TY - JOUR
T1 - Key clinical factors predicting adipokine and oxidative stress marker concentrations among normal, overweight and obese pregnant women using artificial neural networks
AU - Solis-Paredes, Mario
AU - Estrada-Gutierrez, Guadalupe
AU - Perichart-Perera, Otilia
AU - Montoya-Estrada, Araceli
AU - Guzmán-Huerta, Mario
AU - Borboa-Olivares, Héctor
AU - Bravo-Flores, Eyerahi
AU - Cardona-Pérez, Arturo
AU - Zaga-Clavellina, Veronica
AU - Garcia-Latorre, Ethel
AU - Gonzalez-Perez, Gabriela
AU - Hernández-Pérez, José Alfredo
AU - Irles, Claudine
N1 - Publisher Copyright:
© 2017 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2018/1
Y1 - 2018/1
N2 - 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-20-deoxyguanosine prediction, the most significant variable was age (37%). These models become relevant to improve clinical and nutrition interventions in prenatal care.
AB - 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-20-deoxyguanosine prediction, the most significant variable was age (37%). These models become relevant to improve clinical and nutrition interventions in prenatal care.
KW - Adipokines
KW - Artificial neural networks
KW - Obesity
KW - Oxidative stress markers
KW - Pregnancy
UR - http://www.scopus.com/inward/record.url?scp=85039859529&partnerID=8YFLogxK
U2 - 10.3390/ijms19010086
DO - 10.3390/ijms19010086
M3 - Artículo
C2 - 29283404
SN - 1661-6596
VL - 19
JO - International Journal of Molecular Sciences
JF - International Journal of Molecular Sciences
IS - 1
M1 - 86
ER -