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Estarellas M, Oxtoby NP, Schott JM, Alexander DC, Young AL. Multimodal subtypes identified in Alzheimer's Disease Neuroimaging Initiative participants by missing-data-enabled subtype and stage inference. Brain Commun 2024; 6:fcae219. [PMID: 39035417 PMCID: PMC11259979 DOI: 10.1093/braincomms/fcae219] [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: 05/02/2023] [Revised: 03/14/2024] [Accepted: 06/22/2024] [Indexed: 07/23/2024] Open
Abstract
Alzheimer's disease is a highly heterogeneous disease in which different biomarkers are dynamic over different windows of the decades-long pathophysiological processes, and potentially have distinct involvement in different subgroups. Subtype and Stage Inference is an unsupervised learning algorithm that disentangles the phenotypic heterogeneity and temporal progression of disease biomarkers, providing disease insight and quantitative estimates of individual subtype and stage. However, a key limitation of Subtype and Stage Inference is that it requires a complete set of biomarkers for each subject, reducing the number of datapoints available for model fitting and limiting applications of Subtype and Stage Inference to modalities that are widely collected, e.g. volumetric biomarkers derived from structural MRI. In this study, we adapted the Subtype and Stage Inference algorithm to handle missing data, enabling the application of Subtype and Stage Inference to multimodal data (magnetic resonance imaging, positron emission tomography, cerebrospinal fluid and cognitive tests) from 789 participants in the Alzheimer's Disease Neuroimaging Initiative. Missing-data Subtype and Stage Inference identified five subtypes having distinct progression patterns, which we describe by the earliest unique abnormality as 'Typical AD with Early Tau', 'Typical AD with Late Tau', 'Cortical', 'Cognitive' and 'Subcortical'. These new multimodal subtypes were differentially associated with age, years of education, Apolipoprotein E (APOE4) status, white matter hyperintensity burden and the rate of conversion from mild cognitive impairment to Alzheimer's disease, with the 'Cognitive' subtype showing the fastest clinical progression, and the 'Subcortical' subtype the slowest. Overall, we demonstrate that missing-data Subtype and Stage Inference reveals a finer landscape of Alzheimer's disease subtypes, each of which are associated with different risk factors. Missing-data Subtype and Stage Inference has broad utility, enabling the prediction of progression in a much wider set of individuals, rather than being restricted to those with complete data.
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Affiliation(s)
- Mar Estarellas
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
- School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK
| | - Neil P Oxtoby
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Jonathan M Schott
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Alexandra L Young
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
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Koychev I, Marinov E, Young S, Lazarova S, Grigorova D, Palejev D. Identification of preclinical dementia according to ATN classification for stratified trial recruitment: A machine learning approach. PLoS One 2023; 18:e0288039. [PMID: 37856502 PMCID: PMC10586674 DOI: 10.1371/journal.pone.0288039] [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: 11/11/2022] [Accepted: 06/19/2023] [Indexed: 10/21/2023] Open
Abstract
INTRODUCTION The Amyloid/Tau/Neurodegeneration (ATN) framework was proposed to identify the preclinical biological state of Alzheimer's disease (AD). We investigated whether ATN phenotype can be predicted using routinely collected research cohort data. METHODS 927 EPAD LCS cohort participants free of dementia or Mild Cognitive Impairment were separated into 5 ATN categories. We used machine learning (ML) methods to identify a set of significant features separating each neurodegeneration-related group from controls (A-T-(N)-). Random Forest and linear-kernel SVM with stratified 5-fold cross validations were used to optimize model whose performance was then tested in the ADNI database. RESULTS Our optimal results outperformed ATN cross-validated logistic regression models by between 2.2% and 8.3%. The optimal feature sets were not consistent across the 4 models with the AD pathologic change vs controls set differing the most from the rest. Because of that we have identified a subset of 10 features that yield results very close or identical to the optimal. DISCUSSION Our study demonstrates the gains offered by ML in generating ATN risk prediction over logistic regression models among pre-dementia individuals.
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Affiliation(s)
- Ivan Koychev
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Evgeniy Marinov
- Big Data for Smart Society (GATE) Institute, Sofia University, Sofia, Bulgaria
| | - Simon Young
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Sophia Lazarova
- Big Data for Smart Society (GATE) Institute, Sofia University, Sofia, Bulgaria
| | - Denitsa Grigorova
- Big Data for Smart Society (GATE) Institute, Sofia University, Sofia, Bulgaria
- Faculty of Mathematics and Informatics, Sofia University, Sofia, Bulgaria
| | - Dean Palejev
- Big Data for Smart Society (GATE) Institute, Sofia University, Sofia, Bulgaria
- Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, Sofia, Bulgaria
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Sirkis DW, Bonham LW, Johnson TP, La Joie R, Yokoyama JS. Dissecting the clinical heterogeneity of early-onset Alzheimer's disease. Mol Psychiatry 2022; 27:2674-2688. [PMID: 35393555 PMCID: PMC9156414 DOI: 10.1038/s41380-022-01531-9] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 03/07/2022] [Accepted: 03/16/2022] [Indexed: 12/14/2022]
Abstract
Early-onset Alzheimer's disease (EOAD) is a rare but particularly devastating form of AD. Though notable for its high degree of clinical heterogeneity, EOAD is defined by the same neuropathological hallmarks underlying the more common, late-onset form of AD. In this review, we describe the various clinical syndromes associated with EOAD, including the typical amnestic phenotype as well as atypical variants affecting visuospatial, language, executive, behavioral, and motor functions. We go on to highlight advances in fluid biomarker research and describe how molecular, structural, and functional neuroimaging can be used not only to improve EOAD diagnostic acumen but also enhance our understanding of fundamental pathobiological changes occurring years (and even decades) before the onset of symptoms. In addition, we discuss genetic variation underlying EOAD, including pathogenic variants responsible for the well-known mendelian forms of EOAD as well as variants that may increase risk for the much more common forms of EOAD that are either considered to be sporadic or lack a clear autosomal-dominant inheritance pattern. Intriguingly, specific pathogenic variants in PRNP and MAPT-genes which are more commonly associated with other neurodegenerative diseases-may provide unexpectedly important insights into the formation of AD tau pathology. Genetic analysis of the atypical clinical syndromes associated with EOAD will continue to be challenging given their rarity, but integration of fluid biomarker data, multimodal imaging, and various 'omics techniques and their application to the study of large, multicenter cohorts will enable future discoveries of fundamental mechanisms underlying the development of EOAD and its varied clinical presentations.
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Affiliation(s)
- Daniel W Sirkis
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, 94158, USA
| | - Luke W Bonham
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, 94158, USA
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, 94158, USA
| | - Taylor P Johnson
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, 94158, USA
| | - Renaud La Joie
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, 94158, USA
| | - Jennifer S Yokoyama
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, 94158, USA.
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, 94158, USA.
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Golriz Khatami S, Salimi Y, Hofmann-Apitius M, Oxtoby NP, Birkenbihl C. Comparison and aggregation of event sequences across ten cohorts to describe the consensus biomarker evolution in Alzheimer's disease. Alzheimers Res Ther 2022; 14:55. [PMID: 35443691 PMCID: PMC9020023 DOI: 10.1186/s13195-022-01001-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 04/06/2022] [Indexed: 11/10/2022]
Abstract
BACKGROUND Previous models of Alzheimer's disease (AD) progression were primarily hypothetical or based on data originating from single cohort studies. However, cohort datasets are subject to specific inclusion and exclusion criteria that influence the signals observed in their collected data. Furthermore, each study measures only a subset of AD-relevant variables. To gain a comprehensive understanding of AD progression, the heterogeneity and robustness of estimated progression patterns must be understood, and complementary information contained in cohort datasets be leveraged. METHODS We compared ten event-based models that we fit to ten independent AD cohort datasets. Additionally, we designed and applied a novel rank aggregation algorithm that combines partially overlapping, individual event sequences into a meta-sequence containing the complementary information from each cohort. RESULTS We observed overall consistency across the ten event-based model sequences (average pairwise Kendall's tau correlation coefficient of 0.69 ± 0.28), despite variance in the positioning of mainly imaging variables. The changes described in the aggregated meta-sequence are broadly consistent with the current understanding of AD progression, starting with cerebrospinal fluid amyloid beta, followed by tauopathy, memory impairment, FDG-PET, and ultimately brain deterioration and impairment of visual memory. CONCLUSION Overall, the event-based models demonstrated similar and robust disease cascades across independent AD cohorts. Aggregation of data-driven results can combine complementary strengths and information of patient-level datasets. Accordingly, the derived meta-sequence draws a more complete picture of AD pathology compared to models relying on single cohorts.
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Affiliation(s)
- Sepehr Golriz Khatami
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757, Sankt Augustin, Germany.
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53115, Bonn, Germany.
| | - Yasamin Salimi
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757, Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53115, Bonn, Germany
| | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757, Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53115, Bonn, Germany
| | - Neil P Oxtoby
- Centre for Medical Image Computing and Department of Computer Science, University College London, Gower St, London, WC1E 6BT, UK
| | - Colin Birkenbihl
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757, Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53115, Bonn, Germany
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Mao C, Sha L, Li J, Huang X, Chu S, Lei D, Wang J, Dong L, Liu C, Xu Q, Peng B, Gao J. Relationship Between General Cognition, Visual Assessed Cortical Atrophy, and Cerebrospinal Fluid Biomarkers in Alzheimer's Disease: A Cross-Sectional Study from a Chinese PUMCH Cohort. J Alzheimers Dis 2021; 82:205-214. [PMID: 34024840 DOI: 10.3233/jad-210344] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
BACKGROUND Cerebrospinal fluid (CSF) biomarkers are widely accepted as manifestations of Alzheimer's disease (AD) pathogenesis and incorporated into biological definition of AD. However, the correlations between CSF and other biomarkers such as neuroimaging and neuropsychiatric evaluation are complicated and inconsistent. OBJECTIVE We aimed to better interpreting CSF biomarkers results accompanying with other indexes in improving accurate diagnosis of AD. METHODS 112 AD patients and 30 cognitive normal controls were selected. Commercial accessible ELISA kits were introduced for measurement of CSF t-tau, p-tau181, Aβ1-42, and NfL based on standard protocol. MRI examinations were performed using a 3-T MRI scanner and visual rating scales including medial temporal atrophy score and Koedam's scale were used to evaluate medial temporal atrophy and posterior region atrophy. RESULTS CSF biomarkers' profile including decreased concentration of Aβ1-42, increased concentration of t-tau, p-tau181, t-tau/Aβ 1-42, and NfL were diagnostic between AD and control. CSF biomarkers profile was not influenced by the APOE genotype. Increased concentration of t-tau and NfL, as well as ratio of t-tau/Aβ 1-42 were related to decrease of Mini-Mental State Examination (MMSE) score while concentration of Aβ1-42 not. Visual assessed cortical atrophy was related to MMSE score, but most of the CSF biomarkers were not related to atrophy, except that increased concentration of p-tau181 was significantly associated with atrophy of posterior cortical region. CONCLUSION Our results supported CSF biomarkers were helpful in diagnosis of AD. However, CSF biomarkers were cross-sectional reflection of pathogenesis, which did not correlate well with clinical progression. CSF biomarkers should be interpreted in combination with MRI and cognitive evaluation in clinical use.
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Affiliation(s)
- Chenhui Mao
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing, China
| | - Longze Sha
- State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China
| | - Jie Li
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing, China
| | - Xinying Huang
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing, China
| | - Shanshan Chu
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing, China
| | - Dan Lei
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing, China
| | - Jie Wang
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing, China
| | - Liling Dong
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing, China
| | - Caiyan Liu
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing, China
| | - Qi Xu
- State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China
| | - Bin Peng
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing, China
| | - Jing Gao
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing, China
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Qing Z, Chen F, Lu J, Lv P, Li W, Liang X, Wang M, Wang Z, Zhang X, Zhang B. Causal structural covariance network revealing atrophy progression in Alzheimer's disease continuum. Hum Brain Mapp 2021; 42:3950-3962. [PMID: 33978292 PMCID: PMC8288084 DOI: 10.1002/hbm.25531] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Revised: 04/10/2021] [Accepted: 04/26/2021] [Indexed: 01/24/2023] Open
Abstract
The structural covariance network (SCN) has provided a perspective on the large‐scale brain organization impairment in the Alzheimer's Disease (AD) continuum. However, the successive structural impairment across brain regions, which may underlie the disrupted SCN in the AD continuum, is not well understood. In the current study, we enrolled 446 subjects with AD, mild cognitive impairment (MCI) or normal aging (NA) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The SCN as well as a casual SCN (CaSCN) based on Granger causality analysis were applied to the T1‐weighted structural magnetic resonance images of the subjects. Compared with that of the NAs, the SCN was disrupted in the MCI and AD subjects, with the hippocampus and left middle temporal lobe being the most impaired nodes, which is in line with previous studies. In contrast, according to the 194 subjects with records on CSF amyloid and Tau, the CaSCN revealed that during AD progression, the CaSCN was enhanced. Specifically, the hippocampus, thalamus, and precuneus/posterior cingulate cortex (PCC) were identified as the core regions in which atrophy originated and could predict atrophy in other brain regions. Taken together, these findings provide a comprehensive view of brain atrophy in the AD continuum and the relationships among the brain atrophy in different regions, which may provide novel insight into the progression of AD.
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Affiliation(s)
- Zhao Qing
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China.,Institute of Brain Science, Nanjing University, Nanjing, China
| | - Feng Chen
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Jiaming Lu
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Pin Lv
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Weiping Li
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Xue Liang
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Maoxue Wang
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Zhengge Wang
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Xin Zhang
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Bing Zhang
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China.,Institute of Brain Science, Nanjing University, Nanjing, China
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Chen X, Li Z, Cheng Y, Kardami E, Loh YP. Low and High Molecular Weight FGF-2 Have Differential Effects on Astrocyte Proliferation, but Are Both Protective Against Aβ-Induced Cytotoxicity. Front Mol Neurosci 2020; 12:328. [PMID: 32038161 PMCID: PMC6992557 DOI: 10.3389/fnmol.2019.00328] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2019] [Accepted: 12/23/2019] [Indexed: 12/17/2022] Open
Abstract
Astrocytes are the most abundant type of glial cells in the brain, and they play a key role in Alzheimer’s disease (AD). Fibroblast Growth Factor-2 (FGF-2) has been implicated as a potential therapeutic agent for treating AD. In the present study, we investigated the protective effects of low molecular weight (LMW; 17 KDa) and high molecular weight (HMW; 23 KDa) forms of FGF-2 on Aβ1–42-induced toxicity, and proliferation in astrocytes. We show that both isoforms of FGF-2 have similar protective effects against Aβ1–42-induced cytotoxicity in primary cultured cortical astrocytes as measured by Lactate Dehydrogenase (LDH) release assay. Additionally, 17 KDa FGF-2 significantly promoted astrocyte proliferation as measured by Trypan Blue, DRAQ5 and 5-ethynyl-2’-deoxyuridine (EdU) staining, but not 23 kDa FGF-2. Furthermore, our results demonstrated that AKT signaling pathway was required for the protective and proliferative effects of FGF-2. Downstream effector studies indicated that 17 kDa FGF-2 promoted astrocyte proliferation by enhanced expression of c-Myc, Cyclin D1, Cyclin E. Furthermore, our data suggested that Cyclin D1 was required for the proliferative effect of LMW FGF2 in astrocytes. Taken together, our findings provide important information for the similarities and differences between 23 kDa and17 kDa isoforms of FGF-2 on astrocyte survival and proliferation.
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Affiliation(s)
- Xi Chen
- Key Laboratory of Ethnomedicine for Ministry of Education, Center on Translational Neuroscience, College of Life and Environmental Sciences, Minzu University of China, Beijing, China
| | - Zhaojin Li
- Section on Cellular Neurobiology, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, United States
| | - Yong Cheng
- Key Laboratory of Ethnomedicine for Ministry of Education, Center on Translational Neuroscience, College of Life and Environmental Sciences, Minzu University of China, Beijing, China.,Section on Cellular Neurobiology, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, United States
| | - Elissavet Kardami
- Institute of Cardiovascular Sciences, St. Boniface Hospital Albrechtsen Research Centre, University of Manitoba, Winnipeg, MB, Canada
| | - Y Peng Loh
- Section on Cellular Neurobiology, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, United States
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Lipid peroxidation biomarkers correlation with medial temporal atrophy in early Alzheimer Disease. Neurochem Int 2019; 129:104519. [DOI: 10.1016/j.neuint.2019.104519] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Revised: 07/10/2019] [Accepted: 08/05/2019] [Indexed: 12/11/2022]
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Mendez MF, Moheb N, Desarzant RE, Teng EH. The Progressive Acalculia Presentation of Parietal Variant Alzheimer's Disease. J Alzheimers Dis 2019; 63:941-948. [PMID: 29710718 DOI: 10.3233/jad-180024] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
BACKGROUND Many patients with early-onset Alzheimer's disease (EOAD; age of onset <65 years) have non-amnestic presentations involving language (logopenic primary progressive aphasia, lvPPA), visuospatial abilities (posterior cortical atrophy, PCA), and even asymmetric symptoms consistent with corticobasal syndrome (CBS). An inferior parietal lobule variant of EOAD commonly presents with progressive difficulty with calculations. METHODS We reviewed 276 EOAD patients for presentations with predominant acalculia. These patients were diagnosed with clinically probable Alzheimer's disease (AD) verified by positron emission tomography (PET) or cerebrospinal fluid amyloid-β or tau biomarkers. RESULTS We identified 18 (9M/9F) (6.5%) EOAD patients with progressive acalculia that did not meet most criteria for lvPPA, visual PCA, or CBS. Their ages of onset and presentation were 56.6 (5.0) and 59.4 (6.5), respectively. Their acalculia was consistent with a primary acalculia ("anarithmetia") not explained by language or visuospatial impairments. Many also had anomia (14/18), ideomotor apraxia (13/18), and the complete Gerstmann's syndrome (7/18). Visual analysis of their diverse magnetic resonance imaging disclosed biparietal atrophy, disproportionately worse on the left. CONCLUSIONS Primary acalculia may be the most common manifestation of an inferior parietal presentation of EOAD affecting the left intraparietal sulcus. This parietal variant also commonly involves progressive anomia, ideomotor apraxia, and other elements of Gerstmann's syndrome. The early recognition of patients with this variant, which is distinguishable from lvPPA, visual PCA, or CBS, would be facilitated by its recognition as a unique subtype of EOAD.
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Affiliation(s)
- Mario F Mendez
- Department of Neurology, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA.,Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA.,Neurobehavior Unit, V.A. Greater Los Angeles Healthcare System, Los Angeles, CA, USA
| | - Negar Moheb
- Department of Neurology, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA
| | - Randy E Desarzant
- Department of Neurology, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA.,Neurobehavior Unit, V.A. Greater Los Angeles Healthcare System, Los Angeles, CA, USA
| | - Edmond H Teng
- Department of Neurology, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA.,Neurobehavior Unit, V.A. Greater Los Angeles Healthcare System, Los Angeles, CA, USA
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