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Colvee-Martin H, Parra JR, Gonzalez GA, Barker W, Duara R. Neuropathology, Neuroimaging, and Fluid Biomarkers in Alzheimer's Disease. Diagnostics (Basel) 2024; 14:704. [PMID: 38611617 PMCID: PMC11012058 DOI: 10.3390/diagnostics14070704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 02/05/2024] [Accepted: 02/17/2024] [Indexed: 04/14/2024] Open
Abstract
An improved understanding of the pathobiology of Alzheimer's disease (AD) should lead ultimately to an earlier and more accurate diagnosis of AD, providing the opportunity to intervene earlier in the disease process and to improve outcomes. The known hallmarks of Alzheimer's disease include amyloid-β plaques and neurofibrillary tau tangles. It is now clear that an imbalance between production and clearance of the amyloid beta protein and related Aβ peptides, especially Aβ42, is a very early, initiating factor in Alzheimer's disease (AD) pathogenesis, leading to aggregates of hyperphosphorylation and misfolded tau protein, inflammation, and neurodegeneration. In this article, we review how the AD diagnostic process has been transformed in recent decades by our ability to measure these various elements of the pathological cascade through the use of imaging and fluid biomarkers. The more recently developed plasma biomarkers, especially phosphorylated-tau217 (p-tau217), have utility for screening and diagnosis of the earliest stages of AD. These biomarkers can also be used to measure target engagement by disease-modifying therapies and the response to treatment.
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Affiliation(s)
- Helena Colvee-Martin
- Wien Center for Alzheimer’s Disease & Memory Disorders, Mount Sinai Medical Center, Miami Beach, FL 33140, USA; (H.C.-M.); (W.B.)
| | - Juan Rayo Parra
- Human & Molecular Genetics, Florida International University, Miami, FL 33199, USA; (J.R.P.); (G.A.G.)
| | - Gabriel Antonio Gonzalez
- Human & Molecular Genetics, Florida International University, Miami, FL 33199, USA; (J.R.P.); (G.A.G.)
| | - Warren Barker
- Wien Center for Alzheimer’s Disease & Memory Disorders, Mount Sinai Medical Center, Miami Beach, FL 33140, USA; (H.C.-M.); (W.B.)
| | - Ranjan Duara
- Wien Center for Alzheimer’s Disease & Memory Disorders, Mount Sinai Medical Center, Miami Beach, FL 33140, USA; (H.C.-M.); (W.B.)
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Srikrishna M, Ashton NJ, Moscoso A, Pereira JB, Heckemann RA, van Westen D, Volpe G, Simrén J, Zettergren A, Kern S, Wahlund L, Gyanwali B, Hilal S, Ruifen JC, Zetterberg H, Blennow K, Westman E, Chen C, Skoog I, Schöll M. CT-based volumetric measures obtained through deep learning: Association with biomarkers of neurodegeneration. Alzheimers Dement 2024; 20:629-640. [PMID: 37767905 PMCID: PMC10916947 DOI: 10.1002/alz.13445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 06/29/2023] [Accepted: 08/01/2023] [Indexed: 09/29/2023]
Abstract
INTRODUCTION Cranial computed tomography (CT) is an affordable and widely available imaging modality that is used to assess structural abnormalities, but not to quantify neurodegeneration. Previously we developed a deep-learning-based model that produced accurate and robust cranial CT tissue classification. MATERIALS AND METHODS We analyzed 917 CT and 744 magnetic resonance (MR) scans from the Gothenburg H70 Birth Cohort, and 204 CT and 241 MR scans from participants of the Memory Clinic Cohort, Singapore. We tested associations between six CT-based volumetric measures (CTVMs) and existing clinical diagnoses, fluid and imaging biomarkers, and measures of cognition. RESULTS CTVMs differentiated cognitively healthy individuals from dementia and prodromal dementia patients with high accuracy levels comparable to MR-based measures. CTVMs were significantly associated with measures of cognition and biochemical markers of neurodegeneration. DISCUSSION These findings suggest the potential future use of CT-based volumetric measures as an informative first-line examination tool for neurodegenerative disease diagnostics after further validation. HIGHLIGHTS Computed tomography (CT)-based volumetric measures can distinguish between patients with neurodegenerative disease and healthy controls, as well as between patients with prodromal dementia and controls. CT-based volumetric measures associate well with relevant cognitive, biochemical, and neuroimaging markers of neurodegenerative diseases. Model performance, in terms of brain tissue classification, was consistent across two cohorts of diverse nature. Intermodality agreement between our automated CT-based and established magnetic resonance (MR)-based image segmentations was stronger than the agreement between visual CT and MR imaging assessment.
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Chen S, Zhang D, Zheng H, Cao T, Xia K, Su M, Meng Q. The association between retina thinning and hippocampal atrophy in Alzheimer's disease and mild cognitive impairment: a meta-analysis and systematic review. Front Aging Neurosci 2023; 15:1232941. [PMID: 37680540 PMCID: PMC10481874 DOI: 10.3389/fnagi.2023.1232941] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 07/31/2023] [Indexed: 09/09/2023] Open
Abstract
Introduction The retina is the "window" of the central nervous system. Previous studies discovered that retinal thickness degenerates through the pathological process of the Alzheimer's disease (AD) continuum. Hippocampal atrophy is one of the typical clinical features and diagnostic criteria of AD. Former studies have described retinal thinning in normal aging subjects and AD patients, yet the association between retinal thickness and hippocampal atrophy in AD is unclear. The optical coherence tomography (OCT) technique has access the non-invasive to retinal images and magnetic resonance imaging can outline the volume of the hippocampus. Thus, we aim to quantify the correlation between these two parameters to identify whether the retina can be a new biomarker for early AD detection. Methods We systematically searched the PubMed, Embase, and Web of Science databases from inception to May 2023 for studies investigating the correlation between retinal thickness and hippocampal volume. The Newcastle-Ottawa Quality Assessment Scale (NOS) was used to assess the study quality. Pooled correlation coefficient r values were combined after Fisher's Z transformation. Moderator effects were detected through subgroup analysis and the meta-regression method. Results Of the 1,596 citations initially identified, we excluded 1,062 studies after screening the titles and abstract (animal models, n = 99; irrelevant literature, n = 963). Twelve studies met the inclusion criteria, among which three studies were excluded due to unextractable data. Nine studies were eligible for this meta-analysis. A positive moderate correlation between the retinal thickness was discovered in all participants of with AD, mild cognitive impairment (MCI), and normal controls (NC) (r = 0.3469, 95% CI: 0.2490-0.4377, I2 = 5.0%), which was significantly higher than that of the AD group (r = 0.1209, 95% CI:0.0905-0.1510, I2 = 0.0%) (p < 0.05). Among different layers, the peripapillary retinal nerve fiber layer (pRNFL) indicated a moderate positive correlation with hippocampal volume (r = 0.1209, 95% CI:0.0905-0.1510, I2 = 0.0%). The retinal pigmented epithelium (RPE) was also positively correlated [r = 0.1421, 95% CI:(-0.0447-0.3192), I2 = 84.1%]. The retinal layers and participants were the main overall heterogeneity sources. Correlation in the bilateral hemisphere did not show a significant difference. Conclusion The correlation between RNFL thickness and hippocampal volume is more predominant in both NC and AD groups than other layers. Whole retinal thickness is positively correlated to hippocampal volume not only in AD continuum, especially in MCI, but also in NC. Systematic review registration https://www.crd.york.ac.uk/PROSPERO/, CRD42022328088.
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Affiliation(s)
- Shuntai Chen
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China
| | - Dian Zhang
- Department of Neurology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Honggang Zheng
- Department of Oncology, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Tianyu Cao
- Department of Neurology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Kun Xia
- Department of Respiratory, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Mingwan Su
- Department of Respiratory, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Qinggang Meng
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China
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Arnoldy L, Gauci S, Young LM, Marx W, Macpherson H, Pipingas A, Civier O, White DJ. The association of dietary and nutrient patterns on neurocognitive decline: A systematic review of MRI and PET studies. Ageing Res Rev 2023; 87:101892. [PMID: 36878405 DOI: 10.1016/j.arr.2023.101892] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 02/14/2023] [Accepted: 02/27/2023] [Indexed: 03/08/2023]
Abstract
BACKGROUND As the global population ages, there has been a growing incidence of neurodegenerative diseases such as Alzheimer's. More recently, studies exploring the relationship between dietary patterns and neuroimaging outcomes have received particular attention. This systematic literature review provides a structured overview of the association between dietary and nutrient patterns on neuroimaging outcomes and cognitive markers in middle-aged to older adults. A comprehensive literature search was conducted to find relevant articles published from 1999 to date using the following databases Ovid MEDLINE, Embase, PubMed, Scopus and Web of Science. The inclusion criteria for the articles comprised studies reporting on the association between dietary patterns and neuroimaging outcomes, which includes both specific pathological hallmarks of neurodegenerative diseases such as Aβ and tau and nonspecific markers such as structural MRI and glucose metabolism. The risk of bias was evaluated using the Quality Assessment tool from the National Heart, Lung, and Blood Institute of the National Institutes of Health. The results were then organized into a summary of results table, collated based on synthesis without meta-analysis. After conducting the search, 6050 records were extracted and screened for eligibility, with 107 eligible for full-text screening and 42 articles ultimately being included in this review. The results of the systematic review indicate that there is some evidence suggesting that healthy dietary and nutrient patterns were associated with neuroimaging measures, indicative of a protective influence on neurodegeneration and brain ageing. Conversely, unhealthy dietary and nutrient patterns showed evidence pointing to decreased brain volumes, poorer cognition and increased Aβ deposition. Future research should focus on sensitive neuroimaging acquisition and analysis methods, to study early neurodegenerative changes and identify critical periods for interventions and prevention. SYSTEMATIC REVIEW REGISTRATION PROSPERO registration no, CRD42020194444).
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Affiliation(s)
- Lizanne Arnoldy
- Centre of Human Psychopharmacology, Centre for Mental Health and Brain Sciences, Swinburne University, Melbourne Australia.
| | - Sarah Gauci
- Centre of Human Psychopharmacology, Centre for Mental Health and Brain Sciences, Swinburne University, Melbourne Australia; IMPACT - the Institute for Mental and Physical Health and Clinical Translation, Food & Mood Centre, School of Medicine, Deakin University, Geelong, Australia
| | - Lauren M Young
- Centre of Human Psychopharmacology, Centre for Mental Health and Brain Sciences, Swinburne University, Melbourne Australia; IMPACT - the Institute for Mental and Physical Health and Clinical Translation, Food & Mood Centre, School of Medicine, Deakin University, Geelong, Australia
| | - Wolfgang Marx
- IMPACT - the Institute for Mental and Physical Health and Clinical Translation, Food & Mood Centre, School of Medicine, Deakin University, Geelong, Australia
| | - Helen Macpherson
- Institute for Physical Activity and Nutrition (IPAN), School of Exercise and Nutrition Sciences, Deakin University, Geelong, VIC, Australia
| | - Andrew Pipingas
- Centre of Human Psychopharmacology, Centre for Mental Health and Brain Sciences, Swinburne University, Melbourne Australia
| | - Oren Civier
- Swinburne Neuroimaging, Swinburne University, Melbourne, Australia
| | - David J White
- Centre of Human Psychopharmacology, Centre for Mental Health and Brain Sciences, Swinburne University, Melbourne Australia; Swinburne Neuroimaging, Swinburne University, Melbourne, Australia
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Wang C, Kravets S, Sethi A, Espeland MA, Pasquale LR, Rapp SR, Klein BE, Meuer SM, Haan MN, Maki PM, Hallak JA, Vajaranant TS. An Association Between Large Optic Cupping and Total and Regional Brain Volume: The Women's Health Initiative. Am J Ophthalmol 2023; 249:21-28. [PMID: 36638905 DOI: 10.1016/j.ajo.2022.12.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 12/02/2022] [Accepted: 12/04/2022] [Indexed: 01/12/2023]
Abstract
PURPOSE To investigate the relationships between optic nerve cupping and total and regional brain volumes. DESIGN Secondary analysis of randomized clinical trial data. METHODS Women 65 to 79 years of age without glaucoma with cup-to-disc ratio (CDR) measurements from the Women's Health Initiative (WHI) Sight Examination study and magnetic resonance imaging (MRI)-based total and regional brain volumes from the WHI Memory Study MRI-1 were included. Large CDR was defined as 0.6 or greater in either eye. Generalized estimating equation models were used to account for intra-brain correlations between the right and left sides. The final analysis was adjusted for demographic and clinical characteristics and for total brain volume (for regional analyses). RESULTS Final analyses included 471 women, with the mean age ± SD was 69.2 ± 3.6 years; 92.8% of the subjects were white. Of 471 women, 34 (7.2%) had large CDR. Controlling for total brain volume and for demographic and clinical characteristics, lateral ventricle volume was 3.01 cc larger for subjects with large CDR compared to those without large CDR (95% CI = 0.02 to 5.99; P = .048). Furthermore, frontal lobe volume was 4.78 cc lower for subjects with large CDR compared to those without (95% CI = -8.71, -0.84; P = 0.02), and occipital lobe volume was 1.86 cc lower for those with large CDR compared to those without (95% CI = -3.39, -0.3; P =.02). CONCLUSIONS Our analysis suggests that in women aged 65 years or more, large CDR is associated with lower relative total brain volume and absolute regional volume in the frontal and occipital lobes. Enlarged CDR in individuals without glaucoma may represent a sign of optic nerve and brain aging, although more longitudinal data are needed.
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Affiliation(s)
- Catherine Wang
- From the Illinois Eye and Ear Infirmary (C.W., S.K., A.S., J.A.H., T.S.V.), Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, Illinois, USA; College of Medicine (C.W., A.S.), University of Illinois at Chicago, Chicago, Ilinois, USA
| | - Sasha Kravets
- From the Illinois Eye and Ear Infirmary (C.W., S.K., A.S., J.A.H., T.S.V.), Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, Illinois, USA; Division of Epidemiology and Biostatistics (S.K.), School of Public Health, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Abhishek Sethi
- From the Illinois Eye and Ear Infirmary (C.W., S.K., A.S., J.A.H., T.S.V.), Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, Illinois, USA; College of Medicine (C.W., A.S.), University of Illinois at Chicago, Chicago, Ilinois, USA
| | - Mark A Espeland
- Departments of Internal Medicine and Biostatistics and Data Science (M.A.E.), Wake Forest University Health Sciences, Winston Salem, North Carolina, USA
| | - Louis R Pasquale
- Department of Ophthalmology (L.R.P.), Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Stephen R Rapp
- Psychiatry and Behavioral Medicine (S.R.R.), Wake Forest University Health Sciences, Winston Salem, North Carolina, USA
| | - Barbara E Klein
- Department of Ophthalmology and Visual Sciences (B.E.K., S.M.M.), University of Wisconsin, Madison, Wisconsin, USA
| | - Stacy M Meuer
- Department of Ophthalmology and Visual Sciences (B.E.K., S.M.M.), University of Wisconsin, Madison, Wisconsin, USA
| | - Mary N Haan
- Department of Epidemiology and Biostatistics (M.N.H.), University of California at San Francisco, San Francisco, California, USA
| | - Pauline M Maki
- Department of Psychiatry (P.M.M.), University of Illinois at Chicago, Chicago, Illinois, USA
| | - Joelle A Hallak
- From the Illinois Eye and Ear Infirmary (C.W., S.K., A.S., J.A.H., T.S.V.), Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Thasarat Sutabutr Vajaranant
- From the Illinois Eye and Ear Infirmary (C.W., S.K., A.S., J.A.H., T.S.V.), Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, Illinois, USA.
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Parent C, Rousseau LS, Predovan D, Duchesne S, Hudon C. Longitudinal association between ß-amyloid accumulation and cognitive decline in cognitively healthy older adults: A systematic review. AGING BRAIN 2023; 3:100074. [PMID: 37180874 PMCID: PMC10173297 DOI: 10.1016/j.nbas.2023.100074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 03/21/2023] [Accepted: 04/11/2023] [Indexed: 05/16/2023] Open
Abstract
This systematic review examined the longitudinal association between amyloid-β (Aβ) accumulation and cognitive decline in cognitively healthy adults. It was conducted using the PubMed, Embase, PsycInfo, and Web of Science databases. The methodological quality of the selected articles was assessed. In fine, seventeen longitudinal clinical studies were included in this review. A minority (seven out of 17) of studies reported a statistically significant association or prediction of cognitive decline with Aβ change, measured by positron emission tomography (PET; n = 6) and lumbar puncture (n = 1), with a mean follow-up duration of 3.17 years for cognition and 2.99 years for Aβ. The studies reporting significant results with PET found differences in the frontal, posterior cingular, lateral parietal and global (whole brain) cortices as well as in the precuneus. Significant associations were found with episodic memory (n = 6) and global cognition (n = 1). Five of the seven studies using a composite cognitive score found significant results. A quality assessment revealed widespread methodological biases, such as failure to report or account for loss-to follow up and missing data, and failure to report p-values and effect sizes of non-significant results. Overall, the longitudinal association between Aβ accumulation and cognitive decline in preclinical Alzheimer's disease remains unclear. The discrepancy in results between studies may be explained in part by the choice of neuroimaging technique used to measure Aβ change, the duration of longitudinal studies, the heterogeneity of the healthy preclinical population, and importantly, the use of a composite score to capture cognitive changes with increased sensitivity. More longitudinal studies with larger sample sizes are needed to elucidate this relationship.
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Affiliation(s)
- Camille Parent
- École de psychologie, Université Laval, Québec, QC, Canada
- Centre de recherche CERVO, Institut universitaire de santé mentale de Québec, Québec, QC, Canada
| | - Louis-Simon Rousseau
- École de psychologie, Université Laval, Québec, QC, Canada
- Centre de recherche CERVO, Institut universitaire de santé mentale de Québec, Québec, QC, Canada
| | - David Predovan
- Centre de recherche CERVO, Institut universitaire de santé mentale de Québec, Québec, QC, Canada
- Département de radiologie et médecine nucléaire, Université Laval, Québec, QC, Canada
| | - Simon Duchesne
- Centre de recherche CERVO, Institut universitaire de santé mentale de Québec, Québec, QC, Canada
- Département de radiologie et médecine nucléaire, Université Laval, Québec, QC, Canada
| | - Carol Hudon
- École de psychologie, Université Laval, Québec, QC, Canada
- Centre de recherche CERVO, Institut universitaire de santé mentale de Québec, Québec, QC, Canada
- Centre de recherche VITAM du Centre intégré universitaire de santé et de services sociaux de la Capitale-Nationale, Québec, QC, Canada
- Corresponding author.
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Qiu A, Xu L, Liu C. Predicting diagnosis 4 years prior to Alzheimer's disease incident. Neuroimage Clin 2022; 34:102993. [PMID: 35344803 PMCID: PMC8958535 DOI: 10.1016/j.nicl.2022.102993] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 03/23/2022] [Accepted: 03/23/2022] [Indexed: 11/24/2022]
Abstract
This study employed a deep learning longitudinal model, graph convolutional and recurrent neural network (graph-CNN-RNN), on a series of brain structural MRI scans for AD prognosis. It characterized whole-brain morphology via incorporating longitudinal cortical and subcortical morphology and defined a probabilistic risk for the prediction of AD as a function of age prior to clinical diagnosis. The graph-CNN-RNN model was trained on half of the Alzheimer's Disease Neuroimaging Initiative dataset (ADNI, n = 1559) and validated on the other half of the ADNI dataset and the Open Access Series of Imaging Studies-3 (OASIS-3, n = 930). Our findings demonstrated that the graph-CNN-RNN can reliably and robustly diagnose AD at the accuracy rate of 85% and above across all the time points for both datasets. The graph-CNN-RNN predicted the AD conversion from 0 to 4 years before the AD onset at ∼80% of accuracy. The AD probabilistic risk was associated with clinical traits, cognition, and amyloid burden assessed using [18F]-Florbetapir (AV45) positron emission tomography (PET) across all the time points. The graph-CNN-RNN provided the quantitative trajectory of brain morphology from prognosis to overt stages of AD. Such a deep learning tool and the AD probabilistic risk have great potential in clinical applications for AD prognosis.
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Affiliation(s)
- Anqi Qiu
- Department of Biomedical Engineering, National University of Singapore, Singapore; The N.1 Institute for Health, National University of Singapore, Singapore; Institute of Data Science, National University of Singapore, Singapore; NUS (Suzhou) Research Institute, Suzhou, China; School of Computer Engineering and Science, Shanghai University, China; Department of Biomedical Engineering, the Johns Hopkins University, USA.
| | - Liyuan Xu
- School of Computer Engineering and Science, Shanghai University, China
| | - Chaoqiang Liu
- Department of Biomedical Engineering, National University of Singapore, Singapore
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Wu L, Wang X, Ye Y, Liu C. Association of Osteoarthritis With Changes in Structural Neuroimaging Markers Over Time Among Non-demented Older Adults. Front Aging Neurosci 2021; 13:664443. [PMID: 34447303 PMCID: PMC8383489 DOI: 10.3389/fnagi.2021.664443] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Accepted: 07/13/2021] [Indexed: 01/12/2023] Open
Abstract
Objective: Although emerging evidence suggests that both osteoarthritis (OA) and brain atrophy (as assessed by structural neuroimaging markers) are associated with the risk of dementia, little is known about the association between OA and structural neuroimaging markers. This study aimed to examine the association of OA with changes in structural neuroimaging markers among non-demented older people. Methods: We examined the cross-sectional and longitudinal associations between OA and structural neuroimaging markers (hippocampal volume, entorhinal volume, ventricular volume, and volume of gray matter of the whole brain) among non-demented older people. We categorized our participants as those without OA (OA-) and those with OA (OA+). At baseline, we included 1,281 non-demented older adults, including 1,050 without OA and 231 with OA. Results: In the cross-sectional analysis, we did not observe any significant difference in structural neuroimaging markers between the two OA groups. In the longitudinal analysis, we found that compared to participants without OA, those with OA showed a steeper decline in volumes of the gray matter of the whole brain among non-demented older adults. Conclusions: OA was associated with a steeper decline in volumes of the gray matter of the whole brain over time among non-demented older people.
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Affiliation(s)
- Lichuang Wu
- Department of Orthopaedics, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xiang Wang
- Department of Orthopaedics, The First People’s Hospital of Longwan District, Wenzhou, China
| | - Yiheng Ye
- Department of Orthopaedics, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Cailong Liu
- Department of Orthopaedics, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
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Mao C, Sha L, Li J, Huang X, Chu S, Lei D, Wang J, Dong L, Liu C, Xu Q, Peng B, Gao J. Relationship Between General Cognition, Visual Assessed Cortical Atrophy, and Cerebrospinal Fluid Biomarkers in Alzheimer's Disease: A Cross-Sectional Study from a Chinese PUMCH Cohort. J Alzheimers Dis 2021; 82:205-214. [PMID: 34024840 DOI: 10.3233/jad-210344] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
BACKGROUND Cerebrospinal fluid (CSF) biomarkers are widely accepted as manifestations of Alzheimer's disease (AD) pathogenesis and incorporated into biological definition of AD. However, the correlations between CSF and other biomarkers such as neuroimaging and neuropsychiatric evaluation are complicated and inconsistent. OBJECTIVE We aimed to better interpreting CSF biomarkers results accompanying with other indexes in improving accurate diagnosis of AD. METHODS 112 AD patients and 30 cognitive normal controls were selected. Commercial accessible ELISA kits were introduced for measurement of CSF t-tau, p-tau181, Aβ1-42, and NfL based on standard protocol. MRI examinations were performed using a 3-T MRI scanner and visual rating scales including medial temporal atrophy score and Koedam's scale were used to evaluate medial temporal atrophy and posterior region atrophy. RESULTS CSF biomarkers' profile including decreased concentration of Aβ1-42, increased concentration of t-tau, p-tau181, t-tau/Aβ 1-42, and NfL were diagnostic between AD and control. CSF biomarkers profile was not influenced by the APOE genotype. Increased concentration of t-tau and NfL, as well as ratio of t-tau/Aβ 1-42 were related to decrease of Mini-Mental State Examination (MMSE) score while concentration of Aβ1-42 not. Visual assessed cortical atrophy was related to MMSE score, but most of the CSF biomarkers were not related to atrophy, except that increased concentration of p-tau181 was significantly associated with atrophy of posterior cortical region. CONCLUSION Our results supported CSF biomarkers were helpful in diagnosis of AD. However, CSF biomarkers were cross-sectional reflection of pathogenesis, which did not correlate well with clinical progression. CSF biomarkers should be interpreted in combination with MRI and cognitive evaluation in clinical use.
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Affiliation(s)
- Chenhui Mao
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing, China
| | - Longze Sha
- State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China
| | - Jie Li
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing, China
| | - Xinying Huang
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing, China
| | - Shanshan Chu
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing, China
| | - Dan Lei
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing, China
| | - Jie Wang
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing, China
| | - Liling Dong
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing, China
| | - Caiyan Liu
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing, China
| | - Qi Xu
- State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China
| | - Bin Peng
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing, China
| | - Jing Gao
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing, China
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Wang X, Li W, Marcus J, Pearson M, Song L, Smith K, Terracina G, Lee J, Hong KLK, Lu SX, Hyde L, Chen SC, Kinsley D, Melchor JP, Rubins DJ, Meng X, Hostetler E, Sur C, Zhang L, Schachter JB, Hess JF, Selnick HG, Vocadlo DJ, McEachern EJ, Uslaner JM, Duffy JL, Smith SM. MK-8719, a Novel and Selective O-GlcNAcase Inhibitor That Reduces the Formation of Pathological Tau and Ameliorates Neurodegeneration in a Mouse Model of Tauopathy. J Pharmacol Exp Ther 2020; 374:252-263. [PMID: 32493725 DOI: 10.1124/jpet.120.266122] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Accepted: 06/01/2020] [Indexed: 01/01/2023] Open
Abstract
Deposition of hyperphosphorylated and aggregated tau protein in the central nervous system is characteristic of Alzheimer disease and other tauopathies. Tau is subject to O-linked N-acetylglucosamine (O-GlcNAc) modification, and O-GlcNAcylation of tau has been shown to influence tau phosphorylation and aggregation. Inhibition of O-GlcNAcase (OGA), the enzyme that removes O-GlcNAc moieties, is a novel strategy to attenuate the formation of pathologic tau. Here we described the in vitro and in vivo pharmacological properties of a novel and selective OGA inhibitor, MK-8719. In vitro, this compound is a potent inhibitor of the human OGA enzyme with comparable activity against the corresponding enzymes from mouse, rat, and dog. In vivo, oral administration of MK-8719 elevates brain and peripheral blood mononuclear cell O-GlcNAc levels in a dose-dependent manner. In addition, positron emission tomography imaging studies demonstrate robust target engagement of MK-8719 in the brains of rats and rTg4510 mice. In the rTg4510 mouse model of human tauopathy, MK-8719 significantly increases brain O-GlcNAc levels and reduces pathologic tau. The reduction in tau pathology in rTg4510 mice is accompanied by attenuation of brain atrophy, including reduction of forebrain volume loss as revealed by volumetric magnetic resonance imaging analysis. These findings suggest that OGA inhibition may reduce tau pathology in tauopathies. However, since hundreds of O-GlcNAcylated proteins may be influenced by OGA inhibition, it will be critical to understand the physiologic and toxicological consequences of chronic O-GlcNAc elevation in vivo. SIGNIFICANCE STATEMENT: MK-8719 is a novel, selective, and potent O-linked N-acetylglucosamine (O-GlcNAc)-ase (OGA) inhibitor that inhibits OGA enzyme activity across multiple species with comparable in vitro potency. In vivo, MK-8719 elevates brain O-GlcNAc levels, reduces pathological tau, and ameliorates brain atrophy in the rTg4510 mouse model of tauopathy. These findings indicate that OGA inhibition may be a promising therapeutic strategy for the treatment of Alzheimer disease and other tauopathies.
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Affiliation(s)
- Xiaohai Wang
- MRL, Merck & Co., Inc., Kenilworth, New Jersey (X.W., W.L., J.M., M.P., L.S., K.S., G.T., J.L., K.-L.K.H., S.X.L., L.H., S.-C.C., D.K., J.P.M., D.J.R., X.M., E.H., C.S., L.Z., J.B.S., J.F.H., H.G.S., J.M.U., J.L.D., S.M.S.) and Alectos Therapeutics Inc., Burnaby, British Columbia, Canada (D.J.V., E.J.M.)
| | - Wenping Li
- MRL, Merck & Co., Inc., Kenilworth, New Jersey (X.W., W.L., J.M., M.P., L.S., K.S., G.T., J.L., K.-L.K.H., S.X.L., L.H., S.-C.C., D.K., J.P.M., D.J.R., X.M., E.H., C.S., L.Z., J.B.S., J.F.H., H.G.S., J.M.U., J.L.D., S.M.S.) and Alectos Therapeutics Inc., Burnaby, British Columbia, Canada (D.J.V., E.J.M.)
| | - Jacob Marcus
- MRL, Merck & Co., Inc., Kenilworth, New Jersey (X.W., W.L., J.M., M.P., L.S., K.S., G.T., J.L., K.-L.K.H., S.X.L., L.H., S.-C.C., D.K., J.P.M., D.J.R., X.M., E.H., C.S., L.Z., J.B.S., J.F.H., H.G.S., J.M.U., J.L.D., S.M.S.) and Alectos Therapeutics Inc., Burnaby, British Columbia, Canada (D.J.V., E.J.M.)
| | - Michelle Pearson
- MRL, Merck & Co., Inc., Kenilworth, New Jersey (X.W., W.L., J.M., M.P., L.S., K.S., G.T., J.L., K.-L.K.H., S.X.L., L.H., S.-C.C., D.K., J.P.M., D.J.R., X.M., E.H., C.S., L.Z., J.B.S., J.F.H., H.G.S., J.M.U., J.L.D., S.M.S.) and Alectos Therapeutics Inc., Burnaby, British Columbia, Canada (D.J.V., E.J.M.)
| | - Lixin Song
- MRL, Merck & Co., Inc., Kenilworth, New Jersey (X.W., W.L., J.M., M.P., L.S., K.S., G.T., J.L., K.-L.K.H., S.X.L., L.H., S.-C.C., D.K., J.P.M., D.J.R., X.M., E.H., C.S., L.Z., J.B.S., J.F.H., H.G.S., J.M.U., J.L.D., S.M.S.) and Alectos Therapeutics Inc., Burnaby, British Columbia, Canada (D.J.V., E.J.M.)
| | - Karen Smith
- MRL, Merck & Co., Inc., Kenilworth, New Jersey (X.W., W.L., J.M., M.P., L.S., K.S., G.T., J.L., K.-L.K.H., S.X.L., L.H., S.-C.C., D.K., J.P.M., D.J.R., X.M., E.H., C.S., L.Z., J.B.S., J.F.H., H.G.S., J.M.U., J.L.D., S.M.S.) and Alectos Therapeutics Inc., Burnaby, British Columbia, Canada (D.J.V., E.J.M.)
| | - Giuseppe Terracina
- MRL, Merck & Co., Inc., Kenilworth, New Jersey (X.W., W.L., J.M., M.P., L.S., K.S., G.T., J.L., K.-L.K.H., S.X.L., L.H., S.-C.C., D.K., J.P.M., D.J.R., X.M., E.H., C.S., L.Z., J.B.S., J.F.H., H.G.S., J.M.U., J.L.D., S.M.S.) and Alectos Therapeutics Inc., Burnaby, British Columbia, Canada (D.J.V., E.J.M.)
| | - Julie Lee
- MRL, Merck & Co., Inc., Kenilworth, New Jersey (X.W., W.L., J.M., M.P., L.S., K.S., G.T., J.L., K.-L.K.H., S.X.L., L.H., S.-C.C., D.K., J.P.M., D.J.R., X.M., E.H., C.S., L.Z., J.B.S., J.F.H., H.G.S., J.M.U., J.L.D., S.M.S.) and Alectos Therapeutics Inc., Burnaby, British Columbia, Canada (D.J.V., E.J.M.)
| | - Kwok-Lam Karen Hong
- MRL, Merck & Co., Inc., Kenilworth, New Jersey (X.W., W.L., J.M., M.P., L.S., K.S., G.T., J.L., K.-L.K.H., S.X.L., L.H., S.-C.C., D.K., J.P.M., D.J.R., X.M., E.H., C.S., L.Z., J.B.S., J.F.H., H.G.S., J.M.U., J.L.D., S.M.S.) and Alectos Therapeutics Inc., Burnaby, British Columbia, Canada (D.J.V., E.J.M.)
| | - Sherry X Lu
- MRL, Merck & Co., Inc., Kenilworth, New Jersey (X.W., W.L., J.M., M.P., L.S., K.S., G.T., J.L., K.-L.K.H., S.X.L., L.H., S.-C.C., D.K., J.P.M., D.J.R., X.M., E.H., C.S., L.Z., J.B.S., J.F.H., H.G.S., J.M.U., J.L.D., S.M.S.) and Alectos Therapeutics Inc., Burnaby, British Columbia, Canada (D.J.V., E.J.M.)
| | - Lynn Hyde
- MRL, Merck & Co., Inc., Kenilworth, New Jersey (X.W., W.L., J.M., M.P., L.S., K.S., G.T., J.L., K.-L.K.H., S.X.L., L.H., S.-C.C., D.K., J.P.M., D.J.R., X.M., E.H., C.S., L.Z., J.B.S., J.F.H., H.G.S., J.M.U., J.L.D., S.M.S.) and Alectos Therapeutics Inc., Burnaby, British Columbia, Canada (D.J.V., E.J.M.)
| | - Shu-Cheng Chen
- MRL, Merck & Co., Inc., Kenilworth, New Jersey (X.W., W.L., J.M., M.P., L.S., K.S., G.T., J.L., K.-L.K.H., S.X.L., L.H., S.-C.C., D.K., J.P.M., D.J.R., X.M., E.H., C.S., L.Z., J.B.S., J.F.H., H.G.S., J.M.U., J.L.D., S.M.S.) and Alectos Therapeutics Inc., Burnaby, British Columbia, Canada (D.J.V., E.J.M.)
| | - David Kinsley
- MRL, Merck & Co., Inc., Kenilworth, New Jersey (X.W., W.L., J.M., M.P., L.S., K.S., G.T., J.L., K.-L.K.H., S.X.L., L.H., S.-C.C., D.K., J.P.M., D.J.R., X.M., E.H., C.S., L.Z., J.B.S., J.F.H., H.G.S., J.M.U., J.L.D., S.M.S.) and Alectos Therapeutics Inc., Burnaby, British Columbia, Canada (D.J.V., E.J.M.)
| | - Jerry P Melchor
- MRL, Merck & Co., Inc., Kenilworth, New Jersey (X.W., W.L., J.M., M.P., L.S., K.S., G.T., J.L., K.-L.K.H., S.X.L., L.H., S.-C.C., D.K., J.P.M., D.J.R., X.M., E.H., C.S., L.Z., J.B.S., J.F.H., H.G.S., J.M.U., J.L.D., S.M.S.) and Alectos Therapeutics Inc., Burnaby, British Columbia, Canada (D.J.V., E.J.M.)
| | - Daniel J Rubins
- MRL, Merck & Co., Inc., Kenilworth, New Jersey (X.W., W.L., J.M., M.P., L.S., K.S., G.T., J.L., K.-L.K.H., S.X.L., L.H., S.-C.C., D.K., J.P.M., D.J.R., X.M., E.H., C.S., L.Z., J.B.S., J.F.H., H.G.S., J.M.U., J.L.D., S.M.S.) and Alectos Therapeutics Inc., Burnaby, British Columbia, Canada (D.J.V., E.J.M.)
| | - Xiangjun Meng
- MRL, Merck & Co., Inc., Kenilworth, New Jersey (X.W., W.L., J.M., M.P., L.S., K.S., G.T., J.L., K.-L.K.H., S.X.L., L.H., S.-C.C., D.K., J.P.M., D.J.R., X.M., E.H., C.S., L.Z., J.B.S., J.F.H., H.G.S., J.M.U., J.L.D., S.M.S.) and Alectos Therapeutics Inc., Burnaby, British Columbia, Canada (D.J.V., E.J.M.)
| | - Eric Hostetler
- MRL, Merck & Co., Inc., Kenilworth, New Jersey (X.W., W.L., J.M., M.P., L.S., K.S., G.T., J.L., K.-L.K.H., S.X.L., L.H., S.-C.C., D.K., J.P.M., D.J.R., X.M., E.H., C.S., L.Z., J.B.S., J.F.H., H.G.S., J.M.U., J.L.D., S.M.S.) and Alectos Therapeutics Inc., Burnaby, British Columbia, Canada (D.J.V., E.J.M.)
| | - Cyrille Sur
- MRL, Merck & Co., Inc., Kenilworth, New Jersey (X.W., W.L., J.M., M.P., L.S., K.S., G.T., J.L., K.-L.K.H., S.X.L., L.H., S.-C.C., D.K., J.P.M., D.J.R., X.M., E.H., C.S., L.Z., J.B.S., J.F.H., H.G.S., J.M.U., J.L.D., S.M.S.) and Alectos Therapeutics Inc., Burnaby, British Columbia, Canada (D.J.V., E.J.M.)
| | - Lili Zhang
- MRL, Merck & Co., Inc., Kenilworth, New Jersey (X.W., W.L., J.M., M.P., L.S., K.S., G.T., J.L., K.-L.K.H., S.X.L., L.H., S.-C.C., D.K., J.P.M., D.J.R., X.M., E.H., C.S., L.Z., J.B.S., J.F.H., H.G.S., J.M.U., J.L.D., S.M.S.) and Alectos Therapeutics Inc., Burnaby, British Columbia, Canada (D.J.V., E.J.M.)
| | - Joel B Schachter
- MRL, Merck & Co., Inc., Kenilworth, New Jersey (X.W., W.L., J.M., M.P., L.S., K.S., G.T., J.L., K.-L.K.H., S.X.L., L.H., S.-C.C., D.K., J.P.M., D.J.R., X.M., E.H., C.S., L.Z., J.B.S., J.F.H., H.G.S., J.M.U., J.L.D., S.M.S.) and Alectos Therapeutics Inc., Burnaby, British Columbia, Canada (D.J.V., E.J.M.)
| | - J Fred Hess
- MRL, Merck & Co., Inc., Kenilworth, New Jersey (X.W., W.L., J.M., M.P., L.S., K.S., G.T., J.L., K.-L.K.H., S.X.L., L.H., S.-C.C., D.K., J.P.M., D.J.R., X.M., E.H., C.S., L.Z., J.B.S., J.F.H., H.G.S., J.M.U., J.L.D., S.M.S.) and Alectos Therapeutics Inc., Burnaby, British Columbia, Canada (D.J.V., E.J.M.)
| | - Harold G Selnick
- MRL, Merck & Co., Inc., Kenilworth, New Jersey (X.W., W.L., J.M., M.P., L.S., K.S., G.T., J.L., K.-L.K.H., S.X.L., L.H., S.-C.C., D.K., J.P.M., D.J.R., X.M., E.H., C.S., L.Z., J.B.S., J.F.H., H.G.S., J.M.U., J.L.D., S.M.S.) and Alectos Therapeutics Inc., Burnaby, British Columbia, Canada (D.J.V., E.J.M.)
| | - David J Vocadlo
- MRL, Merck & Co., Inc., Kenilworth, New Jersey (X.W., W.L., J.M., M.P., L.S., K.S., G.T., J.L., K.-L.K.H., S.X.L., L.H., S.-C.C., D.K., J.P.M., D.J.R., X.M., E.H., C.S., L.Z., J.B.S., J.F.H., H.G.S., J.M.U., J.L.D., S.M.S.) and Alectos Therapeutics Inc., Burnaby, British Columbia, Canada (D.J.V., E.J.M.)
| | - Ernest J McEachern
- MRL, Merck & Co., Inc., Kenilworth, New Jersey (X.W., W.L., J.M., M.P., L.S., K.S., G.T., J.L., K.-L.K.H., S.X.L., L.H., S.-C.C., D.K., J.P.M., D.J.R., X.M., E.H., C.S., L.Z., J.B.S., J.F.H., H.G.S., J.M.U., J.L.D., S.M.S.) and Alectos Therapeutics Inc., Burnaby, British Columbia, Canada (D.J.V., E.J.M.)
| | - Jason M Uslaner
- MRL, Merck & Co., Inc., Kenilworth, New Jersey (X.W., W.L., J.M., M.P., L.S., K.S., G.T., J.L., K.-L.K.H., S.X.L., L.H., S.-C.C., D.K., J.P.M., D.J.R., X.M., E.H., C.S., L.Z., J.B.S., J.F.H., H.G.S., J.M.U., J.L.D., S.M.S.) and Alectos Therapeutics Inc., Burnaby, British Columbia, Canada (D.J.V., E.J.M.)
| | - Joseph L Duffy
- MRL, Merck & Co., Inc., Kenilworth, New Jersey (X.W., W.L., J.M., M.P., L.S., K.S., G.T., J.L., K.-L.K.H., S.X.L., L.H., S.-C.C., D.K., J.P.M., D.J.R., X.M., E.H., C.S., L.Z., J.B.S., J.F.H., H.G.S., J.M.U., J.L.D., S.M.S.) and Alectos Therapeutics Inc., Burnaby, British Columbia, Canada (D.J.V., E.J.M.)
| | - Sean M Smith
- MRL, Merck & Co., Inc., Kenilworth, New Jersey (X.W., W.L., J.M., M.P., L.S., K.S., G.T., J.L., K.-L.K.H., S.X.L., L.H., S.-C.C., D.K., J.P.M., D.J.R., X.M., E.H., C.S., L.Z., J.B.S., J.F.H., H.G.S., J.M.U., J.L.D., S.M.S.) and Alectos Therapeutics Inc., Burnaby, British Columbia, Canada (D.J.V., E.J.M.)
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Fiford CM, Nicholas JM, Biessels GJ, Lane CA, Cardoso MJ, Barnes J. High blood pressure predicts hippocampal atrophy rate in cognitively impaired elders. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2020; 12:e12035. [PMID: 32587882 PMCID: PMC7308793 DOI: 10.1002/dad2.12035] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Revised: 02/26/2020] [Accepted: 02/26/2020] [Indexed: 11/12/2022]
Abstract
INTRODUCTION Understanding relationships among blood pressure (BP), cognition, and brain volume could inform Alzheimer's disease (AD) management. METHODS We investigated Alzheimer's Disease Neuroimaging Initiative (ADNI) participants: 200 controls, 346 mild cognitive impairment (MCI), and 154 AD. National Alzheimer's Co-ordinating Center (NACC) participants were separately analyzed: 1098 controls, 2297 MCI, and 4845 AD. Relationships between cognition and BP were assessed in both cohorts and BP and atrophy rates in ADNI. Multivariate mixed linear-regression models were fitted with joint outcomes of BP (systolic, diastolic, and pulse pressure), cognition (Mini-Mental State Examination, Logical Memory, and Digit Symbol) and atrophy rate (whole-brain, hippocampus). RESULTS ADNI MCI and AD patients with greater baseline systolic BP had higher hippocampal atrophy rates ([r, P value]; 0.2, 0.005 and 0.2, 0.04, respectively). NACC AD patients with lower systolic BP had lower cognitive scores (0.1, 0.0003). DISCUSSION Higher late-life BP may be associated with faster decline in cognitively impaired elders.
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Affiliation(s)
- Cassidy M. Fiford
- Department of Neurodegenerative Disease, Dementia Research CentreUCL Institute of NeurologyLondonUK
| | | | - Geert Jan Biessels
- Department of Neurology and NeurosurgeryBrain Center Rudolf MagnusUniversity Medical CenterUtrechtthe Netherlands
| | - Christopher A. Lane
- Department of Neurodegenerative Disease, Dementia Research CentreUCL Institute of NeurologyLondonUK
| | - M. Jorge Cardoso
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
- Department of Medical Physics and Biomedical EngineeringUniversity College LondonLondonUK
| | - Josephine Barnes
- Department of Neurodegenerative Disease, Dementia Research CentreUCL Institute of NeurologyLondonUK
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12
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De Guio F, Duering M, Fazekas F, De Leeuw FE, Greenberg SM, Pantoni L, Aghetti A, Smith EE, Wardlaw J, Jouvent E. Brain atrophy in cerebral small vessel diseases: Extent, consequences, technical limitations and perspectives: The HARNESS initiative. J Cereb Blood Flow Metab 2020; 40:231-245. [PMID: 31744377 PMCID: PMC7370623 DOI: 10.1177/0271678x19888967] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Brain atrophy is increasingly evaluated in cerebral small vessel diseases. We aim at systematically reviewing the available data regarding its extent, correlates and cognitive consequences. Given that in this context, brain atrophy measures might be biased, the first part of the review focuses on technical aspects. Thereafter, data from the literature are analyzed in light of these potential limitations, to better understand the relationships between brain atrophy and other MRI markers of cerebral small vessel diseases. In the last part, we review the links between brain atrophy and cognitive alterations in patients with cerebral small vessel diseases.
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Affiliation(s)
- François De Guio
- Department of Neurology and Referral Center for Rare Vascular Diseases of the Brain and Retina (CERVCO), APHP, Lariboisière Hospital, Paris, DHU NeuroVasc, Univ Paris Diderot, and U1141 INSERM, France
| | - Marco Duering
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Germany.,Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Franz Fazekas
- Department of Neurology, Medical University of Graz, Graz, Austria
| | - Frank-Erik De Leeuw
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Centre for Neuroscience, Radboud University, Nijmegen, The Netherlands
| | - Steven M Greenberg
- Department of Neurology, Stroke Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Leonardo Pantoni
- "Luigi Sacco" Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy
| | - Agnès Aghetti
- Department of Neurology and Referral Center for Rare Vascular Diseases of the Brain and Retina (CERVCO), APHP, Lariboisière Hospital, Paris, DHU NeuroVasc, Univ Paris Diderot, and U1141 INSERM, France
| | - Eric E Smith
- Department of Clinical Neurosciences and Hotchkiss Brain Institute, University of Calgary, Calgary, Canada
| | - Joanna Wardlaw
- Centre for Clinical Brain Sciences, Edinburgh Imaging, UK Dementia Research Institute, University of Edinburgh, Edinburgh, UK
| | - Eric Jouvent
- Department of Neurology and Referral Center for Rare Vascular Diseases of the Brain and Retina (CERVCO), APHP, Lariboisière Hospital, Paris, DHU NeuroVasc, Univ Paris Diderot, and U1141 INSERM, France
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13
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La Joie R, Visani AV, Baker SL, Brown JA, Bourakova V, Cha J, Chaudhary K, Edwards L, Iaccarino L, Janabi M, Lesman-Segev OH, Miller ZA, Perry DC, O'Neil JP, Pham J, Rojas JC, Rosen HJ, Seeley WW, Tsai RM, Miller BL, Jagust WJ, Rabinovici GD. Prospective longitudinal atrophy in Alzheimer's disease correlates with the intensity and topography of baseline tau-PET. Sci Transl Med 2020; 12:eaau5732. [PMID: 31894103 PMCID: PMC7035952 DOI: 10.1126/scitranslmed.aau5732] [Citation(s) in RCA: 314] [Impact Index Per Article: 78.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2018] [Revised: 09/13/2019] [Accepted: 11/13/2019] [Indexed: 12/16/2022]
Abstract
β-Amyloid plaques and tau-containing neurofibrillary tangles are the two neuropathological hallmarks of Alzheimer's disease (AD) and are thought to play crucial roles in a neurodegenerative cascade leading to dementia. Both lesions can now be visualized in vivo using positron emission tomography (PET) radiotracers, opening new opportunities to study disease mechanisms and improve patients' diagnostic and prognostic evaluation. In a group of 32 patients at early symptomatic AD stages, we tested whether β-amyloid and tau-PET could predict subsequent brain atrophy measured using longitudinal magnetic resonance imaging acquired at the time of PET and 15 months later. Quantitative analyses showed that the global intensity of tau-PET, but not β-amyloid-PET, signal predicted the rate of subsequent atrophy, independent of baseline cortical thickness. Additional investigations demonstrated that the specific distribution of tau-PET signal was a strong indicator of the topography of future atrophy at the single patient level and that the relationship between baseline tau-PET and subsequent atrophy was particularly strong in younger patients. These data support disease models in which tau pathology is a major driver of local neurodegeneration and highlight the relevance of tau-PET as a precision medicine tool to help predict individual patient's progression and design future clinical trials.
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Affiliation(s)
- Renaud La Joie
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA.
| | - Adrienne V Visani
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Suzanne L Baker
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Jesse A Brown
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Viktoriya Bourakova
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Jungho Cha
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Kiran Chaudhary
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Lauren Edwards
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Leonardo Iaccarino
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Mustafa Janabi
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Orit H Lesman-Segev
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Zachary A Miller
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - David C Perry
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - James P O'Neil
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Julie Pham
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Julio C Rojas
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Howard J Rosen
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - William W Seeley
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Richard M Tsai
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Bruce L Miller
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - William J Jagust
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Gil D Rabinovici
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA
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14
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15
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Niemantsverdriet E, Ribbens A, Bastin C, Benoit F, Bergmans B, Bier JC, Bladt R, Claes L, De Deyn PP, Deryck O, Hanseeuw B, Ivanoiu A, Lemper JC, Mormont E, Picard G, Salmon E, Segers K, Sieben A, Smeets D, Struyfs H, Thiery E, Tournoy J, Triau E, Vanbinst AM, Versijpt J, Bjerke M, Engelborghs S. A Retrospective Belgian Multi-Center MRI Biomarker Study in Alzheimer's Disease (REMEMBER). J Alzheimers Dis 2019; 63:1509-1522. [PMID: 29782314 PMCID: PMC6004934 DOI: 10.3233/jad-171140] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Background: Magnetic resonance imaging (MRI) acquisition/processing techniques assess brain volumes to explore neurodegeneration in Alzheimer’s disease (AD). Objective: We examined the clinical utility of MSmetrix and investigated if automated MRI volumes could discriminate between groups covering the AD continuum and could be used as a predictor for clinical progression. Methods: The Belgian Dementia Council initiated a retrospective, multi-center study and analyzed whole brain (WB), grey matter (GM), white matter (WM), cerebrospinal fluid (CSF), cortical GM (CGM) volumes, and WM hyperintensities (WMH) using MSmetrix in the AD continuum. Baseline (n = 887) and follow-up (FU, n = 95) T1-weighted brain MRIs and time-linked neuropsychological data were available. Results: The cohort consisted of cognitively healthy controls (HC, n = 93), subjective cognitive decline (n = 102), mild cognitive impairment (MCI, n = 379), and AD dementia (n = 313). Baseline WB and GM volumes could accurately discriminate between clinical diagnostic groups and were significantly decreased with increasing cognitive impairment. MCI patients had a significantly larger change in WB, GM, and CGM volumes based on two MRIs (n = 95) compared to HC (FU>24months, p = 0.020). Linear regression models showed that baseline atrophy of WB, GM, CGM, and increased CSF volumes predicted cognitive impairment. Conclusion: WB and GM volumes extracted by MSmetrix could be used to define the clinical spectrum of AD accurately and along with CGM, they are able to predict cognitive impairment based on (decline in) MMSE scores. Therefore, MSmetrix can support clinicians in their diagnostic decisions, is able to detect clinical disease progression, and is of help to stratify populations for clinical trials.
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Affiliation(s)
- Ellis Niemantsverdriet
- Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | | | - Christine Bastin
- GIGA Cyclotron Research Centre in vivo Imaging, University of Liège, Liège, Belgium
| | - Florence Benoit
- Department of Geriatrics, Centre Hospitalier Universitaire (CHU) Brugmann, Brussels, Belgium
| | - Bruno Bergmans
- Department of Neurology and Center for Cognitive Disorders, AZ Sint-Jan Brugge-Oostende AV, Brugge, Belgium
| | | | - Roxanne Bladt
- Department of Radiology, Vrije Universiteit Brussel (VUB), UZ Brussel, Brussels, Belgium
| | | | - Peter Paul De Deyn
- Department of Neurology and Memory Clinic, Hospital Network Antwerp (ZNA) Middelheim and Hoge Beuken, Antwerp, Belgium
| | - Olivier Deryck
- Department of Neurology and Center for Cognitive Disorders, AZ Sint-Jan Brugge-Oostende AV, Brugge, Belgium
| | - Bernard Hanseeuw
- Department of Neurology, Cliniques Universitaires St Luc and Institute of Neuroscience, Université catholique de Louvain, Woluwe-Saint-Lambert (Brussels), Belgium
| | - Adrian Ivanoiu
- Department of Neurology, Cliniques Universitaires St Luc and Institute of Neuroscience, Université catholique de Louvain, Woluwe-Saint-Lambert (Brussels), Belgium
| | - Jean-Claude Lemper
- Department of Geriatrics, UZ Brussel, Brussels, Belgium.,Silva medical Scheutbos, Molenbeek-Saint-Jean (Brussels), Belgium
| | - Eric Mormont
- Department of Neurology, Centre Hospitalier Universitaire (CHU) Namur, Université catholique de Louvain, Yvoir, Belgium.,Université catholique de Louvain, Institute of Neuroscience (IoNS), Louvain-la-Neuve (Brussels), Belgium
| | - Gaëtane Picard
- Department of Neurology, Clinique Saint-Pierre, Ottignies, Belgium
| | - Eric Salmon
- GIGA Cyclotron Research Centre in vivo Imaging, University of Liège, Liège, Belgium.,Department of Neurology, Memory Clinic, Centre Hospitalier Universitaire (CHU) Liège, Liège, Belgium
| | - Kurt Segers
- Department of Neurology, Centre Hospitalier Universitaire (CHU) Brugmann, Brussels, Belgium
| | - Anne Sieben
- Department of Neurology, University Hospital Ghent, Ghent University, Ghent, Belgium
| | | | - Hanne Struyfs
- Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | - Evert Thiery
- Department of Neurology, University Hospital Ghent, Ghent University, Ghent, Belgium
| | - Jos Tournoy
- Gerontology and Geriatrics, Department of Clinical and Experimental Medicine, KU Leuven, Leuven, Belgium.,Geriatric Medicine and Memory Clinic, University Hospital Leuven, Leuven, Belgium
| | | | - Anne-Marie Vanbinst
- Department of Radiology, Vrije Universiteit Brussel (VUB), UZ Brussel, Brussels, Belgium
| | - Jan Versijpt
- Department of Neurology, Vrije Universiteit Brussel (VUB), UZ Brussel, Brussels, Belgium
| | - Maria Bjerke
- Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | - Sebastiaan Engelborghs
- Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium.,Department of Neurology and Memory Clinic, Hospital Network Antwerp (ZNA) Middelheim and Hoge Beuken, Antwerp, Belgium
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16
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Grajauskas LA, Siu W, Medvedev G, Guo H, D’Arcy RC, Song X. MRI-based evaluation of structural degeneration in the ageing brain: Pathophysiology and assessment. Ageing Res Rev 2019; 49:67-82. [PMID: 30472216 DOI: 10.1016/j.arr.2018.11.004] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2018] [Revised: 11/08/2018] [Accepted: 11/21/2018] [Indexed: 12/13/2022]
Abstract
Advances in MRI technology have significantly contributed to our ability to understand the process of brain ageing, allowing us to track and assess changes that occur during normal ageing and neurological conditions. This paper focuses on reviewing structural changes of the ageing brain that are commonly seen using MRI, summarizing the pathophysiology, prevalence, and neuroanatomical distribution of changes including atrophy, lacunes, white matter lesions, and dilated perivascular spaces. We also review the clinically accessible methodology for assessing these MRI-based changes, covering visual rating scales, as well computer-aided and fully automated methods. Subsequently, we consider novel assessment methods designed to evaluate changes across the whole brain, and finally discuss new directions in this field of research.
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17
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Fletcher E, Gavett B, Harvey D, Farias ST, Olichney J, Beckett L, DeCarli C, Mungas D. Brain volume change and cognitive trajectories in aging. Neuropsychology 2018; 32:436-449. [PMID: 29494196 PMCID: PMC6525569 DOI: 10.1037/neu0000447] [Citation(s) in RCA: 80] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
OBJECTIVE Examine how longitudinal cognitive trajectories relate to brain baseline measures and change in lobar volumes in a racially/ethnically and cognitively diverse sample of older adults. METHOD Participants were 460 older adults enrolled in a longitudinal aging study. Cognitive outcomes were measures of episodic memory, semantic memory, executive function, and spatial ability derived from the Spanish and English Neuropsychological Assessment Scales (SENAS). Latent variable multilevel modeling of the four cognitive outcomes as parallel longitudinal processes identified intercepts for each outcome and a second order global change factor explaining covariance among the highly correlated slopes. We examined how baseline brain volumes (lobar gray matter, hippocampus, and white matter hyperintensity) and change in brain volumes (lobar gray matter) were associated with cognitive intercepts and global cognitive change. Lobar volumes were dissociated into global and specific components using latent variable methods. RESULTS Cognitive change was most strongly associated with brain gray matter volume change, with strong independent effects of global gray matter change and specific temporal lobe gray matter change. Baseline white matter hyperintensity and hippocampal volumes had significant incremental effects on cognitive decline beyond gray matter change. Baseline lobar gray matter was related to cognitive decline, but did not contribute beyond gray matter change. CONCLUSION Cognitive decline was strongly influenced by gray matter volume change and, especially, temporal lobe change. The strong influence of temporal lobe gray matter change on cognitive decline may reflect involvement of temporal lobe structures that are critical for late life cognitive health but also are vulnerable to diseases of aging. (PsycINFO Database Record
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18
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Lawrence E, Vegvari C, Ower A, Hadjichrysanthou C, De Wolf F, Anderson RM. A Systematic Review of Longitudinal Studies Which Measure Alzheimer's Disease Biomarkers. J Alzheimers Dis 2018; 59:1359-1379. [PMID: 28759968 PMCID: PMC5611893 DOI: 10.3233/jad-170261] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Alzheimer’s disease (AD) is a progressive and fatal neurodegenerative disease, with no effective treatment or cure. A gold standard therapy would be treatment to slow or halt disease progression; however, knowledge of causation in the early stages of AD is very limited. In order to determine effective endpoints for possible therapies, a number of quantitative surrogate markers of disease progression have been suggested, including biochemical and imaging biomarkers. The dynamics of these various surrogate markers over time, particularly in relation to disease development, are, however, not well characterized. We reviewed the literature for studies that measured cerebrospinal fluid or plasma amyloid-β and tau, or took magnetic resonance image or fluorodeoxyglucose/Pittsburgh compound B-positron electron tomography scans, in longitudinal cohort studies. We summarized the properties of the major cohort studies in various countries, commonly used diagnosis methods and study designs. We have concluded that additional studies with repeat measures over time in a representative population cohort are needed to address the gap in knowledge of AD progression. Based on our analysis, we suggest directions in which research could move in order to advance our understanding of this complex disease, including repeat biomarker measurements, standardization and increased sample sizes.
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Affiliation(s)
- Emma Lawrence
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
| | - Carolin Vegvari
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
| | - Alison Ower
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
| | | | - Frank De Wolf
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK.,Janssen Prevention Center, Leiden, The Netherlands
| | - Roy M Anderson
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
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19
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Malpas CB, Saling MM, Velakoulis D, Desmond P, Hicks RJ, Zetterberg H, Blennow K, O’Brien TJ. Cerebrospinal Fluid Biomarkers are Differentially Related to Structural and Functional Changes in Dementia of the Alzheimer’s Type. J Alzheimers Dis 2018; 62:417-427. [DOI: 10.3233/jad-170250] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Charles B. Malpas
- Department of Medicine, Royal Melbourne Hospital, VIC, Australia
- Melbourne School of Psychological Sciences, The University of Melbourne, VIC, Australia
- Developmental Imaging, Murdoch Children’s Research Institute, Melbourne, VIC, Australia
| | - Michael M. Saling
- Melbourne School of Psychological Sciences, The University of Melbourne, VIC, Australia
| | | | - Patricia Desmond
- Department of Radiology, University of Melbourne, VIC, Australia
| | - Rodney J. Hicks
- Department of Radiology, University of Melbourne, VIC, Australia
- Centre for Molecular Imaging, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Molecular Neuroscience, UCL Institute of Neurology, Queen Square, London, UK
- UK Dementia Research Institute at UCL, London, UK
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Terence J. O’Brien
- Department of Medicine, Royal Melbourne Hospital, VIC, Australia
- Departments of Neuroscience and Neurology, The Central Clinical School and The Alfred Hospital, Monash University, Melbourne, VIC, Australia
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20
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van der Flier WM, Scheltens P. Amsterdam Dementia Cohort: Performing Research to Optimize Care. J Alzheimers Dis 2018; 62:1091-1111. [PMID: 29562540 PMCID: PMC5870023 DOI: 10.3233/jad-170850] [Citation(s) in RCA: 203] [Impact Index Per Article: 33.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/04/2017] [Indexed: 01/01/2023]
Abstract
The Alzheimer center of the VU University Medical Center opened in 2000 and was initiated to combine both patient care and research. Together, to date, all patients forming the Amsterdam Dementia Cohort number almost 6,000 individuals. In this cohort profile, we provide an overview of the results produced based on the Amsterdam Dementia Cohort. We describe the main results over the years in each of these research lines: 1) early diagnosis, 2) heterogeneity, and 3) vascular factors. Among the most important research efforts that have also impacted patients' lives and/or the research field, we count the development of novel, easy to use diagnostic measures such as visual rating scales for MRI and the Amsterdam IADL Questionnaire, insight in different subgroups of AD, and findings on incidence and clinical sequelae of microbleeds. Finally, we describe in the outlook how our research endeavors have improved the lives of our patients.
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Affiliation(s)
- Wiesje M. van der Flier
- Department of Neurology, Alzheimer Center, VU University Medical Center, Amsterdam Neuroscience, Amsterdam, The Netherlands
- Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, The Netherlands
| | - Philip Scheltens
- Department of Neurology, Alzheimer Center, VU University Medical Center, Amsterdam Neuroscience, Amsterdam, The Netherlands
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21
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Mak E, Su L, Williams GB, Firbank MJ, Lawson RA, Yarnall AJ, Duncan GW, Mollenhauer B, Owen AM, Khoo TK, Brooks DJ, Rowe JB, Barker RA, Burn DJ, O'Brien JT. Longitudinal whole-brain atrophy and ventricular enlargement in nondemented Parkinson's disease. Neurobiol Aging 2017; 55:78-90. [PMID: 28431288 PMCID: PMC5454799 DOI: 10.1016/j.neurobiolaging.2017.03.012] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2016] [Revised: 03/05/2017] [Accepted: 03/08/2017] [Indexed: 11/28/2022]
Abstract
We investigated whole-brain atrophy and ventricular enlargement over 18 months in nondemented Parkinson's disease (PD) and examined their associations with clinical measures and baseline CSF markers. PD subjects (n = 100) were classified at baseline into those with mild cognitive impairment (MCI; PD-MCI, n = 36) and no cognitive impairment (PD-NC, n = 64). Percentage of whole-brain volume change (PBVC) and ventricular expansion over 18 months were assessed with FSL-SIENA and ventricular enlargement (VIENA) respectively. PD-MCI showed increased global atrophy (-1.1% ± 0.8%) and ventricular enlargement (6.9 % ± 5.2%) compared with both PD-NC (PBVC: -0.4 ± 0.5, p < 0.01; VIENA: 2.1% ± 4.3%, p < 0.01) and healthy controls. In a subset of 35 PD subjects, CSF levels of tau, and Aβ42/Aβ40 ratio were correlated with PBVC and ventricular enlargement respectively. The sample size required to demonstrate a 20% reduction in PBVC and VIENA was approximately 1/15th of that required to detect equivalent changes in cognitive decline. These findings suggest that longitudinal MRI measurements have potential to serve as surrogate markers to complement clinical assessments for future disease-modifying trials in PD.
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Affiliation(s)
- Elijah Mak
- Department of Psychiatry, University of Cambridge, Cambridgeshire, UK
| | - Li Su
- Department of Psychiatry, University of Cambridge, Cambridgeshire, UK
| | - Guy B Williams
- Wolfson Brain Imaging Centre, University of Cambridge, Cambridgeshire, UK
| | - Michael J Firbank
- Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK
| | - Rachael A Lawson
- Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK
| | - Alison J Yarnall
- Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK
| | - Gordon W Duncan
- Medicine of the Elderly, Western General Hospital, Edinburgh, UK
| | - Brit Mollenhauer
- Paracelsus-Elena-Klinik, Kassel, Germany; University Medical Center Goettingen, Institute of Neuropathology, Goettingen, Germany
| | - Adrian M Owen
- Brain and Mind Institute, University of Western Ontario, London, Canada; Department of Psychology, University of Western Ontario, London, Canada
| | - Tien K Khoo
- Menzies Health Institute, Queensland and School of Medicine, Griffith University, Gold Coast, Australia
| | - David J Brooks
- Division of Neuroscience, Imperial College London, London, UK; Institute of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - James B Rowe
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK; Medical Research Council, Cognition and Brain Sciences Unit, Cambridge, UK; Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK
| | - Roger A Barker
- John van Geest Centre for Brain Repair, University of Cambridge, Cambridge, UK
| | - David J Burn
- Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK
| | - John T O'Brien
- Department of Psychiatry, University of Cambridge, Cambridgeshire, UK.
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22
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Lajoie I, Nugent S, Debacker C, Dyson K, Tancredi FB, Badhwar A, Belleville S, Deschaintre Y, Bellec P, Doyon J, Bocti C, Gauthier S, Arnold D, Kergoat MJ, Chertkow H, Monchi O, Hoge RD. Application of calibrated fMRI in Alzheimer's disease. NEUROIMAGE-CLINICAL 2017; 15:348-358. [PMID: 28560160 PMCID: PMC5443910 DOI: 10.1016/j.nicl.2017.05.009] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2017] [Revised: 04/26/2017] [Accepted: 05/15/2017] [Indexed: 02/07/2023]
Abstract
Calibrated fMRI based on arterial spin-labeling (ASL) and blood oxygen-dependent contrast (BOLD), combined with periods of hypercapnia and hyperoxia, can provide information on cerebrovascular reactivity (CVR), resting blood flow (CBF), oxygen extraction fraction (OEF), and resting oxidative metabolism (CMRO2). Vascular and metabolic integrity are believed to be affected in Alzheimer's disease (AD), thus, the use of calibrated fMRI in AD may help understand the disease and monitor therapeutic responses in future clinical trials. In the present work, we applied a calibrated fMRI approach referred to as Quantitative O2 (QUO2) in a cohort of probable AD dementia and age-matched control participants. The resulting CBF, OEF and CMRO2 values fell within the range from previous studies using positron emission tomography (PET) with 15O labeling. Moreover, the typical parietotemporal pattern of hypoperfusion and hypometabolism in AD was observed, especially in the precuneus, a particularly vulnerable region. We detected no deficit in frontal CBF, nor in whole grey matter CVR, which supports the hypothesis that the effects observed were associated specifically with AD rather than generalized vascular disease. Some key pitfalls affecting both ASL and BOLD methods were encountered, such as prolonged arterial transit times (particularly in the occipital lobe), the presence of susceptibility artifacts obscuring medial temporal regions, and the challenges associated with the hypercapnic manipulation in AD patients and elderly participants. The present results are encouraging and demonstrate the promise of calibrated fMRI measurements as potential biomarkers in AD. Although CMRO2 can be imaged with 15O PET, the QUO2 method uses more widely available imaging infrastructure, avoids exposure to ionizing radiation, and integrates with other MRI-based measures of brain structure and function.
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Affiliation(s)
- Isabelle Lajoie
- Département de Pharmacologie et physiologie, Université de Montréal, Montreal, QC, Canada; Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montreal, QC, Canada; Montreal Neurological Institute, Montreal, QC, Canada.
| | - Scott Nugent
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montreal, QC, Canada; Montreal Neurological Institute, Montreal, QC, Canada; Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Clément Debacker
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montreal, QC, Canada; Montreal Neurological Institute, Montreal, QC, Canada; Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Kenneth Dyson
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montreal, QC, Canada; Montreal Neurological Institute, Montreal, QC, Canada; Department of Physiology, McGill University, Montreal, QC, Canada
| | - Felipe B Tancredi
- Departamento de Radiologia, Centro de Pesquisa em Imagem, Hospital Israelita Albert Einstein, São Palo, SP, Brazil
| | - AmanPreet Badhwar
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montreal, QC, Canada; Department of Computer Science and Operations Research, Université de Montréal, Montreal, QC, Canada
| | - Sylvie Belleville
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montreal, QC, Canada; Département de Psychologie, Université de Montréal, Montreal, QC, Canada
| | - Yan Deschaintre
- Département de Neurosciences, Université de Montréal, Montreal, QC, Canada; Service de neurologie, Département de Médecine, CHUM Notre-Dame, Montréal, QC, Canada
| | - Pierre Bellec
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montreal, QC, Canada; Department of Computer Science and Operations Research, Université de Montréal, Montreal, QC, Canada
| | - Julien Doyon
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montreal, QC, Canada; Département de Psychologie, Université de Montréal, Montreal, QC, Canada
| | - Christian Bocti
- Division de Neurologie, Faculté de Médecine et des Sciences de la Santé & Research Centre on Aging, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Serge Gauthier
- McGill Center for Studies in Aging, Douglas Mental Health Research Institute, Montreal, QC, Canada
| | - Douglas Arnold
- Montreal Neurological Institute, Montreal, QC, Canada; Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Marie-Jeanne Kergoat
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montreal, QC, Canada; Département de Médecine, Université de Montréal, Montreal, QC, Canada
| | - Howard Chertkow
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montreal, QC, Canada; Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada; Department of Medicine, Sir Mortimer B. Davis-Jewish General Hospital, McGill University, Montreal, QC, Canada
| | - Oury Monchi
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montreal, QC, Canada; Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada; McGill Center for Studies in Aging, Douglas Mental Health Research Institute, Montreal, QC, Canada; Department of Clinical Neurosciences and Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Richard D Hoge
- Département de Pharmacologie et physiologie, Université de Montréal, Montreal, QC, Canada; Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montreal, QC, Canada; Montreal Neurological Institute, Montreal, QC, Canada; Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
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23
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Webster L, Groskreutz D, Grinbergs-Saull A, Howard R, O'Brien JT, Mountain G, Banerjee S, Woods B, Perneczky R, Lafortune L, Roberts C, McCleery J, Pickett J, Bunn F, Challis D, Charlesworth G, Featherstone K, Fox C, Goodman C, Jones R, Lamb S, Moniz-Cook E, Schneider J, Shepperd S, Surr C, Thompson-Coon J, Ballard C, Brayne C, Burke O, Burns A, Clare L, Garrard P, Kehoe P, Passmore P, Holmes C, Maidment I, Murtagh F, Robinson L, Livingston G. Development of a core outcome set for disease modification trials in mild to moderate dementia: a systematic review, patient and public consultation and consensus recommendations. Health Technol Assess 2017; 21:1-192. [PMID: 28625273 PMCID: PMC5494514 DOI: 10.3310/hta21260] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND There is currently no disease-modifying treatment available to halt or delay the progression of the disease pathology in dementia. An agreed core set of the best-available and most appropriate outcomes for disease modification would facilitate the design of trials and ensure consistency across disease modification trials, as well as making results comparable and meta-analysable in future trials. OBJECTIVES To agree a set of core outcomes for disease modification trials for mild to moderate dementia with the UK dementia research community and patient and public involvement (PPI). DATA SOURCES We included disease modification trials with quantitative outcomes of efficacy from (1) references from related systematic reviews in workstream 1; (2) searches of the Cochrane Dementia and Cognitive Improvement Group study register, Cochrane Central Register of Controlled Trials, Cumulative Index to Nursing and Allied Health Literature, EMBASE, Latin American and Caribbean Health Sciences Literature and PsycINFO on 11 December 2015, and clinical trial registries [International Standard Randomised Controlled Trial Number (ISRCTN) and clinicaltrials.gov] on 22 and 29 January 2016; and (3) hand-searches of reference lists of relevant systematic reviews from database searches. REVIEW METHODS The project consisted of four workstreams. (1) We obtained related core outcome sets and work from co-applicants. (2) We systematically reviewed published and ongoing disease modification trials to identify the outcomes used in different domains. We extracted outcomes used in each trial, recording how many used each outcome and with how many participants. We divided outcomes into the domains measured and searched for validation data. (3) We consulted with PPI participants about recommended outcomes. (4) We presented all the synthesised information at a conference attended by the wider body of National Institute for Health Research (NIHR) dementia researchers to reach consensus on a core set of outcomes. RESULTS We included 149 papers from the 22,918 papers screened, referring to 125 individual trials. Eighty-one outcomes were used across trials, including 72 scales [31 cognitive, 12 activities of daily living (ADLs), 10 global, 16 neuropsychiatric and three quality of life] and nine biological techniques. We consulted with 18 people for PPI. The conference decided that only cognition and biological markers are core measures of disease modification. Cognition should be measured by the Mini Mental State Examination (MMSE) or the Alzheimer's Disease Assessment Scale - Cognitive subscale (ADAS-Cog), and brain changes through structural magnetic resonance imaging (MRI) in a subset of participants. All other domains are important but not core. We recommend using the Neuropsychiatric Inventory for neuropsychiatric symptoms: the Disability Assessment for Dementia for ADLs, the Dementia Quality of Life Measure for quality of life and the Clinical Dementia Rating scale to measure dementia globally. LIMITATIONS Most of the trials included participants with Alzheimer's disease, so recommendations may not apply to other types of dementia. We did not conduct economic analyses. The PPI consultation was limited to members of the Alzheimer's Society Research Network. CONCLUSIONS Cognitive outcomes and biological markers form the core outcome set for future disease modification trials, measured by the MMSE or ADAS-Cog, and structural MRI in a subset of participants. FUTURE WORK We envisage that the core set may be superseded in the future, particularly for other types of dementia. There is a need to develop an algorithm to compare scores on the MMSE and ADAS-Cog. STUDY REGISTRATION The project was registered with Core Outcome Measures in Effectiveness Trials [ www.comet-initiative.org/studies/details/819?result=true (accessed 7 April 2016)]. The systematic review protocol is registered as PROSPERO CRD42015027346. FUNDING The National Institute for Health Research Health Technology Assessment programme.
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Affiliation(s)
- Lucy Webster
- Division of Psychiatry, University College London, London, UK
| | - Derek Groskreutz
- Division of Psychology and Language Sciences, University College London, London, UK
| | | | - Rob Howard
- Division of Psychiatry, University College London, London, UK
| | - John T O'Brien
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Gail Mountain
- School of Health and Related Research, University of Sheffield, Sheffield, UK
| | - Sube Banerjee
- Brighton and Sussex Medical School, University of Sussex, Brighton, UK
| | - Bob Woods
- Dementia Services Development Centre Wales, Bangor University, Bangor, UK
| | - Robert Perneczky
- Faculty of Medicine, School of Public Health, Imperial College London, London, UK
| | - Louise Lafortune
- Cambridge Institute of Public Health, University of Cambridge, Cambridge, UK
| | - Charlotte Roberts
- International Consortium for Health Outcomes Measurement, London, UK
| | | | | | - Frances Bunn
- Centre for Research in Primary and Community Care, University of Hertfordshire, Hatfield, UK
| | - David Challis
- Personal Social Services Research Unit, University of Manchester, Manchester, UK
| | - Georgina Charlesworth
- Research Department of Clinical, Educational, and Health Psychology, University College London, London, UK
| | | | - Chris Fox
- Norwich Medical School, University of East Anglia, Norwich, UK
| | - Claire Goodman
- Centre for Research in Primary and Community Care, University of Hertfordshire, Hatfield, UK
| | - Roy Jones
- Research Institute for the Care of Older People, University of Bath, Bath, UK
| | - Sallie Lamb
- Oxford Clinical Trials Research Unit, University of Oxford, Oxford, UK
| | - Esme Moniz-Cook
- Faculty of Health and Social Care, University of Hull, Hull, UK
| | - Justine Schneider
- Institute of Mental Health, University of Nottingham, Nottingham, UK
| | - Sasha Shepperd
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Claire Surr
- School of Health & Community Studies, Leeds Beckett University, Leeds, UK
| | - Jo Thompson-Coon
- Collaboration for Leadership in Applied Health Research and Care South West Peninsula, University of Exeter, Exeter, UK
| | - Clive Ballard
- Wolfson Centre for Age-Related Diseases, King's College London, London, UK
| | - Carol Brayne
- Cambridge Institute of Public Health, University of Cambridge, Cambridge, UK
| | - Orlaith Burke
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Alistair Burns
- Institute of Brain, Behaviour and Mental Health, University of Manchester, Manchester, UK
| | - Linda Clare
- Collaboration for Leadership in Applied Health Research and Care South West Peninsula, University of Exeter, Exeter, UK
- School of Psychology, University of Exeter, Exeter, UK
- Centre for Research in Ageing and Cognitive Health, University of Exeter Medical School, Exeter, UK
| | - Peter Garrard
- Neuroscience Research Centre, St George's, University of London, UK
| | - Patrick Kehoe
- School of Clinical Sciences, University of Bristol, Bristol, UK
| | - Peter Passmore
- Centre for Public Health, Queen's University Belfast, Belfast, UK
| | - Clive Holmes
- School of Medicine, University of Southampton, Southampton, UK
| | - Ian Maidment
- Aston Research Centre for Healthy Ageing, Aston University, Birmingham, UK
| | - Fliss Murtagh
- Cicely Saunders Institute, King's College London, London, UK
| | - Louise Robinson
- Institute of Health and Society, Newcastle University, Newcastle upon Tyne, UK
| | - Gill Livingston
- Division of Psychiatry, University College London, London, UK
- Camden and Islington NHS Foundation Trust, London, UK
- North Thames Collaboration for Leadership in Applied Health Research and Care, London, UK
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Ritchie C, Smailagic N, Noel‐Storr AH, Ukoumunne O, Ladds EC, Martin S. CSF tau and the CSF tau/ABeta ratio for the diagnosis of Alzheimer's disease dementia and other dementias in people with mild cognitive impairment (MCI). Cochrane Database Syst Rev 2017; 3:CD010803. [PMID: 28328043 PMCID: PMC6464349 DOI: 10.1002/14651858.cd010803.pub2] [Citation(s) in RCA: 90] [Impact Index Per Article: 12.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
BACKGROUND Research suggests that measurable change in cerebrospinal fluid (CSF) biomarkers occurs years in advance of the onset of clinical symptoms (Beckett 2010). In this review, we aimed to assess the ability of CSF tau biomarkers (t-tau and p-tau) and the CSF tau (t-tau or p-tau)/ABeta ratio to enable the detection of Alzheimer's disease pathology in patients with mild cognitive impairment (MCI). These biomarkers have been proposed as important in new criteria for Alzheimer's disease dementia that incorporate biomarker abnormalities. OBJECTIVES To determine the diagnostic accuracy of 1) CSF t-tau, 2) CSF p-tau, 3) the CSF t-tau/ABeta ratio and 4) the CSF p-tau/ABeta ratio index tests for detecting people with MCI at baseline who would clinically convert to Alzheimer's disease dementia or other forms of dementia at follow-up. SEARCH METHODS The most recent search for this review was performed in January 2013. We searched MEDLINE (OvidSP), Embase (OvidSP), BIOSIS Previews (Thomson Reuters Web of Science), Web of Science Core Collection, including Conference Proceedings Citation Index (Thomson Reuters Web of Science), PsycINFO (OvidSP), and LILACS (BIREME). We searched specialized sources of diagnostic test accuracy studies and reviews. We checked reference lists of relevant studies and reviews for additional studies. We contacted researchers for possible relevant but unpublished data. We did not apply any language or data restriction to the electronic searches. We did not use any methodological filters as a method to restrict the search overall. SELECTION CRITERIA We selected those studies that had prospectively well-defined cohorts with any accepted definition of MCI and with CSF t-tau or p-tau and CSF tau (t-tau or p-tau)/ABeta ratio values, documented at or around the time the MCI diagnosis was made. We also included studies which looked at data from those cohorts retrospectively, and which contained sufficient data to construct two by two tables expressing those biomarker results by disease status. Moreover, studies were only selected if they applied a reference standard for Alzheimer's disease dementia diagnosis, for example, the NINCDS-ADRDA or Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) criteria. DATA COLLECTION AND ANALYSIS We screened all titles generated by the electronic database searches. Two review authors independently assessed the abstracts of all potentially relevant studies, and the full papers for eligibility. Two independent assessors performed data extraction and quality assessment. Where data allowed, we derived estimates of sensitivity at fixed values of specificity from the model we fitted to produce the summary receiver operating characteristic (ROC) curve. MAIN RESULTS In total, 1282 participants with MCI at baseline were identified in the 15 included studies of which 1172 had analysable data; 430 participants converted to Alzheimer's disease dementia and 130 participants to other forms of dementia. Follow-up ranged from less than one year to over four years for some participants, but in the majority of studies was in the range one to three years. Conversion to Alzheimer's disease dementia The accuracy of the CSF t-tau was evaluated in seven studies (291 cases and 418 non-cases).The sensitivity values ranged from 51% to 90% while the specificity values ranged from 48% to 88%. At the median specificity of 72%, the estimated sensitivity was 75% (95% CI 67 to 85), the positive likelihood ratio was 2.72 (95% CI 2.43 to 3.04), and the negative likelihood ratio was 0.32 (95% CI 0.22 to 0.47).Six studies (164 cases and 328 non-cases) evaluated the accuracy of the CSF p-tau. The sensitivities were between 40% and 100% while the specificities were between 22% and 86%. At the median specificity of 47.5%, the estimated sensitivity was 81% (95% CI: 64 to 91), the positive likelihood ratio was 1.55 (CI 1.31 to 1.84), and the negative likelihood ratio was 0.39 (CI: 0.19 to 0.82).Five studies (140 cases and 293 non-cases) evaluated the accuracy of the CSF p-tau/ABeta ratio. The sensitivities were between 80% and 96% while the specificities were between 33% and 95%. We did not conduct a meta-analysis because the studies were few and small. Only one study reported the accuracy of CSF t-tau/ABeta ratio.Our findings are based on studies with poor reporting. A significant number of studies had unclear risk of bias for the reference standard, participant selection and flow and timing domains. According to the assessment of index test domain, eight of 15 studies were of poor methodological quality.The accuracy of these CSF biomarkers for 'other dementias' had not been investigated in the included primary studies. Investigation of heterogeneity The main sources of heterogeneity were thought likely to be reference standards used for the target disorders, sources of recruitment, participant sampling, index test methodology and aspects of study quality (particularly, inadequate blinding).We were not able to formally assess the effect of each potential source of heterogeneity as planned, due to the small number of studies available to be included. AUTHORS' CONCLUSIONS The insufficiency and heterogeneity of research to date primarily leads to a state of uncertainty regarding the value of CSF testing of t-tau, p-tau or p-tau/ABeta ratio for the diagnosis of Alzheimer's disease in current clinical practice. Particular attention should be paid to the risk of misdiagnosis and overdiagnosis of dementia (and therefore over-treatment) in clinical practice. These tests, like other biomarker tests which have been subject to Cochrane DTA reviews, appear to have better sensitivity than specificity and therefore might have greater utility in ruling out Alzheimer's disease as the aetiology to the individual's evident cognitive impairment, as opposed to ruling it in. The heterogeneity observed in the few studies awaiting classification suggests our initial summary will remain valid. However, these tests may have limited clinical value until uncertainties have been addressed. Future studies with more uniformed approaches to thresholds, analysis and study conduct may provide a more homogenous estimate than the one that has been available from the included studies we have identified.
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Affiliation(s)
- Craig Ritchie
- University of EdinburghCentre for Clinical Brain SciencesEdinburghUK
| | - Nadja Smailagic
- University of CambridgeInstitute of Public HealthForvie SiteRobinson WayCambridgeUKCB2 0SR
| | - Anna H Noel‐Storr
- University of OxfordRadcliffe Department of MedicineRoom 4401c (4th Floor)John Radcliffe Hospital, HeadingtonOxfordUKOX3 9DU
| | - Obioha Ukoumunne
- University of Exeter Medical School, University of ExeterNIHR CLAHRC South West Peninsula (PenCLAHRC)Veysey BuildingSalmon Pool LaneExeterDevonUKEX2 4SG
| | - Emma C Ladds
- North Bristol NHS TrustSouthmead hospitalBristolUK
| | - Steven Martin
- University of CambridgeInstitute of Public HealthForvie SiteRobinson WayCambridgeUKCB2 0SR
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25
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Liu K, Chen K, Yao L, Guo X. Prediction of Mild Cognitive Impairment Conversion Using a Combination of Independent Component Analysis and the Cox Model. Front Hum Neurosci 2017; 11:33. [PMID: 28220065 PMCID: PMC5292818 DOI: 10.3389/fnhum.2017.00033] [Citation(s) in RCA: 49] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2016] [Accepted: 01/16/2017] [Indexed: 12/13/2022] Open
Abstract
Mild cognitive impairment (MCI) represents a transitional stage from normal aging to Alzheimer’s disease (AD) and corresponds to a higher risk of developing AD. Thus, it is necessary to explore and predict the onset of AD in MCI stage. In this study, we propose a combination of independent component analysis (ICA) and the multivariate Cox proportional hazards regression model to investigate promising risk factors associated with MCI conversion among 126 MCI converters and 108 MCI non-converters from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Using structural magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET) data, we extracted brain networks from AD and normal control groups via ICA and then constructed Cox models that included network-based neuroimaging factors for the MCI group. We carried out five separate Cox analyses and the two-modality neuroimaging Cox model identified three significant network-based risk factors with higher prediction performance (accuracy = 73.50%) than those in either single-modality model (accuracy = 68.80%). Additionally, the results of the comprehensive Cox model, including significant neuroimaging factors and clinical variables, demonstrated that MCI individuals with reduced gray matter volume in a temporal lobe-related network of structural MRI [hazard ratio (HR) = 8.29E-05 (95% confidence interval (CI), 5.10E- 07 ~ 0.013)], low glucose metabolism in the posterior default mode network based on FDG-PET [HR = 0.066 (95% CI, 4.63E-03 ~ 0.928)], positive apolipoprotein E ε4-status [HR = 1. 988 (95% CI, 1.531 ~ 2.581)], increased Alzheimer’s Disease Assessment Scale-Cognitive Subscale scores [HR = 1.100 (95% CI, 1.059 ~ 1.144)] and Sum of Boxes of Clinical Dementia Rating scores [HR = 1.622 (95% CI, 1.364 ~ 1.930)] were more likely to convert to AD within 36 months after baselines. These significant risk factors in such comprehensive Cox model had the best prediction ability (accuracy = 84.62%, sensitivity = 86.51%, specificity = 82.41%) compared to either neuroimaging factors or clinical variables alone. These results suggested that a combination of ICA and Cox model analyses could be used successfully in survival analysis and provide a network-based perspective of MCI progression or AD-related studies.
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Affiliation(s)
- Ke Liu
- College of Information Science and Technology, Beijing Normal University Beijing, China
| | - Kewei Chen
- Banner Alzheimer's Institute and Banner Good Samaritan PET Center, Phoenix AZ, USA
| | - Li Yao
- College of Information Science and Technology, Beijing Normal University Beijing, China
| | - Xiaojuan Guo
- College of Information Science and Technology, Beijing Normal University Beijing, China
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26
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Gertje EC, Pluta J, Das S, Mancuso L, Kliot D, Yushkevich P, Wolk D. Clinical Application of Automatic Segmentation of Medial Temporal Lobe Subregions in Prodromal and Dementia-Level Alzheimer's Disease. J Alzheimers Dis 2016; 54:1027-1037. [PMID: 27567809 DOI: 10.3233/jad-160014] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Volumetry of medial temporal lobe (MTL) structures to diagnose Alzheimer's disease (AD) in its earliest symptomatic stage could be of great importance for interventions or disease modifying pharmacotherapy. OBJECTIVE This study aimed to demonstrate the first application of an automatic segmentation method of MTL subregions in a clinical population. Automatic segmentation of magnetic resonance images (MRIs) in a research population has previously been shown to detect evidence of neurodegeneration in MTL subregions and to help discriminate AD and mild cognitive impairment (MCI) from a healthy comparison group. METHODS Clinical patients were selected and T2-weighted MRI scan quality was checked. An automatic segmentation method of hippocampal subfields (ASHS) was applied to scans of 67 AD patients, 38 amnestic MCI patients, and 57 healthy controls. Hippocampal subfields, entorhinal cortex (ERC), and perirhinal cortex were automatically labeled and subregion volumes were compared between groups. RESULTS One fourth of all scans were excluded due to bad scan quality. There were significant volume reductions in all subregions, except BA36, in aMCIs (p < 0.001), most prominently in Cornu Ammonis 1 (CA1) and ERC, and in all subregions in AD. However, sensitivity of CA1 and ERC hardly differed from sensitivity of WH in aMCI and AD. CONCLUSION Applying automatic segmentation of MTL subregions in a clinical setting as a potential biomarker for prodromal AD is feasible, but issues of image quality due to motion remain to be addressed. CA1 and ERC provided strongest group discrimination in differentiating aMCIs from controls, but discriminatory power of different subfields was low overall.
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Affiliation(s)
- Eske Christiane Gertje
- Department of Internal Medicine, Skåne University Hospital, Lund, Sweden.,Department of Neurology, University of Oldenburg, Oldenburg, Germany.,Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - John Pluta
- Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Sandhitsu Das
- Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania, Philadelphia, USA.,Penn Memory Center, Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | - Lauren Mancuso
- Penn Memory Center, Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | - Dasha Kliot
- Penn Memory Center, Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | - Paul Yushkevich
- Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - David Wolk
- Penn Memory Center, Department of Neurology, University of Pennsylvania, Philadelphia, USA
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27
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Khan TK, Alkon DL. Alzheimer's Disease Cerebrospinal Fluid and Neuroimaging Biomarkers: Diagnostic Accuracy and Relationship to Drug Efficacy. J Alzheimers Dis 2016; 46:817-36. [PMID: 26402622 DOI: 10.3233/jad-150238] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Widely researched Alzheimer's disease (AD) biomarkers include in vivo brain imaging with PET and MRI, imaging of amyloid plaques, and biochemical assays of Aβ 1 - 42, total tau, and phosphorylated tau (p-tau-181) in cerebrospinal fluid (CSF). In this review, we critically evaluate these biomarkers and discuss their clinical utility for the differential diagnosis of AD. Current AD biomarker tests are either highly invasive (requiring CSF collection) or expensive and labor-intensive (neuroimaging), making them unsuitable for use in the primary care, clinical office-based setting, or to assess drug efficacy in clinical trials. In addition, CSF and neuroimaging biomarkers continue to face challenges in achieving required sensitivity and specificity and minimizing center-to-center variability (for CSF-Aβ 1 - 42 biomarkers CV = 26.5% ; http://www.alzforum.org/news/conference-coverage/paris-standardization-hurdle-spinal-fluid-imaging-markers). Although potentially useful for selecting patient populations for inclusion in AD clinical trials, the utility of CSF biomarkers and neuroimaging techniques as surrogate endpoints of drug efficacy needs to be validated. Recent trials of β- and γ-secretase inhibitors and Aβ immunization-based therapies in AD showed no significant cognitive improvements, despite changes in CSF and neuroimaging biomarkers. As we learn more about the dysfunctional cellular and molecular signaling processes that occur in AD, and how these processes are manifested in tissues outside of the brain, new peripheral biomarkers may also be validated as non-invasive tests to diagnose preclinical and clinical AD.
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28
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Degerman Gunnarsson M, Ingelsson M, Blennow K, Basun H, Lannfelt L, Kilander L. High tau levels in cerebrospinal fluid predict nursing home placement and rapid progression in Alzheimer's disease. ALZHEIMERS RESEARCH & THERAPY 2016; 8:22. [PMID: 27263933 PMCID: PMC4893835 DOI: 10.1186/s13195-016-0191-0] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2015] [Accepted: 04/29/2016] [Indexed: 12/20/2022]
Abstract
Background Increased concentrations of cerebrospinal fluid (CSF) total tau (t-tau) and phosphorylated tau, as well as decreased amyloid-β 42 peptide, are biomarkers of Alzheimer’s disease (AD) pathology, but few studies have shown an association with AD progression rate. We hypothesized that high CSF tau, as a marker of ongoing neurodegeneration, would predict a more aggressive course of AD, using time to nursing home placement (NHP) as the main outcome. Methods Our sample inlcuded 234 patients with mild cognitive impairment (MCI) due to AD (n = 134) or mild to moderate AD (n = 100) who underwent lumbar puncture at a memory clinic and were followed for 2–11 years (median 4.9 years). Results Individuals with CSF t-tau in the highest quartile (≥900 ng/L) had a higher ratio of NHP, both in the total cohort and in patients with MCI only (adjusted HR 2.17 [95 % CI 1.24–3.80]; HR 2.37 [95 % CI 1.10–5.09], respectively), than the lowest quartile. The association between high t-tau levels and future steep deterioration was confirmed in analyses with conversion to moderate dementia (HR 1.66; 95 % CI 1.08–2.56), rapid decline in Mini Mental State Examination score (≥4-point drop/12 months), and dying in severe dementia as outcomes. Conclusions To our knowledge, this is the first study to show that high CSF t-tau levels predict early NHP and conversion to moderate dementia in an AD cohort. Selecting patients with high CSF t-tau, indicating more aggressive neurodegeneration and steeper decline, for AD immunotherapy trials might increase the possibility of showing contrast between active treatment and placebo. Electronic supplementary material The online version of this article (doi:10.1186/s13195-016-0191-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Malin Degerman Gunnarsson
- Department of Public Health and Caring Sciences/Geriatrics, Uppsala University, Uppsala Science Park, SE-751 85, Uppsala, Sweden.
| | - Martin Ingelsson
- Department of Public Health and Caring Sciences/Geriatrics, Uppsala University, Uppsala Science Park, SE-751 85, Uppsala, Sweden
| | - Kaj Blennow
- Clinical Neurochemistry Laboratory, Institute of Neuroscience and Physiology, University of Gothenburg, Sahlgrenska University Hospital, SE-43180, Mölndal, Sweden
| | - Hans Basun
- Department of Public Health and Caring Sciences/Geriatrics, Uppsala University, Uppsala Science Park, SE-751 85, Uppsala, Sweden
| | - Lars Lannfelt
- Department of Public Health and Caring Sciences/Geriatrics, Uppsala University, Uppsala Science Park, SE-751 85, Uppsala, Sweden
| | - Lena Kilander
- Department of Public Health and Caring Sciences/Geriatrics, Uppsala University, Uppsala Science Park, SE-751 85, Uppsala, Sweden
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Orellana C, Ferreira D, Muehlboeck JS, Mecocci P, Vellas B, Tsolaki M, Kłoszewska I, Soininen H, Lovestone S, Simmons A, Wahlund LO, Westman E. Measuring Global Brain Atrophy with the Brain Volume/Cerebrospinal Fluid Index: Normative Values, Cut-Offs and Clinical Associations. NEURODEGENER DIS 2015; 16:77-86. [PMID: 26726737 DOI: 10.1159/000442443] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2015] [Accepted: 11/11/2015] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Global brain atrophy is present in normal aging and different neurodegenerative disorders such as Alzheimer's disease (AD) and is becoming widely used to monitor disease progression. SUMMARY The brain volume/cerebrospinal fluid index (BV/CSF index) is validated in this study as a measurement of global brain atrophy. We tested the ability of the BV/CSF index to detect global brain atrophy, investigated the influence of confounders, provided normative values and cut-offs for mild, moderate and severe brain atrophy, and studied associations with different outcome variables. A total of 1,009 individuals were included [324 healthy controls, 408 patients with mild cognitive impairment (MCI) and 277 patients with AD]. Magnetic resonance images were segmented using FreeSurfer, and the BV/CSF index was calculated and studied both cross-sectionally and longitudinally (1-year follow-up). Both AD patients and MCI patients who progressed to AD showed greater global brain atrophy compared to stable MCI patients and controls. Atrophy was associated with older age, larger intracranial volume, less education and presence of the ApoE ε4 allele. Significant correlations were found with clinical variables, CSF biomarkers and several cognitive tests. KEY MESSAGES The BV/CSF index may be useful for staging individuals according to the degree of global brain atrophy, and for monitoring disease progression. It also shows potential for predicting clinical changes and for being used in the clinical routine.
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Lambert C, Sam Narean J, Benjamin P, Zeestraten E, Barrick TR, Markus HS. Characterising the grey matter correlates of leukoaraiosis in cerebral small vessel disease. Neuroimage Clin 2015; 9:194-205. [PMID: 26448913 PMCID: PMC4564392 DOI: 10.1016/j.nicl.2015.07.002] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2015] [Revised: 06/30/2015] [Accepted: 07/03/2015] [Indexed: 01/05/2023]
Abstract
Cerebral small vessel disease (SVD) is a heterogeneous group of pathological disorders that affect the small vessels of the brain and are an important cause of cognitive impairment. The ischaemic consequences of this disease can be detected using MRI, and include white matter hyperintensities (WMH), lacunar infarcts and microhaemorrhages. The relationship between SVD disease severity, as defined by WMH volume, in sporadic age-related SVD and cortical thickness has not been well defined. However, regional cortical thickness change would be expected due to associated phenomena such as underlying ischaemic white matter damage, and the observation that widespread cortical thinning is observed in the related genetic condition CADASIL (Righart et al., 2013). Using MRI data, we have developed a semi-automated processing pipeline for the anatomical analysis of individuals with cerebral small vessel disease and applied it cross-sectionally to 121 subjects diagnosed with this condition. Using a novel combined automated white matter lesion segmentation algorithm and lesion repair step, highly accurate warping to a group average template was achieved. The volume of white matter affected by WMH was calculated, and used as a covariate of interest in a voxel-based morphometry and voxel-based cortical thickness analysis. Additionally, Gaussian Process Regression (GPR) was used to assess if the severity of SVD, measured by WMH volume, could be predicted from the morphometry and cortical thickness measures. We found significant (Family Wise Error corrected p < 0.05) volumetric decline with increasing lesion load predominately in the parietal lobes, anterior insula and caudate nuclei bilaterally. Widespread significant cortical thinning was found bilaterally in the dorsolateral prefrontal, parietal and posterio-superior temporal cortices. These represent distinctive patterns of cortical thinning and volumetric reduction compared to ageing effects in the same cohort, which exhibited greater changes in the occipital and sensorimotor cortices. Using GPR, the absolute WMH volume could be significantly estimated from the grey matter density and cortical thickness maps (Pearson's coefficients 0.80 and 0.75 respectively). We demonstrate that SVD severity is associated with regional cortical thinning. Furthermore a quantitative measure of SVD severity (WMH volume) can be predicted from grey matter measures, supporting an association between white and grey matter damage. The pattern of cortical thinning and volumetric decline is distinctive for SVD severity compared to ageing. These results, taken together, suggest that there is a phenotypic pattern of atrophy associated with SVD severity.
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Affiliation(s)
- Christian Lambert
- Neurosciences Research Centre, Cardiovascular and Cell Sciences Research Institute, St George's University of London, United Kingdom
| | - Janakan Sam Narean
- Neurosciences Research Centre, Cardiovascular and Cell Sciences Research Institute, St George's University of London, United Kingdom
| | - Philip Benjamin
- Neurosciences Research Centre, Cardiovascular and Cell Sciences Research Institute, St George's University of London, United Kingdom
| | - Eva Zeestraten
- Neurosciences Research Centre, Cardiovascular and Cell Sciences Research Institute, St George's University of London, United Kingdom
| | - Thomas R. Barrick
- Neurosciences Research Centre, Cardiovascular and Cell Sciences Research Institute, St George's University of London, United Kingdom
| | - Hugh S. Markus
- Neurosciences Research Centre, Cardiovascular and Cell Sciences Research Institute, St George's University of London, United Kingdom
- Stroke Research Group, Division of Clinical Neurosciences, University of Cambridge, United Kingdom
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Farzan A, Mashohor S, Ramli AR, Mahmud R. Boosting diagnosis accuracy of Alzheimer's disease using high dimensional recognition of longitudinal brain atrophy patterns. Behav Brain Res 2015; 290:124-30. [PMID: 25889456 DOI: 10.1016/j.bbr.2015.04.010] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2015] [Revised: 04/04/2015] [Accepted: 04/06/2015] [Indexed: 02/01/2023]
Abstract
OBJECTIVE Boosting accuracy in automatically discriminating patients with Alzheimer's disease (AD) and normal controls (NC), based on multidimensional classification of longitudinal whole brain atrophy rates and their intermediate counterparts in analyzing magnetic resonance images (MRI). METHOD Longitudinal percentage of brain volume changes (PBVC) in two-year follow up and its intermediate counterparts in early 6-month and late 18-month are used as features in supervised and unsupervised classification procedures based on K-mean, fuzzy clustering method (FCM) and support vector machine (SVM). The most relevant features for classification are selected using discriminative analysis (DA) of features and their principal components (PC). Accuracy of the proposed method is evaluated in a group of 30 patients with AD (16 males, 14 females, age±standard-deviation (SD)=75±1.36 years) and 30 normal controls (15 males, 15 females, age±SD=77±0.88 years) using leave-one-out cross-validation. RESULTS Results indicate superiority of supervised machine learning techniques over unsupervised ones in diagnosing AD and withal, predominance of RBF kernel over lineal one. Accuracies of 83.3%, 83.3%, 90% and 91.7% are achieved in classification by K-mean, FCM, linear SVM and SVM with radial based function (RBF) respectively. CONCLUSION Evidence that SVM classification of longitudinal atrophy rates may results in high accuracy is given. Additionally, it is realized that use of intermediate atrophy rates and their principal components improves diagnostic accuracy.
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Affiliation(s)
- Ali Farzan
- Faculty of Computer Engineering, IAU, Shabestar Branch, Iran.
| | - Syansiah Mashohor
- Department of Computer & Communication Systems, Faculty of Engineering, University of Putra Malaysia, 43400 Serdang, Selangor, Malaysia; Institute of Advanced Technology, UPM, Malaysia
| | - Abd Rahman Ramli
- Department of Computer & Communication Systems, Faculty of Engineering, University of Putra Malaysia, 43400 Serdang, Selangor, Malaysia
| | - Rozi Mahmud
- Faculty of Radiology, University Putra Malaysia (UPM), 43400 Serdang, Selangor D.E., Malaysia
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32
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Haris M, Yadav SK, Rizwan A, Singh A, Cai K, Kaura D, Wang E, Davatzikos C, Trojanowski JQ, Melhem ER, Marincola FM, Borthakur A. T1rho MRI and CSF biomarkers in diagnosis of Alzheimer's disease. NEUROIMAGE-CLINICAL 2015; 7:598-604. [PMID: 25844314 PMCID: PMC4375645 DOI: 10.1016/j.nicl.2015.02.016] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2014] [Revised: 02/22/2015] [Accepted: 02/23/2015] [Indexed: 01/14/2023]
Abstract
In the current study, we have evaluated the performance of magnetic resonance (MR) T1rho (T1ρ) imaging and CSF biomarkers (T-tau, P-tau and Aβ-42) in characterization of Alzheimer's disease (AD) patients from mild cognitive impairment (MCI) and control subjects. With informed consent, AD (n = 27), MCI (n = 17) and control (n = 17) subjects underwent a standardized clinical assessment and brain MRI on a 1.5-T clinical-scanner. T1ρ images were obtained at four different spin-lock pulse duration (10, 20, 30 and 40 ms). T1ρ maps were generated by pixel-wise fitting of signal intensity as a function of the spin-lock pulse duration. T1ρ values from gray matter (GM) and white matter (WM) of medial temporal lobe were calculated. The binary logistic regression using T1ρ and CSF biomarkers as variables was performed to classify each group. T1ρ was able to predict 77.3% controls and 40.0% MCI while CSF biomarkers predicted 81.8% controls and 46.7% MCI. T1ρ and CSF biomarkers in combination predicted 86.4% controls and 66.7% MCI. When comparing controls with AD, T1ρ predicted 68.2% controls and 73.9% AD, while CSF biomarkers predicted 77.3% controls and 78.3% for AD. Combination of T1ρ and CSF biomarkers improved the prediction rate to 81.8% for controls and 82.6% for AD. Similarly, on comparing MCI with AD, T1ρ predicted 35.3% MCI and 81.9% AD, whereas CSF biomarkers predicted 53.3% MCI and 83.0% AD. Collectively CSF biomarkers and T1ρ were able to predict 59.3% MCI and 84.6% AD. On receiver operating characteristic analysis T1ρ showed higher sensitivity while CSF biomarkers showed greater specificity in delineating MCI and AD from controls. No significant correlation between T1ρ and CSF biomarkers, between T1ρ and age, and between CSF biomarkers and age was observed. The combined use of T1ρ and CSF biomarkers have promise to improve the early and specific diagnosis of AD. Furthermore, disease progression form MCI to AD might be easily tracked using these two parameters in combination. Increased T1rho was observed in MCI and AD compared to controls. Increased T-tau and P-tau and decreased Aβ1-42 were observed in MCI and AD. Combined biomarkers have promise to improve early and specific diagnosis of AD. MCI to AD progression might be tracked using these two biomarkers in combination.
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Key Words
- AD, Alzheimer's disease
- Alzheimer's disease
- Aβ1-42, amyloid beta 42
- CSF biomarkers
- CSF, cerebrospinal fluid
- FOV, field of view
- GM, gray matter
- MCI, mild cognitive impairment
- MMSE, Mini-Mental State Examination
- MPRAGE, magnetization prepared rapid acquisition gradient-echo
- MRI, magnetic resonance imaging
- MTL, medial temporal lobe
- Medial temporal lobe
- Mild cognitive impairment
- PET, positron emission tomography
- ROC, receiver operating characteristic.
- T-tau, total tau
- T1rho
- T1ρ, T1rho
- TE, echo time
- TI, inversion time
- TR, repetition time
- TSL, total spin lock
- WM, white matter
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Affiliation(s)
- Mohammad Haris
- Research Branch, Sidra Medical and Research Center, Doha, Qatar ; Center for Magnetic Resonance and Optical Imaging, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Santosh K Yadav
- Research Branch, Sidra Medical and Research Center, Doha, Qatar
| | - Arshi Rizwan
- All India Institute of Medical Science, Ansari Nagar East, New Delhi, Delhi 110029, India
| | - Anup Singh
- Center for Magnetic Resonance and Optical Imaging, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA ; Center for Biomedical Engineering, Indian institute of Technology, New Delhi, India
| | - Kejia Cai
- Center for Magnetic Resonance and Optical Imaging, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA ; Center for Magnetic Resonance Research, Radiology Department, University of Illinois at Chicago, IL, USA
| | - Deepak Kaura
- Research Branch, Sidra Medical and Research Center, Doha, Qatar
| | - Ena Wang
- Research Branch, Sidra Medical and Research Center, Doha, Qatar
| | - Christos Davatzikos
- Section of Biomedical Image Analysis, University of Pennsylvania, Philadelphia, PA, USA
| | - John Q Trojanowski
- Department of Pathology & Lab Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Elias R Melhem
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Arijitt Borthakur
- Center for Magnetic Resonance and Optical Imaging, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
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Dowling NM, Johnson SC, Gleason CE, Jagust WJ. The mediational effects of FDG hypometabolism on the association between cerebrospinal fluid biomarkers and neurocognitive function. Neuroimage 2015; 105:357-68. [PMID: 25450107 PMCID: PMC4262609 DOI: 10.1016/j.neuroimage.2014.10.050] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2014] [Revised: 10/17/2014] [Accepted: 10/20/2014] [Indexed: 01/09/2023] Open
Abstract
Positive cerebrospinal fluid (CSF) biomarkers of tau and amyloid beta42 suggest possible active underlying Alzheimer's disease (AD) including neurometabolic dysfunction and neurodegeneration leading to eventual cognitive decline. But the temporal relationship between CSF, imaging markers of neural function, and cognition has not been described. Using a statistical mediation model, we examined relationships between cerebrospinal fluid (CSF) analytes (hyperphosphorylated tau (p-Tau(181p)), β-amyloid peptides 1-42 (Aβ(1-42)), total tau (t-Tau), and their ratios); change in cognitive function; and change in [18F]fluorodeoxyglucose (FDG) uptake using positron emission tomography (PET). We hypothesized that a) abnormal CSF protein values at baseline, result in cognitive declines by decreasing neuronal glucose metabolism across time, and b) the role of altered glucose metabolism in the assumed causal chain varies by brain region and the nature of CSF protein alteration. Data from 412 individuals participating in Alzheimer's Disease Neuroimaging (ADNI) cohort studies were included in analyses. At baseline, individuals were cognitively normal (N = 82), or impaired: 241 with mild cognitive impairment, and 89 with Alzheimer's disease. A parallel-process latent growth curve model was used to test mediational effects of changes in regional FDG-PET uptake over time in relation to baseline CSF biomarkers and changes in cognition, measured with the 13-item Alzheimer Disease's Assessment Scale-cognitive subscale (ADAS-Cog). Findings suggested a causal sequence of events; specifically, FDG hypometabolism acted as a mediator between antecedent CSF biomarker alterations and subsequent cognitive impairment. Higher baseline concentrations of t-Tau, and p-Tau(181p) were more predictive of decline in cerebral glucose metabolism than lower baseline concentrations of Aβ(1-42). FDG-PET changes appeared to mediate t-Tau or t-Tau/Aβ(1-42)-associated cognitive change across all brain regions examined. Significant direct effects of alterations in Aβ(1-42) levels on hypometabolism were observed in a single brain region: middle/inferior temporal gyrus. Results support a temporal framework model in which reduced CSF amyloid-related biomarkers occur earlier in the pathogenic pathway, ultimately leading to detrimental cognitive effects. Also consistent with this temporal framework model, baseline markers of neurofibrillary degeneration predicted changes in brain glucose metabolism in turn causing longitudinal cognitive changes, suggesting that tau-related burden precedes neurometabolic dysfunction. While intriguing, the hypothesized mediational relationships require further validation.
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Affiliation(s)
- N Maritza Dowling
- Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI, USA; Alzheimer's Disease Research Center, University of Wisconsin, Madison, WI, USA.
| | - Sterling C Johnson
- Alzheimer's Disease Research Center, University of Wisconsin, Madison, WI, USA; Geriatric Research Education and Clinical Center, William S. Middleton Memorial Veterans Hospital, Madison, WI, USA
| | - Carey E Gleason
- Alzheimer's Disease Research Center, University of Wisconsin, Madison, WI, USA; Geriatric Research Education and Clinical Center, William S. Middleton Memorial Veterans Hospital, Madison, WI, USA
| | - William J Jagust
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, USA; Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
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Prados F, Cardoso MJ, Leung KK, Cash DM, Modat M, Fox NC, Wheeler-Kingshott CAM, Ourselin S. Measuring brain atrophy with a generalized formulation of the boundary shift integral. Neurobiol Aging 2015; 36 Suppl 1:S81-90. [PMID: 25264346 PMCID: PMC4288791 DOI: 10.1016/j.neurobiolaging.2014.04.035] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2013] [Revised: 04/10/2014] [Accepted: 04/15/2014] [Indexed: 11/28/2022]
Abstract
Brain atrophy measured using structural magnetic resonance imaging (MRI) has been widely used as an imaging biomarker for disease diagnosis and tracking of pathologic progression in neurodegenerative diseases. In this work, we present a generalized and extended formulation of the boundary shift integral (gBSI) using probabilistic segmentations to estimate anatomic changes between 2 time points. This method adaptively estimates a non-binary exclusive OR region of interest from probabilistic brain segmentations of the baseline and repeat scans to better localize and capture the brain atrophy. We evaluate the proposed method by comparing the sample size requirements for a hypothetical clinical trial of Alzheimer's disease to that needed for the current implementation of BSI as well as a fuzzy implementation of BSI. The gBSI method results in a modest but reduced sample size, providing increased sensitivity to disease changes through the use of the probabilistic exclusive OR region.
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Affiliation(s)
- Ferran Prados
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK; NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, London, UK.
| | - Manuel Jorge Cardoso
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
| | - Kelvin K Leung
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
| | - David M Cash
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK; Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
| | - Marc Modat
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK; Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
| | - Nick C Fox
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
| | | | - Sebastien Ourselin
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK; Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
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Degerman Gunnarsson M, Lannfelt L, Ingelsson M, Basun H, Kilander L. High tau levels in cerebrospinal fluid predict rapid decline and increased dementia mortality in Alzheimer's disease. Dement Geriatr Cogn Disord 2014; 37:196-206. [PMID: 24157938 DOI: 10.1159/000355556] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/10/2013] [Indexed: 11/19/2022] Open
Abstract
OBJECTIVE Cerebrospinal fluid (CSF) amyloid β42 (Aβ42), total tau (t-tau) and phosphorylated tau (p-tau) are useful as predictors of conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) dementia. However, results are contradictory as to whether these biomarkers reflect the future rate of clinical decline. METHODS This is a retrospective study on 196 patients with AD [mild/moderate AD (n = 72) or AD-MCI (n = 124) at baseline] with a follow-up period of 2-9 years' duration (median 6 years). Lumbar punctures were performed at baseline as a part of the diagnostic procedure. RESULTS We found an increased risk of rapid cognitive decline defined as a drop in the Mini-Mental State Examination score of ≥ 4 points/year in patients with CSF t-tau concentrations above the median (OR 3.31, 95% CI 1.53-7.16) and CSF p-tau above the median (OR 2.53, 95% CI 1.21-5.26). Patients with CSF t-tau in the highest quartile had a higher risk of dying in severe dementia (HR 4.67, 95% CI 1.16-18.82). CONCLUSIONS In this large AD cohort, we found an association between high levels of CSF t-tau and p-tau and a more aggressive course of the disease, measured as a rapid cognitive decline and a higher risk of dying in severe dementia.
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Affiliation(s)
- M Degerman Gunnarsson
- Department of Public Health and Caring Sciences/Geriatrics, Uppsala University, Uppsala, Sweden
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36
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Gross AL, Sherva R, Mukherjee S, Newhouse S, Kauwe JSK, Munsie LM, Waterston LB, Bennett DA, Jones RN, Green RC, Crane PK. Calibrating longitudinal cognition in Alzheimer's disease across diverse test batteries and datasets. Neuroepidemiology 2014; 43:194-205. [PMID: 25402421 PMCID: PMC4297570 DOI: 10.1159/000367970] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2014] [Accepted: 08/23/2014] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND We sought to identify optimal approaches by calibrating longitudinal cognitive performance across studies with different neuropsychological batteries. METHODS We examined four approaches to calibrate cognitive performance in nine longitudinal studies of Alzheimer's disease (AD) (n = 10,875): (1) common test, (2) standardize and average available tests, (3) confirmatory factor analysis (CFA) with continuous indicators, and (4) CFA with categorical indicators. To compare precision, we determined the minimum sample sizes needed to detect 25% cognitive decline with 80% power. To compare criterion validity, we correlated cognitive change from each approach with 6-year changes in average cortical thickness and hippocampal volume using available MRI data from the AD Neuroimaging Initiative. RESULTS CFA with categorical indicators required the smallest sample size to detect 25% cognitive decline with 80% power (n = 232) compared to common test (n = 277), standardize-and-average (n = 291), and CFA with continuous indicators (n = 315) approaches. Associations with changes in biomarkers changes were the strongest for CFA with categorical indicators. CONCLUSIONS CFA with categorical indicators demonstrated greater power to detect change and superior criterion validity compared to other approaches. It has wide applicability to directly compare cognitive performance across studies, making it a good way to obtain operational phenotypes for genetic analyses of cognitive decline among people with AD.
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Affiliation(s)
- Alden L Gross
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Md., USA
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Wang Y, Resnick SM, Davatzikos C. Analysis of spatio-temporal brain imaging patterns by Hidden Markov Models and serial MRI images. Hum Brain Mapp 2014; 35:4777-94. [PMID: 24706564 PMCID: PMC4190046 DOI: 10.1002/hbm.22511] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2012] [Revised: 02/21/2014] [Accepted: 03/18/2014] [Indexed: 12/20/2022] Open
Abstract
Brain changes due to development and maturation, normal aging, or degenerative disease are continuous, gradual, and variable across individuals. To quantify the individual progression of brain changes, we propose a spatio-temporal methodology based on Hidden Markov Models (HMM), and apply it on four-dimensional structural brain magnetic resonance imaging series of older individuals. First, regional brain features are extracted in order to reduce image dimensionality. This process is guided by the objective of the study or the specific imaging patterns whose progression is of interest, for example, the evaluation of Alzheimer-like patterns of brain change in normal individuals. These regional features are used in conjunction with HMMs, which aim to measure the dynamic association between brain structure changes and progressive stages of disease over time. A bagging framework is used to obtain models with good generalization capability, since in practice the number of serial scans is limited. An application of the proposed methodology was to detect individuals with the risk of developing MCI, and therefore it was tested on modeling the progression of brain atrophy patterns in older adults. With HMM models, the state-transition paths corresponding to longitudinal brain changes were constructed from two completely independent datasets, the Alzheimer Disease Neuroimaging Initiative and the Baltimore Longitudinal Study of Aging. The statistical analysis of HMM-state paths among the normal, progressive MCI, and MCI groups indicates that, HMM-state index 1 is likely to be a predictor of the conversion from cognitively normal to MCI, potentially many years before clinical symptoms become measurable.
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Affiliation(s)
- Ying Wang
- Section of Biomedical Image Analysis, Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvania
| | - Susan M. Resnick
- Laboratory of Behavioral NeuroscienceNational Institute on AgingBaltimoreMaryland
| | - Christos Davatzikos
- Section of Biomedical Image Analysis, Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvania
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Hohman TJ, Koran MEI, Thornton-Wells TA. Genetic variation modifies risk for neurodegeneration based on biomarker status. Front Aging Neurosci 2014; 6:183. [PMID: 25140149 PMCID: PMC4121544 DOI: 10.3389/fnagi.2014.00183] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2014] [Accepted: 07/08/2014] [Indexed: 12/14/2022] Open
Abstract
Background: While a great deal of work has gone into understanding the relationship between Cerebrospinal fluid (CSF) biomarkers, brain atrophy, and disease progression, less work has attempted to investigate how genetic variation modifies these relationships. The goal of this study was two-fold. First, we sought to identify high-risk vs. low-risk individuals based on their CSF tau and Aβ load and characterize these individuals with regard to brain atrophy in an AD-relevant region of interest. Next, we sought to identify genetic variants that modified the relationship between biomarker classification and neurodegeneration. Methods: Participants were categorized based on established cut-points for biomarker positivity. Mixed model regression was used to quantify longitudinal change in the left inferior lateral ventricle. Interaction analyses between single nucleotide polymorphisms (SNPs) and biomarker group status were performed using a genome wide association study (GWAS) approach. Correction for multiple comparisons was performed using the Bonferroni procedure. Results: One intergenic SNP (rs4866650) and one SNP within the SPTLC1 gene (rs7849530) modified the association between amyloid positivity and neurodegeneration. A transcript variant of WDR11-AS1 gene (rs12261764) modified the association between tau positivity and neurodegeneration. These effects were consistent across the two sub-datasets and explained approximately 3% of variance in ventricular dilation. One additional SNP (rs6887649) modified the association between amyloid positivity and baseline ventricular volume, but was not observed consistently across the sub-datasets. Conclusions: Genetic variation modifies the association between AD biomarkers and neurodegeneration. Genes that regulate the molecular response in the brain to oxidative stress may be particularly relevant to neural vulnerability to the damaging effects of amyloid-β.
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Affiliation(s)
- Timothy J Hohman
- Department of Molecular Physiology and Biophysics, Center for Human Genetics and Research, Vanderbilt University School of Medicine Nashville, TN, USA
| | - Mary Ellen I Koran
- Department of Molecular Physiology and Biophysics, Center for Human Genetics and Research, Vanderbilt University School of Medicine Nashville, TN, USA
| | - Tricia A Thornton-Wells
- Department of Molecular Physiology and Biophysics, Center for Human Genetics and Research, Vanderbilt University School of Medicine Nashville, TN, USA
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Tang W, Huang Q, Wang Y, Wang ZY, Yao YY. Assessment of CSF Aβ42 as an aid to discriminating Alzheimer's disease from other dementias and mild cognitive impairment: a meta-analysis of 50 studies. J Neurol Sci 2014; 345:26-36. [PMID: 25086857 DOI: 10.1016/j.jns.2014.07.015] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2014] [Revised: 05/27/2014] [Accepted: 07/07/2014] [Indexed: 01/08/2023]
Abstract
Mild Alzheimer's disease (AD) is usually difficult to differentiate from other dementias or mild cognitive impairment (MCI). The aim of our study is to evaluate the clinical importance of cerebrospinal fluid (CSF) β-amyloid 42 (Aβ42) in MCI, AD and other dementias, more specifically: frontotemporal dementia (FTD), dementia with Lewy bodies (DLB), Parkinson's disease (PD) with dementia (PDD) and vascular dementia (VaD). Fifty eligible articles were identified by search of databases including PubMed, EMBASE, Elsevier, Springer Link and the Cochrane Library, from January 1990 to May 2014. The random effects model was used to calculate the standardized mean difference (SMD) with corresponding 95% CI by STATA 9.0 software. The subgroup analyses were made on the method (ELISA, xMAP). We found that CSF Aβ42 concentrations were significantly lower in AD compared to MCI (SMD: -0.68, 95% CI: [-0.80, -0.56], z=11.34, P<0.001), FTD (SMD: -1.09, 95% CI: [-1.41, -0.76], z=6.62, P<0.001), PDD (SMD: -0.75, 95% CI: [-1.39, -0.10], z=2.27, P=0.023), VaD (SMD: -0.95, 95% CI: [-1.30, -0.61], z=5.43, P<0.001). In addition, compared to DLB, Aβ42 concentrations are moderately lower in AD (SMD: -0.27, 95% CI: [-0.51, -0.03], z=2.20, P=0.028). Results from this meta-analysis hinted that CSF Aβ42 is a good biomarker for discriminating Alzheimer's disease from other dementias and MCI.
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Affiliation(s)
- Wei Tang
- Department of Clinical Laboratory Medicine, School of Public Health, Anhui Medical University, No. 81 Meishan Road, Hefei 230032, Anhui, China
| | - Qiong Huang
- AnQing City Affiliated Hospital of Anhui Medical University, No. 352 Renmin Road, AnQing 246003, Anhui, China
| | - Yan Wang
- Department of General Surgery, The Second Affiliated Hospital of Anhui Medical University, No. 678 Furong Road, Hefei 230601, Anhui, China
| | - Zheng-Yu Wang
- Department of Clinical Laboratory Medicine, School of Public Health, Anhui Medical University, No. 81 Meishan Road, Hefei 230032, Anhui, China
| | - Yu-You Yao
- Department of Clinical Laboratory Medicine, School of Public Health, Anhui Medical University, No. 81 Meishan Road, Hefei 230032, Anhui, China.
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Tang W, Huang Q, Yao YY, Wang Y, Wu YL, Wang ZY. Does CSF p-tau181 help to discriminate Alzheimer's disease from other dementias and mild cognitive impairment? A meta-analysis of the literature. J Neural Transm (Vienna) 2014; 121:1541-53. [PMID: 24817210 DOI: 10.1007/s00702-014-1226-y] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2014] [Accepted: 04/20/2014] [Indexed: 12/11/2022]
Abstract
To evaluate the clinical importance of cerebrospinal fluid (CSF) phosphorylated tau 181 (p-tau181) in mild cognitive impairment (MCI), Alzheimer's disease (AD) and other dementias, more specifically: frontotemporal degeneration (FTD), dementia with Lewy bodies (DLB), vascular dementia (VaD) and Parkinson's disease (PD) with dementia (PDD). Fifty eligible articles were identified by search of databases including PubMed, EMBASE, Elsevier, Springer Link and the Cochrane Library, up to December 2013. The random effects model was used to calculate the standardized mean difference (SMD) with corresponding 95% CI by STATA 9.0 software. The subgroup analyses were made on the methods or PD with dementia. We found that CSF p-tau181 concentrations were significantly higher in AD compared to MCI [SMD: 0.61, 95% CI: (0.46, 0.76), z = 8.07, P < 0.001], FTD [SMD: 1.23, 95% CI: (0.89, 1.56), z = 7.19, P < 0.001], DLB [SMD: 1.08, 95% CI: (0.80, 1.37), z = 7.41, P < 0.001], PDD [SMD: 1.05, 95% CI: (0.02, 2.07), z = 2.00, P = 0.045] and VaD [SMD: 1.28, 95% CI: (0.68, 1.88), z = 4.19, P < 0.001]. Results from this meta-analysis implied that CSF p-tau181 is a good biomarker for discriminating Alzheimer's disease from other dementias and mild cognitive impairment.
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Affiliation(s)
- Wei Tang
- Department of Clinical Laboratory Medicine, School of Public Health, Anhui Medical University, No. 81 Meishan road, Hefei, 230032, Anhui, China
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Rosa-Neto P, Hsiung GYR, Masellis M. Fluid biomarkers for diagnosing dementia: rationale and the Canadian Consensus on Diagnosis and Treatment of Dementia recommendations for Canadian physicians. Alzheimers Res Ther 2013; 5:S8. [PMID: 24565514 PMCID: PMC3980280 DOI: 10.1186/alzrt223] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Fluid biomarkers improve the diagnostic accuracy in dementia and provide an objective measure potentially useful as a therapeutic response in clinical trials. The role of fluid biomarkers in patient care is a rapidly evolving field. Here, we provide a review and recommendations regarding the use of fluid biomarkers in clinical practice as discussed at the Fourth Canadian Consensus Conference on the Diagnosis and Treatment of Dementia (CCCDTD4) convened in Montreal, 4 to 5 May 2012. At present, there is no consensus regarding the optimal methodology for conducting quantification of plasma amyloid-beta (Aβ) peptides. In addition, since there is insufficient evidence supporting clinical applications for plasma Aβ-peptide measures, the CCCDTD4 does not recommended plasma biomarkers either for primary care or for specialists. Evidence for CSF Aβ1-42, total tau and phosphorylated tau in the diagnosis of Alzheimer pathology is much stronger, and can be considered at the tertiary care level for selected cases to improve diagnostic certainty, particularly in those cases presenting atypical clinical features.
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Affiliation(s)
- Pedro Rosa-Neto
- McGill Centre for Studies in Aging, McGill University, 6825 LaSalle Boulevard, Verdun, Montreal, Quebec, Canada H4H 1R3
- Douglas Research Institute, McGill University, 6875 LaSalle Blvd, FBC room 1144, F-0105 Montréal (Verdun), QC, Canada H4H 1R3
| | - Ging-Yuek Robin Hsiung
- Division of Neurology, Department of Medicine, University of British Columbia, S162 - 2211 Wesbrook Mall, UBC Hospital, Vancouver BC, Canada V6T 2B5
| | - Mario Masellis
- L.C. Campbell Cognitive Neurology Research Unit, Brain Sciences Program, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, 2075 Bayview Avenue Toronto, Ontario, Canada M4N 3M5
- Department of Medicine, Division of Neurology, University of Toronto, 2075 Bayview Avenue, Toronto, Ontario, M4N 3M5, Canada
- Neurogenetics Section, Centre for Addiction and Mental Health (Queen and Ossington) 1001 Queen Street West; 30, 40, 50 and 60 White Squirrel Way; 100 and 101 Stokes Street; 80 Workman Way, Toronto, Ontario M6J 1H4, Canada
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Prestia A, Caroli A, Herholz K, Reiman E, Chen K, Jagust WJ, Frisoni GB. Diagnostic accuracy of markers for prodromal Alzheimer's disease in independent clinical series. Alzheimers Dement 2013; 9:677-86. [PMID: 23375562 PMCID: PMC4058442 DOI: 10.1016/j.jalz.2012.09.016] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2012] [Revised: 09/12/2012] [Accepted: 09/19/2012] [Indexed: 11/29/2022]
Abstract
OBJECTIVE To capitalize on data from different clinical series to compare sensitivity and specificity of individual biomarkers for predicting mild cognitive impairment (MCI) progression to Alzheimer's disease (AD). METHODS Medial temporal atrophy, cortical hypometabolism, and cerebrospinal fluid biomarkers were assessed in 18 patients with mild cognitive impairment (MCI) with prodromal AD (pAD; conversion time, 26 ± 12 months) and 18 stable MCI (sMCI) patients from the Translational Outpatient Memory Clinic cohort, as well as in 24 pAD patients (conversion time, 36 ± 12 months) and 33 sMCI patients from the Alzheimer's Disease Neuroimaging Initiative cohort. Medial temporal atrophy was measured by manual, semi-automated, and automated hippocampal volumetry; cortical hypometabolism was measured using several indices of AD-related hypometabolism pattern; and cerebrospinal fluid markers were amyloid β (Aβ)42 and total tau protein concentrations. For each biomarker, sensitivity for pAD, specificity for sMCI, and diagnostic accuracy were computed. RESULTS Sensitivity to predict MCI conversion to AD in the Alzheimer's Disease Neuroimaging Initiative and Translational Outpatient Memory Clinic cohorts was 79% and 94% based on Aβ42, 46% and 28% based on hippocampal volumes, 33% to 66% and 56% to 78% based on different hypometabolism indices, and 46% and 61% based on total tau levels, respectively. Specificity to exclude sMCI was 27% and 50% based on Aβ42, 76% and 94% based on hippocampal volumes, 58% to 67% and 55% to 83% based on different hypometabolism indices, and 61% and 83% based on total tau levels, respectively. CONCLUSIONS Current findings suggest that Aβ42 concentrations and hippocampal volumes may be used in combination to best identify pAD.
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Affiliation(s)
- A Prestia
- LENITEM – Laboratory of Epidemiology Neuroimaging and Telemedicine, and Unit for the Clinical Translation of Research, IRCCS Centro San Giovanni di Dio FBF, Brescia, Italy
| | - A Caroli
- LENITEM – Laboratory of Epidemiology Neuroimaging and Telemedicine, and Unit for the Clinical Translation of Research, IRCCS Centro San Giovanni di Dio FBF, Brescia, Italy
- Medical Imaging Unit, Biomedical Engineering Department, Mario Negri Institute for pharmacological research, Bergamo, Italy
| | | | - E Reiman
- Banner Alzheimer’s Institute, Phoenix (AZ), USA
| | - K Chen
- Banner Alzheimer’s Institute, Phoenix (AZ), USA
| | - WJ Jagust
- Helen Wills Neuroscience Institute, University of California, Berkeley (CA), USA
| | - GB Frisoni
- LENITEM – Laboratory of Epidemiology Neuroimaging and Telemedicine, and Unit for the Clinical Translation of Research, IRCCS Centro San Giovanni di Dio FBF, Brescia, Italy
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Guzman VA, Carmichael OT, Schwarz C, Tosto G, Zimmerman ME, Brickman AM. White matter hyperintensities and amyloid are independently associated with entorhinal cortex volume among individuals with mild cognitive impairment. Alzheimers Dement 2013; 9:S124-31. [PMID: 23375566 PMCID: PMC3663926 DOI: 10.1016/j.jalz.2012.11.009] [Citation(s) in RCA: 53] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2012] [Revised: 10/25/2012] [Accepted: 11/07/2012] [Indexed: 01/18/2023]
Abstract
BACKGROUND Current hypothetical models of Alzheimer's disease (AD) pathogenesis emphasize the role of β-amyloid (Aβ), tau deposition, and neurodegenerative changes in the mesial temporal lobe, particularly the entorhinal cortex and hippocampus. However, many individuals with clinical AD who come to autopsy also exhibit cerebrovascular disease. The relationship between AD and vascular pathology is unclear, especially whether they represent additive and independent effects on neuronal injury. We used data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to (1) confirm whether entorhinal cortex and hippocampal volume are associated with memory among individuals with amnestic mild cognitive impairment (MCI) who are at risk for AD; and (2) determine whether regional white matter hyperintensity (WMH) volume, a radiological marker of small-vessel cerebrovascular disease, is associated with entorhinal cortex and hippocampal volume independent of putative AD biomarkers in this group. METHODS Cognitive test scores, entorhinal cortex volume, hippocampus volume, intracranial volume, and cerebrospinal fluid-derived phosphorylated tau and Aβ1-42 protein levels were measured in 199 subjects with amnestic MCI (mean age = 74.89 ± 7.47). Lobar WMH volumes were derived from T1-, proton-density-, and T2-weighted magnetic resonance imaging scans. We examined the association between entorhinal cortex volume and cognition. Next, we examined the association of tau and Aβ1-42 with entorhinal cortex volume and between lobar WMH and entorhinal cortex volume. Finally, tau, Aβ1-42, and regional WMH volumes were entered simultaneously to predict entorhinal cortex volume. We repeated the analyses with hippocampal volume instead of entorhinal cortex volume. The analyses were also repeated with the sample restricted to those MCI patients who transitioned to AD on subsequent ADNI follow-up visits (n = 86). RESULTS Larger entorhinal cortex volume was associated with better memory but not with performance on a task of executive functioning. Lower levels of Aβ1-42 and higher temporal WMH volumes were associated with smaller entorhinal cortex volume. When entered simultaneously, temporal lobe WMH volume was more reliably associated with entorhinal cortex volume than was Aβ1-42. The findings were similar for hippocampus volume and when the sample was restricted to MCI patients who subsequently transitioned to AD. CONCLUSIONS The findings confirm the role of entorhinal cortex and hippocampus volume in influencing memory decline in amnestic MCI, and emphasize that even in this nominally AD prodromal condition, WMH may be influencing regional neurodegeneration.
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Affiliation(s)
- Vanessa A. Guzman
- Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, College of Physicians and Surgeons, Columbia University, New York, NY USA
| | - Owen T. Carmichael
- Center for Neuroscience and Neurology Department, University of California, Davis, CA USA
| | | | - Giuseppe Tosto
- Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, College of Physicians and Surgeons, Columbia University, New York, NY USA
- Gertrude H. Sergievsky Center and Department of Neurology, College of Physicians and Surgeons, Columbia University, New York, NY USA
| | - Molly E. Zimmerman
- The Saul R. Korey Department of Neurology, Albert Einstein College of Medicine, New York, NY USA
| | - Adam M. Brickman
- Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, College of Physicians and Surgeons, Columbia University, New York, NY USA
- Gertrude H. Sergievsky Center and Department of Neurology, College of Physicians and Surgeons, Columbia University, New York, NY USA
- Department of Neurology, College of Physicians and Surgeons, Columbia University, New York, NY USA
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Mattsson N, Insel P, Nosheny R, Trojanowski JQ, Shaw LM, Jack CR, Tosun D, Weiner M. Effects of cerebrospinal fluid proteins on brain atrophy rates in cognitively healthy older adults. Neurobiol Aging 2013; 35:614-22. [PMID: 24094581 DOI: 10.1016/j.neurobiolaging.2013.08.027] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2013] [Revised: 08/01/2013] [Accepted: 08/23/2013] [Indexed: 02/02/2023]
Abstract
Biomarkers associated with Alzheimer's disease (AD)-like brain atrophy in healthy individuals may identify mechanisms involved in early stage AD. Aside from cerebrospinal fluid (CSF) β-amyloid42 (Aβ42) and tau, no studies have tested associations between CSF proteins and AD-like brain atrophy. We studied 90 healthy elders, who underwent lumbar puncture at baseline, and serial magnetic resonance imaging scans for up to 4 years. We tested statistical effects of baseline CSF proteins (N = 70 proteins related to Aβ42-metabolism, microglial activity, and synaptic/neuronal function) on atrophy rates in 7 AD-related regions. Besides the effects of Aβ42 and phosphorylated tau (P-tau) that were seen in several regions, novel CSF proteins were found to have effects in inferior and middle temporal cortex (including apolipoprotein CIII, apolipoprotein D, and apolipoprotein H). Several proteins (including S100β and matrix metalloproteinase-3) had effects that depended on the presence of brain Aβ pathology, as measured by CSF Aβ42. Other proteins (including P-tau and apolipoprotein D) had effects even after adjusting for CSF Aβ42. The statistical effects in this exploratory study were mild and not significant after correction for multiple comparisons, but some of the identified proteins may be associated with brain atrophy in healthy persons. Proteins interacting with CSF Aβ42 may be related to Aβ brain pathology, whereas proteins associated with atrophy even after adjusting for CSF Aβ42 may be related to Aβ-independent mechanisms.
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Affiliation(s)
- Niklas Mattsson
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA; Clinical Neurochemistry Laboratory, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden; Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA.
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45
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Barnes J, Carmichael OT, Leung KK, Schwarz C, Ridgway GR, Bartlett JW, Malone IB, Schott JM, Rossor MN, Biessels GJ, DeCarli C, Fox NC. Vascular and Alzheimer's disease markers independently predict brain atrophy rate in Alzheimer's Disease Neuroimaging Initiative controls. Neurobiol Aging 2013; 34:1996-2002. [PMID: 23522844 PMCID: PMC3810644 DOI: 10.1016/j.neurobiolaging.2013.02.003] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2012] [Revised: 01/29/2013] [Accepted: 02/09/2013] [Indexed: 01/18/2023]
Abstract
This study assessed relationships among white matter hyperintensities (WMH), cerebrospinal fluid (CSF), Alzheimer's disease (AD) pathology markers, and brain volume loss. Subjects included 197 controls, 331 individuals with mild cognitive impairment (MCI), and 146 individuals with AD with serial volumetric 1.5-T MRI. CSF Aβ1-42 (n = 351) and tau (n = 346) were measured. Brain volume change was quantified using the boundary shift integral (BSI). We assessed the association between baseline WMH volume and annualized BSI, adjusting for intracranial volume. We also performed multiple regression analyses in the CSF subset, assessing the relationships of WMH and Aβ1-42 and/or tau with BSI. WMH burden was positively associated with BSI in controls (p = 0.02) but not MCI or AD. In multivariable models, WMH (p = 0.003) and Aβ1-42 (p = 0.001) were independently associated with BSI in controls; in MCI Aβ1-42 (p < 0.001) and tau (p = 0.04) were associated with BSI. There was no evidence of independent effects of WMH or CSF measures on BSI in AD. These data support findings that vascular damage is associated with increased brain atrophy in the context of AD pathology in pre-dementia stages.
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Affiliation(s)
- Josephine Barnes
- Dementia Research Centre, Department of Neurodegenerative Disease, University College London Institute of Neurology, London, UK.
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46
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Cerebrospinal fluid proteins predict longitudinal hippocampal degeneration in early-stage dementia of the Alzheimer type. Alzheimer Dis Assoc Disord 2013; 26:314-21. [PMID: 22156755 DOI: 10.1097/wad.0b013e31823c0cf4] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
OBJECTIVE Biomarkers are needed to improve the sensitivity and accuracy of diagnosis, and also prognosis, in individuals with early Alzheimer disease (AD). Measures of brain structure and disease-related proteins in the cerebrospinal fluid (CSF) have been proposed as biomarkers, yet relatively little is known about the relationships between such measures. The present study was conducted to assess the relationship between CSF Aβ and tau protein levels and longitudinal measures of hippocampal structure in individuals with and without very mild dementia of the Alzheimer type. DESIGN A single CSF sample and longitudinal magnetic resonance scans were collected. The CSF samples were assayed for tau, phosphorylated tau181 (p-tau181), Aβ1-42, and Aβ1-40 using an enzyme-linked immunosorbent assay. Large-deformation diffeomorphic metric mapping was used to generate hippocampal surfaces, and a composite hippocampal surface (previously constructed from 86 healthy participants) was used as a structural reference. PATIENTS OR OTHER PARTICIPANTS Thirteen participants with very mild AD (Clinical Dementia Rating, CDR 0.5) and 11 cognitively normal participants (CDR 0). INTERVENTION None. MAIN OUTCOME MEASURES Initial and rate-of-change measures of total hippocampal volume and displacement of the hippocampal surface within zones overlying the CA1, subiculum, and CA2-4+DG cellular subfields, and their correlations with initial CSF measures. RESULTS Lower CSF Αβ1-42 levels and higher tau/Αβ1-42 and p-tau181/Αβ1-42 ratios were strongly correlated with decreases in hippocampal volume and measures of progressive inward deformations of the CA1 subfield in participants with early AD, but not in cognitively normal participants. CONCLUSIONS Despite the small sample size, we found that Αβ1-42 related and tau-related CSF measures were associated with hippocampal degeneration in individuals with clinically diagnosed early AD and may reflect an association with a common underlying disease mechanism.
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47
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Zhang Y, Schuff N, Camacho M, Chao LL, Fletcher TP, Yaffe K, Woolley SC, Madison C, Rosen HJ, Miller BL, Weiner MW. MRI markers for mild cognitive impairment: comparisons between white matter integrity and gray matter volume measurements. PLoS One 2013; 8:e66367. [PMID: 23762488 PMCID: PMC3675142 DOI: 10.1371/journal.pone.0066367] [Citation(s) in RCA: 54] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2012] [Accepted: 05/07/2013] [Indexed: 11/18/2022] Open
Abstract
The aim of the study was to evaluate the value of assessing white matter integrity using diffusion tensor imaging (DTI) for classification of mild cognitive impairment (MCI) and prediction of cognitive impairments in comparison to brain atrophy measurements using structural MRI. Fifty-one patients with MCI and 66 cognitive normal controls (CN) underwent DTI and T1-weighted structural MRI. DTI measures included fractional anisotropy (FA) and radial diffusivity (DR) from 20 predetermined regions-of-interest (ROIs) in the commissural, limbic and association tracts, which are thought to be involved in Alzheimer's disease; measures of regional gray matter (GM) volume included 21 ROIs in medial temporal lobe, parietal cortex, and subcortical regions. Significant group differences between MCI and CN were detected by each MRI modality: In particular, reduced FA was found in splenium, left isthmus cingulum and fornix; increased DR was found in splenium, left isthmus cingulum and bilateral uncinate fasciculi; reduced GM volume was found in bilateral hippocampi, left entorhinal cortex, right amygdala and bilateral thalamus; and thinner cortex was found in the left entorhinal cortex. Group classifications based on FA or DR was significant and better than classifications based on GM volume. Using either DR or FA together with GM volume improved classification accuracy. Furthermore, all three measures, FA, DR and GM volume were similarly accurate in predicting cognitive performance in MCI patients. Taken together, the results imply that DTI measures are as accurate as measures of GM volume in detecting brain alterations that are associated with cognitive impairment. Furthermore, a combination of DTI and structural MRI measurements improves classification accuracy.
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Affiliation(s)
- Yu Zhang
- Center for Imaging of Neurodegenerative Diseases, San Francisco VA Medical Center, San Francisco, California, United States of America.
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Randall C, Mosconi L, de Leon M, Glodzik L. Cerebrospinal fluid biomarkers of Alzheimer's disease in healthy elderly. FRONT BIOSCI-LANDMRK 2013; 18:1150-73. [PMID: 23747874 PMCID: PMC3904672 DOI: 10.2741/4170] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Numerous studies have shown that Alzheimer's Disease (AD) pathology begins before the onset of clinical symptoms. Because therapies are likely to be more effective if they are implemented early in the disease progression, it is necessary to identify reliable biomarkers to detect AD pathology in the early stages of the disease, ideally in presymptomatic individuals. Recent research has identified three candidate cerebrospinal fluid (CSF) biomarkers that reflect AD pathology: amyloid beta, total tau protein (t-tau), and tau protein phosphorylated at AD-specific epitopes (p-tau). They are useful in supporting the AD diagnosis and have predictive value for AD when patients are in the stage of mild cognitive impairment (MCI). However, their predictive utility in cognitively healthy subjects is still being evaluated. We conducted a review of studies published between 1993 and 2011 and summarized their findings on the role of CSF biomarkers for AD in healthy elderly.
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Affiliation(s)
- Catherine Randall
- Center for Brain Health, 145 East 32nd Street, 5th floor. New York, NY 10016
| | - Lisa Mosconi
- Center for Brain Health, 145 East 32nd Street, 5th floor. New York, NY 10016
| | - Mony de Leon
- Center for Brain Health, 145 East 32nd Street, 5th floor. New York, NY 10016
| | - Lidia Glodzik
- Center for Brain Health, 145 East 32nd Street, 5th floor. New York, NY 10016
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Giorgio A, De Stefano N. Clinical use of brain volumetry. J Magn Reson Imaging 2013; 37:1-14. [PMID: 23255412 DOI: 10.1002/jmri.23671] [Citation(s) in RCA: 82] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2011] [Accepted: 03/12/2012] [Indexed: 12/13/2022] Open
Abstract
Magnetic resonance imaging (MRI)-based brain volumetry is increasingly being used in the clinical setting to assess brain volume changes from structural MR images in a range of neurologic conditions. Measures of brain volumes have been shown to be valid biomarkers of the clinical state and progression by offering high reliability and robust inferences on the underlying disease-related mechanisms. This review critically examines the different scenarios of the application of MRI-based brain volumetry in neurology: 1) supporting disease diagnosis, 2) understanding mechanisms and tracking clinical progression of disease, and 3) monitoring treatment effect. These aspects will be discussed in a wide range of neurologic conditions, with particular emphasis on Alzheimer's disease and multiple sclerosis.
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Affiliation(s)
- Antonio Giorgio
- Department of Neurological and Behavioral Sciences, University of Siena, Italy
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50
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Chiang GC, Insel PS, Tosun D, Schuff N, Truran-Sacrey D, Raptentsetsang ST, Thompson PM, Reiman EM, Jack CR, Fox NC, Jagust WJ, Harvey DJ, Beckett LA, Gamst A, Aisen PS, Petersen RC, Weiner MW. Impact of apolipoprotein E4-cerebrospinal fluid β-amyloid interaction on hippocampal volume loss over 1 year in mild cognitive impairment. Alzheimers Dement 2012; 7:514-20. [PMID: 21889115 DOI: 10.1016/j.jalz.2010.12.010] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2010] [Revised: 09/05/2010] [Accepted: 12/02/2010] [Indexed: 10/17/2022]
Abstract
BACKGROUND The majority of studies relating amyloid pathology with brain volumes have been cross-sectional. Apolipoprotein ɛ4 (APOE ɛ4), a genetic risk factor for Alzheimer's disease, is also known to be associated with hippocampal volume loss. No studies have considered the effects of amyloid pathology and APOE ɛ4 together on longitudinal volume loss. METHODS We evaluated whether an abnormal level of cerebrospinal fluid beta-amyloid (CSF Aβ) and APOE ɛ4 carrier status were independently associated with greater hippocampal volume loss over 1 year. We then assessed whether APOE ɛ4 status and CSF Aβ acted synergistically, testing the significance of an interaction term in the regression analysis. We included 297 participants: 77 cognitively normal, 144 with mild cognitive impairment (MCI), and 76 with Alzheimer's disease. RESULTS An abnormal CSF Aβ level was found to be associated with greater hippocampal volume loss over 1 year in each group. APOE ɛ4 was associated with hippocampal volume loss only in the cognitively normal and MCI groups. APOE ɛ4 carriers with abnormal CSF Aβ in the MCI group acted synergistically to produce disproportionately greater volume loss than noncarriers. CONCLUSION Baseline CSF Aβ predicts progression of hippocampal volume loss. APOE ɛ4 carrier status amplifies the degree of neurodegeneration in MCI. Understanding the effect of interactions between genetic risk and amyloid pathology will be important in clinical trials and our understanding of the disease process.
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Affiliation(s)
- Gloria C Chiang
- Department of Radiology, University of California, San Francisco, USA.
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