Integrating Artificial Intelligence in the Diagnosis and Management of Metabolic Syndrome: A Comprehensive Review

IF 4.6 2区 医学 Q1 ENDOCRINOLOGY & METABOLISM
Jingjing Liu, Zhangdaihong Liu, Chang Liu, Hong Sun, Xiaoguang Li, Yang Yang
{"title":"Integrating Artificial Intelligence in the Diagnosis and Management of Metabolic Syndrome: A Comprehensive Review","authors":"Jingjing Liu,&nbsp;Zhangdaihong Liu,&nbsp;Chang Liu,&nbsp;Hong Sun,&nbsp;Xiaoguang Li,&nbsp;Yang Yang","doi":"10.1002/dmrr.70039","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>Metabolic syndrome (MetS) is a progressive chronic pathophysiological state characterised by abdominal obesity, hypertension, hyperglycaemia, and dyslipidaemia. It is recognised as one of the major clinical syndromes affecting human health, with approximately one-quarter of the global population impacted. MetS increases the risk of developing cardiovascular diseases (CVDs), stroke, type 2 diabetes mellitus (T2DM), and diverse metabolic diseases. Early diagnosis of MetS could potentially reduce the prevalence of these diseases. However, care for the MetS population faces significant challenges due to (i) a lack of comprehensive understanding of the full spectrum of associated diseases, stemming from unclear pathophysiological mechanisms and (ii) frequent underdiagnosis or misdiagnosis of MetS in clinical settings due to inconsistent screening guidelines, limited medical resources, time constraints in clinical practice, and insufficient awareness and training. The increasing availability of healthcare and medical data presents opportunities to apply and innovate with artificial intelligence (AI) in addressing these challenges. This review aims to (i) summarise the spectrum of diseases associated with MetS and (ii) review the diverse AI models applied to MetS and metabolic syndrome-related diseases (MetSRD), where MetSRD collectively refers to diseases and conditions directly associated with MetS.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>Our review consists of two phases. Initially, we conducted a literature review on MetS to narrow down the spectrum of MetSRD based on the strength of clinical evidence. We then used the terms ‘Metabolic Syndrome’ and ‘Machine Learning’ in combination with the identified MetSRD for further refinement. In total, we identified 52 related diseases in the first phase and 36 articles in the second phase.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>We identified a total of 52 MetSRD after the first phase, with T2DM, CVDs, and cancer being the top three. Among the 36 articles obtained in the second phase, we observed the following: (i) The criteria for MetS were inconsistent across the studies. (ii) The primary purpose of AI applications was to identify risk factors for diseases, thereby improving predictions for MetS or MetSRD. Traditional machine learning models, such as Random Forest and Logistic Regression, were found to be the most effective. (iii) In addition to the MetS criteria, AI models explored other factors, including demographic and physiological variables, dietary influences, lipidomic and proteomic indicators, and more.</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>This review underscores the significant link between MetS and a spectrum of diseases, with a particular focus on underreported conditions such as non-alcoholic fatty liver disease and stroke. Through the analysis of data from diverse sources, AI models, and MetS diagnostic criteria, additional indicators beyond traditional measures have been identified, emphasising the importance of combining both traditional and non-traditional markers to enhance the diagnostic and predictive capabilities for MetS and MetSRD. AI shows great potential in MetS research, particularly through the integration of multi-source data, including clinical metrics, genetic information, and omics data. The amalgamation of traditional machine learning and modern machine learning models is particularly promising, offering a balanced approach to model performance and data complexity. While international definitions provide global applicability, they may not be suitable for all populations and scenarios, necessitating flexible diagnostic criteria and adaptive, explainable algorithms. Ultimately, these will enable personalised diagnostics and targeted interventions.</p>\n </section>\n </div>","PeriodicalId":11335,"journal":{"name":"Diabetes/Metabolism Research and Reviews","volume":"41 4","pages":""},"PeriodicalIF":4.6000,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/dmrr.70039","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Diabetes/Metabolism Research and Reviews","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/dmrr.70039","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENDOCRINOLOGY & METABOLISM","Score":null,"Total":0}
引用次数: 0

Abstract

Background

Metabolic syndrome (MetS) is a progressive chronic pathophysiological state characterised by abdominal obesity, hypertension, hyperglycaemia, and dyslipidaemia. It is recognised as one of the major clinical syndromes affecting human health, with approximately one-quarter of the global population impacted. MetS increases the risk of developing cardiovascular diseases (CVDs), stroke, type 2 diabetes mellitus (T2DM), and diverse metabolic diseases. Early diagnosis of MetS could potentially reduce the prevalence of these diseases. However, care for the MetS population faces significant challenges due to (i) a lack of comprehensive understanding of the full spectrum of associated diseases, stemming from unclear pathophysiological mechanisms and (ii) frequent underdiagnosis or misdiagnosis of MetS in clinical settings due to inconsistent screening guidelines, limited medical resources, time constraints in clinical practice, and insufficient awareness and training. The increasing availability of healthcare and medical data presents opportunities to apply and innovate with artificial intelligence (AI) in addressing these challenges. This review aims to (i) summarise the spectrum of diseases associated with MetS and (ii) review the diverse AI models applied to MetS and metabolic syndrome-related diseases (MetSRD), where MetSRD collectively refers to diseases and conditions directly associated with MetS.

Methods

Our review consists of two phases. Initially, we conducted a literature review on MetS to narrow down the spectrum of MetSRD based on the strength of clinical evidence. We then used the terms ‘Metabolic Syndrome’ and ‘Machine Learning’ in combination with the identified MetSRD for further refinement. In total, we identified 52 related diseases in the first phase and 36 articles in the second phase.

Results

We identified a total of 52 MetSRD after the first phase, with T2DM, CVDs, and cancer being the top three. Among the 36 articles obtained in the second phase, we observed the following: (i) The criteria for MetS were inconsistent across the studies. (ii) The primary purpose of AI applications was to identify risk factors for diseases, thereby improving predictions for MetS or MetSRD. Traditional machine learning models, such as Random Forest and Logistic Regression, were found to be the most effective. (iii) In addition to the MetS criteria, AI models explored other factors, including demographic and physiological variables, dietary influences, lipidomic and proteomic indicators, and more.

Conclusion

This review underscores the significant link between MetS and a spectrum of diseases, with a particular focus on underreported conditions such as non-alcoholic fatty liver disease and stroke. Through the analysis of data from diverse sources, AI models, and MetS diagnostic criteria, additional indicators beyond traditional measures have been identified, emphasising the importance of combining both traditional and non-traditional markers to enhance the diagnostic and predictive capabilities for MetS and MetSRD. AI shows great potential in MetS research, particularly through the integration of multi-source data, including clinical metrics, genetic information, and omics data. The amalgamation of traditional machine learning and modern machine learning models is particularly promising, offering a balanced approach to model performance and data complexity. While international definitions provide global applicability, they may not be suitable for all populations and scenarios, necessitating flexible diagnostic criteria and adaptive, explainable algorithms. Ultimately, these will enable personalised diagnostics and targeted interventions.

Abstract Image

人工智能在代谢综合征诊断和治疗中的应用综述
代谢综合征(MetS)是一种进行性慢性病理生理状态,以腹部肥胖、高血压、高血糖和血脂异常为特征。它被认为是影响人类健康的主要临床综合症之一,全球约有四分之一的人口受到影响。MetS增加了患心血管疾病(cvd)、中风、2型糖尿病(T2DM)和多种代谢疾病的风险。早期诊断MetS可能潜在地降低这些疾病的患病率。然而,由于(i)不清楚的病理生理机制,对相关疾病的全谱缺乏全面的了解,以及(ii)由于不一致的筛查指南、有限的医疗资源、临床实践的时间限制以及认识和培训不足,在临床环境中经常误诊或误诊MetS,因此对MetS人群的护理面临着重大挑战。越来越多的医疗保健和医疗数据的可用性为应用和创新人工智能(AI)来应对这些挑战提供了机会。本综述旨在(i)总结与MetS相关的疾病谱,(ii)回顾应用于MetS和代谢综合征相关疾病(MetSRD)的各种人工智能模型,其中MetSRD统称为与MetS直接相关的疾病和病症。方法本研究分为两个阶段。最初,我们根据临床证据的强度对MetS进行了文献综述,以缩小MetSRD的范围。然后,我们将术语“代谢综合征”和“机器学习”与确定的MetSRD结合使用,以进一步细化。第一阶段共鉴定出52种相关疾病,第二阶段共鉴定出36篇相关文章。第一阶段后,我们共发现52例MetSRD,其中T2DM、cvd和癌症是前三名。在第二阶段获得的36篇文章中,我们观察到以下情况:(i)研究中MetS的标准不一致。㈡人工智能应用的主要目的是确定疾病的风险因素,从而改进对MetS或MetSRD的预测。传统的机器学习模型,如随机森林和逻辑回归,被发现是最有效的。(iii)除了MetS标准外,人工智能模型还探讨了其他因素,包括人口统计学和生理变量、饮食影响、脂质组学和蛋白质组学指标等。结论:本综述强调了MetS与一系列疾病之间的重要联系,特别关注未被报道的疾病,如非酒精性脂肪性肝病和中风。通过对来自不同来源的数据、人工智能模型和MetS诊断标准的分析,已经确定了传统措施之外的其他指标,强调了结合传统和非传统标志物以增强MetS和MetSRD诊断和预测能力的重要性。人工智能在MetS研究中显示出巨大的潜力,特别是通过整合多源数据,包括临床指标、遗传信息和组学数据。传统机器学习和现代机器学习模型的融合特别有前途,为模型性能和数据复杂性提供了一种平衡的方法。虽然国际定义具有全球适用性,但它们可能并不适用于所有人群和情景,因此需要灵活的诊断标准和自适应的、可解释的算法。最终,这些将使个性化诊断和有针对性的干预成为可能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Diabetes/Metabolism Research and Reviews
Diabetes/Metabolism Research and Reviews 医学-内分泌学与代谢
CiteScore
17.20
自引率
2.50%
发文量
84
审稿时长
4-8 weeks
期刊介绍: Diabetes/Metabolism Research and Reviews is a premier endocrinology and metabolism journal esteemed by clinicians and researchers alike. Encompassing a wide spectrum of topics including diabetes, endocrinology, metabolism, and obesity, the journal eagerly accepts submissions ranging from clinical studies to basic and translational research, as well as reviews exploring historical progress, controversial issues, and prominent opinions in the field. Join us in advancing knowledge and understanding in the realm of diabetes and metabolism.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信