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- Diamanti, Klev, 1987-, et al.
(author)
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Integration of whole-body PET/MRI with non-targeted metabolomics provides new insights into insulin sensitivity of various tissues
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Other publication (other academic/artistic)abstract
- Background: Alteration of various metabolites has been linked to type 2 diabetes (T2D) and insulin resistance. However, identifying significant associations between metabolites and tissue-specific alterations is challenging and requires a multi-omics approach. In this study, we aimed at discovering associations of metabolites from subcutaneous adipose tissue (SAT) and plasma with the volume, the fat fraction (FF) and the insulin sensitivity (Ki) of specific tissues using [18F]FDG PET/MRI.Materials and Methods: In a cohort of 42 subjects with different levels of glucose tolerance (normal, prediabetes and T2D) matched for age and body-mass-index (BMI) we calculated associations between parameters of whole-body FDG PET/MRI during clamp and non-targeted metabolomics profiling for SAT and blood plasma. We also used a rule-based classifier to identify a large collection of prevalent patterns of co-dependent metabolites that characterize non-diabetes (ND) and T2D.Results: The plasma metabolomics profiling revealed that hepatic fat content was positively associated with tyrosine, and negatively associated with lysoPC(P-16:0). Ki in visceral adipose tissue (VAT) and SAT, was positively associated with several species of lysophospholipids while the opposite applied to branched-chain amino acids (BCAA) and their intermediates. The adipose tissue metabolomics revealed a positive association between non-esterified fatty acids and, VAT and liver Ki. On the contrary, bile acids and carnitines in adipose tissue were inversely associated with VAT Ki. Finally, we presented a transparent machine-learning model that predicted ND or T2D in “unseen” data with an accuracy of 78%.Conclusions: Novel associations of several metabolites from SAT and plasma with the FF, volume and insulin senstivity of various tissues throughout the body were discovered using PET/MRI and a new integrative multi-omics approach. A promising computational model that predicted ND and T2D with high certainty, suggested novel non-linear interdependencies of metabolites.
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- Sarsenbayeva, Assel, et al.
(author)
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Role of glucocorticoids in adipose tissue fibrosis and interplay between macrophages and adipose cells
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Other publication (other academic/artistic)abstract
- Background and aimsExcessive endogenous production and administration of glucocorticoids leads to adipose tissue fibrosis as well as metabolic complications. Alternatively activated M2 macrophages are known to interfere with adipocyte differentiation and to promote phenotypic switch of preadipocytes into pro-fibrotic myofibroblasts. Glucocorticoids are known to promote M2 phenotype in macrophages. Therefore, the aim of this study was to investigate whether the effects of synthetic glucocorticoid dexamethasone on adipose tissue fibrosis are mediated through on macrophages to adipocyte communication. MethodsTranscriptomics analysis was performed on human adipose tissue treated without and with dexamethasone. We tested the effects of dexamethasone on differentiation of human SGBS adipocyte cells in the absence or presence of macrophages derived from THP-1 monocytic cell line plated on inserts. The differentiation rate was assessed by measuring cell lipid accumulation, expression of markers of adipogenesis PPARG and CEBPA. Acquisition of myofibroblast phenotype by preadipocytes was assessed by measuring the expression of myofibroblast marker α-smooth muscle actin. The pro-fibrotic activity of these cells was measured by the expression of collagen VI.ResultsOur transcriptomics data demonstrated that dexamethasone is able to directly double the expression of pro-fibrotic genes, such as CTGF, COL6A3, FN1, in adipose tissue, compared to control (p<0.05). Expression of CD163, a marker of M2 macrophages, positively correlated with the components of extracellular matrix COL6A3 and FN1 (p<0.01), and with the pro-fibrotic genes CTGF and AXL (p<0.01) in human adipose tissue. In addition, dexamethasone induced the expression of CD163 and MRC1 in THP-1 macrophages (p<0.05), suggesting that dexamethasone drives M2 phenotype in macrophages. We observed that dexamethasone inhibited adipogenesis by ~30% (p<0.01). Macrophages almost completely abolished differentiation of adipocytes by ~90% (p<0.01). Additionally, macrophages induced 2-3 fold increase in the expression of ACTA2 gene and protein in adipocytes (p<0.01). Dexamethasone alone did not affect ACTA2 in adipocytes,but in the presence of macrophages, increased ACTA2, when compared to macrophages alone (p<0.05). A similar nominal increase was observed in COL6A3. ConclusionOur data show that dexamethasone has a direct pro-fibrotic effect on adipose tissue and promotes M2 phenotype in macrophages. Furthermore, dexamethasone potentiated macrophage-induced phenotype switch of adipocytes to myofibroblasts, which suggests that its effect on adipose tissue fibrosis is mediated though macrophage-adipocyte communication.
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- Visvanathar, Robin, et al.
(author)
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Exploration of whole-body PET/MRI and clinical variables in type 2 diabetes for data-driven hypothesis generation
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Other publication (other academic/artistic)abstract
- Aim To explore the feasibility of using the automated holistic image analysis approach Imiomics in voxel-level association screening with clinical variables for hypothesis-generation in whole-body [18F]fluorodeoxyglucose (FDG) PET/MR images. Material and methods Three experimental groups consisting of healthy individuals (n=12), individuals with prediabetes (n=16) and individuals with type 2 diabetes (n=13) were examined with simultaneous whole-body PET/MRI during hyperinsulinemic euglycemic clamp. The Imiomics-framework was utilised to create correlation maps between the clinical biomarkers and PET/MRI data. Results Multiple significant moderate-strong associations were detected, the inflammatory biomarkers (P-CRP, B-Leukocytes and B-Neutrophils) were positively associated with visceral adipose tissue (VAT) volume and inversely associated with skeletal muscle Ki. B-monocytes were positively associated with VAT volume, and negatively associated with gluteofemoral SAT volume. Furthermore, insulin sensitivity (M-value) was shown to be negatively associated with brain Ki. Of the plasma lipids, HDL was positively associated with Ki in the liver, VAT and skeletal muscle. Several additional confirmatory and distinct findings are reported. Conclusions An Imiomics-based data-driven exploratory approach allows for rapid and holistic analysis of the massive image datasets generated.
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