Estimating Caloric Intake per Breastfeeding Session in Infants: A Probabilistic Approach

Background/Objectives: Accurate estimation of caloric intake from breastfeeding is essential for understanding infant nutrition during early life. However, most existing models rely on fixed assumptions and do not reflect the natural variability in feeding behaviors and human milk composition. This study aims to provide a realistic estimation of breast milk (BM) caloric intake throughout infancy using a probabilistic approach based on empirical data. Methods: A probabilistic model was developed using four variables: feeding frequency, volume per feeding, caloric density, and infant weight. Systematic reviews were conducted to inform the input values of the first three variables, and meta-analyses were performed when feasible. Infant weight was based on World Health Organization (WHO) growth standards. Variables were stratified by age and integrated into the model through appropriate probability distributions. Monte Carlo simulations were conducted to estimate caloric intake per kilogram of body weight, expressed both per day and per feeding, across all age groups. Results: The model showed a progressive decline in daily caloric intake per kilogram with age, consistent with decreasing feeding frequency and the introduction of complementary foods. In contrast, caloric intake per feeding increased with age. These findings align with WHO energy intake targets during exclusive breastfeeding and reflect expected physiological changes in infant growth and feeding behavior. Conclusions: This study provides a probabilistic framework for estimating BM caloric intake across infancy, accounting for interindividual and age-related variability. It offers a valuable research tool to support future studies on infant nutrition and feeding behavior using realistic, data-driven assumptions.