Key Clinical Factors Predicting Adipokine and Oxidative Stress Marker Concentrations among Normal, Overweight and Obese Pregnant Women Using Artificial Neural Networks

Abstract

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.

Estimation of Neonatal Intestinal Perforation Associated with Necrotizing Enterocolitis by Machine Learning Reveals New Key Factors

“…The input variables were normalized in the range of 0.1 to 0.9, as previously described [ 26 ], in order to prevent within-patient differences in variation and amplitude among variables, and the output variable was not normalized. The Back-propagation neural network (BPNN) was used to train and test ANN models using the Levenberg-Marquardt algorithm [ 30 ], as previously explained [ 26 , 27 ]. Briefly, in the hidden layer, one to <5 neurons were applied until the Root Mean Square Error (RMSE) between the experimental data (Target) and predicted values (network) was <10 −12 , as well as validation of the model by the slope and intercept statistical test (see Section 2.3.3 ) and avoiding overfitting (performance evaluation of the model through training, testing, and validation).…”

“…In order to identify key factors that play an important role in predicting intestinal perforation associated with NEC, we performed a sensitivity analysis to the trained and validated neural network, as previously described ([ 26 , 27 ] and Garson algorithm in Appendix B ), allowing to determine which input variables (maternal and neonatal parameters) are more important (or sensible) to attain precise output values (diagnosis).…”

“…The value of each neuron is the argument of the transfer functions ( f and g ): IP, NEC or no NEC (output) = g ( Wo × f ( Wi × In + b 1 ) + b 2 ) f is a hyperbolic tangent transfer function (TANSIG) and g is a linear transfer function (PURELIN) or Log-Sigmoid function (LOGSIG). We applied different transfer functions in both ANN models and the best performance was found with TANSIG-LOGSIG therefore, Equation (A2) is now Equation (A3): Where n output is: ANN learning In this work, to change the weights and biases, we applied the Levenberg-Marquardt algorithm, following our previously reported methods [ 26 , 27 ]. This uses the adaptation as follows: where: J is the Jacobian matrix (first derivative) e is a vector of network errors μ is the combination coefficient with a value of 0.001 I is the identity matrix.…”

“…The short-elapsed time needed to calculate the IP diagnosis makes the online application of such models possible. We have previously used BPNN in a biomedical context, with accurate results for the prediction of neonatal and maternal biochemical blood parameters [ 26 , 27 ]. Such models will be a key contribution for prevention, early diagnosis, follow-up and quality improvement in medical practice in a clinical setting.…”

Comparison of artificial neural network and logistic regression model for factors affecting birth weight

“…One of the causes of adverse neonatal outcomes is maternal obesity. Therefore, at the end of pregnancy, ANN models based on maternal weight status and clinical data have been developed to estimate reliable maternal blood concentrations of oxidative stress and adipokines biomarkers [36].…”