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Hou R, Dou J, Wu L, Zhang X, Li C, Wang W, Gao Z, Tang X, Yan L, Wan Q, Luo Z, Qin G, Chen L, Ji J, He Y, Wang W, Mu Y, Zheng D. Development and validation of a machine learning-based model to predict isolated post-challenge hyperglycemia in middle-aged and elder adults: Analysis from a multicentric study. Diabetes Metab Res Rev 2024; 40:e3832. [PMID: 39031573 DOI: 10.1002/dmrr.3832] [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: 09/27/2023] [Revised: 01/02/2024] [Accepted: 05/31/2024] [Indexed: 07/22/2024]
Abstract
INTRODUCTION Due to the high cost and complexity, the oral glucose tolerance test is not adopted as the screening method for identifying diabetes patients, which leads to the misdiagnosis of patients with isolated post-challenge hyperglycemia (IPH), that is., patients with normal fasting plasma glucose (<7.0 mmoL/L) and abnormal 2-h postprandial blood glucose (≥11.1 mmoL/L). We aimed to develop a model to differentiate individuals with IPH from the normal population. METHODS Data from 54301 eligible participants were obtained from the Risk Evaluation of Cancers in Chinese Diabetic Individuals: a longitudinal (REACTION) study in China. Data from 37740 participants were used to develop the diagnostic system. External validation was performed among 16561 participants. Three machine learning algorithms were used to create the predictive models, which were further evaluated by various classification algorithms to establish the best predictive model. RESULTS Ten features were selected to develop an IPH diagnosis system (IPHDS) based on an artificial neural network. In external validation, the AUC of the IPHDS was 0.823 (95% CI 0.811-0.836), which was significantly higher than the AUC of the Taiwan model [0.799 (0.786-0.813)] and that of the Chinese Diabetes Risk Score model [0.648 (0.635-0.662)]. The IPHDS model had a sensitivity of 75.6% and a specificity of 74.6%. This model outperformed the Taiwan and CDRS models in subgroup analyses. An online site with instant predictions was deployed at https://app-iphds-e1fc405c8a69.herokuapp.com/. CONCLUSIONS The proposed IPHDS could be a convenient and user-friendly screening tool for diabetes during health examinations in a large general population.
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Affiliation(s)
- Rui Hou
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China
- Beijing Center for Disease Prevention and Control, Beijing, China
| | - Jingtao Dou
- Department of Endocrinology, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Lijuan Wu
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China
| | - Xiaoyu Zhang
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
| | - Changwei Li
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, USA
| | - Weiqing Wang
- National Clinical Research Center for Metabolic Diseases, State Key Laboratory of Medical Genomics, Shanghai Clinical Center for Endocrine and Metabolic Diseases, Shanghai Institute for Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Zhengnan Gao
- Dalian Central Hospital, Dalian, Liaoning, China
| | - Xulei Tang
- First Hospital of Lanzhou University, Lanzhou, Gansu, China
| | - Li Yan
- Zhongshan University Sun Yat-sen Memorial Hospital, Guangzhou, Guangdong, China
| | - Qin Wan
- Southwest Medical University Affiliated Hospital, Luzhou, Sichuan, China
| | - Zuojie Luo
- First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Guijun Qin
- First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Lulu Chen
- Wuhan Union Hospital, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Jianguang Ji
- Center for Primary Health Care Research, Lund University/Region Skåne, Malmö, Sweden
| | - Yan He
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China
| | - Wei Wang
- Centre for Precision Health, Edith Cowan University, Perth, Western Australia, Australia
| | - Yiming Mu
- Department of Endocrinology, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Deqiang Zheng
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China
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Bergman M, Manco M, Satman I, Chan J, Schmidt MI, Sesti G, Vanessa Fiorentino T, Abdul-Ghani M, Jagannathan R, Kumar Thyparambil Aravindakshan P, Gabriel R, Mohan V, Buysschaert M, Bennakhi A, Pascal Kengne A, Dorcely B, Nilsson PM, Tuomi T, Battelino T, Hussain A, Ceriello A, Tuomilehto J. International Diabetes Federation Position Statement on the 1-hour post-load plasma glucose for the diagnosis of intermediate hyperglycaemia and type 2 diabetes. Diabetes Res Clin Pract 2024; 209:111589. [PMID: 38458916 DOI: 10.1016/j.diabres.2024.111589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/10/2024]
Abstract
Many individuals with intermediate hyperglycaemia (IH), including impaired fasting glycaemia (IFG) and impaired glucose tolerance (IGT), as presently defined, will progress to type 2 diabetes (T2D). There is confirmatory evidence that T2D can be prevented by lifestyle modification and/or medications, in people with IGT diagnosed by 2-h plasma glucose (PG) during a 75-gram oral glucose tolerance test (OGTT). Over the last 40 years, a wealth of epidemiological data has confirmed the superior value of 1-h plasma glucose (PG) over fasting PG (FPG), glycated haemoglobin (HbA1c) and 2-h PG in populations of different ethnicity, sex and age in predicting diabetes and associated complications including death. Given the relentlessly rising prevalence of diabetes, a more sensitive, practical method is needed to detect people with IH and T2D for early prevention or treatment in the often lengthy trajectory to T2D and its complications. The International Diabetes Federation (IDF) Position Statement reviews findings that the 1-h post-load PG ≥ 155 mg/dL (8.6 mmol/L) in people with normal glucose tolerance (NGT) during an OGTT is highly predictive for detecting progression to T2D, micro- and macrovascular complications, obstructive sleep apnoea, cystic fibrosis-related diabetes mellitus, metabolic dysfunction-associated steatotic liver disease, and mortality in individuals with risk factors. The 1-h PG of 209 mg/dL (11.6 mmol/L) is also diagnostic of T2D. Importantly, the 1-h PG cut points for diagnosing IH and T2D can be detected earlier than the recommended 2-h PG thresholds. Taken together, the 1-h PG provides an opportunity to avoid misclassification of glycaemic status if FPG or HbA1c alone are used. The 1-h PG also allows early detection of high-risk people for intervention to prevent progression to T2D which will benefit the sizeable and growing population of individuals at increased risk of T2D. Using a 1-h OGTT, subsequent to screening with a non-laboratory diabetes risk tool, and intervening early will favourably impact the global diabetes epidemic. Health services should consider developing a policy for screening for IH based on local human and technical resources. People with a 1-h PG ≥ 155 mg/dL (8.6 mmol/L) are considered to have IH and should be prescribed lifestyle intervention and referred to a diabetes prevention program. People with a 1-h PG ≥ 209 mg/dL (11.6 mmol/L) are considered to have T2D and should have a repeat test to confirm the diagnosis of T2D and then referred for further evaluation and treatment. The substantive data presented in the Position Statement provides strong evidence for redefining current diagnostic criteria for IH and T2D by adding the 1-h PG.
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Affiliation(s)
- Michael Bergman
- NYU Grossman School of Medicine, Departments of Medicine and of Population Health, Division of Endocrinology, Diabetes and Metabolism, VA New York Harbor Healthcare System, New York, NY, USA.
| | - Melania Manco
- Predictive and Preventive Medicine Research Unit, Bambino Gesù Children's Hospital IRCCS, Rome, Italy
| | - Ilhan Satman
- Istanbul University Faculty of Medicine, Department of Internal Medicine, Division of Endocrinology and Metabolism, Istanbul, Turkey
| | - Juliana Chan
- The Chinese University of Hong Kong, Faculty of Medicine, Department of Medicine and Therapeutics, Hong Kong Institute of Diabetes and Obesity, Hong Kong, China
| | - Maria Inês Schmidt
- Postgraduate Program in Epidemiology, School of Medicine and Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Giorgio Sesti
- Department of Clinical and Molecular Medicine, University of Rome-Sapienza, 00189 Rome, Italy
| | - Teresa Vanessa Fiorentino
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, 88100 Catanzaro, Italy
| | - Muhammad Abdul-Ghani
- Division of Diabetes, University of Texas Health Science Center at San Antonio, San Antonio Texas, USA
| | - Ram Jagannathan
- Hubert Department of Global Health Rollins, School of Public Health, Emory University, Atlanta, GA, USA
| | | | - Rafael Gabriel
- Department of International Health, National School of Public Health, Instituto de Salud Carlos III, Madrid, Spain
| | - Viswanathan Mohan
- Dr. Mohan's Diabetes Specialities Centre and Madras Diabetes Research Foundation, Chennai, India
| | - Martin Buysschaert
- Department of Endocrinology and Diabetology, Université Catholique de Louvain, University, Clinic Saint-Luc, Brussels, Belgium
| | - Abdullah Bennakhi
- Dasman Diabetes Institute Office of Regulatory Affairs, Ethics Review Committee, Kuwait
| | - Andre Pascal Kengne
- South African Medical Research Council, Francie Van Zijl Dr, Parow Valley, Cape Town, 7501, South Africa
| | - Brenda Dorcely
- NYU Grossman School of Medicine, Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, New York, NY, USA
| | - Peter M Nilsson
- Department of Clinical Sciences and Lund University Diabetes Centre, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Tiinamaija Tuomi
- Folkhälsan Research Center, Helsinki, Finland; Abdominal Center, Endocrinology, Helsinki University Central Hospital, Research Program for Diabetes and Obesity, Center of Helsinki, Helsinki, Finland
| | | | - Akhtar Hussain
- Faculty of Health Sciences, Nord University, Bodø, Norway; Faculty of Medicine, Federal University of Ceará (FAMED-UFC), Brazil; International Diabetes Federation (IDF), Brussels, Belgium; Diabetes in Asia Study Group, Post Box: 752, Doha-Qatar; Centre for Global Health Research, Diabetic Association of Bangladesh, Dhaka, Bangladesh
| | | | - Jaakko Tuomilehto
- Department of International Health, National School of Public Health, Instituto de Salud Carlos III, Madrid, Spain; Public Health Promotion Unit, Finnish Institute for Health and Welfare, Helsinki, Finland; Department of Public Health, University of Helsinki, Helsinki, Finland; Saudi Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia
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郭 成, 张 晓, 玉 应, 谢 岚, 常 翠. [Effects of chlorogenic acid on glucose tolerance and its curve characteristics in high-fat diet-induced obesity rats]. BEIJING DA XUE XUE BAO. YI XUE BAN = JOURNAL OF PEKING UNIVERSITY. HEALTH SCIENCES 2020; 52:269-274. [PMID: 32306009 PMCID: PMC7433441 DOI: 10.19723/j.issn.1671-167x.2020.02.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 01/28/2019] [Indexed: 06/11/2023]
Abstract
OBJECTIVE To observe the effect of chlorogenic acid (chlorogenic acid, CGA) on the glucose tolerance and its curve characteristics in high fat diet-induced obesity (diet-induced-obesity, DIO) rats, so as to provide scientific grounds for the development and utilization of CGA in early prevention and reversal of prediabetes. METHODS Eight of forty-six male Sprague-Dawley rats were randomly selected as the normal diet group (CON group), and the rest were fed with high-fat diet. After 4 weeks, 24 high-fat-induced obese rats were screened according to the criteria and then randomly divided into high fat diet group (HFD group), 50% CGA group and 98% CGA group. The CGA groups received intragastric administrations of 50% CGA and 98% CGA orally via a gavage needle once a day for 8 weeks, respectively, while the CON and HFD groups received a carrier solution (phosphate buffer saline, PBS). Their body weights were measured weekly and oral glucose tolerance test (OGTT) was performed every 4 weeks. Fasting insulin and insulin release were measured at the end of the study. Meanwhile, HOMA-IR and visceral fat percentage were calculated. Histopathological examination by hematoxylin and eosin staining method were evaluated in the pancreatic tissues. RESULTS Before the intervention of chlorogenic acid, blood glucose levels 120 min after glucose loading (P<0.05) and AUC-G (P<0.05) were increased in the HFD group when compared with the CON group, and the time to glucose peak was delayed after 4 weeks of chlorogenic acid intervention (P<0.05). After 8 weeks of intervention, the HOMA-IR index, the insulin levels at 0 min, 30 min, 60 min, and 120 min after glucose loading and AUC-I increased (P<0.05), and the histopathological examination showed obvious hyperplasia of pancreatic islets (P<0.05). Compared with the HFD group, there was no significant change in glucose tolerance and glucose peak time in 50%CGA and 98%CGA groups at the end of 4 weeks of intervention. However, after 8 weeks of intervention, OGTT-60min,OGTT-120min blood glucose (P<0.05) were lower, HOMA-IR index and OGTT-0min, OGTT-120min serum insulin level decreased (P<0.05), the time to glucose peak shifted to an earlier timepoint (P<0.05), abnormal islet hyperplasia attenuated (P<0.05) in 50% CGA and 98% CGA groups. Also, the OGTT-30min serum insulin level was decreased (P<0.05) in 50% CGA group. CONCLUSION Delay in time to glucose peak during the OGTT was one of the manifestations of impaired glucose tolerance in DIO rats, and 50% and 98% CGA could improve the glucose tolerance and delay in glucose peak time.
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Affiliation(s)
- 成成 郭
- />北京大学第三医院运动医学研究所,北京 100191Institute of Sports Medicine, Peking University Third Hospital, Beijing 100191, China
| | - 晓圆 张
- />北京大学第三医院运动医学研究所,北京 100191Institute of Sports Medicine, Peking University Third Hospital, Beijing 100191, China
| | - 应香 玉
- />北京大学第三医院运动医学研究所,北京 100191Institute of Sports Medicine, Peking University Third Hospital, Beijing 100191, China
| | - 岚 谢
- />北京大学第三医院运动医学研究所,北京 100191Institute of Sports Medicine, Peking University Third Hospital, Beijing 100191, China
| | - 翠青 常
- />北京大学第三医院运动医学研究所,北京 100191Institute of Sports Medicine, Peking University Third Hospital, Beijing 100191, China
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Feng XY, Ding TT, Liu YY, Xu WR, Cheng XC. In-silico identification of peroxisome proliferator-activated receptor (PPAR)α/γ agonists from Ligand Expo Components database. J Biomol Struct Dyn 2020; 39:1853-1864. [DOI: 10.1080/07391102.2020.1745279] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- Xiao-Yan Feng
- Tianjin Key Laboratory on Technologies Enabling Development of Clinical Therapeutics and Diagnostics (Theranostics), School of Pharmacy, Tianjin Medical University, Tianjin, China
| | - Ting-Ting Ding
- Tianjin Key Laboratory on Technologies Enabling Development of Clinical Therapeutics and Diagnostics (Theranostics), School of Pharmacy, Tianjin Medical University, Tianjin, China
| | - Ya-Ya Liu
- Tianjin Key Laboratory on Technologies Enabling Development of Clinical Therapeutics and Diagnostics (Theranostics), School of Pharmacy, Tianjin Medical University, Tianjin, China
| | - Wei-Ren Xu
- Tianjin Key Laboratory of Molecular Design and Drug Discovery, Tianjin Institute of Pharmaceutical Research, Tianjin, China
| | - Xian-Chao Cheng
- Tianjin Key Laboratory on Technologies Enabling Development of Clinical Therapeutics and Diagnostics (Theranostics), School of Pharmacy, Tianjin Medical University, Tianjin, China
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