Cognitive Reserve and Mild Cognitive Impairment

Abstract

Background and Objectives:Little is known about the effect of education or other indicators of cognitive reserve on the rate of reversion from mild cognitive impairment (MCI) to normal cognition (NC), or the relative rate of reversion from MCI to NC vs. progression from MCI to dementia. Our objectives were to: 1) estimate transition rates from MCI to NC and dementia, and 2) determine the effect of age, apolipoprotein E (APOE), and indicators of cognitive reserve on the relative rate (RR) of reversion vs. progression using multistate Markov modelling.Methods:We estimated instantaneous transition rates between NC, MCI and dementia after accounting for transition to death across up to 12 assessments in the Nun Study, a cohort study of religious sisters aged 75+ years. We estimated RRs of reversion vs. progression for age, APOE, and potential cognitive reserve indicators: education, academic performance (high school grades), and written language skills (idea density, grammatical complexity).Results:Of the 619 participants, 472 were assessed with MCI during the study period. Of these 472, 143 (30.3%) experienced at least one reverse transition to NC, and 120 of the 143 (83.9%) never developed dementia (mean follow-up=8.6 years). In models adjusted for age group and APOE, higher levels of education more than doubled the RR ratio of reversion vs. progression. Novel cognitive reserve indicators were significantly associated with a higher adjusted RR of reversion vs. progression (higher vs. lower levels for English grades: RR Ratio = 1.83; idea density: RR ratio=3.93; and grammatical complexity: RR ratio=5.78).Discussion:Knowledge of frequent reversion from MCI to NC may alleviate concerns of inevitable cognitive decline in those with MCI. Identification of characteristics predicting the rate of reversion from MCI to NC vs. progression from MCI to dementia may guide population-level interventions targeting these characteristics to prevent or postpone MCI and dementia. Research on cognitive trajectories would benefit from incorporating predictors of reverse transitions and competing events, such as death, into statistical modelling. Finally, these results may inform the design and interpretation of MCI clinical trials, given that a substantial proportion of participants may experience improvement without intervention.

Multimorbidity patterns and 18‐year transitions from normal cognition to dementia and death: A population‐based study

“…To study cognitive transitions, we used multistate Markov models [18]. These models are used to describe how individuals move across different states over time and have been previously applied to the study of the cognitive continuum [19]. A four‐state model was used considering three cognitive states (1: normal cognition, 2: CIND, and 3: dementia) and death as an absorbing state (state 4) (Fig.…”

The Association Between Emotional Expressivity in Autobiographies from Early Adulthood and the Risk of Dementia in the Context of Written Language Skills

“…Grammatical complexity was measured on an eight-level scale ranging from simple one-clause sentences to complex multi-clause sentences [ 36 ]. Aside from raw scores used for descriptive purposes, idea density and grammatical complexity were ranked within convents and categorized as “high” (i.e., upper three quartiles) or “low” (i.e., bottom quartile), consistent with previous studies [ 9, 15 ]. Scoring of idea density and grammatical complexity showed high agreement between coders (correlations of 0.88 and 0.93, respectively) [ 22 ].…”

“…Early-life intellectual pursuits have shown particular promise in positively impacting late-life cognitive outcomes [ 1, 9, 12 ], which are dynamic and can fluctuate between normal cognition and varying degrees of impairment within an individual over time [ 9, 13, 14 ]. Low education has been associated with a 7% population attributable risk of dementia worldwide, demonstrating the potential impact of early-life intellectual factors to reduce the incidence of dementia [ 1, 3 ].…”

A Machine Learning Classification of Individuals with Mild Cognitive Impairment into Variants from Writing

“…Also, depending on the number of cognitive domains impaired, individuals can be categorized into single-domain and multi-domain MCI. Although a higher risk of developing dementia characterizes individuals with MCI, not all individuals with MCI will progress to dementia; some may remain stable, and others even regress to a condition of healthy aging [10][11][12]. Therefore, it is essential to discriminate against those who are more likely to progress to dementia for early intervention since most treatment strategies are more effective in the presymptomatic stage of dementia [13].…”