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Hok-A-Hin YS, Vermunt L, Peeters CF, van der Ende EL, de Boer SC, Meeter LH, van Swieten JC, Hu WT, Lleó A, Alcolea D, Engelborghs S, Sieben A, Chen-Plotkin A, Irwin DJ, van der Flier WM, Pijnenburg YA, Teunissen CE, del Campo M. Large-scale CSF proteome profiling identifies biomarkers for accurate diagnosis of Frontotemporal Dementia. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.08.19.24312100. [PMID: 39228745 PMCID: PMC11370532 DOI: 10.1101/2024.08.19.24312100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/05/2024]
Abstract
Diagnosis of Frontotemporal dementia (FTD) and the specific underlying neuropathologies (frontotemporal lobar degeneration; FTLD- Tau and FTLD-TDP) is challenging, and thus fluid biomarkers are needed to improve diagnostic accuracy. We used proximity extension assays to analyze 665 proteins in cerebrospinal fluid (CSF) samples from a multicenter cohort including patients with FTD (n = 189), Alzheimer's Disease dementia (AD; n = 232), and cognitively unimpaired individuals (n = 196). In a subset, FTLD neuropathology was determined based on phenotype or genotype (FTLD-Tau = 87 and FTLD-TDP = 68). Forty three proteins were differentially regulated in FTD compared to controls and AD, reflecting axon development, regulation of synapse assembly, and cell-cell adhesion mediator activity pathways. Classification analysis identified a 14- and 13-CSF protein panel that discriminated FTD from controls (AUC: 0.96) or AD (AUC: 0.91). Custom multiplex panels confirmed the highly accurate discrimination between FTD and controls (AUCs > 0.96) or AD (AUCs > 0.88) in three validation cohorts, including one with autopsy confirmation (AUCs > 0.90). Six proteins were differentially regulated between FTLD-TDP and FTLD-Tau, but no reproducible classification model could be generated (AUC: 0.80). Overall, this study introduces novel FTD-specific biomarker panels with potential use in diagnostic setting.
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Affiliation(s)
- Yanaika S. Hok-A-Hin
- Neurochemistry Laboratory, Department of Laboratory Medicine, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Lisa Vermunt
- Neurochemistry Laboratory, Department of Laboratory Medicine, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Alzheimer Center and Department of Neurology, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, The Netherlands
| | - Carel F.W. Peeters
- Mathematical & Statistical Methods group – Biometris, Wageningen University & Research, Wageningen, The Netherlands
| | - Emma L. van der Ende
- Neurochemistry Laboratory, Department of Laboratory Medicine, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Sterre C.M. de Boer
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Alzheimer Center and Department of Neurology, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, The Netherlands
- School of Psychology and Brain & Mind Centre, The University of Sydney, Sydney, Australia
| | - Lieke H. Meeter
- Alzheimer center and department of Neurology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - John C. van Swieten
- Alzheimer center and department of Neurology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - William T. Hu
- Department of Neurology, Center for Neurodegenerative Diseases Research, Emory University School of Medicine, Atlanta, USA
| | - Alberto Lleó
- Department of Neurology, Institut d’Investigacions Biomèdiques Sant Pau (IIB SANT PAU) - Hospital de Sant Pau, Universitat Autònoma de Barcelona, Hospital de la Santa Creu i Sant Pau, Barcelona, Catalunya, Spain
- Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED), Madrid, Spain
| | - Daniel Alcolea
- Department of Neurology, Institut d’Investigacions Biomèdiques Sant Pau (IIB SANT PAU) - Hospital de Sant Pau, Universitat Autònoma de Barcelona, Hospital de la Santa Creu i Sant Pau, Barcelona, Catalunya, Spain
- Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED), Madrid, Spain
| | - Sebastiaan Engelborghs
- Reference Center for Biological Markers of Dementia (BIODEM), Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Vrije Universiteit Brussel, Center for Neurosciences (C4N), Neuroprotection and Neuromodulation Research Group (NEUR), Brussels, Belgium
- Universitair Ziekenhuis Brussel, Department of Neurology, Brussels, Belgium
| | - Anne Sieben
- Lab of neuropathology, Neurobiobank, Institute Born-Bunge, Antwerp University, Edegem, Belgium
| | - Alice Chen-Plotkin
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - David J. Irwin
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Wiesje M. van der Flier
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Alzheimer Center and Department of Neurology, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, The Netherlands
| | - Yolande A.L. Pijnenburg
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Alzheimer Center and Department of Neurology, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, The Netherlands
| | - Charlotte E. Teunissen
- Neurochemistry Laboratory, Department of Laboratory Medicine, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Marta del Campo
- Neurochemistry Laboratory, Department of Laboratory Medicine, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, The Netherlands
- Barcelonaßeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain
- Departamento de Ciencias Farmacéuticas y de la Salud, Facultad de Farmacia, Universidad San PabloCEU, CEU Universities, Madrid, Spain
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Cartas-Cejudo P, Cortés A, Lachén-Montes M, Anaya-Cubero E, Peral E, Ausín K, Díaz-Peña R, Fernández-Irigoyen J, Santamaría E. Mapping the human brain proteome: opportunities, challenges, and clinical potential. Expert Rev Proteomics 2024; 21:55-63. [PMID: 38299555 DOI: 10.1080/14789450.2024.2313073] [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: 09/25/2023] [Accepted: 01/24/2024] [Indexed: 02/02/2024]
Abstract
INTRODUCTION Due to the segmented functions and complexity of the human brain, the characterization of molecular profiles within specific areas such as brain structures and biofluids is essential to unveil the molecular basis for structure specialization as well as the molecular imbalance associated with neurodegenerative and psychiatric diseases. AREAS COVERED Much of our knowledge about brain functionality derives from neurophysiological, anatomical, and transcriptomic approaches. More recently, laser capture and imaging proteomics, technological and computational developments in LC-MS/MS, as well as antibody/aptamer-based platforms have allowed the generation of novel cellular, spatial, and posttranslational dimensions as well as innovative facets in biomarker validation and druggable target identification. EXPERT OPINION Proteomics is a powerful toolbox to functionally characterize, quantify, and localize the extensive protein catalog of the human brain across physiological and pathological states. Brain function depends on multi-dimensional protein homeostasis, and its elucidation will help us to characterize biological pathways that are essential to properly maintain cognitive functions. In addition, comprehensive human brain pathological proteomes may be the basis in computational drug-repositioning methods as a strategy for unveiling potential new therapies in neurodegenerative and psychiatric disorders.
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Affiliation(s)
- Paz Cartas-Cejudo
- Clinical Neuroproteomics Unit, Navarrabiomed, Hospital Universitario de Navarra (HUN), Universidad Pública de Navarra (UPNA), Instituto de Investigación Sanitaria de Navarra (IdiSNA), Pamplona, Spain
| | - Adriana Cortés
- Clinical Neuroproteomics Unit, Navarrabiomed, Hospital Universitario de Navarra (HUN), Universidad Pública de Navarra (UPNA), Instituto de Investigación Sanitaria de Navarra (IdiSNA), Pamplona, Spain
| | - Mercedes Lachén-Montes
- Clinical Neuroproteomics Unit, Navarrabiomed, Hospital Universitario de Navarra (HUN), Universidad Pública de Navarra (UPNA), Instituto de Investigación Sanitaria de Navarra (IdiSNA), Pamplona, Spain
| | - Elena Anaya-Cubero
- Clinical Neuroproteomics Unit, Navarrabiomed, Hospital Universitario de Navarra (HUN), Universidad Pública de Navarra (UPNA), Instituto de Investigación Sanitaria de Navarra (IdiSNA), Pamplona, Spain
| | - Erika Peral
- Proteomics Platform, Navarrabiomed, Hospital Universitario de Navarra (HUN), Universidad Pública de Navarra (UPNA), Instituto de Investigación Sanitaria de Navarra (IdiSNA), Pamplona, Spain
| | - Karina Ausín
- Proteomics Platform, Navarrabiomed, Hospital Universitario de Navarra (HUN), Universidad Pública de Navarra (UPNA), Instituto de Investigación Sanitaria de Navarra (IdiSNA), Pamplona, Spain
| | - Ramón Díaz-Peña
- Proteomics Platform, Navarrabiomed, Hospital Universitario de Navarra (HUN), Universidad Pública de Navarra (UPNA), Instituto de Investigación Sanitaria de Navarra (IdiSNA), Pamplona, Spain
| | - Joaquín Fernández-Irigoyen
- Proteomics Platform, Navarrabiomed, Hospital Universitario de Navarra (HUN), Universidad Pública de Navarra (UPNA), Instituto de Investigación Sanitaria de Navarra (IdiSNA), Pamplona, Spain
| | - Enrique Santamaría
- Clinical Neuroproteomics Unit, Navarrabiomed, Hospital Universitario de Navarra (HUN), Universidad Pública de Navarra (UPNA), Instituto de Investigación Sanitaria de Navarra (IdiSNA), Pamplona, Spain
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Kamenskikh EM, Krygina AY, Gomboeva SC, Zhailebaeva D, Koval DP, Kicherov NA, Otchurzhap CN, Birulina YG, Alifirova VM. [Biobanking in clinical trials involving multiple sclerosis patients]. Zh Nevrol Psikhiatr Im S S Korsakova 2024; 124:7-15. [PMID: 39175234 DOI: 10.17116/jnevro20241240727] [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: 08/24/2024]
Abstract
Investigation of multiple sclerosis (MS) pathogenesis requires sophisticated analytical tools of precision medicine, such as omics research, which include genomics, microbiomics and metabolomics (proteomics, lipidomics and glycomics). Such sensitive methods are based on careful preanalytical work with biomaterials to maintain quality and obtain objective results. Implementation of biobanking as a universal method for working with biomaterials will help to standardize the stages of research, compare different scientific team's results. Collaboration of MS researchers with large biobanks can also help to conduct multicenter and long-term prospective studies, to include a wide number of patients. In this article, we analyze the experience of biobanking practice technologies in studies of MS patients and share the experience of partnership between the Center for MS of the Tomsk Region and the Bank of Biological Material of the Siberian State Medical University.
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Affiliation(s)
| | - A Yu Krygina
- Siberian State Medical University, Tomsk, Russia
| | | | | | - D P Koval
- Siberian State Medical University, Tomsk, Russia
| | - N A Kicherov
- Siberian State Medical University, Tomsk, Russia
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Bivona G, Iemmolo M, Ghersi G. Cerebrospinal and Blood Biomarkers in Alzheimer's Disease: Did Mild Cognitive Impairment Definition Affect Their Clinical Usefulness? Int J Mol Sci 2023; 24:16908. [PMID: 38069230 PMCID: PMC10706963 DOI: 10.3390/ijms242316908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 11/26/2023] [Accepted: 11/27/2023] [Indexed: 12/18/2023] Open
Abstract
Despite Alzheimer's Disease (AD) being known from the times of Alois Alzheimer, who lived more than one century ago, many aspects of the disease are still obscure, including the pathogenesis, the clinical spectrum definition, and the therapeutic approach. Well-established biomarkers for AD come from the histopathological hallmarks of the disease, which are Aβ and phosphorylated Tau protein aggregates. Consistently, cerebrospinal fluid (CSF) Amyloid β (Aβ) and phosphorylated Tau level measurements are currently used to detect AD presence. However, two central biases affect these biomarkers. Firstly, incomplete knowledge of the pathogenesis of diseases legitimates the search for novel molecules that, reasonably, could be expressed by neurons and microglia and could be detected in blood simpler and earlier than the classical markers and in a higher amount. Further, studies have been performed to evaluate whether CSF biomarkers can predict AD onset in Mild Cognitive Impairment (MCI) patients. However, the MCI definition has changed over time. Hence, the studies on MCI patients seem to be biased at the beginning due to the imprecise enrollment and heterogeneous composition of the miscellaneous MCI subgroup. Plasma biomarkers and novel candidate molecules, such as microglia biomarkers, have been tentatively investigated and could represent valuable targets for diagnosing and monitoring AD. Also, novel AD markers are urgently needed to identify molecular targets for treatment strategies. This review article summarizes the main CSF and blood AD biomarkers, underpins their advantages and flaws, and mentions novel molecules that can be used as potential biomarkers for AD.
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Affiliation(s)
- Giulia Bivona
- Department of Biomedicine, Neurosciences and Advanced Diagnostics, University of Palermo, 90127 Palermo, Italy
| | - Matilda Iemmolo
- Department of Biological, Chemical and Pharmaceutical Sciences and Technologies (STEBICEF), University of Palermo, 90128 Palermo, Italy
| | - Giulio Ghersi
- Department of Biological, Chemical and Pharmaceutical Sciences and Technologies (STEBICEF), University of Palermo, 90128 Palermo, Italy
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van der Ende EL, In ‘t Veld SGJG, Hanskamp I, van der Lee S, Dijkstra JIR, Hok-A-Hin YS, Blujdea ER, van Swieten JC, Irwin DJ, Chen-Plotkin A, Hu WT, Lemstra AW, Pijnenburg YAL, van der Flier WM, del Campo M, Teunissen CE, Vermunt L. CSF proteomics in autosomal dominant Alzheimer's disease highlights parallels with sporadic disease. Brain 2023; 146:4495-4507. [PMID: 37348871 PMCID: PMC10629764 DOI: 10.1093/brain/awad213] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 05/10/2023] [Accepted: 05/11/2023] [Indexed: 06/24/2023] Open
Abstract
Autosomal dominant Alzheimer's disease (ADAD) offers a unique opportunity to study pathophysiological changes in a relatively young population with few comorbidities. A comprehensive investigation of proteome changes occurring in ADAD could provide valuable insights into AD-related biological mechanisms and uncover novel biomarkers and therapeutic targets. Furthermore, ADAD might serve as a model for sporadic AD, but in-depth proteome comparisons are lacking. We aimed to identify dysregulated CSF proteins in ADAD and determine the degree of overlap with sporadic AD. We measured 1472 proteins in CSF of PSEN1 or APP mutation carriers (n = 22) and age- and sex-matched controls (n = 20) from the Amsterdam Dementia Cohort using proximity extension-based immunoassays (PEA). We compared protein abundance between groups with two-sided t-tests and identified enriched biological pathways. Using the same protein panels in paired plasma samples, we investigated correlations between CSF proteins and their plasma counterparts. Finally, we compared our results with recently published PEA data from an international cohort of sporadic AD (n = 230) and non-AD dementias (n = 301). All statistical analyses were false discovery rate-corrected. We detected 66 differentially abundant CSF proteins (65 increased, 1 decreased) in ADAD compared to controls (q < 0.05). The most strongly upregulated proteins (fold change >1.8) were related to immunity (CHIT1, ITGB2, SMOC2), cytoskeletal structure (MAPT, NEFL) and tissue remodelling (TMSB10, MMP-10). Significant CSF-plasma correlations were found for the upregulated proteins SMOC2 and LILR1B. Of the 66 differentially expressed proteins, 36 had been measured previously in the sporadic dementias cohort, 34 of which (94%) were also significantly upregulated in sporadic AD, with a strong correlation between the fold changes of these proteins in both cohorts (rs = 0.730, P < 0.001). Twenty-nine of the 36 proteins (81%) were also upregulated among non-AD patients with suspected AD co-pathology. This CSF proteomics study demonstrates substantial biochemical similarities between ADAD and sporadic AD, suggesting involvement of the same biological processes. Besides known AD-related proteins, we identified several relatively novel proteins, such as TMSB10, MMP-10 and SMOC2, which have potential as novel biomarkers. With shared pathophysiological CSF changes, ADAD study findings might be translatable to sporadic AD, which could greatly expedite therapy development.
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Affiliation(s)
- Emma L van der Ende
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Sjors G J G In ‘t Veld
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Iris Hanskamp
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Sven van der Lee
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam UMC, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
- Genomics of Neurodegenerative Diseases and Aging, Human Genetics, Amsterdam UMC, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Janna I R Dijkstra
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam UMC, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
- Genomics of Neurodegenerative Diseases and Aging, Human Genetics, Amsterdam UMC, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Yanaika S Hok-A-Hin
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Elena R Blujdea
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - John C van Swieten
- Alzheimer Center and Department of Neurology, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands
| | - David J Irwin
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Alice Chen-Plotkin
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - William T Hu
- Department of Neurology, Emory University School of Medicine, Atlanta, GA 30307, USA
| | - Afina W Lemstra
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam UMC, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Yolande A L Pijnenburg
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam UMC, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Wiesje M van der Flier
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam UMC, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
- Department of Epidemiology and Data Science, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Marta del Campo
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
- Departamento de Ciencias Farmacéuticas y de la Salud, Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, 28003 Madrid, Spain
- Barcelonabeta Brain Research Center (BBRC), Pasqual Maragall Foundation, 08005 Barcelona, Spain
| | - Charlotte E Teunissen
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Lisa Vermunt
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam UMC, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
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Botter SM, Kessler TM. Neuro-Urology and Biobanking: An Integrated Approach for Advancing Research and Improving Patient Care. Int J Mol Sci 2023; 24:14281. [PMID: 37762582 PMCID: PMC10531693 DOI: 10.3390/ijms241814281] [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: 08/14/2023] [Revised: 09/12/2023] [Accepted: 09/14/2023] [Indexed: 09/29/2023] Open
Abstract
Understanding the molecular mechanisms underlying neuro-urological disorders is crucial for the development of targeted therapeutic interventions. Through the establishment of comprehensive biobanks, researchers can collect and store various biological specimens, including urine, blood, tissue, and DNA samples, to study these mechanisms. In the context of neuro-urology, biobanking facilitates the identification of genetic variations, epigenetic modifications, and gene expression patterns associated with neurogenic lower urinary tract dysfunction. These conditions often present as symptoms of neurological diseases such as Alzheimer's disease, multiple sclerosis, Parkinson's disease, spinal cord injury, and many others. Biobanking of tissue specimens from such patients is essential to understand why these diseases cause the respective symptoms and what can be done to alleviate them. The utilization of high-throughput technologies, such as next-generation sequencing and gene expression profiling, enables researchers to explore the molecular landscape of these conditions in an unprecedented manner. The development of specific and reliable biomarkers resulting from these efforts may help in early detection, accurate diagnosis, and effective monitoring of neuro-urological conditions, leading to improved patient care and management. Furthermore, these biomarkers could potentially facilitate the monitoring of novel therapies currently under investigation in neuro-urological clinical trials. This comprehensive review explores the synergistic integration of neuro-urology and biobanking, with particular emphasis on the translation of biobanking approaches in molecular research in neuro-urology. We discuss the advantages of biobanking in neuro-urological studies, the types of specimens collected and their applications in translational research. Furthermore, we highlight the importance of standardization and quality assurance when collecting samples and discuss challenges that may compromise sample quality and impose limitations on their subsequent utilization. Finally, we give recommendations for sampling in multicenter studies, examine sustainability issues associated with biobanking, and provide future directions for this dynamic field.
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Affiliation(s)
- Sander M. Botter
- Swiss Center for Musculoskeletal Biobanking, Balgrist Campus AG, 8008 Zürich, Switzerland
| | - Thomas M. Kessler
- Department of Neuro-Urology, Balgrist University Hospital, University of Zürich, 8008 Zürich, Switzerland;
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Hok‐A‐Hin YS, Bolsewig K, Ruiters DN, Lleó A, Alcolea D, Lemstra AW, van der Flier WM, Teunissen CE, del Campo M. Thimet oligopeptidase as a potential CSF biomarker for Alzheimer's disease: A cross-platform validation study. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2023; 15:e12456. [PMID: 37502019 PMCID: PMC10369371 DOI: 10.1002/dad2.12456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 06/14/2023] [Accepted: 06/16/2023] [Indexed: 07/29/2023]
Abstract
INTRODUCTION Our previous antibody-based cerebrospinal fluid (CSF) proteomics study showed that Thimet oligopeptidase (THOP1), an amyloid beta (Aβ) neuropeptidase, was increased in mild cognitive impairment with amyloid pathology (MCI-Aβ+) and Alzheimer's disease (AD) dementia compared with controls and dementia with Lewy bodies (DLB), highlighting the potential of CSF THOP1 as an early specific biomarker for AD. We aimed to develop THOP1 immunoassays for large-scale analysis and validate our proteomics findings in two independent cohorts. METHODS We developed in-house CSF THOP1 immunoassays on automated Ella and Simoa platforms. The performance of the different assays were compared using Passing-Bablok regression analysis in a subset of CSF samples from the discovery cohort (n = 72). Clinical validation was performed in two independent cohorts (cohort 1: n = 200; cohort 2: n = 165) using the Ella platform. RESULTS THOP1 concentrations moderately correlated between proteomics analysis and our novel assays (Rho > 0.580). In both validation cohorts, CSF THOP1 was increased in MCI-Aβ+ (>1.3-fold) and AD (>1.2-fold) compared with controls; and between MCI-Aβ+ and DLB (>1.2-fold). Higher THOP1 concentrations were detected in AD compared with DLB only when both cohorts were analyzed together. In both cohorts, THOP1 correlated with CSF total tau (t-tau), phosphorylated tau (p-tau), and Aβ40 (Rho > 0.540) but not Aβ42. DISCUSSION Validation of our proteomics findings underpins the potential of CSF THOP1 as an early specific biomarker associated with AD pathology. The use of antibody-based platforms in both the discovery and validation phases facilitated the translation of proteomics findings, providing an additional workflow that may accelerate the development of biofluid-based biomarkers.
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Affiliation(s)
- Yanaika S. Hok‐A‐Hin
- Neurochemistry Laboratory, Department of Laboratory Medicine, Amsterdam NeuroscienceVU University Medical Center, Amsterdam UMCAmsterdamThe Netherlands
| | - Katharina Bolsewig
- Neurochemistry Laboratory, Department of Laboratory Medicine, Amsterdam NeuroscienceVU University Medical Center, Amsterdam UMCAmsterdamThe Netherlands
| | - Daimy N. Ruiters
- Neurochemistry Laboratory, Department of Laboratory Medicine, Amsterdam NeuroscienceVU University Medical Center, Amsterdam UMCAmsterdamThe Netherlands
| | - Alberto Lleó
- Department of Neurology, Institut d'Investigacions Biomèdiques Sant Pau ‐ Hospital de Sant PauUniversitat Autònoma de Barcelona, Hospital de la Santa Creu i Sant PauBarcelonaSpain
| | - Daniel Alcolea
- Department of Neurology, Institut d'Investigacions Biomèdiques Sant Pau ‐ Hospital de Sant PauUniversitat Autònoma de Barcelona, Hospital de la Santa Creu i Sant PauBarcelonaSpain
| | - Afina W. Lemstra
- Alzheimer Center Amsterdam, Department of NeurologyAmsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMCAmsterdamThe Netherlands
| | - Wiesje M. van der Flier
- Alzheimer Center Amsterdam, Department of NeurologyAmsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMCAmsterdamThe Netherlands
- Department of Epidemiology and Data ScienceVU University Medical CentersAmsterdamThe Netherlands
| | - Charlotte E. Teunissen
- Neurochemistry Laboratory, Department of Laboratory Medicine, Amsterdam NeuroscienceVU University Medical Center, Amsterdam UMCAmsterdamThe Netherlands
| | - Marta del Campo
- Neurochemistry Laboratory, Department of Laboratory Medicine, Amsterdam NeuroscienceVU University Medical Center, Amsterdam UMCAmsterdamThe Netherlands
- Departamento de Ciencias Farmacéuticas y de la Salud, Facultad de FarmaciaUniversidad San Pablo‐CEU, CEU UniversitiesMadridSpain
- Bareclonaβeta Brain Research Center (BBRC)Pasqual Maragall FoundationBarcelonaSpain
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8
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Hanin A, Cespedes J, Pulluru Y, Gopaul M, Aronica E, Decampo D, Helbig I, Howe CL, Huttner A, Koh S, Navarro V, Taraschenko O, Vezzani A, Wilson MR, Xian J, Gaspard N, Hirsch LJ. Review and standard operating procedures for collection of biospecimens and analysis of biomarkers in new onset refractory status epilepticus. Epilepsia 2023; 64:1444-1457. [PMID: 37039049 PMCID: PMC10756682 DOI: 10.1111/epi.17600] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 03/27/2023] [Accepted: 03/28/2023] [Indexed: 04/12/2023]
Abstract
New onset refractory status epilepticus (NORSE), including its subtype with a preceding febrile illness known as febrile infection-related epilepsy syndrome (FIRES), is one of the most severe forms of status epilepticus. The exact causes of NORSE are currently unknown, and there is so far no disease-specific therapy. Identifying the underlying pathophysiology and discovering specific biomarkers, whether immunologic, infectious, genetic, or other, may help physicians in the management of patients with NORSE. A broad spectrum of biomarkers has been proposed for status epilepticus patients, some of which were evaluated for patients with NORSE. Nonetheless, none has been validated, due to significant variabilities in study cohorts, collected biospecimens, applied analytical methods, and defined outcome endpoints, and to small sample sizes. The NORSE Institute established an open NORSE/FIRES biorepository for health-related data and biological samples allowing the collection of biospecimens worldwide, promoting multicenter research and sharing of data and specimens. Here, we suggest standard operating procedures for biospecimen collection and biobanking in this rare condition. We also propose criteria for the appropriate use of previously collected biospecimens. We predict that the widespread use of standardized procedures will reduce heterogeneity, facilitate the future identification of validated biomarkers for NORSE, and provide a better understanding of the pathophysiology and best clinical management for these patients.
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Affiliation(s)
- Aurélie Hanin
- Department of Neurology and Immunobiology, Yale University School of Medicine, New Haven, Connecticut, USA
- Sorbonne Université, Institut du Cerveau ICM, Paris Brain Institute, Inserm, CNRS, Assistance Publique -Hôpitaux de Paris, Hôpital de la Pitié-Salpêtrière, DMU Neurosciences 6, Paris, France
- Assistance Publique -Hôpitaux de Paris, Hôpital de la Pitié-Salpêtrière, DMU Neurosciences 6, Epilepsy Unit and Department of Clinical Neurophysiology, Paris, France
| | - Jorge Cespedes
- Comprehensive Epilepsy Center, Department of Neurology, Yale University School of Medicine, New Haven, Connecticut, USA
- Universidad Autonoma de Centro America, School of Medicine, San Jose, Costa Rica
| | - Yashwanth Pulluru
- Comprehensive Epilepsy Center, Department of Neurology, Yale University School of Medicine, New Haven, Connecticut, USA
- Nebraska Medical Center, Omaha, Nebraska, USA
| | - Margaret Gopaul
- Comprehensive Epilepsy Center, Department of Neurology, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Eleonora Aronica
- Department of (Neuro) Pathology, Amsterdam Neuroscience, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Danielle Decampo
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Epilepsy NeuroGenetics Initiative, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ingo Helbig
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Epilepsy NeuroGenetics Initiative, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Charles L. Howe
- Division of Experimental Neurology, Mayo Clinic, Rochester, Minnesota, USA
- Center for Multiple Sclerosis and Autoimmune Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Anita Huttner
- Department of Pathology, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Sookyong Koh
- Department of Pediatrics, Children’s Hospital Medical Center, University of Nebraska, Omaha, Nebraska, USA
| | - Vincent Navarro
- Sorbonne Université, Institut du Cerveau ICM, Paris Brain Institute, Inserm, CNRS, Assistance Publique -Hôpitaux de Paris, Hôpital de la Pitié-Salpêtrière, DMU Neurosciences 6, Paris, France
- Assistance Publique -Hôpitaux de Paris, Hôpital de la Pitié-Salpêtrière, DMU Neurosciences 6, Epilepsy Unit and Department of Clinical Neurophysiology, Paris, France
| | - Olga Taraschenko
- Department of Neurological Sciences, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Annamaria Vezzani
- Department of Acute Brain Injury, Istituto di Recerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Michael R. Wilson
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, California, San Francisco, USA
| | - Julie Xian
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Epilepsy NeuroGenetics Initiative, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Nicolas Gaspard
- Comprehensive Epilepsy Center, Department of Neurology, Yale University School of Medicine, New Haven, Connecticut, USA
- Université Libre de Bruxelles, Hôpital Erasme, Brussels, Belgium
| | - Lawrence J. Hirsch
- Comprehensive Epilepsy Center, Department of Neurology, Yale University School of Medicine, New Haven, Connecticut, USA
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9
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Hok-A-Hin YS, Del Campo M, Boiten WA, Stoops E, Vanhooren M, Lemstra AW, van der Flier WM, Teunissen CE. Neuroinflammatory CSF biomarkers MIF, sTREM1, and sTREM2 show dynamic expression profiles in Alzheimer's disease. J Neuroinflammation 2023; 20:107. [PMID: 37147668 PMCID: PMC10163795 DOI: 10.1186/s12974-023-02796-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 04/27/2023] [Indexed: 05/07/2023] Open
Abstract
BACKGROUND There is a need for novel fluid biomarkers tracking neuroinflammatory responses in Alzheimer's disease (AD). Our recent cerebrospinal fluid (CSF) proteomics study revealed that migration inhibitory factor (MIF) and soluble triggering receptor expressed on myeloid cells 1 (sTREM1) increased along the AD continuum. We aimed to assess the potential use of these proteins, in addition to sTREM2, as CSF biomarkers to monitor inflammatory processes in AD. METHODS We included cognitively unimpaired controls (n = 67, 63 ± 9 years, 24% females, all amyloid negative), patients with mild cognitive impairment (MCI; n = 92, 65 ± 7 years, 47% females, 65% amyloid positive), AD (n = 38, 67 ± 6 years, 8% females, all amyloid positive), and DLB (n = 50, 67 ± 6 years, 5% females, 54% amyloid positive). MIF, sTREM1, and sTREM2 levels were measured by validated immunoassays. Differences in protein levels between groups were tested with analysis of covariance (corrected for age and sex). Spearman correlation analysis was performed to evaluate the association between these neuroinflammatory markers with AD-CSF biomarkers (Aβ42, tTau, pTau) and mini-mental state examination (MMSE) scores. RESULTS MIF levels were increased in MCI (p < 0.01), AD (p < 0.05), and DLB (p > 0.05) compared to controls. Levels of sTREM1 were specifically increased in AD compared to controls (p < 0.01), MCI (p < 0.05), and DLB patients (p > 0.05), while sTREM2 levels were increased specifically in MCI compared to all other groups (all p < 0.001). Neuroinflammatory proteins were highly correlated with CSF pTau levels (MIF: all groups; sTREM1: MCI, AD and DLB; sTREM2: controls, MCI and DLB). Correlations with MMSE scores were observed in specific clinical groups (MIF in controls, sTREM1 in AD, and sTREM2 in DLB). CONCLUSION Inflammatory-related proteins show diverse expression profiles along different AD stages, with increased protein levels in the MCI stage (MIF and sTREM2) and AD stage (MIF and sTREM1). The associations of these inflammatory markers primarily with CSF pTau levels indicate an intertwined relationship between tau pathology and inflammation. These neuroinflammatory markers might be useful in clinical trials to capture dynamics in inflammatory responses or monitor drug-target engagement of inflammatory modulators.
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Affiliation(s)
- Yanaika S Hok-A-Hin
- Neurochemistry Laboratory, Department of Laboratory Medicine, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, Amsterdam, The Netherlands.
| | - Marta Del Campo
- Neurochemistry Laboratory, Department of Laboratory Medicine, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, Amsterdam, The Netherlands
- Departamento de Ciencias Farmacéuticas y de la Salud, Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Madrid, Spain
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
| | - Walter A Boiten
- Neurochemistry Laboratory, Department of Laboratory Medicine, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, Amsterdam, The Netherlands
| | | | | | - Afina W Lemstra
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Wiesje M van der Flier
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Department of Epidemiology and Data Science, VU University Medical Centers, Amsterdam, The Netherlands
| | - Charlotte E Teunissen
- Neurochemistry Laboratory, Department of Laboratory Medicine, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, Amsterdam, The Netherlands
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10
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Gogishvili D, Vromen EM, Koppes-den Hertog S, Lemstra AW, Pijnenburg YAL, Visser PJ, Tijms BM, Del Campo M, Abeln S, Teunissen CE, Vermunt L. Discovery of novel CSF biomarkers to predict progression in dementia using machine learning. Sci Rep 2023; 13:6531. [PMID: 37085545 PMCID: PMC10121677 DOI: 10.1038/s41598-023-33045-x] [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: 12/15/2022] [Accepted: 04/06/2023] [Indexed: 04/23/2023] Open
Abstract
Providing an accurate prognosis for individual dementia patients remains a challenge since they greatly differ in rates of cognitive decline. In this study, we used machine learning techniques with the aim to identify cerebrospinal fluid (CSF) biomarkers that predict the rate of cognitive decline within dementia patients. First, longitudinal mini-mental state examination scores (MMSE) of 210 dementia patients were used to create fast and slow progression groups. Second, we trained random forest classifiers on CSF proteomic profiles and obtained a well-performing prediction model for the progression group (ROC-AUC = 0.82). As a third step, Shapley values and Gini feature importance measures were used to interpret the model performance and identify top biomarker candidates for predicting the rate of cognitive decline. Finally, we explored the potential for each of the 20 top candidates in internal sensitivity analyses. TNFRSF4 and TGF [Formula: see text]-1 emerged as the top markers, being lower in fast-progressing patients compared to slow-progressing patients. Proteins of which a low concentration was associated with fast progression were enriched for cell signalling and immune response pathways. None of our top markers stood out as strong individual predictors of subsequent cognitive decline. This could be explained by small effect sizes per protein and biological heterogeneity among dementia patients. Taken together, this study presents a novel progression biomarker identification framework and protein leads for personalised prediction of cognitive decline in dementia.
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Affiliation(s)
- Dea Gogishvili
- Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
| | - Eleonora M Vromen
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Sascha Koppes-den Hertog
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Afina W Lemstra
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Yolande A L Pijnenburg
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Pieter Jelle Visser
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Alzheimer Center Limburg, School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
- Department of Neurobiology, Care Sciences and Society, Division of Neurogeriatrics, Karolinska Institutet, Stockholm, Sweden
| | - Betty M Tijms
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Marta Del Campo
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Barcelonabeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain
- Departamento de Ciencias Farmacéuticas y de la Salud, Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Madrid, Spain
| | - Sanne Abeln
- Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- CWI, Amsterdam , The Netherlands
| | - Charlotte E Teunissen
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Lisa Vermunt
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
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11
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Thevarkalam M, Krishnan S, Shanmughan LI, Mathai A, Leelamani JV, Kannoth S, Bhaskaran R, Iype T, Panda S. Determination of sensitivities and specificities of cerebrospinal fluid free light chains to diagnose multiple sclerosis- a multicentric case-control study. Mult Scler Relat Disord 2023; 74:104717. [PMID: 37062197 DOI: 10.1016/j.msard.2023.104717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 04/06/2023] [Accepted: 04/10/2023] [Indexed: 04/18/2023]
Abstract
BACKGROUND CSF free light chains help diagnose multiple sclerosis, but no data is available on the Asian population. Our objective was to study the diagnostic utility of CSF free light chains for diagnosing multiple sclerosis in Indian patients. METHODS Prospective multicentric case-control study. Cases included those who were tested for oligoclonal bands and fulfilled the modified McDonald criteria 2017 for multiple sclerosis and clinically isolated syndromes. Those tested for oligoclonal bands (OCB) but with other diagnoses- inflammatory and non-inflammatory were included as controls. Clinical details were collected from electronic medical records. CSF and serum kappa and lambda free light chains were measured, apart from oligoclonal bands, immunoglobulin, and albumin in paired serum and CSF samples. RESULTS There were 70 patients (31 cases and 39 controls). The mean age was 43.41(SD 16.073) years, and 43(61.4%) were females. CSF kappa showed highest specificity 97.4%, at a cut off 2.06 mg/L (sensitivity 71%) and highest sensitivity 90.3%, at a cut off 0.47 mg/L (specificity 79.5%). Best balance of sensitivity and specificity for CSF kappa was seen at a cut-off of ≥ 0.63 mg/L {sensitivity 87·1 (CI - 70.17-96.37), and specificity 87·18 (CI -72.57-95.70)}. The ratio of Kappa/lambda showed highest specificity of 100%(similar to OCB) with a sensitivity of 71% at a cut off of 1.72. The ratio of sum of kappa and lambda light chains, and Qalb (∑CSF FLC/Qalb), showed the highest specificity (94.87%)among the blood brain barrier corrected ratios. CONCLUSION This study showed that the diagnostic utility of CSF kappa was comparable to OCB to diagnose multiple sclerosis in sensitivity, but not specificity, so can be a screening test before testing for OCB in our population.
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Affiliation(s)
- Meena Thevarkalam
- Department of Biochemistry, Amrita Institute of Medical Sciences, Amrita Viswavidyapeetham University, Kochi, Kerala, India 682041
| | - Sajitha Krishnan
- Department of Biochemistry, Amrita Institute of Medical Sciences, Amrita Viswavidyapeetham University, Kochi, Kerala, India 682041.
| | - Layana I Shanmughan
- Department of Biochemistry, Amrita Institute of Medical Sciences, Amrita Viswavidyapeetham University, Kochi, Kerala, India 682041
| | - Annamma Mathai
- Neuroimmunology Laboratory, Department of Neurology, Amrita Institute of Medical Sciences, Amrita Viswavidyapeetham University, Kochi, Kerala, India 682041
| | - Jyothi V Leelamani
- Neuroimmunology Laboratory, Department of Neurology, Amrita Institute of Medical Sciences, Amrita Viswavidyapeetham University, Kochi, Kerala, India 682041
| | - Sudheeran Kannoth
- Neuroimmunology Laboratory, Department of Neurology, Amrita Institute of Medical Sciences, Amrita Viswavidyapeetham University, Kochi, Kerala, India 682041
| | - Renjitha Bhaskaran
- Department of biostatistics, Amrita Institute of Medical Sciences, Amrita Viswavidyapeetham University, Kochi, Kerala, India 682041
| | - Thomas Iype
- Department of Neurology, Government Medical College, Thiruvananthapuram, Kerala, India. 695011
| | - Samhita Panda
- Department of Neurology, All India Institute of Medical Sciences, Jodhpur, Rajasthan, India 342005
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12
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Giannella E, Notarangelo V, Motta C, Sancesario G. Biobanking for Neurodegenerative Diseases: Challenge for Translational Research and Data Privacy. Neuroscientist 2023; 29:190-201. [PMID: 34353130 DOI: 10.1177/10738584211036693] [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: 11/15/2022]
Abstract
Biobanking has emerged as a strategic challenge to promote knowledge on neurological diseases, by the application of translational research. Due to the inaccessibility of the central nervous system, the advent of biobanks, as structure collecting biospecimens and associated data, are essential to turn experimental results into clinical practice. Findings from basic research, omics sciences, and in silico studies, definitely require validation in clinically well-defined cohorts of patients, even more valuable when longitudinal, or including preclinical and asymptomatic individuals. Finally, collecting biological samples requires a great effort to guarantee respect for transparency and protection of sensitive data of patients and donors. Since the European General Data Protection Regulation 2016/679 has been approved, concerns about the use of data in biomedical research have emerged. In this narrative review, we focus on the essential role of biobanking for translational research on neurodegenerative diseases. Moreover, we address considerations for biological samples and data collection, the importance of standardization in the preanalytical phase, data protection (ethical and legal) and the role of donors in improving research in this field.
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Affiliation(s)
- Emilia Giannella
- Biobank, IRCCS Santa Lucia Foundation, Rome, Italy.,Experimental Neuroscience, European Center for Brain Research, Rome, Italy
| | | | - Caterina Motta
- Dept Clinical and Behavioural Neurology, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Giulia Sancesario
- Biobank, IRCCS Santa Lucia Foundation, Rome, Italy.,Experimental Neuroscience, European Center for Brain Research, Rome, Italy
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13
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Leuzy A, Mattsson-Carlgren N, Cullen NC, Stomrud E, Palmqvist S, La Joie R, Iaccarino L, Zetterberg H, Rabinovici G, Blennow K, Janelidze S, Hansson O. Robustness of CSF Aβ42/40 and Aβ42/P-tau181 measured using fully automated immunoassays to detect AD-related outcomes. Alzheimers Dement 2023. [PMID: 36681387 DOI: 10.1002/alz.12897] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 10/26/2022] [Accepted: 11/10/2022] [Indexed: 01/23/2023]
Abstract
INTRODUCTION This study investigated the comparability of cerebrospinal fluid (CSF) cutoffs for Elecsys immunoassays for amyloid beta (Aβ)42/Aβ40 or Aβ42/phosphorylated tau (p-tau)181 and the effects of measurement variability when predicting Alzheimer's disease (AD)-related outcomes (i.e., Aβ-positron emission tomography [PET] visual read and AD neuropathology). METHODS We studied 750 participants (BioFINDER study, Alzheimer's Disease Neuroimaging Initiative [ADNI], and University of California San Francisco [UCSF]). Youden's index was used to identify cutoffs and to calculate accuracy (Aβ-PET visual read as outcome). Using longitudinal variability in Aβ-negative controls, we identified a gray zone around cut-points where the risk of an inconsistent predicted outcome was >5%. RESULTS For Aβ42/Aβ40, cutoffs across cohorts were <0.059 (BioFINDER), <0.057 (ADNI), and <0.058 (UCSF). For Aβ42/p-tau181, cutoffs were <41.90 (BioFINDER), <39.20 (ADNI), and <46.02 (UCSF). Accuracy was ≈90% for both Aβ42/Aβ40 and Aβ42/p-tau181 using these cutoffs. Using Aβ-PET as an outcome, 8.7% of participants fell within a gray zone interval for Aβ42/Aβ40, compared to 4.5% for Aβ42/p-tau181. Similar findings were observed using a measure of overall AD neuropathologic change (7.7% vs. 3.3%). In a subset with CSF and plasma Aβ42/40, the number of individuals within the gray zone was ≈1.5 to 3 times greater when using plasma Aβ42/40. DISCUSSION CSF Aβ42/p-tau181 was more robust to the effects of measurement variability, suggesting that it may be the preferred Elecsys-based measure in clinical practice and trials.
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Affiliation(s)
- Antoine Leuzy
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Niklas Mattsson-Carlgren
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden.,Department of Neurology, Skåne University Hospital, Lund, Sweden.,Wallenberg Center for Molecular Medicine, Lund University, Lund, Sweden
| | - Nicholas C Cullen
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Erik Stomrud
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden.,Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Sebastian Palmqvist
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden.,Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Renaud La Joie
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, California, USA
| | - Leonardo Iaccarino
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, California, USA
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden.,Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK.,UK Dementia Research Institute at UCL, London, UK.,Hong Kong Center for Neurodegenerative Diseases, Hong Kong, China
| | - Gil Rabinovici
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, California, USA.,Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA.,Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, California, USA.,Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Shorena Janelidze
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden.,Memory Clinic, Skåne University Hospital, Malmö, Sweden
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14
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del Campo M, Zetterberg H, Gandy S, Onyike CU, Oliveira F, Udeh‐Momoh C, Lleó A, Teunissen CE, Pijnenburg Y. New developments of biofluid-based biomarkers for routine diagnosis and disease trajectories in frontotemporal dementia. Alzheimers Dement 2022; 18:2292-2307. [PMID: 35235699 PMCID: PMC9790674 DOI: 10.1002/alz.12643] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 02/04/2022] [Accepted: 02/04/2022] [Indexed: 01/31/2023]
Abstract
Frontotemporal dementia (FTD) covers a spectrum of neurodegenerative disorders with different phenotypes, genetic backgrounds, and pathological states. Its clinicopathological diversity challenges the diagnostic process and the execution of clinical trials, calling for specific diagnostic biomarkers of pathologic FTD types. There is also a need for biomarkers that facilitate disease staging, quantification of severity, monitoring in clinics and observational studies, and for evaluation of target engagement and treatment response in clinical trials. This review discusses current FTD biofluid-based biomarker knowledge taking into account the differing applications. The limitations, knowledge gaps, and challenges for the development and implementation of such markers are also examined. Strategies to overcome these hurdles are proposed, including the technologies available, patient cohorts, and collaborative research initiatives. Access to robust and reliable biomarkers that define the exact underlying pathophysiological FTD process will meet the needs for specific diagnosis, disease quantitation, clinical monitoring, and treatment development.
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Affiliation(s)
- Marta del Campo
- Departamento de Ciencias Farmacéuticas y de la SaludFacultad de FarmaciaUniversidad San Pablo‐CEUCEU UniversitiesMadridSpain
| | - Henrik Zetterberg
- Institute of Neuroscience and PhysiologyThe Sahlgrenska Academy at the University of GothenburgGothenburgSweden,Clinical Neurochemistry LaboratorySahlgrenska University HospitalMölndalSweden,UK Dementia Research Institute at UCLLondonUK,Department of Neurodegenerative DiseaseUCL Institute of NeurologyLondonUK,Hong Kong Center for Neurodegenerative DiseasesHong KongChina
| | - Sam Gandy
- Department of NeurologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Chiadi U Onyike
- Division of Geriatric Psychiatry and NeuropsychiatryThe Johns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Fabricio Oliveira
- Department of Neurology and NeurosurgeryEscola Paulista de MedicinaFederal University of São Paulo (UNIFESP)São PauloSão PauloBrazil
| | - Chi Udeh‐Momoh
- Ageing Epidemiology Research UnitSchool of Public HealthFaculty of MedicineImperial College LondonLondonUK,Translational Health SciencesFaculty of MedicineUniversity of BristolBristolUK
| | - Alberto Lleó
- Neurology DepartmentHospital de la Santa Creu I Sant PauBarcelonaSpain
| | - Charlotte E. Teunissen
- Neurochemistry LaboratoryDepartment of Clinical ChemistryAmsterdam NeuroscienceAmsterdam University Medical CentersVrije UniversiteitAmsterdamthe Netherlands
| | - Yolande Pijnenburg
- Alzheimer Center AmsterdamDepartment of NeurologyAmsterdam NeuroscienceVrije Universiteit AmsterdamAmsterdam UMCAmsterdamthe Netherlands
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15
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Huang S, Wang YJ, Guo J. Biofluid Biomarkers of Alzheimer’s Disease: Progress, Problems, and Perspectives. Neurosci Bull 2022; 38:677-691. [PMID: 35306613 PMCID: PMC9206048 DOI: 10.1007/s12264-022-00836-7] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 11/25/2021] [Indexed: 12/19/2022] Open
Abstract
Since the establishment of the biomarker-based A-T-N (Amyloid/Tau/Neurodegeneration) framework in Alzheimer’s disease (AD), the diagnosis of AD has become more precise, and cerebrospinal fluid tests and positron emission tomography examinations based on this framework have become widely accepted. However, the A-T-N framework does not encompass the whole spectrum of AD pathologies, and problems with invasiveness and high cost limit the application of the above diagnostic methods aimed at the central nervous system. Therefore, we suggest the addition of an “X” to the A-T-N framework and a focus on peripheral biomarkers in the diagnosis of AD. In this review, we retrospectively describe the recent progress in biomarkers based on the A-T-N-X framework, analyze the problems, and present our perspectives on the diagnosis of AD.
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16
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Mommaerts K, Willemse EAJ, Marchese M, Larue C, van der Flier WM, Betsou F, Teunissen CE. A Cystatin C Cleavage ELISA Assay as a Quality Control Tool for Determining Sub-Optimal Storage Conditions of Cerebrospinal Fluid Samples in Alzheimer's Disease Research. J Alzheimers Dis 2021; 83:1367-1377. [PMID: 34420976 PMCID: PMC8673510 DOI: 10.3233/jad-210741] [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] [Indexed: 11/28/2022]
Abstract
Background: An N-terminal octapeptide cleavage of the cystatin C protein was discovered by mass spectrometry when cerebrospinal fluid (CSF) was stored at –20°C for 3 months, which did not occur when CSF was stored at –80°C. Objective: The aim was to develop an immunoassay as quality assessment tool to detect this –20°C cleavage of cystatin C in CSF and support Alzheimer’s disease research. Methods: A specific monoclonal antibody and a double indirect sandwich ELISA were developed: one assay quantifies the octapeptide uncleaved protein specifically and the other quantifies the total cystatin C present in the biological fluid (both cleaved and uncleaved forms). The ratio of these concentrations was calculated to assess the extent of cleavage of cystatin C. The novel ELISA was validated and applied in a short-term (up to 4 weeks) and mid-term (up to one year) stability study of CSF stored at 4°C, –20°C, –80°C, and liquid nitrogen. Impact of freeze-thaw cycles, adsorption, and protease inhibitors were tested. Results: The ratio of truncated protein was modified following –20°C storage and seemed to reach a plateau after 6 months. The ratio was impacted neither by freeze-thaw cycles nor adsorption. The –20°C specific cleavage was found to be protease related. Conclusion: Using this novel double indirect sandwich ELISA, absolute levels of the total and uncleaved cystatin C and the ratio of truncated cystatin C can be measured. This assay is an easily applicable tool which can be used to confirm that CSF biospecimen are fit-for-purpose for Alzheimer’s disease research.
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Affiliation(s)
- Kathleen Mommaerts
- Biospecimen Research Group, Integrated Biobank of Luxembourg, Luxembourg Institute of Health, Luxembourg.,Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg
| | - Eline A J Willemse
- Department of Clinical Chemistry, Neurochemistry Laboratory, Amsterdam Neuroscience, Amsterdam University Medical Center, VU University, Amsterdam, the Netherlands
| | - Monica Marchese
- Translational Biomarker Group, Integrated Biobank of Luxembourg, Luxembourg Institute of Health, Luxembourg
| | - Catherine Larue
- Integrated Biobank of Luxembourg, Luxembourg Institute of Health, Luxembourg
| | - Wiesje M van der Flier
- Alzheimer Center Amsterdam, Amsterdam Neuroscience, Amsterdam University Medical Center, VU University, Amsterdam, the Netherlands.,Department of Epidemiology and Biostatistics, Amsterdam University Medical Center, VU Amsterdam, Amsterdam, the Netherlands
| | - Fay Betsou
- Integrated Biobank of Luxembourg, Luxembourg Institute of Health, Luxembourg
| | - Charlotte E Teunissen
- Department of Clinical Chemistry, Neurochemistry Laboratory, Amsterdam Neuroscience, Amsterdam University Medical Center, VU University, Amsterdam, the Netherlands
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17
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Scheltens P, De Strooper B, Kivipelto M, Holstege H, Chételat G, Teunissen CE, Cummings J, van der Flier WM. Alzheimer's disease. Lancet 2021; 397:1577-1590. [PMID: 33667416 PMCID: PMC8354300 DOI: 10.1016/s0140-6736(20)32205-4] [Citation(s) in RCA: 1986] [Impact Index Per Article: 496.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Revised: 08/21/2020] [Accepted: 10/15/2020] [Indexed: 12/16/2022]
Abstract
In this Seminar, we highlight the main developments in the field of Alzheimer's disease. The most recent data indicate that, by 2050, the prevalence of dementia will double in Europe and triple worldwide, and that estimate is 3 times higher when based on a biological (rather than clinical) definition of Alzheimer's disease. The earliest phase of Alzheimer's disease (cellular phase) happens in parallel with accumulating amyloid β, inducing the spread of tau pathology. The risk of Alzheimer's disease is 60-80% dependent on heritable factors, with more than 40 Alzheimer's disease-associated genetic risk loci already identified, of which the APOE alleles have the strongest association with the disease. Novel biomarkers include PET scans and plasma assays for amyloid β and phosphorylated tau, which show great promise for clinical and research use. Multidomain lifestyle-based prevention trials suggest cognitive benefits in participants with increased risk of dementia. Lifestyle factors do not directly affect Alzheimer's disease pathology, but can still contribute to a positive outcome in individuals with Alzheimer's disease. Promising pharmacological treatments are poised at advanced stages of clinical trials and include anti-amyloid β, anti-tau, and anti-inflammatory strategies.
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Affiliation(s)
- Philip Scheltens
- Alzheimer Centre Amsterdam, Amsterdam University Medical Centers, Amsterdam, Netherlands; Department of Neurology, Amsterdam University Medical Centers, Amsterdam, Netherlands; Life Science Partners, Amsterdam, Netherlands.
| | - Bart De Strooper
- VIB Center for Brain and Disease Research, Leuven, Belgium; KU Leuven Department for Neurology, Leuven, Belgium; Dementia Research Institute, University College London, London, UK
| | - Miia Kivipelto
- Division of Clinical Geriatrics and Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska University Hospital, Stockholm, Sweden; Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland; Ageing and Epidemiology Research Unit, School of Public Health, Imperial College London, London, UK
| | - Henne Holstege
- Alzheimer Centre Amsterdam, Amsterdam University Medical Centers, Amsterdam, Netherlands; Department of Clinical Genetics, Amsterdam University Medical Centers, Amsterdam, Netherlands
| | - Gael Chételat
- Normandie Université, Université de Caen, Institut National de la Santé et de la Recherche Médicale, Groupement d'Intérêt Public Cyceron, Caen, France
| | - Charlotte E Teunissen
- Department of Clinical Chemistry, Amsterdam University Medical Centers, Amsterdam, Netherlands
| | - Jeffrey Cummings
- Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, University of Nevada, Las Vegas, NV, USA; Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA
| | - Wiesje M van der Flier
- Alzheimer Centre Amsterdam, Amsterdam University Medical Centers, Amsterdam, Netherlands; Department of Epidemiology and Datascience, Amsterdam University Medical Centers, Amsterdam, Netherlands
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18
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Hansson O, Batrla R, Brix B, Carrillo MC, Corradini V, Edelmayer RM, Esquivel RN, Hall C, Lawson J, Bastard NL, Molinuevo JL, Nisenbaum LK, Rutz S, Salamone SJ, Teunissen CE, Traynham C, Umek RM, Vanderstichele H, Vandijck M, Wahl S, Weber CJ, Zetterberg H, Blennow K. The Alzheimer's Association international guidelines for handling of cerebrospinal fluid for routine clinical measurements of amyloid β and tau. Alzheimers Dement 2021; 17:1575-1582. [PMID: 33788410 DOI: 10.1002/alz.12316] [Citation(s) in RCA: 64] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Accepted: 01/29/2021] [Indexed: 01/01/2023]
Abstract
The core cerebrospinal fluid (CSF) Alzheimer's disease (AD) biomarkers amyloid beta (Aβ42 and Aβ40), total tau, and phosphorylated tau, have been extensively clinically validated, with very high diagnostic performance for AD, including the early phases of the disease. However, between-center differences in pre-analytical procedures may contribute to variability in measurements across laboratories. To resolve this issue, a workgroup was led by the Alzheimer's Association with experts from both academia and industry. The aim of the group was to develop a simplified and standardized pre-analytical protocol for CSF collection and handling before analysis for routine clinical use, and ultimately to ensure high diagnostic performance and minimize patient misclassification rates. Widespread application of the protocol would help minimize variability in measurements, which would facilitate the implementation of unified cut-off levels across laboratories, and foster the use of CSF biomarkers in AD diagnostics for the benefit of the patients.
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Affiliation(s)
- Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden.,Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | | | | | | | | | | | | | | | - John Lawson
- Fujirebio Diagnostics Inc, Malvern, Pennsylvania, USA
| | | | - José Luis Molinuevo
- BarcelonaBeta Brain Research Center, Pasqual Maragall Foundation Barcelona, Barcelona, Spain.,AD and Other Cognitive Disorders Unit Hospital Clinic, Barcelona, Spain
| | | | | | | | - Charlotte E Teunissen
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | | | | | | | | | - Simone Wahl
- Saladax Biomedical, Inc. Bethlehem, Bethlehem, Pennsylvania, USA
| | | | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience & Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden.,Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK.,UK Dementia Research Institute at UCL, London, UK
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience & Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
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19
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Bruschi M, Petretto A, Cama A, Pavanello M, Bartolucci M, Morana G, Ramenghi LA, Garré ML, Ghiggeri GM, Panfoli I, Candiano G. Potential biomarkers of childhood brain tumor identified by proteomics of cerebrospinal fluid from extraventricular drainage (EVD). Sci Rep 2021; 11:1818. [PMID: 33469081 PMCID: PMC7815722 DOI: 10.1038/s41598-020-80647-w] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Accepted: 11/24/2020] [Indexed: 11/10/2022] Open
Abstract
Brain tumors are the most common solid tumors in childhood. There is the need for biomarkers of residual disease, therapy response and recurrence. Cerebrospinal fluid (CSF) is a source of brain tumor biomarkers. We analyzed the proteome of waste CSF from extraventricular drainage (EVD) from 29 children bearing different brain tumors and 17 controls needing EVD insertion for unrelated causes. 1598 and 1526 proteins were identified by liquid chromatography-coupled tandem mass spectrometry proteomics in CSF control and brain tumor patients, respectively, 263 and 191 proteins being exclusive of either condition. Bioinformatic analysis revealed promising protein biomarkers for the discrimination between control and tumor (TATA-binding protein-associated factor 15 and S100 protein B). Moreover, Thymosin beta-4 (TMSB4X) and CD109, and 14.3.3 and HSP90 alpha could discriminate among other brain tumors and low-grade gliomas plus glyoneuronal tumors/pilocytic astrocytoma, or embryonal tumors/medulloblastoma. Biomarkers were validated by ELISA assay. Our method was able to distinguish among brain tumor vs non-tumor/hemorrhagic conditions (controls) and to differentiate two large classes of brain tumors. Further prospective studies may assess whether the biomarkers proposed by our discovery approach can be identified in other bodily fluids, therefore less invasively, and are useful to guide therapy and predict recurrences.
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Affiliation(s)
- Maurizio Bruschi
- Laboratory of Molecular Nephrology, IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | - Andrea Petretto
- Core Facilities-Clinical Proteomics and Metabolomics, IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | - Armando Cama
- Department of Neurosurgery, IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | - Marco Pavanello
- Department of Neurosurgery, IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | - Martina Bartolucci
- Core Facilities-Clinical Proteomics and Metabolomics, IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | - Giovanni Morana
- Unit of Neuroradiology, IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | | | - Maria Luisa Garré
- Department of Neuroncology, IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | - Gian Marco Ghiggeri
- UO of Nephrology, Dialysis and Transplantation, IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | - Isabella Panfoli
- Dipartimento di Farmacia (DIFAR), Università di Genova, V.le Benedetto XV, 3, 16132, Genoa, Italy.
| | - Giovanni Candiano
- Laboratory of Molecular Nephrology, IRCCS Istituto Giannina Gaslini, Genoa, Italy
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