부산대학교 도서관

Background: Deep vein thrombosis (DVT) is a major health concern with significant morbidity and mortality. The etiology of deep vein thrombosis is complex and multifactorial, including both genetic and environmental factors. Nonspecific symptoms and the lack of a blood biomarker that could con-firm DVT makes the diagnosis challenging. New molecular technologies and techniques have entered the scientific arena which offer the op-portunity to revisit this important clinical need. Metabolomics have succeeded the genomics era. Genomic studies can improve the prediction of the disease but are not able to provide any feed-back on the way the genome interacts with the environment and the lifestyle, what we call phe-notype. Metabolomics uses high throughput technologies to identify all the intermediates and end products of a biological process carrying the imprints of genetic and environmental influ-ences. Metabolomic science can identify novel, unrecognized metabolites and pathways, eluci-dating the pathophysiology of the disease. Aims: To investigate metabonomics technologies as a reliable tool for DVT biomarker investigation and identify specific metabolic profiles related to DVT and thrombus aging. Through the process, different metabolites can be suggested as possible DVT biomarkers. Methods: Given the variability and complexity of the disease, it was decided to apply metabolomic analysis on an established DVT animal experimental model to identify the signature of acute DVT and examine the metabolic differences of the DVT profiling over time. Metabolomic analysis was al-so used to investigate the presence of a DVT metabolic phenotype in biofluids of humans with acute DVT compared to controls and patients with similar symptoms but excluded DVT. Untar-geted metabolomic strategies using Nuclear Magnetic Resonance (NMR) spectrometry, and Mass spectrometry (MS) were applied. The untargeted approach, is a hypothesis generating method to identify clustering between the examined groups and possible metabolites responsible for the clustering. Results: Metabolomic platforms appears to be a valuable tool in DVT metabolomic research. In animals, a different metabolic profile is found related to DVT in blood and vein wall tissue extract. The metabolic difference and clustering of the groups was stronger at early time points, specifically at 3 hours and 6 hours after DVT induction. The identified metabolites include lactate, acetoace-tate, several lipid moieties, N-acetylglucosamines related mostly to energy pathways. Vein wall extract analysis showed that ceramides, carnitines and lipid moieties are involved in vein wall changes during thrombus formation. In humans, NMR analysis showed that there is a clear meta-bolic signature of acute DVT. N-acetylglucosamine, glutamine, alanine, glucose and lipids were the molecules found to drive the difference between the clinical groups. Conclusion: Metabolomic strategy is a robust and valuable tool to investigate metabolic profiles of DVT. That was evident in both humans and mice. Moreover, this work reveals that chronicity of the clot is characterised by specific metabolic changes that eventually can lead to identification of clot aging.