Abstract
Background
We know that the neuropathological changes associated with Alzheimer's disease (AD) occur decades before functional decline. Therefore to intervene prior to irreversible damage, early diagnosis is essential. This requires identification of those in the healthy group at high and low risk of cognitive decline. The Australian Imaging, Biomarkers and Lifestyle (AIBL) study is a longitudinal study of ageing and AD, and more than 70% of this cohort was recruited with healthy cognition. In this analysis we examine the healthy control (HC) group with the aim of determining those at risk of conversion to mild cognitive impairment (MCI) or Alzheimer's disease (AD).
Methods
704 of the 1112 AIBL participants were in the healthy control group and had complete data on the 5 parameters utilized in the classification model. The model uses a multivariate latent class regression model to investigate heterogeneity in the HC group and to examine factors associated with classes. Association between class and risk of transition from baseline clinical state (HC) to cognitive impairment (MCI or AD) within 18 months of follow-up was approximated by an odds ratio (OR) using logistic regression. The OR was adjusted for age (<65 yrs, 65-74 yrs, > 74 yrs), category of education and APOE and reported with 95% confidence intervals (CI).
Results
59% of the healthy cohort performed consistently significantly better on neuropsychological variables. Being in the higher performing group was associated with a lower risk of conversion from HC to MCI or AD classes within 18 months with an OR = 0.12 (95% CI: 0.03, 0.36). Whilst there was an association with education and sex of being in the higher performing healthy class, the class status was not determined by these factors. There was no association with apolipoprotein E (APOE) genetic status.
Conclusions
We have identified a subgroup within the healthy cohort which is performing consistently better on cognitive testing and has one tenth the risk of progressing to diagnosed disease within 18 months. Identification of this AIBL subgroup offers the potential for risk-based “filtering” in clinical trials, allowing focus on the remaining individuals with a relative greater risk of conversion to disease in the cohort.