Abstract
We aimed to leverage trajectories of decline in cognition to refine Alzheimer’s disease (AD) risk predictions and increase the understanding of the clinical meaningfulness of cognitive decline in the pre-clinical stage of AD with regards to relevant long-term outcomes. We developed a joint model to fit natural history data on two outcomes together: i) longitudinal cognitive changes as captured by the Alzheimer’s Prevention Initiative Preclinical Composite (APCC) and ii) time to the first diagnosis of mild cognitive impairment (MCI) or AD dementia. Clinically-relevant static covariates, such as age at inclusion, apolipoprotein ε4 (APOE4) status and years of education were added as intercept and/or slope effects in the mixed model as guided by exploratory analyses of the two outcomes. A time-dependent APCC covariate accounted for the hypothesized dependence of time to diagnosis on APCC decline. The model was fitted with the {JM} package from R v3.4.3 on 2047 subjects with at least two APCC measurements and who were cognitively unimpaired and less than 90 years old at inclusion. An internal validation was performed via predictive checks. The model captured the link between risk factors, early cognitive decline and time to diagnosis. The model-estimated longitudinal cognitive decline (population average and individual trajectories) and overall risk of developing MCI/AD symptoms provided a good fit to the data. As expected, the model estimated a higher risk of developing MCI/AD symptoms for older subjects and for APOE4 carriers. A significant association was observed between decline in APCC and the risk of developing MCI/AD symptoms. The joint model also adequately captured the individual dynamic risk of developing MCI/AD symptoms using personal longitudinal APCC data until a given observation time. This novel method provides a tool to dynamically estimate the individual risk of developing MCI/AD for cognitively unimpaired subjects who have longitudinal measures from sensitive neuro-psychological tests.
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