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Johansson C, Thordardottir S, Laffita-Mesa J, Pannee J, Rodriguez-Vieitez E, Zetterberg H, Blennow K, Graff C. Gene-variant specific effects of plasma amyloid-β levels in Swedish autosomal dominant Alzheimer disease. Alzheimers Res Ther 2024; 16:207. [PMID: 39322953 PMCID: PMC11423518 DOI: 10.1186/s13195-024-01574-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2024] [Accepted: 09/11/2024] [Indexed: 09/27/2024]
Abstract
BACKGROUND Several blood-based biomarkers offer the opportunity of in vivo detection of brain pathology and neurodegeneration in Alzheimer disease with high specificity and sensitivity, but the performance of amyloid-β (Aβ) measurements remains under evaluation. Autosomal dominant Alzheimer disease (ADAD) with mutations in PSEN1, PSEN2 and APP can be studied as a model for sporadic Alzheimer disease. However, clarifying the genetic effects on the Aβ-levels in different matrices such as cerebrospinal fluid or plasma is crucial for generalizability and utility of data. We aimed to explore plasma Aβ concentrations over the Alzheimer disease continuum in a longitudinal cohort of genetic Alzheimer disease. METHODS 92 plasma samples were collected from at-risk individuals (n = 47) in a Swedish cohort of ADAD, including 18 mutation carriers (13 APPswe (p.KM670/671NL) MC), 5 PSEN1 (p.H163Y) MC) and 29 non-carriers (NC) as the reference group. Concentrations of Aβ1-38, Aβ1-40 and Aβ1-42 were analyzed in plasma using immunoprecipitation coupled to tandem liquid chromatography mass spectrometry (IP-LC-MS/MS). Cross-sectional and repeated-measures data analyses were investigated family-wise, applying non-parametric tests as well as mixed-effects models. RESULTS Cross-sectional analysis at baseline showed more than a 3-fold increase in all plasma Aβ peptides in APPswe MC, regardless of clinical status, compared to controls (p < 0.01). PSEN1 (p.H163Y) presymptomatic MC had a decrease of plasma Aβ1-38 compared to controls (p < 0.05). There was no difference in Aβ1-42/1-40 ratio between APPswe MC (PMC and SMC), PSEN1 MC (PMC) and controls at baseline. Notably, both cross-sectional data and repeated-measures analysis suggested that APPswe MC have a stable Aβ1-42/1-40 ratio with increasing age, in contrast to the decrease seen with aging in both controls and PSEN1 (p.H163Y) MC. CONCLUSION These data show very strong mutation-specific effects on Aβ profiles in blood, most likely due to a ubiquitous production outside of the CNS. Hence, analyses in an unselected clinical setting might unintentionally disclose genetic status. Furthermore, our findings suggest that the Aβ ratio might be a poor indicator of brain Aβ pathology in selected genetic cases. The very small sample size is a limitation that needs to be considered but reflects the scarcity of longitudinal in vivo data from genetic cohorts.
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Affiliation(s)
- Charlotte Johansson
- Department NVS, Division of Neurogeriatrics, Center for Alzheimer Research, Karolinska Institutet, Visionsgatan 4, Bioclinicum, Solna, J10:20, 171 64, Sweden
- Theme Inflammation and Aging, Karolinska University Hospital, Stockholm, Sweden
| | - Steinunn Thordardottir
- Department NVS, Division of Neurogeriatrics, Center for Alzheimer Research, Karolinska Institutet, Visionsgatan 4, Bioclinicum, Solna, J10:20, 171 64, Sweden
| | - José Laffita-Mesa
- Department NVS, Division of Neurogeriatrics, Center for Alzheimer Research, Karolinska Institutet, Visionsgatan 4, Bioclinicum, Solna, J10:20, 171 64, Sweden
| | - Josef Pannee
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
| | - Elena Rodriguez-Vieitez
- Department NVS, Division of Neurogeriatrics, Center for Alzheimer Research, Karolinska Institutet, Visionsgatan 4, Bioclinicum, Solna, J10:20, 171 64, Sweden
| | - Henrik Zetterberg
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
- UK Dementia Research Institute at UCL, London, UK
| | - Kaj Blennow
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
- Pitié-Salpêtrière Hospital, Paris Brain Institute, ICM, Sorbonne University, Paris, France
- Neurodegenerative Disorder Research Center, Division of Life Sciences and Medicine, Department of Neurology, Institute on Aging and Brain Disorders, University of Science and Technology of China and First Affiliated Hospital of USTC, Hefei, P.R. China
| | - Caroline Graff
- Department NVS, Division of Neurogeriatrics, Center for Alzheimer Research, Karolinska Institutet, Visionsgatan 4, Bioclinicum, Solna, J10:20, 171 64, Sweden.
- Theme Inflammation and Aging, Karolinska University Hospital, Stockholm, Sweden.
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Abdul Manap AS, Almadodi R, Sultana S, Sebastian MG, Kavani KS, Lyenouq VE, Shankar A. Alzheimer's disease: a review on the current trends of the effective diagnosis and therapeutics. Front Aging Neurosci 2024; 16:1429211. [PMID: 39185459 PMCID: PMC11341404 DOI: 10.3389/fnagi.2024.1429211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Accepted: 07/25/2024] [Indexed: 08/27/2024] Open
Abstract
The most prevalent cause of dementia is Alzheimer's disease. Cognitive decline and accelerating memory loss characterize it. Alzheimer's disease advances sequentially, starting with preclinical stages, followed by mild cognitive and/or behavioral impairment, and ultimately leading to Alzheimer's disease dementia. In recent years, healthcare providers have been advised to make an earlier diagnosis of Alzheimer's, prior to individuals developing Alzheimer's disease dementia. Regrettably, the identification of early-stage Alzheimer's disease in clinical settings can be arduous due to the tendency of patients and healthcare providers to disregard symptoms as typical signs of aging. Therefore, accurate and prompt diagnosis of Alzheimer's disease is essential in order to facilitate the development of disease-modifying and secondary preventive therapies prior to the onset of symptoms. There has been a notable shift in the goal of the diagnosis process, transitioning from merely confirming the presence of symptomatic AD to recognizing the illness in its early, asymptomatic phases. Understanding the evolution of disease-modifying therapies and putting effective diagnostic and therapeutic management into practice requires an understanding of this concept. The outcomes of this study will enhance in-depth knowledge of the current status of Alzheimer's disease's diagnosis and treatment, justifying the necessity for the quest for potential novel biomarkers that can contribute to determining the stage of the disease, particularly in its earliest stages. Interestingly, latest clinical trial status on pharmacological agents, the nonpharmacological treatments such as behavior modification, exercise, and cognitive training as well as alternative approach on phytochemicals as neuroprotective agents have been covered in detailed.
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Affiliation(s)
- Aimi Syamima Abdul Manap
- Department of Biomedical Science, College of Veterinary Medicine, King Faisal University, Al-Ahsa, Saudi Arabia
| | - Reema Almadodi
- Faculty of Pharmacy and Biomedical Sciences, MAHSA University, Selangor, Malaysia
| | - Shirin Sultana
- Faculty of Pharmacy and Biomedical Sciences, MAHSA University, Selangor, Malaysia
| | | | | | - Vanessa Elle Lyenouq
- Faculty of Pharmacy and Biomedical Sciences, MAHSA University, Selangor, Malaysia
| | - Aravind Shankar
- Faculty of Pharmacy and Biomedical Sciences, MAHSA University, Selangor, Malaysia
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Cyr B, Curiel Cid R, Loewenstein D, Vontell RT, Dietrich WD, Keane RW, de Rivero Vaccari JP. The Inflammasome Adaptor Protein ASC in Plasma as a Biomarker of Early Cognitive Changes. Int J Mol Sci 2024; 25:7758. [PMID: 39063000 PMCID: PMC11276719 DOI: 10.3390/ijms25147758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2024] [Revised: 07/13/2024] [Accepted: 07/14/2024] [Indexed: 07/28/2024] Open
Abstract
Dementia is a group of symptoms including memory loss, language difficulties, and other types of cognitive and functional impairments that affects 57 million people worldwide, with the incidence expected to double by 2040. Therefore, there is an unmet need to develop reliable biomarkers to diagnose early brain impairments so that emerging interventions can be applied before brain degeneration. Here, we performed biomarker analyses for apoptosis-associated speck-like protein containing a caspase recruitment domain (ASC), neurofilament light chain (NfL), glial fibrillary acidic protein (GFAP), and amyloid-β 42/40 (Aβ42/40) ratio in the plasma of older adults. Participants had blood drawn at baseline and underwent two annual clinical and cognitive evaluations. The groups tested either cognitively normal on both evaluations (NN), cognitively normal year 1 but cognitively impaired year 2 (NI), or cognitively impaired on both evaluations (II). ASC was elevated in the plasma of the NI group compared to the NN and II groups. Additionally, Aβ42 was increased in the plasma in the NI and II groups compared to the NN group. Importantly, the area under the curve (AUC) for ASC in participants older than 70 years old in NN vs. NI groups was 0.81, indicating that ASC is a promising plasma biomarker for early detection of cognitive decline.
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Affiliation(s)
- Brianna Cyr
- The Miami Project to Cure Paralysis, Department of Neurological Surgery, University of Miami, Miami, FL 33136, USA; (B.C.); (W.D.D.); (R.W.K.)
| | - Rosie Curiel Cid
- Center for Cognitive Neuroscience and Aging, University of Miami, Miami, FL 33136, USA; (R.C.C.); (D.L.)
| | - David Loewenstein
- Center for Cognitive Neuroscience and Aging, University of Miami, Miami, FL 33136, USA; (R.C.C.); (D.L.)
| | | | - W. Dalton Dietrich
- The Miami Project to Cure Paralysis, Department of Neurological Surgery, University of Miami, Miami, FL 33136, USA; (B.C.); (W.D.D.); (R.W.K.)
| | - Robert W. Keane
- The Miami Project to Cure Paralysis, Department of Neurological Surgery, University of Miami, Miami, FL 33136, USA; (B.C.); (W.D.D.); (R.W.K.)
- Department of Physiology and Biophysics, University of Miami, Miami, FL 33136, USA
| | - Juan Pablo de Rivero Vaccari
- The Miami Project to Cure Paralysis, Department of Neurological Surgery, University of Miami, Miami, FL 33136, USA; (B.C.); (W.D.D.); (R.W.K.)
- Center for Cognitive Neuroscience and Aging, University of Miami, Miami, FL 33136, USA; (R.C.C.); (D.L.)
- Department of Physiology and Biophysics, University of Miami, Miami, FL 33136, USA
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Duan S, Cai T, Liu F, Li Y, Yuan H, Yuan W, Huang K, Hoettges K, Chen M, Lim EG, Zhao C, Song P. Automatic offline-capable smartphone paper-based microfluidic device for efficient biomarker detection of Alzheimer's disease. Anal Chim Acta 2024; 1308:342575. [PMID: 38740448 DOI: 10.1016/j.aca.2024.342575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 03/25/2024] [Accepted: 04/02/2024] [Indexed: 05/16/2024]
Abstract
BACKGROUND Alzheimer's disease (AD) is a prevalent neurodegenerative disease with no effective treatment. Efficient and rapid detection plays a crucial role in mitigating and managing AD progression. Deep learning-assisted smartphone-based microfluidic paper analysis devices (μPADs) offer the advantages of low cost, good sensitivity, and rapid detection, providing a strategic pathway to address large-scale disease screening in resource-limited areas. However, existing smartphone-based detection platforms usually rely on large devices or cloud servers for data transfer and processing. Additionally, the implementation of automated colorimetric enzyme-linked immunoassay (c-ELISA) on μPADs can further facilitate the realization of smartphone μPADs platforms for efficient disease detection. RESULTS This paper introduces a new deep learning-assisted offline smartphone platform for early AD screening, offering rapid disease detection in low-resource areas. The proposed platform features a simple mechanical rotating structure controlled by a smartphone, enabling fully automated c-ELISA on μPADs. Our platform successfully applied sandwich c-ELISA for detecting the β-amyloid peptide 1-42 (Aβ 1-42, a crucial AD biomarker) and demonstrated its efficacy in 38 artificial plasma samples (healthy: 19, unhealthy: 19, N = 6). Moreover, we employed the YOLOv5 deep learning model and achieved an impressive 97 % accuracy on a dataset of 1824 images, which is 10.16 % higher than the traditional method of curve-fitting results. The trained YOLOv5 model was seamlessly integrated into the smartphone using the NCNN (Tencent's Neural Network Inference Framework), enabling deep learning-assisted offline detection. A user-friendly smartphone application was developed to control the entire process, realizing a streamlined "samples in, answers out" approach. SIGNIFICANCE This deep learning-assisted, low-cost, user-friendly, highly stable, and rapid-response automated offline smartphone-based detection platform represents a good advancement in point-of-care testing (POCT). Moreover, our platform provides a feasible approach for efficient AD detection by examining the level of Aβ 1-42, particularly in areas with low resources and limited communication infrastructure.
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Affiliation(s)
- Sixuan Duan
- School of Advanced Technology, Xi'an Jiaotong-Liverpool University, 111 Ren'ai Road, Suzhou, 215000, China; Department of Electrical and Electronic Engineering, University of Liverpool, Foundation Building, Brownlow Hill, Liverpool, L69 7ZX, UK; Key Laboratory of Bionic Engineering, Jilin University, 5988 Renmin Street, Changchun, 130022, China
| | - Tianyu Cai
- School of Advanced Technology, Xi'an Jiaotong-Liverpool University, 111 Ren'ai Road, Suzhou, 215000, China
| | - Fuyuan Liu
- School of Advanced Technology, Xi'an Jiaotong-Liverpool University, 111 Ren'ai Road, Suzhou, 215000, China; Department of Electrical and Electronic Engineering, University of Liverpool, Foundation Building, Brownlow Hill, Liverpool, L69 7ZX, UK
| | - Yifan Li
- School of Advanced Technology, Xi'an Jiaotong-Liverpool University, 111 Ren'ai Road, Suzhou, 215000, China; Department of Electrical and Electronic Engineering, University of Liverpool, Foundation Building, Brownlow Hill, Liverpool, L69 7ZX, UK
| | - Hang Yuan
- School of Advanced Technology, Xi'an Jiaotong-Liverpool University, 111 Ren'ai Road, Suzhou, 215000, China
| | - Wenwen Yuan
- School of Advanced Technology, Xi'an Jiaotong-Liverpool University, 111 Ren'ai Road, Suzhou, 215000, China; Department of Electrical and Electronic Engineering, University of Liverpool, Foundation Building, Brownlow Hill, Liverpool, L69 7ZX, UK; State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, No.28 Xianning West Road, Xi'an, 710079, China
| | - Kaizhu Huang
- Department of Electrical and Computer Engineering, Duke Kunshan University, 8 Duke Avenue, Kunshan, 215316, China
| | - Kai Hoettges
- Department of Electrical and Electronic Engineering, University of Liverpool, Foundation Building, Brownlow Hill, Liverpool, L69 7ZX, UK
| | - Min Chen
- School of Advanced Technology, Xi'an Jiaotong-Liverpool University, 111 Ren'ai Road, Suzhou, 215000, China; Department of Electrical and Electronic Engineering, University of Liverpool, Foundation Building, Brownlow Hill, Liverpool, L69 7ZX, UK
| | - Eng Gee Lim
- School of Advanced Technology, Xi'an Jiaotong-Liverpool University, 111 Ren'ai Road, Suzhou, 215000, China; Department of Electrical and Electronic Engineering, University of Liverpool, Foundation Building, Brownlow Hill, Liverpool, L69 7ZX, UK
| | - Chun Zhao
- School of Advanced Technology, Xi'an Jiaotong-Liverpool University, 111 Ren'ai Road, Suzhou, 215000, China; Department of Electrical and Electronic Engineering, University of Liverpool, Foundation Building, Brownlow Hill, Liverpool, L69 7ZX, UK
| | - Pengfei Song
- School of Advanced Technology, Xi'an Jiaotong-Liverpool University, 111 Ren'ai Road, Suzhou, 215000, China; Department of Electrical and Electronic Engineering, University of Liverpool, Foundation Building, Brownlow Hill, Liverpool, L69 7ZX, UK.
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Kim AY, Al Jerdi S, MacDonald R, Triggle CR. Alzheimer's disease and its treatment-yesterday, today, and tomorrow. Front Pharmacol 2024; 15:1399121. [PMID: 38868666 PMCID: PMC11167451 DOI: 10.3389/fphar.2024.1399121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Accepted: 04/25/2024] [Indexed: 06/14/2024] Open
Abstract
Alois Alzheimer described the first patient with Alzheimer's disease (AD) in 1907 and today AD is the most frequently diagnosed of dementias. AD is a multi-factorial neurodegenerative disorder with familial, life style and comorbidity influences impacting a global population of more than 47 million with a projected escalation by 2050 to exceed 130 million. In the USA the AD demographic encompasses approximately six million individuals, expected to increase to surpass 13 million by 2050, and the antecedent phase of AD, recognized as mild cognitive impairment (MCI), involves nearly 12 million individuals. The economic outlay for the management of AD and AD-related cognitive decline is estimated at approximately 355 billion USD. In addition, the intensifying prevalence of AD cases in countries with modest to intermediate income countries further enhances the urgency for more therapeutically and cost-effective treatments and for improving the quality of life for patients and their families. This narrative review evaluates the pathophysiological basis of AD with an initial focus on the therapeutic efficacy and limitations of the existing drugs that provide symptomatic relief: acetylcholinesterase inhibitors (AChEI) donepezil, galantamine, rivastigmine, and the N-methyl-D-aspartate receptor (NMDA) receptor allosteric modulator, memantine. The hypothesis that amyloid-β (Aβ) and tau are appropriate targets for drugs and have the potential to halt the progress of AD is critically analyzed with a particular focus on clinical trial data with anti-Aβ monoclonal antibodies (MABs), namely, aducanumab, lecanemab and donanemab. This review challenges the dogma that targeting Aβ will benefit the majority of subjects with AD that the anti-Aβ MABs are unlikely to be the "magic bullet". A comparison of the benefits and disadvantages of the different classes of drugs forms the basis for determining new directions for research and alternative drug targets that are undergoing pre-clinical and clinical assessments. In addition, we discuss and stress the importance of the treatment of the co-morbidities, including hypertension, diabetes, obesity and depression that are known to increase the risk of developing AD.
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Affiliation(s)
- A. Y. Kim
- Medical Education, Weill Cornell Medicine—Qatar, Doha, Qatar
| | | | - R. MacDonald
- Health Sciences Library, Weill Cornell Medicine—Qatar, Doha, Qatar
| | - C. R. Triggle
- Department of Pharmacology and Medical Education, Weill Cornell Medicine—Qatar, Doha, Qatar
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Gin A, Nguyen PD, Serrano G, Alexander G, Su J. Towards Early Diagnosis and Screening of Alzheimer's Disease Using Frequency Locked Whispering Gallery Mode Microtoroid Biosensors. RESEARCH SQUARE 2024:rs.3.rs-4355995. [PMID: 38798660 PMCID: PMC11118682 DOI: 10.21203/rs.3.rs-4355995/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Alzheimer's disease (AD) is a progressive form of dementia affecting almost 55 million people worldwide. It is characterized by the abnormal deposition of amyloid plaques and neurofibrillary tangles within the brain, leading to a pathological cascade of neuron degeneration and death as well as memory loss and cognitive decline. Amyloid beta (Aβ) is an AD biomarker present in cerebrospinal fluid and blood serum and correlates with the presence of amyloid plaques and tau tangles in the brain. Measuring the levels of Aβ can help with early diagnosis of AD, which is key for studying novel AD drugs and delaying the symptoms of dementia. However, this goal is difficult to achieve due to the low levels of AD biomarkers in biofluids. Here we demonstrate for the first time the use of FLOWER (frequency locked optical whispering evanescent resonator) for quantifying the levels of post-mortem cerebrospinal fluid (CSF) Aβ42 in clinicopathologically classified control, mild cognitive impairment (MCI), and AD participants. FLOWER is capable of measuring CSF Aβ42 (area under curve, AUC = 0.92) with higher diagnostic performance than standard ELISA (AUC = 0.82) and was also able to distinguish between control and MCI samples. Our results demonstrate the capability of FLOWER for screening CSF samples for early diagnosis of Alzheimer's pathology.
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Schraen-Maschke S, Duhamel A, Vidal JS, Ramdane N, Vaudran L, Dussart C, Buée L, Sablonnière B, Delaby C, Allinquant B, Gabelle A, Bombois S, Lehmann S, Hanon O. The free plasma amyloid Aβ 1-42/Aβ 1-40 ratio predicts conversion to dementia for subjects with mild cognitive impairment with performance equivalent to that of the total plasma Aβ 1-42/Aβ 1-40 ratio. The BALTAZAR study. Neurobiol Dis 2024; 193:106459. [PMID: 38423192 DOI: 10.1016/j.nbd.2024.106459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 02/26/2024] [Accepted: 02/26/2024] [Indexed: 03/02/2024] Open
Abstract
BACKGROUND AND PURPOSE Blood-based biomarkers are a non-invasive solution to predict the risk of conversion of mild cognitive impairment (MCI) to dementia. The utility of free plasma amyloid peptides (not bound to plasma proteins and/or cells) as an early indicator of conversion to dementia is still debated, as the results of studies have been contradictory. In this context, we investigated whether plasma levels of the free amyloid peptides Aβ1-42 and Aβ1-40 and the free plasma Aβ1-42/Aβ1-40 ratio are associated with the conversion of MCI to dementia, in particular AD, over three years of follow-up in a subgroup of the BALTAZAR cohort. We also compared their predictive value to that of total plasma Aβ1-42 and Aβ1-40 levels and the total plasma Aβ1-42/Aβ1-40 ratio. METHODS The plasma Aβ1-42 and Aβ1-40 peptide assay was performed using the INNO-BIA kit (Fujirebio Europe). Free amyloid levels (defined by the amyloid fraction directly accessible to antibodies of the assay) were obtained with the undiluted plasma, whereas total amyloid levels were obtained after the dilution of plasma (1/3) with a denaturing buffer. Free and total Aβ1-42 and Aβ1-40 levels were measured at inclusion for a subgroup of participants (N = 106) with mild cognitive impairment (MCI) from the BALTAZAR study (a large-scale longitudinal multicenter cohort with a three-year follow-up). Associations between conversion and the free/total plasma Aβ1-42 and Aβ1-40 levels and Aβ1-42/Aβ1-40 ratio were analyzed using logistic and Cox Proportional Hazards models. Demographic, clinical, cognitive (MMSE, ADL and IADL), APOE, and MRI characteristics (relative hippocampal volume) were compared using non-parametric (Mann-Whitney) or parametric (Student) tests for quantitative variables and Chi-square or Fisher exact tests for qualitative variables. RESULTS The risk of conversion to dementia was lower for patients in the highest quartile of free plasma Aβ1-42/Aβ1-40 (≥ 25.8%) than those in the three lower quartiles: hazard ratio = 0.36 (95% confidence interval [0.15-0.87]), after adjustment for age, sex, education, and APOE ε4 (p-value = 0.022). This was comparable to the risk of conversion in the highest quartile of total plasma Aβ1-42/Aβ1-40: hazard ratio = 0.37 (95% confidence interval [0.16-0.89], p-value = 0.027). However, while patients in the highest quartile of total plasma Aβ1-42/Aβ1-40 showed higher MMSE scores and a higher hippocampal volume than patients in the three lowest quartiles of total plasma Aβ1-42/Aβ1-40, as well as normal CSF biomarker levels, the patients in the highest quartile of free plasma Aβ1-42/Aβ1-40 did not show any significant differences in MMSE scores, hippocampal volume, or CSF biomarker levels relative to the three lowest quartiles of free plasma Aβ1-42/Aβ1-40. CONCLUSION The free plasma Aβ1-42/Aβ1-40 ratio is associated with a risk of conversion from MCI to dementia within three years, with performance comparable to that of the total plasma Aβ1-42/Aβ1-40 ratio. Threshold levels of the free and total plasma Aβ1-42/Aβ1-40 ratio could be determined, with a 60% lower risk of conversion for patients above the threshold than those below.
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Affiliation(s)
- S Schraen-Maschke
- Univ. Lille, Inserm, CHU Lille, UMR-S1172, LiCEND, Lille Neuroscience & Cognition, LabEx DISTALZ, Lille, France.
| | - A Duhamel
- Univ. Lille, CHU Lille, ULR 2694-METRICS: Évaluation des Technologies de Santé et des Pratiques Médicales, Lille, France
| | - J S Vidal
- Université de Paris, EA 4468 and APHP, Hôpital Broca, Memory Resource and Research Centre of de Paris-Broca-Ile de France, Paris, France
| | - N Ramdane
- Univ. Lille, CHU Lille, ULR 2694-METRICS: Évaluation des Technologies de Santé et des Pratiques Médicales, Lille, France
| | - L Vaudran
- Univ. Lille, Inserm, CHU Lille, UMR-S1172, LiCEND, Lille Neuroscience & Cognition, LabEx DISTALZ, Lille, France
| | - C Dussart
- Univ. Lille, Inserm, CHU Lille, UMR-S1172, LiCEND, Lille Neuroscience & Cognition, LabEx DISTALZ, Lille, France
| | - L Buée
- Univ. Lille, Inserm, CHU Lille, UMR-S1172, LiCEND, Lille Neuroscience & Cognition, LabEx DISTALZ, Lille, France
| | - B Sablonnière
- Univ. Lille, Inserm, CHU Lille, UMR-S1172, LiCEND, Lille Neuroscience & Cognition, LabEx DISTALZ, Lille, France
| | - C Delaby
- LBPC-PPC, Université de Montpellier, INM INSERM, IRMB CHU de Montpellier, Montpellier, France
| | - B Allinquant
- UMR-S1266, Université Paris Cité, Institute of Psychiatry and Neurosciences, Inserm, Paris, France
| | - A Gabelle
- CMRR, Université de Montpellier, INM INSERM, CHU de Montpellier, Montpellier, France
| | - S Bombois
- Univ. Lille, Inserm, CHU Lille, UMR-S1172, LiCEND, Lille Neuroscience & Cognition, LabEx DISTALZ, Lille, France; Assistance Publique-Hôpitaux de Paris (AP-HP), Département de Neurologie, Centre des Maladies Cognitives et Comportementales, GH Pitié-Salpêtrière, Paris, France
| | - S Lehmann
- LBPC-PPC, Université de Montpellier, INM INSERM, IRMB CHU de Montpellier, Montpellier, France
| | - O Hanon
- Université de Paris, EA 4468 and APHP, Hôpital Broca, Memory Resource and Research Centre of de Paris-Broca-Ile de France, Paris, France.
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Ma X, Shyer M, Harris K, Wang D, Hsu YC, Farrell C, Goodwin N, Anjum S, Bukhbinder AS, Dean S, Khan T, Hunter D, Schulz PE, Jiang X, Kim Y. Deep learning to predict rapid progression of Alzheimer's disease from pooled clinical trials: A retrospective study. PLOS DIGITAL HEALTH 2024; 3:e0000479. [PMID: 38598464 PMCID: PMC11006164 DOI: 10.1371/journal.pdig.0000479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 02/26/2024] [Indexed: 04/12/2024]
Abstract
The rate of progression of Alzheimer's disease (AD) differs dramatically between patients. Identifying the most is critical because when their numbers differ between treated and control groups, it distorts the outcome, making it impossible to tell whether the treatment was beneficial. Much recent effort, then, has gone into identifying RPs. We pooled de-identified placebo-arm data of three randomized controlled trials (RCTs), EXPEDITION, EXPEDITION 2, and EXPEDITION 3, provided by Eli Lilly and Company. After processing, the data included 1603 mild-to-moderate AD patients with 80 weeks of longitudinal observations on neurocognitive health, brain volumes, and amyloid-beta (Aβ) levels. RPs were defined by changes in four neurocognitive/functional health measures. We built deep learning models using recurrent neural networks with attention mechanisms to predict RPs by week 80 based on varying observation periods from baseline (e.g., 12, 28 weeks). Feature importance scores for RP prediction were computed and temporal feature trajectories were compared between RPs and non-RPs. Our evaluation and analysis focused on models trained with 28 weeks of observation. The models achieved robust internal validation area under the receiver operating characteristic (AUROCs) ranging from 0.80 (95% CI 0.79-0.82) to 0.82 (0.81-0.83), and the area under the precision-recall curve (AUPRCs) from 0.34 (0.32-0.36) to 0.46 (0.44-0.49). External validation AUROCs ranged from 0.75 (0.70-0.81) to 0.83 (0.82-0.84) and AUPRCs from 0.27 (0.25-0.29) to 0.45 (0.43-0.48). Aβ plasma levels, regional brain volumetry, and neurocognitive health emerged as important factors for the model prediction. In addition, the trajectories were stratified between predicted RPs and non-RPs based on factors such as ventricular volumes and neurocognitive domains. Our findings will greatly aid clinical trialists in designing tests for new medications, representing a key step toward identifying effective new AD therapies.
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Affiliation(s)
- Xiaotian Ma
- Department of Health Data Science and Artificial Intelligence, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Madison Shyer
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Kristofer Harris
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Dulin Wang
- Department of Health Data Science and Artificial Intelligence, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Yu-Chun Hsu
- Department of Health Data Science and Artificial Intelligence, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Christine Farrell
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Nathan Goodwin
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Sahar Anjum
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Avram S. Bukhbinder
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
- Division of Pediatric Neurology, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Sarah Dean
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Tanveer Khan
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - David Hunter
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Paul E. Schulz
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Xiaoqian Jiang
- Department of Health Data Science and Artificial Intelligence, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Yejin Kim
- Department of Health Data Science and Artificial Intelligence, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
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9
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Muir RT, Ismail Z, Black SE, Smith EE. Comparative methods for quantifying plasma biomarkers in Alzheimer's disease: Implications for the next frontier in cerebral amyloid angiopathy diagnostics. Alzheimers Dement 2024; 20:1436-1458. [PMID: 37908054 PMCID: PMC10916950 DOI: 10.1002/alz.13510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Revised: 08/09/2023] [Accepted: 08/13/2023] [Indexed: 11/02/2023]
Abstract
Plasma amyloid beta (Aβ) and tau are emerging as accessible biomarkers for Alzheimer's disease (AD). However, many assays exist with variable test performances, highlighting the need for a comparative assessment to identify the most valid assays for future use in AD and to apply to other settings in which the same biomarkers may be useful, namely, cerebral amyloid angiopathy (CAA). CAA is a progressive cerebrovascular disease characterized by deposition of Aβ40 and Aβ42 in cortical and leptomeningeal vessels. Novel immunotherapies for AD can induce amyloid-related imaging abnormalities resembling CAA-related inflammation. Few studies have evaluated plasma biomarkers in CAA. Identifying a CAA signature could facilitate diagnosis, prognosis, and a safer selection of patients with AD for emerging immunotherapies. This review evaluates studies that compare the diagnostic test performance of plasma biomarker techniques in AD and cerebrovascular and plasma biomarker profiles of CAA; it also discusses novel hypotheses and future avenues for plasma biomarker research in CAA.
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Affiliation(s)
- Ryan T. Muir
- Calgary Stroke ProgramDepartment of Clinical NeurosciencesUniversity of CalgaryCalgaryAlbertaCanada
- Department of Community Health SciencesUniversity of CalgaryCalgaryAlbertaCanada
- Hotchkiss Brain InstituteUniversity of CalgaryCalgaryAlbertaCanada
| | - Zahinoor Ismail
- Department of Community Health SciencesUniversity of CalgaryCalgaryAlbertaCanada
- Hotchkiss Brain InstituteUniversity of CalgaryCalgaryAlbertaCanada
- Department of PsychiatryUniversity of CalgaryCalgaryAlbertaCanada
| | - Sandra E. Black
- Division of NeurologyDepartment of MedicineSunnybrook Health Sciences CentreTorontoOntarioCanada
- LC Campbell Cognitive Neurology Research UnitDr Sandra Black Centre for Brain Resilience and Recovery, and Hurvitz Brain Sciences ProgramSunnybrook Research InstituteUniversity of TorontoTorontoOntarioCanada
| | - Eric E. Smith
- Calgary Stroke ProgramDepartment of Clinical NeurosciencesUniversity of CalgaryCalgaryAlbertaCanada
- Department of Community Health SciencesUniversity of CalgaryCalgaryAlbertaCanada
- Hotchkiss Brain InstituteUniversity of CalgaryCalgaryAlbertaCanada
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10
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Schuurmans IK, Ghanbari M, Cecil CAM, Ikram MA, Luik AI. Plasma neurofilament light chain in association to late-life depression in the general population. Psychiatry Clin Neurosci 2024; 78:97-103. [PMID: 37843431 DOI: 10.1111/pcn.13608] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 09/22/2023] [Accepted: 10/11/2023] [Indexed: 10/17/2023]
Abstract
AIM Investigating what is underlying late-life depression is becoming increasingly important with the rapidly growing elderly population. Yet, the associations between plasma biomarkers of neuroaxonal damage and late-life depression remain largely unclear. Therefore, we determined cross-sectional and longitudinal associations of neurofilament light chain (NfL) with depression in middle-aged and elderly individuals, and total tau, β-amyloid 40 and 42 for comparison. METHODS We included 3,895 participants (71.78 years [SD = 7.37], 53.4% women) from the population-based Rotterdam Study. Between 2002 and 2005, NfL, total tau, β-amyloid 40 and β-amyloid 42 were determined in blood and depressive symptoms were measured with the Center for Epidemiologic Studies Depression scale (CES-D). Incident depressive events (clinically relevant depressive symptoms, depressive syndromes, major depressive disorders) were measured prospectively with the Center for Epidemiologic Studies Depression, a clinical interview and follow-up of medical records over a median follow-up of 7.0 years (interquartile range 1.80). We used linear and Cox proportional hazard regression models. RESULTS Each log2 pg./mL increase in NfL was cross-sectionally associated with more depressive symptoms (adjusted mean difference: 0.32, 95% CI 0.05-0.58), as well as with an increased risk of any incident depressive event over time (hazard ratio: 1.22, 95% CI 1.01-1.47). Further, more amyloid-β 40 was cross-sectionally associated with more depressive symptoms (adjusted mean difference: 0.70, 95% CI 0.15-1.25). CONCLUSION Higher levels of NfL are cross-sectionally associated with more depressive symptoms and a higher risk of incident depressive events longitudinally. The association was stronger for NfL compared to other plasma biomarkers, suggesting a potential role of neuroaxonal damage in developing late-life depression.
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Affiliation(s)
- Isabel K Schuurmans
- Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands
- The Generation R Study Group, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Charlotte A M Cecil
- Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Child and Adolescent Psychiatry and Psychology, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Annemarie I Luik
- Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands
- Trimbos Institute-The Netherlands Institute of Mental Health and Addiction, Utrecht, The Netherlands
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11
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Cournut A, Moustiez P, Coffinier Y, Enjalbal C, Bich C. Innovative SALDI mass spectrometry analysis for Alzheimer's disease synthetic peptides detection. Talanta 2024; 268:125357. [PMID: 37951181 DOI: 10.1016/j.talanta.2023.125357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 10/12/2023] [Accepted: 10/26/2023] [Indexed: 11/13/2023]
Abstract
Alzheimer's disease (AD) is nowadays the prominent cause of senile dementia. This pathology is characterized by aggregation of neurofibrillary tangles in cells and by the accumulation of amyloid plaques in the brain. Noteworthy, a phosphorylated protein (tau protein) and a peptide presenting two overlapping sequences of 40 or 42 residues named β-amyloid peptides 1-40 (Aβ 1-40) and 1-42 (Aβ 1-42), respectively, were related to such deleterious phenomena. Singularly, the neurotoxicity was primarily attributed to the amyloid peptide Aβ 1-42 form due to its capacity to fold into beta-sheets rendering it insoluble thus causing subsequent aggregation and accumulation in vivo. Regarding AD diagnosis relying on mass spectrometry, Aβ 1-42 and/or Aβ 1-40 were considered as relevant biomarkers being measured in cerebrospinal fluids (CSF), blood and urine. Under that context, we aimed at implementing an innovative method to evidence the depletion of circulating Aβ 1-42 amyloid peptide compared to the shorter Aβ 1-40 form indicating a pathologic state. We investigated Surface-Assisted Laser Desorption/Ionization Mass Spectrometry (SALDI-MS) in order to monitor the Aβ 1-42/Aβ 1-40 ratio without any prior sample treatment or enrichment. Taking into account that β-amyloid peptide and 1-42 can aggregate into beta-sheets depending on the experimental conditions, specific attention was devoted to sample integrity monitoring performed by circular dichroism experiments during SALDI-MS method development.
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Affiliation(s)
- Aline Cournut
- Univ Montpellier, IBMM, CNRS, ENSCM, Montpellier, France
| | - Paul Moustiez
- Univ Lille, IEMN, UMR CNRS 8520, Villeneuve d'Ascq, France
| | | | | | - Claudia Bich
- Univ Montpellier, IBMM, CNRS, ENSCM, Montpellier, France.
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12
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Bhalala OG, Watson R, Yassi N. Multi-Omic Blood Biomarkers as Dynamic Risk Predictors in Late-Onset Alzheimer's Disease. Int J Mol Sci 2024; 25:1231. [PMID: 38279230 PMCID: PMC10816901 DOI: 10.3390/ijms25021231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 01/17/2024] [Accepted: 01/18/2024] [Indexed: 01/28/2024] Open
Abstract
Late-onset Alzheimer's disease is the leading cause of dementia worldwide, accounting for a growing burden of morbidity and mortality. Diagnosing Alzheimer's disease before symptoms are established is clinically challenging, but would provide therapeutic windows for disease-modifying interventions. Blood biomarkers, including genetics, proteins and metabolites, are emerging as powerful predictors of Alzheimer's disease at various timepoints within the disease course, including at the preclinical stage. In this review, we discuss recent advances in such blood biomarkers for determining disease risk. We highlight how leveraging polygenic risk scores, based on genome-wide association studies, can help stratify individuals along their risk profile. We summarize studies analyzing protein biomarkers, as well as report on recent proteomic- and metabolomic-based prediction models. Finally, we discuss how a combination of multi-omic blood biomarkers can potentially be used in memory clinics for diagnosis and to assess the dynamic risk an individual has for developing Alzheimer's disease dementia.
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Affiliation(s)
- Oneil G. Bhalala
- Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, Parkville 3052, Australia; (R.W.); (N.Y.)
- Department of Neurology, Melbourne Brain Centre at The Royal Melbourne Hospital, University of Melbourne, Parkville 3050, Australia
| | - Rosie Watson
- Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, Parkville 3052, Australia; (R.W.); (N.Y.)
- Department of Medicine, The Royal Melbourne Hospital, University of Melbourne, Parkville 3050, Australia
| | - Nawaf Yassi
- Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, Parkville 3052, Australia; (R.W.); (N.Y.)
- Department of Neurology, Melbourne Brain Centre at The Royal Melbourne Hospital, University of Melbourne, Parkville 3050, Australia
- Department of Medicine, The Royal Melbourne Hospital, University of Melbourne, Parkville 3050, Australia
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13
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Yang Y, Kim WS, Michaelian JC, Lewis SJG, Phillips CL, D'Rozario AL, Chatterjee P, Martins RN, Grunstein R, Halliday GM, Naismith SL. Predicting neurodegeneration from sleep related biofluid changes. Neurobiol Dis 2024; 190:106369. [PMID: 38049012 DOI: 10.1016/j.nbd.2023.106369] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 11/30/2023] [Accepted: 12/01/2023] [Indexed: 12/06/2023] Open
Abstract
Sleep-wake disturbances are common in neurodegenerative diseases and may occur years before the clinical diagnosis, potentially either representing an early stage of the disease itself or acting as a pathophysiological driver. Therefore, discovering biomarkers that identify individuals with sleep-wake disturbances who are at risk of developing neurodegenerative diseases will allow early diagnosis and intervention. Given the association between sleep and neurodegeneration, the most frequently analyzed fluid biomarkers in people with sleep-wake disturbances to date include those directly associated with neurodegeneration itself, such as neurofilament light chain, phosphorylated tau, amyloid-beta and alpha-synuclein. Abnormalities in these biomarkers in patients with sleep-wake disturbances are considered as evidence of an underlying neurodegenerative process. Levels of hormonal sleep-related biomarkers such as melatonin, cortisol and orexin are often abnormal in patients with clinical neurodegenerative diseases, but their relationships with the more standard neurodegenerative biomarkers remain unclear. Similarly, it is unclear whether other chronobiological/circadian biomarkers, such as disrupted clock gene expression, are causal factors or a consequence of neurodegeneration. Current data would suggest that a combination of fluid biomarkers may identify sleep-wake disturbances that are most predictive for the risk of developing neurodegenerative disease with more optimal sensitivity and specificity.
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Affiliation(s)
- Yue Yang
- Brain and Mind Centre, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2050, Australia.
| | - Woojin Scott Kim
- Brain and Mind Centre, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2050, Australia; School of Medical Sciences, University of New South Wales, Sydney, NSW 2052, Australia.
| | - Johannes C Michaelian
- Healthy Brain Ageing Program, School of Psychology, Brain and Mind Centre & The Charles Perkins Centre, The University of Sydney, Sydney, NSW 2050, Australia.
| | - Simon J G Lewis
- Brain and Mind Centre, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2050, Australia; School of Medical Sciences, University of New South Wales, Sydney, NSW 2052, Australia; Parkinson's Disease Research Clinic, Brain and Mind Centre, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2050, Australia.
| | - Craig L Phillips
- CIRUS, Centre for Sleep and Chronobiology, Woolcock Institute of Medical Research, Macquarie University, Sydney, NSW 2109, Australia; Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, NSW 2109, Australia.
| | - Angela L D'Rozario
- Healthy Brain Ageing Program, School of Psychology, Brain and Mind Centre & The Charles Perkins Centre, The University of Sydney, Sydney, NSW 2050, Australia; CIRUS, Centre for Sleep and Chronobiology, Woolcock Institute of Medical Research, Macquarie University, Sydney, NSW 2109, Australia.
| | - Pratishtha Chatterjee
- Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, NSW 2109, Australia; School of Medical and Health Sciences, Edith Cowan University, Perth, WA 6027, Australia.
| | - Ralph N Martins
- Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, NSW 2109, Australia; School of Medical and Health Sciences, Edith Cowan University, Perth, WA 6027, Australia; School of Psychiatry and Clinical Neurosciences, University of Western Australia, Perth, WA 6009, Australia.
| | - Ron Grunstein
- CIRUS, Centre for Sleep and Chronobiology, Woolcock Institute of Medical Research, Macquarie University, Sydney, NSW 2109, Australia; Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia.
| | - Glenda M Halliday
- Brain and Mind Centre, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2050, Australia; School of Medical Sciences, University of New South Wales, Sydney, NSW 2052, Australia.
| | - Sharon L Naismith
- Healthy Brain Ageing Program, School of Psychology, Brain and Mind Centre & The Charles Perkins Centre, The University of Sydney, Sydney, NSW 2050, Australia.
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14
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Memon A, Moore JA, Kang C, Ismail Z, Forkert ND. Visual Functions Are Associated with Biomarker Changes in Alzheimer's Disease. J Alzheimers Dis 2024; 99:623-637. [PMID: 38669529 DOI: 10.3233/jad-231084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/28/2024]
Abstract
Background While various biomarkers of Alzheimer's disease (AD) have been associated with general cognitive function, their association to visual-perceptive function across the AD spectrum warrant more attention due to its significant impact on quality of life. Thus, this study explores how AD biomarkers are associated with decline in this cognitive domain. Objective To explore associations between various fluid and imaging biomarkers and visual-based cognitive assessments in participants across the AD spectrum. Methods Data from participants (N = 1,460) in the Alzheimer's Disease Neuroimaging Initiative were analyzed, including fluid and imaging biomarkers. Along with the Mini-Mental State Examination (MMSE), three specific visual-based cognitive tests were investigated: Trail Making Test (TMT) A and TMT B, and the Boston Naming Test (BNT). Locally estimated scatterplot smoothing curves and Pearson correlation coefficients were used to examine associations. Results MMSE showed the strongest correlations with most biomarkers, followed by TMT-B. The p-tau181/Aβ1-42 ratio, along with the volume of the hippocampus and entorhinal cortex, had the strongest associations among the biomarkers. Conclusions Several biomarkers are associated with visual processing across the disease spectrum, emphasizing their potential in assessing disease severity and contributing to progression models of visual function and cognition.
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Affiliation(s)
- Ashar Memon
- Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Jasmine A Moore
- Department of Radiology, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Biomedical Engineering Program, University of Calgary, Calgary, AB, Canada
| | - Chris Kang
- Department of Radiology, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Zahinoor Ismail
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Departments of Clinical Neurosciences, Psychiatry, Community Health Sciences, and Pathology and Laboratory Medicine, University of Calgary, Calgary, AB, Canada
| | - Nils D Forkert
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Departments of Clinical Neurosciences, Psychiatry, Community Health Sciences, and Pathology and Laboratory Medicine, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Departments of Clinical Neurosciences, Psychiatry, Community Health Sciences, and Pathology and Laboratory Medicine, University of Calgary, Calgary, AB, Canada
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15
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Lee KH, Hsu MH, Chen HH, Yang SY. Analyzer-to-Analyzer Variations in Assaying Ultralow Concentrated Biomarkers Associated with Neurodegenerative Diseases Using Immunomagnetic Reduction. ACS MEASUREMENT SCIENCE AU 2023; 3:488-495. [PMID: 38145030 PMCID: PMC10740117 DOI: 10.1021/acsmeasuresciau.3c00029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 10/01/2023] [Accepted: 10/02/2023] [Indexed: 12/26/2023]
Abstract
By utilizing a high-temperature superconducting quantum interference device (high-Tc SQUID) magnetometer, an alternating current (AC) magnetosusceptometer, referred to as an analyzer, was developed for ultrasensitive immunoassays. The analyzer has been applied to assay biomarkers in human plasma associated with Alzheimer's disease (AD) and Parkinson's disease (PD). The involved assay methodology is the so-called immunomagnetic reduction (IMR). Such an analyzer has been approved for clinical use in Taiwan and Europe. The mass production of the analyzer is needed for clinical utilities. The issue of exploring analyzer-to-analyzer variations in the performances becomes critical. Unfortunately, there is no standard characterization to determine the variations in performances among analyzers. In this study, key characterizations, such as output signal stability, signal-to-noise ratio, measured concentrations of a control sample, etc., are proposed. In total, three analyzers are characterized in this work. The detected biomarkers include amyloid peptides, total tau protein, phosphorylated tau protein, and α-synuclein protein for AD and PD. Through one-way ANOVA for any of the characterizations among the three analyzers, it was found that there was no significant difference in any of these characterizations among the analyzers (p > 0.05). Furthermore, the three analyzers are applied to assay biomolecules for AD and PD in reference samples. High correlations (r > 0.8) in measured concentrations of any of these biomarkers in reference samples were obtained among the three analyzers. The results demonstrate that the proposed characterizations are feasible for achieving consistent performance among high-Tc SQUID-based AC magnetosusceptometers for assaying biomolecules.
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16
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Vijiaratnam N, Foltynie T. How should we be using biomarkers in trials of disease modification in Parkinson's disease? Brain 2023; 146:4845-4869. [PMID: 37536279 PMCID: PMC10690028 DOI: 10.1093/brain/awad265] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 07/18/2023] [Accepted: 07/22/2023] [Indexed: 08/05/2023] Open
Abstract
The recent validation of the α-synuclein seed amplification assay as a biomarker with high sensitivity and specificity for the diagnosis of Parkinson's disease has formed the backbone for a proposed staging system for incorporation in Parkinson's disease clinical studies and trials. The routine use of this biomarker should greatly aid in the accuracy of diagnosis during recruitment of Parkinson's disease patients into trials (as distinct from patients with non-Parkinson's disease parkinsonism or non-Parkinson's disease tremors). There remain, however, further challenges in the pursuit of biomarkers for clinical trials of disease modifying agents in Parkinson's disease, namely: optimizing the distinction between different α-synucleinopathies; the selection of subgroups most likely to benefit from a candidate disease modifying agent; a sensitive means of confirming target engagement; and the early prediction of longer-term clinical benefit. For example, levels of CSF proteins such as the lysosomal enzyme β-glucocerebrosidase may assist in prognostication or allow enrichment of appropriate patients into disease modifying trials of agents with this enzyme as the target; the presence of coexisting Alzheimer's disease-like pathology (detectable through CSF levels of amyloid-β42 and tau) can predict subsequent cognitive decline; imaging techniques such as free-water or neuromelanin MRI may objectively track decline in Parkinson's disease even in its later stages. The exploitation of additional biomarkers to the α-synuclein seed amplification assay will, therefore, greatly add to our ability to plan trials and assess the disease modifying properties of interventions. The choice of which biomarker(s) to use in the context of disease modifying clinical trials will depend on the intervention, the stage (at risk, premotor, motor, complex) of the population recruited and the aims of the trial. The progress already made lends hope that panels of fluid biomarkers in tandem with structural or functional imaging may provide sensitive and objective methods of confirming that an intervention is modifying a key pathophysiological process of Parkinson's disease. However, correlation with clinical progression does not necessarily equate to causation, and the ongoing validation of quantitative biomarkers will depend on insightful clinical-genetic-pathophysiological comparisons incorporating longitudinal biomarker changes from those at genetic risk with evidence of onset of the pathophysiology and those at each stage of manifest clinical Parkinson's disease.
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Affiliation(s)
- Nirosen Vijiaratnam
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
| | - Thomas Foltynie
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
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17
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Zilinskaite N, Shukla RP, Baradoke A. Use of 3D Printing Techniques to Fabricate Implantable Microelectrodes for Electrochemical Detection of Biomarkers in the Early Diagnosis of Cardiovascular and Neurodegenerative Diseases. ACS MEASUREMENT SCIENCE AU 2023; 3:315-336. [PMID: 37868357 PMCID: PMC10588936 DOI: 10.1021/acsmeasuresciau.3c00028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 08/25/2023] [Accepted: 08/25/2023] [Indexed: 10/24/2023]
Abstract
This Review provides a comprehensive overview of 3D printing techniques to fabricate implantable microelectrodes for the electrochemical detection of biomarkers in the early diagnosis of cardiovascular and neurodegenerative diseases. Early diagnosis of these diseases is crucial to improving patient outcomes and reducing healthcare systems' burden. Biomarkers serve as measurable indicators of these diseases, and implantable microelectrodes offer a promising tool for their electrochemical detection. Here, we discuss various 3D printing techniques, including stereolithography (SLA), digital light processing (DLP), fused deposition modeling (FDM), selective laser sintering (SLS), and two-photon polymerization (2PP), highlighting their advantages and limitations in microelectrode fabrication. We also explore the materials used in constructing implantable microelectrodes, emphasizing their biocompatibility and biodegradation properties. The principles of electrochemical detection and the types of sensors utilized are examined, with a focus on their applications in detecting biomarkers for cardiovascular and neurodegenerative diseases. Finally, we address the current challenges and future perspectives in the field of 3D-printed implantable microelectrodes, emphasizing their potential for improving early diagnosis and personalized treatment strategies.
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Affiliation(s)
- Nemira Zilinskaite
- Wellcome/Cancer
Research UK Gurdon Institute, Henry Wellcome Building of Cancer and
Developmental Biology, University of Cambridge, Tennis Court Road, Cambridge CB2 1QN, U.K.
- Faculty
of Medicine, University of Vilnius, M. K. Čiurlionio g. 21, LT-03101 Vilnius, Lithuania
| | - Rajendra P. Shukla
- BIOS
Lab-on-a-Chip Group, MESA+ Institute for Nanotechnology, Max Planck
Center for Complex Fluid Dynamics, University
of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands
| | - Ausra Baradoke
- Wellcome/Cancer
Research UK Gurdon Institute, Henry Wellcome Building of Cancer and
Developmental Biology, University of Cambridge, Tennis Court Road, Cambridge CB2 1QN, U.K.
- Faculty
of Medicine, University of Vilnius, M. K. Čiurlionio g. 21, LT-03101 Vilnius, Lithuania
- BIOS
Lab-on-a-Chip Group, MESA+ Institute for Nanotechnology, Max Planck
Center for Complex Fluid Dynamics, University
of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands
- Center for
Physical Sciences and Technology, Savanoriu 231, LT-02300 Vilnius, Lithuania
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18
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Ye HT, Lu CQ, Wang C, Zhang D, Li YF, Feng XY, Wang HP, Mao YY, Ji MH, Yang JJ. Plasma Aβ level alterations after sleep deprivation correspond to brain structural remodeling in medical night shift workers. Brain Res Bull 2023; 203:110776. [PMID: 37805053 DOI: 10.1016/j.brainresbull.2023.110776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 09/30/2023] [Accepted: 10/04/2023] [Indexed: 10/09/2023]
Abstract
The relationship between brain structure alteration and metabolic product clearance after night shift work with total sleep deprivation (SD) remains unclear. Twenty-two intensive care unit staff on regularly rotating shift work were implemented with structural and diffusion MRI under both rest wakefulness (RW) and SD conditions. Peripheral blood samples were collected for the measurement of cerebral metabolites. Voxel-based morphometry and diffusion tensor imaging analysis were used to investigate the alterations in the gray matter density (GMD) and mean diffusivity (MD) within the participants. Furthermore, correlation analysis was performed to investigate the relationship between the neuroimaging metrics and hematological parameters. A significant increase in the GMD values was observed in the anterior and peripheral areas of the brain under SD. In contrast, a decrease in the values was observed in the posterior regions, such as the bilateral cerebellum and thalamus. In addition, a significant reduction in the total cerebrospinal fluid volume was observed under SD. The Aβ42/Aβ40 levels in participants under SD were significantly lower than those under RW. The mean MD increment values extracted from the region of interest (ROI) of the anterior brain were negatively correlated with the increment of plasma Aβ42/Aβ40 levels (r = -0.658, P = 0.008). The mean GMD decrement values extracted from the posterior ROI were positively correlated with the increment of plasma Aβ-40 levels (r = 0.601, P = 0.023). The findings of this study suggest that one night of shift work under SD induces extensive and direction-specific structural alterations of the brain, which are associated with aberrant brain metabolic waste clearance.
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Affiliation(s)
- Hao-Tian Ye
- Department of Anesthesiology, Pain and Perioperative Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Chun-Qiang Lu
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, Medical School of Southeast University, Nanjing 210009, China; Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Medical School of Southeast University, Nanjing 210009, China
| | - Cong Wang
- Department of Anesthesiology, Pain and Perioperative Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Di Zhang
- Department of Anesthesiology, Pain and Perioperative Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Yan-Fei Li
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Xiang-Yun Feng
- Department of Anesthesiology, Pain and Perioperative Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Hua-Peng Wang
- Department of Anesthesiology, Pain and Perioperative Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Yuan-Yuan Mao
- Department of Anesthesiology, Pain and Perioperative Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Mu-Huo Ji
- Department of Anesthesiology, The Second Affiliated Hospital of Nanjing Medical University, Nanjing 210003, China.
| | - Jian-Jun Yang
- Department of Anesthesiology, Pain and Perioperative Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China.
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19
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Ferreira PCL, Zhang Y, Snitz B, Chang CCH, Bellaver B, Jacobsen E, Kamboh MI, Zetterberg H, Blennow K, Pascoal TA, Villemagne VL, Ganguli M, Karikari TK. Plasma biomarkers identify older adults at risk of Alzheimer's disease and related dementias in a real-world population-based cohort. Alzheimers Dement 2023; 19:4507-4519. [PMID: 36876954 PMCID: PMC10480336 DOI: 10.1002/alz.12986] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 01/12/2023] [Accepted: 01/15/2023] [Indexed: 03/07/2023]
Abstract
INTRODUCTION Plasma biomarkers-cost effective, non-invasive indicators of Alzheimer's disease (AD) and related disorders (ADRD)-have largely been studied in clinical research settings. Here, we examined plasma biomarker profiles and their associated factors in a population-based cohort to determine whether they could identify an at-risk group, independently of brain and cerebrospinal fluid biomarkers. METHODS We measured plasma phosphorylated tau181 (p-tau181), neurofilament light chain (NfL), glial fibrillary acidic protein (GFAP), and amyloid beta (Aβ)42/40 ratio in 847 participants from a population-based cohort in southwestern Pennsylvania. RESULTS K-medoids clustering identified two distinct plasma Aβ42/40 modes, further categorizable into three biomarker profile groups: normal, uncertain, and abnormal. In different groups, plasma p-tau181, NfL, and GFAP were inversely correlated with Aβ42/40, Clinical Dementia Rating, and memory composite score, with the strongest associations in the abnormal group. DISCUSSION Abnormal plasma Aβ42/40 ratio identified older adult groups with lower memory scores, higher dementia risks, and higher ADRD biomarker levels, with potential implications for population screening. HIGHLIGHTS Population-based plasma biomarker studies are lacking, particularly in cohorts without cerebrospinal fluid or neuroimaging data. In the Monongahela-Youghiogheny Healthy Aging Team study (n = 847), plasma biomarkers associated with worse memory and Clinical Dementia Rating (CDR), apolipoprotein E ε4, and greater age. Plasma amyloid beta (Aβ)42/40 ratio levels allowed clustering participants into abnormal, uncertain, and normal groups. Plasma Aβ42/40 correlated differently with neurofilament light chain, glial fibrillary acidic protein, phosphorylated tau181, memory composite, and CDR in each group. Plasma biomarkers can enable relatively affordable and non-invasive community screening for evidence of Alzheimer's disease and related disorders pathophysiology.
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Affiliation(s)
- Pamela C. L Ferreira
- Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Yingjin Zhang
- Department of Biostatistics, School of Public Health, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Beth Snitz
- Department of Neurology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Chung-Chou H. Chang
- Department of Biostatistics, School of Public Health, University of Pittsburgh, Pittsburgh, PA, 15213, USA
- Department of Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Bruna Bellaver
- Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Erin Jacobsen
- Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - M. Ilyas Kamboh
- Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy, University of Gothenburg, Mölndal, 431 41, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Gothenburg, 431 41, Sweden
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK
- UK Dementia Research Institute at UCL, London, WC1N 3BG, UK
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong, HKG, China
- UW Department of Medicine, School of Medicine and Public Health, Madison, WI, 53726, USA
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy, University of Gothenburg, Mölndal, 431 41, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Gothenburg, 431 41, Sweden
| | - Tharick A. Pascoal
- Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA
- Department of Neurology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Victor L. Villemagne
- Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA
- Department of Neurology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Mary Ganguli
- Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA
- Department of Neurology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA
- Department of Epidemiology, School of Public Health, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Thomas K. Karikari
- Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA
- Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy, University of Gothenburg, Mölndal, 431 41, Sweden
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20
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Sharma A, Feng L, Muresanu DF, Tian ZR, Lafuente JV, Buzoianu AD, Nozari A, Bryukhovetskiy I, Manzhulo I, Wiklund L, Sharma HS. Sleep deprivation enhances amyloid beta peptide, p-tau and serotonin in the brain: Neuroprotective effects of nanowired delivery of cerebrolysin with monoclonal antibodies to amyloid beta peptide, p-tau and serotonin. INTERNATIONAL REVIEW OF NEUROBIOLOGY 2023; 171:125-162. [PMID: 37783554 DOI: 10.1016/bs.irn.2023.05.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
Sleep deprivation is quite frequent in military during combat, intelligence gathering or peacekeeping operations. Even one night of sleep deprivation leads to accumulation of amyloid beta peptide burden that would lead to precipitation of Alzheimer's disease over the years. Thus, efforts are needed to slow down or neutralize accumulation of amyloid beta peptide (AβP) and associated Alzheimer's disease brain pathology including phosphorylated tau (p-tau) within the brain fluid environment. Sleep deprivation also alters serotonin (5-hydroxytryptamine) metabolism in the brain microenvironment and impair upregulation of several neurotrophic factors. Thus, blockade or neutralization of AβP, p-tau and serotonin in sleep deprivation may attenuate brain pathology. In this investigation this hypothesis is examined using nanodelivery of cerebrolysin- a balanced composition of several neurotrophic factors and active peptide fragments together with monoclonal antibodies against AβP, p-tau and serotonin (5-hydroxytryptamine, 5-HT). Our observations suggest that sleep deprivation induced pathophysiology is significantly reduced following nanodelivery of cerebrolysin together with monoclonal antibodies to AβP, p-tau and 5-HT, not reported earlier.
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Affiliation(s)
- Aruna Sharma
- International Experimental Central Nervous System Injury & Repair (IECNSIR), Dept. of Surgical Sciences, Anesthesiology & Intensive Care Medicine, Uppsala University Hospital, Uppsala University, Uppsala, Sweden.
| | - Lianyuan Feng
- Department of Neurology, Bethune International Peace Hospital, Zhongshan Road (West), Shijiazhuang, Hebei Province, P.R. China
| | - Dafin F Muresanu
- Dept. Clinical Neurosciences, University of Medicine & Pharmacy, Cluj-Napoca, Romania; "RoNeuro" Institute for Neurological Research and Diagnostic, Mircea Eliade Street, Cluj-Napoca, Romania
| | - Z Ryan Tian
- Dept. Chemistry & Biochemistry, University of Arkansas, Fayetteville, AR, United States
| | - José Vicente Lafuente
- LaNCE, Dept. Neuroscience, University of the Basque Country (UPV/EHU), Leioa, Bizkaia, Spain
| | - Anca D Buzoianu
- Department of Clinical Pharmacology and Toxicology, "Iuliu Hatieganu" University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Ala Nozari
- Department of Anesthesiology, Boston University, Albany str, Boston MA, USA
| | - Igor Bryukhovetskiy
- Department of Fundamental Medicine, School of Biomedicine, Far Eastern Federal University, Vladivostok, Russia; Laboratory of Pharmacology, National Scientific Center of Marine Biology, Far East Branch of the Russian Academy of Sciences, Vladivostok, Russia
| | - Igor Manzhulo
- Laboratory of Pharmacology, National Scientific Center of Marine Biology, Far East Branch of the Russian Academy of Sciences, Vladivostok, Russia
| | - Lars Wiklund
- International Experimental Central Nervous System Injury & Repair (IECNSIR), Dept. of Surgical Sciences, Anesthesiology & Intensive Care Medicine, Uppsala University Hospital, Uppsala University, Uppsala, Sweden
| | - Hari Shanker Sharma
- International Experimental Central Nervous System Injury & Repair (IECNSIR), Dept. of Surgical Sciences, Anesthesiology & Intensive Care Medicine, Uppsala University Hospital, Uppsala University, Uppsala, Sweden.
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21
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Das SS, Gopal PM, Thomas JV, Mohan MC, Thomas SC, Maliakel BP, Krishnakumar IM, Pulikkaparambil Sasidharan BC. Influence of CurQfen ®-curcumin on cognitive impairment: a randomized, double-blinded, placebo-controlled, 3-arm, 3-sequence comparative study. FRONTIERS IN DEMENTIA 2023; 2:1222708. [PMID: 39081970 PMCID: PMC11285547 DOI: 10.3389/frdem.2023.1222708] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 07/27/2023] [Indexed: 08/02/2024]
Abstract
Background Although curcumin is a blood-brain-barrier permeable molecule with the ability to bind and segregate β-amyloid plaques and neurofibrillary tangles of hyperphosphorylated tau proteins, its poor oral bioavailability, rapid biotransformation to inactive metabolites, fast elimination from the systemic circulation, and hence the poor neuronal uptake has been limiting its clinical efficacy under neurodegenerative conditions. Objective We hypothesized that the highly bioavailable CurQfen-curcumin (CGM), which has been shown to possess significant blood-brain-barrier permeability and brain bioavailability, would ameliorate dementia in neurodegenerative conditions. Methods In the present double-blinded placebo-controlled 3-arm 3-sequence comparative study, 48 subjects characterized with moderate dementia due to the onset of Alzheimer's disease were randomized into three groups (N = 16/group) and supplemented with 400 mg × 2/day of either placebo (MCC), unformulated standard curcumin complex with 95% purity (USC), or CGM as a sachet for six months. The relative changes in cognitive and locomotor functions and biochemical markers were compared. Results Supplementation with CGM produced significant (P < 0.05) improvement in the Mini-Mental State Examination (MMSE) and the Geriatric Locomotive Function Scale (GLFS) scores in both intra- and inter-group comparison by 2 × 2 repeated measures (RM) ANOVA. Further, analysis of the serum levels of specific biomarkers (BDNF, Aβ42, tau protein, IL-6, and TNF-α) also revealed a significant (P < 0.05) improvement among CGM subjects as compared to placebo and the USC groups. Conclusion Supplementation with CGM as sachet was found to offer significant delay in the progress of Alzheimer's disease, as evident from the improvements in locomotive and cognitive functions related to dementia. Clinical trial registration http://ctri.nic.in, identifier: CTRI/2018/03/012410.
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Affiliation(s)
- S. Syam Das
- Akay Natural Ingredients, Kochi, Kerala, India
| | - Prasad M. Gopal
- Alzheimer's and Related Disorders Society of India, Kochi, Kerala, India
- Centre for Neuroscience, Cochin University of Science and Technology, Kochi, Kerala, India
| | - Jestin V. Thomas
- Leads Clinical Research & Bio Services Private Limited, Bengaluru, India
| | - Mohind C. Mohan
- Centre for Neuroscience, Cochin University of Science and Technology, Kochi, Kerala, India
- Department of Biotechnology, Cochin University of Science and Technology, Kochi, Kerala, India
| | - Siju C. Thomas
- Alzheimer's and Related Disorders Society of India, Kochi, Kerala, India
| | | | | | - Baby Chakrapani Pulikkaparambil Sasidharan
- Centre for Neuroscience, Cochin University of Science and Technology, Kochi, Kerala, India
- Department of Biotechnology, Cochin University of Science and Technology, Kochi, Kerala, India
- Centre for Excellence in Neurodegeneration and Brain Health, Kochi, Kerala, India
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22
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de Kort AM, Kuiperij HB, Jäkel L, Kersten I, Rasing I, van Etten ES, van Rooden S, van Osch MJP, Wermer MJH, Terwindt GM, Schreuder FHBM, Klijn CJM, Verbeek MM. Plasma amyloid beta 42 is a biomarker for patients with hereditary, but not sporadic, cerebral amyloid angiopathy. Alzheimers Res Ther 2023; 15:102. [PMID: 37270536 PMCID: PMC10239174 DOI: 10.1186/s13195-023-01245-2] [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: 11/25/2022] [Accepted: 05/18/2023] [Indexed: 06/05/2023]
Abstract
BACKGROUND The diagnosis of probable cerebral amyloid angiopathy (CAA) is currently mostly based on characteristics of brain MRI. Blood biomarkers would be a cost-effective, easily accessible diagnostic method that may complement diagnosis by MRI and aid in monitoring disease progression. We studied the diagnostic potential of plasma Aβ38, Aβ40, and Aβ42 in patients with hereditary Dutch-type CAA (D-CAA) and sporadic CAA (sCAA). METHODS All Aβ peptides were quantified in the plasma by immunoassays in a discovery cohort (11 patients with presymptomatic D-CAA and 24 patients with symptomatic D-CAA, and 16 and 24 matched controls, respectively) and an independent validation cohort (54 patients with D-CAA, 26 presymptomatic and 28 symptomatic, and 39 and 46 matched controls, respectively). In addition, peptides were quantified in the plasma in a group of 61 patients with sCAA and 42 matched controls. We compared Aβ peptide levels between patients and controls using linear regression adjusting for age and sex. RESULTS In the discovery cohort, we found significantly decreased levels of all Aβ peptides in patients with presymptomatic D-CAA (Aβ38: p < 0.001; Aβ40: p = 0.009; Aβ42: p < 0.001) and patients with symptomatic D-CAA (Aβ38: p < 0.001; Aβ40: p = 0.01; Aβ42: p < 0.001) compared with controls. In contrast, in the validation cohort, plasma Aβ38, Aβ40, and Aβ42 were similar in patients with presymptomatic D-CAA and controls (Aβ38: p = 0.18; Aβ40: p = 0.28; Aβ42: p = 0.63). In patients with symptomatic D-CAA and controls, plasma Aβ38 and Aβ40 were similar (Aβ38: p = 0.14; Aβ40: p = 0.38), whereas plasma Aβ42 was significantly decreased in patients with symptomatic D-CAA (p = 0.033). Plasma Aβ38, Aβ40, and Aβ42 levels were similar in patients with sCAA and controls (Aβ38: p = 0.092; Aβ40: p = 0.64. Aβ42: p = 0.68). CONCLUSIONS Plasma Aβ42 levels, but not plasma Aβ38 and Aβ40, may be used as a biomarker for patients with symptomatic D-CAA. In contrast, plasma Aβ38, Aβ40, and Aβ42 levels do not appear to be applicable as a biomarker in patients with sCAA.
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Affiliation(s)
- Anna M de Kort
- Department of Neurology, Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Radboud Alzheimer Centre, P.O. Box 9101, Nijmegen, 6500 HB, The Netherlands
| | - H Bea Kuiperij
- Department of Neurology, Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Radboud Alzheimer Centre, P.O. Box 9101, Nijmegen, 6500 HB, The Netherlands
| | - Lieke Jäkel
- Department of Neurology, Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Radboud Alzheimer Centre, P.O. Box 9101, Nijmegen, 6500 HB, The Netherlands
| | - Iris Kersten
- Department of Neurology, Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Radboud Alzheimer Centre, P.O. Box 9101, Nijmegen, 6500 HB, The Netherlands
| | - Ingeborg Rasing
- Department of Neurology, Leiden University Medical Center, Leiden, the Netherlands
| | - Ellis S van Etten
- Department of Neurology, Leiden University Medical Center, Leiden, the Netherlands
| | - Sanneke van Rooden
- Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | | | - Marieke J H Wermer
- Department of Neurology, Leiden University Medical Center, Leiden, the Netherlands
| | - Gisela M Terwindt
- Department of Neurology, Leiden University Medical Center, Leiden, the Netherlands
| | - Floris H B M Schreuder
- Department of Neurology, Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Radboud Alzheimer Centre, P.O. Box 9101, Nijmegen, 6500 HB, The Netherlands
| | - Catharina J M Klijn
- Department of Neurology, Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Radboud Alzheimer Centre, P.O. Box 9101, Nijmegen, 6500 HB, The Netherlands
| | - Marcel M Verbeek
- Department of Neurology, Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Radboud Alzheimer Centre, P.O. Box 9101, Nijmegen, 6500 HB, The Netherlands.
- Department of Laboratory Medicine, Radboud University Medical Center, Nijmegen, The Netherlands.
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23
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Han X, Gao H, Lai H, Zhu W, Wang Y. Anti-Aβ42 Aggregative Polyketides from the Antarctic Psychrophilic Fungus Pseudogymnoascus sp. OUCMDZ-3578. JOURNAL OF NATURAL PRODUCTS 2023; 86:882-890. [PMID: 36861650 DOI: 10.1021/acs.jnatprod.2c01101] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Seven new polyketides, diphenyl ketone (1), diphenyl ketone glycosides (2-4), diphenyl ketone-diphenyl ether dimer (6), and anthraquinone-diphenyl ketone dimers (7 and 8), together with compound 5, were isolated from the psychrophilic fungus Pseudogymnoascus sp. OUCMDZ-3578 fermented at 16 °C and identified by spectroscopic analysis. The absolute configurations of 2-4 were determined by acid hydrolysis and 1-phenyl-3-methyl-5-pyrazolone precolumn derivatization. The configuration of 5 was first determined by X-ray diffraction analysis. Compounds 6 and 8 showed the highest activity against amyloid beta (Aβ42) aggregation with half-maximal inhibitory concentrations (IC50) of 0.10 and 0.18 μM, respectively. They also showed strong abilities to chelate with metal ions, especially iron, were sensitive to Aβ42 aggregation induced by metal ions, and displayed depolymerizing activity. Compounds 6 and 8 show potential as leads for the treatment of Alzheimer's disease to prevent Aβ42 aggregation.
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Affiliation(s)
- Xiaoling Han
- School of Medicine and Pharmacy, Ocean University of China, Qingdao 266003, China
| | - Hai Gao
- School of Medicine and Pharmacy, Ocean University of China, Qingdao 266003, China
| | - Huanyan Lai
- School of Medicine and Pharmacy, Ocean University of China, Qingdao 266003, China
| | - Weiming Zhu
- School of Medicine and Pharmacy, Ocean University of China, Qingdao 266003, China
- Key Laboratory for Marine Drugs and Bioproducts of Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China
| | - Yi Wang
- School of Medicine and Pharmacy, Ocean University of China, Qingdao 266003, China
- Key Laboratory for Marine Drugs and Bioproducts of Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China
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24
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Nasreddine Z, Garibotto V, Kyaga S, Padovani A. The Early Diagnosis of Alzheimer's Disease: A Patient-Centred Conversation with the Care Team. Neurol Ther 2023; 12:11-23. [PMID: 36528836 PMCID: PMC9837364 DOI: 10.1007/s40120-022-00428-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 11/21/2022] [Indexed: 12/23/2022] Open
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder which accounts for 60-80% of dementia cases, affecting approximately 10 million people in Europe. Neuroimaging techniques and cerebrospinal fluid biomarkers used in combination with cognitive assessment tools open the door to early diagnosis of AD. However, these tools present some challenges that need to be overcome, such as low sensitivity or specificity, high cost, limited availability or invasiveness. Thus, low-cost and non-invasive alternatives, such as plasma biomarkers, have the potential to drive changes in AD screening and diagnosis. In addition to the technical aspects, organisational challenges as well as ethical concerns need to be addressed. In many countries, there is an insufficient number of specialists to recognise, evaluate and diagnose dementia and the waiting times to see a specialist are long. Given that there is currently no cure for AD, it is important to consider the potential psychological impact of an early diagnosis. In addition, counselling before biomarker sampling and during diagnosis disclosure is vital to guarantee that the patients have all the information necessary and their queries are addressed in a sensitive manner. Here, we illustrate (using a clinical vignette) current challenges of diagnosis and discuss some of the benefits and challenges of early diagnosis in AD including the value of biomarkers in combination with clinical evaluation. Lastly, some guidelines for disclosing early diagnosis of AD are provided based on our experiences.
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Affiliation(s)
| | - Valentina Garibotto
- University Hospitals of Geneva and University of Geneva, Geneva, Switzerland
| | - Simon Kyaga
- Biogen International GmbH, Neuhofstrasse 30, 6340, Baar, Switzerland.
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25
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Planche V, Bouteloup V, Pellegrin I, Mangin JF, Dubois B, Ousset PJ, Pasquier F, Blanc F, Paquet C, Hanon O, Bennys K, Ceccaldi M, Annweiler C, Krolak-Salmon P, Godefroy O, Wallon D, Sauvee M, Boutoleau-Bretonnière C, Bourdel-Marchasson I, Jalenques I, Chene G, Dufouil C. Validity and Performance of Blood Biomarkers for Alzheimer Disease to Predict Dementia Risk in a Large Clinic-Based Cohort. Neurology 2023; 100:e473-e484. [PMID: 36261295 PMCID: PMC9931079 DOI: 10.1212/wnl.0000000000201479] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 09/13/2022] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND AND OBJECTIVE Blood biomarkers for Alzheimer disease (AD) have consistently proven to be associated with CSF or PET biomarkers and effectively discriminate AD from other neurodegenerative diseases. Our aim was to test their utility in clinical practice, from a multicentric unselected prospective cohort where patients presented with a large spectrum of cognitive deficits or complaints. METHODS The MEMENTO cohort enrolled 2,323 outpatients with subjective cognitive complaint (SCC) or mild cognitive impairment (MCI) consulting in 26 French memory clinics. Participants had neuropsychological assessments, MRI, and blood sampling at baseline. CSF sampling and amyloid PET were optional. Baseline blood Aβ42/40 ratio, total tau, p181-tau, and neurofilament light chain (NfL) were measured using a Simoa HD-X analyzer. An expert committee validated incident dementia cases during a 5-year follow-up period. RESULTS Overall, 2,277 individuals had at least 1 baseline blood biomarker available (n = 357 for CSF subsample, n = 649 for PET subsample), among whom 257 were diagnosed with clinical AD/mixed dementia during follow-up. All blood biomarkers but total tau were mildly correlated with their equivalence in the CSF (r = 0.33 to 0.46, p < 0.0001) and were associated with amyloid-PET status (p < 0.0001). Blood p181-tau was the best blood biomarker to identify amyloid-PET positivity (area under the curve = 0.74 [95% CI = 0.69; 0.79]). Higher blood and CSF p181-tau and NfL concentrations were associated with accelerated time to AD dementia onset with similar incidence rates, whereas blood Aβ42/40 was less efficient than CSF Aβ42/40. Blood p181-tau alone was the best blood predictor of 5-year AD/mixed dementia risk (c-index = 0.73 [95% CI = 0.69; 0.77]); its accuracy was higher in patients with clinical dementia rating (CDR) = 0 (c-index = 0.83 [95% CI = 0.69; 0.97]) than in patients with CDR = 0.5 (c-index = 0.70 [95% CI = 0.66; 0.74]). A "clinical" reference model (combining demographics and neuropsychological assessment) predicted AD/mixed dementia risk with a c-index = 0.88 [95% CI = 0.86-0.91] and performance increased to 0.90 [95% CI = 0.88; 0.92] when adding blood p181-tau + Aβ42/40. A "research" reference model (clinical model + apolipoprotein E genotype and AD signature on MRI) had a c-index = 0.91 [95% CI = 0.89-0.93] increasing to 0.92 [95% CI = 0.90; 0.93] when adding blood p181-tau + Aβ42/40. Chronic kidney disease and vascular comorbidities did not affect predictive performances. DISCUSSION In a clinic-based cohort of patients with SCC or MCI, blood biomarkers may be good hallmarks of underlying pathology but add little to 5-year dementia risk prediction models including traditional predictors.
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Affiliation(s)
- Vincent Planche
- From the Univ. Bordeaux (V.P.), CNRS UMR 5293, Institut des Maladies Neurodégénératives; CHU de Bordeaux (V.P.), Pôle de Neurosciences Cliniques, Centre Mémoire de Ressources et de Recherche; Univ. Bordeaux (V.B., G.C., C.D.), Inserm U1219, PHARes Team, Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED); CHU Bordeaux (V.B., G.C., C.D.), CIC 1401 EC, Pôle Santé Publique; CHU de Bordeaux (I.P.), Département d'Immunologie et d'Immunogénétique; Univ. Paris-Saclay (J.-F.M.), CEA, CNRS, Baobab UMR9027, Neurospin, CATI Multicenter Neuroimaging Platform, US52, UAR 9031, Gif-sur-Yvette; Sorbonne-Université (B.D.), Service des Maladies Cognitives et Comportementales et Institut de La Mémoire et de La Maladie d'Alzheimer (IM2A), Hôpital de La Salpêtrière, AP-PH, Paris; Univ. Toulouse (P.-J.O.), Inserm U1027, Gérontopôle, Departement de Gériatrie, CHU Purpan, Toulouse; Univ. Lille (F.P.), Inserm U1171, Centre Mémoire de Ressources et de Recherche, CHU Lille, DISTAlz, Lille; Univ. Strasbourg (F.B.), CNRS, ICube Laboratory, UMR 7357, Fédération de Médecine Translationnelle de Strasbourg, Centre Mémoire de Ressources et de Recherche, Pôle de Gériatrie, Strasbourg; Univ. Paris (C.P.), Inserm U1144, Groupe Hospitalier Lariboisière Fernand-Widal, AP-HP; Univ. Paris Cité (O.H.), EA 4468, AP-HP, Hôpitaux Universitaires Paris Centre, Service de Gériatrie, Hôpital Broca; CHU de Montpellier (K.B.), Pôle de Neurosciences, Département de Neurologie, Centre Mémoire de Ressources et de Recherche, Montpellier; Univ. Aix Marseille (M.C.), Inserm UMR 1106, Institut de Neurosciences des Systèmes, Centre Mémoire de Ressources et de Recherche, Département de Neurologie et de Neuropsychologie, AP-HM, Marseille; Univ. Angers (C.A.), UPRES EA 4638, Centre Mémoire de Ressources et de Recherche, Département de Gériatrie, CHU d'Angers, Angers; Univ. Lyon (P.K.-S.), Inserm U1028, CNRS UMR5292, Centre de Recherche en Neurosciences de Lyon, Centre Mémoire Ressource et Recherche de Lyon (CMRR), Hôpital des Charpennes, Hospices Civils de Lyon; Univ. Picardie (O.G.), UR UPJV4559, Laboratoire de Neurosciences Fonctionnelles et Pathologies, Service de Neurologie, CHU Amiens; Univ. Normandie (D.W.), UNIROUEN, Inserm U1245, Departement de Neurologie, CNR-MAJ, CHU de Rouen; Centre Mémoire de Ressources et de Recherche Grenoble Arc Alpin (M.S.), Pôle de Psychiatrie et Neurologie, CHU Grenoble Alpes; CHU de Nantes (C.B.-B.), Département de Neurologie, Centre Mémoire de Ressources et Recherche, Nantes; Univ. Bordeaux (I.B.-M.), CNRS UMR 5536, Centre de Résonance Magnétique des Systèmes Biologiques, Pôle de Gérontologie Clinique, CHU de Bordeaux; and Univ. Clermont Auvergne (I.J.), CNRS, CHU Clermont-Ferrand, Centre Mémoire de Ressources et de Recherche, Service de Psychiatrie de L'Adulte A et Psychologie Médicale, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand.
| | - Vincent Bouteloup
- From the Univ. Bordeaux (V.P.), CNRS UMR 5293, Institut des Maladies Neurodégénératives; CHU de Bordeaux (V.P.), Pôle de Neurosciences Cliniques, Centre Mémoire de Ressources et de Recherche; Univ. Bordeaux (V.B., G.C., C.D.), Inserm U1219, PHARes Team, Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED); CHU Bordeaux (V.B., G.C., C.D.), CIC 1401 EC, Pôle Santé Publique; CHU de Bordeaux (I.P.), Département d'Immunologie et d'Immunogénétique; Univ. Paris-Saclay (J.-F.M.), CEA, CNRS, Baobab UMR9027, Neurospin, CATI Multicenter Neuroimaging Platform, US52, UAR 9031, Gif-sur-Yvette; Sorbonne-Université (B.D.), Service des Maladies Cognitives et Comportementales et Institut de La Mémoire et de La Maladie d'Alzheimer (IM2A), Hôpital de La Salpêtrière, AP-PH, Paris; Univ. Toulouse (P.-J.O.), Inserm U1027, Gérontopôle, Departement de Gériatrie, CHU Purpan, Toulouse; Univ. Lille (F.P.), Inserm U1171, Centre Mémoire de Ressources et de Recherche, CHU Lille, DISTAlz, Lille; Univ. Strasbourg (F.B.), CNRS, ICube Laboratory, UMR 7357, Fédération de Médecine Translationnelle de Strasbourg, Centre Mémoire de Ressources et de Recherche, Pôle de Gériatrie, Strasbourg; Univ. Paris (C.P.), Inserm U1144, Groupe Hospitalier Lariboisière Fernand-Widal, AP-HP; Univ. Paris Cité (O.H.), EA 4468, AP-HP, Hôpitaux Universitaires Paris Centre, Service de Gériatrie, Hôpital Broca; CHU de Montpellier (K.B.), Pôle de Neurosciences, Département de Neurologie, Centre Mémoire de Ressources et de Recherche, Montpellier; Univ. Aix Marseille (M.C.), Inserm UMR 1106, Institut de Neurosciences des Systèmes, Centre Mémoire de Ressources et de Recherche, Département de Neurologie et de Neuropsychologie, AP-HM, Marseille; Univ. Angers (C.A.), UPRES EA 4638, Centre Mémoire de Ressources et de Recherche, Département de Gériatrie, CHU d'Angers, Angers; Univ. Lyon (P.K.-S.), Inserm U1028, CNRS UMR5292, Centre de Recherche en Neurosciences de Lyon, Centre Mémoire Ressource et Recherche de Lyon (CMRR), Hôpital des Charpennes, Hospices Civils de Lyon; Univ. Picardie (O.G.), UR UPJV4559, Laboratoire de Neurosciences Fonctionnelles et Pathologies, Service de Neurologie, CHU Amiens; Univ. Normandie (D.W.), UNIROUEN, Inserm U1245, Departement de Neurologie, CNR-MAJ, CHU de Rouen; Centre Mémoire de Ressources et de Recherche Grenoble Arc Alpin (M.S.), Pôle de Psychiatrie et Neurologie, CHU Grenoble Alpes; CHU de Nantes (C.B.-B.), Département de Neurologie, Centre Mémoire de Ressources et Recherche, Nantes; Univ. Bordeaux (I.B.-M.), CNRS UMR 5536, Centre de Résonance Magnétique des Systèmes Biologiques, Pôle de Gérontologie Clinique, CHU de Bordeaux; and Univ. Clermont Auvergne (I.J.), CNRS, CHU Clermont-Ferrand, Centre Mémoire de Ressources et de Recherche, Service de Psychiatrie de L'Adulte A et Psychologie Médicale, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand
| | - Isabelle Pellegrin
- From the Univ. Bordeaux (V.P.), CNRS UMR 5293, Institut des Maladies Neurodégénératives; CHU de Bordeaux (V.P.), Pôle de Neurosciences Cliniques, Centre Mémoire de Ressources et de Recherche; Univ. Bordeaux (V.B., G.C., C.D.), Inserm U1219, PHARes Team, Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED); CHU Bordeaux (V.B., G.C., C.D.), CIC 1401 EC, Pôle Santé Publique; CHU de Bordeaux (I.P.), Département d'Immunologie et d'Immunogénétique; Univ. Paris-Saclay (J.-F.M.), CEA, CNRS, Baobab UMR9027, Neurospin, CATI Multicenter Neuroimaging Platform, US52, UAR 9031, Gif-sur-Yvette; Sorbonne-Université (B.D.), Service des Maladies Cognitives et Comportementales et Institut de La Mémoire et de La Maladie d'Alzheimer (IM2A), Hôpital de La Salpêtrière, AP-PH, Paris; Univ. Toulouse (P.-J.O.), Inserm U1027, Gérontopôle, Departement de Gériatrie, CHU Purpan, Toulouse; Univ. Lille (F.P.), Inserm U1171, Centre Mémoire de Ressources et de Recherche, CHU Lille, DISTAlz, Lille; Univ. Strasbourg (F.B.), CNRS, ICube Laboratory, UMR 7357, Fédération de Médecine Translationnelle de Strasbourg, Centre Mémoire de Ressources et de Recherche, Pôle de Gériatrie, Strasbourg; Univ. Paris (C.P.), Inserm U1144, Groupe Hospitalier Lariboisière Fernand-Widal, AP-HP; Univ. Paris Cité (O.H.), EA 4468, AP-HP, Hôpitaux Universitaires Paris Centre, Service de Gériatrie, Hôpital Broca; CHU de Montpellier (K.B.), Pôle de Neurosciences, Département de Neurologie, Centre Mémoire de Ressources et de Recherche, Montpellier; Univ. Aix Marseille (M.C.), Inserm UMR 1106, Institut de Neurosciences des Systèmes, Centre Mémoire de Ressources et de Recherche, Département de Neurologie et de Neuropsychologie, AP-HM, Marseille; Univ. Angers (C.A.), UPRES EA 4638, Centre Mémoire de Ressources et de Recherche, Département de Gériatrie, CHU d'Angers, Angers; Univ. Lyon (P.K.-S.), Inserm U1028, CNRS UMR5292, Centre de Recherche en Neurosciences de Lyon, Centre Mémoire Ressource et Recherche de Lyon (CMRR), Hôpital des Charpennes, Hospices Civils de Lyon; Univ. Picardie (O.G.), UR UPJV4559, Laboratoire de Neurosciences Fonctionnelles et Pathologies, Service de Neurologie, CHU Amiens; Univ. Normandie (D.W.), UNIROUEN, Inserm U1245, Departement de Neurologie, CNR-MAJ, CHU de Rouen; Centre Mémoire de Ressources et de Recherche Grenoble Arc Alpin (M.S.), Pôle de Psychiatrie et Neurologie, CHU Grenoble Alpes; CHU de Nantes (C.B.-B.), Département de Neurologie, Centre Mémoire de Ressources et Recherche, Nantes; Univ. Bordeaux (I.B.-M.), CNRS UMR 5536, Centre de Résonance Magnétique des Systèmes Biologiques, Pôle de Gérontologie Clinique, CHU de Bordeaux; and Univ. Clermont Auvergne (I.J.), CNRS, CHU Clermont-Ferrand, Centre Mémoire de Ressources et de Recherche, Service de Psychiatrie de L'Adulte A et Psychologie Médicale, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand
| | - Jean-Francois Mangin
- From the Univ. Bordeaux (V.P.), CNRS UMR 5293, Institut des Maladies Neurodégénératives; CHU de Bordeaux (V.P.), Pôle de Neurosciences Cliniques, Centre Mémoire de Ressources et de Recherche; Univ. Bordeaux (V.B., G.C., C.D.), Inserm U1219, PHARes Team, Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED); CHU Bordeaux (V.B., G.C., C.D.), CIC 1401 EC, Pôle Santé Publique; CHU de Bordeaux (I.P.), Département d'Immunologie et d'Immunogénétique; Univ. Paris-Saclay (J.-F.M.), CEA, CNRS, Baobab UMR9027, Neurospin, CATI Multicenter Neuroimaging Platform, US52, UAR 9031, Gif-sur-Yvette; Sorbonne-Université (B.D.), Service des Maladies Cognitives et Comportementales et Institut de La Mémoire et de La Maladie d'Alzheimer (IM2A), Hôpital de La Salpêtrière, AP-PH, Paris; Univ. Toulouse (P.-J.O.), Inserm U1027, Gérontopôle, Departement de Gériatrie, CHU Purpan, Toulouse; Univ. Lille (F.P.), Inserm U1171, Centre Mémoire de Ressources et de Recherche, CHU Lille, DISTAlz, Lille; Univ. Strasbourg (F.B.), CNRS, ICube Laboratory, UMR 7357, Fédération de Médecine Translationnelle de Strasbourg, Centre Mémoire de Ressources et de Recherche, Pôle de Gériatrie, Strasbourg; Univ. Paris (C.P.), Inserm U1144, Groupe Hospitalier Lariboisière Fernand-Widal, AP-HP; Univ. Paris Cité (O.H.), EA 4468, AP-HP, Hôpitaux Universitaires Paris Centre, Service de Gériatrie, Hôpital Broca; CHU de Montpellier (K.B.), Pôle de Neurosciences, Département de Neurologie, Centre Mémoire de Ressources et de Recherche, Montpellier; Univ. Aix Marseille (M.C.), Inserm UMR 1106, Institut de Neurosciences des Systèmes, Centre Mémoire de Ressources et de Recherche, Département de Neurologie et de Neuropsychologie, AP-HM, Marseille; Univ. Angers (C.A.), UPRES EA 4638, Centre Mémoire de Ressources et de Recherche, Département de Gériatrie, CHU d'Angers, Angers; Univ. Lyon (P.K.-S.), Inserm U1028, CNRS UMR5292, Centre de Recherche en Neurosciences de Lyon, Centre Mémoire Ressource et Recherche de Lyon (CMRR), Hôpital des Charpennes, Hospices Civils de Lyon; Univ. Picardie (O.G.), UR UPJV4559, Laboratoire de Neurosciences Fonctionnelles et Pathologies, Service de Neurologie, CHU Amiens; Univ. Normandie (D.W.), UNIROUEN, Inserm U1245, Departement de Neurologie, CNR-MAJ, CHU de Rouen; Centre Mémoire de Ressources et de Recherche Grenoble Arc Alpin (M.S.), Pôle de Psychiatrie et Neurologie, CHU Grenoble Alpes; CHU de Nantes (C.B.-B.), Département de Neurologie, Centre Mémoire de Ressources et Recherche, Nantes; Univ. Bordeaux (I.B.-M.), CNRS UMR 5536, Centre de Résonance Magnétique des Systèmes Biologiques, Pôle de Gérontologie Clinique, CHU de Bordeaux; and Univ. Clermont Auvergne (I.J.), CNRS, CHU Clermont-Ferrand, Centre Mémoire de Ressources et de Recherche, Service de Psychiatrie de L'Adulte A et Psychologie Médicale, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand
| | - Bruno Dubois
- From the Univ. Bordeaux (V.P.), CNRS UMR 5293, Institut des Maladies Neurodégénératives; CHU de Bordeaux (V.P.), Pôle de Neurosciences Cliniques, Centre Mémoire de Ressources et de Recherche; Univ. Bordeaux (V.B., G.C., C.D.), Inserm U1219, PHARes Team, Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED); CHU Bordeaux (V.B., G.C., C.D.), CIC 1401 EC, Pôle Santé Publique; CHU de Bordeaux (I.P.), Département d'Immunologie et d'Immunogénétique; Univ. Paris-Saclay (J.-F.M.), CEA, CNRS, Baobab UMR9027, Neurospin, CATI Multicenter Neuroimaging Platform, US52, UAR 9031, Gif-sur-Yvette; Sorbonne-Université (B.D.), Service des Maladies Cognitives et Comportementales et Institut de La Mémoire et de La Maladie d'Alzheimer (IM2A), Hôpital de La Salpêtrière, AP-PH, Paris; Univ. Toulouse (P.-J.O.), Inserm U1027, Gérontopôle, Departement de Gériatrie, CHU Purpan, Toulouse; Univ. Lille (F.P.), Inserm U1171, Centre Mémoire de Ressources et de Recherche, CHU Lille, DISTAlz, Lille; Univ. Strasbourg (F.B.), CNRS, ICube Laboratory, UMR 7357, Fédération de Médecine Translationnelle de Strasbourg, Centre Mémoire de Ressources et de Recherche, Pôle de Gériatrie, Strasbourg; Univ. Paris (C.P.), Inserm U1144, Groupe Hospitalier Lariboisière Fernand-Widal, AP-HP; Univ. Paris Cité (O.H.), EA 4468, AP-HP, Hôpitaux Universitaires Paris Centre, Service de Gériatrie, Hôpital Broca; CHU de Montpellier (K.B.), Pôle de Neurosciences, Département de Neurologie, Centre Mémoire de Ressources et de Recherche, Montpellier; Univ. Aix Marseille (M.C.), Inserm UMR 1106, Institut de Neurosciences des Systèmes, Centre Mémoire de Ressources et de Recherche, Département de Neurologie et de Neuropsychologie, AP-HM, Marseille; Univ. Angers (C.A.), UPRES EA 4638, Centre Mémoire de Ressources et de Recherche, Département de Gériatrie, CHU d'Angers, Angers; Univ. Lyon (P.K.-S.), Inserm U1028, CNRS UMR5292, Centre de Recherche en Neurosciences de Lyon, Centre Mémoire Ressource et Recherche de Lyon (CMRR), Hôpital des Charpennes, Hospices Civils de Lyon; Univ. Picardie (O.G.), UR UPJV4559, Laboratoire de Neurosciences Fonctionnelles et Pathologies, Service de Neurologie, CHU Amiens; Univ. Normandie (D.W.), UNIROUEN, Inserm U1245, Departement de Neurologie, CNR-MAJ, CHU de Rouen; Centre Mémoire de Ressources et de Recherche Grenoble Arc Alpin (M.S.), Pôle de Psychiatrie et Neurologie, CHU Grenoble Alpes; CHU de Nantes (C.B.-B.), Département de Neurologie, Centre Mémoire de Ressources et Recherche, Nantes; Univ. Bordeaux (I.B.-M.), CNRS UMR 5536, Centre de Résonance Magnétique des Systèmes Biologiques, Pôle de Gérontologie Clinique, CHU de Bordeaux; and Univ. Clermont Auvergne (I.J.), CNRS, CHU Clermont-Ferrand, Centre Mémoire de Ressources et de Recherche, Service de Psychiatrie de L'Adulte A et Psychologie Médicale, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand
| | - Pierre-Jean Ousset
- From the Univ. Bordeaux (V.P.), CNRS UMR 5293, Institut des Maladies Neurodégénératives; CHU de Bordeaux (V.P.), Pôle de Neurosciences Cliniques, Centre Mémoire de Ressources et de Recherche; Univ. Bordeaux (V.B., G.C., C.D.), Inserm U1219, PHARes Team, Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED); CHU Bordeaux (V.B., G.C., C.D.), CIC 1401 EC, Pôle Santé Publique; CHU de Bordeaux (I.P.), Département d'Immunologie et d'Immunogénétique; Univ. Paris-Saclay (J.-F.M.), CEA, CNRS, Baobab UMR9027, Neurospin, CATI Multicenter Neuroimaging Platform, US52, UAR 9031, Gif-sur-Yvette; Sorbonne-Université (B.D.), Service des Maladies Cognitives et Comportementales et Institut de La Mémoire et de La Maladie d'Alzheimer (IM2A), Hôpital de La Salpêtrière, AP-PH, Paris; Univ. Toulouse (P.-J.O.), Inserm U1027, Gérontopôle, Departement de Gériatrie, CHU Purpan, Toulouse; Univ. Lille (F.P.), Inserm U1171, Centre Mémoire de Ressources et de Recherche, CHU Lille, DISTAlz, Lille; Univ. Strasbourg (F.B.), CNRS, ICube Laboratory, UMR 7357, Fédération de Médecine Translationnelle de Strasbourg, Centre Mémoire de Ressources et de Recherche, Pôle de Gériatrie, Strasbourg; Univ. Paris (C.P.), Inserm U1144, Groupe Hospitalier Lariboisière Fernand-Widal, AP-HP; Univ. Paris Cité (O.H.), EA 4468, AP-HP, Hôpitaux Universitaires Paris Centre, Service de Gériatrie, Hôpital Broca; CHU de Montpellier (K.B.), Pôle de Neurosciences, Département de Neurologie, Centre Mémoire de Ressources et de Recherche, Montpellier; Univ. Aix Marseille (M.C.), Inserm UMR 1106, Institut de Neurosciences des Systèmes, Centre Mémoire de Ressources et de Recherche, Département de Neurologie et de Neuropsychologie, AP-HM, Marseille; Univ. Angers (C.A.), UPRES EA 4638, Centre Mémoire de Ressources et de Recherche, Département de Gériatrie, CHU d'Angers, Angers; Univ. Lyon (P.K.-S.), Inserm U1028, CNRS UMR5292, Centre de Recherche en Neurosciences de Lyon, Centre Mémoire Ressource et Recherche de Lyon (CMRR), Hôpital des Charpennes, Hospices Civils de Lyon; Univ. Picardie (O.G.), UR UPJV4559, Laboratoire de Neurosciences Fonctionnelles et Pathologies, Service de Neurologie, CHU Amiens; Univ. Normandie (D.W.), UNIROUEN, Inserm U1245, Departement de Neurologie, CNR-MAJ, CHU de Rouen; Centre Mémoire de Ressources et de Recherche Grenoble Arc Alpin (M.S.), Pôle de Psychiatrie et Neurologie, CHU Grenoble Alpes; CHU de Nantes (C.B.-B.), Département de Neurologie, Centre Mémoire de Ressources et Recherche, Nantes; Univ. Bordeaux (I.B.-M.), CNRS UMR 5536, Centre de Résonance Magnétique des Systèmes Biologiques, Pôle de Gérontologie Clinique, CHU de Bordeaux; and Univ. Clermont Auvergne (I.J.), CNRS, CHU Clermont-Ferrand, Centre Mémoire de Ressources et de Recherche, Service de Psychiatrie de L'Adulte A et Psychologie Médicale, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand
| | - Florence Pasquier
- From the Univ. Bordeaux (V.P.), CNRS UMR 5293, Institut des Maladies Neurodégénératives; CHU de Bordeaux (V.P.), Pôle de Neurosciences Cliniques, Centre Mémoire de Ressources et de Recherche; Univ. Bordeaux (V.B., G.C., C.D.), Inserm U1219, PHARes Team, Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED); CHU Bordeaux (V.B., G.C., C.D.), CIC 1401 EC, Pôle Santé Publique; CHU de Bordeaux (I.P.), Département d'Immunologie et d'Immunogénétique; Univ. Paris-Saclay (J.-F.M.), CEA, CNRS, Baobab UMR9027, Neurospin, CATI Multicenter Neuroimaging Platform, US52, UAR 9031, Gif-sur-Yvette; Sorbonne-Université (B.D.), Service des Maladies Cognitives et Comportementales et Institut de La Mémoire et de La Maladie d'Alzheimer (IM2A), Hôpital de La Salpêtrière, AP-PH, Paris; Univ. Toulouse (P.-J.O.), Inserm U1027, Gérontopôle, Departement de Gériatrie, CHU Purpan, Toulouse; Univ. Lille (F.P.), Inserm U1171, Centre Mémoire de Ressources et de Recherche, CHU Lille, DISTAlz, Lille; Univ. Strasbourg (F.B.), CNRS, ICube Laboratory, UMR 7357, Fédération de Médecine Translationnelle de Strasbourg, Centre Mémoire de Ressources et de Recherche, Pôle de Gériatrie, Strasbourg; Univ. Paris (C.P.), Inserm U1144, Groupe Hospitalier Lariboisière Fernand-Widal, AP-HP; Univ. Paris Cité (O.H.), EA 4468, AP-HP, Hôpitaux Universitaires Paris Centre, Service de Gériatrie, Hôpital Broca; CHU de Montpellier (K.B.), Pôle de Neurosciences, Département de Neurologie, Centre Mémoire de Ressources et de Recherche, Montpellier; Univ. Aix Marseille (M.C.), Inserm UMR 1106, Institut de Neurosciences des Systèmes, Centre Mémoire de Ressources et de Recherche, Département de Neurologie et de Neuropsychologie, AP-HM, Marseille; Univ. Angers (C.A.), UPRES EA 4638, Centre Mémoire de Ressources et de Recherche, Département de Gériatrie, CHU d'Angers, Angers; Univ. Lyon (P.K.-S.), Inserm U1028, CNRS UMR5292, Centre de Recherche en Neurosciences de Lyon, Centre Mémoire Ressource et Recherche de Lyon (CMRR), Hôpital des Charpennes, Hospices Civils de Lyon; Univ. Picardie (O.G.), UR UPJV4559, Laboratoire de Neurosciences Fonctionnelles et Pathologies, Service de Neurologie, CHU Amiens; Univ. Normandie (D.W.), UNIROUEN, Inserm U1245, Departement de Neurologie, CNR-MAJ, CHU de Rouen; Centre Mémoire de Ressources et de Recherche Grenoble Arc Alpin (M.S.), Pôle de Psychiatrie et Neurologie, CHU Grenoble Alpes; CHU de Nantes (C.B.-B.), Département de Neurologie, Centre Mémoire de Ressources et Recherche, Nantes; Univ. Bordeaux (I.B.-M.), CNRS UMR 5536, Centre de Résonance Magnétique des Systèmes Biologiques, Pôle de Gérontologie Clinique, CHU de Bordeaux; and Univ. Clermont Auvergne (I.J.), CNRS, CHU Clermont-Ferrand, Centre Mémoire de Ressources et de Recherche, Service de Psychiatrie de L'Adulte A et Psychologie Médicale, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand
| | - Frederic Blanc
- From the Univ. Bordeaux (V.P.), CNRS UMR 5293, Institut des Maladies Neurodégénératives; CHU de Bordeaux (V.P.), Pôle de Neurosciences Cliniques, Centre Mémoire de Ressources et de Recherche; Univ. Bordeaux (V.B., G.C., C.D.), Inserm U1219, PHARes Team, Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED); CHU Bordeaux (V.B., G.C., C.D.), CIC 1401 EC, Pôle Santé Publique; CHU de Bordeaux (I.P.), Département d'Immunologie et d'Immunogénétique; Univ. Paris-Saclay (J.-F.M.), CEA, CNRS, Baobab UMR9027, Neurospin, CATI Multicenter Neuroimaging Platform, US52, UAR 9031, Gif-sur-Yvette; Sorbonne-Université (B.D.), Service des Maladies Cognitives et Comportementales et Institut de La Mémoire et de La Maladie d'Alzheimer (IM2A), Hôpital de La Salpêtrière, AP-PH, Paris; Univ. Toulouse (P.-J.O.), Inserm U1027, Gérontopôle, Departement de Gériatrie, CHU Purpan, Toulouse; Univ. Lille (F.P.), Inserm U1171, Centre Mémoire de Ressources et de Recherche, CHU Lille, DISTAlz, Lille; Univ. Strasbourg (F.B.), CNRS, ICube Laboratory, UMR 7357, Fédération de Médecine Translationnelle de Strasbourg, Centre Mémoire de Ressources et de Recherche, Pôle de Gériatrie, Strasbourg; Univ. Paris (C.P.), Inserm U1144, Groupe Hospitalier Lariboisière Fernand-Widal, AP-HP; Univ. Paris Cité (O.H.), EA 4468, AP-HP, Hôpitaux Universitaires Paris Centre, Service de Gériatrie, Hôpital Broca; CHU de Montpellier (K.B.), Pôle de Neurosciences, Département de Neurologie, Centre Mémoire de Ressources et de Recherche, Montpellier; Univ. Aix Marseille (M.C.), Inserm UMR 1106, Institut de Neurosciences des Systèmes, Centre Mémoire de Ressources et de Recherche, Département de Neurologie et de Neuropsychologie, AP-HM, Marseille; Univ. Angers (C.A.), UPRES EA 4638, Centre Mémoire de Ressources et de Recherche, Département de Gériatrie, CHU d'Angers, Angers; Univ. Lyon (P.K.-S.), Inserm U1028, CNRS UMR5292, Centre de Recherche en Neurosciences de Lyon, Centre Mémoire Ressource et Recherche de Lyon (CMRR), Hôpital des Charpennes, Hospices Civils de Lyon; Univ. Picardie (O.G.), UR UPJV4559, Laboratoire de Neurosciences Fonctionnelles et Pathologies, Service de Neurologie, CHU Amiens; Univ. Normandie (D.W.), UNIROUEN, Inserm U1245, Departement de Neurologie, CNR-MAJ, CHU de Rouen; Centre Mémoire de Ressources et de Recherche Grenoble Arc Alpin (M.S.), Pôle de Psychiatrie et Neurologie, CHU Grenoble Alpes; CHU de Nantes (C.B.-B.), Département de Neurologie, Centre Mémoire de Ressources et Recherche, Nantes; Univ. Bordeaux (I.B.-M.), CNRS UMR 5536, Centre de Résonance Magnétique des Systèmes Biologiques, Pôle de Gérontologie Clinique, CHU de Bordeaux; and Univ. Clermont Auvergne (I.J.), CNRS, CHU Clermont-Ferrand, Centre Mémoire de Ressources et de Recherche, Service de Psychiatrie de L'Adulte A et Psychologie Médicale, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand
| | - Claire Paquet
- From the Univ. Bordeaux (V.P.), CNRS UMR 5293, Institut des Maladies Neurodégénératives; CHU de Bordeaux (V.P.), Pôle de Neurosciences Cliniques, Centre Mémoire de Ressources et de Recherche; Univ. Bordeaux (V.B., G.C., C.D.), Inserm U1219, PHARes Team, Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED); CHU Bordeaux (V.B., G.C., C.D.), CIC 1401 EC, Pôle Santé Publique; CHU de Bordeaux (I.P.), Département d'Immunologie et d'Immunogénétique; Univ. Paris-Saclay (J.-F.M.), CEA, CNRS, Baobab UMR9027, Neurospin, CATI Multicenter Neuroimaging Platform, US52, UAR 9031, Gif-sur-Yvette; Sorbonne-Université (B.D.), Service des Maladies Cognitives et Comportementales et Institut de La Mémoire et de La Maladie d'Alzheimer (IM2A), Hôpital de La Salpêtrière, AP-PH, Paris; Univ. Toulouse (P.-J.O.), Inserm U1027, Gérontopôle, Departement de Gériatrie, CHU Purpan, Toulouse; Univ. Lille (F.P.), Inserm U1171, Centre Mémoire de Ressources et de Recherche, CHU Lille, DISTAlz, Lille; Univ. Strasbourg (F.B.), CNRS, ICube Laboratory, UMR 7357, Fédération de Médecine Translationnelle de Strasbourg, Centre Mémoire de Ressources et de Recherche, Pôle de Gériatrie, Strasbourg; Univ. Paris (C.P.), Inserm U1144, Groupe Hospitalier Lariboisière Fernand-Widal, AP-HP; Univ. Paris Cité (O.H.), EA 4468, AP-HP, Hôpitaux Universitaires Paris Centre, Service de Gériatrie, Hôpital Broca; CHU de Montpellier (K.B.), Pôle de Neurosciences, Département de Neurologie, Centre Mémoire de Ressources et de Recherche, Montpellier; Univ. Aix Marseille (M.C.), Inserm UMR 1106, Institut de Neurosciences des Systèmes, Centre Mémoire de Ressources et de Recherche, Département de Neurologie et de Neuropsychologie, AP-HM, Marseille; Univ. Angers (C.A.), UPRES EA 4638, Centre Mémoire de Ressources et de Recherche, Département de Gériatrie, CHU d'Angers, Angers; Univ. Lyon (P.K.-S.), Inserm U1028, CNRS UMR5292, Centre de Recherche en Neurosciences de Lyon, Centre Mémoire Ressource et Recherche de Lyon (CMRR), Hôpital des Charpennes, Hospices Civils de Lyon; Univ. Picardie (O.G.), UR UPJV4559, Laboratoire de Neurosciences Fonctionnelles et Pathologies, Service de Neurologie, CHU Amiens; Univ. Normandie (D.W.), UNIROUEN, Inserm U1245, Departement de Neurologie, CNR-MAJ, CHU de Rouen; Centre Mémoire de Ressources et de Recherche Grenoble Arc Alpin (M.S.), Pôle de Psychiatrie et Neurologie, CHU Grenoble Alpes; CHU de Nantes (C.B.-B.), Département de Neurologie, Centre Mémoire de Ressources et Recherche, Nantes; Univ. Bordeaux (I.B.-M.), CNRS UMR 5536, Centre de Résonance Magnétique des Systèmes Biologiques, Pôle de Gérontologie Clinique, CHU de Bordeaux; and Univ. Clermont Auvergne (I.J.), CNRS, CHU Clermont-Ferrand, Centre Mémoire de Ressources et de Recherche, Service de Psychiatrie de L'Adulte A et Psychologie Médicale, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand
| | - Olivier Hanon
- From the Univ. Bordeaux (V.P.), CNRS UMR 5293, Institut des Maladies Neurodégénératives; CHU de Bordeaux (V.P.), Pôle de Neurosciences Cliniques, Centre Mémoire de Ressources et de Recherche; Univ. Bordeaux (V.B., G.C., C.D.), Inserm U1219, PHARes Team, Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED); CHU Bordeaux (V.B., G.C., C.D.), CIC 1401 EC, Pôle Santé Publique; CHU de Bordeaux (I.P.), Département d'Immunologie et d'Immunogénétique; Univ. Paris-Saclay (J.-F.M.), CEA, CNRS, Baobab UMR9027, Neurospin, CATI Multicenter Neuroimaging Platform, US52, UAR 9031, Gif-sur-Yvette; Sorbonne-Université (B.D.), Service des Maladies Cognitives et Comportementales et Institut de La Mémoire et de La Maladie d'Alzheimer (IM2A), Hôpital de La Salpêtrière, AP-PH, Paris; Univ. Toulouse (P.-J.O.), Inserm U1027, Gérontopôle, Departement de Gériatrie, CHU Purpan, Toulouse; Univ. Lille (F.P.), Inserm U1171, Centre Mémoire de Ressources et de Recherche, CHU Lille, DISTAlz, Lille; Univ. Strasbourg (F.B.), CNRS, ICube Laboratory, UMR 7357, Fédération de Médecine Translationnelle de Strasbourg, Centre Mémoire de Ressources et de Recherche, Pôle de Gériatrie, Strasbourg; Univ. Paris (C.P.), Inserm U1144, Groupe Hospitalier Lariboisière Fernand-Widal, AP-HP; Univ. Paris Cité (O.H.), EA 4468, AP-HP, Hôpitaux Universitaires Paris Centre, Service de Gériatrie, Hôpital Broca; CHU de Montpellier (K.B.), Pôle de Neurosciences, Département de Neurologie, Centre Mémoire de Ressources et de Recherche, Montpellier; Univ. Aix Marseille (M.C.), Inserm UMR 1106, Institut de Neurosciences des Systèmes, Centre Mémoire de Ressources et de Recherche, Département de Neurologie et de Neuropsychologie, AP-HM, Marseille; Univ. Angers (C.A.), UPRES EA 4638, Centre Mémoire de Ressources et de Recherche, Département de Gériatrie, CHU d'Angers, Angers; Univ. Lyon (P.K.-S.), Inserm U1028, CNRS UMR5292, Centre de Recherche en Neurosciences de Lyon, Centre Mémoire Ressource et Recherche de Lyon (CMRR), Hôpital des Charpennes, Hospices Civils de Lyon; Univ. Picardie (O.G.), UR UPJV4559, Laboratoire de Neurosciences Fonctionnelles et Pathologies, Service de Neurologie, CHU Amiens; Univ. Normandie (D.W.), UNIROUEN, Inserm U1245, Departement de Neurologie, CNR-MAJ, CHU de Rouen; Centre Mémoire de Ressources et de Recherche Grenoble Arc Alpin (M.S.), Pôle de Psychiatrie et Neurologie, CHU Grenoble Alpes; CHU de Nantes (C.B.-B.), Département de Neurologie, Centre Mémoire de Ressources et Recherche, Nantes; Univ. Bordeaux (I.B.-M.), CNRS UMR 5536, Centre de Résonance Magnétique des Systèmes Biologiques, Pôle de Gérontologie Clinique, CHU de Bordeaux; and Univ. Clermont Auvergne (I.J.), CNRS, CHU Clermont-Ferrand, Centre Mémoire de Ressources et de Recherche, Service de Psychiatrie de L'Adulte A et Psychologie Médicale, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand
| | - Karim Bennys
- From the Univ. Bordeaux (V.P.), CNRS UMR 5293, Institut des Maladies Neurodégénératives; CHU de Bordeaux (V.P.), Pôle de Neurosciences Cliniques, Centre Mémoire de Ressources et de Recherche; Univ. Bordeaux (V.B., G.C., C.D.), Inserm U1219, PHARes Team, Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED); CHU Bordeaux (V.B., G.C., C.D.), CIC 1401 EC, Pôle Santé Publique; CHU de Bordeaux (I.P.), Département d'Immunologie et d'Immunogénétique; Univ. Paris-Saclay (J.-F.M.), CEA, CNRS, Baobab UMR9027, Neurospin, CATI Multicenter Neuroimaging Platform, US52, UAR 9031, Gif-sur-Yvette; Sorbonne-Université (B.D.), Service des Maladies Cognitives et Comportementales et Institut de La Mémoire et de La Maladie d'Alzheimer (IM2A), Hôpital de La Salpêtrière, AP-PH, Paris; Univ. Toulouse (P.-J.O.), Inserm U1027, Gérontopôle, Departement de Gériatrie, CHU Purpan, Toulouse; Univ. Lille (F.P.), Inserm U1171, Centre Mémoire de Ressources et de Recherche, CHU Lille, DISTAlz, Lille; Univ. Strasbourg (F.B.), CNRS, ICube Laboratory, UMR 7357, Fédération de Médecine Translationnelle de Strasbourg, Centre Mémoire de Ressources et de Recherche, Pôle de Gériatrie, Strasbourg; Univ. Paris (C.P.), Inserm U1144, Groupe Hospitalier Lariboisière Fernand-Widal, AP-HP; Univ. Paris Cité (O.H.), EA 4468, AP-HP, Hôpitaux Universitaires Paris Centre, Service de Gériatrie, Hôpital Broca; CHU de Montpellier (K.B.), Pôle de Neurosciences, Département de Neurologie, Centre Mémoire de Ressources et de Recherche, Montpellier; Univ. Aix Marseille (M.C.), Inserm UMR 1106, Institut de Neurosciences des Systèmes, Centre Mémoire de Ressources et de Recherche, Département de Neurologie et de Neuropsychologie, AP-HM, Marseille; Univ. Angers (C.A.), UPRES EA 4638, Centre Mémoire de Ressources et de Recherche, Département de Gériatrie, CHU d'Angers, Angers; Univ. Lyon (P.K.-S.), Inserm U1028, CNRS UMR5292, Centre de Recherche en Neurosciences de Lyon, Centre Mémoire Ressource et Recherche de Lyon (CMRR), Hôpital des Charpennes, Hospices Civils de Lyon; Univ. Picardie (O.G.), UR UPJV4559, Laboratoire de Neurosciences Fonctionnelles et Pathologies, Service de Neurologie, CHU Amiens; Univ. Normandie (D.W.), UNIROUEN, Inserm U1245, Departement de Neurologie, CNR-MAJ, CHU de Rouen; Centre Mémoire de Ressources et de Recherche Grenoble Arc Alpin (M.S.), Pôle de Psychiatrie et Neurologie, CHU Grenoble Alpes; CHU de Nantes (C.B.-B.), Département de Neurologie, Centre Mémoire de Ressources et Recherche, Nantes; Univ. Bordeaux (I.B.-M.), CNRS UMR 5536, Centre de Résonance Magnétique des Systèmes Biologiques, Pôle de Gérontologie Clinique, CHU de Bordeaux; and Univ. Clermont Auvergne (I.J.), CNRS, CHU Clermont-Ferrand, Centre Mémoire de Ressources et de Recherche, Service de Psychiatrie de L'Adulte A et Psychologie Médicale, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand
| | - Mathieu Ceccaldi
- From the Univ. Bordeaux (V.P.), CNRS UMR 5293, Institut des Maladies Neurodégénératives; CHU de Bordeaux (V.P.), Pôle de Neurosciences Cliniques, Centre Mémoire de Ressources et de Recherche; Univ. Bordeaux (V.B., G.C., C.D.), Inserm U1219, PHARes Team, Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED); CHU Bordeaux (V.B., G.C., C.D.), CIC 1401 EC, Pôle Santé Publique; CHU de Bordeaux (I.P.), Département d'Immunologie et d'Immunogénétique; Univ. Paris-Saclay (J.-F.M.), CEA, CNRS, Baobab UMR9027, Neurospin, CATI Multicenter Neuroimaging Platform, US52, UAR 9031, Gif-sur-Yvette; Sorbonne-Université (B.D.), Service des Maladies Cognitives et Comportementales et Institut de La Mémoire et de La Maladie d'Alzheimer (IM2A), Hôpital de La Salpêtrière, AP-PH, Paris; Univ. Toulouse (P.-J.O.), Inserm U1027, Gérontopôle, Departement de Gériatrie, CHU Purpan, Toulouse; Univ. Lille (F.P.), Inserm U1171, Centre Mémoire de Ressources et de Recherche, CHU Lille, DISTAlz, Lille; Univ. Strasbourg (F.B.), CNRS, ICube Laboratory, UMR 7357, Fédération de Médecine Translationnelle de Strasbourg, Centre Mémoire de Ressources et de Recherche, Pôle de Gériatrie, Strasbourg; Univ. Paris (C.P.), Inserm U1144, Groupe Hospitalier Lariboisière Fernand-Widal, AP-HP; Univ. Paris Cité (O.H.), EA 4468, AP-HP, Hôpitaux Universitaires Paris Centre, Service de Gériatrie, Hôpital Broca; CHU de Montpellier (K.B.), Pôle de Neurosciences, Département de Neurologie, Centre Mémoire de Ressources et de Recherche, Montpellier; Univ. Aix Marseille (M.C.), Inserm UMR 1106, Institut de Neurosciences des Systèmes, Centre Mémoire de Ressources et de Recherche, Département de Neurologie et de Neuropsychologie, AP-HM, Marseille; Univ. Angers (C.A.), UPRES EA 4638, Centre Mémoire de Ressources et de Recherche, Département de Gériatrie, CHU d'Angers, Angers; Univ. Lyon (P.K.-S.), Inserm U1028, CNRS UMR5292, Centre de Recherche en Neurosciences de Lyon, Centre Mémoire Ressource et Recherche de Lyon (CMRR), Hôpital des Charpennes, Hospices Civils de Lyon; Univ. Picardie (O.G.), UR UPJV4559, Laboratoire de Neurosciences Fonctionnelles et Pathologies, Service de Neurologie, CHU Amiens; Univ. Normandie (D.W.), UNIROUEN, Inserm U1245, Departement de Neurologie, CNR-MAJ, CHU de Rouen; Centre Mémoire de Ressources et de Recherche Grenoble Arc Alpin (M.S.), Pôle de Psychiatrie et Neurologie, CHU Grenoble Alpes; CHU de Nantes (C.B.-B.), Département de Neurologie, Centre Mémoire de Ressources et Recherche, Nantes; Univ. Bordeaux (I.B.-M.), CNRS UMR 5536, Centre de Résonance Magnétique des Systèmes Biologiques, Pôle de Gérontologie Clinique, CHU de Bordeaux; and Univ. Clermont Auvergne (I.J.), CNRS, CHU Clermont-Ferrand, Centre Mémoire de Ressources et de Recherche, Service de Psychiatrie de L'Adulte A et Psychologie Médicale, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand
| | - Cédric Annweiler
- From the Univ. Bordeaux (V.P.), CNRS UMR 5293, Institut des Maladies Neurodégénératives; CHU de Bordeaux (V.P.), Pôle de Neurosciences Cliniques, Centre Mémoire de Ressources et de Recherche; Univ. Bordeaux (V.B., G.C., C.D.), Inserm U1219, PHARes Team, Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED); CHU Bordeaux (V.B., G.C., C.D.), CIC 1401 EC, Pôle Santé Publique; CHU de Bordeaux (I.P.), Département d'Immunologie et d'Immunogénétique; Univ. Paris-Saclay (J.-F.M.), CEA, CNRS, Baobab UMR9027, Neurospin, CATI Multicenter Neuroimaging Platform, US52, UAR 9031, Gif-sur-Yvette; Sorbonne-Université (B.D.), Service des Maladies Cognitives et Comportementales et Institut de La Mémoire et de La Maladie d'Alzheimer (IM2A), Hôpital de La Salpêtrière, AP-PH, Paris; Univ. Toulouse (P.-J.O.), Inserm U1027, Gérontopôle, Departement de Gériatrie, CHU Purpan, Toulouse; Univ. Lille (F.P.), Inserm U1171, Centre Mémoire de Ressources et de Recherche, CHU Lille, DISTAlz, Lille; Univ. Strasbourg (F.B.), CNRS, ICube Laboratory, UMR 7357, Fédération de Médecine Translationnelle de Strasbourg, Centre Mémoire de Ressources et de Recherche, Pôle de Gériatrie, Strasbourg; Univ. Paris (C.P.), Inserm U1144, Groupe Hospitalier Lariboisière Fernand-Widal, AP-HP; Univ. Paris Cité (O.H.), EA 4468, AP-HP, Hôpitaux Universitaires Paris Centre, Service de Gériatrie, Hôpital Broca; CHU de Montpellier (K.B.), Pôle de Neurosciences, Département de Neurologie, Centre Mémoire de Ressources et de Recherche, Montpellier; Univ. Aix Marseille (M.C.), Inserm UMR 1106, Institut de Neurosciences des Systèmes, Centre Mémoire de Ressources et de Recherche, Département de Neurologie et de Neuropsychologie, AP-HM, Marseille; Univ. Angers (C.A.), UPRES EA 4638, Centre Mémoire de Ressources et de Recherche, Département de Gériatrie, CHU d'Angers, Angers; Univ. Lyon (P.K.-S.), Inserm U1028, CNRS UMR5292, Centre de Recherche en Neurosciences de Lyon, Centre Mémoire Ressource et Recherche de Lyon (CMRR), Hôpital des Charpennes, Hospices Civils de Lyon; Univ. Picardie (O.G.), UR UPJV4559, Laboratoire de Neurosciences Fonctionnelles et Pathologies, Service de Neurologie, CHU Amiens; Univ. Normandie (D.W.), UNIROUEN, Inserm U1245, Departement de Neurologie, CNR-MAJ, CHU de Rouen; Centre Mémoire de Ressources et de Recherche Grenoble Arc Alpin (M.S.), Pôle de Psychiatrie et Neurologie, CHU Grenoble Alpes; CHU de Nantes (C.B.-B.), Département de Neurologie, Centre Mémoire de Ressources et Recherche, Nantes; Univ. Bordeaux (I.B.-M.), CNRS UMR 5536, Centre de Résonance Magnétique des Systèmes Biologiques, Pôle de Gérontologie Clinique, CHU de Bordeaux; and Univ. Clermont Auvergne (I.J.), CNRS, CHU Clermont-Ferrand, Centre Mémoire de Ressources et de Recherche, Service de Psychiatrie de L'Adulte A et Psychologie Médicale, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand
| | - Pierre Krolak-Salmon
- From the Univ. Bordeaux (V.P.), CNRS UMR 5293, Institut des Maladies Neurodégénératives; CHU de Bordeaux (V.P.), Pôle de Neurosciences Cliniques, Centre Mémoire de Ressources et de Recherche; Univ. Bordeaux (V.B., G.C., C.D.), Inserm U1219, PHARes Team, Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED); CHU Bordeaux (V.B., G.C., C.D.), CIC 1401 EC, Pôle Santé Publique; CHU de Bordeaux (I.P.), Département d'Immunologie et d'Immunogénétique; Univ. Paris-Saclay (J.-F.M.), CEA, CNRS, Baobab UMR9027, Neurospin, CATI Multicenter Neuroimaging Platform, US52, UAR 9031, Gif-sur-Yvette; Sorbonne-Université (B.D.), Service des Maladies Cognitives et Comportementales et Institut de La Mémoire et de La Maladie d'Alzheimer (IM2A), Hôpital de La Salpêtrière, AP-PH, Paris; Univ. Toulouse (P.-J.O.), Inserm U1027, Gérontopôle, Departement de Gériatrie, CHU Purpan, Toulouse; Univ. Lille (F.P.), Inserm U1171, Centre Mémoire de Ressources et de Recherche, CHU Lille, DISTAlz, Lille; Univ. Strasbourg (F.B.), CNRS, ICube Laboratory, UMR 7357, Fédération de Médecine Translationnelle de Strasbourg, Centre Mémoire de Ressources et de Recherche, Pôle de Gériatrie, Strasbourg; Univ. Paris (C.P.), Inserm U1144, Groupe Hospitalier Lariboisière Fernand-Widal, AP-HP; Univ. Paris Cité (O.H.), EA 4468, AP-HP, Hôpitaux Universitaires Paris Centre, Service de Gériatrie, Hôpital Broca; CHU de Montpellier (K.B.), Pôle de Neurosciences, Département de Neurologie, Centre Mémoire de Ressources et de Recherche, Montpellier; Univ. Aix Marseille (M.C.), Inserm UMR 1106, Institut de Neurosciences des Systèmes, Centre Mémoire de Ressources et de Recherche, Département de Neurologie et de Neuropsychologie, AP-HM, Marseille; Univ. Angers (C.A.), UPRES EA 4638, Centre Mémoire de Ressources et de Recherche, Département de Gériatrie, CHU d'Angers, Angers; Univ. Lyon (P.K.-S.), Inserm U1028, CNRS UMR5292, Centre de Recherche en Neurosciences de Lyon, Centre Mémoire Ressource et Recherche de Lyon (CMRR), Hôpital des Charpennes, Hospices Civils de Lyon; Univ. Picardie (O.G.), UR UPJV4559, Laboratoire de Neurosciences Fonctionnelles et Pathologies, Service de Neurologie, CHU Amiens; Univ. Normandie (D.W.), UNIROUEN, Inserm U1245, Departement de Neurologie, CNR-MAJ, CHU de Rouen; Centre Mémoire de Ressources et de Recherche Grenoble Arc Alpin (M.S.), Pôle de Psychiatrie et Neurologie, CHU Grenoble Alpes; CHU de Nantes (C.B.-B.), Département de Neurologie, Centre Mémoire de Ressources et Recherche, Nantes; Univ. Bordeaux (I.B.-M.), CNRS UMR 5536, Centre de Résonance Magnétique des Systèmes Biologiques, Pôle de Gérontologie Clinique, CHU de Bordeaux; and Univ. Clermont Auvergne (I.J.), CNRS, CHU Clermont-Ferrand, Centre Mémoire de Ressources et de Recherche, Service de Psychiatrie de L'Adulte A et Psychologie Médicale, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand
| | - Olivier Godefroy
- From the Univ. Bordeaux (V.P.), CNRS UMR 5293, Institut des Maladies Neurodégénératives; CHU de Bordeaux (V.P.), Pôle de Neurosciences Cliniques, Centre Mémoire de Ressources et de Recherche; Univ. Bordeaux (V.B., G.C., C.D.), Inserm U1219, PHARes Team, Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED); CHU Bordeaux (V.B., G.C., C.D.), CIC 1401 EC, Pôle Santé Publique; CHU de Bordeaux (I.P.), Département d'Immunologie et d'Immunogénétique; Univ. Paris-Saclay (J.-F.M.), CEA, CNRS, Baobab UMR9027, Neurospin, CATI Multicenter Neuroimaging Platform, US52, UAR 9031, Gif-sur-Yvette; Sorbonne-Université (B.D.), Service des Maladies Cognitives et Comportementales et Institut de La Mémoire et de La Maladie d'Alzheimer (IM2A), Hôpital de La Salpêtrière, AP-PH, Paris; Univ. Toulouse (P.-J.O.), Inserm U1027, Gérontopôle, Departement de Gériatrie, CHU Purpan, Toulouse; Univ. Lille (F.P.), Inserm U1171, Centre Mémoire de Ressources et de Recherche, CHU Lille, DISTAlz, Lille; Univ. Strasbourg (F.B.), CNRS, ICube Laboratory, UMR 7357, Fédération de Médecine Translationnelle de Strasbourg, Centre Mémoire de Ressources et de Recherche, Pôle de Gériatrie, Strasbourg; Univ. Paris (C.P.), Inserm U1144, Groupe Hospitalier Lariboisière Fernand-Widal, AP-HP; Univ. Paris Cité (O.H.), EA 4468, AP-HP, Hôpitaux Universitaires Paris Centre, Service de Gériatrie, Hôpital Broca; CHU de Montpellier (K.B.), Pôle de Neurosciences, Département de Neurologie, Centre Mémoire de Ressources et de Recherche, Montpellier; Univ. Aix Marseille (M.C.), Inserm UMR 1106, Institut de Neurosciences des Systèmes, Centre Mémoire de Ressources et de Recherche, Département de Neurologie et de Neuropsychologie, AP-HM, Marseille; Univ. Angers (C.A.), UPRES EA 4638, Centre Mémoire de Ressources et de Recherche, Département de Gériatrie, CHU d'Angers, Angers; Univ. Lyon (P.K.-S.), Inserm U1028, CNRS UMR5292, Centre de Recherche en Neurosciences de Lyon, Centre Mémoire Ressource et Recherche de Lyon (CMRR), Hôpital des Charpennes, Hospices Civils de Lyon; Univ. Picardie (O.G.), UR UPJV4559, Laboratoire de Neurosciences Fonctionnelles et Pathologies, Service de Neurologie, CHU Amiens; Univ. Normandie (D.W.), UNIROUEN, Inserm U1245, Departement de Neurologie, CNR-MAJ, CHU de Rouen; Centre Mémoire de Ressources et de Recherche Grenoble Arc Alpin (M.S.), Pôle de Psychiatrie et Neurologie, CHU Grenoble Alpes; CHU de Nantes (C.B.-B.), Département de Neurologie, Centre Mémoire de Ressources et Recherche, Nantes; Univ. Bordeaux (I.B.-M.), CNRS UMR 5536, Centre de Résonance Magnétique des Systèmes Biologiques, Pôle de Gérontologie Clinique, CHU de Bordeaux; and Univ. Clermont Auvergne (I.J.), CNRS, CHU Clermont-Ferrand, Centre Mémoire de Ressources et de Recherche, Service de Psychiatrie de L'Adulte A et Psychologie Médicale, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand
| | - David Wallon
- From the Univ. Bordeaux (V.P.), CNRS UMR 5293, Institut des Maladies Neurodégénératives; CHU de Bordeaux (V.P.), Pôle de Neurosciences Cliniques, Centre Mémoire de Ressources et de Recherche; Univ. Bordeaux (V.B., G.C., C.D.), Inserm U1219, PHARes Team, Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED); CHU Bordeaux (V.B., G.C., C.D.), CIC 1401 EC, Pôle Santé Publique; CHU de Bordeaux (I.P.), Département d'Immunologie et d'Immunogénétique; Univ. Paris-Saclay (J.-F.M.), CEA, CNRS, Baobab UMR9027, Neurospin, CATI Multicenter Neuroimaging Platform, US52, UAR 9031, Gif-sur-Yvette; Sorbonne-Université (B.D.), Service des Maladies Cognitives et Comportementales et Institut de La Mémoire et de La Maladie d'Alzheimer (IM2A), Hôpital de La Salpêtrière, AP-PH, Paris; Univ. Toulouse (P.-J.O.), Inserm U1027, Gérontopôle, Departement de Gériatrie, CHU Purpan, Toulouse; Univ. Lille (F.P.), Inserm U1171, Centre Mémoire de Ressources et de Recherche, CHU Lille, DISTAlz, Lille; Univ. Strasbourg (F.B.), CNRS, ICube Laboratory, UMR 7357, Fédération de Médecine Translationnelle de Strasbourg, Centre Mémoire de Ressources et de Recherche, Pôle de Gériatrie, Strasbourg; Univ. Paris (C.P.), Inserm U1144, Groupe Hospitalier Lariboisière Fernand-Widal, AP-HP; Univ. Paris Cité (O.H.), EA 4468, AP-HP, Hôpitaux Universitaires Paris Centre, Service de Gériatrie, Hôpital Broca; CHU de Montpellier (K.B.), Pôle de Neurosciences, Département de Neurologie, Centre Mémoire de Ressources et de Recherche, Montpellier; Univ. Aix Marseille (M.C.), Inserm UMR 1106, Institut de Neurosciences des Systèmes, Centre Mémoire de Ressources et de Recherche, Département de Neurologie et de Neuropsychologie, AP-HM, Marseille; Univ. Angers (C.A.), UPRES EA 4638, Centre Mémoire de Ressources et de Recherche, Département de Gériatrie, CHU d'Angers, Angers; Univ. Lyon (P.K.-S.), Inserm U1028, CNRS UMR5292, Centre de Recherche en Neurosciences de Lyon, Centre Mémoire Ressource et Recherche de Lyon (CMRR), Hôpital des Charpennes, Hospices Civils de Lyon; Univ. Picardie (O.G.), UR UPJV4559, Laboratoire de Neurosciences Fonctionnelles et Pathologies, Service de Neurologie, CHU Amiens; Univ. Normandie (D.W.), UNIROUEN, Inserm U1245, Departement de Neurologie, CNR-MAJ, CHU de Rouen; Centre Mémoire de Ressources et de Recherche Grenoble Arc Alpin (M.S.), Pôle de Psychiatrie et Neurologie, CHU Grenoble Alpes; CHU de Nantes (C.B.-B.), Département de Neurologie, Centre Mémoire de Ressources et Recherche, Nantes; Univ. Bordeaux (I.B.-M.), CNRS UMR 5536, Centre de Résonance Magnétique des Systèmes Biologiques, Pôle de Gérontologie Clinique, CHU de Bordeaux; and Univ. Clermont Auvergne (I.J.), CNRS, CHU Clermont-Ferrand, Centre Mémoire de Ressources et de Recherche, Service de Psychiatrie de L'Adulte A et Psychologie Médicale, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand
| | - Mathilde Sauvee
- From the Univ. Bordeaux (V.P.), CNRS UMR 5293, Institut des Maladies Neurodégénératives; CHU de Bordeaux (V.P.), Pôle de Neurosciences Cliniques, Centre Mémoire de Ressources et de Recherche; Univ. Bordeaux (V.B., G.C., C.D.), Inserm U1219, PHARes Team, Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED); CHU Bordeaux (V.B., G.C., C.D.), CIC 1401 EC, Pôle Santé Publique; CHU de Bordeaux (I.P.), Département d'Immunologie et d'Immunogénétique; Univ. Paris-Saclay (J.-F.M.), CEA, CNRS, Baobab UMR9027, Neurospin, CATI Multicenter Neuroimaging Platform, US52, UAR 9031, Gif-sur-Yvette; Sorbonne-Université (B.D.), Service des Maladies Cognitives et Comportementales et Institut de La Mémoire et de La Maladie d'Alzheimer (IM2A), Hôpital de La Salpêtrière, AP-PH, Paris; Univ. Toulouse (P.-J.O.), Inserm U1027, Gérontopôle, Departement de Gériatrie, CHU Purpan, Toulouse; Univ. Lille (F.P.), Inserm U1171, Centre Mémoire de Ressources et de Recherche, CHU Lille, DISTAlz, Lille; Univ. Strasbourg (F.B.), CNRS, ICube Laboratory, UMR 7357, Fédération de Médecine Translationnelle de Strasbourg, Centre Mémoire de Ressources et de Recherche, Pôle de Gériatrie, Strasbourg; Univ. Paris (C.P.), Inserm U1144, Groupe Hospitalier Lariboisière Fernand-Widal, AP-HP; Univ. Paris Cité (O.H.), EA 4468, AP-HP, Hôpitaux Universitaires Paris Centre, Service de Gériatrie, Hôpital Broca; CHU de Montpellier (K.B.), Pôle de Neurosciences, Département de Neurologie, Centre Mémoire de Ressources et de Recherche, Montpellier; Univ. Aix Marseille (M.C.), Inserm UMR 1106, Institut de Neurosciences des Systèmes, Centre Mémoire de Ressources et de Recherche, Département de Neurologie et de Neuropsychologie, AP-HM, Marseille; Univ. Angers (C.A.), UPRES EA 4638, Centre Mémoire de Ressources et de Recherche, Département de Gériatrie, CHU d'Angers, Angers; Univ. Lyon (P.K.-S.), Inserm U1028, CNRS UMR5292, Centre de Recherche en Neurosciences de Lyon, Centre Mémoire Ressource et Recherche de Lyon (CMRR), Hôpital des Charpennes, Hospices Civils de Lyon; Univ. Picardie (O.G.), UR UPJV4559, Laboratoire de Neurosciences Fonctionnelles et Pathologies, Service de Neurologie, CHU Amiens; Univ. Normandie (D.W.), UNIROUEN, Inserm U1245, Departement de Neurologie, CNR-MAJ, CHU de Rouen; Centre Mémoire de Ressources et de Recherche Grenoble Arc Alpin (M.S.), Pôle de Psychiatrie et Neurologie, CHU Grenoble Alpes; CHU de Nantes (C.B.-B.), Département de Neurologie, Centre Mémoire de Ressources et Recherche, Nantes; Univ. Bordeaux (I.B.-M.), CNRS UMR 5536, Centre de Résonance Magnétique des Systèmes Biologiques, Pôle de Gérontologie Clinique, CHU de Bordeaux; and Univ. Clermont Auvergne (I.J.), CNRS, CHU Clermont-Ferrand, Centre Mémoire de Ressources et de Recherche, Service de Psychiatrie de L'Adulte A et Psychologie Médicale, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand
| | - Claire Boutoleau-Bretonnière
- From the Univ. Bordeaux (V.P.), CNRS UMR 5293, Institut des Maladies Neurodégénératives; CHU de Bordeaux (V.P.), Pôle de Neurosciences Cliniques, Centre Mémoire de Ressources et de Recherche; Univ. Bordeaux (V.B., G.C., C.D.), Inserm U1219, PHARes Team, Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED); CHU Bordeaux (V.B., G.C., C.D.), CIC 1401 EC, Pôle Santé Publique; CHU de Bordeaux (I.P.), Département d'Immunologie et d'Immunogénétique; Univ. Paris-Saclay (J.-F.M.), CEA, CNRS, Baobab UMR9027, Neurospin, CATI Multicenter Neuroimaging Platform, US52, UAR 9031, Gif-sur-Yvette; Sorbonne-Université (B.D.), Service des Maladies Cognitives et Comportementales et Institut de La Mémoire et de La Maladie d'Alzheimer (IM2A), Hôpital de La Salpêtrière, AP-PH, Paris; Univ. Toulouse (P.-J.O.), Inserm U1027, Gérontopôle, Departement de Gériatrie, CHU Purpan, Toulouse; Univ. Lille (F.P.), Inserm U1171, Centre Mémoire de Ressources et de Recherche, CHU Lille, DISTAlz, Lille; Univ. Strasbourg (F.B.), CNRS, ICube Laboratory, UMR 7357, Fédération de Médecine Translationnelle de Strasbourg, Centre Mémoire de Ressources et de Recherche, Pôle de Gériatrie, Strasbourg; Univ. Paris (C.P.), Inserm U1144, Groupe Hospitalier Lariboisière Fernand-Widal, AP-HP; Univ. Paris Cité (O.H.), EA 4468, AP-HP, Hôpitaux Universitaires Paris Centre, Service de Gériatrie, Hôpital Broca; CHU de Montpellier (K.B.), Pôle de Neurosciences, Département de Neurologie, Centre Mémoire de Ressources et de Recherche, Montpellier; Univ. Aix Marseille (M.C.), Inserm UMR 1106, Institut de Neurosciences des Systèmes, Centre Mémoire de Ressources et de Recherche, Département de Neurologie et de Neuropsychologie, AP-HM, Marseille; Univ. Angers (C.A.), UPRES EA 4638, Centre Mémoire de Ressources et de Recherche, Département de Gériatrie, CHU d'Angers, Angers; Univ. Lyon (P.K.-S.), Inserm U1028, CNRS UMR5292, Centre de Recherche en Neurosciences de Lyon, Centre Mémoire Ressource et Recherche de Lyon (CMRR), Hôpital des Charpennes, Hospices Civils de Lyon; Univ. Picardie (O.G.), UR UPJV4559, Laboratoire de Neurosciences Fonctionnelles et Pathologies, Service de Neurologie, CHU Amiens; Univ. Normandie (D.W.), UNIROUEN, Inserm U1245, Departement de Neurologie, CNR-MAJ, CHU de Rouen; Centre Mémoire de Ressources et de Recherche Grenoble Arc Alpin (M.S.), Pôle de Psychiatrie et Neurologie, CHU Grenoble Alpes; CHU de Nantes (C.B.-B.), Département de Neurologie, Centre Mémoire de Ressources et Recherche, Nantes; Univ. Bordeaux (I.B.-M.), CNRS UMR 5536, Centre de Résonance Magnétique des Systèmes Biologiques, Pôle de Gérontologie Clinique, CHU de Bordeaux; and Univ. Clermont Auvergne (I.J.), CNRS, CHU Clermont-Ferrand, Centre Mémoire de Ressources et de Recherche, Service de Psychiatrie de L'Adulte A et Psychologie Médicale, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand
| | - Isabelle Bourdel-Marchasson
- From the Univ. Bordeaux (V.P.), CNRS UMR 5293, Institut des Maladies Neurodégénératives; CHU de Bordeaux (V.P.), Pôle de Neurosciences Cliniques, Centre Mémoire de Ressources et de Recherche; Univ. Bordeaux (V.B., G.C., C.D.), Inserm U1219, PHARes Team, Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED); CHU Bordeaux (V.B., G.C., C.D.), CIC 1401 EC, Pôle Santé Publique; CHU de Bordeaux (I.P.), Département d'Immunologie et d'Immunogénétique; Univ. Paris-Saclay (J.-F.M.), CEA, CNRS, Baobab UMR9027, Neurospin, CATI Multicenter Neuroimaging Platform, US52, UAR 9031, Gif-sur-Yvette; Sorbonne-Université (B.D.), Service des Maladies Cognitives et Comportementales et Institut de La Mémoire et de La Maladie d'Alzheimer (IM2A), Hôpital de La Salpêtrière, AP-PH, Paris; Univ. Toulouse (P.-J.O.), Inserm U1027, Gérontopôle, Departement de Gériatrie, CHU Purpan, Toulouse; Univ. Lille (F.P.), Inserm U1171, Centre Mémoire de Ressources et de Recherche, CHU Lille, DISTAlz, Lille; Univ. Strasbourg (F.B.), CNRS, ICube Laboratory, UMR 7357, Fédération de Médecine Translationnelle de Strasbourg, Centre Mémoire de Ressources et de Recherche, Pôle de Gériatrie, Strasbourg; Univ. Paris (C.P.), Inserm U1144, Groupe Hospitalier Lariboisière Fernand-Widal, AP-HP; Univ. Paris Cité (O.H.), EA 4468, AP-HP, Hôpitaux Universitaires Paris Centre, Service de Gériatrie, Hôpital Broca; CHU de Montpellier (K.B.), Pôle de Neurosciences, Département de Neurologie, Centre Mémoire de Ressources et de Recherche, Montpellier; Univ. Aix Marseille (M.C.), Inserm UMR 1106, Institut de Neurosciences des Systèmes, Centre Mémoire de Ressources et de Recherche, Département de Neurologie et de Neuropsychologie, AP-HM, Marseille; Univ. Angers (C.A.), UPRES EA 4638, Centre Mémoire de Ressources et de Recherche, Département de Gériatrie, CHU d'Angers, Angers; Univ. Lyon (P.K.-S.), Inserm U1028, CNRS UMR5292, Centre de Recherche en Neurosciences de Lyon, Centre Mémoire Ressource et Recherche de Lyon (CMRR), Hôpital des Charpennes, Hospices Civils de Lyon; Univ. Picardie (O.G.), UR UPJV4559, Laboratoire de Neurosciences Fonctionnelles et Pathologies, Service de Neurologie, CHU Amiens; Univ. Normandie (D.W.), UNIROUEN, Inserm U1245, Departement de Neurologie, CNR-MAJ, CHU de Rouen; Centre Mémoire de Ressources et de Recherche Grenoble Arc Alpin (M.S.), Pôle de Psychiatrie et Neurologie, CHU Grenoble Alpes; CHU de Nantes (C.B.-B.), Département de Neurologie, Centre Mémoire de Ressources et Recherche, Nantes; Univ. Bordeaux (I.B.-M.), CNRS UMR 5536, Centre de Résonance Magnétique des Systèmes Biologiques, Pôle de Gérontologie Clinique, CHU de Bordeaux; and Univ. Clermont Auvergne (I.J.), CNRS, CHU Clermont-Ferrand, Centre Mémoire de Ressources et de Recherche, Service de Psychiatrie de L'Adulte A et Psychologie Médicale, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand
| | - Isabelle Jalenques
- From the Univ. Bordeaux (V.P.), CNRS UMR 5293, Institut des Maladies Neurodégénératives; CHU de Bordeaux (V.P.), Pôle de Neurosciences Cliniques, Centre Mémoire de Ressources et de Recherche; Univ. Bordeaux (V.B., G.C., C.D.), Inserm U1219, PHARes Team, Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED); CHU Bordeaux (V.B., G.C., C.D.), CIC 1401 EC, Pôle Santé Publique; CHU de Bordeaux (I.P.), Département d'Immunologie et d'Immunogénétique; Univ. Paris-Saclay (J.-F.M.), CEA, CNRS, Baobab UMR9027, Neurospin, CATI Multicenter Neuroimaging Platform, US52, UAR 9031, Gif-sur-Yvette; Sorbonne-Université (B.D.), Service des Maladies Cognitives et Comportementales et Institut de La Mémoire et de La Maladie d'Alzheimer (IM2A), Hôpital de La Salpêtrière, AP-PH, Paris; Univ. Toulouse (P.-J.O.), Inserm U1027, Gérontopôle, Departement de Gériatrie, CHU Purpan, Toulouse; Univ. Lille (F.P.), Inserm U1171, Centre Mémoire de Ressources et de Recherche, CHU Lille, DISTAlz, Lille; Univ. Strasbourg (F.B.), CNRS, ICube Laboratory, UMR 7357, Fédération de Médecine Translationnelle de Strasbourg, Centre Mémoire de Ressources et de Recherche, Pôle de Gériatrie, Strasbourg; Univ. Paris (C.P.), Inserm U1144, Groupe Hospitalier Lariboisière Fernand-Widal, AP-HP; Univ. Paris Cité (O.H.), EA 4468, AP-HP, Hôpitaux Universitaires Paris Centre, Service de Gériatrie, Hôpital Broca; CHU de Montpellier (K.B.), Pôle de Neurosciences, Département de Neurologie, Centre Mémoire de Ressources et de Recherche, Montpellier; Univ. Aix Marseille (M.C.), Inserm UMR 1106, Institut de Neurosciences des Systèmes, Centre Mémoire de Ressources et de Recherche, Département de Neurologie et de Neuropsychologie, AP-HM, Marseille; Univ. Angers (C.A.), UPRES EA 4638, Centre Mémoire de Ressources et de Recherche, Département de Gériatrie, CHU d'Angers, Angers; Univ. Lyon (P.K.-S.), Inserm U1028, CNRS UMR5292, Centre de Recherche en Neurosciences de Lyon, Centre Mémoire Ressource et Recherche de Lyon (CMRR), Hôpital des Charpennes, Hospices Civils de Lyon; Univ. Picardie (O.G.), UR UPJV4559, Laboratoire de Neurosciences Fonctionnelles et Pathologies, Service de Neurologie, CHU Amiens; Univ. Normandie (D.W.), UNIROUEN, Inserm U1245, Departement de Neurologie, CNR-MAJ, CHU de Rouen; Centre Mémoire de Ressources et de Recherche Grenoble Arc Alpin (M.S.), Pôle de Psychiatrie et Neurologie, CHU Grenoble Alpes; CHU de Nantes (C.B.-B.), Département de Neurologie, Centre Mémoire de Ressources et Recherche, Nantes; Univ. Bordeaux (I.B.-M.), CNRS UMR 5536, Centre de Résonance Magnétique des Systèmes Biologiques, Pôle de Gérontologie Clinique, CHU de Bordeaux; and Univ. Clermont Auvergne (I.J.), CNRS, CHU Clermont-Ferrand, Centre Mémoire de Ressources et de Recherche, Service de Psychiatrie de L'Adulte A et Psychologie Médicale, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand
| | - Genevieve Chene
- From the Univ. Bordeaux (V.P.), CNRS UMR 5293, Institut des Maladies Neurodégénératives; CHU de Bordeaux (V.P.), Pôle de Neurosciences Cliniques, Centre Mémoire de Ressources et de Recherche; Univ. Bordeaux (V.B., G.C., C.D.), Inserm U1219, PHARes Team, Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED); CHU Bordeaux (V.B., G.C., C.D.), CIC 1401 EC, Pôle Santé Publique; CHU de Bordeaux (I.P.), Département d'Immunologie et d'Immunogénétique; Univ. Paris-Saclay (J.-F.M.), CEA, CNRS, Baobab UMR9027, Neurospin, CATI Multicenter Neuroimaging Platform, US52, UAR 9031, Gif-sur-Yvette; Sorbonne-Université (B.D.), Service des Maladies Cognitives et Comportementales et Institut de La Mémoire et de La Maladie d'Alzheimer (IM2A), Hôpital de La Salpêtrière, AP-PH, Paris; Univ. Toulouse (P.-J.O.), Inserm U1027, Gérontopôle, Departement de Gériatrie, CHU Purpan, Toulouse; Univ. Lille (F.P.), Inserm U1171, Centre Mémoire de Ressources et de Recherche, CHU Lille, DISTAlz, Lille; Univ. Strasbourg (F.B.), CNRS, ICube Laboratory, UMR 7357, Fédération de Médecine Translationnelle de Strasbourg, Centre Mémoire de Ressources et de Recherche, Pôle de Gériatrie, Strasbourg; Univ. Paris (C.P.), Inserm U1144, Groupe Hospitalier Lariboisière Fernand-Widal, AP-HP; Univ. Paris Cité (O.H.), EA 4468, AP-HP, Hôpitaux Universitaires Paris Centre, Service de Gériatrie, Hôpital Broca; CHU de Montpellier (K.B.), Pôle de Neurosciences, Département de Neurologie, Centre Mémoire de Ressources et de Recherche, Montpellier; Univ. Aix Marseille (M.C.), Inserm UMR 1106, Institut de Neurosciences des Systèmes, Centre Mémoire de Ressources et de Recherche, Département de Neurologie et de Neuropsychologie, AP-HM, Marseille; Univ. Angers (C.A.), UPRES EA 4638, Centre Mémoire de Ressources et de Recherche, Département de Gériatrie, CHU d'Angers, Angers; Univ. Lyon (P.K.-S.), Inserm U1028, CNRS UMR5292, Centre de Recherche en Neurosciences de Lyon, Centre Mémoire Ressource et Recherche de Lyon (CMRR), Hôpital des Charpennes, Hospices Civils de Lyon; Univ. Picardie (O.G.), UR UPJV4559, Laboratoire de Neurosciences Fonctionnelles et Pathologies, Service de Neurologie, CHU Amiens; Univ. Normandie (D.W.), UNIROUEN, Inserm U1245, Departement de Neurologie, CNR-MAJ, CHU de Rouen; Centre Mémoire de Ressources et de Recherche Grenoble Arc Alpin (M.S.), Pôle de Psychiatrie et Neurologie, CHU Grenoble Alpes; CHU de Nantes (C.B.-B.), Département de Neurologie, Centre Mémoire de Ressources et Recherche, Nantes; Univ. Bordeaux (I.B.-M.), CNRS UMR 5536, Centre de Résonance Magnétique des Systèmes Biologiques, Pôle de Gérontologie Clinique, CHU de Bordeaux; and Univ. Clermont Auvergne (I.J.), CNRS, CHU Clermont-Ferrand, Centre Mémoire de Ressources et de Recherche, Service de Psychiatrie de L'Adulte A et Psychologie Médicale, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand
| | - Carole Dufouil
- From the Univ. Bordeaux (V.P.), CNRS UMR 5293, Institut des Maladies Neurodégénératives; CHU de Bordeaux (V.P.), Pôle de Neurosciences Cliniques, Centre Mémoire de Ressources et de Recherche; Univ. Bordeaux (V.B., G.C., C.D.), Inserm U1219, PHARes Team, Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED); CHU Bordeaux (V.B., G.C., C.D.), CIC 1401 EC, Pôle Santé Publique; CHU de Bordeaux (I.P.), Département d'Immunologie et d'Immunogénétique; Univ. Paris-Saclay (J.-F.M.), CEA, CNRS, Baobab UMR9027, Neurospin, CATI Multicenter Neuroimaging Platform, US52, UAR 9031, Gif-sur-Yvette; Sorbonne-Université (B.D.), Service des Maladies Cognitives et Comportementales et Institut de La Mémoire et de La Maladie d'Alzheimer (IM2A), Hôpital de La Salpêtrière, AP-PH, Paris; Univ. Toulouse (P.-J.O.), Inserm U1027, Gérontopôle, Departement de Gériatrie, CHU Purpan, Toulouse; Univ. Lille (F.P.), Inserm U1171, Centre Mémoire de Ressources et de Recherche, CHU Lille, DISTAlz, Lille; Univ. Strasbourg (F.B.), CNRS, ICube Laboratory, UMR 7357, Fédération de Médecine Translationnelle de Strasbourg, Centre Mémoire de Ressources et de Recherche, Pôle de Gériatrie, Strasbourg; Univ. Paris (C.P.), Inserm U1144, Groupe Hospitalier Lariboisière Fernand-Widal, AP-HP; Univ. Paris Cité (O.H.), EA 4468, AP-HP, Hôpitaux Universitaires Paris Centre, Service de Gériatrie, Hôpital Broca; CHU de Montpellier (K.B.), Pôle de Neurosciences, Département de Neurologie, Centre Mémoire de Ressources et de Recherche, Montpellier; Univ. Aix Marseille (M.C.), Inserm UMR 1106, Institut de Neurosciences des Systèmes, Centre Mémoire de Ressources et de Recherche, Département de Neurologie et de Neuropsychologie, AP-HM, Marseille; Univ. Angers (C.A.), UPRES EA 4638, Centre Mémoire de Ressources et de Recherche, Département de Gériatrie, CHU d'Angers, Angers; Univ. Lyon (P.K.-S.), Inserm U1028, CNRS UMR5292, Centre de Recherche en Neurosciences de Lyon, Centre Mémoire Ressource et Recherche de Lyon (CMRR), Hôpital des Charpennes, Hospices Civils de Lyon; Univ. Picardie (O.G.), UR UPJV4559, Laboratoire de Neurosciences Fonctionnelles et Pathologies, Service de Neurologie, CHU Amiens; Univ. Normandie (D.W.), UNIROUEN, Inserm U1245, Departement de Neurologie, CNR-MAJ, CHU de Rouen; Centre Mémoire de Ressources et de Recherche Grenoble Arc Alpin (M.S.), Pôle de Psychiatrie et Neurologie, CHU Grenoble Alpes; CHU de Nantes (C.B.-B.), Département de Neurologie, Centre Mémoire de Ressources et Recherche, Nantes; Univ. Bordeaux (I.B.-M.), CNRS UMR 5536, Centre de Résonance Magnétique des Systèmes Biologiques, Pôle de Gérontologie Clinique, CHU de Bordeaux; and Univ. Clermont Auvergne (I.J.), CNRS, CHU Clermont-Ferrand, Centre Mémoire de Ressources et de Recherche, Service de Psychiatrie de L'Adulte A et Psychologie Médicale, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand
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26
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Solis-Urra P, Rodriguez-Ayllon M, Álvarez-Ortega M, Molina-Hidalgo C, Molina-Garcia P, Arroyo-Ávila C, García-Hermoso A, Collins AM, Jain S, Gispert JD, Liu-Ambrose T, Ortega FB, Erickson KI, Esteban-Cornejo I. Physical Performance and Amyloid-β in Humans: A Systematic Review and Meta-Analysis of Observational Studies. J Alzheimers Dis 2023; 96:1427-1439. [PMID: 38007656 DOI: 10.3233/jad-230586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2023]
Abstract
BACKGROUND Accumulation of amyloid-β (Aβ) plaques is one of the main features of Alzheimer's disease (AD). Physical performance has been related to dementia risk and Aβ, and it has been hypothesized as one of the mechanisms leading to greater accumulation of Aβ. Yet, no evidence synthesis has been performed in humans. OBJECTIVE To investigate the association of physical performance with Aβ in humans, including Aβ accumulation on brain, and Aβ abnormalities measured in cerebrospinal fluid (CSF) and blood. METHODS A systematic review with multilevel meta-analysis was performed from inception to June 16th, 2022. Studies were eligible if they examined the association of physical performance with Aβ levels, including the measure of physical performance as a predictor and the measure of Aβ as an outcome in humans. RESULTS 7 articles including 2,619 participants were included in the meta-analysis. The results showed that physical performance was not associated with accumulation of Aβ in the brain (ES = 0.01; 95% CI -0.21 to 0.24; I2 = 69.9%), in the CSF (ES = -0.28; 95% CI -0.98 to 0.41; I2 = 91.0%) or in the blood (ES = -0.19; 95% CI -0.61 to 0.24; I2 = 99.75%). Significant heterogeneity was found across the results , which posed challenges in arriving at consistent conclusions; and the limited number of studies hindered the opportunity to conduct a moderation analysis. CONCLUSIONS The association between physical performance and Aβ is inconclusive. This uncertainly arises from the limited number of studies, study design limitations, and heterogeneity of measurement approaches. More studies are needed to determine whether physical performance is related to Aβ levels in humans.
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Affiliation(s)
- Patricio Solis-Urra
- Department of Physical Education and Sports, Faculty of Sport Sciences, Sport and Health University Research Institute (iMUDS), University of Granada, Granada, Spain
- Nuclear Medicine Services, "Virgen de Las Nieves", University Hospital, Granada, Spain
- Faculty of Education and Social Sciences, Universidad Andres Bello, Viña del Mar, Chile
| | - María Rodriguez-Ayllon
- Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Miriam Álvarez-Ortega
- Department of Physical Education and Sports, Faculty of Sport Sciences, Sport and Health University Research Institute (iMUDS), University of Granada, Granada, Spain
| | - Cristina Molina-Hidalgo
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA
- AdventHealth Research Institute, Neuroscience, Orlando, FL, USA
| | - Pablo Molina-Garcia
- Department of Physical Education and Sports, Faculty of Sport Sciences, Sport and Health University Research Institute (iMUDS), University of Granada, Granada, Spain
- Physical Medicine and Rehabilitation Service, Virgen de las Nieves University Hospital, Instituto de Investigacion Biosanitaria ibs.GRANADA, Granada, Spain
| | - Cristina Arroyo-Ávila
- Department of Physical Education and Sports, Faculty of Sport Sciences, Sport and Health University Research Institute (iMUDS), University of Granada, Granada, Spain
| | - Antonio García-Hermoso
- Navarrabiomed, Hospital Universitario de Navarra, IdiSNA, Universidad Pública de Navarra (UPNA), Pamplona, Spain
| | | | - Shivangi Jain
- AdventHealth Research Institute, Neuroscience, Orlando, FL, USA
| | - Juan Domingo Gispert
- BarcelonaBeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
| | - Teresa Liu-Ambrose
- Centre for Aging SMART at Vancouver Coastal Health, Vancouver Coastal Health Research Institute, Vancouver, British Columbia, Canada
- Djavad Mowafaghian Centre for Brain Health, Vancouver Coastal Health Research Institute, Vancouver, British Columbia, Canada
- Aging, Mobility, and Cognitive Health Laboratory, Department of Physical Therapy, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Francisco B Ortega
- Department of Physical Education and Sports, Faculty of Sport Sciences, Sport and Health University Research Institute (iMUDS), University of Granada, Granada, Spain
- Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland
- Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y Nutrición (CIBERobn), Instituto de Salud Carlos III, Madrid, Spain
| | - Kirk I Erickson
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA
- AdventHealth Research Institute, Neuroscience, Orlando, FL, USA
| | - Irene Esteban-Cornejo
- Department of Physical Education and Sports, Faculty of Sport Sciences, Sport and Health University Research Institute (iMUDS), University of Granada, Granada, Spain
- Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y Nutrición (CIBERobn), Instituto de Salud Carlos III, Madrid, Spain
- ibs.GRANADA Instituto de Investigación Biosanitaria, Granada, Spain
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27
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Hanon O, Vidal JS, Lehmann S, Bombois S, Allinquant B, Baret-Rose C, Tréluyer JM, Abdoul H, Gelé P, Delmaire C, Blanc F, Mangin JF, Buée L, Touchon J, Hugon J, Vellas B, Galbrun E, Benetos A, Berrut G, Paillaud E, Wallon D, Castelnovo G, Volpe-Gillot L, Paccalin M, Robert P, Godefroy O, Camus V, Belmin J, Vandel P, Novella JL, Duron E, Rigaud AS, Schraen-Maschke S, Gabelle A. Plasma amyloid beta predicts conversion to dementia in subjects with mild cognitive impairment: The BALTAZAR study. Alzheimers Dement 2022; 18:2537-2550. [PMID: 35187794 DOI: 10.1002/alz.12613] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 10/20/2021] [Accepted: 12/10/2021] [Indexed: 01/31/2023]
Abstract
INTRODUCTION Blood-based biomarkers are the next challenge for Alzheimer's disease (AD) diagnosis and prognosis. METHODS Mild cognitive impairment (MCI) participants (N = 485) of the BALTAZAR study, a large-scale longitudinal multicenter cohort, were followed-up for 3 years. A total of 165 of them converted to dementia (95% AD). Associations of conversion and plasma amyloid beta (Aβ)1-42 , Aβ1-40 , Aβ1-42 /Aβ1-40 ratio were analyzed with logistic and Cox models. RESULTS Converters to dementia had lower level of plasma Aβ1-42 (37.1 pg/mL [12.5] vs. 39.2 [11.1] , P value = .03) and lower Aβ1-42 /Aβ1-40 ratio than non-converters (0.148 [0.125] vs. 0.154 [0.076], P value = .02). MCI participants in the highest quartile of Aβ1-42 /Aβ1-40 ratio (>0.169) had a significant lower risk of conversion (hazard ratio adjusted for age, sex, education, apolipoprotein E ε4, hippocampus atrophy = 0.52 (95% confidence interval [0.31-0.86], P value = .01). DISCUSSION In this large cohort of MCI subjects we identified a threshold for plasma Aβ1-42 /Aβ1-40 ratio that may detect patients with a low risk of conversion to dementia within 3 years.
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Affiliation(s)
- Olivier Hanon
- Memory Resource and Research Centre of de Paris-Broca-Ile de France, Université de Paris, EA 4468, APHP, Hopital Broca, Paris, France
| | - Jean-Sébastien Vidal
- Memory Resource and Research Centre of de Paris-Broca-Ile de France, Université de Paris, EA 4468, APHP, Hopital Broca, Paris, France
| | - Sylvain Lehmann
- CHU Montpellier, LBPC, Inserm, Université de Montpellier, Montpellier, France
| | - Stéphanie Bombois
- CHU Lille, U1172-LilNCog, LiCEND, LabEx DISTALZ, Université de Lille, Inserm, Lille, France
| | - Bernadette Allinquant
- UMR-S 1266, Université de Paris, Institute of Psychiatric and Neurosciences, Inserm, Paris, France
| | - Christiane Baret-Rose
- UMR-S 1266, Université de Paris, Institute of Psychiatric and Neurosciences, Inserm, Paris, France
| | - Jean-Marc Tréluyer
- Clinical Research Unit, Université de Paris, APHP, Hôpital Necker, Paris, France
| | - Hendy Abdoul
- Clinical Research Unit, Université de Paris, APHP, Hôpital Necker, Paris, France
| | - Patrick Gelé
- CHU Lille, CRB/CIC1403, Université de Lille, Inserm, Lille, France
| | - Christine Delmaire
- CHU Lille, U1172-LilNCog, LiCEND, LabEx DISTALZ, Université de Lille, Inserm, Lille, France
| | - Fredéric Blanc
- CM2R, pôle de Gériatrie, Laboratoire ICube, FMTS, CNRS, équipe IMIS, Université de Strasbourg, Hôpitaux Universitaires de Strasbourg, Strasbourg, France
| | - Jean-François Mangin
- Neurospin, CEA, CNRS, cati-neuroimaging.com, CATI Multicenter Neuroimaging Platform, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Luc Buée
- CHU Lille, U1172-LilNCog, LiCEND, LabEx DISTALZ, Université de Lille, Inserm, Lille, France
| | - Jacques Touchon
- Department of Neurology, Memory Research and Resources Center of Montpellier, Inserm INM NeuroPEPs Team, Excellence Center of Neurodegenerative Disorders, Université de Montpellier, CHU Montpellier, Montpellier, France
| | - Jacques Hugon
- APHP, Groupe Hospitalier Saint Louis-Lariboisière Fernand Widal, Center of Cognitive Neurology, Université de Paris, Paris, France
| | - Bruno Vellas
- Memory Resource and Research Centre of Midi-Pyrénées, Université de Toulouse III, CHU La Grave-Casselardit, Toulouse, France
| | - Evelyne Galbrun
- Department of Gérontology 2, Sorbonne Université, APHP, Centre Hospitalier Dupuytren, Draveil, France
| | - Athanase Benetos
- Memory Resource and Research Centre of Lorraine, Université de Lorraine, CHRU de Nancy, Vandoeuvre-lès-Nancy, France
| | - Gilles Berrut
- Department of Clinical Gerontology, Memory Research Resource Center of Nantes, Université de Nantes, EA 4334 Movement-Interactions-Performance, CHU Nantes, Nantes, France
| | - Elena Paillaud
- Service de Gériatrie, Université de Paris, APHP, Hôpital Europeen Georges Pompidou, Paris, France
| | - David Wallon
- CHU de Rouen, Department of Neurology and CNR-MAJ, Normandy Center for Genomic and Personalized Medicine, CIC-CRB1404, Normandie Univ, UNIROUEN, Inserm U1245, Rouen, France
| | | | - Lisette Volpe-Gillot
- Service de Neuro-Psycho-Gériatrie, Memory Clinic, Hôpital Léopold Bellan, Paris, France
| | - Marc Paccalin
- Memory Resource and Research Centre of Poitiers, CHU de Poitiers, Poitiers, France
| | - Philippe Robert
- Memory Research Resource Center of Nice, CoBTek lab, Université Côte d'Azur, CHU de Nice, Nice, France
| | - Olivier Godefroy
- Memory Resource and Research Centre of Amiens Picardie, CHU d'Amiens-Picardie, Amiens, France
| | - Vincent Camus
- CHRU de Tours, UMR Inserm U1253, Université François-Rabelais de Tours, Tours, France
| | - Joël Belmin
- Service de Gériatrie Ambulatoire, Sorbonne Université, APHP, Hôpitaux Universitaires Pitie-Salpêtrière-Charles Foix, Paris, France
| | - Pierre Vandel
- Laboratoire de Recherches Intégratives en Neurosciences et Psychologie Cognitive, CHU de Besançon, Memory Resource and Research Centre of Besançon Franche-Comté, Université Bourgogne Franche-Comté, Besançon, France
| | - Jean-Luc Novella
- Memory Resource and Research Centre of Champagne-Ardenne, Université de Reims Champagne-Ardenne, EA 3797, CHU de Reims, Reims, France
| | - Emmanuelle Duron
- Département de gériatrie, Équipe MOODS, Inserm 1178, Université Paris-Saclay, APHP, Hôpital Paul Brousse, Villejuif, France
| | - Anne-Sophie Rigaud
- Memory Resource and Research Centre of de Paris-Broca-Ile de France, Université de Paris, EA 4468, APHP, Hopital Broca, Paris, France
| | | | - Audrey Gabelle
- Department of Neurology, Memory Research and Resources Center of Montpellier, Inserm INM NeuroPEPs Team, Excellence Center of Neurodegenerative Disorders, Université de Montpellier, CHU Montpellier, Montpellier, France
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Chiu PY, Yang FC, Chiu MJ, Lin WC, Lu CH, Yang SY. Relevance of plasma biomarkers to pathologies in Alzheimer's disease, Parkinson's disease and frontotemporal dementia. Sci Rep 2022; 12:17919. [PMID: 36289355 PMCID: PMC9605966 DOI: 10.1038/s41598-022-22647-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 10/18/2022] [Indexed: 01/20/2023] Open
Abstract
Amyloid plaques and tau tangles are pathological hallmarks of Alzheimer's disease (AD). Parkinson's disease (PD) results from the accumulation of α-synuclein. TAR DNA-binding protein (TDP-43) and total tau protein (T-Tau) play roles in FTD pathology. All of the pathological evidence was found in the biopsy. However, it is impossible to perform stein examinations in clinical practice. Assays of biomarkers in plasma would be convenient. It would be better to investigate the combinations of various biomarkers in AD, PD and FTD. Ninety-one subjects without neurodegenerative diseases, 76 patients with amnesic mild cognitive impairment (aMCI) or AD dementia, combined as AD family, were enrolled. One hundred and nine PD patients with normal cognition (PD-NC) or dementia (PDD), combined as PD family, were enrolled. Twenty-five FTD patients were enrolled for assays of plasma amyloid β 1-40 (Aβ1-40), Aβ1-42, T-Tau, α-synuclein and TDP-43 using immunomagnetic reduction (IMR). The results show that Aβs and T-Tau are major domains in AD family. α-synuclein is highly dominant in PD family. FTD is closely associated with TDP-43 and T-Tau. The dominant plasma biomarkers in AD family, PD family and FTD are consistent with pathology. This implies that plasma biomarkers are promising for precise and differential assessments of AD, PD and FTD in clinical practice.
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Affiliation(s)
- Pai-Yi Chiu
- grid.452796.b0000 0004 0634 3637Department of Neurology, Show Chwan Memorial Hospital, Chunghwa, 500 Taiwan ,MR-Guided Focus Ultrasound Center, Chang Bin Shaw Chwan Memorial Hospital, Changhwa, 505 Taiwan
| | - Fu-Chi Yang
- grid.278244.f0000 0004 0638 9360Department of Neurology, Tri-Service General Hospital, National Defense Medical Center, Taipei, 114 Taiwan
| | - Ming-Jang Chiu
- grid.19188.390000 0004 0546 0241Department of Neurology, National Taiwan University Hospital, College of Medicine, National Taiwan University, Taipei, 100 Taiwan ,grid.19188.390000 0004 0546 0241Graduate Institute of Brain and Mind Sciences, College of Medicine, National Taiwan University, Taipei, 100 Taiwan ,grid.19188.390000 0004 0546 0241Department of Psychology, National Taiwan University, Taipei, 106 Taiwan ,grid.19188.390000 0004 0546 0241Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, 106 Taiwan
| | - Wei-Che Lin
- grid.145695.a0000 0004 1798 0922Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital, College of Medicine, Chang Gung University, Kaohsiung, 833 Taiwan
| | - Cheng-Hsien Lu
- grid.145695.a0000 0004 1798 0922Department of Neurology, Kaohsiung Chang Gung Memorial Hospital, College of Medicine, Chang Gung University, Kaohsiung, 833 Taiwan
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29
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Chen HH, Hsu MH, Lee KH, Yang SY. Development of a 36-Channel Instrument for Assaying Biomarkers of Ultralow Concentrations Utilizing Immunomagnetic Reduction. ACS MEASUREMENT SCIENCE AU 2022; 2:485-492. [PMID: 36785659 PMCID: PMC9885996 DOI: 10.1021/acsmeasuresciau.2c00030] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 07/25/2022] [Accepted: 07/27/2022] [Indexed: 05/08/2023]
Abstract
With the demands of the high-throughput assay of biomarkers of ultralow concentrations in clinics, a 36-channel instrument utilizing immunomagnetic reduction (IMR) has been developed. The instrument involves the use of a high-T c superconducting-quantum-interference-device (SQUID) magnetometer to detect the signals due to the associations between target biomarker molecules and the antibody-functionalized magnetic nanoparticles in the reagent of IMR. In addition to illustrating the design and the measurements of the instrument, the assay characterizations for eight kinds of biomarkers related to neurodegenerative disease are investigated. Furthermore, the assay results among three independent instruments were compared. For an instrument, the channel-to-channel variations in measured concentrations of biomarkers are within a range of 2.09 to 5.62%. The assay accuracy was found to be from 99 to 103.7%. The p values in measured concentrations for any of the tested biomarkers were higher than 0.05 among the three instruments. The results demonstrate high throughput, high stability, and high consistency for the SQUID-IMR instruments.
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30
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Xiao Z, Wu W, Ma X, Liang X, Lu J, Zheng L, Ding S, Lei Q, Luo J, Chen K, Ding D, Zhao Q. Plasma Aβ42/Aβ40 and p-tau 181 Predict Long-Term Clinical Progression in a Cohort with Amnestic Mild Cognitive Impairment. Clin Chem 2022; 68:1552-1563. [PMID: 36208187 DOI: 10.1093/clinchem/hvac149] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Accepted: 07/13/2022] [Indexed: 11/13/2022]
Abstract
BACKGROUND Previous studies reported the value of blood-based biomarkers in predicting Alzheimer disease (AD) progression among individuals with different disease stages. However, evidence regarding the value of these markers in those with amnestic mild cognitive impairment (aMCI) is insufficient. METHODS A cohort with 251 aMCI individuals were followed for up to 8 years. Baseline blood biomarkers were measured on a single-molecule array platform. Multipoint clinical diagnosis and domain-specific cognitive functions were assessed to investigate the longitudinal relationship between blood biomarkers and clinical AD progression. RESULTS Individuals with low Aβ42/Aβ40 and high p-tau181 at baseline demonstrated the highest AD risk (hazard ratio = 4.83, 95% CI 2.37-9.86), and the most dramatic decline across cognitive domains. Aβ42/Aβ40 and p-tau181, combined with basic characteristics performed the best in predicting AD conversion (AUC = 0.825, 95% CI 0.771-0.878). CONCLUSIONS Combining Aβ42/Aβ40 and p-tau181 may be a feasible indicator for AD progression in clinical practice, and a potential composite marker in clinical trials.
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Affiliation(s)
- Zhenxu Xiao
- Institute of Neurology, Huashan Hospital, Fudan University, Shanghai, China.,National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China.,National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China
| | - Wanqing Wu
- Institute of Neurology, Huashan Hospital, Fudan University, Shanghai, China.,National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China.,National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China
| | - Xiaoxi Ma
- Institute of Neurology, Huashan Hospital, Fudan University, Shanghai, China.,National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China.,National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China
| | - Xiaoniu Liang
- Institute of Neurology, Huashan Hospital, Fudan University, Shanghai, China.,National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China.,National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China
| | - Jiaying Lu
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Li Zheng
- Institute of Neurology, Huashan Hospital, Fudan University, Shanghai, China.,National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China.,National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China
| | - Saineng Ding
- Institute of Neurology, Huashan Hospital, Fudan University, Shanghai, China.,National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China.,National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China
| | - Qiqi Lei
- Department of Biostatistics, School of Public Health, Fudan University, Shanghai, China
| | - Jianfeng Luo
- Department of Biostatistics, School of Public Health, Fudan University, Shanghai, China
| | - Keliang Chen
- Institute of Neurology, Huashan Hospital, Fudan University, Shanghai, China.,National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China.,National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China
| | - Ding Ding
- Institute of Neurology, Huashan Hospital, Fudan University, Shanghai, China.,National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China.,National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China
| | - Qianhua Zhao
- Institute of Neurology, Huashan Hospital, Fudan University, Shanghai, China.,National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China.,National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China.,MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
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31
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Xu C, Zhao L, Dong C. A Review of Application of Aβ42/40 Ratio in Diagnosis and Prognosis of Alzheimer’s Disease. J Alzheimers Dis 2022; 90:495-512. [DOI: 10.3233/jad-220673] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
The number of patients with Alzheimer’s disease (AD) and non-Alzheimer’s disease (non-AD) has drastically increased over recent decades. The amyloid cascade hypothesis attributes a vital role to amyloid-β protein (Aβ) in the pathogenesis of AD. As the main pathological hallmark of AD, amyloid plaques consist of merely the 42 and 40 amino acid variants of Aβ (Aβ 42 and Aβ 40). The cerebrospinal fluid (CSF) biomarker Aβ 42/40 has been extensively investigated and eventually integrated into important diagnostic tools to support the clinical diagnosis of AD. With the development of highly sensitive assays and technologies, blood-based Aβ 42/40, which was obtained using a minimally invasive and cost-effective method, has been proven to be abnormal in synchrony with CSF biomarker values. This paper presents the recent progress of the CSF Aβ 42/40 ratio and plasma Aβ 42/40 for AD as well as their potential clinical application as diagnostic markers or screening tools for dementia.
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Affiliation(s)
- Chang Xu
- Department of Neurology, the First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Li Zhao
- Department of Neurology, the First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Chunbo Dong
- Department of Neurology, the First Affiliated Hospital, Dalian Medical University, Dalian, China
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32
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Tönges L, Buhmann C, Klebe S, Klucken J, Kwon EH, Müller T, Pedrosa DJ, Schröter N, Riederer P, Lingor P. Blood-based biomarker in Parkinson's disease: potential for future applications in clinical research and practice. J Neural Transm (Vienna) 2022; 129:1201-1217. [PMID: 35428925 PMCID: PMC9463345 DOI: 10.1007/s00702-022-02498-1] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 03/27/2022] [Indexed: 12/12/2022]
Abstract
The clinical presentation of Parkinson's disease (PD) is both complex and heterogeneous, and its precise classification often requires an intensive work-up. The differential diagnosis, assessment of disease progression, evaluation of therapeutic responses, or identification of PD subtypes frequently remains uncertain from a clinical point of view. Various tissue- and fluid-based biomarkers are currently being investigated to improve the description of PD. From a clinician's perspective, signatures from blood that are relatively easy to obtain would have great potential for use in clinical practice if they fulfill the necessary requirements as PD biomarker. In this review article, we summarize the knowledge on blood-based PD biomarkers and present both a researcher's and a clinician's perspective on recent developments and potential future applications.
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Affiliation(s)
- Lars Tönges
- Department of Neurology, Ruhr-University Bochum, St. Josef Hospital, Gudrunstr. 56, 44791, Bochum, Germany.
- Center for Protein Diagnostics (ProDi), Ruhr University Bochum, 44801, Bochum, Nordrhein-Westfalen, Germany.
| | - Carsten Buhmann
- Department of Neurology, University Medical Center Hamburg-Eppendorf, 20246, Hamburg, Germany
| | - Stephan Klebe
- Department of Neurology, University Hospital Essen, 45147, Essen, Germany
| | - Jochen Klucken
- Department of Digital Medicine, University Luxembourg, LCSB, L-4367, Belval, Luxembourg
- Digital Medicine Research Group, Luxembourg Institute of Health, L-1445, Strassen, Luxembourg
- Centre Hospitalier de Luxembourg, Digital Medicine Research Clinic, L-1210, Luxembourg, Luxembourg
| | - Eun Hae Kwon
- Department of Neurology, Ruhr-University Bochum, St. Josef Hospital, Gudrunstr. 56, 44791, Bochum, Germany
| | - Thomas Müller
- Department of Neurology, St. Joseph Hospital Berlin-Weissensee, 13088, Berlin, Germany
| | - David J Pedrosa
- Department of Neurology, Universitätsklinikum Gießen and Marburg, Marburg Site, 35043, Marburg, Germany
- Center of Mind, Brain and Behaviour (CMBB), Philipps-Universität Marburg, 35043, Marburg, Germany
| | - Nils Schröter
- Department of Neurology and Clinical Neuroscience, University of Freiburg, 79106, Freiburg, Germany
| | - Peter Riederer
- Psychosomatics and Psychotherapy, University Hospital Wuerzburg, Clinic and Policlinic for Psychiatry, 97080, Wuerzburg, Germany
- University of Southern Denmark Odense, 5000, Odense, Denmark
| | - Paul Lingor
- School of Medicine, Klinikum Rechts Der Isar, Department of Neurology, Technical University of Munich, 81675, München, Germany
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Morató X, Pytel V, Jofresa S, Ruiz A, Boada M. Symptomatic and Disease-Modifying Therapy Pipeline for Alzheimer's Disease: Towards a Personalized Polypharmacology Patient-Centered Approach. Int J Mol Sci 2022; 23:9305. [PMID: 36012569 PMCID: PMC9409252 DOI: 10.3390/ijms23169305] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 08/11/2022] [Accepted: 08/12/2022] [Indexed: 02/07/2023] Open
Abstract
Since 1906, when Dr. Alois Alzheimer first described in a patient "a peculiar severe disease process of the cerebral cortex", people suffering from this pathology have been waiting for a breakthrough therapy. Alzheimer's disease (AD) is an irreversible, progressive neurodegenerative brain disorder and the most common form of dementia in the elderly with a long presymptomatic phase. Worldwide, approximately 50 million people are living with dementia, with AD comprising 60-70% of cases. Pathologically, AD is characterized by the deposition of amyloid β-peptide (Aβ) in the neuropil (neuritic plaques) and blood vessels (amyloid angiopathy), and by the accumulation of hyperphosphorylated tau in neurons (neurofibrillary tangles) in the brain, with associated loss of synapses and neurons, together with glial activation, and neuroinflammation, resulting in cognitive deficits and eventually dementia. The current competitive landscape in AD consists of symptomatic treatments, of which there are currently six approved medications: three AChEIs (donepezil, rivastigmine, and galantamine), one NMDA-R antagonist (memantine), one combination therapy (memantine/donepezil), and GV-971 (sodium oligomannate, a mixture of oligosaccharides derived from algae) only approved in China. Improvements to the approved therapies, such as easier routes of administration and reduced dosing frequencies, along with the developments of new strategies and combined treatments are expected to occur within the next decade and will positively impact the way the disease is managed. Recently, Aducanumab, the first disease-modifying therapy (DMT) has been approved for AD, and several DMTs are in advanced stages of clinical development or regulatory review. Small molecules, mAbs, or multimodal strategies showing promise in animal studies have not confirmed that promise in the clinic (where small to moderate changes in clinical efficacy have been observed), and therefore, there is a significant unmet need for a better understanding of the AD pathogenesis and the exploration of alternative etiologies and therapeutic effective disease-modifying therapies strategies for AD. Therefore, a critical review of the disease-modifying therapy pipeline for Alzheimer's disease is needed.
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Affiliation(s)
- Xavier Morató
- Research Center and Memory Clinic, Fundació ACE, Institut Català de Neurociències Aplicades, Universitat Internacional de Catalunya, 08017 Barcelona, Spain
| | - Vanesa Pytel
- Research Center and Memory Clinic, Fundació ACE, Institut Català de Neurociències Aplicades, Universitat Internacional de Catalunya, 08017 Barcelona, Spain
| | - Sara Jofresa
- Research Center and Memory Clinic, Fundació ACE, Institut Català de Neurociències Aplicades, Universitat Internacional de Catalunya, 08017 Barcelona, Spain
| | - Agustín Ruiz
- Research Center and Memory Clinic, Fundació ACE, Institut Català de Neurociències Aplicades, Universitat Internacional de Catalunya, 08017 Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Mercè Boada
- Research Center and Memory Clinic, Fundació ACE, Institut Català de Neurociències Aplicades, Universitat Internacional de Catalunya, 08017 Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, 28029 Madrid, Spain
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Plasma Phospho-Tau-181 as a Diagnostic Aid in Alzheimer’s Disease. Biomedicines 2022; 10:biomedicines10081879. [PMID: 36009425 PMCID: PMC9405617 DOI: 10.3390/biomedicines10081879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 07/20/2022] [Accepted: 07/27/2022] [Indexed: 11/16/2022] Open
Abstract
Cerebrospinal fluid (CSF) biomarkers remain the gold standard for fluid-biomarker-based diagnosis of Alzheimer’s disease (AD) during life. Plasma biomarkers avoid lumbar puncture and allow repeated sampling. Changes of plasma phospho-tau-181 in AD are of comparable magnitude and seem to parallel the changes in CSF, may occur in preclinical or predementia stages of the disease, and may differentiate AD from other causes of dementia with adequate accuracy. Plasma phospho-tau-181 may offer a useful alternative to CSF phospho-tau determination, but work still has to be done concerning the optimal method of determination with the highest combination of sensitivity and specificity and cost-effect parameters.
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35
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Park SA, Jang YJ, Kim MK, Lee SM, Moon SY. Promising Blood Biomarkers for Clinical Use in Alzheimer's Disease: A Focused Update. J Clin Neurol 2022; 18:401-409. [PMID: 35796265 PMCID: PMC9262460 DOI: 10.3988/jcn.2022.18.4.401] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 04/29/2022] [Accepted: 04/29/2022] [Indexed: 12/11/2022] Open
Abstract
Alzheimer’s disease (AD) is the most-common cause of neurodegenerative dementia, and it is characterized by abnormal amyloid and tau accumulation, which indicates neurodegeneration. AD has mostly been diagnosed clinically. However, ligand-specific positron emission tomography (PET) imaging, such as amyloid PET, and cerebrospinal fluid (CSF) biomarkers are needed to accurately diagnose AD, since they supplement the shortcomings of clinical diagnoses. Using biomarkers that represent the pathology of AD is essential (particularly when disease-modifying treatment is available) to identify the corresponding pathology of targeted therapy and for monitoring the treatment response. Although imaging and CSF biomarkers are useful, their widespread use is restricted by their high cost and the discomfort during the lumbar puncture, respectively. Recent advances in AD blood biomarkers shed light on their future use for clinical purposes. The amyloid β (Aβ)42/Aβ40 ratio and the concentrations of phosphorylated tau at threonine 181 and at threonine 217, and of neurofilament light in the blood were found to represent the pathology of Aβ, tau, and neurodegeneration in the brain when using automatic electrochemiluminescence technologies, single-molecule arrays, immunoprecipitation coupled with mass spectrometry, etc. These blood biomarkers are imminently expected to be incorporated into clinical practice to predict, diagnose, and determine the stage of AD. In this review we focus on advancements in the measurement technologies for blood biomarkers and the promising biomarkers that are approaching clinical application. We also discuss the current limitations, the needed further investigations, and the perspectives on their use.
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Affiliation(s)
- Sun Ah Park
- Lab for Neurodegenerative Dementia, Department of Anatomy, Ajou University School of Medicine, Suwon, Korea.,Department of Neurology, Ajou University School of Medicine, Suwon, Korea.,Neuroscience Graduate Program, Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Korea.
| | - Yu Jung Jang
- Lab for Neurodegenerative Dementia, Department of Anatomy, Ajou University School of Medicine, Suwon, Korea.,Neuroscience Graduate Program, Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Korea
| | - Min Kyoung Kim
- Lab for Neurodegenerative Dementia, Department of Anatomy, Ajou University School of Medicine, Suwon, Korea.,Neuroscience Graduate Program, Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Korea
| | - Sun Min Lee
- Department of Neurology, Ajou University School of Medicine, Suwon, Korea
| | - So Young Moon
- Department of Neurology, Ajou University School of Medicine, Suwon, Korea
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36
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Kang J, Tian Z, Wei J, Mu Z, Liang J, Li M. Association between obstructive sleep apnea and Alzheimer's disease-related blood and cerebrospinal fluid biomarkers: A meta-analysis. J Clin Neurosci 2022; 102:87-94. [PMID: 35753156 DOI: 10.1016/j.jocn.2022.06.004] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 05/27/2022] [Accepted: 06/10/2022] [Indexed: 10/17/2022]
Abstract
INTRODUCTION Recent studies indicate that Alzheimer's disease- (AD) related biomarkers, including amyloid β (Aβ40 and Aβ42) and tau proteins (P-tau and T-tau), in blood and cerebrospinal fluid (CSF) are associated with obstructive sleep apnea (OSA). However, the results have been inconsistent. Therefore, the primary purpose of this meta-analysis was to determine the relationship between blood and CSF AD-related biomarkers and OSA. METHODS We searched the Embase, PubMed, Scopus, and Cochrane Library databases for relevant articles till February 2022. RESULTS Eight articles were finally included after the literature screening, including 446 patients with OSA and 286 controls. Pooled analysis showed that CSF Aβ42 (SMD = -0.220, P = 0.136), T-tau (SMD = 0.012, P = 0.89), and P-tau (SMD = 0.099, P = 0.274) levels were not different between patients with OSA and controls. In patients with moderate to severe OSA, CSF Aβ42 (SMD = -0.482, P = 0.031) were significantly lower than in controls. Blood T-tau (SMD = 0.560, P = 0.026), P-tau (SMD = 0.621, P < 0.001), and Aβ40 (SMD = 0.656, P < 0.001) levels were significantly higher in patients with OSA than in controls. Blood Aβ42 (SMD = 0.241, P = 0.232) were not different between patients with OSA and controls. CONCLUSION OSA is associated with changes in AD-related markers. Higher OSA severity may be associated with the development of AD. AD-related biomarkers, especially in the blood, are clinically efficient, less invasively assessed and monitored, and may be useful for detecting OSA and related cognitive impairments. Further studies are needed to confirm these results.
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Affiliation(s)
- Jing Kang
- Department of Respiratory, The First Hospital of Jilin University, Changchun, Jilin 130021, China; Jilin Medical University, Jilin, Jilin 132013, China
| | - Zongsheng Tian
- Department of Respiratory, The First Hospital of Jilin University, Changchun, Jilin 130021, China
| | - Jun Wei
- Jilin Medical University, Jilin, Jilin 132013, China
| | - Zhuangzhuang Mu
- Department of Respiratory, The First Hospital of Jilin University, Changchun, Jilin 130021, China
| | - Jianmin Liang
- Department of Pediatric Neurology, The First Hospital of Jilin University, Changchun, Jilin 130021, China.
| | - Mingxian Li
- Department of Respiratory, The First Hospital of Jilin University, Changchun, Jilin 130021, China.
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37
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Increased Levels of Plasma Alzheimer’s Disease Biomarkers and Their Associations with Brain Structural Changes and Carotid Intima-Media Thickness in Cognitively Normal Obstructive Sleep Apnea Patients. Diagnostics (Basel) 2022; 12:diagnostics12071522. [PMID: 35885428 PMCID: PMC9324500 DOI: 10.3390/diagnostics12071522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 06/19/2022] [Accepted: 06/20/2022] [Indexed: 11/17/2022] Open
Abstract
Obstructive sleep apnea (OSA) has been linked to Alzheimer’s disease (AD) and amyloid deposition in the brain. OSA is further linked to the development of cardiovascular and cerebrovascular diseases. In this study, we analyzed the plasma levels of AD neuropathology biomarkers and their relationships with structural changes of the brain and atherosclerosis. Thirty OSA patients with normal cognition and 34 normal controls were enrolled. Cognitive functions were assessed by the Wechsler Adult Intelligence Scale third edition and Cognitive Ability Screening Instrument. Plasma Aβ-40, Aβ-42, and T-tau levels were assayed using immunomagnetic reduction. The carotid intima-media thickness was measured to assess the severity of atherosclerosis. Structural MR images of brain were acquired with voxel-based morphometric analysis of T1 structural images. The OSA patients exhibited significantly elevated plasma levels of Aβ-42 and T-tau, as well as increased gray matter volume in the right precuneus. Plasma T-tau level is associated with carotid intima-media thickness and gray matter volume of the precuneus. These findings may indicate early changes that precede clinically apparent cognitive impairment. The measurement of these biomarkers may aid in the early detection of OSA-associated morbidity and possible treatment planning for the prevention of irreversible neuronal damage and cognitive dysfunction.
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Wu CY, Shapiro L, Ouk M, MacIntosh BJ, Black SE, Shah BR, Swardfager W. Glucose-lowering drugs, cognition, and dementia: The clinical evidence. Neurosci Biobehav Rev 2022; 137:104654. [PMID: 35398114 DOI: 10.1016/j.neubiorev.2022.104654] [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: 02/01/2022] [Revised: 04/01/2022] [Accepted: 04/03/2022] [Indexed: 11/19/2022]
Abstract
Type 2 diabetes mellitus (T2DM) is an important risk factor for dementia. The possibility to mitigate this risk by controlling T2DM is compelling; however, different glucose-lowering drugs have different effects on the brain by virtue of their different mechanisms of action. The clinical and epidemiological data appear mixed, warranting careful critical evaluation of the human studies. Here we examine the evidence in the context of dementia prevention and treatment, both for people with and without T2DM. We discuss the evidence on this scaffold of research directions, identifying methodological complexities in the extant literature (e.g. comparator discrepancies, changes in the therapeutic landscape), and the implications of different outcome measures (e.g. neuropsychological). We consider possible implications of cerebrovascular protection vs. effects on progression of neurodegenerative proteinopathy, and we present a research roadmap for glucose-lowering drugs in cognitive neurology, including neuroimaging, and fluid biomarkers. We conclude that there is great potential to advance personalized strategies to prevent and treat dementia with glucose-lowering drugs.
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Affiliation(s)
- Che-Yuan Wu
- Department of Pharmacology & Toxicology, University of Toronto, Toronto, Ontario, Canada; Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Lila Shapiro
- Department of Pharmacology & Toxicology, University of Toronto, Toronto, Ontario, Canada; Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Michael Ouk
- Department of Pharmacology & Toxicology, University of Toronto, Toronto, Ontario, Canada; Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Bradley J MacIntosh
- Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, Ontario, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada; Canadian Partnership for Stroke Recovery, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Sandra E Black
- Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, Ontario, Canada; Canadian Partnership for Stroke Recovery, Sunnybrook Research Institute, Toronto, Ontario, Canada; Department of Medicine, Division of Neurology, University of Toronto, Toronto, Ontario, Canada; Toronto Dementia Research Alliance, Toronto, Ontario, Canada
| | - Baiju R Shah
- ICES, Toronto, Ontario, Canada; Divisions of Endocrinology and Obstetric Medicine, Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - Walter Swardfager
- Department of Pharmacology & Toxicology, University of Toronto, Toronto, Ontario, Canada; Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, Ontario, Canada; Canadian Partnership for Stroke Recovery, Sunnybrook Research Institute, Toronto, Ontario, Canada; KITE UHN Toronto Rehabilitation Institute, Toronto, Ontario, Canada
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Systemic Inflammation Predicts Alzheimer Pathology in Community Samples without Dementia. Biomedicines 2022; 10:biomedicines10061240. [PMID: 35740262 PMCID: PMC9219863 DOI: 10.3390/biomedicines10061240] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 04/06/2022] [Accepted: 04/18/2022] [Indexed: 02/01/2023] Open
Abstract
Neuroinflammation and oxidative stress (OS) are implicated in the pathophysiology of Alzheimer’s disease (AD). However, it is unclear at what stage of the disease process inflammation first becomes manifest. The aim of this study was to investigate the associations between specific plasma markers of inflammation and OS, tau, and Amyloid-β 38, 40, and 42 levels in cognitively unimpaired middle-age and older individuals. Associations between inflammatory states identified through principal component analysis and AD biomarkers were investigated in middle-age (52–56 years, n = 335, 52% female) and older-age (72–76 years, n = 351, 46% female) participants without dementia. In middle-age, a component reflecting variation in OS was most strongly associated with tau and to a lesser extent amyloid-β levels. In older-age, a similar component to that observed in middle-age was only associated with tau, while another component reflecting heightened inflammation independent of OS, was associated with all AD biomarkers. In middle and older-age, inflammation and OS states are associated with plasma AD biomarkers.
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Li K, Qu H, Ma M, Xia C, Cai M, Han F, Zhang Q, Gu X, Ma Q. Correlation Between Brain Structure Atrophy and Plasma Amyloid-β and Phosphorylated Tau in Patients With Alzheimer’s Disease and Amnestic Mild Cognitive Impairment Explored by Surface-Based Morphometry. Front Aging Neurosci 2022; 14:816043. [PMID: 35547625 PMCID: PMC9083065 DOI: 10.3389/fnagi.2022.816043] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 02/28/2022] [Indexed: 12/27/2022] Open
Abstract
ObjectiveTo investigate the changes in the cortical thickness of the region of interest (ROI) and plasma Aβ40, Aβ42, and phosphorylated Tau (P-Tau) concentrations in patients with Alzheimer’s disease (AD) and amnestic mild cognitive impairment (aMCI) as the disease progressed with surface-based morphometry (SBM), to analyze the correlation between ROI cortical thickness and measured plasma indexes and neuropsychological scales, and to explore the clinical value of ROI cortical thickness combined with plasma Aβ40, Aβ42, and P-Tau in the early recognition and diagnosis of AD.MethodsThis study enrolled 33 patients with AD, 48 patients with aMCI, and 33 healthy controls (normal control, NC). Concentration changes in plasma Aβ42, Aβ40, and P-Tau collected in each group were analyzed. Meanwhile, the whole brain T1 structure images (T1WI-3D-MPRAGE) of each group of patients were collected, and T1 image in AD-aMCI, AD-NC, and aMCI-NC group were analyzed and processed by SBM technology to obtain brain regions with statistical differences as clusters, and the cortical thickness of each cluster was extracted. Multivariate ordered logistic regression analysis was used to screen out the measured plasma indexes and the indexes with independent risk factors in the cortical thickness of each cluster. Three comparative receiver operating characteristic (ROC) curves of AD-aMCI, AD-NC, and aMCI-NC groups were plotted, respectively, to explore the diagnostic value of multi-factor combined prediction for cognitive impairment. The relationship between cortical thickness and plasma indexes, and between cortical thickness and Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) scores were clarified by Pearson correlation analysis.ResultsPlasma Aβ40, Aβ42, and P-Tau proteins in the NC, aMCI, and AD groups increased with the progression of AD (P < 0.01); cortical thickness reductions in the AD-aMCI groups and AD-NC groups mainly involved the bilateral superior temporal gyrus, transverse temporal gyrus, superior marginal gyrus, insula, right entorhinal cortex, right fusiform gyrus, and cingulate gyrus. However, there were no statistical significances in cortical thickness reductions in the aMCI and NC groups. The cortical thickness of the ROI was negatively correlated with plasma Aβ40, Aβ42, and P-Tau concentrations (P < 0.05), and the cortical thickness of the ROI was positively correlated with MMSE and MoCA scores. Independent risk factors such as Aβ40, Aβ42, P-Tau, and AD-NC cluster 1R (right superior temporal gyrus, temporal pole, entorhinal cortex, transverse temporal gyrus, fusiform gyrus, superior marginal gyrus, middle temporal gyrus, and inferior temporal gyrus) were combined to plot ROC curves. The diagnostic efficiency of plasma indexes was higher than that of cortical thickness indexes, the diagnostic efficiency of ROC curves after the combination of cortical thickness and plasma indexes was higher than that of cortical thickness or plasma indexes alone.ConclusionPlasma Aβ40, Aβ42, and P-Tau may be potential biomarkers for early prediction of AD. As the disease progressed, AD patients developed cortical atrophy characterized by atrophy of the medial temporal lobe. The combined prediction of these region and plasma Aβ40, Aβ42, and P-Tau had a higher diagnostic value than single-factor prediction for cognitive decline.
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Affiliation(s)
- Kaidi Li
- Department of Neurology, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, China
| | - Hang Qu
- Department of Imaging, Yangzhou First People’s Hospital, Affiliated Hospital of Yangzhou University, Yangzhou, China
| | - Mingyi Ma
- Department of Molecular and Cellular Biology, University of Illinois Urbana-Champaign, Urbana, IL, United States
| | - Chenyu Xia
- Department of Neurology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Ming Cai
- Department of Neurology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Fang Han
- Department of Imaging, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Qing Zhang
- Department of Imaging, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Xinyi Gu
- Department of Neurology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Qiang Ma
- Department of Neurology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
- *Correspondence: Qiang Ma,
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Li TR, Yang Q, Hu X, Han Y. Biomarkers and Tools for Predicting Alzheimer's Disease in the Preclinical Stage. Curr Neuropharmacol 2022; 20:713-737. [PMID: 34030620 PMCID: PMC9878962 DOI: 10.2174/1570159x19666210524153901] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Revised: 04/27/2021] [Accepted: 05/08/2021] [Indexed: 11/22/2022] Open
Abstract
Alzheimer's disease (AD) is the only leading cause of death for which no disease-modifying therapy is currently available. Over the past decade, a string of disappointing clinical trial results has forced us to shift our focus to the preclinical stage of AD, which represents the most promising therapeutic window. However, the accurate diagnosis of preclinical AD requires the presence of brain β- amyloid deposition determined by cerebrospinal fluid or amyloid-positron emission tomography, significantly limiting routine screening and diagnosis in non-tertiary hospital settings. Thus, an easily accessible marker or tool with high sensitivity and specificity is highly needed. Recently, it has been discovered that individuals in the late stage of preclinical AD may not be truly "asymptomatic" in that they may have already developed subtle or subjective cognitive decline. In addition, advances in bloodderived biomarker studies have also allowed the detection of pathologic changes in preclinical AD. Exosomes, as cell-to-cell communication messengers, can reflect the functional changes of their source cell. Methodological advances have made it possible to extract brain-derived exosomes from peripheral blood, making exosomes an emerging biomarker carrier and liquid biopsy tool for preclinical AD. The eye and its associated structures have rich sensory-motor innervation. In this regard, studies have indicated that they may also provide reliable markers. Here, our report covers the current state of knowledge of neuropsychological and eye tests as screening tools for preclinical AD and assesses the value of blood and brain-derived exosomes as carriers of biomarkers in conjunction with the current diagnostic paradigm.
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Affiliation(s)
- Tao-Ran Li
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China
| | - Qin Yang
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China
| | - Xiaochen Hu
- Department of Psychiatry, University of Cologne, Medical Faculty, Cologne, 50924, Germany
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China;,Center of Alzheimer’s Disease, Beijing Institute for Brain Disorders, Beijing, 100053, China;,National Clinical Research Center for Geriatric Disorders, Beijing, 100053, China;,School of Biomedical Engineering, Hainan University, Haikou, 570228, China;,Address correspondence to this author at the Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China; Tel: +86 13621011941; E-mail:
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Su MT, Jheng YS, Lu CW, Wu WJ, Yang SY, Chuang WC, Lee MC, Wu CH. Neurotherapy of Yi-Gan-San, a Traditional Herbal Medicine, in an Alzheimer's Disease Model of Drosophila melanogaster by Alleviating Aβ 42 Expression. PLANTS (BASEL, SWITZERLAND) 2022; 11:plants11040572. [PMID: 35214904 PMCID: PMC8878444 DOI: 10.3390/plants11040572] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 02/19/2022] [Accepted: 02/19/2022] [Indexed: 05/13/2023]
Abstract
Alzheimer's disease (AD), a main cause of dementia, is the most common neurodegenerative disease that is related to the abnormal accumulation of amyloid β (Aβ) proteins. Yi-Gan-San (YGS), a traditional herbal medicine, has been used for the management of neurodegenerative disorders and for the treatment of neurosis, insomnia and dementia. The aim of this study was to examine antioxidant capacity and cytotoxicity of YGS treatment by using 2,2-Diphenyl-1-picrylhydrazyl (DPPH) and 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assays in vitro. We explored neuroprotective effects of YGS treatment in alleviating Aβ neurotoxicity of Drosophila melanogaster in vivo by comparing survival rate, climbing index, and Aβ expressions through retinal green fluorescent protein (GFP) expression, highly sensitive immunomagnetic reduction (IMR) and Western blotting assays. In the in vitro study, our results showed that scavenging activities of free radical and SH-SY5Y nerve cell viability were increased significantly (p < 0.01-0.05). In the in vivo study, Aβ42-expressing flies (Aβ42-GFP flies) and their WT flies (mCD8-GFP flies) were used as an animal model to examine the neurotherapeutic effects of YGS treatment. Our results showed that, in comparison with those Aβ42 flies under sham treatments, Aβ42 flies under YGS treatments showed a greater survival rate, better climbing speed, and lower Aβ42 aggregation in Drosophila brain tissue (p < 0.01). Our findings suggest that YGS should have a beneficial alternative therapy for AD and dementia via alleviating Aβ neurotoxicity in the brain tissue.
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Affiliation(s)
- Ming-Tsan Su
- School of Life Science, National Taiwan Normal University, Taipei 11677, Taiwan; (M.-T.S.); (Y.-S.J.); (C.-W.L.); (W.-J.W.)
| | - Yong-Sin Jheng
- School of Life Science, National Taiwan Normal University, Taipei 11677, Taiwan; (M.-T.S.); (Y.-S.J.); (C.-W.L.); (W.-J.W.)
| | - Chen-Wen Lu
- School of Life Science, National Taiwan Normal University, Taipei 11677, Taiwan; (M.-T.S.); (Y.-S.J.); (C.-W.L.); (W.-J.W.)
| | - Wen-Jhen Wu
- School of Life Science, National Taiwan Normal University, Taipei 11677, Taiwan; (M.-T.S.); (Y.-S.J.); (C.-W.L.); (W.-J.W.)
| | | | | | - Ming-Chung Lee
- Brion Research Institute of Taiwan, Taipei 23143, Taiwan;
| | - Chung-Hsin Wu
- School of Life Science, National Taiwan Normal University, Taipei 11677, Taiwan; (M.-T.S.); (Y.-S.J.); (C.-W.L.); (W.-J.W.)
- Correspondence:
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Evidence of plasma biomarkers indicating high risk of dementia in cognitively normal subjects. Sci Rep 2022; 12:1192. [PMID: 35075194 PMCID: PMC8786959 DOI: 10.1038/s41598-022-05177-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 01/07/2022] [Indexed: 11/08/2022] Open
Abstract
Subjects with comorbidities are at risk for neurodegeneration. There is a lack of a direct relationship between comorbidities and neurodegeneration. In this study, immunomagnetic reduction (IMR) assays were utilized to assay plasma Aβ1-42 and total tau protein (T-Tau) levels in poststroke (PS, n = 27), family history of Alzheimer's disease (ADFH, n = 35), diabetes (n = 21), end-stage renal disease (ESRD, n = 41), obstructive sleep apnea (OSA, n = 20), Alzheimer's disease (AD, n = 65). Thirty-seven healthy controls (HCs) were enrolled. The measured concentrations of plasma Aβ1-42 were 14.26 ± 1.42, 15.43 ± 1.76, 15.52 ± 1.60, 16.15 ± 1.05, 16.52 ± 0.59, 15.97 ± 0.54 and 20.06 ± 3.09 pg/mL in HC, PS, ADFH, diabetes, ESRD, OSA and AD groups, respectively. The corresponding concentrations of plasma T-Tau were 15.13 ± 3.62, 19.29 ± 8.01, 17.93 ± 6.26, 19.74 ± 2.92, 21.54 ± 2.72, 20.17 ± 2.77 and 41.24 ± 14.64 pg/mL. The plasma levels of Aβ1-42 and T-Tau in were significantly higher in the PS, ADFH, diabetes, ESRD and OSA groups than controls (Aβ1-42 in PS: 15.43 ± 1.76 pg/mL vs. 14.26 ± 1.42 pg/mL, p < 0.005; T-Tau in PS: 19.29 ± 8.01 vs. 15.13 ± 3.62 pg/mL, p < 0.005, Aβ1-42 in ADFH: 15.52 ± 1.60 pg/mL vs. 14.26 ± 1.42 pg/mL, p < 0.001; T-Tau in ADFH: 17.93 ± 6.26 vs. 15.13 ± 3.62 pg/mL, p < 0.005, Aβ1-42 in diabetes: 16.15 ± 1.05 pg/mL vs. 14.26 ± 1.42 pg/mL, p < 0.001; T-Tau in diabetes: 19.74 ± 2.92 vs. 15.13 ± 3.62 pg/mL, p < 0.001, Aβ1-42 in ESRD: 16.52 ± 0.59 pg/mL vs. 14.26 ± 1.42 pg/mL, p < 0.001; T-Tau in ESRD: 21.54 ± 2.72 vs. 15.13 ± 3.62 pg/mL, p < 0.001, Aβ1-42 in OSA: 15.97 ± 0.54 pg/mL vs. 14.26 ± 1.42 pg/mL, p < 0.001; T-Tau in OSA: 20.17 ± 2.77 vs. 15.13 ± 3.62 pg/mL, p < 0.001). This evidence indicates the high risk for dementia in these groups from the perspective of plasma biomarkers.
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Wu Y, Wang Z, Yin J, Yang B, Fan J, Cheng Z. Association Plasma Aβ42 Levels with Alzheimer's Disease and Its Influencing Factors in Chinese Elderly Population. Neuropsychiatr Dis Treat 2022; 18:1831-1841. [PMID: 36043117 PMCID: PMC9420413 DOI: 10.2147/ndt.s374722] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 08/17/2022] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND AND PURPOSE Intracerebral Aβ protein deposition is an important pathological mechanism of Alzheimer's disease (AD) and is one of the indicators of early diagnosis of AD. However, invasive lumbar puncture and Aβ PET are difficult to perform in primary units, resulting delays in early diagnosis of AD. In recent years, it has been found that plasma Aβ can reflect the pathological state of AD in early stage, but the results are not consistent. The objective of this study was to explore the association between plasma Aβ42 levels and AD cognitive impairment and its influencing factors in Chinese elderly population, so as to provide guidance for the clinical application of plasma Aβ42 as a blood biomarker of AD. METHODS This is a cross-sectional study based on the community population. Plasma samples were collected from 604 healthy controls (HC), 508 mild cognitive impairment (MCI) and 202 dementia with Alzheimer's type (DAT) patients from three cities. We analyzed the correlation between plasma Aβ42 levels and cognitive function and the influence of confounding factors on the relationship between plasma Aβ42 levels and AD. The independent influencing factors of plasma Aβ42 levels were determined by covariance and linear regression analysis. RESULTS Our results suggest that there is a special linear relationship between plasma Aβ42 and cognitive impairment of AD in Chinese elderly population, with Aβ42 levels slightly decreased in early AD and significantly increased in moderate-to-severe AD (P<0.01). There are many factors influencing the association between plasma Aβ42 levels and AD cognitive impairment, and sample source, gender and BMI are independent influencing factors of plasma Aβ42. CONCLUSION This indentifies that plasma Aβ42 may be a peripheral biomarker for AD screening in Chinese elderly population, but it is necessary to establish standardized detection methods and establish different demarcation criteria for various influencing factors.
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Affiliation(s)
- Yue Wu
- Department of Geriatric Psychiatry, The Affiliated Wuxi Mental Health Center of Jiangnan University, Wuxi, People's Republic of China
| | - Zhiqiang Wang
- Department of Clinical Psychology, The Affiliated Wuxi Mental Health Center of Jiangnan University, Wuxi, People's Republic of China
| | - Jiajun Yin
- Brain Science Basic Laboratory, The Affiliated Wuxi Mental Health Center of Jiangnan University, Wuxi, People's Republic of China
| | - Bixiu Yang
- Department of Clinical Psychology, The Affiliated Wuxi Mental Health Center of Jiangnan University, Wuxi, People's Republic of China
| | - Jie Fan
- Department of Geriatric Psychiatry, The Affiliated Wuxi Mental Health Center of Jiangnan University, Wuxi, People's Republic of China
| | - Zaohuo Cheng
- Department of Geriatric Psychiatry, The Affiliated Wuxi Mental Health Center of Jiangnan University, Wuxi, People's Republic of China
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Huang KL, Hsiao IT, Chang TY, Yang SY, Chang YJ, Wu HC, Liu CH, Wu YM, Lin KJ, Ho MY, Lee TH. Neurodegeneration and Vascular Burden on Cognition After Midlife: A Plasma and Neuroimaging Biomarker Study. Front Hum Neurosci 2022; 15:735063. [PMID: 34970128 PMCID: PMC8712753 DOI: 10.3389/fnhum.2021.735063] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 11/19/2021] [Indexed: 11/13/2022] Open
Abstract
Background and Objectives: Neurodegeneration and vascular burden are the two most common causes of post-stroke cognitive impairment. However, the interrelationship between the plasma beta-amyloid (Aβ) and tau protein, cortical atrophy and brain amyloid accumulation on PET imaging in stroke patients is undetermined. We aimed to explore: (1) the relationships of cortical thickness and amyloid burden on PET with plasma Aβ40, Aβ42, tau protein and their composite scores in stroke patients; and (2) the associations of post-stroke cognitive presentations with these plasma and neuroimaging biomarkers. Methods: The prospective project recruited first-ever ischemic stroke patients around 3 months after stroke onset. The plasma Aβ40, Aβ42, and total tau protein were measured with the immunomagnetic reduction method. Cortical thickness was evaluated on MRI, and cortical amyloid plaque deposition was evaluated by 18F-florbetapir PET. Cognition was evaluated with Mini-Mental State Examination (MMSE), Geriatric Depression Scale (GDS), Dementia Rating Scale-2 (DRS-2). Results: The study recruited 24 stroke patients and 13 normal controls. The plasma tau and tau*Aβ42 levels were correlated with mean cortical thickness after age adjustment. The Aβ42/Aβ40 ratio was correlated with global cortical 18F-florbetapir uptake value. The DRS-2 and GDS scores were associated with mean cortical thickness and plasma biomarkers, including Aβ42/Aβ40, tau, tau*Aβ42, tau/Aβ42, and tau/Aβ40 levels, in stroke patients. Conclusion: Plasma Aβ, tau, and their composite scores were associated with cognitive performance 3 months after stroke, and these plasma biomarkers were correlated with corresponding imaging biomarkers of neurodegeneration. Further longitudinal studies with a larger sample size are warranted to replicate the study results.
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Affiliation(s)
- Kuo-Lun Huang
- Department of Neurology, Linkou Chang Gung Memorial Hospital, and College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Ing-Tsung Hsiao
- Department of Nuclear Medicine and Molecular Imaging Center, Linkou Chang Gung Memorial Hospital, Taoyuan, Taiwan.,Healthy Aging Research Center and Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Ting-Yu Chang
- Department of Neurology, Linkou Chang Gung Memorial Hospital, and College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | | | - Yeu-Jhy Chang
- Department of Neurology, Linkou Chang Gung Memorial Hospital, and College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Hsiu-Chuan Wu
- Department of Neurology, Linkou Chang Gung Memorial Hospital, and College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Chi-Hung Liu
- Department of Neurology, Linkou Chang Gung Memorial Hospital, and College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Yi-Ming Wu
- Department of Radiology, Linkou Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Kun-Ju Lin
- Department of Nuclear Medicine and Molecular Imaging Center, Linkou Chang Gung Memorial Hospital, Taoyuan, Taiwan.,Healthy Aging Research Center and Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Meng-Yang Ho
- Department of Neurology, Linkou Chang Gung Memorial Hospital, and College of Medicine, Chang Gung University, Taoyuan, Taiwan.,Graduate Institute of Behavioral Sciences, Chang Gung University, Taoyuan, Taiwan
| | - Tsong-Hai Lee
- Department of Neurology, Linkou Chang Gung Memorial Hospital, and College of Medicine, Chang Gung University, Taoyuan, Taiwan
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Hu CJ, Chiu MJ, Pai MC, Yan SH, Wang PN, Chiu PY, Lin CH, Chen TF, Yang FC, Huang KL, Hsu YT, Hou YC, Lin WC, Lu CH, Huang LK, Yang SY. Assessment of High Risk for Alzheimer's Disease Using Plasma Biomarkers in Subjects with Normal Cognition in Taiwan: A Preliminary Study. J Alzheimers Dis Rep 2021; 5:761-770. [PMID: 34870102 PMCID: PMC8609520 DOI: 10.3233/adr-210310] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Background In Alzheimer's disease (AD), cognitive impairment begins 10-15 years later than neurodegeneration in the brain. Plasma biomarkers are promising candidates for assessing neurodegeneration in people with normal cognition. It has been reported that subjects with the concentration of plasma amyloid-β 1-42×total tau protein higher than 455 pg2/ml2 are assessed as having a high risk of amnesic mild impairment or AD, denoted as high risk of AD (HRAD). Objective The prevalence of high-risk for dementia in cognitively normal controls is explored by assaying plasma biomarkers. Methods 422 subjects with normal cognition were enrolled around Taiwan. Plasma Aβ1-40, Aβ1-42, and T-Tau levels were assayed using immunomagnetic reduction to assess the risk of dementia. Results The results showed that 4.6% of young adults (age: 20-44 years), 8.5% of middle-aged adults (age: 45-64 years), and 7.3% of elderly adults (age: 65-90 years) had HRAD. The percentage of individuals with HRAD dramatically increased in middle-aged and elderly adults compared to young adults. Conclusion The percentage of HRAD in cognitively normal subjects are approximately 10%, which reveals that the potentially public-health problem of AD in normal population. Although the subject having abnormal levels of Aβ or tau is not definitely going on to develop cognitive declines or AD, the risk of suffering cognitive impairment in future is relatively high. Suitable managements are suggested for these high-risk cognitively normal population. Worth noting, attention should be paid to preventing cognitive impairment due to AD, not only in elderly adults but also middle-aged adults.
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Affiliation(s)
- Chaur-Jong Hu
- Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.,Department of Neurology, Dementia Center, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan.,Graduate Institute of Neural Regenerative Medicine, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.,Taipei Neuroscience Institute, Taipei Medical University, Taipei, Taiwan
| | - Ming-Jang Chiu
- Department of Neurology, National Taiwan University Hospital, College of Medicine, National Taiwan University, Taipei, Taiwan.,Graduate Institute of Brain and Mind Sciences, College of Medicine, National Taiwan University, Taipei, Taiwan.,Department of Psychology, National Taiwan University, Taipei, Taiwan.,Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
| | - Ming-Chyi Pai
- Division of Behavioral Neurology, Department of Neurology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Sui-Hing Yan
- Department of Neurology, Far Eastern Memorial Hospital, New Taipei City, Taiwan
| | - Pei-Ning Wang
- Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan.,Department of Neurology, School of Medicine, National Yang-Ming University, Taipei, Taiwan.,Brain Research Center, National Yang-Ming University, Taipei, Taiwan
| | - Pai-Yi Chiu
- Department of Neurology, Show Chwan Memorial Hospital, Chunghwa, Taiwan.,MR-guided Focus Ultrasound Center, Chang Bin Show Chwan Memorial Hospital, Chunghwa, Taiwan
| | - Chin-Hsien Lin
- Department of Neurology, National Taiwan University Hospital, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Ta-Fu Chen
- Department of Neurology, National Taiwan University Hospital, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Fu-Chi Yang
- Department of Neurology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Kuo-Lun Huang
- Department of Neurology, Linkou Chang Gung Memorial Hospital, and College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Yi-Ting Hsu
- Department of Neurology, China Medical University Hospital, China Medical University, Taichung, Taiwan
| | - Yi-Chou Hou
- Graduate Institute of Clinical Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.,Department of Internal Medicine, Cardinal Tien Hospital, New Taipei City, Taiwan.,School of Medicine, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Wei-Che Lin
- Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Cheng-Hsien Lu
- Department of Neurology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Li-Kai Huang
- Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.,Department of Neurology, Dementia Center, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
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47
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Bhargavan B, Woollard SM, McMillan JE, Kanmogne GD. CCR5 antagonist reduces HIV-induced amyloidogenesis, tau pathology, neurodegeneration, and blood-brain barrier alterations in HIV-infected hu-PBL-NSG mice. Mol Neurodegener 2021; 16:78. [PMID: 34809709 PMCID: PMC8607567 DOI: 10.1186/s13024-021-00500-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Accepted: 11/03/2021] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Neurocognitive impairment is present in 50% of HIV-infected individuals and is often associated with Alzheimer's Disease (AD)-like brain pathologies, including increased amyloid-beta (Aβ) and Tau hyperphosphorylation. Here, we aimed to determine whether HIV-1 infection causes AD-like pathologies in an HIV/AIDS humanized mouse model, and whether the CCR5 antagonist maraviroc alters HIV-induced pathologies. METHODS NOD/scid-IL-2Rγcnull mice engrafted with human blood leukocytes were infected with HIV-1, left untreated or treated with maraviroc (120 mg/kg twice/day). Human cells in animal's blood were quantified weekly by flow cytometry. Animals were sacrificed at week-3 post-infection; blood and tissues viral loads were quantified using p24 antigen ELISA, RNAscope, and qPCR. Human (HLA-DR+) cells, Aβ-42, phospho-Tau, neuronal markers (MAP 2, NeuN, neurofilament-L), gamma-secretase activating protein (GSAP), and blood-brain barrier (BBB) tight junction (TJ) proteins expression and transcription were quantified in brain tissues by immunohistochemistry, immunofluorescence, immunoblotting, and qPCR. Plasma Aβ-42, Aβ-42 cellular uptake, release and transendothelial transport were quantified by ELISA. RESULTS HIV-1 significantly decreased human (h)CD4+ T-cells and hCD4/hCD8 ratios; decreased the expression of BBB TJ proteins claudin-5, ZO-1, ZO-2; and increased HLA-DR+ cells in brain tissues. Significantly, HIV-infected animals showed increased plasma and brain Aβ-42 and phospho-Tau (threonine181, threonine231, serine396, serine199), associated with transcriptional upregulation of GSAP, an enzyme that catalyzes Aβ formation, and loss of MAP 2, NeuN, and neurofilament-L. Maraviroc treatment significantly reduced blood and brain viral loads, prevented HIV-induced loss of neuronal markers and TJ proteins; decreased HLA-DR+ cells infiltration in brain tissues, significantly reduced HIV-induced increase in Aβ-42, GSAP, and phospho-Tau. Maraviroc also reduced Aβ retention and increased Aβ release in human macrophages; decreased the receptor for advanced glycation end products (RAGE) and increased low-density lipoprotein receptor-related protein-1 (LRP1) expression in human brain endothelial cells. Maraviroc induced Aβ transendothelial transport, which was blocked by LRP1 antagonist but not RAGE antagonist. CONCLUSIONS Maraviroc significantly reduced HIV-induced amyloidogenesis, GSAP, phospho-Tau, neurodegeneration, BBB alterations, and leukocytes infiltration into the CNS. Maraviroc increased cellular Aβ efflux and transendothelial Aβ transport via LRP1 pathways. Thus, therapeutically targeting CCR5 could reduce viremia, preserve the BBB and neurons, increased brain Aβ efflux, and reduce AD-like neuropathologies.
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Affiliation(s)
- Biju Bhargavan
- Department of Pharmacology and Experimental Neuroscience, College of Medicine, University of Nebraska Medical Center, 985800 Nebraska Medical Center, Omaha, NE 68198-5800 USA
| | - Shawna M. Woollard
- Department of Pharmacology and Experimental Neuroscience, College of Medicine, University of Nebraska Medical Center, 985800 Nebraska Medical Center, Omaha, NE 68198-5800 USA
- Huvepharma, 421 W Industrial Lake Drive, Lincoln, NE 68528 USA
| | - Jo Ellyn McMillan
- Department of Pharmacology and Experimental Neuroscience, College of Medicine, University of Nebraska Medical Center, 985800 Nebraska Medical Center, Omaha, NE 68198-5800 USA
| | - Georgette D. Kanmogne
- Department of Pharmacology and Experimental Neuroscience, College of Medicine, University of Nebraska Medical Center, 985800 Nebraska Medical Center, Omaha, NE 68198-5800 USA
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48
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Jiao B, Liu H, Guo L, Liao X, Zhou Y, Weng L, Xiao X, Zhou L, Wang X, Jiang Y, Yang Q, Zhu Y, Zhou L, Zhang W, Wang J, Yan X, Tang B, Shen L. Performance of Plasma Amyloid β, Total Tau, and Neurofilament Light Chain in the Identification of Probable Alzheimer's Disease in South China. Front Aging Neurosci 2021; 13:749649. [PMID: 34776933 PMCID: PMC8579066 DOI: 10.3389/fnagi.2021.749649] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 09/24/2021] [Indexed: 12/31/2022] Open
Abstract
Background: Alzheimer's disease (AD) is the most common type of dementia and has no effective treatment to date. It is essential to develop a minimally invasive blood-based biomarker as a tool for screening the general population, but the efficacy remains controversial. This cross-sectional study aimed to evaluate the ability of plasma biomarkers, including amyloid β (Aβ), total tau (t-tau), and neurofilament light chain (NfL), to detect probable AD in the South Chinese population. Methods: A total of 277 patients with a clinical diagnosis of probable AD and 153 healthy controls with normal cognitive function (CN) were enrolled in this study. The levels of plasma Aβ42, Aβ40, t-tau, and NfL were detected using ultra-sensitive immune-based assays (SIMOA). Lumbar puncture was conducted in 89 patients with AD to detect Aβ42, Aβ40, t-tau, and phosphorylated (p)-tau levels in the cerebrospinal fluid (CSF) and to evaluate the consistency between plasma and CSF biomarkers through correlation analysis. Finally, the diagnostic value of plasma biomarkers was further assessed by constructing a receiver operating characteristic (ROC) curve. Results: After adjusting for age, sex, and the apolipoprotein E (APOE) alleles, compared to the CN group, the plasma t-tau, and NfL were significantly increased in the AD group (p < 0.01, Bonferroni correction). Correlation analysis showed that only the plasma t-tau level was positively correlated with the CSF t-tau levels (r = 0.319, p = 0.003). The diagnostic model combining plasma t-tau and NfL levels, and age, sex, and APOE alleles, showed the best performance for the identification of probable AD [area under the curve (AUC) = 0.89, sensitivity = 82.31%, specificity = 83.66%]. Conclusion: Blood biomarkers can effectively distinguish patients with probable AD from controls and may be a non-invasive and efficient method for AD pre-screening.
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Affiliation(s)
- Bin Jiao
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Central South University, Changsha, China.,Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, China
| | - Hui Liu
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Lina Guo
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Xinxin Liao
- National Clinical Research Center for Geriatric Disorders, Central South University, Changsha, China.,Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, China.,Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, China
| | - Yafang Zhou
- National Clinical Research Center for Geriatric Disorders, Central South University, Changsha, China.,Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, China.,Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, China
| | - Ling Weng
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Central South University, Changsha, China.,Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, China
| | - Xuewen Xiao
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Lu Zhou
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Xin Wang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Yaling Jiang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Qijie Yang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Yuan Zhu
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Lin Zhou
- National Clinical Research Center for Geriatric Disorders, Central South University, Changsha, China.,Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, China.,Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, China
| | - Weiwei Zhang
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Junling Wang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Central South University, Changsha, China.,Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, China
| | - Xinxiang Yan
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Central South University, Changsha, China.,Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, China
| | - Beisha Tang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Central South University, Changsha, China.,Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, China
| | - Lu Shen
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Central South University, Changsha, China.,Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, China.,Key Laboratory of Organ Injury, Aging and Regenerative Medicine of Hunan Province, Changsha, China.,Engineering Research Center of Hunan Province in Cognitive Impairment Disorders, Central South University, Changsha, China.,Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic Diseases, Changsha, China
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49
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Chong JR, Ashton NJ, Karikari TK, Tanaka T, Schöll M, Zetterberg H, Blennow K, Chen CP, Lai MKP. Blood-based high sensitivity measurements of beta-amyloid and phosphorylated tau as biomarkers of Alzheimer's disease: a focused review on recent advances. J Neurol Neurosurg Psychiatry 2021; 92:1231-1241. [PMID: 34510001 DOI: 10.1136/jnnp-2021-327370] [Citation(s) in RCA: 54] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Accepted: 08/30/2021] [Indexed: 01/11/2023]
Abstract
Discovery and development of clinically useful biomarkers for Alzheimer's disease (AD) and related dementias have been the focus of recent research efforts. While cerebrospinal fluid and positron emission tomography or MRI-based neuroimaging markers have made the in vivo detection of AD pathology and its consequences possible, the high cost and invasiveness have limited their widespread use in the clinical setting. On the other hand, advances in potentially more accessible blood-based biomarkers had been impeded by lack of sensitivity in detecting changes in markers of the hallmarks of AD, including amyloid-β (Aβ) peptides and phosphorylated tau (P-tau). More recently, however, emerging technologies with superior sensitivity and specificity for measuring Aβ and P-tau have reported high concordances with AD severity. In this focused review, we describe several emerging technologies, including immunoprecipitation-mass spectrometry (IP-MS), single molecule array and Meso Scale Discovery immunoassay platforms, and appraise the current literature arising from their use to identify plaques, tangles and other AD-associated pathology. While there is potential clinical utility in adopting these technologies, we also highlight the further studies needed to establish Aβ and P-tau as blood-based biomarkers for AD, including validation with existing large sample sets, new independent cohorts from diverse backgrounds as well as population-based longitudinal studies. In conclusion, the availability of sensitive and reliable measurements of Aβ peptides and P-tau species in blood holds promise for the diagnosis, prognosis and outcome assessments in clinical trials for AD.
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Affiliation(s)
- Joyce R Chong
- Memory, Aging and Cognition Centre, National University Health Systems, Singapore.,Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Nicholas J Ashton
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden.,Psychology and Neuroscience, King's College London, Institute of Psychiatry, Maurice Wohl Institute Clinical Neuroscience Institute, London, UK.,NIHR Biomedical Research Centre for Mental Health and Biomedical Research Unit for Dementia, South London and Maudsley NHS Foundation, London, UK.,Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
| | - Thomas K Karikari
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden.,Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Tomotaka Tanaka
- Memory, Aging and Cognition Centre, National University Health Systems, Singapore.,Department of Neurology, National Cerebral and Cardiovascular Center, Suita, Osaka, Japan.,Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Michael Schöll
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden.,Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden.,Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden.,Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Gothenburg, Sweden.,UK Dementia Research Institute at UCL, University College London, London, UK
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden.,UK Dementia Research Institute at UCL, University College London, London, UK
| | - Christopher P Chen
- Memory, Aging and Cognition Centre, National University Health Systems, Singapore.,Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Mitchell K P Lai
- Memory, Aging and Cognition Centre, National University Health Systems, Singapore .,Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
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50
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Park S, Kim Y. Bias-generating factors in biofluid amyloid-β measurements for Alzheimer's disease diagnosis. Biomed Eng Lett 2021; 11:287-295. [PMID: 34616582 DOI: 10.1007/s13534-021-00201-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 08/05/2021] [Accepted: 08/08/2021] [Indexed: 01/03/2023] Open
Abstract
Alzheimer's disease (AD) is the most prevalent cause of dementia worldwide, yet the dearth of readily accessible diagnostic biomarkers is a substantial hindrance towards progressing to effective preventive and therapeutic approaches. Due to a long delay between cerebral amyloid-β (Aβ) accumulation and the onset of cognitive impairments, biomarkers that reflect Aβ pathology and enable routine screening for disease progression are of urgent need for application in the clinical diagnosis of AD. According to accumulating evidences, cerebrospinal fluid (CSF) and plasma offer windows to the brain as they allow monitoring of biochemical changes in the brain. Considering the high availability and accuracy in depicting Aβ deposition in the brain, Aβ levels in CSF and plasma are regarded as promising fluid biomarkers for the diagnosis of AD patients at an early stage. However, clinical data with intra- and interindividual variations in the concentrations of CSF and plasma Aβ implicate the need to reevaluate current Aβ detection methods and establish a standardized operating procedure. Therefore, this review introduces three bias-generating factors in biofluid Aβ measurement that may hamper the accurate Aβ quantification and how such complications can be overcome for the widespread implementation of fluid Aβ detection in clinical practice.
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Affiliation(s)
- Sohui Park
- Department of Pharmacy, Department of Integrative Biotechnology and Translational Medicine, and Yonsei Institute of Pharmaceutical Sciences, College of Pharmacy, Yonsei University, Incheon, 21983 Republic of Korea
| | - YoungSoo Kim
- Department of Pharmacy, Department of Integrative Biotechnology and Translational Medicine, and Yonsei Institute of Pharmaceutical Sciences, College of Pharmacy, Yonsei University, Incheon, 21983 Republic of Korea
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