Thesis

167 Longitudinal change in biomarkers Table 2. Change from negative to positive amyloid status Model 1 Model 2 Age 0.97 (0.88-1.06) 0.99 (0.88-1.09) Sex 0.88 (0.21-3.41) 0.45 (0.07-2.16) Education 0.90 (0.47-1.85) 0.84 (0.37-1.96) Baseline MMSE 1.50 (0.79-3.45) 1.40 (0.70-3.42) APOE ε4 carrier 5.22 (1.23-22.75)* 6.39 (1.26-38.41)* Data shown are odds ratio (95% confidence interval) as estimated by logistic regression. Outcome was conversion from a negative to a positive amyloid status, as compared to remaining amyloid negative. In model 1, age, sex, education, baseline MMSE and APOE ε4 carriership were investigated as predictors individually. In model 2, all variables were included simultaneously. * p <0.01 Table 3. Associations with baseline amyloid burden and amyloid accumulation rate Baseline Longitudinal Model 1 Model 2 Model 1 Model 2 Age 0.02 (0.01) 0.00 (0.01) 0.00 (0.00) 0.00 (0.00) Sex 0.04 (0.22) -0.04 (0.20) 0.05 (0.03) 0.02 (0.03) Education 0.23 (0.11)* 0.15 (0.10) 0.00 (0.02) -0.00 (0.02) Baseline MMSE -0.05 (0.10) -0.09 (0.08) 0.01 (0.01) 0.01 (0.01) APOE ε4 carrier 0.83 (0.20)* 0.85 (0.21)* 0.11 (0.03)* 0.10 (0.03)* Data shown are beta (SE) as estimated by linear mixed models. Outcome was [18F] florbetapir over time in a composite region of interest. Models included the variable of interest, time and their interaction as predictors. In model 1, each variable was investigated as predictor individually. In model 2, all variables were included simultaneously. Baseline estimates represent the association between the predictor and baseline BPND, longitudinal estimates represent the association of the interaction between predictor and time, and reflect the slope of BPND. * p <0.05. 7

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