Suppr超能文献

胰岛素抵抗与代谢综合征的生物标志物及数据可视化:一种适用方法。

Biomarkers and Data Visualization of Insulin Resistance and Metabolic Syndrome: An Applicable Approach.

作者信息

Sotiropoulos Christos, Giormezis Nikolaos, Pertsas Vayianos, Tsirkas Theodoros

机构信息

Department of Microbiology, School of Medicine, University of Patras, 26504 Patras, Greece.

Informatics Department, University of Economics and Business, 10434 Athens, Greece.

出版信息

Life (Basel). 2024 Sep 21;14(9):1197. doi: 10.3390/life14091197.

Abstract

Type 2 diabetes, prediabetes, and insulin resistance (IR) are widespread yet often undetected in their early stages, contributing to a silent epidemic. Metabolic Syndrome (MetS) is also highly prevalent, increasing the chronic disease burden. Annual check-ups are inadequate for early detection due to conventional result formats that lack specific markers and comprehensive visualization. The aim of this study was to evaluate low-budget biochemical and hematological parameters, with data visualization, for identifying IR and MetS in a community-based laboratory. In a cross-sectional study with 1870 participants in Patras, Greece, blood samples were analyzed for key cardiovascular and inflammatory markers. IR diagnostic markers (TyG-Index, TyG-BMI, Triglycerides/HDL ratio, NLR) were compared with HOMA-IR. Innovative data visualization techniques were used to present metabolic profiles. Notable differences in parameters of cardiovascular risk and inflammation were observed between normal-weight and obese people, highlighting BMI as a significant risk factor. Also, the inflammation marker NHR (Neutrophils to HDL-Cholesterol Ratio) Index was successful at distinguishing the obese individuals and those with MetS from normal individuals. Additionally, a new diagnostic index of IR, combining BMI (Body Mass Index) and NHR Index, demonstrated better performance than other well-known indices. Lastly, data visualization significantly helped individuals understand their metabolic health patterns more clearly. BMI and NHR Index could play an essential role in assessing metabolic health patterns. Integrating specific markers and data visualization in routine check-ups enhances the early detection of IR and MetS, aiding in better patient awareness and adherence.

摘要

2型糖尿病、糖尿病前期和胰岛素抵抗(IR)广泛存在,但在早期往往未被发现,从而导致了一场悄无声息的流行疾病。代谢综合征(MetS)也非常普遍,增加了慢性病负担。由于传统的检查结果形式缺乏特定标志物和全面的可视化,年度体检不足以实现早期检测。本研究的目的是评估低成本的生化和血液学参数,并通过数据可视化,在社区实验室中识别胰岛素抵抗和代谢综合征。在一项对希腊帕特雷1870名参与者的横断面研究中,对血液样本进行了关键心血管和炎症标志物分析。将胰岛素抵抗诊断标志物(TyG指数、TyG-BMI、甘油三酯/高密度脂蛋白比值、中性粒细胞与淋巴细胞比值)与稳态模型评估胰岛素抵抗(HOMA-IR)进行比较。采用创新的数据可视化技术展示代谢概况。在正常体重者和肥胖者之间观察到心血管风险和炎症参数的显著差异,突出了体重指数作为一个重要风险因素。此外,炎症标志物中性粒细胞与高密度脂蛋白胆固醇比值(NHR)指数成功地区分了肥胖个体和患有代谢综合征的个体与正常个体。此外,一种结合体重指数(BMI)和NHR指数的新的胰岛素抵抗诊断指数表现优于其他知名指数。最后,数据可视化显著帮助个体更清楚地了解自己的代谢健康模式。体重指数和NHR指数在评估代谢健康模式中可发挥重要作用。在常规检查中整合特定标志物和数据可视化可提高胰岛素抵抗和代谢综合征的早期检测率,有助于提高患者的认知度和依从性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/00cb/11433343/3065d34cbd0e/life-14-01197-g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验