Infant formulas have been designed to mimic human milk for infants who cannot be breastfed. The overall goal is to establish similar functional outcomes to assure optimal growth, development, maturation of the immune system, and programming of the metabolic system. However, after decades of improving infant formula, growth patterns and body composition development are still different in formula-fed infants compared to breastfed infants, which could contribute to an increased risk of obesity among formula-fed infants. It has been hypothesized that the lower protein concentration of breast milk compared to infant formula influences infants’ growth and body composition. Thus, several trials in formula-fed infants with different protein intake levels have been performed to test this hypothesis. In this review, we discuss the current evidence on low-protein infant formula and obesity risk, including future perspectives and implications.

Objective Human milk composition is dynamic. While extensive research has focused on its macronutrient concentrations during the first 6 months of lactation, limited research exists for extended lactation periods. This study aims to examine the nutritional composition of human milk during these longer lactation phases. Design A retrospective longitudinal cohort study performed within the National Dutch Human Milk Bank. Participants We selected donors who had provided milk donations at least once after the 6-month postpartum mark. Main outcome measures The Miris Human Milk Analyser was used to analyse macronutrient concentrations in the milk samples. Linear mixed models were used for longitudinal analysis of these concentrations, factoring in time variables established for six sequential lactation periods. Results We analysed 820 milk samples from 86 women, collected between 5 weeks and 28 months postpartum. Initially, milk protein concentrations dropped over the first 8 months of lactation (diff = −0.19 g/dL, p<0.001) and stabilised between 8 and 18 months before increasing again by 0.21 (95% CI 0.06–0.21) g/dL. Carbohydrate concentrations remained steady throughout the study period. Fat concentrations were stable for the first 8 months but saw an increase afterwards. Post 18 months, the fat content saw a rise of 1.90 (95% CI 1.59–2.21) g/dL. The caloric density mirrored the pattern of the fat concentrations. Conclusion The nutritional content of human milk does not decrease after 6 months of lactation. Therefore, human milk banks may accept donations from mothers up to 2 years post-birth.

Introduction: Formula feeding is associated with an increased obesity risk compared to breastfeeding, possibly due to its higher protein content. We aimed to investigate the influence of feeding a modified, low‐protein infant formula during the first 6 months of life on growth and body composition at 6 years.Methods: Healthy term‐born infants were randomised to receive a low‐protein (mLP) infant formula with modified amino acid composition (n = 90; 1.7 g protein/100 kcal) or a control infant formula (CTRL) (n = 88; 2.1 g protein/100 kcal) up to 6 months. A breastfed (BF, n = 67) group served as a reference. At 6 years, anthropometry and body composition (air‐displacement plethysmography) were measured.Results: A total of 106 infants were measured at 6y follow‐up, (n = 39 mLP; n = 33 CTRL; n = 34 BF). No significant differences were observed in mean weight, length, BMI, or fat mass percentage. However, the mean fat‐free mass was lower in the mLP compared to the CTRL group (−1240 g; 95% CI: −1889 to −591, p < 0.001). Moreover, both formula groups had a higher absolute fat mass when compared to the breastfed group (p = 0.01).Conclusions: Feeding a mLP formula during early life did not have beneficial effects on body composition in a subset of infants studied at age 6 year.

Objectives: High protein intake during infancy has been linked to accelerated weight gain and increased obesity risk. This study aimed to examine the effects of a low‐protein formula during the first 6 months of life on blood metabolic and hormonal markers during early childhood. Methods: Formula‐fed infants (<45 days) were randomized to receive either a low‐protein formula with modified amino acid composition (mLP; n = 90; 1.7 g protein/100 kcal) or a control formula (CTRL; n = 88; 2.1 g protein/100 kcal) until 6 months of age. Breastfed infants served as a reference group ( n = 67). Blood samples were collected in cooperating subjects at 1, 2, and 6 years. We measured insulin‐like growth factor‐1 (IGF‐1), IGF‐binding proteins (BPs), leptin, glucose, and insulin, and calculated Homeostatic‐Model‐Assessment‐of‐Insulin‐Resistance (HOMA‐IR). Data were analyzed using linear mixed models and linear regression, adjusting for confounders. In addition, results were correlated to priorly published body composition measurement. Results: Venous blood was collected from 87 (36%), 77 (31%), and 63 (26%) participants at ages 1, 2, and 6 years, respectively. No differences were found in metabolic markers between the formula groups or compared to the breastfed group at any time point. Furthermore, at 6 years of age, a positive correlation was found between some biomarkers (IGF‐1, leptin, and HOMA‐IR) and body composition measurements, but not all biomarkers showed such an association. Conclusions: In this relatively small study, providing a modified, low‐protein infant formula during the first months of life did not affect hormonal and metabolic markers during early childhood.

Background and aims: The macronutrient composition of donor human milk (DHM) can vary substantially due to several factors such as maternal age, diet, and lactation duration. However, consistent macronutrient levels in DHM facilitate the administration of the required amounts to preterm infants. The current pooling strategy at most human milk banks combines milk from different batches from a single donor. This study aims to stabilize the macronutrient quality of DHM by pooling milk from different donors by utilizing machine learning prediction and optimisation techniques. Methods: The current pooling strategy is compared with a new theoretical approach that pools milk batches from up to 5 donors. To predict the crude protein and energy content, we used the following variables: body mass index, the donor’s diet (vegetarian or non-vegetarian), maternal age, full-term or preterm delivery, lactation stage, and volume pumped. These predictions are then used within an optimisation model to create milk pools that minimize the deviations from the target macronutrient levels (1.0 g protein/100 mL and 70 kcal/100 mL). Results: The prediction model is based on 2236 created single-donor pools from 480 donors. Random forest regression models provided the most accurate predictions of macronutrient content. The new pooling strategy using multiple donors shows reduced deviations from target values compared to the current single-donor approach (average total absolute deviation 0.402 versus 0.664). Conclusion: This study proves the potential of data-driven methods to improve operational efficiency in human milk banks, and improving the consistency of donor human milk.