Department of Human Genetics and Genomics, Instituto Nacional de Perinatología Isidro Espinosa de los Reyes, Mexico City 11000, Mexico.
Posgrado en Ciencias Químico-Biológicas, Escuela Nacional de Ciencias Biológicas, Instituto Politécnico Nacional, Mexico City 11340, Mexico.
Int J Mol Sci. 2017 Dec 28;19(1):86. doi: 10.3390/ijms19010086.
Maternal obesity has been related to adverse neonatal outcomes and fetal programming. Oxidative stress and adipokines are potential biomarkers in such pregnancies; thus, the measurement of these molecules has been considered critical. Therefore, we developed artificial neural network (ANN) models based on maternal weight status and clinical data to predict reliable maternal blood concentrations of these biomarkers at the end of pregnancy. Adipokines (adiponectin, leptin, and resistin), and DNA, lipid and protein oxidative markers (8-oxo-2'-deoxyguanosine, malondialdehyde and carbonylated proteins, respectively) were assessed in blood of normal weight, overweight and obese women in the third trimester of pregnancy. A Back-propagation algorithm was used to train ANN models with four input variables (age, pre-gestational body mass index (p-BMI), weight status and gestational age). ANN models were able to accurately predict all biomarkers with regression coefficients greater than R² = 0.945. P-BMI was the most significant variable for estimating adiponectin and carbonylated proteins concentrations (37%), while gestational age was the most relevant variable to predict resistin and malondialdehyde (34%). Age, gestational age and p-BMI had the same significance for leptin values. Finally, for 8-oxo-2'-deoxyguanosine prediction, the most significant variable was age (37%). These models become relevant to improve clinical and nutrition interventions in prenatal care.
母亲肥胖与不良新生儿结局和胎儿编程有关。氧化应激和脂肪因子是这些妊娠的潜在生物标志物；因此，这些分子的测量被认为是至关重要的。因此，我们根据母体体重状况和临床数据开发了人工神经网络（ANN）模型，以预测这些生物标志物在妊娠末期可靠的母体血液浓度。在妊娠晚期，评估了正常体重、超重和肥胖妇女血液中的脂肪因子（脂联素、瘦素和抵抗素）以及 DNA、脂质和蛋白质氧化标志物（8-氧-2'-脱氧鸟苷、丙二醛和羰基化蛋白）。使用反向传播算法，使用四个输入变量（年龄、孕前体重指数（p-BMI）、体重状况和妊娠周数）训练 ANN 模型。ANN 模型能够准确预测所有具有回归系数大于 R²=0.945 的生物标志物。p-BMI 是估计脂联素和羰基化蛋白浓度的最显著变量（37%），而妊娠周数是预测抵抗素和丙二醛的最相关变量（34%）。年龄、妊娠周数和 p-BMI 对瘦素值具有相同的意义。最后，对于 8-氧-2'-脱氧鸟苷的预测，最显著的变量是年龄（37%）。这些模型对于改善产前保健中的临床和营养干预变得至关重要。
