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. 2019 Feb 28:11:205-215.
doi: 10.1016/j.dadm.2019.01.005. eCollection 2019 Dec.

Predicting time to dementia using a quantitative template of disease progression

Murat Bilgel  1 , Bruno M Jedynak  2
Affiliations

Predicting time to dementia using a quantitative template of disease progression

Murat Bilgel et al. Alzheimers Dement (Amst). .

Abstract

Introduction: Characterization of longitudinal trajectories of biomarkers implicated in sporadic Alzheimer's disease (AD) in decades before clinical diagnosis is important for disease prevention and monitoring.

Methods: We used a multivariate Bayesian model to temporally align 1369 Alzheimer's disease Neuroimaging Initiative participants based on the similarity of their longitudinal biomarker measures and estimated a quantitative template of the temporal evolution of cerebrospinal fluid A β 1 - 42 , p- ta u 181 p , and t-tau and hippocampal volume, brain glucose metabolism, and cognitive measurements. We computed biomarker trajectories as a function of time to AD dementia and predicted AD dementia onset age in a disjoint sample.

Results: Quantitative template showed early changes in verbal memory, cerebrospinal fluid Aβ1-42 and p-tau181p, and hippocampal volume. Mean error in predicted AD dementia onset age was < 1.5 years.

Discussion: Our method provides a quantitative approach for characterizing the natural history of AD starting at preclinical stages despite the lack of individual-level longitudinal data spanning the entire disease timeline.

Keywords: Alzheimer; Biomarkers; Cognition; Dementia; Kaplan-Meier; Longitudinal; Onset; Prediction; Quantitative template.

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Figures

Fig. 1
Fig. 1
Estimated population-level biomarker trajectories as a function of progression score s; s is plotted on a natural logarithm scale so that the x-axis is linear in time. Mean trajectories are plotted in black, along with their 95% credible intervals. Longitudinal data points for 100 randomly sampled individuals per diagnostic category are shown. Biomarker z-scores, shown on the right-hand-side y-axes, were computed using mean and standard deviations at baseline across 1369 in the training set. Abbreviations: Aβ, amyloid β; CSF, cerebrospinal fluid; FDG, fluorodeoxyglucose; PET, positron emission tomography; RAVLT, Rey Auditory Verbal Learning Test; ADAS13, Alzheimer's Disease Assessment Scale-Cognitive 13-item scale; MMSE, Mini–Mental State Examination; CDR-SB, Clinical Dementia Rating-Sum of Boxes.
Fig. 2
Fig. 2
Box and swarm plots of (A) baseline age, (B) estimated γ, and (C) estimated progression score s at baseline by baseline diagnosis for individuals in the training set. γ and s are plotted on a natural logarithm scale. All pairwise comparisons were statistically significant (all P < 0.0013), with the exception of baseline age comparison between NL and AD. Abbreviations: AD, Alzheimer's disease; MCI, mild cognitive impairment; NL, cognitively normal.
Fig. 3
Fig. 3
Estimated biomarker trajectories as a function of time from initial AD dementia diagnosis are shown with the black curves, and the shaded areas depict 95% credible intervals. A value of 0 on the x-axis corresponds to the onset of AD dementia. Negative values are before the onset of AD dementia, and positive values are after the onset of AD dementia. Biomarker z-scores, shown on the right-hand-side y-axes, were computed using mean and standard deviations at baseline across 1369 individuals in the training set. Note that observed time from AD was not used in the estimation of the biomarker trajectories shown; trajectories were obtained using the model fit. Scattergrams of observations are shown as a function of observed time from AD, color-coded by diagnosis, to allow for a visual assessment of the agreement of the estimated trajectories with underlying data. Abbreviations: Aβ, amyloid β; CSF, cerebrospinal fluid; FDG, fluorodeoxyglucose; PET, positron emission tomography; RAVLT, Rey Auditory Verbal Learning Test; ADAS13, Alzheimer's Disease Assessment Scale-Cognitive 13-item scale; MMSE, Mini–Mental State Examination; CDR-SB, Clinical Dementia Rating-Sum of Boxes.
Fig. 4
Fig. 4
(A) AD dementia onset ages predicted using the linear regression model with PS + age vs. observed AD dementia onset age (for individuals with known onset ages in the testing set). Time between baseline age and AD onset age (indicated by the size of the markers) varied between 0.48 and 9.0 years (median 1.6, IQR 1.0-3.0). There were 18, 15, 10, and 3 participants whose true time to diagnosis was in the interval (0.4,1.5), (1.5,2.5), (2.5,5.0), and (5,9.1), respectively. The RMSEs corresponding to these ranges were 1.64, 1.06, and 0.84, 3.14. (B) Kaplan-Meier curves based on observed (red) and predicted (blue) AD dementia onset ages.
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