Our overall objective was to test for evidence of temporal ordering of CSF AB42, t-tau, and HVa (54
). We were limited to evaluating these three AD biomarkers for which independent autopsy cohorts were available to select normal/abnormal cut points in an unbiased manner. A biomarker value for an individual subject at a given point in time (and by extension, the percent abnormal across a group at a given disease stage) is a function of two phenomena: the elapsed time from the initial deviation of the biomarker away from normality to the present, and the average rate of change of the biomarker over this period of time. An analogy to motion would be distance traveled from abnormality, average rate of change, and elapsed time. Our data consisted of measures of each biomarker value at a fixed point or points in time in subjects who entered the study at different stages of the disease. We observed whether a subject had reached a certain distance from normality, but cannot individually identify the contributions from average rate of change and elapsed time. We can, however, draw valid inferences from our data about the combined effect of time elapsed from onset and average rate of change, and refer to this as relative dynamic ordering
The data presented here supports several key principles in our recently proposed hypothetical biomarker cascade model (44
). These include: 1) all biomarkers become progressively more abnormal as subjects worsen clinically. 2) Reduction in CSF Aβ42 denotes an upstream pathophysiological process that significantly departs from normality (i.e., is dynamic) early in the pathophysiological cascade while subjects are clinically asymptomatic, but does not change greatly during the clinically symptomatic MCI and dementia phases of the disease. 3) T-tau and HVa are biomarkers of downstream neurodegenerative pathophysiological process that are dynamic later as subjects approach and move through the clinically symptomatic phases of the disease (56
). 4) Cognitive decline is more closely related to biomarkers of neuronal injury than brain Aβ load (65
). 5) T-tau is more dynamic earlier than HVa, but the proportion of abnormal biomarker studies are similar in symptomatic subjects such that HVa “catches up” to t-tau as symptoms worsen. This is supported visually by the steeper slope of HVa vs ttau in .
The hypothetical model represents an idealized trajectory of an individual subject who progresses to pathologically pure AD dementia. Our sample, however, almost certainly consists of a mixture of subjects, many of whom are on the AD pathway, but many, particularly in the CN and MCI groups, who are not (73
). The fact that both elevated t-tau and hippocampal atrophy can occur in other conditions that lead to dementia (79
) such as cerebro-vascular disease has led to the belief that of the three biomarkers we examined, CSF Aβ42 should have the greatest specificity for AD (80
). Consequently, we performed a sub-analysis of subjects with abnormal CSF Aβ at baseline in order to isolate those subjects who we were somewhat more confident were in the AD pathophysiological pathway (56
). Our results concerning evidence for biomarker ordering led to similar conclusions in the “all-subjects” and the abnormal CSF Aβ 42 samples.
Using the percent-abnormal metric might not seem to be an obvious first choice for comparing biomarkers. Other options however prove to be untenable. For example, using biomarker values in native measurement units precludes direct comparisons of biomarkers because they are not on a common scale. Z scores or percentiles are also not tenable since by construction half the subjects in the sample must be above and half below average for each biomarker. This constraint would make it impossible to test our major question – i.e., is one biomarker abnormal more often than another at different stages of the disease. The obvious advantage of comparing biomarkers on a percent-abnormal basis is that the scoring method is standardized for all biomarkers over the entire cognitive continuum. A limitation is that the results are highly sensitive to the cut point values and hence the validity of the analysis depends on selecting valid cut points for each biomarker. We used cut point values for each biomarker that were based on an independent autopsy verified sample, and used the same pathological criteria for all biomarkers – i.e., NIA Reagan low vs. high AD probability. While using cut points based on diagnostic sensitivity, rather than accuracy, in the autopsy reference standard might seem a better approach, that is not the case. Imagine a biomarker with identical distributions in autopsy proven high vs low probability AD. Fixing a cut point at a sensitivity of 90% in high probability autopsy proven AD would lead to the conclusion that 90% of autopsy low probability AD cases had abnormal biomarker values, and the biomarker in question comes “early” in the pathophysiological cascade. This would clearly be erroneous. Thus our choice of selecting cut points for this study in a manner that is constrained by both sensitivity and specificity, as is done for all clinically applied diagnostic tests, seems prudent.
Our hypothetical model (44
) was intended to represent an idealized trajectory that an individual subject follows from a time prior to appearance of any AD pathophysiology in the brain through end stage AD dementia when all biomarkers have become maximally abnormal. The optimal way to test this model would be to follow the trajectory of multiple biomarkers over several decades in a large group of subjects who enter the study prior to the first appearance of any AD pathophysiology, are followed through the symptomatic stages of the disease to autopsy. Given that the total course of the disease may span 30 years or more it will take many years to accumulate the necessary data to construct a temporally accurate disease model. While the data is being accumulated, perhaps the only realistic approach to empirical analysis is to attempt to build models of disease in a piece-wise fashion from individual subjects who are at various stages in the disease as we have done here.