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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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Jang H, Lee S, An S, Park Y, Kim SJ, Cheon BK, Kim JH, Kim HJ, Na DL, Kim JP, Kim K, Seo SW. Association of Glycemic Variability With Imaging Markers of Vascular Burden, β-Amyloid, Brain Atrophy, and Cognitive Impairment. Neurology 2024; 102:e207806. [PMID: 38165363 PMCID: PMC10834128 DOI: 10.1212/wnl.0000000000207806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 09/27/2023] [Indexed: 01/03/2024] Open
Abstract
BACKGROUND AND OBJECTIVE We aimed to investigate the association between glycemic variability (GV) and neuroimaging markers of white matter hyperintensities (WMH), beta-amyloid (Aβ), brain atrophy, and cognitive impairment. METHODS This was a retrospective cohort study that included participants without dementia from a memory clinic. They all had Aβ PET, brain MRI, and standardized neuropsychological tests and had fasting glucose (FG) levels tested more than twice during the study period. We defined GV as the intraindividual visit-to-visit variability in FG levels. Multivariable linear regression and logistic regression were used to identify whether GV was associated with the presence of severe WMH and Aβ uptake with DM, mean FG levels, age, sex, hypertension, and presence of APOE4 allele as covariates. Mediation analyses were used to investigate the mediating effect of WMH and Aβ uptake on the relationship between GV and brain atrophy and cognition. RESULTS Among the 688 participants, the mean age was 72.2 years, and the proportion of female participants was 51.9%. Increase in GV was predictive of the presence of severe WMH (coefficient [95% CI] 1.032 [1.012-1.054]; p = 0.002) and increased Aβ uptake (1.005 [1.001-1.008]; p = 0.007). Both WMH and increased Aβ uptake partially mediated the relationship between GV and frontal-executive dysfunction (GV → WMH → frontal-executive; direct effect, -0.319 [-0.557 to -0.080]; indirect effect, -0.050 [-0.091 to -0.008]) and memory dysfunction (GV → Aβ → memory; direct effect, -0.182 [-0.338 to -0.026]; indirect effect, -0.067 [-0.119 to -0.015]), respectively. In addition, increased Aβ uptake completely mediated the relationship between GV and hippocampal volume (indirect effect, -1.091 [-2.078 to -0.103]) and partially mediated the relationship between GV and parietal thickness (direct effect, -0.00101 [-0.00185 to -0.00016]; indirect effect, -0.00016 [-0.00032 to -0.000002]). DISCUSSION Our findings suggest that increased GV is related to vascular and Alzheimer risk factors and neurodegenerative markers, which in turn leads to subsequent cognitive impairment. Furthermore, GV can be considered a potentially modifiable risk factor for dementia prevention.
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Affiliation(s)
- Hyemin Jang
- From the Alzheimer's Disease Convergence Research Center (H.J., S.A., Y.P., S.-J.K., B.K.C., J.H.K., H.J.K., D.L.N., J.P.K., S.W.S.), Samsung Medical Center; Department of Digital Health (H.J., S.L., K.K., S.W.S.), Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University; Department of Neurology (H.J., H.J.K., J.P.K., S.W.S.), Samsung Medical Center, Sungkyunkwan University School of Medicine; Neuroscience Center (H.J., H.J.K., J.P.K., S.W.S.), Samsung Medical Center; Happymind Clinic (D.L.N.); Biomedical Statistics Center (K.K.), Research Institute for Future Medicine, Samsung Medical Center; and Department of Data Convergence and Future Medicine (K.K.), Sungkyunkwan University School of Medicine, Seoul, Korea. Dr. Jang is currently at the Department of Neurology, Seoul National University Hospital, Korea
| | - Sungjoo Lee
- From the Alzheimer's Disease Convergence Research Center (H.J., S.A., Y.P., S.-J.K., B.K.C., J.H.K., H.J.K., D.L.N., J.P.K., S.W.S.), Samsung Medical Center; Department of Digital Health (H.J., S.L., K.K., S.W.S.), Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University; Department of Neurology (H.J., H.J.K., J.P.K., S.W.S.), Samsung Medical Center, Sungkyunkwan University School of Medicine; Neuroscience Center (H.J., H.J.K., J.P.K., S.W.S.), Samsung Medical Center; Happymind Clinic (D.L.N.); Biomedical Statistics Center (K.K.), Research Institute for Future Medicine, Samsung Medical Center; and Department of Data Convergence and Future Medicine (K.K.), Sungkyunkwan University School of Medicine, Seoul, Korea. Dr. Jang is currently at the Department of Neurology, Seoul National University Hospital, Korea
| | - Sungsik An
- From the Alzheimer's Disease Convergence Research Center (H.J., S.A., Y.P., S.-J.K., B.K.C., J.H.K., H.J.K., D.L.N., J.P.K., S.W.S.), Samsung Medical Center; Department of Digital Health (H.J., S.L., K.K., S.W.S.), Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University; Department of Neurology (H.J., H.J.K., J.P.K., S.W.S.), Samsung Medical Center, Sungkyunkwan University School of Medicine; Neuroscience Center (H.J., H.J.K., J.P.K., S.W.S.), Samsung Medical Center; Happymind Clinic (D.L.N.); Biomedical Statistics Center (K.K.), Research Institute for Future Medicine, Samsung Medical Center; and Department of Data Convergence and Future Medicine (K.K.), Sungkyunkwan University School of Medicine, Seoul, Korea. Dr. Jang is currently at the Department of Neurology, Seoul National University Hospital, Korea
| | - Yuhyun Park
- From the Alzheimer's Disease Convergence Research Center (H.J., S.A., Y.P., S.-J.K., B.K.C., J.H.K., H.J.K., D.L.N., J.P.K., S.W.S.), Samsung Medical Center; Department of Digital Health (H.J., S.L., K.K., S.W.S.), Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University; Department of Neurology (H.J., H.J.K., J.P.K., S.W.S.), Samsung Medical Center, Sungkyunkwan University School of Medicine; Neuroscience Center (H.J., H.J.K., J.P.K., S.W.S.), Samsung Medical Center; Happymind Clinic (D.L.N.); Biomedical Statistics Center (K.K.), Research Institute for Future Medicine, Samsung Medical Center; and Department of Data Convergence and Future Medicine (K.K.), Sungkyunkwan University School of Medicine, Seoul, Korea. Dr. Jang is currently at the Department of Neurology, Seoul National University Hospital, Korea
| | - Soo-Jong Kim
- From the Alzheimer's Disease Convergence Research Center (H.J., S.A., Y.P., S.-J.K., B.K.C., J.H.K., H.J.K., D.L.N., J.P.K., S.W.S.), Samsung Medical Center; Department of Digital Health (H.J., S.L., K.K., S.W.S.), Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University; Department of Neurology (H.J., H.J.K., J.P.K., S.W.S.), Samsung Medical Center, Sungkyunkwan University School of Medicine; Neuroscience Center (H.J., H.J.K., J.P.K., S.W.S.), Samsung Medical Center; Happymind Clinic (D.L.N.); Biomedical Statistics Center (K.K.), Research Institute for Future Medicine, Samsung Medical Center; and Department of Data Convergence and Future Medicine (K.K.), Sungkyunkwan University School of Medicine, Seoul, Korea. Dr. Jang is currently at the Department of Neurology, Seoul National University Hospital, Korea
| | - Bo Kyoung Cheon
- From the Alzheimer's Disease Convergence Research Center (H.J., S.A., Y.P., S.-J.K., B.K.C., J.H.K., H.J.K., D.L.N., J.P.K., S.W.S.), Samsung Medical Center; Department of Digital Health (H.J., S.L., K.K., S.W.S.), Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University; Department of Neurology (H.J., H.J.K., J.P.K., S.W.S.), Samsung Medical Center, Sungkyunkwan University School of Medicine; Neuroscience Center (H.J., H.J.K., J.P.K., S.W.S.), Samsung Medical Center; Happymind Clinic (D.L.N.); Biomedical Statistics Center (K.K.), Research Institute for Future Medicine, Samsung Medical Center; and Department of Data Convergence and Future Medicine (K.K.), Sungkyunkwan University School of Medicine, Seoul, Korea. Dr. Jang is currently at the Department of Neurology, Seoul National University Hospital, Korea
| | - Ji Hyun Kim
- From the Alzheimer's Disease Convergence Research Center (H.J., S.A., Y.P., S.-J.K., B.K.C., J.H.K., H.J.K., D.L.N., J.P.K., S.W.S.), Samsung Medical Center; Department of Digital Health (H.J., S.L., K.K., S.W.S.), Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University; Department of Neurology (H.J., H.J.K., J.P.K., S.W.S.), Samsung Medical Center, Sungkyunkwan University School of Medicine; Neuroscience Center (H.J., H.J.K., J.P.K., S.W.S.), Samsung Medical Center; Happymind Clinic (D.L.N.); Biomedical Statistics Center (K.K.), Research Institute for Future Medicine, Samsung Medical Center; and Department of Data Convergence and Future Medicine (K.K.), Sungkyunkwan University School of Medicine, Seoul, Korea. Dr. Jang is currently at the Department of Neurology, Seoul National University Hospital, Korea
| | - Hee Jin Kim
- From the Alzheimer's Disease Convergence Research Center (H.J., S.A., Y.P., S.-J.K., B.K.C., J.H.K., H.J.K., D.L.N., J.P.K., S.W.S.), Samsung Medical Center; Department of Digital Health (H.J., S.L., K.K., S.W.S.), Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University; Department of Neurology (H.J., H.J.K., J.P.K., S.W.S.), Samsung Medical Center, Sungkyunkwan University School of Medicine; Neuroscience Center (H.J., H.J.K., J.P.K., S.W.S.), Samsung Medical Center; Happymind Clinic (D.L.N.); Biomedical Statistics Center (K.K.), Research Institute for Future Medicine, Samsung Medical Center; and Department of Data Convergence and Future Medicine (K.K.), Sungkyunkwan University School of Medicine, Seoul, Korea. Dr. Jang is currently at the Department of Neurology, Seoul National University Hospital, Korea
| | - Duk L Na
- From the Alzheimer's Disease Convergence Research Center (H.J., S.A., Y.P., S.-J.K., B.K.C., J.H.K., H.J.K., D.L.N., J.P.K., S.W.S.), Samsung Medical Center; Department of Digital Health (H.J., S.L., K.K., S.W.S.), Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University; Department of Neurology (H.J., H.J.K., J.P.K., S.W.S.), Samsung Medical Center, Sungkyunkwan University School of Medicine; Neuroscience Center (H.J., H.J.K., J.P.K., S.W.S.), Samsung Medical Center; Happymind Clinic (D.L.N.); Biomedical Statistics Center (K.K.), Research Institute for Future Medicine, Samsung Medical Center; and Department of Data Convergence and Future Medicine (K.K.), Sungkyunkwan University School of Medicine, Seoul, Korea. Dr. Jang is currently at the Department of Neurology, Seoul National University Hospital, Korea
| | - Jun Pyo Kim
- From the Alzheimer's Disease Convergence Research Center (H.J., S.A., Y.P., S.-J.K., B.K.C., J.H.K., H.J.K., D.L.N., J.P.K., S.W.S.), Samsung Medical Center; Department of Digital Health (H.J., S.L., K.K., S.W.S.), Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University; Department of Neurology (H.J., H.J.K., J.P.K., S.W.S.), Samsung Medical Center, Sungkyunkwan University School of Medicine; Neuroscience Center (H.J., H.J.K., J.P.K., S.W.S.), Samsung Medical Center; Happymind Clinic (D.L.N.); Biomedical Statistics Center (K.K.), Research Institute for Future Medicine, Samsung Medical Center; and Department of Data Convergence and Future Medicine (K.K.), Sungkyunkwan University School of Medicine, Seoul, Korea. Dr. Jang is currently at the Department of Neurology, Seoul National University Hospital, Korea
| | - Kyunga Kim
- From the Alzheimer's Disease Convergence Research Center (H.J., S.A., Y.P., S.-J.K., B.K.C., J.H.K., H.J.K., D.L.N., J.P.K., S.W.S.), Samsung Medical Center; Department of Digital Health (H.J., S.L., K.K., S.W.S.), Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University; Department of Neurology (H.J., H.J.K., J.P.K., S.W.S.), Samsung Medical Center, Sungkyunkwan University School of Medicine; Neuroscience Center (H.J., H.J.K., J.P.K., S.W.S.), Samsung Medical Center; Happymind Clinic (D.L.N.); Biomedical Statistics Center (K.K.), Research Institute for Future Medicine, Samsung Medical Center; and Department of Data Convergence and Future Medicine (K.K.), Sungkyunkwan University School of Medicine, Seoul, Korea. Dr. Jang is currently at the Department of Neurology, Seoul National University Hospital, Korea
| | - Sang Won Seo
- From the Alzheimer's Disease Convergence Research Center (H.J., S.A., Y.P., S.-J.K., B.K.C., J.H.K., H.J.K., D.L.N., J.P.K., S.W.S.), Samsung Medical Center; Department of Digital Health (H.J., S.L., K.K., S.W.S.), Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University; Department of Neurology (H.J., H.J.K., J.P.K., S.W.S.), Samsung Medical Center, Sungkyunkwan University School of Medicine; Neuroscience Center (H.J., H.J.K., J.P.K., S.W.S.), Samsung Medical Center; Happymind Clinic (D.L.N.); Biomedical Statistics Center (K.K.), Research Institute for Future Medicine, Samsung Medical Center; and Department of Data Convergence and Future Medicine (K.K.), Sungkyunkwan University School of Medicine, Seoul, Korea. Dr. Jang is currently at the Department of Neurology, Seoul National University Hospital, Korea
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Kang SH, Yoo H, Cheon BK, Park YH, Kim SJ, Ham H, Jang H, Kim HJ, Oh K, Koh SB, Na DL, Kim JP, Seo SW. Distinct effects of cholesterol profile components on amyloid and vascular burdens. Alzheimers Res Ther 2023; 15:197. [PMID: 37950256 PMCID: PMC10636929 DOI: 10.1186/s13195-023-01342-2] [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: 02/12/2023] [Accepted: 10/25/2023] [Indexed: 11/12/2023]
Abstract
BACKGROUND Cholesterol plays important roles in β-amyloid (Aβ) metabolism and atherosclerosis. However, the relationships of plasma cholesterol levels with Aβ and cerebral small vessel disease (CSVD) burdens are not fully understood in Asians. Herein, we investigated the relationships between plasma cholesterol profile components and Aβ and CSVD burdens in a large, non-demented Korean cohort. METHODS We enrolled 1,175 non-demented participants (456 with unimpaired cognition [CU] and 719 with mild cognitive impairment [MCI]) aged ≥ 45 years who underwent Aβ PET at the Samsung Medical Center in Korea. We performed linear regression analyses with each cholesterol (low-density lipoprotein cholesterol [LDL-c], high-density lipoprotein cholesterol [HDL-c], and triglyceride) level as a predictor and each image marker (Aβ uptake on PET, white matter hyperintensity [WMH] volume, and hippocampal volume) as an outcome after controlling for potential confounders. RESULTS Increased LDL-c levels (β = 0.014 to 0.115, p = 0.013) were associated with greater Aβ uptake, independent of the APOE e4 allele genotype and lipid-lowering medication. Decreased HDL-c levels (β = - 0.133 to - 0.006, p = 0.032) were predictive of higher WMH volumes. Increased LDL-c levels were also associated with decreased hippocampal volume (direct effect β = - 0.053, p = 0.040), which was partially mediated by Aβ uptake (indirect effect β = - 0.018, p = 0.006). CONCLUSIONS Our findings highlight that increased LDL-c and decreased HDL-c levels are important risk factors for Aβ and CSVD burdens, respectively. Furthermore, considering that plasma cholesterol profile components are potentially modified by diet, exercise, and pharmacological agents, our results provide evidence that regulating LDL-c and HDL-c levels is a potential strategy to prevent dementia.
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Grants
- 2022R1I1A1A01056956 Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education
- HI19C1132 a grant of the Korean Health Technology R&D Project, Ministry of Health & Welfare, Republic of Korea
- grant number: HU20C0111, HU22C0170 a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare and Ministry of science and ICT, Republic of Korea
- NRF-2019R1A5A2027340, NRF-2022R1F1A1063966 the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT)
- 2021-ER1006-01 the "National Institute of Health" research project
- a grant of the Korean Health Technology R&D Project, Ministry of Health & Welfare, Republic of Korea
- a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare and Ministry of science and ICT, Republic of Korea
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Affiliation(s)
- Sung Hoon Kang
- Department of Neurology, Sungkyunkwan University School of Medicine, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Korea
- Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Korea
| | - Heejin Yoo
- Department of Neurology, Sungkyunkwan University School of Medicine, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Korea
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Korea
| | - Bo Kyoung Cheon
- Department of Neurology, Sungkyunkwan University School of Medicine, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Korea
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Korea
| | - Yu Hyun Park
- Department of Neurology, Sungkyunkwan University School of Medicine, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Korea
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea
| | - Soo-Jong Kim
- Department of Neurology, Sungkyunkwan University School of Medicine, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Korea
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea
| | - Hongki Ham
- Department of Neurology, Sungkyunkwan University School of Medicine, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Korea
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Korea
| | - Hyemin Jang
- Department of Neurology, Sungkyunkwan University School of Medicine, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Korea
| | - Hee Jin Kim
- Department of Neurology, Sungkyunkwan University School of Medicine, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Korea
| | - Kyungmi Oh
- Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, 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, Sungkyunkwan University School of Medicine, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Korea
| | - Jun Pyo Kim
- Department of Neurology, Sungkyunkwan University School of Medicine, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Korea.
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Korea.
| | - Sang Won Seo
- Department of Neurology, Sungkyunkwan University School of Medicine, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Korea.
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Korea.
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Korea.
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Korea.
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea.
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Kang SH, Yoo H, Cheon BK, Kim JP, Jang H, Kim HJ, Kang M, Oh K, Koh SB, Na DL, Chang Y, Seo SW. Sex-specific relationship between non-alcoholic fatty liver disease and amyloid-β in cognitively unimpaired individuals. Front Aging Neurosci 2023; 15:1277392. [PMID: 37901792 PMCID: PMC10603302 DOI: 10.3389/fnagi.2023.1277392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 09/25/2023] [Indexed: 10/31/2023] Open
Abstract
Background Non-alcoholic fatty liver disease (NAFLD) is known to be associated with a high risk of clinically diagnosed Alzheimer's disease (AD). Additionally, the prevalence of NAFLD and AD is higher in elderly females than in males. However, a sex-specific association between NAFLD and amyloid-beta (Aβ) deposition remains unclear. Therefore, we investigated the sex-specific relationship between NAFLD and Aβ deposition in a large-sized cohort of cognitively unimpaired (CU) individuals. Methods We enrolled 673 (410 [60.9%] females and 263 [39.1%] males) CU individuals aged ≥45 years who underwent Aβ positron emission tomography (PET). The presence of NAFLD, assessed using the hepatic steatosis index, and the severity of NAFLD, assessed using the Fibrosis-4 index, were considered predictors. Aβ deposition on PET was considered as an outcome. Results Females had a higher frequency of NAFLD than males (48 and 23.2%, p < 0.001). Among females, the presence of NAFLD (β = 0.216, p < 0.001) was predictive of increased Aβ deposition, whereas among males, the presence of NAFLD (β = 0.191, p = 0.064) was not associated with Aβ deposition. Among females, the presence of NAFLD with low (β = 0.254, p = 0.039), intermediate (β = 0.201, p = 0.006), and high fibrosis (β = 0.257, p = 0.027) was predictive of increased Aβ deposition. Aβ deposition also increased as the severity of NAFLD increased in females (p for trend = 0.001). Conclusion We highlight the marked influence of NAFLD and its severity on the risk of Aβ deposition in relation to sex. Furthermore, our findings suggest that sex-specific strategies regarding the management of NAFLD are necessary for the prevention of Aβ deposition.
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Affiliation(s)
- Sung Hoon Kang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Heejin Yoo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Alzheimer’s Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Bo Kyoung Cheon
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Alzheimer’s Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Jun Pyo Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Hyemin Jang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Mira Kang
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
- Center for Health Promotion, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Kyungmi Oh
- Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Seong-Beom Koh
- Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Duk L. Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Yoosoo Chang
- Center for Cohort Studies, Total Healthcare Center, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Alzheimer’s Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Republic of Korea
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Chun MY, Jang H, Kim HJ, Kim JP, Gallacher J, Allué JA, Sarasa L, Castillo S, Pascual-Lucas M, Na DL, Seo SW. Contribution of clinical information to the predictive performance of plasma β-amyloid levels for amyloid positron emission tomography positivity. Front Aging Neurosci 2023; 15:1126799. [PMID: 36998318 PMCID: PMC10044013 DOI: 10.3389/fnagi.2023.1126799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Accepted: 02/24/2023] [Indexed: 03/15/2023] Open
Abstract
BackgroundEarly detection of β-amyloid (Aβ) accumulation, a major biomarker for Alzheimer’s disease (AD), has become important. As fluid biomarkers, the accuracy of cerebrospinal fluid (CSF) Aβ for predicting Aβ deposition on positron emission tomography (PET) has been extensively studied, and the development of plasma Aβ is beginning to receive increased attention recently. In the present study, we aimed to determine whether APOE genotypes, age, and cognitive status increase the predictive performance of plasma Aβ and CSF Aβ levels for Aβ PET positivity.MethodsWe recruited 488 participants who underwent both plasma Aβ and Aβ PET studies (Cohort 1) and 217 participants who underwent both cerebrospinal fluid (CSF) Aβ and Aβ PET studies (Cohort 2). Plasma and CSF samples were analyzed using ABtest-MS, an antibody-free liquid chromatography-differential mobility spectrometry-triple quadrupole mass spectrometry method and INNOTEST enzyme-linked immunosorbent assay kits, respectively. To evaluate the predictive performance of plasma Aβ and CSF Aβ, respectively, logistic regression and receiver operating characteristic analyses were performed.ResultsWhen predicting Aβ PET status, both plasma Aβ42/40 ratio and CSF Aβ42 showed high accuracy (plasma Aβ area under the curve (AUC) 0.814; CSF Aβ AUC 0.848). In the plasma Aβ models, the AUC values were higher than plasma Aβ alone model, when the models were combined with either cognitive stage (p < 0.001) or APOE genotype (p = 0.011). On the other hand, there was no difference between the CSF Aβ models, when these variables were added.ConclusionPlasma Aβ might be a useful predictor of Aβ deposition on PET status as much as CSF Aβ, particularly when considered with clinical information such as APOE genotype and cognitive stage.
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Affiliation(s)
- Min Young Chun
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Department of Neurology, Yonsei University College of Medicine, Seoul, Republic of Korea
- Department of Neurology, Yongin Severance Hospital, Yonsei University Health System, Yongin, Republic of Korea
| | - Hyemin Jang
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
- *Correspondence: Hyemin Jang, ; Sang Won Seo,
| | - Hee Jin Kim
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
| | - Jun Pyo Kim
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Center for Neuroimaging, Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, United States
| | - John Gallacher
- Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, United Kingdom
| | | | | | | | | | - Duk L. Na
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Sang Won Seo
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
- Department of Clinical Research Design and Evaluation, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
- *Correspondence: Hyemin Jang, ; Sang Won Seo,
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Kim YJ, Kim SE, Hahn A, Jang H, Kim JP, Kim HJ, Na DL, Chin J, Seo SW. Classification and prediction of cognitive trajectories of cognitively unimpaired individuals. Front Aging Neurosci 2023; 15:1122927. [PMID: 36993907 PMCID: PMC10040799 DOI: 10.3389/fnagi.2023.1122927] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 02/27/2023] [Indexed: 03/14/2023] Open
Abstract
Objectives Efforts to prevent Alzheimer's disease (AD) would benefit from identifying cognitively unimpaired (CU) individuals who are liable to progress to cognitive impairment. Therefore, we aimed to develop a model to predict cognitive decline among CU individuals in two independent cohorts. Methods A total of 407 CU individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and 285 CU individuals from the Samsung Medical Center (SMC) were recruited in this study. We assessed cognitive outcomes by using neuropsychological composite scores in the ADNI and SMC cohorts. We performed latent growth mixture modeling and developed the predictive model. Results Growth mixture modeling identified 13.8 and 13.0% of CU individuals in the ADNI and SMC cohorts, respectively, as the "declining group." In the ADNI cohort, multivariable logistic regression modeling showed that increased amyloid-β (Aβ) uptake (β [SE]: 4.852 [0.862], p < 0.001), low baseline cognitive composite scores (β [SE]: -0.274 [0.070], p < 0.001), and reduced hippocampal volume (β [SE]: -0.952 [0.302], p = 0.002) were predictive of cognitive decline. In the SMC cohort, increased Aβ uptake (β [SE]: 2.007 [0.549], p < 0.001) and low baseline cognitive composite scores (β [SE]: -4.464 [0.758], p < 0.001) predicted cognitive decline. Finally, predictive models of cognitive decline showed good to excellent discrimination and calibration capabilities (C-statistic = 0.85 for the ADNI model and 0.94 for the SMC model). Conclusion Our study provides novel insights into the cognitive trajectories of CU individuals. Furthermore, the predictive model can facilitate the classification of CU individuals in future primary prevention trials.
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Affiliation(s)
- Young Ju Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Si Eun Kim
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea
| | - Alice Hahn
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Hyemin Jang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of 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, Republic of Korea
- Center for Neuroimaging, Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Duk L. Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Institute of Stem Cell and Regenerative Medicine, Seoul, Republic of Korea
- Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Juhee Chin
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Institute of Stem Cell and Regenerative Medicine, Seoul, Republic of Korea
- Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Center for Clinical Epidemiology, Samsung Medical Center, Seoul, Republic of Korea
- Department of Health Sciences and Technology, Seoul, Republic of Korea
- Clinical Research Design and Evaluation, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Seoul, Republic of Korea
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Quantitative comparative analysis of amyloid PET images using three radiopharmaceuticals. Ann Nucl Med 2023; 37:271-279. [PMID: 36749463 PMCID: PMC10129914 DOI: 10.1007/s12149-023-01824-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 01/31/2023] [Indexed: 02/08/2023]
Abstract
OBJECTIVE Amyloid positron emission tomography (PET) with F-18 florbetaben (FBB), F-18 flutemetamol (FMM), and F-18 florapronol (FPN) is being used clinically for the evaluation of dementia. These radiopharmaceuticals are commonly used to evaluate the accumulation of beta-amyloid plaques in the brain, but there are structural differences between them. We investigated whether there are any differences in the imaging characteristics. METHODS A total of 605 subjects were enrolled retrospectively in this study, including healthy subjects (HS) and patients with mild cognitive impairment or Alzheimer's disease. Participants underwent amyloid PET imaging using one of the three radiopharmaceuticals. The PET images were analyzed visually and semi-quantitatively using a standardized uptake value ratio (SUVR). In addition, we calculated and compared the cut-off SUVR of the representative regions for each radiopharmaceutical that can distinguish between positive and negative scans. RESULTS In the negative images of the HS group, the contrast between the white matter and the gray matter was high in the FMM PET images, while striatal uptake was relatively higher in the FPN PET images. The SUVR showed significant differences across the radiopharmaceuticals in all areas except the temporal lobe, but the range of differences was relatively small. Accuracy levels for the global cut-off SUVR to discriminate between positive and negative images were highest in FMM PET, with a value of 0.989. FBB PET also showed a high value of 0.978, while FPN PET showed a relatively low value of 0.901. CONCLUSIONS Negative amyloid PET images using the three radiopharmaceuticals showed visually and quantitatively similar imaging characteristics except in the striatum. Binary classification using the cut-off of the global cortex showed high accuracy overall, although there were some differences between the three PET images.
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Kim J, Choe YS, Park Y, Kim Y, Kim JP, Jang H, Kim HJ, Na DL, Cho SJ, Moon SH, Seo SW. Clinical outcomes of increased focal amyloid uptake in individuals with subthreshold global amyloid levels. Front Aging Neurosci 2023; 15:1124445. [PMID: 36936497 PMCID: PMC10017468 DOI: 10.3389/fnagi.2023.1124445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 02/14/2023] [Indexed: 03/06/2023] Open
Abstract
Background Although the standardized uptake value ratio (SUVR) method is objective and simple, cut-off optimization using global SUVR values may not reflect focal increased uptake in the cerebrum. The present study investigated clinical and neuroimaging characteristics according to focally increased β-amyloid (Aβ) uptake and global Aβ status. Methods We recruited 968 participants with cognitive continuum. All participants underwent neuropsychological tests and 498 18F-florbetaben (FBB) amyloid positron emission tomography (PET) and 470 18F-flutemetamol (FMM) PET. Each PET scan was assessed in 10 regions (left and right frontal, lateral temporal, parietal, cingulate, and striatum) with focal-quantitative SUVR-based cutoff values for each region by using an iterative outlier approach. Results A total of 62 (6.4%) subjects showed increased focal Aβ uptake with subthreshold global Aβ status [global (-) and focal (+) Aβ group, G(-)F(+) group]. The G(-)F(+) group showed worse performance in memory impairment (p < 0.001), global cognition (p = 0.009), greater hippocampal atrophy (p = 0.045), compared to those in the G(-)F(-). Participants with widespread Aβ involvement in the whole region [G(+)] showed worse neuropsychological (p < 0.001) and neuroimaging features (p < 0.001) than those with focal Aβ involvement G(-)F(+). Conclusion Our findings suggest that individuals show distinctive clinical outcomes according to focally increased Aβ uptake and global Aβ status. Thus, researchers and clinicians should pay more attention to focal increased Aβ uptake in addition to global Aβ status.
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Affiliation(s)
- Jaeho Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Neurology, Dongtan Sacred Heart Hospital, Hallym University College of Medicine, Hwaseong-si, Gyeonggi-do, Republic of 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, Samsung Medical Center, Sungkyunkwan University, Seoul, Republic of Korea
| | - Yuhyun Park
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Department of Health Sciences and Technology, SAIHST, Samsung Medical Center, Sungkyunkwan University, Seoul, Republic of Korea
| | - Yeshin Kim
- Department of Neurology, Kangwon National University College of Medicine, Chuncheon-si, Gangwon-do, Republic of 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, Republic of Korea
- Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Hyemin Jang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Republic of 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, Republic of Korea
- Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Duk L. Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Health Sciences and Technology, SAIHST, Samsung Medical Center, Sungkyunkwan University, Seoul, Republic of Korea
- Samsung Medical Center, Stem Cell and Regenerative Medicine Institute, Seoul, Republic of Korea
| | - Soo-Jin Cho
- Department of Neurology, Dongtan Sacred Heart Hospital, Hallym University College of Medicine, Hwaseong-si, Gyeonggi-do, Republic of Korea
| | - Seung Hwan Moon
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- *Correspondence: Seung Hwan Moon,
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Health Sciences and Technology, SAIHST, Samsung Medical Center, Sungkyunkwan University, Seoul, Republic of Korea
- Department of Clinical Research Design and Evaluation, SAIHST, Samsung Medical Center, Sungkyunkwan University, Seoul, Republic of Korea
- Sang Won Seo,
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Kim SJ, Ham H, Park YH, Choe YS, Kim YJ, Jang H, Na DL, Kim HJ, Moon SH, Seo SW. Development and clinical validation of CT-based regional modified Centiloid method for amyloid PET. Alzheimers Res Ther 2022; 14:157. [PMID: 36266688 PMCID: PMC9585745 DOI: 10.1186/s13195-022-01099-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 10/12/2022] [Indexed: 11/06/2022]
Abstract
Background The standard Centiloid (CL) method was proposed to harmonize and quantify global 18F-labeled amyloid beta (Aβ) PET ligands using MRI as an anatomical reference. However, there is need for harmonizing and quantifying regional Aβ uptakes between ligands using CT as an anatomical reference. In the present study, we developed and validated a CT-based regional direct comparison of 18F-florbetaben (FBB) and 18F-flutemetamol (FMM) Centiloid (rdcCL). Methods For development of MRI-based or CT-based rdcCLs, the cohort consisted of 63 subjects (20 young controls (YC) and 18 old controls (OC), and 25 participants with Alzheimer’s disease dementia (ADD)). We performed a direct comparison of the FMM-FBB rdcCL method using MRI and CT images to define a common target region and the six regional VOIs of frontal, temporal, parietal, posterior cingulate, occipital, and striatal regions. Global and regional rdcCL scales were compared between MRI-based and CT-based methods. For clinical validation, the cohort consisted of 2245 subjects (627 CN, 933 MCI, and 685 ADD). Results Both MRI-based and CT-based rdcCL scales showed that FMM and FBB were highly correlated with each other, globally and regionally (R2 = 0.96~0.99). Both FMM and FBB showed that CT-based rdcCL scales were highly correlated with MRI-based rdcCL scales (R2 = 0.97~0.99). Regarding the absolute difference of rdcCLs between FMM and FBB, the CT-based method was not different from the MRI-based method, globally or regionally (p value = 0.07~0.95). In our clinical validation study, the global negative group showed that the regional positive subgroup had worse neuropsychological performance than the regional negative subgroup (p < 0.05). The global positive group also showed that the striatal positive subgroup had worse neuropsychological performance than the striatal negative subgroup (p < 0.05). Conclusions Our findings suggest that it is feasible to convert regional FMM or FBB rdcSUVR values into rdcCL scales without additional MRI scans. This allows a more easily accessible method for researchers that can be applicable to a variety of different conditions. Supplementary Information The online version contains supplementary material available at 10.1186/s13195-022-01099-0.
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Affiliation(s)
- Soo-Jong Kim
- grid.264381.a0000 0001 2181 989XDepartment of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351 Republic of Korea ,grid.414964.a0000 0001 0640 5613Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea ,grid.264381.a0000 0001 2181 989XDepartment of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea ,grid.264381.a0000 0001 2181 989XDepartment of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Republic of Korea
| | - Hongki Ham
- grid.264381.a0000 0001 2181 989XDepartment of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351 Republic of Korea ,grid.414964.a0000 0001 0640 5613Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea ,grid.264381.a0000 0001 2181 989XDepartment of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Republic of Korea ,grid.264381.a0000 0001 2181 989XDepartment of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
| | - Yu Hyun Park
- grid.264381.a0000 0001 2181 989XDepartment of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351 Republic of Korea ,grid.414964.a0000 0001 0640 5613Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea ,grid.264381.a0000 0001 2181 989XDepartment of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea ,grid.264381.a0000 0001 2181 989XDepartment of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Republic of Korea
| | - Yeong Sim Choe
- grid.264381.a0000 0001 2181 989XDepartment of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351 Republic of Korea ,grid.414964.a0000 0001 0640 5613Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea ,grid.264381.a0000 0001 2181 989XDepartment of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea ,grid.264381.a0000 0001 2181 989XDepartment of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Republic of Korea
| | - Young Ju Kim
- grid.264381.a0000 0001 2181 989XDepartment of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351 Republic of Korea ,grid.414964.a0000 0001 0640 5613Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Hyemin Jang
- grid.264381.a0000 0001 2181 989XDepartment of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351 Republic of Korea ,grid.414964.a0000 0001 0640 5613Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Duk L. Na
- grid.264381.a0000 0001 2181 989XDepartment of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351 Republic of Korea ,grid.414964.a0000 0001 0640 5613Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea ,grid.264381.a0000 0001 2181 989XDepartment of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea ,grid.414964.a0000 0001 0640 5613Stem Cell and Regenerative Medicine Institute, Samsung Medical Center, Seoul, Republic of Korea
| | - Hee Jin Kim
- grid.264381.a0000 0001 2181 989XDepartment of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351 Republic of Korea ,grid.414964.a0000 0001 0640 5613Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Seung Hwan Moon
- grid.264381.a0000 0001 2181 989XDepartment of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Sang Won Seo
- grid.264381.a0000 0001 2181 989XDepartment of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351 Republic of Korea ,grid.264381.a0000 0001 2181 989XDepartment of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea ,grid.264381.a0000 0001 2181 989XDepartment of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Republic of Korea ,grid.264381.a0000 0001 2181 989XDepartment of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea ,grid.414964.a0000 0001 0640 5613Alzheimer’s Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
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Kang SH, Lee KH, Chang Y, Choe YS, Kim JP, Jang H, Shin HY, Kim HJ, Koh SB, Na DL, Seo SW, Kang M. Gender-specific relationship between thigh muscle and fat mass and brain amyloid-β positivity. Alzheimers Res Ther 2022; 14:145. [PMID: 36195949 PMCID: PMC9531420 DOI: 10.1186/s13195-022-01086-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 09/21/2022] [Indexed: 12/02/2022]
Abstract
BACKGROUND The relationship of specific body composition in the thighs and brain amyloid-beta (Aβ) deposition remained unclear, although there were growing evidence that higher muscle and fat mass in thighs had a protective effect against cardiometabolic syndromes. To determine whether muscle mass and fat mass in the thighs affected amyloid-beta (Aβ) positivity differently in relation to gender, we investigated the association of muscle mass and fat mass with Aβ positivity using positron emission tomography (PET) in individuals without dementia. METHODS We recruited 240 participants (134 [55.8%] males, 106 [44.2%] females) without dementia ≥45 years of age who underwent Aβ PET, bioelectrical impedance analysis (BIA) and dual-energy X-ray absorptiometry (DEXA) scans of the hip in the health promotion center at Samsung Medical Center in Seoul, Korea. Lower extremity skeletal muscle mass index (LASMI) was measured using BIA, and gluteofemoral fat percentage (GFFP) was estimated using DEXA scans of the hip. We investigated the associations of LASMI and GFFP with Aβ positivity using logistic regression analyses after controlling for age, APOE4 genotype, and cognitive stage. RESULTS Higher muscle mass in the thighs, measured as LASMI (odds ratio [OR]=0.27, 95% confidence interval [CI] 0.08 to 0.84, p=0.031) was associated with a lesser risk of Aβ positivity in only females. Higher fat mass in the thighs, measured as GFFP (OR=0.84, 95% CI 0.73 to 0.95, p=0.008) was associated with a lesser risk of Aβ positivity in only males. However, the association between LAMSI (p for interaction= 0.810), GFFP (p for interaction= 0.075) and Aβ positivity did not significantly differ by gender. Furthermore, LAMSI only negatively correlated with centiloid (CL) values in females (r=-0.205, p=0.037), and GFFP only negatively correlated with CL values only in males (r=-0.253, p=0.004). CONCLUSIONS Our findings highlight the importance of recognizing that gender differences exist with respect to the specific body composition to potentially protect against Aβ deposition. Therefore, our results may help in designing gender-specific strategies for controlling body composition to prevent Aβ deposition.
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Affiliation(s)
- Sung Hoon Kang
- grid.264381.a0000 0001 2181 989XDepartment of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea ,grid.222754.40000 0001 0840 2678Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, South Korea
| | - Kyung Hyun Lee
- grid.264381.a0000 0001 2181 989XDepartment of Digital Health, SAIHST, Sungkyunkwan University, Seoul, South Korea
| | - Yoosoo Chang
- grid.264381.a0000 0001 2181 989XCenter for Cohort Studies, Total Healthcare Center, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Yeong Sim Choe
- grid.264381.a0000 0001 2181 989XDepartment of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea ,grid.264381.a0000 0001 2181 989XDepartment of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, South Korea
| | - Jun Pyo Kim
- grid.264381.a0000 0001 2181 989XDepartment of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Hyemin Jang
- grid.264381.a0000 0001 2181 989XDepartment of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Hee Young Shin
- grid.264381.a0000 0001 2181 989XCenter for Health Promotion, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Hee Jin Kim
- grid.264381.a0000 0001 2181 989XDepartment of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Seong-Beom Koh
- grid.222754.40000 0001 0840 2678Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, South Korea
| | - Duk L. Na
- grid.264381.a0000 0001 2181 989XDepartment of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Sang Won Seo
- grid.264381.a0000 0001 2181 989XDepartment of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea ,grid.264381.a0000 0001 2181 989XDepartment of Digital Health, SAIHST, Sungkyunkwan University, Seoul, South Korea ,grid.264381.a0000 0001 2181 989XDepartment of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, South Korea ,grid.414964.a0000 0001 0640 5613Alzheimer’s Disease Convergence Research Center, Samsung Medical Center, Seoul, South Korea ,grid.264381.a0000 0001 2181 989XDepartment of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, South Korea
| | - Mira Kang
- grid.264381.a0000 0001 2181 989XDepartment of Digital Health, SAIHST, Sungkyunkwan University, Seoul, South Korea ,grid.264381.a0000 0001 2181 989XCenter for Health Promotion, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea ,grid.264381.a0000 0001 2181 989XDigital Innovation Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
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Park CJ, Seo Y, Choe YS, Jang H, Lee H, Kim JP. Predicting conversion of brain β-amyloid positivity in amyloid-negative individuals. Alzheimers Res Ther 2022; 14:129. [PMID: 36096822 PMCID: PMC9465850 DOI: 10.1186/s13195-022-01067-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 08/16/2022] [Indexed: 11/21/2022]
Abstract
Background Cortical deposition of β-amyloid (Aβ) plaque is one of the main hallmarks of Alzheimer’s disease (AD). While Aβ positivity has been the main concern so far, predicting whether Aβ (−) individuals will convert to Aβ (+) has become crucial in clinical and research aspects. In this study, we aimed to develop a classifier that predicts the conversion from Aβ (−) to Aβ (+) using artificial intelligence. Methods Data were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort regarding patients who were initially Aβ (−). We developed an artificial neural network-based classifier with baseline age, gender, APOE ε4 genotype, and global and regional standardized uptake value ratios (SUVRs) from positron emission tomography. Ten times repeated 10-fold cross-validation was performed for model measurement, and the feature importance was assessed. To validate the prediction model, we recruited subjects at the Samsung Medical Center (SMC). Results A total of 229 participants (53 converters) from the ADNI dataset and a total of 40 subjects (10 converters) from the SMC dataset were included. The average area under the receiver operating characteristic values of three developed models are as follows: Model 1 (age, gender, APOE ε4) of 0.674, Model 2 (age, gender, APOE ε4, global SUVR) of 0.814, and Model 3 (age, gender, APOE ε4, global and regional SUVR) of 0.841. External validation result showed an AUROC of 0.900. Conclusion We developed prediction models regarding Aβ positivity conversion. With the growing recognition of the need for earlier intervention in AD, the results of this study are expected to contribute to the screening of early treatment candidates. Supplementary Information The online version contains supplementary material available at 10.1186/s13195-022-01067-8.
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Pemberton HG, Collij LE, Heeman F, Bollack A, Shekari M, Salvadó G, Alves IL, Garcia DV, Battle M, Buckley C, Stephens AW, Bullich S, Garibotto V, Barkhof F, Gispert JD, Farrar G. Quantification of amyloid PET for future clinical use: a state-of-the-art review. Eur J Nucl Med Mol Imaging 2022; 49:3508-3528. [PMID: 35389071 PMCID: PMC9308604 DOI: 10.1007/s00259-022-05784-y] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 03/25/2022] [Indexed: 12/15/2022]
Abstract
Amyloid-β (Aβ) pathology is one of the earliest detectable brain changes in Alzheimer's disease (AD) pathogenesis. The overall load and spatial distribution of brain Aβ can be determined in vivo using positron emission tomography (PET), for which three fluorine-18 labelled radiotracers have been approved for clinical use. In clinical practice, trained readers will categorise scans as either Aβ positive or negative, based on visual inspection. Diagnostic decisions are often based on these reads and patient selection for clinical trials is increasingly guided by amyloid status. However, tracer deposition in the grey matter as a function of amyloid load is an inherently continuous process, which is not sufficiently appreciated through binary cut-offs alone. State-of-the-art methods for amyloid PET quantification can generate tracer-independent measures of Aβ burden. Recent research has shown the ability of these quantitative measures to highlight pathological changes at the earliest stages of the AD continuum and generate more sensitive thresholds, as well as improving diagnostic confidence around established binary cut-offs. With the recent FDA approval of aducanumab and more candidate drugs on the horizon, early identification of amyloid burden using quantitative measures is critical for enrolling appropriate subjects to help establish the optimal window for therapeutic intervention and secondary prevention. In addition, quantitative amyloid measurements are used for treatment response monitoring in clinical trials. In clinical settings, large multi-centre studies have shown that amyloid PET results change both diagnosis and patient management and that quantification can accurately predict rates of cognitive decline. Whether these changes in management reflect an improvement in clinical outcomes is yet to be determined and further validation work is required to establish the utility of quantification for supporting treatment endpoint decisions. In this state-of-the-art review, several tools and measures available for amyloid PET quantification are summarised and discussed. Use of these methods is growing both clinically and in the research domain. Concurrently, there is a duty of care to the wider dementia community to increase visibility and understanding of these methods.
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Affiliation(s)
- Hugh G Pemberton
- GE Healthcare, Amersham, UK.
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK.
- UCL Queen Square Institute of Neurology, University College London, London, UK.
| | - Lyduine E Collij
- Department of Radiology and Nuclear Medicine, Amsterdam Neurocience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Fiona Heeman
- Department of Radiology and Nuclear Medicine, Amsterdam Neurocience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Ariane Bollack
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
| | - Mahnaz Shekari
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
| | - Gemma Salvadó
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Isadora Lopes Alves
- Department of Radiology and Nuclear Medicine, Amsterdam Neurocience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Brain Research Center, Amsterdam, The Netherlands
| | - David Vallez Garcia
- Department of Radiology and Nuclear Medicine, Amsterdam Neurocience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Mark Battle
- GE Healthcare, Amersham, UK
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | | | | | | | - Valentina Garibotto
- Division of Nuclear Medicine and Molecular Imaging, University Hospitals of Geneva, Geneva, Switzerland
- NIMTLab, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Frederik Barkhof
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- UCL Queen Square Institute of Neurology, University College London, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam Neurocience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
- Centro de Investigación Biomédica en Red Bioingeniería, Biomateriales y Nanomedicina, Madrid, Spain
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Kang SH, Kim JH, Chang Y, Cheon BK, Choe YS, Jang H, Kim HJ, Koh SB, Na DL, Kim K, Seo SW. Independent effect of body mass index variation on amyloid-β positivity. Front Aging Neurosci 2022; 14:924550. [PMID: 35936766 PMCID: PMC9354132 DOI: 10.3389/fnagi.2022.924550] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 07/04/2022] [Indexed: 11/16/2022] Open
Abstract
Objectives The relationship of body mass index (BMI) changes and variability with amyloid-β (Aβ) deposition remained unclear, although there were growing evidence that BMI is associated with the risk of developing cognitive impairment or AD dementia. To determine whether BMI changes and BMI variability affected Aβ positivity, we investigated the association of BMI changes and BMI variability with Aβ positivity, as assessed by PET in a non-demented population. Methods We retrospectively recruited 1,035 non-demented participants ≥50 years of age who underwent Aβ PET and had at least three BMI measurements in the memory clinic at Samsung Medical Center. To investigate the association between BMI change and variability with Aβ deposition, we performed multivariable logistic regression. Further distinctive underlying features of BMI subgroups were examined by employing a cluster analysis model. Results Decreased (odds ratio [OR] = 1.68, 95% confidence interval [CI] 1.16–2.42) or increased BMI (OR = 1.60, 95% CI 1.11–2.32) was associated with a greater risk of Aβ positivity after controlling for age, sex, APOE e4 genotype, years of education, hypertension, diabetes, baseline BMI, and BMI variability. A greater BMI variability (OR = 1.73, 95% CI 1.07–2.80) was associated with a greater risk of Aβ positivity after controlling for age, sex, APOE e4 genotype, years of education, hypertension, diabetes, baseline BMI, and BMI change. We also identified BMI subgroups showing a greater risk of Aβ positivity. Conclusion Our findings suggest that participants with BMI change, especially those with greater BMI variability, are more vulnerable to Aβ deposition regardless of baseline BMI. Furthermore, our results may contribute to the design of strategies to prevent Aβ deposition with respect to weight control.
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Affiliation(s)
- Sung Hoon Kang
- Department of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea
- Neuroscience Center, Samsung Medical Center, Seoul, South Korea
- Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, South Korea
| | - Jong Hyuk Kim
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, South Korea
| | - Yoosoo Chang
- Center for Cohort Studies, Total Healthcare Center, Kangbuk Samsung Hospital, School of Medicine, Sungkyunkwan University, Seoul, South Korea
| | - Bo Kyoung Cheon
- Department of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea
- Neuroscience Center, Samsung Medical Center, Seoul, South Korea
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, South Korea
| | - Yeong Sim Choe
- Department of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea
- Neuroscience Center, Samsung Medical Center, Seoul, South Korea
- Department of Health Sciences & Technology, SAIHST, Sungkyunkwan University, Seoul, South Korea
| | - Hyemin Jang
- Department of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea
- Neuroscience Center, Samsung Medical Center, Seoul, South Korea
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea
- Neuroscience Center, Samsung Medical Center, Seoul, 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, School of Medicine, Sungkyunkwan University, Seoul, South Korea
- Neuroscience Center, Samsung Medical Center, Seoul, South Korea
| | - Kyunga Kim
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, South Korea
- Biomedical Statistics Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, South Korea
- Department of Data Convergence and Future Medicine, School of Medicine, Sungkyunkwan University, Seoul, South Korea
- *Correspondence: Kyunga Kim,
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea
- Neuroscience Center, Samsung Medical Center, Seoul, South Korea
- Department of Health Sciences & Technology, SAIHST, Sungkyunkwan University, Seoul, South Korea
- Samsung Alzheimer Research Center, Center for Clinical Epidemiology Medical Center, Seoul, South Korea
- Department of Intelligent Precision Healthcare Convergence, SAIHST, Sungkyunkwan University, Seoul, South Korea
- Sang Won Seo,
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14
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Kim J, Jung SH, Choe YS, Kim S, Kim B, Kim HR, Son SJ, Hong CH, Na DL, Kim HJ, Cho SJ, Won HH, Seo SW. Ethnic differences in the frequency of β-amyloid deposition in cognitively normal individuals. Neurobiol Aging 2022; 114:27-37. [DOI: 10.1016/j.neurobiolaging.2022.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2021] [Revised: 02/24/2022] [Accepted: 03/01/2022] [Indexed: 10/18/2022]
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Müller EG, Stokke C, Stokmo HL, Edwin TH, Knapskog AB, Revheim ME. Evaluation of semi-quantitative measures of 18F-flutemetamol PET for the clinical diagnosis of Alzheimer's disease. Quant Imaging Med Surg 2022; 12:493-509. [PMID: 34993096 DOI: 10.21037/qims-21-188] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 07/06/2021] [Indexed: 11/06/2022]
Abstract
BACKGROUND 18F-flutemetamol positron emission tomography (PET) is used to assess cortical amyloid-β burden in patients with cognitive impairment to support a clinical diagnosis. Visual classification is the most widely used method in clinical practice although semi-quantification is beneficial to obtain an objective and continuous measure of the Aβ burden. The aims were: first to evaluate the correspondence between standardized uptake value ratios (SUVRs) from three different software, Centiloids and visual classification, second to estimate thresholds for supporting visual classification and last to assess differences in semi-quantitative measures between clinical diagnoses. METHODS This observational study included 195 patients with cognitive impairment who underwent 18F-flutemetamol PET. PET images were semi-quantified with SyngoVia, CortexID suite, and PMOD. Receiver operating characteristics curves were used to compare visual classification with composite SUVR normalized to pons (SUVRpons) and cerebellar cortex (SUVRcer), and Centiloids. We explored correlations and differences between semi-quantitative measures as well as differences in SUVR between two clinical diagnosis groups: Alzheimer's disease-group and non-Alzheimer's disease-group. RESULTS PET images from 191 patients were semi-quantified with SyngoVia and CortexID and 86 PET-magnetic resonance imaging pairs with PMOD. All receiver operating characteristics curves showed a high area under the curve (>0.98). Thresholds for a visually positive PET was for SUVRcer: 1.87 (SyngoVia) and 1.64 (CortexID) and for SUVRpons: 0.54 (SyngoVia) and 0.55 (CortexID). The threshold on the Centiloid scale was 39.6 Centiloids. All semi-quantitative measures showed a very high correlation between different software and normalization methods. Composite SUVRcer was significantly different between SyngoVia and PMOD, SyngoVia and CortexID but not between PMOD and CortexID. Composite SUVRpons were significantly different between all three software. There were significant differences in the mean rank of SUVRpons, SUVRcer, and Centiloid between Alzheimer's disease-group and non-Alzheimer's disease-group. CONCLUSIONS SUVR from different software performed equally well in discriminating visually positive and negative 18F-Flutemetamol PET images. Thresholds should be considered software-specific and cautiously be applied across software without preceding validation to categorize scans as positive or negative. SUVR and Centiloid may be used alongside a thorough clinical evaluation to support a clinical diagnosis.
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Affiliation(s)
- Ebba Gløersen Müller
- Division of Radiology and Nuclear Medicine, Department of Nuclear Medicine, Oslo University Hospital, Oslo, Norway.,Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Caroline Stokke
- Division of Radiology and Nuclear Medicine, Department of Diagnostic Physics, Oslo University Hospital, Oslo, Norway.,Department of Physics, University of Oslo, Oslo, Norway
| | - Henning Langen Stokmo
- Division of Radiology and Nuclear Medicine, Department of Nuclear Medicine, Oslo University Hospital, Oslo, Norway.,Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Trine Holt Edwin
- Institute of Health and Society, Faculty of Medicine, University of Oslo, Oslo, Norway.,Department of Geriatric Medicine, The memory clinic, Oslo University Hospital, Oslo, Norway
| | - Anne-Brita Knapskog
- Department of Geriatric Medicine, The memory clinic, Oslo University Hospital, Oslo, Norway
| | - Mona-Elisabeth Revheim
- Division of Radiology and Nuclear Medicine, Department of Nuclear Medicine, Oslo University Hospital, Oslo, Norway.,Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
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16
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Jang H, Park YH, Choe YS, Kang SH, Kang ES, Lee S, Seo SW, Kim HJ, Na DL. Amyloid Positive Hydrocephalus: A Hydrocephalic Variant of Alzheimer's Disease? J Alzheimers Dis 2021; 85:1467-1479. [PMID: 34958024 DOI: 10.3233/jad-215110] [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 Alzheimer's disease (AD) and normal pressure hydrocephalus (NPH) commonly coexist. OBJECTIVE We aimed to characterize an overlapping syndrome of AD and NPH that presents with gait disturbance, ventriculomegaly on magnetic resonance imaging, and significant amyloid deposition on positron emission tomography (PET). METHODS Of 114 patients who underwent cerebrospinal fluid (CSF) drainage for a possible diagnosis of NPH between 2015 and 2020 in Samsung Medical Center, we identified 24 patients (21.1%) with the NPH patients with amyloid deposition on PET, which we referred to as hydrocephalic AD in this study. We compared their clinical and imaging findings with those of 123 typical AD without hydrocephalic signs/symptoms. We also investigated the frequency and potential predictors of the tap test response in hydrocephalic AD. RESULTS Evans' index was 0.36±0.03, and a disproportionately enlarged subarachnoid space was present in 54.2% of the hydrocephalic AD patients. The mean age (75.2±7.3 years) and the APOE4 frequency (68.2%) did not differ from those of AD controls. However, the hydrocephalic AD patients showed better memory and language performance, and a thinner cingulate cortex. About 42% of the hydrocephalic AD patients responded to the tap test, of whom seven underwent shunt surgery. Cognition did not improve, whereas gait improved after shunt surgery in all. CONCLUSION Hydrocephalic AD has different neuropsychological and imaging characteristics from typical AD. Future studies are warranted to further investigate the effect of CSF removal on their clinical course and to elucidate the pathophysiological interaction between amyloid and NPH.
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Affiliation(s)
- Hyemin Jang
- Department of Neurology, Sungkyunkwan University School of Medicine, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea.,Samsung Alzheimer Research Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Yu-Hyun Park
- Department of Neurology, Sungkyunkwan University School of Medicine, Seoul, Korea.,Samsung Alzheimer Research Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Young Sim Choe
- Department of Neurology, Sungkyunkwan University School of Medicine, Seoul, Korea.,Samsung Alzheimer Research Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Sung Hoon Kang
- Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Korea.,Department of Neurology, Sungkyunkwan University School of Medicine, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea.,Samsung Alzheimer Research Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Eun-Sook Kang
- Laboratory Medicine and Genetics, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Seunghoon Lee
- Neurosurgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Sang Won Seo
- Department of Neurology, Sungkyunkwan University School of Medicine, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea.,Samsung Alzheimer Research Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Department of Clinical Research Design & Evaluation, SAIHST, Sungkyunkwan University, Seoul, Korea.,Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea
| | - Hee Jin Kim
- Department of Neurology, Sungkyunkwan University School of Medicine, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea.,Samsung Alzheimer Research Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Duk L Na
- Department of Neurology, Sungkyunkwan University School of Medicine, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea.,Samsung Alzheimer Research Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Stem Cell & Regenerative Medicine Institute, Samsung Medical Center, Seoul, South Korea.,Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea
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17
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Hong WK, Yoon JH, Jang H, Yoon SJ, Moon SY, Kim HJ, Na DL. Honorific Speech Impairment: A Characteristic Sign of Frontotemporal Dementia. Cogn Behav Neurol 2021; 34:275-287. [PMID: 34851865 DOI: 10.1097/wnn.0000000000000284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Accepted: 01/31/2021] [Indexed: 11/26/2022]
Abstract
BACKGROUND Individuals with the behavioral variant of frontotemporal dementia (bvFTD) exhibit various levels of abulia, disinhibition, impaired judgment, and decline in executive function. Empirical evidence has shown that individuals with bvFTD also often exhibit difficulty using honorific speech, which expresses respect to another party or addressee. OBJECTIVE To analyze differences in the ability to use honorific speech among individuals with bvFTD, individuals with dementia of the Alzheimer type (AD dementia), and individuals with normal cognition (NC). METHOD A total of 53 native Korean speakers (13 bvFTD, 20 AD dementia, and 20 NC) completed an experimental honorific speech task (HST) that involved both expressive and receptive tasks. We analyzed the number of correct responses and error patterns separately for an expressive task and for a receptive task. RESULTS The bvFTD group had significantly fewer correct responses on the HST compared with the AD dementia and NC groups. The bvFTD group exhibited more misjudgment errors in identifying nonhonorific speech as honorific speech in the expressive task, and significantly longer response times in the receptive task, than the AD dementia and NC groups. Significant associations were identified between HST scores and cortical atrophy in the temporal and frontotemporal lobes. CONCLUSION A decline in the ability to use honorific speech may be a diagnosable behavioral and psychiatric symptom for bvFTD in Korean-speaking individuals. This decline in individuals with bvFTD could be attributed to multiple factors, including social manners (politeness) and impaired social language use ability (pragmatics).
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Affiliation(s)
- Weon Kyeong Hong
- Department of Speech-Language Pathology and Audiology, Graduate School of Hallym University, Chuncheon, Republic of Korea
| | - Ji Hye Yoon
- Division of Speech Pathology and Audiology, College of Natural Sciences, Hallym University, Chuncheon, Republic of Korea
- Audiology and Speech Pathology Research Institute, Hallym University, Chuncheon, Republic of Korea
| | - Hyemin Jang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Samsung Alzheimer's Research Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Soo Jin Yoon
- Department of Neurology, Eulji University Hospital, Eulji University School of Medicine, Daejeon, Republic of Korea
| | - So Young Moon
- Department of Neurology, Ajou University School of Medicine, Suwon, Republic of 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, Republic of Korea
| | - Duk L Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Health Sciences and Technology, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul, Republic of Korea
- Stem Cell & Regenerative Medicine Institute, Samsung Medical Center, Seoul, Republic of Korea
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18
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Jang H, Kim JS, Lee HJ, Kim CH, Na DL, Kim HJ, Allué JA, Sarasa L, Castillo S, Pesini P, Gallacher J, Seo SW. Performance of the plasma Aβ42/Aβ40 ratio, measured with a novel HPLC-MS/MS method, as a biomarker of amyloid PET status in a DPUK-KOREAN cohort. ALZHEIMERS RESEARCH & THERAPY 2021; 13:179. [PMID: 34686209 PMCID: PMC8540152 DOI: 10.1186/s13195-021-00911-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 10/02/2021] [Indexed: 12/20/2022]
Abstract
Background We assessed the feasibility of plasma Aβ42/Aβ40 determined using a novel liquid chromatography-mass spectrometry method (LC-MS) as a useful biomarker of PET status in a Korean cohort from the DPUK Study. Methods A total of 580 participants belonging to six groups, Alzheimer’s disease dementia (ADD, n = 134), amnestic mild cognitive impairment (aMCI, n = 212), old controls (OC, n = 149), young controls (YC, n = 15), subcortical vascular cognitive impairment (SVCI, n = 58), and cerebral amyloid angiopathy (CAA, n = 12), were included in this study. Plasma Aβ40 and Aβ42 were quantitated using a new antibody-free, LC-MS, which drastically reduced the sample preparation time and cost. We performed receiver operating characteristic (ROC) analysis to develop the cutoff of Aβ42/Aβ40 and investigated its performance predicting centiloid-based PET positivity (PET+). Results Plasma Aβ42/Aβ40 were lower for PET+ individuals in ADD, aMCI, OC, and SVCI (p < 0.001), but not in CAA (p = 0.133). In the group of YC, OC, aMCI, and ADD groups, plasma Aβ42/Aβ40 predicted PET+ with an area under the ROC curve (AUC) of 0.814 at a cutoff of 0.2576. When adding age, APOE4, and diagnosis, the AUC significantly improved to 0.912. Conclusion Plasma Aβ42/Aβ40, as measured by this novel LC-MS method, showed good discriminating performance based on PET positivity. Supplementary Information The online version contains supplementary material available at 10.1186/s13195-021-00911-7.
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Affiliation(s)
- Hyemin Jang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Neuroscience Center, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Alzheimer's Disease Convergence Research Center, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Ji Sun Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Neuroscience Center, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Hye Joo Lee
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Alzheimer's Disease Convergence Research Center, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Chi-Hun Kim
- Department of Neurology, School of Medicine, Kyungpook National University, Kyungpook National University Chilgok Hospital, Daegu, South Korea.,Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, UK
| | - Duk L Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Neuroscience Center, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Alzheimer's Disease Convergence Research Center, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Stem Cell & Regenerative Medicine Institute, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Department of Health Sciences and Technology, Seoul, Republic of Korea
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Neuroscience Center, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Alzheimer's Disease Convergence Research Center, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | | | - Leticia Sarasa
- Araclon Biotech-Grifols, Vía Hispanidad, 21, 50009, Zaragoza, Spain
| | - Sergio Castillo
- Araclon Biotech-Grifols, Vía Hispanidad, 21, 50009, Zaragoza, Spain
| | - Pedro Pesini
- Araclon Biotech-Grifols, Vía Hispanidad, 21, 50009, Zaragoza, Spain
| | - John Gallacher
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, UK
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea. .,Neuroscience Center, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea. .,Alzheimer's Disease Convergence Research Center, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea. .,Department of Clinical Research Design & Evaluation, SAIHST, Sungkyunkwan University, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea. .,Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
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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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20
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Kang SH, Kim ME, Jang H, Kwon H, Lee H, Kim HJ, Seo SW, Na DL. Amyloid Positivity in the Alzheimer/Subcortical-Vascular Spectrum. Neurology 2021; 96:e2201-e2211. [PMID: 33722997 DOI: 10.1212/wnl.0000000000011833] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Accepted: 01/28/2021] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVE We investigated the frequency of β-amyloid (Aβ) positivity in 9 groups classified according to a combination of 3 different cognition states and 3 distinct levels of white matter hyperintensities (WMH) (minimal, moderate, and severe) and aimed to determine which factors were associated with Aβ after controlling for WMH and vice versa. METHODS A total of 1,047 individuals with subjective cognitive decline (SCD, n = 294), mild cognitive impairment (MCI, n = 237), or dementia (n = 516) who underwent Aβ PET scans were recruited from the memory clinic at Samsung Medical Center in Seoul, Korea. We investigated the following: (1) Aβ positivity in the 9 groups, (2) the relationship between Aβ positivity and WMH severity, and (3) clinical and genetic factors independently associated with Aβ or WMH. RESULTS Aβ positivity increased as the severity of cognitive impairment increased (SCD [15.7%], MCI [43.5%], and dementia [76.2%]), whereas it decreased as the severity of WMH increased (minimal [54.5%], moderate [53.9%], and severe [41.0%]) or the number of lacunes (0 [59.0%], 1-3 [42.0%], and >3 [23.4%]) increased. Aβ positivity was associated with higher education, absence of diabetes, and presence of APOE ε4 after controlling for cognitive and WMH status. CONCLUSION Our analysis of Aβ positivity involving a large sample classified according to the stratified cognitive states and WMH severity indicates that Alzheimer and cerebral small vessel diseases lie on a continuum. Our results offer clinicians insightful information about the association among Aβ, WMH, and cognition.
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Affiliation(s)
- Sung Hoon Kang
- From the Department of Neurology, Sungkyunkwan University School of Medicine (S.H.K., H.J., H.L., H.J.K., S.W.S., D.L.N.), Neuroscience Center (S.H.K., H.J., H.L., H.J.K., S.W.S., D.L.N.), and Stem Cell & Regenerative Medicine Institute (D.L.N.), Samsung Medical Center; Department of Neurology (S.H.K.), Korea University Guro Hospital, Korea University College of Medicine, Seoul; Chicago College of Osteopathic Medicine (M.E.K.), Midwestern University, IL; New York University (H.K.), NY; and Department of Health Sciences and Technology, SAIHST (D.L.N.), Sungkyunkwan University, Seoul, Korea
| | - Monica Eunseo Kim
- From the Department of Neurology, Sungkyunkwan University School of Medicine (S.H.K., H.J., H.L., H.J.K., S.W.S., D.L.N.), Neuroscience Center (S.H.K., H.J., H.L., H.J.K., S.W.S., D.L.N.), and Stem Cell & Regenerative Medicine Institute (D.L.N.), Samsung Medical Center; Department of Neurology (S.H.K.), Korea University Guro Hospital, Korea University College of Medicine, Seoul; Chicago College of Osteopathic Medicine (M.E.K.), Midwestern University, IL; New York University (H.K.), NY; and Department of Health Sciences and Technology, SAIHST (D.L.N.), Sungkyunkwan University, Seoul, Korea
| | - Hyemin Jang
- From the Department of Neurology, Sungkyunkwan University School of Medicine (S.H.K., H.J., H.L., H.J.K., S.W.S., D.L.N.), Neuroscience Center (S.H.K., H.J., H.L., H.J.K., S.W.S., D.L.N.), and Stem Cell & Regenerative Medicine Institute (D.L.N.), Samsung Medical Center; Department of Neurology (S.H.K.), Korea University Guro Hospital, Korea University College of Medicine, Seoul; Chicago College of Osteopathic Medicine (M.E.K.), Midwestern University, IL; New York University (H.K.), NY; and Department of Health Sciences and Technology, SAIHST (D.L.N.), Sungkyunkwan University, Seoul, Korea
| | - Hojeong Kwon
- From the Department of Neurology, Sungkyunkwan University School of Medicine (S.H.K., H.J., H.L., H.J.K., S.W.S., D.L.N.), Neuroscience Center (S.H.K., H.J., H.L., H.J.K., S.W.S., D.L.N.), and Stem Cell & Regenerative Medicine Institute (D.L.N.), Samsung Medical Center; Department of Neurology (S.H.K.), Korea University Guro Hospital, Korea University College of Medicine, Seoul; Chicago College of Osteopathic Medicine (M.E.K.), Midwestern University, IL; New York University (H.K.), NY; and Department of Health Sciences and Technology, SAIHST (D.L.N.), Sungkyunkwan University, Seoul, Korea
| | - Hyejoo Lee
- From the Department of Neurology, Sungkyunkwan University School of Medicine (S.H.K., H.J., H.L., H.J.K., S.W.S., D.L.N.), Neuroscience Center (S.H.K., H.J., H.L., H.J.K., S.W.S., D.L.N.), and Stem Cell & Regenerative Medicine Institute (D.L.N.), Samsung Medical Center; Department of Neurology (S.H.K.), Korea University Guro Hospital, Korea University College of Medicine, Seoul; Chicago College of Osteopathic Medicine (M.E.K.), Midwestern University, IL; New York University (H.K.), NY; and Department of Health Sciences and Technology, SAIHST (D.L.N.), Sungkyunkwan University, Seoul, Korea
| | - Hee Jin Kim
- From the Department of Neurology, Sungkyunkwan University School of Medicine (S.H.K., H.J., H.L., H.J.K., S.W.S., D.L.N.), Neuroscience Center (S.H.K., H.J., H.L., H.J.K., S.W.S., D.L.N.), and Stem Cell & Regenerative Medicine Institute (D.L.N.), Samsung Medical Center; Department of Neurology (S.H.K.), Korea University Guro Hospital, Korea University College of Medicine, Seoul; Chicago College of Osteopathic Medicine (M.E.K.), Midwestern University, IL; New York University (H.K.), NY; and Department of Health Sciences and Technology, SAIHST (D.L.N.), Sungkyunkwan University, Seoul, Korea
| | - Sang Won Seo
- From the Department of Neurology, Sungkyunkwan University School of Medicine (S.H.K., H.J., H.L., H.J.K., S.W.S., D.L.N.), Neuroscience Center (S.H.K., H.J., H.L., H.J.K., S.W.S., D.L.N.), and Stem Cell & Regenerative Medicine Institute (D.L.N.), Samsung Medical Center; Department of Neurology (S.H.K.), Korea University Guro Hospital, Korea University College of Medicine, Seoul; Chicago College of Osteopathic Medicine (M.E.K.), Midwestern University, IL; New York University (H.K.), NY; and Department of Health Sciences and Technology, SAIHST (D.L.N.), Sungkyunkwan University, Seoul, Korea
| | - Duk L Na
- From the Department of Neurology, Sungkyunkwan University School of Medicine (S.H.K., H.J., H.L., H.J.K., S.W.S., D.L.N.), Neuroscience Center (S.H.K., H.J., H.L., H.J.K., S.W.S., D.L.N.), and Stem Cell & Regenerative Medicine Institute (D.L.N.), Samsung Medical Center; Department of Neurology (S.H.K.), Korea University Guro Hospital, Korea University College of Medicine, Seoul; Chicago College of Osteopathic Medicine (M.E.K.), Midwestern University, IL; New York University (H.K.), NY; and Department of Health Sciences and Technology, SAIHST (D.L.N.), Sungkyunkwan University, Seoul, Korea.
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21
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Bourgeat P, Doré V, Doecke J, Ames D, Masters CL, Rowe CC, Fripp J, Villemagne VL. Non-negative matrix factorisation improves Centiloid robustness in longitudinal studies. Neuroimage 2021; 226:117593. [PMID: 33248259 PMCID: PMC8049633 DOI: 10.1016/j.neuroimage.2020.117593] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2020] [Revised: 10/12/2020] [Accepted: 11/17/2020] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND Centiloid was introduced to harmonise β-Amyloid (Aβ) PET quantification across different tracers, scanners and analysis techniques. Unfortunately, Centiloid still suffers from some quantification disparities in longitudinal analysis when normalising data from different tracers or scanners. In this work, we aim to reduce this variability using a different analysis technique applied to the existing calibration data. METHOD All PET images from the Centiloid calibration dataset, along with 3762 PET images from the AIBL study were analysed using the recommended SPM pipeline. The PET images were SUVR normalised using the whole cerebellum. All SUVR normalised PiB images from the calibration dataset were decomposed using non-negative matrix factorisation (NMF). The NMF coefficients related to the first component were strongly correlated with global SUVR and were subsequently used as a surrogate for Aβ retention. For each tracer of the calibration dataset, the components of the NMF were computed in a way such that the coefficients of the first component would match those of the corresponding PiB. Given the strong correlations between the SUVR and the NMF coefficients on the calibration dataset, all PET images from AIBL were subsequently decomposed using the computed NMF, and their coefficients transformed into Centiloids. RESULTS Using the AIBL data, the correlation between the standard Centiloid and the novel NMF-based Centiloid was high in each tracer. The NMF-based Centiloids showed a reduction of outliers, and improved longitudinal consistency. Furthermore, it removed the effects of switching tracers from the longitudinal variance of the Centiloid measure, when assessed using a linear mixed effects model. CONCLUSION We here propose a novel image driven method to perform the Centiloid quantification. The methods is highly correlated with standard Centiloids while improving the longitudinal reliability when switching tracers. Implementation of this method across multiple studies may lend to more robust and comparable data for future research.
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Affiliation(s)
| | - Vincent Doré
- CSIRO Health and Biosecurity, Brisbane, Australia; Department of Molecular Imaging & Therapy, Austin Health, Melbourne, Australia
| | - James Doecke
- CSIRO Health and Biosecurity, Brisbane, Australia
| | - David Ames
- University of Melbourne, Academic Unit for Psychiatry of Old Age, St George's Hospital, Kew, Australia
| | - Colin L Masters
- The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Melbourne, Australia
| | - Christopher C Rowe
- Department of Molecular Imaging & Therapy, Austin Health, Melbourne, Australia; Department of Medicine, University of Melbourne, Melbourne, Australia
| | - Jurgen Fripp
- CSIRO Health and Biosecurity, Brisbane, Australia
| | - Victor L Villemagne
- Department of Molecular Imaging & Therapy, Austin Health, Melbourne, Australia; Department of Medicine, University of Melbourne, Melbourne, Australia
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22
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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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23
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Visual interpretation of [18F]Florbetaben PET supported by deep learning–based estimation of amyloid burden. Eur J Nucl Med Mol Imaging 2020; 48:1116-1123. [DOI: 10.1007/s00259-020-05044-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Accepted: 09/15/2020] [Indexed: 12/11/2022]
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24
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Cho SH, Choe YS, Park S, Kim YJ, Kim HJ, Jang H, Kim SJ, Kim JP, Jung YH, Kim BC, Na DL, Moon SH, Seo SW. Appropriate reference region selection of 18F-florbetaben and 18F-flutemetamol beta-amyloid PET expressed in Centiloid. Sci Rep 2020; 10:14950. [PMID: 32917930 PMCID: PMC7486392 DOI: 10.1038/s41598-020-70978-z] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 07/27/2020] [Indexed: 12/01/2022] Open
Abstract
The Centiloid (CL) is a method for standardizing amyloid beta (Aβ) quantification through different ligands and methods. To find the most appropriate reference region to reduce the variance in the Aβ CL unit between 18F-florbetaben (FBB) and 18F-flutemetamol (FMM), we conducted head-to-head comparisons from 56 participants using the direct comparison of FBB-FMM CL (dcCL) method with four reference regions: cerebellar gray (CG), whole cerebellum (WC), WC with brainstem (WC + B), and pons. The FBB and FMM dcCL units were highly correlated in four reference regions: WC (R2 = 0.97), WC + B (R2 = 0.98), CG (R2 = 0.92), and pons (R2 = 0.98). WC showed the largest effect size in both FBB and FMM. Comparison of the variance of the dcCL values within the young control group showed that with FBB, WC + B had the smallest variance and with FMM, the WC had the smallest variance. Additionally, WC + B showed the smallest absolute difference between FBB and FMM, followed by the WC, pons, and CG. We found that it would be reasonable to use the WC or WC + B as the reference region when converting FBB and FMM SUVRs into dcCL, which can increase the accuracy of standardizing FBB and FMM PET results.
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Affiliation(s)
- Soo Hyun Cho
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Department of Neurology, Chonnam National University Hospital, Chonnam National University Medical School, Gwangju, Republic of Korea
| | - Yeong Sim Choe
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Seongbeom Park
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Young Ju Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Hyemin Jang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Seung Joo Kim
- Department of Neurology, Gyeongsang National University School of Medicine, Gyeongsang National University Changwon Hospital, Changwon, Republic of Korea
| | - Jun Pyo Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Young Hee Jung
- Department of Neurology, Myoungji Hospital, Hanyang University, Goyangsi, Republic of Korea
| | - Byeong C Kim
- Department of Neurology, Chonnam National University Hospital, Chonnam National University Medical School, Gwangju, Republic of Korea
| | - Duk L Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea.,Stem Cell and Regenerative Medicine Institute, Samsung Medical Center, Seoul, Republic of Korea
| | - Seung Hwan Moon
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea. .,Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea. .,Department of Clinical Research Design and Evaluation, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea. .,Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Republic of Korea. .,Center for Clinical Epidemiology, Samsung Medical Center, Seoul, Republic of Korea.
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