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Int. J. Mol. Sci. 2018, 19(1), 86; https://doi.org/10.3390/ijms19010086

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

1
Department of Human Genetics and Genomics, Instituto Nacional de Perinatología Isidro Espinosa de los Reyes, Mexico City 11000, Mexico
2
Posgrado en Ciencias Químico-Biológicas, Escuela Nacional de Ciencias Biológicas, Instituto Politécnico Nacional, Mexico City 11340, Mexico
3
Research Division, Instituto Nacional de Perinatología Isidro Espinosa de los Reyes, Mexico City 11000, Mexico
4
Department of Nutrition and Bioprogramming, Instituto Nacional de Perinatología Isidro Espinosa de los Reyes, Mexico City 11000, Mexico
5
Department of Inmunobiochemistry, Instituto Nacional de Perinatología Isidro Espinosa de los Reyes, Mexico City 11000, Mexico
6
Posgrado en Ciencias Biológicas, Universidad Nacional Autónoma de Mexico, Mexico City 04510, Mexico
7
Department of Physiology and Cellular Development, Instituto Nacional de Perinatología Isidro Espinosa de los Reyes, Mexico City 11000, Mexico
8
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
*
Author to whom correspondence should be addressed.
Received: 16 October 2017 / Revised: 2 December 2017 / Accepted: 5 December 2017 / Published: 28 December 2017
(This article belongs to the Section Biochemistry, Molecular and Cellular Biology)
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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. View Full-Text
Keywords: artificial neural networks; pregnancy; oxidative stress markers; adipokines; obesity artificial neural networks; pregnancy; oxidative stress markers; adipokines; obesity
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Solis-Paredes, M.; Estrada-Gutierrez, G.; Perichart-Perera, O.; Montoya-Estrada, A.; Guzmán-Huerta, M.; Borboa-Olivares, H.; Bravo-Flores, E.; Cardona-Pérez, A.; Zaga-Clavellina, V.; Garcia-Latorre, E.; Gonzalez-Perez, G.; Hernández-Pérez, J.A.; Irles, C. Key Clinical Factors Predicting Adipokine and Oxidative Stress Marker Concentrations among Normal, Overweight and Obese Pregnant Women Using Artificial Neural Networks. Int. J. Mol. Sci. 2018, 19, 86.

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