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Integration of whole-body [18F]FDG PET/MRI with non-targeted metabolomics can provide new insights on tissue-specific insulin resistance in type 2 diabetes.

Authors :
Diamanti, Klev
Visvanathar, Robin
Pereira, Maria J.
Cavalli, Marco
Pan, Gang
Kumar, Chanchal
Skrtic, Stanko
Risérus, Ulf
Eriksson, Jan W.
Kullberg, Joel
Komorowski, Jan
Wadelius, Claes
Ahlström, Håkan
Source :
Scientific Reports; 5/20/2020, Vol. 10 Issue 1, p1-9, 9p
Publication Year :
2020

Abstract

Alteration of various metabolites has been linked to type 2 diabetes (T2D) and insulin resistance. However, identifying significant associations between metabolites and tissue-specific phenotypes requires a multi-omics approach. In a cohort of 42 subjects with different levels of glucose tolerance (normal, prediabetes and T2D) matched for age and body mass index, we calculated associations between parameters of whole-body positron emission tomography (PET)/magnetic resonance imaging (MRI) during hyperinsulinemic euglycemic clamp and non-targeted metabolomics profiling for subcutaneous adipose tissue (SAT) and plasma. Plasma metabolomics profiling revealed that hepatic fat content was positively associated with tyrosine, and negatively associated with lysoPC(P-16:0). Visceral adipose tissue (VAT) and SAT insulin sensitivity (K<subscript>i</subscript>), were positively associated with several lysophospholipids, while the opposite applied to branched-chain amino acids. The adipose tissue metabolomics revealed a positive association between non-esterified fatty acids and, VAT and liver K<subscript>i</subscript>. Bile acids and carnitines in adipose tissue were inversely associated with VAT K<subscript>i</subscript>. Furthermore, we detected several metabolites that were significantly higher in T2D than normal/prediabetes. In this study we present novel associations between several metabolites from SAT and plasma with the fat fraction, volume and insulin sensitivity of various tissues throughout the body, demonstrating the benefit of an integrative multi-omics approach. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20452322
Volume :
10
Issue :
1
Database :
Complementary Index
Journal :
Scientific Reports
Publication Type :
Academic Journal
Accession number :
143359123
Full Text :
https://doi.org/10.1038/s41598-020-64524-0