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Introduction: Head and neck cancer patients are at high risk of anorexia-cachexia syndrome and literature shows that Eicosapentaenoic acid (EPA) could regulate it. We aim to determine the EPA effect on body composition and pro-inflammatory markers in patients with head neck cancer. Materials and methods: A randomized single-blind placebo-controlled clinical trial was conducted in patients with head and neck squamous cell cancer who received a polymeric diet with 2 g of EPA or a standard polymeric diet for six weeks before antineoplastic treatment. We assessed body composition by bioelectrical impedance analysis and determined IL-1β, IL-6, TNF-α and IFN-γ, CRP, serum proteins, and blood count at baseline and at the end of the study. Results: 32 patients received EPA (2 g/day) and 32 became controls. A decrease in serum levels of IL-1β, IL-6, TNF-α, and IFN-γ was observed in the experimental group, as well as regulation of body weight (¡0.3 § 5.9 vs. ¡2.1 § 3.7), lean body mass (¡0.2 § 3.8 vs. ¡1.3 § 3.6), body fat mass (0.2 § 3.5 vs. ¡1.2 § 3.8), and quality of life (10 § 33 vs. 5 § 34). Conclusion: Supplementing with 2 g/day of EPA to head and neck cancer patient during antineoplastic treatment regulates serum pro-inflammatory cytokines, body weight, lean body mass, and improve quality of life. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]