Elucidating metabolomics correlates with Autonomic physiology in ME/CFS (Myalgic Encephalomyelitis/Chronic Fatigue Syndrome)
I am currently working in collaboration with the Oxford Chemistry Department, and Nicoloaus Copernicus University, Torun Poland, to analyse a comprehensive dataset comprising variables of symptomatology, autonomic physiology with each case matched to their respective plasma metabolome.
Our initial analysis led by Professor James McCullagh (Chemistry) focused on the coverage of a broad spectrum of metabolites in plasma, quantified under different methods, to include comprehensive coverage of the TCA cycle, which combined with other methods, further covering Amino Acids and Lipids and glycolytic intermediates enabled surrogate indications of reactions pertaining to anaplerotic metabolism. Multivariate models were devised using Principal Component Analysis (PCA) and Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) to enable differentiation of cohorts.
My work at present builds on the data yielded from the multivariate analyses to determine correlation of parameters of physiology and symptomatology with the metabolomics datasets, ultimately enabling us to advance toward potential candidate metabolites that may be implicated in the mediation of clinical sequelae.
We aim to build on and adopt a similar approach in our upcoming project in collaboration with the London School of Hygeine and Tropical Medicine (LSHTM) and sponsored by the ME Association (MEA) which will comprise a larger cohort, with a fatigued control group (Multiple Sclerosis), in addition to healthy controls.