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- Diamanti, Klev, 1987-, et al.
(författare)
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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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Annan publikation (övrigt vetenskapligt/konstnärligt)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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- Visvanathar, Robin, et al.
(författare)
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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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Annan publikation (övrigt vetenskapligt/konstnärligt)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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- Ås, Joel, et al.
(författare)
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Network-Based Analysis of Protein Interactions among Drugs and Adverse Reactions: Identifying Phenotype-Groupings and Key Genes
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Background:Adverse drug reactions (ADRs) present a significant healthcare challenge, leading to morbidity, hospitalizations, and even fatalities. Serious ADRs are in general infrequent, since drugs with a high risk-benefit ratio are rarely approved by the authorities.Genetic factors contribute to serious ADRs, driving pharmacogenomic research to investigate drug-ADR-genetic relationships. These relationships are, however, still largely unstudied due to the scarcity of cases. This scarcity, coupled with the multiple hypothesis problem of genetic studies, poses challenges for these studies. One approach is to group similar ADRs or drugs to bolster sample sizes. However, grouping of drugs and ADRs requires caution to avoid including biologically ill-fitting cases. The objective of our study is to cluster drugs and ADRs based on previous genetic associations and shared protein interactions to propose phenotype groups and genetic targets for investigation.Methods:We developed a Bayesian probability model to substantiate protein-protein interactions across different drugs or ADRs. Subsequently, these proximity values were utilised for spectral clustering to form phenotype-groups. Once obtained, the model was reformulated to rank shared proteins for each cluster.Results:Permutation analysis demonstrated high sensitivity in correctly clustering drugs into therapeutic groups (sensitivity 94-97%) - outperforming other proposed methods - and assigning ADRs to clusters (sensitivity 86%). The model's reformulation enabled the ranking of shared proteins within each cluster, revealing enrichment in KEGG pathways relevant to therapeutic classifications. Discussion:This method successfully replicated known therapeutic drug classifications with high sensitivity, using shared protein interactions among KEGG pathways associated with drug functions. Using the proximity score and spectral clustering we propose phenotype groups and genetic targets for investigations. However, further studies are needed to assess the method's utility for the selection of cases and for target identification in less homogeneous drug-ADR scenarios.
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