Metabolic maturation in the first 2 years of life in resource-constrained settings and its association with postnatal growth

Abstract

Malnutrition continues to affect the growth and development of millions of children worldwide, and chronic undernutrition has proven to be largely refractory to interventions. Improved understanding of metabolic development in infancy and how it differs in growth-constrained children may provide insights to inform more timely, targeted, and effective interventions. Here, the metabolome of healthy infants was compared to that of growth-constrained infants from three continents over the first 2 years of life to identify metabolic signatures of aging. Predictive models demonstrated that growth-constrained children lag in their metabolic maturity relative to their healthier peers and that metabolic maturity can predict growth 6 months into the future. Our results provide a metabolic framework from which future nutritional programs may be more precisely constructed and evaluated.

Plasma lipids and growth faltering: A longitudinal cohort study in rural Gambian children

“…We first characterized the children’s growth patterns in clusters using latent class mixed modeling. Previous studies that have compared growth parameters in children with metabolites have typically characterized children as either growth impaired (e.g., stunted or underweight) or healthy, and determined which metabolites or lipids are able to classify them based on this binary classification ( 7 , 26 ) even for studies that observed children over a period of time ( 14 ). Using latent class modeling, we showed that the children from rural Gambia experienced a general decline in growth outcome over time, albeit at different trajectories.…”

Plasma lipids and growth faltering: a longitudinal cohort study in rural Gambian children

“…Previous studies that have compared growth parameters in children with metabolites have typically characterized children as either growth impaired (e.g. stunted or underweight) or healthy, and determined which metabolites or lipids are able to classify them based on this binary classification 7,26 , even for studies that observed children over a period of time 14 . Using latent class modelling, we showed that the children from rural Gambia experienced a general decline in growth outcome over time, albeit at different trajectories.…”

“…However, most of these studies focused on well-nourished populations. A notable exception to this was the study by Giallourou et al 14 that followed the changes that occur in the metabolome of children by analysing urine and plasma samples at 3, 6, 9, 15 and 24 months of age among children in 3 resource-constrained countries (Peru, Bangladesh and Tanzania) 14 . The authors used a phenome-for-age z-score (PAZ) and found that PAZ of stunted children lagged compared to healthy children indicating poor metabolic maturity.…”

“…However, longitudinal analysis of high-dimensional data in fieldbased settings and among populations most at risk from undernutrition, especially metabolomics and lipidomics, remains challenging. Further, where longitudinal analyses exist, data analysis methods employed have been limited to assessing the progression of metabolic features over time, ranking the most dynamic features [11][12][13][14][15] , and not exploring potential causality or associations among the different metabolic features over time.…”

Gut microbiome contributions to altered metabolism in a pig model of undernutrition

“…gut microbiome | malnutrition | carbohydrate-active enzymes | metabolic regulation | feature selection/information theory U ndernutrition afflicts over 200 million children worldwide and accounts for 45% of mortality in children under 5 y (1). Children with acute malnutrition exhibit wasting (impaired ponderal growth), often accompanied by stunting (reduced linear growth), deficits in bone development, neurodevelopment, and immunity, as well as perturbed metabolism (2,3). Epidemiologic studies indicate that acute malnutrition in children is not due to food insecurity alone and that perturbed gut microbial community development is a contributing factor; children with severe acute malnutrition (SAM) and moderate acute malnutrition (MAM; weight-for-length z-scores are, respectively, 2 to 3 and >3 SDs below World Health Organization mean values) have microbiota that appear "younger" (more immature) compared to those of chronologically agedmatched healthy children (4)(5)(6).…”