Metabolomics signatures in type 2 diabetes: a systematic review and integrative analysis
Sun, Yue et al. (2020), Metabolomics signatures in type 2 diabetes: a systematic review and integrative analysis, Dryad, Dataset, https://doi.org/10.5061/dryad.2fqz612k4
Metabolic signatures have emerged as valuable signaling molecules in the biochemical process of type 2 diabetes (T2D). To summarize and identify metabolic biomarkers in T2D, we performed a systematic review and meta-analysis of the associations between metabolites and T2D using high-throughput metabolomics techniques.
We searched relevant studies from MEDLINE (PubMed), Embase, Web of Science, and Cochrane Library as well as Chinese databases (Wanfang, Vip, and CNKI) inception through 31 December 2018. Meta-analysis was conducted using STATA 14.0 under random effect. Besides, bioinformatic analysis was performed to explore molecule mechanism by MetaboAnalyst and R 3.5.2.
Finally, 46 articles were included in this review on metabolites involved amino acids, acylcarnitines, lipids, carbohydrates, organic acids, and others. Results of meta-analysis in prospective studies indicated that isoleucine, leucine, valine, tyrosine, phenylalanine, glutamate, alanine, valerylcarnitine (C5), palmitoylcarnitine (C16), palmitic acid, and linoleic acid were associated with higher T2D risk. Conversely, serine, glutamine, and lysophosphatidylcholine C18:2 decreased risk of T2D. Arginine and glycine increased risk of T2D in the Western countries subgroup, and betaine was negatively correlated with T2D in nested case-control subgroup. In addition, slight improvements in T2D prediction beyond traditional risk factors were observed when adding these metabolites in predictive analysis. Pathway analysis identified 17 metabolic pathways may alter in the process of T2D and metabolite-related genes were also enriched in functions and pathways associated with T2D.
Several metabolites and metabolic pathways associated with T2D have been identified, which provide valuable biomarkers and novel targets for prevention and drug therapy.
Supplementary materials for a systematic review and integrative analysis of the associations between metabolites and T2D using high-throughput metabolomics techniques.
National Natural Science Foundation of China, Award: 8,177,351,181,573,210
Beijing Municipal Natural Science Foundation, Award: 7162020