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| http://dx.doi.org/10.1161/JAHA.119.013032 | DOI Listing |
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Background: The new anti-Aβ antibody drugs aducanumab and lecanemab (approved in the US, not yet in Europe) must be followed up closely and regularly long-term. Previous knowledge on progression of AD in routine clinical settings longterm is crucial when introducing new dementia medications. The Swedish national quality database on dementia/cognitive disorders, SveDem, where data on different dementia disorders at the time of the dementia diagnosis since 2007 and on mild cognitive impairment (MCI) since 2021 with annual follow-ups of MMSE scores can provide this unique information.
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Laboratory of Neuro Imaging (LONI), University of Southern California, Los Angeles, CA, USA.
Background: Anti-amyloid therapy appears to have an increased effect on reducing cognitive decline in amyloid- and tau-positive individuals. However, clinical trials inclusion criteria require solely amyloid positivity. Herein, we developed a machine-learning prediction model to identify tau positivity in amyloid-positive individuals using clinical variables.
View Article and Find Full Text PDFComparison of Associations of Food Security Instruments and Mediators With Premature All-Cause and Cardiovascular Disease Death in US Adults.
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January 2025
Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA. (L.T., K.S.D., K.P.T., J.D.B.).
Background: Food insecurity is associated with high morbidity and mortality and is typically measured with the 10-item US Adult Food Security Survey Module. Shorter instruments may capture similar information, but this has not been validated against mortality in general populations.
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Laboratory of Neuro Imaging (LONI), University of Southern California, Los Angeles, CA, USA.
Background: Screen failure due to amyloid negativity is yet a problem in clinical trials for anti-amyloid drugs. In this context, clinical characteristics of patients presenting with cognitive decline may decrease the screen failure ratio by increasing the odds of selecting individuals with brain amyloid pathology. Herein, we aimed at estimating amyloid and tau positivity in individuals using clinical variables in a machine learning model of prediction.
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