• Clifford R Jack, Terry M Therneau, Heather J Wiste, Stephen D Weigand, David S Knopman, Val J Lowe, Michelle M Mielke, Prashanthi Vemuri, Rosebud O Roberts, Mary M Machulda, Matthew L Senjem, Jeffrey L Gunter, Walter A Rocca, and Ronald C Petersen.
• Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, USA. Electronic address: jack.clifford@mayo.edu.
• Lancet Neurol. 2016 Jan 1; 15 (1): 56-64.

BackgroundIn a 2014 cross-sectional analysis, we showed that amyloid and neurodegeneration biomarker states in participants with no clinical impairment varied greatly with age, suggesting dynamic within-person processes. In this longitudinal study, we aimed to estimate rates of transition from a less to a more abnormal biomarker state by age in individuals without dementia, as well as to assess rates of transition to dementia from an abnormal state.MethodsParticipants from the Mayo Clinic Study of Aging (Olmsted County, MN, USA) without dementia at baseline were included in this study, a subset of whom agreed to multimodality imaging. Amyloid PET (with (11)C-Pittsburgh compound B) was used to classify individuals as amyloid positive (A(+)) or negative (A(-)). (18)F-fluorodeoxyglucose ((18)F-FDG)-PET and MRI were used to classify individuals as neurodegeneration positive (N(+)) or negative (N(-)). We used all observations, including those from participants who did not have imaging results, to construct a multistate Markov model to estimate four different age-specific biomarker state transition rates: A(-)N(-) to A(+)N(-); A(-)N(-) to A(-)N(+) (suspected non-Alzheimer's pathology); A(+)N(-) to A(+)N(+); and A(-)N(+) to A(+)N(+). We also estimated two age-specific rates to dementia: A(+)N(+) to dementia and A(-)N(+) to dementia. Using these state-to-state transition rates, we estimated biomarker state frequencies by age.FindingsAt baseline (between Nov 29, 2004, to March 7, 2015), 4049 participants did not have dementia (3512 [87%] were clinically normal and 537 [13%] had mild cognitive impairment). 1541 individuals underwent imaging between March 28, 2006, to April 30, 2015. Transition rates were low at age 50 years and, with one exception, exponentially increased with age. At age 85 years compared with age 65 years, the rate was nearly 11-times higher (17.2 vs 1.6 per 100 person-years) for the transition from A(-)N(-) to A(-)N(+), three-times higher (20.8 vs 6.1) for A(+)N(-) to A(+)N(+), and five-times higher (13.2 vs 2.6) for A(-)N(+) to A(+)N(+). The rate of transition was also increased at age 85 years compared with age 65 years for A(+)N(+) to dementia (7.0 vs 0.8) and for A(-)N(+) to dementia (1.7 vs 0.6). 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 years to 6.9 transitions per 100 person-years at age 75 and then plateaued beyond that age. Estimated biomarker frequencies by age from the multistate model were similar to cross-sectional biomarker frequencies.InterpretationOur transition rates suggest that brain ageing is a nearly inevitable acceleration toward worse biomarker and clinical states. The one exception is the transition to amyloidosis without neurodegeneration, which is most dynamic from age 60 years to 70 years and then plateaus beyond that age. We found that simple transition rates can explain complex, highly interdependent biomarker state frequencies in our population.FundingNational Institute on Aging, Alexander Family Professorship of Alzheimer's Disease Research, the GHR Foundation.Copyright © 2016 Elsevier Ltd. All rights reserved.

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