Post-Graduate Program in Technological Development and Innovation in Medicines, Federal University of Rio Grande do Norte, Natal, RN, 59072-970, Brazil.
Biological Chemistry and Chemometrics, Institute of Chemistry, Federal University of Rio Grande do Norte, Natal, RN, 59072-970, Brazil.
Sci Rep. 2020 Nov 6;10(1):19259. doi: 10.1038/s41598-020-75539-y.
Gestational diabetes mellitus (GDM) is a hyperglycaemic imbalance first recognized during pregnancy, and affects up to 22% of pregnancies worldwide, bringing negative maternal-fetal consequences in the short- and long-term. In order to better characterize GDM in pregnant women, 100 blood plasma samples (50 GDM and 50 healthy pregnant control group) were submitted Attenuated Total Reflection Fourier-transform infrared (ATR-FTIR) spectroscopy, using chemometric approaches, including feature selection algorithms associated with discriminant analysis, such as Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) and Support Vector Machines (SVM), analyzed in the biofingerprint region between 1800 and 900 cm followed by Savitzky-Golay smoothing, baseline correction and normalization to Amide-I band (~ 1650 cm). An initial exploratory analysis of the data by Principal Component Analysis (PCA) showed a separation tendency between the two groups, which were then classified by supervised algorithms. Overall, the results obtained by Genetic Algorithm Linear Discriminant Analysis (GA-LDA) were the most satisfactory, with an accuracy, sensitivity and specificity of 100%. The spectral features responsible for group differentiation were attributed mainly to the lipid/protein regions (1462-1747 cm). These findings demonstrate, for the first time, the potential of ATR-FTIR spectroscopy combined with multivariate analysis as a screening tool for fast and low-cost GDM detection.
妊娠期糖尿病（GDM）是一种在怀孕期间首次发现的高血糖失衡，影响全球多达 22%的妊娠，给母婴带来短期和长期的负面影响。为了更好地描述妊娠期糖尿病患者的特征，我们采集了 100 份血浆样本（50 份 GDM 和 50 份健康妊娠对照组），并采用衰减全反射傅里叶变换红外（ATR-FTIR）光谱技术结合化学计量学方法进行分析，包括与判别分析相关的特征选择算法，如线性判别分析（LDA）、二次判别分析（QDA）和支持向量机（SVM），分析范围为 1800 到 900cm 之间的生物指纹区域，然后进行 Savitzky-Golay 平滑、基线校正和酰胺 I 带（~1650cm）归一化。通过主成分分析（PCA）对数据进行初步探索性分析显示，两组之间存在分离趋势，然后通过有监督算法进行分类。总的来说，遗传算法线性判别分析（GA-LDA）的结果最为令人满意，准确率、灵敏度和特异性均为 100%。导致组间差异的光谱特征主要归因于脂质/蛋白质区域（1462-1747cm）。这些发现首次证明了 ATR-FTIR 光谱结合多元分析作为一种快速、低成本的 GDM 检测筛查工具的潜力。
