Rates of transition between amyloid and neurodegeneration biomarker states and to dementia among non-demented individuals: a population-based cohort study

BACKGROUND: We previously observed in a cross-sectional analysis that frequencies of amyloid and neurodegeneration biomarker states varied greatly by age among cognitively non-impaired participants, suggesting dynamic within-person processes. Our objective in this longitudinal study was to estimate rates of transitioning from a less- to a more-abnormal biomarker state by age among non-demented individuals, as well as rates of transitioning to dementia by biomarker state. METHODS: All participants (n=4049) were non-demented at baseline. A subset of 1541 underwent multi-modality imaging. Amyloid PET was used to classify individuals as amyloid positive (A+) or negative (A−). FDG PET and MRI were used to classify individuals as neurodegeneration positive (N+) or negative (N−). All observations from the 4049 individuals were used in a multi-state model to estimate four different age-specific biomarker state transition rates among non-demented individuals: A−N− to A+N−; A−N− to A−N+ (suspected non-Alzheimer pathology, SNAP); A+N− to A+N+; A−N+ (SNAP) to A+N+. We also estimated two age-specific rates to dementia: A+N+ to dementia; and A−N+ (SNAP) to dementia. Using these state-to-state transition rates, we estimated biomarker state frequencies by age. FINDINGS: All transition rates were low at age 50 and (with one exception) were well-characterized by an exponential increase with age. The rates per 100-person years at ages 65 versus 85 were 1.6 versus 17.2 for A−N− to A−N+, 6.1 versus 20.8 for A+N− to A+N+, 2.6 versus 13.2 for A−N+ to A+N+, 0.8 versus 7.0 for A+N+ to dementia, and 0.6 versus 1.7 for A−N+ to dementia. The one exception to an exponential increase with age was the transition rate from A−N− to A+N− which increased from 4.0 transitions per 100 person-years at age 65 to approximately 7 transitions per 100 person-years in the 70s and then plateaued beyond that age. Estimated biomarker frequencies by age from the multistate model were similar to cross-sectional biomarker frequencies. INTERPRETATION: Dynamic state-to-state transition rates illustrate important measurable aspects of the changing biology underlying brain aging. The biomarker states we describe relate to both AD and non-AD processes. Our transition rates suggest that brain aging can be conceptualized as a nearly inevitable acceleration toward worse biomarker and clinical states. The one exception was that transition to amyloidosis without neurodegeneration was most dynamic from age 60 to 70 and then plateaued beyond that age. We found that simple transition rates can explain complex, highly interdependent biomarker state frequencies in our sample.