Fact-based nutrition for infants and lactating mothers – The NUTRISHIELD study

Background: Human milk (HM) is the ideal source of nutrients for infants. Its composition is highly variable according to the infant’s needs. When not enough own mother’s milk (OMM) is available, the administration of pasteurized donor human milk (DHM) is considered a suitable alternative for preterm infants. This study protocol describes the NUTRISHIELD clinical study. The aim of this study is to evaluate the influence of diet, lifestyle habits, psychological stress, and pasteurization on the milk composition, and how it modulates infant’s growth, health, and development.

Methods and design: NUTRISHIELD is a prospective mother-infant birth cohort in the Spanish-Mediterranean area including three groups: preterm infants <32 weeks of gestation (i) exclusively receiving OMM, and (ii) exclusively receiving DHM, and (iii) term infants exclusively receiving OMM, as well as their mothers. Biological samples and nutritional, clinical, and anthropometric characteristics are collected at six time points covering the period from birth and until six months of infant’s age. The genotype, metabolome, and microbiota as well as the HM composition (i.e., macronutrients, fatty acids, vitamins, human milk oligosaccharides, and steroids) are characterized. Portable sensor prototypes for the analysis of HM and urine are benchmarked. Additionally, maternal psychosocial status is measured at the beginning of the study and at month six, including social support, family functioning, perceived stress, anxiety, and depression symptoms, and traumatic life events. Mother-infant postpartum bonding and parental stress are also examined. At six months, infant neurodevelopment scales are applied. Mother’s concerns and attitudes to breastfeeding are also registered through a specific questionnaire.

Discussion: NUTRISHIELD provides an in-depth longitudinal study of the mother-infant-microbiota triad combining multiple biological matrices, newly developed analytical methods, and ad-hoc designed sensor prototypes with a wide range of clinical outcome measures. Data obtained from this study will be used to train a machine-learning algorithm for providing dietary advice to lactating mothers and will be implemented in a user-friendly platform based on a combination of user-provided information and biomarker analysis. A better understanding of the factors affecting milk’s composition, together with the health implications for infants plays an important role in developing improved strategies of nutraceutical management in infant care.