identification of undiagnosed and high-risk individuals is imperative. Multivariable diabetes risk prediction models have gained popularity during the past two decades. These have been shown to predict incident or prevalent diabetes through a simple and affordable risk scoring system accurately. Their development requires cohort or cross-sectional type studies with a variable combination, number and definition of included risk factors, with their performance chiefly measured by discrimination and calibration. Models can be used in clinical and public health settings. However, the impact of their use on outcomes in real-world settings needs to be evaluated before widespread implementation.
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Affiliation(s)
- Katya L Masconi
- a 1 Division of Chemical Pathology, Faculty of Health Sciences, National Health Laboratory Service (NHLS) and University of Stellenbosch, Cape Town, South Africa
- b 2 Non-Communicable Diseases Research Unit, South African Medical Research Council, Cape Town, South Africa
| | - Justin Basile Echouffo-Tcheugui
- c 3 Hubert Department of Public Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
- d 4 Department of Medicine, MedStar Health System, Baltimore, MD, USA
| | - Tandi E Matsha
- e 5 Department of Biomedical Technology, Faculty of Health and Wellness Sciences, Cape Peninsula University of Technology, Cape Town, South Africa
| | - Rajiv T Erasmus
- a 1 Division of Chemical Pathology, Faculty of Health Sciences, National Health Laboratory Service (NHLS) and University of Stellenbosch, Cape Town, South Africa
| | - Andre Pascal Kengne
- b 2 Non-Communicable Diseases Research Unit, South African Medical Research Council, Cape Town, South Africa
- f 6 Department of Medicine, University of Cape Town, Cape Town, South Africa
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Masconi KL, Matsha TE, Echouffo-Tcheugui JB, Erasmus RT, Kengne AP. Reporting and handling of missing data in predictive research for prevalent undiagnosed type 2 diabetes mellitus: a systematic review. EPMA J 2015; 6:7. [PMID: 25829972 PMCID: PMC4380106 DOI: 10.1186/s13167-015-0028-0] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2014] [Accepted: 02/07/2015] [Indexed: 01/10/2023]
Abstract
Missing values are common in health research and omitting participants with missing data often leads to loss of statistical power, biased estimates and, consequently, inaccurate inferences. We critically reviewed the challenges posed by missing data in medical research and approaches to address them. To achieve this more efficiently, these issues were analyzed and illustrated through a systematic review on the reporting of missing data and imputation methods (prediction of missing values through relationships within and between variables) undertaken in risk prediction studies of undiagnosed diabetes. Prevalent diabetes risk models were selected based on a recent comprehensive systematic review, supplemented by an updated search of English-language studies published between 1997 and 2014. Reporting of missing data has been limited in studies of prevalent diabetes prediction. Of the 48 articles identified, 62.5% (n = 30) did not report any information on missing data or handling techniques. In 21 (43.8%) studies, researchers opted out of imputation, completing case-wise deletion of participants missing any predictor values. Although imputation methods are encouraged to handle missing data and ensure the accuracy of inferences, this has seldom been the case in studies of diabetes risk prediction. Hence, we elaborated on the various types and patterns of missing data, the limitations of case-wise deletion and state-of the-art methods of imputations and their challenges. This review highlights the inexperience or disregard of investigators of the effect of missing data in risk prediction research. Formal guidelines may enhance the reporting and appropriate handling of missing data in scientific journals.
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Affiliation(s)
- Katya L Masconi
- Division of Chemical Pathology, Faculty of Health Sciences, National Health Laboratory Service (NHLS) and University of Stellenbosch, Cape Town, South Africa ; Non-Communicable Diseases Research Unit, South African Medical Research Council, PO Box 19070, , Tygerberg, 7505 Cape Town, South Africa
| | - Tandi E Matsha
- Department of Biomedical Technology, Faculty of Health and Wellness Sciences, Cape Peninsula University of Technology, Cape Town, South Africa
| | - Justin B Echouffo-Tcheugui
- Hubert Department of Public Health, Rollins School of Public Health, Emory University, Atlanta, GA USA ; Department of Medicine, MedStar Health System, Baltimore, MD USA
| | - Rajiv T Erasmus
- Division of Chemical Pathology, Faculty of Health Sciences, National Health Laboratory Service (NHLS) and University of Stellenbosch, Cape Town, South Africa
| | - Andre P Kengne
- Non-Communicable Diseases Research Unit, South African Medical Research Council, PO Box 19070, , Tygerberg, 7505 Cape Town, South Africa ; Department of Medicine, University of Cape Town, Cape Town, South Africa
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Prediction of 4-year incident diabetes in older Chinese: recalibration of the Framingham diabetes score on Guangzhou Biobank Cohort Study. Prev Med 2014; 69:63-8. [PMID: 25239055 DOI: 10.1016/j.ypmed.2014.09.004] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/25/2014] [Revised: 09/04/2014] [Accepted: 09/08/2014] [Indexed: 11/23/2022]
Abstract
OBJECTIVE To recalibrate and modify the Framingham diabetes mellitus (DM) function and establish a simple point score for predicting near-term incident diabetes in a large sample of Chinese. METHODS A total of 16,043 participants aged 50years or above without diabetes at baseline from the Guangzhou Biobank Cohort Study (GBCS) were recruited from 2003 to 2008 and followed up until 31 December 2012, with an average follow-up period of 4.1years. A randomly selected sub-sample of 8000 participants was used to calculate the predictive model and the remaining sample including 8043 participants was used for validating the prediction model. RESULTS During follow-up, 5.2% (95% confidence interval (CI) 4.6-5.9) of men and 5.2% (95% CI 4.8-5.6) of women developed diabetes. A GBCS point score prediction model was constructed based on the Framingham DM function risk factors using the randomly selected sub-sample. Compared with the Framingham DM risk score (AUC 0.740, 95% CI 0.715-0.766), the GBCS point score prediction model predicted the development of diabetes well, with an AUC of 0.779 (95% CI 0.756-0.801, P for comparison <0.001). Validation analysis showed that the new GBCS function had satisfactory predictive ability for actual DM incidence and improved the calibration substantially. The original Framingham DM score underestimated diabetes incidence in the GBCS sample. CONCLUSIONS The constructed GBCS point score prediction model based on GBCS coefficients could be more useful for identifying high risk individuals in Chinese populations than the original Framingham DM score.
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Mustafina SV, Simonova GI, Rymar OD. Comparative characteristics of diabetes risk scores. DIABETES MELLITUS 2014. [DOI: 10.14341/dm2014317-22] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
The worldwide prevalence of diabetes among adults (aged 20?79 years) was 8.35% in 2013, and this is expected to increase by 55% (592 million adults) by 2035. To avoid the increase in the prevalence of diabetes, primary prevention and early diagnosis of prediabetes are required. It is important to identify individuals at a high risk of hyperglycaemia using inexpensive and available methods. At present, risk score is an alternative to identify the risk of developing diabetes. There are approximately 10 types of risk scores in the world, and further research for the development and adaptation of risk scores for various populations are being conducted. The use of risk score methods for prediction allows the setting of the level of total risk, identification of high-risk patients and prescription of necessary preventive measures. Actual validation of existing diabetes risk score for the Russian population is being conducted. Assessment of the risk of diabetes is simple, fast, inexpensive, non-invasive and reliable.
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Ye X, Zong G, Liu X, Liu G, Gan W, Zhu J, Lu L, Sun L, Li H, Hu FB, Lin X. Development of a new risk score for incident type 2 diabetes using updated diagnostic criteria in middle-aged and older chinese. PLoS One 2014; 9:e97042. [PMID: 24819157 PMCID: PMC4018395 DOI: 10.1371/journal.pone.0097042] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2014] [Accepted: 04/14/2014] [Indexed: 01/19/2023] Open
Abstract
Type 2 diabetes mellitus (T2DM) reaches an epidemic proportion among adults in China. However, no simple score has been created for the prediction of T2DM incidence diagnosed by updated criteria with hemoglobin A1c (HbA1c) ≥6.5% included in Chinese. In a 6-year follow-up cohort in Beijing and Shanghai, China, we recruited a total of 2529 adults aged 50–70 years in 2005 and followed them up in 2011. Fasting plasma glucose (FPG), HbA1c, and C-reactive protein (CRP) were measured and incident diabetes was identified by the recently updated criteria. Of the 1912 participants without T2DM at baseline, 924 were identified as having T2DM at follow-up, and most of them (72.4%) were diagnosed using the HbA1c criterion. Baseline body mass index, FPG, HbA1c, CRP, hypertension, and female gender were all significantly associated with incident T2DM. Based upon these risk factors, a simple score was developed with an estimated area under the receiver operating characteristic curve of 0.714 (95% confidence interval: 0.691, 0.737), which performed better than most of existing risk score models developed for eastern Asian populations. This simple, newly constructed score of six parameters may be useful in predicting T2DM in middle-aged and older Chinese.
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Affiliation(s)
- Xingwang Ye
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and University of Chinese Academy of Sciences, Shanghai, China
- SIBS-Novo Nordisk Translational Research Centre for PreDiabetes, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Geng Zong
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and University of Chinese Academy of Sciences, Shanghai, China
| | - Xin Liu
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and University of Chinese Academy of Sciences, Shanghai, China
| | - Gang Liu
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and University of Chinese Academy of Sciences, Shanghai, China
| | - Wei Gan
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and University of Chinese Academy of Sciences, Shanghai, China
| | - Jingwen Zhu
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and University of Chinese Academy of Sciences, Shanghai, China
| | - Ling Lu
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and University of Chinese Academy of Sciences, Shanghai, China
| | - Liang Sun
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and University of Chinese Academy of Sciences, Shanghai, China
- SIBS-Novo Nordisk Translational Research Centre for PreDiabetes, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Huaixing Li
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and University of Chinese Academy of Sciences, Shanghai, China
| | - Frank B. Hu
- Departments of Nutrition and Epidemiology, Harvard School of Public Health, Boston, Massachusetts, United States of America
| | - Xu Lin
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and University of Chinese Academy of Sciences, Shanghai, China
- * E-mail:
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Fu Q, Sun M, Tang W, Wang Z, Cao M, Zhu Z, Lu L, Bi Y, Ning G, Yang T. A Chinese risk score model for identifying postprandial hyperglycemia without oral glucose tolerance test. Diabetes Metab Res Rev 2014; 30:284-90. [PMID: 24154991 DOI: 10.1002/dmrr.2490] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2013] [Revised: 09/27/2013] [Accepted: 10/16/2013] [Indexed: 11/06/2022]
Abstract
BACKGROUND The aim of the study is to develop a risk score model for identifying postprandial hyperglycemia without oral glucose tolerance tests (OGTT) in Chinese population, and minimize the number of subjects needing further OGTT. METHODS Multivariable stepwise logistic regression was used to develop risk score models in a derivation cohort (7953 participants without known diabetes). The developed models were verified in a validation cohort (another 1455 subjects without known diabetes). All subjects had completed questionnaires, physical examination and OGTT. Performances of the risk score models were estimated using receiver operating characteristic curves. RESULTS Two risk score models for screening postprandial hyperglycemia were developed. The simple model used non-invasive risk factors (age, height, weight, waist, systolic blood pressure, pulse, hypertension, dyslipidemia and family history of diabetes mellitus), and the full model contained additional variables [fasting blood glucose (FBG), triglyceride/high density lipoprotein cholesterol] obtainable by invasive laboratory tests. The area under receiver operating characteristic curve (AUC) of simple model was similar to FBG and glycated haemoglobin. The full model has the largest AUC [0.799 (0.789-0.809) and 0.730 (0.702-0.758)] in both derivation and validation cohorts (p < 0.001 compared with simple model, FBG, and glycated haemoglobin). At a cutoff point of 80, the sensitivity, specificity and percentage that needed subsequent OGTT were 75.97, 67.56 and 48.38%, respectively. CONCLUSIONS We developed a risk score model for screening postprandial hyperglycemia based on routine clinical information. It could effectively identify patients at high-risk for postprandial hyperglycemia and remarkably reduce the number of subjects requiring OGTT.
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Affiliation(s)
- Qi Fu
- Department of Endocrinology and Metabolism, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
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Cichosz SL, Johansen MD, Ejskjaer N, Hansen TK, Hejlesen OK. Improved diabetes screening using an extended predictive feature search. Diabetes Technol Ther 2014; 16:166-71. [PMID: 24224751 DOI: 10.1089/dia.2013.0255] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
BACKGROUND Screening entire populations for diabetes is not cost-effective. Hence, an efficient screening process must select those people who are at high risk for diabetes. In this study, we investigated whether screening procedures could be improved using an extended predictive feature search. MATERIALS AND METHODS In order to develop our model and identify persons with diabetes (prevalence) we used data from years of the National Health and Nutrition Examination Survey (2005-2010), which has not been explored for this purpose before. We calculated all combinations of predictors in order to identify the optimal subset, and we used a linear logistic classification model to predict diabetes. V-fold cross-validation was used for the process of including variables and for validating the final models. This new model was compared with two established models. RESULTS In total, 5,398 participants were included in this study. Among these, 478 participants had unidentified diabetes. The established models had a receiver operating characteristics curve for the area under the curve (AUC) of 0.74 and 0.71 compared with an AUC of 0.78 for the new model, showing a significant difference (P<0.05). A proposed cutoff point for the established models yielded respective sensitivities/specificities of 63%/72% and 40%/72% compared with the new model, which had a sensitivity/specificity of 70%/72%. CONCLUSIONS Our data indicate that simple healthcare and economic information such as ratio of family income to poverty can add value in deciding who is at risk of unknown diabetes by using extended investigations of predictor combinations.
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Affiliation(s)
- Simon Lebech Cichosz
- 1 Department of Health Science and Technology, Aalborg University , Aalborg, Denmark
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Dhippayom T, Fuangchan A, Tunpichart S, Chaiyakunapruk N. Opportunistic screening and health promotion for type 2 diabetes: an expanding public health role for the community pharmacist. J Public Health (Oxf) 2012; 35:262-9. [PMID: 22976588 DOI: 10.1093/pubmed/fds078] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Early detection to identify people at risk of diabetes is an important approach to prevent or delay type 2 diabetes. This study aimed to implement the Diabetes Prevention Program in community pharmacy using a diabetes risk prediction tool. METHODS The program was conducted in seven pharmacies in Bangkok, Thailand. Participants were the pharmacy's clients aged ≥ 35 years without the history of diabetes. A validated risk prediction tool was used to assess individuals' diabetes risk. Educational information was offered to all participants. Those with a high risk score (≥ 9 out of 17) were offered a self-check of fasting capillary blood glucose (CBG). A referral was made for those with CBG ≥ 126 mg/dl. RESULTS During a 3-month service, 397 individuals participated in the program. Nearly half of the participants (49.4%) were at a high risk of diabetes (risk score: ≥ 9). Ninety five (48.5%) of these high risk individuals undertook fasting CBG. Elevated fasting CBG (≥ 126 mg/dl) was found in 12 persons (12.7%). Overall, two patients with diabetes were identified during the provision of the program. CONCLUSIONS The Diabetes Prevention Program in community pharmacies uncovered half of the clients who were at risk of diabetes and provided an opportunity for participants to learn more about the prevention of diabetes.
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Affiliation(s)
- Teerapon Dhippayom
- Pharmaceutical Care Research Unit (PRU), Faculty of Pharmaceutical Sciences, Naresuan University, Phitsanulok, Thailand
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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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