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Thurston RC, Maki P, Chang Y, Wu M, Aizenstein HJ, Derby CA, Karikari TK. Menopausal vasomotor symptoms and plasma Alzheimer disease biomarkers. Am J Obstet Gynecol 2024; 230:342.e1-342.e8. [PMID: 37939982 PMCID: PMC10939914 DOI: 10.1016/j.ajog.2023.11.002] [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: 07/20/2023] [Revised: 10/30/2023] [Accepted: 11/01/2023] [Indexed: 11/10/2023]
Abstract
BACKGROUND Identifying risk factors for Alzheimer disease in women is important as women compose two-thirds of individuals with Alzheimer disease. Previous work links vasomotor symptoms, the cardinal menopausal symptom, with poor memory performance and alterations in brain structure, function, and connectivity. These associations are evident when vasomotor symptoms are monitored objectively with ambulatory skin conductance monitors. OBJECTIVE This study aimed to determine whether vasomotor symptoms are associated with Alzheimer disease biomarkers. STUDY DESIGN Between 2017 and 2020, the MsBrain study enrolled 274 community-dwelling women aged 45 to 67 years who had a uterus and at least 1 ovary and were late perimenopausal or postmenopausal status. The key exclusion criteria included neurologic disorder, surgical menopause, and recent use of hormonal or nonhormonal vasomotor symptom treatment. Women underwent 24 hours of ambulatory skin conductance monitoring to assess vasomotor symptoms. Plasma concentrations of Alzheimer disease biomarkers, including amyloid β 42-to-amyloid β 40 ratio, phosphorylated tau (181 and 231), glial fibrillary acidic protein, and neurofilament light, were measured using a single-molecule array (Simoa) technology. Associations between vasomotor symptoms and Alzheimer disease biomarkers were assessed via linear regression models adjusted for age, race and ethnicity, education, body mass index, and apolipoprotein E4 status. Additional models adjusted for estradiol and sleep. RESULTS A total of 248 (mean age, 59.06 years; 81% White; 99% postmenopausal status) of enrolled MsBrain participants contributed data. Objectively assessed vasomotor symptoms occurring during sleep were associated with significantly lower amyloid β 42/amyloid β 40, (beta, -.0010 [standard error, .0004]; P=.018; multivariable), suggestive of greater brain amyloid β pathology. The findings remained significant after additional adjustments for estradiol and sleep. CONCLUSION Nighttime vasomotor symptoms may be a marker of women at risk of Alzheimer disease. It is yet unknown if these associations are causal.
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Affiliation(s)
- Rebecca C Thurston
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA; Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA; Department of Psychology, University of Pittsburgh, Pittsburgh, PA.
| | - Pauline Maki
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL; Department of Psychology, University of Illinois at Chicago, Chicago, IL; Department of Obstetrics and Gynecology, University of Illinois at Chicago, Chicago, IL
| | - Yuefang Chang
- Department of Neurosurgery, University of Pittsburgh, Pittsburgh, PA
| | - Minjie Wu
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA
| | | | - Carol A Derby
- Departments of Neurology and Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY
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Wu LY, Chai YL, Cheah IK, Chia RSL, Hilal S, Arumugam TV, Chen CP, Lai MKP. Blood-based biomarkers of cerebral small vessel disease. Ageing Res Rev 2024; 95:102247. [PMID: 38417710 DOI: 10.1016/j.arr.2024.102247] [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: 04/10/2023] [Revised: 02/12/2024] [Accepted: 02/22/2024] [Indexed: 03/01/2024]
Abstract
Age-associated cerebral small vessel disease (CSVD) represents a clinically heterogenous condition, arising from diverse microvascular mechanisms. These lead to chronic cerebrovascular dysfunction and carry a substantial risk of subsequent stroke and vascular cognitive impairment in aging populations. Owing to advances in neuroimaging, in vivo visualization of cerebral vasculature abnormities and detection of CSVD, including lacunes, microinfarcts, microbleeds and white matter lesions, is now possible, but remains a resource-, skills- and time-intensive approach. As a result, there has been a recent proliferation of blood-based biomarker studies for CSVD aimed at developing accessible screening tools for early detection and risk stratification. However, a good understanding of the pathophysiological processes underpinning CSVD is needed to identify and assess clinically useful biomarkers. Here, we provide an overview of processes associated with CSVD pathogenesis, including endothelial injury and dysfunction, neuroinflammation, oxidative stress, perivascular neuronal damage as well as cardiovascular dysfunction. Then, we review clinical studies of the key biomolecules involved in the aforementioned processes. Lastly, we outline future trends and directions for CSVD biomarker discovery and clinical validation.
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Affiliation(s)
- Liu-Yun Wu
- Memory Aging and Cognition Centre, National University Health System, Singapore; Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Yuek Ling Chai
- Memory Aging and Cognition Centre, National University Health System, Singapore; Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Irwin K Cheah
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Neurobiology Programme, Centre for Life Sciences, National University of Singapore, Singapore
| | - Rachel S L Chia
- Memory Aging and Cognition Centre, National University Health System, Singapore; Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Saima Hilal
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Kent Ridge, Singapore
| | - Thiruma V Arumugam
- School of Pharmacy, Sungkyunkwan University, Suwon, Republic of Korea; Centre for Cardiovascular Biology and Disease Research, Department of Microbiology, Anatomy, Physiology and Pharmacology, School of Agriculture, Biomedicine and Environment, La Trobe University, Bundoora, VIC, Australia
| | - Christopher P Chen
- Memory Aging and Cognition Centre, National University Health System, 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 System, Singapore; Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
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Xiong X, He H, Ye Q, Qian S, Zhou S, Feng F, Fang EF, Xie C. Alzheimer's disease diagnostic accuracy by fluid and neuroimaging ATN framework. CNS Neurosci Ther 2024; 30:e14357. [PMID: 37438991 PMCID: PMC10848089 DOI: 10.1111/cns.14357] [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] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Revised: 06/21/2023] [Accepted: 07/01/2023] [Indexed: 07/14/2023] Open
Abstract
OBJECTIVES The ATN's different modalities (fluids and neuroimaging) for each of the Aβ (A), tau (T), and neurodegeneration (N) elements are used for the biological diagnosis of Alzheimer's disease (AD). We aim to identify which ATN category achieves the highest potential for diagnosis and predictive accuracy of longitudinal cognitive decline. METHODS Based on the availability of plasma ATN biomarkers (plasma-derived Aβ42/40 , p-tau181, NFL, respectively), CSF ATN biomarkers (CSF-derived Aβ42 /Aβ40 , p-tau181, NFL), and neuroimaging ATN biomarkers (18F-florbetapir (FBP) amyloid-PET, 18F-flortaucipir (FTP) tau-PET, and fluorodeoxyglucose (FDG)-PET), a total of 2340 participants were selected from ADNI. RESULTS Our data analysis indicates that the area under curves (AUCs) of CSF-A, neuroimaging-T, and neuroimaging-N were ranked the top three ATN candidates for accurate diagnosis of AD. Moreover, neuroimaging ATN biomarkers display the best predictive ability for longitudinal cognitive decline among the three categories. To note, neuroimaging-T correlates well with cognitive performances in a negative correlation manner. Meanwhile, participants in the "N" element positive group, especially the CSF-N positive group, experience the fastest cognitive decline compared with other groups defined by ATN biomarkers. In addition, the voxel-wise analysis showed that CSF-A related to tau accumulation and FDG-PET indexes more strongly in subjects with MCI stage. According to our analysis of the data, the best three ATN candidates for a precise diagnosis of AD are CSF-A, neuroimaging-T, and neuroimaging-N. CONCLUSIONS Collectively, our findings suggest that plasma, CSF, and neuroimaging biomarkers differ considerably within the ATN framework; the most accurate target biomarkers for diagnosing AD were the CSF-A, neuroimaging-T, and neuroimaging-N within each ATN modality. Moreover, neuroimaging-T and CSF-N both show excellent ability in the prediction of cognitive decline in two different dimensions.
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Affiliation(s)
- Xi Xiong
- Department of NeurologyThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouChina
| | - Haijun He
- Department of NeurologyThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouChina
| | - Qianqian Ye
- Department of NeurologyThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouChina
| | - Shuangjie Qian
- Department of NeurologyThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouChina
| | - Shuoting Zhou
- Department of NeurologyThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouChina
| | - Feifei Feng
- Department of NeurologyThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouChina
| | - Evandro F. Fang
- Department of Clinical Molecular BiologyAkershus University Hospital, University of OsloLørenskogNorway
- The Norwegian Centre on Healthy Ageing (NO‐Age)OsloNorway
| | - Chenglong Xie
- Department of NeurologyThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouChina
- Key Laboratory Of Alzheimer's Disease Of Zhejiang ProvinceWenzhouChina
- Institute of AgingWenzhou Medical UniversityWenzhouChina
- Key Laboratory of Intelligent Treatment and Life Support for Critical Diseases of Zhejiang ProvinceWenzhouChina
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Huang L, Li Q, Lu Y, Pan F, Cui L, Wang Y, Miao Y, Chen T, Li Y, Wu J, Chen X, Jia J, Guo Q. Consensus on rapid screening for prodromal Alzheimer's disease in China. Gen Psychiatr 2024; 37:e101310. [PMID: 38313393 PMCID: PMC10836380 DOI: 10.1136/gpsych-2023-101310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 12/19/2023] [Indexed: 02/06/2024] Open
Abstract
Alzheimer's disease (AD) is a common cause of dementia, characterised by cerebral amyloid-β deposition, pathological tau and neurodegeneration. The prodromal stage of AD (pAD) refers to patients with mild cognitive impairment (MCI) and evidence of AD's pathology. At this stage, disease-modifying interventions should be used to prevent the progression to dementia. Given the inherent heterogeneity of MCI, more specific biomarkers are needed to elucidate the underlying AD's pathology. Although the uses of cerebrospinal fluid and positron emission tomography are widely accepted methods for detecting AD's pathology, their clinical applications are limited by their high costs and invasiveness, particularly in low-income areas in China. Therefore, to improve the early detection of Alzheimer's disease (AD) pathology through cost-effective screening methods, a panel of 45 neurologists, psychiatrists and gerontologists was invited to establish a formal consensus on the screening of pAD in China. The supportive evidence and grades of recommendations are based on a systematic literature review and focus group discussion. National meetings were held to allow participants to review, vote and provide their expert opinions to reach a consensus. A majority (two-thirds) decision was used for questions for which consensus could not be reached. Recommended screening methods are presented in this publication, including neuropsychological assessment, peripheral biomarkers and brain imaging. In addition, a general workflow for screening pAD in China is established, which will help clinicians identify individuals at high risk and determine therapeutic targets.
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Affiliation(s)
- Lin Huang
- Department of Gerontology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qinjie Li
- Department of Gerontology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yao Lu
- Department of Gerontology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Fengfeng Pan
- Department of Gerontology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Liang Cui
- Department of Gerontology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ying Wang
- Department of Gerontology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ya Miao
- Department of Gerontology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tianlu Chen
- Center for Translational Medicine and Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yatian Li
- Shanghai BestCovered, Shanghai, China
| | | | - Xiaochun Chen
- Department of Neurology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Jianping Jia
- Department of Neurology, National Clinical Research Center for Geriatric Diseases, Beijing, China
| | - Qihao Guo
- Department of Gerontology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
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5
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Adewale BA, Coker MM, Ogunniyi A, Kalaria RN, Akinyemi RO. Biomarkers and Risk Assessment of Alzheimer's Disease in Low- and Middle-Income Countries. J Alzheimers Dis 2023; 95:1339-1349. [PMID: 37694361 DOI: 10.3233/jad-221030] [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: 09/12/2023]
Abstract
Dementia is a chronic syndrome which is common among the elderly and is associated with significant morbidity and mortality for patients and their caregivers. Alzheimer's disease (AD), the most common form of clinical dementia, is biologically characterized by the deposition of amyloid-β plaques and neurofibrillary tangles in the brain. The onset of AD begins decades before manifestation of symptoms and clinical diagnosis, underlining the need to shift from clinical diagnosis of AD to a more objective diagnosis using biomarkers. Having performed a literature search of original articles and reviews on PubMed and Google Scholar, we present this review detailing the existing biomarkers and risk assessment tools for AD. The prevalence of dementia in low- and middle-income countries (LMICs) is predicted to increase over the next couple of years. Thus, we aimed to identify potential biomarkers that may be appropriate for use in LMICs, considering the following factors: sensitivity, specificity, invasiveness, and affordability of the biomarkers. We also explored risk assessment tools and the potential use of artificial intelligence/machine learning solutions for diagnosing, assessing risks, and monitoring the progression of AD in low-resource settings. Routine use of AD biomarkers has yet to gain sufficient ground in clinical settings. Therefore, clinical diagnosis of AD will remain the mainstay in LMICs for the foreseeable future. Efforts should be made towards the development of low-cost, easily administered risk assessment tools to identify individuals who are at risk of AD in the population. We recommend that stakeholders invest in education, research and development targeted towards effective risk assessment and management.
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Affiliation(s)
- Boluwatife Adeleye Adewale
- Faculty of Clinical Sciences, College of Medicine, University of Ibadan, Ibadan, Nigeria
- Neuroscience and Ageing Research Unit, Institute for Advanced Medical Research and Training (IAMRAT), College of Medicine, University of Ibadan, Ibadan, Nigeria
| | - Motunrayo Mojoyin Coker
- Neuroscience and Ageing Research Unit, Institute for Advanced Medical Research and Training (IAMRAT), College of Medicine, University of Ibadan, Ibadan, Nigeria
| | - Adesola Ogunniyi
- Neuroscience and Ageing Research Unit, Institute for Advanced Medical Research and Training (IAMRAT), College of Medicine, University of Ibadan, Ibadan, Nigeria
- Department of Neurology, University College Hospital, Ibadan, Nigeria
| | - Rajesh N Kalaria
- Neuroscience and Ageing Research Unit, Institute for Advanced Medical Research and Training (IAMRAT), College of Medicine, University of Ibadan, Ibadan, Nigeria
- Translational and Clinical Research Institute, Newcastle University, United Kingdom
| | - Rufus Olusola Akinyemi
- Neuroscience and Ageing Research Unit, Institute for Advanced Medical Research and Training (IAMRAT), College of Medicine, University of Ibadan, Ibadan, Nigeria
- Department of Neurology, University College Hospital, Ibadan, Nigeria
- Centre for Genomic and Precision Medicine, College of Medicine, University of Ibadan, Ibadan, Nigeria
- Translational and Clinical Research Institute, Newcastle University, United Kingdom
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Brand AL, Lawler PE, Bollinger JG, Li Y, Schindler SE, Li M, Lopez S, Ovod V, Nakamura A, Shaw LM, Zetterberg H, Hansson O, Bateman RJ. The performance of plasma amyloid beta measurements in identifying amyloid plaques in Alzheimer's disease: a literature review. Alzheimers Res Ther 2022; 14:195. [PMID: 36575454 PMCID: PMC9793600 DOI: 10.1186/s13195-022-01117-1] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 11/06/2022] [Indexed: 12/28/2022]
Abstract
The extracellular buildup of amyloid beta (Aβ) plaques in the brain is a hallmark of Alzheimer's disease (AD). Detection of Aβ pathology is essential for AD diagnosis and for identifying and recruiting research participants for clinical trials evaluating disease-modifying therapies. Currently, AD diagnoses are usually made by clinical assessments, although detection of AD pathology with positron emission tomography (PET) scans or cerebrospinal fluid (CSF) analysis can be used by specialty clinics. These measures of Aβ aggregation, e.g. plaques, protofibrils, and oligomers, are medically invasive and often only available at specialized medical centers or not covered by medical insurance, and PET scans are costly. Therefore, a major goal in recent years has been to identify blood-based biomarkers that can accurately detect AD pathology with cost-effective, minimally invasive procedures.To assess the performance of plasma Aβ assays in predicting amyloid burden in the central nervous system (CNS), this review compares twenty-one different manuscripts that used measurements of 42 and 40 amino acid-long Aβ (Aβ42 and Aβ40) in plasma to predict CNS amyloid status. Methodologies that quantitate Aβ42 and 40 peptides in blood via immunoassay or immunoprecipitation-mass spectrometry (IP-MS) were considered, and their ability to distinguish participants with amyloidosis compared to amyloid PET and CSF Aβ measures as reference standards was evaluated. Recent studies indicate that some IP-MS assays perform well in accurately and precisely measuring Aβ and detecting brain amyloid aggregates.
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Affiliation(s)
- Abby L. Brand
- grid.4367.60000 0001 2355 7002Department of Neurology, Washington University School of Medicine, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002The Tracy Family SILQ Center, Washington University School of Medicine, St. Louis, MO USA
| | - Paige E. Lawler
- grid.4367.60000 0001 2355 7002Department of Neurology, Washington University School of Medicine, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002The Tracy Family SILQ Center, Washington University School of Medicine, St. Louis, MO USA
| | - James G. Bollinger
- grid.4367.60000 0001 2355 7002Department of Neurology, Washington University School of Medicine, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002The Tracy Family SILQ Center, Washington University School of Medicine, St. Louis, MO USA
| | - Yan Li
- grid.4367.60000 0001 2355 7002Department of Neurology, Washington University School of Medicine, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002The Tracy Family SILQ Center, Washington University School of Medicine, St. Louis, MO USA
| | - Suzanne E. Schindler
- grid.4367.60000 0001 2355 7002Department of Neurology, Washington University School of Medicine, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002Knight Alzheimer’s Disease Research Center, Washington University School of Medicine, St. Louis, MO USA
| | - Melody Li
- grid.4367.60000 0001 2355 7002Department of Neurology, Washington University School of Medicine, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002The Tracy Family SILQ Center, Washington University School of Medicine, St. Louis, MO USA
| | - Samir Lopez
- grid.4367.60000 0001 2355 7002Department of Neurology, Washington University School of Medicine, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002The Tracy Family SILQ Center, Washington University School of Medicine, St. Louis, MO USA
| | - Vitaliy Ovod
- grid.4367.60000 0001 2355 7002Department of Neurology, Washington University School of Medicine, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002The Tracy Family SILQ Center, Washington University School of Medicine, St. Louis, MO USA
| | - Akinori Nakamura
- grid.419257.c0000 0004 1791 9005Department of Biomarker Research, National Center for Geriatrics and Gerontology, Obu, Japan ,grid.27476.300000 0001 0943 978XDepartment of Cognition and Behavior Science, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Leslie M. Shaw
- grid.25879.310000 0004 1936 8972Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
| | - Henrik Zetterberg
- grid.8761.80000 0000 9919 9582Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden ,grid.1649.a000000009445082XClinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden ,grid.83440.3b0000000121901201Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK ,grid.83440.3b0000000121901201UK Dementia Research Institute at UCL, London, UK ,grid.24515.370000 0004 1937 1450Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, China
| | - Oskar Hansson
- grid.4514.40000 0001 0930 2361Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden ,grid.411843.b0000 0004 0623 9987Memory Clinic, Skåne University Hospital, Lund, Sweden
| | - Randall J. Bateman
- grid.4367.60000 0001 2355 7002Department of Neurology, Washington University School of Medicine, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002The Tracy Family SILQ Center, Washington University School of Medicine, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002Knight Alzheimer’s Disease Research Center, Washington University School of Medicine, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO USA
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Pang T, Chong EJY, Wong ZX, Chew KA, Venketasubramanian N, Chen C, Xu X. Validation of the Informant Quick Dementia Rating System (QDRS) among Older Adults in Singapore. J Alzheimers Dis 2022; 89:1323-1330. [DOI: 10.3233/jad-220520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: The Quick Dementia Rating System (QDRS) is a brief and rapid tool that can be administered by an informant without the need for a trained assessor. Objective: Our objective was to examine the validity, reliability, and cost-effectiveness of the informant QDRS in a Singapore memory clinic sample. Methods: We assessed a total of 177 older adults, among whom, 32 had no cognitive impairment (NCI), 61 had mild cognitive impairment (MCI), and 84 had dementia. Elderly underwent 1) the informant QDRS, 2) the Clinical Dementia Rating (CDR) as the gold standard diagnosis, 3) the Mini-Mental State Examination (MMSE), and 4) the Ascertain Dementia 8 (AD8) as comparisons to the QDRS. The extent to which the QDRS may reduce the recruitment cost (time) of clinical trials was also calculated. Results: The QDRS had excellent internal consistency (Cronbach alpha = 0.939). It correlated highly with the CDR-global (R = 0.897), CDR Sum-of-Boxes (R = 0.915), MMSE (R = –0.848), and the AD8 (R = 0.747), showing good concurrent validity. With an optimal cut-off of 1.5 for MCI (sensitivity 85.2%, specificity 96.3%) and 6 for dementia (sensitivity 90.1%, specificity 89.2%), the QDRS achieved a higher overall accuracy of 85.0%, as compared to MMSE (71.2%) and AD8 (73.4%). A simulated clinical trial recruitment scenario demonstrated that pre-screening with the QDRS followed by a confirmatory CDR would reduce the time needed to identify NCI subjects by 23.3% and MCI subjects by 75.3%. Conclusion: The QDRS is a reliable cognitive impairment screening tool which is suitable for informant-administration, especially for identification of MCI.
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Affiliation(s)
- Ting Pang
- School of Public Health and the 2nd Affiliated Hospital of School of Medicine, Zhejiang University, China
| | - Eddie Jun Yi Chong
- Memory, Ageing and Cognition Centre, Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Zi Xuen Wong
- Memory, Ageing and Cognition Centre, Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Kimberly Ann Chew
- Memory, Ageing and Cognition Centre, Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Narayanaswamy Venketasubramanian
- Memory, Ageing and Cognition Centre, Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Christopher Chen
- Memory, Ageing and Cognition Centre, Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Xin Xu
- School of Public Health and the 2nd Affiliated Hospital of School of Medicine, Zhejiang University, China
- Memory, Ageing and Cognition Centre, Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
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Current trends in blood biomarker detection and imaging for Alzheimer’s disease. Biosens Bioelectron 2022; 210:114278. [DOI: 10.1016/j.bios.2022.114278] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 03/21/2022] [Accepted: 04/09/2022] [Indexed: 12/28/2022]
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9
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Pan FF, Huang Q, Wang Y, Wang YF, Guan YH, Xie F, Guo QH. Non-linear Character of Plasma Amyloid Beta Over the Course of Cognitive Decline in Alzheimer’s Continuum. Front Aging Neurosci 2022; 14:832700. [PMID: 35401142 PMCID: PMC8984285 DOI: 10.3389/fnagi.2022.832700] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 02/28/2022] [Indexed: 12/14/2022] Open
Abstract
Plasma amyloid-β (Aβ) was associated with brain Aβ deposition and Alzheimer’s disease (AD) development. However, changes of plasma Aβ over the course of cognitive decline in the Alzheimer’s continuum remained uncertain. We recruited 449 participants to this study, including normal controls (NC), subjective cognitive decline (SCD), mild cognitive impairment (MCI), AD, and non-AD dementia. All the participants underwent plasma Aβ42, Aβ40, and t-tau measurements with single-molecule array (Simoa) immunoassay and PET scan with 18F-florbetapir amyloid tracer. In the subgroup of Aβ-PET positive, plasma Aβ42 and Aβ42/Aβ40 ratio was significantly lower in AD than NC, SCD and MCI, yet SCD had significantly higher levels of plasma Aβ42 than both NC and MCI. In the diagnostic groups of MCI and dementia, participants with Aβ-PET positive had lower plasma Aβ42 and Aβ42/40 ratio than participants with Aβ-PET negative, and the increasing levels of plasma Aβ42 and Aβ42/40 ratio indicated lower risks of Aβ-PET positive. However, in the participants with SCD, plasma Aβ42 and Aβ40 were higher in the subgroup of Aβ-PET positive than Aβ-PET negative, and the increasing levels of plasma Aβ42 and Aβ40 indicated higher risks of Aβ-PET positive. No significant association was observed between plasma Aβ and Aβ-PET status in normal controls. These findings showed that, in the continuum of AD, plasma Aβ42 had a significantly increasing trend from NC to SCD before decreasing in MCI and AD. Furthermore, the predictive values of plasma Aβ for brain amyloid deposition were inconsistent over the course of cognitive decline.
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Affiliation(s)
- Feng-Feng Pan
- Department of Gerontology, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
| | - Qi Huang
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Ying Wang
- Department of Gerontology, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
| | - Yi-Fan Wang
- Department of Gerontology, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
| | - Yi-Hui Guan
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Fang Xie
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
- Fang Xie,
| | - Qi-Hao Guo
- Department of Gerontology, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
- *Correspondence: Qi-Hao Guo,
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Álvarez-Sánchez L, Peña-Bautista C, Baquero M, Cháfer-Pericás C. Novel Ultrasensitive Detection Technologies for the Identification of Early and Minimally Invasive Alzheimer's Disease Blood Biomarkers. J Alzheimers Dis 2022; 86:1337-1369. [PMID: 35213367 DOI: 10.3233/jad-215093] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
BACKGROUND Single molecule array (SIMOA) and other ultrasensitive detection technologies have allowed the determination of blood-based biomarkers of Alzheimer's disease (AD) for diagnosis and monitoring, thereby opening up a promising field of research. OBJECTIVE To review the published bibliography on plasma biomarkers in AD using new ultrasensitive techniques. METHODS A systematic review of the PubMed database was carried out to identify reports on the use of blood-based ultrasensitive technology to identify biomarkers for AD. RESULTS Based on this search, 86 works were included and classified according to the biomarker determined. First, plasma amyloid-β showed satisfactory accuracy as an AD biomarker in patients with a high risk of developing dementia. Second, plasma t-Tau displayed good sensitivity in detecting different neurodegenerative diseases. Third, plasma p-Tau was highly specific for AD. Fourth, plasma NfL was highly sensitive for distinguishing between patients with neurodegenerative diseases and healthy controls. In general, the simultaneous determination of several biomarkers facilitated greater accuracy in diagnosing AD (Aβ42/Aβ40, p-Tau181/217). CONCLUSION The recent development of ultrasensitive technology allows the determination of blood-based biomarkers with high sensitivity, thus facilitating the early detection of AD through the analysis of easily obtained biological samples. In short, as a result of this knowledge, pre-symptomatic and early AD diagnosis may be possible, and the recruitment process for future clinical trials could be more precise. However, further studies are necessary to standardize levels of blood-based biomarkers in the general population and thus achieve reproducible results among different laboratories.
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Affiliation(s)
| | - Carmen Peña-Bautista
- Alzheimer Disease Research Group, Health Research Institute La Fe, Valencia, Spain
| | - Miguel Baquero
- Division of Neurology, University and Polytechnic Hospital La Fe, Valencia, Spain
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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: 51] [Impact Index Per Article: 17.0] [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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