The application of nontargeted metabolomic profiling has recently become a powerful noninvasive tool to discover new clinical biomarkers. This study aimed to identify metabolic pathways that could be exploited for prognostic and therapeutic purposes in hepatorenal dysfunction in cirrhosis. One hundred three subjects with cirrhosis had glomerular filtration rate (GFR) measured using iothalamate plasma clearance, and were followed until death, transplantation, or the last encounter. Concomitantly, plasma metabolomic profiling was performed using ultrahigh performance liquid chromatography-tandem mass spectrometry to identify preliminary metabolomic biomarker candidates. Among the 1028 metabolites identified, 34 were significantly increased in subjects with high liver and kidney disease severity compared with those with low liver and kidney disease severity. The highest average fold-change (2.39) was for 4-acetamidobutanoate. Metabolite-based enriched pathways were significantly associated with the identified metabolomic signature (P values ranged from 2.07E-06 to 0.02919). Ascorbate and aldarate metabolism, methylation, and glucuronidation were among the most significant protein-based enriched pathways associated with this metabolomic signature (P values ranged from 1.09E-18 to 7.61E-05). Erythronate had the highest association with measured GFR (R-square = 0.571, P <0.0001). Erythronate (R = 0.594, P <0.0001) and N6-carbamoylthreonyladenosine (R = 0.591, P <0.0001) showed stronger associations with measured GFR compared with creatinine (R = 0.588, P <0.0001) even after controlling for age, gender, and race. The 5 most significant metabolites that predicted mortality independent of kidney disease and demographics were S-adenosylhomocysteine (P = 0.0003), glucuronate (P = 0.0006), trans-aconitate (P = 0.0018), 3-ureidopropionate (P = 0.0021), and 3-(4-hydroxyphenyl)lactate (P = 0.0047). A unique metabolomic signature associated with hepatorenal dysfunction in cirrhosis was identified for further investigations that provide potentially important mechanistic insights into cirrhosis-altered metabolism.
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http://dx.doi.org/10.1016/j.trsl.2017.12.002 | DOI Listing |
Heliyon
January 2025
Children's Brain Tumour Research Centre, School of Medicine, Biodiscovery Institute, University of Nottingham, UK.
Isocitrate dehydrogenase wild-type glioblastoma (GBM) is characterised by a heterogeneous genetic landscape resulting from dynamic competition between tumour subclones to survive selective pressures. Improvements in metabolite identification and metabolome coverage have led to increased interest in clinically relevant applications of metabolomics. Here, we use liquid chromatography-mass spectrometry and gene expression microarray to profile integrated intratumour metabolic heterogeneity, as a direct functional readout of adaptive responses of subclones to the tumour microenvironment.
View Article and Find Full Text PDFNat Methods
January 2025
Statistical Center for Single-Cell and Spatial Genomics, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Spatial molecular profiling has provided biomedical researchers valuable opportunities to better understand the relationship between cellular localization and tissue function. Effectively modeling multimodal spatial omics data is crucial for understanding tissue complexity and underlying biology. Furthermore, improvements in spatial resolution have led to the advent of technologies that can generate spatial molecular data with subcellular resolution, requiring the development of computationally efficient methods that can handle the resulting large-scale datasets.
View Article and Find Full Text PDFFunct Integr Genomics
January 2025
Department of Exercise Science and Health Promotion, Florida Atlantic University, Boca Raton, FL, USA.
Large-scale, pan-cancer analysis is enabled by data driven knowledge bases that link tumor molecular profiles with phenotypes. A debilitating cancer-related phenotype is skeletal muscle loss, or cachexia, which occurs partly from tumor products secreted into circulation. Using the LinkedOmicsKB knowledge base assembled from the Clinical Proteomics Tumor Analysis Consortium proteogenomic analysis, along with catalogs of human secretome proteins, ligand-receptor pairs and molecular signatures, we sought to identify candidate pan-cancer proteins secreted to blood that could regulate skeletal muscle phenotypes in multiple solid cancers.
View Article and Find Full Text PDFJ Inflamm Res
January 2025
Department of Dermatology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, People's Republic of China.
Purpose: Psoriasis is a complex inflammatory skin disorder that is closely associated with metabolic syndrome (MetS). Limited information is available on skin metabolic changes in psoriasis; the effect of concurrent MetS on psoriatic skin metabolite levels is unknown. We aimed to expand this information through skin metabolomic analysis.
View Article and Find Full Text PDFDiabetes Metab Res Rev
January 2025
Division of Research, Kaiser Permanente Northern California, Pleasanton, California, USA.
Aims: Gestational diabetes mellitus (GDM) poses a significant risk for developing type 2 diabetes mellitus (T2D) and exhibits heterogeneity. However, understanding the link between different types of post-GDM individuals without diabetes and their progression to T2D is crucial to advance personalised medicine approaches.
Materials And Methods: We employed a discovery-based unsupervised machine learning clustering method to generate clustering models for analysing metabolomics, clinical, and biochemical datasets.
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