Vascular risk modulates the relationship between cerebral amyloid deposition and subjective memory complaints

Abstract

Purpose

We aimed to investigate the relationships of cerebral amyloid beta (Aβ) deposition and neurodegeneration (ND) with subjective memory complaints (SMCs) in cognitively normal (CN) individuals, focusing specially on the modulating effects of vascular risk (VR) on those relationships.

Participants and methods

A total of 230 CN elderly individuals underwent comprehensive clinical assessments including the Subjective Memory Complaints Questionnaire (SMCQ), VR assessment, and multimodal brain imaging including [¹¹C] Pittsburgh compound B positron emission tomography (PET), [¹⁸F] fluorodeoxyglucose-PET, and magnetic resonance imaging.

Results

We found a significant overall positive association between cerebral Aβ retention and SMCQ score. In addition, we found a significant cerebral Aβ retention × VR interaction effect on the SMCQ score. Subgroup analyses showed that the Aβ–SMC association was found only in VR-negative, and not in VR-positive, individuals. We found no relationship between ND and SMCQ.

Conclusion

Our findings suggest that SMC in CN elderly individuals reflects early accumulation of Aβ in the brain. Given the modulating effect of VR on the Aβ–SMC relationship, SMC can be used as a meaningful marker of early Aβ deposition in individuals without VR.

Keywords:

• subjective memory complaints
• amyloid beta
• vascular risk
• Alzheimer’s disease
• cognitively normal

Supplementary material

Image acquisition and preprocessing

[¹¹C] Pittsburg compound B (PiB) – positron emission tomography (PET) image acquisition and preprocessing

Participants underwent simultaneous three-dimensional (3D) PiB-PET and 3D T1-weighted magnetic resonance (MR) imaging using a 3.0T Biograph mMR scanner (PET-MR scanner; Siemens, Washington DC, USA) according to the manufacturer’s approved guidelines. After intravenous administration of 555 MBq of [¹¹C] PiB (range, 450–610 MBq), a 30-minute emission scan was obtained 40 minutes after injection. The PiB-PET data collected in list mode were processed for routine corrections such as uniformity, ultrashort echo time-based attenuation, and decay corrections and were reconstructed into a 256×256 image matrix using iterative methods (6 iterations with 21 subsets). The following image preprocessing steps were performed using Statistical Parametric Mapping 8 (SPM8; http://www.fil.ion.ucl.ac.uk/spm) implemented in Matlab 2014a (Mathworks, Natick, MA, USA). Static PiB-PET images were co-registered to individual T1 structural images and transformation parameters for the spatial normalization of individual T1 images to a standard Montreal Neurological Institute (MNI) template were calculated. Using IBASPM software, we used the inverse transformation parameters to transform coordinates from the automatic anatomic labeling (AAL) 116 atlas⁵ into an individual space for each subject (a resampling voxel size =1×0.98×0.98 mm), and the non-gray matter portions of the atlas were individually masked using the cerebral gray matter segment image from each subject. The mean regional ¹¹C-PiB uptake values from cerebral regions were extracted using the individual AAL116 atlas from T1-coregistered PiB-PET images. Cerebellar gray matter was used as the reference region for quantitative normalization of cerebral PiB uptake values due to its relatively low Aβ deposition.³ To measure PiB uptake in the cerebellar gray matter regions, a probabilistic cerebellar atlas (Institute of Cognitive Neuroscience, UCL; Cognitive Neuroscience Laboratory, Royal Holloway) was transformed into individual space in the same manner as described above. Of the 28 anatomical structural regions in the cerebellar atlas, all cerebellar lobular regions except the vermis were included to extract the mean cerebellar uptake values.

[¹⁸F] Fluorodeoxyglucose (FDG)-PET image acquisition and preprocessing

The participants fasted for at least 6 hours and rested in a waiting room for 40 minutes prior to the scans after intravenous administration of 0.1 mCi/kg of [¹⁸F] FDG radioligands. The PET data collected in list mode (5 minutes × 4 frames) were processed for routine corrections such as uniformity, ultrashort echo time-based attenuation, and decay corrections. After inspecting the data for any significant head movements, we reconstructed them into a 20-minute summed image using iterative methods (6 iterations with 21 subsets). The following image processing steps were performed using SPM12 implemented in Matlab 2014a (Mathworks). First, static FDG-PET images were co-registered to individual T1 structural images, and transformation parameters for the spatial normalization of individual T1 images to a standard MNI template were calculated and used to spatially normalize the PET images to the MNI template. After smoothing the spatially normalized FDG-PET images with a 12-mm Gaussian filter, intensity normalization was performed using the pons as the reference region.

MR image acquisition and preprocessing

All T1-weighted images were acquired in the sagittal orientation using the abovementioned 3.0T PET-MR machine. MR image acquisition parameters were as follows: repetition time =1,670 ms, echo time =1.89 ms, field of view 250 mm, and 256×256 matrix with 1.0-mm slice thickness. All MR images were automatically segmented using Free-Surfer version 5.3 (http://surfer.nmr.mgh.harvard.edu/) with manual correction of minor segmentation errors. Based on the Desikan–Killiany atlas,¹ mean cortical thickness values were obtained from Alzheimer’s disease (AD)-signature regions, including the entorhinal, inferior temporal, middle temporal, and fusiform gyrus according to a previous study.²

Definition of threshold for neurodegeneration biomarkers abnormality

Receiver operating characteristic (ROC) curve analyses of two neurodegeneration biomarkers – cerebral glucose metabolism (CMglu) in the AD-signature FDG region of interest (ROI) and AD-signature cortical thickness – were performed to determine the optimal threshold that can distinguish the AD dementia from the cognitively normal (CN) elderly individuals. For these ROC analyses, data from AD dementia subjects and CN subjects in KBASE cohort were used. Inclusion criteria for the AD dementia group were as follows: 1) aged 55–90 years (inclusive), 2) Clinical Dementia Rating (CDR) score 0.5 or 1, and 3) Probable AD dementia according to the National Institute of Aging and the Alzheimer’s Association (NIA-AA) diagnostic criteria for AD.⁴ Inclusion criteria for CN and exclusion criteria for both the groups are described in the manuscript.

To set the threshold of the standardized uptake value ratio (SUVR) value in the AD-signature FDG ROI that can distinguish AD dementia from CN, data from 58 AD dementia subjects (mean age: 72.9±8.1 years; female/male: 41/17; global CDR 0.8±0.2) and 260 CN subjects (mean age: 68.7±8.0 years; female/male: 134/126; global CDR 0.0±0.0) were used. Using CN subjects as the reference group, we set the optimal cutoff point of SUVR in the AD-signature FDG ROI based on the Youden index at 1.386 (sensitivity 91.4%). In terms of cutoff point of AD-signature cortical thickness used to distinguish AD dementia from CN, data from 52 subjects with AD dementia (mean age: 72.5±8.2 years; female/male: 36/16; global CDR 0.8±0.2) and 254 CN subjects (mean age: 68.6±8.0 years; female/male: 132/122; global CDR 0.0±0.0) were used for ROC curve analysis. Using CN subjects as the reference group, we set the optimal cutoff point of AD-signature cortical thickness based on the Youden index at 2.666 mm (sensitivity 92.3%).

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Acknowledgments

This study was supported by a grant from the Ministry of Science, ICT, and Future Planning (Grant No: NRF-2014M3C7A1046042). The funding sources had no role in the study design, data collection, data analysis, data interpretation, writing of the manuscript, or decision to submit it for publication. The authors thank the coinvestigators of the KBASE Research Group who are listed at http://kbase.kr/eng/about/research.php.

Author contributions

JWK and DYL designed the study and JWK wrote the study protocol. JWK and DYL wrote the draft of the manuscript and undertook the statistical analyses. MSB, DY, JHL, KK, and GJ collected and analyzed the data. All authors contributed toward data analysis, drafting and critically revising the paper, gave final approval of the version to be published, and agreed to be accountable for all aspects of the work.

Disclosure

The authors report no conflicts of interest in this work.