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. 2012 Sep;22(9):1993-2004.
doi: 10.1093/cercor/bhr271. Epub 2011 Oct 29.

CSF biomarker and PIB-PET-derived beta-amyloid signature predicts metabolic, gray matter, and cognitive changes in nondemented subjects

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CSF biomarker and PIB-PET-derived beta-amyloid signature predicts metabolic, gray matter, and cognitive changes in nondemented subjects

Michael Ewers et al. Cereb Cortex. 2012 Sep.

Abstract

Beta-amyloid (Aβ) is a histopathological hallmark of Alzheimer's disease dementia, but high levels of Aβ in the brain can also be found in a substantial proportion of nondemented subjects. Here we investigated which 2-year rate of brain and cognitive changes are present in nondemented subjects with high and low Aβ levels, as assessed with cerebrospinal fluid and molecular positron emission tomography (PET)-based biomarkers of Aβ. In subjects with mild cognitive impairment, increased brain Aβ levels were associated with significantly faster cognitive decline, progression of gray matter atrophy within temporal and parietal brain regions, and a trend for a faster decline in parietal Fludeoxyglucose (FDG)-PET metabolism. Changes in gray matter and FDG-PET mediated the association between Aβ and cognitive decline. In contrast, elderly cognitively healthy controls (HC) with high Aβ levels showed only a faster medial temporal lobe and precuneus volume decline compared with HC with low Aβ. In conclusion, the current results suggest not only that both functional and volumetric brain changes are associated with high Aβ years before the onset of dementia but also that HC with substantial Aβ levels show higher Aβ pathology resistance, lack other pathologies that condition neurotoxic effects of Aβ, or accumulated Aβ for a shorter time period.

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Figures

Figure 1.
Figure 1.
Flow chart of subjects included in the current study.
Figure 2.
Figure 2.
Frequency plot of average PIB-PET scores and a fitted smoothed curve of the distribution. The cutoff point of 1.6 (red vertical line) was derived to optimally separate the data of the bimodal distribution into PIB-PET(+) and PIB-PET(−) groups.
Figure 3.
Figure 3.
Regression plot of estimated longitudinal decline in the ADAS-cog (A and B) and delayed RAVLT scores (C and D) over the follow-up period (years) for iPIB-PET(+) subjects (red line) versus iPIB-PET(−) subjects (black line) in MCI (A and C) and HC (B and D). The difference in the rate of decline between iPIB-PET groups was statistically significant for measures, except for ADAS-cog in the HC group (see Results).
Figure 4.
Figure 4.
Survival plot for conversion from MCI to AD for iPIB-PET(+) shown in red and iPIB-PET(−) shown in black.
Figure 5.
Figure 5.
Regression plot of estimated longitudinal decline of gray matter volume in MCI subjects for the inferior parietal cortex (A), entorhinal cortex (B), parahippocampus (C), and middle temporal gyrus (D). The iPIB-PET(+)–associated acceleration in the decline of each volume was significantly larger in MCI than in HC subjects in whom the iPIB-PET–associated difference was not significant (not shown). Note that the scale of the plots varies due to the different sizes of the brain structures.

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