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Han SW, Lee SH, Kim JH, Lee JJ, Park YH, Kim S, Nho K, Sohn JH. Association of liver function markers and apolipoprotein E ε4 with pathogenesis and cognitive decline in Alzheimer's disease. Front Aging Neurosci 2024; 16:1411466. [PMID: 39114318 PMCID: PMC11303325 DOI: 10.3389/fnagi.2024.1411466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Accepted: 07/09/2024] [Indexed: 08/10/2024] Open
Abstract
Background Alzheimer's disease (AD) is a complex neurodegenerative disorder influenced by various factors, including liver function, which may impact the clearance of amyloid-β (Aβ) in the brain. This study aimed to explore how the apolipoprotein E (APOE) ε4 allele affects the relationship of liver function markers with AD pathology and cognition. Methods We analyzed data from two independent cohorts, including 732 participants from the Hallym University Medical Center and 483 from the Alzheimer's Disease Neuroimaging Initiative, each group consisting of individuals with and without the APOE ε4 allele. Cross-sectional analyses evaluated the associations of liver enzymes (aspartate aminotransferase [AST], alanine aminotransferase [ALT], alkaline phosphatase, total bilirubin, and albumin) with AD diagnosis, amyloid positron emission tomography (PET) burden, and cerebrospinal fluid biomarkers for AD (Aβ42, total tau, and phosphorylated tau181) at baseline. Longitudinally, we investigated the associations between these liver enzymes and changes in cognitive performance over the course of a year. Logistic and linear regression models were used to analyze these associations and mediation analyses were conducted to assess whether age and amyloid PET burden mediated these associations. Results Only in the APOE ε4 carrier group, a high AST to ALT ratio and low ALT levels were significantly associated with AD diagnosis, increased amyloid PET burden, and faster longitudinal decline in cognitive function in both cohorts. In particular, the AST to ALT ratio was associated with cerebrospinal fluid Aβ42 levels exclusively in the APOE ε4 carrier group in the Alzheimer's Disease Neuroimaging Initiative cohort but not with phosphorylated tau181 or total tau levels. Moreover, mediation analyses from both cohorts revealed that in the APOE ε4 carriers group, age did not mediate the associations between liver enzymes and AD diagnosis or amyloid PET burden. However, amyloid PET burden partially mediated the association between liver enzymes and AD diagnosis exclusively in the APOE ε4 carriers group. Conclusion This study provides valuable insights into the significant association of the APOE ε4 allele with liver enzymes and their potential role in Aβ-related pathogenesis and cognition in AD. Further research is required to elucidate the underlying mechanisms and potential therapeutic implications of these findings.
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Affiliation(s)
- Sang-Won Han
- Department of Neurology, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon-si, Gangwon-do, Republic of Korea
| | - Sang-Hwa Lee
- Department of Neurology, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon-si, Gangwon-do, Republic of Korea
| | - Jong Ho Kim
- Department of Anesthesiology and Pain Medicine, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon-si, Gangwon-do, Republic of Korea
| | - Jae-Jun Lee
- Department of Anesthesiology and Pain Medicine, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon-si, Gangwon-do, Republic of Korea
- Institute of New Frontier Research Team, Hallym University, Chuncheon-si, Gangwon-do, Republic of Korea
| | - Young Ho Park
- Department of Neurology, Seoul National University College of Medicine and Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Republic of Korea
| | - SangYun Kim
- Department of Neurology, Seoul National University College of Medicine and Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Republic of Korea
| | - Kwangsik Nho
- Department of Radiology and Imaging Sciences, Center for Computational Biology and Bioinformatics, Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Jong-Hee Sohn
- Department of Neurology, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon-si, Gangwon-do, Republic of Korea
- Institute of New Frontier Research Team, Hallym University, Chuncheon-si, Gangwon-do, Republic of Korea
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Jung YH, Jang H, Park S, Kim HJ, Seo SW, Kim GB, Shon YM, Kim S, Na DL. Effectiveness of Personalized Hippocampal Network-Targeted Stimulation in Alzheimer Disease: A Randomized Clinical Trial. JAMA Netw Open 2024; 7:e249220. [PMID: 38709534 PMCID: PMC11074813 DOI: 10.1001/jamanetworkopen.2024.9220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 03/01/2024] [Indexed: 05/07/2024] Open
Abstract
Importance Repetitive transcranial magnetic stimulation (rTMS) has emerged as a safe and promising intervention for Alzheimer disease (AD). Objective To investigate the effect of a 4-week personalized hippocampal network-targeted rTMS on cognitive and functional performance, as well as functional connectivity in AD. Design, Setting, and Participants This randomized clinical trial, which was sham-controlled and masked to participants and evaluators, was conducted between May 2020 and April 2022 at a single Korean memory clinic. Eligible participants were between ages 55 and 90 years and had confirmed early AD with evidence of an amyloid biomarker. Participants who met the inclusion criteria were randomly assigned to receive hippocampal network-targeted rTMS or sham stimulation. Participants received 4-week rTMS treatment, with assessment conducted at weeks 4 and 8. Data were analyzed between April 2022 and January 2024. Interventions Each patient received 20 sessions of personalized rTMS targeting the left parietal area, functionally connected to the hippocampus, based on fMRI connectivity analysis over 4 weeks. The sham group underwent the same procedure, excluding actual magnetic stimulation. A personalized 3-dimensional printed frame to fix the TMS coil to the optimal target site was produced. Main Outcomes and Measures The primary outcome was the change in the AD Assessment Scale-Cognitive Subscale test (ADAS-Cog) after 8 weeks from baseline. Secondary outcomes included changes in the Clinical Dementia Rating-Sum of Boxes (CDR-SOB) and Seoul-Instrumental Activity Daily Living (S-IADL) scales, as well as resting-state fMRI connectivity between the hippocampus and cortical areas. Results Among 30 participants (18 in the rTMS group; 12 in the sham group) who completed the 8-week trial, the mean (SD) age was 69.8 (9.1) years; 18 (60%) were female. As the primary outcome, the change in ADAS-Cog at the eighth week was significantly different between the rTMS and sham groups (coefficient [SE], -5.2 [1.6]; P = .002). The change in CDR-SOB (-4.5 [1.4]; P = .007) and S-IADL (1.7 [0.7]; P = .004) were significantly different between the groups favoring rTMS groups. The fMRI connectivity analysis revealed that rTMS increased the functional connectivity between the hippocampus and precuneus, with its changes associated with improvements in ADAS-Cog (r = -0.57; P = .005). Conclusions and Relevance This randomized clinical trial demonstrated the positive effects of rTMS on cognitive and functional performance, and the plastic changes in the hippocampal-cortical network. Our results support the consideration of rTMS as a potential treatment for AD. Trial Registration ClinicalTrials.gov Identifier: NCT04260724.
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Affiliation(s)
- Young Hee Jung
- Department of Neurology, Myongji Hospital, Hanyang University, Goyang, Korea
| | - Hyemin Jang
- Department of Neurology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Samsung Medical Center, Seoul, Korea
- Samsung Alzheimer Research Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Korea
| | - Sungbeen Park
- Department of Artificial Intelligence, Hanyang University, Seoul, Korea
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Samsung Medical Center, Seoul, Korea
- Samsung Alzheimer Research Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Korea
- Department of Health Science and Technology, Samsung Advanced Institute for Health Science & Technology, Sungkyunkwan University, Seoul, Korea
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Samsung Medical Center, Seoul, Korea
- Samsung Alzheimer Research Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Korea
- Department of Clinical Research Design & Evaluation, Samsung Advanced Institute for Health Science & Technology, Sungkyunkwan University, Seoul, Korea
- Department of Health Science and Technology, Samsung Advanced Institute for Health Science & Technology, Sungkyunkwan University, Seoul, Korea
| | | | - Young-Min Shon
- Department of Health Science and Technology, Samsung Advanced Institute for Health Science & Technology, Sungkyunkwan University, Seoul, Korea
- Smart Healthcare Research Institute, Samsung Medical Center, Seoul, Korea
| | - Sungshin Kim
- Department of Artificial Intelligence, Hanyang University, Seoul, Korea
- Smart Healthcare Research Institute, Samsung Medical Center, Seoul, Korea
- Department of Data Science, Hanyang University, Seoul, Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Korea
| | - Duk L. Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Samsung Medical Center, Seoul, Korea
- Happymind Clinic, Seoul, Korea
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3
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Kang SH, Kim J, Kim JP, Cho SH, Choe YS, Jang H, Kim HJ, Koh SB, Na DL, Seong JK, Seo SW. Harmonisation of PET imaging features with different amyloid ligands using machine learning-based classifier. Eur J Nucl Med Mol Imaging 2021; 49:321-330. [PMID: 34328533 DOI: 10.1007/s00259-021-05499-6] [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: 02/22/2021] [Accepted: 07/14/2021] [Indexed: 10/20/2022]
Abstract
PURPOSE In this study, we used machine learning to develop a new method derived from a ligand-independent amyloid (Aβ) positron emission tomography (PET) classifier to harmonise different Aβ ligands. METHODS We obtained 107 paired 18F-florbetaben (FBB) and 18F-flutemetamol (FMM) PET images at the Samsung Medical Centre. To apply the method to FMM ligand, we transferred the previously developed FBB PET classifier to test similar features from the FMM PET images for application to FMM, which in turn developed a ligand-independent Aβ PET classifier. We explored the concordance rates of our classifier in detecting cortical and striatal Aβ positivity. We investigated the correlation of machine learning-based cortical tracer uptake (ML-CTU) values quantified by the classifier between FBB and FMM. RESULTS This classifier achieved high classification accuracy (area under the curve = 0.958) even with different Aβ PET ligands. In addition, the concordance rate of FBB and FMM using the classifier (87.5%) was good to excellent, which seemed to be higher than that in visual assessment (82.7%) and lower than that in standardised uptake value ratio cut-off categorisation (93.3%). FBB and FMM ML-CTU values were highly correlated with each other (R = 0.903). CONCLUSION Our findings suggested that our novel classifier may harmonise FBB and FMM ligands in the clinical setting which in turn facilitate the biomarker-guided diagnosis and trials of anti-Aβ treatment in the research field.
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Affiliation(s)
- Sung Hoon Kang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, 06351, Seoul, South Korea
- Neuroscience Center, Samsung Medical Center, Seoul, 06351, South Korea
- Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, South Korea
| | - Jeonghun Kim
- Medical & Health Device Division, Korea Testing Laboratory, Seoul, South Korea
| | - Jun Pyo Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, 06351, Seoul, South Korea
- Neuroscience Center, Samsung Medical Center, Seoul, 06351, South Korea
| | - Soo Hyun Cho
- Department of Neurology, Chonnam National University Hospital, Chonnam National University Medical School, Gwangju, South Korea
| | - Yeong Sim Choe
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, 06351, Seoul, South Korea
- Neuroscience Center, Samsung Medical Center, Seoul, 06351, South Korea
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, South Korea
| | - Hyemin Jang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, 06351, Seoul, South Korea
- Neuroscience Center, Samsung Medical Center, Seoul, 06351, South Korea
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, 06351, Seoul, South Korea
- Neuroscience Center, Samsung Medical Center, Seoul, 06351, South Korea
| | - Seong-Beom Koh
- Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, South Korea
| | - Duk L Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, 06351, Seoul, South Korea
- Neuroscience Center, Samsung Medical Center, Seoul, 06351, South Korea
| | - Joon-Kyung Seong
- Department of Artificial Intelligence, Korea University, Seoul, South Korea.
- School of Biomedical Engineering, Korea University, Seoul, South Korea.
- Interdisciplinary Program in Precision Public Health, Korea University, Seoul, South Korea.
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, 06351, Seoul, South Korea.
- Neuroscience Center, Samsung Medical Center, Seoul, 06351, South Korea.
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, South Korea.
- Department of Intelligent Precision Healthcare Convergence, SAIHST, Sungkyunkwan University, Seoul, South Korea.
- Samsung Alzheimer Research Center, Center for Clinical Epidemiology Medical Center, Seoul, South Korea.
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4
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Kang SH, Cho H, Shin J, Kim HR, Noh Y, Kim EJ, Lyoo CH, Jang H, Kim HJ, Koh SB, Na DL, Suh MK, Seo SW. Clinical Characteristic in Primary Progressive Aphasia in Relation to Alzheimer's Disease Biomarkers. J Alzheimers Dis 2021; 84:633-645. [PMID: 34569949 DOI: 10.3233/jad-210392] [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: 11/15/2022]
Abstract
BACKGROUND Primary progressive aphasia (PPA) is associated with amyloid-β (Aβ) pathology. However, clinical feature of PPA based on Aβ positivity remains unclear. OBJECTIVE We aimed to assess the prevalence of Aβ positivity in patients with PPA and compare the clinical characteristics of patients with Aβ-positive (A+) and Aβ-negative (A-) PPA. Further, we applied Aβ and tau classification system (AT system) in patients with PPA for whom additional information of in vivo tau biomarker was available. METHODS We recruited 110 patients with PPA (41 semantic [svPPA], 27 non-fluent [nfvPPA], 32 logopenic [lvPPA], and 10 unclassified [ucPPA]) who underwent Aβ-PET imaging at multi centers. The extent of language impairment and cortical atrophy were compared between the A+ and A-PPA subgroups using general linear models. RESULTS The prevalence of Aβ positivity was highest in patients with lvPPA (81.3%), followed by ucPPA (60.0%), nfvPPA (18.5%), and svPPA (9.8%). The A+ PPA subgroup manifested cortical atrophy mainly in the left superior temporal/inferior parietal regions and had lower repetition scores compared to the A-PPA subgroup. Further, we observed that more than 90% (13/14) of the patients with A+ PPA had tau deposition. CONCLUSION Our findings will help clinicians understand the patterns of language impairment and cortical atrophy in patients with PPA based on Aβ deposition. Considering that most of the A+ PPA patents are tau positive, understanding the influence of Alzheimer's disease biomarkers on PPA might provide an opportunity for these patients to participate in clinical trials aimed for treating atypical Alzheimer's disease.
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Affiliation(s)
- Sung Hoon Kang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea.,Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Korea
| | - Hanna Cho
- Department of Neurology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Jiho Shin
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Hang-Rai Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea.,Department of Neurology, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang, Korea
| | - Young Noh
- Department of Neurology, Gachon University Gil Medical Center, Incheon, Korea
| | - Eun-Joo Kim
- Department of Neurology, Pusan National University Hospital, Pusan National University School of Medicine and Medical Research Institute, Busan, Korea
| | - Chul Hyoung Lyoo
- Department of Neurology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Hyemin Jang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Seong-Beom Koh
- Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Korea
| | - Duk L Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Mee Kyung Suh
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea.,Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea.,Samsung Alzheimer Research Center and Center for Clinical Epidemiology Medical Center, Seoul, Korea.,Department of Intelligent Precision Healthcare Convergence, SAIHST, Sungkyunkwan University, Seoul, Korea
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5
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Kang SH, Cheon BK, Kim JS, Jang H, Kim HJ, Park KW, Noh Y, Lee JS, Ye BS, Na DL, Lee H, Seo SW. Machine Learning for the Prediction of Amyloid Positivity in Amnestic Mild Cognitive Impairment. J Alzheimers Dis 2021; 80:143-157. [PMID: 33523003 DOI: 10.3233/jad-201092] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
BACKGROUND Amyloid-β (Aβ) evaluation in amnestic mild cognitive impairment (aMCI) patients is important for predicting conversion to Alzheimer's disease. However, Aβ evaluation through Aβ positron emission tomography (PET) is limited due to high cost and safety issues. OBJECTIVE We therefore aimed to develop and validate prediction models of Aβ positivity for aMCI using optimal interpretable machine learning (ML) approaches utilizing multimodal markers. METHODS We recruited 529 aMCI patients from multiple centers who underwent Aβ PET. We trained ML algorithms using a training cohort (324 aMCI from Samsung medical center) with two-phase modelling: model 1 included age, gender, education, diabetes, hypertension, apolipoprotein E genotype, and neuropsychological test scores; model 2 included the same variables as model 1 with additional MRI features. We used four-fold cross-validation during the modelling and evaluated the models on an external validation cohort (187 aMCI from the other centers). RESULTS Model 1 showed good accuracy (area under the receiver operating characteristic curve [AUROC] 0.837) in cross-validation, and fair accuracy (AUROC 0.765) in external validation. Model 2 led to improvement in the prediction performance with good accuracy (AUROC 0.892) in cross validation compared to model 1. Apolipoprotein E genotype, delayed recall task scores, and interaction between cortical thickness in the temporal region and hippocampal volume were the most important predictors of Aβ positivity. CONCLUSION Our results suggest that ML models are effective in predicting Aβ positivity at the individual level and could help the biomarker-guided diagnosis of prodromal AD.
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Affiliation(s)
- Sung Hoon Kang
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea.,Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Korea
| | - Bo Kyoung Cheon
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea.,Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Korea
| | - Ji-Sun Kim
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Hyemin Jang
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Hee Jin Kim
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Kyung Won Park
- Department of Neurology, Dong-A University Medical Center, Dong-A University College of Medicine, Busan, Korea
| | - Young Noh
- Department of Neurology, Gachon University Gil Medical Center, Incheon, Korea
| | - Jin San Lee
- Department of Neurology, Kyung Hee University Hospital, Seoul, Korea
| | - Byoung Seok Ye
- Department of Neurology, Severance hospital, Yonsei University School of Medicine, Seoul, Korea
| | - Duk L Na
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Hyejoo Lee
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Sang Won Seo
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea.,Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Korea.,Samsung Alzheimer Research Center and Center for Clinical Epidemiology Medical Center, Seoul, Korea.,Department of Intelligent Precision Healthcare Convergence, SAIHST, Sungkyunkwan University, Seoul, Korea
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Bucci M, Savitcheva I, Farrar G, Salvadó G, Collij L, Doré V, Gispert JD, Gunn R, Hanseeuw B, Hansson O, Shekari M, Lhommel R, Molinuevo JL, Rowe C, Sur C, Whittington A, Buckley C, Nordberg A. A multisite analysis of the concordance between visual image interpretation and quantitative analysis of [ 18F]flutemetamol amyloid PET images. Eur J Nucl Med Mol Imaging 2021; 48:2183-2199. [PMID: 33844055 PMCID: PMC8175298 DOI: 10.1007/s00259-021-05311-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Accepted: 03/09/2021] [Indexed: 12/18/2022]
Abstract
BACKGROUND [18F]flutemetamol PET scanning provides information on brain amyloid load and has been approved for routine clinical use based upon visual interpretation as either negative (equating to none or sparse amyloid plaques) or amyloid positive (equating to moderate or frequent plaques). Quantitation is however fundamental to the practice of nuclear medicine and hence can be used to supplement amyloid reading methodology especially in unclear cases. METHODS A total of 2770 [18F]flutemetamol images were collected from 3 clinical studies and 6 research cohorts with available visual reading of [18F]flutemetamol and quantitative analysis of images. These were assessed further to examine both the discordance and concordance between visual and quantitative imaging primarily using thresholds robustly established using pathology as the standard of truth. Scans covered a wide range of cases (i.e. from cognitively unimpaired subjects to patients attending the memory clinics). Methods of quantifying amyloid ranged from using CE/510K cleared marked software (e.g. CortexID, Brass), to other research-based methods (e.g. PMOD, CapAIBL). Additionally, the clinical follow-up of two types of discordance between visual and quantitation (V+Q- and V-Q+) was examined with competing risk regression analysis to assess possible differences in prediction for progression to Alzheimer's disease (AD) and other diagnoses (OD). RESULTS Weighted mean concordance between visual and quantitation using the autopsy-derived threshold was 94% using pons as the reference region. Concordance from a sensitivity analysis which assessed the maximum agreement for each cohort using a range of cut-off values was also estimated at approximately 96% (weighted mean). Agreement was generally higher in clinical cases compared to research cases. V-Q+ discordant cases were 11% more likely to progress to AD than V+Q- for the SUVr with pons as reference region. CONCLUSIONS Quantitation of amyloid PET shows a high agreement vs binary visual reading and also allows for a continuous measure that, in conjunction with possible discordant analysis, could be used in the future to identify possible earlier pathological deposition as well as monitor disease progression and treatment effectiveness.
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Affiliation(s)
- Marco Bucci
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.
| | - Irina Savitcheva
- Medical Radiation Physics and Nuclear Medicine, Section for Nuclear Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - Gill Farrar
- Pharmaceutical Diagnostics, GE Healthcare, Amersham, UK
| | - Gemma Salvadó
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain.,IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
| | - Lyduine Collij
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan, 1117, Amsterdam, Netherlands
| | - Vincent Doré
- Austin Health, University of Melbourne, Melbourne, Australia.,Health and Biosecurity, CSIRO, Parkville, Australia
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain.,IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain.,Universitat Pompeu Fabra, Barcelona, Spain.,Centro de Investigación Biomédica en Red Bioingenieriá, Biomateriales y Nanomedicina, (CIBER-BBN), Barcelona, Spain
| | - Roger Gunn
- Invicro, London, UK.,Division of Brain Sciences, Department of Medicine, Imperial College, London, UK
| | - Bernard Hanseeuw
- Neurology and Nuclear Medicine Departments, Saint-Luc University Hospital, Av. Hippocrate, 10, 1200, Brussels, Belgium.,Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmo, Lund University, Lund, Sweden
| | - Mahnaz Shekari
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain.,IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain.,Universitat Pompeu Fabra, Barcelona, Spain
| | - Renaud Lhommel
- Neurology and Nuclear Medicine Departments, Saint-Luc University Hospital, Av. Hippocrate, 10, 1200, Brussels, Belgium
| | - José Luis Molinuevo
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain.,IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain.,Universitat Pompeu Fabra, Barcelona, Spain.,Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain
| | - Christopher Rowe
- Austin Health, University of Melbourne, Melbourne, Australia.,Department of Medicine, The University of Melbourne, Melbourne, Australia
| | | | | | | | - Agneta Nordberg
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden. .,Department of Aging, Karolinska University Hospital, Stockholm, Sweden.
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7
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Kim JP, Kim J, Jang H, Kim J, Kang SH, Kim JS, Lee J, Na DL, Kim HJ, Seo SW, Park H. Predicting amyloid positivity in patients with mild cognitive impairment using a radiomics approach. Sci Rep 2021; 11:6954. [PMID: 33772041 PMCID: PMC7997887 DOI: 10.1038/s41598-021-86114-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Accepted: 02/23/2021] [Indexed: 02/01/2023] Open
Abstract
Predicting amyloid positivity in patients with mild cognitive impairment (MCI) is crucial. In the present study, we predicted amyloid positivity with structural MRI using a radiomics approach. From MR images (including T1, T2 FLAIR, and DTI sequences) of 440 MCI patients, we extracted radiomics features composed of histogram and texture features. These features were used alone or in combination with baseline non-imaging predictors such as age, sex, and ApoE genotype to predict amyloid positivity. We used a regularized regression method for feature selection and prediction. The performance of the baseline non-imaging model was at a fair level (AUC = 0.71). Among single MR-sequence models, T1 and T2 FLAIR radiomics models also showed fair performances (AUC for test = 0.71-0.74, AUC for validation = 0.68-0.70) in predicting amyloid positivity. When T1 and T2 FLAIR radiomics features were combined, the AUC for test was 0.75 and AUC for validation was 0.72 (p vs. baseline model < 0.001). The model performed best when baseline features were combined with a T1 and T2 FLAIR radiomics model (AUC for test = 0.79, AUC for validation = 0.76), which was significantly better than those of the baseline model (p < 0.001) and the T1 + T2 FLAIR radiomics model (p < 0.001). In conclusion, radiomics features showed predictive value for amyloid positivity. It can be used in combination with other predictive features and possibly improve the prediction performance.
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Affiliation(s)
- Jun Pyo Kim
- grid.264381.a0000 0001 2181 989XDepartment of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea ,grid.414964.a0000 0001 0640 5613Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Korea ,grid.414964.a0000 0001 0640 5613Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Jonghoon Kim
- grid.264381.a0000 0001 2181 989XDepartment of Electronic and Computer Engineering, Sungkyunkwan University, Suwon, Korea
| | - Hyemin Jang
- grid.264381.a0000 0001 2181 989XDepartment of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea ,grid.414964.a0000 0001 0640 5613Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Korea ,grid.414964.a0000 0001 0640 5613Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Jaeho Kim
- grid.264381.a0000 0001 2181 989XDepartment of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea ,grid.414964.a0000 0001 0640 5613Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Korea ,grid.414964.a0000 0001 0640 5613Neuroscience Center, Samsung Medical Center, Seoul, Korea ,grid.256753.00000 0004 0470 5964Department of Neurology, Dongtan Sacred Heart Hospital, Hallym University College of Medicine, Hwaseong, Korea
| | - Sung Hoon Kang
- grid.264381.a0000 0001 2181 989XDepartment of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea ,grid.414964.a0000 0001 0640 5613Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Korea ,grid.414964.a0000 0001 0640 5613Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Ji Sun Kim
- grid.264381.a0000 0001 2181 989XDepartment of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea ,grid.414964.a0000 0001 0640 5613Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Korea ,grid.414964.a0000 0001 0640 5613Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Jongmin Lee
- grid.264381.a0000 0001 2181 989XDepartment of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea ,grid.414964.a0000 0001 0640 5613Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Korea ,grid.414964.a0000 0001 0640 5613Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Duk L. Na
- grid.264381.a0000 0001 2181 989XDepartment of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea ,grid.414964.a0000 0001 0640 5613Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Korea ,grid.414964.a0000 0001 0640 5613Neuroscience Center, Samsung Medical Center, Seoul, Korea ,grid.264381.a0000 0001 2181 989XDepartment of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea
| | - Hee Jin Kim
- grid.264381.a0000 0001 2181 989XDepartment of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea ,grid.414964.a0000 0001 0640 5613Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Korea ,grid.414964.a0000 0001 0640 5613Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Sang Won Seo
- grid.264381.a0000 0001 2181 989XDepartment of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea ,grid.414964.a0000 0001 0640 5613Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Korea ,grid.414964.a0000 0001 0640 5613Neuroscience Center, Samsung Medical Center, Seoul, Korea ,grid.264381.a0000 0001 2181 989XDepartment of Clinical Research Design and Evaluation, SAIHST, Sungkyunkwan University, Seoul, Korea ,grid.414964.a0000 0001 0640 5613Center for Clinical Epidemiology, Samsung Medical Center, Seoul, Korea ,grid.264381.a0000 0001 2181 989XDepartment of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon-si, Korea
| | - Hyunjin Park
- grid.410720.00000 0004 1784 4496Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Korea ,grid.264381.a0000 0001 2181 989XSchool of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon-si, Republic of Korea
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8
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Collij LE, Salvadó G, Shekari M, Lopes Alves I, Reimand J, Wink AM, Zwan M, Niñerola-Baizán A, Perissinotti A, Scheltens P, Ikonomovic MD, Smith APL, Farrar G, Molinuevo JL, Barkhof F, Buckley CJ, van Berckel BNM, Gispert JD. Visual assessment of [ 18F]flutemetamol PET images can detect early amyloid pathology and grade its extent. Eur J Nucl Med Mol Imaging 2021; 48:2169-2182. [PMID: 33615397 PMCID: PMC8175297 DOI: 10.1007/s00259-020-05174-2] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Accepted: 12/20/2020] [Indexed: 11/08/2022]
Abstract
Purpose To investigate the sensitivity of visual read (VR) to detect early amyloid pathology and the overall utility of regional VR. Methods [18F]Flutemetamol PET images of 497 subjects (ALFA+ N = 352; ADC N = 145) were included. Scans were visually assessed according to product guidelines, recording the number of positive regions (0–5) and a final negative/positive classification. Scans were quantified using the standard and regional Centiloid (CL) method. The agreement between VR-based classification and published CL-based cut-offs for early (CL = 12) and established (CL = 30) pathology was determined. An optimal CL cut-off maximizing Youden’s index was derived. Global and regional CL quantification was compared to VR. Finally, 28 post-mortem cases from the [18F]flutemetamol phase III trial were included to assess the percentage agreement between VR and neuropathological classification of neuritic plaque density. Results VR showed excellent agreement against CL = 12 (κ = .89, 95.2%) and CL = 30 (κ = .88, 95.4%) cut-offs. ROC analysis resulted in an optimal CL = 17 cut-off against VR (sensitivity = 97.9%, specificity = 97.8%). Each additional positive VR region corresponded to a clear increase in global CL. Regional VR was also associated with regional CL quantification. Compared to mCERADSOT-based classification (i.e., any region mCERADSOT > 1.5), VR was in agreement in 89.3% of cases, with 13 true negatives, 12 true positives, and 3 false positives (FP). Regional sparse-to-moderate neuritic and substantial diffuse Aβ plaque was observed in all FP cases. Regional VR was also associated with regional plaque density. Conclusion VR is an appropriate method for assessing early amyloid pathology and that grading the extent of visual amyloid positivity could present clinical value. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-020-05174-2.
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Affiliation(s)
- Lyduine E Collij
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan, 1117, Amsterdam, Netherlands
| | - Gemma Salvadó
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain.,IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
| | - Mahnaz Shekari
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
| | - Isadora Lopes Alves
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan, 1117, Amsterdam, Netherlands
| | - Juhan Reimand
- Alzheimer Center and department of Neurology, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan, 1117, Amsterdam, Netherlands.,Department of Health Technologies, Tallinn University of Technology, Tallinn, Estonia.,Radiology Centre, North Estonia Medical Centre, Tallinn, Estonia
| | - Alle Meije Wink
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan, 1117, Amsterdam, Netherlands
| | - Marissa Zwan
- Alzheimer Center and department of Neurology, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan, 1117, Amsterdam, Netherlands
| | - Aida Niñerola-Baizán
- Nuclear Medicine Department, Hospital Clínic Barcelona & Biomedical Research Networking Center of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Barcelona, Spain
| | - Andrés Perissinotti
- Nuclear Medicine Department, Hospital Clínic Barcelona & Biomedical Research Networking Center of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Barcelona, Spain
| | - Philip Scheltens
- Alzheimer Center and department of Neurology, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan, 1117, Amsterdam, Netherlands
| | - Milos D Ikonomovic
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA.,Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA.,Department of Geriatric Research Education and Clinical Center, VA Pittsburgh HS, Pittsburgh, PA, USA
| | | | | | - José Luis Molinuevo
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain.,IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain.,Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA.,Universitat Pompeu Fabra, Barcelona, Spain.,CIBER Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan, 1117, Amsterdam, Netherlands.,Centre for Medical Image Computing, and Queen Square Institute of Neurology, UCL, London, UK
| | | | - Bart N M van Berckel
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan, 1117, Amsterdam, Netherlands. .,Department of Radiology and Nuclear Medicine, VU University Medical Center, De Boelelaan 1117, 1108 HV, Amsterdam, The Netherlands.
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain. .,IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain. .,Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA. .,Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain. .,Alzheimer Prevention Program, BarcelonaBeta Brain Research Center (BBRC), C/ Wellington, 30, 08005, Barcelona, Spain.
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9
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Cho SH, Choe YS, Kim YJ, Lee B, Kim HJ, Jang H, Kim JP, Jung YH, Kim SJ, Kim BC, Farrar G, Na DL, Moon SH, Seo SW. Concordance in detecting amyloid positivity between 18F-florbetaben and 18F-flutemetamol amyloid PET using quantitative and qualitative assessments. Sci Rep 2020; 10:19576. [PMID: 33177593 PMCID: PMC7658982 DOI: 10.1038/s41598-020-76102-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Accepted: 10/20/2020] [Indexed: 01/19/2023] Open
Abstract
We aimed to quantitatively and qualitatively assess whether there is a discrepancy in detecting amyloid beta (Aβ) positivity between 18F-florbetaben (FBB) and 18F-flutemetamol (FMM) positron emission tomography (PET). We obtained paired FBB and FMM PET images from 107 participants. Three experts visually quantified the Aβ deposition as positive or negative. Quantitative assessment was performed using global cortical standardized uptake value ratio (SUVR) with the whole cerebellum as the reference region. Inter-rater agreement was excellent for FBB and FMM. The concordance rates between FBB and FMM were 94.4% (101/107) for visual assessment and 98.1% (105/107) for SUVR cut-off categorization. Both FBB and FMM showed high agreement rates between visual assessment and SUVR positive or negative categorization (93.5% in FBB and 91.2% in FMM). When the two ligands were compared based on SUVR cut-off categorization as standard of truth, although not statistically significant, the false-positive rate was higher in FMM (9.1%) than in FBB (1.8%) (p = 0.13). Our findings suggested that both FBB and FMM had excellent agreement when used to quantitatively and qualitatively evaluate Aβ deposits, thus, combining amyloid PET data associated with the use of different ligands from multi-centers is feasible.
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Affiliation(s)
- Soo Hyun Cho
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.,Department of Neurology, Chonnam National University Medical School, Chonnam National University Hospital, Gwangju, Korea
| | - Yeong Sim Choe
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.,Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Young Ju Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Byungju Lee
- Department of Neurology, Yuseong Geriatric Rehabilitation Hospital, Pohang, Korea
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Hyemin Jang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Jun Pyo Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Young Hee Jung
- Department of Neurology, Myoungji Hospital, Hanyang University, Goyangsi, Korea
| | - Soo-Jong Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.,Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Byeong C Kim
- Department of Neurology, Chonnam National University Medical School, Chonnam National University Hospital, Gwangju, Korea
| | - Gill Farrar
- Pharmaceutical Diagnostics, GE Healthcare, Chalfont St Giles, UK
| | - Duk L Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.,Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea.,Stem Cell and Regenerative Medicine Institute, Samsung Medical Center, Seoul, Korea
| | - Seung Hwan Moon
- Department of Nuclear Medicine, Sungkyunkwan University School of Medicine, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea. .,Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea. .,Neuroscience Center, Samsung Medical Center, Seoul, Korea. .,Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Korea. .,Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University School of Medicine, Suwon, Korea.
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10
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Thomas J, Ooms SJ, Mentink LJ, Booij J, Olde Rikkert MGM, Overeem S, Kessels RPC, Claassen JAHR. Effects of long-term sleep disruption on cognitive function and brain amyloid-β burden: a case-control study. ALZHEIMERS RESEARCH & THERAPY 2020; 12:101. [PMID: 32847615 PMCID: PMC7450576 DOI: 10.1186/s13195-020-00668-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Accepted: 08/13/2020] [Indexed: 12/20/2022]
Abstract
Background Recent evidence indicates that disrupted sleep could contribute to the development of Alzheimer’s disease by influencing the production and/or clearance of the amyloid-β protein. We set up a case-control study to investigate the association between long-term work-induced sleep disruption, cognitive function, and brain amyloid-β burden. Methods Nineteen male maritime pilots (aged 48–60 years) with chronic work-related sleep disruption and a sex-, age-, and education-matched control sample (n = 16, aged 50–60 years) with normal sleep completed the study. Primary sleep disorders were ruled out with in-lab polysomnography. Additional sleep measurements were obtained at home using actigraphy, sleep-wake logs, and a single-lead EEG device. Cognitive function was assessed with a neuropsychological test battery, sensitive to early symptomatic Alzheimer’s disease. Brain amyloid-β burden was assessed in maritime pilots using 18F-flutemetamol amyloid PET-CT. Results Maritime pilots reported significantly worse sleep quality (Pittsburgh Sleep Quality Index (PSQI) = 8.8 ± 2.9) during work weeks, compared to controls (PSQI = 3.2 ± 1.4; 95% CI 0.01 to 2.57; p = 0.049). This was confirmed with actigraphy-based sleep efficiency (86% ± 3.8 vs. 89.3% ± 4.3; 95% CI 0.43 to 6.03; p = 0.03). Home-EEG recordings showed less total sleep time (TST) and deep sleep time (DST) during work weeks compared to rest weeks (TST 318.56 (250.21–352.93) vs. TST 406.17 (340–425.98); p = 0.001; DST 36.75 (32.30–58.58) vs. DST 51.34 (48.37–69.30); p = 0.005)). There were no differences in any of the cognitive domains between the groups. For brain amyloid-β levels, mean global cortical standard uptake value ratios of 18F-flutemetamol were all in the normal range (1.009 ± 0.059; 95% CI 0.980 to 1.037), confirmed by visual reads. Conclusions Capitalizing on the particular work-rest schedule of maritime pilots, this study with a small sample size observed that long-term intermittent sleep disruption had no effects on global brain amyloid-β levels or cognitive function.
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Affiliation(s)
- Jana Thomas
- Department of Geriatric Medicine, Radboud University Medical Center, 6525, GC, Nijmegen, The Netherlands. .,Donders Institute for Brain, Cognition and Behaviour, 6525, HR, Nijmegen, The Netherlands. .,Radboud Alzheimer Centre, 6525, GA, Nijmegen, The Netherlands.
| | - Sharon J Ooms
- Donders Institute for Brain, Cognition and Behaviour, 6525, HR, Nijmegen, The Netherlands.,Radboud Alzheimer Centre, 6525, GA, Nijmegen, The Netherlands
| | - Lara J Mentink
- Department of Geriatric Medicine, Radboud University Medical Center, 6525, GC, Nijmegen, The Netherlands.,Donders Institute for Brain, Cognition and Behaviour, 6525, HR, Nijmegen, The Netherlands.,Radboud Alzheimer Centre, 6525, GA, Nijmegen, The Netherlands
| | - Jan Booij
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, 6525, GC, Nijmegen, The Netherlands.,Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Academic Medical Center, 1105, AZ, Amsterdam, The Netherlands
| | - Marcel G M Olde Rikkert
- Department of Geriatric Medicine, Radboud University Medical Center, 6525, GC, Nijmegen, The Netherlands.,Donders Institute for Brain, Cognition and Behaviour, 6525, HR, Nijmegen, The Netherlands.,Radboud Alzheimer Centre, 6525, GA, Nijmegen, The Netherlands
| | - Sebastiaan Overeem
- Sleep Medicine Centre Kempenhaeghe, 5591, VE, Heeze, The Netherlands.,Eindhoven University of Technology, 5612, AZ, Eindhoven, The Netherlands
| | - Roy P C Kessels
- Donders Institute for Brain, Cognition and Behaviour, 6525, HR, Nijmegen, The Netherlands.,Radboud Alzheimer Centre, 6525, GA, Nijmegen, The Netherlands.,Department of Medical Psychology, Radboud University Medical Center, 6525, GA, Nijmegen, The Netherlands
| | - Jurgen A H R Claassen
- Department of Geriatric Medicine, Radboud University Medical Center, 6525, GC, Nijmegen, The Netherlands. .,Donders Institute for Brain, Cognition and Behaviour, 6525, HR, Nijmegen, The Netherlands. .,Radboud Alzheimer Centre, 6525, GA, Nijmegen, The Netherlands.
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