Expresiones explícitas

In an ideal scenario, AD biomarkers will identify individuals during preclinical disease, well before the neuronal death that drives cognitive impairments and within a window allowing for secondary, or even primary, prevention of the disease. In 2009, the first of a series of

hypothetical models was published showing how markers such as CSF Aβ42 and tau(s), fMRI, or FDG PET might change along the course of AD161. Biomarker behavior as a whole was in an early stage of exploring preclinical AD, and most biomarkers are grouped together or shown as changing in the very mild stage of AD.

The second group of figures (Figure 1.2 and 1.3) were published in 2010 and 2013,

respectively. The first of these figures is similar, grouping markers by Aβ, tau, brain structure, and clinical markers162. Both Figures 1.1 and 1.2 propose a temporal ordering of biomarkers shifting from normal to abnormal along a disease continuum, but the level of detail was necessarily low given the state of the field at the time.

20

Figure 1.1 Hypothesized Relationship Between the Timecourse of Changes in Various

Biomarkers in Relation to the Neuropathology and Clinical Changes of Alzheimer’s Disease

The first in a series of evolving proposed biomarker curves. From Craig-Schapiro et al., 2009161.

Figure 1.2 Dynamic Biomarkers of the Alzheimer’s Pathological Cascade

The second AD biomarker curve evolution. From Jack et al. 2010162: “Aβ is identified by CSF Aβ42 or PET amyloid imaging. Tau-mediated neuronal injury and dysfunction is identified by CSF tau or fluorodeoxyglucose-PET. Brain structure is measured by use of structural MRI. Aβ=β-amyloid. MCI=mild cognitive impairment.”

21

Figure 1.3 Revised Model of Dynamic Biomarkers of the Alzheimer’s Disease Pathological

Cascade

The third AD biomarker curve evolution. Adapted from Jack et al. 2013163: “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.” The bottom axis reflects time rather than disease stage.

The third iteration of hypothetical curves attempted to further incorporate known difficulties in representing a population through a single set of curves, as well as incorporated more recent findings for the temporal ordering of biomarkers163. For instance, CSF Aβ42 and amyloid PET reflect evidence of CSF amyloid abnormalities being detectable earlier than PET amyloid abnormalities. The sigmoidal curve shapes are also updated to reflect potential differing rates of change between the visualized biomarkers. Lastly, the uncertainty in individual cognitive reserve was acknowledged by adding a high- to low-risk development of cognitive impairment. As a reflection of this uncertainty, the x-axis was represented simply as “time” rather than “disease state”. The premise was to define the temporal ordering of biomarkers for eventual application on a person-by-person basis. The last, most recently updated model (Figure 1.4) was published in 20173. This model steps back from individual biomarkers to the combined groups of (1) amyloid, (2) cognitive performance, FDG-PET, tau PET, atrophy, and (3) clinical function,

22

reflecting the uncertainty in the application of biomarkers toward very specific disease-states or temporal orderings. The continued exploration of non-core CSF biomarkers such as VILIP-1, SNAP-25, Ng, YKL-40, and other non-CSF biomarkers has indeed introduced a higher level of uncertainty as to our ability to accurately pinpoint – if not diagnosis – prognosis in a single individual.

These models are incredibly useful in allowing researchers a common visual scale with which to compare biomarkers and biomarker modalities with respect to clinical status or time. However, each of these sets of curves is modeled off of cross-sectional studies, with relatively little input from within-person longitudinal studies of biomarker change – a caveat that has been clearly acknowledged163. Having a clear picture of the general longitudinal, temporal changes in AD, as well as their relationship with the clinical syndrome of AD, is an area of incredibly active research. Constant thought is given as to the best translation from hypothetical models such as those shown in Figures 1.1-1.4 to functional clinical practice. The first example of data-driven biomarker curves was published using DIAN data, using cross-sectional data derive biomarker curves over the span of autosomal dominant AD (ADAD) by using the time difference in

baseline biomarker measurements and each individuals parental age of AD onset as the basis for modeling biomarker changes over time, shown in Figure 1.527. Data from the DIAN study was a step toward indicating temporal changes in biomarkers from CSF Aβ42 to amyloid imaging changes, to CSF Tau, hippocampal atrophy and hypometabolism, and finally to mild cognitive decline. What remains are studies of within-person longitudinal changes that will best showcase temporal biomarker trajectories for diagnostic, prognostic, or theragnostic utility.

23

Figure 1.4 Change in Biomarkers Over Time

The latest biomarker curve evolution. Adapted from Aisen et al. 20173: “Modified graph showing that amyloid accumulation (measured as low CSF Aβ or elevated amyloid PET standard uptake value ratio) occurs first and functional decline occurs late in the continuum of AD (as before), bug cognitive performance, FDG-PET, tau PET, and MRI atrophy change along a common, gradually steepening curve”.

Figure 1.5 Comparison of Clinical, Cognitive, Structural, Metabolic, and Biochemical Changes

as a Function of Estimated Years from Expected Symptom Onset

Data derived biomarker curves from cross-sectional data using the DIAN cohort. From Bateman et al. 201227: The normalized differences between mutation carriers and noncarriers are shown versus estimated years from expected symptom onset and plotted with a fitted curve. The order of differences suggests decreasing Aβ42 in the CSF (CSF Aβ42), followed by fibrillar Aβ deposition, then increased tau in the CSF (CSF tau), followed by hippocampal atrophy and hypometabolism, with cognitive and clinical changes (as measured by the Clinical Dementia Rating–Sum of Boxes [CDR-SOB]) occurring later. Mild dementia (CDR 1) occurred an average of 3.3 years before expected symptom onset.

24