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Shi Z, Yang L, Xu T, Jia J, Yang S, Yang B, Yang W, Yang C, Peng Y, Gu H, Liu C, Wei S. Development of a risk score for myopia: A cohort study conducted among school-aged children in China. Indian J Ophthalmol 2024; 72:S265-S272. [PMID: 38271422 DOI: 10.4103/ijo.ijo_2077_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 12/04/2023] [Indexed: 01/27/2024] Open
Abstract
PURPOSE To evaluate the myopia risk in school-aged children one year after lifting a pandemic-related lockdown and develop a tool to identify high-risk groups. METHODS In total, 38,079 children without myopia from 38 schools were included. The outcomes were myopia incidence and progression in 1 year after the COVID-19 lockdown was lifted, both obtained by the spherical equivalent refraction (SER). We separated the population into an exploratory (75%) and a validation sample (25%) to construct the risk score model. RESULTS In total, 9811 (29.57%) students became myopic, and the overall myopia progression was 0.22 ± 0.62 D. Even less myopia progression was noted in the pre-myopia group at baseline (All: P = 0.045, Boy: P = 0.005). The risk score model included seven predictors: gender, grade, SER at baseline, residence, parental myopia, eye discomfort symptoms, and online courses. The model had a score range of 0-46 and an optimal cutoff of 34. The area under the receiver operating curve of the model was 0.726 (0.719-0.732) for the exploratory sample and 0.731 (0.720-0.742) for the validation sample. CONCLUSIONS The risk score can serve as a practical tool for classifying the risk of myopia in school-aged children.
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Affiliation(s)
- Ziwei Shi
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, PR China
| | - Lihua Yang
- Wuhan Center for Adolescent Poor Vision Prevention and Control, Wuhan, Hubei, PR China
| | - Ting Xu
- Wuhan Center for Adolescent Poor Vision Prevention and Control, Wuhan, Hubei, PR China
| | - Jing Jia
- Wuhan Center for Adolescent Poor Vision Prevention and Control, Wuhan, Hubei, PR China
| | - Song Yang
- Wuhan Center for Adolescent Poor Vision Prevention and Control, Wuhan, Hubei, PR China
| | - Bo Yang
- Wuhan Center for Adolescent Poor Vision Prevention and Control, Wuhan, Hubei, PR China
| | - Wei Yang
- Wuhan Center for Adolescent Poor Vision Prevention and Control, Wuhan, Hubei, PR China
| | - Changchun Yang
- Wuhan Center for Adolescent Poor Vision Prevention and Control, Wuhan, Hubei, PR China
| | - Yan Peng
- Wuhan Center for Adolescent Poor Vision Prevention and Control, Wuhan, Hubei, PR China
| | - Hong Gu
- Wuhan Center for Adolescent Poor Vision Prevention and Control, Wuhan, Hubei, PR China
| | - Caiping Liu
- Wuhan Center for Adolescent Poor Vision Prevention and Control, Wuhan, Hubei, PR China
| | - Sheng Wei
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, PR China
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Chen H, She Y, Dai S, Wang L, Tao N, Huang S, Xu S, Lou Y, Hu F, Li L, Wang C. Predicting the Risk of Type 2 Diabetes Mellitus with the New Chinese Diabetes Risk Score in a Cohort Study. Int J Public Health 2023; 68:1605611. [PMID: 37180612 PMCID: PMC10166829 DOI: 10.3389/ijph.2023.1605611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 03/27/2023] [Indexed: 05/16/2023] Open
Abstract
Objectives: The New Chinese Diabetes Risk Score (NCDRS) is a noninvasive tool to assess the risk of type 2 diabetes mellitus (T2DM) in the Chinese population. Our study aimed to evaluate the performance of the NCDRS in predicting T2DM risk with a large cohort. Methods: The NCDRS was calculated, and participants were categorized into groups by optimal cutoff or quartiles. Hazard ratios (HRs) and 95% confidential intervals (CIs) in Cox proportional hazards models were used to estimate the association between the baseline NCDRS and the risk of T2DM. The performance of the NCDRS was assessed by the area under the curve (AUC). Results: The T2DM risk was significantly increased in participants with NCDRS ≥25 (HR = 2.12, 95% CI 1.88-2.39) compared with NCDRS <25 after adjusting for potential confounders. T2DM risk also showed a significant increasing trend from the lowest to the highest quartile of NCDRS. The AUC was 0.777 (95% CI 0.640-0.786) with a cutoff of 25.50. Conclusion: The NCDRS had a significant positive association with T2DM risk, and the NCDRS is valid for T2DM screening in China.
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Affiliation(s)
- Hongen Chen
- Department of Non-Communicable Disease Prevention and Control, Shenzhen Nanshan Center for Chronic Disease Control, Shenzhen, China
| | - Yuhang She
- Injury Prevention Research Center, Shantou University Medical College, Shantou, China
- School of Public Health, Shantou University, Shantou, China
| | - Shuhong Dai
- Department of Non-Communicable Disease Prevention and Control, Shenzhen Nanshan Center for Chronic Disease Control, Shenzhen, China
| | - Li Wang
- Department of Non-Communicable Disease Prevention and Control, Shenzhen Nanshan Center for Chronic Disease Control, Shenzhen, China
| | - Na Tao
- Department of Pharmacy, Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China
| | - Shaofen Huang
- Shenzhen Nanshan District Shekou People’s Hospital, Shenzhen, China
| | - Shan Xu
- Department of Non-Communicable Disease Prevention and Control, Shenzhen Nanshan Center for Chronic Disease Control, Shenzhen, China
| | - Yanmei Lou
- Department of Health Management, Beijing Xiao Tang Shan Hospital, Beijing, China
| | - Fulan Hu
- Department of Epidemiology and Health Statistics, School of Public Health, Shenzhen University Health Science Center, Shenzhen, China
| | - Liping Li
- Injury Prevention Research Center, Shantou University Medical College, Shantou, China
- School of Public Health, Shantou University, Shantou, China
| | - Changyi Wang
- Department of Non-Communicable Disease Prevention and Control, Shenzhen Nanshan Center for Chronic Disease Control, Shenzhen, China
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Xu S, Coleman RL, Wan Q, Gu Y, Meng G, Song K, Shi Z, Xie Q, Tuomilehto J, Holman RR, Niu K, Tong N. Risk prediction models for incident type 2 diabetes in Chinese people with intermediate hyperglycemia: a systematic literature review and external validation study. Cardiovasc Diabetol 2022; 21:182. [PMID: 36100925 PMCID: PMC9472437 DOI: 10.1186/s12933-022-01622-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 09/07/2022] [Indexed: 11/23/2022] Open
Abstract
Background People with intermediate hyperglycemia (IH), including impaired fasting glucose and/or impaired glucose tolerance, are at higher risk of developing type 2 diabetes (T2D) than those with normoglycemia. We aimed to evaluate the performance of published T2D risk prediction models in Chinese people with IH to inform them about the choice of primary diabetes prevention measures. Methods A systematic literature search was conducted to identify Asian-derived T2D risk prediction models, which were eligible if they were built on a prospective cohort of Asian adults without diabetes at baseline and utilized routinely-available variables to predict future risk of T2D. These Asian-derived and five prespecified non-Asian derived T2D risk prediction models were divided into BASIC (clinical variables only) and EXTENDED (plus laboratory variables) versions, with validation performed on them in three prospective Chinese IH cohorts: ACE (n = 3241), Luzhou (n = 1333), and TCLSIH (n = 1702). Model performance was assessed in terms of discrimination (C-statistic) and calibration (Hosmer–Lemeshow test). Results Forty-four Asian and five non-Asian studies comprising 21 BASIC and 46 EXTENDED T2D risk prediction models for validation were identified. The majority were at high (n = 43, 87.8%) or unclear (n = 3, 6.1%) risk of bias, while only three studies (6.1%) were scored at low risk of bias. BASIC models showed poor-to-moderate discrimination with C-statistics 0.52–0.60, 0.50–0.59, and 0.50–0.64 in the ACE, Luzhou, and TCLSIH cohorts respectively. EXTENDED models showed poor-to-acceptable discrimination with C-statistics 0.54–0.73, 0.52–0.67, and 0.59–0.78 respectively. Fifteen BASIC and 40 EXTENDED models showed poor calibration (P < 0.05), overpredicting or underestimating the observed diabetes risk. Most recalibrated models showed improved calibration but modestly-to-severely overestimated diabetes risk in the three cohorts. The NAVIGATOR model showed the best discrimination in the three cohorts but had poor calibration (P < 0.05). Conclusions In Chinese people with IH, previously published BASIC models to predict T2D did not exhibit good discrimination or calibration. Several EXTENDED models performed better, but a robust Chinese T2D risk prediction tool in people with IH remains a major unmet need. Supplementary Information The online version contains supplementary material available at 10.1186/s12933-022-01622-5.
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Affiliation(s)
- Shishi Xu
- Division of Endocrinology and Metabolism, Center for Diabetes and Metabolism Research, Laboratory of Diabetes and Islet Transplantation Research, West China Medical School, West China Hospital, Sichuan University, Guo Xue Lane 37, Chengdu, China.,Diabetes Trials Unit, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Ruth L Coleman
- Diabetes Trials Unit, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Qin Wan
- Department of Endocrine and Metabolic Diseases, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Yeqing Gu
- Nutrition and Radiation Epidemiology Research Center, Institute of Radiation Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Ge Meng
- Nutritional Epidemiology Institute and School of Public Health, Tianjin Medical University, Tianjin, China
| | - Kun Song
- Health Management Centre, Tianjin Medical University General Hospital, Tianjin, China
| | - Zumin Shi
- Human Nutrition Department, College of Health Sciences, QU Health, Qatar University, Doha, Qatar
| | - Qian Xie
- Department of General Practice, People's Hospital of LeShan, LeShan, China
| | - Jaakko Tuomilehto
- Department of Public Health, University of Helsinki, Helsinki, Finland.,Population Health Unit, Finnish Institute for Health and Welfare, Helsinki, Finland.,Saudi Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Rury R Holman
- Diabetes Trials Unit, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Kaijun Niu
- Nutrition and Radiation Epidemiology Research Center, Institute of Radiation Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China. .,Nutritional Epidemiology Institute and School of Public Health, Tianjin Medical University, Tianjin, China.
| | - Nanwei Tong
- Division of Endocrinology and Metabolism, Center for Diabetes and Metabolism Research, Laboratory of Diabetes and Islet Transplantation Research, West China Medical School, West China Hospital, Sichuan University, Guo Xue Lane 37, Chengdu, China.
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4
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Dong W, Cheng WHG, Tse ETY, Mi Y, Wong CKH, Tang EHM, Yu EYT, Chin WY, Bedford LE, Ko WWK, Chao DVK, Tan KCB, Lam CLK. Development and validation of a diabetes mellitus and prediabetes risk prediction function for case finding in primary care in Hong Kong: a cross-sectional study and a prospective study protocol paper. BMJ Open 2022; 12:e059430. [PMID: 35613775 PMCID: PMC9131118 DOI: 10.1136/bmjopen-2021-059430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
Abstract
INTRODUCTION Diabetes mellitus (DM) is a major non-communicable disease with an increasing prevalence. Undiagnosed DM is not uncommon and can lead to severe complications and mortality. Identifying high-risk individuals at an earlier disease stage, that is, pre-diabetes (pre-DM), is crucial in delaying progression. Existing risk models mainly rely on non-modifiable factors to predict only the DM risk, and few apply to Chinese people. This study aims to develop and validate a risk prediction function that incorporates modifiable lifestyle factors to detect DM and pre-DM in Chinese adults in primary care. METHODS AND ANALYSIS A cross-sectional study to develop DM/Pre-DM risk prediction functions using data from the Hong Kong's Population Health Survey (PHS) 2014/2015 and a 12-month prospective study to validate the functions in case finding of individuals with DM/pre-DM. Data of 1857 Chinese adults without self-reported DM/Pre-DM will be extracted from the PHS 2014/2015 to develop DM/Pre-DM risk models using logistic regression and machine learning methods. 1014 Chinese adults without a known history of DM/Pre-DM will be recruited from public and private primary care clinics in Hong Kong. They will complete a questionnaire on relevant risk factors and blood tests on Oral Glucose Tolerance Test (OGTT) and haemoglobin A1C (HbA1c) on recruitment and, if the first blood test is negative, at 12 months. A positive case is DM/pre-DM defined by OGTT or HbA1c in any blood test. Area under receiver operating characteristic curve, sensitivity, specificity, positive predictive value and negative predictive value of the models in detecting DM/pre-DM will be calculated. ETHICS AND DISSEMINATION Ethics approval has been received from The University of Hong Kong/Hong Kong Hospital Authority Hong Kong West Cluster (UW19-831) and Hong Kong Hospital Authority Kowloon Central/Kowloon East Cluster (REC(KC/KE)-21-0042/ER-3). The study results will be submitted for publication in a peer-reviewed journal. TRIAL REGISTRATION NUMBER US ClinicalTrial.gov: NCT04881383; HKU clinical trials registry: HKUCTR-2808; Pre-results.
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Affiliation(s)
- Weinan Dong
- Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Will Ho Gi Cheng
- Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Emily Tsui Yee Tse
- Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
- Department of Family Medicine, The University of Hong Kong Shenzhen Hospital, Shenzhen, People's Republic of China
| | - Yuqi Mi
- Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Carlos King Ho Wong
- Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
- Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Eric Ho Man Tang
- Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Esther Yee Tak Yu
- Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Weng Yee Chin
- Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Laura Elizabeth Bedford
- Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Welchie Wai Kit Ko
- Family Medicine and Primary Healthcare Department, Queen Mary Hospital, Hong Kong West Cluster, Hospital Authority, Hong Kong, People's Republic of China
| | - David Vai Kiong Chao
- Department of Family Medicine & Primary Health Care, United Christian Hospital, Kowloon East Cluster, Hospital Authority, Hong Kong, People's Republic of China
- Department of Family Medicine & Primary Health Care, Tseung Kwan O Hospital, Kowloon East Cluster, Hospital Authority, Hong Kong, People's Republic of China
| | - Kathryn Choon Beng Tan
- Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Cindy Lo Kuen Lam
- Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
- Department of Family Medicine, The University of Hong Kong Shenzhen Hospital, Shenzhen, People's Republic of China
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Asgari S, Khalili D, Hosseinpanah F, Hadaegh F. Prediction Models for Type 2 Diabetes Risk in the General Population: A Systematic Review of Observational Studies. Int J Endocrinol Metab 2021; 19:e109206. [PMID: 34567135 PMCID: PMC8453657 DOI: 10.5812/ijem.109206] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Revised: 02/07/2021] [Accepted: 02/13/2021] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVES This study aimed to provide an overview of prediction models of undiagnosed type 2 diabetes mellitus (U-T2DM) or the incident T2DM (I-T2DM) using the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) checklist and the prediction model risk of the bias assessment tool (PROBAST). DATA SOURCES Both PUBMED and EMBASE databases were searched to guarantee adequate and efficient coverage. STUDY SELECTION Articles published between December 2011 and October 2019 were considered. DATA EXTRACTION For each article, information on model development requirements, discrimination measures, calibration, overall performance, clinical usefulness, overfitting, and risk of bias (ROB) was reported. RESULTS The median (interquartile range; IQR) number of the 46 study populations for model development was 5711 (1971 - 27426) and 2457 (2060 - 6995) individuals for I-T2DM and U-T2DM, respectively. The most common reported predictors were age and body mass index, and only the Qrisk-2017 study included social factors (e.g., Townsend score). Univariable analysis was reported in 46% of the studies, and the variable selection procedure was not clear in 17.4% of them. Moreover, internal and external validation was reported in 43% the studies, while over 63% of them reported calibration. The median (IQR) of AUC for I-T2DM models was 0.78 (0.74 - 0.82); the corresponding value for studies derived before October 2011 was 0.80 (0.77 - 0.83). The highest discrimination index was reported for Qrisk-2017 with C-statistics of 0.89 for women and 0.87 for men. Low ROB for I-T2DM and U-T2DM was assessed at 18% and 41%, respectively. CONCLUSIONS Among prediction models, an intermediate to poor quality was reassessed in several aspects of model development and validation. Generally, despite its new risk factors or new methodological aspects, the newly developed model did not increase our capability in screening/predicting T2DM, mainly in the analysis part. It was due to the lack of external validation of the prediction models.
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Affiliation(s)
- Samaneh Asgari
- Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Davood Khalili
- Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Farhad Hosseinpanah
- Obesity Research Center, Research Institute for Endocrine Sciences, Shaheed Beheshti University of Medical Sciences, Tehran, Iran
| | - Farzad Hadaegh
- Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- Corresponding Author: Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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Lin CH, Wei JN, Fan KC, Fang CT, Wu WC, Yang CY, Lin MS, Shih SR, Hua CH, Hsein YC, Lin JW, Chuang LM, Li HY. Different cutoffs of hypertension, risk of incident diabetes and progression of insulin resistance: A prospective cohort study. J Formos Med Assoc 2021; 121:193-201. [PMID: 33766449 DOI: 10.1016/j.jfma.2021.02.022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Revised: 12/29/2020] [Accepted: 02/23/2021] [Indexed: 10/21/2022] Open
Abstract
BACKGROUND/PURPOSE Hypertension is a risk factor of incident diabetes. In 2017, the ACC/AHA updated the definition of hypertension to above 130/80 mmHg, while the 2018 ESC/ESH guideline and the JNC7 criteria remained the cutoff of 140/90 mmHg. This study was aimed to investigate how different cutoffs of hypertension affect the association of hypertension to incident diabetes and the progression of insulin resistance. METHODS A total of 1177 subjects without diabetes at baseline were followed for 4.5 years. Diabetes was diagnosed by the results of oral glucose tolerance tests and hemoglobin A1c, or if anti-diabetic agents were used. RESULTS Hypertension by both criteria was associated with incident diabetes. Change of HOMA2-IR every 5 years (ΔHOMA2-IR/5 yr) was higher in subjects with hypertension than those without (adjusted p = 0.044). Subjects with treated hypertension had the highest risk of diabetes (HR 2.98, p < 0.001) and ΔHOMA2-IR/5 yr, compared with subjects with normal blood pressure. However, the associations of hypertension, HR of incident diabetes and ΔHOMA2-IR/5 yr were attenuated by the 2017 ACC/AHA criteria, as compared with that by the JNC7 and 2018 ESC/ESH criteria. CONCLUSION Hypertension by both criteria is associated with incident diabetes and accelerated progression of insulin resistance, and the associations are attenuated by the 2017 ACC/AHA criteria.
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Affiliation(s)
- Chia-Hung Lin
- Division of Endocrinology and Metabolism, Department of Internal Medicine, National Taiwan University Hospital, Hsin-Chu Branch, Hsin-Chu, Taiwan
| | - Jung-Nan Wei
- Chia-Nan University of Pharmacy and Science, Tainan, Taiwan
| | - Kang-Chih Fan
- Division of Endocrinology and Metabolism, Department of Internal Medicine, National Taiwan University Hospital, Hsin-Chu Branch, Hsin-Chu, Taiwan
| | - Chi-Tai Fang
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan; Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Wan-Chen Wu
- Division of Endocrinology and Metabolism, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Chung-Yi Yang
- Department of Medical Imaging, E-Da Hospital, I-Shou University, Kaohsiung, Taiwan
| | - Mao-Shin Lin
- Division of Cardiology, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Shyang-Rong Shih
- Division of Endocrinology and Metabolism, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan; Department of Internal Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan; Center of Anti-Aging and Health Consultation, National Taiwan University Hospital, Taipei, Taiwan
| | - Cyue-Huei Hua
- Division of Clinical Pathology National Taiwan University Hospital, Yun-Lin Branch, Yun-Lin, Taiwan
| | - Yenh-Chen Hsein
- Division of Clinical Pathology National Taiwan University Hospital, Yun-Lin Branch, Yun-Lin, Taiwan
| | - Jou-Wei Lin
- Division of Cardiology, Department of Internal Medicine, National Taiwan University Hospital, Yun-Lin Branch, Yun-Lin, Taiwan
| | - Lee-Ming Chuang
- Division of Endocrinology and Metabolism, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan; Department of Internal Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Hung-Yuan Li
- Division of Endocrinology and Metabolism, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan.
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Lee PN, Coombs KJ. Systematic review with meta-analysis of the epidemiological evidence relating smoking to type 2 diabetes. World J Meta-Anal 2020; 8:119-152. [DOI: 10.13105/wjma.v8.i2.119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Revised: 04/02/2020] [Accepted: 04/20/2020] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Evidence relating tobacco smoking to type 2 diabetes has accumulated rapidly in the last few years, rendering earlier reviews considerably incomplete.
AIM To review and meta-analyse evidence from prospective studies of the relationship between smoking and the onset of type 2 diabetes.
METHODS Prospective studies were selected if the population was free of type 2 diabetes at baseline and evidence was available relating smoking to onset of the disease. Papers were identified from previous reviews, searches on Medline and Embase and reference lists. Data were extracted on a range of study characteristics and relative risks (RRs) were extracted comparing current, ever or former smokers with never smokers, and current smokers with non-current smokers, as well as by amount currently smoked and duration of quitting. Fixed- and random-effects estimates summarized RRs for each index of smoking overall and by various subdivisions of the data: Sex; continent; publication year; method of diagnosis; nature of the baseline population (inclusion/exclusion of pre-diabetes); number of adjustment factors; cohort size; number of type 2 diabetes cases; age; length of follow-up; definition of smoking; and whether or not various factors were adjusted for. Tests of heterogeneity and publication bias were also conducted.
RESULTS The literature searches identified 157 relevant publications providing results from 145 studies. Fifty-three studies were conducted in Asia and 53 in Europe, with 32 in North America, and seven elsewhere. Twenty-four were in males, 10 in females and the rest in both sexes. Fifteen diagnosed type 2 diabetes from self-report by the individuals, 79 on medical records, and 51 on both. Studies varied widely in size of the cohort, number of cases, length of follow-up, and age. Overall, random-effects estimates of the RR were 1.33 [95% confidence interval (CI): 1.28-1.38] for current vs never smoking, 1.28 (95%CI: 1.24-1.32) for current vs non-smoking, 1.13 (95%CI: 1.11-1.16) for former vs never smoking, and 1.25 (95%CI: 1.21-1.28) for ever vs never smoking based on, respectively, 99, 156, 100 and 100 individual risk estimates. Risk estimates were generally elevated in each subdivision of the data by the various factors considered (exceptions being where numbers of estimates in the subsets were very low), though there was significant (P < 0.05) evidence of variation by level for some factors. Dose-response analysis showed a clear trend of increasing risk with increasing amount smoked by current smokers and of decreasing risk with increasing time quit. There was limited evidence of publication bias.
CONCLUSION The analyses confirmed earlier reports of a modest dose-related association of current smoking and a weaker dose-related association of former smoking with type 2 diabetes risk.
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Affiliation(s)
- Peter N Lee
- Department of Statistics, P.N. Lee Statistics and Computing Ltd., Sutton SM2 5DA, Surrey, United Kingdom
| | - Katharine J Coombs
- Department of Statistics, P.N. Lee Statistics and Computing Ltd., Sutton SM2 5DA, Surrey, United Kingdom
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Liu X, Li Z, Zhang J, Chen S, Tao L, Luo Y, Xu X, Fine JP, Li X, Guo X. A Novel Risk Score for Type 2 Diabetes Containing Sleep Duration: A 7-Year Prospective Cohort Study among Chinese Participants. J Diabetes Res 2020; 2020:2969105. [PMID: 31998805 PMCID: PMC6964717 DOI: 10.1155/2020/2969105] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Revised: 10/08/2019] [Accepted: 12/05/2019] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Sleep duration is associated with type 2 diabetes (T2D). However, few T2D risk scores include sleep duration. We aimed to develop T2D scores containing sleep duration and to estimate the additive value of sleep duration. METHODS We used data from 43,404 adults without T2D in the Beijing Health Management Cohort study. The participants were surveyed approximately every 2 years from 2007/2008 to 2014/2015. Sleep duration was calculated from the self-reported usual time of going to bed and waking up at baseline. Logistic regression was employed to construct the risk scores. Integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were used to estimate the additional value of sleep duration. RESULTS After a median follow-up of 6.8 years, we recorded 2623 (6.04%) new cases of T2D. Shorter (both 6-8 h/night and <6 h/night) sleep durations were associated with an increased risk of T2D (odds ratio (OR) = 1.43, 95% confidence interval (CI) = 1.30-1.59; OR = 1.98, 95%CI = 1.63-2.41, respectively) compared with a sleep duration of >8 h/night in the adjusted model. Seven variables, including age, education, waist-hip ratio, body mass index, parental history of diabetes, fasting plasma glucose, and sleep duration, were selected to form the comprehensive score; the C-index was 0.74 (95% CI: 0.71-0.76) for the test set. The IDI and NRI values for sleep duration were 0.017 (95% CI: 0.012-0.022) and 0.619 (95% CI: 0.518-0.695), respectively, suggesting good improvement in the predictive ability of the comprehensive nomogram. The decision curves showed that women and individuals older than 50 had more net benefit. CONCLUSIONS The performance of T2D risk scores developed in the study could be improved by containing the shorter estimated sleep duration, particularly in women and individuals older than 50.
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Affiliation(s)
- Xiangtong Liu
- School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Zhiwei Li
- School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Jingbo Zhang
- Beijing Physical Examination Center, Beijing 100077, China
| | - Shuo Chen
- Beijing Physical Examination Center, Beijing 100077, China
| | - Lixin Tao
- School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Yanxia Luo
- School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Xiaolin Xu
- The University of Queensland, Brisbane, Australia
| | | | - Xia Li
- La Trobe University, Melbourne, Australia
| | - Xiuhua Guo
- School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
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Hou J, Sun H, Zhou Y, Zhang Y, Yin W, Xu T, Cheng J, Chen W, Yuan J. Environmental exposure to polycyclic aromatic hydrocarbons, kitchen ventilation, fractional exhaled nitric oxide, and risk of diabetes among Chinese females. INDOOR AIR 2018; 28:383-393. [PMID: 29444361 DOI: 10.1111/ina.12453] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2017] [Accepted: 02/06/2018] [Indexed: 06/08/2023]
Abstract
Diabetes is related to exposure to polycyclic aromatic hydrocarbons (PAHs), inflammation in the body, and housing characters. However, associations of urinary monohydroxy-PAHs (OH-PAHs) or fractional exhaled nitric oxide (FeNO) with diabetes risk in relation to housing characters are unclear. In this study, 2645 individuals were drawn from the baseline survey of the Wuhan-Zhuhai Cohort Study. Associations of diabetes with urinary OH-PAHs or FeNO among cooking participants were estimated using logistic regression models. Among women with self-cooking meals, urinary OH-PAH levels were positively associated with diabetes risk (P < .05); the cooking women with high FeNO (≥25 ppb) had a 59% increase in the risk of diabetes (OR: 1.59, 95% CI: 1.06, 2.38), compared with those with low FeNO (<25 ppb). The cooking women with use of kitchen exhaust fans/hoods had a 52% decrease in the risk of diabetes (OR: 0.48, 95% CI: 0.27, 0.84), compared with those with nonuse of kitchen exhaust fans/hoods. The results indicated that the cooking women had an elevated risk of diabetes, which may be partly explained by an increase in the PAH body burden and higher inflammatory responses. Use of kitchen exhaust fan/hood can be associated with a lower risk of diabetes.
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Affiliation(s)
- J Hou
- Department of Occupational and Environmental Health, Huazhong University of Science and Technology, Wuhan, China
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - H Sun
- Department of Occupational and Environmental Health, Huazhong University of Science and Technology, Wuhan, China
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Huizhen Sun, Hubei Center for Disease Control and Prevention, Wuhan, China
| | - Y Zhou
- Department of Occupational and Environmental Health, Huazhong University of Science and Technology, Wuhan, China
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Y Zhang
- Department of Occupational and Environmental Health, Huazhong University of Science and Technology, Wuhan, China
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - W Yin
- Department of Occupational and Environmental Health, Huazhong University of Science and Technology, Wuhan, China
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - T Xu
- Department of Occupational and Environmental Health, Huazhong University of Science and Technology, Wuhan, China
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - J Cheng
- Department of Occupational and Environmental Health, Huazhong University of Science and Technology, Wuhan, China
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - W Chen
- Department of Occupational and Environmental Health, Huazhong University of Science and Technology, Wuhan, China
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - J Yuan
- Department of Occupational and Environmental Health, Huazhong University of Science and Technology, Wuhan, China
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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