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Liu J, Wang L, Shen B, Gong Y, Guo X, Shen Q, Yang M, Dong Y, Liu Y, Chen H, Yang Z, Liu Y, Zhu X, Ma H, Jin G, Qian Y. Association of serum metal levels with type 2 diabetes: A prospective cohort and mediating effects of metabolites analysis in Chinese population. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2024; 279:116470. [PMID: 38772147 DOI: 10.1016/j.ecoenv.2024.116470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 05/13/2024] [Accepted: 05/14/2024] [Indexed: 05/23/2024]
Abstract
Several studies have suggested an association between exposure to various metals and the onset of type 2 diabetes (T2D). However, the results vary across different studies. We aimed to investigate the associations between serum metal concentrations and the risk of developing T2D among 8734 participants using a prospective cohort study design. We utilized inductively coupled plasmamass spectrometry (ICP-MS) to assess the serum concentrations of 27 metals. Cox regression was applied to calculate the hazard ratios (HRs) for the associations between serum metal concentrations on the risk of developing T2D. Additionally, 196 incident T2D cases and 208 healthy control participants were randomly selected for serum metabolite measurement using an untargeted metabolomics approach to evaluate the mediating role of serum metabolite in the relationship between serum metal concentrations and the risk of developing T2D with a nested casecontrol study design. In the cohort study, after Bonferroni correction, the serum concentrations of zinc (Zn), mercury (Hg), and thallium (Tl) were positively associated with the risk of developing T2D, whereas the serum concentrations of manganese (Mn), molybdenum (Mo), barium (Ba), lutetium (Lu), and lead (Pb) were negatively associated with the risk of developing T2D. After adding these eight metals, the predictive ability increased significantly compared with that of the traditional clinical model (AUC: 0.791 vs. 0.772, P=8.85×10-5). In the nested casecontrol study, a machine learning analysis revealed that the serum concentrations of 14 out of 1579 detected metabolites were associated with the risk of developing T2D. According to generalized linear regression models, 7 of these metabolites were significantly associated with the serum concentrations of the identified metals. The mediation analysis showed that two metabolites (2-methyl-1,2-dihydrophthalazin-1-one and mestranol) mediated 46.81% and 58.70%, respectively, of the association between the serum Pb concentration and the risk of developing T2D. Our study suggested that serum Mn, Zn, Mo, Ba, Lu, Hg, Tl, and Pb were associated with T2D risk. Two metabolites mediated the associations between the serum Pb concentration and the risk of developing T2D.
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Affiliation(s)
- Jia Liu
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University (Wuxi Center for Disease Control and Prevention), Wuxi, Jiangsu 214023, China
| | - Lu Wang
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University (Wuxi Center for Disease Control and Prevention), Wuxi, Jiangsu 214023, China
| | - Bohui Shen
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University (Wuxi Center for Disease Control and Prevention), Wuxi, Jiangsu 214023, China
| | - Yan Gong
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University (Wuxi Center for Disease Control and Prevention), Wuxi, Jiangsu 214023, China
| | - Xiangxin Guo
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China
| | - Qian Shen
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University (Wuxi Center for Disease Control and Prevention), Wuxi, Jiangsu 214023, China
| | - Man Yang
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University (Wuxi Center for Disease Control and Prevention), Wuxi, Jiangsu 214023, China
| | - Yunqiu Dong
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University (Wuxi Center for Disease Control and Prevention), Wuxi, Jiangsu 214023, China
| | - Yongchao Liu
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University (Wuxi Center for Disease Control and Prevention), Wuxi, Jiangsu 214023, China
| | - Hai Chen
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University (Wuxi Center for Disease Control and Prevention), Wuxi, Jiangsu 214023, China
| | - Zhijie Yang
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University (Wuxi Center for Disease Control and Prevention), Wuxi, Jiangsu 214023, China
| | - Yaqi Liu
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University (Wuxi Center for Disease Control and Prevention), Wuxi, Jiangsu 214023, China
| | - Xiaowei Zhu
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University (Wuxi Center for Disease Control and Prevention), Wuxi, Jiangsu 214023, China
| | - Hongxia Ma
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China
| | - Guangfu Jin
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China
| | - Yun Qian
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University (Wuxi Center for Disease Control and Prevention), Wuxi, Jiangsu 214023, China.
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Wang SCY, Nickel G, Venkatesh KP, Raza MM, Kvedar JC. AI-based diabetes care: risk prediction models and implementation concerns. NPJ Digit Med 2024; 7:36. [PMID: 38361152 PMCID: PMC10869708 DOI: 10.1038/s41746-024-01034-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Accepted: 02/05/2024] [Indexed: 02/17/2024] Open
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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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Liu J, Wang L, Cui X, Shen Q, Wu D, Yang M, Dong Y, Liu Y, Chen H, Yang Z, Liu Y, Zhu M, Ma H, Jin G, Qian Y. Polygenic Risk Score, Lifestyles, and Type 2 Diabetes Risk: A Prospective Chinese Cohort Study. Nutrients 2023; 15:2144. [PMID: 37432247 DOI: 10.3390/nu15092144] [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: 04/15/2023] [Revised: 04/28/2023] [Accepted: 04/28/2023] [Indexed: 07/12/2023] Open
Abstract
The aim of this study was to generate a polygenic risk score (PRS) for type 2 diabetes (T2D) and test whether it could be used in identifying high-risk individuals for lifestyle intervention in a Chinese cohort. We genotyped 80 genetic variants among 5024 participants without non-communicable diseases at baseline in the Wuxi Non-Communicable Diseases cohort (Wuxi NCDs cohort). During the follow-up period of 14 years, 440 cases of T2D were newly diagnosed. Using Cox regression, we found that the PRS of 46 SNPs identified by the East Asians was relevant to the future T2D. Participants with a high PRS (top quintile) had a two-fold higher risk of T2D than the bottom quintile (hazard ratio: 2.06, 95% confidence interval: 1.42-2.97). Lifestyle factors were considered, including cigarette smoking, alcohol consumption, physical exercise, diet, body mass index (BMI), and waist circumference (WC). Among high-PRS individuals, the 10-year incidence of T2D slumped from 6.77% to 3.28% for participants having ideal lifestyles (4-6 healthy lifestyle factors) compared with poor lifestyles (0-2 healthy lifestyle factors). When integrating the high PRS, the 10-year T2D risk of low-clinical-risk individuals exceeded that of high-clinical-risk individuals with a low PRS (3.34% vs. 2.91%). These findings suggest that the PRS of 46 SNPs could be used in identifying high-risk individuals and improve the risk stratification defined by traditional clinical risk factors for T2D. Healthy lifestyles can reduce the risk of a high PRS, which indicates the potential utility in early screening and precise prevention.
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Affiliation(s)
- Jia Liu
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University (Wuxi Center for Disease Control and Prevention), Wuxi 214023, China
| | - Lu Wang
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University (Wuxi Center for Disease Control and Prevention), Wuxi 214023, China
| | - Xuan Cui
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Qian Shen
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University (Wuxi Center for Disease Control and Prevention), Wuxi 214023, China
| | - Dun Wu
- College of Arts and Science, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Man Yang
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University (Wuxi Center for Disease Control and Prevention), Wuxi 214023, China
| | - Yunqiu Dong
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University (Wuxi Center for Disease Control and Prevention), Wuxi 214023, China
| | - Yongchao Liu
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University (Wuxi Center for Disease Control and Prevention), Wuxi 214023, China
| | - Hai Chen
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University (Wuxi Center for Disease Control and Prevention), Wuxi 214023, China
| | - Zhijie Yang
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University (Wuxi Center for Disease Control and Prevention), Wuxi 214023, China
| | - Yaqi Liu
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University (Wuxi Center for Disease Control and Prevention), Wuxi 214023, China
| | - Meng Zhu
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Hongxia Ma
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Guangfu Jin
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Yun Qian
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University (Wuxi Center for Disease Control and Prevention), Wuxi 214023, China
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