Temporal modeling and AT profiles in the early phase of Alzheimer's disease

Resumen

Background: Identifying markers that have predictive value for disease progression and clinical manifestations, such as mild cognitive impairment (MCI), is important to detect Alzheimer's disease (AD) early.
Objective: In this study, we combined biomarkers from amyloid and tau pathologies (AT profiles) with the prediction of diagnosis and the temporal evolution of clinical symptoms.
Methods Multiple disease progression models were developed through supervised and unsupervised techniques via a two-stage data-driven approach. Models whose natural histories most closely resembled the diagnostic predictions were selected. Finally, various hypotheses regarding the progression of cognitive decline were tested using longitudinal patient data and AT profiles.
Results: At baseline, 22.5% of the cognitively unimpaired (CU) individuals and 46.2% of the CU converters (who progressed to MCI during the first four years) in the studied population had amyloid pathology (A+). Only a small subset of neuropsychological measures was used to predict cognitive decline and its timeline. The study revealed that amyloid pathology occurred approximately 5 years after cognitive decline in the population under investigation. Additionally, patients who were A+ at baseline experienced a more accelerated progression toward cognitive decline than those who were not (A-).
Conclusions: Based on the proposed natural histories and cross-sectional and longitudinal analysis of AD markers, the results indicate that only a single cerebrospinal fluid sample is necessary during the early phase of AD. The progression from CU to MCI and its timeline can be predicted exclusively through neuropsychological measures.