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Background. Alzheimer's disease and related dementia (ADRD) are characterizedby multiple and progressive anatomo clinical changes. Yet, modeling changesover disease course from cohort data is challenging as the usual timescales areinappropriate and time-to-clinical diagnosis is available on small subsamplesof participants with short follow-up durations prior to diagnosis. One solutionto circumvent this challenge is to define the disease time as a latentvariable.  Methods: We developed a multivariate mixed model approach that realignsindividual trajectories into the latent disease time to describe diseaseprogression. Our methodology exploits the clinical diagnosis information as apartially observed and approximate reference to guide the estimation of thelatent disease time. The model estimation was carried out in the BayesianFramework using Stan. We applied the methodology to 2186 participants of theMEMENTO study with 5-year follow-up. Repeated measures of 12 ADRD markersstemmed from cerebrospinal fluid (CSF), brain imaging and cognitive tests wereanalyzed.  Result: The estimated latent disease time spanned over twenty years beforethe clinical diagnosis. Considering the profile of a woman aged 70 with a highlevel of education and APOE4 carrier (the main genetic risk factor for ADRD),CSF markers of tau proteins accumulation preceded markers of brain atrophy by 5years and cognitive decline by 10 years. We observed that individualcharacteristics could substantially modify the sequence and timing of thesechanges.  Conclusion: Our disease progression model does not only realign trajectoriesinto the most homogeneous way. It accounts for the inherent residualinter-individual variability in dementia progression to describe the long-termchanges according to the years preceding clinical diagnosis, and to provideclinically meaningful information on the sequence of events.

Disease progression model anchored around clinical diagnosis in longitudinal cohorts: example of Alzheimer's disease and related dementia