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Mravinacová S, Alanko V, Bergström S, Bridel C, Pijnenburg Y, Hagman G, Kivipelto M, Teunissen C, Nilsson P, Matton A, Månberg A. CSF protein ratios with enhanced potential to reflect Alzheimer's disease pathology and neurodegeneration. Mol Neurodegener 2024; 19:15. [PMID: 38350954 PMCID: PMC10863228 DOI: 10.1186/s13024-024-00705-z] [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: 08/31/2023] [Accepted: 01/23/2024] [Indexed: 02/15/2024] Open
Abstract
BACKGROUND Amyloid and tau aggregates are considered to cause neurodegeneration and consequently cognitive decline in individuals with Alzheimer's disease (AD). Here, we explore the potential of cerebrospinal fluid (CSF) proteins to reflect AD pathology and cognitive decline, aiming to identify potential biomarkers for monitoring outcomes of disease-modifying therapies targeting these aggregates. METHOD We used a multiplex antibody-based suspension bead array to measure the levels of 49 proteins in CSF from the Swedish GEDOC memory clinic cohort at the Karolinska University Hospital. The cohort comprised 148 amyloid- and tau-negative individuals (A-T-) and 65 amyloid- and tau-positive individuals (A+T+). An independent sample set of 26 A-T- and 26 A+T+ individuals from the Amsterdam Dementia Cohort was used for validation. The measured proteins were clustered based on their correlation to CSF amyloid beta peptides, tau and NfL levels. Further, we used support vector machine modelling to identify protein pairs, matched based on their cluster origin, that reflect AD pathology and cognitive decline with improved performance compared to single proteins. RESULTS The protein-clustering revealed 11 proteins strongly correlated to t-tau and p-tau (tau-associated group), including mainly synaptic proteins previously found elevated in AD such as NRGN, GAP43 and SNCB. Another 16 proteins showed predominant correlation with Aβ42 (amyloid-associated group), including PTPRN2, NCAN and CHL1. Support vector machine modelling revealed that proteins from the two groups combined in pairs discriminated A-T- from A+T+ individuals with higher accuracy compared to single proteins, as well as compared to protein pairs composed of proteins originating from the same group. Moreover, combining the proteins from different groups in ratios (tau-associated protein/amyloid-associated protein) significantly increased their correlation to cognitive decline measured with cognitive scores. The results were validated in an independent cohort. CONCLUSIONS Combining brain-derived proteins in pairs largely enhanced their capacity to discriminate between AD pathology-affected and unaffected individuals and increased their correlation to cognitive decline, potentially due to adjustment of inter-individual variability. With these results, we highlight the potential of protein pairs to monitor neurodegeneration and thereby possibly the efficacy of AD disease-modifying therapies.
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Affiliation(s)
- Sára Mravinacová
- Division of Affinity Proteomics, Department of Protein Science, KTH Royal Institute of Technology, SciLifeLab, Stockholm, Sweden
| | - Vilma Alanko
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Division of Neurogeriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Sofia Bergström
- Division of Affinity Proteomics, Department of Protein Science, KTH Royal Institute of Technology, SciLifeLab, Stockholm, Sweden
| | - Claire Bridel
- Neurochemistry Lab, Department of Laboratory Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit, Amsterdam, Netherlands
| | - Yolande Pijnenburg
- Department of Neurology, Alzheimer Centre, Amsterdam UMC, Vrije Universiteit, Amsterdam, Netherlands
| | - Göran Hagman
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Theme Inflammation and Aging, Karolinska University Hospital, Stockholm, Sweden
| | - Miia Kivipelto
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Ageing Epidemiology (AGE) Research Unit, Imperial College London, London, United Kingdom
- Theme Inflammation and Aging, Karolinska University Hospital, Stockholm, Sweden
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland
| | - Charlotte Teunissen
- Neurochemistry Lab, Department of Laboratory Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit, Amsterdam, Netherlands
| | - Peter Nilsson
- Division of Affinity Proteomics, Department of Protein Science, KTH Royal Institute of Technology, SciLifeLab, Stockholm, Sweden
| | - Anna Matton
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Division of Neurogeriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Ageing Epidemiology (AGE) Research Unit, Imperial College London, London, United Kingdom
| | - Anna Månberg
- Division of Affinity Proteomics, Department of Protein Science, KTH Royal Institute of Technology, SciLifeLab, Stockholm, Sweden.
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2
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Teunissen CE, Kimble L, Bayoumy S, Bolsewig K, Burtscher F, Coppens S, Das S, Gogishvili D, Fernandes Gomes B, Gómez de San José N, Mavrina E, Meda FJ, Mohaupt P, Mravinacová S, Waury K, Wojdała AL, Abeln S, Chiasserini D, Hirtz C, Gaetani L, Vermunt L, Bellomo G, Halbgebauer S, Lehmann S, Månberg A, Nilsson P, Otto M, Vanmechelen E, Verberk IMW, Willemse E, Zetterberg H. Methods to Discover and Validate Biofluid-Based Biomarkers in Neurodegenerative Dementias. Mol Cell Proteomics 2023; 22:100629. [PMID: 37557955 PMCID: PMC10594029 DOI: 10.1016/j.mcpro.2023.100629] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 07/24/2023] [Accepted: 07/31/2023] [Indexed: 08/11/2023] Open
Abstract
Neurodegenerative dementias are progressive diseases that cause neuronal network breakdown in different brain regions often because of accumulation of misfolded proteins in the brain extracellular matrix, such as amyloids or inside neurons or other cell types of the brain. Several diagnostic protein biomarkers in body fluids are being used and implemented, such as for Alzheimer's disease. However, there is still a lack of biomarkers for co-pathologies and other causes of dementia. Such biofluid-based biomarkers enable precision medicine approaches for diagnosis and treatment, allow to learn more about underlying disease processes, and facilitate the development of patient inclusion and evaluation tools in clinical trials. When designing studies to discover novel biofluid-based biomarkers, choice of technology is an important starting point. But there are so many technologies to choose among. To address this, we here review the technologies that are currently available in research settings and, in some cases, in clinical laboratory practice. This presents a form of lexicon on each technology addressing its use in research and clinics, its strengths and limitations, and a future perspective.
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Affiliation(s)
- Charlotte E Teunissen
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; Neurochemistry Lab, Department of Laboratory Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit, Amsterdam, Netherlands.
| | - Leighann Kimble
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; KIN Center for Digital Innovation, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Sherif Bayoumy
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; Neurochemistry Lab, Department of Laboratory Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit, Amsterdam, Netherlands
| | - Katharina Bolsewig
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; Neurochemistry Lab, Department of Laboratory Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit, Amsterdam, Netherlands
| | - Felicia Burtscher
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Salomé Coppens
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; National Measurement Laboratory at LGC, Teddington, United Kingdom
| | - Shreyasee Das
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; ADx NeuroSciences, Gent, Belgium
| | - Dea Gogishvili
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Bárbara Fernandes Gomes
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
| | - Nerea Gómez de San José
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; Department of Neurology, University of Ulm, Ulm, Germany
| | - Ekaterina Mavrina
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; KIN Center for Digital Innovation, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Francisco J Meda
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
| | - Pablo Mohaupt
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; LBPC-PPC, IRMB CHU Montpellier, INM INSERM, Université de Montpellier, Montpellier, France
| | - Sára Mravinacová
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; Division of Affinity Proteomics, Department of Protein Science, KTH Royal Institute of Technology, SciLifeLab, Stockholm, Sweden
| | - Katharina Waury
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Anna Lidia Wojdała
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; Section of Neurology, Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | - Sanne Abeln
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Davide Chiasserini
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; Section of Physiology and Biochemistry, Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | - Christophe Hirtz
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; LBPC-PPC, IRMB CHU Montpellier, INM INSERM, Université de Montpellier, Montpellier, France
| | - Lorenzo Gaetani
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; Section of Neurology, Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | - Lisa Vermunt
- Neurochemistry Lab, Department of Laboratory Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit, Amsterdam, Netherlands
| | - Giovanni Bellomo
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; Section of Neurology, Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | - Steffen Halbgebauer
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; Department of Neurology, University of Ulm, Ulm, Germany; German Center for Neurodegenerative Diseases (DZNE e.V.), Ulm, Germany
| | - Sylvain Lehmann
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; LBPC-PPC, IRMB CHU Montpellier, INM INSERM, Université de Montpellier, Montpellier, France
| | - Anna Månberg
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; Division of Affinity Proteomics, Department of Protein Science, KTH Royal Institute of Technology, SciLifeLab, Stockholm, Sweden
| | - Peter Nilsson
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; Division of Affinity Proteomics, Department of Protein Science, KTH Royal Institute of Technology, SciLifeLab, Stockholm, Sweden
| | - Markus Otto
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; Department of Neurology, University of Ulm, Ulm, Germany; Department of Neurology, Martin-Luther-University Halle-Wittenberg, Halle, Germany
| | - Eugeen Vanmechelen
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; ADx NeuroSciences, Gent, Belgium
| | - Inge M W Verberk
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; Neurochemistry Lab, Department of Laboratory Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit, Amsterdam, Netherlands
| | - Eline Willemse
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; Neurochemistry Lab, Department of Laboratory Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit, Amsterdam, Netherlands
| | - Henrik Zetterberg
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; Department of Psychiatry and Neurochemistry, Institute of Neuroscience and 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, London, UK; UK Dementia Research Institute at UCL, London, UK; Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, China; Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, USA
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Wang L, Wang B, Wu C, Wang J, Sun M. Autism Spectrum Disorder: Neurodevelopmental Risk Factors, Biological Mechanism, and Precision Therapy. Int J Mol Sci 2023; 24:ijms24031819. [PMID: 36768153 PMCID: PMC9915249 DOI: 10.3390/ijms24031819] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 01/12/2023] [Accepted: 01/13/2023] [Indexed: 01/19/2023] Open
Abstract
Autism spectrum disorder (ASD) is a heterogeneous, behaviorally defined neurodevelopmental disorder. Over the past two decades, the prevalence of autism spectrum disorders has progressively increased, however, no clear diagnostic markers and specifically targeted medications for autism have emerged. As a result, neurobehavioral abnormalities, neurobiological alterations in ASD, and the development of novel ASD pharmacological therapy necessitate multidisciplinary collaboration. In this review, we discuss the development of multiple animal models of ASD to contribute to the disease mechanisms of ASD, as well as new studies from multiple disciplines to assess the behavioral pathology of ASD. In addition, we summarize and highlight the mechanistic advances regarding gene transcription, RNA and non-coding RNA translation, abnormal synaptic signaling pathways, epigenetic post-translational modifications, brain-gut axis, immune inflammation and neural loop abnormalities in autism to provide a theoretical basis for the next step of precision therapy. Furthermore, we review existing autism therapy tactics and limits and present challenges and opportunities for translating multidisciplinary knowledge of ASD into clinical practice.
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Tan WY, Hargreaves C, Chen C, Hilal S. A Machine Learning Approach for Early Diagnosis of Cognitive Impairment Using Population-Based Data. J Alzheimers Dis 2023; 91:449-461. [PMID: 36442196 PMCID: PMC9881033 DOI: 10.3233/jad-220776] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
BACKGROUND The major mechanisms of dementia and cognitive impairment are vascular and neurodegenerative processes. Early diagnosis of cognitive impairment can facilitate timely interventions to mitigate progression. OBJECTIVE This study aims to develop a reliable machine learning (ML) model using socio-demographics, vascular risk factors, and structural neuroimaging markers for early diagnosis of cognitive impairment in a multi-ethnic Asian population. METHODS The study consisted of 911 participants from the Epidemiology of Dementia in Singapore study (aged 60- 88 years, 49.6% male). Three ML classifiers, logistic regression, support vector machine, and gradient boosting machine, were developed. Prediction results of independent classifiers were combined in a final ensemble model. Model performances were evaluated on test data using F1 score and area under the receiver operating curve (AUC) methods. Post modelling, SHapely Additive exPlanation (SHAP) was applied on the prediction results to identify the predictors that contribute most to the cognitive impairment prediction. FINDINGS The final ensemble model achieved a F1 score and AUC of 0.87 and 0.80 respectively. Accuracy (0.83), sensitivity (0.86), specificity (0.74) and predictive values (positive 0.88 negative 0.72) of the ensemble model were higher compared to the independent classifiers. Age, ethnicity, highest education attainment and neuroimaging markers were identified as important predictors of cognitive impairment. CONCLUSION This study demonstrates the feasibility of using ML tools to integrate multiple domains of data for reliable diagnosis of early cognitive impairment. The ML model uses easy-to-obtain variables and is scalable for screening individuals with a high risk of developing dementia in a population-based setting.
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Affiliation(s)
- Wei Ying Tan
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore,
Institute of Data Science, National University of Singapore, Singapore
| | - Carol Hargreaves
- Data Analytics Consulting Centre, Faculty of Science, National University of Singapore, Singapore
| | - Christopher Chen
- Department of Pharmacology, National University of Singapore, Singapore,
Memory Aging and Cognition Center, National University Health System, Singapore
| | - Saima Hilal
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore,
Department of Pharmacology, National University of Singapore, Singapore,
Memory Aging and Cognition Center, National University Health System, Singapore,Correspondence to: Saima Hilal, PhD, Saw Swee Hock School of Public Health, National University of
Singapore, Tahir Foundation Building, 12 Science Drive 2, #10-03T, 117549, Singapore. E-mail: ; Department of Pharmacology, Yong Loo Lin School of Medicine, National
University of Singapore, Level 4, Block MD3, 16 Medical Drive, 117600, Singapore. Tel.: +65 65165885;
E-mail:
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5
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Zhong X, Yu J, Jiang F, Chen H, Wang Z, Teng J, Jiao H. A risk prediction model based on machine learning for early cognitive impairment in hypertension: Development and validation study. Front Public Health 2023; 11:1143019. [PMID: 36969637 PMCID: PMC10034177 DOI: 10.3389/fpubh.2023.1143019] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 02/13/2023] [Indexed: 03/29/2023] Open
Abstract
Background Clinical practice guidelines recommend early identification of cognitive impairment in individuals with hypertension with the help of risk prediction tools based on risk factors. Objective The aim of this study was to develop a superior machine learning model based on easily collected variables to predict the risk of early cognitive impairment in hypertensive individuals, which could be used to optimize early cognitive impairment risk assessment strategies. Methods For this cross-sectional study, 733 patients with hypertension (aged 30-85, 48.98% male) enrolled in multi-center hospitals in China were divided into a training group (70%) and a validation group (30%). After least absolute shrinkage and selection operator (LASSO) regression analysis with 5-fold cross-validation determined the modeling variables, three machine learning classifiers, logistic regression (LR), XGBoost (XGB), and gaussian naive bayes (GNB), were developed. The area under the ROC curve (AUC), accuracy, sensitivity, specificity, and F1 score were used to evaluate the model performance. Shape Additive explanation (SHAP) analysis was performed to rank feature importance. Further decision curve analysis (DCA) assessed the clinical performance of the established model and visualized it by nomogram. Results Hip circumference, age, education levels, and physical activity were considered significant predictors of early cognitive impairment in hypertension. The AUC (0.88), F1 score (0.59), accuracy (0.81), sensitivity (0.84), and specificity (0.80) of the XGB model were superior to LR and GNB classifiers. Conclusion The XGB model based on hip circumference, age, educational level, and physical activity has superior predictive performance and it shows promise in predicting the risk of cognitive impairment in hypertensive clinical settings.
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Affiliation(s)
- Xia Zhong
- Department of First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
| | - Jie Yu
- Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
| | - Feng Jiang
- Department of Cardiology, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
| | - Haoyu Chen
- Department of Cardiology, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
| | - Zhenyuan Wang
- Department of Cardiology, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
| | - Jing Teng
- Department of First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
| | - Huachen Jiao
- Department of Cardiology, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
- *Correspondence: Huachen Jiao
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Mavrina E, Kimble L, Waury K, Gogishvili D, Gómez de San José N, Das S, Coppens S, Fernandes Gomes B, Mravinacová S, Wojdała AL, Bolsewig K, Bayoumy S, Burtscher F, Mohaupt P, Willemse E, Teunissen C. Multi-Omics Interdisciplinary Research Integration to Accelerate Dementia Biomarker Development (MIRIADE). Front Neurol 2022; 13:890638. [PMID: 35903119 PMCID: PMC9315267 DOI: 10.3389/fneur.2022.890638] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 05/31/2022] [Indexed: 11/13/2022] Open
Abstract
Proteomics studies have shown differential expression of numerous proteins in dementias but have rarely led to novel biomarker tests for clinical use. The Marie Curie MIRIADE project is designed to experimentally evaluate development strategies to accelerate the validation and ultimate implementation of novel biomarkers in clinical practice, using proteomics-based biomarker development for main dementias as experimental case studies. We address several knowledge gaps that have been identified in the field. First, there is the technology-translation gap of different technologies for the discovery (e.g., mass spectrometry) and the large-scale validation (e.g., immunoassays) of biomarkers. In addition, there is a limited understanding of conformational states of biomarker proteins in different matrices, which affect the selection of reagents for assay development. In this review, we aim to understand the decisions taken in the initial steps of biomarker development, which is done via an interim narrative update of the work of each ESR subproject. The results describe the decision process to shortlist biomarkers from a proteomics to develop immunoassays or mass spectrometry assays for Alzheimer's disease, Lewy body dementia, and frontotemporal dementia. In addition, we explain the approach to prepare the market implementation of novel biomarkers and assays. Moreover, we describe the development of computational protein state and interaction prediction models to support biomarker development, such as the prediction of epitopes. Lastly, we reflect upon activities involved in the biomarker development process to deduce a best-practice roadmap for biomarker development.
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Affiliation(s)
- Ekaterina Mavrina
- MIRIADE Consortium: Multiomics Interdisciplinary Research Integration to Address DEmentia Diagnosis,KIN Center for Digital Innovation, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Leighann Kimble
- MIRIADE Consortium: Multiomics Interdisciplinary Research Integration to Address DEmentia Diagnosis,KIN Center for Digital Innovation, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Katharina Waury
- MIRIADE Consortium: Multiomics Interdisciplinary Research Integration to Address DEmentia Diagnosis,Centre for Integrative Bioinformatics VU (IBIVU) – Center for Integrative Bioinformatics, Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Dea Gogishvili
- MIRIADE Consortium: Multiomics Interdisciplinary Research Integration to Address DEmentia Diagnosis,Centre for Integrative Bioinformatics VU (IBIVU) – Center for Integrative Bioinformatics, Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Nerea Gómez de San José
- MIRIADE Consortium: Multiomics Interdisciplinary Research Integration to Address DEmentia Diagnosis,Department of Neurology, University of Ulm, Ulm, Germany
| | - Shreyasee Das
- MIRIADE Consortium: Multiomics Interdisciplinary Research Integration to Address DEmentia Diagnosis,ADx NeuroSciences, Gent, Belgium
| | - Salomé Coppens
- MIRIADE Consortium: Multiomics Interdisciplinary Research Integration to Address DEmentia Diagnosis,National Measurement Laboratory at Laboratory of the Government Chemist (LGC), Teddington, United Kingdom
| | - Bárbara Fernandes Gomes
- MIRIADE Consortium: Multiomics Interdisciplinary Research Integration to Address DEmentia Diagnosis,Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
| | - Sára Mravinacová
- MIRIADE Consortium: Multiomics Interdisciplinary Research Integration to Address DEmentia Diagnosis,Division of Affinity Proteomics, Department of Protein Science, Kungliga Tekniska Högskolan (KTH) Royal Institute of Technology, SciLifeLab, Stockholm, Sweden
| | - Anna Lidia Wojdała
- MIRIADE Consortium: Multiomics Interdisciplinary Research Integration to Address DEmentia Diagnosis,Laboratory of Clinical Neurochemistry, Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | - Katharina Bolsewig
- MIRIADE Consortium: Multiomics Interdisciplinary Research Integration to Address DEmentia Diagnosis,Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Sherif Bayoumy
- MIRIADE Consortium: Multiomics Interdisciplinary Research Integration to Address DEmentia Diagnosis,Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Felicia Burtscher
- MIRIADE Consortium: Multiomics Interdisciplinary Research Integration to Address DEmentia Diagnosis,Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Pablo Mohaupt
- MIRIADE Consortium: Multiomics Interdisciplinary Research Integration to Address DEmentia Diagnosis,Institute for Regenerative Medicine and Biotherapy - Plateforme de Protéomique Clinique (IRMB-PPC), Institute for Neurosciences of Montpellier (INM), Université de Montpellier, Centre Hospitalier Universitaire de Montpellier, Institut National de la Santé et de la Recherche Médicale (INSERM) Centre National de la Recherche Scientifique (CNRS), Montpellier, France
| | - Eline Willemse
- MIRIADE Consortium: Multiomics Interdisciplinary Research Integration to Address DEmentia Diagnosis,Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Charlotte Teunissen
- MIRIADE Consortium: Multiomics Interdisciplinary Research Integration to Address DEmentia Diagnosis,Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands,*Correspondence: Charlotte Teunissen
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Yu D, Wang L, Kong D, Zhu H. Mapping the Genetic-Imaging-Clinical Pathway with Applications to Alzheimer’s Disease. J Am Stat Assoc 2022; 117:1656-1668. [PMID: 37009529 PMCID: PMC10062702 DOI: 10.1080/01621459.2022.2087658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Alzheimer's disease is a progressive form of dementia that results in problems with memory, thinking, and behavior. It often starts with abnormal aggregation and deposition of β amyloid and tau, followed by neuronal damage such as atrophy of the hippocampi, leading to Alzheimers Disease (AD). The aim of this paper is to map the genetic-imaging-clinical pathway for AD in order to delineate the genetically-regulated brain changes that drive disease progression based on the Alzheimers Disease Neuroimaging Initiative (ADNI) dataset. We develop a novel two-step approach to delineate the association between high-dimensional 2D hippocampal surface exposures and the Alzheimers Disease Assessment Scale (ADAS) cognitive score, while taking into account the ultra-high dimensional clinical and genetic covariates at baseline. Analysis results suggest that the radial distance of each pixel of both hippocampi is negatively associated with the severity of behavioral deficits conditional on observed clinical and genetic covariates. These associations are stronger in Cornu Ammonis region 1 (CA1) and subiculum subregions compared to Cornu Ammonis region 2 (CA2) and Cornu Ammonis region 3 (CA3) subregions. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
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Affiliation(s)
- Dengdeng Yu
- Department of Mathematics, University of Texas at Arlington
| | - Linbo Wang
- Department of Statistical Sciences, University of Toronto
| | - Dehan Kong
- Department of Statistical Sciences, University of Toronto
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina, Chapel Hill for the Alzheimer’s Disease Neuroimaging Initiative*
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Bergström S, Öijerstedt L, Remnestål J, Olofsson J, Ullgren A, Seelaar H, van Swieten JC, Synofzik M, Sanchez-Valle R, Moreno F, Finger E, Masellis M, Tartaglia C, Vandenberghe R, Laforce R, Galimberti D, Borroni B, Butler CR, Gerhard A, Ducharme S, Rohrer JD, Månberg A, Graff C, Nilsson P. A panel of CSF proteins separates genetic frontotemporal dementia from presymptomatic mutation carriers: a GENFI study. Mol Neurodegener 2021; 16:79. [PMID: 34838088 PMCID: PMC8626910 DOI: 10.1186/s13024-021-00499-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Accepted: 11/01/2021] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND A detailed understanding of the pathological processes involved in genetic frontotemporal dementia is critical in order to provide the patients with an optimal future treatment. Protein levels in CSF have the potential to reflect different pathophysiological processes in the brain. We aimed to identify and evaluate panels of CSF proteins with potential to separate symptomatic individuals from individuals without clinical symptoms (unaffected), as well as presymptomatic individuals from mutation non-carriers. METHODS A multiplexed antibody-based suspension bead array was used to analyse levels of 111 proteins in CSF samples from 221 individuals from families with genetic frontotemporal dementia. The data was explored using LASSO and Random forest. RESULTS When comparing affected individuals with unaffected individuals, 14 proteins were identified as potentially important for the separation. Among these, four were identified as most important, namely neurofilament medium polypeptide (NEFM), neuronal pentraxin 2 (NPTX2), neurosecretory protein VGF (VGF) and aquaporin 4 (AQP4). The combined profile of these four proteins successfully separated the two groups, with higher levels of NEFM and AQP4 and lower levels of NPTX2 in affected compared to unaffected individuals. VGF contributed to the models, but the levels were not significantly lower in affected individuals. Next, when comparing presymptomatic GRN and C9orf72 mutation carriers in proximity to symptom onset with mutation non-carriers, six proteins were identified with a potential to contribute to a separation, including progranulin (GRN). CONCLUSION In conclusion, we have identified several proteins with the combined potential to separate affected individuals from unaffected individuals, as well as proteins with potential to contribute to the separation between presymptomatic individuals and mutation non-carriers. Further studies are needed to continue the investigation of these proteins and their potential association to the pathophysiological mechanisms in genetic FTD.
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Affiliation(s)
- Sofia Bergström
- Division of Affinity Proteomics, Department of Protein Science, KTH Royal Institute of Technology, SciLifeLab, Stockholm, Sweden
- Swedish FTD Initiative, Stockholm, Sweden
| | - Linn Öijerstedt
- Swedish FTD Initiative, Stockholm, Sweden
- Department of Neurobiology, Care Sciences and Society, Division of Neurogeriatrics, Karolinska Institutet, Unit of Hereditary Dementias, Theme Aging, Karolinska University Hospital, Solna, Sweden
- Unit for Hereditary Dementias, Theme Aging, Karolinska University Hospital, Solna, Sweden
| | - Julia Remnestål
- Division of Affinity Proteomics, Department of Protein Science, KTH Royal Institute of Technology, SciLifeLab, Stockholm, Sweden
- Swedish FTD Initiative, Stockholm, Sweden
| | - Jennie Olofsson
- Division of Affinity Proteomics, Department of Protein Science, KTH Royal Institute of Technology, SciLifeLab, Stockholm, Sweden
- Swedish FTD Initiative, Stockholm, Sweden
| | - Abbe Ullgren
- Swedish FTD Initiative, Stockholm, Sweden
- Department of Neurobiology, Care Sciences and Society, Division of Neurogeriatrics, Karolinska Institutet, Unit of Hereditary Dementias, Theme Aging, Karolinska University Hospital, Solna, Sweden
| | - Harro Seelaar
- Department of Neurology, Erasmus Medical Centre, Rotterdam, Netherlands
| | | | - Matthis Synofzik
- Department of Neurodegenerative Diseases, Hertie-Institute for Clinical Brain Research and Center of Neurology, University of Tübingen, Tübingen, Germany
- Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
| | - Raquel Sanchez-Valle
- Alzheimer’s disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic, Institut d’Investigacións Biomèdiques August Pi I Sunyer, University of Barcelona, Barcelona, Spain
| | - Fermin Moreno
- Cognitive Disorders Unit, Department of Neurology, Donostia University Hospital, San Sebastian, Gipuzkoa Spain
- Neuroscience Area, Biodonostia Health Research Institute, San Sebastian, Gipuzkoa Spain
| | - Elizabeth Finger
- Department of Clinical Neurological Sciences, University of Western Ontario, London, Ontario Canada
| | - Mario Masellis
- Sunnybrook Health Sciences Centre, Sunnybrook Research Institute, University of Toronto, Toronto, Canada
| | - Carmela Tartaglia
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, Canada
| | - Rik Vandenberghe
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium
- Neurology Service, University Hospitals Leuven, Leuven, Belgium
- Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - Robert Laforce
- Clinique Interdisciplinaire de Mémoire, Département des Sciences Neurologiques, CHU de Québec, and Faculté de Médecine, Université Laval, QC, Canada
| | - Daniela Galimberti
- Fondazione IRCCS Ospedale Policlinico, Milan, Italy
- University of Milan, Centro Dino Ferrari, Milan, Italy
| | - Barbara Borroni
- Centre for Neurodegenerative Disorders, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Chris R. Butler
- Nuffield Department of Clinical Neurosciences, Medical Sciences Division, University of Oxford, Oxford, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Alexander Gerhard
- Division of Neuroscience and Experimental Psychology, Wolfson Molecular Imaging Centre, University of Manchester, Manchester, UK
- Departments of Geriatric Medicine and Nuclear Medicine, University of Duisburg- Essen, Duisburg, Germany
| | - Simon Ducharme
- Department of Psychiatry, Douglas Mental Health University Institute, McGill University, Montreal, Québec Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec Canada
| | - Jonathan D. Rohrer
- Department of Neurodegenerative Disease, Dementia Research Centre, UCL Institute of Neurology, Queen Square, London, UK
| | - Anna Månberg
- Division of Affinity Proteomics, Department of Protein Science, KTH Royal Institute of Technology, SciLifeLab, Stockholm, Sweden
- Swedish FTD Initiative, Stockholm, Sweden
| | - Caroline Graff
- Swedish FTD Initiative, Stockholm, Sweden
- Department of Neurobiology, Care Sciences and Society, Division of Neurogeriatrics, Karolinska Institutet, Unit of Hereditary Dementias, Theme Aging, Karolinska University Hospital, Solna, Sweden
- Unit for Hereditary Dementias, Theme Aging, Karolinska University Hospital, Solna, Sweden
| | - Peter Nilsson
- Division of Affinity Proteomics, Department of Protein Science, KTH Royal Institute of Technology, SciLifeLab, Stockholm, Sweden
- Swedish FTD Initiative, Stockholm, Sweden
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