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Cerebral Folate Metabolism in Post-Mortem Alzheimer's Disease Tissues: A Small Cohort Study. Int J Mol Sci 2022; 24:ijms24010660. [PMID: 36614107 PMCID: PMC9820589 DOI: 10.3390/ijms24010660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 12/27/2022] [Indexed: 01/03/2023] Open
Abstract
We investigated the cerebral folate system in post-mortem brains and matched cerebrospinal fluid (CSF) samples from subjects with definite Alzheimer's disease (AD) (n = 21) and neuropathologically normal brains (n = 21) using immunohistochemistry, Western blot and dot blot. In AD the CSF showed a significant decrease in 10-formyl tetrahydrofolate dehydrogenase (FDH), a critical folate binding protein and enzyme in the CSF, as well as in the main folate transporter, folate receptor alpha (FRα) and folate. In tissue, we found a switch in the pathway of folate supply to the cerebral cortex in AD compared to neurologically normal brains. FRα switched from entry through FDH-positive astrocytes in normal, to entry through glial fibrillary acidic protein (GFAP)-positive astrocytes in the AD cortex. Moreover, this switch correlated with an apparent change in metabolic direction to hypermethylation of neurons in AD. Our data suggest that the reduction in FDH in CSF prohibits FRα-folate entry via FDH-positive astrocytes and promotes entry through the GFAP pathway directly to neurons for hypermethylation. This data may explain some of the cognitive decline not attributable to the loss of neurons alone and presents a target for potential treatment.
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Folate Related Pathway Gene Analysis Reveals a Novel Metabolic Variant Associated with Alzheimer’s Disease with a Change in Metabolic Profile. Metabolites 2022; 12:metabo12060475. [PMID: 35736408 PMCID: PMC9230919 DOI: 10.3390/metabo12060475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 05/20/2022] [Accepted: 05/20/2022] [Indexed: 11/30/2022] Open
Abstract
Metabolic disorders may be important potential causative pathways to Alzheimer’s disease (AD). Cerebrospinal fluid (CSF) decreasing output, raised intracranial pressure, and ventricular enlargement have all been linked to AD. Cerebral folate metabolism may be a key player since this is significantly affected by such changes in CSF, and genetic susceptibilities may exist in this pathway. In the current study, we aimed to identify whether any single nucleotide polymorphism (SNPs) affecting folate and the associated metabolic pathways were significantly associated with AD. We took a functional nutrigenomics approach to look for SNPs in genes for the linked folate, methylation, and biogenic amine neurotransmitter pathways. Changes in metabolism were found with the SNPs identified. An abnormal SNP in methylene tetrahydrofolate dehydrogenase 1 (MTHFD1) was significantly predictive of AD and associated with an increase in tissue glutathione. Individuals without these SNPs had normal levels of glutathione but significantly raised MTHFD1. Both changes would serve to decrease potentially neurotoxic levels of homocysteine. Seven additional genes were associated with Alzheimer’s and five with normal ageing. MTHFD1 presents a strong prediction of susceptibility and disease among the SNPs associated with AD. Associated physiological changes present potential biomarkers for identifying at-risk individuals.
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3
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Dong Q, Zhang W, Stonnington CM, Wu J, Gutman BA, Chen K, Su Y, Baxter LC, Thompson PM, Reiman EM, Caselli RJ, Wang Y. Applying surface-based morphometry to study ventricular abnormalities of cognitively unimpaired subjects prior to clinically significant memory decline. NEUROIMAGE-CLINICAL 2020; 27:102338. [PMID: 32683323 PMCID: PMC7371915 DOI: 10.1016/j.nicl.2020.102338] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Revised: 06/15/2020] [Accepted: 07/02/2020] [Indexed: 12/31/2022]
Abstract
A completely automated surface-based ventricular morphometry system. Generate a whole connected 3D ventricular shape model. Test-retest the system in two independent CU subject cohorts. Subregional ventricular abnormalities prior to clinically memory decline.
Ventricular volume (VV) is a widely used structural magnetic resonance imaging (MRI) biomarker in Alzheimer’s disease (AD) research. Abnormal enlargements of VV can be detected before clinically significant memory decline. However, VV does not pinpoint the details of subregional ventricular expansions. Here we introduce a ventricular morphometry analysis system (VMAS) that generates a whole connected 3D ventricular shape model and encodes a great deal of ventricular surface deformation information that is inaccessible by VV. VMAS contains an automated segmentation approach and surface-based multivariate morphometry statistics. We applied VMAS to two independent datasets of cognitively unimpaired (CU) groups. To our knowledge, it is the first work to detect ventricular abnormalities that distinguish normal aging subjects from those who imminently progress to clinically significant memory decline. Significant bilateral ventricular morphometric differences were first shown in 38 members of the Arizona APOE cohort, which included 18 CU participants subsequently progressing to the clinically significant memory decline within 2 years after baseline visits (progressors), and 20 matched CU participants with at least 4 years of post-baseline cognitive stability (non-progressors). VMAS also detected significant differences in bilateral ventricular morphometry in 44 Alzheimer’s Disease Neuroimaging Initiative (ADNI) subjects (18 CU progressors vs. 26 CU non-progressors) with the same inclusion criterion. Experimental results demonstrated that the ventricular anterior horn regions were affected bilaterally in CU progressors, and more so on the left. VMAS may track disease progression at subregional levels and measure the effects of pharmacological intervention at a preclinical stage.
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Affiliation(s)
- Qunxi Dong
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Wen Zhang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | | | - Jianfeng Wu
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Boris A Gutman
- Armour College of Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Kewei Chen
- Banner Alzheimer's Institute, Phoenix, AZ, USA
| | - Yi Su
- Banner Alzheimer's Institute, Phoenix, AZ, USA
| | - Leslie C Baxter
- Human Brain Imaging Laboratory, Barrow Neurological Institute, Phoenix, AZ, USA
| | - Paul M Thompson
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
| | | | | | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.
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4
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Smith CD, Van Eldik LJ, Jicha GA, Schmitt FA, Nelson PT, Abner EL, Kryscio RJ, Murphy RR, Andersen AH. Brain structure changes over time in normal and mildly impaired aged persons. AIMS Neurosci 2020; 7:120-135. [PMID: 32607416 PMCID: PMC7321765 DOI: 10.3934/neuroscience.2020009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Accepted: 05/08/2020] [Indexed: 01/25/2023] Open
Abstract
Structural brain changes in aging are known to occur even in the absence of dementia, but the magnitudes and regions involved vary between studies. To further characterize these changes, we analyzed paired MRI images acquired with identical protocols and scanner over a median 5.8-year interval. The normal study group comprised 78 elders (25M 53F, baseline age range 70–78 years) who underwent an annual standardized expert assessment of cognition and health and who maintained normal cognition for the duration of the study. We found a longitudinal grey matter (GM) loss rate of 2.56 ± 0.07 ml/year (0.20 ± 0.04%/year) and a cerebrospinal fluid (CSF) expansion rate of 2.97 ± 0.07 ml/year (0.22 ± 0.04%/year). Hippocampal volume loss rate was higher than the GM and CSF global rates, 0.0114 ± 0.0004 ml/year (0.49 ± 0.04%/year). Regions of greatest GM loss were posterior inferior frontal lobe, medial parietal lobe and dorsal cerebellum. Rates of GM loss and CSF expansion were on the low end of the range of other published values, perhaps due to the relatively good health of the elder volunteers in this study. An additional smaller group of 6 subjects diagnosed with MCI at baseline were followed as well, and comparisons were made with the normal group in terms of both global and regional GM loss and CSF expansion rates. An increased rate of GM loss was found in the hippocampus bilaterally for the MCI group.
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Affiliation(s)
- Charles D Smith
- Department of Neurology, University of Kentucky College of Medicine, Lexington, Kentucky, USA.,Magnetic Resonance Imaging and Spectroscopy Center, University of Kentucky, Lexington, Kentucky, USA
| | - Linda J Van Eldik
- Alzheimer's Disease Center, Sanders-Brown Center on Aging, University of Kentucky, Lexington, Kentucky, USA.,Department of Neuroscience, University of Kentucky, Lexington, Kentucky, USA
| | - Gregory A Jicha
- Department of Neurology, University of Kentucky College of Medicine, Lexington, Kentucky, USA.,Alzheimer's Disease Center, Sanders-Brown Center on Aging, University of Kentucky, Lexington, Kentucky, USA
| | - Frederick A Schmitt
- Department of Neurology, University of Kentucky College of Medicine, Lexington, Kentucky, USA.,Alzheimer's Disease Center, Sanders-Brown Center on Aging, University of Kentucky, Lexington, Kentucky, USA
| | - Peter T Nelson
- Alzheimer's Disease Center, Sanders-Brown Center on Aging, University of Kentucky, Lexington, Kentucky, USA.,Department of Pathology and Laboratory Medicine, University of Kentucky, Lexington, Kentucky, USA
| | - Erin L Abner
- Alzheimer's Disease Center, Sanders-Brown Center on Aging, University of Kentucky, Lexington, Kentucky, USA.,Department of Epidemiology, University of Kentucky, Lexington, Kentucky, USA
| | - Richard J Kryscio
- Alzheimer's Disease Center, Sanders-Brown Center on Aging, University of Kentucky, Lexington, Kentucky, USA.,Department of Statistics, University of Kentucky, Lexington, Kentucky, USA
| | - Ronan R Murphy
- Department of Neurology, University of Kentucky College of Medicine, Lexington, Kentucky, USA.,Alzheimer's Disease Center, Sanders-Brown Center on Aging, University of Kentucky, Lexington, Kentucky, USA
| | - Anders H Andersen
- Magnetic Resonance Imaging and Spectroscopy Center, University of Kentucky, Lexington, Kentucky, USA.,Department of Neuroscience, University of Kentucky, Lexington, Kentucky, USA
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5
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Oschwald J, Guye S, Liem F. Brain structure and cognitive ability in healthy aging: a review on longitudinal correlated change. Rev Neurosci 2019; 31:1-57. [PMID: 31194693 PMCID: PMC8572130 DOI: 10.1515/revneuro-2018-0096] [Citation(s) in RCA: 115] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2018] [Accepted: 03/02/2019] [Indexed: 12/20/2022]
Abstract
Little is still known about the neuroanatomical substrates related to changes in specific cognitive abilities in the course of healthy aging, and the existing evidence is predominantly based on cross-sectional studies. However, to understand the intricate dynamics between developmental changes in brain structure and changes in cognitive ability, longitudinal studies are needed. In the present article, we review the current longitudinal evidence on correlated changes between magnetic resonance imaging-derived measures of brain structure (e.g. gray matter/white matter volume, cortical thickness), and laboratory-based measures of fluid cognitive ability (e.g. intelligence, memory, processing speed) in healthy older adults. To theoretically embed the discussion, we refer to the revised Scaffolding Theory of Aging and Cognition. We found 31 eligible articles, with sample sizes ranging from n = 25 to n = 731 (median n = 104), and participant age ranging from 19 to 103. Several of these studies report positive correlated changes for specific regions and specific cognitive abilities (e.g. between structures of the medial temporal lobe and episodic memory). However, the number of studies presenting converging evidence is small, and the large methodological variability between studies precludes general conclusions. Methodological and theoretical limitations are discussed. Clearly, more empirical evidence is needed to advance the field. Therefore, we provide guidance for future researchers by presenting ideas to stimulate theory and methods for development.
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Affiliation(s)
- Jessica Oschwald
- University Research Priority Program ‘Dynamics of Healthy Aging’, University of Zurich, Andreasstrasse 15, CH-8050 Zurich, Switzerland
| | - Sabrina Guye
- University Research Priority Program ‘Dynamics of Healthy Aging’, University of Zurich, Andreasstrasse 15, CH-8050 Zurich, Switzerland
| | - Franziskus Liem
- University Research Priority Program ‘Dynamics of Healthy Aging’, University of Zurich, Andreasstrasse 15, CH-8050 Zurich, Switzerland
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6
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Sügis E, Dauvillier J, Leontjeva A, Adler P, Hindie V, Moncion T, Collura V, Daudin R, Loe-Mie Y, Herault Y, Lambert JC, Hermjakob H, Pupko T, Rain JC, Xenarios I, Vilo J, Simonneau M, Peterson H. HENA, heterogeneous network-based data set for Alzheimer's disease. Sci Data 2019; 6:151. [PMID: 31413325 PMCID: PMC6694132 DOI: 10.1038/s41597-019-0152-0] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Accepted: 06/18/2019] [Indexed: 12/13/2022] Open
Abstract
Alzheimer's disease and other types of dementia are the top cause for disabilities in later life and various types of experiments have been performed to understand the underlying mechanisms of the disease with the aim of coming up with potential drug targets. These experiments have been carried out by scientists working in different domains such as proteomics, molecular biology, clinical diagnostics and genomics. The results of such experiments are stored in the databases designed for collecting data of similar types. However, in order to get a systematic view of the disease from these independent but complementary data sets, it is necessary to combine them. In this study we describe a heterogeneous network-based data set for Alzheimer's disease (HENA). Additionally, we demonstrate the application of state-of-the-art graph convolutional networks, i.e. deep learning methods for the analysis of such large heterogeneous biological data sets. We expect HENA to allow scientists to explore and analyze their own results in the broader context of Alzheimer's disease research.
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Affiliation(s)
- Elena Sügis
- Quretec Ltd., Ülikooli 6a, 51003, Tartu, Estonia
- Institute of Computer Science, University of Tartu, J. Liivi 2, 50409, Tartu, Estonia
| | - Jerome Dauvillier
- Swiss Institute of Bioinformatics, Vital-IT group, Unil Quartier Sorge, Genopode building, CH-1015, Lausanne, Switzerland
| | - Anna Leontjeva
- CSIRO Data 61, 5/13 Garden St, Eveleigh, NSW, 2015, Australia
| | - Priit Adler
- Quretec Ltd., Ülikooli 6a, 51003, Tartu, Estonia
- Institute of Computer Science, University of Tartu, J. Liivi 2, 50409, Tartu, Estonia
| | - Valerie Hindie
- Hybrigenics SA, 3-5 Impasse Reille, 75014, Paris, France
| | - Thomas Moncion
- Hybrigenics SA, 3-5 Impasse Reille, 75014, Paris, France
| | | | - Rachel Daudin
- Institut national de la santé et de la recherche médicale, INSERM U894 2 ter rue d'Alésia, 75014, Paris, France
- Laboratoire Aimé Cotton, Centre National Recherche Scientifique, Université Paris-Sud, Ecole Normale Supérieure Paris-Saclay, Université Paris-Saclay, 91405, Orsay, France
| | - Yann Loe-Mie
- (Epi)genomics of Animal Development Unit, Institut Pasteur, CNRS UMR3738, Paris, 75015, France
| | - Yann Herault
- Centre Européen de Recherche en Biologie et Médecine, 1 rue Laurent Fries, 67404, Illkirch, France
| | - Jean-Charles Lambert
- Institut Pasteur de Lille, UMR 744 1 rue du Pr. Calmette BP 245, 59019, Lille cedex, France
| | - Henning Hermjakob
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, CB10 1SD, Hinxton, United Kingdom
| | - Tal Pupko
- George S. Wise Faculty of Life Sciences, School of Molecular Cell Biology and Biotechnology, Tel Aviv University, P.O. Box 39040, 6997801, Tel Aviv, Israel
| | | | - Ioannis Xenarios
- Center for Integrative Genomics University of Lausanne, Genopode, 1015, Lausanne, Switzerland
- Genome Center Health 2030, Analytical Platform Department, Chemin des Mines 9, 1202, Genève, Switzerland
- DFR CHUV, Rue du Bugnon 21, 1011, Lausanne, Switzerland
- Agora Center, LICR/Department of Oncology, Rue du Bugnon 25A, 1005, Lausanne, Switzerland
| | - Jaak Vilo
- Quretec Ltd., Ülikooli 6a, 51003, Tartu, Estonia
- Institute of Computer Science, University of Tartu, J. Liivi 2, 50409, Tartu, Estonia
| | - Michel Simonneau
- Institut national de la santé et de la recherche médicale, INSERM U894 2 ter rue d'Alésia, 75014, Paris, France.
- Laboratoire Aimé Cotton, Centre National Recherche Scientifique, Université Paris-Sud, Ecole Normale Supérieure Paris-Saclay, Université Paris-Saclay, 91405, Orsay, France.
| | - Hedi Peterson
- Quretec Ltd., Ülikooli 6a, 51003, Tartu, Estonia.
- Institute of Computer Science, University of Tartu, J. Liivi 2, 50409, Tartu, Estonia.
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7
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Huang H, Tanner J, Parvataneni H, Rice M, Horgas A, Ding M, Price C. Impact of Total Knee Arthroplasty with General Anesthesia on Brain Networks: Cognitive Efficiency and Ventricular Volume Predict Functional Connectivity Decline in Older Adults. J Alzheimers Dis 2018; 62:319-333. [PMID: 29439328 PMCID: PMC5827939 DOI: 10.3233/jad-170496] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Using resting state functional magnetic resonance imaging (RS-fMRI), we explored: 1) pre- to post-operative changes in functional connectivity in default mode, salience, and central executive networks after total knee arthroplasty (TKA) with general anesthesia, and 2) the contribution of cognitive/brain reserve metrics these resting state functional declines. Individuals age 60 and older electing unilateral total knee arthroplasty (TKA; n = 48) and non-surgery peers with osteoarthritis (n = 45) completed baseline cognitive testing and baseline and post-surgery (post-baseline, 48-h post-surgery) brain MRI. We acquired cognitive and brain estimates for premorbid (vocabulary, reading, education, intracranial volume) and current (working memory, processing speed, declarative memory, ventricular volume) reserve. Functional network analyses corrected for pain severity and pain medication. The surgery group declined in every functional network of interest (p < 0.001). Relative to non-surgery peers, 23% of surgery participants declined in at least one network and 15% of the total TKA sample declined across all networks. Larger preoperative ventricular volume and lower scores on preoperative metrics of processing speed and working memory predicted default mode network connectivity decline. Premorbid cognitive and premorbid brain reserve did not predict decline. Within 48 hours after surgery, at least one fourth of the older adult sample showed significant functional network decline. Metrics of current brain status (ventricular volume), working memory, and processing speed predicted the severity of default mode network connectivity decline. These findings demonstrate the relevance of preoperative cognition and brain integrity on acute postoperative functional network change.
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Affiliation(s)
- Haiqing Huang
- Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA
| | - Jared Tanner
- Department of Clinical and Health Psychology, University of Florida, Gainesville, FL, USA
| | - Hari Parvataneni
- Department of Orthopedic Surgery, University of Florida, Gainesville, FL, USA
| | - Mark Rice
- Department of Anesthesiology, University of Florida, Gainesville, FL, USA
| | - Ann Horgas
- College of Nursing, University of Florida, Gainesville, FL, USA
| | - Mingzhou Ding
- Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA
| | - Catherine Price
- Department of Clinical and Health Psychology, University of Florida, Gainesville, FL, USA
- Department of Anesthesiology, University of Florida, Gainesville, FL, USA
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8
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Tuokkola T, Koikkalainen J, Parkkola R, Karrasch M, Lötjönen J, Rinne JO. Visual rating method and tensor-based morphometry in the diagnosis of mild cognitive impairment and Alzheimer's disease: a comparative magnetic resonance imaging study. Acta Radiol 2016; 57:348-55. [PMID: 25977576 DOI: 10.1177/0284185115584656] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2014] [Accepted: 03/19/2015] [Indexed: 11/17/2022]
Abstract
BACKGROUND Atrophy of the medial temporal lobe (MTL) is the main structural magnetic resonance imaging (MRI) finding in the brain of patients with Alzheimer's disease (AD). However, evaluating the degree of atrophy is still demanding. PURPOSE The visual rating method (VRM) was compared with multi-template tensor-based morphometry (TBM), in terms of its efficacy in diagnosing of mild cognitive impairment (MCI) and AD. MATERIAL AND METHODS Forty-seven patients with MCI, 80 patients with AD and 84 controls were studied. RESULTS TBM seems to be more sensitive than VRM at the early stage of dementia in the areas of MTL and ventricles. The methods were equally good in distinguishing controls and the MCI group from the AD group. At the frontal areas TBM was better than VRM in all comparisons. CONCLUSION A user-friendly VRM is still useful for the clinical evaluation of MCI patients, but multi-template TBM is more sensitive for diagnosing the early stages of dementia. However, TBM is currently too demanding to use for daily clinical work.
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Affiliation(s)
- Terhi Tuokkola
- Turku PET Centre, Turku University Hospital, Finland University of Turku, Turku, Finland
| | | | - Riitta Parkkola
- Department of Radiology, University of Turku and Turku University Hospital, Finland
| | - Mira Karrasch
- Department of Psychology and Logopedics, Abo Akademi University, Turku, Finland
| | - Jyrki Lötjönen
- VTT Technical Research Centre of Finland, Tampere, Finland
| | - Juha O Rinne
- Turku PET Centre, Turku University Hospital, Finland Division of Clinical Neurosciences, University of Turku and Turku University Hospital, Finland
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9
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Callahan BL, Ramirez J, Berezuk C, Duchesne S, Black SE. Predicting Alzheimer's disease development: a comparison of cognitive criteria and associated neuroimaging biomarkers. ALZHEIMERS RESEARCH & THERAPY 2015; 7:68. [PMID: 26537709 PMCID: PMC4634913 DOI: 10.1186/s13195-015-0152-z] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/13/2015] [Accepted: 09/30/2015] [Indexed: 01/18/2023]
Abstract
Introduction The definition of “objective cognitive impairment” in current criteria for mild cognitive impairment (MCI) varies considerably between research groups and clinics. This study aims to compare different methods of defining memory impairment to improve prediction models for the development of Alzheimer’s disease (AD) from baseline to 24 months. Methods The sensitivity and specificity of six methods of defining episodic memory impairment (< −1, −1.5 or −2 standard deviations [SD] on one or two memory tests) were compared in 494 non-demented seniors from the Alzheimer’s Disease Neuroimaging Initiative using the area under the curve (AUC) for receiver operating characteristic analysis. The added value of non-memory measures (language and executive function) and biomarkers (hippocampal and white-matter hyperintensity volume, brain parenchymal fraction [BPF], and APOEε4 status) was investigated using logistic regression. Results Baseline scores < −1 SD on two memory tests predicted AD with 75.91 % accuracy (AUC = 0.80). Only APOE ε4 status further improved prediction (B = 1.10, SE = 0.45, p = .016). A < −1.5 SD cut-off on one test had 66.60 % accuracy (AUC = 0.77). Prediction was further improved using Trails B/A ratio (B = 0.27, SE = 0.13, p = .033), BPF (B = −15.97, SE = 7.58, p = .035), and APOEε4 status (B = 1.08, SE = 0.45, p = .017). A cut-off of < −2 SD on one memory test (AUC = 0.77, SE = 0.03, 95 % CI 0.72-0.82) had 76.52 % accuracy in predicting AD. Trails B/A ratio (B = 0.31, SE = 0.13, p = .017) and APOE ε4 status (B = 1.07, SE = 0.46, p = .019) improved predictive accuracy. Conclusions Episodic memory impairment in MCI should be defined as scores < −1 SD below normative references on at least two measures. Clinicians or researchers who administer a single test should opt for a more stringent cut-off and collect and analyze whole-brain volume. When feasible, ascertaining APOE ε4 status can further improve prediction.
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Affiliation(s)
- Brandy L Callahan
- LC Campbell Cognitive Neurology Research Unit, Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, Rm A4 21, Toronto, Ontario, M4N 3 M5, Canada. .,Heart & Stroke Foundation Canadian Partnership in Stroke Recovery, Sunnybrook Health Sciences Centre, Toronto, Canada. .,Sunnybrook Health Sciences Centre, Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, Canada. .,Université Laval, Faculté de médecine (Radiologie), Québec, Canada. .,Centre de recherche de l'Institut universitaire en santé mentale de Québec, Québec, Canada.
| | - Joel Ramirez
- LC Campbell Cognitive Neurology Research Unit, Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, Rm A4 21, Toronto, Ontario, M4N 3 M5, Canada. .,Heart & Stroke Foundation Canadian Partnership in Stroke Recovery, Sunnybrook Health Sciences Centre, Toronto, Canada. .,Sunnybrook Health Sciences Centre, Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, Canada.
| | - Courtney Berezuk
- LC Campbell Cognitive Neurology Research Unit, Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, Rm A4 21, Toronto, Ontario, M4N 3 M5, Canada. .,Heart & Stroke Foundation Canadian Partnership in Stroke Recovery, Sunnybrook Health Sciences Centre, Toronto, Canada. .,Sunnybrook Health Sciences Centre, Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, Canada.
| | - Simon Duchesne
- Université Laval, Faculté de médecine (Radiologie), Québec, Canada. .,Centre de recherche de l'Institut universitaire en santé mentale de Québec, Québec, Canada.
| | - Sandra E Black
- LC Campbell Cognitive Neurology Research Unit, Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, Rm A4 21, Toronto, Ontario, M4N 3 M5, Canada. .,Heart & Stroke Foundation Canadian Partnership in Stroke Recovery, Sunnybrook Health Sciences Centre, Toronto, Canada. .,Sunnybrook Health Sciences Centre, Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, Canada. .,Department of Medicine (Neurology), University of Toronto, Institute of Medical Science, Québec, Canada.
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10
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Combined effects of Alzheimer risk variants in the CLU and ApoE genes on ventricular expansion patterns in the elderly. J Neurosci 2014; 34:6537-45. [PMID: 24806679 DOI: 10.1523/jneurosci.5236-13.2014] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
The C allele at the rs11136000 locus in the clusterin (CLU) gene is the third strongest known genetic risk factor for late-onset Alzheimer's disease (LOAD). A recent genome-wide association study of LOAD found the strongest evidence of association with CLU at rs1532278, in high linkage disequilibrium with rs11136000. Brain structure and function are related to the CLU risk alleles, not just in LOAD patients but also in healthy young adults. We tracked the volume of the lateral ventricles across baseline, 1-year, and 2-year follow-up scans in a large sample of elderly human participants (N = 736 at baseline), from the Alzheimer's Disease Neuroimaging Initiative, to determine whether these CLU risk variants predicted longitudinal ventricular expansion. The rs11136000 major C allele-previously linked with reduced CLU expression and with increased risk for dementia-predicted faster expansion, independently of dementia status or ApoE genotype. Further analyses revealed that the CLU and ApoE risk variants had combined effects on both volumetric expansion and lateral ventricle surface morphology. The rs1532278 locus strongly resembles a regulatory element. Its association with ventricular expansion was slightly stronger than that of rs11136000 in our analyses, suggesting that it may be closer to a functional variant. Clusterin affects inflammation, immune responses, and amyloid clearance, which in turn may result in neurodegeneration. Pharmaceutical agents such as valproate, which counteract the effects of genetically determined reduced clusterin expression, may help to achieve neuroprotection and contribute to the prevention of dementia, especially in carriers of these CLU risk variants.
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11
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Roussotte FF, Gutman BA, Hibar DP, Jahanshad N, Madsen SK, Jack CR, Weiner MW, Thompson PM. A single nucleotide polymorphism associated with reduced alcohol intake in the RASGRF2 gene predicts larger cortical volumes but faster longitudinal ventricular expansion in the elderly. Front Aging Neurosci 2013; 5:93. [PMID: 24409144 PMCID: PMC3867747 DOI: 10.3389/fnagi.2013.00093] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2013] [Accepted: 11/30/2013] [Indexed: 11/23/2022] Open
Abstract
A recent genome-wide association meta-analysis showed a suggestive association between alcohol intake in humans and a common single nucleotide polymorphism in the ras-specific guanine nucleotide releasing factor 2 gene. Here, we tested whether this variant – associated with lower alcohol consumption – showed associations with brain structure and longitudinal ventricular expansion over time, across two independent elderly cohorts, totaling 1,032 subjects. We first examined a large sample of 738 elderly participants with neuroimaging and genetic data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI1). Then, we assessed the generalizability of the findings by testing this polymorphism in a replication sample of 294 elderly subjects from a continuation of the first ADNI project (ADNI2) to minimize the risk of reporting false positive results. The minor allele – previously linked with lower alcohol intake – was associated with larger volumes in various cortical regions, notably the medial prefrontal cortex and cingulate gyrus in both cohorts. Intriguingly, the same allele also predicted faster ventricular expansion rates in the ADNI1 cohort at 1- and 2-year follow up. Despite a lack of alcohol consumption data in this study cohort, these findings, combined with earlier functional imaging investigations of the same gene, suggest the existence of reciprocal interactions between genes, brain, and drinking behavior.
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Affiliation(s)
- Florence F Roussotte
- Imaging Genetics Center, University of Southern California Los Angeles, CA, USA ; Departments of Neurology and Psychiatry, David Geffen School of Medicine at University of California Los Angeles Los Angeles, CA, USA
| | - Boris A Gutman
- Imaging Genetics Center, University of Southern California Los Angeles, CA, USA
| | - Derrek P Hibar
- Imaging Genetics Center, University of Southern California Los Angeles, CA, USA
| | - Neda Jahanshad
- Imaging Genetics Center, University of Southern California Los Angeles, CA, USA
| | - Sarah K Madsen
- Imaging Genetics Center, University of Southern California Los Angeles, CA, USA
| | | | - Michael W Weiner
- Departments of Radiology, Medicine, Psychiatry, University of California San Francisco San Francisco, CA, USA ; Department of Veterans Affairs Medical Center San Francisco, CA, USA
| | - Paul M Thompson
- Imaging Genetics Center, University of Southern California Los Angeles, CA, USA ; Departments of Neurology and Psychiatry, David Geffen School of Medicine at University of California Los Angeles Los Angeles, CA, USA ; Departments of Neurology, Psychiatry, Pediatrics, Engineering, Radiology, and Ophthalmology, Keck University of Southern California School of Medicine, University of Southern California , Los Angeles, CA, USA
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Roussotte FF, Gutman BA, Madsen SK, Colby JB, Narr KL, Thompson PM. Apolipoprotein E epsilon 4 allele is associated with ventricular expansion rate and surface morphology in dementia and normal aging. Neurobiol Aging 2013; 35:1309-17. [PMID: 24411483 DOI: 10.1016/j.neurobiolaging.2013.11.030] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2013] [Revised: 11/20/2013] [Accepted: 11/29/2013] [Indexed: 01/12/2023]
Abstract
The apolipoprotein E epsilon 4 allele (ApoE-ε4) is the strongest known genetic risk factor for late onset Alzheimer's disease. Expansion of the lateral ventricles occurs with normal aging, but dementia accelerates this process. Brain structure and function depend on ApoE genotype not just for Alzheimer's disease patients but also in healthy elderly individuals, and even in asymptomatic young individuals. Therefore, we hypothesized that the ApoE-ε4 allele is associated with altered patterns of longitudinal ventricular expansion, in dementia and normal aging. We tested this hypothesis in a large sample of elderly participants, using a linear discriminant analysis-based approach. Carrying more ApoE-ε4 alleles was associated with faster ventricular expansion bilaterally and with regional patterns of lateral ventricle morphology at 1- and 2-year follow up, after controlling for sex, age, and dementia status. ApoE genotyping is considered critical in clinical trials of Alzheimer's disease. These findings, combined with earlier investigations showing that ApoE is also directly implicated in other conditions, suggest that the selective enrollment of ApoE-ε4 carriers may empower clinical trials of other neurological disorders.
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Affiliation(s)
- Florence F Roussotte
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Boris A Gutman
- Department of Psychiatry, Semel Institute, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA; Imaging Genetics Center, USC Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Sarah K Madsen
- Department of Psychiatry, Semel Institute, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA; Imaging Genetics Center, USC Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - John B Colby
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Katherine L Narr
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Paul M Thompson
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA; Department of Psychiatry, Semel Institute, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA; Imaging Genetics Center, USC Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Department of Neurology, USC Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Department of Psychiatry, USC Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Department of Radiology, USC Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Department of Engineering, USC Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Department of Pediatrics, USC Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Department of Ophthalmology, USC Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
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