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

International Journal of Molecular Sciences

José Alfredo Hernández-Pérez¹  Mario Solis-Paredes²  Eyerahi Bravo-Flores³  Veronica Zaga-Clavellina³  Araceli Montoya-Estrada³  Héctor Borboa-Olivares⁴  Otilia Perichart-Perera⁴  Claudine Irles⁵  Gabriela Gonzalez-Perez⁵  Ethel Garcia-Latorre⁶  Mario Guzmán-Huerta⁷  Arturo Cardona-Pérez⁷  Guadalupe Estrada-Gutierrez⁷ 

[1] Centro de Investigación en Ingeniería y Ciencias Aplicadas-Instituto de Investigación en Ciencias Básicas y Aplicadas (CIICAp-IICBA), Universidad Autónoma de Morelos, Cuernavaca 62209, Mexico;Department of Human Genetics and Genomics, Instituto Nacional de Perinatología Isidro Espinosa de los Reyes, Mexico City 11000, Mexico;Department of Inmunobiochemistry, Instituto Nacional de Perinatología Isidro Espinosa de los Reyes, Mexico City 11000, Mexico;Department of Nutrition and Bioprogramming, Instituto Nacional de Perinatología Isidro Espinosa de los Reyes, Mexico City 11000, Mexico;Department of Physiology and Cellular Development, 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;Research Division, Instituto Nacional de Perinatología Isidro Espinosa de los Reyes, Mexico City 11000, Mexico;

关键词: artificial neural networks;    pregnancy;    oxidative stress markers;    adipokines;    obesity;   

DOI  :  10.3390/ijms19010086

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown