Alzheimer 2013 July-10


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

Lancet Neurol. 2013 Feb;12(2):207-16.

Jack CR Jr, Knopman DS, Jagust WJ, Petersen RC, Weiner MW, Aisen PS, Shaw LM, Vemuri P, Wiste HJ, Weigand SD, Lesnick TG, Pankratz VS, Donohue MC, Trojanowski JQ.

Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA.


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. Copyright © 2013 Elsevier Ltd. All rights reserved.

PMID: 23332364


Supplementary pictures:

Denise A Reyes-fig5

Figure 1: Revised model of dynamic biomarkers of the Alzheimer’s disease pathological cascade

(A and B) Neurodegeneration is measured by FDG PET and structural MRI, which are drawn concordantly (dark blue). By definition, all curves converge at the top right-hand corner of the plot, the point of maximum abnormality. Cognitive impairment is illustrated as a zone (light green-filled area) with low-risk and high-risk borders. (B) Operational use of the 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. People who are at high risk of cognitive impairment due to Alzheimer’s disease pathophysiology are shown with a cognitive impairment curve that is shifted to the left. By contrast, the cognitive impairment curve is shifted to the right in people with a protective genetic profile, high cognitive reserve, and the absence of comorbid pathological changes in the brain, showing that two patients with the same biomarker profile (at time T) can have different cognitive outcomes (denoted by grey circles at the intersection of time T). Aβ=amyloid β. FDG=fluorodeoxyglucose. MCI=mild cognitive impairment.


Denise A Reyes-fig6

Figure 2: Model integrating Alzheimer’s disease immunohistology and biomarkers

The threshold for biomarker detection of pathophysiological changes is denoted by the black horizontal line. The grey area denotes the zone in which abnormal pathophysiological changes lie below the biomarker detection threshold. In this figure, tau pathology precedes Aβ deposition in time, but only early on at a subthreshold biomarker detection level. Aβ deposition then occurs independently and rises above the biomarker detection threshold (purple and red arrows). This induces acceleration of tauopathy and CSF tau then rises above the detection threshold (light blue arrow). Later still, FDG PET and MRI (dark blue arrow) rise above the detection threshold. Finally, cognitive impairment becomes evident (green arrow), with a range of cognitive responses that depend on the individual’s risk profile (light green-filled area). Aβ=amyloid β. FDG=fluorodeoxyglucose. MCI=mild cognitive impairment.

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