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Mukherjee A, Biswas S, Roy I. Immunotherapy: An emerging treatment option for neurodegenerative diseases. Drug Discov Today 2024; 29:103974. [PMID: 38555032 DOI: 10.1016/j.drudis.2024.103974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 03/18/2024] [Accepted: 03/26/2024] [Indexed: 04/02/2024]
Abstract
Accumulation of misfolded proteins and protein aggregates leading to degeneration of neurons is a hallmark of several neurodegenerative diseases. Therapy mostly relies on symptomatic relief. Immunotherapy offers a promising approach for the development of disease-modifying routes. Such strategies have shown remarkable results in oncology, and this promise is increasingly being realized for neurodegenerative diseases in advanced preclinical and clinical studies. This review highlights cases of passive and active immunotherapies in Parkinson's and Alzheimer's diseases. The reasons for success and failure, wherever available, and strategies to cross the blood-brain barrier, are discussed. The need for conditional modulation of the immune response is also reflected on.
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Affiliation(s)
- Abhiyanta Mukherjee
- Department of Biotechnology, National Institute of Pharmaceutical Education and Research, Sector 67, S.A.S. Nagar, Punjab 160062, India
| | - Soumojit Biswas
- Department of Biotechnology, National Institute of Pharmaceutical Education and Research, Sector 67, S.A.S. Nagar, Punjab 160062, India
| | - Ipsita Roy
- Department of Biotechnology, National Institute of Pharmaceutical Education and Research, Sector 67, S.A.S. Nagar, Punjab 160062, India.
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Wang C, Tachimori H, Yamaguchi H, Sekiguchi A, Li Y, Yamashita Y. A multimodal deep learning approach for the prediction of cognitive decline and its effectiveness in clinical trials for Alzheimer's disease. Transl Psychiatry 2024; 14:105. [PMID: 38383536 PMCID: PMC10882004 DOI: 10.1038/s41398-024-02819-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 02/01/2024] [Accepted: 02/07/2024] [Indexed: 02/23/2024] Open
Abstract
Alzheimer's disease is one of the most important health-care challenges in the world. For decades, numerous efforts have been made to develop therapeutics for Alzheimer's disease, but most clinical trials have failed to show significant treatment effects on slowing or halting cognitive decline. Among several challenges in such trials, one recently noticed but unsolved is biased allocation of fast and slow cognitive decliners to treatment and placebo groups during randomization caused by the large individual variation in the speed of cognitive decline. This allocation bias directly results in either over- or underestimation of the treatment effect from the outcome of the trial. In this study, we propose a stratified randomization method using the degree of cognitive decline predicted by an artificial intelligence model as a stratification index to suppress the allocation bias in randomization and evaluate its effectiveness by simulation using ADNI data set.
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Affiliation(s)
- Caihua Wang
- Bio Science & Engineering Laboratories, FUJIFILM Corporation, Ashigarakami-gun, Kanagawa, Japan
| | - Hisateru Tachimori
- Department of Information Medicine, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Tokyo, Japan
- Endowed Course for Health System Innovation, Keio University School of Medicine, Tokyo, Japan
| | - Hiroyuki Yamaguchi
- Department of Information Medicine, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Tokyo, Japan
- Department of Psychiatry, Yokohama City University School of Medicine, Yokohama, Japan
| | - Atsushi Sekiguchi
- Department of Behavioral Medicine, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Yuanzhong Li
- Bio Science & Engineering Laboratories, FUJIFILM Corporation, Ashigarakami-gun, Kanagawa, Japan.
| | - Yuichi Yamashita
- Department of Information Medicine, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Tokyo, Japan
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Basheer N, Smolek T, Hassan I, Liu F, Iqbal K, Zilka N, Novak P. Does modulation of tau hyperphosphorylation represent a reasonable therapeutic strategy for Alzheimer's disease? From preclinical studies to the clinical trials. Mol Psychiatry 2023; 28:2197-2214. [PMID: 37264120 PMCID: PMC10611587 DOI: 10.1038/s41380-023-02113-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 04/28/2023] [Accepted: 05/05/2023] [Indexed: 06/03/2023]
Abstract
Protein kinases (PKs) have emerged as one of the most intensively investigated drug targets in current pharmacological research, with indications ranging from oncology to neurodegeneration. Tau protein hyperphosphorylation was the first pathological post-translational modification of tau protein described in Alzheimer's disease (AD), highlighting the role of PKs in neurodegeneration. The therapeutic potential of protein kinase inhibitors (PKIs)) and protein phosphatase 2 A (PP2A) activators in AD has recently been explored in several preclinical and clinical studies with variable outcomes. Where a number of preclinical studies demonstrate a visible reduction in the levels of phospho-tau in transgenic tauopathy models, no reduction in neurofibrillary lesions is observed. Amongst the few PKIs and PP2A activators that progressed to clinical trials, most failed on the efficacy front, with only a few still unconfirmed and potential positive trends. This suggests that robust preclinical and clinical data is needed to unequivocally evaluate their efficacy. To this end, we take a systematic look at the results of preclinical and clinical studies of PKIs and PP2A activators, and the evidence they provide regarding the utility of this approach to evaluate the potential of targeting tau hyperphosphorylation as a disease modifying therapy.
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Affiliation(s)
- Neha Basheer
- Institute of Neuroimmunology, Slovak Academy of Sciences, Bratislava, 845 10, Slovakia
| | - Tomáš Smolek
- Institute of Neuroimmunology, Slovak Academy of Sciences, Bratislava, 845 10, Slovakia
| | - Imtaiyaz Hassan
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, Jamia Nagar, New Delhi, 110025, India
| | - Fei Liu
- Department of Neurochemistry, Inge Grundke-Iqbal Research Floor, New York State Institute for Basic Research in Developmental Disabilities, 1050 Forest Hill Road, Staten Island, NY, 10314, USA
| | - Khalid Iqbal
- Department of Neurochemistry, Inge Grundke-Iqbal Research Floor, New York State Institute for Basic Research in Developmental Disabilities, 1050 Forest Hill Road, Staten Island, NY, 10314, USA
| | - Norbert Zilka
- Institute of Neuroimmunology, Slovak Academy of Sciences, Bratislava, 845 10, Slovakia.
- AXON Neuroscience R&D Services SE, Bratislava, 811 02, Slovakia.
| | - Petr Novak
- Institute of Neuroimmunology, Slovak Academy of Sciences, Bratislava, 845 10, Slovakia.
- AXON Neuroscience CRM Services SE, Bratislava, 811 02, Slovakia.
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Lasch F, Guizzaro L, Pétavy F, Gallo C. A simulation study on the estimation of the effect in the hypothetical scenario of no use of symptomatic treatment in trials for disease-modifying agents for Alzheimer’s disease. Stat Biopharm Res 2022. [DOI: 10.1080/19466315.2022.2055633] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Affiliation(s)
- Florian Lasch
- European Medicines Agency, Amsterdam, The Netherlands
- Hannover Medical School, Hannover, Germany
| | - Lorenzo Guizzaro
- European Medicines Agency, Amsterdam, The Netherlands
- Università della Campania “Luigi Vanvitelli”, Italy
| | - Frank Pétavy
- European Medicines Agency, Amsterdam, The Netherlands
| | - Ciro Gallo
- Università della Campania “Luigi Vanvitelli”, Italy
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Hill KR, Gardus JD, Bartlett EA, Perlman G, Parsey RV, DeLorenzo C. Measuring brain glucose metabolism in order to predict response to antidepressant or placebo: A randomized clinical trial. NEUROIMAGE: CLINICAL 2022; 32:102858. [PMID: 34689056 PMCID: PMC8551925 DOI: 10.1016/j.nicl.2021.102858] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 08/18/2021] [Accepted: 10/12/2021] [Indexed: 01/09/2023] Open
Abstract
There is critical need for a clinically useful tool to predict antidepressant treatment outcome in major depressive disorder (MDD) to reduce suffering and mortality. This analysis sought to build upon previously reported antidepressant treatment efficacy prediction from 2-[18F]-fluorodeoxyglucose - Positron Emission Tomography (FDG-PET) using metabolic rate of glucose uptake (MRGlu) from dynamic FDG-PET imaging with the goal of translation to clinical utility. This investigation is a randomized, double-blind placebo-controlled trial. All participants were diagnosed with MDD and received an FDG-PET scan before randomization and after treatment. Hamilton Depression Rating Scale (HDRS-17) was completed in participants diagnosed with MDD before and after 8 weeks of escitalopram, or placebo. MRGlu (mg/(min*100 ml)) was estimated within the raphe nuclei, right insula, and left ventral Prefrontal Cortex in 63 individuals. Linear regression was used to examine the association between pretreatment MRGlu and percent decrease in HDRS-17. Additionally, the association between percent decrease in HDRS-17 and percent change in MRGlu between pretreatment scan and post-treatment scan was examined. Covariates were treatment type (SSRI/placebo), handedness, sex, and age. Depression severity decrease (n = 63) was not significantly associated with pretreatment MRGlu in the raphe nuclei (β = -2.61e-03 [-0.26, 0.25], p = 0.98), right insula (β = 0.05 [-0.23, 0.32], p = 0.72), or ventral prefrontal cortex (β = 0.06 [-0.23, 0.34], p = 0.68) where β is the standardized estimated coefficient, with a 95% confidence interval, or in whole brain voxelwise analysis (family-wise error correction, alpha = 0.05). MRGlu percent change was not significantly associated with depression severity decrease (n = 58) before multiple comparison correction in the RN (β = 0.20 [-0.07, 0.47], p = 0.15), right insula (β = 0.24 [-0.03, 0.51], p = 0.08), or vPFC (β = 0.22 [-0.06, 0.50], p = 0.12). We propose that FDG-PET imaging does not indicate a clinically relevant biomarker of escitalopram or placebo treatment response in heterogeneous major depressive disorder cohorts. Future directions include focusing on potential biologically-based subtypes of major depressive disorder by implementing biomarker stratified designs.
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Affiliation(s)
- Kathryn R Hill
- Department of Psychiatry, Renaissance School of Medicine at Stony Brook University, 101 Nicolls Rd, Stony Brook, NY, 11794, USA.
| | - John D Gardus
- Department of Psychiatry, Renaissance School of Medicine at Stony Brook University, 101 Nicolls Rd, Stony Brook, NY, 11794, USA.
| | - Elizabeth A Bartlett
- Molecular Imaging and Neuropathology Area, New York State Psychiatric Institute, 1051 Riverside Dr, New York, NY 10032, USA; Department of Psychiatry, Columbia University Medical Center, 1051 Riverside Dr, New York, NY 10032, USA.
| | - Greg Perlman
- Department of Psychiatry, Renaissance School of Medicine at Stony Brook University, 101 Nicolls Rd, Stony Brook, NY, 11794, USA.
| | - Ramin V Parsey
- Department of Psychiatry, Renaissance School of Medicine at Stony Brook University, 101 Nicolls Rd, Stony Brook, NY, 11794, USA.
| | - Christine DeLorenzo
- Department of Psychiatry, Renaissance School of Medicine at Stony Brook University, 101 Nicolls Rd, Stony Brook, NY, 11794, USA; Department of Psychiatry, Columbia University Medical Center, 1051 Riverside Dr, New York, NY 10032, USA.
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Duara R, Barker W. Heterogeneity in Alzheimer's Disease Diagnosis and Progression Rates: Implications for Therapeutic Trials. Neurotherapeutics 2022; 19:8-25. [PMID: 35084721 PMCID: PMC9130395 DOI: 10.1007/s13311-022-01185-z] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/05/2022] [Indexed: 01/03/2023] Open
Abstract
The clinical presentation and the pathological processes underlying Alzheimer's disease (AD) can be very heterogeneous in severity, location, and composition including the amount and distribution of AB deposition and spread of neurofibrillary tangles in different brain regions resulting in atypical clinical patterns and the existence of distinct AD variants. Heterogeneity in AD may be related to demographic factors (such as age, sex, educational and socioeconomic level) and genetic factors, which influence underlying pathology, the cognitive and behavioral phenotype, rate of progression, the occurrence of neuropsychiatric features, and the presence of comorbidities (e.g., vascular disease, neuroinflammation). Heterogeneity is also manifest in the individual resilience to the development of neuropathology (brain reserve) and the ability to compensate for its cognitive and functional impact (cognitive and functional reserve). The variability in specific cognitive profiles and types of functional impairment may be associated with different progression rates, and standard measures assessing progression may not be equivalent for individual cognitive and functional profiles. Other factors, which may govern the presence, rate, and type of progression of AD, include the individuals' general medical health, the presence of specific systemic conditions, and lifestyle factors, including physical exercise, cognitive and social stimulation, amount of leisure activities, environmental stressors, such as toxins and pollution, and the effects of medications used to treat medical and behavioral conditions. These factors that affect progression are important to consider while designing a clinical trial to ensure, as far as possible, well-balanced treatment and control groups.
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Affiliation(s)
- Ranjan Duara
- Wien Center for Alzheimer's Disease and Memory Disorders, Mount Sinai Medical Center, Miami Beach, FL, USA
- Departments of Neurology, University of Florida College of Medicine, Gainesville, FL, USA
- Herbert Wertheim College of Medicine, Florida International University, Miami, FL, USA
| | - Warren Barker
- Wien Center for Alzheimer's Disease and Memory Disorders, Mount Sinai Medical Center, Miami Beach, FL, USA.
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Dong M, Xie L, Das SR, Wang J, Wisse LEM, deFlores R, Wolk DA, Yushkevich PA. DeepAtrophy: Teaching a neural network to detect progressive changes in longitudinal MRI of the hippocampal region in Alzheimer's disease. Neuroimage 2021; 243:118514. [PMID: 34450261 PMCID: PMC8604562 DOI: 10.1016/j.neuroimage.2021.118514] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 08/18/2021] [Accepted: 08/23/2021] [Indexed: 11/26/2022] Open
Abstract
Measures of change in hippocampal volume derived from longitudinal MRI are a well-studied biomarker of disease progression in Alzheimer's disease (AD) and are used in clinical trials to track therapeutic efficacy of disease-modifying treatments. However, longitudinal MRI change measures based on deformable registration can be confounded by MRI artifacts, resulting in over-estimation or underestimation of hippocampal atrophy. For example, the deformation-based-morphometry method ALOHA (Das et al., 2012) finds an increase in hippocampal volume in a substantial proportion of longitudinal scan pairs from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, unexpected, given that the hippocampal gray matter is lost with age and disease progression. We propose an alternative approach to quantify disease progression in the hippocampal region: to train a deep learning network (called DeepAtrophy) to infer temporal information from longitudinal scan pairs. The underlying assumption is that by learning to derive time-related information from scan pairs, the network implicitly learns to detect progressive changes that are related to aging and disease progression. Our network is trained using two categorical loss functions: one that measures the network's ability to correctly order two scans from the same subject, input in arbitrary order; and another that measures the ability to correctly infer the ratio of inter-scan intervals between two pairs of same-subject input scans. When applied to longitudinal MRI scan pairs from subjects unseen during training, DeepAtrophy achieves greater accuracy in scan temporal ordering and interscan interval inference tasks than ALOHA (88.5% vs. 75.5% and 81.1% vs. 75.0%, respectively). A scalar measure of time-related change in a subject level derived from DeepAtrophy is then examined as a biomarker of disease progression in the context of AD clinical trials. We find that this measure performs on par with ALOHA in discriminating groups of individuals at different stages of the AD continuum. Overall, our results suggest that using deep learning to infer temporal information from longitudinal MRI of the hippocampal region has good potential as a biomarker of disease progression, and hints that combining this approach with conventional deformation-based morphometry algorithms may lead to improved biomarkers in the future.
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Affiliation(s)
- Mengjin Dong
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States.
| | - Long Xie
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States; Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, United States
| | - Sandhitsu R Das
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States; Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, United States; Penn Memory Center, University of Pennsylvania, Philadelphia, Pennsylvania, United States
| | - Jiancong Wang
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Laura E M Wisse
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States; Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, United States; Department of Diagnostic Radiology, Lund University, Lund, Sweden
| | - Robin deFlores
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, United States; Penn Memory Center, University of Pennsylvania, Philadelphia, Pennsylvania, United States; Institut National de la Santé et de la Recherche Médicale (INSERM), Caen, France
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, United States; Penn Memory Center, University of Pennsylvania, Philadelphia, Pennsylvania, United States
| | - Paul A Yushkevich
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States; Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, United States
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Kühnel L, Bouteloup V, Lespinasse J, Chêne G, Dufouil C, Molinuevo JL, Raket LL. Personalized prediction of progression in pre-dementia patients based on individual biomarker profile: A development and validation study. Alzheimers Dement 2021; 17:1938-1949. [PMID: 34581496 DOI: 10.1002/alz.12363] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 03/24/2021] [Accepted: 04/01/2021] [Indexed: 11/10/2022]
Abstract
INTRODUCTION The prognosis of patients at the pre-dementia stage is difficult to define. The aim of this study is to develop and validate a biomarker-based continuous model for predicting the individual cognitive level at any future moment. In addition to personalized prognosis, such a model could reduce trial sample size requirements by allowing inclusion of a homogenous patient population. METHODS Disease-progression modeling of longitudinal cognitive scores of pre-dementia patients (baseline Clinical Dementia Rating ≤ 0.5) was used to derive a biomarker profile that was predictive of patient's cognitive progression along the dementia continuum. The biomarker profile model was developed and validated in the MEMENTO cohort and externally validated in the Alzheimer's Disease Neuroimaging Initiative. RESULTS Of nine candidate biomarkers in the development analysis, three cerebrospinal fluid and two magnetic resonance imaging measures were selected to form the final biomarker profile. The model-based prognosis of individual future cognitive deficit was shown to significantly improve when incorporating biomarker information on top of cognition and demographic data. In trial power calculations, adjusting the primary analysis for the baseline biomarker profile reduced sample size requirements by ≈10%. Compared to conventional cognitive cut-offs, inclusion criteria based on biomarker-profile cut-offs resulted in up to 28% reduced sample size requirements due to increased homogeneity in progression patterns. DISCUSSION The biomarker profile allows prediction of personalized trajectories of future cognitive progression. This enables accurate personalized prognosis in clinical care and better selection of patient populations for clinical trials. A web-based application for prediction of patients' future cognitive progression is available online.
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Affiliation(s)
- Line Kühnel
- H. Lundbeck A/S, Copenhagen, Denmark.,Department of Mathematical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Vincent Bouteloup
- Inserm, Population Health Research Center, University of Bordeaux, Bordeaux, France.,CHU de Bordeaux, Pole Santé Publique, Talence, France
| | - Jérémie Lespinasse
- Inserm, Population Health Research Center, University of Bordeaux, Bordeaux, France.,CHU de Bordeaux, Pole Santé Publique, Talence, France
| | - Geneviève Chêne
- Inserm, Population Health Research Center, University of Bordeaux, Bordeaux, France.,CHU de Bordeaux, Pole Santé Publique, Talence, France
| | - Carole Dufouil
- Inserm, Population Health Research Center, University of Bordeaux, Bordeaux, France.,CHU de Bordeaux, Pole Santé Publique, Talence, France
| | | | - Lars Lau Raket
- H. Lundbeck A/S, Copenhagen, Denmark.,Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden
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Schaible BJ, Yin J. Joint confidence region estimation on predictive values. Pharm Stat 2021; 20:1147-1167. [PMID: 34021708 DOI: 10.1002/pst.2131] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 04/29/2021] [Accepted: 05/01/2021] [Indexed: 01/04/2023]
Abstract
For evaluating diagnostic accuracy of inherently continuous diagnostic tests/biomarkers, sensitivity and specificity are well-known measures both of which depend on a diagnostic cut-off, which is usually estimated. Sensitivity (specificity) is the conditional probability of testing positive (negative) given the true disease status. However, a more relevant question is "what is the probability of having (not having) a disease if a test is positive (negative)?". Such post-test probabilities are denoted as positive predictive value (PPV) and negative predictive value (NPV). The PPV and NPV at the same estimated cut-off are correlated, hence it is desirable to make the joint inference on PPV and NPV to account for such correlation. Existing inference methods for PPV and NPV focus on the individual confidence intervals and they were developed under binomial distribution assuming binary instead of continuous test results. Several approaches are proposed to estimate the joint confidence region as well as the individual confidence intervals of PPV and NPV. Simulation results indicate the proposed approaches perform well with satisfactory coverage probabilities for normal and non-normal data and, additionally, outperform existing methods with improved coverage as well as narrower confidence intervals for PPV and NPV. The Alzheimer's Disease Neuroimaging Initiative (ADNI) data set is used to illustrate the proposed approaches and compare them with the existing methods.
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Affiliation(s)
- Braydon J Schaible
- Department of Biostatistics, Epidemiology and Environmental Health Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, Georgia, USA
| | - Jingjing Yin
- Department of Biostatistics, Epidemiology and Environmental Health Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, Georgia, USA
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Rosenberg A, Solomon A, Soininen H, Visser PJ, Blennow K, Hartmann T, Kivipelto M. Research diagnostic criteria for Alzheimer's disease: findings from the LipiDiDiet randomized controlled trial. ALZHEIMERS RESEARCH & THERAPY 2021; 13:64. [PMID: 33766132 PMCID: PMC7995792 DOI: 10.1186/s13195-021-00799-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 02/23/2021] [Indexed: 12/18/2022]
Abstract
Background To explore the utility of the International Working Group (IWG)-1 criteria in recruitment for Alzheimer’s disease (AD) clinical trials, we applied the more recently proposed research diagnostic criteria to individuals enrolled in a randomized controlled prevention trial (RCT) and assessed their disease progression. Methods The multinational LipiDiDiet RCT targeted 311 individuals with IWG-1 defined prodromal AD. Based on centrally analyzed baseline biomarkers, participants were classified according to the IWG-2 and National Institute on Aging–Alzheimer’s Association (NIA-AA) 2011 and 2018 criteria. Linear mixed models were used to investigate the 2-year change in cognitive and functional performance (Neuropsychological Test Battery NTB Z scores, Clinical Dementia Rating-Sum of Boxes CDR-SB) (criteria × time interactions; baseline score, randomization group, sex, Mini-Mental State Examination (MMSE), and age also included in the models). Cox models adjusted for randomization group, MMSE, sex, age, and study site were used to investigate the risk of progression to dementia over 2 years. Results In total, 88%, 86%, and 69% of participants had abnormal cerebrospinal fluid (CSF) β-amyloid, total tau, and phosphorylated tau, respectively; 64% had an A+T+N+ profile (CSF available for N = 107). Cognitive-functional decline appeared to be more pronounced in the IWG-2 prodromal AD, NIA-AA 2011 high and intermediate AD likelihood, and NIA-AA 2018 AD groups, but few significant differences were observed between the groups within each set of criteria. Hazard ratio (95% CI) for dementia was 4.6 (1.6–13.7) for IWG-2 prodromal AD (reference group no prodromal AD), 7.4 (1.0–54.7) for NIA-AA 2011 high AD likelihood (reference group suspected non-AD pathology SNAP), and 9.4 (1.2–72.7) for NIA-AA 2018 AD (reference group non-Alzheimer’s pathologic change). Compared with the NIA-AA 2011 high AD likelihood group (abnormal β-amyloid and neuronal injury markers), disease progression was similar in the intermediate AD likelihood group (medial temporal lobe atrophy; no CSF available). Conclusions Despite being less restrictive than the other criteria, the IWG-1 criteria reliably identified individuals with AD pathology. More pragmatic and easily applicable selection criteria might be preferred due to feasibility in certain situations, e.g., in multidomain prevention trials that do not specifically target β-amyloid/tau pathologies. Trial registration Netherlands Trial Register, NL1620. Registered on 9 March 2009
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Affiliation(s)
- Anna Rosenberg
- Department of Neurology, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland.
| | - Alina Solomon
- Department of Neurology, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland.,Division of Clinical Geriatrics, Centre for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.,Theme Aging, Karolinska University Hospital, Stockholm, Sweden
| | - Hilkka Soininen
- Department of Neurology, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland.,Neurocenter, Department of Neurology, Kuopio University Hospital, Kuopio, Finland
| | - Pieter Jelle Visser
- Department of Psychiatry and Neuropsychology, Alzheimer Centre Limburg, University of Maastricht, Maastricht, Netherlands.,Department of Neurology, Alzheimer Centre, Amsterdam Neuroscience, VU University Medical Centre, Amsterdam, Netherlands
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg, Mölndal, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Tobias Hartmann
- Deutsches Institut für Demenz Prävention (DIDP), Medical Faculty, and Department of Experimental Neurology, Saarland University, Homburg, Germany
| | - Miia Kivipelto
- Department of Neurology, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland.,Division of Clinical Geriatrics, Centre for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.,Theme Aging, Karolinska University Hospital, Stockholm, Sweden.,Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland.,Ageing Epidemiology Research Unit, School of Public Health, Imperial College London, London, UK
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Bjorkli C, Sandvig A, Sandvig I. Bridging the Gap Between Fluid Biomarkers for Alzheimer's Disease, Model Systems, and Patients. Front Aging Neurosci 2020; 12:272. [PMID: 32982716 PMCID: PMC7492751 DOI: 10.3389/fnagi.2020.00272] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Accepted: 08/06/2020] [Indexed: 12/12/2022] Open
Abstract
Alzheimer’s disease (AD) is a debilitating neurodegenerative disease characterized by the accumulation of two proteins in fibrillar form: amyloid-β (Aβ) and tau. Despite decades of intensive research, we cannot yet pinpoint the exact cause of the disease or unequivocally determine the exact mechanism(s) underlying its progression. This confounds early diagnosis and treatment of the disease. Cerebrospinal fluid (CSF) biomarkers, which can reveal ongoing biochemical changes in the brain, can help monitor developing AD pathology prior to clinical diagnosis. Here we review preclinical and clinical investigations of commonly used biomarkers in animals and patients with AD, which can bridge translation from model systems into the clinic. The core AD biomarkers have been found to translate well across species, whereas biomarkers of neuroinflammation translate to a lesser extent. Nevertheless, there is no absolute equivalence between biomarkers in human AD patients and those examined in preclinical models in terms of revealing key pathological hallmarks of the disease. In this review, we provide an overview of current but also novel AD biomarkers and how they relate to key constituents of the pathological cascade, highlighting confounding factors and pitfalls in interpretation, and also provide recommendations for standardized procedures during sample collection to enhance the translational validity of preclinical AD models.
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Affiliation(s)
- Christiana Bjorkli
- Sandvig Group, Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Axel Sandvig
- Sandvig Group, Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway.,Institute of Neuromedicine and Movement Science, Department of Neurology, St. Olavs Hospital, Trondheim, Norway.,Department of Pharmacology and Clinical Neurosciences, Division of Neuro, Head, and Neck, University Hospital of Umeå, Umeå, Sweden
| | - Ioanna Sandvig
- Sandvig Group, Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
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12
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Xie L, Wisse LEM, Das SR, Vergnet N, Dong M, Ittyerah R, de Flores R, Yushkevich PA, Wolk DA. Longitudinal atrophy in early Braak regions in preclinical Alzheimer's disease. Hum Brain Mapp 2020; 41:4704-4717. [PMID: 32845545 PMCID: PMC7555086 DOI: 10.1002/hbm.25151] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 07/10/2020] [Accepted: 07/18/2020] [Indexed: 01/01/2023] Open
Abstract
A major focus of Alzheimer's disease (AD) research has been finding sensitive outcome measures to disease progression in preclinical AD, as intervention studies begin to target this population. We hypothesize that tailored measures of longitudinal change of the medial temporal lobe (MTL) subregions (the sites of earliest cortical tangle pathology) are more sensitive to disease progression in preclinical AD compared to standard cognitive and plasma NfL measures. Longitudinal T1-weighted MRI of 337 participants were included, divided into amyloid-β negative (Aβ-) controls, cerebral spinal fluid p-tau positive (T+) and negative (T-) preclinical AD (Aβ+ controls), and early prodromal AD. Anterior/posterior hippocampus, entorhinal cortex, Brodmann areas (BA) 35 and 36, and parahippocampal cortex were segmented in baseline MRI using a novel pipeline. Unbiased change rates of subregions were estimated using MRI scans within a 2-year-follow-up period. Experimental results showed that longitudinal atrophy rates of all MTL subregions were significantly higher for T+ preclinical AD and early prodromal AD than controls, but not for T- preclinical AD. Posterior hippocampus and BA35 demonstrated the largest group differences among hippocampus and MTL cortex respectively. None of the cross-sectional MTL measures, longitudinal cognitive measures (PACC, ADAS-Cog) and cross-sectional or longitudinal plasma NfL reached significance in preclinical AD. In conclusion, longitudinal atrophy measurements reflect active neurodegeneration and thus are more directly linked to active disease progression than cross-sectional measurements. Moreover, accelerated atrophy in preclinical AD seems to occur only in the presence of concomitant tau pathology. The proposed longitudinal measurements may serve as efficient outcome measures in clinical trials.
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Affiliation(s)
- Long Xie
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Laura E M Wisse
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Diagnostic Radiology, Lund University, Lund, Sweden
| | - Sandhitsu R Das
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Penn Memory Center, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Nicolas Vergnet
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Mengjin Dong
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ranjit Ittyerah
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Robin de Flores
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Penn Memory Center, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Paul A Yushkevich
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Penn Memory Center, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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13
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Vuoksimaa E, McEvoy LK, Holland D, Franz CE, Kremen WS. Modifying the minimum criteria for diagnosing amnestic MCI to improve prediction of brain atrophy and progression to Alzheimer's disease. Brain Imaging Behav 2020; 14:787-796. [PMID: 30511118 PMCID: PMC7275013 DOI: 10.1007/s11682-018-0019-6] [Citation(s) in RCA: 10] [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] [Indexed: 12/15/2022]
Abstract
Mild cognitive impairment (MCI) is a heterogeneous condition with variable outcomes. Improving diagnosis to increase the likelihood that MCI reliably reflects prodromal Alzheimer's Disease (AD) would be of great benefit for clinical practice and intervention trials. In 230 cognitively normal (CN) and 394 MCI individuals from the Alzheimer's Disease Neuroimaging Initiative, we studied whether an MCI diagnostic requirement of impairment on at least two episodic memory tests improves 3-year prediction of medial temporal lobe atrophy and progression to AD. Based on external age-adjusted norms for delayed free recall on the Rey Auditory Verbal Learning Test (AVLT), MCI participants were further classified as having normal (AVLT+, above -1 SD, n = 121) or impaired (AVLT -, -1 SD or below, n = 273) AVLT performance. CN, AVLT+, and AVLT- groups differed significantly on baseline brain (hippocampus, entorhinal cortex) and cerebrospinal fluid (amyloid, tau, p-tau) biomarkers, with the AVLT- group being most abnormal. The AVLT- group had significantly more medial temporal atrophy and a substantially higher AD progression rate than the AVLT+ group (51% vs. 16%, p < 0.001). The AVLT+ group had similar medial temporal trajectories compared to CN individuals. Results were similar even when restricted to individuals with above average (based on the CN group mean) baseline medial temporal volume/thickness. Requiring impairment on at least two memory tests for MCI diagnosis can markedly improve prediction of medial temporal atrophy and conversion to AD, even in the absence of baseline medial temporal atrophy. This modification constitutes a practical and cost-effective approach for clinical and research settings.
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Affiliation(s)
- Eero Vuoksimaa
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, P.O. Box 20 (Tukholmankatu 8), 00014, Helsinki, Finland.
| | - Linda K McEvoy
- Department of Radiology, University of California, San Diego, La Jolla, CA, USA
| | - Dominic Holland
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA
| | - Carol E Franz
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
- Center for Behavior Genetics of Aging, University of California, San Diego, CA, USA
| | - William S Kremen
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
- Center for Behavior Genetics of Aging, University of California, San Diego, CA, USA
- Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, San Diego, CA, USA
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14
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Polosecki P, Castro E, Rish I, Pustina D, Warner JH, Wood A, Sampaio C, Cecchi GA. Resting-state connectivity stratifies premanifest Huntington's disease by longitudinal cognitive decline rate. Sci Rep 2020; 10:1252. [PMID: 31988371 PMCID: PMC6985137 DOI: 10.1038/s41598-020-58074-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Accepted: 01/10/2020] [Indexed: 11/17/2022] Open
Abstract
Patient stratification is critical for the sensitivity of clinical trials at early stages of neurodegenerative disorders. In Huntington’s disease (HD), genetic tests make cognitive, motor and brain imaging measurements possible before symptom manifestation (pre-HD). We evaluated pre-HD stratification models based on single visit resting-state functional MRI (rs-fMRI) data that assess observed longitudinal motor and cognitive change rates from the multisite Track-On HD cohort (74 pre-HD, 79 control participants). We computed longitudinal performance change on 10 tasks (including visits from the preceding TRACK-HD study when available), as well as functional connectivity density (FCD) maps in single rs-fMRI visits, which showed high test-retest reliability. We assigned pre-HD subjects to subgroups of fast, intermediate, and slow change along single tasks or combinations of them, correcting for expectations based on aging; and trained FCD-based classifiers to distinguish fast- from slow-progressing individuals. For robustness, models were validated across imaging sites. Stratification models distinguished fast- from slow-changing participants and provided continuous assessments of decline applicable to the whole pre-HD population, relying on previously-neglected white matter functional signals. These results suggest novel correlates of early deterioration and a robust stratification strategy where a single MRI measurement provides an estimate of multiple ongoing longitudinal changes.
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Affiliation(s)
- Pablo Polosecki
- IBM T.J. Watson Research Center, Yorktown Heights, Yorktown, NY, USA.
| | - Eduardo Castro
- IBM T.J. Watson Research Center, Yorktown Heights, Yorktown, NY, USA
| | - Irina Rish
- IBM T.J. Watson Research Center, Yorktown Heights, Yorktown, NY, USA
| | | | | | - Andrew Wood
- CHDI Management/CHDI Foundation, Princeton, NJ, USA
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15
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Identifying an Optimal Cutoff of the Montreal Cognitive Assessment to Predict Amyloid-PET Positivity in a Referral Memory Clinic. Alzheimer Dis Assoc Disord 2019; 33:194-199. [PMID: 31305321 DOI: 10.1097/wad.0000000000000330] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
BACKGROUND Brain amyloid- positron emission tomography (PET) imaging is highly sensitive for identifying Alzheimer disease. Currently, there is a lack of insight on the association between amyloid-PET status and the widely used Montreal cognitive assessment (MoCA). Studying this relationship may optimize the clinical use of amyloid-PET imaging. OBJECTIVES To evaluate the relationship between amyloid-PET status and MoCA scores and to identify a MoCA score cutoff that translates to amyloid-PET positivity. METHODS Using retrospective chart review, patients from 2010 to 2017 with amyloid-PET scans (positive or negative) and MoCA test scores were included. We studied the relationship between amyloid-PET status and MoCA scores and the influence of age, sex, education, and race. A MoCA score cutoff for amyloid-PET positivity was estimated. RESULTS Among the 684 clinic patients with dementia, 99 fulfilled inclusion criteria. Amyloid-PET positivity was associated significantly with lower MoCA scores (median=19, U=847, P=0.01). The MoCA score cutoff (25) used for minimal cognitive impairment (MCI) predicted amyloid-PET positivity suboptimally (sensitivity=94.6%, specificity=13.9%). A MoCA score cutoff of 20 patients had optimal sensitivity (64.2%) and specificity (67.4%). CONCLUSIONS Amyloid-PET positivity is associated with lower MoCA scores. Clinical utility of amyloid-PET scan is likely to be suboptimal at the MoCA score cutoff for minimal cognitive impairment.
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16
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Prodromes and Preclinical Detection of Brain Diseases: Surveying the Ethical Landscape of Predicting Brain Health. eNeuro 2019; 6:ENEURO.0439-18.2019. [PMID: 31221862 PMCID: PMC6658915 DOI: 10.1523/eneuro.0439-18.2019] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Revised: 05/16/2019] [Accepted: 06/02/2019] [Indexed: 02/08/2023] Open
Abstract
The future of medicine lies not primarily in cures but in disease modification and prevention. While the science of preclinical detection is young, it is moving rapidly. Preclinical interventions offer hope to decrease the severity of a disease or delay the development of a disorder. With such promise, the research and practice of detecting brain disorders at a preclinical stage present unique ethical challenges that must be addressed to ensure the benefit of these technologies. Direct brain interventions have the potential to impact not just what a patient has but who they are and who they could become. Further, receiving an assessment for a preclinical or prodromal state has potential to impact perceptions about capacity, autonomy and personhood and could become entangled with stigma and discrimination. Exploring ethical issues alongside and integrated into the experimental design and research of these technologies is critical. This review will highlight ethical issues attendant to the current and near future states of preclinical detection across the life span, specifically as it relates to autism spectrum disorder (ASD), schizophrenia, and Alzheimer’s disease.
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17
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Márquez F, Yassa MA. Neuroimaging Biomarkers for Alzheimer's Disease. Mol Neurodegener 2019; 14:21. [PMID: 31174557 PMCID: PMC6555939 DOI: 10.1186/s13024-019-0325-5] [Citation(s) in RCA: 116] [Impact Index Per Article: 23.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Accepted: 05/28/2019] [Indexed: 12/11/2022] Open
Abstract
Currently, over five million Americans suffer with Alzheimer’s disease (AD). In the absence of a cure, this number could increase to 13.8 million by 2050. A critical goal of biomedical research is to establish indicators of AD during the preclinical stage (i.e. biomarkers) allowing for early diagnosis and intervention. Numerous advances have been made in developing biomarkers for AD using neuroimaging approaches. These approaches offer tremendous versatility in terms of targeting distinct age-related and pathophysiological mechanisms such as structural decline (e.g. volumetry, cortical thinning), functional decline (e.g. fMRI activity, network correlations), connectivity decline (e.g. diffusion anisotropy), and pathological aggregates (e.g. amyloid and tau PET). In this review, we survey the state of the literature on neuroimaging approaches to developing novel biomarkers for the amnestic form of AD, with an emphasis on combining approaches into multimodal biomarkers. We also discuss emerging methods including imaging epigenetics, neuroinflammation, and synaptic integrity using PET tracers. Finally, we review the complementary information that neuroimaging biomarkers provide, which highlights the potential utility of composite biomarkers as suitable outcome measures for proof-of-concept clinical trials with experimental therapeutics.
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Affiliation(s)
- Freddie Márquez
- Department of Neurobiology and Behavior, Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine, CA, 92697, USA.
| | - Michael A Yassa
- Department of Neurobiology and Behavior, Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine, CA, 92697, USA.
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18
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Iaccarino L, Sala A, Perani D. Predicting long-term clinical stability in amyloid-positive subjects by FDG-PET. Ann Clin Transl Neurol 2019; 6:1113-1120. [PMID: 31211176 PMCID: PMC6562030 DOI: 10.1002/acn3.782] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2018] [Revised: 03/13/2019] [Accepted: 04/01/2019] [Indexed: 12/20/2022] Open
Abstract
Imaging biomarkers can be used to screen participants for Alzheimer's disease clinical trials. To test the predictive values in clinical progression of neuropathology change (amyloid-PET) or brain metabolism as neurodegeneration biomarker ([18F]FDG-PET), we evaluated data from N = 268 healthy controls and N = 519 mild cognitive impairment subjects. Despite being a significant risk factor, amyloid positivity was not associated with clinical progression in the majority (≥60%) of subjects. Notably, a negative [18F]FDG-PET scan at baseline strongly predicted clinical stability with high negative predictive values (>0.80) for both groups. We suggest [18F]FDG-PET brain metabolism or other neurodegeneration measures should be coupled to amyloid-PET to exclude clinically stable individuals from clinical trials.
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Affiliation(s)
- Leonardo Iaccarino
- Vita-Salute San Raffaele University Milan Italy.,In vivo Human Molecular and Structural Neuroimaging Unit Division of Neuroscience San Raffaele Scientific Institute Milan 20132 Italy.,Memory and Aging Center University of California San Francisco San Francisco California 94158
| | - Arianna Sala
- Vita-Salute San Raffaele University Milan Italy.,In vivo Human Molecular and Structural Neuroimaging Unit Division of Neuroscience San Raffaele Scientific Institute Milan 20132 Italy
| | - Daniela Perani
- Vita-Salute San Raffaele University Milan Italy.,In vivo Human Molecular and Structural Neuroimaging Unit Division of Neuroscience San Raffaele Scientific Institute Milan 20132 Italy.,Nuclear Medicine Unit IRCCS San Raffaele Hospital Milan 20132 Italy
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19
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Marizzoni M, Ferrari C, Jovicich J, Albani D, Babiloni C, Cavaliere L, Didic M, Forloni G, Galluzzi S, Hoffmann KT, Molinuevo JL, Nobili F, Parnetti L, Payoux P, Ribaldi F, Rossini PM, Schönknecht P, Salvatore M, Soricelli A, Hensch T, Tsolaki M, Visser PJ, Wiltfang J, Richardson JC, Bordet R, Blin O, Frisoni GB. Predicting and Tracking Short Term Disease Progression in Amnestic Mild Cognitive Impairment Patients with Prodromal Alzheimer’s Disease: Structural Brain Biomarkers. J Alzheimers Dis 2019; 69:3-14. [DOI: 10.3233/jad-180152] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Affiliation(s)
- Moira Marizzoni
- Laboratory of Neuroimaging and Alzheimer’s Epidemiology, IRCCS Istituto Centro San Giovanni diDio Fatebenefratelli, Brescia, Italy
| | - Clarissa Ferrari
- Unit of Statistics, IRCCS Istituto Centro San Giovanni diDio Fatebenefratelli, Brescia, Italy
| | - Jorge Jovicich
- Center for Mind/Brain Sciences, University of Trento, Italy
| | - Diego Albani
- Neuroscience Department, IRCCS - Istituto di Ricerche Farmacologiche Mario Negri, Milano, Italy
| | - Claudio Babiloni
- Department of Physiology and Pharmacology “V. Erspamer”, Sapienza University of Rome, Rome, Italy
- IRCCS San Raffaele Pisana of Rome, Rome, Italy
| | - Libera Cavaliere
- Laboratory of Neuroimaging and Alzheimer’s Epidemiology, IRCCS Istituto Centro San Giovanni diDio Fatebenefratelli, Brescia, Italy
| | - Mira Didic
- Aix-Marseille Université, Inserm, INS UMR_S 1106, Marseille, France
- APHM, Timone, Service de Neurologie et Neuropsychologie, APHM Hôpital Timone Adultes, Marseille, France
| | - Gianluigi Forloni
- Neuroscience Department, IRCCS - Istituto di Ricerche Farmacologiche Mario Negri, Milano, Italy
| | - Samantha Galluzzi
- Laboratory of Neuroimaging and Alzheimer’s Epidemiology, IRCCS Istituto Centro San Giovanni diDio Fatebenefratelli, Brescia, Italy
| | | | - José Luis Molinuevo
- Alzheimer’s Disease Unit and Other Cognitive Disorders Unit, Hospital Clínic de Barcelona, and Institut d’Investigacions Biomédiques August Pi i Sunyer (IDIBAPS), Barcelona, Catalunya, Spain
| | - Flavio Nobili
- Clinical Neurology, Dept. of Neuroscience (DINOGMI), University of Genoa and IRCCS AOU SanMartino-IST, Genoa, Italy
| | - Lucilla Parnetti
- Clinica Neurologica, Università di Perugia, Ospedale Santa Mariadella Misericordia, Perugia, Italy
| | - Pierre Payoux
- INSERM; Imagerie cérébrale et handicapsneurologiques UMR 825, Toulouse, France
| | - Federica Ribaldi
- Laboratory of Neuroimaging and Alzheimer’s Epidemiology, IRCCS Istituto Centro San Giovanni diDio Fatebenefratelli, Brescia, Italy
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Paolo Maria Rossini
- Area of Neuroscience, Department of Gerontology, Neurosciences & Orthopedics, Catholic University, Policlinic A. Gemelli Foundation Rome, Italy
| | - Peter Schönknecht
- Department of Psychiatry and Psychotherapy, University of Leipzig, Leipzig, Germany
| | - Marco Salvatore
- SDN Istituto di Ricerca Diagnostica e Nucleare, Napoli, Italy
| | | | - Tilman Hensch
- Department of Psychiatry and Psychotherapy, University of Leipzig, Leipzig, Germany
| | - Magda Tsolaki
- 3rd Neurologic Clinic, Medical School, G. Papanikolaou Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Pieter Jelle Visser
- Department of Neurology, Alzheimer Centre, VU Medical Centre, Amsterdam, The Netherlands
| | - Jens Wiltfang
- LVR-Hospital Essen, Department of Psychiatry and Psychotherapy, Faculty of Medicine, University of Duisburg-Essen, Essen, Germany
- Department of Psychiatry and Psychotherapy, University Medical Center (UMG), Georg-August-University, Goettingen, Germany
- iBiMED, Medical Sciences Department, University of Aveiro, Aveiro, Portugal
| | - Jill C. Richardson
- Neurosciences Therapeutic Area, GlaxoSmithKline R&D, Gunnels Wood Road, Stevenage, United Kingdom
| | - Régis Bordet
- University of Lille, Inserm, CHU Lille, U1171 - Degenerative and vascular cognitive disorders, Lille, France
| | - Olivier Blin
- Aix Marseille University, UMR-CNRS 7289, Service de Pharmacologie Clinique, AP-HM, Marseille, France
| | - Giovanni B. Frisoni
- Laboratory of Neuroimaging and Alzheimer’s Epidemiology, IRCCS Istituto Centro San Giovanni diDio Fatebenefratelli, Brescia, Italy
- Memory Clinic and LANVIE - Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland
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20
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DIVE: A spatiotemporal progression model of brain pathology in neurodegenerative disorders. Neuroimage 2019; 192:166-177. [DOI: 10.1016/j.neuroimage.2019.02.053] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2018] [Revised: 02/18/2019] [Accepted: 02/20/2019] [Indexed: 11/17/2022] Open
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21
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Leuzy A, Heurling K, Ashton NJ, Schöll M, Zimmer ER. In vivo Detection of Alzheimer's Disease. THE YALE JOURNAL OF BIOLOGY AND MEDICINE 2018; 91:291-300. [PMID: 30258316 PMCID: PMC6153625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
Recent revisions to the diagnostic criteria for Alzheimer's disease (AD) incorporated conceptual advances in the field. Specifically, AD is now recognized to encompass a continuum, spanning from preclinical (accruing brain pathology in the absence of symptoms) through symptomatic predementia (prodromal AD, mild cognitive impairment) and dementia phases. The role of biological markers (biomarkers) of both the underlying molecular pathologies and related neurodegenerative changes has also been acknowledged. In this abridged review, we provide an overview of fluid (cerebrospinal fluid and blood) and molecular imaging-based biomarkers used within the field and discuss the potential role of computer driven artificial intelligence approaches for both the early and accurate identification of AD and as a tool for population enrichment in clinical trials testing candidate disease modifying therapies.
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Affiliation(s)
- Antoine Leuzy
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Huddinge, Sweden
| | - Kerstin Heurling
- Wallenberg Centre for Molecular and Translational Medicine, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Sweden
| | - Nicholas J. Ashton
- Wallenberg Centre for Molecular and Translational Medicine, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Sweden
| | - Michael Schöll
- Wallenberg Centre for Molecular and Translational Medicine, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Sweden,Clinical Memory Research Unit, Lund University, Sweden
| | - Eduardo R. Zimmer
- Department of Pharmacology, Federal University of Rio Grande do Sul, Porto Alegre, Brazil,Graduate Program in Biological Sciences: Biochemistry, Federal University of Rio Grande do Sul, Porto Alegre, Brazil,Brain Institute of Rio Grande do Sul (BraIns), Pontifical Catholic University of Rio Grande do Sul, Porto Alegre, Brazil,To whom all correspondence should be addressed: Eduardo R. Zimmer, PhD, Department of Pharmacology, Federal University of Rio Grande do Sul, 500 Sarmento Leite Street, 90050-170, Porto Alegre, RS, Brazil; Tel: +55 51 33085558,
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22
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O'Donoghue MC, Murphy SE, Zamboni G, Nobre AC, Mackay CE. APOE genotype and cognition in healthy individuals at risk of Alzheimer's disease: A review. Cortex 2018; 104:103-123. [DOI: 10.1016/j.cortex.2018.03.025] [Citation(s) in RCA: 85] [Impact Index Per Article: 14.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2017] [Revised: 02/02/2018] [Accepted: 03/19/2018] [Indexed: 01/22/2023]
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23
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Lee JH, Jahrling JB, Denner L, Dineley KT. Targeting Insulin for Alzheimer’s Disease: Mechanisms, Status and Potential Directions. J Alzheimers Dis 2018; 64:S427-S453. [DOI: 10.3233/jad-179923] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Jung Hyun Lee
- Department of Neurology, University of Texas Medical Branch, Galveston, TX, USA
| | - Jordan B. Jahrling
- Department of Neurology, University of Texas Medical Branch, Galveston, TX, USA
| | - Larry Denner
- Department of Internal Medicine, University of Texas Medical Branch, Galveston, TX, USA
| | - Kelly T. Dineley
- Department of Neurology, University of Texas Medical Branch, Galveston, TX, USA
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24
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Jaafaru MS, Nordin N, Shaari K, Rosli R, Abdull Razis AF. Isothiocyanate from Moringa oleifera seeds mitigates hydrogen peroxide-induced cytotoxicity and preserved morphological features of human neuronal cells. PLoS One 2018; 13:e0196403. [PMID: 29723199 PMCID: PMC5933767 DOI: 10.1371/journal.pone.0196403] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2018] [Accepted: 04/12/2018] [Indexed: 01/12/2023] Open
Abstract
Reactive oxygen species are well known for induction of oxidative stress conditions through oxidation of vital biomarkers leading to cellular death via apoptosis and other process, thereby causing devastative effects on the host organs. This effect is believed to be linked with pathological alterations seen in several neurodegenerative disease conditions. Many phytochemical compounds proved to have robust antioxidant activities that deterred cells against cytotoxic stress environment, thus protect apoptotic cell death. In view of that we studied the potential of glucomoringin-isothiocyanate (GMG-ITC) or moringin to mitigate the process that lead to neurodegeneration in various ways. Neuroprotective effect of GMG-ITC was performed on retinoic acid (RA) induced differentiated neuroblastoma cells (SHSY5Y) via cell viability assay, flow cytometry analysis and fluorescence microscopy by means of acridine orange and propidium iodide double staining, to evaluate the anti-apoptotic activity and morphology conservation ability of the compound. Additionally, neurite surface integrity and ultrastructural analysis were carried out by means of scanning and transmission electron microscopy to assess the orientation of surface and internal features of the treated neuronal cells. GMG-ITC pre-treated neuron cells showed significant resistance to H2O2-induced apoptotic cell death, revealing high level of protection by the compound. Increase of intracellular oxidative stress induced by H2O2 was mitigated by GMG-ITC. Thus, pre-treatment with the compound conferred significant protection to cytoskeleton and cytoplasmic inclusion coupled with conservation of surface morphological features and general integrity of neuronal cells. Therefore, the collective findings in the presence study indicated the potentials of GMG-ITC to protect the integrity of neuron cells against induced oxidative-stress related cytotoxic processes, the hallmark of neurodegenerative diseases.
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Affiliation(s)
- Mohammed Sani Jaafaru
- UPM-MAKNA Cancer Research Laboratory, Institute of Bioscience, Universiti Putra Malaysia, UPM Serdang, Selangor, Malaysia
- Department of Biochemistry, Kaduna State University, Main Campus, Kaduna, Nigeria
| | - Norshariza Nordin
- Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, UPM Serdang, Selangor, Malaysia
| | - Khozirah Shaari
- Laboratory of Natural Product, Institute of Bioscience, Universiti Putra Malaysia, UPM Serdang, Selangor, Malaysia
- Department of Chemistry, Faculty of Science, Universiti Putra Malaysia, UPM Serdang, Selangor, Malaysia
| | - Rozita Rosli
- UPM-MAKNA Cancer Research Laboratory, Institute of Bioscience, Universiti Putra Malaysia, UPM Serdang, Selangor, Malaysia
- Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, UPM Serdang, Selangor, Malaysia
| | - Ahmad Faizal Abdull Razis
- Laboratory of Molecular Biomedicine, Institute of Bioscience, Universiti Putra Malaysia, UPM Serdang, Selangor, Malaysia
- Institute of Tropical Agriculture and Food Security, Universiti Putra Malaysia, UPM Serdang, Selangor, Malaysia
- Department of Food Science, Faculty of Food Science and Technology, Universiti Putra Malaysia, UPM Serdang, Selangor, Malaysia
- * E-mail:
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Kauppi K, Fan CC, McEvoy LK, Holland D, Tan CH, Chen CH, Andreassen OA, Desikan RS, Dale AM. Combining Polygenic Hazard Score With Volumetric MRI and Cognitive Measures Improves Prediction of Progression From Mild Cognitive Impairment to Alzheimer's Disease. Front Neurosci 2018; 12:260. [PMID: 29760643 PMCID: PMC5937163 DOI: 10.3389/fnins.2018.00260] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2018] [Accepted: 04/04/2018] [Indexed: 01/18/2023] Open
Abstract
Improved prediction of progression to Alzheimer's Disease (AD) among older individuals with mild cognitive impairment (MCI) is of high clinical and societal importance. We recently developed a polygenic hazard score (PHS) that predicted age of AD onset above and beyond APOE. Here, we used data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to further explore the potential clinical utility of PHS for predicting AD development in older adults with MCI. We examined the predictive value of PHS alone and in combination with baseline structural magnetic resonance imaging (MRI) data on performance on the Mini-Mental State Exam (MMSE). In survival analyses, PHS significantly predicted time to progression from MCI to AD over 120 months (p = 1.07e-5), and PHS was significantly more predictive than APOE alone (p = 0.015). Combining PHS with baseline brain atrophy score and/or MMSE score significantly improved prediction compared to models without PHS (three-factor model p = 4.28e-17). Prediction model accuracies, sensitivities and area under the curve were also improved by including PHS in the model, compared to only using atrophy score and MMSE. Further, using linear mixed-effect modeling, PHS improved the prediction of change in the Clinical Dementia Rating-Sum of Boxes (CDR-SB) score and MMSE over 36 months in patients with MCI at baseline, beyond both APOE and baseline levels of brain atrophy. These results illustrate the potential clinical utility of PHS for assessment of risk for AD progression among individuals with MCI both alone, or in conjunction with clinical measures of prodromal disease including measures of cognitive function and regional brain atrophy.
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Affiliation(s)
- Karolina Kauppi
- Department of Radiology, University of California, San Diego, La Jolla, CA, United States
- Department of Radiation Sciences, University of Umea, Umea, Sweden
| | - Chun Chieh Fan
- Department of Radiology, University of California, San Diego, La Jolla, CA, United States
- Department of Cognitive Sciences, University of California, San Diego, La Jolla, CA, United States
| | - Linda K. McEvoy
- Department of Radiology, University of California, San Diego, La Jolla, CA, United States
| | - Dominic Holland
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, United States
| | - Chin Hong Tan
- Neuroradiology Section, Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Chi-Hua Chen
- Department of Radiology, University of California, San Diego, La Jolla, CA, United States
| | - Ole A. Andreassen
- NORMENT, Institute of Clinical Medicine, Division of Mental Health and Addiction, University of Oslo, Oslo University Hospital, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Rahul S. Desikan
- Neuroradiology Section, Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Anders M. Dale
- Department of Radiology, University of California, San Diego, La Jolla, CA, United States
- Department of Cognitive Sciences, University of California, San Diego, La Jolla, CA, United States
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, United States
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Amyloid and tau signatures of brain metabolic decline in preclinical Alzheimer's disease. Eur J Nucl Med Mol Imaging 2018; 45:1021-1030. [PMID: 29396637 PMCID: PMC5915512 DOI: 10.1007/s00259-018-3933-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2017] [Accepted: 01/02/2018] [Indexed: 01/18/2023]
Abstract
Purpose We aimed to determine the amyloid (Aβ) and tau biomarker levels associated with imminent Alzheimer’s disease (AD) - related metabolic decline in cognitively normal individuals. Methods A threshold analysis was performed in 120 cognitively normal elderly individuals by modelling 2-year declines in brain glucose metabolism measured with [18F]fluorodeoxyglucose ([18F]FDG) as a function of [18F]florbetapir Aβ positron emission tomography (PET) and cerebrospinal fluid phosphorylated tau biomarker thresholds. Additionally, using a novel voxel-wise analytical framework, we determined the sample sizes needed to test an estimated 25% drugeffect with 80% of power on changes in FDG uptake over 2 years at every brain voxel. Results The combination of [18F]florbetapir standardized uptake value ratios and phosphorylated-tau levels more than one standard deviation higher than their respective thresholds for biomarker abnormality was the best predictor of metabolic decline in individuals with preclinical AD. We also found that a clinical trial using these thresholds would require as few as 100 individuals to test a 25% drug effect on AD-related metabolic decline over 2 years. Conclusions These results highlight the new concept that combined Aβ and tau thresholds can predict imminent neurodegeneration as an alternative framework with a high statistical power for testing the effect of disease-modifying therapies on [18F]FDG uptake decline over a typical 2-year clinical trial period in individuals with preclinical AD. Electronic supplementary material The online version of this article (10.1007/s00259-018-3933-3) contains supplementary material, which is available to authorized users.
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27
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Mathotaarachchi S, Pascoal TA, Shin M, Benedet AL, Kang MS, Beaudry T, Fonov VS, Gauthier S, Rosa-Neto P. Identifying incipient dementia individuals using machine learning and amyloid imaging. Neurobiol Aging 2017; 59:80-90. [DOI: 10.1016/j.neurobiolaging.2017.06.027] [Citation(s) in RCA: 69] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2017] [Revised: 06/20/2017] [Accepted: 06/30/2017] [Indexed: 01/18/2023]
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Bertens D, Tijms BM, Vermunt L, Prins ND, Scheltens P, Visser PJ. The effect of diagnostic criteria on outcome measures in preclinical and prodromal Alzheimer's disease: Implications for trial design. ALZHEIMERS & DEMENTIA-TRANSLATIONAL RESEARCH & CLINICAL INTERVENTIONS 2017; 3:513-523. [PMID: 29124109 PMCID: PMC5671625 DOI: 10.1016/j.trci.2017.08.005] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
Introduction We investigated the influence of different inclusion criteria for preclinical and prodromal Alzheimer's disease (AD) on changes in biomarkers and cognitive markers and on trial sample size estimates. Methods We selected 522 cognitively normal subjects and 872 subjects with mild cognitive impairment from the Alzheimer's Disease Neuroimaging Initiative study. Compared inclusion criteria were (1) preclinical or prodromal AD (amyloid marker abnormal); (2) preclinical or prodromal AD stage-1 (amyloid marker abnormal, injury marker normal); and (3) preclinical or prodromal AD stage-2 (amyloid and injury markers abnormal). Outcome measures were amyloid, neuronal injury, and cognitive markers. Results In both subjects with preclinical and prodromal AD stage-2, inclusion criteria resulted in the largest observed decline in brain volumetric measures on magnetic resonance imaging and cognitive markers. Discussion Inclusion criteria influence the observed rate of worsening in outcome measures. This has implications for trial design.
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Affiliation(s)
- Daniela Bertens
- Alzheimer Centre, Department of Neurology, VU University Medical Centre, Amsterdam, The Netherlands
| | - Betty M Tijms
- Alzheimer Centre, Department of Neurology, VU University Medical Centre, Amsterdam, The Netherlands
| | - Lisa Vermunt
- Alzheimer Centre, Department of Neurology, VU University Medical Centre, Amsterdam, The Netherlands
| | - Niels D Prins
- Alzheimer Centre, Department of Neurology, VU University Medical Centre, Amsterdam, The Netherlands.,Alzheimer Research Center, Amsterdam The Netherlands
| | - Philip Scheltens
- Alzheimer Centre, Department of Neurology, VU University Medical Centre, Amsterdam, The Netherlands
| | - Pieter Jelle Visser
- Alzheimer Centre, Department of Neurology, VU University Medical Centre, Amsterdam, The Netherlands.,Alzheimer Centre, School for Mental Health and Neuroscience (MHeNS), University Medical Centre, Maastricht, The Netherlands
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29
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Duke Han S, Nguyen CP, Stricker NH, Nation DA. Detectable Neuropsychological Differences in Early Preclinical Alzheimer's Disease: A Meta-Analysis. Neuropsychol Rev 2017; 27:305-325. [PMID: 28497179 DOI: 10.1007/s11065-017-9345-5] [Citation(s) in RCA: 65] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2016] [Accepted: 04/23/2017] [Indexed: 12/20/2022]
Abstract
The development of methods for in vivo detection of cerebral beta amyloid retention and tau accumulation have been increasingly useful in characterizing preclinical Alzheimer's disease (AD). While the association between these biomarkers and eventual AD has been demonstrated among cognitively intact older adults, the link between biomarkers and neurocognitive ability remains unclear. We conducted a meta-analysis to test the hypothesis that cognitively intact older adults would show statistically discernable differences in neuropsychological performance by amyloid status (amyloid negative = A-, amyloid positive = A+). We secondarily hypothesized a third group characterized by either CSF tau pathology or neurodegeneration, in addition to amyloidosis (A+/N+ or Stage 2), would show lower neuropsychology scores than the amyloid positive group (A+/N- or Stage 1) when compared to the amyloid negative group. Pubmed, PsychINFO, and other sources were searched for relevant articles, yielding 775 total sources. After review for inclusion/exclusion criteria, duplicates, and risk of bias, 61 studies were utilized in the final meta-analysis. Results showed A+ was associated with poorer performance in the domains of global cognitive function, memory, language, visuospatial ability, processing speed, and attention/working memory/executive functions when compared to A-. A+/N+ showed lower performances on memory measures when compared to A+/N- in secondary analyses based on a smaller subset of studies. Results support the notion that neuropsychological measures are sensitive to different stages of preclinical AD among cognitively intact older adults. Further research is needed to determine what constitutes meaningful differences in neuropsychological performance among cognitively intact older adults.
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Affiliation(s)
- S Duke Han
- Department of Family Medicine, USC Keck School of Medicine, 1000 S. Fremont Avenue, Unit 22, HSA Building A-6, 4th Floor, Room 6437A, Alhambra, CA, 91803, USA. .,Department of Neurology, USC Keck School of Medicine, Los Angeles, CA, USA. .,Department of Psychology, USC Dornsife College, Los Angeles, CA, USA. .,USC School of Gerontology, Los Angeles, CA, USA.
| | - Caroline P Nguyen
- Department of Family Medicine, USC Keck School of Medicine, 1000 S. Fremont Avenue, Unit 22, HSA Building A-6, 4th Floor, Room 6437A, Alhambra, CA, 91803, USA
| | - Nikki H Stricker
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Daniel A Nation
- Department of Psychology, USC Dornsife College, Los Angeles, CA, USA
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Dubois B, Hampel H, Feldman HH, Scheltens P, Aisen P, Andrieu S, Bakardjian H, Benali H, Bertram L, Blennow K, Broich K, Cavedo E, Crutch S, Dartigues JF, Duyckaerts C, Epelbaum S, Frisoni GB, Gauthier S, Genthon R, Gouw AA, Habert MO, Holtzman DM, Kivipelto M, Lista S, Molinuevo JL, O'Bryant SE, Rabinovici GD, Rowe C, Salloway S, Schneider LS, Sperling R, Teichmann M, Carrillo MC, Cummings J, Jack CR. Preclinical Alzheimer's disease: Definition, natural history, and diagnostic criteria. Alzheimers Dement 2016; 12:292-323. [PMID: 27012484 DOI: 10.1016/j.jalz.2016.02.002] [Citation(s) in RCA: 1117] [Impact Index Per Article: 139.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
During the past decade, a conceptual shift occurred in the field of Alzheimer's disease (AD) considering the disease as a continuum. Thanks to evolving biomarker research and substantial discoveries, it is now possible to identify the disease even at the preclinical stage before the occurrence of the first clinical symptoms. This preclinical stage of AD has become a major research focus as the field postulates that early intervention may offer the best chance of therapeutic success. To date, very little evidence is established on this "silent" stage of the disease. A clarification is needed about the definitions and lexicon, the limits, the natural history, the markers of progression, and the ethical consequence of detecting the disease at this asymptomatic stage. This article is aimed at addressing all the different issues by providing for each of them an updated review of the literature and evidence, with practical recommendations.
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Affiliation(s)
- Bruno Dubois
- Institute of Memory and Alzheimer's Disease (IM2A) and Brain and Spine Institute (ICM) UMR S 1127 Frontlab, Department of Neurology, AP_HP, Pitié-Salpêtrière University Hospital, Sorbonne Universities, Pierre et Marie Curie University, Paris 06, Paris, France.
| | - Harald Hampel
- Institute of Memory and Alzheimer's Disease (IM2A) and Brain and Spine Institute (ICM) UMR S 1127 Frontlab, Department of Neurology, AP_HP, Pitié-Salpêtrière University Hospital, Sorbonne Universities, Pierre et Marie Curie University, Paris 06, Paris, France; AXA Research Fund & UPMC Chair, Paris, France
| | | | - Philip Scheltens
- Department of Neurology and Alzheimer Center, VU University Medical Center and Neuroscience Campus, Amsterdam, The Netherlands
| | - Paul Aisen
- University of Southern California San Diego, CA, USA
| | - Sandrine Andrieu
- UMR1027, INSERM, Université Toulouse III, Toulouse University Hospital, France
| | - Hovagim Bakardjian
- IHU-A-ICM-Institut des Neurosciences translationnelles de Paris, Paris, France
| | - Habib Benali
- INSERM U1146-CNRS UMR 7371-UPMC UM CR2, Site Pitié-Salpêtrière, Paris, France
| | - Lars Bertram
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), Institutes of Neurogenetics and Integrative and Experimental Genomics, University of Lübeck, Lübeck, Germany; School of Public Health, Faculty of Medicine, Imperial College London, London, UK
| | - Kaj Blennow
- Clinical Neurochemistry Lab, Department of Neuroscience and Physiology, University of Gothenburg, Mölndal Hospital, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Karl Broich
- Federal Institute for Drugs and Medical Devices, Bonn, Germany
| | - Enrica Cavedo
- AXA Research Fund & UPMC Chair, Paris, France; Laboratory of Alzheimer's Neuroimaging and Epidemiology, IRCCS Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Sebastian Crutch
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, University College London, London, UK
| | | | - Charles Duyckaerts
- University Pierre et Marie Curie, Assistance Publique des Hôpitaux de Paris, Alzheimer-Prion Team Institut du Cerveau et de la Moelle (ICM), Paris, France
| | - Stéphane Epelbaum
- Institute of Memory and Alzheimer's Disease (IM2A) and Brain and Spine Institute (ICM) UMR S 1127 Frontlab, Department of Neurology, AP_HP, Pitié-Salpêtrière University Hospital, Sorbonne Universities, Pierre et Marie Curie University, Paris 06, Paris, France
| | - Giovanni B Frisoni
- University Hospitals and University of Geneva, Geneva, Switzerland; IRCCS Fatebenefratelli, Brescia, Italy
| | - Serge Gauthier
- McGill Center for Studies in Aging, Douglas Mental Health Research Institute, Montreal, Canada
| | - Remy Genthon
- Fondation pour la Recherche sur Alzheimer, Hôpital Pitié-Salpêtrière, Paris, France
| | - Alida A Gouw
- UMR1027, INSERM, Université Toulouse III, Toulouse University Hospital, France; Department of Clinical Neurophysiology/MEG Center, VU University Medical Center, Amsterdam
| | - Marie-Odile Habert
- Sorbonne Universités, UPMC Univ Paris 06, CNRS, INSERM, Laboratoire d'Imagerie Biomédicale, Paris, France; AP-HP, Hôpital Pitié-Salpêtrière, Département de Médecine Nucléaire, Paris, France
| | - David M Holtzman
- Department of Neurology, Washington University, Hope Center for Neurological Disorders, St. Louis, MO, USA; Department of Neurology, Washington University, Knight Alzheimer's Disease Research Center, St. Louis, MO, USA
| | - Miia Kivipelto
- Center for Alzheimer Research, Karolinska Institutet, Department of Geriatric Medicine, Karolinska University Hospital, Stockholm, Sweden; Institute of Clinical Medicine/ Neurology, University of Eastern Finland, Kuopio, Finland
| | | | - José-Luis Molinuevo
- Alzheimer's Disease and Other Cognitive Disorders Unit, Hospital Clínic, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain
| | - Sid E O'Bryant
- Center for Alzheimer's & Neurodegenerative Disease Research, University of North Texas Health Science Center, TX, USA
| | - Gil D Rabinovici
- Memory & Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Christopher Rowe
- Department of Molecular Imaging, Austin Health, University of Melbourne, Australia
| | - Stephen Salloway
- Memory and Aging Program, Butler Hospital, Alpert Medical School of Brown University, USA; Department of Neurology, Alpert Medical School of Brown University, USA; Department of Psychiatry, Alpert Medical School of Brown University, USA
| | - Lon S Schneider
- Keck School of Medicine of the University of Southern California, Los Angeles, CA, USA
| | - Reisa Sperling
- Harvard Medical School, Memory Disorders Unit, Center for Alzheimer Research and Treatment, Brigham and Women's Hospital, Boston, USA; Harvard Medical School, Memory Disorders Unit, Center for Alzheimer Research and Treatment, Massachusetts General Hospital, Boston, USA
| | - Marc Teichmann
- Institute of Memory and Alzheimer's Disease (IM2A) and Brain and Spine Institute (ICM) UMR S 1127 Frontlab, Department of Neurology, AP_HP, Pitié-Salpêtrière University Hospital, Sorbonne Universities, Pierre et Marie Curie University, Paris 06, Paris, France
| | - Maria C Carrillo
- The Alzheimer's Association Division of Medical & Scientific Relations, Chicago, USA
| | - Jeffrey Cummings
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA
| | - Cliff R Jack
- Department of Radiology, Mayo Clinic, Rochester MN, USA
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Wolz R, Schwarz AJ, Gray KR, Yu P, Hill DLG. Enrichment of clinical trials in MCI due to AD using markers of amyloid and neurodegeneration. Neurology 2016; 87:1235-41. [PMID: 27558378 PMCID: PMC5035986 DOI: 10.1212/wnl.0000000000003126] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2016] [Accepted: 06/07/2016] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVE To investigate the effect of enriching mild cognitive impairment (MCI) clinical trials using combined markers of amyloid pathology and neurodegeneration. METHODS We evaluate an implementation of the recent National Institute for Aging-Alzheimer's Association (NIA-AA) diagnostic criteria for MCI due to Alzheimer disease (AD) as inclusion criteria in clinical trials and assess the effect of enrichment with amyloid (A+), neurodegeneration (N+), and their combination (A+N+) on the rate of clinical progression, required sample sizes, and estimates of trial time and cost. RESULTS Enrichment based on an individual marker (A+ or N+) substantially improves all assessed trial characteristics. Combined enrichment (A+N+) further improves these results with a reduction in required sample sizes by 45% to 60%, depending on the endpoint. CONCLUSIONS Operationalizing the NIA-AA diagnostic criteria for clinical trial screening has the potential to substantially improve the statistical power of trials in MCI due to AD by identifying a more rapidly progressing patient population.
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Affiliation(s)
- Robin Wolz
- From IXICO Plc (R.W., K.R.G., D.L.G.H.), London, UK; Department of Computing (R.W., K.R.G.,), Imperial College London, UK; Eli Lilly and Company (A.J.S., P.Y.), Indianapolis, IN; Department of Psychology and Brain Sciences (A.J.S.), Indiana University, Bloomington; and Department of Radiology and Imaging Sciences (A.J.S.), Indiana University School of Medicine, Indianapolis.
| | - Adam J Schwarz
- From IXICO Plc (R.W., K.R.G., D.L.G.H.), London, UK; Department of Computing (R.W., K.R.G.,), Imperial College London, UK; Eli Lilly and Company (A.J.S., P.Y.), Indianapolis, IN; Department of Psychology and Brain Sciences (A.J.S.), Indiana University, Bloomington; and Department of Radiology and Imaging Sciences (A.J.S.), Indiana University School of Medicine, Indianapolis
| | - Katherine R Gray
- From IXICO Plc (R.W., K.R.G., D.L.G.H.), London, UK; Department of Computing (R.W., K.R.G.,), Imperial College London, UK; Eli Lilly and Company (A.J.S., P.Y.), Indianapolis, IN; Department of Psychology and Brain Sciences (A.J.S.), Indiana University, Bloomington; and Department of Radiology and Imaging Sciences (A.J.S.), Indiana University School of Medicine, Indianapolis
| | - Peng Yu
- From IXICO Plc (R.W., K.R.G., D.L.G.H.), London, UK; Department of Computing (R.W., K.R.G.,), Imperial College London, UK; Eli Lilly and Company (A.J.S., P.Y.), Indianapolis, IN; Department of Psychology and Brain Sciences (A.J.S.), Indiana University, Bloomington; and Department of Radiology and Imaging Sciences (A.J.S.), Indiana University School of Medicine, Indianapolis
| | - Derek L G Hill
- From IXICO Plc (R.W., K.R.G., D.L.G.H.), London, UK; Department of Computing (R.W., K.R.G.,), Imperial College London, UK; Eli Lilly and Company (A.J.S., P.Y.), Indianapolis, IN; Department of Psychology and Brain Sciences (A.J.S.), Indiana University, Bloomington; and Department of Radiology and Imaging Sciences (A.J.S.), Indiana University School of Medicine, Indianapolis
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32
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Menéndez-González M, de Celis Alonso B, Salas-Pacheco J, Arias-Carrión O. Structural Neuroimaging of the Medial Temporal Lobe in Alzheimer's Disease Clinical Trials. J Alzheimers Dis 2016; 48:581-9. [PMID: 26402089 DOI: 10.3233/jad-150226] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Atrophy in the medial temporal lobe (MTA) is being used as a criterion to support a diagnosis of Alzheimer's disease (AD). There are several structural neuroimaging approaches for quantifying MTA, including semiquantitative visual rating scales, volumetry (3D), planimetry (2D), and linear measures (1D). Current applications of structural neuroimaging in Alzheimer's disease clinical trials (ADCTs) incorporate it as a tool for improving the selection of subjects for enrollment or for stratification, for tracking disease progression, or providing evidence of target engagement for new therapeutic agents. It may also be used as a surrogate marker, providing evidence of disease-modifying effects. However, despite the widespread use of volumetric magnetic resonance imaging (MRI) in ADCTs, there are some important challenges and limitations, such as difficulties in the interpretation of results, limitations in translating results into clinical practice, and reproducibility issues, among others. Solutions to these issues may arise from other methodologies that are able to link the results of volumetric MRI from trials with conventional MRIs performed in routine clinical practice (linear or planimetric methods). Also of potential benefit are automated volumetry, using indices for comparing the relative rate of atrophy of different regions instead of absolute rates of atrophy, and combining structural neuroimaging with other biomarkers. In this review, authors present the existing structural neuroimaging approaches for MTA quantification. They then discuss solutions to the limitations of the different techniques as well as the current challenges of the field. Finally, they discuss how the current advances in AD neuroimaging can help AD diagnosis.
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Affiliation(s)
- Manuel Menéndez-González
- Unidad de Neurología, Hospital Álvarez-Buylla, Mieres, Asturias, España.,Departamento de Morfología y Biología Celular, Universidad de Oviedo, Oviedo, Asturias, España.,Instituto de Neurociencias, Universidad de Oviedo, Oviedo, Asturias, España
| | - Benito de Celis Alonso
- Facultad de Ciencias Físico Matemáticas, Benemérita Universidad Autónoma de Puebla, Puebla, México.,Facultad para el Desarrollo, Carlos Sigüenza, Puebla, México
| | - José Salas-Pacheco
- Instituto de Investigación Científica, Universidad Juárez del Estado de Durango, Durango, México
| | - Oscar Arias-Carrión
- Unidad de Trastornos del Movimiento y Sueño (TMS), Hospital General Dr. Manuel Gea González/IFC-UNAM, México DF, México
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Jack CR, Barnes J, Bernstein MA, Borowski BJ, Brewer J, Clegg S, Dale AM, Carmichael O, Ching C, DeCarli C, Desikan RS, Fennema-Notestine C, Fjell AM, Fletcher E, Fox NC, Gunter J, Gutman BA, Holland D, Hua X, Insel P, Kantarci K, Killiany RJ, Krueger G, Leung KK, Mackin S, Maillard P, Malone IB, Mattsson N, McEvoy L, Modat M, Mueller S, Nosheny R, Ourselin S, Schuff N, Senjem ML, Simonson A, Thompson PM, Rettmann D, Vemuri P, Walhovd K, Zhao Y, Zuk S, Weiner M. Magnetic resonance imaging in Alzheimer's Disease Neuroimaging Initiative 2. Alzheimers Dement 2016; 11:740-56. [PMID: 26194310 DOI: 10.1016/j.jalz.2015.05.002] [Citation(s) in RCA: 109] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2015] [Revised: 04/28/2015] [Accepted: 05/05/2015] [Indexed: 01/18/2023]
Abstract
INTRODUCTION Alzheimer's Disease Neuroimaging Initiative (ADNI) is now in its 10th year. The primary objective of the magnetic resonance imaging (MRI) core of ADNI has been to improve methods for clinical trials in Alzheimer's disease (AD) and related disorders. METHODS We review the contributions of the MRI core from present and past cycles of ADNI (ADNI-1, -Grand Opportunity and -2). We also review plans for the future-ADNI-3. RESULTS Contributions of the MRI core include creating standardized acquisition protocols and quality control methods; examining the effect of technical features of image acquisition and analysis on outcome metrics; deriving sample size estimates for future trials based on those outcomes; and piloting the potential utility of MR perfusion, diffusion, and functional connectivity measures in multicenter clinical trials. DISCUSSION Over the past decade the MRI core of ADNI has fulfilled its mandate of improving methods for clinical trials in AD and will continue to do so in the future.
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Affiliation(s)
| | - Josephine Barnes
- Department of Neurodegenerative Disease, Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | | | | | - James Brewer
- Department of Neuroscience, University of California at San Diego, La Jolla, CA, USA
| | - Shona Clegg
- Department of Neurodegenerative Disease, Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Anders M Dale
- Department of Neuroscience, University of California at San Diego, La Jolla, CA, USA
| | - Owen Carmichael
- Department of Neurology, University of California at Davis, Davis, CA, USA
| | - Christopher Ching
- Department of Neurology, Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Marina del Rey, CA, USA
| | - Charles DeCarli
- Department of Neurology, University of California at Davis, Davis, CA, USA; Center for Neuroscience, University of California at Davis, Davis, CA, USA
| | - Rahul S Desikan
- Department of Radiology, University of California at San Diego, La Jolla, CA, USA
| | - Christine Fennema-Notestine
- Department of Radiology, University of California at San Diego, La Jolla, CA, USA; Department of Psychiatry, University of California at San Diego, La Jolla, CA, USA
| | - Anders M Fjell
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Evan Fletcher
- Department of Neurology, University of California at Davis, Davis, CA, USA; Center for Neuroscience, University of California at Davis, Davis, CA, USA
| | - Nick C Fox
- Department of Neurodegenerative Disease, Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Jeff Gunter
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Boris A Gutman
- Department of Neurology, Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Marina del Rey, CA, USA
| | - Dominic Holland
- Department of Neuroscience, University of California at San Diego, La Jolla, CA, USA
| | - Xue Hua
- Department of Neurology, Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Marina del Rey, CA, USA
| | - Philip Insel
- Department of Radiology and Biomedical Imaging, Center for Imaging of Neurodegenerative Diseases, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA
| | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Ron J Killiany
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, USA
| | | | - Kelvin K Leung
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Scott Mackin
- Department of Radiology and Biomedical Imaging, Center for Imaging of Neurodegenerative Diseases, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA; Department of Psychiatry, University of California at San Francisco, San Francisco, CA, USA
| | - Pauline Maillard
- Department of Neurology, University of California at Davis, Davis, CA, USA; Center for Neuroscience, University of California at Davis, Davis, CA, USA
| | - Ian B Malone
- Department of Neurodegenerative Disease, Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Niklas Mattsson
- Clinical Neurochemistry Laboratory, Institute of Neuroscience and Physiology, University of Gothenburg, Mölndal, Sweden
| | - Linda McEvoy
- Department of Radiology, University of California at San Diego, La Jolla, CA, USA
| | - Marc Modat
- Department of Neurodegenerative Disease, Dementia Research Centre, Institute of Neurology, University College London, London, UK; Translational Imaging Group, Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Susanne Mueller
- Department of Radiology and Biomedical Imaging, Center for Imaging of Neurodegenerative Diseases, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA; Department of Radiology, University of California at San Francisco, San Francisco, CA, USA
| | - Rachel Nosheny
- Department of Radiology and Biomedical Imaging, Center for Imaging of Neurodegenerative Diseases, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA; Department of Radiology, University of California at San Francisco, San Francisco, CA, USA
| | - Sebastien Ourselin
- Department of Neurodegenerative Disease, Dementia Research Centre, Institute of Neurology, University College London, London, UK; Translational Imaging Group, Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Norbert Schuff
- Department of Radiology and Biomedical Imaging, Center for Imaging of Neurodegenerative Diseases, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA; Department of Radiology, University of California at San Francisco, San Francisco, CA, USA
| | | | - Alix Simonson
- Department of Radiology and Biomedical Imaging, Center for Imaging of Neurodegenerative Diseases, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA
| | - Paul M Thompson
- Department of Neurology, Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Marina del Rey, CA, USA
| | - Dan Rettmann
- MR Applications and Workflow, GE Healthcare, Rochester, MN, USA
| | | | | | | | - Samantha Zuk
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Michael Weiner
- Department of Radiology and Biomedical Imaging, Center for Imaging of Neurodegenerative Diseases, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA; Department of Psychiatry, University of California at San Francisco, San Francisco, CA, USA; Department of Radiology, University of California at San Francisco, San Francisco, CA, USA; Department of Medicine, University of California at San Francisco, San Francisco, CA, USA; Department of Neurology, University of California at San Francisco, San Francisco, CA, USA
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Weiner MW, Veitch DP, Aisen PS, Beckett LA, Cairns NJ, Cedarbaum J, Donohue MC, Green RC, Harvey D, Jack CR, Jagust W, Morris JC, Petersen RC, Saykin AJ, Shaw L, Thompson PM, Toga AW, Trojanowski JQ. Impact of the Alzheimer's Disease Neuroimaging Initiative, 2004 to 2014. Alzheimers Dement 2016; 11:865-84. [PMID: 26194320 DOI: 10.1016/j.jalz.2015.04.005] [Citation(s) in RCA: 140] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2014] [Revised: 03/04/2015] [Accepted: 04/23/2015] [Indexed: 01/18/2023]
Abstract
INTRODUCTION The Alzheimer's Disease Neuroimaging Initiative (ADNI) was established in 2004 to facilitate the development of effective treatments for Alzheimer's disease (AD) by validating biomarkers for AD clinical trials. METHODS We searched for ADNI publications using established methods. RESULTS ADNI has (1) developed standardized biomarkers for use in clinical trial subject selection and as surrogate outcome measures; (2) standardized protocols for use across multiple centers; (3) initiated worldwide ADNI; (4) inspired initiatives investigating traumatic brain injury and post-traumatic stress disorder in military populations, and depression, respectively, as an AD risk factor; (5) acted as a data-sharing model; (6) generated data used in over 600 publications, leading to the identification of novel AD risk alleles, and an understanding of the relationship between biomarkers and AD progression; and (7) inspired other public-private partnerships developing biomarkers for Parkinson's disease and multiple sclerosis. DISCUSSION ADNI has made myriad impacts in its first decade. A competitive renewal of the project in 2015 would see the use of newly developed tau imaging ligands, and the continued development of recruitment strategies and outcome measures for clinical trials.
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Affiliation(s)
- Michael W Weiner
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA; Department of Radiology, University of California, San Francisco, San Francisco, CA, USA; Department of Medicine, University of California, San Francisco, San Francisco, CA, USA; Department of Psychiatry, University of California, San Francisco, San Francisco, CA, USA; Department of Neurology, University of California, San Francisco, San Francisco, CA, USA.
| | - Dallas P Veitch
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA
| | - Paul S Aisen
- Department of Neurosciences, University of California- San Diego, La Jolla, CA, USA
| | - Laurel A Beckett
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, Davis, CA, USA
| | - Nigel J Cairns
- Department of Neurology, Knight Alzheimer's Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA; Department of Neurology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Jesse Cedarbaum
- Neurology Early Clinical Development, Biogen Idec, Cambridge, MA, USA
| | - Michael C Donohue
- Division of Biostatistics and Bioinformatics, Department of Family Medicine and Public Health, University of California, San Diego, San Diego, CA, USA
| | - Robert C Green
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Danielle Harvey
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, Davis, CA, USA
| | | | - William Jagust
- Helen Wills Neuroscience Institute and the School of Public Health, University of California Berkeley, Berkeley, CA, USA
| | - John C Morris
- Department of Neurology, Washington University School of Medicine, Saint Louis, MO, USA
| | | | - Andrew J Saykin
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Leslie Shaw
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Paul M Thompson
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Marina Del Rey, CA, USA
| | - Arthur W Toga
- Laboratory of Neuroimaging, Institute of Neuroimaging and Informatics, Keck School of Medicine of University of Southern California Los Angeles, CA, USA
| | - John Q Trojanowski
- Department of Pathology and Laboratory Medicine, Center for Neurodegenerative Research, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Institute on Aging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Alzheimer's Disease Core Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Udall Parkinson's Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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Weiner MW, Veitch DP, Aisen PS, Beckett LA, Cairns NJ, Cedarbaum J, Green RC, Harvey D, Jack CR, Jagust W, Luthman J, Morris JC, Petersen RC, Saykin AJ, Shaw L, Shen L, Schwarz A, Toga AW, Trojanowski JQ. 2014 Update of the Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception. Alzheimers Dement 2016; 11:e1-120. [PMID: 26073027 DOI: 10.1016/j.jalz.2014.11.001] [Citation(s) in RCA: 203] [Impact Index Per Article: 25.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Revised: 04/18/2013] [Indexed: 01/18/2023]
Abstract
The Alzheimer's Disease Neuroimaging Initiative (ADNI) is an ongoing, longitudinal, multicenter study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer's disease (AD). The initial study, ADNI-1, enrolled 400 subjects with early mild cognitive impairment (MCI), 200 with early AD, and 200 cognitively normal elderly controls. ADNI-1 was extended by a 2-year Grand Opportunities grant in 2009 and by a competitive renewal, ADNI-2, which enrolled an additional 550 participants and will run until 2015. This article reviews all papers published since the inception of the initiative and summarizes the results to the end of 2013. The major accomplishments of ADNI have been as follows: (1) the development of standardized methods for clinical tests, magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers in a multicenter setting; (2) elucidation of the patterns and rates of change of imaging and CSF biomarker measurements in control subjects, MCI patients, and AD patients. CSF biomarkers are largely consistent with disease trajectories predicted by β-amyloid cascade (Hardy, J Alzheimer's Dis 2006;9(Suppl 3):151-3) and tau-mediated neurodegeneration hypotheses for AD, whereas brain atrophy and hypometabolism levels show predicted patterns but exhibit differing rates of change depending on region and disease severity; (3) the assessment of alternative methods of diagnostic categorization. Currently, the best classifiers select and combine optimum features from multiple modalities, including MRI, [(18)F]-fluorodeoxyglucose-PET, amyloid PET, CSF biomarkers, and clinical tests; (4) the development of blood biomarkers for AD as potentially noninvasive and low-cost alternatives to CSF biomarkers for AD diagnosis and the assessment of α-syn as an additional biomarker; (5) the development of methods for the early detection of AD. CSF biomarkers, β-amyloid 42 and tau, as well as amyloid PET may reflect the earliest steps in AD pathology in mildly symptomatic or even nonsymptomatic subjects and are leading candidates for the detection of AD in its preclinical stages; (6) the improvement of clinical trial efficiency through the identification of subjects most likely to undergo imminent future clinical decline and the use of more sensitive outcome measures to reduce sample sizes. Multimodal methods incorporating APOE status and longitudinal MRI proved most highly predictive of future decline. Refinements of clinical tests used as outcome measures such as clinical dementia rating-sum of boxes further reduced sample sizes; (7) the pioneering of genome-wide association studies that leverage quantitative imaging and biomarker phenotypes, including longitudinal data, to confirm recently identified loci, CR1, CLU, and PICALM and to identify novel AD risk loci; (8) worldwide impact through the establishment of ADNI-like programs in Japan, Australia, Argentina, Taiwan, China, Korea, Europe, and Italy; (9) understanding the biology and pathobiology of normal aging, MCI, and AD through integration of ADNI biomarker and clinical data to stimulate research that will resolve controversies about competing hypotheses on the etiopathogenesis of AD, thereby advancing efforts to find disease-modifying drugs for AD; and (10) the establishment of infrastructure to allow sharing of all raw and processed data without embargo to interested scientific investigators throughout the world.
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Affiliation(s)
- Michael W Weiner
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA; Department of Radiology, University of California, San Francisco, CA, USA; Department of Medicine, University of California, San Francisco, CA, USA; Department of Psychiatry, University of California, San Francisco, CA, USA; Department of Neurology, University of California, San Francisco, CA, USA.
| | - Dallas P Veitch
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA
| | - Paul S Aisen
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA
| | - Laurel A Beckett
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA
| | - Nigel J Cairns
- Knight Alzheimer's Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA; Department of Neurology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Jesse Cedarbaum
- Neurology Early Clinical Development, Biogen Idec, Cambridge, MA, USA
| | - Robert C Green
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Danielle Harvey
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA
| | | | - William Jagust
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, USA
| | - Johan Luthman
- Neuroscience Clinical Development, Neuroscience & General Medicine Product Creation Unit, Eisai Inc., Philadelphia, PA, USA
| | - John C Morris
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA
| | | | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Leslie Shaw
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Li Shen
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Adam Schwarz
- Tailored Therapeutics, Eli Lilly and Company, Indianapolis, IN, USA
| | - Arthur W Toga
- Laboratory of Neuroimaging, Institute of Neuroimaging and Informatics, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA
| | - John Q Trojanowski
- Institute on Aging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Alzheimer's Disease Core Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Udall Parkinson's Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Pathology and Laboratory Medicine, Center for Neurodegenerative Research, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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Clark DG, McLaughlin PM, Woo E, Hwang K, Hurtz S, Ramirez L, Eastman J, Dukes RM, Kapur P, DeRamus TP, Apostolova LG. Novel verbal fluency scores and structural brain imaging for prediction of cognitive outcome in mild cognitive impairment. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2016; 2:113-22. [PMID: 27239542 PMCID: PMC4879664 DOI: 10.1016/j.dadm.2016.02.001] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
INTRODUCTION The objective of this study was to assess the utility of novel verbal fluency scores for predicting conversion from mild cognitive impairment (MCI) to clinical Alzheimer's disease (AD). METHOD Verbal fluency lists (animals, vegetables, F, A, and S) from 107 MCI patients and 51 cognitively normal controls were transcribed into electronic text files and automatically scored with traditional raw scores and five types of novel scores computed using methods from machine learning and natural language processing. Additional scores were derived from structural MRI scans: region of interest measures of hippocampal and ventricular volumes and gray matter scores derived from performing ICA on measures of cortical thickness. Over 4 years of follow-up, 24 MCI patients converted to AD. Using conversion as the outcome variable, ensemble classifiers were constructed by training classifiers on the individual groups of scores and then entering predictions from the primary classifiers into regularized logistic regression models. Receiver operating characteristic curves were plotted, and the area under the curve (AUC) was measured for classifiers trained with five groups of available variables. RESULTS Classifiers trained with novel scores outperformed those trained with raw scores (AUC 0.872 vs 0.735; P < .05 by DeLong test). Addition of structural brain measurements did not improve performance based on novel scores alone. CONCLUSION The brevity and cost profile of verbal fluency tasks recommends their use for clinical decision making. The word lists generated are a rich source of information for predicting outcomes in MCI. Further work is needed to assess the utility of verbal fluency for early AD.
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Affiliation(s)
- David Glenn Clark
- Department of Neurology, Medical University of South Carolina, Charleston, SC, USA
- Department of Neurology, Ralph H. Johnson VA Medical Center, Charleston, SC, USA
| | - Paula M. McLaughlin
- Ontario Neurodegenerative Disease Research Initiative, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Ellen Woo
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Kristy Hwang
- Oakland University William Beaumont School of Medicine, Rochester, MI, USA
| | - Sona Hurtz
- Drexel University College of Medicine, Philadelphia, PA, USA
| | - Leslie Ramirez
- Drexel University College of Medicine, Philadelphia, PA, USA
| | - Jennifer Eastman
- Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Reshil-Marie Dukes
- Department of Neurology, Medical University of South Carolina, Charleston, SC, USA
| | - Puneet Kapur
- Department of Neurology, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Thomas P. DeRamus
- Department of Psychology, University of Alabama at Birmingham, Birmingham, AL, USA
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Abstract
BACKGROUND The aim of this study was to compare the performance and power of the best-established diagnostic biological markers as outcome measures for clinical trials in patients with mild cognitive impairment (MCI). METHODS Magnetic resonance imaging, F-18 fluorodeoxyglucose positron emission tomography markers, and Alzheimer's Disease Assessment Scale-cognitive subscale were compared in terms of effect size and statistical power over different follow-up periods in 2 MCI groups, selected from Alzheimer's Disease Neuroimaging Initiative data set based on cerebrospinal fluid (abnormal cerebrospinal fluid Aβ1-42 concentration-ABETA+) or magnetic resonance imaging evidence of Alzheimer disease (positivity to hippocampal atrophy-HIPPO+). Biomarkers progression was modeled through mixed effect models. Scaled slope was chosen as measure of effect size. Biomarkers power was estimated using simulation algorithms. RESULTS Seventy-four ABETA+ and 51 HIPPO+ MCI patients were included in the study. Imaging biomarkers of neurodegeneration, especially MR measurements, showed highest performance. For all biomarkers and both MCI groups, power increased with increasing follow-up time, irrespective of biomarker assessment frequency. CONCLUSION These findings provide information about biomarker enrichment and outcome measurements that could be employed to reduce MCI patient samples and treatment duration in future clinical trials.
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Kim D, Kim YS, Shin DW, Park CS, Kang JH. Harnessing Cerebrospinal Fluid Biomarkers in Clinical Trials for Treating Alzheimer's and Parkinson's Diseases: Potential and Challenges. J Clin Neurol 2016; 12:381-392. [PMID: 27819412 PMCID: PMC5063862 DOI: 10.3988/jcn.2016.12.4.381] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2015] [Revised: 03/28/2016] [Accepted: 03/29/2016] [Indexed: 01/04/2023] Open
Abstract
No disease-modifying therapies (DMT) for neurodegenerative diseases (NDs) have been established, particularly for Alzheimer's disease (AD) and Parkinson's disease (PD). It is unclear why candidate drugs that successfully demonstrate therapeutic effects in animal models fail to show disease-modifying effects in clinical trials. To overcome this hurdle, patients with homogeneous pathologies should be detected as early as possible. The early detection of AD patients using sufficiently tested biomarkers could demonstrate the potential usefulness of combining biomarkers with clinical measures as a diagnostic tool. Cerebrospinal fluid (CSF) biomarkers for NDs are being incorporated in clinical trials designed with the aim of detecting patients earlier, evaluating target engagement, collecting homogeneous patients, facilitating prevention trials, and testing the potential of surrogate markers relative to clinical measures. In this review we summarize the latest information on CSF biomarkers in NDs, particularly AD and PD, and their use in clinical trials. The large number of issues related to CSF biomarker measurements and applications has resulted in relatively few clinical trials on CSF biomarkers being conducted. However, the available CSF biomarker data obtained in clinical trials support the advantages of incorporating CSF biomarkers in clinical trials, even though the data have mostly been obtained in AD trials. We describe the current issues with and ongoing efforts for the use of CSF biomarkers in clinical trials and the plans to harness CSF biomarkers for the development of DMT and clinical routines. This effort requires nationwide, global, and multidisciplinary efforts in academia, industry, and regulatory agencies to facilitate a new era.
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Affiliation(s)
- Dana Kim
- Department of Pharmacology and Medicinal Toxicology Research Center, Incheon, Korea.,Hypoxia-Related Diseases Research Center, Inha University School of Medicine, Incheon, Korea
| | - Young Sam Kim
- Department of Thoracic Surgery, Inha University Hospital, Inha University, Incheon, Korea
| | - Dong Wun Shin
- Department of Emergency Medicine, Inje University Ilsan Paik Hospital, Goyang, Korea
| | - Chang Shin Park
- Department of Pharmacology and Medicinal Toxicology Research Center, Incheon, Korea.,Hypoxia-Related Diseases Research Center, Inha University School of Medicine, Incheon, Korea
| | - Ju Hee Kang
- Department of Pharmacology and Medicinal Toxicology Research Center, Incheon, Korea.,Hypoxia-Related Diseases Research Center, Inha University School of Medicine, Incheon, Korea.
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Larvie M, Fischl B. Volumetric and fiber-tracing MRI methods for gray and white matter. HANDBOOK OF CLINICAL NEUROLOGY 2016; 135:39-60. [PMID: 27432659 DOI: 10.1016/b978-0-444-53485-9.00003-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Magnetic resonance imaging (MRI) is capable of generating high-resolution brain images with fine anatomic detail and unique tissue contrasts that reveal structures that are not visible to the eye. Sharply defined gray- and white-matter interfaces allow for quantitative anatomic analysis that can be accurately performed with largely automated segmentation methods. In an analogous fashion, diffusion MRI in the brain provides structural information based on contrasts derived from the diffusivity of water in brain tissue, which can highlight the orientation of neuronal axons. Also using largely automated methods, diffusion MRI can be used to generate models of white-matter tracts throughout the brain, a method known as tractography, as well as characterize the microstructural integrity of neuronal axons. Tractographic analysis has helped to define connectivity in the brain that powerfully informs understanding of brain function, and, together with other diffusion metrics, is useful in evaluation of the normal and diseased brain. The quantitative methods of brain segmentation, tractography, and diffusion MRI extend MRI into a realm beyond visual inspection and provide otherwise unachievable sensitivity and specificity in the analysis of brain structure and function.
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Affiliation(s)
- Mykol Larvie
- Divisions of Neuroradiology and Nuclear Medicine and Molecular Imaging, Massachusetts General Hospital, Boston, MA, USA.
| | - Bruce Fischl
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
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Saykin AJ, Shen L, Yao X, Kim S, Nho K, Risacher SL, Ramanan VK, Foroud TM, Faber KM, Sarwar N, Munsie LM, Hu X, Soares HD, Potkin SG, Thompson PM, Kauwe JSK, Kaddurah-Daouk R, Green RC, Toga AW, Weiner MW. Genetic studies of quantitative MCI and AD phenotypes in ADNI: Progress, opportunities, and plans. Alzheimers Dement 2015; 11:792-814. [PMID: 26194313 PMCID: PMC4510473 DOI: 10.1016/j.jalz.2015.05.009] [Citation(s) in RCA: 202] [Impact Index Per Article: 22.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2015] [Revised: 05/08/2015] [Accepted: 05/08/2015] [Indexed: 01/01/2023]
Abstract
INTRODUCTION Genetic data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) have been crucial in advancing the understanding of Alzheimer's disease (AD) pathophysiology. Here, we provide an update on sample collection, scientific progress and opportunities, conceptual issues, and future plans. METHODS Lymphoblastoid cell lines and DNA and RNA samples from blood have been collected and banked, and data and biosamples have been widely disseminated. To date, APOE genotyping, genome-wide association study (GWAS), and whole exome and whole genome sequencing data have been obtained and disseminated. RESULTS ADNI genetic data have been downloaded thousands of times, and >300 publications have resulted, including reports of large-scale GWAS by consortia to which ADNI contributed. Many of the first applications of quantitative endophenotype association studies used ADNI data, including some of the earliest GWAS and pathway-based studies of biospecimen and imaging biomarkers, as well as memory and other clinical/cognitive variables. Other contributions include some of the first whole exome and whole genome sequencing data sets and reports in healthy controls, mild cognitive impairment, and AD. DISCUSSION Numerous genetic susceptibility and protective markers for AD and disease biomarkers have been identified and replicated using ADNI data and have heavily implicated immune, mitochondrial, cell cycle/fate, and other biological processes. Early sequencing studies suggest that rare and structural variants are likely to account for significant additional phenotypic variation. Longitudinal analyses of transcriptomic, proteomic, metabolomic, and epigenomic changes will also further elucidate dynamic processes underlying preclinical and prodromal stages of disease. Integration of this unique collection of multiomics data within a systems biology framework will help to separate truly informative markers of early disease mechanisms and potential novel therapeutic targets from the vast background of less relevant biological processes. Fortunately, a broad swath of the scientific community has accepted this grand challenge.
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Affiliation(s)
- Andrew J Saykin
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA.
| | - Li Shen
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA; Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Xiaohui Yao
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; School of Informatics and Computing, Indiana University, Purdue University - Indianapolis, Indianapolis, IN, USA
| | - Sungeun Kim
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Kwangsik Nho
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Shannon L Risacher
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Vijay K Ramanan
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Tatiana M Foroud
- Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Kelley M Faber
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | | | | | - Xiaolan Hu
- Bristol-Myers Squibb, Wallingford, CT, USA
| | | | - Steven G Potkin
- Department of Psychiatry and Human Behavior, University of California - Irvine, Irvine, CA, USA
| | - Paul M Thompson
- Department of Neurology, Keck School of Medicine of USC, University of Southern California, Marina del Rey, CA, USA; Imaging Genetics Center, Keck School of Medicine of USC, University of Southern California, Marina del Rey, CA, USA
| | - John S K Kauwe
- Department of Biology, Brigham Young University, Provo, UT, USA; Department of Neuroscience, Brigham Young University, Provo, UT, USA
| | - Rima Kaddurah-Daouk
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA; Duke Institute for Brain Sciences, Duke University, Durham, NC, USA
| | - Robert C Green
- Partners Center for Personalized Genetic Medicine, Boston, MA, USA; Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Arthur W Toga
- Laboratory of Neuroimaging, Institute for Neuroimaging and Neuroinformatics, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Michael W Weiner
- Department of Radiology, University of California-San Francisco, San Francisco, CA, USA; Department of Medicine, University of California-San Francisco, San Francisco, CA, USA; Department of Psychiatry, University of California-San Francisco, San Francisco, CA, USA; Center for Imaging of Neurodegenerative Diseases, San Francisco VA Medical Center, San Francisco, CA, USA
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Cheng H, Shang Y, Jiang L, Shi TL, Wang L. The peroxisome proliferators activated receptor-gamma agonists as therapeutics for the treatment of Alzheimer's disease and mild-to-moderate Alzheimer's disease: a meta-analysis. Int J Neurosci 2015; 126:299-307. [PMID: 26001206 DOI: 10.3109/00207454.2015.1015722] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Alzheimer's disease (AD) is a devastating neurodegenerative disease and there is no effective therapy for it. Peroxisome proliferators activated receptor-gamma (PPAR-γ) agonists is a promising therapeutic approach for AD and has been widely studied recently, but no consensus was available up to now. To clarify this point, a meta-analysis was performed. We searched MEDLINE, EMBASE, Cochrane Central database, PUBMED, Springer Link database, SDOS database, CBM, CNKI and Wan fang database by December 2014. Standardized mean difference (SMD), relative risk (RR) and 95% confidence interval (CI) were calculated to assess the strength of the novel therapeutics for AD and mild-to-moderate AD. A total of nine studies comprising 1314 patients and 1311 controls were included in the final meta-analysis. We found the effect of PPAR-γ agonists on Alzheimer's Disease Assessment Scale - Cognitive Subscale (ADAS-cog) scores by using STATA software. There was no evidence for obvious publication bias in the overall meta-analysis. There is insufficient evidence of statistically incognition of AD and mild-to-moderate AD patients have been improved who were treated with PPAR-γ agonists in our research. However, PPAR-γ agonists may be a promising therapeutic approach in future, especially pioglitazone, with large-scale randomized controlled trials to confirm.
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Affiliation(s)
- Huawei Cheng
- a Department of Pharmacy, Anhui Cancer Hospital , Hefei , China
| | - Yuping Shang
- a Department of Pharmacy, Anhui Cancer Hospital , Hefei , China
| | - Ling Jiang
- b Department of Pharmacy, Anhui Provincial Hospital , Hefei , China
| | - Tian-lu Shi
- b Department of Pharmacy, Anhui Provincial Hospital , Hefei , China
| | - Lin Wang
- c Clinical Laboratory, the First Affiliated Hospital of Anhui University of Chinese Medicine , Hefei , China
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Horr T, Messinger-Rapport B, Pillai JA. Systematic review of strengths and limitations of randomized controlled trials for non-pharmacological interventions in mild cognitive impairment: focus on Alzheimer's disease. J Nutr Health Aging 2015; 19:141-53. [PMID: 25651439 DOI: 10.1007/s12603-014-0565-6] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
BACKGROUND Non-pharmacological interventions may improve cognition and quality of life, reduce disruptive behaviors, slow progression from Mild Cognitive Impairment (MCI) to dementia, and delay institutionalization. It is important to look at their trial designs as well as outcomes to understand the state of the evidence supporting non-pharmacological interventions in Alzheimer's disease (AD). An analysis of trial design strengths and limitations may help researchers clarify treatment effect and design future studies of non-pharmacological interventions for MCI related to AD. METHODS A systematic review of the methodology of Randomized Controlled Trials (RCTs) targeting physical activity, cognitive interventions, and socialization among subjects with MCI in AD reported until March 2014 was undertaken. The primary outcome was CONSORT 2010 reporting quality. Secondary outcomes were qualitative assessments of specific methodology problems. RESULTS 23 RCT studies met criteria for this review. Eight focused on physical activity, fourteen on cognitive interventions, and one on the effects of socialization. Most studies found a benefit with the intervention compared to control. CONSORT reporting quality of physical activity interventions was higher than that of cognitive interventions. Reporting quality of recent studies was higher than older studies, particularly with respect to sample size, control characteristics, and methodology of intervention training and delivery. However, the heterogeneity of subjects identified as having MCI and variability in interventions and outcomes continued to limit generalizability. CONCLUSIONS The role for non-pharmacological interventions targeting MCI is promising. Future studies of RCTs for non-pharmacological interventions targeting MCI related to AD may benefit by addressing design limitations.
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Affiliation(s)
- T Horr
- J.A. Pillai, MBBS, PhD, Staff Neurologist, Lou Ruvo Center for Brain Health, Assistant Professor of Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, 9500 Euclid Ave / U10, Cleveland, OH 44195, Tel: 216 636 9467, Fax: 216 445 7013, E-mail:
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Nosheny RL, Insel PS, Truran D, Schuff N, Jack CR, Aisen PS, Shaw LM, Trojanowski JQ, Weiner MW. Variables associated with hippocampal atrophy rate in normal aging and mild cognitive impairment. Neurobiol Aging 2015; 36:273-82. [PMID: 25175807 PMCID: PMC5832349 DOI: 10.1016/j.neurobiolaging.2014.07.036] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2013] [Revised: 07/24/2014] [Accepted: 07/26/2014] [Indexed: 01/18/2023]
Abstract
The goal of this study was to identify factors contributing to hippocampal atrophy rate (HAR) in clinically normal older adults (NC) and participants with mild cognitive impairment (MCI). Longitudinal HAR was measured on T1-weighted magnetic resonance imaging, and the contribution of age, gender, apolipoprotein E (ApoE) ε4 status, intracranial volume, white matter lesions, and β-amyloid (Aβ) levels to HAR was determined using linear regression. Age-related effects of HAR were compared in Aβ positive (Aβ+) and Aβ negative (Aβ-) participants. Age and Aβ levels had independent effects on HAR in NC, whereas gender, ApoE ε4 status, and Aβ levels were associated with HAR in MCI. In multivariable models, Aβ levels were associated with HAR in NC; ApoE ε4 and Aβ levels were associated with HAR in MCI. In MCI, age was a stronger predictor of HAR in Aβ- versus Aβ+ participants. HAR was higher in Aβ+ participants, but most of the HAR was because of factors other than Aβ status. Age-related effects on HAR did not differ between NC versus MCI participants with the same Aβ status. Therefore, we conclude that even when accounting for other covariates, Aβ status, and not age, is a significant predictor of HAR; and that most of the HAR is not accounted for by Aβ status in either NC or MCI.
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Affiliation(s)
- Rachel L Nosheny
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA.
| | - Philip S Insel
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA
| | - Diana Truran
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA
| | - Norbert Schuff
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA
| | | | - Paul S Aisen
- Department of Neurosciences, University of California San Diego School of Medicine, La Jolla, CA, USA
| | - Leslie M Shaw
- Department of Pathology & Laboratory Medicine, Institute on Aging, Center for Neurodegenerative Disease Research, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - John Q Trojanowski
- Department of Pathology & Laboratory Medicine, Institute on Aging, Center for Neurodegenerative Disease Research, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Michael W Weiner
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA; Department of Radiology and Biomedical Imaging, University of California, CA, USA
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Kang JH, Ryoo NY, Shin DW, Trojanowski JQ, Shaw LM. Role of cerebrospinal fluid biomarkers in clinical trials for Alzheimer's disease modifying therapies. THE KOREAN JOURNAL OF PHYSIOLOGY & PHARMACOLOGY : OFFICIAL JOURNAL OF THE KOREAN PHYSIOLOGICAL SOCIETY AND THE KOREAN SOCIETY OF PHARMACOLOGY 2014; 18:447-56. [PMID: 25598657 PMCID: PMC4296032 DOI: 10.4196/kjpp.2014.18.6.447] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/19/2014] [Revised: 09/02/2014] [Accepted: 10/07/2014] [Indexed: 12/27/2022]
Abstract
Until now, a disease-modifying therapy (DMT) that has an ability to slow or arrest Alzheimer's disease (AD) progression has not been developed, and all clinical trials involving AD patients enrolled by clinical assessment alone also have not been successful. Given the growing consensus that the DMT is likely to require treatment initiation well before full-blown dementia emerges, the early detection of AD will provide opportunities to successfully identify new drugs that slow the course of AD pathology. Recent advances in early detection of AD and prediction of progression of the disease using various biomarkers, including cerebrospinal fluid (CSF) Aβ1-42, total tau and p-tau181 levels, and imagining biomarkers, are now being actively integrated into the designs of AD clinical trials. In terms of therapeutic mechanisms, monitoring these markers may be helpful for go/no-go decision making as well as surrogate markers for disease severity or progression. Furthermore, CSF biomarkers can be used as a tool to enrich patients for clinical trials with prospect of increasing statistical power and reducing costs in drug development. However, the standardization of technical aspects of analysis of these biomarkers is an essential prerequisite to the clinical uses. To accomplish this, global efforts are underway to standardize CSF biomarker measurements and a quality control program supported by the Alzheimer's Association. The current review summarizes therapeutic targets of developing drugs in AD pathophysiology, and provides the most recent advances in the
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Affiliation(s)
- Ju-Hee Kang
- Department of Pharmacology and Clinical Pharmacology, Inha University School of Medicine, Incheon 400-712, Korea. ; Hypoxia-related Disease Research Center, Inha University School of Medicine, Incheon 400-712, Korea. ; Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Na-Young Ryoo
- Hypoxia-related Disease Research Center, Inha University School of Medicine, Incheon 400-712, Korea. ; Department of Anatomy, Inha University School of Medicine, Incheon 400-712, Korea
| | - Dong Wun Shin
- Department of Emergency Medicine, Inje University Ilsan Paik Hospital, Ilsan 411-706, Korea
| | - John Q Trojanowski
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA. ; Institute on Aging and Center for Neurodegenerative Disease Research, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Leslie M Shaw
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA. ; Institute on Aging and Center for Neurodegenerative Disease Research, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
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Do patients with young onset Alzheimer's disease deteriorate faster than those with late onset Alzheimer's disease? A review of the literature. Int Psychogeriatr 2014; 26:1945-53. [PMID: 24989902 DOI: 10.1017/s1041610214001173] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
BACKGROUND Young onset Alzheimer's disease (YOAD; onset before 65 years of age) is thought to have a more rapid course and increased rate of progression compared to late onset Alzheimer's disease (LOAD). This assumption appears partly due to important clinical, structural, neuropathological, and neurochemical differences suggesting YOAD is a separate entity to LOAD. The aim in this review was to systematically identify and examine appropriate studies comparing rate of cognitive decline between patients with YOAD and patients with LOAD. METHODS A computer-based literature search was initially undertaken, followed by citation tracking and search of related papers. Primary research studies specifically focused on the rate of cognitive decline between people with YOAD and LOAD were included. Studies were described, critically analyzed, presented, and discussed in the review. RESULTS Four studies were included, of which three were longitudinal and one was a case-control study. Three of the included studies found a faster rate of decline in patients with YOAD, and one found no difference in rate of decline between the two groups. CONCLUSIONS The findings of the review are mixed and conflicting, and limited by the heterogeneity of the included studies. There is a need for future research to design systematic studies that include sufficient sample sizes and follow-up periods, and control for possible confounding factors such as education level, baseline cognitive impairment, and vascular risk factors. This will help to validate the findings so far and improve our understanding of the rate of cognitive decline in people with YOAD and LOAD.
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Guo S, Getsios D, Revankar N, Xu P, Thompson G, Bobula J, Lacey L, Gaudig M. Evaluating disease-modifying agents: a simulation framework for Alzheimer's disease. PHARMACOECONOMICS 2014; 32:1129-1139. [PMID: 25124747 DOI: 10.1007/s40273-014-0203-5] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
BACKGROUND Considerable advances have been made in modeling Alzheimer's disease (AD), with a move towards individual-level rather than cohort models and simulations that consider multiple dimensions when evaluating disease severity. However, the possibility that disease-modifying agents (DMAs) may emerge requires an update of existing modeling frameworks. OBJECTIVES The aim of this study was to develop a simulation allowing for economic evaluation of DMAs in AD. METHODS The model was developed based on a previously published, well-validated, discrete event simulation which measures disease severity on the basis of cognition, behaviour, and function, and captures the interrelated changes in these measures for individuals. The updated model adds one more domain, patient dependence, in addition to cognition, behaviour, and function to better characterize disease severity. Furthermore, the model was modified to have greater flexibility in assessing the impact of various important assumptions, such as the long-term effectiveness of DMAs and their impact on survival, on model outcomes. A validation analysis was performed to examine how well the model predicted change in disease severity among patients not receiving DMA treatment by comparing model results to those observed in two recent phase III clinical trials of bapineuzumab. In addition, various hypothetical scenarios were tested to demonstrate the improved features of the model. RESULTS Validation results show that the model closely predicts the mean changes in disease severity over 18 months. Results from different hypothetical scenarios show that the model allows for credible assessment of those major uncertainties surrounding the long-term effectiveness of DMAs, including the potential impact of improved survival with DMA treatment. They also indicate that varying these assumptions could have a major impact on the value of DMAs. CONCLUSIONS The updated economic model has good predictive power, but validation against longer-term outcomes is still needed. Our analyses also demonstrate the importance of designing a model with sufficient flexibility such that the model allows for assessment of the impact of key sources of uncertainty on the value of DMAs.
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Affiliation(s)
- Shien Guo
- Evidera, 430 Bedford Street, Suite 300, Lexington Office Park, Lexington, MA, 02420, USA,
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Insulin resistance in Alzheimer's disease. Neurobiol Dis 2014; 72 Pt A:92-103. [PMID: 25237037 DOI: 10.1016/j.nbd.2014.09.001] [Citation(s) in RCA: 82] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2014] [Revised: 09/02/2014] [Accepted: 09/05/2014] [Indexed: 12/16/2022] Open
Abstract
Insulin is a key hormone regulating metabolism. Insulin binding to cell surface insulin receptors engages many signaling intermediates operating in parallel and in series to control glucose, energy, and lipids while also regulating mitogenesis and development. Perturbations in the function of any of these intermediates, which occur in a variety of diseases, cause reduced sensitivity to insulin and insulin resistance with consequent metabolic dysfunction. Chronic inflammation ensues which exacerbates compromised metabolic homeostasis. Since insulin has a key role in learning and memory as well as directly regulating ERK, a kinase required for the type of learning and memory compromised in early Alzheimer's disease (AD), insulin resistance has been identified as a major risk factor for the onset of AD. Animal models of AD or insulin resistance or both demonstrate that AD pathology and impaired insulin signaling form a reciprocal relationship. Of note are human and animal model studies geared toward improving insulin resistance that have led to the identification of the nuclear receptor and transcription factor, peroxisome proliferator-activated receptor gamma (PPARγ) as an intervention tool for early AD. Strategic targeting of alternate nodes within the insulin signaling network has revealed disease-stage therapeutic windows in animal models that coalesce with previous and ongoing clinical trial approaches. Thus, exploiting the connection between insulin resistance and AD provides powerful opportunities to delineate therapeutic interventions that slow or block the pathogenesis of AD.
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Maarouf CL, Kokjohn TA, Walker DG, Whiteside CM, Kalback WM, Whetzel A, Sue LI, Serrano G, Jacobson SA, Sabbagh MN, Reiman EM, Beach TG, Roher AE. Biochemical assessment of precuneus and posterior cingulate gyrus in the context of brain aging and Alzheimer's disease. PLoS One 2014; 9:e105784. [PMID: 25166759 PMCID: PMC4148328 DOI: 10.1371/journal.pone.0105784] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2014] [Accepted: 07/24/2014] [Indexed: 12/12/2022] Open
Abstract
Defining the biochemical alterations that occur in the brain during “normal” aging is an important part of understanding the pathophysiology of neurodegenerative diseases and of distinguishing pathological conditions from aging-associated changes. Three groups were selected based on age and on having no evidence of neurological or significant neurodegenerative disease: 1) young adult individuals, average age 26 years (n = 9); 2) middle-aged subjects, average age 59 years (n = 5); 3) oldest-old individuals, average age 93 years (n = 6). Using ELISA and Western blotting methods, we quantified and compared the levels of several key molecules associated with neurodegenerative disease in the precuneus and posterior cingulate gyrus, two brain regions known to exhibit early imaging alterations during the course of Alzheimer’s disease. Our experiments revealed that the bioindicators of emerging brain pathology remained steady or decreased with advancing age. One exception was S100B, which significantly increased with age. Along the process of aging, neurofibrillary tangle deposition increased, even in the absence of amyloid deposition, suggesting the presence of amyloid plaques is not obligatory for their development and that limited tangle density is a part of normal aging. Our study complements a previous assessment of neuropathology in oldest-old subjects, and within the limitations of the small number of individuals involved in the present investigation, it adds valuable information to the molecular and structural heterogeneity observed along the course of aging and dementia. This work underscores the need to examine through direct observation how the processes of amyloid deposition unfold or change prior to the earliest phases of dementia emergence.
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Affiliation(s)
- Chera L. Maarouf
- The Longtine Center for Neurodegenerative Biochemistry, Banner Sun Health Research Institute, Sun City, Arizona, United States of America
| | - Tyler A. Kokjohn
- Department of Microbiology, Midwestern University, Glendale, Arizona, United States of America
| | - Douglas G. Walker
- Laboratory of Neuroinflammation, Banner Sun Health Research Institute, Sun City, Arizona, United States of America
| | - Charisse M. Whiteside
- The Longtine Center for Neurodegenerative Biochemistry, Banner Sun Health Research Institute, Sun City, Arizona, United States of America
| | - Walter M. Kalback
- The Longtine Center for Neurodegenerative Biochemistry, Banner Sun Health Research Institute, Sun City, Arizona, United States of America
| | - Alexis Whetzel
- Laboratory of Neuroinflammation, Banner Sun Health Research Institute, Sun City, Arizona, United States of America
| | - Lucia I. Sue
- Civin Laboratory for Neuropathology, Banner Sun Health Research Institute, Sun City, Arizona, United States of America
| | - Geidy Serrano
- Civin Laboratory for Neuropathology, Banner Sun Health Research Institute, Sun City, Arizona, United States of America
| | - Sandra A. Jacobson
- Cleo Roberts Center for Clinical Research, Banner Sun Health Research Institute, Sun City, Arizona, United States of America
| | - Marwan N. Sabbagh
- Cleo Roberts Center for Clinical Research, Banner Sun Health Research Institute, Sun City, Arizona, United States of America
| | - Eric M. Reiman
- Banner Alzheimer’s Institute, Phoenix, Arizona, United States of America
| | - Thomas G. Beach
- Civin Laboratory for Neuropathology, Banner Sun Health Research Institute, Sun City, Arizona, United States of America
| | - Alex E. Roher
- The Longtine Center for Neurodegenerative Biochemistry, Banner Sun Health Research Institute, Sun City, Arizona, United States of America
- * E-mail:
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Zhang S, Smailagic N, Hyde C, Noel‐Storr AH, Takwoingi Y, McShane R, Feng J. (11)C-PIB-PET for the early diagnosis of Alzheimer's disease dementia and other dementias in people with mild cognitive impairment (MCI). Cochrane Database Syst Rev 2014; 2014:CD010386. [PMID: 25052054 PMCID: PMC6464750 DOI: 10.1002/14651858.cd010386.pub2] [Citation(s) in RCA: 66] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
BACKGROUND According to the latest revised National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer's Disease and Related Disorders Association (now known as the Alzheimer's Association) (NINCDS-ADRDA) diagnostic criteria for Alzheimer's disease dementia, the confidence in diagnosing mild cognitive impairment (MCI) due to Alzheimer's disease dementia is raised with the application of imaging biomarkers. These tests, added to core clinical criteria, might increase the sensitivity or specificity of a testing strategy. However, the accuracy of biomarkers in the diagnosis of Alzheimer's disease dementia and other dementias has not yet been systematically evaluated. A formal systematic evaluation of the sensitivity, specificity, and other properties of positron emission tomography (PET) imaging with the (11)C-labelled Pittsburgh Compound-B ((11)C-PIB) ligand was performed. OBJECTIVES To determine the diagnostic accuracy of the (11)C- PIB-PET scan for detecting participants with MCI at baseline who will clinically convert to Alzheimer's disease dementia or other forms of dementia over a period of time. SEARCH METHODS The most recent search for this review was performed on 12 January 2013. We searched MEDLINE (OvidSP), EMBASE (OvidSP), BIOSIS Previews (ISI Web of Knowledge), Web of Science and Conference Proceedings (ISI Web of Knowledge), PsycINFO (OvidSP), and LILACS (BIREME). We also requested a search of the Cochrane Register of Diagnostic Test Accuracy Studies (managed by the Cochrane Renal Group).No language or date restrictions were applied to the electronic searches and methodological filters were not used so as to maximise sensitivity. SELECTION CRITERIA We selected studies that had prospectively defined cohorts with any accepted definition of MCI with baseline (11)C-PIB-PET scan. In addition, we only selected studies that applied a reference standard for Alzheimer's dementia diagnosis for example NINCDS-ADRDA or Diagnostic and Statistical Manual of Mental Disorders-IV (DSM-IV) criteria. DATA COLLECTION AND ANALYSIS We screened all titles generated by electronic database searches. Two review authors independently assessed the abstracts of all potentially relevant studies. The identified full papers were assessed for eligibility and data were extracted to create two by two tables. Two independent assessors performed quality assessment using the QUADAS 2 tool. We used the hierarchical summary receiver operating characteristic (ROC) model to produce a summary ROC curve. MAIN RESULTS Conversion from MCI to Alzheimer's disease dementia was evaluated in nine studies. The quality of the evidence was limited. Of the 274 participants included in the meta-analysis, 112 developed Alzheimer's dementia. Based on the nine included studies, the median proportion converting was 34%. The studies varied markedly in how the PIB scans were done and interpreted.The sensitivities were between 83% and 100% while the specificities were between 46% and 88%. Because of the variation in thresholds and measures of (11)C-PIB amyloid retention, we did not calculate summary sensitivity and specificity. Although subject to considerable uncertainty, to illustrate the potential strengths and weaknesses of (11)C-PIB-PET scans we estimated from the fitted summary ROC curve that the sensitivity was 96% (95% confidence interval (CI) 87 to 99) at the included study median specificity of 58%. This equated to a positive likelihood ratio of 2.3 and a negative likelihood ratio of 0.07. Assuming a typical conversion rate of MCI to Alzheimer's dementia of 34%, for every 100 PIB scans one person with a negative scan would progress and 28 with a positive scan would not actually progress to Alzheimer's dementia.There were limited data for formal investigation of heterogeneity. We performed two sensitivity analyses to assess the influence of type of reference standard and the use of a pre-specified threshold. There was no effect on our findings. AUTHORS' CONCLUSIONS Although the good sensitivity achieved in some included studies is promising for the value of (11)C-PIB-PET, given the heterogeneity in the conduct and interpretation of the test and the lack of defined thresholds for determination of test positivity, we cannot recommend its routine use in clinical practice.(11)C-PIB-PET biomarker is a high cost investigation, therefore it is important to clearly demonstrate its accuracy and standardise the process of the (11)C-PIB diagnostic modality prior to it being widely used.
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Affiliation(s)
- Shuo Zhang
- China Medical UniversityDepartment of Neurology, Shengjing Hospital36 Shanhao StreetShenyangLiaoningChina110004
| | - Nadja Smailagic
- University of CambridgeInstitute of Public HealthForvie SiteRobinson WayCambridgeUKCB2 0SR
| | - Chris Hyde
- University of Exeter Medical School, University of ExeterInstitute of Health ResearchVeysey BuildingSalmon Pool LaneExeterUKEX2 4SG
| | - Anna H Noel‐Storr
- University of OxfordRadcliffe Department of MedicineRoom 4401c (4th Floor)John Radcliffe Hospital, HeadingtonOxfordUKOX3 9DU
| | - Yemisi Takwoingi
- University of BirminghamPublic Health, Epidemiology and BiostatisticsEdgbastonBirminghamUKB15 2TT
| | - Rupert McShane
- University of OxfordRadcliffe Department of MedicineRoom 4401c (4th Floor)John Radcliffe Hospital, HeadingtonOxfordUKOX3 9DU
| | - Juan Feng
- Shengjing Hospital, China Medical UniversityDepartment of Neurology36 Shanhao StreetShenyangChina110004
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Clark DG, Kapur P, Geldmacher DS, Brockington JC, Harrell L, DeRamus TP, Blanton PD, Lokken K, Nicholas AP, Marson DC. Latent information in fluency lists predicts functional decline in persons at risk for Alzheimer disease. Cortex 2014; 55:202-18. [PMID: 24556551 PMCID: PMC4039569 DOI: 10.1016/j.cortex.2013.12.013] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2013] [Revised: 11/05/2013] [Accepted: 12/25/2013] [Indexed: 01/24/2023]
Abstract
OBJECTIVE We constructed random forest classifiers employing either the traditional method of scoring semantic fluency word lists or new methods. These classifiers were then compared in terms of their ability to diagnose Alzheimer disease (AD) or to prognosticate among individuals along the continuum from cognitively normal (CN) through mild cognitive impairment (MCI) to AD. METHOD Semantic fluency lists from 44 cognitively normal elderly individuals, 80 MCI patients, and 41 AD patients were transcribed into electronic text files and scored by four methods: traditional raw scores, clustering and switching scores, "generalized" versions of clustering and switching, and a method based on independent components analysis (ICA). Random forest classifiers based on raw scores were compared to "augmented" classifiers that incorporated newer scoring methods. Outcome variables included AD diagnosis at baseline, MCI conversion, increase in Clinical Dementia Rating-Sum of Boxes (CDR-SOB) score, or decrease in Financial Capacity Instrument (FCI) score. Receiver operating characteristic (ROC) curves were constructed for each classifier and the area under the curve (AUC) was calculated. We compared AUC between raw and augmented classifiers using Delong's test and assessed validity and reliability of the augmented classifier. RESULTS Augmented classifiers outperformed classifiers based on raw scores for the outcome measures AD diagnosis (AUC .97 vs. .95), MCI conversion (AUC .91 vs. .77), CDR-SOB increase (AUC .90 vs. .79), and FCI decrease (AUC .89 vs. .72). Measures of validity and stability over time support the use of the method. CONCLUSION Latent information in semantic fluency word lists is useful for predicting cognitive and functional decline among elderly individuals at increased risk for developing AD. Modern machine learning methods may incorporate latent information to enhance the diagnostic value of semantic fluency raw scores. These methods could yield information valuable for patient care and clinical trial design with a relatively small investment of time and money.
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Affiliation(s)
- D G Clark
- Birmingham VA Medical Center, USA; Department of Neurology, University of Alabama at Birmingham, USA.
| | - P Kapur
- Department of Biomedical Sciences, Georgia Health Science University, USA
| | - D S Geldmacher
- Department of Neurology, University of Alabama at Birmingham, USA
| | - J C Brockington
- Department of Neurology, University of Alabama at Birmingham, USA
| | - L Harrell
- Department of Neurology, University of Alabama at Birmingham, USA
| | - T P DeRamus
- Department of Psychology and Behavioral Neuroscience, University of Alabama at Birmingham, USA
| | | | - K Lokken
- Birmingham VA Medical Center, USA
| | - A P Nicholas
- Birmingham VA Medical Center, USA; Department of Neurology, University of Alabama at Birmingham, USA
| | - D C Marson
- Department of Neurology, University of Alabama at Birmingham, USA
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