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Chen Y, Wang Y, Zhang M, Zhou Y, Zhang H, Li P, Wu J. The clinical and neuropsychological profiles of Alzheimer's disease with white matter hyperintensity in North China. Front Neurol 2024; 15:1436030. [PMID: 39416665 PMCID: PMC11480061 DOI: 10.3389/fneur.2024.1436030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Accepted: 09/16/2024] [Indexed: 10/19/2024] Open
Abstract
Background Patients with Alzheimer's disease (AD) often exhibit characteristic clinical manifestations, particularly neuropsychiatric symptoms. Previous studies have shown that white matter hyperintensity (WMH) is strongly associated with AD progression, as well as neuropsychiatric symptoms. The purpose of this study was to investigate the clinical and neuropsychological characteristics of AD patients with WMH. Methods This retrospective study involved 104 18-fluorodeoxyglucose-positron emission computed tomography (18FDG-PET-CT)-defined AD patients treated at Tianjin Huanhu Hospital from January 2010 to December 2022. Cranial magnetic resonance imaging (MRI) provided semi-quantitative data on brain structure and WMH. Collect and analyze patient clinical data. Neuropsychological assessments were used to evaluate cognitive function and psychobehavioral traits. Results Among the 104 patients, 66 were in the WMH group (63.5%) and 38 in the non-white matter hyperintensity (non-WMH) group (36.5%). There were no significant differences in gender, age, age of onset, education, BMI, smoking, drinking, diabetes, coronary heart disease, dementia family history, fasting blood glucose, total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C) between the two groups. The WMH group showed higher rates of hypertension, homocysteine (Hcy) levels, NPI, and CDR scores as compared to the non-WMH group (p < 0.05). MMSE and MoCA scores were significantly lower in the WMH group (p < 0.05). In the MMSE subitem analysis, patients in the WMH group showed a decrease in attention, recall, and language scores. In the MOCA subitem analysis, WMH patients had lower scores in executive function, naming, attention, language, abstraction, and orientation (p < 0.05). Furthermore, subgroup analysis of NPI showed a higher incidence of delusions, depression, and apathy in the WMH group (p < 0.05). According to the hierarchical analysis of mild, moderate and severe dementia groups, the hypertension, leukoencephalopathy, Hcy level, Fazekas total score, PWMH and DWMH scores in the severe dementia group were significantly higher than those in the mild and moderate dementia groups (p < 0.05). As the disease progresses, more and more patients show increased white matter hyperintensity. Conclusion White matter lesions are closely correlated with cognitive decline and psychobehavioral symptoms in AD patients, and may be used as an indicator of disease progression. Priority should be given to early screening and prevention of WMH-related risk factors.
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Affiliation(s)
- Yuan Chen
- Clinical College of Neurology, Neurosurgery and Neurorehabilitation, Tianjin Medical University, Tianjin, China
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin, China
- Department of Neurology, Tianjin Huanhu Hospital Affiliated to Tianjin Medical University, Tianjin Huanhu Hospital Affiliated to Nankai University, Tianjin University Huanhu Hospital, Tianjin, China
- Tianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, Tianjin Neurosurgery Institute, Tianjin Huanhu Hospital, Tianjin, China
| | - Yan Wang
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin, China
- Department of Neurology, Tianjin Huanhu Hospital Affiliated to Tianjin Medical University, Tianjin Huanhu Hospital Affiliated to Nankai University, Tianjin University Huanhu Hospital, Tianjin, China
- Tianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, Tianjin Neurosurgery Institute, Tianjin Huanhu Hospital, Tianjin, China
| | - Miao Zhang
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin, China
- Department of Neurology, Tianjin Huanhu Hospital Affiliated to Tianjin Medical University, Tianjin Huanhu Hospital Affiliated to Nankai University, Tianjin University Huanhu Hospital, Tianjin, China
- Tianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, Tianjin Neurosurgery Institute, Tianjin Huanhu Hospital, Tianjin, China
| | - Yuying Zhou
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin, China
- Department of Neurology, Tianjin Huanhu Hospital Affiliated to Tianjin Medical University, Tianjin Huanhu Hospital Affiliated to Nankai University, Tianjin University Huanhu Hospital, Tianjin, China
- Tianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, Tianjin Neurosurgery Institute, Tianjin Huanhu Hospital, Tianjin, China
| | - Huihong Zhang
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin, China
- Department of Neurology, Tianjin Huanhu Hospital Affiliated to Tianjin Medical University, Tianjin Huanhu Hospital Affiliated to Nankai University, Tianjin University Huanhu Hospital, Tianjin, China
- Tianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, Tianjin Neurosurgery Institute, Tianjin Huanhu Hospital, Tianjin, China
| | - Pan Li
- Clinical College of Neurology, Neurosurgery and Neurorehabilitation, Tianjin Medical University, Tianjin, China
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin, China
- Department of Neurology, Tianjin Huanhu Hospital Affiliated to Tianjin Medical University, Tianjin Huanhu Hospital Affiliated to Nankai University, Tianjin University Huanhu Hospital, Tianjin, China
- Tianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, Tianjin Neurosurgery Institute, Tianjin Huanhu Hospital, Tianjin, China
| | - Jialing Wu
- Clinical College of Neurology, Neurosurgery and Neurorehabilitation, Tianjin Medical University, Tianjin, China
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin, China
- Department of Neurology, Tianjin Huanhu Hospital Affiliated to Tianjin Medical University, Tianjin Huanhu Hospital Affiliated to Nankai University, Tianjin University Huanhu Hospital, Tianjin, China
- Tianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, Tianjin Neurosurgery Institute, Tianjin Huanhu Hospital, Tianjin, China
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Huq A, Thompson B, Winship I. Clinical application of whole genome sequencing in young onset dementia: challenges and opportunities. Expert Rev Mol Diagn 2024; 24:659-675. [PMID: 39135326 DOI: 10.1080/14737159.2024.2388765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2024] [Accepted: 08/01/2024] [Indexed: 08/30/2024]
Abstract
INTRODUCTION Young onset dementia (YOD) by its nature is difficult to diagnose. Despite involvement of multidisciplinary neurogenetics services, patients with YOD and their families face significant diagnostic delays. Genetic testing for people with YOD currently involves a staggered, iterative approach. There is currently no optimal single genetic investigation that simultaneously identifies the different genetic variants resulting in YOD. AREAS COVERED This review discusses the advances in clinical genomic testing for people with YOD. Whole genome sequencing (WGS) can be employed as a 'one stop shop' genomic test for YOD. In addition to single nucleotide variants, WGS can reliably detect structural variants, short tandem repeat expansions, mitochondrial genetic variants as well as capture single nucleotide polymorphisms for the calculation of polygenic risk scores. EXPERT OPINION WGS, when used as the initial genetic test, can enhance the likelihood of a precision diagnosis and curtail the time taken to reach this. Finding a clinical diagnosis using WGS can reduce invasive and expensive investigations and could be cost effective. These advances need to be balanced against the limitations of the technology and the genetic counseling needs for these vulnerable patients and their families.
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Affiliation(s)
- Aamira Huq
- Department of Genomic Medicine, Royal Melbourne Hospital, Parkville, Victoria, Australia
- Department of Medicine, University of Melbourne, Parkville, Victoria, Australia
| | - Bryony Thompson
- Department of Medicine, Royal Melbourne Hospital, Parkville, Victoria, Australia
- Department of Pathology, University of Melbourne, Parkville, Victoria, Australia
| | - Ingrid Winship
- Department of Genomic Medicine, Royal Melbourne Hospital, Parkville, Victoria, Australia
- Department of Medicine, University of Melbourne, Parkville, Victoria, Australia
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Hong H, Chen Y, Liu W, Luo X, Zhang M. Distinct patterns of voxel- and connection-based white matter hyperintensity distribution and associated factors in early-onset and late-onset Alzheimer's disease. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2024; 16:e12585. [PMID: 38651161 PMCID: PMC11033836 DOI: 10.1002/dad2.12585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 02/12/2024] [Accepted: 03/15/2024] [Indexed: 04/25/2024]
Abstract
Introduction The distribution of voxel- and connection-based white matter hyperintensity (WMH) patterns in early-onset Alzheimer's disease (EOAD) and late-onset Alzheimer's disease (LOAD), as well as factors associated with these patterns, remain unclear. Method We analyzed the WMH distribution patterns in EOAD and LOAD at the voxel and connection levels, each compared with their age-matched cognitively unimpaired participants. Linear regression assessed the independent effects of amyloid and vascular risk factors on WMH distribution patterns in both groups. Results Patients with EOAD showed increased WMH burden in the posterior region at the voxel level, and in occipital region tracts and visual network at the connection level, compared to controls. LOAD exhibited extensive involvement across various brain areas in both levels. Amyloid accumulation was associated WMH distribution in the early-onset group, whereas the late-onset group demonstrated associations with both amyloid and vascular risk factors. Discussion EOAD showed posterior-focused WMH distribution pattern, whereas LOAD was with a wider distribution. Amyloid accumulation was associated with connection-based WMH patterns in both early-onset and late-onset groups, with additional independent effects of vascular risk factors in late-onset group. Highlights Both early-onset Alzheimer's disease (EOAD) and late-onset AD (LOAD) showed increased white matter hyperintensity (WMH) volume compared with their age-matched cognitively unimpaired participants.EOAD and LOAD exhibited distinct patterns of WMH distribution, with EOAD showing a posterior-focused pattern and LOAD displaying a wider distribution across both voxel- and connection-based levels.In both EOAD and LOAD, amyloid accumulation was associated with connection-based WMH patterns, with additional independent effects of vascular risk factors observed in LOAD.
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Affiliation(s)
- Hui Hong
- Department of RadiologyThe Second Affiliated Hospital of Zhejiang UniversitySchool of MedicineHangzhouChina
- Department of Clinical NeurosciencesUniversity of CambridgeCambridgeUK
| | - Yutong Chen
- Department of Clinical NeurosciencesUniversity of CambridgeCambridgeUK
| | - Weiran Liu
- Department of Clinical NeurosciencesUniversity of CambridgeCambridgeUK
| | - Xiao Luo
- Department of RadiologyThe Second Affiliated Hospital of Zhejiang UniversitySchool of MedicineHangzhouChina
| | - Minming Zhang
- Department of RadiologyThe Second Affiliated Hospital of Zhejiang UniversitySchool of MedicineHangzhouChina
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Lin X, Feng T, Cui E, Li Y, Qin Z, Zhao X. A rat model established by simulating genetic-environmental interactions recapitulates human Alzheimer's disease pathology. Brain Res 2024; 1822:148663. [PMID: 37918702 DOI: 10.1016/j.brainres.2023.148663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 10/16/2023] [Accepted: 10/30/2023] [Indexed: 11/04/2023]
Abstract
BACKGROUND In humans, Alzheimer's disease (AD) is typically sporadic in nature, and its pathology is usually influenced by extensive factors. The study established a rat model based on the genetic-environmental interaction. METHODS A rat model was established by transduction of an adeno-associated virus combined with acrolein treatment. Rats were assigned to the normal control (NC), acrolein group, AAV (-) group, AAV-APP group, and AAV-APP/acrolein group. The success of model construction was verified in multiple ways, including by assessing cognitive function, examining microstructural changes in the brain in vivo, and performing immunohistochemistry. The contribution of genetic (APP mutation) and environmental (acrolein) factors to AD-like phenotypes in the model was explored by factorial analysis. RESULTS 1) The AAV-APP/acrolein group showed a decline in cognitive function, as indicated by a reduced gray matter volume in key cognition-related brain areas, lower FA values in the hippocampus and internal olfactory cortex, and Aβ deposition in the cortex and hippocampus. 2) The AAV-APP group also showed a decline in cognitive function, although the group exhibited atypical brain atrophy in the gray matter and insignificant Aβ deposition. 3) The acrolein group did not show any significant changes in Aβ levels, gray matter volume, or cognitive function. 4) The genetic factor (APP mutation) explained 39.74% of the AD-like phenotypes in the model factors, and the environmental factor (acrolein exposure) explained 33.3%. CONCLUSIONS The genetic-environmental interaction rat model exhibited a phenotype that resembled the features of human AD and will be useful for research on AD.
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Affiliation(s)
- Xiaomei Lin
- Department of Imaging, The Fifth People's Hospital of Shanghai, Fudan University, Shanghai 200000, China
| | - Tianyuyi Feng
- Department of Imaging, The Fifth People's Hospital of Shanghai, Fudan University, Shanghai 200000, China
| | - Erheng Cui
- Department of Imaging, The Fifth People's Hospital of Shanghai, Fudan University, Shanghai 200000, China
| | - Yunfei Li
- Department of Imaging, The Fifth People's Hospital of Shanghai, Fudan University, Shanghai 200000, China
| | - Zhang Qin
- Department of Imaging, The Fifth People's Hospital of Shanghai, Fudan University, Shanghai 200000, China
| | - Xiaohu Zhao
- Department of Imaging, The Fifth People's Hospital of Shanghai, Fudan University, Shanghai 200000, China.
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Eloyan A, Thangarajah M, An N, Borowski BJ, Reddy AL, Aisen P, Dage JL, Foroud T, Ghetti B, Griffin P, Hammers D, Iaccarino L, Jack CR, Kirby K, Kramer J, Koeppe R, Kukull WA, La Joie R, Mundada NS, Murray ME, Nudelman K, Rumbaugh M, Soleimani-Meigooni DN, Toga A, Touroutoglou A, Atri A, Day GS, Duara R, Graff-Radford NR, Honig LS, Jones DT, Masdeu J, Mendez MF, Musiek E, Onyike CU, Rogalski E, Salloway S, Sha S, Turner RS, Wingo TS, Wolk DA, Womack K, Beckett L, Gao S, Carrillo MC, Rabinovici G, Apostolova LG, Dickerson B, Vemuri P. White matter hyperintensities are higher among early-onset Alzheimer's disease participants than their cognitively normal and early-onset nonAD peers: Longitudinal Early-onset Alzheimer's Disease Study (LEADS). Alzheimers Dement 2023; 19 Suppl 9:S89-S97. [PMID: 37491599 PMCID: PMC10808262 DOI: 10.1002/alz.13402] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 05/23/2023] [Accepted: 05/24/2023] [Indexed: 07/27/2023]
Abstract
INTRODUCTION We compared white matter hyperintensities (WMHs) in early-onset Alzheimer's disease (EOAD) with cognitively normal (CN) and early-onset amyloid-negative cognitively impaired (EOnonAD) groups in the Longitudinal Early-Onset Alzheimer's Disease Study. METHODS We investigated the role of increased WMH in cognition and amyloid and tau burden. We compared WMH burden of 205 EOAD, 68 EOnonAD, and 89 CN participants in lobar regions using t-tests and analyses of covariance. Linear regression analyses were used to investigate the association between WMH and cognitive impairment and that between amyloid and tau burden. RESULTS EOAD showed greater WMHs compared with CN and EOnonAD participants across all regions with no significant differences between CN and EOnonAD groups. Greater WMHs were associated with worse cognition. Tau burden was positively associated with WMH burden in the EOAD group. DISCUSSION EOAD consistently showed higher WMH volumes. Overall, greater WMHs were associated with worse cognition and higher tau burden in EOAD. HIGHLIGHTS This study represents a comprehensive characterization of WMHs in sporadic EOAD. WMH volumes are associated with tau burden from positron emission tomography (PET) in EOAD, suggesting WMHs are correlated with increasing burden of AD. Greater WMH volumes are associated with worse performance on global cognitive tests. EOAD participants have higher WMH volumes compared with CN and early-onset amyloid-negative cognitively impaired (EOnonAD) groups across all brain regions.
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Affiliation(s)
- Ani Eloyan
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, Rhode Island, USA, 02903
| | - Maryanne Thangarajah
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, Rhode Island, USA, 02903
| | - Na An
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, Rhode Island, USA, 02903
| | - Bret J. Borowski
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA, 55905
| | - Ashritha L. Reddy
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA, 55905
| | - Paul Aisen
- Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, California, USA, 92121
| | - Jeffrey L. Dage
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA, 46202
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA, 46202
| | - Tatiana Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA, 46202
| | - Bernardino Ghetti
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA, 46202
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA, 46202
- Department of Pathology & Laboratory Medicine Indiana University School of Medicine, Indianapolis, Indiana, USA, 02912
| | - Percy Griffin
- Medical & Scientific Relations Division, Alzheimer’s Association, Chicago, Illinois, USA, 60603
| | - Dustin Hammers
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA, 46202
| | - Leonardo Iaccarino
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA, 94143
| | - Clifford R. Jack
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA, 46202
| | - Kala Kirby
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA, 46202
| | - Joel Kramer
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA, 94143
| | - Robert Koeppe
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA, 48109
| | - Walter A. Kukull
- Department of Epidemiology, University of Washington, Seattle, Washington, USA, 98195
| | - Renaud La Joie
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA, 94143
| | - Nidhi S Mundada
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA, 94143
| | - Melissa E. Murray
- Department of Neuroscience, Mayo Clinic, Jacksonville, Florida, USA, 32224
| | - Kelly Nudelman
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA, 46202
| | - Malia Rumbaugh
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA, 46202
| | | | - Arthur Toga
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles, California, USA, 90033
| | - Alexandra Touroutoglou
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA, 02114
| | - Alireza Atri
- Banner Sun Health Research Institute, Sun City, Arizona, USA, 85351
| | - Gregory S. Day
- Department of Neurology, Mayo Clinic, Jacksonville, Florida, USA, 32224
| | - Ranjan Duara
- Wien Center for Alzheimer’s Disease and Memory Disorders, Mount Sinai Medical Center, Miami, Florida, USA, 33140
| | | | - Lawrence S. Honig
- Taub Institute and Department of Neurology, Columbia University Irving Medical Center, New York, New York, USA,10032
| | - David T. Jones
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA, 55905
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA, 55905
| | - Joseph Masdeu
- Nantz National Alzheimer Center, Houston Methodist and Weill Cornell Medicine, Houston, Texas, USA, 77030
| | - Mario F. Mendez
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California, USA, 90095
| | - Erik Musiek
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA, 63108
| | - Chiadi U. Onyike
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA, 21205
| | - Emily Rogalski
- Department of Psychiatry and Behavioral Sciences, Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA, 60611
| | - Stephen Salloway
- Department of Neurology, Alpert Medical School, Brown University, Providence, Rhode Island, USA, 02912
| | - Sharon Sha
- Department of Neurology & Neurological Sciences, Stanford University, Palo Alto, California, USA, 94304
| | - Raymond S. Turner
- Department of Neurology, Georgetown University, Washington D.C., USA, 20007
| | - Thomas S. Wingo
- Department of Neurology and Human Genetics, Emory University School of Medicine, Atlanta, Georgia, USA, 30322
| | - David A. Wolk
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA, 19104
| | - Kyle Womack
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA, 63108
| | - Laurel Beckett
- Department of Public Health Sciences, University of California – Davis, Davis, California, USA, 95616
| | - Sujuan Gao
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, Indiana, USA, 46202
| | - Maria C. Carrillo
- Medical & Scientific Relations Division, Alzheimer’s Association, Chicago, Illinois, USA, 60603
| | - Gil Rabinovici
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA, 94143
| | - Liana G. Apostolova
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA, 46202
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA, 46202
- Department of Radiology and Imaging Sciences, Center for Neuroimaging, Indiana University School of Medicine Indianapolis, Indianapolis, Indiana, USA, 46202
| | - Brad Dickerson
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA, 02114
| | - Prashanthi Vemuri
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA, 55905
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Mao C, You H, Hou B, Chu S, Jin W, Huang X, Shang L, Feng F, Peng B, Gao J. Differentiation of Alzheimer’s Disease from Frontotemporal Dementia and Mild Cognitive Impairment Based on Arterial Spin Labeling Magnetic Resonance Imaging: A Pilot Cross-Sectional Study from PUMCH Dementia Cohort. J Alzheimers Dis 2023; 93:509-519. [PMID: 37038812 DOI: 10.3233/jad-221023] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/07/2023]
Abstract
Background: Arterial spin labeling (ASL) is helpful in early diagnosis and differential diagnosis of Alzheimer’s disease (AD), with advantages including no exposure to radioactivity, no injection of a contrast agent, more accessible, and relatively less expensive. Objective: To establish the perfusion pattern of different dementia in Chinese population and evaluate the effectiveness of ASL in differentiating AD from cognitive unimpaired (CU), mild cognitive impairment (MCI), and frontotemporal dementia (FTD). Methods: Four groups of participants were enrolled, including AD, FTD, MCI, and CU based on clinical diagnosis from PUMCH dementia cohort. ASL image was collected using 3D spiral fast spin echo–based pseudo-continuous ASL pulse sequence with background suppression and a high resolution T1-weighted scan covering the whole brain. Data processing was performed using Dr. Brain Platform to get cerebral blood flow (ml/100g/min) in every region of interest cortices. Results: Participants included 66 AD, 26 FTD, 21 MCI, and 21 CU. Statistically, widespread hypoperfusion neocortices, most significantly in temporal-parietal-occipital cortices, but not hippocampus and subcortical nucleus were found in AD. Hypoperfusion in parietal lobe was most significantly associated with cognitive decline in AD. Hypoperfusion in parietal lobe was found in MCI and extended to adjacent temporal, occipital and posterior cingulate cortices in AD. Significant reduced perfusion in frontal and temporal cortices, including subcortical nucleus and anterior cingulate cortex were found in FTD. Hypoperfusion regions were relatively symmetrical in AD and left predominant especially in FTD. Conclusion: Specific patterns of ASL hypoperfusion were helpful in differentiating AD from CU, MCI, and FTD.
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Affiliation(s)
- Chenhui Mao
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing, China
| | - Hui You
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of MedicalScience/ Peking Union Medical College, Beijing, China
| | - Bo Hou
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of MedicalScience/ Peking Union Medical College, Beijing, China
| | - Shanshan Chu
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing, China
| | - Wei Jin
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing, China
| | - Xinying Huang
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing, China
| | - Li Shang
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing, China
| | - Feng Feng
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of MedicalScience/ Peking Union Medical College, Beijing, China
| | - Bin Peng
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing, China
| | - Jing Gao
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing, China
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A New Presenilin-1 Missense Variant Associated With a Progressive Supranuclear Palsy-like Phenotype. Alzheimer Dis Assoc Disord 2023; 37:82-84. [PMID: 35383591 DOI: 10.1097/wad.0000000000000503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 01/25/2022] [Indexed: 11/26/2022]
Abstract
Early-onset forms of Alzheimer disease (AD) have been associated with pathogenic variants in the APP , PSEN1 , and PSEN2 genes. Mutations in presenilin-1 ( PSEN1 ) account for the majority of cases of autosomal dominant AD. Numerous phenotypes have been associated with PSEN1 -pathogenic variants, including cerebellar ataxia and spastic paraplegia. Here, we describe a patient with early-onset AD presenting with extrapyramidal symptoms and supranuclear gaze palsy, mimicking progressive supranuclear palsy.
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Mao C, Hou B, Li J, Chu S, Huang X, Wang J, Dong L, Liu C, Feng F, Peng B, Gao J. Distribution of Cortical Atrophy Associated with Cognitive Decline in Alzheimer's Disease: A Cross-Sectional Quantitative Structural MRI Study from PUMCH Dementia Cohort. Curr Alzheimer Res 2022; 19:618-627. [PMID: 36065913 DOI: 10.2174/1567205019666220905145756] [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: 03/05/2022] [Revised: 06/25/2022] [Accepted: 06/28/2022] [Indexed: 01/27/2023]
Abstract
BACKGROUND Quantitative measures of atrophy on structural MRI are sensitive to the neurodegeneration that occurs in AD, and the topographical pattern of atrophy could serve as a sensitive and specific biomarker. OBJECTIVE We aimed to examine the distribution of cortical atrophy associated with cognitive decline and disease stage based on quantitative structural MRI analysis in a Chinese cohort to inform clinical diagnosis and follow-up of AD patients. METHODS One hundred and eleven patients who were clinically diagnosed with probable AD were enrolled. All patients completed a systemic cognitive evaluation and domain-specific batteries. The severity of cognitive decline was defined by MMSE score: 1-10 severe, 11-20 moderate, and 21-30 mild. Cortical volume and thickness determined using 3D-T1 MRI data were analyzed using voxelbased morphometry and surface-based analysis supported by the DR. Brain Platform. RESULTS The male:female ratio was 38:73. The average age was 70.8 ± 10.6 years. The mild: moderate: severe ratio was 48:38:25. Total grey matter volume was significantly related to cognition while the relationship between white matter volume and cognition did not reach statistical significance. The volume of the temporal-parietal-occipital cortex was most strongly associated with cognitive decline in group analysis, while the hippocampus and entorhinal area had a less significant association with cognitive decline. Volume of subcortical grey matter was also associated with cognition. Volume and thickness of temporoparietal cortexes were significantly correlated with the cognitive decline, with a left predominance observed. CONCLUSION Cognitive deterioration was associated with cortical atrophy. Volume and thickness of the left temporal-parietal-occipital cortex were most important in early diagnosis and longitudinal evaluation of AD in clinical practice. Cognitively relevant cortices were left predominant.
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Affiliation(s)
- Chenhui Mao
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing 100730, China
| | - Bo Hou
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing 100730, China
| | - Jie Li
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing 100730, China
| | - Shanshan Chu
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing 100730, China
| | - Xinying Huang
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing 100730, China
| | - Jie Wang
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing 100730, China
| | - Liling Dong
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing 100730, China
| | - Caiyan Liu
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing 100730, China
| | - Feng Feng
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing 100730, China
| | - Bin Peng
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing 100730, China
| | - Jing Gao
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing 100730, China
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