Our major findings were: (1) overall biomarker trajectory shapes were complex and were affected by interactions with age and APOE status. 2) Baseline biomarker values generally worsened (i.e., non-zero slope) with lower baseline MMSE. 3) Baseline hippocampal volume, amyloid PET and FDG PET values plateaued (i.e., non-linear slope) with lower MMSE in one or more analyses. 4) Longitudinally, within-subject rates of biomarker change were associated with worsening MMSE. 5) Non-constant within-subject rates of biomarker change were found in only one model; the rate of hippocampal volume change decelerated with worsening MMSE in amyloid positive e4 negative participants. 6) Trajectories for a given biomarker were often different in ε4 carriers vs non carriers in the overall samples. This was less often so in the amyloid positive sub samples. 7) While most findings were the same between the amyloid positive cohorts and the entire sample there was a slightly greater tendency toward non-linear baseline effects in amyloid positive participants.
Our hypothetical biomarker model27
predicts that each biomarker follows a sigmoid shaped trajectory. The rationale for this prediction starts with the assumption that the rate of change of a biomarker denoting accumulating AD pathophysiology in the brain should be zero from birth through at least early adulthood. At some point, e.g., age 50s – 70s, AD biomarkers deflect from the normal baseline and begin to become abnormal, which by definition represents acceleration in rate. Based on prior evidence that some biomarker rates of change (i.e., amyloid PET, CSF Aβ42 and t-tau) do not accelerate in the dementia phase of the disease31,52
, we presumed that biomarker rates do not continue to accelerate indefinitely, but instead begin to saturate or plateau at some point, which represents deceleration. An initial period of acceleration followed later by deceleration defines a trajectory that is approximately sigmoidal, with the midpoint of the curve defined as the initiation of deceleration. A second reason to suspect that biomarkers should follow a sigmoid shaped trajectory relates to sensitivity limits of any measurement technique at extremes. Floor and ceiling measurement sensitivity effects impart a sigmoid shape to a data distribution.
Sources outside the field of human biomarker studies suggest that amyloid and neurodegenerative biomarkers might follow a sigmoid-shaped function. Inglesson, et al 200428
found in human autopsy studies that amyloid accumulation plateaus with increasing disease duration. Amyloid deposition in transgenic AD mice follows a sigmoidal-shaped function with advancing age53
. Tau fibrillization follows a sigmoid shaped function with time in vitro54
. A cumulative damage model of neurodegenerative disease where the risk of cell death in the vulnerable population of cells changes over time predicts a sigmoid-shaped trajectory of neurodegenerative brain atrophy55,56
Reports from analyses of ADNI data draw somewhat inconsistent conclusions about the shapes of biomarker trajectories. Caroli, et al 201057
analyzed cross-sectional ADNI data and found that mean baseline hippocampal volume, CSF Aβ42, and CSF tau data could be better modeled as a function of worsening cognition with sigmoid-shaped curves compared to linear fits. Lo, et al 201158
examined rates of change of biomarkers in ADNI and illustrated deceleration in CSF Aβ42 but acceleration in hippocampal atrophy rates with advancing disease. Schuff, et at 200959
found acceleration in atrophy rates in MCI and AD ADNI subjects. Sabuncu, et al 201160
examined brain atrophy rates in ADNI participants who had an AD-like CSF profile. They found that atrophy rates in a set of AD-signature ROIs exhibit early acceleration followed by deceleration which was consistent with a sigmoid shaped curve. Conversely, they found rates of hippocampal volume loss exhibited positive acceleration.
In the present study, we fit the models in such a way that would allow us to assess the shape of the biomarker trajectories without imposing a particular structure (i.e. a sigmoid shape) upon the data. Flexible restricted cubic splines allowed for non-linearity if there was evidence for it. Interaction terms allowed for biomarker – MMSE relationships to depend on covariates. This way of modeling let the data “speak for themselves” and was preferred in this study because of several important limitations in the nature of the data. (1) The right and left hand portions of a sigmoid curve are where the maximum inflection occurs and thus the portions of the function where data are most needed to detect acceleration and deceleration. Unfortunately, our data are sparse in these regions. In participants with abnormal biomarkers at baseline, we have no data that would allow us to characterize the initial deviation of biomarkers from their normal baseline. The right-hand tail is equally problematic in that many patients survive a decade or more after the clinical diagnosis of AD dementia is made, but most stop participating in clinical research studies once they become moderately demented. Indeed, the AD subjects in our samples were only mildly demented (median MMSE of 24 for the CSF/MRI cohort and 23 for the PET/MRI cohort). (2) The median follow-up time in our data was only about 1 year with a maximum of only 4 years. This is a small fraction of the total duration of the disease which may span 30 plus years. Examining such a small window of time in each subject makes it difficult to detect acceleration or deceleration in within-subject rates. (3) We lacked a linear clinical measure of disease progression. Every cognitive testing instrument has a non-linear response function with both floor and ceiling effects50,61
. Because subjects spanning the cognitive continuum were combined to estimate biomarker trajectories, a single universal cognitive test was needed to index all subjects on a common axis. The MMSE was the best option that was available in all ADNI and Mayo subjects. However, the limited range of the MMSE in cognitively normal participants (roughly 30–27) in particular made estimation of trajectory shape early in the disease particularly problematic. In many of our CN participants MMSE did not change, or fluctuated randomly from one time point to the next.
Our results do not disagree with sigmoid shaped biomarker trajectories in that most biomarkers worsened as MMSE worsened in both baseline and longitudinal analyses which is consistent with the middle, roughly linear, portion of a sigmoid curve. While cross sectional data may be influenced by cohort effects, we did see some baseline effects that were consistent with a sigmoid-shaped trajectory (i.e. baseline effects that plateaued with worsening MMSE). However, we found non-constant within-subject rates in only one analysis. Several prior studies (including one of our own) have shown that rates of brain atrophy accelerate prior to incident dementia62–65
. However, these earlier MRI studies had considerably more within-subject longitudinal data than we had in the present study. Our failure to detect acceleration or deceleration in within-subject MRI rates may well be due to limited longitudinal data because we only used those time points in individual participants where all biomarkers were available.
AD biomarkers are poised to become an essential component of a comprehensive assessment of the disease. In particular, AD biomarkers constitute a major (some would say only) window into the disease in its long pre-clinical phase. Designing clinical trials in early symptomatic and preclinical disease will depend on acquiring a thorough understanding of the longitudinal trajectory of AD biomarkers. In addition, the notion of biomarker trajectories is central to the staging proposed in the recent preclinical AD research criteria66
. However, creating reliable models that accurately describe the full trajectory shapes of AD biomarkers will require significant additional longitudinal data in individual participants beginning prior to deviation of biomarkers from normality (age 50s) through the end stage of the disease and ultimately to autopsy. Ideally, this data would be acquired in well-defined epidemiological cohorts.