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A multivariate model of time to conversion from mild cognitive impairment to Alzheimer’s disease
Author(s) -
García María Eugenia López,
Turrero Agustín,
Cuesta Pablo,
Rojo Inmaculada Concepción Rodríguez,
Barabash Ana,
Dolado Alberto Marcos,
Maestú Fernando,
Fernandez Alberto
Publication year - 2020
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1002/alz.047537
Subject(s) - dementia , cognition , proportional hazards model , magnetic resonance imaging , medicine , psychology , cardiology , neuropsychology , disease , brain size , alzheimer's disease , cognitive decline , audiology , neuroscience , radiology
Background Alzheimer's disease (AD) is a neurodegenerative disorder, clinically defined by a progressive loss of memory and other cognitive and functional abilities. One of the most studied phases in the prognosis of AD is the Mild cognitive impairment (MCI) since it entails a higher risk of developing this type of dementia. The majority longitudinal studies from MCI to AD utilize both a reduce number of potential prediction markers and a shorten length of follow‐up. Therefore, the present study was aimed at determining which combination of demographic, genetic, cognitive, neurophysiological (i.e. magnetoencephalography, MEG), and neuroanatomical (i.e. magnetic resonance imaging (MRI) volumetry) factors may predict differences in time to progression from MCI to AD during an extended follow‐up. Method To this end, a sample of 121 MCIs was followed‐up during a 5‐years period. According to their clinical outcome, MCIs were divided into two subgroups: (i) the “progressive” MCI (pMCI; n= 46); and (ii) the “stable” MCI group (sMCI; n= 75). Kaplan‐Meier survival analyses were applied to explore each variable’s relationship with the progression to AD. Once potential predictors were detected, Cox regression analyses were utilized to calculate a parsimonious model that may allow the estimation of differences in time to progression. Result Results indicated that the final model included three variables (in order of relevance): Left parahippocampal volume (corrected by intracranial volume, LP_ ICV), Delayed recall (DR), and Left Inferior Occipital lobe individual alpha peak frequency (LIOL_IAF). Those MCIs with LP_ ICV volume, DR score and LIOL_IAPF value lower than the defined cutoff had 6‐times, 5.5‐times and 3‐times higher risk of progression to AD, respectively. Besides, when the categories of the three variables were “unfavourable” (i.e. values below the cutoff), a 100% of cases progressed to AD at the end of follow‐ up, while a combination of “favourable” categories yielded a 94.7% of stable cases at the end of follow‐up. Conclusion Our results highlighted the relevance of neurophysiological markers as predictors of conversion, and the importance of multivariate models that combine markers of different nature to predict time to progression from MCI to dementia.