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Yang H, Chen Z, Huang J, Li S. AWD-stacking: An enhanced ensemble learning model for predicting glucose levels. PLoS One 2024; 19:e0291594. [PMID: 38354168 PMCID: PMC10866533 DOI: 10.1371/journal.pone.0291594] [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: 05/23/2023] [Accepted: 09/01/2023] [Indexed: 02/16/2024] Open
Abstract
Accurate prediction of blood glucose levels is essential for type 1 diabetes optimizing insulin therapy and minimizing complications in patients with type 1 diabetes. Using ensemble learning algorithms is a promising approach. In this regard, this study proposes an improved stacking ensemble learning algorithm for predicting blood glucose level, in which three improved long short-term memory network models are used as the base model, and an improved nearest neighbor propagation clustering algorithm is adaptively weighted to this ensemble model. The OhioT1DM dataset is used to train and evaluate the performance of the proposed model. This study evaluated the performance of the proposed model using the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Matthews Correlation Coefficient (MCC) as the evaluation metrics. The experimental results demonstrate that the proposed model achieves an RMSE of 1.425 mg/dL, MAE of 0.721 mg/dL, and MCC of 0.982 mg/dL for a 30-minute prediction horizon(PH), RMSE of 3.212 mg/dL, MAE of 1.605 mg/dL, and MCC of 0.950 mg/dL for a 45-minute PH; and RMSE of 6.346 mg/dL, MAE of 3.232 mg/dL, and MCC of 0.930 mg/dL for a 60-minute PH. Compared with the best non-ensemble model StackLSTM, the RMSE and MAE were improved by up to 27.92% and 65.32%, respectively. Clarke Error Grid Analysis and critical difference diagram revealed that the model errors were within 10%. The model proposed in this study exhibits state-of-the-art predictive performance, making it suitable for clinical decision-making and of significant importance for the effective treatment of diabetes in patients.
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Affiliation(s)
- HuaZhong Yang
- School of Computer Engineering, Jingchu University of Technology, Jingmen, Hubei, China
- School of Computer Science, Yangtze University, Jingzhou, Hubei, China
| | - Zhongju Chen
- School of Computer Science, Yangtze University, Jingzhou, Hubei, China
| | - Jinfan Huang
- School of Computer Science, Yangtze University, Jingzhou, Hubei, China
| | - Suruo Li
- School of Computer Engineering, Jingchu University of Technology, Jingmen, Hubei, China
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2
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Mohsen F, Al-Absi HRH, Yousri NA, El Hajj N, Shah Z. A scoping review of artificial intelligence-based methods for diabetes risk prediction. NPJ Digit Med 2023; 6:197. [PMID: 37880301 PMCID: PMC10600138 DOI: 10.1038/s41746-023-00933-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Accepted: 09/25/2023] [Indexed: 10/27/2023] Open
Abstract
The increasing prevalence of type 2 diabetes mellitus (T2DM) and its associated health complications highlight the need to develop predictive models for early diagnosis and intervention. While many artificial intelligence (AI) models for T2DM risk prediction have emerged, a comprehensive review of their advancements and challenges is currently lacking. This scoping review maps out the existing literature on AI-based models for T2DM prediction, adhering to the PRISMA extension for Scoping Reviews guidelines. A systematic search of longitudinal studies was conducted across four databases, including PubMed, Scopus, IEEE-Xplore, and Google Scholar. Forty studies that met our inclusion criteria were reviewed. Classical machine learning (ML) models dominated these studies, with electronic health records (EHR) being the predominant data modality, followed by multi-omics, while medical imaging was the least utilized. Most studies employed unimodal AI models, with only ten adopting multimodal approaches. Both unimodal and multimodal models showed promising results, with the latter being superior. Almost all studies performed internal validation, but only five conducted external validation. Most studies utilized the area under the curve (AUC) for discrimination measures. Notably, only five studies provided insights into the calibration of their models. Half of the studies used interpretability methods to identify key risk predictors revealed by their models. Although a minority highlighted novel risk predictors, the majority reported commonly known ones. Our review provides valuable insights into the current state and limitations of AI-based models for T2DM prediction and highlights the challenges associated with their development and clinical integration.
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Affiliation(s)
- Farida Mohsen
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, 34110, Doha, Qatar
| | - Hamada R H Al-Absi
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, 34110, Doha, Qatar
| | - Noha A Yousri
- Genetic Medicine, Weill Cornell Medicine-Qatar, Qatar Foundation, Doha, Qatar
- College of Health and Life Sciences, Hamad Bin Khalifa University, Qatar Foundation, 34110, Doha, Qatar
- Computer and Systems Engineering, Alexandria University, Alexandria, Egypt
| | - Nady El Hajj
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, 34110, Doha, Qatar
- College of Health and Life Sciences, Hamad Bin Khalifa University, Qatar Foundation, 34110, Doha, Qatar
| | - Zubair Shah
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, 34110, Doha, Qatar.
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Guan Z, Li H, Liu R, Cai C, Liu Y, Li J, Wang X, Huang S, Wu L, Liu D, Yu S, Wang Z, Shu J, Hou X, Yang X, Jia W, Sheng B. Artificial intelligence in diabetes management: Advancements, opportunities, and challenges. Cell Rep Med 2023; 4:101213. [PMID: 37788667 PMCID: PMC10591058 DOI: 10.1016/j.xcrm.2023.101213] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 08/07/2023] [Accepted: 09/08/2023] [Indexed: 10/05/2023]
Abstract
The increasing prevalence of diabetes, high avoidable morbidity and mortality due to diabetes and diabetic complications, and related substantial economic burden make diabetes a significant health challenge worldwide. A shortage of diabetes specialists, uneven distribution of medical resources, low adherence to medications, and improper self-management contribute to poor glycemic control in patients with diabetes. Recent advancements in digital health technologies, especially artificial intelligence (AI), provide a significant opportunity to achieve better efficiency in diabetes care, which may diminish the increase in diabetes-related health-care expenditures. Here, we review the recent progress in the application of AI in the management of diabetes and then discuss the opportunities and challenges of AI application in clinical practice. Furthermore, we explore the possibility of combining and expanding upon existing digital health technologies to develop an AI-assisted digital health-care ecosystem that includes the prevention and management of diabetes.
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Affiliation(s)
- Zhouyu Guan
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Huating Li
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Ruhan Liu
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China; MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Furong Laboratory, Changsha, Hunan 41000, China
| | - Chun Cai
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Yuexing Liu
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Jiajia Li
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China; MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xiangning Wang
- Department of Ophthalmology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai 200233, China
| | - Shan Huang
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China; MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Liang Wu
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Dan Liu
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Shujie Yu
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Zheyuan Wang
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China; MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Jia Shu
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China; MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xuhong Hou
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Xiaokang Yang
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Weiping Jia
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China.
| | - Bin Sheng
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China; MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
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Tao X, Jiang M, Liu Y, Hu Q, Zhu B, Hu J, Guo W, Wu X, Xiong Y, Shi X, Zhang X, Han X, Li W, Tong R, Long E. Predicting three-month fasting blood glucose and glycated hemoglobin changes in patients with type 2 diabetes mellitus based on multiple machine learning algorithms. Sci Rep 2023; 13:16437. [PMID: 37777593 PMCID: PMC10543442 DOI: 10.1038/s41598-023-43240-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 09/21/2023] [Indexed: 10/02/2023] Open
Abstract
Fasting blood glucose (FBG) and glycosylated hemoglobin (HbA1c) are key indicators reflecting blood glucose control in type 2 diabetes mellitus (T2DM) patients. The purpose of this study is to establish a predictive model for blood glucose changes in T2DM patients after 3 months of treatment, achieving personalized treatment.A retrospective study was conducted on type 2 diabetes mellitus real-world medical data from 4 cities in Sichuan Province, China from January 2015 to December 2020. After data preprocessing, data inputting, data sampling, and feature screening, 16 kinds of machine learning methods were used to construct prediction models, and 5 prediction models with the best prediction performance were screened respectively. A total of 100,000 cases were included to establish the FBG model, and 2,169 cases were established to establish the HbA1c model. The best prediction model both of FBG and HbA1c finally obtained are realized by ensemble learning and modified random forest inputting, the AUC values are 0.819 and 0.970, respectively. The most important indicators of the FBG and HbA1c prediction model were FBG and HbA1c. Medication compliance, follow-up outcome, dietary habits, BMI, and waist circumference also had a greater impact on FBG levels. The prediction accuracy of the models of the two blood glucose control indicators is high and has certain clinical applicability.HbA1c and FBG are mutually important predictors, and there is a close relationship between them.
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Affiliation(s)
- Xue Tao
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, Sichuan, China
| | - Min Jiang
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, 610044, Sichuan, China
| | - Yumeng Liu
- Department of Pharmacy, Daping Hospital, Army Medical University, Chongqing, 400042, China
| | - Qi Hu
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610072, Sichuan, China
| | - Baoqiang Zhu
- School of Pharmacy, Southwest Medical University, Luzhou, 646000, Sichuan, China
| | - Jiaqiang Hu
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, Sichuan, China
| | - Wenmei Guo
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, Sichuan, China
| | - Xingwei Wu
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, Sichuan, China
| | - Yu Xiong
- Institute of Materia Medica, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing, 100050, China
| | - Xia Shi
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, Sichuan, China
| | - Xueli Zhang
- Sichuan Provincial Health Information Center, Chengdu, 610015, Sichuan, China
| | - Xu Han
- Sichuan Provincial Health Information Center, Chengdu, 610015, Sichuan, China
| | - Wenyuan Li
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, Sichuan, China
| | - Rongsheng Tong
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, Sichuan, China
| | - Enwu Long
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, Sichuan, China.
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5
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Simmons SS. Strikes and Gutters: Biomarkers and anthropometric measures for predicting diagnosed diabetes mellitus in adults in low- and middle-income countries. Heliyon 2023; 9:e19494. [PMID: 37810094 PMCID: PMC10558610 DOI: 10.1016/j.heliyon.2023.e19494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 08/22/2023] [Accepted: 08/24/2023] [Indexed: 10/10/2023] Open
Abstract
The management of diabetes necessitates the requirement of reliable health indices, specifically biomarkers and anthropometric measures, to detect the presence or absence of the disease. Nevertheless, limited robust empirical evidence exists regarding the optimal metrics for predicting diabetes in adults, particularly within low- and middle-income countries. This study investigates objective and subjective indices for screening diabetes in these countries. METHODS Data for this study was sourced from surveys conducted among adults (aged 18 years and above) in seventeen (17) countries. Self-reported diabetes status, fifty-four biomarkers, and twenty-six core and twenty-eight estimated anthropometric indices, including weight, waist circumference, body mass index, glycaemic triglycerides, and fasting blood glucose, were utilised to construct lasso regression models. RESULTS The study revealed variances in diabetes prediction outcomes across different countries. Central adiposity measures, fasting plasma glucose and glycaemic triglycerides demonstrated superior predictive capabilities for diabetes when compared to body mass index. Furthermore, fasting plasma or blood glucose, serving as a biomarker, emerged as the most accurate predictor of diabetes. CONCLUSIONS These findings offer critical insights into both general and context-specific tools for diabetes screening. The study proposes that fasting plasma glucose and central adiposity indices should be considered as routine screening tools for diabetes, both in policy interventions and clinical practice. By identifying adults with or at higher risk of developing diabetes and implementing appropriate interventions, these screening tools possess the potential to mitigate diabetes-related complications in low- and middle-income countries.
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Affiliation(s)
- Sally Sonia Simmons
- Department of Social Policy, London School of Economics and Political Science, London, WC2A 2AE, United Kingdom
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Sun X, Qiu WW, Wu J, Ding SL, Wu RZ. Associations between the levels of circulating inflammatory adipokines and the risk of type 2 diabetes in Chinese male individuals: A case-control study. J Clin Lab Anal 2023; 37:e24875. [PMID: 37003602 PMCID: PMC10156094 DOI: 10.1002/jcla.24875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 01/04/2023] [Accepted: 03/19/2023] [Indexed: 04/03/2023] Open
Abstract
BACKGROUND Whether the levels of circulating inflammatory adipokines affect the progression of type 2 diabetes (T2D) remains unclear. This study aimed to assess the association between circulating inflammatory adipokine levels and risk of T2D. METHODS This case-control study involved 130 individuals consisting of 66 healthy controls (Control group) and 64 patients with T2D (T2D group) in Lishui Municipal Central Hospital from January 2017 to June 2017. Multivariate logistic regression analysis was applied to assess the associations between circulating inflammatory adipokine levels and the risk of T2D. RESULTS There were significant differences in the levels of adiponectin (p = 0.013) and visfatin (p < 0.001) between the T2D and Control groups. In contrast, no significant differences in leptin (p = 0.113), TNF-α (p = 0.632), and IL-6 (p = 0.156) levels were found between the groups. Multivariate logistic regression indicated that elevated visfatin level was associated with an increased risk of T2D (OR: 3.543; 95% CI: 1.771-7.088; p < 0.001), while adiponectin (OR: 1.946; 95% CI: 0.925-4.094; p = 0.079), leptin (OR: 3.723; 95% CI: 0.788-17.583; p = 0.097), TNF-α (OR: 1.081; 95% CI: 0.911-1.281; p = 0.373), and IL-6 (OR: 0.878; 95% CI: 0.657-1.173; p = 0.379) were not associated with the risk of T2D. CONCLUSIONS This study found elevated visfatin levels are associated with an increased risk of T2D, while adiponectin, leptin, TNF-α, and IL-6 are not. These findings should be further verified by a large-scale prospective study.
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Affiliation(s)
- Xia Sun
- Department of Endocrinology, Lishui Hospital of Traditional Chinese Medicine, Lishui, Zhejiang, China
| | - Wei-Wen Qiu
- Department of Neurology, Lishui Hospital of Traditional Chinese Medicine, Lishui, Zhejiang, China
| | - Jing Wu
- Department of Endocrinology, Lishui Hospital of Traditional Chinese Medicine, Lishui, Zhejiang, China
| | - Shi-Ling Ding
- Department of Endocrinology, Lishui Hospital of Traditional Chinese Medicine, Lishui, Zhejiang, China
| | - Rong-Zhen Wu
- Department of Clinical Laboratory, Lishui Municipal Central Hospital, Lishui, Zhejiang, China
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Liu J, Liu M, Chai Z, Li C, Wang Y, Shen M, Zhuang G, Zhang L. Projected rapid growth in diabetes disease burden and economic burden in China: a spatio-temporal study from 2020 to 2030. THE LANCET REGIONAL HEALTH. WESTERN PACIFIC 2023; 33:100700. [PMID: 36817869 PMCID: PMC9932123 DOI: 10.1016/j.lanwpc.2023.100700] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Revised: 01/01/2023] [Accepted: 01/13/2023] [Indexed: 02/05/2023]
Abstract
Background This study projects the trend of disease burden and economic burden of diabetes in 33 Chinese provinces and nationally during 2020-2030 and investigates its spatial disparities. Methods Time series prediction on the prevalence and disability-adjusted life-year (DALY) rates of diabetes was conducted using a Bayesian modelling approach in 2020-2030. The top-down method and the human capital method were used to predict the direct and indirect costs of diabetes for each Chinese province. Global and local spatial autocorrelation analyses were used to identify geographic clusters of low-or high-burden areas. Findings Diabetes prevalence in Chinese adults aged 20-79 years was projected to increase from 8.2% to 9.7% during 2020-2030. During the same period, the total costs of diabetes would increase from $250.2 billion to $460.4 billion, corresponding to an annual growth rate of 6.32%. The total costs of diabetes as a percentage of GDP would increase from 1.58% to 1.69% in China during 2020-2030, suggesting a faster growth in the economic burden of diabetes than China's economic growth. Consistently, the per-capita economic burden of diabetes would increase from $231 to $414 in China during 2020-2030, with an annual growth rate of 6.02%. High disease and economic burden areas were aggregated in Northeast and/or North China. Interpretation Our study projects a significant growth of disease and economic burden of diabetes in China during 2020-2030, with strong spatial aggregation in northern Chinese regions. The increase in the economic burden of diabetes will exceed that of GDP. Funding National Natural Science Foundation of China, Outstanding Young Scholars Funding.
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Affiliation(s)
- Jinli Liu
- China-Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China
| | - Min Liu
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Zhonglin Chai
- Department of Diabetes, Central Clinical School, Monash University, Melbourne, Victoria, Australia
| | - Chao Li
- China-Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China
| | - Yanan Wang
- Med-X Institute, Center for Immunological and Metabolic Diseases, and Department of Endocrinology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Mingwang Shen
- China-Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China
| | - Guihua Zhuang
- China-Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China,Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, China,Corresponding author. China-Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi province, China
| | - Lei Zhang
- China-Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China,Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, China,Melbourne Sexual Health Centre, Alfred Health, Melbourne, Australia,Central Clinical School, Faculty of Medicine, Monash University, Melbourne, Victoria, Australia,Corresponding author. School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, 710061, China.
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8
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Matricciani L, Paquet C, Dumuid D, Lushington K, Olds T. Multidimensional Sleep and Cardiometabolic Risk Factors for Type 2 Diabetes: Examining Self-Report and Objective Dimensions of Sleep. DIABETES EDUCATOR 2022; 48:533-545. [DOI: 10.1177/26350106221137896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Purpose: The purpose of the study was to determine the association between objective and self-report measures of sleep and cardiometabolic risk factors for type 2 diabetes. Methods: This study examines data on Australian adults, collected as part of the Child Health CheckPoint study. Sleep was examined in terms of actigraphy-derived sleep duration, timing, efficiency and variability; and self-report trouble sleeping. Cardiometabolic risk factors for type 2 diabetes were examined in terms of body mass index and biomarkers of inflammation and dyslipidemia. Generalized estimating equations, adjusted for geographic clustering, were used to determine the association between measures of sleep and cardiometabolic risk factors. Results: Complete case analysis was conducted for 1017 parents (87% mothers). Both objective and self-report measures of sleep were significantly but weakly associated with cardiometabolic risk factors. Conclusion: Both objective and self-report measures of sleep are significantly associated with cardiometabolic risk factors for type 2 diabetes. Self-report troubled sleep is associated with poorer cardiometabolic health, independent of actigraphy-derived sleep parameters.
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Affiliation(s)
- Lisa Matricciani
- Clinical & Health Sciences, University of South Australia, Adelaide, Australia
- Alliance for Research in Exercise, Nutrition and Activity (ARENA), University of South Australia, Adelaide, Australia
| | - Catherine Paquet
- Faculté des Sciences Administratives, Université Laval; Centre Nutrition, santé et société (NUTRISS), INAF, Université Laval; Centre de Recherche, Centre Hospitalier Universitaire de Québec - Université Laval
| | - Dorothea Dumuid
- Alliance for Research in Exercise, Nutrition and Activity (ARENA), University of South Australia, Adelaide, Australia
- Allied Health and Human Performance (AHHP), University of South Australia, Adelaide, Australia
- Murdoch Children’s Research Institute, Parkville, Victoria, Australia
| | - Kurt Lushington
- Discipline of Psychology, Justice and Society Unit, University of South Australia
| | - Tim Olds
- Alliance for Research in Exercise, Nutrition and Activity (ARENA), University of South Australia, Adelaide, Australia
- Murdoch Children’s Research Institute, Parkville, Victoria, Australia
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9
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Xue L, Wang H, He Y, Sui M, Li H, Mei L, Ying X. Incidence and risk factors of diabetes mellitus in the Chinese population: a dynamic cohort study. BMJ Open 2022; 12:e060730. [PMID: 36410801 PMCID: PMC9680191 DOI: 10.1136/bmjopen-2021-060730] [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] [Indexed: 11/23/2022] Open
Abstract
OBJECTIVE Diabetes mellitus is a common condition often associated with an ageing population. However, only few longitudinal studies in China have investigated the incidence of diabetes and identified its risk factors. Therefore, this study aimed to investigate the incidence and risk factors of diabetes in Chinese people aged ≥45 years using the harmonised China Health and Retirement Longitudinal Study (CHARLS) data. DESIGN A dynamic cohort study. SETTING The harmonised CHARLS 2011-2018. PARTICIPANTS 19 988 adults aged ≥45 years. PRIMARY OUTCOME MEASURE Incident diabetes from 2011 to 2018. RESULTS The harmonised CHARLS is a representative longitudinal survey of people aged ≥45 years. Using data extracted from the harmonised CHARLS, we calculated the incidence of diabetes and used a competing risk model to determine risk factors of diabetes. In 2011-2013, 2013-2015, 2015-2018, the crude incidence of diabetes among middle-aged and older people in China was 1403.21 (1227.09 to 1604.19), 1673.22 (1485.73 to 1883.92) and 3919.83 (3646.01 to 4213.30) per 100 000 person-years, respectively, with a significant increasing trend. There were no geographical variations in the incidence of diabetes. Age, obesity and alcohol consumption were associated with an increased risk of incident diabetes. CONCLUSION The incidence of diabetes increased annually, without any geographical differences. Age, obesity and alcohol consumption were found to be risk factors for incident diabetes.
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Affiliation(s)
- Long Xue
- Department of Health Economics, Fudan University School of Public Health, Shanghai, China
| | - Huiying Wang
- Department of Medical Services, Huashan Hospital Fudan University, Shanghai, China
| | - YunZhen He
- Department of Health Economics, Fudan University School of Public Health, Shanghai, China
| | - Mengyun Sui
- Department of Health Economics, Fudan University School of Public Health, Shanghai, China
| | - Hongzheng Li
- Department of Health Economics, Fudan University School of Public Health, Shanghai, China
| | - Lin Mei
- Department of Medical Services, Huashan Hospital Fudan University, Shanghai, China
| | - Xiaohua Ying
- Department of Health Economics, Fudan University School of Public Health, Shanghai, China
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10
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Nagpal MS, Barbaric A, Sherifali D, Morita PP, Cafazzo JA. Patient-Generated Data Analytics of Health Behaviors of People Living With Type 2 Diabetes: Scoping Review. JMIR Diabetes 2021; 6:e29027. [PMID: 34783668 PMCID: PMC8726031 DOI: 10.2196/29027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 08/01/2021] [Accepted: 10/31/2021] [Indexed: 11/13/2022] Open
Abstract
Background Complications due to type 2 diabetes (T2D) can be mitigated through proper self-management that can positively change health behaviors. Technological tools are available to help people living with, or at risk of developing, T2D to manage their condition, and such tools provide a large repository of patient-generated health data (PGHD). Analytics can provide insights into the health behaviors of people living with T2D. Objective The aim of this review is to investigate what can be learned about the health behaviors of those living with, or at risk of developing, T2D through analytics from PGHD. Methods A scoping review using the Arksey and O’Malley framework was conducted in which a comprehensive search of the literature was conducted by 2 reviewers. In all, 3 electronic databases (PubMed, IEEE Xplore, and ACM Digital Library) were searched using keywords associated with diabetes, behaviors, and analytics. Several rounds of screening using predetermined inclusion and exclusion criteria were conducted, after which studies were selected. Critical examination took place through a descriptive-analytical narrative method, and data extracted from the studies were classified into thematic categories. These categories reflect the findings of this study as per our objective. Results We identified 43 studies that met the inclusion criteria for this review. Although 70% (30/43) of the studies examined PGHD independently, 30% (13/43) combined PGHD with other data sources. Most of these studies used machine learning algorithms to perform their analysis. The themes identified through this review include predicting diabetes or obesity, deriving factors that contribute to diabetes or obesity, obtaining insights from social media or web-based forums, predicting glycemia, improving adherence and outcomes, analyzing sedentary behaviors, deriving behavior patterns, discovering clinical correlations from behaviors, and developing design principles. Conclusions The increased volume and availability of PGHD have the potential to derive analytical insights into the health behaviors of people living with T2D. From the literature, we determined that analytics can predict outcomes and identify granular behavior patterns from PGHD. This review determined the broad range of insights that can be examined through PGHD, which constitutes a unique source of data for these applications that would not be possible through the use of other data sources.
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Affiliation(s)
- Meghan S Nagpal
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada.,Centre for Global eHealth Innovation, Techna Institute, University Health Network, Toronto, ON, Canada
| | - Antonia Barbaric
- Centre for Global eHealth Innovation, Techna Institute, University Health Network, Toronto, ON, Canada.,Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
| | - Diana Sherifali
- School of Nursing, McMaster University, Hamilton, ON, Canada
| | - Plinio P Morita
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada.,Centre for Global eHealth Innovation, Techna Institute, University Health Network, Toronto, ON, Canada.,School of Public Health and Health Systems, University of Waterloo, Waterloo, ON, Canada
| | - Joseph A Cafazzo
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada.,Centre for Global eHealth Innovation, Techna Institute, University Health Network, Toronto, ON, Canada.,Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada.,Department of Computer Science, University of Toronto, Toronto, ON, Canada
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11
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Yang C, Liu Q, Guo H, Zhang M, Zhang L, Zhang G, Zeng J, Huang Z, Meng Q, Cui Y. Usefulness of Machine Learning for Identification of Referable Diabetic Retinopathy in a Large-Scale Population-Based Study. Front Med (Lausanne) 2021; 8:773881. [PMID: 34977075 PMCID: PMC8717406 DOI: 10.3389/fmed.2021.773881] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 11/11/2021] [Indexed: 11/13/2022] Open
Abstract
Purpose: To development and validation of machine learning-based classifiers based on simple non-ocular metrics for detecting referable diabetic retinopathy (RDR) in a large-scale Chinese population–based survey.Methods: The 1,418 patients with diabetes mellitus from 8,952 rural residents screened in the population-based Dongguan Eye Study were used for model development and validation. Eight algorithms [extreme gradient boosting (XGBoost), random forest, naïve Bayes, k-nearest neighbor (KNN), AdaBoost, Light GBM, artificial neural network (ANN), and logistic regression] were used for modeling to detect RDR in individuals with diabetes. The area under the receiver operating characteristic curve (AUC) and their 95% confidential interval (95% CI) were estimated using five-fold cross-validation as well as an 80:20 ratio of training and validation.Results: The 10 most important features in machine learning models were duration of diabetes, HbA1c, systolic blood pressure, triglyceride, body mass index, serum creatine, age, educational level, duration of hypertension, and income level. Based on these top 10 variables, the XGBoost model achieved the best discriminative performance, with an AUC of 0.816 (95%CI: 0.812, 0.820). The AUCs for logistic regression, AdaBoost, naïve Bayes, and Random forest were 0.766 (95%CI: 0.756, 0.776), 0.754 (95%CI: 0.744, 0.764), 0.753 (95%CI: 0.743, 0.763), and 0.705 (95%CI: 0.697, 0.713), respectively.Conclusions: A machine learning–based classifier that used 10 easily obtained non-ocular variables was able to effectively detect RDR patients. The importance scores of the variables provide insight to prevent the occurrence of RDR. Screening RDR with machine learning provides a useful complementary tool for clinical practice in resource-poor areas with limited ophthalmic infrastructure.
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Affiliation(s)
- Cheng Yang
- Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Eye Institute, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Qingyang Liu
- Department of Ophthalmology, Dongguan People's Hospital, Dongguan, China
| | - Haike Guo
- Shanghai Peace Eye Hospital, Shanghai, China
- Xiamen Eye Center, Xiamen University, Xiamen, China
| | - Min Zhang
- Department of Ophthalmology, Dongguan People's Hospital, Dongguan, China
| | - Lixin Zhang
- Department of Ophthalmology, Hengli Hospital, Dongguan, China
| | - Guanrong Zhang
- Information and Statistical Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Jin Zeng
- Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Eye Institute, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Zhongning Huang
- Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Eye Institute, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Qianli Meng
- Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Eye Institute, Guangdong Academy of Medical Sciences, Guangzhou, China
- *Correspondence: Qianli Meng
| | - Ying Cui
- Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Eye Institute, Guangdong Academy of Medical Sciences, Guangzhou, China
- Ying Cui
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12
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Gautier T, Ziegler LB, Gerber MS, Campos-Náñez E, Patek SD. Artificial intelligence and diabetes technology: A review. Metabolism 2021; 124:154872. [PMID: 34480920 DOI: 10.1016/j.metabol.2021.154872] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 07/27/2021] [Accepted: 08/28/2021] [Indexed: 12/15/2022]
Abstract
Artificial intelligence (AI) is widely discussed in the popular literature and is portrayed as impacting many aspects of human life, both in and out of the workplace. The potential for revolutionizing healthcare is significant because of the availability of increasingly powerful computational platforms and methods, along with increasingly informative sources of patient data, both in and out of clinical settings. This review aims to provide a realistic assessment of the potential for AI in understanding and managing diabetes, accounting for the state of the art in the methodology and medical devices that collect data, process data, and act accordingly. Acknowledging that many conflicting definitions of AI have been put forth, this article attempts to characterize the main elements of the field as they relate to diabetes, identifying the main perspectives and methods that can (i) affect basic understanding of the disease, (ii) affect understanding of risk factors (genetic, clinical, and behavioral) of diabetes development, (iii) improve diagnosis, (iv) improve understanding of the arc of disease (progression and personal/societal impact), and finally (v) improve treatment.
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Affiliation(s)
- Thibault Gautier
- Dexcom/TypeZero, 946 Grady Avenue, Suite 203, Charlottesville, VA 22903, United States of America.
| | - Leah B Ziegler
- Dexcom/TypeZero, 946 Grady Avenue, Suite 203, Charlottesville, VA 22903, United States of America
| | - Matthew S Gerber
- Dexcom/TypeZero, 946 Grady Avenue, Suite 203, Charlottesville, VA 22903, United States of America
| | - Enrique Campos-Náñez
- Dexcom/TypeZero, 946 Grady Avenue, Suite 203, Charlottesville, VA 22903, United States of America
| | - Stephen D Patek
- Dexcom/TypeZero, 946 Grady Avenue, Suite 203, Charlottesville, VA 22903, United States of America
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13
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Wie profitieren Menschen mit Diabetes von Big Data und künstlicher Intelligenz? DIABETOLOGE 2021. [DOI: 10.1007/s11428-021-00818-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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14
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Liu J, Garstka MA, Chai Z, Chen Y, Lipkova V, Cooper ME, Mokoena KK, Wang Y, Zhang L. Marriage contributes to higher obesity risk in China: findings from the China Health and Nutrition Survey. ANNALS OF TRANSLATIONAL MEDICINE 2021; 9:564. [PMID: 33987262 DOI: 10.21037/atm-20-4550] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Background To investigate the association between marriage and the prevalence of overweight and obesity in China. Methods We conducted cross-sectional and retrospective cohort analyses using a nationwide sample of 36,310 individuals from the China Health and Nutrition Survey [2004-2015]. Results The prevalence of overweight and obesity increased from 28.7% to 36.7% and from 8.0% to 14.5% between 2004 and 2015, respectively. The cross-sectional analysis showed that married individuals were at a higher risk of being overweight (OR =2.18; 95% CI, 1.90-2.51) or obese (OR =1.95; 1.57-2.43) than never-married individuals. Divorced/widowed individuals were also at a greater risk of being overweight (OR =1.80; 1.51-2.13) or obese (OR =1.67; 1.28-2.17) than never-married individuals. Retrospective cohort analysis showed that individuals who married during the study were 1.55 (1.13-2.11) times more likely to be overweight than those who remained never-married. Compared to those who remained never-married, individuals who remained married were 1.71 (1.42-2.07) and 1.45 (1.11-1.89) times more likely to be overweight and obese. Individuals who became divorced or widowed were more likely to be overweight (RR =1.59; 1.18-2.15) or obese (RR =1.63; 1.08-2.46) than those who remained never-married. However, the risk of being overweight or obese among those who became divorced or widowed did not differ significantly from the risk among those who remained married. Conclusions Marriage contributes to an increased risk of overweight and obesity in China; however, this risk is not significantly reduced by exiting a marriage.
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Affiliation(s)
- Jinli Liu
- China-Australia Joint Research Centre for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Centre, Xi'an, China
| | - Malgorzata A Garstka
- Core Research Laboratory, Department of Endocrinology, National & Local Joint Engineering Research Center of Biodiagnosis and Biotherapy, The Second Affiliated Hospital, School of Medicine, Xi'an Jiaotong University, Xi'an, China
| | - Zhonglin Chai
- Department of Diabetes, Central Clinical School, Monash University, Melbourne, Australia
| | - Yifan Chen
- Medical Sciences Division, University of Oxford, Oxford, UK
| | | | - Mark E Cooper
- Department of Diabetes, Central Clinical School, Monash University, Melbourne, Australia
| | | | - Youfa Wang
- Global Health Institute, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - Lei Zhang
- China-Australia Joint Research Centre for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Centre, Xi'an, China.,Melbourne Sexual Health Centre, Alfred Health, Melbourne, Australia.,Central Clinical School, Faculty of Medicine, Monash University, Melbourne, Australia.,Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, China
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15
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Wu J, Wang Y, Xiao X, Shang X, He M, Zhang L. Spatial Analysis of Incidence of Diagnosed Type 2 Diabetes Mellitus and Its Association With Obesity and Physical Inactivity. Front Endocrinol (Lausanne) 2021; 12:755575. [PMID: 34777252 PMCID: PMC8581298 DOI: 10.3389/fendo.2021.755575] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 10/08/2021] [Indexed: 01/03/2023] Open
Abstract
OBJECTIVES To investigate the spatial distribution of 10-year incidence of diagnosed type 2 diabetes mellitus (T2DM) and its association with obesity and physical inactivity at a reginal level breakdown. METHODS Demographic, behavioral, medical and pharmaceutical and diagnosed T2DM incidence data were collected from a cohort of 232,064 participants who were free of diabetes at enrolment in the 45 and Up Study, conducted in the state of New South Wales (NSW), Australia. We examined the geographical trend and correlation between obesity prevalence, physical inactivity rate and age-and-gender-adjusted cumulative incidence of T2DM, aggregated based on geographical regions. RESULT The T2DM incidence, prevalence of obesity and physical inactivity rate at baseline were 6.32%, 20.24%, and 18.7%, respectively. The spatial variation of T2DM incidence was significant (Moran's I=0.52; p<0.01), with the lowest incidence of 2.76% in Richmond Valley-Coastal and the highest of 12.27% in Mount Druitt. T2DM incidence was significantly correlated with the prevalence of obesity (Spearman r=0.62, p<0.001), percentage of participants having five sessions of physical activities or less per week (r=0.79, p<0.001) and percentage of participants walked to work (r=-0.44, p<0.001). The geographical variations in obesity prevalence and physical inactivity rate resembled the geographical variation in the incidence of T2DM. CONCLUSION The spatial distribution of T2DM incidence is significantly associated with the geographical prevalence of obesity and physical inactivity rate. Regional campaigns advocating the importance of physical activities in response to the alarming T2DM epidemic should be promoted.
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Affiliation(s)
- Jinrong Wu
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Melbourne, VIC, Australia
- Research Centre for Data Analytics and Cognition, La Trobe University, Melbourne, VIC, Australia
| | - Yang Wang
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Melbourne, VIC, Australia
| | - Xin Xiao
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Melbourne, VIC, Australia
- Center for Optometry and Visual Science, Department of Optometry, People’s Hospital of Guangxi Zhuang Autonomous Region, Guangxi, China
| | - Xianwen Shang
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Melbourne, VIC, Australia
| | - Mingguang He
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Melbourne, VIC, Australia
- Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, VIC, Australia
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
- *Correspondence: Lei Zhang, ; Mingguang He,
| | - Lei Zhang
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Melbourne, VIC, Australia
- Melbourne Sexual Health Centre, Alfred Health, Melbourne, VIC, Australia
- Central Clinical School, Faculty of Medicine, Monash University, Melbourne, VIC, Australia
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, China
- *Correspondence: Lei Zhang, ; Mingguang He,
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16
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Bao Y, Medland NA, Fairley CK, Wu J, Shang X, Chow EPF, Xu X, Ge Z, Zhuang X, Zhang L. Predicting the diagnosis of HIV and sexually transmitted infections among men who have sex with men using machine learning approaches. J Infect 2020; 82:48-59. [PMID: 33189772 DOI: 10.1016/j.jinf.2020.11.007] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 10/30/2020] [Accepted: 11/07/2020] [Indexed: 01/14/2023]
Abstract
OBJECTIVES We aimed to develop machine learning models and evaluate their performance in predicting HIV and sexually transmitted infections (STIs) diagnosis based on a cohort of Australian men who have sex with men (MSM). METHODS We collected clinical records of 21,273 Australian MSM during 2011-2017. We compared accuracies for predicting HIV and STIs (syphilis, gonorrhoea, chlamydia) diagnosis using four machine learning approaches against a multivariable logistic regression (MLR) model. RESULTS Machine learning approaches consistently outperformed MLR. Gradient boosting machine (GBM) achieved the highest area under the receiver operator characteristic curve for HIV (76.3%) and STIs (syphilis, 85.8%; gonorrhoea, 75.5%; chlamydia, 68.0%), followed by extreme gradient boosting (71.1%, 82.2%, 70.3%, 66.4%), random forest (72.0%, 81.9%, 67.2%, 64.3%), deep learning (75.8%, 81.0%, 67.5%, 65.4%) and MLR (69.8%, 80.1%, 67.2%, 63.2%). GBM models demonstrated the ten greatest predictors collectively explained 62.7-73.6% of variations in predicting HIV/STIs. STIs symptoms, past syphilis infection, age, time living in Australia, frequency of condom use with casual male sexual partners during receptive anal sex and the number of casual male sexual partners in the past 12 months were most commonly identified predictors. CONCLUSIONS Machine learning approaches are advantageous over multivariable logistic regression models in predicting HIV/STIs diagnosis.
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Affiliation(s)
- Yining Bao
- China Australia Joint Research Centre for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Centre, Xi'an, Shaanxi 710061, People's Republic of China; Department of Epidemiology and Biostatistics, School of Public Health, Nantong University, No.9 Seyuan Road, Chongchuan District, Nantong, Jiangsu 226019, People's Republic of China; Melbourne Sexual Health Centre, Alfred Health, Melbourne, Australia
| | - Nicholas A Medland
- Melbourne Sexual Health Centre, Alfred Health, Melbourne, Australia; Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia; The Kirby Institute, University of NSW, Sydney, Australia
| | - Christopher K Fairley
- China Australia Joint Research Centre for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Centre, Xi'an, Shaanxi 710061, People's Republic of China; Melbourne Sexual Health Centre, Alfred Health, Melbourne, Australia; Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
| | - Jinrong Wu
- Centre for Eye Research Australia; Ophthalmology, Department of Surgery, The University of Melbourne, Melbourne, Australia; Centre for Data Analytics and Cognition, College of Arts, Social Sciences and Commerce, The La Trobe University, Melbourne, Australia
| | - Xianwen Shang
- Centre for Eye Research Australia; Ophthalmology, Department of Surgery, The University of Melbourne, Melbourne, Australia
| | - Eric P F Chow
- Melbourne Sexual Health Centre, Alfred Health, Melbourne, Australia; Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
| | - Xianglong Xu
- China Australia Joint Research Centre for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Centre, Xi'an, Shaanxi 710061, People's Republic of China; Melbourne Sexual Health Centre, Alfred Health, Melbourne, Australia; Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
| | - Zongyuan Ge
- Monash e-Research Centre, Faculty of Engineering, Airdoc Research, Nvidia AI Technology Research Centre, Monash University, Melbourne, VIC, Australia
| | - Xun Zhuang
- Department of Epidemiology and Biostatistics, School of Public Health, Nantong University, No.9 Seyuan Road, Chongchuan District, Nantong, Jiangsu 226019, People's Republic of China.
| | - Lei Zhang
- China Australia Joint Research Centre for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Centre, Xi'an, Shaanxi 710061, People's Republic of China; Melbourne Sexual Health Centre, Alfred Health, Melbourne, Australia; Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia; Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou 450001, Henan, China.
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17
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Guo S, Yu X, Okan O. Moving Health Literacy Research and Practice towards a Vision of Equity, Precision and Transparency. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:E7650. [PMID: 33092206 PMCID: PMC7589069 DOI: 10.3390/ijerph17207650] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 10/15/2020] [Accepted: 10/16/2020] [Indexed: 02/06/2023]
Abstract
Over the past two decades, health literacy research has gained increasing attention in global health initiatives to reduce health disparities. While it is well-documented that health literacy is associated with health outcomes, most findings are generated from cross-sectional data. Along with the increasing importance of health literacy in policy, there is a lack of specificity and transparency about how to improve health literacy in practice. In this study, we are calling for a shift of current research paradigms from judging health literacy levels towards observing how health literacy skills are developed over the life course and practised in the real world. This includes using a life-course approach, integrating the rationale of precision public health, applying open science practice, and promoting actionable knowledge translation strategies. We show how a greater appreciation for these paradigms promises to advance health literacy research and practice towards an equitable, precise, transparent, and actionable vision.
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Affiliation(s)
- Shuaijun Guo
- Centre for Community Child Health, Murdoch Children’s Research Institute, Royal Children’s Hospital, Melbourne, VIC 3052, Australia
- Department of Pediatrics, University of Melbourne, Melbourne, VIC 3010, Australia
| | - Xiaoming Yu
- Institute of Child and Adolescent Health, School of Public Health, Peking University, Beijing 100191, China;
| | - Orkan Okan
- Centre for Prevention and Intervention in Childhood and Adolescence (CPI), Faculty of Educational Science, Bielefeld University, 33615 Bielefeld, Germany;
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