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Xu L, Ren C, Jing C, Wang G, Wei H, Kong M, Ba M. Predicting amyloid-PET and clinical conversion in apolipoprotein E ε3/ε3 non-demented individuals with multidimensional factors. Eur J Neurosci 2024; 60:3742-3758. [PMID: 38698692 DOI: 10.1111/ejn.16376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 04/14/2024] [Accepted: 04/16/2024] [Indexed: 05/05/2024]
Abstract
The apolipoprotein E (APOE) ε4 is a well-established risk factor of amyloid-β (Aβ) in Alzheimer's disease (AD). However, because of the high prevalence of APOE ε3, there may be a large number of people with APOE ε3/ε3 who are non-demented and have Aβ pathology. There are limited studies on assessing Aβ status and clinical conversion in the APOE ε3/ε3 non-demented population. Two hundred and ninety-three non-demented individuals with APOE ε3/ε3 from ADNI database were divided into Aβ-positron emission tomography (Aβ-PET) positivity (+) and Aβ-PET negativity (-) groups using cut-off value of >1.11. Stepwise regression searched for a single or multidimensional clinical variables for predicting Aβ-PET (+), and the receiver operating characteristic curve (ROC) assessed the accuracy of the predictive models. The Cox regression model explored the risk factors associated with clinical conversion to mild cognitive impairment (MCI) or AD. The results showed that the combination of sex, education, ventricle and white matter hyperintensity (WMH) volume can accurately predict Aβ-PET status in cognitively normal (CN), and the combination of everyday cognition study partner total (EcogSPTotal) score, age, plasma p-tau 181 and WMH can accurately predict Aβ-PET status in MCI individuals. EcogSPTotal score were independent predictors of clinical conversion to MCI or AD. The findings may provide a non-invasive and effective tool to improve the efficiency of screening Aβ-PET (+), accelerate and reduce costs of AD trial recruitment in future secondary prevention trials or help to select patients at high risk of disease progression in clinical trials.
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Affiliation(s)
- Lijuan Xu
- Department of Neurology, the Affiliated Yantai Yuhuangding Hospital of Qingdao University, Shandong, China
| | - Chao Ren
- Department of Neurology, the Affiliated Yantai Yuhuangding Hospital of Qingdao University, Shandong, China
| | - Chenxi Jing
- Department of Neurology, the Affiliated Yantai Yuhuangding Hospital of Qingdao University, Shandong, China
| | - Gang Wang
- School of Ulsan Ship and Ocean College, Ludong University, Yantai, China
| | - Hongchun Wei
- Department of Neurology, the Affiliated Yantai Yuhuangding Hospital of Qingdao University, Shandong, China
| | - Min Kong
- Department of Neurology, Yantaishan Hospital, Yantai City, Shandong, China
| | - Maowen Ba
- Department of Neurology, the Affiliated Yantai Yuhuangding Hospital of Qingdao University, Shandong, China
- Yantai Regional Sub Center of National Center for Clinical Medical Research of Neurological Diseases, Shandong, China
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Nemali A, Vockert N, Berron D, Maas A, Bernal J, Yakupov R, Peters O, Gref D, Cosma N, Preis L, Priller J, Spruth E, Altenstein S, Lohse A, Fliessbach K, Kimmich O, Vogt I, Wiltfang J, Hansen N, Bartels C, Schott BH, Maier F, Meiberth D, Glanz W, Incesoy E, Butryn M, Buerger K, Janowitz D, Pernecky R, Rauchmann B, Burow L, Teipel S, Kilimann I, Göerß D, Dyrba M, Laske C, Munk M, Sanzenbacher C, Müller S, Spottke A, Roy N, Heneka M, Brosseron F, Roeske S, Dobisch L, Ramirez A, Ewers M, Dechent P, Scheffler K, Kleineidam L, Wolfsgruber S, Wagner M, Jessen F, Duzel E, Ziegler G. Gaussian Process-based prediction of memory performance and biomarker status in ageing and Alzheimer's disease-A systematic model evaluation. Med Image Anal 2023; 90:102913. [PMID: 37660483 DOI: 10.1016/j.media.2023.102913] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 06/28/2023] [Accepted: 07/25/2023] [Indexed: 09/05/2023]
Abstract
Neuroimaging markers based on Magnetic Resonance Imaging (MRI) combined with various other measures (such as genetic covariates, biomarkers, vascular risk factors, neuropsychological tests etc.) might provide useful predictions of clinical outcomes during the progression towards Alzheimer's disease (AD). The use of multiple features in predictive frameworks for clinical outcomes has become increasingly prevalent in AD research. However, many studies do not focus on systematically and accurately evaluating combinations of multiple input features. Hence, the aim of the present work is to explore and assess optimal combinations of various features for MR-based prediction of (1) cognitive status and (2) biomarker positivity with a multi-kernel learning Gaussian process framework. The explored features and parameters included (A) combinations of brain tissues, modulation, smoothing, and image resolution; (B) incorporating demographics & clinical covariates; (C) the impact of the size of the training data set; (D) the influence of dimensionality reduction and the choice of kernel types. The approach was tested in a large German cohort including 959 subjects from the multicentric longitudinal study of cognitive impairment and dementia (DELCODE). Our evaluation suggests the best prediction of memory performance was obtained for a combination of neuroimaging markers, demographics, genetic information (ApoE4) and CSF biomarkers explaining 57% of outcome variance in out-of-sample predictions. The highest performance for Aβ42/40 status classification was achieved for a combination of demographics, ApoE4, and a memory score while usage of structural MRI further improved the classification of individual patient's pTau status.
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Affiliation(s)
- A Nemali
- Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University Magdeburg, Germany; German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.
| | - N Vockert
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - D Berron
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - A Maas
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - J Bernal
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - R Yakupov
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - O Peters
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany; Charité-Universitätsmedizin Berlin, Campus Benjamin Franklin, Department of Psychiatry, Hindenburgdamm 30, 12203, Berlin, Germany
| | - D Gref
- Charité-Universitätsmedizin Berlin, Campus Benjamin Franklin, Department of Psychiatry, Hindenburgdamm 30, 12203, Berlin, Germany
| | - N Cosma
- Charité-Universitätsmedizin Berlin, Campus Benjamin Franklin, Department of Psychiatry, Hindenburgdamm 30, 12203, Berlin, Germany
| | - L Preis
- Charité-Universitätsmedizin Berlin, Campus Benjamin Franklin, Department of Psychiatry, Hindenburgdamm 30, 12203, Berlin, Germany
| | - J Priller
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany; Department of Psychiatry and Psychotherapy, Charité, Charitéplatz 1, 10117 Berlin, Germany; School of Medicine, Technical University of Munich; Department of Psychiatry and Psychotherapy, Munich, Germany; University of Edinburgh and UK DRI, Edinburgh, UK
| | - E Spruth
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany; Department of Psychiatry and Psychotherapy, Charité, Charitéplatz 1, 10117 Berlin, Germany
| | - S Altenstein
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany; Department of Psychiatry and Psychotherapy, Charité, Charitéplatz 1, 10117 Berlin, Germany
| | - A Lohse
- Department of Psychiatry and Psychotherapy, Charité, Charitéplatz 1, 10117 Berlin, Germany
| | - K Fliessbach
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Venusberg-Campus 1, 53127 Bonn, Germany; University of Bonn Medical Center, Department of Neurodegenerative Disease and Geriatric Psychiatry/Psychiatry, Venusberg-Campus 1, 53127 Bonn, Germany
| | - O Kimmich
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
| | - I Vogt
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
| | - J Wiltfang
- German Center for Neurodegenerative Diseases (DZNE), Goettingen, Germany; Department of Psychiatry and Psychotherapy, University Medical Center Goettingen, University of Goettingen, Von-Siebold-Str. 5, 37075 Goettingen, Germany; Neurosciences and Signaling Group, Institute of Biomedicine (iBiMED), Department of Medical Sciences, University of Aveiro, Aveiro, Portugal
| | - N Hansen
- Department of Psychiatry and Psychotherapy, University Medical Center Goettingen, University of Goettingen, Von-Siebold-Str. 5, 37075 Goettingen, Germany
| | - C Bartels
- Department of Psychiatry and Psychotherapy, University Medical Center Goettingen, University of Goettingen, Von-Siebold-Str. 5, 37075 Goettingen, Germany
| | - B H Schott
- Leibniz Institute for Neurobiology, Magdeburg, Germany; German Center for Neurodegenerative Diseases (DZNE), Goettingen, Germany; Department of Psychiatry and Psychotherapy, University Medical Center Goettingen, University of Goettingen, Von-Siebold-Str. 5, 37075 Goettingen, Germany
| | - F Maier
- Department of Psychiatry, University of Cologne, Medical Faculty, Kerpener Strasse 62, 50924 Cologne, Germany
| | - D Meiberth
- Department of Psychiatry, University of Cologne, Medical Faculty, Kerpener Strasse 62, 50924 Cologne, Germany
| | - W Glanz
- Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University Magdeburg, Germany
| | - E Incesoy
- Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University Magdeburg, Germany; German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - M Butryn
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - K Buerger
- German Center for Neurodegenerative Diseases (DZNE, Munich), Feodor-Lynen-Strasse 17, 81377 Munich, Germany; Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Feodor-Lynen-Strasse 17, 81377 Munich, Germany
| | - D Janowitz
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Feodor-Lynen-Strasse 17, 81377 Munich, Germany
| | - R Pernecky
- German Center for Neurodegenerative Diseases (DZNE, Munich), Feodor-Lynen-Strasse 17, 81377 Munich, Germany; Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany; Munich Cluster for Systems Neurology (SyNergy) Munich, Munich, Germany; Ageing Epidemiology Research Unit (AGE), School of Public Health, Imperial College London, London, UK
| | - B Rauchmann
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - L Burow
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - S Teipel
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany; Department of Psychosomatic Medicine, Rostock University Medical Center, Gehlsheimer Str. 20, 18147 Rostock, Germany
| | - I Kilimann
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany; Department of Psychosomatic Medicine, Rostock University Medical Center, Gehlsheimer Str. 20, 18147 Rostock, Germany
| | - D Göerß
- Department of Psychosomatic Medicine, Rostock University Medical Center, Gehlsheimer Str. 20, 18147 Rostock, Germany
| | - M Dyrba
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
| | - C Laske
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany; Section for Dementia Research, Hertie Institute for Clinical Brain Research and Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - M Munk
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany; Department of Psychiatry and Psychotherapy, University of Tuebingen, Tuebingen, Germany
| | - C Sanzenbacher
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
| | - S Müller
- Department of Psychiatry and Psychotherapy, University of Tuebingen, Tuebingen, Germany
| | - A Spottke
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Venusberg-Campus 1, 53127 Bonn, Germany; Department of Neurology, University of Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
| | - N Roy
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
| | - M Heneka
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Venusberg-Campus 1, 53127 Bonn, Germany; Department of Psychiatry and Psychotherapy, University of Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
| | - F Brosseron
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
| | - S Roeske
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
| | - L Dobisch
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - A Ramirez
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Venusberg-Campus 1, 53127 Bonn, Germany; Department of Neurology, University of Bonn, Venusberg-Campus 1, 53127 Bonn, Germany; Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Joseph-Stelzmann-Strasse 26, 50931 Köln, Germany; Division of Neurogenetics and Molecular Psychiatry, Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany; Department of Psychiatry & Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, San Antonio, TX, USA
| | - M Ewers
- German Center for Neurodegenerative Diseases (DZNE, Munich), Feodor-Lynen-Strasse 17, 81377 Munich, Germany; Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Feodor-Lynen-Strasse 17, 81377 Munich, Germany
| | - P Dechent
- MR-Research in Neurosciences, Department of Cognitive Neurology, Georg-August-University Goettingen, Germany
| | - K Scheffler
- Department for Biomedical Magnetic Resonance, University of Tübingen, 72076 Tübingen, Germany
| | - L Kleineidam
- University of Bonn Medical Center, Department of Neurodegenerative Disease and Geriatric Psychiatry/Psychiatry, Venusberg-Campus 1, 53127 Bonn, Germany
| | - S Wolfsgruber
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Venusberg-Campus 1, 53127 Bonn, Germany; University of Bonn Medical Center, Department of Neurodegenerative Disease and Geriatric Psychiatry/Psychiatry, Venusberg-Campus 1, 53127 Bonn, Germany
| | - M Wagner
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Venusberg-Campus 1, 53127 Bonn, Germany; University of Bonn Medical Center, Department of Neurodegenerative Disease and Geriatric Psychiatry/Psychiatry, Venusberg-Campus 1, 53127 Bonn, Germany
| | - F Jessen
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Venusberg-Campus 1, 53127 Bonn, Germany; Department of Psychiatry, University of Cologne, Medical Faculty, Kerpener Strasse 62, 50924 Cologne, Germany; Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Joseph-Stelzmann-Strasse 26, 50931 Köln, Germany
| | - E Duzel
- Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University Magdeburg, Germany; German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - G Ziegler
- Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University Magdeburg, Germany; German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
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Balu D, Valencia-Olvera AC, Islam Z, Mielczarek C, Hansen A, Perez Ramos TM, York J, LaDu MJ, Tai LM. APOE genotype and sex modulate Alzheimer's disease pathology in aged EFAD transgenic mice. Front Aging Neurosci 2023; 15:1279343. [PMID: 38020764 PMCID: PMC10644540 DOI: 10.3389/fnagi.2023.1279343] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 10/10/2023] [Indexed: 12/01/2023] Open
Abstract
Increasing evidence supports that age, APOE and sex interact to modulate Alzheimer's disease (AD) risk, however the underlying pathways are unclear. One way that AD risk factors may modulate cognition is by impacting amyloid beta (Aβ) accumulation as plaques, and/or neuroinflammation Therefore, the goal of the present study was to evaluate the extent to which age, APOE and sex modulate Aβ pathology, neuroinflammation and behavior in vivo. To achieve this goal, we utilized the EFAD mice, which express human APOE3 or APOE4 and have five familial AD mutations (FAD) that result in Aβ42 overproduction. We assessed Aβ levels, reactive glia and Morris water maze performance in 6-, 10-, 14-, and 18-month-old EFAD mice. Female APOE4 mice had the highest Aβ deposition, fibrillar amyloid deposits and neuroinflammation as well as earlier behavior deficits. Interestingly, we found that female APOE3 mice and male APOE4 mice had similar levels of pathology. Collectively our data support that the combination of APOE4 and female sex is the most detrimental combination for AD, and that at older ages, female sex may be equivalent to APOE4 genotype.
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Affiliation(s)
- Deebika Balu
- Department of Anatomy and Cell Biology, University of Illinois at Chicago, Chicago, IL, United States
| | - Ana C. Valencia-Olvera
- Department of Anatomy and Cell Biology, University of Illinois at Chicago, Chicago, IL, United States
| | - Zarak Islam
- Department of Anatomy and Cell Biology, University of Illinois at Chicago, Chicago, IL, United States
- University of Illinois College of Medicine, Chicago, IL, United States
| | - Clare Mielczarek
- Department of Anatomy and Cell Biology, University of Illinois at Chicago, Chicago, IL, United States
| | - Allison Hansen
- Department of Anatomy and Cell Biology, University of Illinois at Chicago, Chicago, IL, United States
- University of Illinois College of Medicine, Peoria, IL, United States
| | - Tamara M. Perez Ramos
- Department of Anatomy and Cell Biology, University of Illinois at Chicago, Chicago, IL, United States
- School of Medicine, St. George’s University, St. George’s, Grenada
| | - Jason York
- Department of Anatomy and Cell Biology, University of Illinois at Chicago, Chicago, IL, United States
| | - Mary Jo LaDu
- Department of Anatomy and Cell Biology, University of Illinois at Chicago, Chicago, IL, United States
| | - Leon M. Tai
- Department of Anatomy and Cell Biology, University of Illinois at Chicago, Chicago, IL, United States
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Li B, Shi K, Ren C, Kong M, Ba M. Detection of Tau-PET Positivity in Clinically Diagnosed Mild Cognitive Impairment with Multidimensional Features. J Alzheimers Dis 2023:JAD230180. [PMID: 37334600 DOI: 10.3233/jad-230180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
BACKGROUND The way to evaluate brain tau pathology in vivo is tau positron emission tomography (tau-PET) or cerebrospinal fluid (CSF) analysis. In the clinically diagnosed mild cognitive impairment (MCI), a significant proportion of tau-PET are negative. Interest in less expensive and convenient ways to detect tau pathology in Alzheimer's disease has increased due to the high cost of tau-PET and the invasiveness of lumbar puncture, which typically slows down the cost and enrollment of clinical trials. OBJECTIVE We aimed to investigate one simple and effective method in predicting tau-PET status in MCI individuals. METHODS The sample included 154 individuals which were dichotomized into tau-PET (+) and tau-PET (-) using a cut-off of >1.33. We used stepwise regression to select the unitary or combination of variables that best predicted tau-PET. The receiver operating characteristic curve was used to assess the accuracy of single and multiple clinical markers. RESULTS The combined performance of three variables [Alzheimer's Disease Assessment Scale-Cognitive Subscale 13 (ADAS-Cog13), Mini-Mental State Examination (MMSE), ADNI-Memory summary score (ADNI-MEM)] in neurocognitive measures demonstrated good predictive accuracy of tau-PET status [accuracy = 85.7%, area under the curve (AUC) = 0.879]. The combination of clinical markers model (APOEɛ4, neurocognitive measures and structural MRI imaging of middle temporal) had the best discriminative power (AUC = 0.946). CONCLUSION As a noninvasive test, the combination of APOEɛ4, neurocognitive measures and structural MRI imaging of middle temporal accurately predicts tau-PET status. The finding may provide a non-invasive, cost-effective tool for clinical application in predicting tau pathology among MCI individuals.
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Affiliation(s)
- Bingyu Li
- Department of Neurology, Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, China
| | - Kening Shi
- Department of Neurology, Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, China
| | - Chao Ren
- Department of Neurology, Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, China
| | - Min Kong
- Department of Neurology, Yantaishan Hospital, Yantai, Shandong, China
| | - Maowen Ba
- Department of Neurology, Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, China
- Yantai Regional Sub Center of National Center for Clinical Medical Research of Neurological Diseases, Shandong, China
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Zou Y, Yu S, Ma X, Ma C, Mao C, Mu D, Li L, Gao J, Qiu L. How far is the goal of applying β-amyloid in cerebrospinal fluid for clinical diagnosis of Alzheimer's disease with standardization of measurements? Clin Biochem 2023; 112:33-42. [PMID: 36473516 DOI: 10.1016/j.clinbiochem.2022.11.013] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 11/02/2022] [Accepted: 11/30/2022] [Indexed: 12/12/2022]
Abstract
Cerebrospinal fluid (CSF) β-amyloid (Aβ) is important for early diagnosis of Alzheimer's disease (AD). However, the cohort distributions and cut-off values have large variation across different analytical assays, kits, and laboratories. In this review, we summarize the cut-off values and diagnostic performance for CSF Aβ1-42 and Aβ1-42/Aβ1-40, and explore the important effect factors. Based on the Alzheimer's Association external quality control program (AAQC program), the peer group coefficient of variation of manual ELISA assays for CSF Aβ1-42 was unsatisfied (>20%). Fully automated platforms with better performance have recently been developed, but still not widely applied. In 2020, the certified reference material (CRM) for CSF Aβ1-42 was launched; however, the AAQC 2021-round results did not show effective improvements. Thus, further development and popularization of CRM for CSF Aβ1-42 and Aβ1-40 are urgently required. Standardizing the diagnostic procedures of AD and related status and the pre-analytical protocols of CSF samples, improving detection performance of analytical assays, and popularizing the application of fully automated platforms are also important for the establishment of uniform cut-off values. Moreover, each laboratory should verify the applicability of uniform cut-off values, and evaluate whether it is necessary to establish its own population- and assay-specific cut-off values.
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Affiliation(s)
- Yutong Zou
- Department of Laboratory Medicine, Peking Union Medical College Hospital, Peking Union Medical College & Chinese Academy of Medical Science, Beijing 100730, China
| | - Songlin Yu
- Department of Laboratory Medicine, Peking Union Medical College Hospital, Peking Union Medical College & Chinese Academy of Medical Science, Beijing 100730, China
| | - Xiaoli Ma
- Department of Laboratory Medicine, Peking Union Medical College Hospital, Peking Union Medical College & Chinese Academy of Medical Science, Beijing 100730, China; Medical Science Research Center, Peking Union Medical College Hospital, Peking Union Medical College & Chinese Academy of Medical Science, Beijing 100730, China
| | - Chaochao Ma
- Department of Laboratory Medicine, Peking Union Medical College Hospital, Peking Union Medical College & Chinese Academy of Medical Science, Beijing 100730, China
| | - Chenhui Mao
- Department of Neurology, Peking Union Medical College Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing 100730, China
| | - Danni Mu
- Department of Laboratory Medicine, Peking Union Medical College Hospital, Peking Union Medical College & Chinese Academy of Medical Science, Beijing 100730, China
| | - Lei Li
- Department of Laboratory Medicine, Peking Union Medical College Hospital, Peking Union Medical College & Chinese Academy of Medical Science, Beijing 100730, China
| | - Jing Gao
- Department of Neurology, Peking Union Medical College Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing 100730, China.
| | - Ling Qiu
- Department of Laboratory Medicine, Peking Union Medical College Hospital, Peking Union Medical College & Chinese Academy of Medical Science, Beijing 100730, China; State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing 100730, China.
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Chang HI, Hsu SW, Kao ZK, Lee CC, Huang SH, Lin CH, Liu MN, Chang CC. Impact of Amyloid Pathology in Mild Cognitive Impairment Subjects: The Longitudinal Cognition and Surface Morphometry Data. Int J Mol Sci 2022; 23:ijms232314635. [PMID: 36498962 PMCID: PMC9738566 DOI: 10.3390/ijms232314635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 11/13/2022] [Accepted: 11/18/2022] [Indexed: 11/25/2022] Open
Abstract
The amyloid framework forms the central medical theory related to Alzheimer disease (AD), and the in vivo demonstration of amyloid positivity is essential for diagnosing AD. On the basis of a longitudinal cohort design, the study investigated clinical progressive patterns by obtaining cognitive and structural measurements from a group of patients with amnestic mild cognitive impairment (MCI); the measurements were classified by the positivity (Aβ+) or absence (Aβ-) of the amyloid biomarker. We enrolled 185 patients (64 controls, 121 patients with MCI). The patients with MCI were classified into two groups on the basis of their [18F]flubetaben or [18F]florbetapir amyloid positron-emission tomography scan (Aβ+ vs. Aβ-, 67 vs. 54 patients) results. Data from annual cognitive measurements and three-dimensional T1 magnetic resonance imaging scans were used for between-group comparisons. To obtain longitudinal cognitive test scores, generalized estimating equations were applied. A linear mixed effects model was used to compare the time effect of cortical thickness degeneration. The cognitive decline trajectory of the Aβ+ group was obvious, whereas the Aβ- and control groups did not exhibit a noticeable decline over time. The group effects of cortical thickness indicated decreased entorhinal cortex in the Aβ+ group and supramarginal gyrus in the Aβ- group. The topology of neurodegeneration in the Aβ- group was emphasized in posterior cortical regions. A comparison of the changes in the Aβ+ and Aβ- groups over time revealed a higher rate of cortical thickness decline in the Aβ+ group than in the Aβ- group in the default mode network. The Aβ+ and Aβ- groups experienced different APOE ε4 effects. For cortical-cognitive correlations, the regions associated with cognitive decline in the Aβ+ group were mainly localized in the perisylvian and anterior cingulate regions. By contrast, the degenerative topography of Aβ- MCI was scattered. The memory learning curves, cognitive decline patterns, and cortical degeneration topographies of the two MCI groups were revealed to be different, suggesting a difference in pathophysiology. Longitudinal analysis may help to differentiate between these two MCI groups if biomarker access is unavailable in clinical settings.
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Affiliation(s)
- Hsin-I Chang
- Department of Neurology, Cognition and Aging Center, Institute for Translational Research in Biomedicine, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung 833, Taiwan
| | - Shih-Wei Hsu
- Department of Radiology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung 833, Taiwan
| | - Zih-Kai Kao
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Chen-Chang Lee
- Department of Radiology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung 833, Taiwan
| | - Shu-Hua Huang
- Department of Nuclear Medicine, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung 833, Taiwan
| | - Ching-Heng Lin
- Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Taoyuan 333, Taiwan
- Bachelor Program in Artificial Intelligence, Chang Gung University, Taoyuan 333, Taiwan
| | - Mu-N Liu
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei 112, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Correspondence: (M.-N.L.); (C.-C.C.)
| | - Chiung-Chih Chang
- Department of Neurology, Cognition and Aging Center, Institute for Translational Research in Biomedicine, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung 833, Taiwan
- Correspondence: (M.-N.L.); (C.-C.C.)
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7
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Valerio KE, Prieto S, Hasselbach AN, Moody JN, Hayes SM, Hayes JP. Machine learning identifies novel markers predicting functional decline in older adults. Brain Commun 2021; 3:fcab140. [PMID: 34286271 PMCID: PMC8286801 DOI: 10.1093/braincomms/fcab140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Revised: 04/06/2021] [Accepted: 05/05/2021] [Indexed: 11/13/2022] Open
Abstract
The ability to carry out instrumental activities of daily living, such as paying bills, remembering appointments and shopping alone decreases with age, yet there are remarkable individual differences in the rate of decline among older adults. Understanding variables associated with a decline in instrumental activities of daily living is critical to providing appropriate intervention to prolong independence. Prior research suggests that cognitive measures, neuroimaging and fluid-based biomarkers predict functional decline. However, a priori selection of variables can lead to the over-valuation of certain variables and exclusion of others that may be predictive. In this study, we used machine learning techniques to select a wide range of baseline variables that best predicted functional decline in two years in individuals from the Alzheimer's Disease Neuroimaging Initiative dataset. The sample included 398 individuals characterized as cognitively normal or mild cognitive impairment. Support vector machine classification algorithms were used to identify the most predictive modality from five different data modality types (demographics, structural MRI, fluorodeoxyglucose-PET, neurocognitive and genetic/fluid-based biomarkers). In addition, variable selection identified individual variables across all modalities that best predicted functional decline in a testing sample. Of the five modalities examined, neurocognitive measures demonstrated the best accuracy in predicting functional decline (accuracy = 74.2%; area under the curve = 0.77), followed by fluorodeoxyglucose-PET (accuracy = 70.8%; area under the curve = 0.66). The individual variables with the greatest discriminatory ability for predicting functional decline included partner report of language in the Everyday Cognition questionnaire, the ADAS13, and activity of the left angular gyrus using fluorodeoxyglucose-PET. These three variables collectively explained 32% of the total variance in functional decline. Taken together, the machine learning model identified novel biomarkers that may be involved in the processing, retrieval, and conceptual integration of semantic information and which predict functional decline two years after assessment. These findings may be used to explore the clinical utility of the Everyday Cognition as a non-invasive, cost and time effective tool to predict future functional decline.
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Affiliation(s)
- Kate E Valerio
- Department of Psychology, The Ohio State University, Columbus, OH 43210, USA
| | - Sarah Prieto
- Department of Psychology, The Ohio State University, Columbus, OH 43210, USA
| | | | - Jena N Moody
- Department of Psychology, The Ohio State University, Columbus, OH 43210, USA
| | - Scott M Hayes
- Department of Psychology, The Ohio State University, Columbus, OH 43210, USA
- Chronic Brain Injury Initiative, The Ohio State University, Columbus, OH 43210, USA
| | - Jasmeet P Hayes
- Department of Psychology, The Ohio State University, Columbus, OH 43210, USA
- Chronic Brain Injury Initiative, The Ohio State University, Columbus, OH 43210, USA
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8
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Shan G, Bernick C, Caldwell JZK, Ritter A. Machine learning methods to predict amyloid positivity using domain scores from cognitive tests. Sci Rep 2021; 11:4822. [PMID: 33649452 PMCID: PMC7921140 DOI: 10.1038/s41598-021-83911-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 02/09/2021] [Indexed: 01/31/2023] Open
Abstract
Amyloid-[Formula: see text] (A[Formula: see text]) is the target in many clinical trials for Alzheimer's disease (AD). Preclinical AD patients are heterogeneous with regards to different backgrounds and diagnosis. Accurately predicting A[Formula: see text] status of participants by using machine learning (ML) models based on easily accessible data, could improve the effectiveness of AD clinical trials. We will develop optimal ML models for each subpopulation stratified by sex and disease stages using sub scores from screening neurological tests. Data from the AD Neuroimaging Initiative (ADNI) were used to build the ML models, for three groups: individuals with significant memory concern, early mild cognitive impairment (MCI), and late MCI. Data were further separated into 6 groups by disease stage (3 levels) and sex (2 categories). The outcome was defined as the A[Formula: see text] status confirmed by the PET imaging, and the features include demographic data, newly identified risk factors, screening tests, and the domain scores from screening tests. Monte Carlo simulation studies were used together with k-fold cross-validation technique to compute model performance metric. We also develop a new feature selection method based on the stochastic ordering to avoiding searching all possible combinations of features. Accuracy of the identified optimal model for SMC male was over 90% by using domain scores, and accuracy for LMCI female was above 86%. Domain scores can improve the ML model prediction as compared to the total scores. Accurate ML prediction models can identify the proper population for AD clinical trials.
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Affiliation(s)
- Guogen Shan
- Department of Epidemiology and Biostatistics, School of Public Health, University of Nevada Las Vegas, Las Vegas, NV, 89154, USA.
| | - Charles Bernick
- Cleveland Clinic Lou Ruvo Center for Brain Health, 888 W. Bonneville Avenue, Las Vegas, NV, 89106, USA
| | - Jessica Z K Caldwell
- Cleveland Clinic Lou Ruvo Center for Brain Health, 888 W. Bonneville Avenue, Las Vegas, NV, 89106, USA
| | - Aaron Ritter
- Cleveland Clinic Lou Ruvo Center for Brain Health, 888 W. Bonneville Avenue, Las Vegas, NV, 89106, USA
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Ashford MT, Veitch DP, Neuhaus J, Nosheny RL, Tosun D, Weiner MW. The search for a convenient procedure to detect one of the earliest signs of Alzheimer's disease: A systematic review of the prediction of brain amyloid status. Alzheimers Dement 2021; 17:866-887. [PMID: 33583100 DOI: 10.1002/alz.12253] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Revised: 10/10/2020] [Accepted: 11/03/2020] [Indexed: 12/12/2022]
Abstract
INTRODUCTION Convenient, cost-effective tests for amyloid beta (Aβ) are needed to identify those at higher risk for developing Alzheimer's disease (AD). This systematic review evaluates recent models that predict dichotomous Aβ. (PROSPERO: CRD42020144734). METHODS We searched Embase and identified 73 studies from 29,581 for review. We assessed study quality using established tools, extracted information, and reported results narratively. RESULTS We identified few high-quality studies due to concerns about Aβ determination and analytical issues. The most promising convenient, inexpensive classifiers consist of age, apolipoprotein E genotype, cognitive measures, and/or plasma Aβ. Plasma Aβ may be sufficient if pre-analytical variables are standardized and scalable assays developed. Some models lowered costs associated with clinical trial recruitment or clinical screening. DISCUSSION Conclusions about models are difficult due to study heterogeneity and quality. Promising prediction models used demographic, cognitive/neuropsychological, imaging, and plasma Aβ measures. Further studies using standardized Aβ determination, and improved model validation are required.
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Affiliation(s)
- Miriam T Ashford
- Department of Veterans Affairs Medical Center, Northern California Institute for Research and Education, San Francisco, California, USA.,Department of Veterans Affairs Medical Center, Center for Imaging and Neurodegenerative Diseases, San Francisco, California, USA
| | - Dallas P Veitch
- Department of Veterans Affairs Medical Center, Northern California Institute for Research and Education, San Francisco, California, USA
| | - John Neuhaus
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California, USA
| | - Rachel L Nosheny
- Department of Veterans Affairs Medical Center, Center for Imaging and Neurodegenerative Diseases, San Francisco, California, USA.,Department of Psychiatry, University of California San Francisco, San Francisco, California, USA
| | - Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, USA
| | - Michael W Weiner
- Department of Veterans Affairs Medical Center, Northern California Institute for Research and Education, San Francisco, California, USA.,Department of Veterans Affairs Medical Center, Center for Imaging and Neurodegenerative Diseases, San Francisco, California, USA.,Department of Psychiatry, University of California San Francisco, San Francisco, California, USA.,Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, USA.,Department of Medicine, University of California San Francisco, San Francisco, California, USA.,Department of Neurology, University of California San Francisco, San Francisco, California, USA
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10
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Tsoy E, Strom A, Iaccarino L, Erlhoff SJ, Goode CA, Rodriguez AM, Rabinovici GD, Miller BL, Kramer JH, Rankin KP, La Joie R, Possin KL. Detecting Alzheimer's disease biomarkers with a brief tablet-based cognitive battery: sensitivity to Aβ and tau PET. ALZHEIMERS RESEARCH & THERAPY 2021; 13:36. [PMID: 33557905 PMCID: PMC7871372 DOI: 10.1186/s13195-021-00776-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Accepted: 01/25/2021] [Indexed: 11/29/2022]
Abstract
Background β-amyloid (Aβ) and tau positron emission tomography (PET) detect the pathological changes that define Alzheimer’s disease (AD) in living people. Cognitive measures sensitive to Aβ and tau burden may help streamline identification of cases for confirmatory AD biomarker testing. Methods We examined the association of Brain Health Assessment (BHA) tablet-based cognitive measures with dichotomized Aβ -PET status using logistic regression models in individuals with mild cognitive impairment (MCI) or dementia (N = 140; 43 Aβ-, 97 Aβ+). We also investigated the relationship between the BHA tests and regional patterns of tau-PET signal using voxel-wise regression analyses in a subsample of 60 Aβ+ individuals with MCI or dementia. Results Favorites (associative memory), Match (executive functions and speed), and Everyday Cognition Scale scores were significantly associated with Aβ positivity (area under the curve [AUC] = 0.75 [95% CI 0.66–0.85]). We found significant associations with tau-PET signal in mesial temporal regions for Favorites, frontoparietal regions for Match, and occipitoparietal regions for Line Orientation (visuospatial skills) in a subsample of individuals with MCI and dementia. Conclusion The BHA measures are significantly associated with both Aβ and regional tau in vivo imaging markers and could be used for the identification of patients with suspected AD pathology in clinical practice. Supplementary Information The online version contains supplementary material available at 10.1186/s13195-021-00776-w.
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Affiliation(s)
- Elena Tsoy
- Department of Neurology, Memory and Aging Center, University of California San Francisco, Box 1207, 675 Nelson Rising Lane, Suite 190, San Francisco, CA, 94158, USA
| | - Amelia Strom
- Department of Neurology, Memory and Aging Center, University of California San Francisco, Box 1207, 675 Nelson Rising Lane, Suite 190, San Francisco, CA, 94158, USA
| | - Leonardo Iaccarino
- Department of Neurology, Memory and Aging Center, University of California San Francisco, Box 1207, 675 Nelson Rising Lane, Suite 190, San Francisco, CA, 94158, USA
| | - Sabrina J Erlhoff
- Department of Neurology, Memory and Aging Center, University of California San Francisco, Box 1207, 675 Nelson Rising Lane, Suite 190, San Francisco, CA, 94158, USA
| | - Collette A Goode
- Department of Neurology, Memory and Aging Center, University of California San Francisco, Box 1207, 675 Nelson Rising Lane, Suite 190, San Francisco, CA, 94158, USA
| | - Anne-Marie Rodriguez
- Department of Neurology, Memory and Aging Center, University of California San Francisco, Box 1207, 675 Nelson Rising Lane, Suite 190, San Francisco, CA, 94158, USA
| | - Gil D Rabinovici
- Department of Neurology, Memory and Aging Center, University of California San Francisco, Box 1207, 675 Nelson Rising Lane, Suite 190, San Francisco, CA, 94158, USA.,Department of Radiology and Biomedical Imaging, University of California San Francisco, 1500 Owens Street, 2nd Fl, San Francisco, CA, 94158, USA
| | - Bruce L Miller
- Department of Neurology, Memory and Aging Center, University of California San Francisco, Box 1207, 675 Nelson Rising Lane, Suite 190, San Francisco, CA, 94158, USA.,Global Brain Health Institute, University of California San Francisco, 675 Nelson Rising Lane, San Francisco, CA, 94158, USA
| | - Joel H Kramer
- Department of Neurology, Memory and Aging Center, University of California San Francisco, Box 1207, 675 Nelson Rising Lane, Suite 190, San Francisco, CA, 94158, USA.,Global Brain Health Institute, University of California San Francisco, 675 Nelson Rising Lane, San Francisco, CA, 94158, USA
| | - Katherine P Rankin
- Department of Neurology, Memory and Aging Center, University of California San Francisco, Box 1207, 675 Nelson Rising Lane, Suite 190, San Francisco, CA, 94158, USA
| | - Renaud La Joie
- Department of Neurology, Memory and Aging Center, University of California San Francisco, Box 1207, 675 Nelson Rising Lane, Suite 190, San Francisco, CA, 94158, USA
| | - Katherine L Possin
- Department of Neurology, Memory and Aging Center, University of California San Francisco, Box 1207, 675 Nelson Rising Lane, Suite 190, San Francisco, CA, 94158, USA. .,Global Brain Health Institute, University of California San Francisco, 675 Nelson Rising Lane, San Francisco, CA, 94158, USA.
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11
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Tosun D, Veitch D, Aisen P, Jack CR, Jagust WJ, Petersen RC, Saykin AJ, Bollinger J, Ovod V, Mawuenyega KG, Bateman RJ, Shaw LM, Trojanowski JQ, Blennow K, Zetterberg H, Weiner MW. Detection of β-amyloid positivity in Alzheimer's Disease Neuroimaging Initiative participants with demographics, cognition, MRI and plasma biomarkers. Brain Commun 2021; 3:fcab008. [PMID: 33842885 PMCID: PMC8023542 DOI: 10.1093/braincomms/fcab008] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Revised: 12/02/2020] [Accepted: 12/04/2020] [Indexed: 01/18/2023] Open
Abstract
In vivo gold standard for the ante-mortem assessment of brain β-amyloid pathology is currently β-amyloid positron emission tomography or cerebrospinal fluid measures of β-amyloid42 or the β-amyloid42/β-amyloid40 ratio. The widespread acceptance of a biomarker classification scheme for the Alzheimer's disease continuum has ignited interest in more affordable and accessible approaches to detect Alzheimer's disease β-amyloid pathology, a process that often slows down the recruitment into, and adds to the cost of, clinical trials. Recently, there has been considerable excitement concerning the value of blood biomarkers. Leveraging multidisciplinary data from cognitively unimpaired participants and participants with mild cognitive impairment recruited by the multisite biomarker study of Alzheimer's Disease Neuroimaging Initiative, here we assessed to what extent plasma β-amyloid42/β-amyloid40, neurofilament light and phosphorylated-tau at threonine-181 biomarkers detect the presence of β-amyloid pathology, and to what extent the addition of clinical information such as demographic data, APOE genotype, cognitive assessments and MRI can assist plasma biomarkers in detecting β-amyloid-positivity. Our results confirm plasma β-amyloid42/β-amyloid40 as a robust biomarker of brain β-amyloid-positivity (area under curve, 0.80-0.87). Plasma phosphorylated-tau at threonine-181 detected β-amyloid-positivity only in the cognitively impaired with a moderate area under curve of 0.67, whereas plasma neurofilament light did not detect β-amyloid-positivity in either group of participants. Clinical information as well as MRI-score independently detected positron emission tomography β-amyloid-positivity in both cognitively unimpaired and impaired (area under curve, 0.69-0.81). Clinical information, particularly APOE ε4 status, enhanced the performance of plasma biomarkers in the detection of positron emission tomography β-amyloid-positivity by 0.06-0.14 units of area under curve for cognitively unimpaired, and by 0.21-0.25 units for cognitively impaired; and further enhancement of these models with an MRI-score of β-amyloid-positivity yielded an additional improvement of 0.04-0.11 units of area under curve for cognitively unimpaired and 0.05-0.09 units for cognitively impaired. Taken together, these multi-disciplinary results suggest that when combined with clinical information, plasma phosphorylated-tau at threonine-181 and neurofilament light biomarkers, and an MRI-score could effectively identify β-amyloid+ cognitively unimpaired and impaired (area under curve, 0.80-0.90). Yet, when the MRI-score is considered in combination with clinical information, plasma phosphorylated-tau at threonine-181 and plasma neurofilament light have minimal added value for detecting β-amyloid-positivity. Our systematic comparison of β-amyloid-positivity detection models identified effective combinations of demographics, APOE, global cognition, MRI and plasma biomarkers. Promising minimally invasive and low-cost predictors such as plasma biomarkers of β-amyloid42/β-amyloid40 may be improved by age and APOE genotype.
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Affiliation(s)
- Duygu Tosun
- San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Dallas Veitch
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Paul Aisen
- Alzheimer’s Therapeutic Research Institute (ATRI), Keck School of Medicine, University of Southern California, San Diego, CA, USA
| | | | - William J Jagust
- School of Public Health and Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA
| | - Ronald C Petersen
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Center for Neuroimaging, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - James Bollinger
- 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
| | - Vitaliy Ovod
- 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
| | - Kwasi G Mawuenyega
- 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
| | - Randall J Bateman
- 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
| | - Leslie M Shaw
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - John Q Trojanowski
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
- UK Dementia Research Institute at UCL, London, UK
| | - Michael W Weiner
- San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
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12
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Sánchez‐Benavides G, Salvadó G, Arenaza‐Urquijo EM, Grau‐Rivera O, Suárez‐Calvet M, Milà‐Alomà M, González‐de‐Echávarri JM, Minguillon C, Crous‐Bou M, Niñerola‐Baizán A, Perissinotti A, Gispert JD, Molinuevo JL. Quantitative informant- and self-reports of subjective cognitive decline predict amyloid beta PET outcomes in cognitively unimpaired individuals independently of age and APOE ε4. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2020; 12:e12127. [PMID: 33204815 PMCID: PMC7656171 DOI: 10.1002/dad2.12127] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 10/01/2020] [Accepted: 10/08/2020] [Indexed: 12/02/2022]
Abstract
INTRODUCTION Amyloid beta (Aβ) pathology is an Alzheimer's disease early hallmark. Here we assess the value of longitudinal self- and informant reports of cognitive decline to predict Aβ positron emission tomography (PET) outcome in cognitively unimpaired middle-aged individuals. METHODS A total of 261 participants from the ALFA+ study underwent [18F]flutemetamol PET and Subjective Cognitive Decline Questionnaire (SCD-Q) concurrently, and 3 years before scan. We used logistic regressions to evaluate the ability of SCD-Q scores (self and informant) to predict Aβ PET visual read, and repeated analysis of variance to assess whether changes in SCD-Q scores relate to Aβ status. RESULTS Self-perception of decline in memory (odds ratio [OR] = 1.2), and informant perception of executive decline (OR = 1.6), increased the probability of a positive scan. Informant reports 3 years before scanning predicted Aβ PET outcome. Longitudinal increase of self-reported executive decline was predictive of Aβ in women (P = .003). DISCUSSION Subjective reports of cognitive decline are useful to predict Aβ and may improve recruitment strategies.
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Affiliation(s)
- Gonzalo Sánchez‐Benavides
- Barcelonaβeta Brain Research Center (BBRC)Pasqual Maragall FoundationBarcelonaSpain
- IMIM (Hospital del Mar Medical Research Institute)BarcelonaSpain
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES)MadridSpain
| | - Gemma Salvadó
- Barcelonaβeta Brain Research Center (BBRC)Pasqual Maragall FoundationBarcelonaSpain
- IMIM (Hospital del Mar Medical Research Institute)BarcelonaSpain
| | - Eider M. Arenaza‐Urquijo
- Barcelonaβeta Brain Research Center (BBRC)Pasqual Maragall FoundationBarcelonaSpain
- IMIM (Hospital del Mar Medical Research Institute)BarcelonaSpain
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES)MadridSpain
| | - Oriol Grau‐Rivera
- Barcelonaβeta Brain Research Center (BBRC)Pasqual Maragall FoundationBarcelonaSpain
- IMIM (Hospital del Mar Medical Research Institute)BarcelonaSpain
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES)MadridSpain
- Servei de NeurologiaHospital del MarBarcelonaSpain
| | - Marc Suárez‐Calvet
- Barcelonaβeta Brain Research Center (BBRC)Pasqual Maragall FoundationBarcelonaSpain
- IMIM (Hospital del Mar Medical Research Institute)BarcelonaSpain
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES)MadridSpain
- Servei de NeurologiaHospital del MarBarcelonaSpain
| | - Marta Milà‐Alomà
- Barcelonaβeta Brain Research Center (BBRC)Pasqual Maragall FoundationBarcelonaSpain
- IMIM (Hospital del Mar Medical Research Institute)BarcelonaSpain
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES)MadridSpain
- Universitat Pompeu FabraBarcelonaSpain
| | - José María González‐de‐Echávarri
- Barcelonaβeta Brain Research Center (BBRC)Pasqual Maragall FoundationBarcelonaSpain
- IMIM (Hospital del Mar Medical Research Institute)BarcelonaSpain
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES)MadridSpain
| | - Carolina Minguillon
- Barcelonaβeta Brain Research Center (BBRC)Pasqual Maragall FoundationBarcelonaSpain
- IMIM (Hospital del Mar Medical Research Institute)BarcelonaSpain
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES)MadridSpain
| | - Marta Crous‐Bou
- Barcelonaβeta Brain Research Center (BBRC)Pasqual Maragall FoundationBarcelonaSpain
- Department of EpidemiologyHarvard T.H. Chan School of Public HealthBostonMassachusettsUSA
- Cancer Epidemiology Research ProgramCatalan Institute of Oncology (ICO)Hospitalet del LlobregatSpain
| | | | | | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC)Pasqual Maragall FoundationBarcelonaSpain
- IMIM (Hospital del Mar Medical Research Institute)BarcelonaSpain
- Universitat Pompeu FabraBarcelonaSpain
- CIBER de BioengenieríaBiomateriales y NanomedicinaMadridSpain
| | - José Luis Molinuevo
- Barcelonaβeta Brain Research Center (BBRC)Pasqual Maragall FoundationBarcelonaSpain
- IMIM (Hospital del Mar Medical Research Institute)BarcelonaSpain
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES)MadridSpain
- Universitat Pompeu FabraBarcelonaSpain
| | - for the ALFA Study
- Barcelonaβeta Brain Research Center (BBRC)Pasqual Maragall FoundationBarcelonaSpain
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