Distinct infant feeding type-specific plasma metabolites at age 3 months associate with body composition at 2 years

Beijsterveldt, Inge A.L.P. van; Myers, Pernille Neve; Snowden, Stuart G.; Ong, Ken K; Pedersen, Susanne Brix; Hokken-Koelega, Anita; Koulman, Albert

doi:10.1016/j.clnu.2022.04.015

Cited by 4 publications

(8 citation statements)

References 37 publications

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“…Further, LysoPC (22:2) and dimethylarginine predicted, albeit modestly, the truncal-to-peripheral skinfold ratio at 2 years of age [ 118 ]. At 3 months of age, PC 42:8 levels were higher in exclusively breastfed infants with high abdominal subcutaneous fat at 2 years of age, whereas, at 3 months, PC 38:3 levels were only higher in exclusively formula-fed infants with excessive abdominal subcutaneous fat at 2 years of age [ 119 ]. Several metabolites were linked to visceral fat at 2 years of age: lysophosopholipids (LysoPC 14:0; LysoPC 16:3; LysoPC 16:1; LysoPC 16:0; LysoPS 21:1; LysoPS 25:6 and LysoPA 23:1), dimethylarginine, diacylglycerol (DG 40:10) and sphingomyelin (SM 35:2; O2) [ 119 ].…”

Section: Obesitymentioning

confidence: 99%

“…At 3 months of age, PC 42:8 levels were higher in exclusively breastfed infants with high abdominal subcutaneous fat at 2 years of age, whereas, at 3 months, PC 38:3 levels were only higher in exclusively formula-fed infants with excessive abdominal subcutaneous fat at 2 years of age [ 119 ]. Several metabolites were linked to visceral fat at 2 years of age: lysophosopholipids (LysoPC 14:0; LysoPC 16:3; LysoPC 16:1; LysoPC 16:0; LysoPS 21:1; LysoPS 25:6 and LysoPA 23:1), dimethylarginine, diacylglycerol (DG 40:10) and sphingomyelin (SM 35:2; O2) [ 119 ]. Gestational weight gain (in mothers) and birth weights (children) are predictors of diabetes and cardiovascular disease in mothers and children, respectively.…”

Section: Obesitymentioning

confidence: 99%

“…LysoPA, LysoPE, LysoPG, LysoPS and dimethylarginine are metabolites linked to pro-inflammatory pathways in preadipocytes [ 120 , 121 ] and inflammation [ 122 , 123 ], primarily through interacting with peroxisome proliferator-activated receptor gamma (PPAR-γ) [ 124 ] and Toll-like receptor dimers [ 125 ] linked, via G-protein-coupled receptors, to the pathogenesis of cardiovascular disease [ 126 ]. The metabolites identified at 3 months of age have roles in the development of adiposity and inflammation, which may be initiated early in life [ 119 ]. The link of the metabolite profile at 3 months to body composition at 2 years was dependent on the infant feeding type [ 119 ].…”

Section: Obesitymentioning

confidence: 99%

“…The metabolites identified at 3 months of age have roles in the development of adiposity and inflammation, which may be initiated early in life [ 119 ]. The link of the metabolite profile at 3 months to body composition at 2 years was dependent on the infant feeding type [ 119 ]. The feeding type and composition of milk impact infants’ metabolite profiles [ 116 , 117 , 118 , 127 ], and metabolite profile differences in early life are likely implicated in the adiposity differences in breastfed and formula-fed infants [ 119 ].…”

Section: Obesitymentioning

confidence: 99%

“…The link of the metabolite profile at 3 months to body composition at 2 years was dependent on the infant feeding type [ 119 ]. The feeding type and composition of milk impact infants’ metabolite profiles [ 116 , 117 , 118 , 127 ], and metabolite profile differences in early life are likely implicated in the adiposity differences in breastfed and formula-fed infants [ 119 ]. This distinctive metabolite profile, shaped by nutrition over the early life course, reveals associations with adiposity and inflammation that may trigger the onset of non-communicable diseases later in life.…”

Section: Obesitymentioning

confidence: 99%

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Maternal and Child Health, Non-Communicable Diseases and Metabolites

Cerf

2023

Metabolites

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Mothers influence the health and disease trajectories of their children, particularly during the critical developmental windows of fetal and neonatal life reflecting the gestational–fetal and lactational–neonatal phases. As children grow and develop, they are exposed to various stimuli and insults, such as metabolites, that shape their physiology and metabolism to impact their health. Non-communicable diseases, such as diabetes, cardiovascular disease, cancer and mental illness, have high global prevalence and are increasing in incidence. Non-communicable diseases often overlap with maternal and child health. The maternal milieu shapes progeny outcomes, and some diseases, such as gestational diabetes and preeclampsia, have gestational origins. Metabolite aberrations occur from diets and physiological changes. Differential metabolite profiles can predict the onset of non-communicable diseases and therefore inform prevention and/or better treatment. In mothers and children, understanding the metabolite influence on health and disease can provide insights for maintaining maternal physiology and sustaining optimal progeny health over the life course. The role and interplay of metabolites on physiological systems and signaling pathways in shaping health and disease present opportunities for biomarker discovery and identifying novel therapeutic agents, particularly in the context of maternal and child health, and non-communicable diseases.

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Section: Obesitymentioning

confidence: 99%

Section: Obesitymentioning

confidence: 99%

Section: Obesitymentioning

confidence: 99%

Section: Obesitymentioning

confidence: 99%

Section: Obesitymentioning

confidence: 99%

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Maternal and Child Health, Non-Communicable Diseases and Metabolites

Cerf

2023

Metabolites

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Ultrasound for assessing paediatric body composition and nutritional status: Scoping review and future directions

Ranger,

Lombardi,

Kwon

et al. 2024

Acta Paediatrica

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AimThis scoping review aims to assess the utility of ultrasound as a prospective tool in measuring body composition and nutritional status in the paediatric population. We provide a comprehensive summary of the existing literature, identify gaps, and propose future research directions.MethodsWe conducted a systematic scoping review following the PRISMA Extension for Scoping Reviews guidelines. This involved screening titles and abstracts of relevant studies, followed by a detailed full‐text review and extraction of pertinent data.ResultsWe identified and synthesised 34 articles. The review revealed that while ultrasound has been used to assess body composition and bone properties in children, significant gaps remain in the literature. These include limited studies on ultrasound performance, insufficient attention to relevant sample characteristics, reliance on manual image measurements, and limited sample diversity.ConclusionPoint‐of‐care ultrasound shows significant promise for assessing paediatric body composition and nutritional status. To validate and enhance its effectiveness, further research is needed. Future studies should include larger and more diverse patient cohorts and conduct longitudinal investigations to evaluate nutritional interventions. Additionally, developing artificial intelligence (AI) for standardising and automating data interpretation will be crucial in improving the accuracy and efficiency of ultrasound assessments.

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Artificial intelligence in paediatric endocrinology: conflict or cooperation

Dimitri,

Savage

2024

Journal of Pediatric Endocrinology and Metabolism

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Artificial intelligence (AI) in medicine is transforming healthcare by automating system tasks, assisting in diagnostics, predicting patient outcomes and personalising patient care, founded on the ability to analyse vast datasets. In paediatric endocrinology, AI has been developed for diabetes, for insulin dose adjustment, detection of hypoglycaemia and retinopathy screening; bone age assessment and thyroid nodule screening; the identification of growth disorders; the diagnosis of precocious puberty; and the use of facial recognition algorithms in conditions such as Cushing syndrome, acromegaly, congenital adrenal hyperplasia and Turner syndrome. AI can also predict those most at risk from childhood obesity by stratifying future interventions to modify lifestyle. AI will facilitate personalised healthcare by integrating data from ‘omics’ analysis, lifestyle tracking, medical history, laboratory and imaging, therapy response and treatment adherence from multiple sources. As data acquisition and processing becomes fundamental, data privacy and protecting children’s health data is crucial. Minimising algorithmic bias generated by AI analysis for rare conditions seen in paediatric endocrinology is an important determinant of AI validity in clinical practice. AI cannot create the patient–doctor relationship or assess the wider holistic determinants of care. Children have individual needs and vulnerabilities and are considered in the context of family relationships and dynamics. Importantly, whilst AI provides value through augmenting efficiency and accuracy, it must not be used to replace clinical skills.

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Distinct infant feeding type-specific plasma metabolites at age 3 months associate with body composition at 2 years

Cited by 4 publications

References 37 publications

Maternal and Child Health, Non-Communicable Diseases and Metabolites

Maternal and Child Health, Non-Communicable Diseases and Metabolites

Ultrasound for assessing paediatric body composition and nutritional status: Scoping review and future directions

Artificial intelligence in paediatric endocrinology: conflict or cooperation

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