Insulin resistance and impaired pancreatic insulin secretion are the main precursors of type 2 diabetes and can cause cardiovascular disease. Researchers from Uppsala University/SciLifeLab led by Tove Fall and her doctoral student Christoph Nowak have brought together plasma metabolomics data from several international cohorts to identify metabolic pathways related to insulin resistance and impaired insulin secretion.
The researchers used an innovative open-access bioinformatics pipeline developed by the group to process and annotate liquid chromatography/mass spectrometry data. In non-targeted analysis, the team discovered 52 metabolites related to diabetes pathology that implicated different metabolic pathways for insulin resistance and impaired pancreatic secretory function. In a second step, the researchers collaborated with teams from Stockholm, Finland, Germany and the United States to obtain additional metabolomics data from independent cohorts.
Through Mendelian randomization analysis – a method that uses a person’s genetic background to assess causal relationships between correlated variables – Nowak et al. discovered evidence that insulin resistance reduced circulating levels of the monounsaturated fatty acids (MUFAs) oleate and palmitoleate. These two metabolites are the main MUFAs that are newly produced by the body and have been associated with both beneficial and harmful health effects. Based on publicly available animal experiment data and previous publications, Nowak et al. propose a theory of how an inherited predisposition to insulin resistance may lead to lower MUFA levels thereby simultaneously improving glucose metabolism whilst increasing arterial atherosclerosis risk. The presumed relationships, however, require validation in future studies.
The Uppsala-led international project adds another piece to the puzzle in the larger picture connecting inherited predispositions with changes in physiology and long-term cardiometabolic health. Whilst the discovered effects need to be replicated in experimental models, the study demonstrates the power of multi-disciplinary large-scale “-omics” research and international data sharing to gain new insights into health and disease. The findings were published in PLOS Genetics.
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