OMF Funded Severely Ill Big Data Study Update

On this #OMFScienceWednesday, we review the Severely ill Big Data Study. This study, led by Drs. Davis and Xiao, included over 1,000 tests per patient, producing, to our knowledge, the biggest dataset ever generated in a cohort of ME / CFS patients. This big data study examined the patients’ genome, gene expression, metabolomics, microbiome, cell-free DNA sequencing and quantitation, and cytokines, as well as a range of tests typically performed by clinicians. In 2017, the focus of the study was on analysis, data integration, and making the dataset available to researchers at The Stanford End ME / CFS Data Center(registration required).

•        Differences in metabolites, microbiomes, cytokines, and several clinical test results were observed between patients and controls.
•        No significant differences were found for any major DNA viruses between patients and controls using cell-free DNA from the blood. By using cell-free DNA it was possible to look for even the viruses that can hide behind the blood-brain barrier escaping detection by normal means. In addition, the blood of patients was examined for new pathogens by isolating particles from the blood and using DNA sequencing. No new pathogens were found.
•        SF-36 scores are worse in ME / CFS than in several other major diseases and correlate the least with depression and mental illnesses. (SF-36 is a questionnaire in which patients report on their fatigue and other aspects of their quality of life.)
•        Genetics have been a particularly interesting aspect of this study, as the team has identified several candidate genes that may predispose individuals to develop ME / CFS (or severe ME / CFS). This is exciting because it may tell us about the root cause of the disease – which still remains a mystery.
•        Amping up the analysis is a priority given the complexity of this dataset. OMF has funded a full-time bioinformatician at the Stanford Genome Technology Center to help complete the analysis of this dataset and publish it in the scientific literature, and to continue integrating it with future projects.