Tracking pathophysiological processes in Alzheimer's disease: an updated hypothetical model of dynamic biomarkers

Abstract

In 2010, we put forward a hypothetical model of the major biomarkers of Alzheimer's disease (AD). The model was received with interest because we described the temporal evolution of AD biomarkers in relation to each other and to the onset and progression of clinical symptoms. Since then, evidence has accumulated that supports the major assumptions of this model. Evidence has also appeared that challenges some of our assumptions, which has allowed us to modify our original model. Refinements to our model include indexing of individuals by time rather than clinical symptom severity; incorporation of interindividual variability in cognitive impairment associated with progression of AD pathophysiology; modifications of the specific temporal ordering of some biomarkers; and recognition that the two major proteinopathies underlying AD biomarker changes, amyloid β (Aβ) and tau, might be initiated independently in sporadic AD, in which we hypothesise that an incident Aβ pathophysiology can accelerate antecedent limbic and brainstem tauopathy.

Figure 1. Original Dynamic biomarkers of the AD pathological cascade model – 2010

Aβ amyloid is identified by CSF Aβ42 or PET amyloid imaging. Neuronal injury and dysfunction is identified by CSF tau or FDG-PET. Neurodegenerative atrophy is measured by structural MRI. Reproduced from Jack et al .

Figure 2. Temporal ordering of CSF biomarkers

Mean baseline levels of CSFAβ42 (A), P-tau (B), and t-tau (C) stratified into patients with MCI who developed AD dementia within 0 to 2.5 years (n=28), 2.5 to 5 years (n=32), and 5 to 10 years (n=12). Biomarker levels in a cognitively healthy control group are also given. Levels of Aβ42 did not differ among any of the MCI-AD groups with different intervals to AD dementia. Levels of t-tau and P-tau were significantly lower in late converters (5–10 years) compared with very early converters (0–2.5 years). Error bars represent the SEM. Reproduced with permission from Buchhave et al .

Figure 3. Evidence for temporal ordering of CSF Aβ42, tau and MRI

Estimated probability of abnormality for each AD biomarker). The probability of an abnormal biomarker test (point estimate and 95% CI) is shown by clinical diagnosis (i.e. CN, MCI, or AD) (A) and Mini-Mental State Examination (MMSE) score (B). The cutoffs used are 192 pg/mL for the CSF Aβ42 level, 93 pg/mL for the CSF total tau level (t-tau), and 0.48 for the adjusted hippocampal volume (HVa). Reproduced with permission from Jack et al.

Figure 4

Cross sectional data from the DIAN study indicating temporal ordering of biomarkers in subjects harboring autosomal dominant mutations. Temporal ordering is inferred by anchoring each subject’s current age to the age of dementia onset in his/her affected parent. The proposed order in which biomarkers become abnormal is, CSF Aβ42, amyloid PET, CSF tau, FDG PET and structural MRI, followed by clinical symptoms. Reproduced with permission from Bateman et al.

Figure 5. Revised dynamic biomarkers of the AD pathological cascade model – 2012

For both Figures 5a and 5b, Aβ amyloid is identified by CSF Aβ42 (purple) or PET amyloid imaging (red). Elevated CSF tau (blue). Neurodegeneration is measured by FDG PET and structural MRI respectively which are drawn concordantly (orange). By definition, all curves converge at the top right-hand corner of the plot, the point of maximum abnormality. The horizontal axis of disease progression is expressed as time. Cognitive response is illustrated as a zone (green filled area) with low and high risk boarders. Figure 5b, illustrates operational use of this model. The vertical black line denotes a given time (T). Projection of the intersection of time T with the biomarker curves to the left vertical axis (horizontal dashed arrows) gives values of each biomarker at time T, with the lead biomarker (CSF Aβ42) being most abnormal at any given time in the progression of the disease. Intersection of time T with the cognitive impairment zone gives cognitive impairment at that fixed point in time. Subjects who are at high risk of cognitive impairment due to AD pathophysiology are shown with a cognitive response curve that is shifted to the left. In contrast, the cognitive response curve is shifted to the right in subjects with a protective genetic profile, high cognitive reserve and the absence of comorbid brain pathologies – illustrating that two subjects with the same biomarker profile (at time T) can have different cognitive outcomes (denoted by yellow circles at the intersection of time T and low vs. high risk cognitive profiles).

Figure 6. Model integrating AD immuno-hisotology and biomarkers

The threshold for biomarker detection of pathophysiology is denoted by a horizontal line. The grey area denotes the zone in which abnormal pathophysiology lies below the biomarker detection threshold. In this illustration, tau pathology precedes Aβ amyloid deposition in time – but early on exists at a subthreshold biomarker detection level. Aβ amyloid deposition then occurs independently and rises above biomarker threshold detection (purple and red arrows). This induces acceleration of tauopathy with CSF tau then rising above threshold level (blue arrow). Later still, FDG PET and MRI (orange arrow) rise above threshold detection level. Finally, cognitive impairment becomes evident (green arrow), with a range of cognitive responses that depend on the individual’s risk profile (green filled area).