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Cui L, Zhang Z, Huang YL, Xie F, Guan YH, Lo CYZ, Guo YH, Jiang JH, Guo QH. Brain amyloid-β deposition associated functional connectivity changes of ultra-large structural scale in mild cognitive impairment. Brain Imaging Behav 2023; 17:494-506. [PMID: 37188840 DOI: 10.1007/s11682-023-00780-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/19/2023] [Indexed: 05/17/2023]
Abstract
In preclinical Alzheimer's disease, neuro-functional changes due to amyloid-β (Aβ) deposition are not synchronized in different brain lobes and subcortical nuclei. This study aimed to explore the correlation between brain Aβ burden, connectivity changes in an ultra-large structural scale, and cognitive function in mild cognitive impairment. Participants with mild cognitive impairment were recruited and underwent florbetapir (F18-AV45) PET, resting-state functional MRI, and multidomain neuropsychological tests. AV-45 standardized uptake value ratio (SUVR) and functional connectivity of all participants were calculated. Of the total 144 participants, 72 were put in the low Aβ burden group and 72 in the high Aβ burden group. In the low Aβ burden group, all connectivities between lobes and nuclei had no correlation with SUVR. In the high Aβ burden group, SUVR showed negative correlations with the Subcortical-Occipital connectivity (r=-0.36, P = 0.02) and Subcortical-Parietal connectivity (r=-0.26, P = 0.026). Meanwhile, in the high Aβ burden group, SUVR showed positive correlations with the Temporal-Prefrontal connectivity (r = 0.27, P = 0.023), Temporal-Occipital connectivity (r = 0.24, P = 0.038), and Temporal-Parietal connectivity (r = 0.32, P = 0.006). Subcortical to Occipital and Parietal connectivities had positive correlations with general cognition, language, memory, and executive function. Temporal to Prefrontal, Occipital, and Parietal connectivities had negative correlations with memory function, executive function, and visuospatial function, and a positive correlation with language function. In conclusion, Individuals with mild cognitive impairment with high Aβ burden have Aβ-related bidirectional functional connectivity changes between lobes and subcortical nuclei that are associated with cognitive decline in multiple domains. These connectivity changes reflect neurological impairment and failed compensation.
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Affiliation(s)
- Liang Cui
- Department of Gerontology, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, 600 Yishan Road, Shanghai, 200233, China
| | - Zhen Zhang
- Department of Gerontology, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, 600 Yishan Road, Shanghai, 200233, China
| | - Yan-Lu Huang
- Department of Gerontology, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, 600 Yishan Road, Shanghai, 200233, China
| | - Fang Xie
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, 518 East Wuzhong Road, Shanghai, 200040, China
| | - Yi-Hui Guan
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, 518 East Wuzhong Road, Shanghai, 200040, China
| | - Chun-Yi Zac Lo
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, 200433, China
| | - Yi-Han Guo
- Faculty of Medicine, The University of Queensland, Brisbane, Australia
| | - Jie-Hui Jiang
- Institute of Biomedical Engineering, School of Life Science, Shanghai University, 99 Shangda Road, Shanghai, 200444, China.
| | - Qi-Hao Guo
- Department of Gerontology, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, 600 Yishan Road, Shanghai, 200233, China.
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Luckett ES, Zielonka M, Kordjani A, Schaeverbeke J, Adamczuk K, De Meyer S, Van Laere K, Dupont P, Cleynen I, Vandenberghe R. Longitudinal APOE4- and amyloid-dependent changes in the blood transcriptome in cognitively intact older adults. Alzheimers Res Ther 2023; 15:121. [PMID: 37438770 PMCID: PMC10337180 DOI: 10.1186/s13195-023-01242-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 05/06/2023] [Indexed: 07/14/2023]
Abstract
BACKGROUND Gene expression is dysregulated in Alzheimer's disease (AD) patients, both in peripheral blood and post mortem brain. We investigated peripheral whole-blood gene (co)expression to determine molecular changes prior to symptom onset. METHODS RNA was extracted and sequenced for 65 cognitively healthy F-PACK participants (65 (56-80) years, 34 APOE4 non-carriers, 31 APOE4 carriers), at baseline and follow-up (interval: 5.0 (3.4-8.6) years). Participants received amyloid PET at both time points and amyloid rate of change derived. Accumulators were defined with rate of change ≥ 2.19 Centiloids. We performed differential gene expression and weighted gene co-expression network analysis to identify differentially expressed genes and networks of co-expressed genes, respectively, with respect to traits of interest (APOE4 status, amyloid accumulation (binary/continuous)), and amyloid positivity status, followed by Gene Ontology annotation. RESULTS There were 166 significant differentially expressed genes at follow-up compared to baseline in APOE4 carriers only, whereas 12 significant differentially expressed genes were found only in APOE4 non-carriers, over time. Among the significant genes in APOE4 carriers, several had strong evidence for a pathogenic role in AD based on direct association scores generated from the DISQOVER platform: NGRN, IGF2, GMPR, CLDN5, SMIM24. Top enrichment terms showed upregulated mitochondrial and metabolic pathways, and an exacerbated upregulation of ribosomal pathways in APOE4 carriers compared to non-carriers. Similarly, there were 33 unique significant differentially expressed genes at follow-up compared to baseline in individuals classified as amyloid negative at baseline and positive at follow-up or amyloid positive at both time points and 32 unique significant differentially expressed genes over time in individuals amyloid negative at both time points. Among the significant genes in the first group, the top five with the highest direct association scores were as follows: RPL17-C18orf32, HSP90AA1, MBP, SIRPB1, and GRINA. Top enrichment terms included upregulated metabolism and focal adhesion pathways. Baseline and follow-up gene co-expression networks were separately built. Seventeen baseline co-expression modules were derived, with one significantly negatively associated with amyloid accumulator status (r2 = - 0.25, p = 0.046). This was enriched for proteasomal protein catabolic process and myeloid cell development. Thirty-two follow-up modules were derived, with two significantly associated with APOE4 status: one downregulated (r2 = - 0.27, p = 0.035) and one upregulated (r2 = 0.26, p = 0.039) module. Top enrichment processes for the downregulated module included proteasomal protein catabolic process and myeloid cell homeostasis. Top enrichment processes for the upregulated module included cytoplasmic translation and rRNA processing. CONCLUSIONS We show that there are longitudinal gene expression changes that implicate a disrupted immune system, protein removal, and metabolism in cognitively intact individuals who carry APOE4 or who accumulate in cortical amyloid. This provides insight into the pathophysiology of AD, whilst providing novel targets for drug and therapeutic development.
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Affiliation(s)
- Emma S Luckett
- Laboratory for Cognitive Neurology, Leuven Brain Institute, KU Leuven, Leuven, 3000, Belgium
- Alzheimer Research Centre KU Leuven, Leuven Brain Institute, Leuven, 3000, Belgium
- Laboratory for Complex Genetics, KU Leuven, Leuven, 3000, Belgium
| | - Magdalena Zielonka
- Alzheimer Research Centre KU Leuven, Leuven Brain Institute, Leuven, 3000, Belgium
- Laboratory for the Research of Neurodegenerative Diseases, VIB-KU Leuven, KU Leuven, Leuven, 3000, Belgium
| | - Amine Kordjani
- Laboratory for Complex Genetics, KU Leuven, Leuven, 3000, Belgium
| | - Jolien Schaeverbeke
- Laboratory for Cognitive Neurology, Leuven Brain Institute, KU Leuven, Leuven, 3000, Belgium
- Alzheimer Research Centre KU Leuven, Leuven Brain Institute, Leuven, 3000, Belgium
- Laboratory of Neuropathology, Leuven Brain Institute, KU Leuven, Leuven, 3000, Belgium
| | | | - Steffi De Meyer
- Laboratory for Cognitive Neurology, Leuven Brain Institute, KU Leuven, Leuven, 3000, Belgium
- Alzheimer Research Centre KU Leuven, Leuven Brain Institute, Leuven, 3000, Belgium
- Laboratory of Molecular Neurobiomarker Research, KU Leuven, Leuven, 3000, Belgium
| | - Koen Van Laere
- Division of Nuclear Medicine, UZ Leuven, Leuven, 3000, Belgium
- Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, KU Leuven, Leuven, 3000, Belgium
| | - Patrick Dupont
- Laboratory for Cognitive Neurology, Leuven Brain Institute, KU Leuven, Leuven, 3000, Belgium
- Alzheimer Research Centre KU Leuven, Leuven Brain Institute, Leuven, 3000, Belgium
| | - Isabelle Cleynen
- Laboratory for Complex Genetics, KU Leuven, Leuven, 3000, Belgium
| | - Rik Vandenberghe
- Laboratory for Cognitive Neurology, Leuven Brain Institute, KU Leuven, Leuven, 3000, Belgium.
- Alzheimer Research Centre KU Leuven, Leuven Brain Institute, Leuven, 3000, Belgium.
- Neurology Department, University Hospitals Leuven, Herestraat 49, Leuven, 3000, Belgium.
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Luckett ES, Schaeverbeke J, De Meyer S, Adamczuk K, Van Laere K, Dupont P, Vandenberghe R. Longitudinal changes in 18F-Flutemetamol amyloid load in cognitively intact APOE4 carriers versus noncarriers: Methodological considerations. Neuroimage Clin 2023; 37:103321. [PMID: 36621019 PMCID: PMC9850036 DOI: 10.1016/j.nicl.2023.103321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 12/12/2022] [Accepted: 01/04/2023] [Indexed: 01/07/2023]
Abstract
PURPOSE Measuring longitudinal changes in amyloid load in the asymptomatic stage of Alzheimer's disease is of high relevance for clinical research and progress towards more efficacious, timely treatments. Apolipoprotein E ε4 (APOE4) has a well-established effect on the rate of amyloid accumulation. Here we investigated which region of interest and which reference region perform best at detecting the effect of APOE4 on longitudinal amyloid load in individuals participating in the Flemish Prevent Alzheimer's Disease Cohort KU Leuven (F-PACK). METHODS Ninety cognitively intact F-PACK participants (baseline age: 68 (52-80) years, 46 males, 42 APOE4 carriers) received structural MRI and 18F-Flutemetamol PET scans at baseline and follow-up (6.2 (3.4-10.9) year interval). Standardised uptake value ratios (SUVRs) and Centiloids (CLs) were calculated in a composite cortical volume of interest (SUVRcomp/CL) and in the precuneus (SUVRprec), and amyloid rate of change derived: (follow-up amyloid load - baseline amyloid load) / time interval (years). Four reference regions were used to derive amyloid load: whole cerebellum, cerebellar grey matter, eroded subcortical white matter, and pons. RESULTS When using whole cerebellum or cerebellar grey matter as reference region, APOE4 carriers had a significantly higher SUVRcomp amyloid rate of change than non-carriers (pcorr = 0.004, t = 3.40 (CI 0.005-0.018); pcorr = 0.036, t = 2.66 (CI 0.003-0.018), respectively). Significance was not observed for eroded subcortical white matter or pons (pcorr = 0.144, t = 2.13 (CI 0.0003-0.008); pcorr = 0.116, t = 2.22 (CI 0.005-0.010), respectively). When using CLs as the amyloid measurement, and whole cerebellum, APOE4 carriers had a higher amyloid rate of change than non-carriers (pcorr = 0.012, t = 3.05 (CI 0.499-2.359)). Significance was not observed for the other reference regions. No significance was observed with any of the reference regions and amyloid rate of change in the precuneus (SUVRprec). CONCLUSION In this cognitively intact cohort, a composite neocortical volume of interest together with whole cerebellum or cerebellar grey matter as reference region are the methods of choice for detecting APOE4-dependent differences in amyloid rate of change.
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Affiliation(s)
- Emma S Luckett
- Laboratory for Cognitive Neurology, Department of Neurosciences, Leuven Brain Institute, KU Leuven, Leuven, Belgium; Alzheimer Research Centre KU Leuven, Leuven Brain Institute, Leuven, Belgium
| | - Jolien Schaeverbeke
- Laboratory for Cognitive Neurology, Department of Neurosciences, Leuven Brain Institute, KU Leuven, Leuven, Belgium; Alzheimer Research Centre KU Leuven, Leuven Brain Institute, Leuven, Belgium
| | - Steffi De Meyer
- Laboratory for Cognitive Neurology, Department of Neurosciences, Leuven Brain Institute, KU Leuven, Leuven, Belgium; Alzheimer Research Centre KU Leuven, Leuven Brain Institute, Leuven, Belgium; Laboratory for Molecular Neurobiomarker Research, KU Leuven, Leuven, Belgium
| | | | - Koen Van Laere
- Division of Nuclear Medicine, UZ Leuven, Leuven, Belgium; Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Patrick Dupont
- Laboratory for Cognitive Neurology, Department of Neurosciences, Leuven Brain Institute, KU Leuven, Leuven, Belgium; Alzheimer Research Centre KU Leuven, Leuven Brain Institute, Leuven, Belgium
| | - Rik Vandenberghe
- Laboratory for Cognitive Neurology, Department of Neurosciences, Leuven Brain Institute, KU Leuven, Leuven, Belgium; Alzheimer Research Centre KU Leuven, Leuven Brain Institute, Leuven, Belgium; Neurology Department, University Hospitals Leuven, Leuven, Belgium.
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Sur C, Adamczuk K, Scott D, Kost J, Sampat M, Buckley C, Farrar G, Newton B, Suhy J, Bennacef I, Egan MF. Evaluation of 18F-flutemetamol amyloid PET image analysis parameters on the effect of verubecestat on brain amlyoid load in Alzheimer's disease. Mol Imaging Biol 2022; 24:862-873. [PMID: 35794343 DOI: 10.1007/s11307-022-01735-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 03/17/2022] [Accepted: 04/21/2022] [Indexed: 12/29/2022]
Abstract
PURPOSE The BACE inhibitor verubecestat was previously found to reduce amyloid load as assessed by 18F-flutemetamol positron emission tomography (PET) composite cortical standard uptake value ratio (SUVr) in patients with mild-to-moderate Alzheimer's disease (AD) in a substudy of the EPOCH trial. Here, we report on additional analyses relevant to the EPOCH PET data, to help inform on the use of PET for assessing amlyloid load in AD clinical trials. PROCEDURES The analyses addressed (1) identification of an optimal 18F-flutemetamol reference region, (2) determination of the threshold to characterize the magnitude of the longitudinal change, and (3) the impact of partial volume correction (PVC). Pons and subcortical white matter were evaluated as reference regions. The SUVr cutoffs and final reference region choice were determined using 162 18F-flutemetamol PET scans from the AIBL dataset. 18F-flutemetamol SUVrs were computed at baseline and at Week 78 in EPOCH participants who received verubecestat 12 mg (n = 14), 40 mg (n = 20), or placebo (n = 20). Drug effects on amyloid load were computed using either Meltzer (MZ), or symmetric geometric transfer matrix (SGTM) PVC and compared to uncorrected data. RESULTS The optimal subcortical white matter and pons SUVr cutoffs were determined to be 0.69 and 0.62, respectively. The effect size to detect longitudinal change was higher for subcortical white matter (1.20) than pons (0.45). Hence, subcortical white matter was used as the reference region for the EPOCH PET substudy. In EPOCH, uncorrected baseline SUVr values correlated strongly with MZ PVC (r2 = 0.94) and SGTM PVC (r2 = 0.92) baseline SUVr values, and PVC did not provide improvement for evaluating treatment effects on amyloid load at Week 78. No change from baseline was observed in the placebo group at Week 78, whereas a 0.02 and a 0.04 decrease in SUVr were observed in the 12 mg and 40 mg arms, with the latter representing a 22% reduction in the amyloid load above the detection threshold. CONCLUSIONS Treatment-related 18F-flutemetamol longitudinal changes in AD clinical trials can be quantified using a subcortical white matter reference region without PVC. CLINICAL TRIAL REGISTRATION clinicaltrials.gov NCT01739348.
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Luckett ES, Abakkouy Y, Reinartz M, Adamczuk K, Schaeverbeke J, Verstockt S, De Meyer S, Van Laere K, Dupont P, Cleynen I, Vandenberghe R. Association of Alzheimer’s disease polygenic risk scores with amyloid accumulation in cognitively intact older adults. Alzheimers Res Ther 2022; 14:138. [PMID: 36151568 PMCID: PMC9508733 DOI: 10.1186/s13195-022-01079-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 09/08/2022] [Indexed: 11/10/2022]
Abstract
Abstract
Background
Early detection of individuals at risk for Alzheimer’s disease (AD) is highly important. Amyloid accumulation is an early pathological AD event, but the genetic association with known AD risk variants beyond the APOE4 effect is largely unknown. We investigated the association between different AD polygenic risk scores (PRS) and amyloid accumulation in the Flemish Prevent AD Cohort KU Leuven (F-PACK).
Methods
We calculated PRS with and without the APOE region in 90 cognitively healthy F-PACK participants (baseline age 67.8 (52–80) years, 41 APOE4 carriers), with baseline and follow-up amyloid-PET (time interval 6.1 (3.4–10.9) years). Individuals were genotyped using Illumina GSA and imputed. PRS were calculated using three p-value thresholds (pT) for variant inclusion: 5 × 10−8, 1 × 10−5, and 0.1, based on the stage 1 summary statistics from Kunkle et al. (Nat Genet 51:414–30, 2019). Linear regression models determined if these PRS predicted amyloid accumulation.
Results
A score based on PRS excluding the APOE region at pT = 5 × 10−8 plus the weighted sum of the two major APOE variants (rs429358 and rs7412) was significantly associated with amyloid accumulation (p = 0.0126). The two major APOE variants were also significantly associated with amyloid accumulation (p = 0.0496). The other PRS were not significant.
Conclusions
Specific PRS are associated with amyloid accumulation in the asymptomatic phase of AD.
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Reinartz M, Luckett ES, Schaeverbeke J, De Meyer S, Adamczuk K, Thal DR, Van Laere K, Dupont P, Vandenberghe R. Classification of 18F-Flutemetamol scans in cognitively normal older adults using machine learning trained with neuropathology as ground truth. Eur J Nucl Med Mol Imaging 2022; 49:3772-3786. [PMID: 35522322 PMCID: PMC9399207 DOI: 10.1007/s00259-022-05808-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 04/19/2022] [Indexed: 11/29/2022]
Abstract
Purpose End-of-life studies have validated the binary visual reads of 18F-labeled amyloid PET tracers as an accurate tool for the presence or absence of increased neuritic amyloid plaque density. In this study, the performance of a support vector machine (SVM)-based classifier will be tested against pathological ground truths and its performance determined in cognitively healthy older adults. Methods We applied SVM with a linear kernel to an 18F-Flutemetamol end-of-life dataset to determine the regions with the highest feature weights in a data-driven manner and to compare between two different pathological ground truths: based on neuritic amyloid plaque density or on amyloid phases, respectively. We also trained and tested classifiers based on the 10% voxels with the highest amplitudes of feature weights for each of the two neuropathological ground truths. Next, we tested the classifiers’ diagnostic performance in the asymptomatic Alzheimer’s disease (AD) phase, a phase of interest for future drug development, in an independent dataset of cognitively intact older adults, the Flemish Prevent AD Cohort-KU Leuven (F-PACK). A regression analysis was conducted between the Centiloid (CL) value in a composite volume of interest (VOI), as index for amyloid load, and the distance to the hyperplane for each of the two classifiers, based on the two pathological ground truths. A receiver operating characteristic analysis was also performed to determine the CL threshold that optimally discriminates between neuritic amyloid plaque positivity versus negativity, or amyloid phase positivity versus negativity, within F-PACK. Results The classifiers yielded adequate specificity and sensitivity within the end-of-life dataset (neuritic amyloid plaque density classifier: specificity of 90.2% and sensitivity of 83.7%; amyloid phase classifier: specificity of 98.4% and sensitivity of 84.0%). The regions with the highest feature weights corresponded to precuneus, caudate, anteromedial prefrontal, and also posterior inferior temporal and inferior parietal cortex. In the cognitively normal cohort, the correlation coefficient between CL and distance to the hyperplane was −0.66 for the classifier trained with neuritic amyloid plaque density, and −0.88 for the classifier trained with amyloid phases. This difference was significant. The optimal CL cut-off for discriminating positive versus negative scans was CL = 48–51 for the different classifiers (area under the curve (AUC) = 99.9%), except for the classifier trained with amyloid phases and based on the 10% voxels with highest feature weights. There the cut-off was CL = 26 (AUC = 99.5%), which closely matched the CL threshold for discriminating phases 0–2 from 3–5 based on the end-of-life dataset and the neuropathological ground truth. Discussion Among a set of neuropathologically validated classifiers trained with end-of-life cases, transfer to a cognitively normal population works best for a classifier trained with amyloid phases and using only voxels with the highest amplitudes of feature weights. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-022-05808-7.
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Affiliation(s)
- Mariska Reinartz
- Laboratory for Cognitive Neurology, KU Leuven, Leuven, Belgium.,Alzheimer Research Centre KU Leuven, Leuven Brain Institute, Leuven, Belgium
| | - Emma Susanne Luckett
- Laboratory for Cognitive Neurology, KU Leuven, Leuven, Belgium.,Alzheimer Research Centre KU Leuven, Leuven Brain Institute, Leuven, Belgium
| | - Jolien Schaeverbeke
- Laboratory for Cognitive Neurology, KU Leuven, Leuven, Belgium.,Alzheimer Research Centre KU Leuven, Leuven Brain Institute, Leuven, Belgium
| | - Steffi De Meyer
- Laboratory for Cognitive Neurology, KU Leuven, Leuven, Belgium.,Alzheimer Research Centre KU Leuven, Leuven Brain Institute, Leuven, Belgium
| | | | - Dietmar Rudolf Thal
- Department of Pathology, UZ Leuven, Leuven, Belgium.,Laboratory of Neuropathology, KU Leuven, Leuven, Belgium
| | - Koen Van Laere
- Division of Nuclear Medicine, UZ Leuven, Leuven, Belgium.,Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Patrick Dupont
- Laboratory for Cognitive Neurology, KU Leuven, Leuven, Belgium.,Alzheimer Research Centre KU Leuven, Leuven Brain Institute, Leuven, Belgium
| | - Rik Vandenberghe
- Laboratory for Cognitive Neurology, KU Leuven, Leuven, Belgium. .,Alzheimer Research Centre KU Leuven, Leuven Brain Institute, Leuven, Belgium. .,Neurology Department, University Hospitals Leuven, Herestraat 49, 3000, Leuven, Belgium.
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De Meyer S, Vanbrabant J, Schaeverbeke JM, Reinartz M, Luckett ES, Dupont P, Van Laere K, Stoops E, Vanmechelen E, Poesen K, Vandenberghe R. Phospho-specific plasma p-tau181 assay detects clinical as well as asymptomatic Alzheimer's disease. Ann Clin Transl Neurol 2022; 9:734-746. [PMID: 35502634 PMCID: PMC9082389 DOI: 10.1002/acn3.51553] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 03/17/2022] [Accepted: 03/18/2022] [Indexed: 11/29/2022] Open
Abstract
OBJECTIVE Plasma phosphorylated-tau-181 (p-tau181) reliably detects clinical Alzheimer's disease (AD) as well as asymptomatic amyloid-β (Aβ) pathology, but is consistently quantified with assays using antibody AT270, which cross-reacts with p-tau175. This study investigates two novel phospho-specific assays for plasma p-tau181 and p-tau231 in clinical and asymptomatic AD. METHODS Plasma p-tau species were quantified with Simoa in 44 AD patients, 40 spouse controls and an independent cohort of 151 cognitively unimpaired (CU) elderly who underwent Aβ-PET. Simoa plasma Aβ42 measurements were available in a CU subset (N = 69). Receiver operating characteristics and Aβ-PET associations were used to evaluate biomarker validity. RESULTS The novel plasma p-tau181 and p-tau231 assays did not show cross-reactivity. Plasma p-tau181 accurately detected clinical AD (area under the curve (AUC) = 0.98, 95% CI 0.95-1.00) as well as asymptomatic Aβ pathology (AUC = 0.84, 95% CI 0.76-0.92), while plasma p-tau231 did not (AUC = 0.74, 95% CI 0.63-0.85 and 0.61, 95% CI 0.52-0.71, respectively). Plasma p-tau181, but not p-tau231, detected asymptomatic Aβ pathology more accurately than age, sex and APOE combined (AUC = 0.64). In asymptomatic elderly, correlations between plasma p-tau181 and Aβ pathology were observed throughout the cerebral cortex (ρ = 0.40, p < 0.0001), with focal associations within AD-vulnerable regions, particularly the precuneus. The plasma Aβ42/p-tau181 ratio did not reflect asymptomatic Aβ pathology better than p-tau181 alone. INTERPRETATION The novel plasma p-tau181 assay is an accurate tool to detect clinical as well as asymptomatic AD and provides a phospho-specific alternative to currently employed immunoassays.
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Affiliation(s)
- Steffi De Meyer
- Laboratory for Cognitive Neurology, Department of NeurosciencesKU LeuvenLeuvenBelgium
- Laboratory for Molecular Neurobiomarker Research, Department of NeurosciencesKU LeuvenLeuvenBelgium
- Laboratory Medicine DepartmentUZ LeuvenLeuvenBelgium
- Alzheimer Research CentreLeuven Brain Institute (LBI), KU LeuvenLeuvenBelgium
| | | | - Jolien M. Schaeverbeke
- Laboratory for Cognitive Neurology, Department of NeurosciencesKU LeuvenLeuvenBelgium
- Alzheimer Research CentreLeuven Brain Institute (LBI), KU LeuvenLeuvenBelgium
| | - Mariska Reinartz
- Laboratory for Cognitive Neurology, Department of NeurosciencesKU LeuvenLeuvenBelgium
- Alzheimer Research CentreLeuven Brain Institute (LBI), KU LeuvenLeuvenBelgium
| | - Emma S. Luckett
- Laboratory for Cognitive Neurology, Department of NeurosciencesKU LeuvenLeuvenBelgium
- Alzheimer Research CentreLeuven Brain Institute (LBI), KU LeuvenLeuvenBelgium
| | - Patrick Dupont
- Laboratory for Cognitive Neurology, Department of NeurosciencesKU LeuvenLeuvenBelgium
- Alzheimer Research CentreLeuven Brain Institute (LBI), KU LeuvenLeuvenBelgium
| | - Koen Van Laere
- Alzheimer Research CentreLeuven Brain Institute (LBI), KU LeuvenLeuvenBelgium
- Nuclear Medicine and Molecular Imaging, Department of Imaging and PathologyKU LeuvenLeuvenBelgium
- Division of Nuclear MedicineUZ LeuvenLeuvenBelgium
| | | | | | - Koen Poesen
- Laboratory for Molecular Neurobiomarker Research, Department of NeurosciencesKU LeuvenLeuvenBelgium
- Laboratory Medicine DepartmentUZ LeuvenLeuvenBelgium
- Alzheimer Research CentreLeuven Brain Institute (LBI), KU LeuvenLeuvenBelgium
| | - Rik Vandenberghe
- Laboratory for Cognitive Neurology, Department of NeurosciencesKU LeuvenLeuvenBelgium
- Alzheimer Research CentreLeuven Brain Institute (LBI), KU LeuvenLeuvenBelgium
- Neurology DepartmentUZ LeuvenLeuvenBelgium
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Smith NM, Ford JN, Haghdel A, Glodzik L, Li Y, D’Angelo D, RoyChoudhury A, Wang X, Blennow K, de Leon MJ, Ivanidze J. Statistical Parametric Mapping in Amyloid Positron Emission Tomography. Front Aging Neurosci 2022; 14:849932. [PMID: 35547630 PMCID: PMC9083453 DOI: 10.3389/fnagi.2022.849932] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 03/21/2022] [Indexed: 12/03/2022] Open
Abstract
Alzheimer's disease (AD), the most common cause of dementia, has limited treatment options. Emerging disease modifying therapies are targeted at clearing amyloid-β (Aβ) aggregates and slowing the rate of amyloid deposition. However, amyloid burden is not routinely evaluated quantitatively for purposes of disease progression and treatment response assessment. Statistical Parametric Mapping (SPM) is a technique comparing single-subject Positron Emission Tomography (PET) to a healthy cohort that may improve quantification of amyloid burden and diagnostic performance. While primarily used in 2-[18F]-fluoro-2-deoxy-D-glucose (FDG)-PET, SPM's utility in amyloid PET for AD diagnosis is less established and uncertainty remains regarding optimal normal database construction. Using commercially available SPM software, we created a database of 34 non-APOE ε4 carriers with normal cognitive testing (MMSE > 25) and negative cerebrospinal fluid (CSF) AD biomarkers. We compared this database to 115 cognitively normal subjects with variable AD risk factors. We hypothesized that SPM based on our database would identify more positive scans in the test cohort than the qualitatively rated [11C]-PiB PET (QR-PiB), that SPM-based interpretation would correlate better with CSF Aβ42 levels than QR-PiB, and that regional z-scores of specific brain regions known to be involved early in AD would be predictive of CSF Aβ42 levels. Fisher's exact test and the kappa coefficient assessed the agreement between SPM, QR-PiB PET, and CSF biomarkers. Logistic regression determined if the regional z-scores predicted CSF Aβ42 levels. An optimal z-score cutoff was calculated using Youden's index. We found SPM identified more positive scans than QR-PiB PET (19.1 vs. 9.6%) and that SPM correlated more closely with CSF Aβ42 levels than QR-PiB PET (kappa 0.13 vs. 0.06) indicating that SPM may have higher sensitivity than standard QR-PiB PET images. Regional analysis demonstrated the z-scores of the precuneus, anterior cingulate and posterior cingulate were predictive of CSF Aβ42 levels [OR (95% CI) 2.4 (1.1, 5.1) p = 0.024; 1.8 (1.1, 2.8) p = 0.020; 1.6 (1.1, 2.5) p = 0.026]. This study demonstrates the utility of using SPM with a "true normal" database and suggests that SPM enhances diagnostic performance in AD in the clinical setting through its quantitative approach, which will be increasingly important with future disease-modifying therapies.
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Affiliation(s)
- Natasha M. Smith
- Department of Radiology and MD Program, Weill Cornell Medicine, New York City, NY, United States
| | - Jeremy N. Ford
- Department of Radiology, Weill Cornell Medicine, New York City, NY, United States
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
| | - Arsalan Haghdel
- Department of Radiology and MD Program, Weill Cornell Medicine, New York City, NY, United States
| | - Lidia Glodzik
- Department of Radiology, Weill Cornell Medicine, New York City, NY, United States
| | - Yi Li
- Department of Radiology, Weill Cornell Medicine, New York City, NY, United States
| | - Debra D’Angelo
- Department of Population Health Sciences, Weill Cornell Medicine, New York City, NY, United States
| | - Arindam RoyChoudhury
- Department of Population Health Sciences, Weill Cornell Medicine, New York City, NY, United States
| | - Xiuyuan Wang
- Department of Radiology, Weill Cornell Medicine, New York City, NY, United States
| | - Kaj Blennow
- Department of Neuroscience and Physiology, University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Mony J. de Leon
- Department of Radiology, Weill Cornell Medicine, New York City, NY, United States
| | - Jana Ivanidze
- Department of Radiology, Weill Cornell Medicine, New York City, NY, United States
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9
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Kagerer SM, Schroeder C, van Bergen JMG, Schreiner SJ, Meyer R, Steininger SC, Vionnet L, Gietl AF, Treyer V, Buck A, Pruessmann KP, Hock C, Unschuld PG. Low Subicular Volume as an Indicator of Dementia-Risk Susceptibility in Old Age. Front Aging Neurosci 2022; 14:811146. [PMID: 35309894 PMCID: PMC8926841 DOI: 10.3389/fnagi.2022.811146] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 01/31/2022] [Indexed: 11/13/2022] Open
Abstract
Introduction Hippocampal atrophy is an established Alzheimer’s Disease (AD) biomarker. Volume loss in specific subregions as measurable with ultra-high field magnetic resonance imaging (MRI) may reflect earliest pathological alterations. Methods Data from positron emission tomography (PET) for estimation of cortical amyloid β (Aβ) and high-resolution 7 Tesla T1 MRI for assessment of hippocampal subfield volumes were analyzed in 61 non-demented elderly individuals who were divided into risk-categories as defined by high levels of cortical Aβ and low performance in standardized episodic memory tasks. Results High cortical Aβ and low episodic memory interactively predicted subicular volume [F(3,57) = 5.90, p = 0.018]. The combination of high cortical Aβ and low episodic memory was associated with significantly lower subicular volumes, when compared to participants with high episodic memory (p = 0.004). Discussion Our results suggest that low subicular volume is linked to established indicators of AD risk, such as increased cortical Aβ and low episodic memory. Our data support subicular volume as a marker of dementia-risk susceptibility in old-aged non-demented persons.
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Affiliation(s)
- Sonja M. Kagerer
- Institute for Regenerative Medicine, University of Zurich, Zurich, Switzerland
- Psychogeriatric Medicine, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Clemens Schroeder
- Institute for Regenerative Medicine, University of Zurich, Zurich, Switzerland
| | | | - Simon J. Schreiner
- Institute for Regenerative Medicine, University of Zurich, Zurich, Switzerland
| | - Rafael Meyer
- Institute for Regenerative Medicine, University of Zurich, Zurich, Switzerland
| | - Stefanie C. Steininger
- Psychogeriatric Medicine, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Laetitia Vionnet
- Institute for Biomedical Engineering, University of Zurich and ETH Zürich, Zurich, Switzerland
| | - Anton F. Gietl
- Institute for Regenerative Medicine, University of Zurich, Zurich, Switzerland
- Psychogeriatric Medicine, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Valerie Treyer
- Institute for Regenerative Medicine, University of Zurich, Zurich, Switzerland
- Department of Nuclear Medicine, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Alfred Buck
- Department of Nuclear Medicine, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Klaas P. Pruessmann
- Institute for Biomedical Engineering, University of Zurich and ETH Zürich, Zurich, Switzerland
| | - Christoph Hock
- Institute for Regenerative Medicine, University of Zurich, Zurich, Switzerland
- Neurimmune, Schlieren, Switzerland
| | - Paul G. Unschuld
- Institute for Regenerative Medicine, University of Zurich, Zurich, Switzerland
- Psychogeriatric Medicine, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Institute for Biomedical Engineering, University of Zurich and ETH Zürich, Zurich, Switzerland
- Geriatric Psychiatry, Department of Psychiatry, University Hospitals of Geneva, University of Geneva, Geneva, Switzerland
- *Correspondence: Paul G. Unschuld,
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10
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Emsell L, Vanhaute H, Vansteelandt K, De Winter FL, Christiaens D, Van den Stock J, Vandenberghe R, Van Laere K, Sunaert S, Bouckaert F, Vandenbulcke M. An optimized MRI and PET based clinical protocol for improving the differential diagnosis of geriatric depression and Alzheimer's disease. Psychiatry Res Neuroimaging 2022; 320:111443. [PMID: 35091333 DOI: 10.1016/j.pscychresns.2022.111443] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 09/28/2021] [Accepted: 01/12/2022] [Indexed: 11/16/2022]
Abstract
Amyloid positron emission tomography (PET) and hippocampal volume derived from magnetic resonance imaging may be useful clinical biomarkers for differentiating between geriatric depression and Alzheimer's disease (AD). Here we investigated the incremental value of using hippocampal volume and 18F-flutemetmol amyloid PET measures in tandem and sequentially to improve discrimination in unclassified participants. Two approaches were compared in 41 participants with geriatric depression and 27 participants with probable AD: (1) amyloid and hippocampal volume combined in one model and (2) classification based on hippocampal volume first and then subsequent stratification using standardized uptake value ratio (SUVR)-determined amyloid positivity. Hippocampal volume and amyloid SUVR were significant diagnostic predictors of depression (sensitivity: 95%, specificity: 89%). 51% of participants were correctly classified according to clinical diagnosis based on hippocampal volume alone, increasing to 87% when adding amyloid data (sensitivity: 94%, specificity: 78%). Our results suggest that hippocampal volume may be a useful gatekeeper for identifying depressed individuals at risk for AD who would benefit from additional amyloid biomarkers when available.
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Affiliation(s)
- Louise Emsell
- Geriatric Psychiatry, UPC KU Leuven, Leuven, Belgium; Department of Neurosciences, Neuropsychiatry, KU Leuven, Leuven, Belgium; Department of Imaging & Pathology, Translational MRI, Medical Imaging Research Center, KU Leuven, UZ Leuven (Gasthuisberg), Leuven 3000, Belgium.
| | - Heleen Vanhaute
- Geriatric Psychiatry, UPC KU Leuven, Leuven, Belgium; Department of Imaging & Pathology, Nuclear Medicine and Molecular Imaging, KU Leuven, Leuven, Belgium; Nuclear Medicine, University Hospitals Leuven, Herestraat 49, Leuven 3000, Belgium
| | - Kristof Vansteelandt
- Geriatric Psychiatry, UPC KU Leuven, Leuven, Belgium; Department of Neurosciences, Neuropsychiatry, KU Leuven, Leuven, Belgium.
| | - François-Laurent De Winter
- Geriatric Psychiatry, UPC KU Leuven, Leuven, Belgium; Department of Neurosciences, Neuropsychiatry, KU Leuven, Leuven, Belgium.
| | - Danny Christiaens
- Department of Neurosciences, Neuropsychiatry, KU Leuven, Leuven, Belgium
| | - Jan Van den Stock
- Geriatric Psychiatry, UPC KU Leuven, Leuven, Belgium; Department of Neurosciences, Neuropsychiatry, KU Leuven, Leuven, Belgium.
| | - Rik Vandenberghe
- Department of Neurosciences, Laboratory for Cognitive Neurology, KU Leuven, Leuven, Belgium.
| | - Koen Van Laere
- Department of Imaging & Pathology, Nuclear Medicine and Molecular Imaging, KU Leuven, Leuven, Belgium; Nuclear Medicine, University Hospitals Leuven, Herestraat 49, Leuven 3000, Belgium.
| | - Stefan Sunaert
- Department of Imaging & Pathology, Translational MRI, Medical Imaging Research Center, KU Leuven, UZ Leuven (Gasthuisberg), Leuven 3000, Belgium; Department of Radiology, University Hospitals Leuven, Herestraat 49, Leuven 3000, Belgium.
| | - Filip Bouckaert
- Geriatric Psychiatry, UPC KU Leuven, Leuven, Belgium; Department of Neurosciences, Neuropsychiatry, KU Leuven, Leuven, Belgium.
| | - Mathieu Vandenbulcke
- Geriatric Psychiatry, UPC KU Leuven, Leuven, Belgium; Department of Neurosciences, Neuropsychiatry, KU Leuven, Leuven, Belgium.
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11
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Reinartz M, Gabel S, Schaeverbeke J, Meersmans K, Adamczuk K, Luckett ES, De Meyer S, Van Laere K, Sunaert S, Dupont P, Vandenberghe R. Changes in the language system as amyloid-β accumulates. Brain 2021; 144:3756-3768. [PMID: 34534284 PMCID: PMC8719839 DOI: 10.1093/brain/awab335] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 07/08/2021] [Accepted: 08/12/2021] [Indexed: 11/17/2022] Open
Abstract
Language dysfunction is common in Alzheimer's disease. There is increasing interest in the preclinical or asymptomatic phase of Alzheimer's disease. Here we examined in 35 cognitively intact older adults (age range 52-78 years at baseline, 17 male) in a longitudinal study design the association between accumulation of amyloid over a 5-6-year period, measured using PET, and functional changes in the language network measured over the same time period using task-related functional MRI. In the same participants, we also determined the association between the longitudinal functional MRI changes and a cross-sectional measure of tau load as measured with 18F-AV1451 PET. As predicted, the principal change occurred in posterior temporal cortex. In the cortex surrounding the right superior temporal sulcus, the response amplitude during the associative-semantic versus visuo-perceptual task increased over time as amyloid load accumulated (Pcorrected = 0.008). In a whole-brain voxel-wise analysis, amyloid accumulation was also associated with a decrease in response amplitude in the left inferior frontal sulcus (Pcorrected = 0.009) and the right dorsomedial prefrontal cortex (Pcorrected = 0.005). In cognitively intact older adults, cross-sectional tau load was not associated with longitudinal changes in functional MRI response amplitude. Our findings confirm the central role of the neocortex surrounding the posterior superior temporal sulcus as the area of predilection within the language network in the earliest stages of Alzheimer's disease. Amyloid accumulation has an impact on cognitive brain circuitry in the asymptomatic phase of Alzheimer's disease.
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Affiliation(s)
- Mariska Reinartz
- Laboratory for Cognitive Neurology, Department of Neurosciences, Leuven Brain Institute, KU Leuven, 3000 Leuven, Belgium
| | - Silvy Gabel
- Laboratory for Cognitive Neurology, Department of Neurosciences, Leuven Brain Institute, KU Leuven, 3000 Leuven, Belgium
| | - Jolien Schaeverbeke
- Laboratory for Cognitive Neurology, Department of Neurosciences, Leuven Brain Institute, KU Leuven, 3000 Leuven, Belgium
| | - Karen Meersmans
- Laboratory for Cognitive Neurology, Department of Neurosciences, Leuven Brain Institute, KU Leuven, 3000 Leuven, Belgium
| | | | - Emma Susanne Luckett
- Laboratory for Cognitive Neurology, Department of Neurosciences, Leuven Brain Institute, KU Leuven, 3000 Leuven, Belgium
| | - Steffi De Meyer
- Laboratory for Cognitive Neurology, Department of Neurosciences, Leuven Brain Institute, KU Leuven, 3000 Leuven, Belgium
| | - Koen Van Laere
- Division of Nuclear Medicine, UZ Leuven, 3000 Leuven, Belgium
- Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, KU Leuven, 3000 Leuven, Belgium
| | | | - Patrick Dupont
- Laboratory for Cognitive Neurology, Department of Neurosciences, Leuven Brain Institute, KU Leuven, 3000 Leuven, Belgium
| | - Rik Vandenberghe
- Laboratory for Cognitive Neurology, Department of Neurosciences, Leuven Brain Institute, KU Leuven, 3000 Leuven, Belgium
- Alzheimer Research Centre KU Leuven, Leuven Brain Institute, 3000 Leuven, Belgium
- Neurology Department, University Hospitals Leuven, 3000 Leuven, Belgium
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12
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Xu J, Green R, Kim M, Lord J, Ebshiana A, Westwood S, Baird AL, Nevado-Holgado AJ, Shi L, Hye A, Snowden SG, Bos I, Vos SJB, Vandenberghe R, Teunissen CE, Kate MT, Scheltens P, Gabel S, Meersmans K, Blin O, Richardson J, De Roeck EE, Engelborghs S, Sleegers K, Bordet R, Rami L, Kettunen P, Tsolaki M, Verhey FRJ, Alcolea D, Lleó A, Peyratout G, Tainta M, Johannsen P, Freund-Levi Y, Frölich L, Dobricic V, Frisoni GB, Molinuevo JL, Wallin A, Popp J, Martinez-Lage P, Bertram L, Blennow K, Zetterberg H, Streffer J, Visser PJ, Lovestone S, Proitsi P, Legido-Quigley C. Sex-Specific Metabolic Pathways Were Associated with Alzheimer's Disease (AD) Endophenotypes in the European Medical Information Framework for AD Multimodal Biomarker Discovery Cohort. Biomedicines 2021; 9:1610. [PMID: 34829839 PMCID: PMC8615383 DOI: 10.3390/biomedicines9111610] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 10/22/2021] [Accepted: 10/25/2021] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND physiological differences between males and females could contribute to the development of Alzheimer's Disease (AD). Here, we examined metabolic pathways that may lead to precision medicine initiatives. METHODS We explored whether sex modifies the association of 540 plasma metabolites with AD endophenotypes including diagnosis, cerebrospinal fluid (CSF) biomarkers, brain imaging, and cognition using regression analyses for 695 participants (377 females), followed by sex-specific pathway overrepresentation analyses, APOE ε4 stratification and assessment of metabolites' discriminatory performance in AD. RESULTS In females with AD, vanillylmandelate (tyrosine pathway) was increased and tryptophan betaine (tryptophan pathway) was decreased. The inclusion of these two metabolites (area under curve (AUC) = 0.83, standard error (SE) = 0.029) to a baseline model (covariates + CSF biomarkers, AUC = 0.92, SE = 0.019) resulted in a significantly higher AUC of 0.96 (SE = 0.012). Kynurenate was decreased in males with AD (AUC = 0.679, SE = 0.046). CONCLUSIONS metabolic sex-specific differences were reported, covering neurotransmission and inflammation pathways with AD endophenotypes. Two metabolites, in pathways related to dopamine and serotonin, were associated to females, paving the way to personalised treatment.
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Affiliation(s)
- Jin Xu
- Institute of Pharmaceutical Science, King’s College London, London SE1 9NH, UK; (J.X.); (A.E.); (S.G.S.)
- Institute of Psychiatry, Psychology and Neuroscience, Maurice Wohl Clinical Neuroscience Institute, King’s College London, London SE5 9RT, UK; (R.G.); (J.L.); (A.H.); (S.L.)
| | - Rebecca Green
- Institute of Psychiatry, Psychology and Neuroscience, Maurice Wohl Clinical Neuroscience Institute, King’s College London, London SE5 9RT, UK; (R.G.); (J.L.); (A.H.); (S.L.)
- NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Trust, London SE5 8AF, UK
| | - Min Kim
- Steno Diabetes Center, 2820 Gentofte, Denmark;
| | - Jodie Lord
- Institute of Psychiatry, Psychology and Neuroscience, Maurice Wohl Clinical Neuroscience Institute, King’s College London, London SE5 9RT, UK; (R.G.); (J.L.); (A.H.); (S.L.)
| | - Amera Ebshiana
- Institute of Pharmaceutical Science, King’s College London, London SE1 9NH, UK; (J.X.); (A.E.); (S.G.S.)
| | - Sarah Westwood
- Department of Psychiatry, University of Oxford, Oxford OX3 7JX, UK; (S.W.); (A.L.B.); (A.J.N.-H.); (L.S.)
| | - Alison L. Baird
- Department of Psychiatry, University of Oxford, Oxford OX3 7JX, UK; (S.W.); (A.L.B.); (A.J.N.-H.); (L.S.)
| | - Alejo J. Nevado-Holgado
- Department of Psychiatry, University of Oxford, Oxford OX3 7JX, UK; (S.W.); (A.L.B.); (A.J.N.-H.); (L.S.)
| | - Liu Shi
- Department of Psychiatry, University of Oxford, Oxford OX3 7JX, UK; (S.W.); (A.L.B.); (A.J.N.-H.); (L.S.)
| | - Abdul Hye
- Institute of Psychiatry, Psychology and Neuroscience, Maurice Wohl Clinical Neuroscience Institute, King’s College London, London SE5 9RT, UK; (R.G.); (J.L.); (A.H.); (S.L.)
| | - Stuart G. Snowden
- Institute of Pharmaceutical Science, King’s College London, London SE1 9NH, UK; (J.X.); (A.E.); (S.G.S.)
| | - Isabelle Bos
- Alzheimer Center, VU University Medical Center, 1081 HV Amsterdam, The Netherlands; (I.B.); (R.V.); (M.T.K.); (P.S.); (P.J.V.)
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Alzheimer Centrum Limburg, Maastricht University, 6211 LK Maastricht, The Netherlands; (S.J.B.V.); (F.R.J.V.)
| | - Stephanie J. B. Vos
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Alzheimer Centrum Limburg, Maastricht University, 6211 LK Maastricht, The Netherlands; (S.J.B.V.); (F.R.J.V.)
| | - Rik Vandenberghe
- Alzheimer Center, VU University Medical Center, 1081 HV Amsterdam, The Netherlands; (I.B.); (R.V.); (M.T.K.); (P.S.); (P.J.V.)
| | - Charlotte E. Teunissen
- Department of Radiology and Nuclear Medicine, VU University Medical Center, 1081 HV Amsterdam, The Netherlands;
| | - Mara Ten Kate
- Alzheimer Center, VU University Medical Center, 1081 HV Amsterdam, The Netherlands; (I.B.); (R.V.); (M.T.K.); (P.S.); (P.J.V.)
- Department of Radiology and Nuclear Medicine, VU University Medical Center, 1081 HV Amsterdam, The Netherlands;
| | - Philip Scheltens
- Alzheimer Center, VU University Medical Center, 1081 HV Amsterdam, The Netherlands; (I.B.); (R.V.); (M.T.K.); (P.S.); (P.J.V.)
| | - Silvy Gabel
- Department of Clinical Chemistry, Neurochemistry Laboratory, Amsterdam Neuroscience, Amsterdam University Medical Centers, Vrije Universiteit, 1081 HV Amsterdam, The Netherlands;
- Department of Neurosciences, Laboratory for Cognitive Neurology, KU Leuven, 3000 Leuven, Belgium;
- University Hospital Leuven, 3000 Leuven, Belgium
| | - Karen Meersmans
- Department of Neurosciences, Laboratory for Cognitive Neurology, KU Leuven, 3000 Leuven, Belgium;
- University Hospital Leuven, 3000 Leuven, Belgium
| | - Olivier Blin
- Clinical Pharmacology & Pharmacovigilance Department, Aix-Marseille University-CNRS, 13007 Marseille, France;
| | - Jill Richardson
- Neurosciences Therapeutic Area, GlaxoSmithKline R&D, Stevenage SG1 2NY, UK;
| | - Ellen Elisa De Roeck
- Center for Neurosciences, Vrije Universiteit Brussel, 1050 Brussels, Belgium;
- Reference Center for Biological Markers of Dementia (BIODEM), Institute Born-Bunge, University of Antwerp, 2000 Antwerp, Belgium; (S.E.); (J.S.)
| | - Sebastiaan Engelborghs
- Reference Center for Biological Markers of Dementia (BIODEM), Institute Born-Bunge, University of Antwerp, 2000 Antwerp, Belgium; (S.E.); (J.S.)
- Department of Neurology and Center for Neurosciences (C4N), UZ Brussel and Vrije Universiteit Brussel (VUB), 1050 Brussels, Belgium
| | - Kristel Sleegers
- Department of Neurology and Memory Clinic, Hospital Network Antwerp (ZNA) Middelheim and Hoge Beuken, University of Antwerp, 2000 Antwerp, Belgium;
- Complex Genetics of Alzheimer’s Disease Group, VIB Center for Molecular Neurology, VIB, 2000 Antwerp, Belgium
| | - Régis Bordet
- Department of Medical Pharmacology, Université de Lille, 59000 Lille, France;
| | - Lorena Rami
- Alzheimer’s Disease and Other Cognitive Disorders Unit, Hospital Clínic of Barcelona, August Pi Sunyer Biomedical Research Institute (IDIBAPS), 08036 Barcelona, Spain; (L.R.); (J.L.M.)
| | - Petronella Kettunen
- Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, 405 30 Gothenburg, Sweden; (P.K.); (A.W.)
| | - Magda Tsolaki
- 1st Department of Neurology, AHEPA University Hospital, 546 21 Thessaloniki, Greece;
| | - Frans R. J. Verhey
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Alzheimer Centrum Limburg, Maastricht University, 6211 LK Maastricht, The Netherlands; (S.J.B.V.); (F.R.J.V.)
| | - Daniel Alcolea
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, 08041 Barcelona, Spain; (D.A.); (A.L.)
| | - Alberto Lleó
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, 08041 Barcelona, Spain; (D.A.); (A.L.)
| | | | - Mikel Tainta
- Fundación CITA-Alzhéimer Fundazioa, 20009 San Sebastian, Spain;
| | - Peter Johannsen
- Danish Dementia Research Centre, Rigshospitalet, 2100 Copenhagen, Denmark;
| | - Yvonne Freund-Levi
- Department of Neurobiology, Caring Sciences and Society (NVS), Division of Clinical Geriatrics, Karolinska Institutet, Karolinska University Hospital Huddinge, 141 86 Stockholm, Sweden;
- Department of Old Age Psychiatry, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London SE5 8AF, UK
| | - Lutz Frölich
- Department of Geriatric Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, 68167 Mannheim, Germany;
| | - Valerija Dobricic
- Lübeck Interdisciplinary Platform for Genome Analytics, Institutes of Neurogenetics and Cardiogenetics, University of Lübeck, 23562 Lübeck, Germany; (V.D.); (L.B.)
| | - Giovanni B. Frisoni
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, 1205 Geneva, Switzerland;
- IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, 25125 Brescia, Italy
| | - José Luis Molinuevo
- Alzheimer’s Disease and Other Cognitive Disorders Unit, Hospital Clínic of Barcelona, August Pi Sunyer Biomedical Research Institute (IDIBAPS), 08036 Barcelona, Spain; (L.R.); (J.L.M.)
- Barcelona Beta Brain Research Center, Unversitat Pompeu Fabra, 08002 Barcelona, Spain
| | - Anders Wallin
- Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, 405 30 Gothenburg, Sweden; (P.K.); (A.W.)
| | - Julius Popp
- Old Age Psychiatry, Department of Psychiatry, University Hospital Lausanne, 1011 Lausanne, Switzerland;
- Department of Geriatric Psychiatry, University Hospital of Psychiatry Zürich, 8008 Zürich, Switzerland
| | - Pablo Martinez-Lage
- Center for Research and Advanced Therapies, CITA-Alzheimer Foundation, 20009 San Sebastian, Spain;
| | - Lars Bertram
- Lübeck Interdisciplinary Platform for Genome Analytics, Institutes of Neurogenetics and Cardiogenetics, University of Lübeck, 23562 Lübeck, Germany; (V.D.); (L.B.)
- Department of Psychology, University of Oslo, 0315 Oslo, Norway
| | - Kaj Blennow
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, 413 45 Mölndal, Sweden; (K.B.); (H.Z.)
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, 415 45 Mölndal, Sweden
| | - Henrik Zetterberg
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, 413 45 Mölndal, Sweden; (K.B.); (H.Z.)
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, 415 45 Mölndal, Sweden
- UK Dementia Research Institute at UCL, London WC1E 6BT, UK
- Department of Neurodegenerative Disease, UCL Institute of Neurology, London WC1N 3BG, UK
| | - Johannes Streffer
- Reference Center for Biological Markers of Dementia (BIODEM), Institute Born-Bunge, University of Antwerp, 2000 Antwerp, Belgium; (S.E.); (J.S.)
| | - Pieter Jelle Visser
- Alzheimer Center, VU University Medical Center, 1081 HV Amsterdam, The Netherlands; (I.B.); (R.V.); (M.T.K.); (P.S.); (P.J.V.)
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Alzheimer Centrum Limburg, Maastricht University, 6211 LK Maastricht, The Netherlands; (S.J.B.V.); (F.R.J.V.)
| | - Simon Lovestone
- Institute of Psychiatry, Psychology and Neuroscience, Maurice Wohl Clinical Neuroscience Institute, King’s College London, London SE5 9RT, UK; (R.G.); (J.L.); (A.H.); (S.L.)
- Department of Psychiatry, University of Oxford, Oxford OX3 7JX, UK; (S.W.); (A.L.B.); (A.J.N.-H.); (L.S.)
- Janssen-Cilag UK Ltd., Oxford HP12 4EG, UK
| | - Petroula Proitsi
- Institute of Psychiatry, Psychology and Neuroscience, Maurice Wohl Clinical Neuroscience Institute, King’s College London, London SE5 9RT, UK; (R.G.); (J.L.); (A.H.); (S.L.)
| | - Cristina Legido-Quigley
- Institute of Pharmaceutical Science, King’s College London, London SE1 9NH, UK; (J.X.); (A.E.); (S.G.S.)
- Steno Diabetes Center, 2820 Gentofte, Denmark;
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Pegueroles J, Montal V, Bejanin A, Vilaplana E, Aranha M, Santos‐Santos MA, Alcolea D, Carrió I, Camacho V, Blesa R, Lleó A, Fortea J. AMYQ: An index to standardize quantitative amyloid load across PET tracers. Alzheimers Dement 2021; 17:1499-1508. [PMID: 33797846 PMCID: PMC8519100 DOI: 10.1002/alz.12317] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 01/21/2021] [Accepted: 01/31/2021] [Indexed: 12/17/2022]
Abstract
INTRODUCTION Positron emission tomography (PET) amyloid quantification methods require magnetic resonance imaging (MRI) for spatial registration and a priori reference region to scale the images. Furthermore, different tracers have distinct thresholds for positivity. We propose the AMYQ index, a new measure of amyloid burden, to overcome these limitations. METHODS We selected 18F-amyloid scans from ADNI and Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL) with the corresponding T1-MRI. A subset also had neuropathological data. PET images were normalized, and the AMYQ was calculated based on an adaptive template. We compared AMYQ with the Centiloid scale on clinical and neuropathological diagnostic performance. RESULTS AMYQ was related with amyloid neuropathological burden and had excellent diagnostic performance to discriminate controls from patients with Alzheimer's disease (AD) (area under the curve [AUC] = 0.86). AMYQ had a high agreement with the Centiloid scale (intraclass correlation coefficient [ICC] = 0.88) and AUC between 0.94 and 0.99 to discriminate PET positivity when using different Centiloid cutoffs. DISCUSSION AMYQ is a new MRI-independent index for standardizing and quantifying amyloid load across tracers.
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Affiliation(s)
- Jordi Pegueroles
- Sant Pau Memory Unit, Department of NeurologyHospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de BarcelonaBarcelonaSpain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED)MadridSpain
| | - Victor Montal
- Sant Pau Memory Unit, Department of NeurologyHospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de BarcelonaBarcelonaSpain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED)MadridSpain
| | - Alexandre Bejanin
- Sant Pau Memory Unit, Department of NeurologyHospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de BarcelonaBarcelonaSpain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED)MadridSpain
| | - Eduard Vilaplana
- Sant Pau Memory Unit, Department of NeurologyHospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de BarcelonaBarcelonaSpain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED)MadridSpain
| | - Mateus Aranha
- Sant Pau Memory Unit, Department of NeurologyHospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de BarcelonaBarcelonaSpain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED)MadridSpain
| | - Miguel Angel Santos‐Santos
- Sant Pau Memory Unit, Department of NeurologyHospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de BarcelonaBarcelonaSpain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED)MadridSpain
| | - Daniel Alcolea
- Sant Pau Memory Unit, Department of NeurologyHospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de BarcelonaBarcelonaSpain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED)MadridSpain
| | - Ignasi Carrió
- Department of Nuclear MedicineHospital de la Santa Creu i Sant Pau, Universitat Autònoma de BarcelonaBarcelonaSpain
| | - Valle Camacho
- Department of Nuclear MedicineHospital de la Santa Creu i Sant Pau, Universitat Autònoma de BarcelonaBarcelonaSpain
| | - Rafael Blesa
- Sant Pau Memory Unit, Department of NeurologyHospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de BarcelonaBarcelonaSpain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED)MadridSpain
| | - Alberto Lleó
- Sant Pau Memory Unit, Department of NeurologyHospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de BarcelonaBarcelonaSpain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED)MadridSpain
| | - Juan Fortea
- Sant Pau Memory Unit, Department of NeurologyHospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de BarcelonaBarcelonaSpain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED)MadridSpain
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Schaeverbeke JM, Gabel S, Meersmans K, Luckett ES, De Meyer S, Adamczuk K, Nelissen N, Goovaerts V, Radwan A, Sunaert S, Dupont P, Van Laere K, Vandenberghe R. Baseline cognition is the best predictor of 4-year cognitive change in cognitively intact older adults. Alzheimers Res Ther 2021; 13:75. [PMID: 33827690 PMCID: PMC8028179 DOI: 10.1186/s13195-021-00798-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 02/22/2021] [Indexed: 11/25/2022]
Abstract
BACKGROUND We examined in cognitively intact older adults the relative weight of cognitive, genetic, structural and amyloid brain imaging variables for predicting cognitive change over a 4-year time course. METHODS One hundred-eighty community-recruited cognitively intact older adults (mean age 68 years, range 52-80 years, 81 women) belonging to the Flemish Prevent Alzheimer's Disease Cohort KU Leuven (F-PACK) longitudinal observational cohort underwent a baseline evaluation consisting of detailed cognitive assessment, structural MRI and 18F-flutemetamol PET. At inclusion, subjects were stratified based on Apolipoprotein E (APOE) ε4 and Brain-Derived Neurotrophic Factor (BDNF) val66met polymorphism according to a factorial design. At inclusion, 15% were amyloid-PET positive (Centiloid >23.4). All subjects underwent 2-yearly follow-up of cognitive performance for a 4-year time period. Baseline cognitive scores were analysed using factor analysis. The slope of cognitive change over time was modelled using latent growth curve analysis. Using correlation analysis, hierarchical regression and mediation analysis, we examined the effect of demographic (age, sex, education) and genetic variables, baseline cognition, MRI volumetric (both voxelwise and region-based) as well as amyloid imaging measures on the longitudinal slope of cognitive change. RESULTS A base model of age and sex explained 18.5% of variance in episodic memory decline. This increased to 41.6% by adding baseline episodic memory scores. Adding amyloid load or volumetric measures explained only a negligible additional amount of variance (increase to 42.2%). A mediation analysis indicated that the effect of age on episodic memory scores was partly direct and partly mediated via hippocampal volume. Amyloid load did not play a significant role as mediator between age, hippocampal volume and episodic memory decline. CONCLUSION In cognitively intact older adults, the strongest baseline predictor of subsequent episodic memory decline was the baseline episodic memory score. When this score was included, only very limited explanatory power was added by brain volume or amyloid load measures. The data warn against classifications that are purely biomarker-based and highlight the value of baseline cognitive performance levels in predictive models.
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Affiliation(s)
- Jolien M Schaeverbeke
- Laboratory for Cognitive Neurology, Department of Neurosciences, Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - Silvy Gabel
- Laboratory for Cognitive Neurology, Department of Neurosciences, Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - Karen Meersmans
- Laboratory for Cognitive Neurology, Department of Neurosciences, Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - Emma S Luckett
- Laboratory for Cognitive Neurology, Department of Neurosciences, Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - Steffi De Meyer
- Laboratory for Cognitive Neurology, Department of Neurosciences, Leuven Brain Institute, KU Leuven, Leuven, Belgium
- Laboratory of Molecular Neurobiomarker Research, KU Leuven, Leuven, Belgium
| | - Katarzyna Adamczuk
- Laboratory for Cognitive Neurology, Department of Neurosciences, Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - Natalie Nelissen
- Laboratory for Cognitive Neurology, Department of Neurosciences, Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - Valerie Goovaerts
- Neurology Department, University Hospitals Leuven, Herestraat 49, Leuven, 3000, Belgium
| | - Ahmed Radwan
- Translational MRI, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Stefan Sunaert
- Translational MRI, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Patrick Dupont
- Laboratory for Cognitive Neurology, Department of Neurosciences, Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - Koen Van Laere
- Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, KU Leuven and Division of Nuclear Medicine, University Hospitals Leuven, Leuven, Belgium
| | - Rik Vandenberghe
- Laboratory for Cognitive Neurology, Department of Neurosciences, Leuven Brain Institute, KU Leuven, Leuven, Belgium.
- Neurology Department, University Hospitals Leuven, Herestraat 49, Leuven, 3000, Belgium.
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15
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Abstract
Since many years, magnetic resonance imaging (MRI) and positron emission tomography (PET) have a prominent role in neurodegenerative disorders and dementia, not only in a research setting but also in a clinical setting. For several decades, information from both modalities is combined ranging from individual visual assessments to fully integrating all images. Several tools are available to coregister images from MRI and PET and to covisualize these images. When studying neurodegenerative disorders with PET it is important to perform a partial volume correction and this can be done using the structural information obtained by MRI. With the advent of PET/MR, the question arises in how far this hybrid imaging modality is an added value compared to combining PET and MRI data from two separate modalities. One issue in PET/MR is still not yet completely settled, that is, the attenuation correction. This is of less importance for visual assessments but it can become an issue when combining data from PET/CT and PET/MR scanners in multicenter studies or when using cut-off values to classify patients. Simultaneous imaging has clearly some advantages: for the patient it is beneficial to have only one scan session instead of two but also in cases in which PET data are related to functional of physiological data acquired with MRI (such as functional MRI or arterial spin labeling). However, the most important benefit is currently the more integrated use of PET and MRI. This is also possible with separate measurements but requires more streamlining of the whole process. In that case coregistration of images is mandatory. It needs to be determined in which cases simultaneous PET/MRI leads to new insights or improved diagnosis compared to multimodal imaging using dedicated scanners.
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Affiliation(s)
- Patrick Dupont
- KU Leuven, Leuven Brain Institute, Department of Neurosciences, Laboratory for Cognitive Neurology, Leuven, Belgium; University of Stellenbosch, Department of Nuclear Medicine, Cape Town, South Africa.
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16
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De Meyer S, Schaeverbeke JM, Verberk IMW, Gille B, De Schaepdryver M, Luckett ES, Gabel S, Bruffaerts R, Mauroo K, Thijssen EH, Stoops E, Vanderstichele HM, Teunissen CE, Vandenberghe R, Poesen K. Comparison of ELISA- and SIMOA-based quantification of plasma Aβ ratios for early detection of cerebral amyloidosis. Alzheimers Res Ther 2020; 12:162. [PMID: 33278904 PMCID: PMC7719262 DOI: 10.1186/s13195-020-00728-w] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Accepted: 11/17/2020] [Indexed: 01/25/2023]
Abstract
BACKGROUND Blood-based amyloid biomarkers may provide a non-invasive, cost-effective and scalable manner for detecting cerebral amyloidosis in early disease stages. METHODS In this prospective cross-sectional study, we quantified plasma Aβ1-42/Aβ1-40 ratios with both routinely available ELISAs and novel SIMOA Amyblood assays, and provided a head-to-head comparison of their performances to detect cerebral amyloidosis in a nondemented elderly cohort (n = 199). Participants were stratified according to amyloid-PET status, and the performance of plasma Aβ1-42/Aβ1-40 to detect cerebral amyloidosis was assessed using receiver operating characteristic analysis. We additionally investigated the correlations of plasma Aβ ratios with amyloid-PET and CSF Alzheimer's disease biomarkers, as well as platform agreement using Passing-Bablok regression and Bland-Altman analysis for both Aβ isoforms. RESULTS ELISA and SIMOA plasma Aβ1-42/Aβ1-40 detected cerebral amyloidosis with identical accuracy (ELISA: area under curve (AUC) 0.78, 95% CI 0.72-0.84; SIMOA: AUC 0.79, 95% CI 0.73-0.85), and both increased the performance of a basic demographic model including only age and APOE-ε4 genotype (p ≤ 0.02). ELISA and SIMOA had positive predictive values of respectively 41% and 36% in cognitively normal elderly and negative predictive values all exceeding 88%. Plasma Aβ1-42/Aβ1-40 correlated similarly with amyloid-PET for both platforms (Spearman ρ = - 0.32, p < 0.0001), yet correlations with CSF Aβ1-42/t-tau were stronger for ELISA (ρ = 0.41, p = 0.002) than for SIMOA (ρ = 0.29, p = 0.03). Plasma Aβ levels demonstrated poor agreement between ELISA and SIMOA with concentrations of both Aβ1-42 and Aβ1-40 measured by SIMOA consistently underestimating those measured by ELISA. CONCLUSIONS ELISA and SIMOA demonstrated equivalent performances in detecting cerebral amyloidosis through plasma Aβ1-42/Aβ1-40, both with high negative predictive values, making them equally suitable non-invasive prescreening tools for clinical trials by reducing the number of necessary PET scans for clinical trial recruitment. TRIAL REGISTRATION EudraCT 2009-014475-45 (registered on 23 Sept 2009) and EudraCT 2013-004671-12 (registered on 20 May 2014, https://www.clinicaltrialsregister.eu/ctr-search/trial/2013-004671-12/BE ).
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Affiliation(s)
- Steffi De Meyer
- Laboratory for Molecular Neurobiomarker Research, Department of Neurosciences, KU Leuven, box 7003, Herestraat 49, 3000, Leuven, Belgium
- Laboratory Medicine, UZ Leuven, Leuven, Belgium
- Leuven Brain Institute (LBI), KU Leuven, Leuven, Belgium
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium
| | - Jolien M Schaeverbeke
- Leuven Brain Institute (LBI), KU Leuven, Leuven, Belgium
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium
| | - Inge M W Verberk
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam UMC, Amsterdam, The Netherlands
| | - Benjamin Gille
- Laboratory for Molecular Neurobiomarker Research, Department of Neurosciences, KU Leuven, box 7003, Herestraat 49, 3000, Leuven, Belgium
- Leuven Brain Institute (LBI), KU Leuven, Leuven, Belgium
| | - Maxim De Schaepdryver
- Laboratory for Molecular Neurobiomarker Research, Department of Neurosciences, KU Leuven, box 7003, Herestraat 49, 3000, Leuven, Belgium
- Leuven Brain Institute (LBI), KU Leuven, Leuven, Belgium
| | - Emma S Luckett
- Leuven Brain Institute (LBI), KU Leuven, Leuven, Belgium
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium
| | - Silvy Gabel
- Leuven Brain Institute (LBI), KU Leuven, Leuven, Belgium
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium
| | - Rose Bruffaerts
- Leuven Brain Institute (LBI), KU Leuven, Leuven, Belgium
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium
- Neurology Department, UZ Leuven, Leuven, Belgium
| | | | - Elisabeth H Thijssen
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam UMC, Amsterdam, The Netherlands
| | | | | | - Charlotte E Teunissen
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam UMC, Amsterdam, The Netherlands
| | - Rik Vandenberghe
- Leuven Brain Institute (LBI), KU Leuven, Leuven, Belgium
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium
- Neurology Department, UZ Leuven, Leuven, Belgium
| | - Koen Poesen
- Laboratory for Molecular Neurobiomarker Research, Department of Neurosciences, KU Leuven, box 7003, Herestraat 49, 3000, Leuven, Belgium.
- Laboratory Medicine, UZ Leuven, Leuven, Belgium.
- Leuven Brain Institute (LBI), KU Leuven, Leuven, Belgium.
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17
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Westwood S, Baird AL, Anand SN, Nevado-Holgado AJ, Kormilitzin A, Shi L, Hye A, Ashton NJ, Morgan AR, Bos I, Vos SJ, Baker S, Buckley NJ, Ten Kate M, Scheltens P, Teunissen CE, Vandenberghe R, Gabel S, Meersmans K, Engelborghs S, De Roeck EE, Sleegers K, Frisoni GB, Blin O, Richardson JC, Bordet R, Molinuevo JL, Rami L, Wallin A, Kettunen P, Tsolaki M, Verhey F, Lléo A, Sala I, Popp J, Peyratout G, Martinez-Lage P, Tainta M, Johannsen P, Freund-Levi Y, Frölich L, Dobricic V, Legido-Quigley C, Bertram L, Barkhof F, Zetterberg H, Morgan BP, Streffer J, Visser PJ, Lovestone S. Validation of Plasma Proteomic Biomarkers Relating to Brain Amyloid Burden in the EMIF-Alzheimer's Disease Multimodal Biomarker Discovery Cohort. J Alzheimers Dis 2020; 74:213-225. [PMID: 31985466 PMCID: PMC7175945 DOI: 10.3233/jad-190434] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
We have previously investigated, discovered, and replicated plasma protein biomarkers for use to triage potential trials participants for PET or cerebrospinal fluid measures of Alzheimer's disease (AD) pathology. This study sought to undertake validation of these candidate plasma biomarkers in a large, multi-center sample collection. Targeted plasma analyses of 34 proteins with prior evidence for prediction of in vivo pathology were conducted in up to 1,000 samples from cognitively healthy elderly individuals, people with mild cognitive impairment, and in patients with AD-type dementia, selected from the EMIF-AD catalogue. Proteins were measured using Luminex xMAP, ELISA, and Meso Scale Discovery assays. Seven proteins replicated in their ability to predict in vivo amyloid pathology. These proteins form a biomarker panel that, along with age, could significantly discriminate between individuals with high and low amyloid pathology with an area under the curve of 0.74. The performance of this biomarker panel remained consistent when tested in apolipoprotein E ɛ4 non-carrier individuals only. This blood-based panel is biologically relevant, measurable using practical immunocapture arrays, and could significantly reduce the cost incurred to clinical trials through screen failure.
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Affiliation(s)
| | | | | | | | | | - Liu Shi
- Department of Psychiatry, University of Oxford, UK
| | - Abdul Hye
- Maurice Wohl Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, Kings College London, London, UK
| | - Nicholas J. Ashton
- Maurice Wohl Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, Kings College London, London, UK
- Department of Psychiatry and Neurochemistry, University of Gothenburg, Mölndal, Sweden
- Wallenberg Centre for Molecular & Translational Medicine, University of Gothenburg, Gothenburg, Sweden
| | | | - Isabelle Bos
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Alzheimer Centrum Limburg, Maastricht University, Maastricht, the Netherlands
| | - Stephanie J.B. Vos
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Alzheimer Centrum Limburg, Maastricht University, Maastricht, the Netherlands
| | | | | | - Mara Ten Kate
- Alzheimer Center, VU University Medical Center, Amsterdam, the Netherlands
| | - Philip Scheltens
- Alzheimer Center, VU University Medical Center, Amsterdam, the Netherlands
| | | | | | - Silvy Gabel
- University Hospital Leuven, Leuven, Belgium
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Belgium
| | - Karen Meersmans
- University Hospital Leuven, Leuven, Belgium
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Belgium
| | - Sebastiaan Engelborghs
- Department of Neurology and Memory Clinic, Hospital Network Antwerp (ZNA) Middelheim and Hoge Beuken, University of Antwerp, Antwerp, Belgium
- Center for Neurosciences, Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | - Ellen E. De Roeck
- Department of Neurology and Memory Clinic, Hospital Network Antwerp (ZNA) Middelheim and Hoge Beuken, University of Antwerp, Antwerp, Belgium
- Center for Neurosciences, Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | - Kristel Sleegers
- Center for Neurosciences, Vrije Universiteit Brussel (VUB), Brussels, Belgium
- Neurodegenerative Brain Diseases Group, Center for Molecular Neurology, VIB, Belgium
| | - Giovanni B. Frisoni
- University of Geneva, Geneva, Switzerland
- IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Olivier Blin
- AIX Marseille University, INS, Ap-Hm, Marseille, France
| | | | | | - José L. Molinuevo
- Alzheimer’s Disease & Other Cognitive Disorders Unit, Hopsital Clínic-IDIBAPS, Barcelona, Spain
- Barcelona Beta Brain Research Center, Unversitat Pompeu Fabra, Barcelona, Spain
| | - Lorena Rami
- Barcelona Beta Brain Research Center, Unversitat Pompeu Fabra, Barcelona, Spain
| | - Anders Wallin
- Institute of Neuroscience and Physiology, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
| | - Petronella Kettunen
- Institute of Neuroscience and Physiology, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
| | - Magda Tsolaki
- 1st Department of Neurology, AHEPA University Hospital, Makedonia, Thessaloniki, Greece
| | - Frans Verhey
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Alzheimer Centrum Limburg, Maastricht University, Maastricht, the Netherlands
| | - Alberto Lléo
- Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
| | - Isabel Sala
- Department of Neurology, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
| | - Julius Popp
- University Hospital of Lausanne, Lausanne, Switzerland
- Geriatric Psychiatry, Department of Mental Health and Psychiatry, Geneva University Hospitals, Geneva, Switzerland
| | | | - Pablo Martinez-Lage
- Geriatric Psychiatry, Department of Mental Health and Psychiatry, Geneva University Hospitals, Geneva, Switzerland
| | | | - Peter Johannsen
- Danish Dementia Research Centre, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Yvonne Freund-Levi
- Maurice Wohl Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, Kings College London, London, UK
- Department of Neurobiology, Caring Sciences and Society (NVS), Division of Clinical Geriatrics, Karolinska Institutet, and Department of Geriatric Medicine, Karolinska University Hospital Huddinge, Stockholm, Sweden
| | - Lutz Frölich
- Department of Geriatric Psychiatry, Zentralinstitut für Seelische Gesundheit, University of Heidelberg, Mannheim, Germany
| | - Valerija Dobricic
- Lübeck Interdisciplinary Platform for Genome Analytics, Institutes of Neurogenetics and Cardiogenetics, University of Lübeck, Lübeck, Germany
| | - Cristina Legido-Quigley
- Kings College London, London, UK
- The Systems Medicine Group, Steno Diabetes Center, Gentofte, Denmark
| | - Lars Bertram
- Lübeck Interdisciplinary Platform for Genome Analytics, Institutes of Neurogenetics and Cardiogenetics, University of Lübeck, Lübeck, Germany
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherland
- UCL Institutes of Neurology and Healthcare Engineering, London, UK
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- UK Dementia Research Institute at UCL, London, UK
- Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
| | - B. Paul Morgan
- Dementia Research Institute Cardiff, Cardiff University, Cardiff, UK
| | - Johannes Streffer
- Center for Neurosciences, Vrije Universiteit Brussel (VUB), Brussels, Belgium
- UCB, Braine-l’Alleud, Belgium, formerly Janssen R&D, LLC. Beerse, Belgium at the Time of Study Conduct
| | - Pieter Jelle Visser
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Alzheimer Centrum Limburg, Maastricht University, Maastricht, the Netherlands
- Alzheimer Center, VU University Medical Center, Amsterdam, the Netherlands
| | - Simon Lovestone
- Department of Psychiatry, University of Oxford, UK
- Janssen R&D, UK formerly affiliation (1) at the Time of the Study Conduct
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18
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Chipi E, Salvadori N, Farotti L, Parnetti L. Biomarker-Based Signature of Alzheimer's Disease in Pre-MCI Individuals. Brain Sci 2019; 9:E213. [PMID: 31450744 DOI: 10.3390/brainsci9090213] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2019] [Revised: 08/10/2019] [Accepted: 08/20/2019] [Indexed: 12/11/2022] Open
Abstract
Alzheimer’s disease (AD) pathology begins decades before the onset of clinical symptoms. It is recognized as a clinicobiological entity, being detectable in vivo independently of the clinical stage by means of pathophysiological biomarkers. Accordingly, neuropathological studies that were carried out on healthy elderly subjects, with or without subjective experience of cognitive decline, reported evidence of AD pathology in a high proportion of cases. At present, mild cognitive impairment (MCI) represents the only clinically diagnosed pre-dementia stage. Several attempts have been carried out to detect AD as early as possible, when subtle cognitive alterations, still not fulfilling MCI criteria, appear. Importantly, pre-MCI individuals showing the positivity of pathophysiological AD biomarkers show a risk of progression similar to MCI patients. In view of successful treatment with disease modifying agents, in a clinical setting, a timely diagnosis is mandatory. In clinical routine, biomarkers assessment should be taken into consideration whenever a subject with subtle cognitive deficits (pre-MCI), who is aware of his/her decline, requests to know the cause of such disturbances. In this review, we report the available neuropsychological and biomarkers data that characterize the pre-MCI patients, thus proposing pre-MCI as the first clinical manifestation of AD.
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Bouckaert F, Emsell L, Vansteelandt K, De Winter FL, Van den Stock J, Obbels J, Dols A, Stek M, Adamczuk K, Sunaert S, Van Laere K, Sienaert P, Vandenbulcke M. Electroconvulsive therapy response in late-life depression unaffected by age-related brain changes. J Affect Disord 2019; 251:114-120. [PMID: 30921594 DOI: 10.1016/j.jad.2019.03.055] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Revised: 02/25/2019] [Accepted: 03/19/2019] [Indexed: 12/16/2022]
Abstract
BACKGROUND Gray matter volume decrease, white matter vascular pathology and amyloid accumulation are age-related brain changes that have been related to the pathogenesis of late life depression (LLD). Furthermore, lower hippocampal volume and more white matter hyperintensities (WMH) may contribute to poor response to electroconvulsive therapy (ECT) in severely depressed older adults. We hypothesized that the accumulation of age-related brain changes negatively affects outcome following ECT in LLD. METHODS 34 elderly patients with severe LLD were treated twice weekly with ECT until remission. All had both 3T structural magnetic resonance imaging (MRI) and β-amyloid positron emission tomography (PET) imaging using 18F-flutemetamol at baseline. MADRS and MMSE were obtained weekly which included 1 week prior to ECT (T0), after the sixth ECT (T1), and one week (T2) after the last ECT as well as at four weeks (T3) and 6 months (T4) after the last ECT. We conducted a multiple logistic regression analysis and a survival analysis with neuroimaging measures as predictors, and response, remission and relapse as outcome variable. RESULTS We did not find any association between baseline hippocampal volume, white matter hyperintensity volume and total amyloid load and response or remission at 1 and 4 weeks post ECT, nor with relapse at week 4. LIMITATIONS The present exploratory study was conducted at a single center academic hospital, the sample size was small, the focus was on hippocampal volume and the predictive effect of structural and molecular changes associated with aging were used. CONCLUSIONS Our study shows no evidence of relationship between response to ECT and age-related structural or molecular brain changes, implying that ECT can be applied effectively in depressed patients irrespective of accumulating age-related brain changes.
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Affiliation(s)
- Filip Bouckaert
- KU Leuven, University Psychiatric Center KU Leuven, Department of Old Age Psychiatry, Herestraat 49, 3000 Leuven / Leuvensesteenweg 517, 3070 Kortenberg, Belgium.
| | - Louise Emsell
- KU Leuven, University Psychiatric Center KU Leuven, Department of Old Age Psychiatry, Herestraat 49, 3000 Leuven / Leuvensesteenweg 517, 3070 Kortenberg, Belgium; Translational MRI, Department of Imaging and Pathology, KU Leuven, Radiology, University Hospitals Leuven, and University Psychiatric Center KU Leuven, Belgium
| | - Kristof Vansteelandt
- KU Leuven, University Psychiatric Center KU Leuven, Department of Statistics, Herestraat 49, 3000 Leuven / Leuvensesteenweg 517, 3070 Kortenberg, Belgium
| | - François-Laurent De Winter
- KU Leuven, University Psychiatric Center KU Leuven, Department of Old Age Psychiatry, Herestraat 49, 3000 Leuven / Leuvensesteenweg 517, 3070 Kortenberg, Belgium
| | - Jan Van den Stock
- KU Leuven, University Psychiatric Center KU Leuven, Department of Old Age Psychiatry, Herestraat 49, 3000 Leuven / Leuvensesteenweg 517, 3070 Kortenberg, Belgium
| | - Jasmien Obbels
- KU Leuven, University Psychiatric Center KU Leuven, Academic Center for ECT and Neuromodulation (AcCENT), Leuvensesteenweg 517, 3070 Kortenberg, Belgium
| | - Annemieke Dols
- Department of Psychiatry and the EMGO+ Institute for Health and Care Research, VU University Medical Center Amsterdam, the Netherlands
| | - Max Stek
- Department of Psychiatry and the EMGO+ Institute for Health and Care Research, VU University Medical Center Amsterdam, the Netherlands
| | | | - Stefan Sunaert
- Translational MRI, Department of Imaging and Pathology, KU Leuven, Radiology, University Hospitals Leuven, and University Psychiatric Center KU Leuven, Belgium
| | - Koen Van Laere
- Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, KU Leuven and University Hospitals Leuven, Belgium
| | - Pascal Sienaert
- KU Leuven, University Psychiatric Center KU Leuven, Academic Center for ECT and Neuromodulation (AcCENT), Leuvensesteenweg 517, 3070 Kortenberg, Belgium
| | - Mathieu Vandenbulcke
- KU Leuven, University Psychiatric Center KU Leuven, Department of Old Age Psychiatry, Herestraat 49, 3000 Leuven / Leuvensesteenweg 517, 3070 Kortenberg, Belgium
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20
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Schaeverbeke J, Gille B, Adamczuk K, Vanderstichele H, Chassaing E, Bruffaerts R, Neyens V, Stoops E, Tournoy J, Vandenberghe R, Poesen K. Cerebrospinal fluid levels of synaptic and neuronal integrity correlate with gray matter volume and amyloid load in the precuneus of cognitively intact older adults. J Neurochem 2019; 149:139-157. [PMID: 30720873 DOI: 10.1111/jnc.14680] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2018] [Revised: 12/10/2018] [Accepted: 02/01/2019] [Indexed: 12/18/2022]
Abstract
The main pathophysiological alterations of Alzheimer's disease (AD) include loss of neuronal and synaptic integrity, amyloidogenic processing, and neuroinflammation. Similar alterations can, however, also be observed in cognitively intact older subjects and may prelude the clinical manifestation of AD. The objectives of this prospective cross-sectional study in a cohort of 38 cognitively intact older adults were twofold: (i) to investigate the latent relationship among cerebrospinal fluid (CSF) biomarkers reflecting the main pathophysiological processes of AD, and (ii) to assess the correlation between these biomarkers and gray matter volume as well as amyloid load. All subjects underwent extensive neuropsychological examinations, CSF sampling, [18 F]-flutemetamol amyloid positron emission tomography, and T1 -weighted magnetic resonance imaging. A factor analysis revealed one factor that explained most of the variance in the CSF biomarker dataset clustering t-tau, α-synuclein, p-tau181 , neurogranin, BACE1, visinin-like protein 1, chitinase-3-like protein 1 (YKL-40), Aβ1-40 and Aβ1-38 . Higher scores on this factor correlated with lower gray matter volume and with higher amyloid load in the precuneus. At the level of individual CSF biomarkers, levels of visinin-like protein 1, neurogranin, BACE1, Aβ1-40 , Aβ1-38, and YKL-40 all correlated inversely with gray matter volume of the precuneus. These findings demonstrate that in cognitively intact older subjects, CSF levels of synaptic and neuronal integrity biomarkers, amyloidogenic processing and measures of innate immunity (YKL-40) display a latent structure of common variance, which is associated with loss of structural integrity of brain regions implicated in the earliest stages of AD. OPEN SCIENCE BADGES: This article has received a badge for *Open Materials* because it provided all relevant information to reproduce the study in the manuscript, and for *Preregistration* because the study was pre-registered at https://osf.io/7qm9t/. The complete Open Science Disclosure form for this article can be found at the end of the article. More information about the Open Practices badges can be found at https://cos.io/our-services/open-science-badges/.
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Affiliation(s)
- Jolien Schaeverbeke
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium.,Alzheimer Research Centre KU Leuven, Leuven Institute of Neuroscience and Disease, Leuven, Belgium
| | - Benjamin Gille
- Laboratory for Molecular Neurobiomarker Research, Department of Neurosciences, KU Leuven, Leuven, Belgium.,Department of Chronic disease, Metabolism and Ageing, KU Leuven, Leuven, Belgium
| | - Katarzyna Adamczuk
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium.,Alzheimer Research Centre KU Leuven, Leuven Institute of Neuroscience and Disease, Leuven, Belgium.,Bioclinica LAB, Newark, California, USA
| | | | | | - Rose Bruffaerts
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium.,Department of Neurology, University Hospitals Leuven, Leuven, Belgium
| | - Veerle Neyens
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium.,Alzheimer Research Centre KU Leuven, Leuven Institute of Neuroscience and Disease, Leuven, Belgium
| | | | - Jos Tournoy
- Alzheimer Research Centre KU Leuven, Leuven Institute of Neuroscience and Disease, Leuven, Belgium.,Department of Chronic disease, Metabolism and Ageing, KU Leuven, Leuven, Belgium.,Department of Geriatric Medicine, University Hospitals Leuven, Leuven, Belgium
| | - Rik Vandenberghe
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium.,Alzheimer Research Centre KU Leuven, Leuven Institute of Neuroscience and Disease, Leuven, Belgium.,Department of Neurology, University Hospitals Leuven, Leuven, Belgium
| | - Koen Poesen
- Laboratory for Molecular Neurobiomarker Research, Department of Neurosciences, KU Leuven, Leuven, Belgium.,Department of Chronic disease, Metabolism and Ageing, KU Leuven, Leuven, Belgium.,Laboratory Medicine, University Hospitals Leuven, Leuven, Belgium
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21
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ten Kate M, Redolfi A, Peira E, Bos I, Vos SJ, Vandenberghe R, Gabel S, Schaeverbeke J, Scheltens P, Blin O, Richardson JC, Bordet R, Wallin A, Eckerstrom C, Molinuevo JL, Engelborghs S, Van Broeckhoven C, Martinez-Lage P, Popp J, Tsolaki M, Verhey FRJ, Baird AL, Legido-Quigley C, Bertram L, Dobricic V, Zetterberg H, Lovestone S, Streffer J, Bianchetti S, Novak GP, Revillard J, Gordon MF, Xie Z, Wottschel V, Frisoni G, Visser PJ, Barkhof F. MRI predictors of amyloid pathology: results from the EMIF-AD Multimodal Biomarker Discovery study. Alzheimers Res Ther 2018; 10:100. [PMID: 30261928 PMCID: PMC6161396 DOI: 10.1186/s13195-018-0428-1] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2018] [Accepted: 09/04/2018] [Indexed: 01/30/2023]
Abstract
BACKGROUND With the shift of research focus towards the pre-dementia stage of Alzheimer's disease (AD), there is an urgent need for reliable, non-invasive biomarkers to predict amyloid pathology. The aim of this study was to assess whether easily obtainable measures from structural MRI, combined with demographic data, cognitive data and apolipoprotein E (APOE) ε4 genotype, can be used to predict amyloid pathology using machine-learning classification. METHODS We examined 810 subjects with structural MRI data and amyloid markers from the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery study, including subjects with normal cognition (CN, n = 337, age 66.5 ± 7.2, 50% female, 27% amyloid positive), mild cognitive impairment (MCI, n = 375, age 69.1 ± 7.5, 53% female, 63% amyloid positive) and AD dementia (n = 98, age 67.0 ± 7.7, 48% female, 97% amyloid positive). Structural MRI scans were visually assessed and Freesurfer was used to obtain subcortical volumes, cortical thickness and surface area measures. We first assessed univariate associations between MRI measures and amyloid pathology using mixed models. Next, we developed and tested an automated classifier using demographic, cognitive, MRI and APOE ε4 information to predict amyloid pathology. A support vector machine (SVM) with nested 10-fold cross-validation was applied to identify a set of markers best discriminating between amyloid positive and amyloid negative subjects. RESULTS In univariate associations, amyloid pathology was associated with lower subcortical volumes and thinner cortex in AD-signature regions in CN and MCI. The multi-variable SVM classifier provided an area under the curve (AUC) of 0.81 ± 0.07 in MCI and an AUC of 0.74 ± 0.08 in CN. In CN, selected features for the classifier included APOE ε4, age, memory scores and several MRI measures such as hippocampus, amygdala and accumbens volumes and cortical thickness in temporal and parahippocampal regions. In MCI, the classifier including demographic and APOE ε4 information did not improve after additionally adding imaging measures. CONCLUSIONS Amyloid pathology is associated with changes in structural MRI measures in CN and MCI. An automated classifier based on clinical, imaging and APOE ε4 data can identify the presence of amyloid pathology with a moderate level of accuracy. These results could be used in clinical trials to pre-screen subjects for anti-amyloid therapies.
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Affiliation(s)
- Mara ten Kate
- Alzheimer Center & Department of Neurology, VU University Medical Center, PO Box 7057, 1007 MB Amsterdam, the Netherlands
| | - Alberto Redolfi
- Laboratory of Epidemiology & Neuroimaging, IRCCS San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Enrico Peira
- Laboratory of Epidemiology & Neuroimaging, IRCCS San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Isabelle Bos
- Alzheimer Centrum Limburg, Department of Psychiatry and Neuropsychology, Maastricht University, Maastricht, the Netherlands
| | - Stephanie J. Vos
- Alzheimer Centrum Limburg, Department of Psychiatry and Neuropsychology, Maastricht University, Maastricht, the Netherlands
| | - Rik Vandenberghe
- University Hospital Leuven, Leuven, Belgium
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium
| | - Silvy Gabel
- University Hospital Leuven, Leuven, Belgium
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium
| | - Jolien Schaeverbeke
- University Hospital Leuven, Leuven, Belgium
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium
| | - Philip Scheltens
- Alzheimer Center & Department of Neurology, VU University Medical Center, PO Box 7057, 1007 MB Amsterdam, the Netherlands
| | - Olivier Blin
- AP-HM, CHU Timone, CIC CPCET, Service de Pharmacologie Clinique et Pharmacovigilance, Marseille, France
| | | | - Regis Bordet
- U1171 Inserm, CHU Lille, Degenerative and Vascular Cognitive Disorders, University of Lille, Lille, France
| | - Anders Wallin
- Sahlgrenska Academy, Institute of Neuroscience and Physiology, Section for Psychiatry and Neurochemistry, University of Gothenburg, Gothenburg, Sweden
| | - Carl Eckerstrom
- Sahlgrenska Academy, Institute of Neuroscience and Physiology, Section for Psychiatry and Neurochemistry, University of Gothenburg, Gothenburg, Sweden
| | - José Luis Molinuevo
- Barcelona βeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain
| | - Sebastiaan Engelborghs
- Reference Center for Biological Markers of Dementia (BIODEM), Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
- Department of Neurology and Memory Clinic, Hospital Network Antwerp (ZNA) Middelheim and Hoge Beuken, Antwerp, Belgium
| | - Christine Van Broeckhoven
- Neurodegenerative Brain Diseases, Center for Molecular Neurology, VIB, Antwerp, Belgium
- Laboratory of Neurogenetics, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | - Pablo Martinez-Lage
- Department of Neurology, Center for Research and Advanced Therapies, CITA-Alzheimer Foundation, San Sebastian, Spain
| | - Julius Popp
- Department of Psychiatry, University Hospital of Lausanne, Lausanne, Switzerland
- Geriatric Psychiatry, Department of Mental Health and Psychiatry, Geneva University Hospitals, Geneva, Switzerland
| | - Magdalini Tsolaki
- Memory and Dementia Center, 3rd Department of Neurology, “G Papanicolau” General Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Frans R. J. Verhey
- Alzheimer Centrum Limburg, Department of Psychiatry and Neuropsychology, Maastricht University, Maastricht, the Netherlands
| | | | | | - Lars Bertram
- Lübeck Interdisciplinary Platform for Genome Analytics, University of Lübeck, Lubeck, Germany
- School of Public Health, Imperial College London, London, UK
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Valerija Dobricic
- Lübeck Interdisciplinary Platform for Genome Analytics, University of Lübeck, Lubeck, Germany
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, University of Gothenburg, Mölndal, Sweden
- Department of Molecular Neuroscience, UCL Institute of Neurology, Queen Square, London, UK
- UK Dementia Research Institute at UCL, London, UK
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | | | - Johannes Streffer
- Reference Center for Biological Markers of Dementia (BIODEM), Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
- UCB Biopharma SPRL, Braine-l’Alleud, Belgium
| | - Silvia Bianchetti
- Laboratory of Epidemiology & Neuroimaging, IRCCS San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Gerald P. Novak
- Janssen Pharmaceutical Research and Development, Titusville, NJ USA
| | | | - Mark F. Gordon
- Teva Pharmaceuticals, Inc., Malvern, PA USA
- Boehringer Ingelheim Pharmaceuticals, Inc., Ridgefield, CT USA
| | - Zhiyong Xie
- Worldwide Research and Development, Pfizer Inc, Cambridge, MA USA
| | - Viktor Wottschel
- Department of Radiology and Nuclear Medicine, VUMC, Amsterdam, the Netherlands
| | - Giovanni Frisoni
- Laboratory of Epidemiology & Neuroimaging, IRCCS San Giovanni di Dio Fatebenefratelli, Brescia, Italy
- University of Geneva, Geneva, Switzerland
| | - Pieter Jelle Visser
- Alzheimer Center & Department of Neurology, VU University Medical Center, PO Box 7057, 1007 MB Amsterdam, the Netherlands
- Alzheimer Centrum Limburg, Department of Psychiatry and Neuropsychology, Maastricht University, Maastricht, the Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, VUMC, Amsterdam, the Netherlands
- Institutes of Neurology and Healthcare Engineering, UCL, London, UK
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Schaeverbeke J, Gabel S, Meersmans K, Bruffaerts R, Liuzzi AG, Evenepoel C, Dries E, Van Bouwel K, Sieben A, Pijnenburg Y, Peeters R, Bormans G, Van Laere K, Koole M, Dupont P, Vandenberghe R. Single-word comprehension deficits in the nonfluent variant of primary progressive aphasia. Alzheimers Res Ther 2018; 10:68. [PMID: 30021613 PMCID: PMC6052568 DOI: 10.1186/s13195-018-0393-8] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2018] [Accepted: 05/30/2018] [Indexed: 12/12/2022]
Abstract
BACKGROUND A subset of patients with the nonfluent variant of primary progressive aphasia (PPA) exhibit concomitant single-word comprehension problems, constituting a 'mixed variant' phenotype. This phenotype is rare and currently not fully characterized. The aim of this study was twofold: to assess the prevalence and nature of single-word comprehension problems in the nonfluent variant and to study multimodal imaging characteristics of atrophy, tau, and amyloid burden associated with this mixed phenotype. METHODS A consecutive memory-clinic recruited series of 20 PPA patients (12 nonfluent, five semantic, and three logopenic variants) were studied on neurolinguistic and neuropsychological domains relative to 64 cognitively intact healthy older control subjects. The neuroimaging battery included high-resolution volumetric magnetic resonance imaging processed with voxel-based morphometry, and positron emission tomography with the tau-tracer [18F]-THK5351 and amyloid-tracer [11C]-Pittsburgh Compound B. RESULTS Seven out of 12 subjects who had been classified a priori with nonfluent variant PPA showed deficits on conventional single-word comprehension tasks along with speech apraxia and agrammatism, corresponding to a mixed variant phenotype. These mixed variant cases included three females and four males, with a mean age at onset of 65 years (range 44-77 years). Object knowledge and object recognition were additionally affected, although less severely compared with the semantic variant. The mixed variant was characterized by a distributed atrophy pattern in frontal and temporoparietal regions. A more focal pattern of elevated [18F]-THK5351 binding was present in the supplementary motor area, the left premotor cortex, midbrain, and basal ganglia. This pattern was closely similar to that seen in pure nonfluent variant PPA. At the individual patient level, elevated [18F]-THK5351 binding in the supplementary motor area and premotor cortex was present in six out of seven mixed variant cases and in five and four of these cases, respectively, in the thalamus and midbrain. Amyloid biomarker positivity was present in two out of seven mixed variant cases, compared with none of the five pure nonfluent cases. CONCLUSIONS A substantial proportion of PPA patients with speech apraxia and agrammatism also have single-word comprehension deficits. At the neurobiological level, the mixed variant shows a high degree of similarity with the pure nonfluent variant of PPA. TRIAL REGISTRATION EudraCT, 2014-002976-10 . Registered on 13-01-2015.
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Affiliation(s)
- Jolien Schaeverbeke
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Herestraat 49, 3000 Leuven, Belgium
- Alzheimer Research Centre KU Leuven, Leuven Research Institute for Neuroscience & Disease, KU Leuven, Herestraat 49, 3000 Leuven, Belgium
| | - Silvy Gabel
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Herestraat 49, 3000 Leuven, Belgium
- Alzheimer Research Centre KU Leuven, Leuven Research Institute for Neuroscience & Disease, KU Leuven, Herestraat 49, 3000 Leuven, Belgium
| | - Karen Meersmans
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Herestraat 49, 3000 Leuven, Belgium
| | - Rose Bruffaerts
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Herestraat 49, 3000 Leuven, Belgium
- Alzheimer Research Centre KU Leuven, Leuven Research Institute for Neuroscience & Disease, KU Leuven, Herestraat 49, 3000 Leuven, Belgium
- Neurology Department, University Hospitals Leuven, Herestraat 49 - box 7003, 3000 Leuven, Belgium
| | - Antonietta Gabriella Liuzzi
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Herestraat 49, 3000 Leuven, Belgium
| | - Charlotte Evenepoel
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Herestraat 49, 3000 Leuven, Belgium
- Alzheimer Research Centre KU Leuven, Leuven Research Institute for Neuroscience & Disease, KU Leuven, Herestraat 49, 3000 Leuven, Belgium
| | - Eva Dries
- Neurology Department, University Hospitals Leuven, Herestraat 49 - box 7003, 3000 Leuven, Belgium
| | - Karen Van Bouwel
- Neurology Department, University Hospitals Leuven, Herestraat 49 - box 7003, 3000 Leuven, Belgium
| | - Anne Sieben
- Neurodegenerative Brain Diseases Group, Center for Molecular Neurology, VIB, Universiteitsplein 1, 2610 Antwerp, Belgium
- Institute Born-Bunge, Neuropathology and Laboratory of Neurochemistry and Behavior, University of Antwerp, Universiteitsplein 1, 2610 Antwerp, Belgium
- Neurology Department, University Hospitals Ghent, Corneel Heymanslaan 10, 9000 Ghent, Belgium
| | - Yolande Pijnenburg
- Old Age Psychiatry Department, GGZinGeest, Van Hilligaertstraat 21, 1072 JX Amsterdam, The Netherlands
- Alzheimer Center & Department of Neurology, VU University Medical Center, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
| | - Ronald Peeters
- Radiology Department, University Hospitals Leuven, Herestraat 49, Leuven, 30000 Belgium
| | - Guy Bormans
- Laboratory of Radiopharmaceutical Research, KU Leuven, Herestraat 49, 3000 Leuven, Belgium
- Nuclear Medicine and Molecular Imaging, University Hospitals Leuven, Herestraat 49, 3000 Leuven, Belgium
| | - Koen Van Laere
- Alzheimer Research Centre KU Leuven, Leuven Research Institute for Neuroscience & Disease, KU Leuven, Herestraat 49, 3000 Leuven, Belgium
- Nuclear Medicine and Molecular Imaging, University Hospitals Leuven, Herestraat 49, 3000 Leuven, Belgium
| | - Michel Koole
- Nuclear Medicine and Molecular Imaging, University Hospitals Leuven, Herestraat 49, 3000 Leuven, Belgium
| | - Patrick Dupont
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Herestraat 49, 3000 Leuven, Belgium
- Alzheimer Research Centre KU Leuven, Leuven Research Institute for Neuroscience & Disease, KU Leuven, Herestraat 49, 3000 Leuven, Belgium
| | - Rik Vandenberghe
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Herestraat 49, 3000 Leuven, Belgium
- Alzheimer Research Centre KU Leuven, Leuven Research Institute for Neuroscience & Disease, KU Leuven, Herestraat 49, 3000 Leuven, Belgium
- Neurology Department, University Hospitals Leuven, Herestraat 49 - box 7003, 3000 Leuven, Belgium
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23
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Bos I, Vos S, Vandenberghe R, Scheltens P, Engelborghs S, Frisoni G, Molinuevo JL, Wallin A, Lleó A, Popp J, Martinez-Lage P, Baird A, Dobson R, Legido-Quigley C, Sleegers K, Van Broeckhoven C, Bertram L, Ten Kate M, Barkhof F, Zetterberg H, Lovestone S, Streffer J, Visser PJ. The EMIF-AD Multimodal Biomarker Discovery study: design, methods and cohort characteristics. Alzheimers Res Ther 2018; 10:64. [PMID: 29980228 PMCID: PMC6035398 DOI: 10.1186/s13195-018-0396-5] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/10/2017] [Accepted: 06/08/2018] [Indexed: 02/04/2023]
Abstract
Background There is an urgent need for novel, noninvasive biomarkers to diagnose Alzheimer’s disease (AD) in the predementia stages and to predict the rate of decline. Therefore, we set up the European Medical Information Framework for Alzheimer’s Disease Multimodal Biomarker Discovery (EMIF-AD MBD) study. In this report we describe the design of the study, the methods used and the characteristics of the participants. Methods Participants were selected from existing prospective multicenter and single-center European studies. Inclusion criteria were having normal cognition (NC) or a diagnosis of mild cognitive impairment (MCI) or AD-type dementia at baseline, age above 50 years, known amyloid-beta (Aβ) status, availability of cognitive test results and at least two of the following materials: plasma, DNA, magnetic resonance imaging (MRI) or cerebrospinal fluid (CSF). Targeted and untargeted metabolomic and proteomic analyses were performed in plasma, and targeted and untargeted proteomics were performed in CSF. Genome-wide SNP genotyping, next-generation sequencing and methylation profiling were conducted in DNA. Visual rating and volumetric measures were assessed on MRI. Baseline characteristics were analyzed using ANOVA or chi-square, rate of decline analyzed by linear mixed modeling. Results We included 1221 individuals (NC n = 492, MCI n = 527, AD-type dementia n = 202) with a mean age of 67.9 (SD 8.3) years. The percentage Aβ+ was 26% in the NC, 58% in the MCI, and 87% in the AD-type dementia groups. Plasma samples were available for 1189 (97%) subjects, DNA samples for 929 (76%) subjects, MRI scans for 862 (71%) subjects and CSF samples for 767 (63%) subjects. For 759 (62%) individuals, clinical follow-up data were available. In each diagnostic group, the APOE ε4 allele was more frequent amongst Aβ+ individuals (p < 0.001). Only in MCI was there a difference in baseline Mini Mental State Examination (MMSE) score between the A groups (p < 0.001). Aβ+ had a faster rate of decline on the MMSE during follow-up in the NC (p < 0.001) and MCI (p < 0.001) groups. Conclusions The characteristics of this large cohort of elderly subjects at various cognitive stages confirm the central roles of Aβ and APOE ε4 in AD pathogenesis. The results of the multimodal analyses will provide new insights into underlying mechanisms and facilitate the discovery of new diagnostic and prognostic AD biomarkers. All researchers can apply for access to the EMIF-AD MBD data by submitting a research proposal via the EMIF-AD Catalog. Electronic supplementary material The online version of this article (10.1186/s13195-018-0396-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Isabelle Bos
- Alzheimer Centrum Limburg, Maastricht University, Maastricht, the Netherlands. .,Department of Psychiatry & Neuropsychology, School for Mental Health and Neuroscience, Alzheimer Center Limburg, Maastricht University, Universiteitssingel 40, Box 34, P.O. Box 616, 6200, MD, Maastricht, the Netherlands.
| | - Stephanie Vos
- Alzheimer Centrum Limburg, Maastricht University, Maastricht, the Netherlands
| | - Rik Vandenberghe
- University Hospital Leuven, Leuven, Belgium.,Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium
| | - Philip Scheltens
- Alzheimer Center and Department of Neurology, VU University Medical Center, Amsterdam, the Netherlands
| | - Sebastiaan Engelborghs
- Reference Center for Biological Markers of Dementia (BIODEM), University of Antwerp, Antwerp, Belgium.,Department of Neurology and Memory Clinic, Hospital Network Antwerp (ZNA) Middelheim and Hoge Beuken, Antwerp, Belgium.,University of Antwerp, Antwerp, Belgium
| | - Giovanni Frisoni
- University of Geneva, Geneva, Switzerland.,IRCCS Instituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - José Luis Molinuevo
- Alzheimer's Disease & Other Cognitive Disorders Unit, Hospital Clínic-IDIBAPS, Barcelona, Spain.,Barcelona Beta Brain Research Center, Fundació Pasqual Maragall, Barcelona, Spain
| | - Anders Wallin
- Institute of Neuroscience and Physiology, Moelndal, Sweden
| | - Alberto Lleó
- Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
| | - Julius Popp
- Geriatric Psychiatry, Department of Mental Health and Psychiatry, Geneva University Hospitals, Geneva, Switzerland.,Department of Psychiatry, University Hospital of Lausanne, Lausanne, Switzerland
| | - Pablo Martinez-Lage
- Center for Research and Advanced Therapies, CITA-Alzheimer Foundation, San Sebastian, Spain
| | | | - Richard Dobson
- King's College London, London, UK.,NIHR Biomedical Research Centre for Mental Health and Biomedical Research Unit for Dementia at South London and Maudsley NHS Foundation, London, UK.,Farr Institute of Health Informatics Research, UCL Institute of Health Informatics, University College London, London, UK.,NIHR University College London Hospitals Biomedical Research Centre, London, UK
| | | | - Kristel Sleegers
- Neurodegenerative Brain Diseases Group, VIB-Department of Molecular Genetics, Antwerp, Belgium.,Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | - Christine Van Broeckhoven
- Neurodegenerative Brain Diseases Group, VIB-Department of Molecular Genetics, Antwerp, Belgium.,Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | - Lars Bertram
- Lübeck Interdisciplinary Platform for Genome Analytics, University of Lübeck, Lübeck, Germany.,School of Public Health, Imperial College London, London, UK.,Department of Psychology, University of Oslo, Oslo, Norway
| | - Mara Ten Kate
- Alzheimer Center and Department of Neurology, VU University Medical Center, Amsterdam, the Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, the Netherlands
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, University of Gothenburg, Mölndal, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden.,Department of Molecular Neuroscience, UCL Institute of Neurology, London, UK.,UK Dementia Research Institute, London, UK
| | | | - Johannes Streffer
- Experimental Medicine, Janssen Pharmaceutical Companies, Beerse, Belgium
| | - Pieter Jelle Visser
- Alzheimer Centrum Limburg, Maastricht University, Maastricht, the Netherlands.,Alzheimer Center and Department of Neurology, VU University Medical Center, Amsterdam, the Netherlands
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24
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Bos I, Vos SJB, Jansen WJ, Vandenberghe R, Gabel S, Estanga A, Ecay-Torres M, Tomassen J, den Braber A, Lleó A, Sala I, Wallin A, Kettunen P, Molinuevo JL, Rami L, Chetelat G, de la Sayette V, Tsolaki M, Freund-Levi Y, Johannsen P, Novak GP, Ramakers I, Verhey FR, Visser PJ. Amyloid-β, Tau, and Cognition in Cognitively Normal Older Individuals: Examining the Necessity to Adjust for Biomarker Status in Normative Data. Front Aging Neurosci 2018; 10:193. [PMID: 29988624 PMCID: PMC6027060 DOI: 10.3389/fnagi.2018.00193] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2018] [Accepted: 06/07/2018] [Indexed: 11/13/2022] Open
Abstract
We investigated whether amyloid-β (Aβ) and tau affected cognition in cognitively normal (CN) individuals, and whether norms for neuropsychological tests based on biomarker-negative individuals would improve early detection of dementia. We included 907 CN individuals from 8 European cohorts and from the Alzheimer's disease Neuroimaging Initiative. All individuals were aged above 40, had Aβ status and neuropsychological data available. Linear mixed models were used to assess the associations of Aβ and tau with five neuropsychological tests assessing memory (immediate and delayed recall of Auditory Verbal Learning Test, AVLT), verbal fluency (Verbal Fluency Test, VFT), attention and executive functioning (Trail Making Test, TMT, part A and B). All test except the VFT were associated with Aβ status and this influence was augmented by age. We found no influence of tau on any of the cognitive tests. For the AVLT Immediate and Delayed recall and the TMT part A and B, we calculated norms in individuals without Aβ pathology (Aβ- norms), which we validated in an independent memory-clinic cohort by comparing their predictive accuracy to published norms. For memory tests, the Aβ- norms rightfully identified an additional group of individuals at risk of dementia. For non-memory test we found no difference. We confirmed the relationship between Aβ and cognition in cognitively normal individuals. The Aβ- norms for memory tests in combination with published norms improve prognostic accuracy of dementia.
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Affiliation(s)
- Isabelle Bos
- Department of Psychiatry and Neuropsychology, Alzheimer Center Limburg, School for Mental Health and Neuroscience Maastricht University, Maastricht, Netherlands
| | - Stephanie J B Vos
- Department of Psychiatry and Neuropsychology, Alzheimer Center Limburg, School for Mental Health and Neuroscience Maastricht University, Maastricht, Netherlands
| | - Willemijn J Jansen
- Department of Psychiatry and Neuropsychology, Alzheimer Center Limburg, School for Mental Health and Neuroscience Maastricht University, Maastricht, Netherlands
| | - Rik Vandenberghe
- University Hospital Leuven, Belgium.,Laboratory for Cognitive Neurology, Department of Neurosciences KU Leuven, Leuven, Belgium
| | - Silvy Gabel
- Laboratory for Cognitive Neurology, Department of Neurosciences KU Leuven, Leuven, Belgium.,Alzheimer Research Centre KU Leuven, Leuven, Belgium
| | - Ainara Estanga
- Center for Research and Advanced Therapies CITA-Alzheimer Foundation, San Sebastián, Spain
| | - Mirian Ecay-Torres
- Center for Research and Advanced Therapies CITA-Alzheimer Foundation, San Sebastián, Spain
| | - Jori Tomassen
- Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center VU University Amsterdam, Amsterdam, Netherlands
| | - Anouk den Braber
- Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center VU University Amsterdam, Amsterdam, Netherlands.,Department of Biological Psychology VU University Amsterdam, Amsterdam, Netherlands
| | - Alberto Lleó
- Department of Neurology Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
| | - Isabel Sala
- Department of Neurology Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
| | - Anders Wallin
- Section for Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, University of Gothenburg Sahlgrenska Academy, Gothenburg, Sweden
| | - Petronella Kettunen
- Section for Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, University of Gothenburg Sahlgrenska Academy, Gothenburg, Sweden.,Nuffield Department of Clinical Neurosciences University of Oxford, Oxford, United Kingdom
| | - José L Molinuevo
- Alzheimer's Disease & Other Cognitive Disorders Unit, Hopsital Clínic Consorci Institut D'Investigacions Biomediques August Pi I Sunyer (IDIBAPS), Barcelona, Spain.,Barcelona Beta Brain Research Center Unversitat Pompeu Fabra, Barcelona, Spain
| | - Lorena Rami
- Alzheimer's Disease & Other Cognitive Disorders Unit, Hopsital Clínic Consorci Institut D'Investigacions Biomediques August Pi I Sunyer (IDIBAPS), Barcelona, Spain
| | - Gaël Chetelat
- Institut National de la Santé et de la Recherche Médicale UMR-S U1237, Université de Caen-Normandie GIP Cyceron, Caen, France
| | - Vincent de la Sayette
- Institut National de la Santé et de la Recherche Médicale U1077, Université de Caen Normandie Ecole Pratique des Hautes Etudes, Caen, France.,CHU de Caen Service de Neurologie, Caen, France
| | - Magda Tsolaki
- 1st Department of Neurology University General Hospital of Thessaloniki AHEPA, Thessaloniki, Greece
| | - Yvonne Freund-Levi
- Division of Clinical Geriatrics, Department of Neurobiology, Caring Sciences and Society (NVS) Karolinska Institutet, Stockholm, Sweden.,Department of Geriatric Medicine, Karolinska University Hospital Huddinge Karolinska Institutet, Stockholm, Sweden.,Department of Psychiatry Norrtälje Hospital Tiohundra, Norrtälje, Sweden
| | - Peter Johannsen
- Danish Dementia Research Centre, Rigshospitalet, Copenhagen University Hospital University of Copenhagen, Copenhagen, Denmark
| | | | - Gerald P Novak
- Janssen Pharmaceutical Research and Development Titusville, NJ, United States
| | - Inez Ramakers
- Department of Psychiatry and Neuropsychology, Alzheimer Center Limburg, School for Mental Health and Neuroscience Maastricht University, Maastricht, Netherlands
| | - Frans R Verhey
- Department of Psychiatry and Neuropsychology, Alzheimer Center Limburg, School for Mental Health and Neuroscience Maastricht University, Maastricht, Netherlands
| | - Pieter Jelle Visser
- Department of Psychiatry and Neuropsychology, Alzheimer Center Limburg, School for Mental Health and Neuroscience Maastricht University, Maastricht, Netherlands.,Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center VU University Amsterdam, Amsterdam, Netherlands
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25
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Renard D, Collombier L, Demattei C, Wacongne A, Charif M, Ayrignac X, Azakri S, Gaillard N, Boudousq V, Lehmann S, Menjot de Champfleur N, Thouvenot E. Cerebrospinal Fluid, MRI, and Florbetaben-PET in Cerebral Amyloid Angiopathy-Related Inflammation. J Alzheimers Dis 2018; 61:1107-1117. [DOI: 10.3233/jad-170843] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Dimitri Renard
- Department of Neurology, CHU Nîmes, Hôpital Caremeau, France
| | | | | | - Anne Wacongne
- Department of Neurology, CHU Nîmes, Hôpital Caremeau, France
| | - Mahmoud Charif
- Department of Neurology, CHU Montpellier, Hôpital Gui de Chauliac, France
| | - Xavier Ayrignac
- Department of Neurology, CHU Montpellier, Hôpital Gui de Chauliac, France
| | - Souhayla Azakri
- Department of Neurology, CHU Montpellier, Hôpital Gui de Chauliac, France
| | | | - Vincent Boudousq
- Department of Nuclear Medicine, CHU Nîmes, Hôpital Caremeau, France
| | - Sylvain Lehmann
- Laboratoire de Biochimie-Protéomique Clinique – IRMB – CRB – Inserm U11183, CHU Montpellier, Hôpital St-Eloi – Université Montpellier, France
| | | | - Eric Thouvenot
- Department of Neurology, CHU Nîmes, Hôpital Caremeau, France
- Institut de Génomique Fonctionnelle, UMR5203, Université Montpellier, Montpellier, France
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26
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Chen YJ, Nasrallah IM. Brain amyloid PET interpretation approaches: from visual assessment in the clinic to quantitative pharmacokinetic modeling. Clin Transl Imaging 2017. [DOI: 10.1007/s40336-017-0257-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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27
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Emsell L, Adamson C, De Winter FL, Billiet T, Christiaens D, Bouckaert F, Adamczuk K, Vandenberghe R, Seal ML, Sienaert P, Sunaert S, Vandenbulcke M. Corpus callosum macro and microstructure in late-life depression. J Affect Disord 2017; 222:63-70. [PMID: 28672181 DOI: 10.1016/j.jad.2017.06.063] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2017] [Revised: 05/31/2017] [Accepted: 06/26/2017] [Indexed: 12/13/2022]
Abstract
BACKGROUND Differences in corpus callosum (CC) morphology and microstructure have been implicated in late-life depression and may distinguish between late and early-onset forms of the illness. However, a multimodal approach using complementary imaging techniques is required to disentangle microstructural alterations from macrostructural partial volume effects. METHODS 107 older adults were assessed: 55 currently-depressed patients without dementia and 52 controls without cognitive impairment. We investigated group differences and clinical associations in 7 sub-regions of the mid-sagittal corpus callosum using T1 anatomical data, white matter hyperintensity (WMH) quantification and two different diffusion MRI (dMRI) models (multi-tissue constrained spherical deconvolution, yielding apparent fibre density, AFD; and diffusion tensor imaging, yielding fractional anisotropy, FA and radial diffusivity, RD). RESULTS Callosal AFD was lower in patients compared to controls. There were no group differences in CC thickness, surface area, FA, RD, nor whole brain or WMH volume. Late-onset of depression was associated with lower FA, higher RD and lower AFD. There were no associations between any imaging measures and psychotic features or depression severity as assessed by the geriatric depression scale. WMH volume was associated with lower FA and AFD, and higher RD in patients. LIMITATIONS Patients were predominantly treatment-resistant. Measurements were limited to the mid-sagittal CC. dMRI analysis was performed on a smaller cohort, n=77. AFD was derived from low b-value data. CONCLUSIONS Callosal structure is largely preserved in LLD. WMH burden may impact on CC microstructure in late-onset depression suggesting vascular pathology has additional deleterious effects in these patients.
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Affiliation(s)
- Louise Emsell
- Old Age Psychiatry, University Psychiatric Centre (UPC) - KU Leuven, Belgium; Translational MRI & Radiology, KU Leuven & University Hospital Leuven, Belgium.
| | - Christopher Adamson
- Developmental Imaging, Murdoch Children's Research Institute, Victoria, Australia
| | | | - Thibo Billiet
- Translational MRI & Radiology, KU Leuven & University Hospital Leuven, Belgium
| | - Daan Christiaens
- Department of Electrical Engineering (ESAT), Processing of Speech and Images (PSI), Medical Image Computing, KU Leuven & Medical Imaging Research Center, University Hospital Leuven, Belgium; Division of Imaging Sciences and Biomedical Engineering, Kings College London, UK
| | - Filip Bouckaert
- Old Age Psychiatry, University Psychiatric Centre (UPC) - KU Leuven, Belgium; KU Leuven, University Psychiatric Center KU Leuven, Academic Center for ECT and Neurostimulation (AcCENT), Kortenberg, Belgium
| | - Katarzyna Adamczuk
- Laboratory for Cognitive Neurology, Department of Neurology, KU Leuven & University Hospital Leuven, Belgium; Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, USA
| | - Rik Vandenberghe
- Laboratory for Cognitive Neurology, Department of Neurology, KU Leuven & University Hospital Leuven, Belgium
| | - Marc L Seal
- Developmental Imaging, Murdoch Children's Research Institute, Victoria, Australia; Department of Paediatrics, The University of Melbourne, Victoria, Australia
| | - Pascal Sienaert
- KU Leuven, University Psychiatric Center KU Leuven, Academic Center for ECT and Neurostimulation (AcCENT), Kortenberg, Belgium
| | - Stefan Sunaert
- Translational MRI & Radiology, KU Leuven & University Hospital Leuven, Belgium
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28
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Hammers DB, Atkinson TJ, Dalley BCA, Suhrie KR, Horn KP, Rasmussen KM, Beardmore BE, Burrell LD, Duff K, Hoffman JM. Amyloid Positivity Using [18F]Flutemetamol-PET and Cognitive Deficits in Nondemented Community-Dwelling Older Adults. Am J Alzheimers Dis Other Demen 2017; 32:320-328. [PMID: 28403622 DOI: 10.1177/1533317517698795] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Little research exists examining the relationship between beta-amyloid neuritic plaque density via [18F]flutemetamol binding and cognition; consequently, the purpose of the current study was to compare cognitive performances among individuals having either increased amyloid deposition (Flute+) or minimal amyloid deposition (Flute-). Twenty-seven nondemented community-dwelling adults over the age of 65 underwent [18F]flutemetamol amyloid-positron emission tomography imaging, along with cognitive testing using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) and select behavioral measures. Analysis of variance was used to identify the differences among the cognitive and behavioral measures between Flute+/Flute- groups. Flute+ participants performed significantly worse than Flute- participants on RBANS indexes of immediate memory, language, delayed memory, and total scale score, but no significant group differences in the endorsed level of depression or subjective report of cognitive difficulties were observed. Although these results are preliminary, [18F]flutemetamol accurately tracks cognition in a nondemented elderly sample, which may allow for better prediction of cognitive decline in late life.
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Affiliation(s)
- Dustin B Hammers
- 1 Department of Neurology, Center for Alzheimer's Care, Imaging and Research, University of Utah, Salt Lake City, UT, USA
| | - Taylor J Atkinson
- 1 Department of Neurology, Center for Alzheimer's Care, Imaging and Research, University of Utah, Salt Lake City, UT, USA
| | - Bonnie C A Dalley
- 1 Department of Neurology, Center for Alzheimer's Care, Imaging and Research, University of Utah, Salt Lake City, UT, USA
| | - Kayla R Suhrie
- 1 Department of Neurology, Center for Alzheimer's Care, Imaging and Research, University of Utah, Salt Lake City, UT, USA
| | - Kevin P Horn
- 2 Center for Quantitative Cancer Imaging, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Kelli M Rasmussen
- 2 Center for Quantitative Cancer Imaging, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Britney E Beardmore
- 2 Center for Quantitative Cancer Imaging, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Lance D Burrell
- 2 Center for Quantitative Cancer Imaging, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Kevin Duff
- 1 Department of Neurology, Center for Alzheimer's Care, Imaging and Research, University of Utah, Salt Lake City, UT, USA
| | - John M Hoffman
- 2 Center for Quantitative Cancer Imaging, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
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29
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Chiotis K, Saint-Aubert L, Boccardi M, Gietl A, Picco A, Varrone A, Garibotto V, Herholz K, Nobili F, Nordberg A, Frisoni GB, Winblad B, Jack CR. Clinical validity of increased cortical uptake of amyloid ligands on PET as a biomarker for Alzheimer's disease in the context of a structured 5-phase development framework. Neurobiol Aging 2017; 52:214-227. [DOI: 10.1016/j.neurobiolaging.2016.07.012] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2015] [Revised: 06/10/2016] [Accepted: 07/06/2016] [Indexed: 12/31/2022]
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30
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Vandenbulcke M, Bouckaert F, De Winter FL, Koole M, Adamczuk K, Vandenberghe R, Emsell L, Van Laere K. Asymmetric Amyloid Deposition in the Brain Following Unilateral Electroconvulsive Therapy. Biol Psychiatry 2017; 81:e11-e13. [PMID: 26582587 DOI: 10.1016/j.biopsych.2015.09.016] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2015] [Accepted: 09/30/2015] [Indexed: 10/22/2022]
Affiliation(s)
- Mathieu Vandenbulcke
- Department of Old Age Psychiatry, University Psychiatric Centre, Katholieke Universiteit Leuven, Leuven, Belgium.
| | - Filip Bouckaert
- Department of Old Age Psychiatry, University Psychiatric Centre, Katholieke Universiteit Leuven, Leuven, Belgium
| | - François-Laurent De Winter
- Department of Old Age Psychiatry, University Psychiatric Centre, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Michel Koole
- Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, Katholieke Universiteit Leuven and University Hospitals Leuven, Leuven, Belgium
| | - Katarzyna Adamczuk
- Laboratory for Cognitive Neurology, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Rik Vandenberghe
- Laboratory for Cognitive Neurology, Katholieke Universiteit Leuven, Leuven, Belgium; Neurology Department, University Hospitals Leuven, Leuven, Belgium; Translational MRI, Department of Imaging and Pathology, Katholieke Universiteit Leuven and University Hospitals Leuven, Leuven, Belgium
| | - Louise Emsell
- Department of Old Age Psychiatry, University Psychiatric Centre, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Koen Van Laere
- Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, Katholieke Universiteit Leuven and University Hospitals Leuven, Leuven, Belgium
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31
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Abstract
Amyloid imaging represents a significant advance as an adjunct in the diagnosis of Alzheimer's disease (AD) because it is the first imaging modality that identifies in vivo changes known to be associated with the pathogenesis. Initially, 11C-PIB was developed, which was the prototype for many 18F compounds, including florbetapir, florbetaben, and flutemetamol, among others. Despite the high sensitivity and specificity of amyloid imaging, it is not commonly used in clinical practice, mainly because it is not reimbursed under current Center for Medicare and Medicaid Services guidelines in the USA. To guide the field in who would be most appropriate for the utility of amyloid positron emission tomography, current studies are underway [Imaging Dementia Evidence for Amyloid Scanning (IDEAS) Study] that will inform the field on the utilization of amyloid positron emission tomography in clinical practice. With the advent of monoclonal antibodies that specifically target amyloid antibody, there is an interest, possibly a mandate, to screen potential treatment recipients to ensure that they are suitable for treatment. In this review, we summarize progress in the field to date.
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Affiliation(s)
- Keshav Anand
- St. Joseph’s Hospital and Medical Center, 350 W. Thomas Road, Phoenix, AZ 85013 USA
| | - Marwan Sabbagh
- Alzhiemer’s and Memory Disorders Division, Barrow Neurological Institute, 240 W. Thomas Road, Ste 301, Phoenix, AZ 85013 USA
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32
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Nan DD, Gan CS, Wang CW, Qiao JP, Wang XM, Zhou JN. 6-Methoxy-indanone derivatives as potential probes for β-amyloid plaques in Alzheimer's disease. Eur J Med Chem 2016; 124:117-128. [DOI: 10.1016/j.ejmech.2016.07.069] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2016] [Revised: 07/26/2016] [Accepted: 07/27/2016] [Indexed: 11/16/2022]
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33
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Adamczuk K, Schaeverbeke J, Vanderstichele HMJ, Lilja J, Nelissen N, Van Laere K, Dupont P, Hilven K, Poesen K, Vandenberghe R. Diagnostic value of cerebrospinal fluid Aβ ratios in preclinical Alzheimer's disease. Alzheimers Res Ther 2015; 7:75. [PMID: 26677842 PMCID: PMC4683859 DOI: 10.1186/s13195-015-0159-5] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/18/2015] [Accepted: 10/22/2015] [Indexed: 12/31/2022]
Abstract
Introduction In this study of preclinical Alzheimer’s disease (AD) we assessed the added diagnostic value of using cerebrospinal fluid (CSF) Aβ ratios rather than Aβ42 in isolation for detecting individuals who are positive on amyloid positron emission tomography (PET). Methods Thirty-eight community-recruited cognitively intact older adults (mean age 73, range 65–80 years) underwent 18F-flutemetamol PET and CSF measurement of Aβ1-42, Aβ1-40, Aβ1-38, and total tau (ttau). 18F-flutemetamol retention was quantified using standardized uptake value ratios in a composite cortical region (SUVRcomp) with reference to cerebellar grey matter. Based on a prior autopsy validation study, the SUVRcomp cut-off was 1.57. Sensitivities, specificities and cut-offs were defined based on receiver operating characteristic analysis with CSF analytes as variables of interest and 18F-flutemetamol positivity as the classifier. We also determined sensitivities and CSF cut-off values at fixed specificities of 90 % and 95 %. Results Seven out of 38 subjects (18 %) were positive on amyloid PET. Aβ42/ttau, Aβ42/Aβ40, Aβ42/Aβ38, and Aβ42 had the highest accuracy to identify amyloid-positive subjects (area under the curve (AUC) ≥ 0.908). Aβ40 and Aβ38 had significantly lower discriminative power (AUC = 0.571). When specificity was fixed at 90 % and 95 %, Aβ42/ttau had the highest sensitivity among the different CSF markers (85.71 % and 71.43 %, respectively). Sensitivity of Aβ42 alone was significantly lower under these conditions (57.14 % and 42.86 %, respectively). Conclusion For the CSF-based definition of preclinical AD, if a high specificity is required, our data support the use of Aβ42/ttau rather than using Aβ42 in isolation.
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Affiliation(s)
- Katarzyna Adamczuk
- Laboratory for Cognitive Neurology, KU Leuven, Herestraat 49, 3000, Leuven, Belgium. .,Alzheimer Research Centre KU Leuven, Leuven Institute of Neuroscience and Disease, Herestraat 49, 3000, Leuven, Belgium.
| | - Jolien Schaeverbeke
- Laboratory for Cognitive Neurology, KU Leuven, Herestraat 49, 3000, Leuven, Belgium. .,Alzheimer Research Centre KU Leuven, Leuven Institute of Neuroscience and Disease, Herestraat 49, 3000, Leuven, Belgium.
| | | | - Johan Lilja
- GE Healthcare, Björkgatan 30, 751 25, Uppsala, Sweden. .,Nuclear Medicine and PET, Department of Surgical Sciences, Uppsala University, 751 85, Uppsala, Sweden.
| | - Natalie Nelissen
- Laboratory for Cognitive Neurology, KU Leuven, Herestraat 49, 3000, Leuven, Belgium. .,Department of Psychiatry, Oxford University, Oxford, OX3 7JX, UK.
| | - Koen Van Laere
- Alzheimer Research Centre KU Leuven, Leuven Institute of Neuroscience and Disease, Herestraat 49, 3000, Leuven, Belgium. .,Nuclear Medicine and Molecular Imaging Department, KU Leuven and University Hospitals Leuven, Herestraat 49, 3000, Leuven, Belgium.
| | - Patrick Dupont
- Laboratory for Cognitive Neurology, KU Leuven, Herestraat 49, 3000, Leuven, Belgium. .,Alzheimer Research Centre KU Leuven, Leuven Institute of Neuroscience and Disease, Herestraat 49, 3000, Leuven, Belgium.
| | - Kelly Hilven
- Laboratory for Neuroimmunology, KU Leuven, Herestraat 49, 3000, Leuven, Belgium.
| | - Koen Poesen
- Laboratory for Molecular Neurobiomarker Research, KU Leuven, Herestraat 49, 3000, Leuven, Belgium. .,Laboratory Medicine, UZ Leuven, Herestraat 49, 3000, Leuven, Belgium.
| | - Rik Vandenberghe
- Laboratory for Cognitive Neurology, KU Leuven, Herestraat 49, 3000, Leuven, Belgium. .,Alzheimer Research Centre KU Leuven, Leuven Institute of Neuroscience and Disease, Herestraat 49, 3000, Leuven, Belgium. .,Neurology Department, University Hospitals Leuven, Herestraat 49, 3000, Leuven, Belgium.
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