What time window do AD trajectories depart from normal? Estimating age‐related trajectories to chart preclinical phase of Alzheimer’s disease and select trial‐ready cohorts

Abstract

AbstractBackgroundThe preclinical phase of Alzheimer’s disease (AD), where pathology slowly accumulates years before cognitive impairment becomes apparent, could offer a treatment window with the greatest potential to preserve cognitive function before downstream pathological processes gather momentum. Characterizing when biomarker trajectories deviate from normal ageing, and the heterogeneity therein, could facilitate targeted trial recruitment and improved biomarker‐based evidence of disease modification. However, reliably identifying early abnormal changes can be challenging due to various confounds, such as age and vascular factors, as well as disease heterogeneity.MethodData from the following cohorts were analysed: (1) individuals at risk for, or affected by, autosomal dominant AD (ADAD), including the Dominantly Inherited Alzheimer Network; (2) Insight 46, a neuroimaging substudy of the MRC National Survey of Heath and Development; (3) pre‐randomization study data from the Anti‐Amyloid Treatment in Asymptomatic Alzheimer’s Disease (A4); and (4) the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Linear regression assessed differences between at‐risk groups (biomarker‐ or mutation‐positive) and normal ageing. Longitudinal changepoint models estimated when atrophy rates deviate from normal ageing trajectories. Normative modelling, based on large reference datasets, identified outliers of regional atrophy assessed within‐group heterogeneity at the individual level. Data‐driven disease progression models (DPMs) were used to estimate quantitative signatures of AD, including imaging biomarkers.ResultDPMs reveal that amyloid markers deviate first in ADAD, roughly ten years before neurodegenerative markers. Changepoint models indicate that atrophy rates in ADAD become abnormal 3‐8 years before expected symptom onset, with longitudinal tau PET data suggesting a similar trend. While cross‐sectional volumetric measures showed no evidence of association with amyloid markers, atrophy rates were independently related to both amyloid positivity and markers of cerebrovascular disease, indicating similar, additive effects. Both DPMs and normative modelling found evidence of heterogeneity within imaging biomarkers in ADNI and A4, which explained variability in cognitive decline observed in at‐risk participants.ConclusionBiomarker data from preclinical AD suggests a long temporal gap between amyloidosis and subsequent changes. This represents a treatment opportunity to remove amyloid while minimising irreversible damage. There is evidence of heterogeneity within the biomarker trajectories, which could impact the ability to detect disease modification.