A Latent Transition Analysis Model to Assess Change in Cognitive States over Three Occasions: Results from the Rush Memory and Aging Project

Abstract

Background: Conceptualizing cognitive aging as a step-sequential process is useful in identifying particular stages of cognitive function and impairment. Objectives: We applied Latent Transition Analysis (LTA) to determine i) whether the underlying structure of cognitive profiles found at every measurement occasion are uniform across three waves of assessment, ii) whether class-instability is predictive of distal outcomes, and iii) whether class-reversions from impaired to non-impaired using latent modelling is lower than when using clinical criteria of MCI. Methods: A mover-stayer LTA model with dementia as a distal outcome was specified to model transitions of ten neuropsychological measures over three annual waves in the Rush Memory and Aging Project (n = 1,661). The predictive validity of the mover-stayer status for incident Alzheimer's dementia (AD) was then assessed. Results: We identified a five-class model across the three time-points: Mixed-Domain Impairment, Memory-Specific Impairment, Frontal Impairment, Average and Superior Cognition. None of the individuals in the Impairment classes reverted to the Average or Superior classes. Conventional MCI classification identified 26.4% and 14.1% at Times 1 and 2 as false-positive cases. "Movers" had 87% increased risk of developing dementia compared to those classified as "Stayers". Conclusion: Our findings support the use of latent variable modelling that incorporates comprehensive neuropsychological assessment to identify and classify cognitive impairment.

Latent Class and Transition Analysis of Alzheimer's Disease Data

“…This has important clinical implications as individuals can be classified as experiencing different kinds of cognitive impairment early on (i.e., at baseline) and this categorization does not change significantly across time. Consistent with recent findings (i.e., Zammit et al, 2020)., we also showed that using LCA to classify individuals with MCI or AD remains relatively stable over time (as indicated by LTA) and that LCA might better categorize and reduce the risk of misdiagnosis.…”

“…To our knowledge, this is the first study to successfully use neuropsychological assessments and biomarkers of AD (e.g., Fluorodeoxyglucose, entorhinal cortex volume, and fusiform gyrus volume) to classify and predict individuals likely to transition from MCI to AD. Similar to previous results, we identified multiple classes of cognitive impairment (Scheltens et al, 2016;Zammit et al, 2019bZammit et al, , 2020. For example, the results from our LCA identified 3 classes of individuals at each time point: Class 1, which is more healthy than the other classes representing 63, 59, and 65% of the sample at, respectively, time 1, 2, and time 3; Class 2, which lies in between Class 1 and 3, representing ∼30% of the sample across the three time points; and Class 3, which include the least healthy individuals, representing 7, 10, and 6% of the sample at time 1, 2, and time 3, respectively.…”

“…Our study has replicated previous findings that LCA and LTA can be used to identify homogeneous classes of cognitive impairment (Zammit et al, 2020). However, we have uniquely identified that neuropsychological measures of AD and the associated neurological biomarkers are indicators of an individual's class membership and can predict their likelihood to transition between the healthy class (Class 1), the MCI class (Class 2), and the AD class (Class 3).…”

“…As such, the results are only capturing the class an individual belongs to at a single point in time. Zammit et al (2020) extended their previous work by using latent transition analysis to identify participants within the Rush Memory and Aging Project who transitioned from nonimpairment to cognitive impairment. A second aim was to compare the classification of individuals within the LTA to the clinical criteria of MCI.…”

“…However, their paper does highlight that LCA and LTA are at the forefront of research aiming to improve diagnostic methods and to identify individuals at risk of progressing toward AD. Zammit et al (2020) also note the need to validate these methods through replication of their findings, and efforts to identify homogeneous classes of cognitive impairment using other neuropsychological measures of AD. As mentioned earlier, recent studies using LTA have not included biomarkers of AD.…”