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

• Published in: International journal of molecular sciences Vol. 19; no. 1; p. 86
• Main Authors: Solis-Paredes, Mario, Estrada-Gutierrez, Guadalupe, Perichart-Perera, Otilia, Montoya-Estrada, Araceli, Guzmán-Huerta, Mario, Borboa-Olivares, Héctor, Bravo-Flores, Eyerahi, Cardona-Pérez, Arturo, Zaga-Clavellina, Veronica, Garcia-Latorre, Ethel, Gonzalez-Perez, Gabriela, Hernández-Pérez, José Alfredo, Irles, Claudine
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 28.12.2017; MDPI
• Subjects: adipokines; Adiponectin; Age; artificial neural networks; Back propagation; Biomarkers; Body mass index; Body size; Body weight; Carbonyls; Deoxyguanosine; Deoxyribonucleic acid; DNA; Fetuses; Gestational age; Leptin; Malondialdehyde; Mathematical models; Neonates; Neural networks; Nutrition; Obesity; Oxidative stress; oxidative stress markers; Pregnancy; Proteins; Regression coefficients; artificial neural networks; adipokines; oxidative stress markers; obesity; pregnancy
• ISSN: 1422-0067; 1661-6596; 1422-0067
• 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.