Estimation of umbilical cord blood leptin and insulin based on anthropometric data by means of artificial neural network approach: identifying key maternal and neonatal factors

BMC Pregnancy and Childbirth

Research Article

José Alfredo Hernández¹  Héctor Baptista-González²  José Guzmán-Bárcenas²  Claudine Irles²  Joel Arias-Martínez³  Guillermo Ceballos-Reyes⁴ 

[1] Centro de Investigación en Ingeniería y Ciencias Aplicadas (CIICAp)-Universidad Autónoma del Estado de Morelos (UAEM), Cuernavaca, Morelos, Mexico;Department of Physiology and Cellular Development, Instituto Nacional de Perinatología Isidro Espinoza de los Reyes (INPerIER), Montes Urales 800, Lomas de Virreyes, C.P. 11000, Mexico city, Mexico;Departmento de Ciencias de la Salud-Universidad de Sonora, Campus Cajeme, Sonora, Mexico;Laboratorio Multidisciplinario y Sección de Estudios de Posgrado e Investigación, Escuela Superior de Medicina, Instituto Politécnico Nacional, Mexico City, Mexico;

关键词: Mathematical model;    Leptin;    Insulin;    Neonate;    Artificial neural network;    Umbilical cord blood;    Gestational diabetes;    Maternal obesity;   

DOI  :  10.1186/s12884-016-0967-z

 received in 2015-11-17, accepted in 2016-07-12,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundLeptin and insulin levels are key factors regulating fetal and neonatal energy homeostasis, development and growth. Both biomarkers are used as predictors of weight gain and obesity during infancy. There are currently no prediction algorithms for cord blood (UCB) hormone levels using Artificial Neural Networks (ANN) that have been directly trained with anthropometric maternal and neonatal data, from neonates exposed to distinct metabolic environments during pregnancy (obese with or without gestational diabetes mellitus or lean women). The aims were: 1) to develop ANN models that simulate leptin and insulin concentrations in UCB based on maternal and neonatal data (ANN perinatal model) or from only maternal data during early gestation (ANN prenatal model); 2) To evaluate the biological relevance of each parameter (maternal and neonatal anthropometric variables).MethodsWe collected maternal and neonatal anthropometric data (n = 49) in normoglycemic healthy lean, obese or obese with gestational diabetes mellitus women, as well as determined UCB leptin and insulin concentrations by ELISA. The ANN perinatal model consisted of an input layer of 12 variables (maternal and neonatal anthropometric and biochemical data from early gestation and at term) while the ANN prenatal model used only 6 variables (maternal anthropometric from early gestation) in the input layer. For both networks, the output layer contained 1 variable to UCB leptin or to UCB insulin concentration.ResultsThe best architectures for the ANN perinatal models estimating leptin and insulin were 12-5-1 while for the ANN prenatal models, 6-5-1 and 6-4-1 were found for leptin and insulin, respectively. ANN models presented an excellent agreement between experimental and simulated values. Interestingly, the use of only prenatal maternal anthropometric data was sufficient to estimate UCB leptin and insulin values. Maternal BMI, weight and age as well as neonatal birth were the most influential parameters for leptin while maternal morbidity was the most significant factor for insulin prediction.ConclusionsLow error percentage and short computing time makes these ANN models interesting in a translational research setting, to be applied for the prediction of neonatal leptin and insulin values from maternal anthropometric data, and possibly the on-line estimation during pregnancy.

【 授权许可】

CC BY   
© The Author(s). 2016