Predicting cognitive decline in prodromal synucleinopathies using clinical markers and machine learning

Predicting cognitive decline in prodromal synucleinopathies using clinical markers and machine learning | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Predicting cognitive decline in prodromal synucleinopathies using clinical markers and machine learning Loubna Mekki Berrada, Arthur Dehgan, Ronald Postuma, Yann Harel, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4619161/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract Neuroprotective interventions for dementia with Lewy bodies (DLB) and Parkinson’s disease (PD) are still in their early days. Clinical trials are expected to target idiopathic rapid eye movement sleep behavior disorder (iRBD), their strongest predictor. However, the presentation and progression of symptoms within this population show significant heterogeneity. We used machine learning (ML) to identify the clinical markers that are best at distinguishing iRBD patients (n=156) who developed DLB (n=26) from those who developed PD (n=34) at a mean follow-up of 4.37 years. Our model classified subsequent conversion to DLB versus PD with 0.80 accuracy, with mild cognitive impairment as best predictive feature. Cognitive tests of executive functions and verbal learning also played a major role in classifying other related pathological trajectories. These findings support the use of ML with clinical markers in iRBD, paving the way for a more targeted selection of participants in future neuroprotective trials of synucleinopathies. Health sciences/Biomarkers/Predictive markers Biological sciences/Neuroscience/Diseases of the nervous system/Neurodegeneration Health sciences/Biomarkers/Prognostic markers Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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