Lin Hancheng, Wang Zhimin, Luo Yiwen, Lin Zijie, Hong Guanghui, Deng Kaifei, Huang Ping, Shen Yiwen
Department of Forensic Medicine, School of Basic Medical Sciences, Fudan University, Shanghai 200032, China.
Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Institute of Forensic Science, Ministry of Justice, PRC, Shanghai 200063, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2022 Sep 5;277:121263. doi: 10.1016/j.saa.2022.121263. Epub 2022 Apr 13.
Diabetic cardiomyopathy (DbCM) is a serious complication of diabetes that affects about 12% of the diabetic population. Sensitive detection of diabetes-induced biochemical changes in the heart before symptoms appear can assist clinicians in developing targeted treatment plans and forensic pathologists in making accurate postmortem diagnoses. The Fourier transform infrared (FTIR) spectroscopy-based approach allows for the analysis of the sample biomolecular composition and variations. In the current study, the myocardial tissues of mouse models of type 2 diabetes mellitus (T2DM) at various ages (7, 12, and 21 weeks) were analyzed using FTIR microspectroscopy (FTIRM) in combination with machine learning algorithms. The carbonyl esters, olefinic=CH and CH groups of lipids, total lipids, saccharides, and β-sheet to α-helix conformational transition in proteins increased significantly in diabetic mice myocardial tissues compared to healthy mice. Furthermore, partial least-squares discriminant analysis and random forest-guided partial least-squares discriminant analysis revealed the time-dependent progression of the spectral lipidomic profiles during the development of DbCM. Finally, a random forest classifier was developed for diagnosing DbCM, with 97.1% accuracy. This study demonstrates that FTIRM is a novel method for monitoring early biochemical changes in the myocardia of mice with T2DM.
糖尿病性心肌病（DbCM）是糖尿病的一种严重并发症，影响约12%的糖尿病患者群体。在症状出现之前灵敏检测出糖尿病引起的心脏生化变化，有助于临床医生制定有针对性的治疗方案，也有助于法医病理学家做出准确的死后诊断。基于傅里叶变换红外（FTIR）光谱的方法能够分析样本的生物分子组成及变化。在本研究中，使用傅里叶变换红外显微光谱（FTIRM）结合机器学习算法，对不同年龄（7周、12周和21周）的2型糖尿病（T2DM）小鼠模型的心肌组织进行了分析。与健康小鼠相比，糖尿病小鼠心肌组织中脂质的羰基酯、烯烃类=CH和CH基团、总脂质、糖类以及蛋白质中β-折叠向α-螺旋的构象转变均显著增加。此外，偏最小二乘判别分析和随机森林引导的偏最小二乘判别分析揭示了DbCM发展过程中光谱脂质组学特征随时间的变化。最后，开发了一种用于诊断DbCM的随机森林分类器，准确率达97.1%。本研究表明，FTIRM是监测T2DM小鼠心肌早期生化变化的一种新方法。
