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Insights into the pathophysiology of catch-up compared with non-catch-up growth in children born small for gestational age: an integrated analysis of metabolic and transcriptomic data

Abstract

Small for gestational age (SGA) children exhibiting catch-up (CU) growth have a greater risk of cardiometabolic diseases in later life compared with non-catch-up (NCU) SGA children. The aim of this study was to establish differences in metabolism and gene expression profiles between CU and NCU at age 4–9 years. CU children (n=22) had greater height, weight and body mass index standard deviation scores along with insulin-like growth factor-I (IGF-I) and fasting glucose levels but lower adiponectin values than NCU children (n=11; all P<0.05). Metabolic profiling demonstrated a fourfold decrease of urine myo-inositol in CU compared with NCU (P<0.05). There were 1558 genes differentially expressed in peripheral blood mononuclear cells between the groups (P<0.05). Integrated analysis of data identified myo-inositol related to gene clusters associated with an increase in insulin, growth factor and IGF-I signalling in CU children (P<0.05). Metabolic and transcriptomic profiles in CU SGA children showed changes that may relate to cardiometabolic risk.

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Acknowledgements

AS was supported by Merck Serono SA, Geneva, Switzerland. IB was supported by an unrestricted educational grant from Novo Nordisk UK. CDL was supported by a European Society for Paediatric Endocrinology (ESPE) Research Fellowship, sponsored by Novo Nordisk A/S. WD was supported by the NIHR Manchester Biomedical Research Centre. DH was supported by a Wellcome Trust Institutional Strategic Support Fund (TSSF) award (097820) to the University of Manchester. Recruitment to this study was facilitated by the NIHR Medicines for Children Research Network.

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Stevens, A., Bonshek, C., Whatmore, A. et al. Insights into the pathophysiology of catch-up compared with non-catch-up growth in children born small for gestational age: an integrated analysis of metabolic and transcriptomic data. Pharmacogenomics J 14, 376–384 (2014). https://doi.org/10.1038/tpj.2014.4

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