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Biesbroek JM, Coenen M, DeCarli C, Fletcher EM, Maillard PM, Barkhof F, Barnes J, Benke T, Chen CPLH, Dal‐Bianco P, Dewenter A, Duering M, Enzinger C, Ewers M, Exalto LG, Franzmeier N, Hilal S, Hofer E, Koek HL, Maier AB, McCreary CR, Papma JM, Paterson RW, Pijnenburg YAL, Rubinski A, Schmidt R, Schott JM, Slattery CF, Smith EE, Sudre CH, Steketee RME, Teunissen CE, van den Berg E, van der Flier WM, Venketasubramanian N, Venkatraghavan V, Vernooij MW, Wolters FJ, Xin X, Kuijf HJ, Biessels GJ. Amyloid pathology and vascular risk are associated with distinct patterns of cerebral white matter hyperintensities: A multicenter study in 3132 memory clinic patients. Alzheimers Dement 2024; 20:2980-2989. [PMID: 38477469 PMCID: PMC11032573 DOI: 10.1002/alz.13765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 01/30/2024] [Accepted: 01/31/2024] [Indexed: 03/14/2024]
Abstract
INTRODUCTION White matter hyperintensities (WMH) are associated with key dementia etiologies, in particular arteriolosclerosis and amyloid pathology. We aimed to identify WMH locations associated with vascular risk or cerebral amyloid-β1-42 (Aβ42)-positive status. METHODS Individual patient data (n = 3,132; mean age 71.5 ± 9 years; 49.3% female) from 11 memory clinic cohorts were harmonized. WMH volumes in 28 regions were related to a vascular risk compound score (VRCS) and Aß42 status (based on cerebrospinal fluid or amyloid positron emission tomography), correcting for age, sex, study site, and total WMH volume. RESULTS VRCS was associated with WMH in anterior/superior corona radiata (B = 0.034/0.038, p < 0.001), external capsule (B = 0.052, p < 0.001), and middle cerebellar peduncle (B = 0.067, p < 0.001), and Aß42-positive status with WMH in posterior thalamic radiation (B = 0.097, p < 0.001) and splenium (B = 0.103, p < 0.001). DISCUSSION Vascular risk factors and Aß42 pathology have distinct signature WMH patterns. This regional vulnerability may incite future studies into how arteriolosclerosis and Aß42 pathology affect the brain's white matter. HIGHLIGHTS Key dementia etiologies may be associated with specific patterns of white matter hyperintensities (WMH). We related WMH locations to vascular risk and cerebral Aβ42 status in 11 memory clinic cohorts. Aβ42 positive status was associated with posterior WMH in splenium and posterior thalamic radiation. Vascular risk was associated with anterior and infratentorial WMH. Amyloid pathology and vascular risk have distinct signature WMH patterns.
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Lyu X, Duong MT, Xie L, de Flores R, Richardson H, Hwang G, Wisse LEM, DiCalogero M, McMillan CT, Robinson JL, Xie SX, Lee EB, Irwin DJ, Dickerson BC, Davatzikos C, Nasrallah IM, Yushkevich PA, Wolk DA, Das SR. Tau-neurodegeneration mismatch reveals vulnerability and resilience to comorbidities in Alzheimer's continuum. Alzheimers Dement 2024; 20:1586-1600. [PMID: 38050662 PMCID: PMC10984442 DOI: 10.1002/alz.13559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 10/23/2023] [Accepted: 10/24/2023] [Indexed: 12/06/2023]
Abstract
INTRODUCTION Variability in relationship of tau-based neurofibrillary tangles (T) and neurodegeneration (N) in Alzheimer's disease (AD) arises from non-specific nature of N, modulated by non-AD co-pathologies, age-related changes, and resilience factors. METHODS We used regional T-N residual patterns to partition 184 patients within the Alzheimer's continuum into data-driven groups. These were compared with groups from 159 non-AD (amyloid "negative") patients partitioned using cortical thickness, and groups in 98 patients with ante mortem MRI and post mortem tissue for measuring N and T, respectively. We applied the initial T-N residual model to classify 71 patients in an independent cohort into predefined groups. RESULTS AD groups displayed spatial T-N mismatch patterns resembling neurodegeneration patterns in non-AD groups, similarly associated with non-AD factors and diverging cognitive outcomes. In the autopsy cohort, limbic T-N mismatch correlated with TDP-43 co-pathology. DISCUSSION T-N mismatch may provide a personalized approach for determining non-AD factors associated with resilience/vulnerability in AD.
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Affiliation(s)
- Xueying Lyu
- Departments of BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Michael Tran Duong
- Departments of BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Long Xie
- Departments of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | | | - Hayley Richardson
- Department of Biostatistics, Epidemiology and InformaticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Gyujoon Hwang
- Departments of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | | | - Michael DiCalogero
- Departments of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Corey T. McMillan
- Departments of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - John L. Robinson
- Departments of Pathology and Laboratory MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Sharon X. Xie
- Department of Biostatistics, Epidemiology and InformaticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Edward B. Lee
- Departments of Pathology and Laboratory MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - David J. Irwin
- Departments of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | | | - Christos Davatzikos
- Departments of BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Ilya M. Nasrallah
- Departments of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Paul A. Yushkevich
- Departments of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - David A. Wolk
- Departments of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Sandhitsu R. Das
- Departments of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
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Devanarayan V, Ye Y, Charil A, Andreozzi E, Sachdev P, Llano DA, Tian L, Zhu L, Hampel H, Kramer L, Dhadda S, Irizarry M. Predicting clinical progression trajectories of early Alzheimer's disease patients. Alzheimers Dement 2024; 20:1725-1738. [PMID: 38087949 PMCID: PMC10984448 DOI: 10.1002/alz.13565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 09/06/2023] [Accepted: 11/07/2023] [Indexed: 03/16/2024]
Abstract
BACKGROUND Models for forecasting individual clinical progression trajectories in early Alzheimer's disease (AD) are needed for optimizing clinical studies and patient monitoring. METHODS Prediction models were constructed using a clinical trial training cohort (TC; n = 934) via a gradient boosting algorithm and then evaluated in two validation cohorts (VC 1, n = 235; VC 2, n = 421). Model inputs included baseline clinical features (cognitive function assessments, APOE ε4 status, and demographics) and brain magnetic resonance imaging (MRI) measures. RESULTS The model using clinical features achieved R2 of 0.21 and 0.31 for predicting 2-year cognitive decline in VC 1 and VC 2, respectively. Adding MRI features improved the R2 to 0.29 in VC 1, which employed the same preprocessing pipeline as the TC. Utilizing these model-based predictions for clinical trial enrichment reduced the required sample size by 20% to 49%. DISCUSSION Our validated prediction models enable baseline prediction of clinical progression trajectories in early AD, benefiting clinical trial enrichment and various applications.
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Affiliation(s)
- Viswanath Devanarayan
- Clinical Evidence GenerationEisai Inc.NutleyNew JerseyUSA
- Department of MathematicsStatistics and Computer ScienceUniversity of Illinois ChicagoChicagoIllinoisUSA
| | - Yuanqing Ye
- Clinical Evidence GenerationEisai Inc.NutleyNew JerseyUSA
| | - Arnaud Charil
- Clinical Evidence GenerationEisai Inc.NutleyNew JerseyUSA
| | | | | | - Daniel A. Llano
- Carle Illinois College of MedicineUrbanaIllinoisUSA
- Department of Molecular and Integrative PhysiologyUniversity of Illinois Urbana‐ChampaignUrbanaIllinoisUSA
| | - Lu Tian
- Department of Biomedical Data ScienceStanford University School of MedicinePalo AltoCaliforniaUSA
| | - Liang Zhu
- Clinical Evidence GenerationEisai Inc.NutleyNew JerseyUSA
| | - Harald Hampel
- Clinical Evidence GenerationEisai Inc.NutleyNew JerseyUSA
| | - Lynn Kramer
- Clinical Evidence GenerationEisai Inc.NutleyNew JerseyUSA
| | - Shobha Dhadda
- Clinical Evidence GenerationEisai Inc.NutleyNew JerseyUSA
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Tosun D, Yardibi O, Benzinger TLS, Kukull WA, Masters CL, Perrin RJ, Weiner MW, Simen A, Schwarz AJ. Identifying individuals with non-Alzheimer's disease co-pathologies: A precision medicine approach to clinical trials in sporadic Alzheimer's disease. Alzheimers Dement 2024; 20:421-436. [PMID: 37667412 PMCID: PMC10843695 DOI: 10.1002/alz.13447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 07/14/2023] [Accepted: 08/04/2023] [Indexed: 09/06/2023]
Abstract
INTRODUCTION Biomarkers remain mostly unavailable for non-Alzheimer's disease neuropathological changes (non-ADNC) such as transactive response DNA-binding protein 43 (TDP-43) proteinopathy, Lewy body disease (LBD), and cerebral amyloid angiopathy (CAA). METHODS A multilabel non-ADNC classifier using magnetic resonance imaging (MRI) signatures was developed for TDP-43, LBD, and CAA in an autopsy-confirmed cohort (N = 214). RESULTS A model using demographic, genetic, clinical, MRI, and ADNC variables (amyloid positive [Aβ+] and tau+) in autopsy-confirmed participants showed accuracies of 84% for TDP-43, 81% for LBD, and 81% to 93% for CAA, outperforming reference models without MRI and ADNC biomarkers. In an ADNI cohort (296 cognitively unimpaired, 401 mild cognitive impairment, 188 dementia), Aβ and tau explained 33% to 43% of variance in cognitive decline; imputed non-ADNC explained an additional 16% to 26%. Accounting for non-ADNC decreased the required sample size to detect a 30% effect on cognitive decline by up to 28%. DISCUSSION Our results lead to a better understanding of the factors that influence cognitive decline and may lead to improvements in AD clinical trial design.
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Affiliation(s)
- Duygu Tosun
- Department of Radiology and Biomedical ImagingUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Ozlem Yardibi
- Takeda Pharmaceutical Company LtdCambridgeMassachusettsUSA
| | | | - Walter A. Kukull
- Department of EpidemiologyNational Alzheimer's Coordinating CenterUniversity of WashingtonSeattleWashingtonUSA
| | - Colin L. Masters
- The Florey Institute of Neuroscience and Mental HealthThe University of MelbourneParkvilleVictoriaAustralia
| | - Richard J. Perrin
- Department of Pathology & ImmunologyWashington University in St. LouisSt. LouisMissouriUSA
- Department of NeurologyWashington University in St. LouisSt. LouisMissouriUSA
| | - Michael W. Weiner
- Department of Radiology and Biomedical ImagingUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Arthur Simen
- Takeda Pharmaceutical Company LtdCambridgeMassachusettsUSA
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Albala B, Appelmans E, Burress R, De Santi S, Devins T, Klein G, Logovinsky V, Novak GP, Ribeiro K, Schmidt ME, Schwarz AJ, Scott D, Shcherbinin S, Siemers E, Travaglia A, Weber CJ, White L, Wolf‐Rodda J, Vasanthakumar A. The Alzheimer's Disease Neuroimaging Initiative and the role and contributions of the Private Partners Scientific Board (PPSB). Alzheimers Dement 2024; 20:695-708. [PMID: 37774088 PMCID: PMC10843521 DOI: 10.1002/alz.13483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 08/24/2023] [Accepted: 08/27/2023] [Indexed: 10/01/2023]
Abstract
The Alzheimer's Disease Neuroimaging Initiative (ADNI) Private Partners Scientific Board (PPSB) encompasses members from industry, biotechnology, diagnostic, and non-profit organizations that have until recently been managed by the Foundation for the National Institutes of Health (FNIH) and provided financial and scientific support to ADNI programs. In this article, we review some of the major activities undertaken by the PPSB, focusing on those supporting the most recently completed National Institute on Aging grant, ADNI3, and the impact it has had on streamlining biomarker discovery and validation in Alzheimer's disease. We also provide a perspective on the gaps that may be filled with future PPSB activities as part of ADNI4 and beyond. HIGHLIGHTS: The Private Partners Scientific board (PPSB) continues to play a key role in enabling several Alzheimer's Disease Neuroimaging Initiative (ADNI) activities. PPSB working groups have led landscape assessments to provide valuable feedback on new technologies, platforms, and methods that may be taken up by ADNI in current or future iterations.
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Affiliation(s)
- Bruce Albala
- Eisai Inc.NutleyNew JerseyUSA
- Present address:
Program in Public HealthIrvine and Department of NeurologyUCI School of MedicineUniversity of California856 Health Sciences QuadIrvineCalifornia92697‐3957USA
| | - Eline Appelmans
- Foundation for the National Institutes of HealthNorth BethesdaMarylandUSA
| | - Ramona Burress
- Janssen Research & Development, LLCTitusvilleNew JerseyUSA
- Present address:
Takeda95, Hayden AvenueLexingtonMassachusetts02421USA
| | - Susan De Santi
- Eisai Inc.NutleyNew JerseyUSA
- Life Molecular ImagingBerlinGermany
- Present address:
Eisai Inc.NutleyNew JerseyUSA
| | - Theresa Devins
- Eisai Inc.NutleyNew JerseyUSA
- Present address:
Cognition Therapeutics2500 Westchester AvenuePurchaseNew York10577USA
| | | | - Veronika Logovinsky
- Eisai Inc.NutleyNew JerseyUSA
- Present address:
Lundbeck6 Parkway NDeerfieldIllinois60015USA
| | | | | | | | | | | | | | | | - Alessio Travaglia
- Foundation for the National Institutes of HealthNorth BethesdaMarylandUSA
| | | | - Leah White
- Foundation for the National Institutes of HealthNorth BethesdaMarylandUSA
- Present address:
Veranex5420 Wade Park Blvd Suite 204RaleighNorth Carolina27607USA
| | - Julie Wolf‐Rodda
- Foundation for the National Institutes of HealthNorth BethesdaMarylandUSA
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Fu J, Tzortzakakis A, Barroso J, Westman E, Ferreira D, Moreno R. Fast three-dimensional image generation for healthy brain aging using diffeomorphic registration. Hum Brain Mapp 2023; 44:1289-1308. [PMID: 36468536 PMCID: PMC9921328 DOI: 10.1002/hbm.26165] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 11/15/2022] [Accepted: 11/16/2022] [Indexed: 12/12/2022] Open
Abstract
Predicting brain aging can help in the early detection and prognosis of neurodegenerative diseases. Longitudinal cohorts of healthy subjects scanned through magnetic resonance imaging (MRI) have been essential to understand the structural brain changes due to aging. However, these cohorts suffer from missing data due to logistic issues in the recruitment of subjects. This paper proposes a methodology for filling up missing data in longitudinal cohorts with anatomically plausible images that capture the subject-specific aging process. The proposed methodology is developed within the framework of diffeomorphic registration. First, two novel modules are introduced within Synthmorph, a fast, state-of-the-art deep learning-based diffeomorphic registration method, to simulate the aging process between the first and last available MRI scan for each subject in three-dimensional (3D). The use of image registration also makes the generated images plausible by construction. Second, we used six image similarity measurements to rearrange the generated images to the specific age range. Finally, we estimated the age of every generated image by using the assumption of linear brain decay in healthy subjects. The methodology was evaluated on 2662 T1-weighted MRI scans from 796 healthy participants from 3 different longitudinal cohorts: Alzheimer's Disease Neuroimaging Initiative, Open Access Series of Imaging Studies-3, and Group of Neuropsychological Studies of the Canary Islands (GENIC). In total, we generated 7548 images to simulate the access of a scan per subject every 6 months in these cohorts. We evaluated the quality of the synthetic images using six quantitative measurements and a qualitative assessment by an experienced neuroradiologist with state-of-the-art results. The assumption of linear brain decay was accurate in these cohorts (R2 ∈ [.924, .940]). The experimental results show that the proposed methodology can produce anatomically plausible aging predictions that can be used to enhance longitudinal datasets. Compared to deep learning-based generative methods, diffeomorphic registration is more likely to preserve the anatomy of the different structures of the brain, which makes it more appropriate for its use in clinical applications. The proposed methodology is able to efficiently simulate anatomically plausible 3D MRI scans of brain aging of healthy subjects from two images scanned at two different time points.
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Affiliation(s)
- Jingru Fu
- Division of Biomedical ImagingDepartment of Biomedical Engineering and Health Systems, KTH Royal Institute of TechnologyStockholmSweden
| | - Antonios Tzortzakakis
- Division of RadiologyDepartment for Clinical Science, Intervention and Technology (CLINTEC), Karolinska InstitutetStockholmSweden
- Medical Radiation Physics and Nuclear MedicineFunctional Unit of Nuclear Medicine, Karolinska University HospitalHuddingeStockholmSweden
| | - José Barroso
- Department of PsychologyFaculty of Health Sciences, University Fernando Pessoa CanariasLas PalmasSpain
| | - Eric Westman
- Division of Clinical GeriatricsCentre for Alzheimer Research, Department of Neurobiology, Care Sciences, and Society (NVS), Karolinska InstitutetStockholmSweden
- Department of NeuroimagingCentre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUnited Kingdom
| | - Daniel Ferreira
- Division of Clinical GeriatricsCentre for Alzheimer Research, Department of Neurobiology, Care Sciences, and Society (NVS), Karolinska InstitutetStockholmSweden
| | - Rodrigo Moreno
- Division of Biomedical ImagingDepartment of Biomedical Engineering and Health Systems, KTH Royal Institute of TechnologyStockholmSweden
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Bharthur Sanjay A, Patania A, Yan X, Svaldi D, Duran T, Shah N, Nemes S, Chen E, Apostolova LG. Characterization of gene expression patterns in mild cognitive impairment using a transcriptomics approach and neuroimaging endophenotypes. Alzheimers Dement 2022; 18:2493-2508. [PMID: 35142026 PMCID: PMC10078657 DOI: 10.1002/alz.12587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Revised: 12/10/2021] [Accepted: 12/15/2021] [Indexed: 01/31/2023]
Abstract
INTRODUCTION Identification of novel therapeutics and risk assessment in early stages of Alzheimer's disease (AD) is a crucial aspect of addressing this complex disease. We characterized gene-expression patterns at the mild cognitive impairment (MCI) stage to identify critical mRNA measures and gene clusters associated with AD pathogenesis. METHODS We used a transcriptomics approach, integrating magnetic resonance imaging (MRI) and peripheral blood-based gene expression data using persistent homology (PH) followed by kernel-based clustering. RESULTS We identified three clusters of genes significantly associated with diagnosis of amnestic MCI. The biological processes associated with each cluster were mitochondrial function, NF-kB signaling, and apoptosis. Cluster-level associations with cortical thickness displayed canonical AD-like patterns. Driver genes from clusters were also validated in an external dataset for prediction of amyloidosis and clinical diagnosis. DISCUSSION We found a disease-relevant transcriptomic signature sensitive to prodromal AD and identified a subset of potential therapeutic targets associated with AD pathogenesis.
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Affiliation(s)
| | - Alice Patania
- Indiana University Network Sciences InstituteIndiana UniversityBloomingtonIndianaUSA
| | - Xiaoran Yan
- Indiana University Network Sciences InstituteIndiana UniversityBloomingtonIndianaUSA
| | - Diana Svaldi
- Department of NeurologyIndiana University School of MedicineIndianapolisIndianaUSA
| | - Tugce Duran
- Department of Internal Medicine, Section of Gerontology & Geriatric MedicineWake Forest School of MedicineWinston‐SalemNorth CarolinaUSA
| | - Niraj Shah
- Department of NeurologyIndiana University School of MedicineIndianapolisIndianaUSA
| | - Sara Nemes
- Department of NeurologyIndiana University School of MedicineIndianapolisIndianaUSA
| | - Eric Chen
- Department of NeurologyIndiana University School of MedicineIndianapolisIndianaUSA
| | - Liana G. Apostolova
- Department of NeurologyIndiana University School of MedicineIndianapolisIndianaUSA
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Uretsky M, Gibbons LE, Mukherjee S, Trittschuh EH, Fardo DW, Boyle PA, Keene CD, Saykin AJ, Crane PK, Schneider JA, Mez J. Longitudinal cognitive performance of Alzheimer's disease neuropathological subtypes. Alzheimers Dement (N Y) 2021; 7:e12201. [PMID: 34604500 PMCID: PMC8474122 DOI: 10.1002/trc2.12201] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 05/03/2021] [Accepted: 06/17/2021] [Indexed: 12/31/2022]
Abstract
INTRODUCTION Alzheimer's disease (AD) neuropathological subtypes (limbic predominant [lpAD], hippocampal sparing [HpSpAD], and typical [tAD]), defined by relative neurofibrillary tangle (NFT) burden in limbic and cortical regions, have not been studied in prospectively characterized epidemiological cohorts with robust cognitive assessments. METHODS Two hundred ninety-two participants with neuropathologically confirmed AD from the Religious Orders Study and Memory and Aging Project were categorized by neuropathological subtype based on previously specified diagnostic criteria using quantitative regional NFT counts. Rates of cognitive decline were compared across subtypes using linear mixed-effects models that included subtype, time, and a subtype-time interaction as predictors and four cognitive domain factor scores (memory, executive function, language, visuospatial) and a global score as outcomes. To assess if memory was relatively preserved in HpSpAD, non-memory factor scores were included as covariates in the mixed-effects model with memory as the outcome. RESULTS There were 57 (20%) with lpAD, 22 (8%) with HpSpAD and 213 (73%) with tAD. LpAD died significantly later than the participants with tAD (2.4 years, P = .01) and with HpSpAD (3.8 years, P = .03). Compared to tAD, HpSpAD, but not lpAD, performed significantly worse in all cognitive domains at the time of initial impairment and declined significantly faster in memory, language, and globally. HpSpAD did not have relatively preserved memory performance at any time point. CONCLUSION The relative frequencies of AD neuropathological subtypes in an epidemiological sample were consistent with a previous report in a convenience sample. People with HpSpAD decline rapidly, but may not have a memory-sparing clinical syndrome. Cohort-specific differences in regional tau burden and comorbid neuropathology may explain the lack of clinicopathological correlation.
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Affiliation(s)
- Madeline Uretsky
- Boston University Alzheimer's Disease and CTE CentersBoston University School of MedicineBostonMassachusettsUSA
- Department of NeurologyBoston University School of MedicineBostonMassachusettsUSA
| | - Laura E. Gibbons
- Department of General Internal MedicineUniversity of Washington School of Medicine, University of WashingtonSeattleWashingtonUSA
| | - Shubhabrata Mukherjee
- Department of General Internal MedicineUniversity of Washington School of Medicine, University of WashingtonSeattleWashingtonUSA
| | - Emily H. Trittschuh
- Geriatric Research, Education, and Clinical CenterPuget Sound Veterans Affairs Health Care SystemSeattleWashingtonUSA
- Department of Psychiatry and Behavioral SciencesUniversity of Washington School of MedicineSeattleWashingtonUSA
| | - David W. Fardo
- Sanders‐Brown Center on AgingUniversity of Kentucky College of MedicineLexingtonKentuckyUSA
- College of Public Health and Department of BiostatisticsUniversity of KentuckyLexingtonKentuckyUSA
| | - Patricia A. Boyle
- Rush Alzheimer's Disease CenterRush University Medical CenterChicagoIllinoisUSA
- Division of Behavioral SciencesRush Medical CollegeChicagoIllinoisUSA
| | - C. Dirk Keene
- University of Washington Alzheimer's Disease Research CenterUniversity of Washington School of MedicineSeattleWashingtonUSA
- Department of Laboratory Medicine and PathologyUniversity of Washington School of MedicineSeattleWashingtonUSA
| | - Andrew J. Saykin
- Indiana Alzheimer's Disease Research CenterIndiana University School of MedicineIndianapolisIndianaUSA
- Department of Radiology and Imaging ServicesIndiana University School of MedicineIndianapolisIndianaUSA
- Department of Medical and Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Paul K. Crane
- Department of General Internal MedicineUniversity of Washington School of Medicine, University of WashingtonSeattleWashingtonUSA
- University of Washington Alzheimer's Disease Research CenterUniversity of Washington School of MedicineSeattleWashingtonUSA
| | - Julie A. Schneider
- Rush Alzheimer's Disease CenterRush University Medical CenterChicagoIllinoisUSA
- Department of PathologyRush Medical College, ChicagoIllinoisUSA
- Department of Neurological SciencesRush University Medical CenterChicagoIllinoisUSA
| | - Jesse Mez
- Boston University Alzheimer's Disease and CTE CentersBoston University School of MedicineBostonMassachusettsUSA
- Department of NeurologyBoston University School of MedicineBostonMassachusettsUSA
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