Metabolomics in the developing infant

Fanos, Vassilios; Antonucci, Roberto; Atzori, Luigi

doi:10.1097/mop.0b013e328363ec8b

Cited by 16 publications

(11 citation statements)

References 43 publications

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“…The field of pediatric metabolomics is still being pioneered [ 15 , 16 ]. It is tremendously important to understand the dynamic metabolic changes that occur during infancy and childhood to further metabolomic research in pediatrics.…”

Section: Introductionmentioning

confidence: 99%

Metabolomics Reveals Dynamic Metabolic Changes Associated with Age in Early Childhood

et al. 2016

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ObjectivesA detailed understanding of the metabolic processes governing rapid growth in early life is still lacking. The aim of this study was to investigate the age-related metabolic changes in healthy children throughout early childhood.MethodsHealthy children from a birth cohort were enrolled in this study from birth through 4 years of age. Urinary metabolites were assessed at 6 months, and 1, 2, 3, and 4 yr of age by using 1H-nuclear magnetic resonance (NMR) spectroscopy coupled with multivariate statistical analysis including principal components analysis (PCA) and partial least-squares discriminant analysis (PLS-DA). Metabolic pathway analysis was performed using the MetPA web tool.ResultsA total of 105 urine samples from 30 healthy children were collected and analyzed. Metabolites contributing to the discrimination between age groups were identified by using supervised PLS-DA (Q2 = 0.60; R2 = 0.66). A significantly higher urinary trimethylamine N-oxide (TMAO) and betaine level was found in children aged 6 months. Urinary glycine and glutamine levels declined significantly after 6 months of age and there was a concomitant compensatory increase in urinary creatine and creatinine. Metabolic pathway analysis using MetPA revealed similar nitrogen metabolism associated energy production across all ages assessed. Pathways associated with amino acid metabolism were significantly different between infants aged 6 months and 1 year, whereas pathways associated with carbohydrate metabolism were significantly different between children at ages 2 and 3 years.ConclusionsUrine metabolomics ideally represents dynamic metabolic changes across age. Urinary metabolic profiles change significantly within the first year of life, which can potentially provide crucial information about infant nutrition and growth.

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Section: Introductionmentioning

confidence: 99%

Metabolomics Reveals Dynamic Metabolic Changes Associated with Age in Early Childhood

et al. 2016

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“…In essence, this review concludes that the biological mechanisms linking infant weight gain to child obesity via metabolic programming are due to metabolic characteristics of the given human or formula milk affecting the metabolic, immunological, and anthropometric response of the infant. Although this review is very informative, it documents that, in contrast to the literature on adults [5,6], the power of metabolomics still needs to be fully utilized in pediatrics beyond clinical settings in neonatology [7,8]. Metabolomics has a great potential for the discovery of biomarkers reflective of weight gain and obesity via examination of alterations in amino acid and lipid metabolism [5,6,7,8].…”

Section: Introductionmentioning

confidence: 99%

Rapid Growth and Childhood Obesity Are Strongly Associated with LysoPC(14:0)

Rzehak

Hellmuth²,

Uhl³

et al. 2014

Ann Nutr Metab

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Background: Despite the growing interest in the early-origins-of-later-disease hypothesis, little is known about the metabolic underpinnings linking infant weight gain and childhood obesity. Objective: To discover biomarkers reflective of weight change in the first 6 months and overweight/obesity at age 6 years via a targeted metabolomics approach. Design: This analysis comprised 726 infants from a European multicenter randomized trial (Childhood Obesity Programme, CHOP) for whom plasma blood samples at age 6 months and anthropometric data up to the age of 6 years were available. ‘Rapid growth' was defined as a positive difference in weight within the first 6 months of life standardized to WHO growth standards. Weight change was regressed on each of 168 metabolites (acylcarnitines, lysophosphatidylcholines, sphingomyelins, and amino acids). Metabolites significant after Bonferroni's correction were tested as predictors of later overweight/obesity. Results: Among the overall 19 significant metabolites, 4 were associated with rapid growth and 15 were associated with a less-than-ideal weight change. After adjusting for feeding group, only the lysophosphatidylcholine LPCaC14:0 remained significantly associated with rapid weight gain (β = 0.18). Only LPCaC14:0 at age 6 months was predictive of overweight/obesity at age 6 years (OR 1.33; 95% CI 1.04-1.69). Conclusion: LPCa14:0 is strongly related to rapid growth in infancy and childhood overweight/obesity. This suggests that LPCaC14:0 levels may represent a metabolically programmed effect of infant weight gain on the later obesity risk. However, these results require confirmation by independent cohorts.

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“…Thus, where statistical powering is a key consideration in research metabolomics, clinical metabolomics is a true n‐of‐1 analysis. Untargeted clinical metabolomic profiling of individual patient samples has found early application in the screening of inherited metabolic diseases, but other uses, such as probing of undiagnosed cases for disease insights, as well as health and wellness screenings, have started to emerge …”

Section: Considerations For Metabolomics Applications In Research Andmentioning

confidence: 99%

“…Untargeted clinical metabolomic profiling of individual patient samples has found early application in the screening of inherited metabolic diseases, 26,37,64,65 but other uses, such as probing of undiagnosed cases for disease insights, as well as health and wellness screenings, have started to emerge. [66][67][68][69][70]…”

Section: Introductionmentioning

confidence: 99%

Metabolomics in the clinic: A review of the shared and unique features of untargeted metabolomics for clinical research and clinical testing

et al. 2018

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Metabolomics is the untargeted measurement of the metabolome, which is composed of the complement of small molecules detected in a biological sample. As such, metabolomic analysis produces a global biochemical phenotype. It is a technology that has been utilized in the research setting for over a decade. The metabolome is directly linked to and is influenced by genetics, epigenetics, environmental factors, and the microbiome—all of which affect health. Metabolomics can be applied to human clinical diagnostics and to other fields such as veterinary medicine, nutrition, exercise, physiology, agriculture/plant biochemistry, and toxicology. Applications of metabolomics in clinical testing are emerging, but several aspects of its use as a clinical test differ from applications focused on research or biomarker discovery and need to be considered for metabolomics clinical test data to have optimum impact, be meaningful, and be used responsibly. In this review, we deconstruct aspects and challenges of metabolomics for clinical testing by illustrating the significance of test design, accurate and precise data acquisition, quality control, data processing, n‐of‐1 comparison to a reference population, and biochemical pathway analysis. We describe how metabolomics technology is integral to defining individual biochemical phenotypes, elaborates on human health and disease, and fits within the precision medicine landscape. Finally, we conclude by outlining some future steps needed to bring metabolomics into the clinical space and to be recognized by the broader medical and regulatory fields.

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Metabolomics in the developing infant

Cited by 16 publications

References 43 publications

Metabolomics Reveals Dynamic Metabolic Changes Associated with Age in Early Childhood

Metabolomics Reveals Dynamic Metabolic Changes Associated with Age in Early Childhood

Rapid Growth and Childhood Obesity Are Strongly Associated with LysoPC(14:0)

Metabolomics in the clinic: A review of the shared and unique features of untargeted metabolomics for clinical research and clinical testing

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