Independent Unit of Spectroscopy and Chemical Imaging, Faculty of Biomedical Sciences, Medical University of Lublin, Chodźki 4a, 20-093 Lublin, Poland.
Institute of Nuclear Physics, Polish Academy of Sciences, Walerego Eljasza - Radzikowskiego 152, 31-342 Kraków, Poland; Department of Biochemistry and Molecular Biology, Medical University of Lublin, Chodźki 1, Lublin 20-093, Poland.
Spectrochim Acta A Mol Biomol Spectrosc. 2024 Nov 5;320:124653. doi: 10.1016/j.saa.2024.124653. Epub 2024 Jun 13.
The number of people suffering from type 2 diabetes has rapidly increased. Taking into account, that elevated intracellular lipid concentrations, as well as their metabolism, are correlated with diminished insulin sensitivity, in this study we would like to show lipids spectroscopy markers of diabetes. For this purpose, serum collected from rats (animal model of diabetes) was analyzed using Fourier Transformed Infrared-Attenuated Total Reflection (FTIR-ATR) spectroscopy. Analyzed spectra showed that rats with diabetes presented higher concentration of phospholipids and cholesterol in comparison with non-diabetic rats. Moreover, the analysis of second (II) derivative spectra showed no structural changes in lipids. Machine learning methods showed higher accuracy for II derivative spectra (from 65 % to 89 %) than for absorbance FTIR spectra (53-65 %). Moreover, it was possible to identify significant wavelength intervals from II derivative spectra using random forest-based feature selection algorithm, which further increased the accuracy of the classification (up to 92 % for phospholipid region). Moreover decision tree based on the selected features showed, that peaks at 1016 cm and 2936 cm can be good candidates of lipids marker of diabetes.
患 2 型糖尿病的人数迅速增加。鉴于细胞内脂质浓度及其代谢与胰岛素敏感性降低有关，本研究旨在展示糖尿病的脂质光谱标志物。为此，使用傅里叶变换衰减全反射红外光谱（FTIR-ATR）分析了从大鼠（糖尿病动物模型）收集的血清。分析的光谱表明，与非糖尿病大鼠相比，糖尿病大鼠的磷脂和胆固醇浓度更高。此外，二阶导数光谱分析表明脂质结构没有变化。机器学习方法对二阶导数光谱（65%至 89%）的准确性高于吸光度 FTIR 光谱（53%至 65%）。此外，使用基于随机森林的特征选择算法，可以从二阶导数光谱中识别出重要的波长间隔，从而进一步提高分类的准确性（对于磷脂区域高达 92%）。此外，基于所选特征的决策树表明，在 1016 cm 和 2936 cm 处的峰值可能是糖尿病脂质标志物的良好候选物。
