Metabolic analysis

The lack of sensitive biomarkers for both sarcopenia and cancer cachexia underscores the need for improvement in our understanding of the underlying mechanisms specific to each syndrome and necessitates a new approach to the current problem. Employing multi-omics investigative approach may identify clinically relevant biomarkers and delineate inflammatory and metabolic pathways specific to each syndrome.

I have add a few papers about clinical utility of metabolomics in the sub-folder. Please use them to make a compelling argument that adding metabolomics investigation enhances our ability to 1) better differentiate sarcopenia from cachexia, 2) improve prognosis ability, 3) enable discovery of new compounds to treat the specific disease that is being studied.

Summary the targeted approach used to analyse metabolome

Untargeted metabolomics analyses assay a wide array of metabolites, cinluding those that may not be specific to the disease under investigation; thus, new analytes and biomarkers can be discovered using the untargeted approach (Johnson 2016). In contrast, targeted metabolomics approaches focus on a narrow, specific cluster of metabolites that have been hypothesized to be important players in the studied phenomenon (Johnson 2016). Advantages of the targeted approach include smaller-scale assay and higher sensitivity and specificity that would facilitate detection and quantification of metabolites that have been previously determined to be relevant in the disease being studied (Johnson 2016).

(come after dalle and shih 16). Alzh’s disease . Many studies have utilized metabolomics to idenfity metabolites that differentiated various illnesses from the healthy state. For example, Tasic et al. (2017) found 7 metabolites that were detected only in patients with schizophrenia, and 5 metabolites that were only present in healthy controls (Tasic 2017). Yang et al. (2013) documented 22 serum and 21 urinary metabolites that were significantly differential between healthy controls and schizophrenia patients and suggested a panel of biomarkers for schizophrenia that consists of cystine, glycerate, b-hydroxybutyrate, , hydroxybutyrate, pyruvate, and eicosenoic acid (Yang 2013). Moreover, Zheng et al. (2016) reported 17 metabolites from peripheral blood mononuclear cells that had significantly different concentrations in healthy controls versus patients with major depressive disorder (Zheng 2016). Additionally, Sethi et al. (2017) identified 7 important serum metabolites that separated bipolar disorder from the healthy state (Sethi 2017). Similarly, Chen et al (2014) documented 26 differential urinary metabolites in bipolar disorder vs. in the healthy state. By investigating metabolites in healthy controls vs. patients, investigators are able to identify signature clusters of metabolites that can be developed into useful diagnostic biomarkers for various diseases.

We have demonstrated the effectiveness of lipidomics and targeted metabolomics in a series of pilot studies investigating anorexia nervosa, an illness characterized by rapid weight loss and reduction in food consumption (summarize how metabolite findings showed a pro-inflammatory response to PUFA epoxy fatty acid). Targeted metabolomics was used to assay oxylipin precursors and products of the sEH-catalyzed hydrolytic pathway as well as COX-, LOX-, and CYP450- related oxylipins (Shih 2016). Higher ratios of dihydroxy to epoxy fatty acids were found in anorexia nervosa patients than in controls, suggesting an upregulation of sEH activity, an elevation in pro-inflammatory oxylipins in anorexia nervosa, and a reduction in anti-inflammatory epoxy fatty acids (Shih 2016). Additionally, recovered anorexia nervosa patients showed a restoration of normal PUFA profiles, implying the process of resolution of inflammation. The results of the study indicated possible interactions between PUFAs, PUFA metabolites, and inflammatory pathways that may contribute to disease pathophysiology. As oxylipin changes may affect the inflammation status that underlies disease progression, metabolomics can be applied as an effective tool to to monitor oxylipins as clinical biomarkers that would enable better discrimination of a pathological condition from the healthy state and improve prognosis ability.