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Background: Leptin 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)., Methods: We 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., Results: The 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., Conclusions: Low 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.

Oxidative stress damage to biomolecules has been implicated in several diseases including diabetes mellitus. In the present study, we investigated the effect of oxidative stress in whole blood (WB) from diabetic patients (n = 60) on recombinant human insulin. Insulin was incubated with WB obtained from diabetic patients (DP) who had hyperglycemia (>300 mg/dL) or from 41 healthy volunteers (HV). Whole blood of DP, unlike WB of HV, induced higher values of formazan (142%), dityrosines (279%), and carbonyls (58%) in the insulin residues. Interestingly, the insulin modified by WB of DP showed less hypoglycemic activity in rat (30%) in comparison with insulin incubated with WB of HV. The incubation of insulin in WB from DP induces chemical changes in insulin and a decrease in its biological activity, events that might be associated with the high levels of oxidative stress markers found in the plasma of these patients.