Predicting the risk of mild cognitive impairment

Objective: We sought to develop risk scores for the progression from cognitively normal (CN) to mild cognitive impairment (MCI).

Methods: We recruited into a longitudinal cohort study a randomly selected, population-based sample of Olmsted County, MN, residents, aged 70 to 89 years on October 1, 2004. At baseline and subsequent visits, participants were evaluated for demographic, clinical, and neuropsychological measures, and were classified as CN, MCI, or dementia. Using baseline demographic and clinical variables in proportional hazards models, we derived scores that predicted the risk of progressing from CN to MCI. We evaluated the ability of these risk scores to classify participants for MCI risk.

Results: Of 1,449 CN participants, 401 (27.7%) developed MCI. A basic model had a C statistic of 0.60 (0.58 for women, 0.62 for men); an augmented model resulted in a C statistic of 0.70 (0.69 for women, 0.71 for men). Both men and women in the highest vs lowest sex-specific quartiles of the augmented model's risk scores had an approximately 7-fold higher risk of developing MCI. Adding APOE ε4 carrier status improved the model (p = 0.002).

Conclusions: We have developed MCI risk scores using variables easily assessable in the clinical setting and that may be useful in routine patient care. Because of variability among populations, validation in independent samples is required. These models may be useful in identifying patients who might benefit from more expensive or invasive diagnostic testing, and can inform clinical trial design. Inclusion of biomarkers or other risk factors may further enhance the models.

Reference: Neurology 10.1212/WNL.0000000000001437

Very early brain changes detected in children with genetic predisposition to Alzheimer's

A very interesting study found that infant ε4 carriers had lower MWF and GMV measurements than noncarriers in precuneus, posterior/middle cingulate, lateral temporal, and medial occipitotemporal regions, areas preferentially affected by AD, and greater MWF and GMV measurements in extensive frontal regions and measurements were also significant in the subset of 2- to 6-month-old infants (MWF differences, P<.05, after correction for multiple comparisons; GMV differences, P<.001,uncorrected fo rmultiple comparisons). Infant ε4 carriers also exhibited an attenuated relationship between MWF and age in posterior white matter regions.

This study raises new questions about the role of APOE in normal human brain development, the extent to which these processes are related to subsequent AD pathology, and whether they could be targeted by AD prevention therapies.

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Association of plasma and cortical amyloid beta is modulated by APOE ε4 status

Background

Apolipoprotein E (APOE) ε4 allele's role as a modulator of the relationship between soluble plasma amyloid beta (Aβ) and fibrillar brain Aβ measured by Pittsburgh compound B positron emission tomography ([¹¹C]PiB PET) has not been assessed.

Methods

Ninety-six Alzheimer's Disease Neuroimaging Initiative participants with [¹¹C]PiB scans and plasma Aβ_1–40 and Aβ_1–42 measurements at the time of PET scanning were included. Regional and voxelwise analyses of [¹¹C]PiB data were used to determine the influence of APOE ε4 allele on association of plasma Aβ_1–40, Aβ_1–42, and Aβ_1–40/Aβ_1–42 with [¹¹C]PiB uptake.

Results

In APOE ε4− but not ε4+ participants, positive relationships between plasma Aβ_1–40/Aβ_1–42 and [¹¹C]PiB uptake were observed. Modeling the interaction of APOE and plasma Aβ_1–40/Aβ_1–42 improved the explained variance in [¹¹C]PiB binding compared with using APOE and plasma Aβ_1–40/Aβ_1–42 as separate terms.

Conclusions

The results suggest that plasma Aβ is a potential Alzheimer's disease biomarker and highlight the importance of genetic variation in interpretation of plasma Aβ levels.

Fig. 1. (A–D) Scatterplots of plasma Aβ_1–40/Aβ_1–42 vs average regional [¹¹C]PiB uptake from the (Average regional [¹¹C]PiB uptake = Plasma Aβ_1–40/Aβ_1–42 + APOE ε4 status + [Plasma Aβ_1–40/Aβ_1–42 × APOE ε4 status]) model (A and B), and plasma Aβ_1–40/Aβ_1–42 vs mean [¹¹C]PiB uptake from the cluster identified in the (Voxel [¹¹C]PiB uptake = Plasma Aβ_1–40/Aβ_1–42 + APOE ε4 status + [Plasma Aβ_1–40/Aβ_1–42 × APOE ε4 status]) model (C and D). Aβ, amyloid beta; PiB, Pittsburgh compound B; APOE, apolipoprotein E.

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Fig. 2. Brain regions (R, right; L, left) identified in the (Voxel [¹¹C]PiB uptake = Plasma Aβ_1–40/Aβ_1–42 + APOE ε4 status + [Plasma Aβ_1–40/Aβ_1–42 × APOE ε4 status]) model (voxel-level threshold of P < .005 [uncorrected], cluster size ≥ 200 voxels). The red-to-yellow scale indicates increasing statistical significance of association. PiB, Pittsburgh compound B; Aβ, amyloid beta; APOE, apolipoprotein E.