A multivariate model of time to conversion from mild cognitive impairment to Alzheimer's disease: Developing topics. (7th December 2020)

• Record Type: Journal Article
• Authors: García, María Eugenia López; Turrero, Agustín; Cuesta, Pablo; Rojo, Inmaculada Concepción Rodríguez; Barabash, Ana; Dolado, Alberto Marcos; Maestú, Fernando; Fernandez, Alberto
• Is Part Of: Alzheimer's & dementia. Volume 16(2020)Supplement 5
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-12-07
• Subjects: Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1002/alz.047537
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: 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.