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Ng HY, Chan LTW. Prediabetes in children and adolescents: An updated review. World J Clin Pediatr 2023; 12:263-272. [PMID: 38178932 PMCID: PMC10762598 DOI: 10.5409/wjcp.v12.i5.263] [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: 07/25/2023] [Revised: 10/05/2023] [Accepted: 10/16/2023] [Indexed: 12/08/2023] Open
Abstract
Prediabetes, the precursor of type 2 diabetes mellitus, is an intermediate stage between normal glucose homeostasis and overt diabetes. This asymptomatic metabolic state is increasingly prevalent in pediatric population and is very difficult to detect without appropriate screening. Studies have shown that a certain proportion of children with prediabetes will develop diabetes in a few years. Even more alarming is the evidence that youth-onset diabetes has a more aggressive clinical course with progressive beta-cell decline and accelerated end-organ damage. Despite its importance, several aspects involving prediabetes in childhood are disputed or unknown. This review presents the latest insights into this challenging entity and outlines a simplified screening approach to aid clinical practice. In summary, childhood prediabetes is an important clinical condition indicating the need for proper screening and timely intervention.
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Affiliation(s)
- Hak Yung Ng
- Department of Paediatrics and Adolescent Medicine, Tseung Kwan O Hospital, Hong Kong, China
| | - Louis Tsz Wang Chan
- Department of Paediatrics and Adolescent Medicine, Tseung Kwan O Hospital, Hong Kong, China
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Zheng R, Xu Y, Li M, Gao Z, Wang G, Hou X, Chen L, Huo Y, Qin G, Yan L, Wan Q, Zeng T, Chen L, Shi L, Hu R, Tang X, Su Q, Yu X, Qin Y, Chen G, Gu X, Shen F, Luo Z, Chen Y, Zhang Y, Liu C, Wang Y, Wu S, Yang T, Li Q, Mu Y, Zhao J, Hu C, Jia X, Xu M, Wang T, Zhao Z, Wang S, Lin H, Ning G, Wang W, Lu J, Bi Y. Data-driven subgroups of prediabetes and the associations with outcomes in Chinese adults. Cell Rep Med 2023; 4:100958. [PMID: 36863337 PMCID: PMC10040373 DOI: 10.1016/j.xcrm.2023.100958] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 12/11/2022] [Accepted: 02/04/2023] [Indexed: 03/04/2023]
Abstract
Prediabetes and its pathophysiology remain important issues. We aimed to examine the cluster characteristics of prediabetes and explore their associations with developing diabetes and its complications based on 12 variables representing body fat, glycemic measures, pancreatic β cell function, insulin resistance, blood lipids, and liver enzymes. A total of 55,777 individuals with prediabetes from the China Cardiometabolic Disease and Cancer Cohort (4C) were classified at baseline into six clusters. During a median of 3.1 years of follow-up, significant differences in the risks of diabetes and its complications between clusters were observed. The odds ratios of diabetes stepwisely increase from cluster 1 to cluster 6. Clusters 1, 4, and 6 have increased chronic kidney diseases risks, while the prediabetes in cluster 4, characterized by obesity and insulin resistance, confers higher risks of cardiovascular diseases compared with others. This subcategorization has potential value in developing more precise strategies for targeted prediabetes prevention and treatment.
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Affiliation(s)
- Ruizhi Zheng
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yu Xu
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Mian Li
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhengnan Gao
- Dalian Municipal Central Hospital, Dalian, China
| | - Guixia Wang
- The First Hospital of Jilin University, Changchun, China
| | - Xinguo Hou
- Qilu Hospital of Shandong University, Jinan, China
| | - Li Chen
- Qilu Hospital of Shandong University, Jinan, China
| | - Yanan Huo
- Jiangxi Provincial People's Hospital Affiliated to Nanchang University, Nanchang, China
| | - Guijun Qin
- The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Li Yan
- Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Qin Wan
- The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Tianshu Zeng
- Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Lulu Chen
- Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Lixin Shi
- Affiliated Hospital of Guiyang Medical College, Guiyang, China
| | - Ruying Hu
- Zhejiang Provincial Center for Disease Control and Prevention, China
| | - Xulei Tang
- The First Hospital of Lanzhou University, Lanzhou, China
| | - Qing Su
- Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Xuefeng Yu
- Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yingfen Qin
- The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Gang Chen
- Fujian Provincial Hospital, Fujian Medical University, Fuzhou, China
| | - Xuejiang Gu
- The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Feixia Shen
- The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Zuojie Luo
- The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Yuhong Chen
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yinfei Zhang
- Central Hospital of Shanghai Jiading District, Shanghai, China
| | - Chao Liu
- Jiangsu Province Hospital on Integration of Chinese and Western Medicine, Nanjing, China
| | - Youmin Wang
- The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Shengli Wu
- Karamay Municipal People's Hospital, Xinjiang, China
| | - Tao Yang
- The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Qiang Li
- The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Yiming Mu
- Chinese People's Liberation Army General Hospital, Beijing, China
| | - Jiajun Zhao
- Shandong Provincial Hospital Affiliated to Shandong University, Jinan, China
| | - Chunyan Hu
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaojing Jia
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Min Xu
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tiange Wang
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhiyun Zhao
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shuangyuan Wang
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hong Lin
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Guang Ning
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Weiqing Wang
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Jieli Lu
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Yufang Bi
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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Arranz-Martínez E, Ruiz-García A, García Álvarez JC, Fernández Vicente T, Iturmendi Martínez N, Rivera-Teijido M. Prevalence of prediabetes and association with cardiometabolic and renal factors. SIMETAP-PRED study. CLINICA E INVESTIGACION EN ARTERIOSCLEROSIS : PUBLICACION OFICIAL DE LA SOCIEDAD ESPANOLA DE ARTERIOSCLEROSIS 2022; 34:193-204. [PMID: 35120792 DOI: 10.1016/j.arteri.2021.12.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 11/29/2021] [Accepted: 12/01/2021] [Indexed: 06/14/2023]
Abstract
INTRODUCTION Prediabetes is a major public health problem. The aims of the SIMETAP-PRED study were to determine the prevalence rates of prediabetes according to two diagnostic criteria, and to compare the association of cardiometabolic and renal risk factors between populations with and without prediabetes. METHODS Cross-sectional observational study conducted in Primary Care. Based random sample: 6,588 study subjects (response rate: 66%). Two diagnostic criteria for prediabetes were used: 1) prediabetes according to the Spanish Diabetes Society (PRED-SDS): Fasting plasma glucose (FPG) 110-125mg/dL or HbA1c 6.0% -6.4%; 2) prediabetes according to the American Diabetes Association (PRED-ADA): FPG 100-125mg/dL or HbA1c 5.7%-6.4%. The crude and sex- and age-adjusted prevalence rates, and cardiometabolic and renal variables associated with prediabetes were assessed. RESULTS The crude prevalence rates of PRED-SDS and PRED-ADA were 7.9% (95% CI 7.3-8.6%), and 22.0% (95% CI 21.0-23.0%) respectively, their age-adjusted prevalence rates were 6.6% and 19.1 respectively. The high or very high cardiovascular risk of the PRED-SDS or PRED-ADA populations were 68.6% (95%CI 64.5-72.6%) and 61.7% (95%CI 59.1-64.1%) respectively. Hypertension, hypertriglyceridemia, overweight, obesity, and increased waist-to-height ratio were independently associated with PRED-SDS. In addition to these factors, low glomerular filtration rate and hypercholesterolemia were also independently associated with PRED-ADA. CONCLUSIONS The prevalence of PRED-ADA triples that of PRED-SDS. Two thirds of the population with prediabetes had a high cardiovascular risk. Several cardiometabolic and renal risk factors were associated with prediabetes. Compared to the SDS criteria, the ADA criteria make the diagnosis of prediabetes easier.
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Affiliation(s)
| | - Antonio Ruiz-García
- Unidad de Lípidos y Prevención Cardiovascular, Centro de Salud Universitario Pinto, Pinto, Madrid, España.
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Li N, Lu C, Ma Y, Wang X, Ling Y, Yin Y, Li S, Huang J, Yu L, Dong W, He H, Kang M, Ma L, Gu M, Zhao L, Huang Y, Liu F, Wang Y, Gai X, Jiang J, Peng Y, Ding X. Factors associated with progression of different prediabetic status to Diabetes: A Community-based cohort study. Diabetes Res Clin Pract 2022; 184:109193. [PMID: 35032561 DOI: 10.1016/j.diabres.2022.109193] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Revised: 12/19/2021] [Accepted: 01/10/2022] [Indexed: 12/26/2022]
Abstract
AIMS To examine the predictive factors associated with the progression of different prediabetic status to diabetes. METHODS A two-year retrospective cohort study was conducted on 5741 participants aged 40 years or older. Finally, 1685 participants with prediabetes defined by IFG (impaired fasting glucose), IGT (impaired glucose tolerance) and CGI (combined IFG and IGT) were included. Logistic regression model was used to evaluate the risk of prediabetes progression to diabetes. RESULTS Of the 1685 subjects with prediabetes at baseline, 212 (12.6%) subjects progressed to diabetes and 1473 (87.4%) subjects did not. Logistic regression analysis demonstrated that people with CGI were associated with an increased risk of progressing to diabetes compared to those with IFG (OR, 95% CI: 3.127, 2.047-4.776). Moreover, males, obese people, people with increased BMI and WHR (Waist/ Hip ratio), and hypertension were positively associated with the progression to diabetes, while HOMA-β was negatively associated with the progression to diabetes. CONCLUSIONS Subjects with CGI are prone to progressed to diabetes compared to those with IFG or IGT in middle-aged and older person in China. More attention should be paid to male and obese prediabetic subjects, and measures should be taken to control the increase in their BMI and WHR.
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Affiliation(s)
- Na Li
- Department of Endocrinology and Metabolism, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China
| | - Chunhua Lu
- Community Health Service Center of Sijing, Songjiang District, Shanghai 201601, China
| | - Yuhang Ma
- Department of Endocrinology and Metabolism, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China
| | - Xuejiao Wang
- Department of Endocrinology and Metabolism, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China
| | - Yunxia Ling
- Department of Endocrinology and Metabolism, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China
| | - Yanhua Yin
- Department of Endocrinology and Metabolism, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China
| | - Shumei Li
- Department of Endocrinology and Metabolism, Shanghai Fourth People's Hospital, School of Medicine, Tongji University, Shanghai 200434, China
| | - Jiao Huang
- Department of Endocrinology and Metabolism, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China
| | - Lihua Yu
- Community Health Service Center of Sijing, Songjiang District, Shanghai 201601, China
| | - Weiping Dong
- Department of Endocrinology and Metabolism, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China
| | - Huichen He
- Clinical Research Center, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China
| | - Mei Kang
- Clinical Research Center, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China
| | - Lei Ma
- Clinical Research Center, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China
| | - Mingyu Gu
- Department of Endocrinology and Metabolism, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China
| | - Li Zhao
- Department of Endocrinology and Metabolism, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China
| | - Yunhong Huang
- Department of Endocrinology and Metabolism, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China
| | - Fang Liu
- Department of Endocrinology and Metabolism, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China
| | - Yufan Wang
- Department of Endocrinology and Metabolism, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China
| | - Xianying Gai
- Department of Endocrinology, Sijing Hospital, Songjiang District, Shanghai 201601, China
| | - Junyi Jiang
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University and Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, Changsha 410008, China; Aier Eye Institute, Changsha 410015, China.
| | - Yongde Peng
- Department of Endocrinology and Metabolism, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China.
| | - Xiaoying Ding
- Department of Endocrinology and Metabolism, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China.
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Thamakaison S, Anothaisintawee T, Sukhato K, Unwanatham N, Rattanasiri S, Reutrakul S, Thakkinstian A. Hemoglobin A1c in combination with fasting plasma glucose trumps fasting plasma glucose alone as predictive indicators for diabetes mellitus: an ambidirectional cohort study of Thai people with impaired fasting glucose. BMJ Open Diabetes Res Care 2021; 9:9/2/e002427. [PMID: 34845059 PMCID: PMC8634022 DOI: 10.1136/bmjdrc-2021-002427] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Accepted: 11/05/2021] [Indexed: 01/16/2023] Open
Abstract
INTRODUCTION This ambidirectional cohort study aimed to assess the performance of combining hemoglobin A1c (HbA1c) to fasting plasma glucose (FPG) for estimation of progression rate to diabetes mellitus (DM) and to explore the risk factors of DM in patients with impaired fasting glucose (IFG). RESEARCH DESIGN AND METHODS Patients with IFG were eligible for this study. IFG was defined as FPG of 100-125 mg/dL. Progression rates to DM were estimated using Kaplan-Meier analysis. Risk factors of DM were explored by Cox regression analysis. RESULTS 3011 patients were enrolled with median follow-up time of 8 years (range: 6 months-29 years). Progression rates to DM in patients with FPG 100-109 mg/dL and 110-125 mg/dL were 2.64 and 4.79 per 100 person-years. After adjusting covariables, compared with patients with FPG 100-109 mg/dL plus normal HbA1c (<5.7%), hazard ratios (95% CI) of patients with FPG 110-125 plus normal HbA1c, FBG 100-109 plus abnormal HbA1c (5.7%-6.49%), and FPG 110-125 plus abnormal HbA1c were 5.89 (2.37 to 14.63), 16.30 (8.59 to 30.92), and 33.84 (16.41 to 69.78), respectively. Body mass index ≥27.5 kg/m2, serum triglyceride level ≥150 mg/dL, family history of DM, and low level of high-density lipoprotein-cholesterol were independently associated with risk of DM in patients with IFG. CONCLUSIONS Patients with both IFG and abnormal HbA1c had higher risk of DM than patients with IFG alone. Therefore, performing HbA1c in combination with FPG helps to identify subgroups of people with IFG at highest risk of DM. These patients should have the highest priority in diabetes prevention programs, especially in countries with low and limited resources.
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Affiliation(s)
- Sangsulee Thamakaison
- Department of Family Medicine, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Thunyarat Anothaisintawee
- Department of Family Medicine, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
- Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Kanokporn Sukhato
- Department of Family Medicine, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Nattawut Unwanatham
- Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Sasivimol Rattanasiri
- Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Sirimon Reutrakul
- Department of Endocrinology, Diabetes, and Metabolism, Department of Medicine, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Ammarin Thakkinstian
- Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
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Lim WXJ, Gammon CS, von Hurst P, Chepulis L, Page RA. A Narrative Review of Human Clinical Trials on the Impact of Phenolic-Rich Plant Extracts on Prediabetes and Its Subgroups. Nutrients 2021; 13:nu13113733. [PMID: 34835989 PMCID: PMC8624625 DOI: 10.3390/nu13113733] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 10/17/2021] [Accepted: 10/21/2021] [Indexed: 12/12/2022] Open
Abstract
Phenolic-rich plant extracts have been demonstrated to improve glycemic control in individuals with prediabetes. However, there is increasing evidence that people with prediabetes are not a homogeneous group but exhibit different glycemic profiles leading to the existence of prediabetes subgroups. Prediabetes subgroups have been identified as: isolated impaired fasting glucose (IFG), isolated impaired glucose tolerance (IGT), and combined impaired fasting glucose and glucose intolerance (IFG/IGT). The present review investigates human clinical trials examining the hypoglycemic potential of phenolic-rich plant extracts in prediabetes and prediabetes subgroups. Artemisia princeps Pampanini, soy (Glycine max (L.) Merrill) leaf and Citrus junos Tanaka peel have been demonstrated to improve fasting glycemia and thus may be more useful for individuals with IFG with increasing hepatic insulin resistance. In contrast, white mulberry (Morus alba Linn.) leaf, persimmon (Diospyros kaki) leaf and Acacia. Mearnsii bark were shown to improve postprandial glycemia and hence may be preferably beneficial for individuals with IGT with increasing muscle insulin resistance. Elaeis guineensis leaf was observed to improve both fasting and postprandial glycemic measures depending on the dose. Current evidence remains scarce regarding the impact of the plant extracts on glycemic control in prediabetes subgroups and therefore warrants further study.
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Affiliation(s)
- Wen Xin Janice Lim
- School of Health Sciences, Massey University, Auckland 0632, New Zealand; (W.X.J.L.); (C.S.G.)
- Riddet Institute, Massey University, Palmerston North 4442, New Zealand
| | - Cheryl S. Gammon
- School of Health Sciences, Massey University, Auckland 0632, New Zealand; (W.X.J.L.); (C.S.G.)
| | - Pamela von Hurst
- School of Sport, Exercise and Nutrition, Massey University, Auckland 0632, New Zealand;
| | - Lynne Chepulis
- Waikato Medical Research Centre, Te Huataki Waiora School of Health, University of Waikato, Hamilton 3216, New Zealand;
| | - Rachel A. Page
- School of Health Sciences, Massey University, Wellington 6021, New Zealand
- Centre for Metabolic Health Research, Massey University, Auckland 0632, New Zealand
- Correspondence: ; Tel.: +64-4-801-5799 (ext. 63462)
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Kowall B, Rathmann W, Kuss O, Herder C, Roden M, Stang A, Huth C, Thorand B, Meisinger C, Peters A. Reversion from prediabetes to normoglycaemia after weight change in older persons: The KORA F4/FF4 study. Nutr Metab Cardiovasc Dis 2021; 31:429-438. [PMID: 33594985 DOI: 10.1016/j.numecd.2020.09.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 09/04/2020] [Accepted: 09/07/2020] [Indexed: 12/11/2022]
Abstract
BACKGROUND AND AIMS In a non-interventional study of older persons, we assessed the impact of changes in BMI and waist circumference (WC) on reversion from glucose- and HbA1c-defined prediabetes to normoglycaemia (in short: reversion) and on persistence of normoglycaemia. Moreover, we studied whether reversion reduced cardiovascular risk. METHODS AND RESULTS From the population-based KORA S4/F4/FF4 cohort study conducted in Southern Germany, we utilized data from the second and third visit to the study center (median follow-up 6.5 years). We used two overlapping data sets, one with 563 persons with HbA1c<6.5% (mean age 69 years, 51.5% men), one with 510 persons with glucose-based prediabetes or normal glucose tolerance. We calculated proportions of reversion, and estimated adjusted relative risks for the association between initial BMI/WC and change of BMI/WC, respectively, and reversion (and persistence of normoglycaemia, respectively). We estimated 10-year cardiovascular risks using the Framingham 2008 score. Overall, 27.3% of persons with HbA1c-defined prediabetes and 9.2% of persons with glucose-based prediabetes returned to normoglycaemia during follow-up. Lower initial BMI/WC and reduction of BMI/WC were associated with larger probabilities of returning to normoglycaemia (e.g., for HbA1c 5.7-6.4%, RR = 1.24 (95% CI: 1.09-1.41) per 1 kg/m2 decline of BMI). Moreover, reduction of BMI/WC increased probabilities of maintaining normoglycaemia (e.g., for glucose-based prediabetes, RR = 1.09 (1.02-1.16) per 1 kg/m2 decline of BMI). 10-year cardiovascular risk was 5.6 (1.7-9.6) percentage points lower after reversion from glucose-based prediabetes to normoglycaemia. CONCLUSION In older adults, even moderate weight reduction contributes to reversion from prediabetes to normoglycaemia and to maintaining normoglycaemia.
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Affiliation(s)
- Bernd Kowall
- Center of Clinical Epidemiology, Institute for Medical Informatics, Biometry and Epidemiology, Medical Faculty, University Duisburg-Essen, Essen, Germany.
| | - Wolfgang Rathmann
- Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, Düsseldorf, Germany; German Center for Diabetes Research (DZD), München, Neuherberg, Germany
| | - Oliver Kuss
- Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, Düsseldorf, Germany; German Center for Diabetes Research (DZD), München, Neuherberg, Germany
| | - Christian Herder
- German Center for Diabetes Research (DZD), München, Neuherberg, Germany; Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, Düsseldorf, Germany; Division of Endocrinology and Diabetology, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
| | - Michael Roden
- German Center for Diabetes Research (DZD), München, Neuherberg, Germany; Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, Düsseldorf, Germany; Division of Endocrinology and Diabetology, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
| | - Andreas Stang
- Center of Clinical Epidemiology, Institute for Medical Informatics, Biometry and Epidemiology, Medical Faculty, University Duisburg-Essen, Essen, Germany
| | - Cornelia Huth
- German Center for Diabetes Research (DZD), München, Neuherberg, Germany; Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Barbara Thorand
- German Center for Diabetes Research (DZD), München, Neuherberg, Germany; Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Christa Meisinger
- Chair of Epidemiology, Ludwig-Maximilian-Universität München, UNIKA-T Augsburg, Augsburg, Germany; Independent Research Group Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Annette Peters
- German Center for Diabetes Research (DZD), München, Neuherberg, Germany; Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
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8
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Kowall B, Rathmann W, Kuß O, Herder C, Roden M, Stang A, Erbel R, Huth C, Thorand B, Meisinger C, Jöckel KH, Peters A. Associations between haemoglobin A 1c and mortality rate in the KORA S4 and the Heinz Nixdorf Recall population-based cohort studies. Diabetes Metab Res Rev 2021; 37:e3369. [PMID: 32558166 DOI: 10.1002/dmrr.3369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Revised: 05/30/2020] [Accepted: 06/12/2020] [Indexed: 11/06/2022]
Abstract
BACKGROUND There is limited knowledge about mortality risk in persons with increased haemoglobin A1c (HbA1c ) levels below the diabetes threshold. Moreover, little is known about how associations between increased HbA1c and mortality depend on the length of follow-up. Therefore, we studied associations between HbA1c and mortality over long-term follow-up in persons with and without known diabetes. METHODS We used data from two German population-based cohort studies: KORA S4 Study (Southern Germany, n = 1458, baseline visits in 1999 to 2001, baseline age 55 to 74 years, mortality follow-up 16.8 years) and Heinz Nixdorf Recall (HNR) Study (Ruhr area, n = 4613, baseline visits in 2000 to 2003, baseline age 45 to 75 years, mortality follow-up 17.8 years). Adjusted log-linear models were fitted to estimate relative risks (RRs) with 95% confidence intervals (CI). RESULTS In both cohorts, participants with HbA1c 39 to 41 mmol/mol (5.7%-5.9%) and HbA1c 42 to 46 mmol/mol (6.0% to 6.4%) did not have a larger overall mortality risk than participants with HbA1c < 39 mmol/mol (5.7%): the corresponding adjusted RRs were 1.00 (95% CI: 0.83-1.21) and 1.01 (0.80-1.27) in KORA and 0.99 (0.82-1.21) and 0.83 (0.65-1.07) in the HNR Study. For the pooled cohorts, the RR for HbA1c 39 to 46 mmol/mol (5.7%-6.4%) was 0.96 (0.85-1.07). Associations between newly detected diabetes (HbA1c ≥ 6.5%) and mortality were weak after 4 and 8 years of follow-up, but were stronger after 12 years of follow-up, whereas associations between previously known diabetes (baseline) and mortality decreased. CONCLUSIONS HbA1c -defined pre-diabetes is not associated with overall mortality. For newly detected and previously known diabetes, mortality risks vary with length of follow-up.
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Affiliation(s)
- Bernd Kowall
- Center of Clinical Epidemiology, Institute for Medical Informatics, Biometry and Epidemiology, Medical Faculty, University Duisburg-Essen, Essen, Germany
| | - Wolfgang Rathmann
- Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research, Heinrich Heine University, Düsseldorf, Germany
- German Center for Diabetes Research, Düsseldorf, Germany
| | - Oliver Kuß
- Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research, Heinrich Heine University, Düsseldorf, Germany
- German Center for Diabetes Research, Düsseldorf, Germany
| | - Christian Herder
- German Center for Diabetes Research, Düsseldorf, Germany
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research, Heinrich Heine University, Düsseldorf, Germany
- Division of Endocrinology and Diabetology, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
| | - Michael Roden
- German Center for Diabetes Research, Düsseldorf, Germany
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research, Heinrich Heine University, Düsseldorf, Germany
- Division of Endocrinology and Diabetology, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
| | - Andreas Stang
- Center of Clinical Epidemiology, Institute for Medical Informatics, Biometry and Epidemiology, Medical Faculty, University Duisburg-Essen, Essen, Germany
| | - Raimund Erbel
- Institute for Medical Informatics, Biometry and Epidemiology, University Clinic Essen, University Duisburg-Essen, Essen, Germany
| | - Cornelia Huth
- German Center for Diabetes Research, Düsseldorf, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Barbara Thorand
- German Center for Diabetes Research, Düsseldorf, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Christa Meisinger
- Department of Epidemiology, Ludwig-Maximilian-Universität München, UNIKA-T Augsburg, Augsburg, Germany
- Independent Research Group Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Karl-Heinz Jöckel
- Institute for Medical Informatics, Biometry and Epidemiology, University Clinic Essen, University Duisburg-Essen, Essen, Germany
| | - Annette Peters
- German Center for Diabetes Research, Düsseldorf, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
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9
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Park HY, Han K, Kim Y, Kim YH, Sur YJ. The risk of hip fractures in individuals over 50 years old with prediabetes and type 2 diabetes - A longitudinal nationwide population-based study. Bone 2021; 142:115691. [PMID: 33069920 DOI: 10.1016/j.bone.2020.115691] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 09/07/2020] [Accepted: 10/11/2020] [Indexed: 12/11/2022]
Abstract
BACKGROUND The present study aimed to investigate the association between type 2 diabetes mellitus (T2DM) and hip fractures using a large-scale nationwide population-based cohort that is representative of the Republic of Korea. We determined the risks of hip fractures in individuals with prediabetes and T2DM with different diabetes durations, and compared them with the risks of hip fractures in individuals without T2DM. METHODS A total of 5,761,785 subjects over 50 years old who underwent the National Health Insurance Service medical checkup in 2009-2010 were included. Subjects were classified into 5 groups based on the diabetes status; Normal, Prediabetes, Newly-diagnosed T2DM, T2DM less than 5 years, and T2DM more than 5 years. They were followed from the date of the medical checkup to the end of 2016. The endpoint was a new development of hip fracture during follow-up. The hazard ratios (HRs) and 95% confidence intervals (CIs) of hip fractures for each group were analyzed using Cox proportional hazard regression models after adjusting for age, sex, smoking, alcohol drinking, regular exercise, body mass index, hypertension, dyslipidemia, and chronic kidney disease. RESULTS The HRs of hip fractures were 1 in the Normal group, 1.032 (95% CI: 1.009, 1.056) in the Prediabetes group, 1.168 (95% CI: 1.113, 1.225) in the Newly-diagnosed T2DM2, 1.543 (95% CI: 1.495, 1.592) in the T2DM less than 5 years and 2.105 (95% CI: 2.054, 2.157) in the T2DM more than 5 years. The secular trend of the HRs of hip fractures according to the duration of T2DM was statistically significant (P < .001). Subgroup analyses also showed the same increasing pattern of the HRs of hip fractures according to the duration of T2DM in both sexes and all age groups (50-64 years, 65-74 years, over 75 years). CONCLUSIONS In summary, this large-scale, retrospective, longitudinal, nationwide population-based cohort study of 5,761,785 subjects demonstrated that the risks of hip fractures started to increase in prediabetes and was associated linearly with the duration of T2DM. The secular trend of risks of hip fractures according to the duration of T2DM was consistent in both sexes and all age groups.
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Affiliation(s)
- Ho Youn Park
- Department of Orthopedic Surgery, Uijeongbu St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Kyoungdo Han
- Department of Statistics and Actuarial Science, Soongsil University, Seoul, Republic of Korea
| | - Youngwoo Kim
- Department of Orthopedic Surgery, Uijeongbu St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Yoon Hwan Kim
- Department of Orthopedic Surgery, Uijeongbu St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Yoo Joon Sur
- Department of Orthopedic Surgery, Uijeongbu St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
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Schmidt MI, Bracco PA, Yudkin JS, Bensenor IM, Griep RH, Barreto SM, Castilhos CD, Duncan BB. Intermediate hyperglycaemia to predict progression to type 2 diabetes (ELSA-Brasil): an occupational cohort study in Brazil. Lancet Diabetes Endocrinol 2019; 7:267-277. [PMID: 30803929 DOI: 10.1016/s2213-8587(19)30058-0] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2018] [Revised: 12/24/2018] [Accepted: 01/11/2019] [Indexed: 12/22/2022]
Abstract
BACKGROUND The burden of diabetes is increasing worldwide and diabetes can be prevented with intervention in people with impaired glucose tolerance (IGT). Intermediate hyperglycaemia defined without an oral glucose tolerance test as impaired fasting glucose (IFG) and high HbA1c are also used to characterise risk. We aimed to assess the prognostic properties of five definitions of intermediate hyperglycaemia (also known as prediabetes) on the basis of their ability to predict who will progress to diabetes. METHODS The Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) is an occupational cohort study of active or retired civil servants, aged 35-74 years, recruited from public universities and research institutes in six state capital cities in Brazil. We excluded participants who provided insufficient information to ascertain diabetes status, those without information on relevant covariates, and those with diabetes. We classified type 2 diabetes on the basis of self-report, medication use, measures of fasting plasma glucose (FPG), 2 h plasma glucose, and HbA1c. We used five laboratory definitions of intermediate hyperglycaemia: IGT (2 h plasma glucose ≥7·8 mmol/L [≥140 mg/dL]); IFG based on American Diabetes Association (ADA) criteria (FPG ≥5·5 mmol/L [≥100 mg/dL]); IFG based on WHO criteria (FPG ≥6·1 mmol/L [≥110 mg/dL]); HbA1c based on ADA criteria (HbA1c ≥39 mmol/mol [5·7%]); and HbA1c based on International Expert Committee criteria, IEC-HbA1c, (HbA1c ≥42 mmol/mol [6·0%]). We estimated risk of each definition using Cox regression and overall predictability (area under the receiver operating characteristic curve [AUC]) using logistic regression. FINDINGS We recruited 15 105 participants from Aug 18, 2008, to Dec 20, 2010, and followed up for a mean of 3·7 (SD 0·63) years. Diabetes incidence rate was 2·0 per 100 person-years (95% CI 1·8-2·1). Among the 11 199 eligible participants, 6563 (59%) presented with some form of intermediate hyperglycaemia. ADA-IFG (4870/11 199 [43·5%), IEC-HbA1c (1005 [9·0%]), and ADA-HbA1c (2299 [20·5%]) poorly predicted diabetes (3·5-3·6 per 100 person-years). WHO-IFG (1140 [10·2%]) and IGT (2245 [20·0%]) predicted greater conversion (7·5 per 100 person-years and 5·8 per 100 person-years, respectively). All definitions presented either low sensitivity or specificity. Combinations of tests improved prognostic properties, with the combination of IGT or WHO-IFG showing the best, but still insufficient, predictability (sensitivity 67·7%, 95% CI 64·5-70·1; specificity 77·9%, 77·1-78·7). The AUC for the three underlying glycaemic tests was 65·0% (95% CI 63·0-66·9) for HbA1c, 74·6% (72·7-76·4) for FPG, and 77·1% (75·4-78·8) for 2 h plasma glucose, whereas the AUC for a score composed of clinical information was 71·6% (69·8-73·3). When this score was combined with results of an oral glucose tolerance test, the AUC reached 82·4% (80·9-83·9). INTERPRETATION IFG based on WHO criteria and IGT predict diabetes progression better than do the other three definitions of intermediate hyperglycaemia, but their sensitivity is low. IFG based on ADA criteria has better sensitivity than the others, but classifies almost half of adults as having intermediate hyperglycaemia and poorly predicts diabetes. Combining glycaemic results with clinical information improves prognostic properties of those at risk. FUNDING The Brazilian Ministry of Health (Science and Technology Department), the Brazilian Ministry of Science, Technology and Innovation (Financiadora de Estudos e Projetos and Conselho Nacional de Desenvolvimento Científico e Tecnológico), and the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-Brasil (CAPES).
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Affiliation(s)
- Maria Inês Schmidt
- Postgraduate Studies Program in Epidemiology, School of Medicine and Hospital de Clínicas, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil.
| | - Paula A Bracco
- Postgraduate Studies Program in Epidemiology, School of Medicine and Hospital de Clínicas, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil
| | - John S Yudkin
- Division of Medicine, University College London, London, UK
| | - Isabela M Bensenor
- Center for Clinical and Epidemiologic Research, Universidade de São Paulo, São Paulo, Brazil
| | - Rosane Härter Griep
- Laboratory of Health and Environment Education, Fundação Oswaldo Cruz, Rio de Janeiro, Rio de Janeiro, Brazil
| | - Sandhi Maria Barreto
- Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Cristina D Castilhos
- Postgraduate Studies Program in Epidemiology, School of Medicine and Hospital de Clínicas, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil
| | - Bruce B Duncan
- Postgraduate Studies Program in Epidemiology, School of Medicine and Hospital de Clínicas, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil
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