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Hu Y, Wang J, Zhu H, Li J, Shi J. Cost-Sensitive Weighted Contrastive Learning Based on Graph Convolutional Networks for Imbalanced Alzheimer's Disease Staging. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:3126-3136. [PMID: 38625767 DOI: 10.1109/tmi.2024.3389747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2024]
Abstract
Identifying the progression stages of Alzheimer's disease (AD) can be considered as an imbalanced multi-class classification problem in machine learning. It is challenging due to the class imbalance issue and the heterogeneity of the disease. Recently, graph convolutional networks (GCNs) have been successfully applied in AD classification. However, these works did not handle the class imbalance issue in classification. Besides, they ignore the heterogeneity of the disease. To this end, we propose a novel cost-sensitive weighted contrastive learning method based on graph convolutional networks (CSWCL-GCNs) for imbalanced AD staging using resting-state functional magnetic resonance imaging (rs-fMRI). The proposed method is developed on a multi-view graph constructed by the functional connectivity (FC) and high-order functional connectivity (HOFC) features of the subjects. A novel cost-sensitive weighted contrastive learning procedure is proposed to capture discriminative information from the minority classes, encouraging the samples in the minority class to provide adequate supervision. Considering the heterogeneity of the disease, the weights of the negative pairs are introduced into contrastive learning and they are computed based on the distance to class prototypes, which are automatically learned from the training data. Meanwhile, the cost-sensitive mechanism is further introduced into contrastive learning to handle the class imbalance issue. The proposed CSWCL-GCN is evaluated on 720 subjects (including 184 NCs, 40 SMC patients, 208 EMCI patients, 172 LMCI patients and 116 AD patients) from the ADNI (Alzheimer's Disease Neuroimaging Initiative). Experimental results show that the proposed CSWCL-GCN outperforms state-of-the-art methods on the ADNI database.
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Zhang J, Zhang Y, Wang J, Xia Y, Zhang J, Chen L. Recent advances in Alzheimer's disease: Mechanisms, clinical trials and new drug development strategies. Signal Transduct Target Ther 2024; 9:211. [PMID: 39174535 PMCID: PMC11344989 DOI: 10.1038/s41392-024-01911-3] [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: 11/09/2023] [Revised: 03/18/2024] [Accepted: 07/02/2024] [Indexed: 08/24/2024] Open
Abstract
Alzheimer's disease (AD) stands as the predominant form of dementia, presenting significant and escalating global challenges. Its etiology is intricate and diverse, stemming from a combination of factors such as aging, genetics, and environment. Our current understanding of AD pathologies involves various hypotheses, such as the cholinergic, amyloid, tau protein, inflammatory, oxidative stress, metal ion, glutamate excitotoxicity, microbiota-gut-brain axis, and abnormal autophagy. Nonetheless, unraveling the interplay among these pathological aspects and pinpointing the primary initiators of AD require further elucidation and validation. In the past decades, most clinical drugs have been discontinued due to limited effectiveness or adverse effects. Presently, available drugs primarily offer symptomatic relief and often accompanied by undesirable side effects. However, recent approvals of aducanumab (1) and lecanemab (2) by the Food and Drug Administration (FDA) present the potential in disrease-modifying effects. Nevertheless, the long-term efficacy and safety of these drugs need further validation. Consequently, the quest for safer and more effective AD drugs persists as a formidable and pressing task. This review discusses the current understanding of AD pathogenesis, advances in diagnostic biomarkers, the latest updates of clinical trials, and emerging technologies for AD drug development. We highlight recent progress in the discovery of selective inhibitors, dual-target inhibitors, allosteric modulators, covalent inhibitors, proteolysis-targeting chimeras (PROTACs), and protein-protein interaction (PPI) modulators. Our goal is to provide insights into the prospective development and clinical application of novel AD drugs.
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Affiliation(s)
- Jifa Zhang
- Department of Neurology, Laboratory of Neuro-system and Multimorbidity and State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Yinglu Zhang
- Department of Neurology, Laboratory of Neuro-system and Multimorbidity and State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Jiaxing Wang
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Tennessee Health Science Center, Memphis, 38163, TN, USA
| | - Yilin Xia
- Department of Neurology, Laboratory of Neuro-system and Multimorbidity and State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Jiaxian Zhang
- Department of Neurology, Laboratory of Neuro-system and Multimorbidity and State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Lei Chen
- Department of Neurology, Laboratory of Neuro-system and Multimorbidity and State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China.
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Kang S, Kim SW, Seong JK. Disentangling brain atrophy heterogeneity in Alzheimer's disease: A deep self-supervised approach with interpretable latent space. Neuroimage 2024; 297:120737. [PMID: 39004409 DOI: 10.1016/j.neuroimage.2024.120737] [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: 03/06/2024] [Revised: 07/03/2024] [Accepted: 07/11/2024] [Indexed: 07/16/2024] Open
Abstract
Alzheimer's disease (AD) is heterogeneous, but existing methods for capturing this heterogeneity through dimensionality reduction and unsupervised clustering have limitations when it comes to extracting intricate atrophy patterns. In this study, we propose a deep learning based self-supervised framework that characterizes complex atrophy features using latent space representation. It integrates feature engineering, classification, and clustering to synergistically disentangle heterogeneity in Alzheimer's disease. Through this representation learning, we trained a clustered latent space with distinct atrophy patterns and clinical characteristics in AD, and replicated the findings in prodromal Alzheimer's disease. Moreover, we discovered that these clusters are not solely attributed to subtypes but also reflect disease progression in the latent space, representing the core dimensions of heterogeneity, namely progression and subtypes. Furthermore, longitudinal latent space analysis revealed two distinct disease progression pathways: medial temporal and parietotemporal pathways. The proposed approach enables effective latent representations that can be integrated with individual-level cognitive profiles, thereby facilitating a comprehensive understanding of AD heterogeneity.
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Affiliation(s)
- Sohyun Kang
- Department of Artificial Intelligence, College of Informatics, Korea University, Seoul, 02841, South Korea
| | - Sung-Woo Kim
- School of Biomedical Engineering, College of Health Science, Korea University, Seoul, 02841, South Korea; Department of Neurology, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, Wonju, 26426, South Korea; Research Institute of Metabolism and Inflammation, Yonsei University Wonju College of Medicine, Wonju, 26426, South Korea
| | - Joon-Kyung Seong
- Department of Artificial Intelligence, College of Informatics, Korea University, Seoul, 02841, South Korea; School of Biomedical Engineering, College of Health Science, Korea University, Seoul, 02841, South Korea; Interdisciplinary Program in Precision Public Health, College of Health Science, Korea University, Seoul, 02841, South Korea.
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Lorenzini L, Collij LE, Tesi N, Vilor-Tejedor N, Ingala S, Blennow K, Foley C, Frisoni GB, Haller S, Holstege H, van der van der Lee S, Martinez-Lage P, Marioni RE, McCartney DL, O' Brien J, Oliveira TG, Payoux P, Reinders M, Ritchie C, Scheltens P, Schwarz AJ, Sudre CH, Waldman AD, Wolz R, Chatelat G, Ewers M, Wink AM, Mutsaerts HJMM, Gispert JD, Visser PJ, Tijms BM, Altmann A, Barkhof F. Alzheimer's disease genetic pathways impact cerebrospinal fluid biomarkers and imaging endophenotypes in non-demented individuals. Alzheimers Dement 2024. [PMID: 39073684 DOI: 10.1002/alz.14096] [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: 10/31/2023] [Revised: 03/20/2024] [Accepted: 06/03/2024] [Indexed: 07/30/2024]
Abstract
INTRODUCTION Unraveling how Alzheimer's disease (AD) genetic risk is related to neuropathological heterogeneity, and whether this occurs through specific biological pathways, is a key step toward precision medicine. METHODS We computed pathway-specific genetic risk scores (GRSs) in non-demented individuals and investigated how AD risk variants predict cerebrospinal fluid (CSF) and imaging biomarkers reflecting AD pathology, cardiovascular, white matter integrity, and brain connectivity. RESULTS CSF amyloidbeta and phosphorylated tau were related to most GRSs. Inflammatory pathways were associated with cerebrovascular disease, whereas quantitative measures of white matter lesion and microstructure integrity were predicted by clearance and migration pathways. Functional connectivity alterations were related to genetic variants involved in signal transduction and synaptic communication. DISCUSSION This study reveals distinct genetic risk profiles in association with specific pathophysiological aspects in predementia stages of AD, unraveling the biological substrates of the heterogeneity of AD-associated endophenotypes and promoting a step forward in disease understanding and development of personalized therapies. HIGHLIGHTS Polygenic risk for Alzheimer's disease encompasses six biological pathways that can be quantified with pathway-specific genetic risk scores, and differentially relate to cerebrospinal fluid and imaging biomarkers. Inflammatory pathways are mostly related to cerebrovascular burden. White matter health is associated with pathways of clearance and membrane integrity, whereas functional connectivity measures are related to signal transduction and synaptic communication pathways.
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Affiliation(s)
- Luigi Lorenzini
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Vrije Universiteit, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, The Netherlands
| | - Lyduine E Collij
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Vrije Universiteit, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, The Netherlands
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Niccoló Tesi
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, The Netherlands
- Genomics of Neurodegenerative Diseases and Aging, Human Genetics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands
| | - Natàlia Vilor-Tejedor
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Barcelona, Spain
- Department of Clinical Genetics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Silvia Ingala
- Department of Radiology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Cerebriu A/S, Copenhagen, Denmark
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | | | - Giovanni B Frisoni
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
- University Hospitals and University of Geneva, Geneva, Switzerland
| | - Sven Haller
- CIMC - Centre d'Imagerie Médicale de Cornavin, Geneva, Switzerland
- Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, P. R. China
| | - Henne Holstege
- Genomics of Neurodegenerative Diseases and Aging, Human Genetics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Sven van der van der Lee
- Genomics of Neurodegenerative Diseases and Aging, Human Genetics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Pablo Martinez-Lage
- Centro de Investigación y Terapias Avanzadas, Neurología, CITA-Alzheimer Foundation, San Sebastián, Spain
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Daniel L McCartney
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - John O' Brien
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Tiago Gil Oliveira
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B's - PT Government Associate Laboratory, Braga/Guimarães, Portugal
| | - Pierre Payoux
- Department of Nuclear Medicine, Toulouse University Hospital, Toulouse, France
- ToNIC, Toulouse NeuroImaging Center, University of Toulouse, Inserm, Toulouse, France
| | - Marcel Reinders
- Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands
| | - Craig Ritchie
- Edinburgh Dementia Prevention, Centre for Clinical Brain Sciences, Outpatient Department 2, Western General Hospital, University of Edinburgh, Edinburgh, UK
- Brain Health Scotland, Edinburgh, UK
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Adam J Schwarz
- Takeda Pharmaceuticals Ltd., Cambridge, Massachusetts, USA
| | - Carole H Sudre
- Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing (CMIC), University College London (UCL), London, UK
- MRC Unit for Lifelong Health & Ageing at UCL, University College London, London, UK
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Adam D Waldman
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
- Department of Medicine, Imperial College London, London, UK
| | | | - Gael Chatelat
- Université de Normandie, Unicaen, Inserm, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", institut Blood-and-Brain @ Caen-Normandie, Cyceron, Caen, France
| | - Michael Ewers
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Alle Meije Wink
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Vrije Universiteit, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, The Netherlands
| | - Henk J M M Mutsaerts
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, The Netherlands
- Ghent Institute for Functional and Metabolic Imaging (GIfMI), Ghent University, Ghent, Belgium
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
| | - Pieter Jelle Visser
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Alzheimer Center Limburg, Department of Psychiatry & Neuropsychology, School of Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
- Division of Neurogeriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Betty M Tijms
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Andre Altmann
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Vrije Universiteit, Amsterdam, The Netherlands
- Institutes of Neurology and Healthcare Engineering, University College London, London, UK
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Sun Y, Wang P, Zhao K, Chen P, Qu Y, Li Z, Zhong S, Zhou B, Lu J, Zhang X, Wang D, Han Y, Yao H, Liu Y. Structure-function coupling reveals the brain hierarchical structure dysfunction in Alzheimer's disease: A multicenter study. Alzheimers Dement 2024. [PMID: 39072981 DOI: 10.1002/alz.14123] [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: 05/03/2024] [Revised: 06/10/2024] [Accepted: 06/13/2024] [Indexed: 07/30/2024]
Abstract
BACKGROUND Alzheimer's disease (AD) is a neurodegenerative condition characterized by cognitive decline. To date, the specific dysfunction in the brain's hierarchical structure in AD remains unclear. METHODS We introduced the structural decoupling index (SDI), based on a multi-site data set comprising functional and diffusion-weighted magnetic resonance imaging data from 793 subjects, to assess their brain hierarchy. RESULTS Compared to normal controls (NCs), individuals with AD exhibited increased SDI within the posterior superior temporal sulcus, insular gyrus, precuneus, hippocampus, amygdala, postcentral gyrus, and cingulate gyrus; meanwhile, the patients with AD demonstrated decreased SDI in the frontal lobe. The SDI in those regions also showed a significant correlation with cognitive ability. Moreover, the SDI was a robust AD neuroimaging biomarker capable of accurately distinguishing diagnostic status (area under the curve [AUC] = 0.86). DISCUSSION Our findings revealed the dysfunction of the brain's hierarchical structure in AD. Furthermore, the SDI could serve as a promising neuroimaging biomarker for AD. HIGHLIGHTS This study utilized multi-center, multi-modal data from East Asian populations. We found an increased spatial gradient of the structure decoupling index (SDI) from sensory-motor to higher-order cognitive regions. Changes in SDI are associated with energy metabolism and mitochondria. SDI can identify Alzheimer's disease (AD) and further uncover the disease mechanisms of AD.
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Affiliation(s)
- Yibao Sun
- Center for Artificial Intelligence in Medical Imaging, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Pan Wang
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin, China
| | - Kun Zhao
- Center for Artificial Intelligence in Medical Imaging, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Pindong Chen
- School of Artificial Intelligence, University of Chinese Academy of Sciences & Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Yida Qu
- School of Artificial Intelligence, University of Chinese Academy of Sciences & Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Zhuangzhuang Li
- Center for Artificial Intelligence in Medical Imaging, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Suyu Zhong
- Center for Artificial Intelligence in Medical Imaging, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Bo Zhou
- Department of Neurology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Jie Lu
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Xi Zhang
- Department of Neurology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Dawei Wang
- Department of Radiology, School of Public Health, Qilu Hospital of Shandong University & Department of Epidemiology and Health Statistics, Jinan, China
| | - Ying Han
- School of Biomedical Engineering, Hainan University, Haikou, China
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
- National Clinical Research Center for Geriatric Diseases, Beijing, China
| | - Hongxiang Yao
- Department of Radiology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Yong Liu
- Center for Artificial Intelligence in Medical Imaging, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences & Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
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Meng W, Xu J, Huang Y, Wang C, Song Q, Ma A, Song L, Bian J, Ma Q, Yin R. Autoencoder to Identify Sex-Specific Sub-phenotypes in Alzheimer's Disease Progression Using Longitudinal Electronic Health Records. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.07.07.24310055. [PMID: 39040206 PMCID: PMC11261930 DOI: 10.1101/2024.07.07.24310055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/24/2024]
Abstract
Alzheimer's Disease (AD) is a complex neurodegenerative disorder significantly influenced by sex differences, with approximately two-thirds of AD patients being women. Characterizing the sex-specific AD progression and identifying its progression trajectory is a crucial step to developing effective risk stratification and prevention strategies. In this study, we developed an autoencoder to uncover sex-specific sub-phenotypes in AD progression leveraging longitudinal electronic health record (EHR) data from OneFlorida+ Clinical Research Consortium. Specifically, we first constructed temporal patient representation using longitudinal EHRs from a sex-stratified AD cohort. We used a long short-term memory (LSTM)-based autoencoder to extract and generate latent representation embeddings from sequential clinical records of patients. We then applied hierarchical agglomerative clustering to the learned representations, grouping patients based on their progression sub-phenotypes. The experimental results show we successfully identified five primary sex-based AD sub-phenotypes with corresponding progression pathways with high confidence. These sex-specific sub-phenotypes not only illustrated distinct AD progression patterns but also revealed differences in clinical characteristics and comorbidities between females and males in AD development. These findings could provide valuable insights for advancing personalized AD intervention and treatment strategies.
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Affiliation(s)
- Weimin Meng
- Department of Health Outcomes &Biomedical Informatics, University of Florida, Gainesville, FL, 32610, USA
| | - Jie Xu
- Department of Health Outcomes &Biomedical Informatics, University of Florida, Gainesville, FL, 32610, USA
| | - Yu Huang
- Department of Health Outcomes &Biomedical Informatics, University of Florida, Gainesville, FL, 32610, USA
| | - Cankun Wang
- Department of Biomedical Informatics, Ohio State University, Columbus, OH, 43210, USA
| | - Qianqian Song
- Department of Health Outcomes &Biomedical Informatics, University of Florida, Gainesville, FL, 32610, USA
| | - Anjun Ma
- Department of Biomedical Informatics, Ohio State University, Columbus, OH, 43210, USA
| | - Lixin Song
- School of Nursing, University of Texas Health Science Center at San Antonio, San Antonio, TX, 78229, USA
| | - Jiang Bian
- Department of Health Outcomes &Biomedical Informatics, University of Florida, Gainesville, FL, 32610, USA
| | - Qin Ma
- Department of Biomedical Informatics, Ohio State University, Columbus, OH, 43210, USA
| | - Rui Yin
- Department of Health Outcomes &Biomedical Informatics, University of Florida, Gainesville, FL, 32610, USA
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Sahelijo N, Rajagopalan P, Qian L, Rahman R, Priyadarshi D, Goldstein D, Thomopoulos SI, Bennett DA, Farrer LA, Stein TD, Shen L, Huang H, Nho K, Andrew SJ, Davatzikos C, Thompson PM, Tcw J, Jun GR. Brain Cell-based Genetic Subtyping and Drug Repositioning for Alzheimer Disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.06.21.24309255. [PMID: 38947056 PMCID: PMC11213108 DOI: 10.1101/2024.06.21.24309255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Alzheimer's Disease (AD) is characterized by its complex and heterogeneous etiology and gradual progression, leading to high drug failure rates in late-stage clinical trials. In order to better stratify individuals at risk for AD and discern potential therapeutic targets we employed a novel procedure utilizing cell-based co-regulated gene networks and polygenic risk scores (cbPRSs). After defining genetic subtypes using extremes of cbPRS distributions, we evaluated correlations of the genetic subtypes with previously defined AD subtypes defined on the basis of domain-specific cognitive functioning and neuroimaging biomarkers. Employing a PageRank algorithm, we identified priority gene targets for the genetic subtypes. Pathway analysis of priority genes demonstrated associations with neurodegeneration and suggested candidate drugs currently utilized in diabetes, hypertension, and epilepsy for repositioning in AD. Experimental validation utilizing human induced pluripotent stem cell (hiPSC)-derived astrocytes demonstrated the modifying effects of estradiol, levetiracetam, and pioglitazone on expression of APOE and complement C4 genes, suggesting potential repositioning for AD.
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8
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Huo Y, Jing R, Li P, Chen P, Si J, Liu G, Liu Y. Delineating the Heterogeneity of Alzheimer's Disease and Mild Cognitive Impairment Using Normative Models of Dynamic Brain Functional Networks. Biol Psychiatry 2024:S0006-3223(24)01365-9. [PMID: 38857821 DOI: 10.1016/j.biopsych.2024.05.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 05/15/2024] [Accepted: 05/30/2024] [Indexed: 06/12/2024]
Abstract
BACKGROUND Alzheimer's disease (AD), which has been identified as the most common type of dementia, presents considerable heterogeneity in its clinical manifestations. Early intervention at the stage of mild cognitive impairment (MCI) holds potential in AD prevention. However, characterizing the heterogeneity of neurobiological abnormalities and identifying MCI subtypes pose significant challenges. METHODS We constructed sex-specific normative age models of dynamic brain functional networks and mapped the deviations of the brain characteristics for individuals from multiple datasets, including 295 patients with AD, 441 patients with MCI, and 1160 normal control participants. Then, based on these individual deviation patterns, subtypes for both AD and MCI were identified using the clustering method, and their similarities and differences were comprehensively assessed. RESULTS Individuals with AD and MCI were clustered into 2 subtypes, and these subtypes exhibited significant differences in their intrinsic brain functional phenotypes and spatial atrophy patterns, as well as in disease progression and cognitive decline trajectories. The subtypes with positive deviations in AD and MCI shared similar deviation patterns, as did those with negative deviations. There was a potential transformation of MCI with negative deviation patterns into AD, and participants with MCI had a more severe cognitive decline rate. CONCLUSIONS In this study, we quantified neurophysiological heterogeneity by analyzing deviation patterns from the dynamic functional connectome normative model and identified disease subtypes of AD and MCI using a comprehensive resting-state functional magnetic resonance imaging multicenter dataset. The findings provide new insights for developing early prevention and personalized treatment strategies for AD.
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Affiliation(s)
- Yanxi Huo
- School of Instrument Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, Beijing, China
| | - Rixing Jing
- School of Instrument Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, Beijing, China.
| | - Peng Li
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Chinese Academy of Medical Sciences Research Unit, Peking University, Beijing, China
| | - Pindong Chen
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Juanning Si
- School of Instrument Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, Beijing, China
| | - Guozhong Liu
- School of Instrument Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, Beijing, China
| | - Yong Liu
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.
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Pumo A, Legeay S. The dichotomous activities of microglia: A potential driver for phenotypic heterogeneity in Alzheimer's disease. Brain Res 2024; 1832:148817. [PMID: 38395249 DOI: 10.1016/j.brainres.2024.148817] [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: 10/30/2023] [Revised: 01/28/2024] [Accepted: 02/19/2024] [Indexed: 02/25/2024]
Abstract
Alzheimer's disease (AD) is a leading cause of dementia, characterized by two defining neuropathological hallmarks: amyloid plaques composed of Aβ aggregates and neurofibrillary pathology. Recent research suggests that microglia have both beneficial and detrimental effects in the development of AD. A new theory proposes that microglia play a beneficial role in the early stages of the disease but become harmful in later stages. Further investigations are needed to gain a comprehensive understanding of this shift in microglia's function. This transition is likely influenced by specific conditions, including spatial, temporal, and transcriptional factors, which ultimately lead to the deterioration of microglial functionality. Additionally, recent studies have also highlighted the potential influence of microglia diversity on the various manifestations of AD. By deciphering the multiple states of microglia and the phenotypic heterogeneity in AD, significant progress can be made towards personalized medicine and better treatment outcomes for individuals affected by AD.
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Affiliation(s)
- Anna Pumo
- Université d'Angers, Faculté de Santé, Département Pharmacie, 16, Boulevard Daviers, Angers 49045, France.
| | - Samuel Legeay
- Université d'Angers, Faculté de Santé, Département Pharmacie, 16, Boulevard Daviers, Angers 49045, France; Univ Angers, Inserm, CNRS, MINT, SFR ICAT, Angers F-49000, France
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10
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Choi HK, Chen M, Goldston LL, Lee KB. Extracellular vesicles as nanotheranostic platforms for targeted neurological disorder interventions. NANO CONVERGENCE 2024; 11:19. [PMID: 38739358 DOI: 10.1186/s40580-024-00426-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Accepted: 04/24/2024] [Indexed: 05/14/2024]
Abstract
Central Nervous System (CNS) disorders represent a profound public health challenge that affects millions of people around the world. Diseases such as Alzheimer's disease (AD), Parkinson's disease (PD), and traumatic brain injury (TBI) exemplify the complexities and diversities that complicate their early detection and the development of effective treatments. Amid these challenges, the emergence of nanotechnology and extracellular vesicles (EVs) signals a new dawn for treating and diagnosing CNS ailments. EVs are cellularly derived lipid bilayer nanosized particles that are pivotal in intercellular communication within the CNS and have the potential to revolutionize targeted therapeutic delivery and the identification of novel biomarkers. Integrating EVs with nanotechnology amplifies their diagnostic and therapeutic capabilities, opening new avenues for managing CNS diseases. This review focuses on examining the fascinating interplay between EVs and nanotechnology in CNS theranostics. Through highlighting the remarkable advancements and unique methodologies, we aim to offer valuable perspectives on how these approaches can bring about a revolutionary change in disease management. The objective is to harness the distinctive attributes of EVs and nanotechnology to forge personalized, efficient interventions for CNS disorders, thereby providing a beacon of hope for affected individuals. In short, the confluence of EVs and nanotechnology heralds a promising frontier for targeted and impactful treatments against CNS diseases, which continue to pose significant public health challenges. By focusing on personalized and powerful diagnostic and therapeutic methods, we might improve the quality of patients.
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Affiliation(s)
- Hye Kyu Choi
- Department of Chemistry and Chemical Biology, The State University of New Jersey, 123 Bevier Road, Rutgers, Piscataway, NJ, 08854, USA
| | - Meizi Chen
- Department of Chemistry and Chemical Biology, The State University of New Jersey, 123 Bevier Road, Rutgers, Piscataway, NJ, 08854, USA
| | - Li Ling Goldston
- Department of Chemistry and Chemical Biology, The State University of New Jersey, 123 Bevier Road, Rutgers, Piscataway, NJ, 08854, USA
| | - Ki-Bum Lee
- Department of Chemistry and Chemical Biology, The State University of New Jersey, 123 Bevier Road, Rutgers, Piscataway, NJ, 08854, USA.
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Gustafson D, Kalaria R, O'Brien J, van den Brink H, Hilal S, Marseglia A, ter Telgte A, Skoog I. VasCog 2023: 20 years of research on vascular behavioural and cognitive disorders. CEREBRAL CIRCULATION - COGNITION AND BEHAVIOR 2024; 6:100224. [PMID: 38868624 PMCID: PMC11167242 DOI: 10.1016/j.cccb.2024.100224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Accepted: 05/03/2024] [Indexed: 06/14/2024]
Abstract
This Commentary describes the 20th Anniversary of VasCog 2023, held in Gothenburg, Sweden.
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Affiliation(s)
- D.R. Gustafson
- Department of Neurology, State University of New York Downstate Health Sciences University, MSC 1213, 450 Clarkson Avenue, Brooklyn, NY 11203, USA
| | - R. Kalaria
- Translational and Clinical Research Institute, Newcastle University, United Kingdom
| | - J. O'Brien
- Department of Psychiatry, University of Cambridge School of Clinical Medicine, United Kingdom
| | - H. van den Brink
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - S. Hilal
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
| | - A. Marseglia
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - A. ter Telgte
- VASCage – Center on Clinical Stroke Research, Innsbruck, Austria
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - I. Skoog
- Center for Ageing and Health, Institute for Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
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12
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Karakasli AA, Ozkan E, Karacam Dogan M, Cap D, Karaosmanoglu A, Karahan S, Zorlu N, Saka E, Ayhan Y. Clinical predictors of Alzheimer's disease-like brain atrophy in individuals with memory complaints. Brain Behav 2024; 14:e3506. [PMID: 38688882 PMCID: PMC11061206 DOI: 10.1002/brb3.3506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Revised: 04/03/2024] [Accepted: 04/08/2024] [Indexed: 05/02/2024] Open
Abstract
OBJECTIVES The definition and assessment methods for subjective cognitive decline (SCD) vary among studies. We aimed to investigate which features or assessment methods of SCD best predict Alzheimer's disease (AD)-related structural atrophy patterns. METHODS We assessed 104 individuals aged 55+ with memory complaints but normal cognitive screening. Our research questions were as follows: To improve the prediction of AD related morphological changes, (1) Would the use of a standardized cognitive screening scale be beneficial? (2) Is conducting a thorough neuropsychological evaluation necessary instead of relying solely on cognitive screening tests? (3) Should we apply SCD-plus research criteria, and if so, which criterion would be the most effective? (4) Is it necessary to consider medical and psychiatric comorbidities, vitamin deficiencies, vascular burden on MRI, and family history? We utilized Freesurfer to analyze cortical thickness and regional brain volume meta-scores linked to AD or predicting its development. We employed multiple linear regression models for each variable, with morphology as the dependent variable. RESULTS AD-like morphology was associated with subjective complaints in males, individuals with advanced age, and higher education. Later age of onset for complaints, complaints specifically related to memory, excessive deep white matter vascular lesions, and using medications that have negative implications for cognitive health (according to the Beers criteria) were predictive of AD-related morphology. The subjective cognitive memory questionnaire scores were found to be a better predictor of reduced volumes than a single-question assessment. It is important to note that not all SCD-plus criteria were evaluated in this study, particularly the APOE genotype, amyloid, and tau status, due to resource limitations. CONCLUSIONS The detection of AD-related structural changes is impacted by demographics and assessment methods. Standardizing SCD assessment methods can enhance predictive accuracy.
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Affiliation(s)
| | - Esra Ozkan
- Research Center for Translational Medicine, Koç UniversityİstanbulTurkey
| | | | - Duygu Cap
- Department of PsychologyUfuk UniversityAnkaraTurkey
| | - Ayca Karaosmanoglu
- Department of RadiologyHacettepe University Faculty of MedicineAnkaraTurkey
| | - Sevilay Karahan
- Department of BiostatisticsHacettepe University Faculty of MedicineAnkaraTurkey
| | - Nabi Zorlu
- Department of Psychiatryİzmir Katip Çelebi University Faculty of MedicineİzmirTurkey
| | - Esen Saka
- Department of NeurologyHacettepe University Faculty of MedicineAnkaraTurkey
| | - Yavuz Ayhan
- Department of PsychiatryHacettepe University Faculty of MedicineAnkaraTurkey
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13
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Chun MY, Chae W, Seo SW, Jang H, Yun J, Na DL, Kang D, Lee J, Hammers DB, Apostolova LG, Jang SI, Kim HJ. Effects of risk factors on the development and mortality of early- and late-onset dementia: an 11-year longitudinal nationwide population-based cohort study in South Korea. Alzheimers Res Ther 2024; 16:92. [PMID: 38664771 PMCID: PMC11044300 DOI: 10.1186/s13195-024-01436-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 03/23/2024] [Indexed: 04/28/2024]
Abstract
BACKGROUND Early-onset dementia (EOD, onset age < 65) and late-onset dementia (LOD, onset age ≥ 65) exhibit distinct features. Understanding the risk factors for dementia development and mortality in EOD and LOD respectively is crucial for personalized care. While risk factors are known for LOD development and mortality, their impact on EOD remains unclear. We aimed to investigate how hypertension, diabetes mellitus, hyperlipidemia, atrial fibrillation, and osteoporosis influence the development and mortality of EOD and LOD, respectively. METHODS Using the Korean National Health Insurance Service (NHIS) database, we collected 546,709 dementia-free individuals and followed up for 11 years. In the two study groups, the Younger group (< 65 years old) and the Older group (≥ 65 years old), we applied Cox proportional hazard models to assess risk factors for development of EOD and LOD, respectively. Then, we assessed risk factors for mortality among EOD and LOD. RESULTS Diabetes mellitus and osteoporosis increased the risk of EOD and LOD development. Hypertension increased the risk of EOD, while atrial fibrillation increased the risk of LOD. Conversely, hyperlipidemia exhibited a protective effect against LOD development. Additionally, diabetes mellitus increased mortality in EOD and LOD. Hypertension and atrial fibrillation increased mortality in LOD, while hyperlipidemia decreased mortality in EOD and LOD. CONCLUSIONS Risk factors influencing dementia development and mortality differed in EOD and LOD. Targeted public health interventions addressing age-related risk factors may reduce dementia incidence and mortality.
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Affiliation(s)
- Min Young Chun
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, South Korea
- Department of Neurology, Yonsei University College of Medicine, 50-1, Yonsei-Ro, Seodaemun-gu, Seoul, 03722, South Korea
- Department of Neurology, Yongin Severance Hospital, Yonsei University Health System, 363 Dongbaekjukjeon-daero, Giheung-gu, , Yongin-si, Gyeonggi-do, 16995, South Korea
| | - Wonjeong Chae
- Office of Strategic Planning, Healthcare Policy and Strategy Task Force, Yonsei University Health System, 50-1, Yonsei-Ro, Seodaemun-gu, Seoul, 03722, South Korea
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, South Korea
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, South Korea
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, South Korea
- Department of Digital Health, SAIHST, Sungkyunkwan University, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, South Korea
| | - Hyemin Jang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, South Korea
- Department of Neurology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, South Korea
| | - Jihwan Yun
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, South Korea
- Department of Neurology, Soonchunhyang University Bucheon Hospital, 170, Jomaru-ro, Wonmi-Gu, Bucheon-si, Gyeonggi-do, 14574, South Korea
| | - Duk L Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, South Korea
| | - Dongwoo Kang
- Department of Data Science, Hanmi Pharm. Co., Ltd, 14, Wiryeseong-daero, Songpa-gu, Seoul, South Korea
| | - Jungkuk Lee
- Department of Data Science, Hanmi Pharm. Co., Ltd, 14, Wiryeseong-daero, Songpa-gu, Seoul, South Korea
| | - Dustin B Hammers
- Department of Neurology, Indiana University School of Medicine, 355 W 16th St, Indianapolis, IN, USA
| | - Liana G Apostolova
- Department of Neurology, Indiana University School of Medicine, 355 W 16th St, Indianapolis, IN, USA
- Department of Radiology and Imaging Sciences, Center for Neuroimaging, Indiana University School of Medicine Indianapolis, 355W 16th St, Indianapolis, IN, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, 355W 16th St, Indianapolis, IN, USA
| | - Sung-In Jang
- Department of Preventive Medicine, College of Medicine, Yonsei University, 50-1, Yonsei-Ro, Seodaemun-gu, Seoul, 03722, South Korea.
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, South Korea.
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, South Korea.
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, South Korea.
- Department of Digital Health, SAIHST, Sungkyunkwan University, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, South Korea.
- Department of Neurology, Indiana University School of Medicine, 355 W 16th St, Indianapolis, IN, USA.
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14
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Ma H, Shi Z, Kim M, Liu B, Smith PJ, Liu Y, Wu G. Disentangling sex-dependent effects of APOE on diverse trajectories of cognitive decline in Alzheimer's disease. Neuroimage 2024; 292:120609. [PMID: 38614371 PMCID: PMC11069285 DOI: 10.1016/j.neuroimage.2024.120609] [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: 09/05/2023] [Revised: 04/10/2024] [Accepted: 04/11/2024] [Indexed: 04/15/2024] Open
Abstract
Current diagnostic systems for Alzheimer's disease (AD) rely upon clinical signs and symptoms, despite the fact that the multiplicity of clinical symptoms renders various neuropsychological assessments inadequate to reflect the underlying pathophysiological mechanisms. Since putative neuroimaging biomarkers play a crucial role in understanding the etiology of AD, we sought to stratify the diverse relationships between AD biomarkers and cognitive decline in the aging population and uncover risk factors contributing to the diversities in AD. To do so, we capitalized on a large amount of neuroimaging data from the ADNI study to examine the inflection points along the dynamic relationship between cognitive decline trajectories and whole-brain neuroimaging biomarkers, using a state-of-the-art statistical model of change point detection. Our findings indicated that the temporal relationship between AD biomarkers and cognitive decline may differ depending on the synergistic effect of genetic risk and biological sex. Specifically, tauopathy-PET biomarkers exhibit a more dynamic and age-dependent association with Mini-Mental State Examination scores (p<0.05), with inflection points at 72, 78, and 83 years old, compared with amyloid-PET and neurodegeneration (cortical thickness from MRI) biomarkers. In the landscape of health disparities in AD, our analysis indicated that biological sex moderates the rate of cognitive decline associated with APOE4 genotype. Meanwhile, we found that higher education levels may moderate the effect of APOE4, acting as a marker of cognitive reserve.
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Affiliation(s)
- Haixu Ma
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Zhuoyu Shi
- Department of Psychiatry, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Minjeong Kim
- Department of Computer Science, University of North Carolina at Greensboro, NC 27412, USA
| | - Bin Liu
- Department of Statistics and Data Science, School of Management at Fudan University, Shanghai, 200433, PR China
| | - Patrick J Smith
- Department of Psychiatry, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Yufeng Liu
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, NC 27599, USA; Department of Genetics, Department of Biostatistics, University of North Carolina at Chapel Hill, NC 27599, USA.
| | - Guorong Wu
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, NC 27599, USA; Department of Psychiatry, University of North Carolina at Chapel Hill, NC 27599, USA; Department of Computer Science, University of North Carolina at Chapel Hill, NC 27599, USA; UNC Neuroscience Center, University of North Carolina at Chapel Hill, NC 27599, USA.
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15
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Katariya RA, Sammeta SS, Kale MB, Kotagale NR, Umekar MJ, Taksande BG. Agmatine as a novel intervention for Alzheimer's disease: Pathological insights and cognitive benefits. Ageing Res Rev 2024; 96:102269. [PMID: 38479477 DOI: 10.1016/j.arr.2024.102269] [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: 01/17/2024] [Revised: 03/01/2024] [Accepted: 03/08/2024] [Indexed: 03/24/2024]
Abstract
Alzheimer's disease (AD) is a devastating neurodegenerative disorder characterized by progressive cognitive decline and a significant societal burden. Despite extensive research and efforts of the multidisciplinary scientific community, to date, there is no cure for this debilitating disease. Moreover, the existing pharmacotherapy for AD only provides symptomatic support and does not modify the course of the illness or halt the disease progression. This is a significant limitation as the underlying pathology of the disease continues to progress leading to the deterioration of cognitive functions over time. In this milieu, there is a growing need for the development of new and more efficacious treatments for AD. Agmatine, a naturally occurring molecule derived from L-arginine, has emerged as a potential therapeutic agent for AD. Besides this, agmatine has been shown to modulate amyloid beta (Aβ) production, aggregation, and clearance, key processes implicated in AD pathogenesis. It also exerts neuroprotective effects, modulates neurotransmitter systems, enhances synaptic plasticity, and stimulates neurogenesis. Furthermore, preclinical and clinical studies have provided evidence supporting the cognition-enhancing effects of agmatine in AD. Therefore, this review article explores the promising role of agmatine in AD pathology and cognitive function. However, several limitations and challenges exist, including the need for large-scale clinical trials, optimal dosing, and treatment duration. Future research should focus on mechanistic investigations, biomarker studies, and personalized medicine approaches to fully understand and optimize the therapeutic potential of agmatine. Augmenting the use of agmatine may offer a novel approach to address the unmet medical need in AD and provide cognitive enhancement and disease modification for individuals affected by this disease.
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Affiliation(s)
- Raj A Katariya
- Smt. Kishoritai Bhoyar College of Pharmacy, Kamptee, Nagpur, Maharashtra 441002, India
| | - Shivkumar S Sammeta
- Smt. Kishoritai Bhoyar College of Pharmacy, Kamptee, Nagpur, Maharashtra 441002, India
| | - Mayur B Kale
- Smt. Kishoritai Bhoyar College of Pharmacy, Kamptee, Nagpur, Maharashtra 441002, India
| | - Nandkishor R Kotagale
- Government College of Pharmacy, Kathora Naka, VMV Road, Amravati, Maharashtra 444604, India
| | - Milind J Umekar
- Smt. Kishoritai Bhoyar College of Pharmacy, Kamptee, Nagpur, Maharashtra 441002, India
| | - Brijesh G Taksande
- Smt. Kishoritai Bhoyar College of Pharmacy, Kamptee, Nagpur, Maharashtra 441002, India.
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Miskovic R, Ljubicic J, Bonaci-Nikolic B, Petkovic A, Markovic V, Rankovic I, Djordjevic J, Stankovic A, Klaassen K, Pavlovic S, Stojanovic M. Case report: Rapidly progressive neurocognitive disorder with a fatal outcome in a patient with PU.1 mutated agammaglobulinemia. Front Immunol 2024; 15:1324679. [PMID: 38500873 PMCID: PMC10945545 DOI: 10.3389/fimmu.2024.1324679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 02/19/2024] [Indexed: 03/20/2024] Open
Abstract
Introduction PU.1-mutated agammaglobulinemia (PU.MA) represents a recently described autosomal-dominant form of agammaglobulinemia caused by mutation of the SPI1 gene. This gene codes for PU.1 pioneer transcription factor important for the maturation of monocytes, B lymphocytes, and conventional dendritic cells. Only six cases with PU.MA, presenting with chronic sinopulmonary and systemic enteroviral infections, have been previously described. Accumulating literature evidence suggests a possible relationship between SPI1 mutation, microglial phagocytic dysfunction, and the development of Alzheimer's disease (AD). Case description We present a Caucasian female patient born from a non-consanguineous marriage, who was diagnosed with agammaglobulinemia at the age of 15 years when the immunoglobulin replacement therapy was started. During the following seventeen years, she was treated for recurrent respiratory and intestinal infections. At the age of 33 years, the diagnosis of celiac-like disease was established. Five years later progressive cognitive deterioration, unstable gait, speech disturbances, and behavioral changes developed. Comprehensive microbiological investigations were negative, excluding possible infective etiology. Brain MRI, 18FDG-PET-CT, and neuropsychological testing were suggestive for a diagnosis of a frontal variant of AD. Clinical exome sequencing revealed the presence of a novel frameshift heterozygous variant c.441dup in exon 4 of the SPI1 gene. Despite intensive therapy, the patient passed away a few months after the onset of the first neurological symptoms. Conclusion We describe the first case of PU.MA patient presenting with a rapidly progressive neurocognitive deterioration. The possible role of microglial dysfunction in patients with SPI1 mutation could explain their susceptibility to neurodegenerative diseases thus highlighting the importance of genetic testing in patients with inborn errors of immunity. Since PU.MA represents a newly described form of agammaglobulinemia, our case expands the spectrum of manifestations associated with SPI1 mutation.
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Affiliation(s)
- Rada Miskovic
- Clinic of Allergy and Immunology, University Clinical Center of Serbia, Belgrade, Serbia
- Faculty of Medicine, University of Belgrade, Belgrade, Serbia
| | - Jelena Ljubicic
- Faculty of Medicine, University of Belgrade, Belgrade, Serbia
- Institute of Molecular Genetics and Genetic Engineering, University of Belgrade, Belgrade, Serbia
| | - Branka Bonaci-Nikolic
- Clinic of Allergy and Immunology, University Clinical Center of Serbia, Belgrade, Serbia
- Faculty of Medicine, University of Belgrade, Belgrade, Serbia
| | - Ana Petkovic
- Diagnostic Department, Center of Sterotaxic Radiosurgery, Clinic of Neurosurgery, University Clinical Center of Serbia, Belgrade, Serbia
| | - Vladana Markovic
- Faculty of Medicine, University of Belgrade, Belgrade, Serbia
- Clinic of Neurology, University Clinical Center of Serbia, Belgrade, Serbia
| | - Ivan Rankovic
- Department of Gastroenterology and Liver Unit, Royal Cornwall Hospitals NHS Trust, University of Exeter, Truro, United Kingdom
| | - Jelena Djordjevic
- Clinic of Neurology and Psychiatry for Children and Youth, Belgrade, Serbia
- Faculty of Medical Sciences, University of Kragujevac, Kragujevac, Serbia
| | - Ana Stankovic
- Center for Radiology, University Clinical Center of Serbia, Belgrade, Serbia
| | - Kristel Klaassen
- Institute of Molecular Genetics and Genetic Engineering, University of Belgrade, Belgrade, Serbia
| | - Sonja Pavlovic
- Institute of Molecular Genetics and Genetic Engineering, University of Belgrade, Belgrade, Serbia
| | - Maja Stojanovic
- Clinic of Allergy and Immunology, University Clinical Center of Serbia, Belgrade, Serbia
- Faculty of Medicine, University of Belgrade, Belgrade, Serbia
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17
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Levin F, Grothe MJ, Dyrba M, Franzmeier N, Teipel SJ. Longitudinal trajectories of cognitive reserve in hypometabolic subtypes of Alzheimer's disease. Neurobiol Aging 2024; 135:26-38. [PMID: 38157587 DOI: 10.1016/j.neurobiolaging.2023.12.003] [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: 05/30/2023] [Revised: 11/16/2023] [Accepted: 12/13/2023] [Indexed: 01/03/2024]
Abstract
Previous studies have demonstrated resilience to AD-related neuropathology in a form of cognitive reserve (CR). In this study we investigated a relationship between CR and hypometabolic subtypes of AD, specifically the typical and the limbic-predominant subtypes. We analyzed data from 59 Aβ-positive cognitively normal (CN), 221 prodromal Alzheimer's disease (AD) and 174 AD dementia participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) from ADNI and ADNIGO/2 phases. For replication, we analyzed data from 5 Aβ-positive CN, 89 prodromal AD and 43 AD dementia participants from ADNI3. CR was estimated as standardized residuals in a model predicting cognition from temporoparietal grey matter volumes and covariates. Higher CR estimates predicted slower cognitive decline. Typical and limbic-predominant hypometabolic subtypes demonstrated similar baseline CR, but the results suggested a faster decline of CR in the typical subtype. These findings support the relationship between subtypes and CR, specifically longitudinal trajectories of CR. Results also underline the importance of longitudinal analyses in research on CR.
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Affiliation(s)
- Fedor Levin
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Rostock, Germany.
| | - Michel J Grothe
- Unidad de Trastornos del Movimiento, Servicio de Neurología y Neurofisiología Clínica, Instituto de Biomedicina de Sevilla, Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, Seville, Spain
| | - Martin Dyrba
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Rostock, Germany
| | - Nicolai Franzmeier
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany; Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Sweden; Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Stefan J Teipel
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Rostock, Germany; Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
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18
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Zheng C, Zhao W, Yang Z, Tang D, Feng M, Guo S. Resolving heterogeneity in Alzheimer's disease based on individualized structural covariance network. Prog Neuropsychopharmacol Biol Psychiatry 2024; 129:110873. [PMID: 37827426 DOI: 10.1016/j.pnpbp.2023.110873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Revised: 10/06/2023] [Accepted: 10/08/2023] [Indexed: 10/14/2023]
Abstract
The heterogeneity of Alzheimer's disease (AD) poses a challenge to precision medicine. We aimed to identify distinct subtypes of AD based on the individualized structural covariance network (IDSCN) analysis and to research the underlying neurobiology mechanisms. In this study, 187 patients with AD (age = 73.57 ± 6.00, 50% female) and 143 matched normal controls (age = 74.30 ± 7.80, 44% female) were recruited from the Alzheimer's Disease Neuroimaging Initiative (ADNI) project database, and T1 images were acquired. We utilized the IDSCN analysis to generate individual-level altered structural covariance network and performed k-means clustering to subtype AD based on structural covariance network. Cognition, disease progression, morphological features, and gene expression profiles were further compared between subtypes, to characterize the heterogeneity in AD. Two distinct AD subtypes were identified in a reproducible manner, and we named the two subtypes as slow progression type (subtype 1, n = 104, age = 76.15 ± 6.44, 42% female) and rapid progression type (subtype 2, n = 83, age = 71.98 ± 8.72, 47% female), separately. Subtype 1 had better baseline visuospatial function than subtype 2 (p < 0.05), whereas subtype 2 had better baseline memory function than subtype 1 (p < 0.05). Subtype 2 showed worse progression in memory (p = 0.003), language (p = 0.003), visuospatial function (p = 0.020), and mental state (p = 0.038) than subtype 1. Subtype 1 often shared increased structural covariance network, mainly in the frontal lobe and temporal lobe regions, whereas subtype 2 often shared increased structural covariance network, mainly in occipital lobe regions and temporal lobe regions. Functional annotation further revealed that all differential structural covariance network between the two AD subtypes were mainly implicated in memory, learning, emotion, and cognition. Additionally, differences in gray matter volume (GMV) between AD subtypes were identified, and genes associated with GMV differences were found to be enriched in the terms potassium ion transport, synapse organization, and histone modification and the pathways viral infection, neurodegeneration-multiple diseases, and long-term depression. The two distinct AD subtypes were identified and characterized with neuroanatomy, cognitive trajectories, and gene expression profiles. These comprehensive results have implications for neurobiology mechanisms and precision medicine.
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Affiliation(s)
- Chuchu Zheng
- School of Mathematics and Statistics, Hunan Normal University, Changsha 410006, China; Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha 410006, China
| | - Wei Zhao
- School of Mathematics and Statistics, Hunan Normal University, Changsha 410006, China; Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha 410006, China
| | - Zeyu Yang
- School of Mathematics and Statistics, Hunan Normal University, Changsha 410006, China; Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha 410006, China
| | - Dier Tang
- School of Mathematics, Jilin University, Changchun 130015, China
| | - Muyi Feng
- School of Mathematics and Statistics, Hunan Normal University, Changsha 410006, China; Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha 410006, China
| | - Shuixia Guo
- School of Mathematics and Statistics, Hunan Normal University, Changsha 410006, China; Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha 410006, China.
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19
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Xu E, Zhang J, Li J, Song Q, Yang D, Wu G, Chen M. Pathology steered stratification network for subtype identification in Alzheimer's disease. Med Phys 2024; 51:1190-1202. [PMID: 37522278 PMCID: PMC10828102 DOI: 10.1002/mp.16655] [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: 01/11/2023] [Revised: 06/25/2023] [Accepted: 07/19/2023] [Indexed: 08/01/2023] Open
Abstract
BACKGROUND Alzheimer's disease (AD) is a heterogeneous, multifactorial neurodegenerative disorder characterized by three neurobiological factors beta-amyloid, pathologic tau, and neurodegeneration. There are no effective treatments for AD at a late stage, urging for early detection and prevention. However, existing statistical inference approaches in neuroimaging studies of AD subtype identification do not take into account the pathological domain knowledge, which could lead to ill-posed results that are sometimes inconsistent with the essential neurological principles. PURPOSE Integrating systems biology modeling with machine learning, the study aims to assist clinical AD prognosis by providing a subpopulation classification in accordance with essential biological principles, neurological patterns, and cognitive symptoms. METHODS We propose a novel pathology steered stratification network (PSSN) that incorporates established domain knowledge in AD pathology through a reaction-diffusion model, where we consider non-linear interactions between major biomarkers and diffusion along the brain structural network. Trained on longitudinal multimodal neuroimaging data, the biological model predicts long-term evolution trajectories that capture individual characteristic progression pattern, filling in the gaps between sparse imaging data available. A deep predictive neural network is then built to exploit spatiotemporal dynamics, link neurological examinations with clinical profiles, and generate subtype assignment probability on an individual basis. We further identify an evolutionary disease graph to quantify subtype transition probabilities through extensive simulations. RESULTS Our stratification achieves superior performance in both inter-cluster heterogeneity and intra-cluster homogeneity of various clinical scores. Applying our approach to enriched samples of aging populations, we identify six subtypes spanning AD spectrum, where each subtype exhibits a distinctive biomarker pattern that is consistent with its clinical outcome. CONCLUSIONS The proposed PSSN (i) reduces neuroimage data to low-dimensional feature vectors, (ii) combines AT[N]-Net based on real pathological pathways, (iii) predicts long-term biomarker trajectories, and (iv) stratifies subjects into fine-grained subtypes with distinct neurological underpinnings. PSSN provides insights into pre-symptomatic diagnosis and practical guidance on clinical treatments, which may be further generalized to other neurodegenerative diseases.
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Affiliation(s)
- Enze Xu
- Department of Computer Science, Wake Forest University, Winston-Salem, NC 27109, U.S
| | - Jingwen Zhang
- Department of Computer Science, Wake Forest University, Winston-Salem, NC 27109, U.S
| | - Jiadi Li
- Department of Psychology, Wake Forest University, Winston-Salem, NC 27109, U.S
| | - Qianqian Song
- Department of Cancer Biology, Wake Forest School of Medicine, Winston-Salem, NC 27157, U.S
| | - Defu Yang
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, U.S
| | - Guorong Wu
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, U.S
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, U.S
| | - Minghan Chen
- Department of Computer Science, Wake Forest University, Winston-Salem, NC 27109, U.S
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20
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Muir RT, Ismail Z, Black SE, Smith EE. Comparative methods for quantifying plasma biomarkers in Alzheimer's disease: Implications for the next frontier in cerebral amyloid angiopathy diagnostics. Alzheimers Dement 2024; 20:1436-1458. [PMID: 37908054 PMCID: PMC10916950 DOI: 10.1002/alz.13510] [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: 03/12/2023] [Revised: 08/09/2023] [Accepted: 08/13/2023] [Indexed: 11/02/2023]
Abstract
Plasma amyloid beta (Aβ) and tau are emerging as accessible biomarkers for Alzheimer's disease (AD). However, many assays exist with variable test performances, highlighting the need for a comparative assessment to identify the most valid assays for future use in AD and to apply to other settings in which the same biomarkers may be useful, namely, cerebral amyloid angiopathy (CAA). CAA is a progressive cerebrovascular disease characterized by deposition of Aβ40 and Aβ42 in cortical and leptomeningeal vessels. Novel immunotherapies for AD can induce amyloid-related imaging abnormalities resembling CAA-related inflammation. Few studies have evaluated plasma biomarkers in CAA. Identifying a CAA signature could facilitate diagnosis, prognosis, and a safer selection of patients with AD for emerging immunotherapies. This review evaluates studies that compare the diagnostic test performance of plasma biomarker techniques in AD and cerebrovascular and plasma biomarker profiles of CAA; it also discusses novel hypotheses and future avenues for plasma biomarker research in CAA.
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Affiliation(s)
- Ryan T. Muir
- Calgary Stroke ProgramDepartment of Clinical NeurosciencesUniversity of CalgaryCalgaryAlbertaCanada
- Department of Community Health SciencesUniversity of CalgaryCalgaryAlbertaCanada
- Hotchkiss Brain InstituteUniversity of CalgaryCalgaryAlbertaCanada
| | - Zahinoor Ismail
- Department of Community Health SciencesUniversity of CalgaryCalgaryAlbertaCanada
- Hotchkiss Brain InstituteUniversity of CalgaryCalgaryAlbertaCanada
- Department of PsychiatryUniversity of CalgaryCalgaryAlbertaCanada
| | - Sandra E. Black
- Division of NeurologyDepartment of MedicineSunnybrook Health Sciences CentreTorontoOntarioCanada
- LC Campbell Cognitive Neurology Research UnitDr Sandra Black Centre for Brain Resilience and Recovery, and Hurvitz Brain Sciences ProgramSunnybrook Research InstituteUniversity of TorontoTorontoOntarioCanada
| | - Eric E. Smith
- Calgary Stroke ProgramDepartment of Clinical NeurosciencesUniversity of CalgaryCalgaryAlbertaCanada
- Department of Community Health SciencesUniversity of CalgaryCalgaryAlbertaCanada
- Hotchkiss Brain InstituteUniversity of CalgaryCalgaryAlbertaCanada
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21
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Di Napoli J, Arighi A, Conte G, Carandini T, Sacchi L, Arcaro M, Fenoglio C, Sorrentino F, Mercurio M, Pietroboni AM, Giardinieri G, Triulzi F, Galimberti D, Scarpini E, Fumagalli GG. Predominant right temporal lobe atrophy: Clinical, neuropsychological and structural differences based on amyloid status. Eur J Neurol 2024; 31:e16124. [PMID: 37933893 PMCID: PMC11235810 DOI: 10.1111/ene.16124] [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: 05/31/2023] [Revised: 10/06/2023] [Accepted: 10/17/2023] [Indexed: 11/08/2023]
Abstract
BACKGROUND Predominant right temporal atrophy is a radiological sign usually associated with frontotemporal dementia but this sign can also be present in Alzheimer's disease. Given the overlap of clinical symptoms between the two conditions, it is important to know which characteristics allow them to be differentiated. OBJECTIVES To compare clinical, neuropsychological and structural magnetic resonance imaging (MRI) data of subjects with prominent right anterior temporal atrophy, depending on the status of amyloid biomarkers. METHODS Among patients followed in the dementia center of Ospedale Maggiore Policlinico, subjects with right anterior temporal atrophy, defined as grade 3 or 4 on the corresponding visual rating scale, were identified. Only subjects with both an MRI scan and amyloid status available were considered. For selected subjects, data were extracted from clinical and neuropsychological records at initial presentation and at last available follow-up. Two raters applied a protocol of eight visual rating scales to compare brain atrophy and white matter hyperintensities. RESULTS Of 497 subjects, 17 fulfilled the inclusion criteria: 7 amyloid-positive and 10 amyloid-negative. At initial presentation, executive dysfunction and topographical disorientation were more common in amyloid-positive patients. At follow-up, behavioral symptoms, such as social awkwardness and compulsive attitude, were more frequent in the amyloid-negative patients. Amyloid-positive patients presented an overall worse neuropsychological performance, especially in the language and visuospatial domain, and had higher scores on the right anterior cingulate visual rating scale. CONCLUSION Patients with predominant right temporal atrophy showed clinical, neuropsychological and radiological differences, depending on the status of amyloid biomarkers.
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Affiliation(s)
- Jacopo Di Napoli
- Department of Pathophysiology and TransplantationUniversity of MilanMilanItaly
| | - Andrea Arighi
- Neurodegenerative Diseases UnitFondazione IRCCS Ca' GrandaOspedale Maggiore PoliclinicoMilanItaly
| | - Giorgio Conte
- Neuroradiology UnitFondazione IRCCS Ca' GrandaOspedale Maggiore PoliclinicoMilanItaly
| | - Tiziana Carandini
- Neurodegenerative Diseases UnitFondazione IRCCS Ca' GrandaOspedale Maggiore PoliclinicoMilanItaly
| | - Luca Sacchi
- Department of Pathophysiology and TransplantationUniversity of MilanMilanItaly
| | - Marina Arcaro
- Neurodegenerative Diseases UnitFondazione IRCCS Ca' GrandaOspedale Maggiore PoliclinicoMilanItaly
| | - Chiara Fenoglio
- Department of Pathophysiology and TransplantationUniversity of MilanMilanItaly
| | - Federica Sorrentino
- Department of Biomedical Surgical and Dental SciencesUniversity of MilanMilanItaly
| | - Matteo Mercurio
- Neurodegenerative Diseases UnitFondazione IRCCS Ca' GrandaOspedale Maggiore PoliclinicoMilanItaly
| | - Anna M. Pietroboni
- Neurodegenerative Diseases UnitFondazione IRCCS Ca' GrandaOspedale Maggiore PoliclinicoMilanItaly
| | - Giulia Giardinieri
- Neurodegenerative Diseases UnitFondazione IRCCS Ca' GrandaOspedale Maggiore PoliclinicoMilanItaly
| | - Fabio Triulzi
- Neuroradiology UnitFondazione IRCCS Ca' GrandaOspedale Maggiore PoliclinicoMilanItaly
| | - Daniela Galimberti
- Neurodegenerative Diseases UnitFondazione IRCCS Ca' GrandaOspedale Maggiore PoliclinicoMilanItaly
- Department of Biomedical Surgical and Dental SciencesUniversity of MilanMilanItaly
| | - Elio Scarpini
- Neurodegenerative Diseases UnitFondazione IRCCS Ca' GrandaOspedale Maggiore PoliclinicoMilanItaly
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22
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Bermejo-Pareja F, del Ser T. Controversial Past, Splendid Present, Unpredictable Future: A Brief Review of Alzheimer Disease History. J Clin Med 2024; 13:536. [PMID: 38256670 PMCID: PMC10816332 DOI: 10.3390/jcm13020536] [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: 10/12/2023] [Revised: 12/29/2023] [Accepted: 01/04/2024] [Indexed: 01/24/2024] Open
Abstract
Background: The concept of Alzheimer disease (AD)-since its histological discovery by Alzheimer to the present day-has undergone substantial modifications. Methods: We conducted a classical narrative review of this field with a bibliography selection (giving preference to Medline best match). Results: The following subjects are reviewed and discussed: Alzheimer's discovery, Kraepelin's creation of a new disease that was a rare condition until the 1970's, the growing interest and investment in AD as a major killer in a society with a large elderly population in the second half of the 20th century, the consolidation of the AD clinicopathological model, and the modern AD nosology based on the dominant amyloid hypothesis among many others. In the 21st century, the development of AD biomarkers has supported a novel biological definition of AD, although the proposed therapies have failed to cure this disease. The incidence of dementia/AD has shown a decrease in affluent countries (possibly due to control of risk factors), and mixed dementia has been established as the most frequent etiology in the oldest old. Conclusions: The current concept of AD lacks unanimity. Many hypotheses attempt to explain its complex physiopathology entwined with aging, and the dominant amyloid cascade has yielded poor therapeutic results. The reduction in the incidence of dementia/AD appears promising but it should be confirmed in the future. A reevaluation of the AD concept is also necessary.
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Affiliation(s)
- Félix Bermejo-Pareja
- CIBERNED, Institute of Health Carlos III, 28029 Madrid, Spain
- Institute of Research i+12, University Hospital “12 de Octubre”, 28041 Madrid, Spain
| | - Teodoro del Ser
- Alzheimer’s Centre Reina Sofia—CIEN Foundation, Institute of Health Carlos III, 28031 Madrid, Spain;
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23
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Chen P, Zhang S, Zhao K, Kang X, Rittman T, Liu Y. Robustly uncovering the heterogeneity of neurodegenerative disease by using data-driven subtyping in neuroimaging: A review. Brain Res 2024; 1823:148675. [PMID: 37979603 DOI: 10.1016/j.brainres.2023.148675] [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: 10/19/2023] [Accepted: 11/07/2023] [Indexed: 11/20/2023]
Abstract
Neurodegenerative diseases are associated with heterogeneity in genetics, pathology, and clinical manifestation. Understanding this heterogeneity is particularly relevant for clinical prognosis and stratifying patients for disease modifying treatments. Recently, data-driven methods based on neuroimaging have been applied to investigate the subtyping of neurodegenerative disease, helping to disentangle this heterogeneity. We reviewed brain-based subtyping studies in aging and representative neurodegenerative diseases, including Alzheimer's disease, mild cognitive impairment, frontotemporal dementia, and Lewy body dementia, from January 2000 to November 2022. We summarized clustering methods, validation, robustness, reproducibility, and clinical relevance of 71 eligible studies in the present study. We found vast variations in approaches between studies, including ten neuroimaging modalities, 24 cluster algorithms, and 41 methods of cluster number determination. The clinical relevance of subtyping studies was evaluated by summarizing the analysis method of clinical measurements, showing a relatively low clinical utility in the current studies. Finally, we conclude that future studies of heterogeneity in neurodegenerative disease should focus on validation, comparison between subtyping approaches, and prioritise clinical utility.
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Affiliation(s)
- Pindong Chen
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; Department of Clinical Neurosciences, University of Cambridge, Cambridge, Cambridgeshire, UK
| | - Shirui Zhang
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Kun Zhao
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Xiaopeng Kang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Timothy Rittman
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, Cambridgeshire, UK
| | - Yong Liu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.
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24
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Shui L, Shibata D, Chan KCG, Zhang W, Sung J, Haynor DR. Longitudinal Relationship Between Brain Atrophy Patterns, Cognitive Decline, and Cerebrospinal Fluid Biomarkers in Alzheimer's Disease Explored by Orthonormal Projective Non-Negative Matrix Factorization. J Alzheimers Dis 2024; 98:969-986. [PMID: 38517788 DOI: 10.3233/jad-231149] [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] [Indexed: 03/24/2024]
Abstract
Background Longitudinal magnetic resonance imaging (MRI) has been proposed for tracking the progression of Alzheimer's disease (AD) through the assessment of brain atrophy. Objective Detection of brain atrophy patterns in patients with AD as the longitudinal disease tracker. Methods We used a refined version of orthonormal projective non-negative matrix factorization (OPNMF) to identify six distinct spatial components of voxel-wise volume loss in the brains of 83 subjects with AD from the ADNI3 cohort relative to healthy young controls from the ABIDE study. We extracted non-negative coefficients representing subject-specific quantitative measures of regional atrophy. Coefficients of brain atrophy were compared to subjects with mild cognitive impairment and controls, to investigate the cross-sectional and longitudinal associations between AD biomarkers and regional atrophy severity in different groups. We further validated our results in an independent dataset from ADNI2. Results The six non-overlapping atrophy components represent symmetric gray matter volume loss primarily in frontal, temporal, parietal and cerebellar regions. Atrophy in these regions was highly correlated with cognition both cross-sectionally and longitudinally, with medial temporal atrophy showing the strongest correlations. Subjects with elevated CSF levels of TAU and PTAU and lower baseline CSF Aβ42 values, demonstrated a tendency toward a more rapid increase of atrophy. Conclusions The present study has applied a transferable method to characterize the imaging changes associated with AD through six spatially distinct atrophy components and correlated these atrophy patterns with cognitive changes and CSF biomarkers cross-sectionally and longitudinally, which may help us better understand the underlying pathology of AD.
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Affiliation(s)
- Lan Shui
- Department of Biostatistics, University of Washington, Seattle, WA, USA
- National Alzheimer's Coordinating Center, Seattle, WA, USA
- Department of Biostatistics, MD Anderson Cancer Center, Houston, TX, USA
| | - Dean Shibata
- Department of Radiology, University of Washington, Seattle, WA, USA
- National Alzheimer's Coordinating Center, Seattle, WA, USA
| | - Kwun Chuen Gary Chan
- Department of Biostatistics, University of Washington, Seattle, WA, USA
- National Alzheimer's Coordinating Center, Seattle, WA, USA
| | - Wenbo Zhang
- Department of Statistics, University of California Irvine, CA, USA
| | - Junhyoun Sung
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - David R Haynor
- Department of Biostatistics, MD Anderson Cancer Center, Houston, TX, USA
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Ryoo HG, Choi H, Shi K, Rominger A, Lee DY, Lee DS. Distinct subtypes of spatial brain metabolism patterns in Alzheimer's disease identified by deep learning-based FDG PET clusters. Eur J Nucl Med Mol Imaging 2024; 51:443-454. [PMID: 37735259 DOI: 10.1007/s00259-023-06440-9] [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: 05/24/2023] [Accepted: 09/08/2023] [Indexed: 09/23/2023]
Abstract
PURPOSE Alzheimer's disease (AD) is a heterogeneous disease that presents a broad spectrum of clinicopathologic profiles. To date, objective subtyping of AD independent of disease progression using brain imaging has been required. Our study aimed to extract representations of unique brain metabolism patterns different from disease progression to identify objective subtypes of AD. METHODS A total of 3620 FDG brain PET images with AD, mild cognitive impairment (MCI), and cognitively normal (CN) were obtained from the ADNI database from 1607 participants at enrollment and follow-up visits. A conditional variational autoencoder model was trained on FDG brain PET images of AD patients with the corresponding condition of AD severity score. The k-means algorithm was applied to generate clusters from the encoded representations. The trained deep learning-based cluster model was also transferred to FDG PET of MCI patients and predicted the prognosis of subtypes for conversion from MCI to AD. Spatial metabolism patterns, clinical and biological characteristics, and conversion rate from MCI to AD were compared across the subtypes. RESULTS Four distinct subtypes of spatial metabolism patterns in AD with different brain pathologies and clinical profiles were identified: (i) angular, (ii) occipital, (iii) orbitofrontal, and (iv) minimal hypometabolic patterns. The deep learning model was also successfully transferred for subtyping MCI, and significant differences in frequency (P < 0.001) and risk of conversion (log-rank P < 0.0001) from MCI to AD were observed across the subtypes, highest in S2 (35.7%) followed by S1 (23.4%). CONCLUSION We identified distinct subtypes of AD with different clinicopathologic features. The deep learning-based approach to distinguish AD subtypes on FDG PET could have implications for predicting individual outcomes and provide a clue to understanding the heterogeneous pathophysiology of AD.
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Affiliation(s)
- Hyun Gee Ryoo
- Department of Nuclear Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
- Department of Nuclear Medicine, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
- Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, and College of Medicine or College of Pharmacy, Seoul National University, Seoul, Republic of Korea
| | - Hongyoon Choi
- Department of Nuclear Medicine, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
- Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.
| | - Kuangyu Shi
- Department of Nuclear Medicine, Inselspital, University of Bern, Freiburgstrasse 18, 3010, Bern, Switzerland
| | - Axel Rominger
- Department of Nuclear Medicine, Inselspital, University of Bern, Freiburgstrasse 18, 3010, Bern, Switzerland
| | - Dong Young Lee
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Dong Soo Lee
- Department of Nuclear Medicine, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
- Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, and College of Medicine or College of Pharmacy, Seoul National University, Seoul, Republic of Korea
- Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
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26
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de Veij Mestdagh CF, Smit AB, Henning RH, van Kesteren RE. Mitochondrial Targeting against Alzheimer's Disease: Lessons from Hibernation. Cells 2023; 13:12. [PMID: 38201215 PMCID: PMC10778235 DOI: 10.3390/cells13010012] [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: 11/13/2023] [Revised: 12/14/2023] [Accepted: 12/18/2023] [Indexed: 01/12/2024] Open
Abstract
Alzheimer's disease (AD) is the most common cause of dementia worldwide and yet remains without effective therapy. Amongst the many proposed causes of AD, the mitochondrial cascade hypothesis is gaining attention. Accumulating evidence shows that mitochondrial dysfunction is a driving force behind synaptic dysfunction and cognitive decline in AD patients. However, therapies targeting the mitochondria in AD have proven unsuccessful so far, and out-of-the-box options, such as hibernation-derived mitochondrial mechanisms, may provide valuable new insights. Hibernators uniquely and rapidly alternate between suppression and re-activation of the mitochondria while maintaining a sufficient energy supply and without acquiring ROS damage. Here, we briefly give an overview of mitochondrial dysfunction in AD, how it affects synaptic function, and why mitochondrial targeting in AD has remained unsuccessful so far. We then discuss mitochondria in hibernation and daily torpor in mice, covering current advancements in hibernation-derived mitochondrial targeting strategies. We conclude with new ideas on how hibernation-derived dual mitochondrial targeting of both the ATP and ROS pathways may boost mitochondrial health and induce local synaptic protein translation to increase synaptic function and plasticity. Further exploration of these mechanisms may provide more effective treatment options for AD in the future.
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Affiliation(s)
- Christina F. de Veij Mestdagh
- Department of Molecular and Cellular Neurobiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands; (A.B.S.); (R.E.v.K.)
- Department of Clinical Pharmacy and Pharmacology, University Medical Center Groningen, 9713 GZ Groningen, The Netherlands;
- Alzheimer Center Amsterdam, Amsterdam UMC Location VUmc, 1081 HV Amsterdam, The Netherlands
| | - August B. Smit
- Department of Molecular and Cellular Neurobiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands; (A.B.S.); (R.E.v.K.)
| | - Robert H. Henning
- Department of Clinical Pharmacy and Pharmacology, University Medical Center Groningen, 9713 GZ Groningen, The Netherlands;
| | - Ronald E. van Kesteren
- Department of Molecular and Cellular Neurobiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands; (A.B.S.); (R.E.v.K.)
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27
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Fu Z, Zhao M, Li Y, He Y, Wang X, Zhou Z, Han Y, Li S. Heterogeneity in subjective cognitive decline in the Sino Longitudinal Study on Cognitive Decline(SILCODE): Empirically derived subtypes, structural and functional verification. CNS Neurosci Ther 2023; 29:4032-4042. [PMID: 37475187 PMCID: PMC10651943 DOI: 10.1111/cns.14327] [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: 01/15/2023] [Revised: 03/01/2023] [Accepted: 06/17/2023] [Indexed: 07/22/2023] Open
Abstract
AIMS We evaluated whether Subjective Cognitive Decline (SCD) subtypes could be empirically derived within the Sino Longitudinal Study on Cognitive Decline (SILCODE) SCD cohort and examined associated neuroimaging markers, biomarkers, and clinical outcomes. METHODS A cluster analysis was performed on eight neuropsychological test scores from 124 SCD SILCODE participants and 57 normal control (NC) subjects. Structural and functional neuroimaging indices were used to evaluate the SCD subgroups. RESULTS Four subtypes emerged: (1) dysexecutive/mixed SCD (n = 23), (2) neuropsychiatric SCD (n = 24), (3) amnestic SCD (n = 22), and (4) cluster-derived normal (n = 55) who exhibited normal performance in neuropsychological tests. Compared with the NC group, each subgroup showed distinct patterns in gray matter (GM) volume and the amplitude of low-frequency fluctuations (ALFF). Lower fractional anisotropy (FA) values were only found in the neuropsychiatric SCD group relative to NC. CONCLUSION The identification of empirically derived SCD subtypes demonstrates the presence of heterogeneity in SCD neuropsychological profiles. The cluster-derived normal group may represent the majority of SCD individuals who do not show progressive cognitive decline; the dysexecutive/mixed SCD and amnestic SCD might represent high-risk groups with progressing cognitive decline; and finally, the neuropsychiatric SCD may represent a new topic in SCD research.
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Affiliation(s)
- Zhenrong Fu
- Key Laboratory of Adolescent Cyberpsychology and Behavior (CCNU)Ministry of EducationWuhanChina
- School of Psychology, Key Laboratory of Human Development and Mental Health of Hubei ProvinceCentral China Normal UniversityWuhanChina
| | - Mingyan Zhao
- Department of PsychologyTangshan Gongren HospitalTangshanChina
| | - Yuxia Li
- Department of NeurologyTangshan Central HospitalTangshanChina
- Department of NeurologyXuanwu Hospital of Capital Medical UniversityBeijingChina
| | - Yirong He
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingChina
| | - Xuetong Wang
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingChina
| | - Zongkui Zhou
- Key Laboratory of Adolescent Cyberpsychology and Behavior (CCNU)Ministry of EducationWuhanChina
- School of Psychology, Key Laboratory of Human Development and Mental Health of Hubei ProvinceCentral China Normal UniversityWuhanChina
| | - Ying Han
- Department of NeurologyXuanwu Hospital of Capital Medical UniversityBeijingChina
- Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical EngineeringHainan UniversityHaikouChina
- Center of Alzheimer's DiseaseBeijing Institute for Brain DisordersBeijingChina
- National Clinical Research Center for Geriatric DisordersBeijingChina
- Institute of Biomedical EngineeringShenzhen Bay LaboratoryShenzhenChina
| | - Shuyu Li
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingChina
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28
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Björk L, Shirani H, Todarwal Y, Linares M, Vidal R, Ghetti B, Norman P, Klingstedt T, Nilsson KPR. Distinct Heterocyclic Moieties Govern the Selectivity of Thiophene-Vinylene-Based Ligands Towards Aβ or Tau Pathology in Alzheime's Disease. European J Org Chem 2023; 26:e202300583. [PMID: 38585413 PMCID: PMC10997339 DOI: 10.1002/ejoc.202300583] [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: 06/16/2023] [Indexed: 04/09/2024]
Abstract
Distinct aggregated proteins are correlated with numerous neurodegenerative diseases and the development of ligands that selectively detect these pathological hallmarks is vital. Recently, the synthesis of thiophene-based optical ligands, denoted bi-thiophene-vinyl-benzothiazoles (bTVBTs), that could be utilized for selective assignment of tau pathology in brain tissue with Alzheime's disease (AD) pathology, was reported. Herein, we investigate the ability of these ligands to selectively distinguish tau deposits from aggregated amyloid-β (Aβ), the second AD associated pathological hallmark, when replacing the terminal thiophene moiety with other heterocyclic motifs. The selectivity for tau pathology was reduced when introducing specific heterocyclic motifs, verifying that specific molecular interactions between the ligands and the aggregates are necessary for selective detection of tau deposits. In addition, ligands having certain heterocyclic moieties attached to the central thiophene-vinylene building block displayed selectivity to aggregated Aβ pathology. Our findings provide chemical insights for the development of ligands that can distinguish between aggregated proteinaceous species consisting of different proteins and might also aid in creating novel agents for clinical imaging of tau pathology in AD.
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Affiliation(s)
- Linnea Björk
- Department of Physics, Chemistry and Biology, Linköping University, SE-581 83 Linköping, Sweden
| | - Hamid Shirani
- Department of Physics, Chemistry and Biology, Linköping University, SE-581 83 Linköping, Sweden
| | - Yogesh Todarwal
- Division of Theoretical Chemistry and Biology, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, SE-106 91, Stockholm, Sweden
| | - Mathieu Linares
- Division of Theoretical Chemistry and Biology, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, SE-106 91, Stockholm, Sweden
| | - Ruben Vidal
- Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, 46202 Indiana, USA
| | - Bernardino Ghetti
- Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, 46202 Indiana, USA
| | - Patrick Norman
- Division of Theoretical Chemistry and Biology, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, SE-106 91, Stockholm, Sweden
| | - Therése Klingstedt
- Department of Physics, Chemistry and Biology, Linköping University, SE-581 83 Linköping, Sweden
| | - K Peter R Nilsson
- Department of Physics, Chemistry and Biology, Linköping University, SE-581 83 Linköping, Sweden
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Rosewood TJ, Nho K, Risacher SL, Gao S, Shen L, Foroud T, Saykin AJ. Genome-Wide Association Analysis across Endophenotypes in Alzheimer's Disease: Main Effects and Disease Stage-Specific Interactions. Genes (Basel) 2023; 14:2010. [PMID: 38002954 PMCID: PMC10671827 DOI: 10.3390/genes14112010] [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: 09/11/2023] [Revised: 10/23/2023] [Accepted: 10/23/2023] [Indexed: 11/26/2023] Open
Abstract
The underlying genetic susceptibility for Alzheimer's disease (AD) is not yet fully understood. The heterogeneous nature of the disease challenges genetic association studies. Endophenotype approaches can help to address this challenge by more direct interrogation of biological traits related to the disease. AD endophenotypes based on amyloid-β, tau, and neurodegeneration (A/T/N) biomarkers and cognitive performance were selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort (N = 1565). A genome-wide association study (GWAS) of quantitative phenotypes was performed using an SNP main effect and an SNP by Diagnosis interaction (SNP × DX) model to identify disease stage-specific genetic effects. Nine loci were identified as study-wide significant with one or more A/T/N endophenotypes in the main effect model, as well as additional findings significantly associated with cognitive measures. These nine loci include SNPs in or near the genes APOE, SRSF10, HLA-DQB1, XKR3, and KIAA1671. The SNP × DX model identified three study-wide significant genetic loci (BACH2, EP300, and PACRG-AS1) with a neuroprotective effect in later AD stage endophenotypes. An endophenotype approach identified novel genetic associations and provided insight into the molecular mechanisms underlying the genetic associations that may otherwise be missed using conventional case-control study designs.
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Affiliation(s)
- Thea J. Rosewood
- Indiana Alzheimer’s Disease Research Center, Indianapolis, IN 46202, USA; (T.J.R.); (S.L.R.); (S.G.); (T.F.)
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Kwangsik Nho
- Indiana Alzheimer’s Disease Research Center, Indianapolis, IN 46202, USA; (T.J.R.); (S.L.R.); (S.G.); (T.F.)
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA
- School of Informatics and Computing, Indiana University, Indianapolis, IN 46202, USA
| | - Shannon L. Risacher
- Indiana Alzheimer’s Disease Research Center, Indianapolis, IN 46202, USA; (T.J.R.); (S.L.R.); (S.G.); (T.F.)
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Sujuan Gao
- Indiana Alzheimer’s Disease Research Center, Indianapolis, IN 46202, USA; (T.J.R.); (S.L.R.); (S.G.); (T.F.)
- Department of Biostatistics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, The Perelman School of Medicine, Philadelphia, PA 19104, USA;
| | - Tatiana Foroud
- Indiana Alzheimer’s Disease Research Center, Indianapolis, IN 46202, USA; (T.J.R.); (S.L.R.); (S.G.); (T.F.)
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Andrew J. Saykin
- Indiana Alzheimer’s Disease Research Center, Indianapolis, IN 46202, USA; (T.J.R.); (S.L.R.); (S.G.); (T.F.)
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA
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Nguyen TTT, Lee HH, Huang LK, Hu CJ, Yeh CY, Yang WCV, Lin MC. Heterogeneity of Alzheimer's disease identified by neuropsychological test profiling. PLoS One 2023; 18:e0292527. [PMID: 37797059 PMCID: PMC10553816 DOI: 10.1371/journal.pone.0292527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 09/22/2023] [Indexed: 10/07/2023] Open
Abstract
Alzheimer's disease (AD) is a highly heterogeneous disorder. Untangling this variability could lead to personalized treatments and improve participant recruitment for clinical trials. We investigated the cognitive subgroups by using a data-driven clustering technique in an AD cohort. People with mild-moderate probable AD from Taiwan was included. Neuropsychological test results from the Cognitive Abilities Screening Instrument were clustered using nonnegative matrix factorization. We identified two clusters in 112 patients with predominant deficits in memory (62.5%) and non-memory (37.5%) cognitive domains, respectively. The memory group performed worse in short-term memory and orientation and better in attention than the non-memory group. At baseline, patients in the memory group had worse global cognitive status and dementia severity. Linear mixed effect model did not reveal difference in disease trajectory within 3 years of follow-up between the two clusters. Our results provide insights into the cognitive heterogeneity in probable AD in an Asian population.
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Affiliation(s)
- Truc Tran Thanh Nguyen
- Graduate Institute of Biomedical Informatics, Division of Translational Medicine, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
- Memory and Dementia Center, Hospital 30–4, Ho Chi Minh City, Vietnam
| | - Hsun-Hua Lee
- Department of Neurology, Taipei Medical University Hospital, Taipei Medical University, Taipei, Taiwan
- Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
- Dizziness and Balance Disorder Center, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
- Department of Neurology, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
| | - Li-Kai Huang
- Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
- Department of Neurology, Dementia Center, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
| | - Chaur-Jong Hu
- Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
- Department of Neurology, Dementia Center, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
- Graduate Institute of Neural Regenerative Medicine, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
- Taipei Neuroscience Institute, Taipei Medical University, Taipei, Taiwan
| | - Chih-Yang Yeh
- Graduate Institute of Biomedical Informatics, Division of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
| | - Wei-Chung Vivian Yang
- The PhD Program for Translational Medicine, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
| | - Ming-Chin Lin
- Graduate Institute of Biomedical Informatics, Division of Translational Medicine, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
- Graduate Institute of Biomedical Informatics, Division of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
- Department of Neurosurgery, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
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31
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Hromadkova L, Kim C, Haldiman T, Peng L, Zhu X, Cohen M, de Silva R, Safar JG. Evolving prion-like tau conformers differentially alter postsynaptic proteins in neurons inoculated with distinct isolates of Alzheimer's disease tau. Cell Biosci 2023; 13:174. [PMID: 37723591 PMCID: PMC10507869 DOI: 10.1186/s13578-023-01133-0] [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: 05/03/2023] [Accepted: 09/12/2023] [Indexed: 09/20/2023] Open
Abstract
OBJECTIVES Although accumulation of misfolded tau species has been shown to predict cognitive decline in patients with Alzheimer's disease (AD) and other tauopathies but with the remarkable diversity of clinical manifestations, neuropathology profiles, and time courses of disease progression remaining unexplained by current genetic data. We considered the diversity of misfolded tau conformers present in individual AD cases as an underlying driver of the phenotypic variations of AD and progressive loss of synapses. METHODS To model the mechanism of tau propagation and synaptic toxicity of distinct tau conformers, we inoculated wild-type primary mouse neurons with structurally characterized Sarkosyl-insoluble tau isolates from the frontal cortex of six AD cases and monitored the impact for fourteen days. We analyzed the accumulation rate, tau isoform ratio, and conformational characteristics of de novo-induced tau aggregates with conformationally sensitive immunoassays, and the dynamics of synapse formation, maintenance, and their loss using a panel of pre-and post-synaptic markers. RESULTS At the same concentrations of tau, the different AD tau isolates induced accumulation of misfolded predominantly 4-repeat tau aggregates at different rates in mature neurons, and demonstrated distinct conformational characteristics corresponding to the original AD brain tau. The time-course of the formation of misfolded tau aggregates and colocalization correlated with significant loss of synapses in tau-inoculated cell cultures and the reduction of synaptic connections implicated the disruption of postsynaptic compartment as an early event. CONCLUSIONS The data obtained with mature neurons expressing physiological levels and adult isoforms of tau protein demonstrate markedly different time courses of endogenous tau misfolding and differential patterns of post-synaptic alterations. These and previous biophysical data argue for an ensemble of various misfolded tau aggregates in individual AD brains and template propagation of their homologous conformations in neurons with different rates and primarily postsynaptic interactors. Modeling tau aggregation in mature differentiated neurons provides a platform for investigating divergent molecular mechanisms of tau strain propagation and for identifying common structural features of misfolded tau and critical interactors for new therapeutic targets and approaches in AD.
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Affiliation(s)
- Lenka Hromadkova
- Departments of Pathology, Case Western Reserve University School of Medicine, 2085 Adelbert Rd, Cleveland, OH, 44106, USA
| | - Chae Kim
- Departments of Pathology, Case Western Reserve University School of Medicine, 2085 Adelbert Rd, Cleveland, OH, 44106, USA
| | - Tracy Haldiman
- Departments of Pathology, Case Western Reserve University School of Medicine, 2085 Adelbert Rd, Cleveland, OH, 44106, USA
| | - Lihua Peng
- Departments of Pathology, Case Western Reserve University School of Medicine, 2085 Adelbert Rd, Cleveland, OH, 44106, USA
| | - Xiongwei Zhu
- Departments of Pathology, Case Western Reserve University School of Medicine, 2085 Adelbert Rd, Cleveland, OH, 44106, USA
- Departments of Neurology, Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA
| | - Mark Cohen
- Departments of Pathology, Case Western Reserve University School of Medicine, 2085 Adelbert Rd, Cleveland, OH, 44106, USA
- National Prion Disease Pathology Surveillance Center, Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA
| | - Rohan de Silva
- Reta Lila Weston Institute, UCL Queen Square Institute of Neurology, London, WC1N 1PJ, UK
| | - Jiri G Safar
- Departments of Pathology, Case Western Reserve University School of Medicine, 2085 Adelbert Rd, Cleveland, OH, 44106, USA.
- Departments of Neurology, Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA.
- Departments of Neuroscience, Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA.
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Qiu F, Wang Y, Du Y, Zeng C, Liu Y, Pan H, Ke C. Current evidence for J147 as a potential therapeutic agent in nervous system disease: a narrative review. BMC Neurol 2023; 23:317. [PMID: 37674139 PMCID: PMC10481599 DOI: 10.1186/s12883-023-03358-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 08/03/2023] [Indexed: 09/08/2023] Open
Abstract
Curcumin has anti-inflammatory, antioxidant, and anticancer effects and is used to treat diseases such as dermatological diseases, infection, stress, depression, and anxiety. J147, an analogue of curcumin, is designed and synthesized with better stability and bioavailability. Accumulating evidence demonstrates the potential role of J147 in the prevention and treatment of Alzheimer's disease, diabetic neuropathy, ischemic stroke, depression, anxiety, and fatty liver disease. In this narrative review, we summarized the background and biochemical properties of J147 and discussed the role and mechanism of J147 in different diseases. Overall, the mechanical attributes of J147 connote it as a potential target for the prevention and treatment of neurological diseases.
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Affiliation(s)
- Fang Qiu
- Department of Burn and Plastic Surgery, Shenzhen Longhua District Central Hospital, Shenzhen, 518110, Guangdong, China
- Center for Human Tissues and Organs Degeneration, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, Guangdong, China
| | - Yanmei Wang
- Department of critical care medicine, Shenzhen Longhua District Central Hospital, Shenzhen, Guangdong, China
| | - Yunbo Du
- Department of critical care medicine, Shenzhen Longhua District Central Hospital, Shenzhen, Guangdong, China
| | - Changchun Zeng
- Department of Medical Laboratory, Shenzhen Longhua District Central Hospital, Affiliated Central Hospital of Shenzhen Longhua District, Guangdong Medical University, Shenzhen, Guangdong, China
| | - Yuqiang Liu
- Department of Anesthesiology, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, 518025, Guangdong, China.
| | - Haobo Pan
- Center for Human Tissues and Organs Degeneration, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, Guangdong, China.
| | - Changneng Ke
- Department of Burn and Plastic Surgery, Shenzhen Longhua District Central Hospital, Shenzhen, 518110, Guangdong, China.
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Hui Y, Ma Q, Zhou XR, Wang H, Dong JH, Gao LN, Zhang T, Li YY, Gong T. Immunological characterization and diagnostic models of RNA N6-methyladenosine regulators in Alzheimer's disease. Sci Rep 2023; 13:14588. [PMID: 37666846 PMCID: PMC10477294 DOI: 10.1038/s41598-023-41129-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Accepted: 08/22/2023] [Indexed: 09/06/2023] Open
Abstract
Alzheimer's disease (AD) is the most prevalent form of dementia, and it displays both clinical and molecular variability. RNA N6-methyladenosine (m6A) regulators are involved in a wide range of essential cellular processes. In this study, we aimed to identify molecular signatures associated with m6A in Alzheimer's disease and use those signatures to develop a predictive model. We examined the expression patterns of m6A regulators and immune features in Alzheimer's disease using the GSE33000 dataset. We examined the immune cell infiltration and molecular groups based on m6A-related genes in 310 Alzheimer's disease samples. The WGCNA algorithm was utilized to determine differently expressed genes within each cluster. After evaluating the strengths and weaknesses of the random forest model, the support vector machine model, the generalized linear model, and eXtreme Gradient Boosting, the best machine model was selected. Methods such as nomograms, calibration curves, judgment curve analysis, and the use of independent data sets were used to verify the accuracy of the predictions made. Alzheimer's disease and non-disease Alzheimer's groups were compared to identify dysregulated m6A-related genes and activated immune responses. In Alzheimer's disease, two molecular clusters linked to m6A were identified. Immune infiltration analysis indicated substantial variation in protection between groups. Cluster 1 included processes like the Toll-like receptor signaling cascade, positive regulation of chromatin binding, and numerous malignancies; cluster 2 included processes like the cell cycle, mRNA transport, and ubiquitin-mediated proteolysis. With a lower residual and root mean square error and a larger area under the curve (AUC = 0.951), the Random forest machine model showed the greatest discriminative performance. The resulting random forest model was based on five genes, and it performed well (AUC = 0.894) on external validation datasets. Accuracy in predicting Alzheimer's disease subgroups was also shown by analyses of nomograms, calibration curves, and decision curves. In this research, we methodically outlined the tangled web of connections between m6A and AD and created a promising prediction model for gauging the correlation between m6A subtype risk and AD pathology.
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Affiliation(s)
- Yuan Hui
- School of Integrative Medicine, Gansu University of Traditional Chinese Medicine, Lanzhou, China
| | - Qi Ma
- School of Integrative Medicine, Gansu University of Traditional Chinese Medicine, Lanzhou, China
| | - Xue-Rui Zhou
- School of Integrative Medicine, Gansu University of Traditional Chinese Medicine, Lanzhou, China
| | - Huan Wang
- School of Integrative Medicine, Gansu University of Traditional Chinese Medicine, Lanzhou, China
| | - Jian-Hua Dong
- School of Integrative Medicine, Gansu University of Traditional Chinese Medicine, Lanzhou, China
| | - Li-Na Gao
- School of Integrative Medicine, Gansu University of Traditional Chinese Medicine, Lanzhou, China
| | - Tian Zhang
- School of Integrative Medicine, Gansu University of Traditional Chinese Medicine, Lanzhou, China
| | - Yan-Yi Li
- Department of Encephalopathy II, Gansu Provincial Hospital of Traditional Chinese Medicine, Lanzhou, 730050, China
| | - Ting Gong
- Department of Encephalopathy II, Gansu Provincial Hospital of Traditional Chinese Medicine, Lanzhou, 730050, China.
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Hu Z, Li Y, Wang Z, Zhang S, Hou W. Conv-Swinformer: Integration of CNN and shift window attention for Alzheimer's disease classification. Comput Biol Med 2023; 164:107304. [PMID: 37549456 DOI: 10.1016/j.compbiomed.2023.107304] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 07/14/2023] [Accepted: 07/28/2023] [Indexed: 08/09/2023]
Abstract
Deep learning (DL) algorithms based on brain MRI images have achieved great success in the prediction of Alzheimer's disease (AD), with classification accuracy exceeding even that of the most experienced clinical experts. As a novel feature fusion method, Transformer has achieved excellent performance in many computer vision tasks, which also greatly promotes the application of Transformer in medical images. However, when Transformer is used for 3D MRI image feature fusion, existing DL models treat the input local features equally, which is inconsistent with the fact that adjacent voxels have stronger semantic connections than spatially distant voxels. In addition, due to the relatively small size of the dataset for medical images, it is difficult to capture local lesion features in limited iterative training by treating all input features equally. This paper proposes a deep learning model Conv-Swinformer that focuses on extracting and integrating local fine-grained features. Conv-Swinformer consists of a CNN module and a Transformer encoder module. The CNN module summarizes the planar features of the MRI slices, and the Transformer module establishes semantic connections in 3D space for these planar features. By introducing the shift window attention mechanism in the Transformer encoder, the attention is focused on a small spatial area of the MRI image, which effectively reduces unnecessary background semantic information and enables the model to capture local features more accurately. In addition, the layer-by-layer enlarged attention window can further integrate local fine-grained features, thus enhancing the model's attention ability. Compared with DL algorithms that indiscriminately fuse local features of MRI images, Conv-Swinformer can fine-grained extract local lesion features, thus achieving better classification results.
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Affiliation(s)
- Zhentao Hu
- School of Artificial Intelligence, Henan University, Zhengzhou, 450046, China
| | - Yanyang Li
- School of Artificial Intelligence, Henan University, Zhengzhou, 450046, China
| | - Zheng Wang
- School of Artificial Intelligence, Henan University, Zhengzhou, 450046, China.
| | - Shuo Zhang
- School of Artificial Intelligence, Henan University, Zhengzhou, 450046, China
| | - Wei Hou
- College of Computer and Information Engineering, Henan University, Kaifeng, 475004, China
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Williams E, Theys C, McAuliffe M. Lexical-semantic properties of verbs and nouns used in conversation by people with Alzheimer's disease. PLoS One 2023; 18:e0288556. [PMID: 37535626 PMCID: PMC10399788 DOI: 10.1371/journal.pone.0288556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Accepted: 06/29/2023] [Indexed: 08/05/2023] Open
Abstract
Alzheimer's disease (AD) is accompanied by language impairments and communicative breakdowns. Research into language processing by people with AD (pwAD) has focused largely on production of nouns in isolation. However, impairments are consistently found in verb production at word and sentence levels, and comparatively little is known about word use by pwAD in conversation. This study investigated differences between pwAD and cognitively healthy controls in conversational use of nouns, verbs, and pronouns. Speech samples produced by 12 pwAD and 12 controls for the Carolinas Conversations Collection were analysed for noun, verb and pronoun counts and ratios, lexical diversity overall and among nouns and verbs, copula use, and frequencies and ages of acquisition (AoA) of nouns and verbs produced. pwAD used fewer nouns and a narrower range of words than controls, exhibiting signs of increased reliance on pronouns and decreased noun diversity. Age affected noun frequencies differently within each group-pwAD produced nouns of lower frequencies with age, while controls produced nouns of higher frequencies. pwAD produced nouns of higher AoA than controls. Verb use differed little by group. These findings highlight the need to account for differences between nouns and verbs, including in frequency, AoA, proportion of all words spoken, and context-dependent processing demands, when drawing conclusions on language use by pwAD. They also suggest potential for communicative interventions targeting contextual use of both nouns and verbs.
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Affiliation(s)
- Eric Williams
- School of Psychology, Speech and Hearing, University of Canterbury, Christchurch, New Zealand
| | - Catherine Theys
- School of Psychology, Speech and Hearing, University of Canterbury, Christchurch, New Zealand
- New Zealand Institute of Language, Brain and Behaviour, University of Canterbury, Christchurch, New Zealand
| | - Megan McAuliffe
- School of Psychology, Speech and Hearing, University of Canterbury, Christchurch, New Zealand
- New Zealand Institute of Language, Brain and Behaviour, University of Canterbury, Christchurch, New Zealand
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Li Q, Yang X, Xu J, Guo Y, He X, Hu H, Lyu T, Marra D, Miller A, Smith G, DeKosky S, Boyce RD, Schliep K, Shenkman E, Maraganore D, Wu Y, Bian J. Early prediction of Alzheimer's disease and related dementias using real-world electronic health records. Alzheimers Dement 2023; 19:3506-3518. [PMID: 36815661 PMCID: PMC10976442 DOI: 10.1002/alz.12967] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 12/31/2022] [Accepted: 01/05/2023] [Indexed: 02/24/2023]
Abstract
INTRODUCTION This study aims to explore machine learning (ML) methods for early prediction of Alzheimer's disease (AD) and related dementias (ADRD) using the real-world electronic health records (EHRs). METHODS A total of 23,835 ADRD and 1,038,643 control patients were identified from the OneFlorida+ Research Consortium. Two ML methods were used to develop the prediction models. Both knowledge-driven and data-driven approaches were explored. Four computable phenotyping algorithms were tested. RESULTS The gradient boosting tree (GBT) models trained with the data-driven approach achieved the best area under the curve (AUC) scores of 0.939, 0.906, 0.884, and 0.854 for early prediction of ADRD 0, 1, 3, or 5 years before diagnosis, respectively. A number of important clinical and sociodemographic factors were identified. DISCUSSION We tested various settings and showed the predictive ability of using ML approaches for early prediction of ADRD with EHRs. The models can help identify high-risk individuals for early informed preventive or prognostic clinical decisions.
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Affiliation(s)
- Qian Li
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Xi Yang
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Jie Xu
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Yi Guo
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Xing He
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Hui Hu
- Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Tianchen Lyu
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - David Marra
- Department of Psychology, VA Boston Healthcare System, Boston, Massachusetts, USA
| | - Amber Miller
- Department of Neurology, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Glenn Smith
- Department of Clinical and Health Psychology, University of Florida, Gainesville, Florida, USA
| | - Steven DeKosky
- Department of Neurology, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Richard D. Boyce
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Karen Schliep
- Department of Family and Preventive Medicine, University of Utah, Salt Lake City, Utah, USA
| | - Elizabeth Shenkman
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Demetrius Maraganore
- Department of Neurology, School of Medicine, Tulane University, New Orleans, Louisiana, USA
| | - Yonghui Wu
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA
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Fotuhi M, Khorrami ND, Raji CA. Benefits of a 12-Week Non-Drug "Brain Fitness Program" for Patients with Attention-Deficit/Hyperactive Disorder, Post-Concussion Syndrome, or Memory Loss. J Alzheimers Dis Rep 2023; 7:675-697. [PMID: 37483322 PMCID: PMC10357116 DOI: 10.3233/adr-220091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 05/25/2023] [Indexed: 07/25/2023] Open
Abstract
Background Non-pharmacologic interventions can potentially improve cognitive function, sleep, and/or mood in patients with attention-deficit/hyperactive disorder (ADHD), post-concussion syndrome (PCS), or memory loss. Objective We evaluated the benefits of a brain rehabilitation program in an outpatient neurology practice that consists of targeted cognitive training, lifestyle coaching, and electroencephalography (EEG)-based neurofeedback, twice weekly (90 minutes each), for 12 weeks. Methods 223 child and adult patients were included: 71 patients with ADHD, 88 with PCS, and 64 with memory loss (mild cognitive impairment or subjective cognitive decline). Patients underwent a complete neurocognitive evaluation, including tests for Verbal Memory, Complex Attention, Processing Speed, Executive Functioning, and Neurocognition Index. They completed questionnaires about sleep, mood, diet, exercise, anxiety levels, and depression-as well as underwent quantitative EEG-at the beginning and the end of the program. Results Pre-post test score comparison demonstrated that all patient subgroups experienced statistically significant improvements on most measures, especially the PCS subgroup, which experienced significant score improvement on all measures tested (p≤0.0011; dz≥0.36). After completing the program, 60% to 90% of patients scored higher on cognitive tests and reported having fewer cognitive and emotional symptoms. The largest effect size for pre-post score change was improved executive functioning in all subgroups (ADHD dz= 0.86; PCS dz= 0.83; memory dz= 1.09). Conclusion This study demonstrates that a multimodal brain rehabilitation program can have benefits for patients with ADHD, PCS, or memory loss and supports further clinical trials in this field.
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Affiliation(s)
- Majid Fotuhi
- Department of Psychological & Brain Sciences, George Washington University, Washington, DC, USA
- NeuroGrow Brain Fitness Center, McLean, VA, USA
| | | | - Cyrus A. Raji
- Mallinckrodt Institute of Radiology, Washington University, St. Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
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Jing R, Chen P, Wei Y, Si J, Zhou Y, Wang D, Song C, Yang H, Zhang Z, Yao H, Kang X, Fan L, Han T, Qin W, Zhou B, Jiang T, Lu J, Han Y, Zhang X, Liu B, Yu C, Wang P, Liu Y. Altered large-scale dynamic connectivity patterns in Alzheimer's disease and mild cognitive impairment patients: A machine learning study. Hum Brain Mapp 2023; 44:3467-3480. [PMID: 36988434 PMCID: PMC10203807 DOI: 10.1002/hbm.26291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 02/27/2023] [Accepted: 03/15/2023] [Indexed: 03/30/2023] Open
Abstract
Alzheimer's disease (AD) is a common neurodegeneration disease associated with substantial disruptions in the brain network. However, most studies investigated static resting-state functional connections, while the alteration of dynamic functional connectivity in AD remains largely unknown. This study used group independent component analysis and the sliding-window method to estimate the subject-specific dynamic connectivity states in 1704 individuals from three data sets. Informative inherent states were identified by the multivariate pattern classification method, and classifiers were built to distinguish ADs from normal controls (NCs) and to classify mild cognitive impairment (MCI) patients with informative inherent states similar to ADs or not. In addition, MCI subgroups with heterogeneous functional states were examined in the context of different cognition decline trajectories. Five informative states were identified by feature selection, mainly involving functional connectivity belonging to the default mode network and working memory network. The classifiers discriminating AD and NC achieved the mean area under the receiver operating characteristic curve of 0.87 with leave-one-site-out cross-validation. Alterations in connectivity strength, fluctuation, and inter-synchronization were found in AD and MCIs. Moreover, individuals with MCI were clustered into two subgroups, which had different degrees of atrophy and different trajectories of cognition decline progression. The present study uncovered the alteration of dynamic functional connectivity in AD and highlighted that the dynamic states could be powerful features to discriminate patients from NCs. Furthermore, it demonstrated that these states help to identify MCIs with faster cognition decline and might contribute to the early prevention of AD.
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Affiliation(s)
- Rixing Jing
- School of Instrument Science and Opto‐Electronics EngineeringBeijing Information Science and Technology UniversityBeijingChina
| | - Pindong Chen
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingChina
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijingChina
| | - Yongbin Wei
- School of Artificial IntelligenceBeijing University of Posts and TelecommunicationsBeijingChina
| | - Juanning Si
- School of Instrument Science and Opto‐Electronics EngineeringBeijing Information Science and Technology UniversityBeijingChina
| | - Yuying Zhou
- Department of NeurologyTianjin Huanhu Hospital, Tianjin UniversityTianjinChina
| | - Dawei Wang
- Department of RadiologyQilu Hospital of Shandong UniversityJi'nanChina
| | - Chengyuan Song
- Department of NeurologyQilu Hospital of Shandong UniversityJi'nanChina
| | - Hongwei Yang
- Department of RadiologyXuanwu Hospital of Capital Medical UniversityBeijingChina
| | | | - Hongxiang Yao
- Department of Radiology, the Second Medical CentreNational Clinical Research Centre for Geriatric Diseases, Chinese PLA General HospitalBeijingChina
| | - Xiaopeng Kang
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingChina
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijingChina
| | - Lingzhong Fan
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingChina
| | - Tong Han
- Department of RadiologyTianjin Huanhu HospitalTianjinChina
| | - Wen Qin
- Department of RadiologyTianjin Medical University General HospitalTianjinChina
| | - Bo Zhou
- Department of Neurologythe Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General HospitalBeijingChina
| | - Tianzi Jiang
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingChina
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijingChina
| | - Jie Lu
- Department of RadiologyXuanwu Hospital of Capital Medical UniversityBeijingChina
| | - Ying Han
- Department of NeurologyXuanwu Hospital of Capital Medical UniversityBeijingChina
- Beijing Institute of GeriatricsBeijingChina
- National Clinical Research Center for Geriatric DisordersBeijingChina
| | - Xi Zhang
- Department of Neurologythe Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General HospitalBeijingChina
| | - Bing Liu
- State Key Laboratory of Cognition Neuroscience & LearningBeijing Normal UniversityBeijingChina
| | - Chunshui Yu
- Department of RadiologyTianjin Medical University General HospitalTianjinChina
| | - Pan Wang
- Department of NeurologyTianjin Huanhu Hospital, Tianjin UniversityTianjinChina
| | - Yong Liu
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingChina
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijingChina
- School of Artificial IntelligenceBeijing University of Posts and TelecommunicationsBeijingChina
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Verdi S, Kia SM, Yong KXX, Tosun D, Schott JM, Marquand AF, Cole JH. Revealing Individual Neuroanatomical Heterogeneity in Alzheimer Disease Using Neuroanatomical Normative Modeling. Neurology 2023; 100:e2442-e2453. [PMID: 37127353 PMCID: PMC10264044 DOI: 10.1212/wnl.0000000000207298] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 03/02/2023] [Indexed: 05/03/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Alzheimer disease (AD) is highly heterogeneous, with marked individual differences in clinical presentation and neurobiology. To explore this, we used neuroanatomical normative modeling to index regional patterns of variability in cortical thickness. We aimed to characterize individual differences and outliers in cortical thickness in patients with AD, people with mild cognitive impairment (MCI), and controls. Furthermore, we assessed the relationships between cortical thickness heterogeneity and cognitive function, β-amyloid, phosphorylated-tau, and ApoE genotype. Finally, we examined whether cortical thickness heterogeneity was predictive of conversion from MCI to AD. METHODS Cortical thickness measurements across 148 brain regions were obtained from T1-weighted MRI scans from 62 sites of the Alzheimer's Disease Neuroimaging Initiative. AD was determined by clinical and neuropsychological examination with no comorbidities present. Participants with MCI had reported memory complaints, and controls were cognitively normal. A neuroanatomical normative model indexed cortical thickness distributions using a separate healthy reference data set (n = 33,072), which used hierarchical Bayesian regression to predict cortical thickness per region using age and sex, while adjusting for site noise. Z-scores per region were calculated, resulting in a Z-score brain map per participant. Regions with Z-scores <-1.96 were classified as outliers. RESULTS Patients with AD (n = 206) had a median of 12 outlier regions (out of a possible 148), with the highest proportion of outliers (47%) in the parahippocampal gyrus. For 62 regions, over 90% of these patients had cortical thicknesses within the normal range. Patients with AD had more outlier regions than people with MCI (n = 662) or controls (n = 159) (F(2, 1,022) = 95.39, p = 2.0 × 10-16). They were also more dissimilar to each other than people with MCI or controls (F(2, 1,024) = 209.42, p = 2.2 × 10-16). A greater number of outlier regions were associated with worse cognitive function, CSF protein concentrations, and an increased risk of converting from MCI to AD within 3 years (hazard ratio 1.028, 95% CI 1.016-1.039, p = 1.8 × 10-16). DISCUSSION Individualized normative maps of cortical thickness highlight the heterogeneous effect of AD on the brain. Regional outlier estimates have the potential to be a marker of disease and could be used to track an individual's disease progression or treatment response in clinical trials.
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Affiliation(s)
- Serena Verdi
- From the Centre for Medical Image Computing (S.V., J.H.C.), Medical Physics and Biomedical Engineering, University College London; Dementia Research Centre (S.V., K.X.X.Y., J.M.S., J.H.C.), UCL Queen Square Institute of Neurology, London, United Kingdom; Donders Centre for Cognitive Neuroimaging (S.M.K., A.F.M.), Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen; Department of Psychiatry (S.M.K.), University Medical Centre Utrecht, the Netherlands; Department of Radiology and Biomedical Imaging (D.T.), University of California, San Francisco; and Department of Cognitive Neuroscience (A.F.M.), Radboud University Medical Centre, Nijmegen, the Netherlands
| | - Seyed Mostafa Kia
- From the Centre for Medical Image Computing (S.V., J.H.C.), Medical Physics and Biomedical Engineering, University College London; Dementia Research Centre (S.V., K.X.X.Y., J.M.S., J.H.C.), UCL Queen Square Institute of Neurology, London, United Kingdom; Donders Centre for Cognitive Neuroimaging (S.M.K., A.F.M.), Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen; Department of Psychiatry (S.M.K.), University Medical Centre Utrecht, the Netherlands; Department of Radiology and Biomedical Imaging (D.T.), University of California, San Francisco; and Department of Cognitive Neuroscience (A.F.M.), Radboud University Medical Centre, Nijmegen, the Netherlands
| | - Keir X X Yong
- From the Centre for Medical Image Computing (S.V., J.H.C.), Medical Physics and Biomedical Engineering, University College London; Dementia Research Centre (S.V., K.X.X.Y., J.M.S., J.H.C.), UCL Queen Square Institute of Neurology, London, United Kingdom; Donders Centre for Cognitive Neuroimaging (S.M.K., A.F.M.), Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen; Department of Psychiatry (S.M.K.), University Medical Centre Utrecht, the Netherlands; Department of Radiology and Biomedical Imaging (D.T.), University of California, San Francisco; and Department of Cognitive Neuroscience (A.F.M.), Radboud University Medical Centre, Nijmegen, the Netherlands
| | - Duygu Tosun
- From the Centre for Medical Image Computing (S.V., J.H.C.), Medical Physics and Biomedical Engineering, University College London; Dementia Research Centre (S.V., K.X.X.Y., J.M.S., J.H.C.), UCL Queen Square Institute of Neurology, London, United Kingdom; Donders Centre for Cognitive Neuroimaging (S.M.K., A.F.M.), Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen; Department of Psychiatry (S.M.K.), University Medical Centre Utrecht, the Netherlands; Department of Radiology and Biomedical Imaging (D.T.), University of California, San Francisco; and Department of Cognitive Neuroscience (A.F.M.), Radboud University Medical Centre, Nijmegen, the Netherlands
| | - Jonathan M Schott
- From the Centre for Medical Image Computing (S.V., J.H.C.), Medical Physics and Biomedical Engineering, University College London; Dementia Research Centre (S.V., K.X.X.Y., J.M.S., J.H.C.), UCL Queen Square Institute of Neurology, London, United Kingdom; Donders Centre for Cognitive Neuroimaging (S.M.K., A.F.M.), Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen; Department of Psychiatry (S.M.K.), University Medical Centre Utrecht, the Netherlands; Department of Radiology and Biomedical Imaging (D.T.), University of California, San Francisco; and Department of Cognitive Neuroscience (A.F.M.), Radboud University Medical Centre, Nijmegen, the Netherlands
| | - Andre F Marquand
- From the Centre for Medical Image Computing (S.V., J.H.C.), Medical Physics and Biomedical Engineering, University College London; Dementia Research Centre (S.V., K.X.X.Y., J.M.S., J.H.C.), UCL Queen Square Institute of Neurology, London, United Kingdom; Donders Centre for Cognitive Neuroimaging (S.M.K., A.F.M.), Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen; Department of Psychiatry (S.M.K.), University Medical Centre Utrecht, the Netherlands; Department of Radiology and Biomedical Imaging (D.T.), University of California, San Francisco; and Department of Cognitive Neuroscience (A.F.M.), Radboud University Medical Centre, Nijmegen, the Netherlands
| | - James H Cole
- From the Centre for Medical Image Computing (S.V., J.H.C.), Medical Physics and Biomedical Engineering, University College London; Dementia Research Centre (S.V., K.X.X.Y., J.M.S., J.H.C.), UCL Queen Square Institute of Neurology, London, United Kingdom; Donders Centre for Cognitive Neuroimaging (S.M.K., A.F.M.), Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen; Department of Psychiatry (S.M.K.), University Medical Centre Utrecht, the Netherlands; Department of Radiology and Biomedical Imaging (D.T.), University of California, San Francisco; and Department of Cognitive Neuroscience (A.F.M.), Radboud University Medical Centre, Nijmegen, the Netherlands.
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Chen Z, Yang Y, Zhang D, Guo J, Guo Y, Hu X, Chen Y, Bian J. Predicting the Risk of Alzheimer's Disease and Related Dementia in Patients with Mild Cognitive Impairment Using a Semi-Competing Risk Approach. INFORMATICS (MDPI) 2023; 10:46. [PMID: 38919750 PMCID: PMC11198980 DOI: 10.3390/informatics10020046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/27/2024]
Abstract
Alzheimer's disease (AD) and AD-related dementias (AD/ADRD) are a group of progressive neurodegenerative diseases. The progression of AD can be conceptualized as a continuum in which patients progress from normal cognition to preclinical AD (i.e., no symptoms but biological changes in the brain) to mild cognitive impairment (MCI) due to AD (i.e., mild symptoms but not interfere with daily activities), followed by increasing severity of dementia due to AD. Early detection and prediction models for the transition of MCI to AD/ADRD are needed, and efforts have been made to build predictions of MCI conversion to AD/ADRD. However, most existing studies developing such prediction models did not consider the competing risks of death, which may result in biased risk estimates. In this study, we aim to develop a prediction model for AD/ADRD among patients with MCI considering the competing risks of death using a semi-competing risk approach.
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Affiliation(s)
- Zhaoyi Chen
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL 32611, USA
| | - Yuchen Yang
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Dazheng Zhang
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jingchuan Guo
- Department of Pharmaceutical Outcomes & Policy, University of Florida, Gainesville, FL 32611, USA
| | - Yi Guo
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL 32611, USA
| | - Xia Hu
- Department of Computer Science, Rice University, Houston, TX 77005, USA
| | - Yong Chen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL 32611, USA
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Björk L, Klingstedt T, Nilsson KPR. Thiophene-Based Ligands: Design, Synthesis and Their Utilization for Optical Assignment of Polymorphic-Disease-Associated Protein Aggregates. Chembiochem 2023; 24:e202300044. [PMID: 36891883 PMCID: PMC10404026 DOI: 10.1002/cbic.202300044] [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: 01/20/2023] [Revised: 03/03/2023] [Accepted: 03/08/2023] [Indexed: 03/10/2023]
Abstract
The development of ligands for detecting protein aggregates is of great interest, as these aggregated proteinaceous species are the pathological hallmarks of several devastating diseases, including Alzheimer's disease. In this regard, thiophene-based ligands have emerged as powerful tools for fluorescent assessment of these pathological entities. The intrinsic conformationally sensitive photophysical properties of poly- and oligothiophenes have allowed optical assignment of disease-associated protein aggregates in tissue sections, as well as real-time in vivo imaging of protein deposits. Herein, we recount the chemical evolution of different generations of thiophene-based ligands, and exemplify their use for the optical distinction of polymorphic protein aggregates. Furthermore, the chemical determinants for achieving a superior fluorescent thiophene-based ligand, as well as the next generation of thiophene-based ligands targeting distinct aggregated species are described. Finally, the directions for future research into the chemical design of thiophene-based ligands that can aid in resolving the scientific challenges around protein aggregation diseases are discussed.
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Affiliation(s)
- Linnea Björk
- Department of Physics, Chemistry and Biology, Linköping University, 581 83, Linköping, Sweden
| | - Therése Klingstedt
- Department of Physics, Chemistry and Biology, Linköping University, 581 83, Linköping, Sweden
| | - K Peter R Nilsson
- Department of Physics, Chemistry and Biology, Linköping University, 581 83, Linköping, Sweden
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Chotiyanonta JS, Onda K, Nowrangi MA, Li X, Xu X, Adams R, Lyketsos CG, Zandi P, Oishi K. Translating clinical notes into quantitative measures-a real-world observation on the response to cholinesterase inhibitors or selective serotonin reuptake inhibitors prescribed to outpatients with dementia using electronic medical records. Front Pharmacol 2023; 14:1177026. [PMID: 37234714 PMCID: PMC10206004 DOI: 10.3389/fphar.2023.1177026] [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: 03/01/2023] [Accepted: 04/24/2023] [Indexed: 05/28/2023] Open
Abstract
Objective: Cholinesterase inhibitors (CEIs) are prescribed for dementia to maintain or improve memory. Selective serotonin reuptake inhibitors (SSRIs) are also prescribed to manage psychiatric symptoms seen in dementia. What proportion of outpatients actually responds to these drugs is still unclear. Our objective was to investigate the responder rates of these medications in an outpatient setting using the electronic medical record (EMR). Methods: We used the Johns Hopkins EMR system to identify patients with dementia who were prescribed a CEI or SSRI for the first time between 2010 and 2021. Treatment effects were assessed through routinely documented clinical notes and free-text entries in which healthcare providers record clinical findings and impressions of patients. Responses were scored using a three-point Likert scale named the NOte-based evaluation method for Treatment Efficacy (NOTE) in addition to the Clinician's Interview-Based Impression of Change Plus caregiver input (CIBIC-plus), a seven-point Likert scale used in clinical trials. To validate NOTE, the relationships between NOTE and CIBIC-plus and between NOTE and change in MMSE (Mini-Mental State Examination) before and after medication were examined. Inter-rater reliability was evaluated using Krippendorff's alpha. The responder rates were calculated. Results: NOTE showed excellent inter-rater reliability and correlated well with CIBIC-plus and changes in MMSEs. Out of 115 CEI cases, 27.0% reported improvement and 34.8% reported stable symptoms in cognition; out of 225 SSRI cases, 69.3% reported an improvement in neuropsychiatric symptoms. Conclusion: NOTE showed high validity in measuring the pharmacotherapy effects based on unstructured clinical entries. Although our real-world observation included various types of dementia, the results were remarkably similar to what was reported in controlled clinical trials of Alzheimer's disease and its related neuropsychiatric symptoms.
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Affiliation(s)
- Jill S. Chotiyanonta
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Kengo Onda
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Milap A. Nowrangi
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Richman Family Precision Medicine Center of Excellence in Alzheimer’s Disease, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Xin Li
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Xin Xu
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Roy Adams
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Richman Family Precision Medicine Center of Excellence in Alzheimer’s Disease, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Constantine G. Lyketsos
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Richman Family Precision Medicine Center of Excellence in Alzheimer’s Disease, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Peter Zandi
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Richman Family Precision Medicine Center of Excellence in Alzheimer’s Disease, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Kenichi Oishi
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Richman Family Precision Medicine Center of Excellence in Alzheimer’s Disease, Johns Hopkins University School of Medicine, Baltimore, MD, United States
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Crane PK, Choi SE, Lee M, Scollard P, Sanders RE, Klinedinst B, Nakano C, Trittschuh EH, Mez J, Saykin AJ, Gibbons LE, Wang C, Mungas D, Zhu R, Foldi NS, Lamar M, Jutten R, Sikkes SA, Grandoit E, Rabin LA, Jones RN, Tommet D. Measurement precision across cognitive domains in the Alzheimer's Disease Neuroimaging Initiative (ADNI) data set. Neuropsychology 2023; 37:373-382. [PMID: 37276134 PMCID: PMC10247161 DOI: 10.1037/neu0000901] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2023] Open
Abstract
OBJECTIVE To demonstrate measurement precision of cognitive domains in the Alzheimer's Disease Neuroimaging Initiative (ADNI) data set. METHOD Participants with normal cognition (NC), mild cognitive impairment (MCI), and Alzheimer's disease (AD) were included from all ADNI waves. We used data from each person's last study visit to calibrate scores for memory, executive function, language, and visuospatial functioning. We extracted item information functions for each domain and used these to calculate standard errors of measurement. We derived scores for each domain for each diagnostic group and plotted standard errors of measurement for the observed range of scores. RESULTS Across all waves, there were 961 people with NC, 825 people with MCI, and 694 people with AD at their most recent study visit (data pulled February 25, 2019). Across ADNI's battery there were 34 memory items, 18 executive function items, 20 language items, and seven visuospatial items. Scores for each domain were highest on average for people with NC, intermediate for people with MCI, and lowest for people with AD, with most scores across all groups in the range of -1 to +1. Standard error of measurement in the range from -1 to +1 was highest for memory, intermediate for language and executive functioning, and lowest for visuospatial. CONCLUSION Modern psychometric approaches provide tools to help understand measurement precision of the scales used in studies. In ADNI, there are important differences in measurement precision across cognitive domains. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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Affiliation(s)
- Paul K. Crane
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - Seo-Eun Choi
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - Michael Lee
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - Phoebe Scollard
- Department of Medicine, University of Washington, Seattle, WA, USA
| | | | | | - Connie Nakano
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - Emily H. Trittschuh
- Department of Psychiatry and Behavioral Sciences, University of Washington, and VA Puget Sound Health Care System, Geriatrics Research, Education, and Clinical Core (GRECC), both in Seattle, WA, USA
| | - Jesse Mez
- Department of Neurology, Boston University, Boston, MA, USA
| | | | - Laura E. Gibbons
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - Chun Wang
- College of Education, University of Washington, Seattle, WA, USA
| | - Dan Mungas
- Department of Neurology, University of California at Davis, Sacramento, CA, USA
| | - Ruoyi Zhu
- College of Education, University of Washington, Seattle, WA, USA
| | - Nancy S. Foldi
- Department of Psychology, Queens College and The Graduate Center, City University of New York & Department of Radiology, Brain Health Imaging Institute, Weill Cornell Medicine, New York, NY
| | - Melissa Lamar
- Rush University Alzheimer’s Center and the Department of Psychiatry and Behavioral Sciences, Rush University Medical Center, Chicago, IL, USA
| | - Roos Jutten
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Sietske A.M. Sikkes
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location, VUmc & Amsterdam Neuroscience, Neurodegeneration & Department of Clinical, Neuro, and Developmental Psychology, Vrije Universitiet Amsterdam, Amsterdam, NL
| | - Evan Grandoit
- Department of Psychology, Northwestern University, Chicago, IL, USA
| | - Laura A. Rabin
- Department of Psychology, Brooklyn College and the Graduate Center, City University of New York, Brooklyn, NY, USA
| | | | - Doug Tommet
- Department of Psychiatry, Brown University, Providence, RI, USA
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Ruan Y, Lv W, Li S, Cheng Y, Wang D, Zhang C, Shimizu K. Identification of telomere-related genes associated with aging-related molecular clusters and the construction of a diagnostic model in Alzheimer's disease based on a bioinformatic analysis. Comput Biol Med 2023; 159:106922. [PMID: 37094463 DOI: 10.1016/j.compbiomed.2023.106922] [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: 12/19/2022] [Revised: 03/07/2023] [Accepted: 04/13/2023] [Indexed: 04/26/2023]
Abstract
BACKGROUND Alzheimer's disease (AD) is a neurodegenerative disease that is strongly associated with aging. Telomeres are DNA sequences that protect chromosomes from damage and shorten with age. Telomere-related genes (TRGs) may play a role in AD's pathogenesis. OBJECTIVES To identify TRGs related to aging clusters in AD patients, explore their immunological characteristics, and build a TRG-based prediction model for AD and AD subtypes. METHODS We analyzed the gene expression profiles of 97 AD samples from the GSE132903 dataset, using aging-related genes (ARGs) as clustering variables. We also assessed immune-cell infiltration in each cluster. We performed a weighted gene co-expression network analysis to identify cluster-specific differentially expressed TRGs. We compared four machine-learning models (random forest, generalized linear model [GLM], gradient boosting model, and support vector machine) for predicting AD and AD subtypes based on TRGs and validated TRGs by conducting an artificial neural network (ANN) analysis and a nomogram model. RESULTS We identified two aging clusters in AD patients with distinct immunological features: Cluster A had higher immune scores than Cluster B. Cluster A and the immune system are intimately associated, and this association could affect immunological function and result in AD via the digestive system. The GLM predicted AD and AD subtypes most accurately and was validated by the ANN analysis and nomogram model. CONCLUSION Our analyses revealed novel TRGs associated with aging clusters in AD patients and their immunological characteristics. We also developed a promising prediction model based on TRGs for assessing AD risk.
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Affiliation(s)
- Yang Ruan
- Laboratory of Systematic Forest and Forest Products Sciences, Graduate School of Bioresource and Bioenvironmental Sciences, Kyushu University, Fukuoka, 819-0395, Japan
| | - Weichao Lv
- Sino-Jan Joint Lab of Natural Health Products Research, School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing, 210009, China
| | - Shuaiyu Li
- Saigo Laboratory, School of Information Science, Kyushu University, Fukuoka, 819-0395, Japan
| | - Yuzhong Cheng
- Joint Graduate School of Mathematics for Innovation, Kyushu University, Fukuoka, 819-0395, Japan
| | - Duanyang Wang
- Laboratory of Systematic Forest and Forest Products Sciences, Graduate School of Bioresource and Bioenvironmental Sciences, Kyushu University, Fukuoka, 819-0395, Japan
| | - Chaofeng Zhang
- Sino-Jan Joint Lab of Natural Health Products Research, School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing, 210009, China
| | - Kuniyoshi Shimizu
- Laboratory of Systematic Forest and Forest Products Sciences, Graduate School of Bioresource and Bioenvironmental Sciences, Kyushu University, Fukuoka, 819-0395, Japan.
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Jang S, Chorna N, Rodríguez-Graciani KM, Inyushin M, Fossati S, Javadov S. The Effects of Amyloid-β on Metabolomic Profiles of Cardiomyocytes and Coronary Endothelial Cells. J Alzheimers Dis 2023; 93:307-319. [PMID: 36970904 DOI: 10.3233/jad-221199] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/14/2023]
Abstract
BACKGROUND An increasing number of experimental and clinical studies show a link between Alzheimer's disease and heart diseases such as heart failure, ischemic heart disease, and atrial fibrillation. However, the mechanisms underlying the potential role of amyloid-β (Aβ) in the pathogenesis of cardiac dysfunction in Alzheimer's disease remain unknown. We have recently shown the effects of Aβ 1 - 40 and Aβ 1 - 42 on cell viability and mitochondrial function in cardiomyocytes and coronary artery endothelial cells. OBJECTIVE In this study, we investigated the effects of Aβ 1 - 40 and Aβ 1 - 42 on the metabolism of cardiomyocytes and coronary artery endothelial cells. METHODS Gas chromatography-mass spectrometry was used to analyze metabolomic profiles of cardiomyocytes and coronary artery endothelial cells treated with Aβ 1 - 40 and Aβ 1 - 42. In addition, we determined mitochondrial respiration and lipid peroxidation in these cells. RESULTS We found that the metabolism of different amino acids was affected by Aβ 1 - 42 in each cell type, whereas the fatty acid metabolism is consistently disrupted in both types of cells. Lipid peroxidation was significantly increased, whereas mitochondrial respiration was reduced in both cell types in response to Aβ 1 - 42. CONCLUSION This study revealed the disruptive effects of Aβ on lipid metabolism and mitochondria function in cardiac cells.
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Affiliation(s)
- Sehwan Jang
- Department of Physiology, University of Puerto Rico School of Medicine, San Juan, PR, USA
| | - Nataliya Chorna
- Department of Biochemistry, University of Puerto Rico School of Medicine, San Juan, PR, USA
| | | | - Mikhail Inyushin
- Department of Physiology, School of Medicine, Universidad Central del Caribe, Bayamon, PR, USA
| | - Silvia Fossati
- Alzheimer's Center at Temple, Department of Neural Sciences, Lewis Katz School of Medicine, Temple University, Philadelphia, PA, USA
| | - Sabzali Javadov
- Department of Physiology, University of Puerto Rico School of Medicine, San Juan, PR, USA
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Hollenbenders Y, Pobiruchin M, Reichenbach A. Two Routes to Alzheimer's Disease Based on Differential Structural Changes in Key Brain Regions. J Alzheimers Dis 2023; 92:1399-1412. [PMID: 36911937 DOI: 10.3233/jad-221061] [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: 03/09/2023]
Abstract
BACKGROUND Alzheimer's disease (AD) is a neurodegenerative disorder with homogenous disease patterns. Neuropathological changes precede symptoms by up to two decades making neuroimaging biomarkers a prime candidate for early diagnosis, prognosis, and patient stratification. OBJECTIVE The goal of the study was to discern intermediate AD stages and their precursors based on neuroanatomical features for stratifying patients on their progression through different stages. METHODS Data include grey matter features from 14 brain regions extracted from longitudinal structural MRI and cognitive data obtained from 1,017 healthy controls and AD patients of ADNI. AD progression was modeled with a Hidden Markov Model, whose hidden states signify disease stages derived from the neuroanatomical data. To tie the progression in brain atrophy to a behavioral marker, we analyzed the ADAS-cog sub-scores in the stages. RESULTS The optimal model consists of eight states with differentiable neuroanatomical features, forming two routes crossing once at a very early point and merging at the final state. The cortical route is characterized by early and sustained atrophy in cortical regions. The limbic route is characterized by early decrease in limbic regions. Cognitive differences between the two routes are most noticeable in the memory domain with subjects from the limbic route experiencing stronger memory impairments. CONCLUSION Our findings corroborate that more than one pattern of grey matter deterioration with several discernable stages can be identified in the progression of AD. These neuroanatomical subtypes are behaviorally meaningful and provide a door into early diagnosis of AD and prognosis of the disease's progression.
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Affiliation(s)
- Yasmin Hollenbenders
- Medical Faculty Heidelberg, Heidelberg University, Germany.,Faculty of Computer Science, Heilbronn University of Applied Sciences, Germany.,Center for Machine Learning, Heilbronn University of Applied Sciences, Germany
| | - Monika Pobiruchin
- Faculty of Computer Science, Heilbronn University of Applied Sciences, Germany.,GECKO Institute for Medicine, Informatics and Economics, Heilbronn University of Applied Sciences, Germany
| | - Alexandra Reichenbach
- Medical Faculty Heidelberg, Heidelberg University, Germany.,Faculty of Computer Science, Heilbronn University of Applied Sciences, Germany.,Center for Machine Learning, Heilbronn University of Applied Sciences, Germany
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Everly J, Plummer J, Lohman M, Neils-Strunjas J. A Tutorial for Speech-Language Pathologists: Physical Activity and Social Engagement to Prevent or Slow Cognitive Decline in Older Adults. AMERICAN JOURNAL OF SPEECH-LANGUAGE PATHOLOGY 2023; 32:83-95. [PMID: 36450149 DOI: 10.1044/2022_ajslp-22-00035] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
PURPOSE This tutorial provides an overview of two behavioral approaches, physical activity and social engagement, to prevent or slow cognitive decline in older adults and to increase awareness in the field of speech-language pathology of the important role that speech-language pathologists (SLPs) play in an interprofessional team working with this population. METHOD A review of exercise science, neuroscience, and social science literature was used to synthesize evidence and to outline the impact of physical activity and social engagement on cognition. The following topics were explored: How do exercise and social engagement support cognition? What are modifiable risk factors of dementia? What is the impact of inactivity and isolation on cognition? What is the potential role of the SLP on an interprofessional team focusing on preventive measures for cognitive decline? What is the impact of physical exercise and social engagement on nursing home residents? RESULTS Research increasingly points to the critical importance of physical activity and social engagement to prevent cognitive decline in normal aging and to slow cognitive decline associated with mild cognitive impairment and dementia. Research suggests that physical activity maintains or improves memory, attention, executive function, visuospatial function, speed of processing, and general cognitive function. Social engagement has been found to maintain and improve general cognitive function. CONCLUSIONS Behavioral interventions are an effective strategy to prevent or slow cognitive decline in the older adult population. SLPs have a role to play on an interprofessional team that works to prevent cognitive decline. By considering factors that play a role in the prevention of cognitive decline, such as physical activity and social engagement, the quality of life and overall health of older adults can be improved. Areas of improvement include memory, attention, executive function, visuospatial function, speed of processing, and general cognitive function.
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Affiliation(s)
- Janet Everly
- Department of Communication Sciences and Disorders, Arnold School of Public Health, University of South Carolina, Columbia
| | - Jamie Plummer
- Department of Communication Sciences and Disorders, Arnold School of Public Health, University of South Carolina, Columbia
| | - Matthew Lohman
- Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia
| | - Jean Neils-Strunjas
- Department of Communication Sciences and Disorders, Arnold School of Public Health, University of South Carolina, Columbia
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Abbate C, Trimarchi PD, Fumagalli GG, Gallucci A, Tomasini E, Fracchia S, Rebecchi I, Morello E, Fontanella A, Parisi PM, Tartarone F, Giunco F, Ciccone S, Nicolini P, Lucchi T, Arosio B, Inglese S, Rossi PD. Diencephalic versus Hippocampal Amnesia in Alzheimer's Disease: The Possible Confabulation-Misidentification Phenotype. J Alzheimers Dis 2023; 91:363-388. [PMID: 36442200 PMCID: PMC9881034 DOI: 10.3233/jad-220919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
BACKGROUND Alzheimer's disease (AD) is clinically heterogeneous, including the classical-amnesic (CA-) phenotype and some variants. OBJECTIVE We aim to describe a further presentation we (re)named confabulation-misidentification (CM-) phenotype. METHODS We performed a retrospective longitudinal case-series study of 17 AD outpatients with the possible CM-phenotype (CM-ADs). Then, in a cross-sectional study, we compared the CM-ADs to a sample of 30 AD patients with the CA-phenotype (CA-ADs). The primary outcome was the frequency of cognitive and behavioral features. Data were analyzed as differences in percentage by non-parametric Chi Square and mean differences by parametric T-test. RESULTS Anterograde amnesia (100%) with early confabulation (88.2%), disorientation (88.2%) and non-infrequently retrograde amnesia (64.7%) associated with reduced insight (88.2%), moderate prefrontal executive impairment (94.1%) and attention deficits (82.3%) dominated the CM-phenotype. Neuropsychiatric features with striking misidentification (52.9%), other less-structured delusions (70.6%), and brief hallucinations (64.7%) were present. Marked behavioral disturbances were present early in some patients and very common at later stages. At the baseline, the CM-ADs showed more confabulation (p < 0.001), temporal disorientation (p < 0.02), misidentification (p = 0.013), other delusions (p = 0.002), and logorrhea (p = 0.004) than the CA-ADs. In addition, more social disinhibition (p = 0.018), reduction of insight (p = 0.029), and hallucination (p = 0.03) persisted at 12 months from baseline. Both the CA- and CM-ADs showed anterior and medial temporal atrophy. Compared to HCs, the CM-ADs showed more right fronto-insular atrophy, while the CA-ADs showed more dorsal parietal, precuneus, and right parietal atrophy. CONCLUSION We described an AD phenotype resembling diencephalic rather than hippocampal amnesia and overlapping the past-century description of presbyophrenia.
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Affiliation(s)
- Carlo Abbate
- Istituto Palazzolo, IRCCS Fondazione Don Carlo Gnocchi Onlus, Milan, Italy,Correspondence to: Carlo Abbate, PhD, Istituto Palazzolo, IRCCS
Fondazione Don Carlo Gnocchi ONLUS, via Don Luigi Palazzolo, 21,
20149 Milan, Italy. Tel.: +39 02 39703657; E-mail:
| | | | - Giorgio G. Fumagalli
- Neurology Unit, IRCCS Fondazione Ca’ Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Alessia Gallucci
- Istituto Palazzolo, IRCCS Fondazione Don Carlo Gnocchi Onlus, Milan, Italy,Ph.D. Program in Neuroscience, School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
| | - Emanuele Tomasini
- Istituto Palazzolo, IRCCS Fondazione Don Carlo Gnocchi Onlus, Milan, Italy
| | - Stefania Fracchia
- Istituto Palazzolo, IRCCS Fondazione Don Carlo Gnocchi Onlus, Milan, Italy
| | - Isabella Rebecchi
- Istituto Palazzolo, IRCCS Fondazione Don Carlo Gnocchi Onlus, Milan, Italy
| | - Elisabetta Morello
- Istituto Palazzolo, IRCCS Fondazione Don Carlo Gnocchi Onlus, Milan, Italy
| | - Anna Fontanella
- Istituto Palazzolo, IRCCS Fondazione Don Carlo Gnocchi Onlus, Milan, Italy
| | - Paola M.R. Parisi
- Istituto Palazzolo, IRCCS Fondazione Don Carlo Gnocchi Onlus, Milan, Italy
| | - Federica Tartarone
- Istituto Palazzolo, IRCCS Fondazione Don Carlo Gnocchi Onlus, Milan, Italy
| | - Fabrizio Giunco
- Istituto Palazzolo, IRCCS Fondazione Don Carlo Gnocchi Onlus, Milan, Italy
| | - Simona Ciccone
- Geriatric Unit, IRCCS Fondazione Ca’ Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Paola Nicolini
- Geriatric Unit, IRCCS Fondazione Ca’ Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Tiziano Lucchi
- Geriatric Unit, IRCCS Fondazione Ca’ Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Beatrice Arosio
- Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy
| | - Silvia Inglese
- Geriatric Unit, IRCCS Fondazione Ca’ Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Paolo D. Rossi
- Geriatric Unit, IRCCS Fondazione Ca’ Granda, Ospedale Maggiore Policlinico, Milan, Italy
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Oatman SR, Reddy JS, Quicksall Z, Carrasquillo MM, Wang X, Liu CC, Yamazaki Y, Nguyen TT, Malphrus K, Heckman M, Biswas K, Nho K, Baker M, Martens YA, Zhao N, Kim JP, Risacher SL, Rademakers R, Saykin AJ, DeTure M, Murray ME, Kanekiyo T, Dickson DW, Bu G, Allen M, Ertekin-Taner N. Genome-wide association study of brain biochemical phenotypes reveals distinct genetic architecture of Alzheimer's disease related proteins. Mol Neurodegener 2023; 18:2. [PMID: 36609403 PMCID: PMC9825010 DOI: 10.1186/s13024-022-00592-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 12/19/2022] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND Alzheimer's disease (AD) is neuropathologically characterized by amyloid-beta (Aβ) plaques and neurofibrillary tangles. The main protein components of these hallmarks include Aβ40, Aβ42, tau, phosphor-tau, and APOE. We hypothesize that genetic variants influence the levels and solubility of these AD-related proteins in the brain; identifying these may provide key insights into disease pathogenesis. METHODS Genome-wide genotypes were collected from 441 AD cases, imputed to the haplotype reference consortium (HRC) panel, and filtered for quality and frequency. Temporal cortex levels of five AD-related proteins from three fractions, buffer-soluble (TBS), detergent-soluble (Triton-X = TX), and insoluble (Formic acid = FA), were available for these same individuals. Variants were tested for association with each quantitative biochemical measure using linear regression, and GSA-SNP2 was used to identify enriched Gene Ontology (GO) terms. Implicated variants and genes were further assessed for association with other relevant variables. RESULTS We identified genome-wide significant associations at seven novel loci and the APOE locus. Genes and variants at these loci also associate with multiple AD-related measures, regulate gene expression, have cell-type specific enrichment, and roles in brain health and other neuropsychiatric diseases. Pathway analysis identified significant enrichment of shared and distinct biological pathways. CONCLUSIONS Although all biochemical measures tested reflect proteins core to AD pathology, our results strongly suggest that each have unique genetic architecture and biological pathways that influence their specific biochemical states in the brain. Our novel approach of deep brain biochemical endophenotype GWAS has implications for pathophysiology of proteostasis in AD that can guide therapeutic discovery efforts focused on these proteins.
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Affiliation(s)
- Stephanie R. Oatman
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Joseph S. Reddy
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL USA
| | - Zachary Quicksall
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL USA
| | | | - Xue Wang
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL USA
| | - Chia-Chen Liu
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Yu Yamazaki
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Thuy T. Nguyen
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Kimberly Malphrus
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Michael Heckman
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL USA
| | - Kristi Biswas
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Kwangsik Nho
- Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN USA
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN USA
- School of Informatics and Computing, Indiana University School of Medicine, Indianapolis, IN USA
| | - Matthew Baker
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Yuka A. Martens
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Na Zhao
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Jun Pyo Kim
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN USA
| | - Shannon L. Risacher
- Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN USA
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN USA
| | - Rosa Rademakers
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
- VIB-UA Center for Molecular Neurology, VIB, University of Antwerp, Antwerp, Belgium
| | - Andrew J. Saykin
- Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN USA
| | - Michael DeTure
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Melissa E. Murray
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Takahisa Kanekiyo
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - for the Alzheimer’s Disease Neuroimaging Initiative
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL USA
- Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN USA
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN USA
- School of Informatics and Computing, Indiana University School of Medicine, Indianapolis, IN USA
- VIB-UA Center for Molecular Neurology, VIB, University of Antwerp, Antwerp, Belgium
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN USA
- Department of Neurology, Mayo Clinic, 4500 San Pablo Road, Birdsall 3, Jacksonville, FL 32224 USA
| | - Dennis W. Dickson
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Guojun Bu
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Mariet Allen
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Nilüfer Ertekin-Taner
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
- Department of Neurology, Mayo Clinic, 4500 San Pablo Road, Birdsall 3, Jacksonville, FL 32224 USA
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Zhang J, Li X, Xiao J, Xiang Y, Ye F. Analysis of gene expression profiles in Alzheimer's disease patients with different lifespan: A bioinformatics study focusing on the disease heterogeneity. Front Aging Neurosci 2023; 15:1072184. [PMID: 36909942 PMCID: PMC9995587 DOI: 10.3389/fnagi.2023.1072184] [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: 10/17/2022] [Accepted: 01/30/2023] [Indexed: 02/25/2023] Open
Abstract
Objective Alzheimer's disease (AD) as the most frequent neurodegenerative disease is featured by gradual decline of cognition and social function in the elderly. However, there have been few studies focusing on AD heterogeneity which exists both genetically and clinically, leading to the difficulties of AD researches. As one major kind of clinical heterogeneity, the lifespan of AD patients varies significantly. Aiming to investigate the potential driving factors, the current research identified the differentially expressed genes (DEGs) between longer-lived AD patients and shorter-lived ones via bioinformatics analyses. Methods Qualified datasets of gene expression profiles were identified in National Center of Biotechnology Information Gene Expression Omnibus (NCBI-GEO). The data of the temporal lobes of patients above 60 years old were used. Two groups were divided according to the lifespan: the group ≥85 years old and the group <85 years old. Then GEO2R online software and R package of Robust Rank Aggregation (RRA) were used to screen DEGs. Bioinformatic tools were adopted to identify possible pathways and construct protein-protein interaction network. Result Sixty-seven AD cases from four qualified datasets (GSE28146, GSE5281, GSE48350, and GSE36980) were included in this study. 740 DEGs were identified with 361 upregulated and 379 downregulated when compared longer-lived AD patients with shorter-lived ones. These DEGs were primarily involved in the pathways directly or indirectly associated with the regulation of neuroinflammation and cancer pathogenesis, as shown by pathway enrichment analysis. Among the DEGs, the top 15 hub genes were identified from the PPI network. Notably, the same bioinformatic procedures were conducted in 62 non-AD individuals (serving as controls of AD patients in the four included studies) with distinctly different findings from AD patients, indicating different regulatory mechanisms of lifespan between non-AD controls and AD, reconfirming the necessity of the present study. Conclusion These results shed some lights on lifespan-related regulatory mechanisms in AD patients, which also indicated that AD heterogeneity should be more taken into account in future investigations.
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Affiliation(s)
- Ji Zhang
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, China
| | - Xiaojia Li
- Department of Neurology, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, China.,Department of Neurology, Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Chengdu, China
| | - Jun Xiao
- Department of Neurology, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, China.,Department of Neurology, Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Chengdu, China
| | - Yang Xiang
- Department of Neurology, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, China.,Department of Neurology, Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Chengdu, China
| | - Fang Ye
- Department of Neurology, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, China.,Department of Neurology, Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Chengdu, China
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