A systematic review of metabolite profiling in gestational diabetes mellitus

Abstract

Aims/hypothesis: Gestational diabetes mellitus is associated with adverse maternal and fetal outcomes during, as well as subsequent to, pregnancy, including increased risk of type 2 diabetes and cardiovascular disease. Because of the importance of early risk stratification in preventing these complications, improved first-trimester biomarker determination for diagnosing gestational diabetes would enhance our ability to optimise both maternal and fetal health. Metabolomic profiling, the systematic study of small molecule products of biochemical pathways, has shown promise in the identification of key metabolites associated with the pathogenesis of several metabolic diseases, including gestational diabetes. This article provides a systematic review of the current state of research on biomarkers and gestational diabetes and discusses the clinical relevance of metabolomics in the prediction, diagnosis and management of gestational diabetes.

Methods: We conducted a systematic search of MEDLINE (PubMed) up to the end of February 2014 using the key term combinations of 'metabolomics,' 'metabonomics,' 'nuclear magnetic spectroscopy,' 'mass spectrometry,' 'metabolic profiling' and 'amino acid profile' combined (AND) with 'gestational diabetes'. Additional articles were identified through searching the reference lists from included studies. Quality assessment of included articles was conducted through the use of QUADOMICS.

Results: This systematic review included 17 articles. The biomarkers most consistently associated with gestational diabetes were asymmetric dimethylarginine and NEFAs. After QUADOMICS analysis, 13 of the 17 included studies were classified as 'high quality'.

Conclusions/interpretation: Existing metabolomic studies of gestational diabetes present inconsistent findings regarding metabolite profile characteristics. Further studies are needed in larger, more racially/ethnically diverse populations.

Fig. 1

Systematic review study selection algorithm. The 17 studies that fulfilled the inclusion/exclusion criteria for this systematic review were selected from among 69 articles identified in the initial PubMed literature review. The process for selecting the 17 articles is displayed