Roberto Santangelo1,2, Federico Masserini1, Federica Agosta2,3, Arianna Sala2,4, Silvia P Caminiti2,4, Giordano Cecchetti1,3, Francesca Caso1, Vittorio Martinelli1, Patrizia Pinto5, Gabriella Passerini6, Daniela Perani2,4,7, Giuseppe Magnani1, Massimo Filippi8,9,10,11. 1. Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy. 2. Vita-Salute San Raffaele University, Milan, Italy. 3. Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy. 4. In vivo human molecular and structural neuroimaging Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy. 5. Department of Neurology, Papa Giovanni XXIII Hospital, Bergamo, Italy. 6. Department of Laboratory Medicine, IRCCS San Raffaele Scientific Institute, Milan, Italy. 7. Nuclear Medicine Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy. 8. Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy. filippi.massimo@hsr.it. 9. Vita-Salute San Raffaele University, Milan, Italy. filippi.massimo@hsr.it. 10. Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy. filippi.massimo@hsr.it. 11. Neurophysiology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy. filippi.massimo@hsr.it.
Abstract
PURPOSE: To know whether mild cognitive impairment (MCI) patients will develop Alzheimer's disease (AD) dementia in very short time or remain stable is of crucial importance, also considering new experimental drugs usually tested within very short time frames. Here we combined cerebrospinal fluid (CSF) AD biomarkers and a neurodegeneration marker such as brain FDG-PET to define an objective algorithm, suitable not only to reliably detect MCI converters to AD dementia but also to predict timing of conversion. METHODS: We included 77 consecutive MCI patients with neurological/neuropsychological assessment, brain 18F-FDG-PET and CSF analysis available at diagnosis and a neuropsychological/neurological evaluation every 6 months for a medium- to a long-term follow-up (at least 2 and up to 8 years). Binomial logistic regression models and Kaplan-Meier survival analyses were performed to determine the best biomarker (or combination of biomarkers) in detecting MCI converters to AD dementia and then, among the converters, those who converted in short time frames. RESULTS: Thirty-five out of 77 MCI patients (45%) converted to AD dementia, with an average conversion time since MCI diagnosis of 26.07 months. CSF p-tau/Aβ42 was the most accurate predictor of conversion from MCI to AD dementia (82.9% sensitivity; 90% specificity). CSF p-tau/Aβ42 and FDG-PET-positive MCIs converted to AD dementia significantly earlier than the CSF-positive-only MCIs (median conversion time, 17.1 vs 31.3 months). CONCLUSIONS: CSF p-tau/Aβ42 ratio and brain FDG-PET may predict both occurrence and timing of MCI conversion to full-blown AD dementia. MCI patients with both biomarkers suggestive for AD will likely develop an AD dementia shortly, thus representing the ideal target for any new experimental drug requiring short periods to be tested for.
PURPOSE: To know whether mild cognitive impairment (MCI) patients will develop Alzheimer's disease (AD) dementia in very short time or remain stable is of crucial importance, also considering new experimental drugs usually tested within very short time frames. Here we combined cerebrospinal fluid (CSF) AD biomarkers and a neurodegeneration marker such as brain FDG-PET to define an objective algorithm, suitable not only to reliably detect MCI converters to AD dementia but also to predict timing of conversion. METHODS: We included 77 consecutive MCI patients with neurological/neuropsychological assessment, brain 18F-FDG-PET and CSF analysis available at diagnosis and a neuropsychological/neurological evaluation every 6 months for a medium- to a long-term follow-up (at least 2 and up to 8 years). Binomial logistic regression models and Kaplan-Meier survival analyses were performed to determine the best biomarker (or combination of biomarkers) in detecting MCI converters to AD dementia and then, among the converters, those who converted in short time frames. RESULTS: Thirty-five out of 77 MCI patients (45%) converted to AD dementia, with an average conversion time since MCI diagnosis of 26.07 months. CSF p-tau/Aβ42 was the most accurate predictor of conversion from MCI to AD dementia (82.9% sensitivity; 90% specificity). CSF p-tau/Aβ42 and FDG-PET-positive MCIs converted to AD dementia significantly earlier than the CSF-positive-only MCIs (median conversion time, 17.1 vs 31.3 months). CONCLUSIONS: CSF p-tau/Aβ42 ratio and brain FDG-PET may predict both occurrence and timing of MCI conversion to full-blown AD dementia. MCI patients with both biomarkers suggestive for AD will likely develop an AD dementia shortly, thus representing the ideal target for any new experimental drug requiring short periods to be tested for.
Entities:
Keywords:
Alzheimer’s disease; Brain FDG-PET; Cerebrospinal fluid biomarkers; MCI (mild cognitive impairment); MCI converters to AD
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