Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2013 Dec 18;80(6):1347-58.
doi: 10.1016/j.neuron.2013.12.003.

Biomarker modeling of Alzheimer's disease

Clifford R Jack Jr  1 , David M Holtzman  2
Affiliations
Review

Biomarker modeling of Alzheimer's disease

Clifford R Jack Jr et al. Neuron. .

Abstract

Alzheimer's disease (AD) is a slowly progressing disorder in which pathophysiological abnormalities, detectable in vivo by biomarkers, precede overt clinical symptoms by many years to decades. Five AD biomarkers are sufficiently validated to have been incorporated into clinical diagnostic criteria and commonly used in therapeutic trials. Current AD biomarkers fall into two categories: biomarkers of amyloid-β plaques and of tau-related neurodegeneration. Three of the five are imaging measures and two are cerebrospinal fluid analytes. AD biomarkers do not evolve in an identical manner but rather in a sequential but temporally overlapping manner. Models of the temporal evolution of AD biomarkers can take the form of plots of biomarker severity (degree of abnormality) versus time. In this Review, we discuss several time-dependent models of AD that take into consideration varying age of onset (early versus late) and the influence of aging and co-occurring brain pathologies that commonly arise in the elderly.

PubMed Disclaimer

Figures

Figure 1
Figure 1. Voxel based comparisons of amyloid PET (Fig 1a), FDG PET (Fig 1b) and structural MRI (Fig 1c) illustrate differences between subjects with Alzheimer's disease (n=50) and cognitively normal elderly (n=50)
AD and cognitively normal elderly subjects were from the Mayo Clinic and were matched on age and gender. Fig 1a, amyloid PET maps (thresholded at FWE, P < 0.001 without partial volume correction) illustrate greater retention of Pittsburgh Compound B (PIB) in AD vs cognitively normal elderly in most brain areas; primary sensory motor, visual and the medial temporal lobe are spared. Fig 1b, FDG PET maps (thresholded at FWE, P < 0.001 without partial volume correction) illustrate decreased FDG uptake in the basal temporal, lateral temporal – parietal, lateral pre-frontal, and posterior cingulate-precuneus in AD compared to cognitively normal elderly. This spatial pattern constitutes an “AD-signature” in FDG PET. Fig 1c, structural MRI maps (thresholded at FWE, P < 0.05) illustrate grey matter loss in the medial, basal and lateral temporal, lateral parietal, occipital, insula and precuneus in AD compared to cognitively normal elderly. This spatial pattern constitutes an “AD-signature” in structural MRI. All voxel wise comparisons generated with SPM5. 3D displays by Brain Net Viewer (http://www.nitrc.org/projects/bnv/). The color bar scale indicates the t-test differences between the groups.
Figure 2
Figure 2. Temporal evolution of biomarkers
Fig 2a, biomarker model of pure AD. The horizontal axis is time and the vertical axis severity of biomarker abnormality from completely normal (min) to abnormal (max). 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. Amyloid biomarkers become abnomral first, followed by CSF tau, followed by FDH PET and MRI. Cognitive impairment (green filled area) is the last event in the progression of the disease. A range of cognitive responses are possible that depend on the individual's risk profile. The cognitive response curve is shifted to the left for those with low cognitive reserve and to the right for those with high cognitive reserve. At a given point on the disease time line, a person with high cognitive reserve can be cognitively normal while a low cognitive response person with the same biomarker profile can be impaired. All biomarker curves (as well as cognitive impairment) are configured as sigmoids, but the curves have a progressively steeper slope in the right-hand tail for later-changing biomarkers. The right hand tails of the curves are dashed indicating that biomarker trajectories in end stage dementia are unknown at this time. MCI = mild cognitive impairment. Fig 2b, amyloid-first biomarker model of late onset AD where comorbid pathologies are likely. Figure labels are as in Fig 2a, except here the CSF tau, structural MRI and FDG PET are grouped under the generic neurodegenerative label. This reflects the fact that neurodegenerative biomarkers can be non-specific and that the proportional contribution of various possible etiologies to neurodegeneration cannot be known in vivo. We assume that in elderly individuals tauopathy and in many individuals non-AD neurodegenerative pathologies arise first but lie beneath the detection threshold of biomarkers of neurodegeneration. Aβopathy arises later independently and is the first AD biomarker to become positive. The neurodegeneration curve has a shallow slope initially, but the slope steepens after the onset of amyloidosis. Fig 2c, neurodegeneration-first biomarker model of late onset AD where comorbid pathologies are likely. Figure labels are as in Fig 2a, except here one or more neurodegenerative biomarkers become abnormal first reflecting the onset of tauopathy and in some individuals other neurodegenerative pathologies prior to amyloid. Observing the onset of an abnormal neurodegenerative biomarker prior to an abnormal amyloid biomarker does not alter the role of amyloid as an inducer of neurodegeneration. This is indicated by showing the neurodegeneration curve with a shallow slope initially which steepens after the onset of amyloidosis.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Albert MS, DeKosky ST, Dickson D, Dubois B, Feldman HH, Fox NC, Gamst A, Holtzman DM, Jagust WJ, Petersen RC, Snyder PJ, Phelps CH. The diagnosis of mild cognitive impairment due to Alzheimer's disease: Recommendations from the National Institute on Aging and Alzheimer's Association Workgroup. Alzheimers Dement. 2011;7:270–279. - PMC - PubMed
    1. Bateman RJ, Xiong C, Benzinger TL, Fagan AM, Goate A, Fox NC, Marcus DS, Cairns NJ, Xie X, Blazey TM, Holtzman DM, Santacruz A, Buckles V, Oliver A, Moulder K, Aisen PS, Ghetti B, Klunk WE, McDade E, Martins RN, Masters CL, Mayeux R, Ringman JM, Rossor MN, Schofield PR, Sperling RA, Salloway S, Morris JC. Clinical and Biomarker Changes in Dominantly Inherited Alzheimer's Disease. The New England journal of medicine. 2012;367:795–804. - PMC - PubMed
    1. Bennett DA, Schneider JA, Wilson RS, Bienias JL, Arnold SE. Neurofibrillary tangles mediate the association of amyloid load with clinical Alzheimer disease and level of cognitive function. Arch Neurol. 2004;61:378–384. - PubMed
    1. Bertram L, Lange C, Mullin K, Parkinson M, Hsiao M, Hogan MF, Schjeide BM, Hooli B, Divito J, Ionita I, Jiang H, Laird N, Moscarillo T, Ohlsen KL, Elliott K, Wang X, Hu-Lince D, Ryder M, Murphy A, Wagner SL, Blacker D, Becker KD, Tanzi RE. Genome-wide association analysis reveals putative Alzheimer's disease susceptibility loci in addition to APOE. American journal of human genetics. 2008;83:623–632. - PMC - PubMed
    1. Bobinski M, de Leon MJ, Wegiel J, Desanti S, Convit A, Saint Louis LA, Rusinek H, Wisniewski HM. The histological validation of post mortem magnetic resonance imaging-determined hippocampal volume in Alzheimer's disease. Neuroscience. 2000;95:721–725. - PubMed

Publication types

MeSH terms