Predicting Short-term MCI-to-AD Progression Using Imaging, CSF, Genetic Factors, Cognitive Resilience, and Demographics
| Autor: | Varatharajah, Yogatheesan, Ramanan, Vijay K., Iyer, Ravishankar, Vemuri, Prashanthi, Weiner, Michael W., Aisen, Paul, Petersen, Ronald, Jack, Clifford R., Saykin, Andrew J., Jagust, William, Trojanowki, John Q., Toga, Arthur W., Beckett, Laurel, Green, Robert C., Morris, John, Shaw, Leslie M., Khachaturian, Zaven, Sorensen, Greg, Carrillo, Maria, Kuller, Lew, Raichle, Marc, Paul, Steven, Davies, Peter, Fillit, Howard, Hefti, Franz, Holtzman, David, Mesulam, M. Marcel, Potter, William, Snyder, Peter, Schwartz, Adam, Montine, Tom, Thomas, Ronald G. |
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| Rok vydání: | 2019 |
| Předmět: |
0301 basic medicine
Oncology Male Aging Complement receptor 1 lcsh:Medicine Disease Neurodegenerative Alzheimer's Disease 0302 clinical medicine Cognition Models 80 and over 2.1 Biological and endogenous factors Alzheimer's Disease including Alzheimer's Disease Related Dementias Aetiology lcsh:Science Cognitive impairment media_common Aged 80 and over screening and diagnosis Multidisciplinary biology Resilience Psychological Middle Aged 3. Good health Other Physical Sciences Detection Neurological Female Psychological resilience Alzheimer's Disease Neuroimaging Initiative medicine.medical_specialty Demographics media_common.quotation_subject Models Neurological and over Article 03 medical and health sciences Genetic Alzheimer Disease Clinical Research Internal medicine mental disorders medicine Acquired Cognitive Impairment Dementia Humans Genetic Predisposition to Disease Cognitive Dysfunction Aged Models Genetic Resilience business.industry Prevention lcsh:R Neurosciences Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD) Alzheimer’s Disease Neuroimaging Initiative medicine.disease Brain Disorders 4.1 Discovery and preclinical testing of markers and technologies 030104 developmental biology Psychological lcsh:Q Biochemistry and Cell Biology biology.gene business 030217 neurology & neurosurgery |
| Zdroj: | Scientific reports, vol 9, iss 1 Scientific Reports Medical Biophysics Publications Varatharajah, Yogatheesan; Ramanan, Vijay K; Iyer, Ravishankar; Vemuri, Prashanthi; & Alzheimer’s Disease Neuroimaging Initiative,. (2019). Predicting Short-term MCI-to-AD Progression Using Imaging, CSF, Genetic Factors, Cognitive Resilience, and Demographics.. Scientific reports, 9(1), 2235. doi: 10.1038/s41598-019-38793-3. UCSF: Retrieved from: http://www.escholarship.org/uc/item/63h3f0tp Scientific Reports, Vol 9, Iss 1, Pp 1-15 (2019) |
| DOI: | 10.1038/s41598-019-38793-3. |
| Popis: | In the Alzheimer’s disease (AD) continuum, the prodromal state of mild cognitive impairment (MCI) precedes AD dementia and identifying MCI individuals at risk of progression is important for clinical management. Our goal was to develop generalizable multivariate models that integrate high-dimensional data (multimodal neuroimaging and cerebrospinal fluid biomarkers, genetic factors, and measures of cognitive resilience) for identification of MCI individuals who progress to AD within 3 years. Our main findings were i) we were able to build generalizable models with clinically relevant accuracy (~93%) for identifying MCI individuals who progress to AD within 3 years; ii) markers of AD pathophysiology (amyloid, tau, neuronal injury) accounted for large shares of the variance in predicting progression; iii) our methodology allowed us to discover that expression of CR1 (complement receptor 1), an AD susceptibility gene involved in immune pathways, uniquely added independent predictive value. This work highlights the value of optimized machine learning approaches for analyzing multimodal patient information for making predictive assessments. |
| Databáze: | OpenAIRE |
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