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Koychev I, Adler AI, Edison P, Tom B, Milton JE, Butchart J, Hampshire A, Marshall C, Coulthard E, Zetterberg H, Hellyer P, Cormack F, Underwood BR, Mummery CJ, Holman RR. Protocol for a double-blind placebo-controlled randomised controlled trial assessing the impact of oral semaglutide in amyloid positivity (ISAP) in community dwelling UK adults. BMJ Open 2024; 14:e081401. [PMID: 38908839 DOI: 10.1136/bmjopen-2023-081401] [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] [Indexed: 06/24/2024] Open
Abstract
INTRODUCTION Glucagon-like peptide-1 receptor agonists (GLP-1 RAs), currently marketed for type 2 diabetes and obesity, may offer novel mechanisms to delay or prevent neurotoxicity associated with Alzheimer's disease (AD). The impact of semaglutide in amyloid positivity (ISAP) trial is investigating whether the GLP-1 RA semaglutide reduces accumulation in the brain of cortical tau protein and neuroinflammation in individuals with preclinical/prodromal AD. METHODS AND ANALYSIS ISAP is an investigator-led, randomised, double-blind, superiority trial of oral semaglutide compared with placebo. Up to 88 individuals aged ≥55 years with brain amyloid positivity as assessed by positron emission tomography (PET) or cerebrospinal fluid, and no or mild cognitive impairment, will be randomised. People with the low-affinity binding variant of the rs6971 allele of the Translocator Protein 18 kDa (TSPO) gene, which can interfere with interpreting TSPO PET scans (a measure of neuroinflammation), will be excluded.At baseline, participants undergo tau, TSPO PET and MRI scanning, and provide data on physical activity and cognition. Eligible individuals are randomised in a 1:1 ratio to once-daily oral semaglutide or placebo, starting at 3 mg and up-titrating to 14 mg over 8 weeks. They will attend safety visits and provide blood samples to measure AD biomarkers at weeks 4, 8, 26 and 39. All cognitive assessments are repeated at week 26. The last study visit will be at week 52, when all baseline measurements will be repeated. The primary end point is the 1-year change in tau PET signal. ETHICS AND DISSEMINATION The study was approved by the West Midlands-Edgbaston Research Ethics Committee (22/WM/0013). The results of the study will be disseminated through scientific presentations and peer-reviewed publications. TRIAL REGISTRATION NUMBER ISRCTN71283871.
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Affiliation(s)
- Ivan Koychev
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Amanda I Adler
- Diabetes Trials Unit, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Paul Edison
- Faculty of Medicine, Department of Brain Sciences, Imperial College London, London, UK
| | - Brian Tom
- Medical Research Council Biostatistics Unit, University of Cambridge, UK
| | - Joanne E Milton
- Diabetes Trials Unit, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Joe Butchart
- Royal Devon University Healthcare Foundation Trust, Exeter, UK
- University of Exeter Medical School, Exeter, UK
| | - Adam Hampshire
- Faculty of Medicine, Department of Brain Sciences, Imperial College London, London, UK
| | - Charles Marshall
- Preventive Neurology Unit, Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | | | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK
- UK Dementia Research Institute at UCL, London, UK
- Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, People's Republic of China
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA18 Dementia Research Centre, Institute of Neurology, University College London, Queen Square, London, UK
| | - Peter Hellyer
- Faculty of Medicine, Department of Brain Sciences, Imperial College London, London, UK
| | | | - Benjamin R Underwood
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridgeshire and Peterborough NHS Foundation trust, Cambridge, UK
| | - Catherine J Mummery
- Dementia Research Centre, Institute of Neurology, University College London, Queen Square, London, UK
| | - Rury R Holman
- Diabetes Trials Unit, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
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Kim HW, Kim HJ, Lee H, Yang H, Rieu Z, Lee JH. Magnetic resonance image-based brain age as a discriminator of dementia conversion in patients with amyloid-negative amnestic mild cognitive impairment. Sci Rep 2023; 13:22467. [PMID: 38105274 PMCID: PMC10725862 DOI: 10.1038/s41598-023-49465-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 12/08/2023] [Indexed: 12/19/2023] Open
Abstract
Patients with amyloid-negative amnestic mild cognitive impairment (MCI) have a conversion rate of approximately 10% to dementia within 2 years. We aimed to investigate whether brain age is an important factor in predicting conversion to dementia in patients with amyloid-negative amnestic MCI. We conducted a retrospective cohort study of patients with amyloid-negative amnestic MCI. All participants underwent detailed neuropsychological evaluation, brain magnetic resonance imaging (MRI), and [18F]-florbetaben positron emission tomography. Brain age was determined by the volumetric assessment of 12 distinct brain regions using an automatic segmentation software. During the follow-up period, 38% of the patients converted from amnestic MCI to dementia. Further, 73% of patients had a brain age greater than their actual chronological age. When defining 'survival' as the non-conversion of MCI to dementia, these groups differed significantly in survival probability (p = 0.036). The low-educated female group with a brain age greater than their actual age had the lowest survival rate among all groups. Our findings suggest that the MRI-based brain age used in this study can contribute to predicting conversion to dementia in patients with amyloid-negative amnestic MCI.
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Affiliation(s)
- Hye Weon Kim
- Research Institute, Neurophet Inc., Seoul, 06234, Korea
| | - Hyung-Ji Kim
- Department of Neurology, Uijeongbu Eulji Medical Center, Eulji University, Uijeongbu, Korea
| | - Hyunji Lee
- Research Institute, Neurophet Inc., Seoul, 06234, Korea
| | - Hyeonsik Yang
- Research Institute, Neurophet Inc., Seoul, 06234, Korea
| | - ZunHyan Rieu
- Research Institute, Neurophet Inc., Seoul, 06234, Korea
| | - Jae-Hong Lee
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Korea.
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Perovnik M, Tang CC, Namías M, Eidelberg D. Longitudinal changes in metabolic network activity in early Alzheimer's disease. Alzheimers Dement 2023; 19:4061-4072. [PMID: 37204815 DOI: 10.1002/alz.13137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 04/19/2023] [Accepted: 04/20/2023] [Indexed: 05/20/2023]
Abstract
INTRODUCTION The progression of Alzheimer's disease (AD) has been linked to two metabolic networks, the AD-related pattern (ADRP) and the default mode network (DMN). METHODS Converting and clinically stable cognitively normal subjects (n = 47) and individuals with mild cognitive impairment (n = 96) underwent 2-[18 F]fluoro-2-deoxy-D-glucose (FDG) positron emission tomography (PET) three or more times over 6 years (nscans = 705). Expression levels for ADRP and DMN were measured in each subject and time point, and the resulting changes were correlated with cognitive performance. The role of network expression in predicting conversion to dementia was also evaluated. RESULTS Longitudinal increases in ADRP expression were observed in converters, while age-related DMN loss was seen in converters and nonconverters. Cognitive decline correlated with increases in ADRP and declines in DMN, but conversion to dementia was predicted only by baseline ADRP levels. DISCUSSION The results point to the potential utility of ADRP as an imaging biomarker of AD progression.
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Affiliation(s)
- Matej Perovnik
- Department of Neurology, University Medical Center Ljubljana, Ljubljana, Slovenia
- Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, New York, USA
| | - Chris C Tang
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, New York, USA
| | - Mauro Namías
- Fundación Centro Diagnóstico Nuclear, Buenos Aires, Argentina
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, New York, USA
- Molecular Medicine and Neurology, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York, USA
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Casagrande CC, Rempe MP, Springer SD, Wilson TW. Comprehensive review of task-based neuroimaging studies of cognitive deficits in Alzheimer's disease using electrophysiological methods. Ageing Res Rev 2023; 88:101950. [PMID: 37156399 PMCID: PMC10261850 DOI: 10.1016/j.arr.2023.101950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 03/27/2023] [Accepted: 05/05/2023] [Indexed: 05/10/2023]
Abstract
With an aging population, cognitive decline and neurodegenerative disorders are an emerging public health crises with enormous, yet still under-recognized burdens. Alzheimer's disease (AD) is the most common type of dementia, and the number of cases is expected to dramatically rise in the upcoming decades. Substantial efforts have been placed into understanding the disease. One of the primary avenues of research is neuroimaging, and while positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) are most common, crucial recent advancements in electrophysiological methods such as magnetoencephalography (MEG) and electroencephalography (EEG) have provided novel insight into the aberrant neural dynamics at play in AD pathology. In this review, we outline task-based M/EEG studies published since 2010 using paradigms probing the cognitive domains most affected by AD, including memory, attention, and executive functioning. Furthermore, we provide important recommendations for adapting cognitive tasks for optimal use in this population and adjusting recruitment efforts to improve and expand future neuroimaging work.
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Affiliation(s)
- Chloe C Casagrande
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE 68010, USA
| | - Maggie P Rempe
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE 68010, USA; College of Medicine, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Seth D Springer
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE 68010, USA; College of Medicine, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Tony W Wilson
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE 68010, USA; Department of Pharmacology & Neuroscience, Creighton University, Omaha, NE 68178, USA.
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Blum D, Hepp T, Belov V, Goya-Maldonado R, la Fougère C, Reimold M. Estimating uncertainty in read-out patterns: Application to controls-based denoising and voxel-based morphometry patterns in neurodegenerative and neuropsychiatric diseases. Hum Brain Mapp 2023; 44:2802-2814. [PMID: 36947555 PMCID: PMC10089107 DOI: 10.1002/hbm.26246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 01/13/2023] [Accepted: 02/11/2023] [Indexed: 03/23/2023] Open
Abstract
Quantifying pathology-related patterns in patient data with pattern expression score (PES) is a standard approach in medical image analysis. In order to estimate the PES error, we here propose to express the uncertainty contained in read-out patterns in terms of the expected squared Euclidean distance between the read-out pattern and the unknown "true" pattern (squared standard error of the read-out pattern, SE2 ). Using SE2 , we predicted and optimized the net benefit (NBe) of the recently suggested method controls-based denoising (CODE) by weighting patterns of nonpathological variance (NPV). Multi-center MRI (1192 patients with various neurodegenerative and neuropsychiatric diseases, 1832 healthy controls) were analysed with voxel-based morphometry. For each pathology, accounting for SE2 , NBe correctly predicted classification improvement and allowed to optimize NPV pattern weights. Using these weights, CODE improved classification performances in all but one analyses, for example, for prediction of conversion to Alzheimer's disease (AUC 0.81 vs. 0.75, p = .01), diagnosis of autism (AUC 0.66 vs. 0.60, p < .001), and of major depressive disorder (AUC 0.62 vs. 0.50, p = .03). We conclude that the degree of uncertainty in a read-out pattern should generally be reported in PES-based analyses and suggest using weighted CODE as a complement to PES-based analyses.
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Affiliation(s)
- Dominik Blum
- Department of Nuclear Medicine and Clinical Molecular Imaging, University Hospital Tübingen, Tübingen, Germany
| | - Tobias Hepp
- Department of Radiology, University Hospital Tübingen, Tübingen, Germany
- Max Planck Institute for Intelligent Systems, Tübingen, Germany
| | - Valdimir Belov
- Laboratory of Systems Neuroscience and Imaging in Psychiatry, University Medical Center Göttingen, Göttingen, Germany
| | - Roberto Goya-Maldonado
- Laboratory of Systems Neuroscience and Imaging in Psychiatry, University Medical Center Göttingen, Göttingen, Germany
| | - Christian la Fougère
- Department of Nuclear Medicine and Clinical Molecular Imaging, University Hospital Tübingen, Tübingen, Germany
| | - Matthias Reimold
- Department of Nuclear Medicine and Clinical Molecular Imaging, University Hospital Tübingen, Tübingen, Germany
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Prosser L, Macdougall A, Sudre CH, Manning EN, Malone IB, Walsh P, Goodkin O, Pemberton H, Barkhof F, Biessels GJ, Cash DM, Barnes J. Predicting Cognitive Decline in Older Adults Using Baseline Metrics of AD Pathologies, Cerebrovascular Disease, and Neurodegeneration. Neurology 2023; 100:e834-e845. [PMID: 36357185 PMCID: PMC9984210 DOI: 10.1212/wnl.0000000000201572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 09/28/2022] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND AND OBJECTIVES Dementia is a growing socioeconomic challenge that requires early intervention. Identifying biomarkers that reliably predict clinical progression early in the disease process would better aid selection of individuals for future trial participation. Here, we compared the ability of baseline, single time-point biomarkers (CSF amyloid 1-42, CSF ptau-181, white matter hyperintensities (WMH), cerebral microbleeds, whole-brain volume, and hippocampal volume) to predict decline in cognitively normal individuals who later converted to mild cognitive impairment (MCI) (CNtoMCI) and those with MCI who later converted to an Alzheimer disease (AD) diagnosis (MCItoAD). METHODS Standardized baseline biomarker data from AD Neuroimaging Initiative 2 (ADNI2)/GO and longitudinal diagnostic data (including ADNI3) were used. Cox regression models assessed biomarkers in relation to time to change in clinical diagnosis using all follow-up time points available. Models were fit for biomarkers univariately and together in a multivariable model. Hazard ratios (HRs) were compared to evaluate biomarkers. Analyses were performed separately in CNtoMCI and MCItoAD groups. RESULTS For CNtoMCI (n = 189), there was strong evidence that higher WMH volume (individual model: HR 1.79, p = 0.002; fully adjusted model: HR 1.98, p = 0.003) and lower hippocampal volume (individual: HR 0.54, p = 0.001; fully adjusted: HR 0.40, p < 0.001) were associated with conversion to MCI individually and independently. For MCItoAD (n = 345), lower hippocampal (individual model: HR 0.45, p < 0.001; fully adjusted model: HR 0.55, p < 0.001) and whole-brain volume (individual: HR 0.31, p < 0.001; fully adjusted: HR 0.48, p = 0.02), increased CSF ptau (individual: HR 1.88, p < 0.001; fully adjusted: HR 1.61, p < 0.001), and lower CSF amyloid (individual: HR 0.37, p < 0.001; fully adjusted: HR 0.62, p = 0.008) were most strongly associated with conversion to AD individually and independently. DISCUSSION Lower hippocampal volume was a consistent predictor of clinical conversion to MCI and AD. CSF and brain volume biomarkers were predictive of conversion to AD from MCI, whereas WMH were predictive of conversion to MCI from cognitively normal. The predictive ability of WMH in the CNtoMCI group may be interpreted as some being on a different pathologic pathway, such as vascular cognitive impairment.
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Affiliation(s)
- Lloyd Prosser
- From the Department of Neurodegenerative Disease (L.P., A.M., C.H.S., E.N.M., I.B.M., P.W., H.P., D.M.C., J.B.), Dementia Research Centre, UCL Queen Square Institute of Neurology, London; Medical Statistics (A.M.), London School of Hygiene and Tropical Medicine; School of Biomedical Engineering and Imaging Sciences (C.H.S.), King's College London; Centre for Medical Image Computing (C.H.S., O.G., H.P., F.B.) and Department of Population Sciences and Experimental Medicine (C.H.S.), MRC Unit for Lifelong Health and Ageing at UCL, University College London, United Kingdom; Department of Radiology and Nuclear Medicine (F.B.), VU University Medical Center, Amsterdam Neuroscience; and Department of Neurology and Neurosurgery (G.J.B.), UMC Utrecht Brain Center, University Medical Center Utrecht, the Netherlands.
| | - Amy Macdougall
- From the Department of Neurodegenerative Disease (L.P., A.M., C.H.S., E.N.M., I.B.M., P.W., H.P., D.M.C., J.B.), Dementia Research Centre, UCL Queen Square Institute of Neurology, London; Medical Statistics (A.M.), London School of Hygiene and Tropical Medicine; School of Biomedical Engineering and Imaging Sciences (C.H.S.), King's College London; Centre for Medical Image Computing (C.H.S., O.G., H.P., F.B.) and Department of Population Sciences and Experimental Medicine (C.H.S.), MRC Unit for Lifelong Health and Ageing at UCL, University College London, United Kingdom; Department of Radiology and Nuclear Medicine (F.B.), VU University Medical Center, Amsterdam Neuroscience; and Department of Neurology and Neurosurgery (G.J.B.), UMC Utrecht Brain Center, University Medical Center Utrecht, the Netherlands
| | - Carole H Sudre
- From the Department of Neurodegenerative Disease (L.P., A.M., C.H.S., E.N.M., I.B.M., P.W., H.P., D.M.C., J.B.), Dementia Research Centre, UCL Queen Square Institute of Neurology, London; Medical Statistics (A.M.), London School of Hygiene and Tropical Medicine; School of Biomedical Engineering and Imaging Sciences (C.H.S.), King's College London; Centre for Medical Image Computing (C.H.S., O.G., H.P., F.B.) and Department of Population Sciences and Experimental Medicine (C.H.S.), MRC Unit for Lifelong Health and Ageing at UCL, University College London, United Kingdom; Department of Radiology and Nuclear Medicine (F.B.), VU University Medical Center, Amsterdam Neuroscience; and Department of Neurology and Neurosurgery (G.J.B.), UMC Utrecht Brain Center, University Medical Center Utrecht, the Netherlands
| | - Emily N Manning
- From the Department of Neurodegenerative Disease (L.P., A.M., C.H.S., E.N.M., I.B.M., P.W., H.P., D.M.C., J.B.), Dementia Research Centre, UCL Queen Square Institute of Neurology, London; Medical Statistics (A.M.), London School of Hygiene and Tropical Medicine; School of Biomedical Engineering and Imaging Sciences (C.H.S.), King's College London; Centre for Medical Image Computing (C.H.S., O.G., H.P., F.B.) and Department of Population Sciences and Experimental Medicine (C.H.S.), MRC Unit for Lifelong Health and Ageing at UCL, University College London, United Kingdom; Department of Radiology and Nuclear Medicine (F.B.), VU University Medical Center, Amsterdam Neuroscience; and Department of Neurology and Neurosurgery (G.J.B.), UMC Utrecht Brain Center, University Medical Center Utrecht, the Netherlands
| | - Ian B Malone
- From the Department of Neurodegenerative Disease (L.P., A.M., C.H.S., E.N.M., I.B.M., P.W., H.P., D.M.C., J.B.), Dementia Research Centre, UCL Queen Square Institute of Neurology, London; Medical Statistics (A.M.), London School of Hygiene and Tropical Medicine; School of Biomedical Engineering and Imaging Sciences (C.H.S.), King's College London; Centre for Medical Image Computing (C.H.S., O.G., H.P., F.B.) and Department of Population Sciences and Experimental Medicine (C.H.S.), MRC Unit for Lifelong Health and Ageing at UCL, University College London, United Kingdom; Department of Radiology and Nuclear Medicine (F.B.), VU University Medical Center, Amsterdam Neuroscience; and Department of Neurology and Neurosurgery (G.J.B.), UMC Utrecht Brain Center, University Medical Center Utrecht, the Netherlands
| | - Phoebe Walsh
- From the Department of Neurodegenerative Disease (L.P., A.M., C.H.S., E.N.M., I.B.M., P.W., H.P., D.M.C., J.B.), Dementia Research Centre, UCL Queen Square Institute of Neurology, London; Medical Statistics (A.M.), London School of Hygiene and Tropical Medicine; School of Biomedical Engineering and Imaging Sciences (C.H.S.), King's College London; Centre for Medical Image Computing (C.H.S., O.G., H.P., F.B.) and Department of Population Sciences and Experimental Medicine (C.H.S.), MRC Unit for Lifelong Health and Ageing at UCL, University College London, United Kingdom; Department of Radiology and Nuclear Medicine (F.B.), VU University Medical Center, Amsterdam Neuroscience; and Department of Neurology and Neurosurgery (G.J.B.), UMC Utrecht Brain Center, University Medical Center Utrecht, the Netherlands
| | - Olivia Goodkin
- From the Department of Neurodegenerative Disease (L.P., A.M., C.H.S., E.N.M., I.B.M., P.W., H.P., D.M.C., J.B.), Dementia Research Centre, UCL Queen Square Institute of Neurology, London; Medical Statistics (A.M.), London School of Hygiene and Tropical Medicine; School of Biomedical Engineering and Imaging Sciences (C.H.S.), King's College London; Centre for Medical Image Computing (C.H.S., O.G., H.P., F.B.) and Department of Population Sciences and Experimental Medicine (C.H.S.), MRC Unit for Lifelong Health and Ageing at UCL, University College London, United Kingdom; Department of Radiology and Nuclear Medicine (F.B.), VU University Medical Center, Amsterdam Neuroscience; and Department of Neurology and Neurosurgery (G.J.B.), UMC Utrecht Brain Center, University Medical Center Utrecht, the Netherlands
| | - Hugh Pemberton
- From the Department of Neurodegenerative Disease (L.P., A.M., C.H.S., E.N.M., I.B.M., P.W., H.P., D.M.C., J.B.), Dementia Research Centre, UCL Queen Square Institute of Neurology, London; Medical Statistics (A.M.), London School of Hygiene and Tropical Medicine; School of Biomedical Engineering and Imaging Sciences (C.H.S.), King's College London; Centre for Medical Image Computing (C.H.S., O.G., H.P., F.B.) and Department of Population Sciences and Experimental Medicine (C.H.S.), MRC Unit for Lifelong Health and Ageing at UCL, University College London, United Kingdom; Department of Radiology and Nuclear Medicine (F.B.), VU University Medical Center, Amsterdam Neuroscience; and Department of Neurology and Neurosurgery (G.J.B.), UMC Utrecht Brain Center, University Medical Center Utrecht, the Netherlands
| | - Frederik Barkhof
- From the Department of Neurodegenerative Disease (L.P., A.M., C.H.S., E.N.M., I.B.M., P.W., H.P., D.M.C., J.B.), Dementia Research Centre, UCL Queen Square Institute of Neurology, London; Medical Statistics (A.M.), London School of Hygiene and Tropical Medicine; School of Biomedical Engineering and Imaging Sciences (C.H.S.), King's College London; Centre for Medical Image Computing (C.H.S., O.G., H.P., F.B.) and Department of Population Sciences and Experimental Medicine (C.H.S.), MRC Unit for Lifelong Health and Ageing at UCL, University College London, United Kingdom; Department of Radiology and Nuclear Medicine (F.B.), VU University Medical Center, Amsterdam Neuroscience; and Department of Neurology and Neurosurgery (G.J.B.), UMC Utrecht Brain Center, University Medical Center Utrecht, the Netherlands
| | - Geert Jan Biessels
- From the Department of Neurodegenerative Disease (L.P., A.M., C.H.S., E.N.M., I.B.M., P.W., H.P., D.M.C., J.B.), Dementia Research Centre, UCL Queen Square Institute of Neurology, London; Medical Statistics (A.M.), London School of Hygiene and Tropical Medicine; School of Biomedical Engineering and Imaging Sciences (C.H.S.), King's College London; Centre for Medical Image Computing (C.H.S., O.G., H.P., F.B.) and Department of Population Sciences and Experimental Medicine (C.H.S.), MRC Unit for Lifelong Health and Ageing at UCL, University College London, United Kingdom; Department of Radiology and Nuclear Medicine (F.B.), VU University Medical Center, Amsterdam Neuroscience; and Department of Neurology and Neurosurgery (G.J.B.), UMC Utrecht Brain Center, University Medical Center Utrecht, the Netherlands
| | - David M Cash
- From the Department of Neurodegenerative Disease (L.P., A.M., C.H.S., E.N.M., I.B.M., P.W., H.P., D.M.C., J.B.), Dementia Research Centre, UCL Queen Square Institute of Neurology, London; Medical Statistics (A.M.), London School of Hygiene and Tropical Medicine; School of Biomedical Engineering and Imaging Sciences (C.H.S.), King's College London; Centre for Medical Image Computing (C.H.S., O.G., H.P., F.B.) and Department of Population Sciences and Experimental Medicine (C.H.S.), MRC Unit for Lifelong Health and Ageing at UCL, University College London, United Kingdom; Department of Radiology and Nuclear Medicine (F.B.), VU University Medical Center, Amsterdam Neuroscience; and Department of Neurology and Neurosurgery (G.J.B.), UMC Utrecht Brain Center, University Medical Center Utrecht, the Netherlands
| | - Josephine Barnes
- From the Department of Neurodegenerative Disease (L.P., A.M., C.H.S., E.N.M., I.B.M., P.W., H.P., D.M.C., J.B.), Dementia Research Centre, UCL Queen Square Institute of Neurology, London; Medical Statistics (A.M.), London School of Hygiene and Tropical Medicine; School of Biomedical Engineering and Imaging Sciences (C.H.S.), King's College London; Centre for Medical Image Computing (C.H.S., O.G., H.P., F.B.) and Department of Population Sciences and Experimental Medicine (C.H.S.), MRC Unit for Lifelong Health and Ageing at UCL, University College London, United Kingdom; Department of Radiology and Nuclear Medicine (F.B.), VU University Medical Center, Amsterdam Neuroscience; and Department of Neurology and Neurosurgery (G.J.B.), UMC Utrecht Brain Center, University Medical Center Utrecht, the Netherlands
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Cano A, Esteban-de-Antonio E, Bernuz M, Puerta R, García-González P, de Rojas I, Olivé C, Pérez-Cordón A, Montrreal L, Núñez-Llaves R, Sotolongo-Grau Ó, Alarcón-Martín E, Valero S, Alegret M, Martín E, Martino-Adami PV, Ettcheto M, Camins A, Vivas A, Gomez-Chiari M, Tejero MÁ, Orellana A, Tárraga L, Marquié M, Ramírez A, Martí M, Pividori MI, Boada M, Ruíz A. Plasma extracellular vesicles reveal early molecular differences in amyloid positive patients with early-onset mild cognitive impairment. J Nanobiotechnology 2023; 21:54. [PMID: 36788617 PMCID: PMC9930227 DOI: 10.1186/s12951-023-01793-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 01/24/2023] [Indexed: 02/16/2023] Open
Abstract
In the clinical course of Alzheimer's disease (AD) development, the dementia phase is commonly preceded by a prodromal AD phase, which is mainly characterized by reaching the highest levels of Aβ and p-tau-mediated neuronal injury and a mild cognitive impairment (MCI) clinical status. Because of that, most AD cases are diagnosed when neuronal damage is already established and irreversible. Therefore, a differential diagnosis of MCI causes in these prodromal stages is one of the greatest challenges for clinicians. Blood biomarkers are emerging as desirable tools for pre-screening purposes, but the current results are still being analyzed and much more data is needed to be implemented in clinical practice. Because of that, plasma extracellular vesicles (pEVs) are gaining popularity as a new source of biomarkers for the early stages of AD development. To identify an exosome proteomics signature linked to prodromal AD, we performed a cross-sectional study in a cohort of early-onset MCI (EOMCI) patients in which 184 biomarkers were measured in pEVs, cerebrospinal fluid (CSF), and plasma samples using multiplex PEA technology of Olink© proteomics. The obtained results showed that proteins measured in pEVs from EOMCI patients with established amyloidosis correlated with CSF p-tau181 levels, brain ventricle volume changes, brain hyperintensities, and MMSE scores. In addition, the correlations of pEVs proteins with different parameters distinguished between EOMCI Aβ( +) and Aβ(-) patients, whereas the CSF or plasma proteome did not. In conclusion, our findings suggest that pEVs may be able to provide information regarding the initial amyloidotic changes of AD. Circulating exosomes may acquire a pathological protein signature of AD before raw plasma, becoming potential biomarkers for identifying subjects at the earliest stages of AD development.
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Affiliation(s)
- Amanda Cano
- Ace Alzheimer Center Barcelona - International University of Catalunya (UIC), C/Marquès de Sentmenat, 57, 08029, Barcelona, Spain. .,Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), Madrid, Spain.
| | - Ester Esteban-de-Antonio
- grid.410675.10000 0001 2325 3084Ace Alzheimer Center Barcelona – International University of Catalunya (UIC), C/Marquès de Sentmenat, 57, 08029 Barcelona, Spain
| | - Mireia Bernuz
- grid.7080.f0000 0001 2296 0625Grup de Sensors I Biosensors, Departament de Química, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain
| | - Raquel Puerta
- grid.410675.10000 0001 2325 3084Ace Alzheimer Center Barcelona – International University of Catalunya (UIC), C/Marquès de Sentmenat, 57, 08029 Barcelona, Spain
| | - Pablo García-González
- grid.410675.10000 0001 2325 3084Ace Alzheimer Center Barcelona – International University of Catalunya (UIC), C/Marquès de Sentmenat, 57, 08029 Barcelona, Spain ,grid.418264.d0000 0004 1762 4012Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), Madrid, Spain
| | - Itziar de Rojas
- grid.410675.10000 0001 2325 3084Ace Alzheimer Center Barcelona – International University of Catalunya (UIC), C/Marquès de Sentmenat, 57, 08029 Barcelona, Spain ,grid.418264.d0000 0004 1762 4012Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), Madrid, Spain
| | - Claudia Olivé
- grid.410675.10000 0001 2325 3084Ace Alzheimer Center Barcelona – International University of Catalunya (UIC), C/Marquès de Sentmenat, 57, 08029 Barcelona, Spain
| | - Alba Pérez-Cordón
- grid.410675.10000 0001 2325 3084Ace Alzheimer Center Barcelona – International University of Catalunya (UIC), C/Marquès de Sentmenat, 57, 08029 Barcelona, Spain
| | - Laura Montrreal
- grid.410675.10000 0001 2325 3084Ace Alzheimer Center Barcelona – International University of Catalunya (UIC), C/Marquès de Sentmenat, 57, 08029 Barcelona, Spain
| | - Raúl Núñez-Llaves
- grid.410675.10000 0001 2325 3084Ace Alzheimer Center Barcelona – International University of Catalunya (UIC), C/Marquès de Sentmenat, 57, 08029 Barcelona, Spain
| | - Óscar Sotolongo-Grau
- grid.410675.10000 0001 2325 3084Ace Alzheimer Center Barcelona – International University of Catalunya (UIC), C/Marquès de Sentmenat, 57, 08029 Barcelona, Spain
| | - Emilio Alarcón-Martín
- grid.410675.10000 0001 2325 3084Ace Alzheimer Center Barcelona – International University of Catalunya (UIC), C/Marquès de Sentmenat, 57, 08029 Barcelona, Spain
| | - Sergi Valero
- grid.410675.10000 0001 2325 3084Ace Alzheimer Center Barcelona – International University of Catalunya (UIC), C/Marquès de Sentmenat, 57, 08029 Barcelona, Spain ,grid.418264.d0000 0004 1762 4012Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), Madrid, Spain
| | - Montserrat Alegret
- grid.410675.10000 0001 2325 3084Ace Alzheimer Center Barcelona – International University of Catalunya (UIC), C/Marquès de Sentmenat, 57, 08029 Barcelona, Spain ,grid.418264.d0000 0004 1762 4012Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), Madrid, Spain
| | - Elvira Martín
- grid.410675.10000 0001 2325 3084Ace Alzheimer Center Barcelona – International University of Catalunya (UIC), C/Marquès de Sentmenat, 57, 08029 Barcelona, Spain
| | - Pamela V. Martino-Adami
- grid.6190.e0000 0000 8580 3777Division of Neurogenetics and Molecular Psychiatry, Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50937 Cologne, Germany
| | - Miren Ettcheto
- grid.418264.d0000 0004 1762 4012Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), Madrid, Spain ,grid.5841.80000 0004 1937 0247Department of Pharmacology, Toxicology and Therapeutic Chemistry, Faculty of Pharmacy and Food Sciences, University of Barcelona, 08028 Barcelona, Spain ,grid.5841.80000 0004 1937 0247Institute of Neurosciences, University of Barcelona, Barcelona, Spain
| | - Antonio Camins
- grid.418264.d0000 0004 1762 4012Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), Madrid, Spain ,grid.5841.80000 0004 1937 0247Department of Pharmacology, Toxicology and Therapeutic Chemistry, Faculty of Pharmacy and Food Sciences, University of Barcelona, 08028 Barcelona, Spain ,grid.5841.80000 0004 1937 0247Institute of Neurosciences, University of Barcelona, Barcelona, Spain
| | - Assumpta Vivas
- Departament de Diagnòstic Per La Imatge, Clínica Corachan, Barcelona, Spain
| | - Marta Gomez-Chiari
- Departament de Diagnòstic Per La Imatge, Clínica Corachan, Barcelona, Spain
| | | | - Adelina Orellana
- grid.410675.10000 0001 2325 3084Ace Alzheimer Center Barcelona – International University of Catalunya (UIC), C/Marquès de Sentmenat, 57, 08029 Barcelona, Spain ,grid.418264.d0000 0004 1762 4012Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), Madrid, Spain
| | - Lluís Tárraga
- grid.410675.10000 0001 2325 3084Ace Alzheimer Center Barcelona – International University of Catalunya (UIC), C/Marquès de Sentmenat, 57, 08029 Barcelona, Spain ,grid.418264.d0000 0004 1762 4012Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), Madrid, Spain
| | - Marta Marquié
- grid.410675.10000 0001 2325 3084Ace Alzheimer Center Barcelona – International University of Catalunya (UIC), C/Marquès de Sentmenat, 57, 08029 Barcelona, Spain ,grid.418264.d0000 0004 1762 4012Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), Madrid, Spain
| | - Alfredo Ramírez
- grid.6190.e0000 0000 8580 3777Division of Neurogenetics and Molecular Psychiatry, Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50937 Cologne, Germany ,grid.15090.3d0000 0000 8786 803XDepartment of Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Medical Faculty, 53127 Bonn, Germany ,grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany ,Department of Psychiatry and Glenn, Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, San Antonio, TX 78229 USA ,grid.6190.e0000 0000 8580 3777Cluster of Excellence Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, 50931 Cologne, Germany
| | - Mercè Martí
- grid.7080.f0000 0001 2296 0625Grup de Sensors I Biosensors, Departament de Química, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain
| | - María Isabel Pividori
- grid.7080.f0000 0001 2296 0625Grup de Sensors I Biosensors, Departament de Química, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain ,grid.7080.f0000 0001 2296 0625Biosensing and Bioanalysis Group, Institut de Biotecnologia I de Biomedicina (IBB-UAB), Mòdul B Parc de Recerca UAB, Campus Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain
| | - Mercè Boada
- grid.410675.10000 0001 2325 3084Ace Alzheimer Center Barcelona – International University of Catalunya (UIC), C/Marquès de Sentmenat, 57, 08029 Barcelona, Spain ,grid.418264.d0000 0004 1762 4012Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), Madrid, Spain
| | - Agustín Ruíz
- Ace Alzheimer Center Barcelona - International University of Catalunya (UIC), C/Marquès de Sentmenat, 57, 08029, Barcelona, Spain. .,Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), Madrid, Spain.
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8
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Perovnik M, Rus T, Schindlbeck KA, Eidelberg D. Functional brain networks in the evaluation of patients with neurodegenerative disorders. Nat Rev Neurol 2023; 19:73-90. [PMID: 36539533 DOI: 10.1038/s41582-022-00753-3] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/21/2022] [Indexed: 12/24/2022]
Abstract
Network analytical tools are increasingly being applied to brain imaging maps of resting metabolic activity (PET) or blood oxygenation-dependent signals (functional MRI) to characterize the abnormal neural circuitry that underlies brain diseases. This approach is particularly valuable for the study of neurodegenerative disorders, which are characterized by stereotyped spread of pathology along discrete neural pathways. Identification and validation of disease-specific brain networks facilitate the quantitative assessment of pathway changes over time and during the course of treatment. Network abnormalities can often be identified before symptom onset and can be used to track disease progression even in the preclinical period. Likewise, network activity can be modulated by treatment and might therefore be used as a marker of efficacy in clinical trials. Finally, early differential diagnosis can be achieved by simultaneously measuring the activity levels of multiple disease networks in an individual patient's scans. Although these techniques were originally developed for PET, over the past several years analogous methods have been introduced for functional MRI, a more accessible non-invasive imaging modality. This advance is expected to broaden the application of network tools to large and diverse patient populations.
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Affiliation(s)
- Matej Perovnik
- Department of Neurology, University Medical Center Ljubljana, Ljubljana, Slovenia.,Medical Faculty, University of Ljubljana, Ljubljana, Slovenia
| | - Tomaž Rus
- Department of Neurology, University Medical Center Ljubljana, Ljubljana, Slovenia.,Medical Faculty, University of Ljubljana, Ljubljana, Slovenia
| | | | - David Eidelberg
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY, USA.
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9
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Kim HJ, Oh JS, Lim JS, Lee S, Jo S, Chung EN, Shim WH, Oh M, Kim JS, Roh JH, Lee JH. The impact of subthreshold levels of amyloid deposition on conversion to dementia in patients with amyloid-negative amnestic mild cognitive impairment. Alzheimers Res Ther 2022; 14:93. [PMID: 35821150 PMCID: PMC9277922 DOI: 10.1186/s13195-022-01035-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 06/25/2022] [Indexed: 11/26/2022]
Abstract
BACKGROUND About 40-50% of patients with amnestic mild cognitive impairment (MCI) are found to have no significant Alzheimer's pathology based on amyloid PET positivity. Notably, conversion to dementia in this population is known to occur much less often than in amyloid-positive MCI. However, the relationship between MCI and brain amyloid deposition remains largely unknown. Therefore, we investigated the influence of subthreshold levels of amyloid deposition on conversion to dementia in amnestic MCI patients with negative amyloid PET scans. METHODS This study was a retrospective cohort study of patients with amyloid-negative amnestic MCI who visited the memory clinic of Asan Medical Center. All participants underwent detailed neuropsychological testing, brain magnetic resonance imaging, and [18F]-florbetaben (FBB) positron emission tomography scan (PET). Conversion to dementia was determined by a neurologist based on a clinical interview with a detailed neuropsychological test or a decline in the Korean version of the Mini-Mental State Examination score of more than 4 points per year combined with impaired activities of daily living. Regional cortical amyloid levels were calculated, and a receiver operating characteristic (ROC) curve for conversion to dementia was obtained. To increase the reliability of the results of the study, we analyzed the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset together. RESULTS During the follow-up period, 36% (39/107) of patients converted to dementia from amnestic MCI. The dementia converter group displayed increased standardized uptake value ratio (SUVR) values of FBB on PET in the bilateral temporal, parietal, posterior cingulate, occipital, and left precuneus cortices as well as increased global SUVR. Among volume of interests, the left parietal SUVR predicted conversion to dementia with the highest accuracy in the ROC analysis (area under the curve [AUC] = 0.762, P < 0.001). The combination of precuneus, parietal cortex, and FBB composite SUVRs also showed a higher accuracy in predicting conversion to dementia than other models (AUC = 0.763). Of the results of ADNI data, the SUVR of the left precuneus SUVR showed the highest AUC (AUC = 0.596, P = 0.006). CONCLUSION Our findings suggest that subthreshold amyloid levels may contribute to conversion to dementia in patients with amyloid-negative amnestic MCI.
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Affiliation(s)
- Hyung-Ji Kim
- Department of Neurology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea
- Department of Neurology, Uijeongbu Eulji Medical Center, Eulji University School of Medicine, Uijeongbu, South Korea
| | - Jungsu S Oh
- Department of Nuclear Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea
| | - Jae-Sung Lim
- Department of Neurology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea
| | - Sunju Lee
- Department of Neurology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea
| | - Sungyang Jo
- Department of Neurology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea
| | - E-Nae Chung
- Health Innovation Bigdata Center, Asan Institute for Lifesciences, Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea
| | - Woo-Hyun Shim
- Health Innovation Bigdata Center, Asan Institute for Lifesciences, Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea
- Asan Institute for Life Sciences, Asan Medical Center, Seoul, South Korea
| | - Minyoung Oh
- Department of Nuclear Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea
| | - Jae Seung Kim
- Department of Nuclear Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea
| | - Jee Hoon Roh
- Neuroscience Institute, Korea University College of Medicine and School of Medicine, Seoul, South Korea
| | - Jae-Hong Lee
- Department of Neurology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea.
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10
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Wang SS, Liu ZK, Liu JJ, Cheng Q, Wang YX, Liu Y, Ni WW, Chen HZ, Song M. Imaging asparaginyl endopeptidase (AEP) in the live brain as a biomarker for Alzheimer's disease. J Nanobiotechnology 2021; 19:249. [PMID: 34412639 PMCID: PMC8375181 DOI: 10.1186/s12951-021-00988-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 08/05/2021] [Indexed: 12/05/2022] Open
Abstract
BACKGROUND Discovery of early-stage biomarkers is a long-sought goal of Alzheimer's disease (AD) diagnosis. Age is the greatest risk factor for most AD and accumulating evidence suggests that age-dependent elevation of asparaginyl endopeptidase (AEP) in the brain may represent a new biological marker for predicting AD. However, this speculation remains to be explored with an appropriate assay method because mammalian AEP exists in many organs and the level of AEP in body fluid isn't proportional to its concentration in brain parenchyma. To this end, we here modified gold nanoparticle (AuNPs) into an AEP-responsive imaging probe and choose transgenic APPswe/PS1dE9 (APP/PS1) mice as an animal model of AD. Our aim is to determine whether imaging of brain AEP can be used to predict AD pathology. RESULTS This AEP-responsive imaging probe AuNPs-Cy5.5-A&C consisted of two particles, AuNPs-Cy5.5-AK and AuNPs-Cy5.5-CABT, which were respectively modified with Ala-Ala-Asn-Cys-Lys (AK) and 2-cyano-6-aminobenzothiazole (CABT). We showed that AuNPs-Cy5.5-A&C could be selectively activated by AEP to aggregate and emit strong fluorescence. Moreover, AuNPs-Cy5.5-A&C displayed a general applicability in various cell lines and its florescence intensity correlated well with AEP activity in these cells. In the brain of APP/PS1 transgenic mice , AEP activity was increased at an early disease stage of AD that precedes formation of senile plaques and cognitive impairment. Pharmacological inhibition of AEP with δ-secretase inhibitor 11 (10 mg kg-1, p.o.) reduced production of β-amyloid (Aβ) and ameliorated memory loss. Therefore, elevation of AEP is an early sign of AD onset. Finally, we showed that live animal imaging with this AEP-responsive probe could monitor the up-regulated AEP in the brain of APP/PS1 mice. CONCLUSIONS The current work provided a proof of concept that assessment of brain AEP activity by in vivo imaging assay is a potential biomarker for early diagnosis of AD.
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Affiliation(s)
- Shan-Shan Wang
- Department of Pharmacology and Chemical Biology, Institute of Medical Sciences, Shanghai Jiao Tong University School of Medicine, 280 South Chongqing Road, Shanghai, 200025, China
| | - Zi-Kai Liu
- Department of Pharmacology and Chemical Biology, Institute of Medical Sciences, Shanghai Jiao Tong University School of Medicine, 280 South Chongqing Road, Shanghai, 200025, China
| | - Jing-Jing Liu
- Department of Pharmacology and Chemical Biology, Institute of Medical Sciences, Shanghai Jiao Tong University School of Medicine, 280 South Chongqing Road, Shanghai, 200025, China
| | - Qing Cheng
- Department of Pharmacology and Chemical Biology, Institute of Medical Sciences, Shanghai Jiao Tong University School of Medicine, 280 South Chongqing Road, Shanghai, 200025, China
| | - Yan-Xia Wang
- Department of Pharmacology and Chemical Biology, Institute of Medical Sciences, Shanghai Jiao Tong University School of Medicine, 280 South Chongqing Road, Shanghai, 200025, China
| | - Yan Liu
- Department of Pharmacology and Chemical Biology, Institute of Medical Sciences, Shanghai Jiao Tong University School of Medicine, 280 South Chongqing Road, Shanghai, 200025, China
| | - Wen-Wen Ni
- Department of Pharmacology and Chemical Biology, Institute of Medical Sciences, Shanghai Jiao Tong University School of Medicine, 280 South Chongqing Road, Shanghai, 200025, China
| | - Hong-Zhuan Chen
- Institute of Interdisciplinary Integrative Biomedical Research, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Shanghai, 201210, China.
| | - Mingke Song
- Department of Pharmacology and Chemical Biology, Institute of Medical Sciences, Shanghai Jiao Tong University School of Medicine, 280 South Chongqing Road, Shanghai, 200025, China.
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