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.
Biochim Biophys Acta Mol Basis Dis. 2022 Sep 1;1868(9):166445. doi: 10.1016/j.bbadis.2022.166445. Epub 2022 May 14.
Early identification of diabetic cardiomyopathy (DCM) can help clinicians develop targeted treatment plans and forensic pathologists make accurate postmortem diagnoses. In the present study, diabetes-induced metabolic abnormalities in the myocardium and biofluids (plasma, urine, and saliva) of db/db mice of various ages (7, 12, and 21 weeks) were investigated by attenuated total reflection (ATR)-Fourier transform infrared (FTIR) spectroscopy. The results indicated that the diabetic and control groups had significantly different changes in the function groups of lipids, phosphate macromolecules (mostly nucleic acids), protein compositions and conformations, and carbohydrates (primarily glucose) in the myocardium and biofluids. The prediction model for quantifying DCM severity was developed on db/db mice's myocardial spectra using a genetic algorithm (GA)-partial least squares (PLS) regression method. Following that, the linear correlations between the predicted values for DCM severity and spectra for db/db biofluids were evaluated using the GA-PLS regression algorithm. The results showed there were good linear correlations between the predicted values for DCM severity and spectra for plasma (R = 0.929), saliva (R = 0.967), urine (R = 0.954), and combination of plasma and saliva (R = 0.980). This study provides a novel perspective on detecting diabetes-related biofluid and cardiac metabolic abnormalities and demonstrates the potential of biofluid infrared spectro-diagnostic models for non/mini-invasive assessment of DCM.
早期识别糖尿病性心肌病（DCM）有助于临床医生制定有针对性的治疗计划，法医病理学家做出准确的死后诊断。本研究采用衰减全反射（ATR）-傅里叶变换红外（FTIR）光谱法，研究了不同年龄（7、12 和 21 周）db/db 小鼠心肌和生物流体（血浆、尿液和唾液）中糖尿病引起的代谢异常。结果表明，糖尿病组和对照组心肌和生物流体的脂质、磷酸盐大分子（主要是核酸）、蛋白质组成和构象以及碳水化合物（主要是葡萄糖）功能基团存在显著差异。采用遗传算法（GA）-偏最小二乘（PLS）回归法，基于 db/db 小鼠心肌光谱建立了定量 DCM 严重程度的预测模型。然后，采用 GA-PLS 回归算法评估了 db/db 生物流体光谱与 DCM 严重程度预测值之间的线性相关性。结果表明，DCM 严重程度预测值与血浆（R=0.929）、唾液（R=0.967）、尿液（R=0.954）以及血浆和唾液组合（R=0.980）的光谱之间存在良好的线性关系。本研究为检测糖尿病相关生物流体和心脏代谢异常提供了新视角，并展示了生物流体红外光谱诊断模型在非/微创评估 DCM 方面的潜力。
