{"id":20972,"date":"2024-01-17T14:21:57","date_gmt":"2024-01-17T14:21:57","guid":{"rendered":"https:\/\/clinlabint.com\/?p=20972"},"modified":"2024-01-17T14:31:37","modified_gmt":"2024-01-17T14:31:37","slug":"precisionlife-project-awarded-innovate-uk-grant-to-improve-diagnosis-and-treatment-of-me-cfs-and-long-covid","status":"publish","type":"post","link":"https:\/\/clinlabint.com\/precisionlife-project-awarded-innovate-uk-grant-to-improve-diagnosis-and-treatment-of-me-cfs-and-long-covid\/","title":{"rendered":"PrecisionLife project awarded Innovate UK Grant to improve diagnosis and treatment of ME\/CFS and long Covid"},"content":{"rendered":"
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PrecisionLife project awarded Innovate UK Grant to improve diagnosis and treatment of ME\/CFS and long Covid<\/h1>\/ in E-News<\/a> <\/span><\/span><\/header>\n<\/div><\/section>
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Leading computational biology company PrecisionLife, which is driving precision medicine in complex chronic diseases, the ME\/CFS (myalgic encephalomyelitis\/chronic fatigue syndrome) charity Action for M.E.,
\nand the MRC Human Genetics Unit at the Institute of Genetics and Cancer, University of Edinburgh, have been awarded a \u00a3622,000 (about US$794,000) grant by Innovate UK\u2019s Advancing Precision Medicine programme to improve diagnosis and treatment for the millions of people affected by ME\/CFS and long Covid.<\/h3>\n

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PrecisionLife\u2019s platform recently identified the first reproducible genetic associations with ME\/CFS [1] and long Covid [2]. Before then, little was known about the genetic causes of either disease, which have no effective diagnostic tools or disease modifying therapies.<\/p>\n

The LOCOME (LOng COvid and Myalgic Encephalomyelitis diagnostics and stratification) project will extend PrecisionLife\u2019s previous analyses to include deeper multimodal data for a wider group of patients
\nfrom long Covid datasets and the world\u2019s largest study of ME\/CFS, led by the DecodeME Partnership, which includes Action for M.E.
\nand the MRC Human Genetics Unit.<\/p>\n

PrecisionLife will use its precision medicine and mechanistic patient stratification approach to identify the factors driving disease in different patient subgroups through combinatorial analysis of the DecodeME data. The insights and biomarkers that this generates will be used to create the first predictive diagnostic tools to rapidly triage people presenting with potential ME\/CFS or long Covid symptoms and identify novel repurposing opportunities to accelerate access to disease modifying treatments for patients.<\/p>\n

\u201cLOCOME will demonstrate a powerful new ability to generate clinically useful disease insights from patient datasets that can be applied in a rapid and cost-efficient manner in healthcare settings to improve diagnosis and treatment of the most challenging and costly diseases which have huge unmet medical need. Upon demonstrating success for ME\/CFS and long Covid patients, we hope to also apply this precision medicine approach to multiple diseases of ageing in respiratory, dementia, autoimmune, and metabolic diseases to benefit millions more people,\u201d said Steve Gardner, PhD, CEO, PrecisionLife.<\/p>\n

Sonya Chowdhury, PhD, chief executive, Action for M.E., commented: \u201cWe are delighted to partner with PrecisionLife, through the Genetic Centre of Excellence that we are establishing with the University
\nof Edinburgh. People with M.E. deserve greater investment in research to help identify a diagnostic test and treatments and this is another step on that journey. By working together, we aim to accelerate research into personalised diagnosis and treatment.\u201d<\/p>\n

References:<\/strong><\/em>
\n1. Das, S., Taylor, K., Kozubek, J. et al. Genetic risk factors for ME\/CFS identified using combinatorial analysis. J Transl Med 20, 598 (2022). https:\/\/bit.ly\/48RlIx5<\/em>
\n2. Taylor, K., Pearson, M., Das, S. et al. Genetic risk factors for severe and fatigue dominant long COVID and commonalities with ME\/CFS identified by combinatorial analysis. J Transl Med 21, 775 (2023). <\/em>
\nhttps:\/\/bit.ly\/3Or1xyn<\/a><\/em><\/p>\n<\/div><\/section>
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