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Welhaf MS, Wilks H, Aschenbrenner AJ, Balota DA, Schindler SE, Benzinger TLS, Gordon BA, Cruchaga C, Xiong C, Morris JC, Hassenstab J. Naturalistic assessment of reaction time variability in older adults at risk for Alzheimer's disease. J Int Neuropsychol Soc 2024; 30:428-438. [PMID: 38282413 PMCID: PMC11078617 DOI: 10.1017/s1355617723011475] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/30/2024]
Abstract
OBJECTIVE Maintaining attention underlies many aspects of cognition and becomes compromised early in neurodegenerative diseases like Alzheimer's disease (AD). The consistency of maintaining attention can be measured with reaction time (RT) variability. Previous work has focused on measuring such fluctuations during in-clinic testing, but recent developments in remote, smartphone-based cognitive assessments can allow one to test if these fluctuations in attention are evident in naturalistic settings and if they are sensitive to traditional clinical and cognitive markers of AD. METHOD Three hundred and seventy older adults (aged 75.8 +/- 5.8 years) completed a week of remote daily testing on the Ambulatory Research in Cognition (ARC) smartphone platform and also completed clinical, genetic, and conventional in-clinic cognitive assessments. RT variability was assessed in a brief (20-40 seconds) processing speed task using two different measures of variability, the Coefficient of Variation (CoV) and the Root Mean Squared Successive Difference (RMSSD) of RTs on correct trials. RESULTS Symptomatic participants showed greater variability compared to cognitively normal participants. When restricted to cognitively normal participants, APOE ε4 carriers exhibited greater variability than noncarriers. Both CoV and RMSSD showed significant, and similar, correlations with several in-clinic cognitive composites. Finally, both RT variability measures significantly mediated the relationship between APOE ε4 status and several in-clinic cognition composites. CONCLUSIONS Attentional fluctuations over 20-40 seconds assessed in daily life, are sensitive to clinical status and genetic risk for AD. RT variability appears to be an important predictor of cognitive deficits during the preclinical disease stage.
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Affiliation(s)
- Matthew S Welhaf
- Department of Psychological & Brain Sciences, Washington University in St. Louis. St. Louis, MO, USA
| | - Hannah Wilks
- Department of Psychological & Brain Sciences, Washington University in St. Louis. St. Louis, MO, USA
| | - Andrew J Aschenbrenner
- Department of Neurology. Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - David A Balota
- Department of Psychological & Brain Sciences, Washington University in St. Louis. St. Louis, MO, USA
| | - Suzanne E Schindler
- Department of Neurology. Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Tammie L S Benzinger
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Brian A Gordon
- Department of Psychological & Brain Sciences, Washington University in St. Louis. St. Louis, MO, USA
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Chengjie Xiong
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
| | - John C Morris
- Department of Neurology. Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Jason Hassenstab
- Department of Psychological & Brain Sciences, Washington University in St. Louis. St. Louis, MO, USA
- Department of Neurology. Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
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2
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Salvadó G, Horie K, Barthélemy NR, Vogel JW, Pichet Binette A, Chen CD, Aschenbrenner AJ, Gordon BA, Benzinger TLS, Holtzman DM, Morris JC, Palmqvist S, Stomrud E, Janelidze S, Ossenkoppele R, Schindler SE, Bateman RJ, Hansson O. Disease staging of Alzheimer's disease using a CSF-based biomarker model. Nat Aging 2024; 4:694-708. [PMID: 38514824 DOI: 10.1038/s43587-024-00599-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 02/20/2024] [Indexed: 03/23/2024]
Abstract
Biological staging of individuals with Alzheimer's disease (AD) may improve diagnostic and prognostic workup of dementia in clinical practice and the design of clinical trials. In this study, we used the Subtype and Stage Inference (SuStaIn) algorithm to establish a robust biological staging model for AD using cerebrospinal fluid (CSF) biomarkers. Our analysis involved 426 participants from BioFINDER-2 and was validated in 222 participants from the Knight Alzheimer Disease Research Center cohort. SuStaIn identified a singular biomarker sequence and revealed that five CSF biomarkers effectively constituted a reliable staging model (ordered: Aβ42/40, pT217/T217, pT205/T205, MTBR-tau243 and non-phosphorylated mid-region tau). The CSF stages (0-5) demonstrated a correlation with increased abnormalities in other AD-related biomarkers, such as Aβ-PET and tau-PET, and aligned with longitudinal biomarker changes reflective of AD progression. Higher CSF stages at baseline were associated with an elevated hazard ratio of clinical decline. This study highlights a common molecular pathway underlying AD pathophysiology across all patients, suggesting that a single CSF collection can accurately indicate the presence of AD pathologies and characterize the stage of disease progression. The proposed staging model has implications for enhancing diagnostic and prognostic assessments in both clinical practice and the design of clinical trials.
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Affiliation(s)
- Gemma Salvadó
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden.
| | - Kanta Horie
- Tracy Family Stable Isotope Labeling Quantitation (SILQ) Center, Washington University School of Medicine, St. Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Eisai, Inc., Nutley, NJ, USA
| | - Nicolas R Barthélemy
- Tracy Family Stable Isotope Labeling Quantitation (SILQ) Center, Washington University School of Medicine, St. Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Jacob W Vogel
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Department of Clinical Science, Malmö, SciLifeLab, Lund University, Lund, Sweden
| | - Alexa Pichet Binette
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Charles D Chen
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Andrew J Aschenbrenner
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Brian A Gordon
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Tammie L S Benzinger
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - David M Holtzman
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - John C Morris
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Sebastian Palmqvist
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Erik Stomrud
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Shorena Janelidze
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Rik Ossenkoppele
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Suzanne E Schindler
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Randall J Bateman
- Tracy Family Stable Isotope Labeling Quantitation (SILQ) Center, Washington University School of Medicine, St. Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden.
- Memory Clinic, Skåne University Hospital, Malmö, Sweden.
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3
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Li Y, Yen D, Hendrix RD, Gordon BA, Dlamini S, Barthélemy NR, Aschenbrenner AJ, Henson RL, Herries EM, Volluz K, Kirmess K, Eastwood S, Meyer M, Heller M, Jarrett L, McDade E, Holtzman DM, Benzinger TL, Morris JC, Bateman RJ, Xiong C, Schindler SE. Timing of Biomarker Changes in Sporadic Alzheimer's Disease in Estimated Years from Symptom Onset. Ann Neurol 2024; 95:951-965. [PMID: 38400792 PMCID: PMC11060905 DOI: 10.1002/ana.26891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 12/26/2023] [Accepted: 01/30/2024] [Indexed: 02/26/2024]
Abstract
OBJECTIVE A clock relating amyloid positron emission tomography (PET) to time was used to estimate the timing of biomarker changes in sporadic Alzheimer disease (AD). METHODS Research participants were included who underwent cerebrospinal fluid (CSF) collection within 2 years of amyloid PET. The ages at amyloid onset and AD symptom onset were estimated for each individual. The timing of change for plasma, CSF, imaging, and cognitive measures was calculated by comparing restricted cubic splines of cross-sectional data from the amyloid PET positive and negative groups. RESULTS The amyloid PET positive sub-cohort (n = 118) had an average age of 70.4 ± 7.4 years (mean ± standard deviation) and 16% were cognitively impaired. The amyloid PET negative sub-cohort (n = 277) included individuals with low levels of amyloid plaque burden at all scans who were cognitively unimpaired at the time of the scans. Biomarker changes were detected 15-19 years before estimated symptom onset for CSF Aβ42/Aβ40, plasma Aβ42/Aβ40, CSF pT217/T217, and amyloid PET; 12-14 years before estimated symptom onset for plasma pT217/T217, CSF neurogranin, CSF SNAP-25, CSF sTREM2, plasma GFAP, and plasma NfL; and 7-9 years before estimated symptom onset for CSF pT205/T205, CSF YKL-40, hippocampal volumes, and cognitive measures. INTERPRETATION The use of an amyloid clock enabled visualization and analysis of biomarker changes as a function of estimated years from symptom onset in sporadic AD. This study demonstrates that estimated years from symptom onset based on an amyloid clock can be used as a continuous staging measure for sporadic AD and aligns with findings in autosomal dominant AD. ANN NEUROL 2024;95:951-965.
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Affiliation(s)
- Yan Li
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Daniel Yen
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Rachel D. Hendrix
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Brian A. Gordon
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Sibonginkhosi Dlamini
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Nicolas R. Barthélemy
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | | | - Rachel L. Henson
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Elizabeth M. Herries
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Katherine Volluz
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | | | | | | | - Maren Heller
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Lea Jarrett
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Eric McDade
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - David M. Holtzman
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Tammie L.S. Benzinger
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - John C. Morris
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Randall J. Bateman
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Chengjie Xiong
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
| | - Suzanne E. Schindler
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
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4
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Buckley RF, Schindler SE. Lower Accuracy Alzheimer Disease Blood Tests Will Never Be "Ready for Prime Time". Neurology 2024; 102:e209295. [PMID: 38527244 DOI: 10.1212/wnl.0000000000209295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 01/26/2024] [Indexed: 03/27/2024] Open
Affiliation(s)
- Rachel F Buckley
- From the Massachusetts General Hospital (R.F.B.); Washington University School of Medicine (S.E.S.)
| | - Suzanne E Schindler
- From the Massachusetts General Hospital (R.F.B.); Washington University School of Medicine (S.E.S.)
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5
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McKay NS, Millar PR, Nicosia J, Aschenbrenner AJ, Gordon BA, Benzinger TLS, Cruchaga CC, Schindler SE, Morris JC, Hassenstab J. Pick a PACC: Comparing domain-specific and general cognitive composites in Alzheimer disease research. Neuropsychology 2024:2024-72440-001. [PMID: 38602816 DOI: 10.1037/neu0000949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/13/2024] Open
Abstract
OBJECTIVE We aimed to illustrate how complex cognitive data can be used to create domain-specific and general cognitive composites relevant to Alzheimer disease research. METHOD Using equipercentile equating, we combined data from the Charles F. and Joanne Knight Alzheimer Disease Research Center that spanned multiple iterations of the Uniform Data Set. Exploratory factor analyses revealed four domain-specific composites representing episodic memory, semantic memory, working memory, and attention/processing speed. The previously defined preclinical Alzheimer disease cognitive composite (PACC) and a novel alternative, the Knight-PACC, were also computed alongside a global composite comprising all available tests. These three composites allowed us to compare the usefulness of domain and general composites in the context of predicting common Alzheimer disease biomarkers. RESULTS General composites slightly outperformed domain-specific metrics in predicting imaging-derived amyloid, tau, and neurodegeneration burden. Power analyses revealed that the global, Knight-PACC, and attention and processing speed composites would require the smallest sample sizes to detect cognitive change in a clinical trial, while the Alzheimer Disease Cooperative Study-PACC required two to three times as many participants. CONCLUSIONS Analyses of cognition with the Knight-PACC and our domain-specific composites offer researchers flexibility by providing validated outcome assessments that can equate across test versions to answer a wide range of questions regarding cognitive decline in normal aging and neurodegenerative disease. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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Affiliation(s)
- Nicole S McKay
- Mallinckrodt Institute of Radiology, Washington University School of Medicine
| | - Peter R Millar
- Department of Neurology, Washington University School of Medicine
| | - Jessica Nicosia
- Department of Neurology, Washington University School of Medicine
| | | | - Brian A Gordon
- Mallinckrodt Institute of Radiology, Washington University School of Medicine
| | | | | | | | - John C Morris
- Department of Neurology, Washington University School of Medicine
| | - Jason Hassenstab
- Department of Neurology, Washington University School of Medicine
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6
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Barthélemy NR, Salvadó G, Schindler SE, He Y, Janelidze S, Collij LE, Saef B, Henson RL, Chen CD, Gordon BA, Li Y, La Joie R, Benzinger TLS, Morris JC, Mattsson-Carlgren N, Palmqvist S, Ossenkoppele R, Rabinovici GD, Stomrud E, Bateman RJ, Hansson O. Highly accurate blood test for Alzheimer's disease is similar or superior to clinical cerebrospinal fluid tests. Nat Med 2024; 30:1085-1095. [PMID: 38382645 PMCID: PMC11031399 DOI: 10.1038/s41591-024-02869-z] [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: 10/24/2023] [Accepted: 02/12/2024] [Indexed: 02/23/2024]
Abstract
With the emergence of Alzheimer's disease (AD) disease-modifying therapies, identifying patients who could benefit from these treatments becomes critical. In this study, we evaluated whether a precise blood test could perform as well as established cerebrospinal fluid (CSF) tests in detecting amyloid-β (Aβ) plaques and tau tangles. Plasma %p-tau217 (ratio of phosporylated-tau217 to non-phosphorylated tau) was analyzed by mass spectrometry in the Swedish BioFINDER-2 cohort (n = 1,422) and the US Charles F. and Joanne Knight Alzheimer Disease Research Center (Knight ADRC) cohort (n = 337). Matched CSF samples were analyzed with clinically used and FDA-approved automated immunoassays for Aβ42/40 and p-tau181/Aβ42. The primary and secondary outcomes were detection of brain Aβ or tau pathology, respectively, using positron emission tomography (PET) imaging as the reference standard. Main analyses were focused on individuals with cognitive impairment (mild cognitive impairment and mild dementia), which is the target population for available disease-modifying treatments. Plasma %p-tau217 was clinically equivalent to FDA-approved CSF tests in classifying Aβ PET status, with an area under the curve (AUC) for both between 0.95 and 0.97. Plasma %p-tau217 was generally superior to CSF tests in classification of tau-PET with AUCs of 0.95-0.98. In cognitively impaired subcohorts (BioFINDER-2: n = 720; Knight ADRC: n = 50), plasma %p-tau217 had an accuracy, a positive predictive value and a negative predictive value of 89-90% for Aβ PET and 87-88% for tau PET status, which was clinically equivalent to CSF tests, further improving to 95% using a two-cutoffs approach. Blood plasma %p-tau217 demonstrated performance that was clinically equivalent or superior to clinically used FDA-approved CSF tests in the detection of AD pathology. Use of high-performance blood tests in clinical practice can improve access to accurate AD diagnosis and AD-specific treatments.
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Affiliation(s)
- Nicolas R Barthélemy
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Tracy Family Stable Isotope Labeling Quantitation (SILQ) Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Gemma Salvadó
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund University, Lund, Sweden
| | - Suzanne E Schindler
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Charles F. and Joanne Knight Alzheimer Disease Research Center (Knight ADRC), Washington University School of Medicine, St. Louis, MO, USA
| | - Yingxin He
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Tracy Family Stable Isotope Labeling Quantitation (SILQ) Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Shorena Janelidze
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund University, Lund, Sweden
| | - Lyduine E Collij
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund University, Lund, Sweden
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, The Netherlands
| | - Benjamin Saef
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Rachel L Henson
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Charles D Chen
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Brian A Gordon
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Yan Li
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Division of Biostatistics, Washington University in St. Louis, St. Louis, MO, USA
| | - Renaud La Joie
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Tammie L S Benzinger
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - John C Morris
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Charles F. and Joanne Knight Alzheimer Disease Research Center (Knight ADRC), Washington University School of Medicine, St. Louis, MO, USA
| | - Niklas Mattsson-Carlgren
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund University, Lund, Sweden
- Wallenberg Center for Molecular Medicine, Lund University, Lund, Sweden
- Department of Neurology, Skåne University Hospital, Lund University, Lund, Sweden
| | - Sebastian Palmqvist
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Rik Ossenkoppele
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund University, Lund, Sweden
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, The Netherlands
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC, location VUmc, Amsterdam, The Netherlands
| | - Gil D Rabinovici
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA
| | - Erik Stomrud
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Randall J Bateman
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA.
- Tracy Family Stable Isotope Labeling Quantitation (SILQ) Center, Washington University School of Medicine, St. Louis, MO, USA.
- Charles F. and Joanne Knight Alzheimer Disease Research Center (Knight ADRC), Washington University School of Medicine, St. Louis, MO, USA.
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund University, Lund, Sweden.
- Memory Clinic, Skåne University Hospital, Malmö, Sweden.
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7
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Therriault J, Schindler SE, Salvadó G, Pascoal TA, Benedet AL, Ashton NJ, Karikari TK, Apostolova L, Murray ME, Verberk I, Vogel JW, La Joie R, Gauthier S, Teunissen C, Rabinovici GD, Zetterberg H, Bateman RJ, Scheltens P, Blennow K, Sperling R, Hansson O, Jack CR, Rosa-Neto P. Biomarker-based staging of Alzheimer disease: rationale and clinical applications. Nat Rev Neurol 2024; 20:232-244. [PMID: 38429551 DOI: 10.1038/s41582-024-00942-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/05/2024] [Indexed: 03/03/2024]
Abstract
Disease staging, whereby the spatial extent and load of brain pathology are used to estimate the severity of Alzheimer disease (AD), is pivotal to the gold-standard neuropathological diagnosis of AD. Current in vivo diagnostic frameworks for AD are based on abnormal concentrations of amyloid-β and tau in the cerebrospinal fluid or on PET scans, and breakthroughs in molecular imaging have opened up the possibility of in vivo staging of AD. Focusing on the key principles of disease staging shared across several areas of medicine, this Review highlights the potential for in vivo staging of AD to transform our understanding of preclinical AD, refine enrolment criteria for trials of disease-modifying therapies and aid clinical decision-making in the era of anti-amyloid therapeutics. We provide a state-of-the-art review of recent biomarker-based AD staging systems and highlight their contributions to the understanding of the natural history of AD. Furthermore, we outline hypothetical frameworks to stage AD severity using more accessible fluid biomarkers. In addition, by applying amyloid PET-based staging to recently published anti-amyloid therapeutic trials, we highlight how biomarker-based disease staging frameworks could illustrate the numerous pathological changes that have already taken place in individuals with mildly symptomatic AD. Finally, we discuss challenges related to the validation and standardization of disease staging and provide a forward-looking perspective on potential clinical applications.
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Affiliation(s)
- Joseph Therriault
- Translational Neuroimaging Laboratory, McGill Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, Montreal, Quebec, Canada.
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada.
| | - Suzanne E Schindler
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Gemma Salvadó
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Tharick A Pascoal
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Andréa Lessa Benedet
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
| | - Nicholas J Ashton
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
- NIHR Biomedical Research Centre, South London and Maudsley NHS Foundation, London, UK
| | - Thomas K Karikari
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
| | - Liana Apostolova
- Department of Neurology, University of Indiana School of Medicine, Indianapolis, IN, USA
| | | | - Inge Verberk
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Amsterdam, Netherlands
| | - Jacob W Vogel
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Department of Clinical Sciences, Malmö, SciLifeLab, Lund University, Lund, Sweden
| | - Renaud La Joie
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Serge Gauthier
- Translational Neuroimaging Laboratory, McGill Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, Montreal, Quebec, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Charlotte Teunissen
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Amsterdam, Netherlands
| | - Gil D Rabinovici
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Gothenburg, Sweden
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- UK Dementia Research Institute at UCL, London, UK
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong, China
| | - Randall J Bateman
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
- Tracy Family SILQ Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Philip Scheltens
- Alzheimer Centre Amsterdam, Amsterdam Neuroscience, Amsterdam, Netherlands
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
| | - Reisa Sperling
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | | | - Pedro Rosa-Neto
- Translational Neuroimaging Laboratory, McGill Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, Montreal, Quebec, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
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8
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Meeker KL, Luckett PH, Barthélemy NR, Hobbs DA, Chen C, Bollinger J, Ovod V, Flores S, Keefe S, Henson RL, Herries EM, McDade E, Hassenstab JJ, Xiong C, Cruchaga C, Benzinger TLS, Holtzman DM, Schindler SE, Bateman RJ, Morris JC, Gordon BA, Ances BM. Comparison of cerebrospinal fluid, plasma and neuroimaging biomarker utility in Alzheimer's disease. Brain Commun 2024; 6:fcae081. [PMID: 38505230 PMCID: PMC10950051 DOI: 10.1093/braincomms/fcae081] [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] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 02/01/2024] [Accepted: 03/14/2024] [Indexed: 03/21/2024] Open
Abstract
Alzheimer's disease biomarkers are crucial to understanding disease pathophysiology, aiding accurate diagnosis and identifying target treatments. Although the number of biomarkers continues to grow, the relative utility and uniqueness of each is poorly understood as prior work has typically calculated serial pairwise relationships on only a handful of markers at a time. The present study assessed the cross-sectional relationships among 27 Alzheimer's disease biomarkers simultaneously and determined their ability to predict meaningful clinical outcomes using machine learning. Data were obtained from 527 community-dwelling volunteers enrolled in studies at the Charles F. and Joanne Knight Alzheimer Disease Research Center at Washington University in St Louis. We used hierarchical clustering to group 27 imaging, CSF and plasma measures of amyloid beta, tau [phosphorylated tau (p-tau), total tau t-tau)], neuronal injury and inflammation drawn from MRI, PET, mass-spectrometry assays and immunoassays. Neuropsychological and genetic measures were also included. Random forest-based feature selection identified the strongest predictors of amyloid PET positivity across the entire cohort. Models also predicted cognitive impairment across the entire cohort and in amyloid PET-positive individuals. Four clusters emerged reflecting: core Alzheimer's disease pathology (amyloid and tau), neurodegeneration, AT8 antibody-associated phosphorylated tau sites and neuronal dysfunction. In the entire cohort, CSF p-tau181/Aβ40lumi and Aβ42/Aβ40lumi and mass spectrometry measurements for CSF pT217/T217, pT111/T111, pT231/T231 were the strongest predictors of amyloid PET status. Given their ability to denote individuals on an Alzheimer's disease pathological trajectory, these same markers (CSF pT217/T217, pT111/T111, p-tau/Aβ40lumi and t-tau/Aβ40lumi) were largely the best predictors of worse cognition in the entire cohort. When restricting analyses to amyloid-positive individuals, the strongest predictors of impaired cognition were tau PET, CSF t-tau/Aβ40lumi, p-tau181/Aβ40lumi, CSF pT217/217 and pT205/T205. Non-specific CSF measures of neuronal dysfunction and inflammation were poor predictors of amyloid PET and cognitive status. The current work utilized machine learning to understand the interrelationship structure and utility of a large number of biomarkers. The results demonstrate that, although the number of biomarkers has rapidly expanded, many are interrelated and few strongly predict clinical outcomes. Examining the entire corpus of available biomarkers simultaneously provides a meaningful framework to understand Alzheimer's disease pathobiological change as well as insight into which biomarkers may be most useful in Alzheimer's disease clinical practice and trials.
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Affiliation(s)
- Karin L Meeker
- Department of Neurology, Washington University in St Louis, St Louis, MO 63110, USA
| | - Patrick H Luckett
- Department of Neurosurgery, Washington University in St Louis, St Louis, MO 63110, USA
| | - Nicolas R Barthélemy
- Department of Neurology, Washington University in St Louis, St Louis, MO 63110, USA
| | - Diana A Hobbs
- Department of Radiology, Washington University in St Louis, St Louis, MO 63110, USA
| | - Charles Chen
- Department of Radiology, Washington University in St Louis, St Louis, MO 63110, USA
| | - James Bollinger
- Department of Neurology, Washington University in St Louis, St Louis, MO 63110, USA
| | - Vitaliy Ovod
- Department of Neurology, Washington University in St Louis, St Louis, MO 63110, USA
| | - Shaney Flores
- Department of Radiology, Washington University in St Louis, St Louis, MO 63110, USA
| | - Sarah Keefe
- Department of Radiology, Washington University in St Louis, St Louis, MO 63110, USA
| | - Rachel L Henson
- Department of Neurology, Washington University in St Louis, St Louis, MO 63110, USA
| | - Elizabeth M Herries
- Department of Neurology, Washington University in St Louis, St Louis, MO 63110, USA
| | - Eric McDade
- Department of Neurology, Washington University in St Louis, St Louis, MO 63110, USA
| | - Jason J Hassenstab
- Department of Neurology, Washington University in St Louis, St Louis, MO 63110, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St Louis, MO 63110, USA
| | - Chengjie Xiong
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St Louis, MO 63110, USA
- Division of Biostatistics, Washington University in St Louis, St Louis, MO 63110, USA
| | - Carlos Cruchaga
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St Louis, MO 63110, USA
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO 63110, USA
| | - Tammie L S Benzinger
- Department of Radiology, Washington University in St Louis, St Louis, MO 63110, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St Louis, MO 63110, USA
| | - David M Holtzman
- Department of Neurology, Washington University in St Louis, St Louis, MO 63110, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St Louis, MO 63110, USA
| | - Suzanne E Schindler
- Department of Neurology, Washington University in St Louis, St Louis, MO 63110, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St Louis, MO 63110, USA
| | - Randall J Bateman
- Department of Neurology, Washington University in St Louis, St Louis, MO 63110, USA
| | - John C Morris
- Department of Neurology, Washington University in St Louis, St Louis, MO 63110, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St Louis, MO 63110, USA
| | - Brian A Gordon
- Department of Radiology, Washington University in St Louis, St Louis, MO 63110, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St Louis, MO 63110, USA
| | - Beau M Ances
- Department of Neurology, Washington University in St Louis, St Louis, MO 63110, USA
- Department of Radiology, Washington University in St Louis, St Louis, MO 63110, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St Louis, MO 63110, USA
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9
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Bonomi S, Lu R, Schindler SE, Bui Q, Lah JJ, Wolk D, Gleason CE, Sperling R, Roberson ED, Levey AI, Shaw L, Van Hulle C, Benzinger T, Adams M, Manzanares C, Qiu D, Hassenstab J, Moulder KL, Balls-Berry JE, Johnson K, Johnson SC, Murchison CF, Luo J, Gremminger E, Agboola F, Grant EA, Hornbeck R, Massoumzadeh P, Keefe S, Dierker D, Gray JD, Henson RL, Streitz M, Mechanic-Hamilton D, Morris JC, Xiong C. Relationships of Cognitive Measures with Cerebrospinal Fluid but Not Imaging Biomarkers of Alzheimer Disease Vary between Black and White Individuals. Ann Neurol 2024; 95:495-506. [PMID: 38038976 PMCID: PMC10922199 DOI: 10.1002/ana.26838] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 11/02/2023] [Accepted: 11/10/2023] [Indexed: 12/02/2023]
Abstract
OBJECTIVE Biomarkers of Alzheimer disease vary between groups of self-identified Black and White individuals in some studies. This study examined whether the relationships between biomarkers or between biomarkers and cognitive measures varied by racialized groups. METHODS Cerebrospinal fluid (CSF), amyloid positron emission tomography (PET), and magnetic resonance imaging measures were harmonized across four studies of memory and aging. Spearman correlations between biomarkers and between biomarkers and cognitive measures were calculated within each racialized group, then compared between groups by standard normal tests after Fisher's Z-transformations. RESULTS The harmonized dataset included at least one biomarker measurement from 495 Black and 2,600 White participants. The mean age was similar between racialized groups. However, Black participants were less likely to have cognitive impairment (28% vs 36%) and had less abnormality of some CSF biomarkers including CSF Aβ42/40, total tau, p-tau181, and neurofilament light. CSF Aβ42/40 was negatively correlated with total tau and p-tau181 in both groups, but at a smaller magnitude in Black individuals. CSF Aβ42/40, total tau, and p-tau181 had weaker correlations with cognitive measures, especially episodic memory, in Black than White participants. Correlations of amyloid measures between CSF (Aβ42/40, Aβ42) and PET imaging were also weaker in Black than White participants. Importantly, no differences based on race were found in correlations between different imaging biomarkers, or in correlations between imaging biomarkers and cognitive measures. INTERPRETATION Relationships between CSF biomarkers but not imaging biomarkers varied by racialized groups. Imaging biomarkers performed more consistently across racialized groups in associations with cognitive measures. ANN NEUROL 2024;95:495-506.
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Affiliation(s)
- Samuele Bonomi
- Department of Neurology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Ruijin Lu
- Division of Biostatistics, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Suzanne E. Schindler
- Department of Neurology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Quoc Bui
- Division of Biostatistics, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - James J. Lah
- Department of Neurology, Emory University School of Medicine, Atlanta, Georgia, USA
- Goizueta Alzheimer’s Disease Research Center, Emory University, Atlanta, GA
| | - David Wolk
- Department of Neurology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Carey E. Gleason
- Division of Geriatrics and Gerontology, Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
- Wisconsin Alzheimer’s Disease Research Center, Madison, Wisconsin, USA
- Geriatric Research, Education and Clinical Center, William S. Middleton Memorial Veterans Hospital, Madison, Wisconsin, USA
| | - Reisa Sperling
- Department of Neurology, Center for Alzheimer Research and Treatment, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Erik D. Roberson
- Center for Neurodegeneration and Experimental Therapeutics, Alzheimer’s Disease Center, Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Allan I. Levey
- Department of Neurology, Emory University School of Medicine, Atlanta, Georgia, USA
- Goizueta Alzheimer’s Disease Research Center, Emory University, Atlanta, GA
| | - Leslie Shaw
- Department of Neurology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Carol Van Hulle
- Division of Geriatrics and Gerontology, Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
- Wisconsin Alzheimer’s Disease Research Center, Madison, Wisconsin, USA
| | - Tammie Benzinger
- Knight Alzheimer Disease Research Center, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Morgann Adams
- Department of Neurology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Cecelia Manzanares
- Department of Neurology, Emory University School of Medicine, Atlanta, Georgia, USA
- Goizueta Alzheimer’s Disease Research Center, Emory University, Atlanta, GA
| | - Deqiang Qiu
- Goizueta Alzheimer’s Disease Research Center, Emory University, Atlanta, GA
| | - Jason Hassenstab
- Department of Neurology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Krista L. Moulder
- Department of Neurology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Joyce E. Balls-Berry
- Department of Neurology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Keith Johnson
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Sterling C. Johnson
- Division of Geriatrics and Gerontology, Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
- Wisconsin Alzheimer’s Disease Research Center, Madison, Wisconsin, USA
- Geriatric Research, Education and Clinical Center, William S. Middleton Memorial Veterans Hospital, Madison, Wisconsin, USA
| | - Charles F. Murchison
- Center for Neurodegeneration and Experimental Therapeutics, Alzheimer’s Disease Center, Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Jingqin Luo
- Division of Biostatistics, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Emily Gremminger
- Department of Neurology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Folasade Agboola
- Division of Biostatistics, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Elizabeth A. Grant
- Division of Biostatistics, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Russ Hornbeck
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Parinaz Massoumzadeh
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Sarah Keefe
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Donna Dierker
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Julia D. Gray
- Knight Alzheimer Disease Research Center, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Rachel L. Henson
- Department of Neurology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Marissa Streitz
- Department of Neurology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Dawn Mechanic-Hamilton
- Department of Neurology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - John C. Morris
- Department of Neurology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Chengjie Xiong
- Division of Biostatistics, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
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10
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Wilks H, Benzinger TLS, Schindler SE, Cruchaga C, Morris JC, Hassenstab J. Predictors and outcomes of fluctuations in the clinical dementia rating scale. Alzheimers Dement 2024; 20:2080-2088. [PMID: 38224146 PMCID: PMC10984446 DOI: 10.1002/alz.13679] [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: 09/08/2023] [Revised: 11/15/2023] [Accepted: 12/03/2023] [Indexed: 01/16/2024]
Abstract
INTRODUCTION Reversion, or change in cognitive status from impaired to normal, is common in aging and dementia studies, but it remains unclear what factors predict reversion. METHODS We investigated whether reverters, defined as those who revert from a Clinical Dementia Rating® (CDR®) scale score of 0.5 to CDR 0) differed on cognition and biomarkers from unimpaired participants (always CDR 0) and impaired participants (converted to CDR > 0 and had no reversion events). Models evaluated relationships between biomarker status, apolipoprotein E (APOE) ε4 status, and cognition. Additional models described predictors of reversion and predictors of eventual progression to CDR > 0. RESULTS CDR reversion was associated with younger age, better cognition, and negative amyloid biomarker status. Reverters that eventually progressed to CDR > 0 had more visits, were older, and were more likely to have an APOE ε4 allele. DISCUSSION CDR reversion occupies a transitional phase in disease progression between cognitive normality and overt dementia. Reverters may be ideal candidates for secondary prevention Alzheimer's disease (AD) trials. HIGHLIGHTS Reverters had more longitudinal cognitive decline than those who remained cognitively normal. Predictors of reversion: younger age, better cognition, and negative amyloid biomarker status. Reverting from CDR 0.5 to 0 is a risk factor for future conversion to CDR > 0. CDR reversion may be a transitional phase in Alzheimer's Disease progression. CDR reverters may be ideal for Alzheimer's disease secondary prevention trials.
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Affiliation(s)
- Hannah Wilks
- Department of Psychological & Brain SciencesWashington University in St. LouisSt. LouisMissouriUSA
- Charles F. and Joanne Knight Alzheimer Disease Research CenterDepartment of NeurologyWashington University School of MedicineSt. LouisMissouriUSA
| | - Tammie L. S. Benzinger
- Charles F. and Joanne Knight Alzheimer Disease Research CenterDepartment of NeurologyWashington University School of MedicineSt. LouisMissouriUSA
- Department of RadiologyWashington University School of MedicineSt. LouisMissouriUSA
| | - Suzanne E. Schindler
- Charles F. and Joanne Knight Alzheimer Disease Research CenterDepartment of NeurologyWashington University School of MedicineSt. LouisMissouriUSA
| | - Carlos Cruchaga
- Charles F. and Joanne Knight Alzheimer Disease Research CenterDepartment of NeurologyWashington University School of MedicineSt. LouisMissouriUSA
- Department of PsychiatryWashington University School of Medicine1 Barnes Jewish Hospital PlazaSt. LouisMissouriUSA
| | - John C. Morris
- Charles F. and Joanne Knight Alzheimer Disease Research CenterDepartment of NeurologyWashington University School of MedicineSt. LouisMissouriUSA
| | - Jason Hassenstab
- Department of Psychological & Brain SciencesWashington University in St. LouisSt. LouisMissouriUSA
- Charles F. and Joanne Knight Alzheimer Disease Research CenterDepartment of NeurologyWashington University School of MedicineSt. LouisMissouriUSA
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11
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Sexton CE, Bitan G, Bowles KR, Brys M, Buée L, Maina MB, Clelland CD, Cohen AD, Crary JF, Dage JL, Diaz K, Frost B, Gan L, Goate AM, Golbe LI, Hansson O, Karch CM, Kolb HC, La Joie R, Lee SE, Matallana D, Miller BL, Onyike CU, Quiroz YT, Rexach JE, Rohrer JD, Rommel A, Sadri‐Vakili G, Schindler SE, Schneider JA, Sperling RA, Teunissen CE, Weninger SC, Worley SL, Zheng H, Carrillo MC. Novel avenues of tau research. Alzheimers Dement 2024; 20:2240-2261. [PMID: 38170841 PMCID: PMC10984447 DOI: 10.1002/alz.13533] [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: 03/27/2023] [Revised: 10/03/2023] [Accepted: 10/05/2023] [Indexed: 01/05/2024]
Abstract
INTRODUCTION The pace of innovation has accelerated in virtually every area of tau research in just the past few years. METHODS In February 2022, leading international tau experts convened to share selected highlights of this work during Tau 2022, the second international tau conference co-organized and co-sponsored by the Alzheimer's Association, CurePSP, and the Rainwater Charitable Foundation. RESULTS Representing academia, industry, and the philanthropic sector, presenters joined more than 1700 registered attendees from 59 countries, spanning six continents, to share recent advances and exciting new directions in tau research. DISCUSSION The virtual meeting provided an opportunity to foster cross-sector collaboration and partnerships as well as a forum for updating colleagues on research-advancing tools and programs that are steadily moving the field forward.
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Affiliation(s)
| | - Gal Bitan
- Department of NeurologyDavid Geffen School of MedicineBrain Research InstituteMolecular Biology InstituteUniversity of California Los Angeles (UCLA)Los AngelesCaliforniaUSA
| | - Kathryn R. Bowles
- UK Dementia Research Institute at the University of EdinburghCentre for Discovery Brain SciencesUniversity of EdinburghEdinburghUK
| | | | - Luc Buée
- Univ LilleInsermCHU‐LilleLille Neuroscience and CognitionLabEx DISTALZPlace de VerdunLilleFrance
| | - Mahmoud Bukar Maina
- Sussex NeuroscienceSchool of Life SciencesUniversity of SussexFalmerUK
- Biomedical Science Research and Training CentreYobe State UniversityDamaturuNigeria
| | - Claire D. Clelland
- Memory and Aging CenterDepartment of NeurologyWeill Institute for NeurosciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Ann D. Cohen
- University of PittsburghSchool of MedicineDepartment of Psychiatry and Alzheimer's disease Research CenterPittsburghPennsylvaniaUSA
| | - John F. Crary
- Departments of PathologyNeuroscience, and Artificial Intelligence & Human HealthIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Jeffrey L. Dage
- Department of NeurologyIndiana University School of MedicineIndianapolisIndianaUSA
| | | | - Bess Frost
- Sam & Ann Barshop Institute for Longevity & Aging Studies Glenn Biggs Institute for Alzheimer's & Neurodegenerative Disorders Department of Cell Systems and Anatomy University of Texas Health San AntonioSan AntonioTexasUSA
| | - Li Gan
- Helen and Robert Appel Alzheimer Disease Research InstituteFeil Family Brain and Mind Research InstituteWeill Cornell MedicineNew YorkNew YorkUSA
| | - Alison M Goate
- Department of Genetics & Genomic SciencesRonald M. Loeb Center for Alzheimer's diseaseIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Lawrence I. Golbe
- CurePSPIncNew YorkNew YorkUSA
- Rutgers Robert Wood Johnson Medical SchoolNew BrunswickNew JerseyUSA
| | - Oskar Hansson
- Clinical Memory Research UnitDepartment of Clinical Sciences MalmöLund UniversityLundSweden
| | - Celeste M. Karch
- Department of PsychiatryWashington University in St. LouisSt. LouisMissouriUSA
| | | | - Renaud La Joie
- Memory and Aging CenterDepartment of NeurologyWeill Institute for NeurosciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Suzee E. Lee
- Memory and Aging CenterDepartment of NeurologyWeill Institute for NeurosciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Diana Matallana
- Aging InstituteNeuroscience ProgramPsychiatry DepartmentSchool of MedicinePontificia Universidad JaverianaBogotáColombia
- Mental Health DepartmentHospital Universitario Fundaciòn Santa FeBogotaColombia
| | - Bruce L. Miller
- Memory and Aging CenterDepartment of NeurologyWeill Institute for NeurosciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
- Department of Psychiatry and Behavioral SciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Chiadi U. Onyike
- Division of Geriatric Psychiatry and NeuropsychiatryJohns Hopkins University School of MedicineBaltimoreBaltimoreMarylandUSA
| | - Yakeel T. Quiroz
- Departments of Psychiatry and NeurologyMassachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Jessica E. Rexach
- Program in NeurogeneticsDepartment of NeurologyDavid Geffen School of MedicineUniversity of CaliforniaLos AngelesCaliforniaUSA
| | - Jonathan D. Rohrer
- Department of Neurodegenerative DiseaseDementia Research CentreUniversity College London Institute of Neurology, Queen SquareLondonUK
| | - Amy Rommel
- Rainwater Charitable FoundationFort WorthTexasUSA
| | - Ghazaleh Sadri‐Vakili
- Sean M. Healey &AMG Center for ALS at Mass GeneralMassachusetts General HospitalBostonMassachusettsUSA
| | - Suzanne E. Schindler
- Department of NeurologyWashington University School of MedicineSt. LouisMissouriUSA
| | | | - Reisa A. Sperling
- Center for Alzheimer Research and TreatmentBrigham and Women's HospitalMassachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Charlotte E. Teunissen
- Neurochemistry LaboratoryClinical Chemistry departmentAmsterdam NeuroscienceProgram NeurodegenerationAmsterdam University Medical CentersVrije Universiteit AmsterdamAmsterdamThe Netherlands
| | | | | | - Hui Zheng
- Huffington Center on AgingBaylor College of MedicineHoustonTexasUSA
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12
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Wang Q, Schindler SE, Chen G, Mckay NS, McCullough A, Flores S, Liu J, Sun Z, Wang S, Wang W, Hassenstab J, Cruchaga C, Perrin RJ, Fagan AM, Morris JC, Wang Y, Benzinger TLS. Investigating White Matter Neuroinflammation in Alzheimer Disease Using Diffusion-Based Neuroinflammation Imaging. Neurology 2024; 102:e208013. [PMID: 38315956 PMCID: PMC10890836 DOI: 10.1212/wnl.0000000000208013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 10/13/2023] [Indexed: 02/07/2024] Open
Abstract
BACKGROUND AND OBJECTIVES Alzheimer disease (AD) is primarily associated with accumulations of amyloid plaques and tau tangles in gray matter, however, it is now acknowledged that neuroinflammation, particularly in white matter (WM), significantly contributes to the development and progression of AD. This study aims to investigate WM neuroinflammation in the continuum of AD and its association with AD pathologies and cognition using diffusion-based neuroinflammation imaging (NII). METHODS This is a cross-sectional, single-center, retrospective evaluation conducted on an observational study of 310 older research participants who were enrolled in the Knight Alzheimer's Disease Research Center cohort. Hindered water ratio (HR), an index of WM neuroinflammation, was quantified by a noninvasive diffusion MRI method, NII. The alterations of NII-HR were investigated at different AD stages, classified based on CSF concentrations of β-amyloid (Aβ) 42/Aβ40 for amyloid and phosphorylated tau181 (p-tau181) for tau. On the voxel and regional levels, the relationship between NII-HR and CSF markers of amyloid, tau, and neuroinflammation were examined, as well as cognition. RESULTS This cross-sectional study included 310 participants (mean age 67.1 [±9.1] years), with 52 percent being female. Subgroups included 120 individuals (38.7%) with CSF measures of soluble triggering receptor expressed on myeloid cells 2, 80 participants (25.8%) with CSF measures of chitinase-3-like protein 1, and 110 individuals (35.5%) with longitudinal cognitive measures. The study found that cognitively normal individuals with positive CSF Aβ42/Aβ40 and p-tau181 had higher HR than healthy controls and those with positive CSF Aβ42/Aβ40 but negative p-tau181. WM tracts with elevated NII-HR in individuals with positive CSF Aβ42/Aβ40 and p-tau181 were primarily located in the posterior brain regions while those with elevated NII-HR in individuals with positive CSF Aβ42/Aβ40 and p-tau181 connected the posterior and anterior brain regions. A significant negative correlation between NII-HR and CSF Aβ42/Aβ40 was found in individuals with positive CSF Aβ42/Aβ40. Baseline NII-HR correlated with baseline cognitive composite score and predicted longitudinal cognitive decline. DISCUSSION Those findings suggest that WM neuroinflammation undergoes alterations before the onset of AD clinical symptoms and that it interacts with amyloidosis. This highlights the potential value of noninvasive monitoring of WM neuroinflammation in AD progression and treatment.
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Affiliation(s)
- Qing Wang
- From the Mallinckrodt Institute of Radiology (Q.W., G.C., N.S.M., A.M., S.F., Y.W., T.L.S.B.), Knight Alzheimer Disease Research Center (Q.W., S.E.S., G.C., N.S.M., A.M., J.H., R.J.P., A.M.F., J.C.M., T.L.S.B.), Department of Neurology (S.E.S., J.H., C.C., A.M.F., J.C.M.), Department of Surgery (J.L.), Department of Biomedical Engineering (Z.S.), Department of Electrical and System Engineering (S.W., W.W., Y.W.), Department of Psychiatry (C.C.), Department of Pathology & Immunology (R.J.P.), Department of Obstetrics & Gynecology (Y.W.), and Department of Neurosurgery (T.L.S.B.), Washington University School of Medicine, St. Louis, MO
| | - Suzanne E Schindler
- From the Mallinckrodt Institute of Radiology (Q.W., G.C., N.S.M., A.M., S.F., Y.W., T.L.S.B.), Knight Alzheimer Disease Research Center (Q.W., S.E.S., G.C., N.S.M., A.M., J.H., R.J.P., A.M.F., J.C.M., T.L.S.B.), Department of Neurology (S.E.S., J.H., C.C., A.M.F., J.C.M.), Department of Surgery (J.L.), Department of Biomedical Engineering (Z.S.), Department of Electrical and System Engineering (S.W., W.W., Y.W.), Department of Psychiatry (C.C.), Department of Pathology & Immunology (R.J.P.), Department of Obstetrics & Gynecology (Y.W.), and Department of Neurosurgery (T.L.S.B.), Washington University School of Medicine, St. Louis, MO
| | - Gengsheng Chen
- From the Mallinckrodt Institute of Radiology (Q.W., G.C., N.S.M., A.M., S.F., Y.W., T.L.S.B.), Knight Alzheimer Disease Research Center (Q.W., S.E.S., G.C., N.S.M., A.M., J.H., R.J.P., A.M.F., J.C.M., T.L.S.B.), Department of Neurology (S.E.S., J.H., C.C., A.M.F., J.C.M.), Department of Surgery (J.L.), Department of Biomedical Engineering (Z.S.), Department of Electrical and System Engineering (S.W., W.W., Y.W.), Department of Psychiatry (C.C.), Department of Pathology & Immunology (R.J.P.), Department of Obstetrics & Gynecology (Y.W.), and Department of Neurosurgery (T.L.S.B.), Washington University School of Medicine, St. Louis, MO
| | - Nicole S Mckay
- From the Mallinckrodt Institute of Radiology (Q.W., G.C., N.S.M., A.M., S.F., Y.W., T.L.S.B.), Knight Alzheimer Disease Research Center (Q.W., S.E.S., G.C., N.S.M., A.M., J.H., R.J.P., A.M.F., J.C.M., T.L.S.B.), Department of Neurology (S.E.S., J.H., C.C., A.M.F., J.C.M.), Department of Surgery (J.L.), Department of Biomedical Engineering (Z.S.), Department of Electrical and System Engineering (S.W., W.W., Y.W.), Department of Psychiatry (C.C.), Department of Pathology & Immunology (R.J.P.), Department of Obstetrics & Gynecology (Y.W.), and Department of Neurosurgery (T.L.S.B.), Washington University School of Medicine, St. Louis, MO
| | - Austin McCullough
- From the Mallinckrodt Institute of Radiology (Q.W., G.C., N.S.M., A.M., S.F., Y.W., T.L.S.B.), Knight Alzheimer Disease Research Center (Q.W., S.E.S., G.C., N.S.M., A.M., J.H., R.J.P., A.M.F., J.C.M., T.L.S.B.), Department of Neurology (S.E.S., J.H., C.C., A.M.F., J.C.M.), Department of Surgery (J.L.), Department of Biomedical Engineering (Z.S.), Department of Electrical and System Engineering (S.W., W.W., Y.W.), Department of Psychiatry (C.C.), Department of Pathology & Immunology (R.J.P.), Department of Obstetrics & Gynecology (Y.W.), and Department of Neurosurgery (T.L.S.B.), Washington University School of Medicine, St. Louis, MO
| | - Shaney Flores
- From the Mallinckrodt Institute of Radiology (Q.W., G.C., N.S.M., A.M., S.F., Y.W., T.L.S.B.), Knight Alzheimer Disease Research Center (Q.W., S.E.S., G.C., N.S.M., A.M., J.H., R.J.P., A.M.F., J.C.M., T.L.S.B.), Department of Neurology (S.E.S., J.H., C.C., A.M.F., J.C.M.), Department of Surgery (J.L.), Department of Biomedical Engineering (Z.S.), Department of Electrical and System Engineering (S.W., W.W., Y.W.), Department of Psychiatry (C.C.), Department of Pathology & Immunology (R.J.P.), Department of Obstetrics & Gynecology (Y.W.), and Department of Neurosurgery (T.L.S.B.), Washington University School of Medicine, St. Louis, MO
| | - Jingxia Liu
- From the Mallinckrodt Institute of Radiology (Q.W., G.C., N.S.M., A.M., S.F., Y.W., T.L.S.B.), Knight Alzheimer Disease Research Center (Q.W., S.E.S., G.C., N.S.M., A.M., J.H., R.J.P., A.M.F., J.C.M., T.L.S.B.), Department of Neurology (S.E.S., J.H., C.C., A.M.F., J.C.M.), Department of Surgery (J.L.), Department of Biomedical Engineering (Z.S.), Department of Electrical and System Engineering (S.W., W.W., Y.W.), Department of Psychiatry (C.C.), Department of Pathology & Immunology (R.J.P.), Department of Obstetrics & Gynecology (Y.W.), and Department of Neurosurgery (T.L.S.B.), Washington University School of Medicine, St. Louis, MO
| | - Zhexian Sun
- From the Mallinckrodt Institute of Radiology (Q.W., G.C., N.S.M., A.M., S.F., Y.W., T.L.S.B.), Knight Alzheimer Disease Research Center (Q.W., S.E.S., G.C., N.S.M., A.M., J.H., R.J.P., A.M.F., J.C.M., T.L.S.B.), Department of Neurology (S.E.S., J.H., C.C., A.M.F., J.C.M.), Department of Surgery (J.L.), Department of Biomedical Engineering (Z.S.), Department of Electrical and System Engineering (S.W., W.W., Y.W.), Department of Psychiatry (C.C.), Department of Pathology & Immunology (R.J.P.), Department of Obstetrics & Gynecology (Y.W.), and Department of Neurosurgery (T.L.S.B.), Washington University School of Medicine, St. Louis, MO
| | - Sicheng Wang
- From the Mallinckrodt Institute of Radiology (Q.W., G.C., N.S.M., A.M., S.F., Y.W., T.L.S.B.), Knight Alzheimer Disease Research Center (Q.W., S.E.S., G.C., N.S.M., A.M., J.H., R.J.P., A.M.F., J.C.M., T.L.S.B.), Department of Neurology (S.E.S., J.H., C.C., A.M.F., J.C.M.), Department of Surgery (J.L.), Department of Biomedical Engineering (Z.S.), Department of Electrical and System Engineering (S.W., W.W., Y.W.), Department of Psychiatry (C.C.), Department of Pathology & Immunology (R.J.P.), Department of Obstetrics & Gynecology (Y.W.), and Department of Neurosurgery (T.L.S.B.), Washington University School of Medicine, St. Louis, MO
| | - Wenshang Wang
- From the Mallinckrodt Institute of Radiology (Q.W., G.C., N.S.M., A.M., S.F., Y.W., T.L.S.B.), Knight Alzheimer Disease Research Center (Q.W., S.E.S., G.C., N.S.M., A.M., J.H., R.J.P., A.M.F., J.C.M., T.L.S.B.), Department of Neurology (S.E.S., J.H., C.C., A.M.F., J.C.M.), Department of Surgery (J.L.), Department of Biomedical Engineering (Z.S.), Department of Electrical and System Engineering (S.W., W.W., Y.W.), Department of Psychiatry (C.C.), Department of Pathology & Immunology (R.J.P.), Department of Obstetrics & Gynecology (Y.W.), and Department of Neurosurgery (T.L.S.B.), Washington University School of Medicine, St. Louis, MO
| | - Jason Hassenstab
- From the Mallinckrodt Institute of Radiology (Q.W., G.C., N.S.M., A.M., S.F., Y.W., T.L.S.B.), Knight Alzheimer Disease Research Center (Q.W., S.E.S., G.C., N.S.M., A.M., J.H., R.J.P., A.M.F., J.C.M., T.L.S.B.), Department of Neurology (S.E.S., J.H., C.C., A.M.F., J.C.M.), Department of Surgery (J.L.), Department of Biomedical Engineering (Z.S.), Department of Electrical and System Engineering (S.W., W.W., Y.W.), Department of Psychiatry (C.C.), Department of Pathology & Immunology (R.J.P.), Department of Obstetrics & Gynecology (Y.W.), and Department of Neurosurgery (T.L.S.B.), Washington University School of Medicine, St. Louis, MO
| | - Carlos Cruchaga
- From the Mallinckrodt Institute of Radiology (Q.W., G.C., N.S.M., A.M., S.F., Y.W., T.L.S.B.), Knight Alzheimer Disease Research Center (Q.W., S.E.S., G.C., N.S.M., A.M., J.H., R.J.P., A.M.F., J.C.M., T.L.S.B.), Department of Neurology (S.E.S., J.H., C.C., A.M.F., J.C.M.), Department of Surgery (J.L.), Department of Biomedical Engineering (Z.S.), Department of Electrical and System Engineering (S.W., W.W., Y.W.), Department of Psychiatry (C.C.), Department of Pathology & Immunology (R.J.P.), Department of Obstetrics & Gynecology (Y.W.), and Department of Neurosurgery (T.L.S.B.), Washington University School of Medicine, St. Louis, MO
| | - Richard J Perrin
- From the Mallinckrodt Institute of Radiology (Q.W., G.C., N.S.M., A.M., S.F., Y.W., T.L.S.B.), Knight Alzheimer Disease Research Center (Q.W., S.E.S., G.C., N.S.M., A.M., J.H., R.J.P., A.M.F., J.C.M., T.L.S.B.), Department of Neurology (S.E.S., J.H., C.C., A.M.F., J.C.M.), Department of Surgery (J.L.), Department of Biomedical Engineering (Z.S.), Department of Electrical and System Engineering (S.W., W.W., Y.W.), Department of Psychiatry (C.C.), Department of Pathology & Immunology (R.J.P.), Department of Obstetrics & Gynecology (Y.W.), and Department of Neurosurgery (T.L.S.B.), Washington University School of Medicine, St. Louis, MO
| | - Anne M Fagan
- From the Mallinckrodt Institute of Radiology (Q.W., G.C., N.S.M., A.M., S.F., Y.W., T.L.S.B.), Knight Alzheimer Disease Research Center (Q.W., S.E.S., G.C., N.S.M., A.M., J.H., R.J.P., A.M.F., J.C.M., T.L.S.B.), Department of Neurology (S.E.S., J.H., C.C., A.M.F., J.C.M.), Department of Surgery (J.L.), Department of Biomedical Engineering (Z.S.), Department of Electrical and System Engineering (S.W., W.W., Y.W.), Department of Psychiatry (C.C.), Department of Pathology & Immunology (R.J.P.), Department of Obstetrics & Gynecology (Y.W.), and Department of Neurosurgery (T.L.S.B.), Washington University School of Medicine, St. Louis, MO
| | - John C Morris
- From the Mallinckrodt Institute of Radiology (Q.W., G.C., N.S.M., A.M., S.F., Y.W., T.L.S.B.), Knight Alzheimer Disease Research Center (Q.W., S.E.S., G.C., N.S.M., A.M., J.H., R.J.P., A.M.F., J.C.M., T.L.S.B.), Department of Neurology (S.E.S., J.H., C.C., A.M.F., J.C.M.), Department of Surgery (J.L.), Department of Biomedical Engineering (Z.S.), Department of Electrical and System Engineering (S.W., W.W., Y.W.), Department of Psychiatry (C.C.), Department of Pathology & Immunology (R.J.P.), Department of Obstetrics & Gynecology (Y.W.), and Department of Neurosurgery (T.L.S.B.), Washington University School of Medicine, St. Louis, MO
| | - Yong Wang
- From the Mallinckrodt Institute of Radiology (Q.W., G.C., N.S.M., A.M., S.F., Y.W., T.L.S.B.), Knight Alzheimer Disease Research Center (Q.W., S.E.S., G.C., N.S.M., A.M., J.H., R.J.P., A.M.F., J.C.M., T.L.S.B.), Department of Neurology (S.E.S., J.H., C.C., A.M.F., J.C.M.), Department of Surgery (J.L.), Department of Biomedical Engineering (Z.S.), Department of Electrical and System Engineering (S.W., W.W., Y.W.), Department of Psychiatry (C.C.), Department of Pathology & Immunology (R.J.P.), Department of Obstetrics & Gynecology (Y.W.), and Department of Neurosurgery (T.L.S.B.), Washington University School of Medicine, St. Louis, MO
| | - Tammie L S Benzinger
- From the Mallinckrodt Institute of Radiology (Q.W., G.C., N.S.M., A.M., S.F., Y.W., T.L.S.B.), Knight Alzheimer Disease Research Center (Q.W., S.E.S., G.C., N.S.M., A.M., J.H., R.J.P., A.M.F., J.C.M., T.L.S.B.), Department of Neurology (S.E.S., J.H., C.C., A.M.F., J.C.M.), Department of Surgery (J.L.), Department of Biomedical Engineering (Z.S.), Department of Electrical and System Engineering (S.W., W.W., Y.W.), Department of Psychiatry (C.C.), Department of Pathology & Immunology (R.J.P.), Department of Obstetrics & Gynecology (Y.W.), and Department of Neurosurgery (T.L.S.B.), Washington University School of Medicine, St. Louis, MO
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13
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Kuchenbecker LA, Tipton PW, Martens Y, Brier MR, Satyadev N, Dunham SR, Lazar EB, Dacquel MV, Henson RL, Bu G, Geschwind MD, Morris JC, Schindler SE, Herries E, Graff-Radford NR, Day GS. Diagnostic Utility of Cerebrospinal Fluid Biomarkers in Patients with Rapidly Progressive Dementia. Ann Neurol 2024; 95:299-313. [PMID: 37897306 PMCID: PMC10842089 DOI: 10.1002/ana.26822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 10/13/2023] [Accepted: 10/22/2023] [Indexed: 10/30/2023]
Abstract
OBJECTIVE This study was undertaken to apply established and emerging cerebrospinal fluid (CSF) biomarkers to improve diagnostic accuracy in patients with rapidly progressive dementia (RPD). Overlap in clinical presentation and results of diagnostic tests confounds etiologic diagnosis in patients with RPD. Objective measures are needed to improve diagnostic accuracy and to recognize patients with potentially treatment-responsive causes of RPD. METHODS Biomarkers of Alzheimer disease neuropathology (amyloid-β 42/40 ratio, phosphorylated tau [p-tau181, p-tau231]), neuroaxonal/neuronal injury (neurofilament light chain [NfL], visinin-like protein-1 [VILIP-1], total tau), neuroinflammation (chitinase-3-like protein [YKL-40], soluble triggering receptor expressed on myeloid cells 2 [sTREM2], glial fibrillary acidic protein [GFAP], monocyte chemoattractant protein-1 [MCP-1]), and synaptic dysfunction (synaptosomal-associated protein 25kDa, neurogranin) were measured in CSF obtained at presentation from 78 prospectively accrued patients with RPD due to neurodegenerative, vascular, and autoimmune/inflammatory diseases; 35 age- and sex-matched patients with typically progressive neurodegenerative disease; and 72 cognitively normal controls. Biomarker levels were compared across etiologic diagnoses, by potential treatment responsiveness, and between patients with typical and rapidly progressive presentations of neurodegenerative disease. RESULTS Alzheimer disease biomarkers were associated with neurodegenerative causes of RPD. High NfL, sTREM2, and YKL-40 and low VILIP-1 identified patients with autoimmune/inflammatory diseases. MCP-1 levels were highest in patients with vascular causes of RPD. A multivariate model including GFAP, MCP-1, p-tau181, and sTREM2 identified the 44 patients with treatment-responsive causes of RPD with 89% accuracy. Minimal differences were observed between typical and rapidly progressive presentations of neurodegenerative disease. INTERPRETATION Selected CSF biomarkers at presentation were associated with etiologic diagnoses and treatment responsiveness in patients with heterogeneous causes of RPD. The ability of cross-sectional biomarkers to inform upon mechanisms that drive rapidly progressive neurodegenerative disease is less clear. ANN NEUROL 2024;95:299-313.
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Affiliation(s)
| | - Philip W Tipton
- Mayo Clinic Florida, Department of Neurology; Jacksonville, FL 32224, USA
| | - Yuka Martens
- Mayo Clinic Florida, Department of Neuroscience; Jacksonville, FL 32224, USA
| | - Matthew R Brier
- Washington University School of Medicine, Department of Neurology, Saint Louis, MO 63110, USA
| | - Nihal Satyadev
- Mayo Clinic Florida, Department of Neurology; Jacksonville, FL 32224, USA
| | - S Richard Dunham
- Washington University School of Medicine, Department of Neurology, Saint Louis, MO 63110, USA
| | - Evelyn B Lazar
- Mayo Clinic Florida, Department of Neurology; Jacksonville, FL 32224, USA
- Hackensack Meridian JFK University Medical Center, Edison, NJ 08820, USA
| | - Maxwell V Dacquel
- Mayo Clinic Florida, Department of Neuroscience; Jacksonville, FL 32224, USA
| | - Rachel L Henson
- Washington University School of Medicine, Department of Neurology, Saint Louis, MO 63110, USA
| | - Guojun Bu
- Mayo Clinic Florida, Department of Neuroscience; Jacksonville, FL 32224, USA
| | - Michael D Geschwind
- University of California San Francisco, Department of Neurology, San Francisco, CA 94143, USA
| | - John C Morris
- Washington University School of Medicine, Department of Neurology, Saint Louis, MO 63110, USA
| | - Suzanne E Schindler
- Washington University School of Medicine, Department of Neurology, Saint Louis, MO 63110, USA
| | - Elizabeth Herries
- Washington University School of Medicine, Department of Neurology, Saint Louis, MO 63110, USA
| | | | - Gregory S Day
- Mayo Clinic Florida, Department of Neurology; Jacksonville, FL 32224, USA
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14
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Xiong C, Schindler SE, Henson RL, Wolk D, Shaw LM, Agboola F, Morris JC, Lu R, Luo J. Correlational analyses of biomarkers that are harmonized through a bridging study due to measurement errors. Stat Methods Med Res 2024; 33:185-202. [PMID: 37994004 PMCID: PMC10939855 DOI: 10.1177/09622802231215810] [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] [Indexed: 11/24/2023]
Abstract
Evaluating correlations between disease biomarkers and clinical outcomes is crucial in biomedical research. During the early stages of many chronic diseases, changes in biomarkers and clinical outcomes are often subtle. A major challenge to detecting subtle correlations is that studies with large sample sizes are usually needed to achieve sufficient statistical power. This challenge is even greater when biofluid and imaging biomarker data are used because the required procedures are burdensome, perceived as invasive, and/or expensive, limiting sample sizes in individual studies. Combining data across multiple studies may increase statistical power, but biomarker data may be generated using different assay platforms, scanner types, or processing protocols, which may affect measured biomarker values. Therefore, harmonizing biomarker data is essential to combining data across studies. Bridging studies involve re-processing of a subset of samples or imaging scans to evaluate how biomarker values vary by studies. This presents an analytic challenge on how to best harmonize biomarker data across studies to allow unbiased and optimal estimates of their correlations with standardized clinical outcomes. We conceptualize that a latent biomarker underlies the observed biomarkers across studies, and propose a novel approach that integrates the data in the bridging study with the study-specific biomarker data for estimating the biological correlations between biomarkers and clinical outcomes. Through extensive simulations, we compare our method to several alternative methods/algorithms often used to estimate the correlations. Finally, we demonstrate the application of this methodology to a real-world multi-center Alzheimer's disease biomarker study to correlate cerebrospinal fluid biomarker concentrations with cognitive outcomes.
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Affiliation(s)
- Chengjie Xiong
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Suzanne E. Schindler
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Rachel L. Henson
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - David Wolk
- Perelman School of Medicine, University of Pennsylvania
| | - Leslie M. Shaw
- Perelman School of Medicine, University of Pennsylvania
- Department of Pathology and Laboratory Medicine, University of Pennsylvania
| | - Folasade Agboola
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - John C. Morris
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Departments of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA
| | - Ruijin Lu
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Jingqin Luo
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
- Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA
- Siteman Cancer Center Biostatistics Core, Washington University School of Medicine, St. Louis, MO, USA
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15
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Chen G, McKay NS, Gordon BA, Liu J, Joseph-Mathurin N, Schindler SE, Hassenstab J, Aschenbrenner AJ, Wang Q, Schultz SA, Su Y, LaMontagne PJ, Keefe SJ, Massoumzadeh P, Cruchaga C, Xiong C, Morris JC, Benzinger TLS. Predicting cognitive decline: Which is more useful, baseline amyloid levels or longitudinal change? Neuroimage Clin 2023; 41:103551. [PMID: 38150745 PMCID: PMC10788301 DOI: 10.1016/j.nicl.2023.103551] [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: 12/08/2023] [Accepted: 12/10/2023] [Indexed: 12/29/2023]
Abstract
The use of biomarkers for the early detection of Alzheimer's disease (AD) is crucial for developing potential therapeutic treatments. Positron Emission Tomography (PET) is a well-established tool used to detect β-amyloid (Aβ) plaques in the brain. Previous studies have shown that cross-sectional biomarkers can predict cognitive decline (Schindler et al.,2021). However, it is still unclear whether longitudinal Aβ-PET may have additional value for predicting time to cognitive impairment in AD. The current study aims to evaluate the ability of baseline- versus longitudinal rate of change in-11C-Pittsburgh compound B (PiB) Aβ-PET to predict cognitive decline. A cohort of 153 participants who previously underwent PiB-PET scans and comprehensive clinical assessments were used in this study. Our analyses revealed that baseline Aβ is significantly associated with the rate of change in cognitive composite scores, with cognition declining more rapidly when baseline PiB Aβ levels were higher. In contrast, no signification association was identified between the rate of change in PiB-PET Aβ and cognitive decline. Additionally, the ability of the rate of change in the PiB-PET measures to predict cognitive decline was significantly influenced by APOE ε4 carrier status. These results suggest that a single PiB-PET scan is sufficient to predict cognitive decline and that longitudinal measures of Aβ accumulation do not improve the prediction of cognitive decline once someone is amyloid positive.
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Affiliation(s)
- Gengsheng Chen
- Departments of Radiology, Washington University in St. Louis School of Medicine, St. Louis, MO, USA; Knight Alzheimer's Disease Research Center, Washington University in St. Louis School of Medicine, St. Louis, MO, USA
| | - Nicole S McKay
- Departments of Radiology, Washington University in St. Louis School of Medicine, St. Louis, MO, USA; Knight Alzheimer's Disease Research Center, Washington University in St. Louis School of Medicine, St. Louis, MO, USA
| | - Brian A Gordon
- Departments of Radiology, Washington University in St. Louis School of Medicine, St. Louis, MO, USA; Knight Alzheimer's Disease Research Center, Washington University in St. Louis School of Medicine, St. Louis, MO, USA; Hope Center for Neurological Disorders, Washington University in St. Louis School of Medicine, St. Louis, MO, USA
| | - Jingxia Liu
- Department of Surgery, Washington University in St. Louis School of Medicine, St. Louis, MO, USA
| | - Nelly Joseph-Mathurin
- Departments of Radiology, Washington University in St. Louis School of Medicine, St. Louis, MO, USA; Knight Alzheimer's Disease Research Center, Washington University in St. Louis School of Medicine, St. Louis, MO, USA
| | - Suzanne E Schindler
- Department of Neurology, Washington University in St. Louis School of Medicine, St. Louis, MO, USA
| | - Jason Hassenstab
- Department of Neurology, Washington University in St. Louis School of Medicine, St. Louis, MO, USA
| | - Andrew J Aschenbrenner
- Knight Alzheimer's Disease Research Center, Washington University in St. Louis School of Medicine, St. Louis, MO, USA; Department of Neurology, Washington University in St. Louis School of Medicine, St. Louis, MO, USA
| | - Qing Wang
- Departments of Radiology, Washington University in St. Louis School of Medicine, St. Louis, MO, USA; Knight Alzheimer's Disease Research Center, Washington University in St. Louis School of Medicine, St. Louis, MO, USA
| | - Stephanie A Schultz
- Department of Neurology Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Yi Su
- Banner Alzheimer's Institute, Phoenix, AZ, USA
| | - Pamela J LaMontagne
- Departments of Radiology, Washington University in St. Louis School of Medicine, St. Louis, MO, USA; Knight Alzheimer's Disease Research Center, Washington University in St. Louis School of Medicine, St. Louis, MO, USA
| | - Sarah J Keefe
- Departments of Radiology, Washington University in St. Louis School of Medicine, St. Louis, MO, USA; Knight Alzheimer's Disease Research Center, Washington University in St. Louis School of Medicine, St. Louis, MO, USA
| | - Parinaz Massoumzadeh
- Departments of Radiology, Washington University in St. Louis School of Medicine, St. Louis, MO, USA; Knight Alzheimer's Disease Research Center, Washington University in St. Louis School of Medicine, St. Louis, MO, USA
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University in St. Louis School of Medicine, St. Louis, MO, USA
| | - Chengjie Xiong
- Divison of Biostatistics, Washington University in St. Louis School of Medicine, St. Louis, MO, USA
| | - John C Morris
- Departments of Radiology, Washington University in St. Louis School of Medicine, St. Louis, MO, USA; Knight Alzheimer's Disease Research Center, Washington University in St. Louis School of Medicine, St. Louis, MO, USA; Hope Center for Neurological Disorders, Washington University in St. Louis School of Medicine, St. Louis, MO, USA
| | - Tammie L S Benzinger
- Departments of Radiology, Washington University in St. Louis School of Medicine, St. Louis, MO, USA; Knight Alzheimer's Disease Research Center, Washington University in St. Louis School of Medicine, St. Louis, MO, USA; Hope Center for Neurological Disorders, Washington University in St. Louis School of Medicine, St. Louis, MO, USA.
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Li M, Li Y, Schindler SE, Yen D, Sutcliffe S, Babulal GM, Benzinger TL, Lenze EJ, Bateman RJ. Design and feasibility of an Alzheimer's disease blood test study in a diverse community-based population. Alzheimers Dement 2023; 19:5387-5398. [PMID: 37204806 PMCID: PMC10657331 DOI: 10.1002/alz.13125] [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: 02/03/2023] [Revised: 04/06/2023] [Accepted: 04/07/2023] [Indexed: 05/20/2023]
Abstract
INTRODUCTION Alzheimer's disease (AD) blood tests are likely to become increasingly important in clinical practice, but they need to be evaluated in diverse groups before use in the general population. METHODS This study enrolled a community-based sample of older adults in the St. Louis, Missouri, USA area. Participants completed a blood draw, Eight-Item Informant Interview to Differentiate Aging and Dementia (AD8® ), Montreal Cognitive Assessment (MoCA), and survey about their perceptions of the blood test. A subset of participants completed additional blood collection, amyloid positron emission tomography (PET), magnetic resonance imaging (MRI), and Clinical Dementia Rating (CDR® ). RESULTS Of the 859 participants enrolled in this ongoing study, 20.6% self-identified as Black or African American. The AD8 and MoCA correlated moderately with the CDR. The blood test was well accepted by the cohort, but it was perceived more positively by White and highly educated individuals. DISCUSSION Studying an AD blood test in a diverse population is feasible and may accelerate accurate diagnosis and implementation of effective treatments. HIGHLIGHTS A diverse group of older adults was recruited to evaluate a blood amyloid test. The enrollment rate was high and the blood test was well accepted by participants. Cognitive impairment screens have moderate performance in a diverse population. Alzheimer's disease blood tests are likely to be feasible for use in real-world settings.
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Affiliation(s)
- Melody Li
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, 63110, USA
- The Tracy Family Stable Isotope Labeling Quantitation Center for Neurodegenerative Biology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Yan Li
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, 63110, USA
- The Tracy Family Stable Isotope Labeling Quantitation Center for Neurodegenerative Biology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Suzanne E. Schindler
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Daniel Yen
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Siobhan Sutcliffe
- Department of Surgery – Public Health Sciences, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Ganesh M. Babulal
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Tammie L.S. Benzinger
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Eric J. Lenze
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Randall J. Bateman
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, 63110, USA
- The Tracy Family Stable Isotope Labeling Quantitation Center for Neurodegenerative Biology, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, 63110, USA
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Bonomi S, Gupta MR, Schindler SE. Inadequate reimbursement for lumbar puncture is a potential barrier to accessing new Alzheimer's disease treatments. Alzheimers Dement 2023; 19:5849-5851. [PMID: 37718645 DOI: 10.1002/alz.13473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 08/20/2023] [Indexed: 09/19/2023]
Affiliation(s)
- Samuele Bonomi
- Department of Neurology, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
| | - Mahendra R Gupta
- Olin Business School, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Suzanne E Schindler
- Department of Neurology, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
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Dage JL, Eloyan A, Thangarajah M, Hammers DB, Fagan AM, Gray JD, Schindler SE, Snoddy C, Nudelman KNH, Faber KM, Foroud T, Aisen P, Griffin P, Grinberg LT, Iaccarino L, Kirby K, Kramer J, Koeppe R, Kukull WA, Joie RL, Mundada NS, Murray ME, Rumbaugh M, Soleimani-Meigooni DN, Toga AW, Touroutoglou A, Vemuri P, Atri A, Beckett LA, Day GS, Graff-Radford NR, Duara R, Honig LS, Jones DT, Masdeu JC, Mendez MF, Musiek E, Onyike CU, Riddle M, Rogalski E, Salloway S, Sha SJ, Turner RS, Wingo TS, Wolk DA, Womack KB, Carrillo MC, Dickerson BC, Rabinovici GD, Apostolova LG. Cerebrospinal fluid biomarkers in the Longitudinal Early-onset Alzheimer's Disease Study. Alzheimers Dement 2023; 19 Suppl 9:S115-S125. [PMID: 37491668 PMCID: PMC10877673 DOI: 10.1002/alz.13399] [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: 03/08/2023] [Revised: 05/23/2023] [Accepted: 05/23/2023] [Indexed: 07/27/2023]
Abstract
INTRODUCTION One goal of the Longitudinal Early Onset Alzheimer's Disease Study (LEADS) is to define the fluid biomarker characteristics of early-onset Alzheimer's disease (EOAD). METHODS Cerebrospinal fluid (CSF) concentrations of Aβ1-40, Aβ1-42, total tau (tTau), pTau181, VILIP-1, SNAP-25, neurogranin (Ng), neurofilament light chain (NfL), and YKL-40 were measured by immunoassay in 165 LEADS participants. The associations of biomarker concentrations with diagnostic group and standard cognitive tests were evaluated. RESULTS Biomarkers were correlated with one another. Levels of CSF Aβ42/40, pTau181, tTau, SNAP-25, and Ng in EOAD differed significantly from cognitively normal and early-onset non-AD dementia; NfL, YKL-40, and VILIP-1 did not. Across groups, all biomarkers except SNAP-25 were correlated with cognition. Within the EOAD group, Aβ42/40, NfL, Ng, and SNAP-25 were correlated with at least one cognitive measure. DISCUSSION This study provides a comprehensive analysis of CSF biomarkers in sporadic EOAD that can inform EOAD clinical trial design.
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Affiliation(s)
- Jeffrey L. Dage
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Ani Eloyan
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, Rhode Island, USA
| | - Maryanne Thangarajah
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, Rhode Island, USA
| | - Dustin B. Hammers
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Anne M. Fagan
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Julia D. Gray
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Suzanne E. Schindler
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Casey Snoddy
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Kelly N. H. Nudelman
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Kelley M. Faber
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Tatiana Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Paul Aisen
- Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, California, USA
| | - Percy Griffin
- Medical & Scientific Relations Division, Alzheimer’s Association, Chicago, Illinois, USA
| | - Lea T. Grinberg
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
- Department of Pathology, University of California – San Francisco, San Francisco, California, USA
| | - Leonardo Iaccarino
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | - Kala Kirby
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Joel Kramer
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | - Robert Koeppe
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Walter A. Kukull
- Department of Epidemiology, University of Washington, Seattle, Washington, USA
| | - Renaud La Joie
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | - Nidhi S Mundada
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | | | - Malia Rumbaugh
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | | | - Arthur W. Toga
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles, California, USA
| | - Alexandra Touroutoglou
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | | | - Alireza Atri
- Banner Sun Health Research Institute, Sun City, Arizona, USA
| | - Laurel A. Beckett
- Department of Public Health Sciences, University of California-Davis, Davis, California, USA
| | - Gregory S. Day
- Department of Neurology, Mayo Clinic, Jacksonville, Florida, USA
| | | | - Ranjan Duara
- Wien Center for Alzheimer’s Disease and Memory Disorders, Mount Sinai Medical Center, Miami, Florida, USA
| | - Lawrence S. Honig
- Taub Institute and Department of Neurology, Columbia University Irving Medical Center, New York, New York, USA
| | - David T. Jones
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Joseph C. Masdeu
- Nantz National Alzheimer Center, Houston Methodist and Weill Cornell Medicine, Houston, Texas, USA
| | - Mario F. Mendez
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - Erik Musiek
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Chiadi U. Onyike
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Meghan Riddle
- Department of Neurology, Alpert Medical School, Brown University, Providence, Rhode Island, USA
| | - Emily Rogalski
- Department of Psychiatry and Behavioral Sciences, Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Stephen Salloway
- Department of Neurology, Alpert Medical School, Brown University, Providence, Rhode Island, USA
| | - Sharon J. Sha
- Department of Neurology & Neurological Sciences, Stanford University, Palo Alto, California, USA
| | - Raymond S. Turner
- Department of Neurology, Georgetown University, Washington, D.C., USA
| | - Thomas S. Wingo
- Department of Neurology and Human Genetics, Emory University School of Medicine, Atlanta, Georgia, USA
| | - David A. Wolk
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Kyle B. Womack
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Maria C. Carrillo
- Medical & Scientific Relations Division, Alzheimer’s Association, Chicago, Illinois, USA
| | - Bradford C. Dickerson
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Gil D. Rabinovici
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | - Liana G. Apostolova
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, California, USA
- Department of Radiology and Imaging Sciences, Center for Neuroimaging, Indiana University School of Medicine Indianapolis, Indianapolis, Indiana, USA
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Llibre-Guerra JJ, Iaccarino L, Coble D, Edwards L, Li Y, McDade E, Strom A, Gordon B, Mundada N, Schindler SE, Tsoy E, Ma Y, Lu R, Fagan AM, Benzinger TLS, Soleimani-Meigooni D, Aschenbrenner AJ, Miller Z, Wang G, Kramer JH, Hassenstab J, Rosen HJ, Morris JC, Miller BL, Xiong C, Perrin RJ, Allegri R, Chrem P, Surace E, Berman SB, Chhatwal J, Masters CL, Farlow MR, Jucker M, Levin J, Fox NC, Day G, Gorno-Tempini ML, Boxer AL, La Joie R, Rabinovici GD, Bateman R. Longitudinal clinical, cognitive and biomarker profiles in dominantly inherited versus sporadic early-onset Alzheimer's disease. Brain Commun 2023; 5:fcad280. [PMID: 37942088 PMCID: PMC10629466 DOI: 10.1093/braincomms/fcad280] [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] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 10/02/2023] [Accepted: 10/17/2023] [Indexed: 11/10/2023] Open
Abstract
Approximately 5% of Alzheimer's disease cases have an early age at onset (<65 years), with 5-10% of these cases attributed to dominantly inherited mutations and the remainder considered as sporadic. The extent to which dominantly inherited and sporadic early-onset Alzheimer's disease overlap is unknown. In this study, we explored the clinical, cognitive and biomarker profiles of early-onset Alzheimer's disease, focusing on commonalities and distinctions between dominantly inherited and sporadic cases. Our analysis included 117 participants with dominantly inherited Alzheimer's disease enrolled in the Dominantly Inherited Alzheimer Network and 118 individuals with sporadic early-onset Alzheimer's disease enrolled at the University of California San Francisco Alzheimer's Disease Research Center. Baseline differences in clinical and biomarker profiles between both groups were compared using t-tests. Differences in the rates of decline were compared using linear mixed-effects models. Individuals with dominantly inherited Alzheimer's disease exhibited an earlier age-at-symptom onset compared with the sporadic group [43.4 (SD ± 8.5) years versus 54.8 (SD ± 5.0) years, respectively, P < 0.001]. Sporadic cases showed a higher frequency of atypical clinical presentations relative to dominantly inherited (56.8% versus 8.5%, respectively) and a higher frequency of APOE-ε4 (50.0% versus 28.2%, P = 0.001). Compared with sporadic early onset, motor manifestations were higher in the dominantly inherited cohort [32.5% versus 16.9% at baseline (P = 0.006) and 46.1% versus 25.4% at last visit (P = 0.001)]. At baseline, the sporadic early-onset group performed worse on category fluency (P < 0.001), Trail Making Test Part B (P < 0.001) and digit span (P < 0.001). Longitudinally, both groups demonstrated similar rates of cognitive and functional decline in the early stages. After 10 years from symptom onset, dominantly inherited participants experienced a greater decline as measured by Clinical Dementia Rating Sum of Boxes [3.63 versus 1.82 points (P = 0.035)]. CSF amyloid beta-42 levels were comparable [244 (SD ± 39.3) pg/ml dominantly inherited versus 296 (SD ± 24.8) pg/ml sporadic early onset, P = 0.06]. CSF phosphorylated tau at threonine 181 levels were higher in the dominantly inherited Alzheimer's disease cohort (87.3 versus 59.7 pg/ml, P = 0.005), but no significant differences were found for t-tau levels (P = 0.35). In summary, sporadic and inherited Alzheimer's disease differed in baseline profiles; sporadic early onset is best distinguished from dominantly inherited by later age at onset, high frequency of atypical clinical presentations and worse executive performance at baseline. Despite these differences, shared pathways in longitudinal clinical decline and CSF biomarkers suggest potential common therapeutic targets for both populations, offering valuable insights for future research and clinical trial design.
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Affiliation(s)
| | - Leonardo Iaccarino
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Dean Coble
- Division of Biostatistics, Washington University in St Louis, St Louis, MO 63108, USA
| | - Lauren Edwards
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Yan Li
- Division of Biostatistics, Washington University in St Louis, St Louis, MO 63108, USA
| | - Eric McDade
- Department of Neurology, Washington University in St Louis, St Louis, MO 63108, USA
| | - Amelia Strom
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Brian Gordon
- Malinckrodt Institute of Radiology, Washington University in St Louis, St Louis, MO 63108, USA
| | - Nidhi Mundada
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Suzanne E Schindler
- Department of Neurology, Washington University in St Louis, St Louis, MO 63108, USA
| | - Elena Tsoy
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Yinjiao Ma
- Division of Biostatistics, Washington University in St Louis, St Louis, MO 63108, USA
| | - Ruijin Lu
- Division of Biostatistics, Washington University in St Louis, St Louis, MO 63108, USA
| | - Anne M Fagan
- Department of Neurology, Washington University in St Louis, St Louis, MO 63108, USA
| | - Tammie L S Benzinger
- Malinckrodt Institute of Radiology, Washington University in St Louis, St Louis, MO 63108, USA
| | - David Soleimani-Meigooni
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | | | - Zachary Miller
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Guoqiao Wang
- Division of Biostatistics, Washington University in St Louis, St Louis, MO 63108, USA
| | - Joel H Kramer
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Jason Hassenstab
- Department of Neurology, Washington University in St Louis, St Louis, MO 63108, USA
| | - Howard J Rosen
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - John C Morris
- Department of Neurology, Washington University in St Louis, St Louis, MO 63108, USA
| | - Bruce L Miller
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Chengjie Xiong
- Division of Biostatistics, Washington University in St Louis, St Louis, MO 63108, USA
| | - Richard J Perrin
- Department of Neurology, Washington University in St Louis, St Louis, MO 63108, USA
- Department of Pathology and Immunology, Washington University in St Louis, St. Louis, MO 63108, USA
| | - Ricardo Allegri
- Department of Cognitive Neurology, Institute for Neurological Research Fleni, Buenos Aires, Argentina
| | - Patricio Chrem
- Department of Cognitive Neurology, Institute for Neurological Research Fleni, Buenos Aires, Argentina
| | - Ezequiel Surace
- Department of Cognitive Neurology, Institute for Neurological Research Fleni, Buenos Aires, Argentina
| | - Sarah B Berman
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Jasmeer Chhatwal
- Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Colin L Masters
- Florey Institute, The University of Melbourne, Melbourne 3052, Australia
| | - Martin R Farlow
- Neuroscience Center, Indiana University School of Medicine at Indianapolis, IN 46202, USA
| | - Mathias Jucker
- DZNE-German Center for Neurodegenerative Diseases, Tübingen 72076, Germany
- Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen 72076, Germany
| | - Johannes Levin
- Department of Neurology, Ludwig-Maximilians-University, Munich 80539, Germany
- German Center for Neurodegenerative Diseases, Munich 81377, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich 81377, Germany
| | - Nick C Fox
- Dementia Research Centre, Department of Neurodegenerative Disease, University College London Institute of Neurology, London WC1N 3BG, UK
| | - Gregory Day
- Department of Neurology, Mayo Clinic Florida, Jacksonville, FL 33224, USA
| | - Maria Luisa Gorno-Tempini
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Adam L Boxer
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Renaud La Joie
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Gil D Rabinovici
- Department of Neurology, UCSF 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
| | - Randall Bateman
- Department of Neurology, Washington University in St Louis, St Louis, MO 63108, USA
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20
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Lewis A, Gupta A, Oh I, Schindler SE, Ghoshal N, Abrams Z, Foraker R, Snider BJ, Morris JC, Balls-Berry J, Gupta M, Payne PRO, Lai AM. Association Between Socioeconomic Factors, Race, and Use of a Specialty Memory Clinic. Neurology 2023; 101:e1424-e1433. [PMID: 37532510 PMCID: PMC10573139 DOI: 10.1212/wnl.0000000000207674] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 06/06/2023] [Indexed: 08/04/2023] Open
Abstract
BACKGROUND AND OBJECTIVES The capacity of specialty memory clinics in the United States is very limited. If lower socioeconomic status or minoritized racial group is associated with reduced use of memory clinics, this could exacerbate health care disparities, especially if more effective treatments of Alzheimer disease become available. We aimed to understand how use of a memory clinic is associated with neighborhood-level measures of socioeconomic factors and the intersectionality of race. METHODS We conducted an observational cross-sectional study using electronic health record data to compare the neighborhood advantage of patients seen at the Washington University Memory Diagnostic Center with the catchment area using a geographical information system. Furthermore, we compared the severity of dementia at the initial visit between patients who self-identified as Black or White. We used a multinomial logistic regression model to assess the Clinical Dementia Rating at the initial visit and t tests to compare neighborhood characteristics, including Area Deprivation Index, with those of the catchment area. RESULTS A total of 4,824 patients seen at the memory clinic between 2008 and 2018 were included in this study (mean age 72.7 [SD 11.0] years, 2,712 [56%] female, 543 [11%] Black). Most of the memory clinic patients lived in more advantaged neighborhoods within the overall catchment area. The percentage of patients self-identifying as Black (11%) was lower than the average percentage of Black individuals by census tract in the catchment area (16%) (p < 0.001). Black patients lived in less advantaged neighborhoods, and Black patients were more likely than White patients to have moderate or severe dementia at their initial visit (odds ratio 1.59, 95% CI 1.11-2.25). DISCUSSION This study demonstrates that patients living in less affluent neighborhoods were less likely to be seen in one large memory clinic. Black patients were under-represented in the clinic, and Black patients had more severe dementia at their initial visit. These findings suggest that patients with a lower socioeconomic status and who identify as Black are less likely to be seen in memory clinics, which are likely to be a major point of access for any new Alzheimer disease treatments that may become available.
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Affiliation(s)
- Abigail Lewis
- From the Division of Computational and Data Sciences (A.L.), Washington University in St. Louis; Institute for Informatics (A.L., A.G., I.O., Z.A., R.F., P.R.O.P., A.M.L.), Department of Neurology (S.E.S., N.G., B.J.S., J.C.M., J.B.-B.), and Department of Psychiatry (N.G.), Washington University School of Medicine, St. Louis; and Olin Business School (M.G.), Washington University in St. Louis, MO
| | - Aditi Gupta
- From the Division of Computational and Data Sciences (A.L.), Washington University in St. Louis; Institute for Informatics (A.L., A.G., I.O., Z.A., R.F., P.R.O.P., A.M.L.), Department of Neurology (S.E.S., N.G., B.J.S., J.C.M., J.B.-B.), and Department of Psychiatry (N.G.), Washington University School of Medicine, St. Louis; and Olin Business School (M.G.), Washington University in St. Louis, MO
| | - Inez Oh
- From the Division of Computational and Data Sciences (A.L.), Washington University in St. Louis; Institute for Informatics (A.L., A.G., I.O., Z.A., R.F., P.R.O.P., A.M.L.), Department of Neurology (S.E.S., N.G., B.J.S., J.C.M., J.B.-B.), and Department of Psychiatry (N.G.), Washington University School of Medicine, St. Louis; and Olin Business School (M.G.), Washington University in St. Louis, MO
| | - Suzanne E Schindler
- From the Division of Computational and Data Sciences (A.L.), Washington University in St. Louis; Institute for Informatics (A.L., A.G., I.O., Z.A., R.F., P.R.O.P., A.M.L.), Department of Neurology (S.E.S., N.G., B.J.S., J.C.M., J.B.-B.), and Department of Psychiatry (N.G.), Washington University School of Medicine, St. Louis; and Olin Business School (M.G.), Washington University in St. Louis, MO
| | - Nupur Ghoshal
- From the Division of Computational and Data Sciences (A.L.), Washington University in St. Louis; Institute for Informatics (A.L., A.G., I.O., Z.A., R.F., P.R.O.P., A.M.L.), Department of Neurology (S.E.S., N.G., B.J.S., J.C.M., J.B.-B.), and Department of Psychiatry (N.G.), Washington University School of Medicine, St. Louis; and Olin Business School (M.G.), Washington University in St. Louis, MO
| | - Zachary Abrams
- From the Division of Computational and Data Sciences (A.L.), Washington University in St. Louis; Institute for Informatics (A.L., A.G., I.O., Z.A., R.F., P.R.O.P., A.M.L.), Department of Neurology (S.E.S., N.G., B.J.S., J.C.M., J.B.-B.), and Department of Psychiatry (N.G.), Washington University School of Medicine, St. Louis; and Olin Business School (M.G.), Washington University in St. Louis, MO
| | - Randi Foraker
- From the Division of Computational and Data Sciences (A.L.), Washington University in St. Louis; Institute for Informatics (A.L., A.G., I.O., Z.A., R.F., P.R.O.P., A.M.L.), Department of Neurology (S.E.S., N.G., B.J.S., J.C.M., J.B.-B.), and Department of Psychiatry (N.G.), Washington University School of Medicine, St. Louis; and Olin Business School (M.G.), Washington University in St. Louis, MO
| | - Barbara Joy Snider
- From the Division of Computational and Data Sciences (A.L.), Washington University in St. Louis; Institute for Informatics (A.L., A.G., I.O., Z.A., R.F., P.R.O.P., A.M.L.), Department of Neurology (S.E.S., N.G., B.J.S., J.C.M., J.B.-B.), and Department of Psychiatry (N.G.), Washington University School of Medicine, St. Louis; and Olin Business School (M.G.), Washington University in St. Louis, MO
| | - John C Morris
- From the Division of Computational and Data Sciences (A.L.), Washington University in St. Louis; Institute for Informatics (A.L., A.G., I.O., Z.A., R.F., P.R.O.P., A.M.L.), Department of Neurology (S.E.S., N.G., B.J.S., J.C.M., J.B.-B.), and Department of Psychiatry (N.G.), Washington University School of Medicine, St. Louis; and Olin Business School (M.G.), Washington University in St. Louis, MO
| | - Joyce Balls-Berry
- From the Division of Computational and Data Sciences (A.L.), Washington University in St. Louis; Institute for Informatics (A.L., A.G., I.O., Z.A., R.F., P.R.O.P., A.M.L.), Department of Neurology (S.E.S., N.G., B.J.S., J.C.M., J.B.-B.), and Department of Psychiatry (N.G.), Washington University School of Medicine, St. Louis; and Olin Business School (M.G.), Washington University in St. Louis, MO
| | - Mahendra Gupta
- From the Division of Computational and Data Sciences (A.L.), Washington University in St. Louis; Institute for Informatics (A.L., A.G., I.O., Z.A., R.F., P.R.O.P., A.M.L.), Department of Neurology (S.E.S., N.G., B.J.S., J.C.M., J.B.-B.), and Department of Psychiatry (N.G.), Washington University School of Medicine, St. Louis; and Olin Business School (M.G.), Washington University in St. Louis, MO
| | - Philip R O Payne
- From the Division of Computational and Data Sciences (A.L.), Washington University in St. Louis; Institute for Informatics (A.L., A.G., I.O., Z.A., R.F., P.R.O.P., A.M.L.), Department of Neurology (S.E.S., N.G., B.J.S., J.C.M., J.B.-B.), and Department of Psychiatry (N.G.), Washington University School of Medicine, St. Louis; and Olin Business School (M.G.), Washington University in St. Louis, MO
| | - Albert M Lai
- From the Division of Computational and Data Sciences (A.L.), Washington University in St. Louis; Institute for Informatics (A.L., A.G., I.O., Z.A., R.F., P.R.O.P., A.M.L.), Department of Neurology (S.E.S., N.G., B.J.S., J.C.M., J.B.-B.), and Department of Psychiatry (N.G.), Washington University School of Medicine, St. Louis; and Olin Business School (M.G.), Washington University in St. Louis, MO.
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21
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Stojanovic M, Schindler SE, Morris JC, Head D. Effect of exercise engagement and cardiovascular risk on neuronal injury. Alzheimers Dement 2023; 19:4454-4462. [PMID: 37534906 PMCID: PMC10592382 DOI: 10.1002/alz.13400] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 06/26/2023] [Accepted: 06/28/2023] [Indexed: 08/04/2023]
Abstract
INTRODUCTION Neuronal health as a potential underlying mechanism of the beneficial effects of exercise has been understudied in humans. Furthermore, there has been limited consideration of potential moderators (e.g., cardiovascular health) on the effects of exercise. METHODS Clinically normal middle-aged and older adults completed a validated questionnaire about exercise engagement over a 10-year period (n = 75; age 63 ± 8 years). A composite estimate of neuronal injury was formulated that included cerebrospinal fluid-based measures of visinin-like protein-1, neurogranin, synaptosomal-associated protein 25, and neurofilament light chain. Cardiovascular risk was estimated using the Framingham Risk Score. RESULTS Cross-sectional analyses showed that greater exercise engagement was associated with less neuronal injury in the group with lower cardiovascular risk (p = 0.008), but not the group with higher cardiovascular risk (p = 0.209). DISCUSSION Cardiovascular risk is an important moderator to consider when examining the effects of exercise on cognitive and neural health, and may be relevant to personalized exercise recommendations. HIGHLIGHTS We examined the association between exercise engagement and neuronal injury. Vascular risk moderated the association between exercise and neuronal injury. Cardiovascular risk may be relevant to personalized exercise recommendations.
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Affiliation(s)
- Marta Stojanovic
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, 63105
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, 63110
| | - Suzanne E. Schindler
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, 63110
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University in St. Louis, St. Louis, MO, 63110
| | - John C. Morris
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, 63110
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University in St. Louis, St. Louis, MO, 63110
| | - Denise Head
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, 63105
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University in St. Louis, St. Louis, MO, 63110
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, 63110
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22
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Hampel H, Hu Y, Cummings J, Mattke S, Iwatsubo T, Nakamura A, Vellas B, O'Bryant S, Shaw LM, Cho M, Batrla R, Vergallo A, Blennow K, Dage J, Schindler SE. Blood-based biomarkers for Alzheimer's disease: Current state and future use in a transformed global healthcare landscape. Neuron 2023; 111:2781-2799. [PMID: 37295421 PMCID: PMC10720399 DOI: 10.1016/j.neuron.2023.05.017] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Revised: 03/03/2023] [Accepted: 05/19/2023] [Indexed: 06/12/2023]
Abstract
Timely detection of the pathophysiological changes and cognitive impairment caused by Alzheimer's disease (AD) is increasingly pressing because of the advent of biomarker-guided targeted therapies that may be most effective when provided early in the disease. Currently, diagnosis and management of early AD are largely guided by clinical symptoms. FDA-approved neuroimaging and cerebrospinal fluid biomarkers can aid detection and diagnosis, but the clinical implementation of these testing modalities is limited because of availability, cost, and perceived invasiveness. Blood-based biomarkers (BBBMs) may enable earlier and faster diagnoses as well as aid in risk assessment, early detection, prognosis, and management. Herein, we review data on BBBMs that are closest to clinical implementation, particularly those based on measures of amyloid-β peptides and phosphorylated tau species. We discuss key parameters and considerations for the development and potential deployment of these BBBMs under different contexts of use and highlight challenges at the methodological, clinical, and regulatory levels.
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Affiliation(s)
- Harald Hampel
- Alzheimer's Disease and Brain Health, Eisai Inc., Nutley, NJ, USA.
| | - Yan Hu
- Alzheimer's Disease and Brain Health, Eisai Inc., Nutley, NJ, USA.
| | - Jeffrey Cummings
- Chambers-Grundy Center for Transformative Neuroscience, Pam Quirk Brain Health and Biomarker Laboratory, Department of Brain Health, School of Integrated Health Sciences, University of Nevada Las Vegas (UNLV), Las Vegas, NV, USA
| | - Soeren Mattke
- Center for Improving Chronic Illness Care, University of Southern California, Los Angeles, CA, USA
| | - Takeshi Iwatsubo
- Department of Neuropathology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Akinori Nakamura
- Department of Biomarker Research, National Center for Geriatrics and Gerontology, Obu, Japan; Department of Cognition and Behavior Science, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Bruno Vellas
- University Paul Sabatier, Gérontopôle, Toulouse University Hospital, UMR INSERM 1285, Toulouse, France
| | - Sid O'Bryant
- Institute for Translational Research, Texas College of Osteopathic Medicine, Department of Family Medicine, University of North Texas Health Science Center, Fort Worth, TX, USA
| | - Leslie M Shaw
- Perelman School of Medicine, Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Min Cho
- Alzheimer's Disease and Brain Health, Eisai Inc., Nutley, NJ, USA
| | - Richard Batrla
- Alzheimer's Disease and Brain Health, Eisai Inc., Nutley, NJ, USA
| | - Andrea Vergallo
- Alzheimer's Disease and Brain Health, Eisai Inc., Nutley, NJ, USA
| | - Kaj Blennow
- Clinical Neurochemistry Laboratory, Institute of Neuroscience and Physiology, University of Gothenburg, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Jeffrey Dage
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Suzanne E Schindler
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
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23
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Horie K, Salvadó G, Barthélemy NR, Janelidze S, Li Y, He Y, Saef B, Chen CD, Jiang H, Strandberg O, Pichet Binette A, Palmqvist S, Sato C, Sachdev P, Koyama A, Gordon BA, Benzinger TLS, Holtzman DM, Morris JC, Mattsson-Carlgren N, Stomrud E, Ossenkoppele R, Schindler SE, Hansson O, Bateman RJ. CSF MTBR-tau243 is a specific biomarker of tau tangle pathology in Alzheimer's disease. Nat Med 2023; 29:1954-1963. [PMID: 37443334 PMCID: PMC10427417 DOI: 10.1038/s41591-023-02443-z] [Citation(s) in RCA: 29] [Impact Index Per Article: 29.0] [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: 01/02/2023] [Accepted: 06/05/2023] [Indexed: 07/15/2023]
Abstract
Aggregated insoluble tau is one of two defining features of Alzheimer's disease. Because clinical symptoms are strongly correlated with tau aggregates, drug development and clinical diagnosis need cost-effective and accessible specific fluid biomarkers of tau aggregates; however, recent studies suggest that the fluid biomarkers currently available cannot specifically track tau aggregates. We show that the microtubule-binding region (MTBR) of tau containing the residue 243 (MTBR-tau243) is a new cerebrospinal fluid (CSF) biomarker specific for insoluble tau aggregates and compared it to multiple other phosphorylated tau measures (p-tau181, p-tau205, p-tau217 and p-tau231) in two independent cohorts (BioFINDER-2, n = 448; and Knight Alzheimer Disease Research Center, n = 219). MTBR-tau243 was most strongly associated with tau-positron emission tomography (PET) and cognition, whereas showing the lowest association with amyloid-PET. In combination with p-tau205, MTBR-tau243 explained most of the total variance in tau-PET burden (0.58 ≤ R2 ≤ 0.75) and the performance in predicting cognitive measures (0.34 ≤ R2 ≤ 0.48) approached that of tau-PET (0.44 ≤ R2 ≤ 0.52). MTBR-tau243 levels longitudinally increased with insoluble tau aggregates, unlike CSF p-tau species. CSF MTBR-tau243 is a specific biomarker of tau aggregate pathology, which may be utilized in interventional trials and in the diagnosis of patients. Based on these findings, we propose to revise the A/T/(N) criteria to include MTBR-tau243 as representing insoluble tau aggregates ('T').
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Grants
- P30 AG066444 NIA NIH HHS
- R01 AG070941 NIA NIH HHS
- P01 AG003991 NIA NIH HHS
- P01 AG026276 NIA NIH HHS
- P30 NS048056 NINDS NIH HHS
- S10 OD025214 NIH HHS
- The Tracy Family SILQ Center established by the Tracy Family, Richard Frimel and Gary Werths, GHR Foundation, David Payne, and the Willman Family brought together by The Foundation for Barnes-Jewish Hospital.
- Eisai industry grant
- The European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie action grant agreement No 101061836, from Greta och Johan Kocks research grants and, travel grants from the Strategic Research Area MultiPark (Multidisciplinary Research in Parkinson’s disease) at Lund University
- U.S. Department of Health & Human Services | National Institutes of Health (NIH)
- The Swedish Research Council (2016-00906), the Knut and Alice Wallenberg foundation (2017-0383), the Marianne and Marcus Wallenberg foundation (2015.0125), the Strategic Research Area MultiPark (Multidisciplinary Research in Parkinson’s disease) at Lund University, the Swedish Alzheimer Foundation (AF-939932), the Swedish Brain Foundation (FO2021-0293), The Parkinson foundation of Sweden (1280/20), the Cure Alzheimer’s fund, the Konung Gustaf V:s och Drottning Victorias Frimurarestiftelse, the Skåne University Hospital Foundation (2020-O000028), Regionalt Forskningsstöd (2020-0314) and the Swedish federal government under the ALF agreement (2018-Projekt0279)
- The Knight ADRC developmental project
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Affiliation(s)
- Kanta Horie
- The Tracy Family SILQ Center, Washington University School of Medicine, St Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Eisai Inc., Nutley, NJ, USA
| | - Gemma Salvadó
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Nicolas R Barthélemy
- The Tracy Family SILQ Center, Washington University School of Medicine, St Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Shorena Janelidze
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Yan Li
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Yingxin He
- The Tracy Family SILQ Center, Washington University School of Medicine, St Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Benjamin Saef
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Charles D Chen
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Hong Jiang
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Olof Strandberg
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Alexa Pichet Binette
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Sebastian Palmqvist
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Chihiro Sato
- The Tracy Family SILQ Center, Washington University School of Medicine, St Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | | | | | - Brian A Gordon
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Tammie L S Benzinger
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, USA
| | - David M Holtzman
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, USA
| | - John C Morris
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Niklas Mattsson-Carlgren
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Wallenberg Center for Molecular Medicine, Lund University, Lund, Sweden
- Department of Neurology, Skåne University Hospital, Lund, Sweden
| | - Erik Stomrud
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Rik Ossenkoppele
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Suzanne E Schindler
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden.
- Memory Clinic, Skåne University Hospital, Malmö, Sweden.
| | - Randall J Bateman
- The Tracy Family SILQ Center, Washington University School of Medicine, St Louis, MO, USA.
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA.
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA.
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, USA.
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24
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Bradley J, Gorijala P, Schindler SE, Sung YJ, Ances B, Fernandez MV, Cruchaga C. Genetic architecture of plasma Alzheimer disease biomarkers. Hum Mol Genet 2023; 32:2532-2543. [PMID: 37208024 PMCID: PMC10360384 DOI: 10.1093/hmg/ddad087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 04/29/2023] [Accepted: 05/16/2023] [Indexed: 05/21/2023] Open
Abstract
Genome-wide association studies (GWAS) of cerebrospinal fluid (CSF) Alzheimer's Disease (AD) biomarker levels have identified novel genes implicated in disease risk, onset and progression. However, lumbar punctures have limited availability and may be perceived as invasive. Blood collection is readily available and well accepted, but it is not clear whether plasma biomarkers will be informative for genetic studies. Here we perform genetic analyses on concentrations of plasma amyloid-β peptides Aβ40 (n = 1,467) and Aβ42 (n = 1,484), Aβ42/40 (n = 1467) total tau (n = 504), tau phosphorylated (p-tau181; n = 1079) and neurofilament light (NfL; n = 2,058). GWAS and gene-based analysis was used to identify single variant and genes associated with plasma levels. Finally, polygenic risk score and summary statistics were used to investigate overlapping genetic architecture between plasma biomarkers, CSF biomarkers and AD risk. We found a total of six genome-wide significant signals. APOE was associated with plasma Aβ42, Aβ42/40, tau, p-tau181 and NfL. We proposed 10 candidate functional genes on the basis of 12 single nucleotide polymorphism-biomarker pairs and brain differential gene expression analysis. We found a significant genetic overlap between CSF and plasma biomarkers. We also demonstrate that it is possible to improve the specificity and sensitivity of these biomarkers, when genetic variants regulating protein levels are included in the model. This current study using plasma biomarker levels as quantitative traits can be critical to identification of novel genes that impact AD and more accurate interpretation of plasma biomarker levels.
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Affiliation(s)
- Joseph Bradley
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO 63110, USA
- Hope Center for Neurologic Diseases, Washington University in St. Louis, St. Louis, MO 63110, USA
| | - Priyanka Gorijala
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Suzanne E Schindler
- Hope Center for Neurologic Diseases, Washington University in St. Louis, St. Louis, MO 63110, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Yun J Sung
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO 63110, USA
- Hope Center for Neurologic Diseases, Washington University in St. Louis, St. Louis, MO 63110, USA
| | - Beau Ances
- Hope Center for Neurologic Diseases, Washington University in St. Louis, St. Louis, MO 63110, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Maria V Fernandez
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO 63110, USA
- Hope Center for Neurologic Diseases, Washington University in St. Louis, St. Louis, MO 63110, USA
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25
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Salvadó G, Horie K, Barthélemy NR, Vogel JW, Binette AP, Chen CD, Aschenbrenner AJ, Gordon BA, Benzinger TL, Holtzman DM, Morris JC, Palmqvist S, Stomrud E, Janelidze S, Ossenkoppele R, Schindler SE, Bateman RJ, Hansson O. Novel CSF tau biomarkers can be used for disease staging of sporadic Alzheimer's disease. medRxiv 2023:2023.07.14.23292650. [PMID: 37503281 PMCID: PMC10370223 DOI: 10.1101/2023.07.14.23292650] [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] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Biological staging of individuals with Alzheimer's disease (AD) may improve diagnostic and prognostic work-up of dementia in clinical practice and the design of clinical trials. Here, we created a staging model using the Subtype and Stage Inference (SuStaIn) algorithm by evaluating cerebrospinal fluid (CSF) amyloid-β (Aβ) and tau biomarkers in 426 participants from BioFINDER-2, that represent the entire spectrum of AD. The model composition and main analyses were replicated in 222 participants from the Knight ADRC cohort. SuStaIn revealed in the two cohorts that the data was best explained by a single biomarker sequence (one subtype), and that five CSF biomarkers (ordered: Aβ42/40, tau phosphorylation occupancies at the residues 217 and 205 [pT217/T217 and pT205/T205], microtubule-binding region of tau containing the residue 243 [MTBR-tau243], and total tau) were sufficient to create an accurate disease staging model. Increasing CSF stages (0-5) were associated with increased abnormality in other AD-related biomarkers, such as Aβ- and tau-PET, and aligned with different phases of longitudinal biomarker changes consistent with current models of AD progression. Higher CSF stages at baseline were associated with higher hazard ratio of clinical decline. Our findings indicate that a common pathophysiologic molecular pathway develops across all AD patients, and that a single CSF collection is sufficient to reliably indicate the presence of both AD pathologies and the degree and stage of disease progression.
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Affiliation(s)
- Gemma Salvadó
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Kanta Horie
- The Tracy Family SILQ Center, Washington University School of Medicine, St Louis, MO, United States
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
- Eisai Inc., Nutley, NJ, United States
| | - Nicolas R. Barthélemy
- The Tracy Family SILQ Center, Washington University School of Medicine, St Louis, MO, United States
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
| | - Jacob W. Vogel
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Department of Clinical Science, Malmö, SciLifeLab, Lund University, Lund, Sweden
| | - Alexa Pichet Binette
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Charles D. Chen
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Andrew J Aschenbrenner
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Brian A. Gordon
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Tammie L.S. Benzinger
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - David M. Holtzman
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - John C. Morris
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Sebastian Palmqvist
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Erik Stomrud
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Shorena Janelidze
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Rik Ossenkoppele
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Suzanne E. Schindler
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Randall J. Bateman
- The Tracy Family SILQ Center, Washington University School of Medicine, St Louis, MO, United States
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
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26
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Phillips B, Western D, Wang L, Timsina J, Sun Y, Gorijala P, Yang C, Do A, Nykänen NP, Alvarez I, Aguilar M, Pastor P, Morris JC, Schindler SE, Fagan AM, Puerta R, García-González P, de Rojas I, Marquié M, Boada M, Ruiz A, Perlmutter JS, Ibanez L, Perrin RJ, Sung YJ, Cruchaga C. Proteome wide association studies of LRRK2 variants identify novel causal and druggable proteins for Parkinson's disease. NPJ Parkinsons Dis 2023; 9:107. [PMID: 37422510 PMCID: PMC10329646 DOI: 10.1038/s41531-023-00555-4] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 06/29/2023] [Indexed: 07/10/2023] Open
Abstract
Common and rare variants in the LRRK2 locus are associated with Parkinson's disease (PD) risk, but the downstream effects of these variants on protein levels remain unknown. We performed comprehensive proteogenomic analyses using the largest aptamer-based CSF proteomics study to date (7006 aptamers (6138 unique proteins) in 3107 individuals). The dataset comprised six different and independent cohorts (five using the SomaScan7K (ADNI, DIAN, MAP, Barcelona-1 (Pau), and Fundació ACE (Ruiz)) and the PPMI cohort using the SomaScan5K panel). We identified eleven independent SNPs in the LRRK2 locus associated with the levels of 25 proteins as well as PD risk. Of these, only eleven proteins have been previously associated with PD risk (e.g., GRN or GPNMB). Proteome-wide association study (PWAS) analyses suggested that the levels of ten of those proteins were genetically correlated with PD risk, and seven were validated in the PPMI cohort. Mendelian randomization analyses identified GPNMB, LCT, and CD68 causal for PD and nominate one more (ITGB2). These 25 proteins were enriched for microglia-specific proteins and trafficking pathways (both lysosome and intracellular). This study not only demonstrates that protein phenome-wide association studies (PheWAS) and trans-protein quantitative trail loci (pQTL) analyses are powerful for identifying novel protein interactions in an unbiased manner, but also that LRRK2 is linked with the regulation of PD-associated proteins that are enriched in microglial cells and specific lysosomal pathways.
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Affiliation(s)
- Bridget Phillips
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, 63110, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Daniel Western
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, 63110, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Lihua Wang
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, 63110, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Jigyasha Timsina
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, 63110, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Yichen Sun
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, 63110, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Priyanka Gorijala
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, 63110, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Chengran Yang
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, 63110, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Anh Do
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, 63110, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Division of Biostatistics, Washington University, St. Louis, MO, 63110, USA
| | - Niko-Petteri Nykänen
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, 63110, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Ignacio Alvarez
- Memory Disorders Unit, Department of Neurology, University Hospital Mutua Terrassa, Terrassa, Spain
| | - Miquel Aguilar
- Memory Disorders Unit, Department of Neurology, University Hospital Mutua Terrassa, Terrassa, Spain
| | - Pau Pastor
- Unit of Neurodegenerative diseases, Department of Neurology, University Hospital Germans Trias i Pujol and The Germans Trias i Pujol Research Institute (IGTP) Badalona, Barcelona, Spain
| | - John C Morris
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Suzanne E Schindler
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Anne M Fagan
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Raquel Puerta
- Ace Alzheimer Center Barcelona - Universitat Internacional de Catalunya, Barcelona, Spain
- Networking Research Center On Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Pablo García-González
- Ace Alzheimer Center Barcelona - Universitat Internacional de Catalunya, Barcelona, Spain
- Networking Research Center On Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Itziar de Rojas
- Ace Alzheimer Center Barcelona - Universitat Internacional de Catalunya, Barcelona, Spain
- Networking Research Center On Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Marta Marquié
- Ace Alzheimer Center Barcelona - Universitat Internacional de Catalunya, Barcelona, Spain
- Networking Research Center On Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Mercè Boada
- Ace Alzheimer Center Barcelona - Universitat Internacional de Catalunya, Barcelona, Spain
- Networking Research Center On Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Agustin Ruiz
- Ace Alzheimer Center Barcelona - Universitat Internacional de Catalunya, Barcelona, Spain
- Networking Research Center On Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Joel S Perlmutter
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Laura Ibanez
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, 63110, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Richard J Perrin
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Yun Ju Sung
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, 63110, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Division of Biostatistics, Washington University, St. Louis, MO, 63110, USA
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, 63110, USA.
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, 63110, USA.
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, 63110, USA.
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27
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Sung YJ, Yang C, Norton J, Johnson M, Fagan A, Bateman RJ, Perrin RJ, Morris JC, Farlow MR, Chhatwal JP, Schofield PR, Chui H, Wang F, Novotny B, Eteleeb A, Karch C, Schindler SE, Rhinn H, Johnson EC, Se-Hwee Oh H, Rutledge JE, Dammer EB, Seyfried NT, Wyss-Coray T, Harari O, Cruchaga C. Proteomics of brain, CSF, and plasma identifies molecular signatures for distinguishing sporadic and genetic Alzheimer's disease. Sci Transl Med 2023; 15:eabq5923. [PMID: 37406134 PMCID: PMC10803068 DOI: 10.1126/scitranslmed.abq5923] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 06/13/2023] [Indexed: 07/07/2023]
Abstract
Proteomic studies for Alzheimer's disease (AD) are instrumental in identifying AD pathways but often focus on single tissues and sporadic AD cases. Here, we present a proteomic study analyzing 1305 proteins in brain tissue, cerebrospinal fluid (CSF), and plasma from patients with sporadic AD, TREM2 risk variant carriers, patients with autosomal dominant AD (ADAD), and healthy individuals. We identified 8 brain, 40 CSF, and 9 plasma proteins that were altered in individuals with sporadic AD, and we replicated these findings in several external datasets. We identified a proteomic signature that differentiated TREM2 variant carriers from both individuals with sporadic AD and healthy individuals. The proteins associated with sporadic AD were also altered in patients with ADAD, but with a greater effect size. Brain-derived proteins associated with ADAD were also replicated in additional CSF samples. Enrichment analyses highlighted several pathways, including those implicated in AD (calcineurin and Apo E), Parkinson's disease (α-synuclein and LRRK2), and innate immune responses (SHC1, ERK-1, and SPP1). Our findings suggest that combined proteomics across brain tissue, CSF, and plasma can be used to identify markers for sporadic and genetically defined AD.
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Affiliation(s)
- Yun Ju Sung
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO 63108, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO 63108, USA
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Chengran Yang
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO 63108, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Joanne Norton
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO 63108, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Matt Johnson
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO 63108, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Anne Fagan
- The Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO 63108, USA
- Department of Neurology, Washington University School of Medicine, St Louis, MO 63108, USA
| | - Randall J. Bateman
- The Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO 63108, USA
- Department of Neurology, Washington University School of Medicine, St Louis, MO 63108, USA
| | - Richard J. Perrin
- The Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO 63108, USA
- Department of Neurology, Washington University School of Medicine, St Louis, MO 63108, USA
- Department of Pathology & Immunology, Washington University School of Medicine, St Louis, MO 63108, USA
| | - John C. Morris
- The Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO 63108, USA
- Department of Neurology, Washington University School of Medicine, St Louis, MO 63108, USA
- Department of Pathology & Immunology, Washington University School of Medicine, St Louis, MO 63108, USA
| | - Martin R. Farlow
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Jasmeer P. Chhatwal
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Peter R. Schofield
- Neuroscience Research Australia, Randwick, NSW, 2031, Australia
- School of Biomedical Sciences, University of New South Wales, Sydney, NSW, 2052, Australia
| | - Helena Chui
- Department of Neurology, University of Southern California, Los Angeles, CA 90089, USA
| | - Fengxian Wang
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO 63108, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Brenna Novotny
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO 63108, USA
| | - Abdallah Eteleeb
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO 63108, USA
| | - Celeste Karch
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO 63108, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Suzanne E. Schindler
- The Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO 63108, USA
- Department of Neurology, Washington University School of Medicine, St Louis, MO 63108, USA
| | - Herve Rhinn
- Department of Bioinformatics. Alector, Inc. 151 Oyster Point Blvd. #300 South San Francisco CA 94080, USA
| | - Erik C.B. Johnson
- Goizueta Alzheimer’s Disease Research Center, Emory University School of Medicine, Atlanta, GA 30329, USA
| | - Hamilton Se-Hwee Oh
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA 94304, USA
| | - Jarod Evert Rutledge
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA 94304, USA
| | - Eric B Dammer
- Goizueta Alzheimer’s Disease Research Center, Emory University School of Medicine, Atlanta, GA 30329, USA
| | - Nicholas T. Seyfried
- Goizueta Alzheimer’s Disease Research Center, Emory University School of Medicine, Atlanta, GA 30329, USA
- Department of Biochemistry, Emory School of Medicine, Atlanta, GA 30329, USA
| | - Tony Wyss-Coray
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA 94304, USA
| | - Oscar Harari
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO 63108, USA
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO 63108, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO 63108, USA
- The Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO 63108, USA
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28
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Wisch JK, Butt OH, Gordon BA, Schindler SE, Fagan AM, Henson RL, Yang C, Boerwinkle AH, Benzinger TLS, Holtzman DM, Morris JC, Cruchaga C, Ances BM. Proteomic clusters underlie heterogeneity in preclinical Alzheimer's disease progression. Brain 2023; 146:2944-2956. [PMID: 36542469 PMCID: PMC10316757 DOI: 10.1093/brain/awac484] [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: 04/05/2022] [Revised: 11/21/2022] [Accepted: 11/30/2022] [Indexed: 12/24/2022] Open
Abstract
Heterogeneity in progression to Alzheimer's disease (AD) poses challenges for both clinical prognosis and clinical trial implementation. Multiple AD-related subtypes have previously been identified, suggesting differences in receptivity to drug interventions. We identified early differences in preclinical AD biomarkers, assessed patterns for developing preclinical AD across the amyloid-tau-(neurodegeneration) [AT(N)] framework, and considered potential sources of difference by analysing the CSF proteome. Participants (n = 10) enrolled in longitudinal studies at the Knight Alzheimer Disease Research Center completed four or more lumbar punctures. These individuals were cognitively normal at baseline. Cerebrospinal fluid measures of amyloid-β (Aβ)42, phosphorylated tau (pTau181), and neurofilament light chain (NfL) as well as proteomics values were evaluated. Imaging biomarkers, including PET amyloid and tau, and structural MRI, were repeatedly obtained when available. Individuals were staged according to the amyloid-tau-(neurodegeneration) framework. Growth mixture modelling, an unsupervised clustering technique, identified three patterns of biomarker progression as measured by CSF pTau181 and Aβ42. Two groups (AD Biomarker Positive and Intermediate AD Biomarker) showed distinct progression from normal biomarker status to having biomarkers consistent with preclinical AD. A third group (AD Biomarker Negative) did not develop abnormal AD biomarkers over time. Participants grouped by CSF trajectories were re-classified using only proteomic profiles (AUCAD Biomarker Positive versus AD Biomarker Negative = 0.857, AUCAD Biomarker Positive versus Intermediate AD Biomarkers = 0.525, AUCIntermediate AD Biomarkers versus AD Biomarker Negative = 0.952). We highlight heterogeneity in the development of AD biomarkers in cognitively normal individuals. We identified some individuals who became amyloid positive before the age of 50 years. A second group, Intermediate AD Biomarkers, developed elevated CSF ptau181 significantly before becoming amyloid positive. A third group were AD Biomarker Negative over repeated testing. Our results could influence the selection of participants for specific treatments (e.g. amyloid-reducing versus other agents) in clinical trials. CSF proteome analysis highlighted additional non-AT(N) biomarkers for potential therapies, including blood-brain barrier-, vascular-, immune-, and neuroinflammatory-related targets.
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Affiliation(s)
- Julie K Wisch
- Department of Neurology, Washington University in St. Louis, St. Louis, MO 63110, USA
| | - Omar H Butt
- Department of Neurology, Washington University in St. Louis, St. Louis, MO 63110, USA
| | - Brian A Gordon
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110, USA
- Hope Center, Washington University in Saint Louis, St. Louis, MO 63110, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Suzanne E Schindler
- Department of Neurology, Washington University in St. Louis, St. Louis, MO 63110, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Anne M Fagan
- Department of Neurology, Washington University in St. Louis, St. Louis, MO 63110, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Rachel L Henson
- Department of Neurology, Washington University in St. Louis, St. Louis, MO 63110, USA
| | - Chengran Yang
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Anna H Boerwinkle
- Department of Neurology, Washington University in St. Louis, St. Louis, MO 63110, USA
| | - Tammie L S Benzinger
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - David M Holtzman
- Department of Neurology, Washington University in St. Louis, St. Louis, MO 63110, USA
- Hope Center, Washington University in Saint Louis, St. Louis, MO 63110, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - John C Morris
- Department of Neurology, Washington University in St. Louis, St. Louis, MO 63110, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Carlos Cruchaga
- Hope Center, Washington University in Saint Louis, St. Louis, MO 63110, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Beau M Ances
- Department of Neurology, Washington University in St. Louis, St. Louis, MO 63110, USA
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110, USA
- Hope Center, Washington University in Saint Louis, St. Louis, MO 63110, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, USA
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29
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Xiong C, McCue LM, Buckles V, Grant E, Agboola F, Coble D, Bateman RJ, Fagan AM, Benzinger TL, Hassenstab J, Schindler SE, McDade E, Moulder K, Gordon BA, Cruchaga C, Day GS, Ikeuchi T, Suzuki K, Allegri RF, Vöglein J, Levin J, Morris JC. Cross-sectional and longitudinal comparisons of biomarkers and cognition among asymptomatic middle-aged individuals with a parental history of either autosomal dominant or late-onset Alzheimer's disease. Alzheimers Dement 2023; 19:2923-2932. [PMID: 36640138 PMCID: PMC10345163 DOI: 10.1002/alz.12912] [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: 05/05/2022] [Revised: 11/21/2022] [Accepted: 11/23/2022] [Indexed: 01/15/2023]
Abstract
BACKGROUND Comparisons of late-onset Alzheimer's disease (LOAD) and autosomal dominant AD (ADAD) are confounded by age. METHODS We compared biomarkers from cerebrospinal fluid (CSF), magnetic resonance imaging, and amyloid imaging with Pittsburgh Compound-B (PiB) across four groups of 387 cognitively normal participants, 42 to 65 years of age, in the Dominantly Inherited Alzheimer Network (DIAN) and the Adult Children Study (ACS) of LOAD: DIAN mutation carriers (MCs) and non-carriers (NON-MCs), and ACS participants with a positive (FH+) and negative (FH-) family history of LOAD. RESULTS At baseline, MCs had the lowest age-adjusted level of CSF Aβ42 and the highest levels of total and phosphorylated tau-181, and PiB uptake. Longitudinally, MC had similar increase in PiB uptake to FH+, but drastically faster decline in hippocampal volume than others, and was the only group showing cognitive decline. DISCUSSION Preclinical ADAD and LOAD share many biomarker signatures, but cross-sectional and longitudinal differences may exist.
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Affiliation(s)
- Chengjie Xiong
- Knight Alzheimer Disease Research Center, Washington University, St. Louis, Missouri, USA
- The Dominantly Inherited Alzheimer Network, Washington University, St. Louis, Missouri, USA
- Department of Neurology, Washington University, St. Louis, Missouri, USA
- Division of Biostatistics, Washington University, St. Louis, Missouri, USA
| | - Lena M. McCue
- Division of Biostatistics, Washington University, St. Louis, Missouri, USA
| | - Virginia Buckles
- Knight Alzheimer Disease Research Center, Washington University, St. Louis, Missouri, USA
- The Dominantly Inherited Alzheimer Network, Washington University, St. Louis, Missouri, USA
- Department of Neurology, Washington University, St. Louis, Missouri, USA
| | - Elizabeth Grant
- Division of Biostatistics, Washington University, St. Louis, Missouri, USA
| | - Folasade Agboola
- Division of Biostatistics, Washington University, St. Louis, Missouri, USA
| | - Dean Coble
- Division of Biostatistics, Washington University, St. Louis, Missouri, USA
| | - Randall J. Bateman
- Knight Alzheimer Disease Research Center, Washington University, St. Louis, Missouri, USA
- The Dominantly Inherited Alzheimer Network, Washington University, St. Louis, Missouri, USA
- Department of Neurology, Washington University, St. Louis, Missouri, USA
| | - Anne M Fagan
- Knight Alzheimer Disease Research Center, Washington University, St. Louis, Missouri, USA
- The Dominantly Inherited Alzheimer Network, Washington University, St. Louis, Missouri, USA
- Department of Neurology, Washington University, St. Louis, Missouri, USA
| | - Tammie L.S. Benzinger
- Knight Alzheimer Disease Research Center, Washington University, St. Louis, Missouri, USA
- The Dominantly Inherited Alzheimer Network, Washington University, St. Louis, Missouri, USA
- Department of Radiology, Washington University, St. Louis, Missouri, USA
- Department of Neurological Surgery, Washington University, St. Louis, Missouri, USA
| | - Jason Hassenstab
- Knight Alzheimer Disease Research Center, Washington University, St. Louis, Missouri, USA
- The Dominantly Inherited Alzheimer Network, Washington University, St. Louis, Missouri, USA
- Department of Neurology, Washington University, St. Louis, Missouri, USA
- Department of Psychology, Washington University, St. Louis, Missouri, USA
| | - Suzanne E. Schindler
- Knight Alzheimer Disease Research Center, Washington University, St. Louis, Missouri, USA
- The Dominantly Inherited Alzheimer Network, Washington University, St. Louis, Missouri, USA
- Department of Neurology, Washington University, St. Louis, Missouri, USA
| | - Eric McDade
- Knight Alzheimer Disease Research Center, Washington University, St. Louis, Missouri, USA
- The Dominantly Inherited Alzheimer Network, Washington University, St. Louis, Missouri, USA
- Department of Neurology, Washington University, St. Louis, Missouri, USA
| | - Krista Moulder
- Knight Alzheimer Disease Research Center, Washington University, St. Louis, Missouri, USA
- The Dominantly Inherited Alzheimer Network, Washington University, St. Louis, Missouri, USA
- Department of Neurology, Washington University, St. Louis, Missouri, USA
| | - Brian A. Gordon
- Knight Alzheimer Disease Research Center, Washington University, St. Louis, Missouri, USA
- The Dominantly Inherited Alzheimer Network, Washington University, St. Louis, Missouri, USA
- Department of Psychology, Washington University, St. Louis, Missouri, USA
- Department of Radiology, Washington University, St. Louis, Missouri, USA
| | - Carlos Cruchaga
- Knight Alzheimer Disease Research Center, Washington University, St. Louis, Missouri, USA
- Department of Psychiatry, Washington University, St. Louis, Missouri, USA
| | - Gregory S. Day
- Department of Neurology, Mayo Clinic in Florida, Jacksonville, FL, USA
| | - Takeshi Ikeuchi
- Department of Molecular Genetics, Brain Research Institute, Niigata University, Niigata, JAPAN
| | | | | | - Jonathan Vöglein
- Department of Neurology, Ludwig-Maximilians-Universität München, Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Johannes Levin
- Department of Neurology, Ludwig-Maximilians-Universität München, Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - John C. Morris
- Knight Alzheimer Disease Research Center, Washington University, St. Louis, Missouri, USA
- The Dominantly Inherited Alzheimer Network, Washington University, St. Louis, Missouri, USA
- Department of Neurology, Washington University, St. Louis, Missouri, USA
- Department of Pathology and Immunology, Washington University, St. Louis, Missouri, USA
- Department of Physical Therapy, Washington University, St. Louis, Missouri, USA
- Department of Occupational Therapy, Washington University, St. Louis, Missouri, USA
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30
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Ferreiro AL, Choi J, Ryou J, Newcomer EP, Thompson R, Bollinger RM, Hall-Moore C, Ndao IM, Sax L, Benzinger TLS, Stark SL, Holtzman DM, Fagan AM, Schindler SE, Cruchaga C, Butt OH, Morris JC, Tarr PI, Ances BM, Dantas G. Gut microbiome composition may be an indicator of preclinical Alzheimer's disease. Sci Transl Med 2023; 15:eabo2984. [PMID: 37315112 PMCID: PMC10680783 DOI: 10.1126/scitranslmed.abo2984] [Citation(s) in RCA: 38] [Impact Index Per Article: 38.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 05/26/2023] [Indexed: 06/16/2023]
Abstract
Alzheimer's disease (AD) pathology is thought to progress from normal cognition through preclinical disease and ultimately to symptomatic AD with cognitive impairment. Recent work suggests that the gut microbiome of symptomatic patients with AD has an altered taxonomic composition compared with that of healthy, cognitively normal control individuals. However, knowledge about changes in the gut microbiome before the onset of symptomatic AD is limited. In this cross-sectional study that accounted for clinical covariates and dietary intake, we compared the taxonomic composition and gut microbial function in a cohort of 164 cognitively normal individuals, 49 of whom showed biomarker evidence of early preclinical AD. Gut microbial taxonomic profiles of individuals with preclinical AD were distinct from those of individuals without evidence of preclinical AD. The change in gut microbiome composition correlated with β-amyloid (Aβ) and tau pathological biomarkers but not with biomarkers of neurodegeneration, suggesting that the gut microbiome may change early in the disease process. We identified specific gut bacterial taxa associated with preclinical AD. Inclusion of these microbiome features improved the accuracy, sensitivity, and specificity of machine learning classifiers for predicting preclinical AD status when tested on a subset of the cohort (65 of the 164 participants). Gut microbiome correlates of preclinical AD neuropathology may improve our understanding of AD etiology and may help to identify gut-derived markers of AD risk.
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Affiliation(s)
- Aura L. Ferreiro
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA
- Department of Pathology and Immunology, Division of Laboratory and Genomic Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - JooHee Choi
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Jian Ryou
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Erin P. Newcomer
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Regina Thompson
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Rebecca M. Bollinger
- Program in Occupational Therapy, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Carla Hall-Moore
- Division of Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - I. Malick Ndao
- Division of Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Laurie Sax
- Division of Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Tammie L. S. Benzinger
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Charles F. and Joanne Knight Alzheimer’s Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Susan L. Stark
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Program in Occupational Therapy, Washington University School of Medicine, St. Louis, MO 63110, USA
- Charles F. and Joanne Knight Alzheimer’s Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - David M. Holtzman
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Charles F. and Joanne Knight Alzheimer’s Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, USA
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Anne M. Fagan
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Charles F. and Joanne Knight Alzheimer’s Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, USA
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Suzanne E. Schindler
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Charles F. and Joanne Knight Alzheimer’s Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Carlos Cruchaga
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO 63110, USA
- NeuroGenomics and Informatics, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Omar H. Butt
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - John C. Morris
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Charles F. and Joanne Knight Alzheimer’s Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Phillip I. Tarr
- Division of Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Molecular Microbiology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Beau M. Ances
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Charles F. and Joanne Knight Alzheimer’s Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, USA
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Molecular Microbiology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Gautam Dantas
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA
- Department of Pathology and Immunology, Division of Laboratory and Genomic Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Molecular Microbiology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, USA
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Cooley SA, Nelson B, Boerwinkle A, Yarasheski KE, Kirmess KM, Meyer MR, Schindler SE, Morris JC, Fagan A, Ances BM, O’Halloran JA. Plasma Aβ42/Aβ40 Ratios in Older People With Human Immunodeficiency Virus. Clin Infect Dis 2023; 76:1776-1783. [PMID: 36610788 PMCID: PMC10209437 DOI: 10.1093/cid/ciad001] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [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: 10/24/2022] [Revised: 12/19/2022] [Accepted: 01/03/2023] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND As people with human immunodeficiency virus (HIV) (PWH) age, it remains unclear whether they are at higher risk for age-related neurodegenerative disorders-for example, Alzheimer disease (AD)-and, if so, how to differentiate HIV-associated neurocognitive impairment from AD. We examined a clinically available blood biomarker test for AD (plasma amyloid-β [Aβ] 42/Aβ40 ratio) in PWH who were cognitively normal (PWH_CN) or cognitively impaired (PWH_CI) and people without HIV (PWoH) who were cognitively normal (PWoH_CN) or had symptomatic AD (PWoH_AD). METHODS A total of 66 PWH (age >40 years) (HIV RNA <50 copies/mL) and 195 PWoH provided blood samples, underwent magnetic resonance imaging, and completed a neuropsychological battery or clinical dementia rating scale. Participants were categorized by impairment (PWH_CN, n = 43; PWH_CI, n = 23; PWoH_CN, n = 138; PWoH_AD, n = 57). Plasma Aβ42 and Aβ40 concentrations were obtained using a liquid chromatography-tandem mass spectrometry method to calculate the PrecivityAD amyloid probability score (APS). The APS incorporates age and apolipoprotein E proteotype into a risk score for brain amyloidosis. Plasma Aβ42/Aβ40 ratios and APSs were compared between groups and assessed for relationships with hippocampal volumes or cognition and HIV clinical characteristics (PWH only). RESULTS The plasma Aβ42/Aβ40 ratio was significantly lower, and the APS higher, in PWoH_AD than in other groups. A lower Aβ42/Aβ40 ratio and higher APS was associated with smaller hippocampal volumes for PWoH_AD. The Aβ42/Aβ40 ratio and APS were not associated with cognition or HIV clinical measures for PWH. CONCLUSIONS The plasma Aβ42/Aβ40 ratio can serve as a screening tool for AD and may help differentiate effects of HIV from AD within PWH, but larger studies with older PWH are needed.
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Affiliation(s)
- Sarah A Cooley
- Department of Neurology, Washington University in St Louis, St Louis, Missouri, USA
| | - Brittany Nelson
- Department of Neurology, Washington University in St Louis, St Louis, Missouri, USA
| | - Anna Boerwinkle
- Department of Neurology, Washington University in St Louis, St Louis, Missouri, USA
| | | | | | | | - Suzanne E Schindler
- Department of Neurology, Washington University in St Louis, St Louis, Missouri, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St Louis, Missouri, USA
| | - John C Morris
- Department of Neurology, Washington University in St Louis, St Louis, Missouri, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St Louis, Missouri, USA
- Department of Radiology, Washington University in St Louis, St Louis, Missouri, USA
| | - Anne Fagan
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St Louis, Missouri, USA
| | - Beau M Ances
- Department of Neurology, Washington University in St Louis, St Louis, Missouri, USA
| | - Jane A O’Halloran
- Division of Infectious Diseases, Department of Medicine, Washington University School of Medicine, St Louis, Missouri, USA
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Abstract
A major transformation in dementia diagnosis and care appears imminent and will depend on three major types of biomarkers: molecular imaging, blood-based biomarkers, and cerebrospinal fluid biomarkers. Each modality has unique strengths and limitations that suggest its optimal uses in research, clinical trials, and clinical diagnosis.
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Affiliation(s)
- Suzanne E Schindler
- Department of Neurology, Washington University in St Louis, St Louis, MO, USA.
- Charles F. and Joanne Knight Alzheimer Disease Research Center, St Louis, MO, USA.
| | - Alireza Atri
- Banner Sun Health Research Institute, Banner Health, Sun City, AZ, USA
- Center for Brain/Mind Medicine, Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
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Doherty JM, Murphy SA, Bayat S, Wisch JK, Johnson AM, Walker A, Schindler SE, Ances BM, Morris JC, Babulal GM. Adverse driving behaviors increase over time as a function of preclinical Alzheimer's disease biomarkers. Alzheimers Dement 2023; 19:2014-2023. [PMID: 36419201 PMCID: PMC10182221 DOI: 10.1002/alz.12852] [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: 07/01/2022] [Revised: 10/02/2022] [Accepted: 10/05/2022] [Indexed: 11/25/2022]
Abstract
INTRODUCTION We investigated the relationship between preclinical Alzheimer's disease (AD) biomarkers and adverse driving behaviors in a longitudinal analysis of naturalistic driving data. METHODS Naturalistic driving data collected using in-vehicle dataloggers from 137 community-dwelling older adults (65+) were used to model driving behavior over time. Cerebrospinal fluid (CSF) biomarkers were used to identify individuals with preclinical AD. Additionally, hippocampal volume and cognitive biomarkers for AD were investigated in exploratory analyses. RESULTS CSF biomarkers predicted the longitudinal trajectory of the incidence of adverse driving behavior. Abnormal amyloid beta (Aβ42 /Aβ40 ) ratio was associated with an increase in adverse driving behaviors over time compared to ratios in the normal/lower range. DISCUSSION Preclinical AD is associated with increased adverse driving behavior over time that cannot be explained by cognitive changes. Driving behavior as a functional, neurobehavioral marker may serve as an early detection for decline in preclinical AD. Screening may also help prolong safe driving as older drivers age.
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Affiliation(s)
- Jason M. Doherty
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Samantha A. Murphy
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Sayeh Bayat
- Department of Biomedical Engineering, University of Calgary, Calgary, AB, Canada
- Department of Geomatics Engineering, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Julie K. Wisch
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Ann M. Johnson
- Center for Clinical Studies, Washington University in St. Louis, St. Louis, MO, USA
| | - Alexis Walker
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Suzanne E. Schindler
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
- Hope Center for Neurological Disorders, Washington University in St. Louis, MO, USA
- Knight Alzheimer’s Disease Research Center, Washington University in St. Louis, MO, USA
| | - Beau M. Ances
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
- Hope Center for Neurological Disorders, Washington University in St. Louis, MO, USA
- Knight Alzheimer’s Disease Research Center, Washington University in St. Louis, MO, USA
| | - John C. Morris
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
- Hope Center for Neurological Disorders, Washington University in St. Louis, MO, USA
- Knight Alzheimer’s Disease Research Center, Washington University in St. Louis, MO, USA
| | - Ganesh M. Babulal
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
- Knight Alzheimer’s Disease Research Center, Washington University in St. Louis, MO, USA
- Institute of Public Health, Washington University in St. Louis, St. Louis, MO, USA
- Department of Psychology, Faculty of Humanities, University of Johannesburg, South Africa
- Department of Clinical Research and Leadership, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA
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Lawler PE, Bollinger JG, Schindler SE, Hodge CR, Iglesias NJ, Krishnan V, Coulton JB, Li Y, Holtzman DM, Bateman RJ. Apolipoprotein E O-glycosylation is associated with amyloid plaques and APOE genotype. Anal Biochem 2023; 672:115156. [PMID: 37072097 DOI: 10.1016/j.ab.2023.115156] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 03/12/2023] [Accepted: 04/12/2023] [Indexed: 04/20/2023]
Abstract
Although the APOE ε4 allele is the strongest genetic risk factor for sporadic Alzheimer's disease (AD), the relationship between apolipoprotein (apoE) and AD pathophysiology is not yet fully understood. Relatively little is known about the apoE protein species, including post-translational modifications, that exist in the human periphery and CNS. To better understand these apoE species, we developed a LC-MS/MS assay that simultaneously quantifies both unmodified and O-glycosylated apoE peptides. The study cohort included 47 older individuals (age 75.6 ± 5.7 years [mean ± standard deviation]), including 23 individuals (49%) with cognitive impairment. Paired plasma and cerebrospinal fluid samples underwent analysis. We quantified O-glycosylation of two apoE protein residues - one in the hinge region and one in the C-terminal region - and found that glycosylation occupancy of the hinge region in the plasma was significantly correlated with plasma total apoE levels, APOE genotype and amyloid status as determined by CSF Aβ42/Aβ40. A model with plasma glycosylation occupancy, plasma total apoE concentration, and APOE genotype distinguished amyloid status with an AUROC of 0.89. These results suggest that plasma apoE glycosylation levels could be a marker of brain amyloidosis, and that apoE glycosylation may play a role in the pathophysiology of AD.
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Affiliation(s)
- Paige E Lawler
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA; The Tracy Family SILQ Center, Washington University School of Medicine, St. Louis, MO, USA
| | - James G Bollinger
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA; The Tracy Family SILQ Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Suzanne E Schindler
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA; Knight Alzheimer's Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Cynthia R Hodge
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA; The Tracy Family SILQ Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Nicolas J Iglesias
- School of Medicine, University of Texas Medical Branch, Galveston, TX, USA
| | - Vishal Krishnan
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - John B Coulton
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA; The Tracy Family SILQ Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Yan Li
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA; The Tracy Family SILQ Center, Washington University School of Medicine, St. Louis, MO, USA
| | - David M Holtzman
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA; Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, USA; Knight Alzheimer's Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Randall J Bateman
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA; The Tracy Family SILQ Center, Washington University School of Medicine, St. Louis, MO, USA; Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, USA; Knight Alzheimer's Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA.
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Hartz SM, Mozersky J, Schindler SE, Linnenbringer E, Wang J, Gordon BA, Raji CA, Moulder KL, West T, Benzinger TL, Cruchaga C, Hassenstab JJ, Bierut LJ, Xiong C, Morris JC. A flexible modeling approach for biomarker-based computation of absolute risk of Alzheimer's disease dementia. Alzheimers Dement 2023; 19:1452-1465. [PMID: 36178120 PMCID: PMC10060442 DOI: 10.1002/alz.12781] [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: 11/19/2021] [Revised: 06/15/2022] [Accepted: 07/21/2022] [Indexed: 01/19/2023]
Abstract
INTRODUCTION As Alzheimer's disease (AD) biomarkers rapidly develop, tools are needed that accurately and effectively communicate risk of AD dementia. METHODS We analyzed longitudinal data from >10,000 cognitively unimpaired older adults. Five-year risk of AD dementia was modeled using survival analysis. RESULTS A demographic model was developed and validated on independent data with area under the receiver operating characteristic curve (AUC) for 5-year prediction of AD dementia of 0.79. Clinical and cognitive variables (AUC = 0.79), and apolipoprotein E genotype (AUC = 0.76) were added to the demographic model. We then incorporated the risk computed from the demographic model with hazard ratios computed from independent data for amyloid positron emission tomography status and magnetic resonance imaging hippocampal volume (AUC = 0.84), and for plasma amyloid beta (Aβ)42/Aβ40 (AUC = 0.82). DISCUSSION An adaptive tool was developed and validated to compute absolute risks of AD dementia. This approach allows for improved accuracy and communication of AD risk among cognitively unimpaired older adults.
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Affiliation(s)
- Sarah M. Hartz
- Washington University School of Medicine, St. Louis, Missouri, USA
| | - Jessica Mozersky
- Washington University School of Medicine, St. Louis, Missouri, USA
| | | | | | - Junwei Wang
- Washington University School of Medicine, St. Louis, Missouri, USA
| | - Brian A. Gordon
- Washington University School of Medicine, St. Louis, Missouri, USA
| | - Cyrus A. Raji
- Washington University School of Medicine, St. Louis, Missouri, USA
| | | | - Tim West
- C2N Diagnostics, St. Louis, Missouri USA
| | | | - Carlos Cruchaga
- Washington University School of Medicine, St. Louis, Missouri, USA
| | | | - Laura J. Bierut
- Washington University School of Medicine, St. Louis, Missouri, USA
| | - Chengjie Xiong
- Washington University School of Medicine, St. Louis, Missouri, USA
| | - John C. Morris
- Washington University School of Medicine, St. Louis, Missouri, USA
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Barthélemy NR, Saef B, Li Y, Gordon BA, He Y, Horie K, Stomrud E, Salvadó G, Janelidze S, Sato C, Ovod V, Henson RL, Fagan AM, Benzinger TLS, Xiong C, Morris JC, Hansson O, Bateman RJ, Schindler SE. CSF tau phosphorylation occupancies at T217 and T205 represent improved biomarkers of amyloid and tau pathology in Alzheimer's disease. Nat Aging 2023; 3:391-401. [PMID: 37117788 PMCID: PMC10154225 DOI: 10.1038/s43587-023-00380-7] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 02/03/2023] [Indexed: 04/30/2023]
Abstract
Cerebrospinal fluid (CSF) amyloid-β peptide (Aβ)42/Aβ40 and the concentration of tau phosphorylated at site 181 (p-tau181) are well-established biomarkers of Alzheimer's disease (AD). The present study used mass spectrometry to measure concentrations of nine phosphorylated and five nonphosphorylated tau species and phosphorylation occupancies (percentage phosphorylated/nonphosphorylated) at ten sites. In the present study we show that, in 750 individuals with a median age of 71.2 years, CSF pT217/T217 predicted the presence of brain amyloid by positron emission tomography (PET) slightly better than Aβ42/Aβ40 (P = 0.02). Furthermore, for individuals with positive brain amyloid by PET (n = 263), CSF pT217/T217 was more strongly correlated with the amount of amyloid (Spearman's ρ = 0.69) than Aβ42/Aβ40 (ρ = -0.42, P < 0.0001). In two independent cohorts of participants with symptoms of AD dementia (n = 55 and n = 90), CSF pT217/T217 and pT205/T205 were better correlated with tau PET measures than CSF p-tau181 concentration. These findings suggest that CSF pT217/T217 and pT205/T205 represent improved CSF biomarkers of amyloid and tau pathology in AD.
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Affiliation(s)
- Nicolas R Barthélemy
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA.
- Tracy Family SILQ Center for Neurodegenerative Biology, St. Louis, MO, USA.
| | - Benjamin Saef
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Yan Li
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Brian A Gordon
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Yingxin He
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Tracy Family SILQ Center for Neurodegenerative Biology, St. Louis, MO, USA
| | - Kanta Horie
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Tracy Family SILQ Center for Neurodegenerative Biology, St. Louis, MO, USA
| | - Erik Stomrud
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Gemma Salvadó
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Shorena Janelidze
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Chihiro Sato
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Tracy Family SILQ Center for Neurodegenerative Biology, St. Louis, MO, USA
| | - Vitaliy Ovod
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Tracy Family SILQ Center for Neurodegenerative Biology, St. Louis, MO, USA
| | - Rachel L Henson
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Anne M Fagan
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Tammie L S Benzinger
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Chengjie Xiong
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
| | - John C Morris
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Randall J Bateman
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Tracy Family SILQ Center for Neurodegenerative Biology, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Suzanne E Schindler
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA.
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA.
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Oh IY, Schindler SE, Ghoshal N, Lai AM, Payne PRO, Gupta A. Extraction of clinical phenotypes for Alzheimer's disease dementia from clinical notes using natural language processing. JAMIA Open 2023; 6:ooad014. [PMID: 36844369 PMCID: PMC9952043 DOI: 10.1093/jamiaopen/ooad014] [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] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 01/27/2023] [Accepted: 02/10/2023] [Indexed: 02/28/2023] Open
Abstract
Objectives There is much interest in utilizing clinical data for developing prediction models for Alzheimer's disease (AD) risk, progression, and outcomes. Existing studies have mostly utilized curated research registries, image analysis, and structured electronic health record (EHR) data. However, much critical information resides in relatively inaccessible unstructured clinical notes within the EHR. Materials and Methods We developed a natural language processing (NLP)-based pipeline to extract AD-related clinical phenotypes, documenting strategies for success and assessing the utility of mining unstructured clinical notes. We evaluated the pipeline against gold-standard manual annotations performed by 2 clinical dementia experts for AD-related clinical phenotypes including medical comorbidities, biomarkers, neurobehavioral test scores, behavioral indicators of cognitive decline, family history, and neuroimaging findings. Results Documentation rates for each phenotype varied in the structured versus unstructured EHR. Interannotator agreement was high (Cohen's kappa = 0.72-1) and positively correlated with the NLP-based phenotype extraction pipeline's performance (average F1-score = 0.65-0.99) for each phenotype. Discussion We developed an automated NLP-based pipeline to extract informative phenotypes that may improve the performance of eventual machine learning predictive models for AD. In the process, we examined documentation practices for each phenotype relevant to the care of AD patients and identified factors for success. Conclusion Success of our NLP-based phenotype extraction pipeline depended on domain-specific knowledge and focus on a specific clinical domain instead of maximizing generalizability.
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Affiliation(s)
- Inez Y Oh
- Corresponding Author: Inez Y. Oh, Institute for Informatics, Washington University School of Medicine, 660 S. Euclid Ave, Campus Box 8132, St Louis, MO 63110, USA;
| | - Suzanne E Schindler
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Nupur Ghoshal
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, USA,Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Albert M Lai
- Institute for Informatics, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Philip R O Payne
- Institute for Informatics, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Aditi Gupta
- Institute for Informatics, Washington University School of Medicine, St. Louis, Missouri, USA,Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri, USA
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Millar PR, Gordon BA, Luckett PH, Benzinger TLS, Cruchaga C, Fagan AM, Hassenstab JJ, Perrin RJ, Schindler SE, Allegri RF, Day GS, Farlow MR, Mori H, Nübling G, Bateman RJ, Morris JC, Ances BM. Multimodal brain age estimates relate to Alzheimer disease biomarkers and cognition in early stages: a cross-sectional observational study. eLife 2023; 12:e81869. [PMID: 36607335 PMCID: PMC9988262 DOI: 10.7554/elife.81869] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [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: 07/14/2022] [Accepted: 12/30/2022] [Indexed: 01/07/2023] Open
Abstract
Background Estimates of 'brain-predicted age' quantify apparent brain age compared to normative trajectories of neuroimaging features. The brain age gap (BAG) between predicted and chronological age is elevated in symptomatic Alzheimer disease (AD) but has not been well explored in presymptomatic AD. Prior studies have typically modeled BAG with structural MRI, but more recently other modalities, including functional connectivity (FC) and multimodal MRI, have been explored. Methods We trained three models to predict age from FC, structural (S), or multimodal MRI (S+FC) in 390 amyloid-negative cognitively normal (CN/A-) participants (18-89 years old). In independent samples of 144 CN/A-, 154 CN/A+, and 154 cognitively impaired (CI; CDR > 0) participants, we tested relationships between BAG and AD biomarkers of amyloid and tau, as well as a global cognitive composite. Results All models predicted age in the control training set, with the multimodal model outperforming the unimodal models. All three BAG estimates were significantly elevated in CI compared to controls. FC-BAG was significantly reduced in CN/A+ participants compared to CN/A-. In CI participants only, elevated S-BAG and S+FC BAG were associated with more advanced AD pathology and lower cognitive performance. Conclusions Both FC-BAG and S-BAG are elevated in CI participants. However, FC and structural MRI also capture complementary signals. Specifically, FC-BAG may capture a unique biphasic response to presymptomatic AD pathology, while S-BAG may capture pathological progression and cognitive decline in the symptomatic stage. A multimodal age-prediction model improves sensitivity to healthy age differences. Funding This work was supported by the National Institutes of Health (P01-AG026276, P01- AG03991, P30-AG066444, 5-R01-AG052550, 5-R01-AG057680, 1-R01-AG067505, 1S10RR022984-01A1, and U19-AG032438), the BrightFocus Foundation (A2022014F), and the Alzheimer's Association (SG-20-690363-DIAN).
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Affiliation(s)
- Peter R Millar
- Department of Neurology, Washington University in St. LouisSt LouisUnited States
| | - Brian A Gordon
- Department of Radiology, Washington University in St. LouisSt LouisUnited States
| | - Patrick H Luckett
- Department of Neurosurgery, Washington University in St. LouisSt LouisUnited States
| | - Tammie LS Benzinger
- Department of Radiology, Washington University in St. LouisSt LouisUnited States
- Department of Neurosurgery, Washington University in St. LouisSt LouisUnited States
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University in St. LouisSt LouisUnited States
| | - Anne M Fagan
- Department of Neurology, Washington University in St. LouisSt LouisUnited States
| | - Jason J Hassenstab
- Department of Neurology, Washington University in St. LouisSt LouisUnited States
| | - Richard J Perrin
- Department of Neurology, Washington University in St. LouisSt LouisUnited States
- Department of Pathology and Immunology, Washington University in St. LouisSt LouisUnited States
| | - Suzanne E Schindler
- Department of Neurology, Washington University in St. LouisSt LouisUnited States
| | - Ricardo F Allegri
- Department of Cognitive Neurology, Institute for Neurological Research (FLENI)Buenos AiresArgentina
| | - Gregory S Day
- Department of Neurology, Mayo Clinic FloridaJacksonvilleUnited States
| | - Martin R Farlow
- Department of Neurology, Indiana University School of MedicineIndianapolisUnited States
| | - Hiroshi Mori
- Department of Clinical Neuroscience, Osaka Metropolitan University Medical School, Nagaoka Sutoku UniversityOsakaJapan
| | - Georg Nübling
- Department of Neurology, Ludwig-Maximilians UniversityMunichGermany
- German Center for Neurodegenerative DiseasesMunichGermany
| | - Randall J Bateman
- Department of Neurology, Washington University in St. LouisSt LouisUnited States
| | - John C Morris
- Department of Neurology, Washington University in St. LouisSt LouisUnited States
| | - Beau M Ances
- Department of Neurology, Washington University in St. LouisSt LouisUnited States
- Department of Radiology, Washington University in St. LouisSt LouisUnited States
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Chen CD, Ponisio MR, Lang JA, Flores S, Schindler SE, Fagan AM, Morris JC, Benzinger TL. Comparing Tau PET Visual Interpretation with Tau PET Quantification, Cerebrospinal Fluid Biomarkers, and Longitudinal Clinical Assessment. J Alzheimers Dis 2023; 93:765-777. [PMID: 37092225 PMCID: PMC10200228 DOI: 10.3233/jad-230032] [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] [Accepted: 03/14/2023] [Indexed: 04/25/2023]
Abstract
BACKGROUND 18F-flortaucipir PET received FDA approval to visualize aggregated neurofibrillary tangles (NFTs) in brains of adult patients with cognitive impairment being evaluated for Alzheimer's disease (AD). However, manufacturer's guidelines for visual interpretation of 18F-flortaucipir PET differ from how 18F-flortaucipir PET has been measured in research settings using standardized uptake value ratios (SUVRs). How visual interpretation relates to 18F-flortaucipir PET SUVR, cerebrospinal fluid (CSF) biomarkers, or longitudinal clinical assessment is not well understood. OBJECTIVE We compare various diagnostic methods in participants enrolled in longitudinal observational studies of aging and memory (n = 189, 23 were cognitively impaired). METHODS Participants had tau PET, Aβ PET, MRI, and clinical and cognitive evaluation within 18 months (n = 189); the majority (n = 144) also underwent lumbar puncture. Two radiologists followed manufacturer's guidelines for 18F-flortaucipir PET visual interpretation. RESULTS Visual interpretation had high agreement with SUVR (98.4%)and moderate agreement with CSF p-tau181 (86.1%). Two participants demonstrated 18F-flortaucipir uptake from meningiomas. Visual interpretation could not predict follow-up clinical assessment in 9.52% of cases. CONCLUSION Visual interpretation was highly consistent with SUVR (discordant participants had hemorrhagic infarcts or occipital-predominant AD NFT deposition) and moderately consistent with CSF p-tau181 (discordant participants had AD pathophysiology not detectable on tau PET). However, close association between AD NFT deposition and clinical onset in group-level studies does not necessarily hold at the individual level, with discrepancies arising from atypical AD, vascular dementia, or frontotemporal dementia. A better understanding of relationships across imaging, CSF biomarkers, and clinical assessment is needed to provide appropriate diagnoses for these individuals.
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Affiliation(s)
- Charles D. Chen
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Maria Rosana Ponisio
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Jordan A. Lang
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Shaney Flores
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | | | - Anne M. Fagan
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - John C. Morris
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Tammie L.S. Benzinger
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
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40
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Wisch JK, Gordon BA, Boerwinkle AH, Luckett PH, Bollinger JG, Ovod V, Li Y, Henson RL, West T, Meyer MR, Kirmess KM, Benzinger TL, Fagan AM, Morris JC, Bateman RJ, Ances BM, Schindler SE. Predicting continuous amyloid PET values with CSF and plasma Aβ42/Aβ40. Alzheimers Dement (Amst) 2023; 15:e12405. [PMID: 36874595 PMCID: PMC9980305 DOI: 10.1002/dad2.12405] [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] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 12/14/2022] [Accepted: 01/19/2023] [Indexed: 03/06/2023]
Abstract
Introduction Continuous measures of amyloid burden as measured by positron emission tomography (PET) are being used increasingly to stage Alzheimer's disease (AD). This study examined whether cerebrospinal fluid (CSF) and plasma amyloid beta (Aβ)42/Aβ40 could predict continuous values for amyloid PET. Methods CSF Aβ42 and Aβ40 were measured with automated immunoassays. Plasma Aβ42 and Aβ40 were measured with an immunoprecipitation-mass spectrometry assay. Amyloid PET was performed with Pittsburgh compound B (PiB). The continuous relationships of CSF and plasma Aβ42/Aβ40 with amyloid PET burden were modeled. Results Most participants were cognitively normal (427 of 491 [87%]) and the mean age was 69.0 ± 8.8 years. CSF Aβ42/Aβ40 predicted amyloid PET burden until a relatively high level of amyloid accumulation (69.8 Centiloids), whereas plasma Aβ42/Aβ40 predicted amyloid PET burden until a lower level (33.4 Centiloids). Discussion CSF Aβ42/Aβ40 predicts the continuous level of amyloid plaque burden over a wider range than plasma Aβ42/Aβ40 and may be useful in AD staging. Highlights Cerebrospinal fluid (CSF) amyloid beta (Aβ)42/Aβ40 predicts continuous amyloid positron emission tomography (PET) values up to a relatively high burden.Plasma Aβ42/Aβ40 is a comparatively dichotomous measure of brain amyloidosis.Models can predict regional amyloid PET burden based on CSF Aβ42/Aβ40.CSF Aβ42/Aβ40 may be useful in staging AD.
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Affiliation(s)
- Julie K. Wisch
- Department of NeurologyWashington University in Saint LouisSt. LouisMissouriUSA
| | - Brian A. Gordon
- Department of RadiologyWashington University in Saint LouisSt. LouisMissouriUSA
- Hope CenterWashington University in Saint LouisSt. LouisMissouriUSA
- Knight Alzheimer Disease Research CenterWashington University School of MedicineSt LouisMissouriUSA
| | - Anna H. Boerwinkle
- Department of NeurologyWashington University in Saint LouisSt. LouisMissouriUSA
| | - Patrick H. Luckett
- Department of NeurologyWashington University in Saint LouisSt. LouisMissouriUSA
| | - James G. Bollinger
- Department of NeurologyWashington University in Saint LouisSt. LouisMissouriUSA
- The Tracy Family SILQ Center for Neurodegenerative BiologySt. LouisMissouriUSA
| | - Vitaliy Ovod
- Department of NeurologyWashington University in Saint LouisSt. LouisMissouriUSA
- The Tracy Family SILQ Center for Neurodegenerative BiologySt. LouisMissouriUSA
| | - Yan Li
- Department of RadiologyWashington University in Saint LouisSt. LouisMissouriUSA
| | - Rachel L. Henson
- Department of NeurologyWashington University in Saint LouisSt. LouisMissouriUSA
| | - Tim West
- C2N DiagnosticsSt. LouisMissouriUSA
| | | | | | - Tammie L.S. Benzinger
- Department of RadiologyWashington University in Saint LouisSt. LouisMissouriUSA
- Knight Alzheimer Disease Research CenterWashington University School of MedicineSt LouisMissouriUSA
| | - Anne M. Fagan
- Department of NeurologyWashington University in Saint LouisSt. LouisMissouriUSA
- Knight Alzheimer Disease Research CenterWashington University School of MedicineSt LouisMissouriUSA
| | - John C. Morris
- Department of NeurologyWashington University in Saint LouisSt. LouisMissouriUSA
- Knight Alzheimer Disease Research CenterWashington University School of MedicineSt LouisMissouriUSA
| | - Randall J. Bateman
- Department of NeurologyWashington University in Saint LouisSt. LouisMissouriUSA
- The Tracy Family SILQ Center for Neurodegenerative BiologySt. LouisMissouriUSA
| | - Beau M. Ances
- Department of NeurologyWashington University in Saint LouisSt. LouisMissouriUSA
- Department of RadiologyWashington University in Saint LouisSt. LouisMissouriUSA
- Hope CenterWashington University in Saint LouisSt. LouisMissouriUSA
- Knight Alzheimer Disease Research CenterWashington University School of MedicineSt LouisMissouriUSA
| | - Suzanne E. Schindler
- Department of NeurologyWashington University in Saint LouisSt. LouisMissouriUSA
- Knight Alzheimer Disease Research CenterWashington University School of MedicineSt LouisMissouriUSA
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41
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Brand AL, Lawler PE, Bollinger JG, Li Y, Schindler SE, Li M, Lopez S, Ovod V, Nakamura A, Shaw LM, Zetterberg H, Hansson O, Bateman RJ. The performance of plasma amyloid beta measurements in identifying amyloid plaques in Alzheimer's disease: a literature review. Alzheimers Res Ther 2022; 14:195. [PMID: 36575454 PMCID: PMC9793600 DOI: 10.1186/s13195-022-01117-1] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 11/06/2022] [Indexed: 12/28/2022]
Abstract
The extracellular buildup of amyloid beta (Aβ) plaques in the brain is a hallmark of Alzheimer's disease (AD). Detection of Aβ pathology is essential for AD diagnosis and for identifying and recruiting research participants for clinical trials evaluating disease-modifying therapies. Currently, AD diagnoses are usually made by clinical assessments, although detection of AD pathology with positron emission tomography (PET) scans or cerebrospinal fluid (CSF) analysis can be used by specialty clinics. These measures of Aβ aggregation, e.g. plaques, protofibrils, and oligomers, are medically invasive and often only available at specialized medical centers or not covered by medical insurance, and PET scans are costly. Therefore, a major goal in recent years has been to identify blood-based biomarkers that can accurately detect AD pathology with cost-effective, minimally invasive procedures.To assess the performance of plasma Aβ assays in predicting amyloid burden in the central nervous system (CNS), this review compares twenty-one different manuscripts that used measurements of 42 and 40 amino acid-long Aβ (Aβ42 and Aβ40) in plasma to predict CNS amyloid status. Methodologies that quantitate Aβ42 and 40 peptides in blood via immunoassay or immunoprecipitation-mass spectrometry (IP-MS) were considered, and their ability to distinguish participants with amyloidosis compared to amyloid PET and CSF Aβ measures as reference standards was evaluated. Recent studies indicate that some IP-MS assays perform well in accurately and precisely measuring Aβ and detecting brain amyloid aggregates.
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Affiliation(s)
- Abby L. Brand
- grid.4367.60000 0001 2355 7002Department of Neurology, Washington University School of Medicine, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002The Tracy Family SILQ Center, Washington University School of Medicine, St. Louis, MO USA
| | - Paige E. Lawler
- grid.4367.60000 0001 2355 7002Department of Neurology, Washington University School of Medicine, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002The Tracy Family SILQ Center, Washington University School of Medicine, St. Louis, MO USA
| | - James G. Bollinger
- grid.4367.60000 0001 2355 7002Department of Neurology, Washington University School of Medicine, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002The Tracy Family SILQ Center, Washington University School of Medicine, St. Louis, MO USA
| | - Yan Li
- grid.4367.60000 0001 2355 7002Department of Neurology, Washington University School of Medicine, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002The Tracy Family SILQ Center, Washington University School of Medicine, St. Louis, MO USA
| | - Suzanne E. Schindler
- grid.4367.60000 0001 2355 7002Department of Neurology, Washington University School of Medicine, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002Knight Alzheimer’s Disease Research Center, Washington University School of Medicine, St. Louis, MO USA
| | - Melody Li
- grid.4367.60000 0001 2355 7002Department of Neurology, Washington University School of Medicine, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002The Tracy Family SILQ Center, Washington University School of Medicine, St. Louis, MO USA
| | - Samir Lopez
- grid.4367.60000 0001 2355 7002Department of Neurology, Washington University School of Medicine, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002The Tracy Family SILQ Center, Washington University School of Medicine, St. Louis, MO USA
| | - Vitaliy Ovod
- grid.4367.60000 0001 2355 7002Department of Neurology, Washington University School of Medicine, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002The Tracy Family SILQ Center, Washington University School of Medicine, St. Louis, MO USA
| | - Akinori Nakamura
- grid.419257.c0000 0004 1791 9005Department of Biomarker Research, National Center for Geriatrics and Gerontology, Obu, Japan ,grid.27476.300000 0001 0943 978XDepartment of Cognition and Behavior Science, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Leslie M. Shaw
- grid.25879.310000 0004 1936 8972Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
| | - Henrik Zetterberg
- grid.8761.80000 0000 9919 9582Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden ,grid.1649.a000000009445082XClinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden ,grid.83440.3b0000000121901201Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK ,grid.83440.3b0000000121901201UK Dementia Research Institute at UCL, London, UK ,grid.24515.370000 0004 1937 1450Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, China
| | - Oskar Hansson
- grid.4514.40000 0001 0930 2361Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden ,grid.411843.b0000 0004 0623 9987Memory Clinic, Skåne University Hospital, Lund, Sweden
| | - Randall J. Bateman
- grid.4367.60000 0001 2355 7002Department of Neurology, Washington University School of Medicine, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002The Tracy Family SILQ Center, Washington University School of Medicine, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002Knight Alzheimer’s Disease Research Center, Washington University School of Medicine, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO USA
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Long JM, Coble DW, Xiong C, Schindler SE, Perrin RJ, Gordon BA, Benzinger TLS, Grant E, Fagan AM, Harari O, Cruchaga C, Holtzman DM, Morris JC. Preclinical Alzheimer's disease biomarkers accurately predict cognitive and neuropathological outcomes. Brain 2022; 145:4506-4518. [PMID: 35867858 PMCID: PMC10200309 DOI: 10.1093/brain/awac250] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [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: 01/25/2022] [Revised: 05/30/2022] [Accepted: 07/20/2022] [Indexed: 01/24/2023] Open
Abstract
Alzheimer's disease biomarkers are widely accepted as surrogate markers of underlying neuropathological changes. However, few studies have evaluated whether preclinical Alzheimer's disease biomarkers predict Alzheimer's neuropathology at autopsy. We sought to determine whether amyloid PET imaging or CSF biomarkers accurately predict cognitive outcomes and Alzheimer's disease neuropathological findings. This study included 720 participants, 42-91 years of age, who were enrolled in longitudinal studies of memory and aging in the Washington University Knight Alzheimer Disease Research Center and were cognitively normal at baseline, underwent amyloid PET imaging and/or CSF collection within 1 year of baseline clinical assessment, and had subsequent clinical follow-up. Cognitive status was assessed longitudinally by Clinical Dementia Rating®. Biomarker status was assessed using predefined cut-offs for amyloid PET imaging or CSF p-tau181/amyloid-β42. Subsequently, 57 participants died and underwent neuropathologic examination. Alzheimer's disease neuropathological changes were assessed using standard criteria. We assessed the predictive value of Alzheimer's disease biomarker status on progression to cognitive impairment and for presence of Alzheimer's disease neuropathological changes. Among cognitively normal participants with positive biomarkers, 34.4% developed cognitive impairment (Clinical Dementia Rating > 0) as compared to 8.4% of those with negative biomarkers. Cox proportional hazards modelling indicated that preclinical Alzheimer's disease biomarker status, APOE ɛ4 carrier status, polygenic risk score and centred age influenced risk of developing cognitive impairment. Among autopsied participants, 90.9% of biomarker-positive participants and 8.6% of biomarker-negative participants had Alzheimer's disease neuropathological changes. Sensitivity was 87.0%, specificity 94.1%, positive predictive value 90.9% and negative predictive value 91.4% for detection of Alzheimer's disease neuropathological changes by preclinical biomarkers. Single CSF and amyloid PET baseline biomarkers were also predictive of Alzheimer's disease neuropathological changes, as well as Thal phase and Braak stage of pathology at autopsy. Biomarker-negative participants who developed cognitive impairment were more likely to exhibit non-Alzheimer's disease pathology at autopsy. The detection of preclinical Alzheimer's disease biomarkers is strongly predictive of future cognitive impairment and accurately predicts presence of Alzheimer's disease neuropathology at autopsy.
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Affiliation(s)
- Justin M Long
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
- Department of Neurology, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
| | - Dean W Coble
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
- Division of Biostatistics, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
| | - Chengjie Xiong
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
- Division of Biostatistics, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
| | - Suzanne E Schindler
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
- Department of Neurology, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
| | - Richard J Perrin
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
- Department of Neurology, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
- Department of Pathology and Immunology, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
| | - Brian A Gordon
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
| | - Tammie L S Benzinger
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
| | - Elizabeth Grant
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
- Division of Biostatistics, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
| | - Anne M Fagan
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
- Department of Neurology, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
| | - Oscar Harari
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
- Department of Psychiatry, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
| | - Carlos Cruchaga
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
- Department of Psychiatry, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
| | - David M Holtzman
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
- Department of Neurology, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
| | - John C Morris
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
- Department of Neurology, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
- Department of Pathology and Immunology, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
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43
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Gordon BA, Wisch JK, Hobbs DA, McKay NS, Schultz SA, Flores S, Dincer A, Keefe SJ, Ances BM, Morris JC, Schindler SE, Fagan AM, Benzinger TL. Comparing neuroimaging and cerebrospinal fluid markers of amyloid, tau, and neurodegenerative biomarkers: implications for understanding the biology of the disease. Alzheimers Dement 2022. [DOI: 10.1002/alz.062634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Brian A. Gordon
- Washington University in St. Louis School of Medicine St. Louis MO USA
| | - Julie K. Wisch
- Washington University in St. Louis School of Medicine St. Louis MO USA
| | - Diana A. Hobbs
- Washington University School of Medicine St. Louis MO USA
| | | | - Stephanie A. Schultz
- Massachusetts General Hospital, Brigham and Women’s Hospital, Harvard Medical School Boston MA USA
| | - Shaney Flores
- Washington University in St. Louis School of Medicine St. Louis MO USA
| | - Aylin Dincer
- Washington University in St. Louis School of Medicine St. Louis MO USA
| | - Sarah J. Keefe
- Washington University in St. Louis School of Medicine St. Louis MO USA
| | - Beau M Ances
- Washington University in St. Louis School of Medicine St. Louis MO USA
| | - John C. Morris
- Washington University in St. Louis School of Medicine St. Louis MO USA
| | | | - Anne M. Fagan
- Washington University in St. Louis School of Medicine St. Louis MO USA
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Xiong C, Wolk DA, Lah JJ, Gleason CE, Roberson ED, Benzinger TL, Schindler SE, Fagan AM, Hassenstab JJ, Moulder KL, Balls‐Berry JE, Sperling RA, Johnson KA, Levey AI, Johnson SC, Luo J, Gremminger E, Agboola F, Grant EA, Ances BM, Gordon BA, Hornbeck RC, Massoumzadeh P, Keefe SJ, Dierker D, Gray JD, Andrews J, Henson RL, Streitz M, Manzanares C, Qiu D, Mechanic‐Hamilton D, Stites SD, Shaw LM, Midgett S, Morris JC. SORTOUT‐AB: A Study of Race to Understand Alzheimer Biomarkers. Alzheimers Dement 2022. [DOI: 10.1002/alz.066301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Chengjie Xiong
- Knight Alzheimer Disease Research Center St. Louis MO USA
- Department of Biostatistics, Washington University St. Louis MO USA
| | - David A. Wolk
- Penn Alzheimer’s Disease Research Center, University of Pennsylvania Philadelphia PA USA
| | - James J. Lah
- Emory Goizueta Alzheimer's Disease Research Center Atlanta GA USA
| | - Carey E. Gleason
- University of Wisconsin School of Medicine and Public Health Madison WI USA
- Geriatric Research, Education, and Clinical Center (GRECC), Middleton Memorial Veterans Hospital Madison WI USA
- University of Wisconsin School of Medicine and Public Health Alzheimer's Disease Research Center Madison WI USA
| | - Erik D Roberson
- Department of Neurology, University of Alabama at Birmingham Birmingham AL USA
| | - Tammie L.S. Benzinger
- Knight Alzheimer Disease Research Center St. Louis MO USA
- Mallinckrodt Institute of Radiology Saint Louis MO USA
| | | | - Anne M. Fagan
- Knight Alzheimer Disease Research Center St. Louis MO USA
| | | | | | | | | | | | - Allan I. Levey
- Emory Goizueta Alzheimer's Disease Research Center Atlanta GA USA
| | - Sterling C. Johnson
- William S. Middleton Memorial Veterans Hospital Madison WI USA
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin‐Madison Madison WI USA
- Department of Medicine, University of Wisconsin‐Madison School of Medicine and Public Health Madison WI USA
| | - Jingqin Luo
- Department of Surgery, Washington University St. Louis MO USA
| | - Emily Gremminger
- Division of Biostatistics, Washington University School of Medic St. Louis MO USA
| | | | | | - Beau M Ances
- Knight Alzheimer Disease Research Center Saint Louis MO USA
- Washington University at St. Louis, Department of Neurology St. Louis MO USA
| | - Brian A. Gordon
- Knight Alzheimer Disease Research Center St. Louis MO USA
- Washington University School of Medicine St. Louis MO USA
| | - Russ C. Hornbeck
- Knight Alzheimer Disease Research Center St. Louis MO USA
- Washington University in St. Louis School of Medicine St. Louis MO USA
| | | | - Sarah J. Keefe
- Washington University School of Medicine St. Louis MO USA
| | - Donna Dierker
- Washington University School of Medic St. Louis MO USA
| | - Julia D Gray
- Washington University School of Medicine St. Louis MO USA
| | | | | | | | - Cecelia Manzanares
- Emory University Goizueta Alzheimer’s Disease Research Center Atlanta GA USA
| | - Deqiang Qiu
- Emory University School of Medicine Atlanta GA USA
| | - Dawn Mechanic‐Hamilton
- Penn Alzheimer’s Disease Research Center, University of Pennsylvania Philadelphia PA USA
| | | | - Leslie M. Shaw
- Department of Pathology and Laboratory Medicine Philadelphia PA USA
- Perelman School of Medicine, University of Pennsylvania Philadelphia PA USA
| | | | - John C. Morris
- Knight Alzheimer Disease Research Center St. Louis MO USA
- Washington University School of Medicine St. Louis MO USA
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45
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Saef BA, Henson RL, Volluz K, Yarasheski KE, West T, Kirmess K, Meyer MR, Gordon BA, Benzinger TL, Morris JC, Fagan AM, Schindler SE. Raindrop animation: Visualizing change in longitudinal biomarker data. Alzheimers Dement 2022. [DOI: 10.1002/alz.067300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Benjamin A. Saef
- Knight Alzheimer Disease Research Center St. Louis MO USA
- Washington University in St. Louis Saint Louis MO USA
| | - Rachel L. Henson
- Washington University in St. Louis Saint Louis MO USA
- Washington University School of Medicine Saint Louis MO USA
- Knight Alzheimer Disease Research Center Saint Louis MO USA
| | - Katherine Volluz
- Washington University in St. Louis Saint Louis MO USA
- Knight Alzheimer Disease Research Center Saint Louis MO USA
| | - Kevin E. Yarasheski
- Washington University School of Medicine St. Louis MO USA
- C2N Diagnostics, LLC Saint Louis MO USA
| | - Tim West
- C2N Diagnostics, LLC Saint Louis MO USA
| | | | | | | | - Tammie L.S. Benzinger
- Knight Alzheimer Disease Research Center St. Louis MO USA
- Hope Center for Neurological Disorders Saint Louis MO USA
- Mallinckrodt Institute of Radiology Saint Louis MO USA
- Washington University School of Medicine St. Louis MO USA
| | - John C. Morris
- Knight Alzheimer Disease Research Center St. Louis MO USA
- Hope Center for Neurological Disorders Saint Louis MO USA
- Washington University in St. Louis School of Medicine St. Louis MO USA
| | - Anne M. Fagan
- Knight Alzheimer Disease Research Center St. Louis MO USA
- Hope Center for Neurological Disorders Saint Louis MO USA
- Washington University in St. Louis St. Louis MO USA
| | - Suzanne E. Schindler
- Knight Alzheimer Disease Research Center St. Louis MO USA
- Hope Center for Neurological Disorders Saint Louis MO USA
- Washington University in St. Louis School of Medicine St. Louis MO USA
- Washington University in St. Louis St. Louis MO USA
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46
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Lokhande VS, Wang J, Soldan A, Pettigrew C, Moghekar A, Morris JC, Fagan AM, Schindler SE, Xiong C, Albert MS, Johnson SC, Singh V. Pooling CSF Measurements Across Multiple Cohorts in the Preclinical Alzheimer's Consortium. Alzheimers Dement 2022. [DOI: 10.1002/alz.067977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
| | - Jiangxia Wang
- Johns Hopkins Bloomberg School of Public Health Baltimore MD USA
| | - Anja Soldan
- Johns Hopkins University School of Medicine Baltimore MD USA
| | | | - Abhay Moghekar
- Johns Hopkins University School of Medicine Baltimore MD USA
| | - John C. Morris
- Washington University School of Medicine St. Louis MO USA
| | - Anne M. Fagan
- Washington University School of Medicine St. Louis MO USA
| | | | | | | | - Sterling C. Johnson
- University of Wisconsin‐Madison School of Medicine and Public Health Madison WI USA
| | - Vikas Singh
- University of Wisconsin‐Madison Madison WI USA
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47
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Gordon BA, Wisch JK, Hobbs DA, McKay NS, Schultz SA, Flores S, Keefe SJ, Dincer A, Ances BM, Morris JC, Schindler SE, Fagan AM, Benzinger TL. Comparing neuroimaging and cerebrospinal fluid markers of amyloid, tau, and neurodegenerative biomarkers: implications for understanding the biology of the disease. Alzheimers Dement 2022. [DOI: 10.1002/alz.062589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Brian A. Gordon
- Washington University in St. Louis School of Medicine St. Louis MO USA
| | - Julie K. Wisch
- Washington University in St. Louis School of Medicine St. Louis MO USA
| | - Diana A. Hobbs
- Washington University School of Medicine St. Louis MO USA
| | | | - Stephanie A. Schultz
- Massachusetts General Hospital, Brigham and Women’s Hospital, Harvard Medical School Boston MA USA
| | - Shaney Flores
- Washington University in St. Louis School of Medicine St. Louis MO USA
| | - Sarah J. Keefe
- Washington University in St. Louis School of Medicine St. Louis MO USA
| | - Aylin Dincer
- Washington University in St. Louis School of Medicine St. Louis MO USA
| | - Beau M Ances
- Washington University in St. Louis School of Medicine St. Louis MO USA
| | - John C. Morris
- Washington University in St. Louis School of Medicine St. Louis MO USA
| | | | - Anne M. Fagan
- Washington University in St. Louis School of Medicine St. Louis MO USA
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48
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Wang Q, Wang W, Schindler SE, McKay NS, Chen G, Liu J, Wang S, Sun Z, Hassenstab JJ, Fagan AM, Morris JC, Wang Y, Benzinger TL. Baseline White Matter Neuroinflammation Predicts Cognitive Decline in Alzheimer Disease. Alzheimers Dement 2022. [DOI: 10.1002/alz.063781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Qing Wang
- Washington University in St. Louis School of Medicine St. Louis MO USA
- Knight Alzheimer Disease Research Center St. Louis MO USA
| | - Wenshang Wang
- Washington University School of Medicine St. Louis MO USA
| | - Suzanne E. Schindler
- Washington University in St. Louis School of Medicine St. Louis MO USA
- Hope Center for Neurological Disorders St. Louis MO USA
| | - Nicole S. McKay
- Washington University School of Medicine St. Louis MO USA
- Knight Alzheimer Disease Research Center St. Louis MO USA
| | - Gengsheng Chen
- Washington University School of Medicine St. Louis MO USA
| | - Jingxia Liu
- Washington University in St. Louis School of Medicine St. Louis MO USA
| | - Sicheng Wang
- Washington University School of Medicine St. Louis MO USA
| | - Zhexian Sun
- Washington University School of Medicine St. Louis MO USA
| | - Jason J. Hassenstab
- Washington University in St. Louis School of Medicine St. Louis MO USA
- Knight Alzheimer Disease Research Center St. Louis MO USA
| | - Anne M. Fagan
- Washington University in St. Louis School of Medicine St. Louis MO USA
- Knight Alzheimer Disease Research Center St. Louis MO USA
| | - John C. Morris
- Washington University in St. Louis School of Medicine St. Louis MO USA
- Knight Alzheimer Disease Research Center St. Louis MO USA
| | - Yong Wang
- Washington University School of Medicine St. Louis MO USA
| | - Tammie L.S. Benzinger
- Washington University in St. Louis School of Medicine St. Louis MO USA
- Knight Alzheimer Disease Research Center St. Louis MO USA
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49
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Henson RL, Volluz K, Saef BA, Sutphen CL, Herries EM, Morris JC, Fagan AM, Xiong C, Schindler SE. A methodology for normalizing fluid biomarker concentrations across reagent lots. Alzheimers Dement 2022. [DOI: 10.1002/alz.066912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Rachel L. Henson
- Washington University in St. Louis Saint Louis MO USA
- Knight Alzheimer Disease Research Center Saint Louis MO USA
| | - Katherine Volluz
- Washington University in St. Louis Saint Louis MO USA
- Knight Alzheimer Disease Research Center Saint Louis MO USA
| | - Benjamin A. Saef
- Washington University in St. Louis Saint Louis MO USA
- Knight Alzheimer Disease Research Center St. Louis MO USA
| | - Courtney L. Sutphen
- Washington University in St. Louis Saint Louis MO USA
- Knight Alzheimer Disease Research Center Saint Louis MO USA
| | - Elizabeth M. Herries
- Washington University in St. Louis Saint Louis MO USA
- Knight Alzheimer Disease Research Center Saint Louis MO USA
| | - John C. Morris
- Washington University in St. Louis Saint Louis MO USA
- Knight Alzheimer Disease Research Center St. Louis MO USA
| | - Anne M. Fagan
- Knight Alzheimer Disease Research Center St. Louis MO USA
- Washington University in St. Louis St. Louis MO USA
| | - Chengjie Xiong
- Knight Alzheimer Disease Research Center St. Louis MO USA
- Washington University in St. Louis St. Louis MO USA
| | - Suzanne E. Schindler
- Knight Alzheimer Disease Research Center St. Louis MO USA
- Washington University in St. Louis St. Louis MO USA
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50
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Keleman AA, Wisch JK, Bollinger RM, Schindler SE, Morris JC, Ances BM, Stark SL. Worse Performance of Complex Daily Activities is associated with Plasma Neurofilament Light. Alzheimers Dement 2022. [DOI: 10.1002/alz.064814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Audrey A. Keleman
- Washington University in St. Louis School of Medicine St. Louis MO USA
| | - Julie K. Wisch
- Washington University in St. Louis School of Medicine St. Louis MO USA
| | | | | | - John C. Morris
- Washington University in St. Louis School of Medicine St. Louis MO USA
- Knight Alzheimer Disease Research Center St. Louis MO USA
| | - Beau M Ances
- Washington University in St. Louis School of Medicine St. Louis MO USA
| | - Susan L. Stark
- Washington University in St. Louis School of Medicine St. Louis MO USA
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