Next Article in Journal
Omentin-A Novel Adipokine in Respiratory Diseases
Previous Article in Journal
New Method for Differentiation of Granuloviruses (Betabaculoviruses) Based on Multitemperature Single Stranded Conformational Polymorphism
Open AccessArticle

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.
Int. J. Mol. Sci. 2018, 19(1), 86; https://doi.org/10.3390/ijms19010086
Received: 16 October 2017 / Revised: 2 December 2017 / Accepted: 5 December 2017 / Published: 28 December 2017
(This article belongs to the Section Biochemistry)
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
Show Figures

Figure 1

MDPI and ACS Style

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. https://doi.org/10.3390/ijms19010086

AMA Style

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 JA, Irles C. 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. 2018; 19(1):86. https://doi.org/10.3390/ijms19010086

Chicago/Turabian Style

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 A.; Irles, Claudine. 2018. "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. 19, no. 1: 86. https://doi.org/10.3390/ijms19010086

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop