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Juengling F, Wuest F, Schirrmacher R, Abele J, Thiel A, Soucy JP, Camicioli R, Garibotto V. PET Imaging in Dementia: Mini-Review and Canadian Perspective for Clinical Use. Can J Neurol Sci 2024:1-13. [PMID: 38433571 DOI: 10.1017/cjn.2024.31] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2024]
Abstract
PET imaging is increasingly recognized as an important diagnostic tool to investigate patients with cognitive disturbances of possible neurodegenerative origin. PET with 2-[18F]fluoro-2-deoxy-D-glucose ([18F]FDG), assessing glucose metabolism, provides a measure of neurodegeneration and allows a precise differential diagnosis among the most common neurodegenerative diseases, such as Alzheimer's disease, frontotemporal dementia or dementia with Lewy bodies. PET tracers specific for the pathological deposits characteristic of different neurodegenerative processes, namely amyloid and tau deposits typical of Alzheimer's Disease, allow the visualization of these aggregates in vivo. [18F]FDG and amyloid PET imaging have reached a high level of clinical validity and are since 2022 investigations that can be offered to patients in standard clinical care in most of Canada.This article will briefly review and summarize the current knowledge on these diagnostic tools, their integration into diagnostic algorithms as well as perspectives for future developments.
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Affiliation(s)
- Freimut Juengling
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada
- Division of Oncologic Imaging and Radionuclide Therapy, Cross Cancer Institute, Edmonton, AB, Canada
- Medical Faculty, University of Bern, Bern, Switzerland
| | - Frank Wuest
- Division of Oncologic Imaging and Radionuclide Therapy, Cross Cancer Institute, Edmonton, AB, Canada
| | - Ralf Schirrmacher
- Division of Oncologic Imaging and Radionuclide Therapy, Cross Cancer Institute, Edmonton, AB, Canada
- Medical Isotope and Cyclotron Facility, University of Alberta, Edmonton, AB, Canada
| | - Jonathan Abele
- Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, AB, Canada
| | - Alexander Thiel
- Department of Neurology and Neurosurgery, Lady Davis Institute for Medical Research, McGill University, Montréal, QC, Canada
| | - Jean-Paul Soucy
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Richard Camicioli
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada
- Department of Medicine, Division of Neurology, University of Alberta, Edmonton, AB, Canada
| | - Valentina Garibotto
- Diagnostic Department, Nuclear Medicine and Molecular Imaging Division, University Hospitals of Geneva, Geneva, Switzerland
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Rogeau A, Hives F, Bordier C, Lahousse H, Roca V, Lebouvier T, Pasquier F, Huglo D, Semah F, Lopes R. A 3D convolutional neural network to classify subjects as Alzheimer's disease, frontotemporal dementia or healthy controls using brain 18F-FDG PET. Neuroimage 2024; 288:120530. [PMID: 38311126 DOI: 10.1016/j.neuroimage.2024.120530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 02/01/2024] [Accepted: 02/02/2024] [Indexed: 02/06/2024] Open
Abstract
With the arrival of disease-modifying drugs, neurodegenerative diseases will require an accurate diagnosis for optimal treatment. Convolutional neural networks are powerful deep learning techniques that can provide great help to physicians in image analysis. The purpose of this study is to introduce and validate a 3D neural network for classification of Alzheimer's disease (AD), frontotemporal dementia (FTD) or cognitively normal (CN) subjects based on brain glucose metabolism. Retrospective [18F]-FDG-PET scans of 199 CE, 192 FTD and 200 CN subjects were collected from our local database, Alzheimer's disease and frontotemporal lobar degeneration neuroimaging initiatives. Training and test sets were created using randomization on a 90 %-10 % basis, and training of a 3D VGG16-like neural network was performed using data augmentation and cross-validation. Performance was compared to clinical interpretation by three specialists in the independent test set. Regions determining classification were identified in an occlusion experiment and Gradient-weighted Class Activation Mapping. Test set subjects were age- and sex-matched across categories. The model achieved an overall 89.8 % accuracy in predicting the class of test scans. Areas under the ROC curves were 93.3 % for AD, 95.3 % for FTD, and 99.9 % for CN. The physicians' consensus showed a 69.5 % accuracy, and there was substantial agreement between them (kappa = 0.61, 95 % CI: 0.49-0.73). To our knowledge, this is the first study to introduce a deep learning model able to discriminate AD and FTD based on [18F]-FDG PET scans, and to isolate CN subjects with excellent accuracy. These initial results are promising and hint at the potential for generalization to data from other centers.
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Affiliation(s)
- Antoine Rogeau
- Department of Nuclear Medicine, Lille University Hospitals, Lille, France; Institute of Nuclear Medicine, University College London Hospitals NHS Foundation Trust, London, UK.
| | - Florent Hives
- Department of Nuclear Medicine, Lille University Hospitals, Lille, France
| | - Cécile Bordier
- University of Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, Lille, France; Institut Pasteur de Lille, University of Lille, CNRS, Inserm, CHU Lille, US 41 - UAR 2014 - PLBS, Lille F-59000, France
| | - Hélène Lahousse
- Department of Nuclear Medicine, Lille University Hospitals, Lille, France
| | - Vincent Roca
- University of Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, Lille, France; Institut Pasteur de Lille, University of Lille, CNRS, Inserm, CHU Lille, US 41 - UAR 2014 - PLBS, Lille F-59000, France
| | - Thibaud Lebouvier
- University of Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, Lille, France; Memory Clinic, Lille University Hospitals, Lille, France
| | - Florence Pasquier
- University of Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, Lille, France; Memory Clinic, Lille University Hospitals, Lille, France
| | - Damien Huglo
- Department of Nuclear Medicine, Lille University Hospitals, Lille, France; Inserm, CHU Lille, University of Lille, U1189 OncoTHAI, Lille, France
| | - Franck Semah
- Department of Nuclear Medicine, Lille University Hospitals, Lille, France; University of Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, Lille, France
| | - Renaud Lopes
- University of Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, Lille, France; Institut Pasteur de Lille, University of Lille, CNRS, Inserm, CHU Lille, US 41 - UAR 2014 - PLBS, Lille F-59000, France
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Liu F, Shi Y, Wu Q, Chen H, Wang Y, Cai L, Zhang N. The value of FDG combined with PiB PET in the diagnosis of patients with cognitive impairment in a memory clinic. CNS Neurosci Ther 2024; 30:e14418. [PMID: 37602885 PMCID: PMC10848040 DOI: 10.1111/cns.14418] [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: 06/05/2023] [Revised: 07/12/2023] [Accepted: 08/07/2023] [Indexed: 08/22/2023] Open
Abstract
AIMS To analyze the value of 18 F-fluorodeoxyglucose (FDG) positron emission tomography (PET) combined with amyloid PET in cognitive impairment diagnosis. METHODS A total of 187 patients with dementia or mild cognitive impairment (MCI) who underwent 11 C-Pittsburgh compound B (PiB) and FDG PET scans in a memory clinic were included in the final analysis. RESULTS Amyloid-positive and amyloid-negative dementia patient groups showed a significant difference in the proportion of individuals presenting temporoparietal cortex (p < 0.001) and posterior cingulate/precuneus cortex (p < 0.001) hypometabolism. The sensitivity and specificity of this hypometabolic pattern for identifying amyloid pathology were 72.61% and 77.97%, respectively, in patients clinically diagnosed with AD and 60.87% and 76.19%, respectively, in patients with MCI. The initial diagnosis was changed in 32.17% of patients with dementia after considering both PiB and FDG results. There was a significant difference in both the proportion of patients showing the hypometabolic pattern and PiB positivity between dementia conversion patients and patients with a stable diagnosis of MCI (p < 0.05). CONCLUSION Temporoparietal and posterior cingulate/precuneus cortex hypometabolism on FDG PET suggested amyloid pathology in patients with cognitive impairment and is helpful in diagnostic decision-making and predicting AD dementia conversion from MCI, particularly when combined with amyloid PET.
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Affiliation(s)
- Fang Liu
- Department of NeurologyTianjin Neurological Institute, Tianjin Medical University General HospitalTianjinChina
| | - Yudi Shi
- Department of NeurologyTianjin Neurological Institute, Tianjin Medical University General HospitalTianjinChina
- Health Management CenterTianjin Medical University General Hospital Airport SiteTianjinChina
| | - Qiuyan Wu
- Department of NeurologyTianjin Neurological Institute, Tianjin Medical University General HospitalTianjinChina
| | - Huifeng Chen
- Department of NeurologyTianjin Neurological Institute, Tianjin Medical University General HospitalTianjinChina
- Department of NeurologyTianjin Medical University General Hospital Airport SiteTianjinChina
| | - Ying Wang
- PET/CT CenterTianjin Medical University General HospitalTianjinChina
| | - Li Cai
- PET/CT CenterTianjin Medical University General HospitalTianjinChina
| | - Nan Zhang
- Department of NeurologyTianjin Neurological Institute, Tianjin Medical University General HospitalTianjinChina
- Department of NeurologyTianjin Medical University General Hospital Airport SiteTianjinChina
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Na S, Kang DW, Kim GH, Kim KW, Kim Y, Kim HJ, Park KH, Park YH, Byeon G, Suh J, Shin JH, Shim Y, Yang Y, Um YH, Oh SI, Wang SM, Yoon B, Yoon HJ, Lee SM, Lee J, Lee JS, Rhee HY, Lim JS, Jung YH, Chin J, Hong YJ, Jang H, Choi H, Choi M, Jang JW. The Usefulness of 18F-FDG PET to Differentiate Subtypes of Dementia: The Systematic Review and Meta-Analysis. Dement Neurocogn Disord 2024; 23:54-66. [PMID: 38362056 PMCID: PMC10864694 DOI: 10.12779/dnd.2024.23.1.54] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 01/22/2024] [Accepted: 01/22/2024] [Indexed: 02/17/2024] Open
Abstract
Background and Purpose Dementia subtypes, including Alzheimer's dementia (AD), dementia with Lewy bodies (DLB), and frontotemporal dementia (FTD), pose diagnostic challenges. This review examines the effectiveness of 18F-Fluorodeoxyglucose Positron Emission Tomography (18F-FDG PET) in differentiating these subtypes for precise treatment and management. Methods A systematic review following Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines was conducted using databases like PubMed and Embase to identify studies on the diagnostic utility of 18F-FDG PET in dementia. The search included studies up to November 16, 2022, focusing on peer-reviewed journals and applying the gold-standard clinical diagnosis for dementia subtypes. Results From 12,815 articles, 14 were selected for final analysis. For AD versus FTD, the sensitivity was 0.96 (95% confidence interval [CI], 0.88-0.98) and specificity was 0.84 (95% CI, 0.70-0.92). In the case of AD versus DLB, 18F-FDG PET showed a sensitivity of 0.93 (95% CI 0.88-0.98) and specificity of 0.92 (95% CI, 0.70-0.92). Lastly, when differentiating AD from non-AD dementias, the sensitivity was 0.86 (95% CI, 0.80-0.91) and the specificity was 0.88 (95% CI, 0.80-0.91). The studies mostly used case-control designs with visual and quantitative assessments. Conclusions 18F-FDG PET exhibits high sensitivity and specificity in differentiating dementia subtypes, particularly AD, FTD, and DLB. This method, while not a standalone diagnostic tool, significantly enhances diagnostic accuracy in uncertain cases, complementing clinical assessments and structural imaging.
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Affiliation(s)
- Seunghee Na
- Department of Neurology, Incheon St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Incheon, Korea
| | - Dong Woo Kang
- Department of Psychiatry, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Geon Ha Kim
- Department of Neurology, Ewha Womans University Mokdong Hospital, Ewha Womans University College of Medicine, Seoul, Korea
| | - Ko Woon Kim
- Department of Neurology, Jeonbuk National University Hospital, Jeonbuk National University College of Medicine, Jeonju, Korea
| | - Yeshin Kim
- Department of Neurology, Kangwon National University Hospital, Kangwon National University College of Medicine, Chuncheon, Korea
| | - Hee-Jin Kim
- Department of Neurology, Hanyang University Hospital, College of Medicine, Hanyang University, Seoul, Korea
| | - Kee Hyung Park
- Department of Neurology, College of Medicine, Gachon University Gil Medical Center, Incheon, Korea
| | - Young Ho Park
- Department of Neurology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
| | - Gihwan Byeon
- Department of Psychiatry, Kangwon National University Hospital, Kangwon National University College of Medicine, Chuncheon, Korea
| | - Jeewon Suh
- Department of Neurology, National Medical Center, Seoul, Korea
| | | | - YongSoo Shim
- Department of Neurology, Eunpyeong St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - YoungSoon Yang
- Department of Neurology, Soonchunhyang University Cheonan Hospital, Soonchunhyang University School of Medicine, Cheonan, Korea
| | - Yoo Hyun Um
- Department of Psychiatry, St. Vincent’s Hospital, College of Medicine, The Catholic University of Korea, Suwon, Korea
| | - Seong-il Oh
- Department of Neurology, Kyung Hee University Hospital, Kyung Hee University College of Medicine, Seoul, Korea
| | - Sheng-Min Wang
- Department of Psychiatry, Yeouido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Bora Yoon
- Department of Neurology, Konyang University Hospital, Konyang University College of Medicine, Daejeon, Korea
| | - Hai-Jeon Yoon
- Department of Nuclear Medicine, Ewha Womans University College of Medicine, Seoul, Korea
| | - Sun Min Lee
- Department of Neurology, Ajou University Hospital, Ajou University School of Medicine, Suwon, Korea
| | - Juyoun Lee
- Department of Neurology, Chungnam National University Hospital, Chungnam National University School of Medicine, Daejeon, Korea
| | - Jin San Lee
- Department of Neurology, Kyung Hee University Hospital, Kyung Hee University College of Medicine, Seoul, Korea
| | - Hak Young Rhee
- Department of Neurology, Kyung Hee University Hospital at Gangdong, Kyung Hee University College of Medicine, Seoul, Korea
| | - Jae-Sung Lim
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Young Hee Jung
- Department of Neurology, Myungji Hospital, Goyang, Korea
| | - Juhee Chin
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Yun Jeong Hong
- Department of Neurology, Uijeongbu St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Uijeongbu, Korea
| | - Hyemin Jang
- Department of Neurology, Seoul National University Hospital, Seoul, Korea
| | - Hongyoon Choi
- Department of Nuclear Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
| | - Miyoung Choi
- Division of Healthcare Technology Assessment Research, National Evidence-based Healthcare Collaborating Agency, Seoul, Korea
| | - Jae-Won Jang
- Department of Neurology, Kangwon National University Hospital, Kangwon National University College of Medicine, Chuncheon, Korea
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Lee YG, Jeon S, Kang SW, Ye BS. Effects of amyloid beta and dopaminergic depletion on perfusion and clinical symptoms. Alzheimers Dement 2023; 19:5719-5729. [PMID: 37422287 DOI: 10.1002/alz.13379] [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: 02/14/2023] [Revised: 05/23/2023] [Accepted: 06/07/2023] [Indexed: 07/10/2023]
Abstract
INTRODUCTION Although mixed pathologies are common in Alzheimer's disease (AD) and dementia with Lewy bodies (DLB), the effects of amyloid beta and dopaminergic depletion on brain perfusion and clinical symptoms have not been elucidated. METHODS In 99 cognitive impairment patients due to AD and/or DLB and 32 controls, 18F-florbetaben (FBB) and dual-phase dopamine transporter (DAT) positron emission tomography (PET) were performed to measure the FBB standardized uptake value ratio (SUVR), striatal DAT uptakes, and brain perfusion. RESULTS Higher FBB-SUVR and lower ventral striatal DAT uptake were intercorrelated and, respectively, associated with left entorhinal/temporo-parietal-centered hypoperfusion and vermis/hippocampal-centered hyperperfusion, whereas regional perfusion mediated clinical symptoms and cognition. DISCUSSION Amyloid beta deposition and striatal dopaminergic depletion contribute to regional perfusion changes, clinical symptoms, and cognition in the spectrum of normal aging and cognitive impairment due to AD and/or LBD. HIGHLIGHTS Amyloid beta (Aβ) deposition was associated with ventral striatal dopaminergic depletion. Aβ deposition and dopaminergic depletion correlated with perfusion. Aβ deposition correlated with hypoperfusion centered in the left entorhinal cortex. Dopaminergic depletion correlated with hyperperfusion centered in the vermis. Perfusion mediated the Aβ deposition/dopaminergic depletion's effects on cognition.
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Affiliation(s)
- Young-Gun Lee
- Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea
- Department of Neurology, Ilsan Paik Hospital, Inje University College of Medicine, Goyang, South Korea
| | - Seun Jeon
- Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea
- Brain Research Institute, Yonsei University College of Medicine, Seoul, South Korea
- Metabolism-Dementia Research Institute, Yonsei University College of Medicine, Seoul, South Korea
| | - Sung Woo Kang
- Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea
| | - Byoung Seok Ye
- Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea
- Metabolism-Dementia Research Institute, Yonsei University College of Medicine, Seoul, South Korea
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Li L, Ji B, Zhao M, Bai L, Chen B. Nonfluent Variant Primary Progressive Aphasia on FDG, 11 C-PIB, and 18 F-APN-1607 PET Imaging. Clin Nucl Med 2023; 48:e539-e541. [PMID: 37756439 DOI: 10.1097/rlu.0000000000004853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/29/2023]
Abstract
ABSTRACT A 61-year-old right-handed man presented with decreased cognitive function, short-term memory, fluent speech disorders, and grammatical errors for 1 year. The patient underwent PET imaging with 11 C-PIB, 18 F-FDG, and 18 F-APN-1607. The 11 C-PIB PET showed no amyloid accumulation; the 18 F-FDG PET showed hypometabolism in the bilateral frontal lobe, temporal lobe, and midbrain; and the 18 F-APN-1607 PET showed tau accumulation in the brainstem, basal ganglia, and left inferior frontal gyrus. These findings suggested a diagnosis of nonfluent variant primary progressive aphasia. This case emphasizes the value of combined imaging of glucose metabolism, Aβ, and tau PET in the diagnosis of nonfluent variant primary progressive aphasia.
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Affiliation(s)
- Lingchao Li
- From the Department of Nuclear Medicine, China-Japan Union Hospital of Jilin University, Changchun, China
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Bucci M, Bluma M, Savitcheva I, Ashton NJ, Chiotis K, Matton A, Kivipelto M, Di Molfetta G, Blennow K, Zetterberg H, Nordberg A. Profiling of plasma biomarkers in the context of memory assessment in a tertiary memory clinic. Transl Psychiatry 2023; 13:268. [PMID: 37491358 PMCID: PMC10368630 DOI: 10.1038/s41398-023-02558-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Revised: 06/24/2023] [Accepted: 07/03/2023] [Indexed: 07/27/2023] Open
Abstract
Plasma biomarkers have shown promising performance in research cohorts in discriminating between different stages of Alzheimer's disease (AD). Studies in clinical populations are necessary to provide insights on the clinical utility of plasma biomarkers before their implementation in real-world settings. Here we investigated plasma biomarkers (glial fibrillary acidic protein (GFAP), tau phosphorylated at 181 and 231 (pTau181, pTau231), amyloid β (Aβ) 42/40 ratio, neurofilament light) in 126 patients (age = 65 ± 8) who were admitted to the Clinic for Cognitive Disorders, at Karolinska University Hospital. After extensive clinical assessment (including CSF analysis), patients were classified as: mild cognitive impairment (MCI) (n = 75), AD (n = 25), non-AD dementia (n = 16), no dementia (n = 9). To refine the diagnosis, patients were examined with [18F]flutemetamol PET (Aβ-PET). Aβ-PET images were visually rated for positivity/negativity and quantified in Centiloid. Accordingly, 68 Aβ+ and 54 Aβ- patients were identified. Plasma biomarkers were measured using single molecule arrays (SIMOA). Receiver-operated curve (ROC) analyses were performed to detect Aβ-PET+ using the different biomarkers. In the whole cohort, the Aβ-PET centiloid values correlated positively with plasma GFAP, pTau231, pTau181, and negatively with Aβ42/40 ratio. While in the whole MCI group, only GFAP was associated with Aβ PET centiloid. In ROC analyses, among the standalone biomarkers, GFAP showed the highest area under the curve discriminating Aβ+ and Aβ- compared to other plasma biomarkers. The combination of plasma biomarkers via regression was the most predictive of Aβ-PET, especially in the MCI group (prior to PET, n = 75) (sensitivity = 100%, specificity = 82%, negative predictive value = 100%). In our cohort of memory clinic patients (mainly MCI), the combination of plasma biomarkers was sensitive in ruling out Aβ-PET negative individuals, thus suggesting a potential role as rule-out tool in clinical practice.
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Affiliation(s)
- Marco Bucci
- Department of Neurobiology, Care Sciences and Society, Centre for Alzheimer Research, Division of Clinical Geriatrics, Karolinska Institutet, SE-14183, Stockholm, Sweden
- Theme Inflammation and Aging, Karolinska University Hospital, SE-14186, Stockholm, Sweden
| | - Marina Bluma
- Department of Neurobiology, Care Sciences and Society, Centre for Alzheimer Research, Division of Clinical Geriatrics, Karolinska Institutet, SE-14183, Stockholm, Sweden
| | - Irina Savitcheva
- Medical Radiation Physics and Nuclear Medicine, Karolinska University, SE-14186, Stockholm, Sweden
| | - Nicholas J Ashton
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, SE-43180, Mölndal, Sweden
| | - Konstantinos Chiotis
- Department of Neurobiology, Care Sciences and Society, Centre for Alzheimer Research, Division of Clinical Geriatrics, Karolinska Institutet, SE-14183, Stockholm, Sweden
- Department of Neurology, Karolinska University Hospital, SE-14186, Stockholm, Sweden
| | - Anna Matton
- Department of Neurobiology, Care Sciences and Society, Centre for Alzheimer Research, Division of Clinical Geriatrics, Karolinska Institutet, SE-14183, Stockholm, Sweden
| | - Miia Kivipelto
- Department of Neurobiology, Care Sciences and Society, Centre for Alzheimer Research, Division of Clinical Geriatrics, Karolinska Institutet, SE-14183, Stockholm, Sweden
- Theme Inflammation and Aging, Karolinska University Hospital, SE-14186, Stockholm, Sweden
| | - Guglielmo Di Molfetta
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, SE-43180, Mölndal, Sweden
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, SE-43180, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, SE-43180, Mölndal, Sweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, SE-43180, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, SE-43180, Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, WC1N 3BG, UK
- UK Dementia Research Institute at UCL, London, WC1N 3BG, UK
- Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, China
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, 53792, USA
| | - Agneta Nordberg
- Department of Neurobiology, Care Sciences and Society, Centre for Alzheimer Research, Division of Clinical Geriatrics, Karolinska Institutet, SE-14183, Stockholm, Sweden.
- Theme Inflammation and Aging, Karolinska University Hospital, SE-14186, Stockholm, Sweden.
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Lu J, Ma X, Zhang H, Xiao Z, Li M, Wu J, Ju Z, Chen L, Zheng L, Ge J, Liang X, Bao W, Wu P, Ding D, Yen TC, Guan Y, Zuo C, Zhao Q. Head-to-head comparison of plasma and PET imaging ATN markers in subjects with cognitive complaints. Transl Neurodegener 2023; 12:34. [PMID: 37381042 PMCID: PMC10308642 DOI: 10.1186/s40035-023-00365-x] [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: 02/19/2023] [Accepted: 06/02/2023] [Indexed: 06/30/2023] Open
Abstract
BACKGROUND Gaining more information about the reciprocal associations between different biomarkers within the ATN (Amyloid/Tau/Neurodegeneration) framework across the Alzheimer's disease (AD) spectrum is clinically relevant. We aimed to conduct a comprehensive head-to-head comparison of plasma and positron emission tomography (PET) ATN biomarkers in subjects with cognitive complaints. METHODS A hospital-based cohort of subjects with cognitive complaints with a concurrent blood draw and ATN PET imaging (18F-florbetapir for A, 18F-Florzolotau for T, and 18F-fluorodeoxyglucose [18F-FDG] for N) was enrolled (n = 137). The β-amyloid (Aβ) status (positive versus negative) and the severity of cognitive impairment served as the main outcome measures for assessing biomarker performances. RESULTS Plasma phosphorylated tau 181 (p-tau181) level was found to be associated with PET imaging of ATN biomarkers in the entire cohort. Plasma p-tau181 level and PET standardized uptake value ratios of AT biomarkers showed a similarly excellent diagnostic performance for distinguishing between Aβ+ and Aβ- subjects. An increased tau burden and glucose hypometabolism were significantly associated with the severity of cognitive impairment in Aβ+ subjects. Additionally, glucose hypometabolism - along with elevated plasma neurofilament light chain level - was related to more severe cognitive impairment in Aβ- subjects. CONCLUSION Plasma p-tau181, as well as 18F-florbetapir and 18F-Florzolotau PET imaging can be considered as interchangeable biomarkers in the assessment of Aβ status in symptomatic stages of AD. 18F-Florzolotau and 18F-FDG PET imaging could serve as biomarkers for the severity of cognitive impairment. Our findings have implications for establishing a roadmap to identifying the most suitable ATN biomarkers for clinical use.
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Affiliation(s)
- Jiaying Lu
- Department of Nuclear Medicine and PET Center, Huashan Hospital, Fudan University, Shanghai, China
- National Clinical Research Center for Aging and Medicine and National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China
| | - Xiaoxi Ma
- National Clinical Research Center for Aging and Medicine and National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - Huiwei Zhang
- Department of Nuclear Medicine and PET Center, Huashan Hospital, Fudan University, Shanghai, China
- National Clinical Research Center for Aging and Medicine and National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China
| | - Zhenxu Xiao
- National Clinical Research Center for Aging and Medicine and National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - Ming Li
- Department of Nuclear Medicine and PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Jie Wu
- National Clinical Research Center for Aging and Medicine and National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - Zizhao Ju
- Department of Nuclear Medicine and PET Center, Huashan Hospital, Fudan University, Shanghai, China
- National Clinical Research Center for Aging and Medicine and National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China
| | - Li Chen
- Department of Ultrasound, Huashan Hospital, Fudan University, Shanghai, China
| | - Li Zheng
- National Clinical Research Center for Aging and Medicine and National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - Jingjie Ge
- Department of Nuclear Medicine and PET Center, Huashan Hospital, Fudan University, Shanghai, China
- National Clinical Research Center for Aging and Medicine and National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China
| | - Xiaoniu Liang
- National Clinical Research Center for Aging and Medicine and National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - Weiqi Bao
- Department of Nuclear Medicine and PET Center, Huashan Hospital, Fudan University, Shanghai, China
- National Clinical Research Center for Aging and Medicine and National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China
| | - Ping Wu
- Department of Nuclear Medicine and PET Center, Huashan Hospital, Fudan University, Shanghai, China
- National Clinical Research Center for Aging and Medicine and National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China
| | - Ding Ding
- National Clinical Research Center for Aging and Medicine and National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | | | - Yihui Guan
- Department of Nuclear Medicine and PET Center, Huashan Hospital, Fudan University, Shanghai, China.
- National Clinical Research Center for Aging and Medicine and National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China.
| | - Chuantao Zuo
- Department of Nuclear Medicine and PET Center, Huashan Hospital, Fudan University, Shanghai, China.
- National Clinical Research Center for Aging and Medicine and National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China.
- Human Phenome Institute, Fudan University, Shanghai, China.
| | - Qianhua Zhao
- National Clinical Research Center for Aging and Medicine and National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China.
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China.
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.
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9
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Largent EA, Grill JD, O'Brien K, Wolk D, Harkins K, Karlawish J. Testing for Alzheimer Disease Biomarkers and Disclosing Results Across the Disease Continuum. Neurology 2023; 100:1010-1019. [PMID: 36720642 PMCID: PMC10238153 DOI: 10.1212/wnl.0000000000206891] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 12/20/2022] [Indexed: 02/02/2023] Open
Abstract
Three pathologic processes are characteristic of Alzheimer disease (AD): β-amyloid, hyperphosphorylated tau, and neurodegeneration. Our understanding of AD is undergoing a transformation due to our ability to measure biomarkers of these processes across different stages of cognitive impairment. There is growing interest in using AD biomarker tests in care and research and, with this, a growing need for guidance on how to return these sensitive results to patients and participants. Here, we propose a 5-step approach informed by clinical and research experience designing and implementing AD biomarker disclosure processes, extant evidence describing how individuals react to AD biomarker information, ethics, law, and the literature on breaking bad news. The clinician should (1) determine the appropriateness of AD biomarker testing and return of results for the particular patient or research participant. If testing is appropriate, the next steps are to (2) provide pretest education and seek consent for testing from the individual and their support person, (3) administer testing, (4) return the results to the individual and their support person, and (5) follow-up to promote the recipient's well-being.
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Affiliation(s)
- Emily A Largent
- From the Department of Medical Ethics and Health Policy (E.A.L., J.K.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Department of Psychiatry and Human Behavior (J.D.G.), and Department of Neurobiology and Behavior (J.D.G.), University of California, Irvine; Department of Neurology (K.O.B., D.W., J.K.), and Department of Medicine (K.H., J.K.), University of Pennsylvania Perelman School of Medicine, Philadelphia.
| | - Joshua D Grill
- From the Department of Medical Ethics and Health Policy (E.A.L., J.K.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Department of Psychiatry and Human Behavior (J.D.G.), and Department of Neurobiology and Behavior (J.D.G.), University of California, Irvine; Department of Neurology (K.O.B., D.W., J.K.), and Department of Medicine (K.H., J.K.), University of Pennsylvania Perelman School of Medicine, Philadelphia
| | - Kyra O'Brien
- From the Department of Medical Ethics and Health Policy (E.A.L., J.K.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Department of Psychiatry and Human Behavior (J.D.G.), and Department of Neurobiology and Behavior (J.D.G.), University of California, Irvine; Department of Neurology (K.O.B., D.W., J.K.), and Department of Medicine (K.H., J.K.), University of Pennsylvania Perelman School of Medicine, Philadelphia
| | - David Wolk
- From the Department of Medical Ethics and Health Policy (E.A.L., J.K.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Department of Psychiatry and Human Behavior (J.D.G.), and Department of Neurobiology and Behavior (J.D.G.), University of California, Irvine; Department of Neurology (K.O.B., D.W., J.K.), and Department of Medicine (K.H., J.K.), University of Pennsylvania Perelman School of Medicine, Philadelphia
| | - Kristin Harkins
- From the Department of Medical Ethics and Health Policy (E.A.L., J.K.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Department of Psychiatry and Human Behavior (J.D.G.), and Department of Neurobiology and Behavior (J.D.G.), University of California, Irvine; Department of Neurology (K.O.B., D.W., J.K.), and Department of Medicine (K.H., J.K.), University of Pennsylvania Perelman School of Medicine, Philadelphia
| | - Jason Karlawish
- From the Department of Medical Ethics and Health Policy (E.A.L., J.K.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Department of Psychiatry and Human Behavior (J.D.G.), and Department of Neurobiology and Behavior (J.D.G.), University of California, Irvine; Department of Neurology (K.O.B., D.W., J.K.), and Department of Medicine (K.H., J.K.), University of Pennsylvania Perelman School of Medicine, Philadelphia
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10
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Aramadaka S, Mannam R, Sankara Narayanan R, Bansal A, Yanamaladoddi VR, Sarvepalli SS, Vemula SL. Neuroimaging in Alzheimer's Disease for Early Diagnosis: A Comprehensive Review. Cureus 2023; 15:e38544. [PMID: 37273363 PMCID: PMC10239271 DOI: 10.7759/cureus.38544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/03/2023] [Indexed: 06/06/2023] Open
Abstract
Alzheimer's disease (AD) is the most common cause of dementia in the elderly, affecting roughly half of those over the age of 85. We briefly discussed the risk factors, epidemiology, and treatment options for AD. The development of therapeutic therapies operating very early in the disease cascade has been spurred by the realization that the disease process begins at least a decade or more before the manifestation of symptoms. Thus, the clinical significance of early diagnosis was emphasized. Using various keywords, a literature search was carried out using PubMed and other databases. For inclusion, pertinent articles were chosen and reviewed. This article has reviewed different neuroimaging techniques that are considered advanced tools to aid in establishing a diagnosis and highlighted the advantages as well as disadvantages of those techniques. Besides, the prevalence of several in vivo biomarkers aided in discriminating affected individuals from healthy controls in the early stages of the disease. Each imaging method has its advantages and disadvantages, hence no single imaging approach can be the optimum modality for diagnosis. This article also commented on a better approach to using these techniques to increase the likelihood of an early diagnosis.
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Affiliation(s)
| | - Raam Mannam
- Research, Narayana Medical College, Nellore, IND
| | | | - Arpit Bansal
- Research, Narayana Medical College, Nellore, IND
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11
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Lv X, Chu M, Liu Y, Jing D, Liu L, Cui Y, Wang Y, Jiang D, Song W, Yang C, Wu L. Neurofunctional Correlates of Activities of Daily Living in Patients with Posterior Cortical Atrophy. J Alzheimers Dis 2023; 93:295-305. [PMID: 36970906 DOI: 10.3233/jad-221229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
BACKGROUND Research on posterior cortical atrophy (PCA) has focused on cognitive decline, especially visual processing deficits. However, few studies have examined the impact of PCA on activities of daily living (ADL) and the neurofunctional and neuroanatomic bases of ADL. OBJECTIVE To identify brain regions associated with ADL in PCA patients. METHODS A total of 29 PCA patients, 35 typical Alzheimer's disease (tAD) patients, and 26 healthy volunteers were recruited. Each subject completed an ADL questionnaire that included basic and instrumental subscales (BADL and IADL, respectively), and underwent hybrid magnetic resonance imaging and 18F fluorodeoxyglucose positron emission tomography. Voxel-wise regression multivariable analysis was conducted to identify specific brain regions associated with ADL. RESULTS General cognitive status was similar between PCA and tAD patients; however, the former had lower total ADL scores and BADL and IADL scores. All three scores were associated with hypometabolism in bilateral parietal lobes (especially bilateral superior parietal gyri) at the whole-brain level, PCA-related hypometabolism level, and PCA-specific hypometabolism level. A cluster that included the right superior parietal gyrus showed an ADL×group interaction effect that was correlated with the total ADL score in the PCA group (r = -0.6908, p = 9.3599e-5) but not in the tAD group (r = 0.1006, p = 0.5904). There was no significant association between gray matter density and ADL scores. CONCLUSION Hypometabolism in bilateral superior parietal lobes contributes to a decline in ADL in patients with PCA and can potentially be targeted by noninvasive neuromodulatory interventions.
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Affiliation(s)
- Xuedan Lv
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
- Department of Rehabilitation, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Min Chu
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Yang Liu
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
- Department of Neurology, Beijing Sixth Hospital, Beijing, China
| | - Donglai Jing
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
- Department of Neurology, Rongcheng People's Hospital, Hebei, China
| | - Li Liu
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Yue Cui
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Yihao Wang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Deming Jiang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Weiqun Song
- Department of Rehabilitation, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Caishui Yang
- State Key Laboratory of Cognitive Neuroscience and Learning, Faculty of Psychology, Beijing Normal University, Beijing, China
- School of Systems Science, Beijing Normal University, Beijing, China
| | - Liyong Wu
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
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12
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PET imaging in dementia. Nucl Med Mol Imaging 2022. [DOI: 10.1016/b978-0-12-822960-6.00089-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
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13
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Garibotto V, Boccardi M, Chiti A, Frisoni GB. Molecular imaging and fluid biomarkers of Alzheimer's disease neuropathology: an opportunity for integrated diagnostics. Eur J Nucl Med Mol Imaging 2021; 48:2067-2069. [PMID: 33688995 DOI: 10.1007/s00259-020-05116-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Affiliation(s)
- Valentina Garibotto
- NIMTlab - Neuroimaging and Innovative Molecular Tracers Laboratory, University of Geneva, Geneva, Switzerland. .,Nuclear Medicine and Molecular Division, University Hospitals of Geneva, Rue Gabrielle-Perret-Gentil 4, 1205, Geneva, Switzerland.
| | - Marina Boccardi
- German Center for Neurodegenerative Diseases (DZNE) Rostock-Greifswald, Rostock, Germany
| | - Arturo Chiti
- Department of Biomedical Sciences, Humanitas University, Milan, Italy.,Humanitas Clinical and Research Center, IRCCS, Milan, Italy
| | - Giovanni B Frisoni
- Memory Clinic, University Hospital, Geneva, Switzerland.,LANVIE - Laboratory of Neuroimaging of Aging, University of Geneva, Geneva, Switzerland
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14
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Serum Corticosterone and Insulin Resistance as Early Biomarkers in the hAPP23 Overexpressing Mouse Model of Alzheimer's Disease. Int J Mol Sci 2021; 22:ijms22136656. [PMID: 34206322 PMCID: PMC8269119 DOI: 10.3390/ijms22136656] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 06/16/2021] [Accepted: 06/18/2021] [Indexed: 12/21/2022] Open
Abstract
Increasing epidemiological evidence highlights the association between systemic insulin resistance and Alzheimer’s disease (AD). As insulin resistance can be caused by high-stress hormone levels and since hypercortisolism appears to be an important risk factor of AD, we aimed to investigate the systemic insulin functionality and circulating stress hormone levels in a mutant humanized amyloid precursor protein (APP) overexpressing (hAPP23+/−) AD mouse model. Memory and spatial learning of male hAPP23+/− and C57BL/6 (wild type, WT) mice were assessed by a Morris Water Maze (MWM) test at the age of 4 and 12 months. The systemic metabolism was examined by intraperitoneal glucose and insulin tolerance tests (GTT, ITT). Insulin and corticosterone levels were determined in serum. In the hippocampus, parietal and occipital cortex of hAPP23+/− brains, amyloid-beta (Aβ) deposits were present at 12 months of age. MWM demonstrated a cognitive decline in hAPP23+/− mice at 12 but not at 4 months, evidenced by increasing total path lengths and deteriorating probe trials compared to WT mice. hAPP23+/− animals presented increased serum corticosterone levels compared to WT mice at both 4 and 12 months. hAPP23+/− mice exhibited peripheral insulin resistance compared to WT mice at 4 months, which stabilized at 12 months of age. Serum insulin levels were similar between genotypes at 4 months of age but were significantly higher in hAPP23+/− mice at 12 months of age. Peripheral glucose homeostasis remained unchanged. These results indicate that peripheral insulin resistance combined with elevated circulating stress hormone levels could be potential biomarkers of the pre-symptomatic phase of AD.
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15
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Damian A, Portugal F, Niell N, Quagliata A, Bayardo K, Alonso O, Ferrando R. Clinical Impact of PET With 18F-FDG and 11C-PIB in Patients With Dementia in a Developing Country. Front Neurol 2021; 12:630958. [PMID: 34017300 PMCID: PMC8129494 DOI: 10.3389/fneur.2021.630958] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Accepted: 04/06/2021] [Indexed: 12/03/2022] Open
Abstract
Introduction: The objective of this study was to evaluate the clinical impact PET with 18F-FDG and 11C-PIB in patients with dementia in a developing country. Methodology: Retrospective study of the patients referred for the evaluation of dementia to the only PET center in Uruguay. A total of 248 patients were identified, from which 70 patients were included based on the availability of medical history and clinical follow-up. Main outcomes included change in diagnosis, diagnostic dilemma and AD treatment. We evaluated the association of clinical outcomes with PET concordance with baseline diagnosis, diagnostic dilemma, level of education, AD pathology/Non-AD pathology (AD/Non-AD), baseline diagnosis and 11C-PIB PET result. Results: Baseline clinical diagnosis was concordant with 18F-FDG and 11C-PIB PET results in 64.7 and 77.1% of the patients, respectively. Change in diagnosis after PET was identified in 30.0% of the patients and was associated with discordant 18F-FDG (p = 0.002) and 11C-PIB (p < 0.001) PET results, previous diagnostic dilemma (p = 0.005), low education (p = 0.027), Non-AD baseline diagnosis (p = 0.027), and negative 11C-PIB PET result (p < 0.001). Only the last variable remained significant in the multivariate analysis (adjusted p = 0.038). Diagnostic dilemma decreased after PET from 15.7 to 7.1% (p = 0.11) and was associated with Non-AD diagnosis (p = 0.002) and negative 11C-PIB PET result (p = 0.003). Change in AD treatment after PET occurred in 45.7% of the patients. Conclusion:18F-FDG and 11C-PIB PET had a significant clinical impact in terms of change in diagnosis and treatment in patients with dementia in a developing country, similar to that reported in high-income countries.
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Affiliation(s)
- Andres Damian
- Centro Uruguayo de Imagenología Molecular (CUDIM), Montevideo, Uruguay.,Centro de Medicina Nuclear e Imagenología Molecular, Hospital de Clínicas, Universidad de la República (UdelaR), Montevideo, Uruguay
| | - Fabiola Portugal
- Centro de Medicina Nuclear e Imagenología Molecular, Hospital de Clínicas, Universidad de la República (UdelaR), Montevideo, Uruguay
| | - Nicolas Niell
- Centro Uruguayo de Imagenología Molecular (CUDIM), Montevideo, Uruguay.,Centro de Medicina Nuclear e Imagenología Molecular, Hospital de Clínicas, Universidad de la República (UdelaR), Montevideo, Uruguay
| | - Adriana Quagliata
- Centro Uruguayo de Imagenología Molecular (CUDIM), Montevideo, Uruguay
| | - Karina Bayardo
- Centro de Medicina Nuclear e Imagenología Molecular, Hospital de Clínicas, Universidad de la República (UdelaR), Montevideo, Uruguay
| | - Omar Alonso
- Centro Uruguayo de Imagenología Molecular (CUDIM), Montevideo, Uruguay.,Centro de Medicina Nuclear e Imagenología Molecular, Hospital de Clínicas, Universidad de la República (UdelaR), Montevideo, Uruguay
| | - Rodolfo Ferrando
- Centro Uruguayo de Imagenología Molecular (CUDIM), Montevideo, Uruguay.,Centro de Medicina Nuclear e Imagenología Molecular, Hospital de Clínicas, Universidad de la República (UdelaR), Montevideo, Uruguay
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16
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Abstract
Alzheimer's disease (AD) is the most common cause of dementia and accounts for approximately 50% to 80% of all cases of dementia. The diagnosis of probable AD is based on clinical criteria and overlapping clinical features pose a challenge to accurate diagnosis. However, neuroimaging has been included as a biomarker in various published criteria for the diagnosis of probable AD, in the absence of a confirmatory diagnostic test during life. Advances in neuroimaging techniques and their inclusion in diagnostic and research criteria for the diagnosis of AD includes the use of positron emission tomography (PET) imaging as a biomarker in various therapeutic and prognostic studies in AD. The development and application of a range of PET tracers will allow more detailed assessment of people with AD and will improve diagnostic specificity and targeted therapy of AD. The aim of this review is to summarize current evidence on PET imaging using the non-specific tracer [18F]fluorodeoxyglucose and specific tracers that target amyloid and tau pathology in people with AD.
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Affiliation(s)
- Shailendra Mohan Tripathi
- Aberdeen Biomedical Imaging Centre, Institute of Medical Sciences, University of Aberdeen, Foresterhill, Aberdeen, UK
| | - Alison D Murray
- Aberdeen Biomedical Imaging Centre, Institute of Medical Sciences, University of Aberdeen, Foresterhill, Aberdeen, UK
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