Cord Blood Metabolites Associated with Newborn Adiposity and Hyperinsulinemia

To evaluate the association between cord blood amino acid and acylcarnitine profiles and measures of adiposity and hyperinsulinemia in healthy newborns. A cross-sectional study of 118 full-term infants born to mothers without gestational diabetes was performed. Cord blood leptin, C-peptide, acylcarn...

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Veröffentlicht in:The Journal of pediatrics 2018-12, Vol.203, p.144-149.e1
Hauptverfasser: Kadakia, Rachel, Scholtens, Denise M., Rouleau, Gerald W., Talbot, Octavious, Ilkayeva, Olga R., George, Tabitha, Josefson, Jami L.
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Sprache:eng
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Zusammenfassung:To evaluate the association between cord blood amino acid and acylcarnitine profiles and measures of adiposity and hyperinsulinemia in healthy newborns. A cross-sectional study of 118 full-term infants born to mothers without gestational diabetes was performed. Cord blood leptin, C-peptide, acylcarnitine, and amino acid levels were measured. Body composition was measured by air displacement plethysmography. Multivariate linear regression and principal component analysis were used to analyze associations of cord blood metabolites with newborn anthropometrics, leptin, and C-peptide. Acylcarnitines AC C2, AC C4-DC/Ci4-DC, and AC C8:1-OH/C6:1-DC were positively associated with leptin, and AC C14, AC C14:2, AC C16, AC C18, and AC C18:2 were negatively associated with C-peptide (P ≤ .0016). Principal component analysis revealed a positive association between factor 1(AC C2, AC C3, AC C5, AC C4/Ci4, AC C4-OH, AC C4-DC/Ci4-DC, glutamate/glutamine, and glycine) and adiposity measures. The positive association of AC C2 and AC C4-DC/Ci4-DC levels with leptin may reflect excess fat stores, higher fatty acid oxidation rate, and mitochondrial dysfunction leading to accumulation of acylcarnitine intermediates. Principal component analysis revealed a positive association between branched chain amino acid and ketone body metabolites and adiposity, confirming prior findings in adults. Cord blood acylcarnitine profiles may identify at-risk children before obesity or insulin resistance develops.
ISSN:0022-3476
1097-6833
DOI:10.1016/j.jpeds.2018.07.056