The quantity and quality of dietary lipids in infant formulae have a significant impact on health outcomes, especially when fat storing and/or absorption are limited (e.g., preterm birth and short bowel disease) or when fat byproducts may help to prevent some pathologies (e.g., atopy). The lipid composition of infant formulae varies according to the different fat sources used, and the potential biological effects are related to the variety of saturated and unsaturated fatty acids. For example, since lipids are the main source of energy when the normal absorptive capacity of the digestive tract is compromised, medium-chain saturated fatty acids might cover this requirement. Instead, ruminant-derived trans fatty acids and metabolites of n-3 long-chain polyunsaturated fatty acids with their anti-inflammatory properties can modulate immune function. Furthermore, dietary fats may influence the nutrient profile of formulae, improving the acceptance of these products and the compliance with dietary schedules.

The metabolic response to stress and infection is closely related to the corresponding requirements of energy and nutrients. On a general level, the response is driven by a complex endocrine network and related to the nature and severity of the insult. On an individual level, the effects of nutritional interventions are highly variable and a possible source of complications. This narrative review aims to discuss the metabolic changes in critically-ill children and the potential of developing personalized nutritional interventions. Through a literature search strategy, we have investigated the importance of blood glucose levels, the nutritional aspects of the different phases of acute stress response, and the reliability of the available tools to assess the energy expenditure. The dynamics of metabolism during stressful events reveals the difficult balance between risk of hypo- or hyperglycemia and under- or overfeeding. Within this context, individualized and accurate measurement of energy expenditure may help in defining the metabolic needs of patients. Given the variability of the metabolic response in critical conditions, randomized clinical studies in ill children are needed to evaluate the effect of individualized nutritional intervention on health outcomes.

The inaccuracy of resting energy expenditure (REE) prediction formulae to calculate energy metabolism in children may lead to either under- or overestimated real caloric needs with clinical consequences. The aim of this paper was to apply artificial neural networks algorithms (ANNs) to REE prediction. We enrolled 561 healthy children (2–17 years). Nutritional status was classified according to World Health Organization (WHO) criteria, and 113 were obese. REE was measured using indirect calorimetry and estimated with WHO, Harris–Benedict, Schofield, and Oxford formulae. The ANNs considered specific anthropometric data to model REE. The mean absolute error (mean ± SD) of the prediction was 95.8 ± 80.8 and was strongly correlated with REE values (R² = 0.88). The performance of ANNs was higher in the subgroup of obese children (101 ± 91.8) with a lower grade of imprecision (5.4%). ANNs as a novel approach may give valuable information regarding energy requirements and weight management in children.