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1. Whole-genome sequencing analysis reveals new susceptibility loci and structural variants associated with progressive supranuclear palsy

Author

Wang, Hui, Chang, Timothy S, Dombroski, Beth A, Cheng, Po-Liang, Patil, Vishakha, Valiente-Banuet, Leopoldo, Farrell, Kurt, Mclean, Catriona, Molina-Porcel, Laura, Rajput, Alex, De Deyn, Peter Paul, Le Bastard, Nathalie, Gearing, Marla, Kaat, Laura Donker, Van Swieten, John C, Dopper, Elise, Ghetti, Bernardino F, Newell, Kathy L, Troakes, Claire, de Yébenes, Justo G, Rábano-Gutierrez, Alberto, Meller, Tina, Oertel, Wolfgang H, Respondek, Gesine, Stamelou, Maria, Arzberger, Thomas, Roeber, Sigrun, Müller, Ulrich, Hopfner, Franziska, Pastor, Pau, Brice, Alexis, Durr, Alexandra, Le Ber, Isabelle, Beach, Thomas G, Serrano, Geidy E, Hazrati, Lili-Naz, Litvan, Irene, Rademakers, Rosa, Ross, Owen A, Galasko, Douglas, Boxer, Adam L, Miller, Bruce L, Seeley, Willian W, Van Deerlin, Vivanna M, Lee, Edward B, White, Charles L, Morris, Huw, de Silva, Rohan, Crary, John F, Goate, Alison M, Friedman, Jeffrey S, Leung, Yuk Yee, Coppola, Giovanni, Naj, Adam C, Wang, Li-San, Dalgard, Clifton, Dickson, Dennis W, Höglinger, Günter U, Schellenberg, Gerard D, Geschwind, Daniel H, and Lee, Wan-Ping

Subjects
Biological Sciences, Genetics, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Neurodegenerative, Acquired Cognitive Impairment, Frontotemporal Dementia (FTD), Rare Diseases, Neurosciences, Dementia, Human Genome, Brain Disorders, Biotechnology, Aging, Alzheimer's Disease Related Dementias (ADRD), Alzheimer's Disease, 2.1 Biological and endogenous factors, Neurological, Progressive Supranuclear Palsy, Whole-Genome Sequencing, Genome-Wide Association Study, Structural Variants, Apolipoprotein E, P. S. P. genetics study group, Humans, Supranuclear Palsy, Progressive, Genetic Predisposition to Disease, Polymorphism, Single Nucleotide, Aged, Aged, 80 and over, Middle Aged, Female, Male, Whole Genome Sequencing, Clinical Sciences, Neurology & Neurosurgery, Biochemistry and cell biology
Abstract

BackgroundProgressive supranuclear palsy (PSP) is a rare neurodegenerative disease characterized by the accumulation of aggregated tau proteins in astrocytes, neurons, and oligodendrocytes. Previous genome-wide association studies for PSP were based on genotype array, therefore, were inadequate for the analysis of rare variants as well as larger mutations, such as small insertions/deletions (indels) and structural variants (SVs).MethodIn this study, we performed whole genome sequencing (WGS) and conducted association analysis for single nucleotide variants (SNVs), indels, and SVs, in a cohort of 1,718 cases and 2,944 controls of European ancestry. Of the 1,718 PSP individuals, 1,441 were autopsy-confirmed and 277 were clinically diagnosed.ResultsOur analysis of common SNVs and indels confirmed known genetic loci at MAPT, MOBP, STX6, SLCO1A2, DUSP10, and SP1, and further uncovered novel signals in APOE, FCHO1/MAP1S, KIF13A, TRIM24, TNXB, and ELOVL1. Notably, in contrast to Alzheimer's disease (AD), we observed the APOE ε2 allele to be the risk allele in PSP. Analysis of rare SNVs and indels identified significant association in ZNF592 and further gene network analysis identified a module of neuronal genes dysregulated in PSP. Moreover, seven common SVs associated with PSP were observed in the H1/H2 haplotype region (17q21.31) and other loci, including IGH, PCMT1, CYP2A13, and SMCP. In the H1/H2 haplotype region, there is a burden of rare deletions and duplications (P = 6.73 × 10-3) in PSP.ConclusionsThrough WGS, we significantly enhanced our understanding of the genetic basis of PSP, providing new targets for exploring disease mechanisms and therapeutic interventions.

Published
2024

2. An Interpretable Machine Learning Model with Deep Learning-based Imaging Biomarkers for Diagnosis of Alzheimer's Disease

Author

Kang, Wenjie, Li, Bo, Papma, Janne M., Jiskoot, Lize C., De Deyn, Peter Paul, Biessels, Geert Jan, Claassen, Jurgen A. H. R., Middelkoop, Huub A. M., van der Flier, Wiesje M., Ramakers, Inez H. G. B., Klein, Stefan, and Bron, Esther E.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Machine learning methods have shown large potential for the automatic early diagnosis of Alzheimer's Disease (AD). However, some machine learning methods based on imaging data have poor interpretability because it is usually unclear how they make their decisions. Explainable Boosting Machines (EBMs) are interpretable machine learning models based on the statistical framework of generalized additive modeling, but have so far only been used for tabular data. Therefore, we propose a framework that combines the strength of EBM with high-dimensional imaging data using deep learning-based feature extraction. The proposed framework is interpretable because it provides the importance of each feature. We validated the proposed framework on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, achieving accuracy of 0.883 and area-under-the-curve (AUC) of 0.970 on AD and control classification. Furthermore, we validated the proposed framework on an external testing set, achieving accuracy of 0.778 and AUC of 0.887 on AD and subjective cognitive decline (SCD) classification. The proposed framework significantly outperformed an EBM model using volume biomarkers instead of deep learning-based features, as well as an end-to-end convolutional neural network (CNN) with optimized architecture., Comment: 11 pages, 5 figures

Published
2023

3. New insights into the genetic etiology of Alzheimer’s disease and related dementias

Author

Bellenguez, Céline, Küçükali, Fahri, Jansen, Iris E, Kleineidam, Luca, Moreno-Grau, Sonia, Amin, Najaf, Naj, Adam C, Campos-Martin, Rafael, Grenier-Boley, Benjamin, Andrade, Victor, Holmans, Peter A, Boland, Anne, Damotte, Vincent, van der Lee, Sven J, Costa, Marcos R, Kuulasmaa, Teemu, Yang, Qiong, de Rojas, Itziar, Bis, Joshua C, Yaqub, Amber, Prokic, Ivana, Chapuis, Julien, Ahmad, Shahzad, Giedraitis, Vilmantas, Aarsland, Dag, Garcia-Gonzalez, Pablo, Abdelnour, Carla, Alarcón-Martín, Emilio, Alcolea, Daniel, Alegret, Montserrat, Alvarez, Ignacio, Álvarez, Victoria, Armstrong, Nicola J, Tsolaki, Anthoula, Antúnez, Carmen, Appollonio, Ildebrando, Arcaro, Marina, Archetti, Silvana, Pastor, Alfonso Arias, Arosio, Beatrice, Athanasiu, Lavinia, Bailly, Henri, Banaj, Nerisa, Baquero, Miquel, Barral, Sandra, Beiser, Alexa, Pastor, Ana Belén, Below, Jennifer E, Benchek, Penelope, Benussi, Luisa, Berr, Claudine, Besse, Céline, Bessi, Valentina, Binetti, Giuliano, Bizarro, Alessandra, Blesa, Rafael, Boada, Mercè, Boerwinkle, Eric, Borroni, Barbara, Boschi, Silvia, Bossù, Paola, Bråthen, Geir, Bressler, Jan, Bresner, Catherine, Brodaty, Henry, Brookes, Keeley J, Brusco, Luis Ignacio, Buiza-Rueda, Dolores, Bûrger, Katharina, Burholt, Vanessa, Bush, William S, Calero, Miguel, Cantwell, Laura B, Chene, Geneviève, Chung, Jaeyoon, Cuccaro, Michael L, Carracedo, Ángel, Cecchetti, Roberta, Cervera-Carles, Laura, Charbonnier, Camille, Chen, Hung-Hsin, Chillotti, Caterina, Ciccone, Simona, Claassen, Jurgen AHR, Clark, Christopher, Conti, Elisa, Corma-Gómez, Anaïs, Costantini, Emanuele, Custodero, Carlo, Daian, Delphine, Dalmasso, Maria Carolina, Daniele, Antonio, Dardiotis, Efthimios, Dartigues, Jean-François, de Deyn, Peter Paul, de Paiva Lopes, Katia, de Witte, Lot D, Debette, Stéphanie, Deckert, Jürgen, and del Ser, Teodoro

Subjects
Biochemistry and Cell Biology, Genetics, Biological Sciences, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Dementia, Neurodegenerative, Alzheimer's Disease, Human Genome, Prevention, Aging, Brain Disorders, Acquired Cognitive Impairment, Neurosciences, Genetic Testing, 2.1 Biological and endogenous factors, Aetiology, Neurological, Alzheimer Disease, Cognitive Dysfunction, Genome-Wide Association Study, Humans, tau Proteins, EADB, GR@ACE, DEGESCO, EADI, GERAD, Demgene, FinnGen, ADGC, CHARGE, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract

Characterization of the genetic landscape of Alzheimer's disease (AD) and related dementias (ADD) provides a unique opportunity for a better understanding of the associated pathophysiological processes. We performed a two-stage genome-wide association study totaling 111,326 clinically diagnosed/'proxy' AD cases and 677,663 controls. We found 75 risk loci, of which 42 were new at the time of analysis. Pathway enrichment analyses confirmed the involvement of amyloid/tau pathways and highlighted microglia implication. Gene prioritization in the new loci identified 31 genes that were suggestive of new genetically associated processes, including the tumor necrosis factor alpha pathway through the linear ubiquitin chain assembly complex. We also built a new genetic risk score associated with the risk of future AD/dementia or progression from mild cognitive impairment to AD/dementia. The improvement in prediction led to a 1.6- to 1.9-fold increase in AD risk from the lowest to the highest decile, in addition to effects of age and the APOE ε4 allele.

Published
2022

4. Tau-targeting antisense oligonucleotide MAPTRx in mild Alzheimer’s disease: a phase 1b, randomized, placebo-controlled trial

Author

Mummery, Catherine J., Börjesson-Hanson, Anne, Blackburn, Daniel J., Vijverberg, Everard G. B., De Deyn, Peter Paul, Ducharme, Simon, Jonsson, Michael, Schneider, Anja, Rinne, Juha O., Ludolph, Albert C., Bodenschatz, Ralf, Kordasiewicz, Holly, Swayze, Eric E., Fitzsimmons, Bethany, Mignon, Laurence, Moore, Katrina M., Yun, Chris, Baumann, Tiffany, Li, Dan, Norris, Daniel A., Crean, Rebecca, Graham, Danielle L., Huang, Ellen, Ratti, Elena, Bennett, C. Frank, Junge, Candice, and Lane, Roger M.

Published
2023
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5. Cross-Cohort Generalizability of Deep and Conventional Machine Learning for MRI-based Diagnosis and Prediction of Alzheimer's Disease

Author

Bron, Esther E., Klein, Stefan, Papma, Janne M., Jiskoot, Lize C., Venkatraghavan, Vikram, Linders, Jara, Aalten, Pauline, De Deyn, Peter Paul, Biessels, Geert Jan, Claassen, Jurgen A. H. R., Middelkoop, Huub A. M., Smits, Marion, Niessen, Wiro J., van Swieten, John C., van der Flier, Wiesje M., Ramakers, Inez H. G. B., and van der Lugt, Aad

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Physics - Medical Physics
Abstract

This work validates the generalizability of MRI-based classification of Alzheimer's disease (AD) patients and controls (CN) to an external data set and to the task of prediction of conversion to AD in individuals with mild cognitive impairment (MCI). We used a conventional support vector machine (SVM) and a deep convolutional neural network (CNN) approach based on structural MRI scans that underwent either minimal pre-processing or more extensive pre-processing into modulated gray matter (GM) maps. Classifiers were optimized and evaluated using cross-validation in the ADNI (334 AD, 520 CN). Trained classifiers were subsequently applied to predict conversion to AD in ADNI MCI patients (231 converters, 628 non-converters) and in the independent Health-RI Parelsnoer data set. From this multi-center study representing a tertiary memory clinic population, we included 199 AD patients, 139 participants with subjective cognitive decline, 48 MCI patients converting to dementia, and 91 MCI patients who did not convert to dementia. AD-CN classification based on modulated GM maps resulted in a similar AUC for SVM (0.940) and CNN (0.933). Application to conversion prediction in MCI yielded significantly higher performance for SVM (0.756) than for CNN (0.742). In external validation, performance was slightly decreased. For AD-CN, it again gave similar AUCs for SVM (0.896) and CNN (0.876). For prediction in MCI, performances decreased for both SVM (0.665) and CNN (0.702). Both with SVM and CNN, classification based on modulated GM maps significantly outperformed classification based on minimally processed images. Deep and conventional classifiers performed equally well for AD classification and their performance decreased only slightly when applied to the external cohort. We expect that this work on external validation contributes towards translation of machine learning to clinical practice.

Published
2020

7. PapRIV, a BV-2 microglial cell activating quorum sensing peptide.

Author

Janssens, Yorick, Debunne, Nathan, De Spiegeleer, Anton, Wynendaele, Evelien, Planas, Marta, Feliu, Lidia, Quarta, Alessandra, Claes, Christel, Van Dam, Debby, De Deyn, Peter Paul, Ponsaerts, Peter, Blurton-Jones, Matthew, and De Spiegeleer, Bart

Subjects
Blood-Brain Barrier, Brain, Microglia, Peptides, Inflammation Mediators, Culture Media, Conditioned, Protein Transport, Quorum Sensing, Feedback, Physiological, Biomarkers, Gastrointestinal Microbiome, Host Microbial Interactions, Neurosciences, Infectious Diseases, 1.1 Normal biological development and functioning
Abstract

Quorum sensing peptides (QSPs) are bacterial peptides produced by Gram-positive bacteria to communicate with their peers in a cell-density dependent manner. These peptides do not only act as interbacterial communication signals, but can also have effects on the host. Compelling evidence demonstrates the presence of a gut-brain axis and more specifically, the role of the gut microbiota in microglial functioning. The aim of this study is to investigate microglial activating properties of a selected QSP (PapRIV) which is produced by Bacillus cereus species. PapRIV showed in vitro activating properties of BV-2 microglia cells and was able to cross the in vitro Caco-2 cell model and reach the brain. In vivo peptide presence was also demonstrated in mouse plasma. The peptide caused induction of IL-6, TNFα and ROS expression and increased the fraction of ameboid BV-2 microglia cells in an NF-κB dependent manner. Different metabolites were identified in serum, of which the main metabolite still remained active. PapRIV is thus able to cross the gastro-intestinal tract and the blood-brain barrier and shows in vitro activating properties in BV-2 microglia cells, hereby indicating a potential role of this quorum sensing peptide in gut-brain interaction.

Published
2021

8. Author Correction: Tau-targeting antisense oligonucleotide MAPTRx in mild Alzheimer’s disease: a phase 1b, randomized, placebo-controlled trial

Author

Mummery, Catherine J., Börjesson-Hanson, Anne, Blackburn, Daniel J., Vijverberg, Everard G. B., De Deyn, Peter Paul, Ducharme, Simon, Jonsson, Michael, Schneider, Anja, Rinne, Juha O., Ludolph, Albert C., Bodenschatz, Ralf, Kordasiewicz, Holly, Swayze, Eric E., Fitzsimmons, Bethany, Mignon, Laurence, Moore, Katrina M., Yun, Chris, Baumann, Tiffany, Li, Dan, Norris, Daniel A., Crean, Rebecca, Graham, Danielle L., Huang, Ellen, Ratti, Elena, Bennett, C. Frank, Junge, Candice, and Lane, Roger M.

Published
2024
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9. Genome-wide meta-analysis for Alzheimer’s disease cerebrospinal fluid biomarkers

Author

Jansen, Iris E., van der Lee, Sven J., Gomez-Fonseca, Duber, de Rojas, Itziar, Dalmasso, Maria Carolina, Grenier-Boley, Benjamin, Zettergren, Anna, Mishra, Aniket, Ali, Muhammad, Andrade, Victor, Bellenguez, Céline, Kleineidam, Luca, Küçükali, Fahri, Sung, Yun Ju, Tesí, Niccolo, Vromen, Ellen M., Wightman, Douglas P., Alcolea, Daniel, Alegret, Montserrat, Alvarez, Ignacio, Amouyel, Philippe, Athanasiu, Lavinia, Bahrami, Shahram, Bailly, Henri, Belbin, Olivia, Bergh, Sverre, Bertram, Lars, Biessels, Geert Jan, Blennow, Kaj, Blesa, Rafael, Boada, Mercè, Boland, Anne, Buerger, Katharina, Carracedo, Ángel, Cervera-Carles, Laura, Chene, Geneviève, Claassen, Jurgen A. H. R., Debette, Stephanie, Deleuze, Jean-Francois, de Deyn, Peter Paul, Diehl-Schmid, Janine, Djurovic, Srdjan, Dols-Icardo, Oriol, Dufouil, Carole, Duron, Emmanuelle, Düzel, Emrah, Fladby, Tormod, Fortea, Juan, Frölich, Lutz, García-González, Pablo, Garcia-Martinez, Maria, Giegling, Ina, Goldhardt, Oliver, Gobom, Johan, Grimmer, Timo, Haapasalo, Annakaisa, Hampel, Harald, Hanon, Olivier, Hausner, Lucrezia, Heilmann-Heimbach, Stefanie, Helisalmi, Seppo, Heneka, Michael T., Hernández, Isabel, Herukka, Sanna-Kaisa, Holstege, Henne, Jarholm, Jonas, Kern, Silke, Knapskog, Anne-Brita, Koivisto, Anne M., Kornhuber, Johannes, Kuulasmaa, Teemu, Lage, Carmen, Laske, Christoph, Leinonen, Ville, Lewczuk, Piotr, Lleó, Alberto, de Munain, Adolfo López, Lopez-Garcia, Sara, Maier, Wolfgang, Marquié, Marta, Mol, Merel O., Montrreal, Laura, Moreno, Fermin, Moreno-Grau, Sonia, Nicolas, Gael, Nöthen, Markus M., Orellana, Adelina, Pålhaugen, Lene, Papma, Janne M., Pasquier, Florence, Perneczky, Robert, Peters, Oliver, Pijnenburg, Yolande A. L., Popp, Julius, Posthuma, Danielle, Pozueta, Ana, Priller, Josef, Puerta, Raquel, Quintela, Inés, Ramakers, Inez, Rodriguez-Rodriguez, Eloy, Rujescu, Dan, Saltvedt, Ingvild, Sanchez-Juan, Pascual, Scheltens, Philip, Scherbaum, Norbert, Schmid, Matthias, Schneider, Anja, Selbæk, Geir, Selnes, Per, Shadrin, Alexey, Skoog, Ingmar, Soininen, Hilkka, Tárraga, Lluís, Teipel, Stefan, Tijms, Betty, Tsolaki, Magda, Van Broeckhoven, Christine, Van Dongen, Jasper, van Swieten, John C., Vandenberghe, Rik, Vidal, Jean-Sébastien, Visser, Pieter J., Vogelgsang, Jonathan, Waern, Margda, Wagner, Michael, Wiltfang, Jens, Wittens, Mandy M. J., Zetterberg, Henrik, Zulaica, Miren, van Duijn, Cornelia M., Bjerke, Maria, Engelborghs, Sebastiaan, Jessen, Frank, Teunissen, Charlotte E., Pastor, Pau, Hiltunen, Mikko, Ingelsson, Martin, Andreassen, Ole A., Clarimón, Jordi, Sleegers, Kristel, Ruiz, Agustín, Ramirez, Alfredo, Cruchaga, Carlos, Lambert, Jean-Charles, and van der Flier, Wiesje

Published
2022
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10. Psychiatric Disorders in Dementia

Author

Vermeiren, Yannick, Van Dam, Debby, de Vries, Maartje, De Deyn, Peter Paul, Dierckx, Rudi A.J.O., editor, Otte, Andreas, editor, de Vries, Erik F. J., editor, van Waarde, Aren, editor, and Sommer, Iris E., editor

Published
2021
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11. SPECT and PET in Late-Life Depression

Author

Vieira, Tiago S., Voshaar, Richard Oude, De Deyn, Peter Paul, Dierckx, Rudi A. J. O., van Waarde, Aren, Dierckx, Rudi A.J.O., editor, Otte, Andreas, editor, de Vries, Erik F. J., editor, van Waarde, Aren, editor, and Sommer, Iris E., editor

Published
2021
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13. Animal Models for Brain Research

Author

Van Dam, Debby, De Deyn, Peter Paul, Dierckx, Rudi A.J.O., editor, Otte, Andreas, editor, de Vries, Erik F.J., editor, van Waarde, Aren, editor, and Lammertsma, Adriaan A., editor

Published
2021
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15. Nuclear Imaging in Frontotemporal Dementia

Author

Reesink, Fransje E., Stormezand, Gilles N., Dierckx, Rudi A. J. O., De Deyn, Peter Paul, Dierckx, Rudi A. J. O., editor, Otte, Andreas, editor, de Vries, Erik F. J., editor, van Waarde, Aren, editor, and Leenders, Klaus L., editor

Published
2021
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19. Early imaging biomarkers of lung cancer, COPD and coronary artery disease in the general population : rationale and design of the ImaLife (Imaging in Lifelines) Study

Author

Xia, Congying, Rook, Mieneke, Pelgrim, Gert Jan, Sidorenkov, Grigory, Wisselink, Hendrik J., van Bolhuis, Jurjen N., van Ooijen, Peter M. A., Guo, Jiapan, Oudkerk, Matthijs, Groen, Harry, van den Berge, Maarten, van der Harst, Pim, Dijkstra, Hildebrand, Vonder, Marleen, Heuvelmans, Marjolein A., Dorrius, Monique D., De Deyn, Peter Paul, de Bock, Geertruida H., Dotinga, Aafje, and Vliegenthart, Rozemarijn

Published
2020

21. Brain age as a biomarker for pathological versus healthy ageing – a REMEMBER study

Author

Wittens, Mandy Melissa Jane, Denissen, Stijn, Sima, Diana Maria, Fransen, Erik, Niemantsverdriet, Ellis, Bastin, Christine, Benoit, Florence, Bergmans, B., Bier, Jean Christophe, De Deyn, Peter Paul, Deryck, Olivier, Hanseeuw, Bernard, Ivanoiu, Adrian, Picard, Gaëtane, Ribbens, Annemie, Salmon, Eric, Segers, Kurt, Sieben, Anne, Struyfs, Hanne, Thiery, Evert, Tournoy, Jos, Van Binst, Anne, Versijpt, Jan, Smeets, Dirk, Bjerke, M., Nagels, Guy, Engelborghs, Sebastiaan, Wittens, Mandy Melissa Jane, Denissen, Stijn, Sima, Diana Maria, Fransen, Erik, Niemantsverdriet, Ellis, Bastin, Christine, Benoit, Florence, Bergmans, B., Bier, Jean Christophe, De Deyn, Peter Paul, Deryck, Olivier, Hanseeuw, Bernard, Ivanoiu, Adrian, Picard, Gaëtane, Ribbens, Annemie, Salmon, Eric, Segers, Kurt, Sieben, Anne, Struyfs, Hanne, Thiery, Evert, Tournoy, Jos, Van Binst, Anne, Versijpt, Jan, Smeets, Dirk, Bjerke, M., Nagels, Guy, and Engelborghs, Sebastiaan

Abstract

Objectives: This study aimed to evaluate the potential clinical value of a new brain age prediction model as a single interpretable variable representing the condition of our brain. Among many clinical use cases, brain age could be a novel outcome measure to assess the preventive effect of life-style interventions. Methods: The REMEMBER study population (N = 742) consisted of cognitively healthy (HC,N = 91), subjective cognitive decline (SCD,N = 65), mild cognitive impairment (MCI,N = 319) and AD dementia (ADD,N = 267) subjects. Automated brain volumetry of global, cortical, and subcortical brain structures computed by the CE-labeled and FDA-cleared software icobrain dm (dementia) was retrospectively extracted from T1-weighted MRI sequences that were acquired during clinical routine at participating memory clinics from the Belgian Dementia Council. The volumetric features, along with sex, were combined into a weighted sum using a linear model, and were used to predict ‘brain age’ and ‘brain predicted age difference’ (BPAD = brain age–chronological age) for every subject. Results: MCI and ADD patients showed an increased brain age compared to their chronological age. Overall, brain age outperformed BPAD and chronological age in terms of classification accuracy across the AD spectrum. There was a weak-to-moderate correlation between total MMSE score and both brain age (r = -0.38,p <.001) and BPAD (r = -0.26,p <.001). Noticeable trends, but no significant correlations, were found between BPAD and incidence of conversion from MCI to ADD, nor between BPAD and conversion time from MCI to ADD. BPAD was increased in heavy alcohol drinkers compared to non-/sporadic (p =.014) and moderate (p =.040) drinkers. Conclusions: Brain age and associated BPAD have the potential to serve as indicators for, and to evaluate the impact of lifestyle modifications or interventions on, brain health., SCOPUS: ar.j, info:eu-repo/semantics/published

Published
2024

22. Accumulation of Ambient Black Carbon Particles Within Key Memory-Related Brain Regions

Author

Vanbrabant, Kenneth, van Dam, Debby, Bongaerts, Eva, Vermeiren, Yannick, Bové, Hannelore, Hellings, Niels, Ameloot, Marcel, Plusquin, Michelle, de Deyn, Peter Paul, Nawrot, Tim, Vanbrabant, Kenneth, van Dam, Debby, Bongaerts, Eva, Vermeiren, Yannick, Bové, Hannelore, Hellings, Niels, Ameloot, Marcel, Plusquin, Michelle, de Deyn, Peter Paul, and Nawrot, Tim

Abstract

Importance Ambient air pollution is a worldwide problem, not only related to respiratory and cardiovascular diseases but also to neurodegenerative disorders. Different pathways on how air pollutants could affect the brain are already known, but direct evidence of the presence of ambient particles (or nanoparticles) in the human adult brain is limited.Objective To examine whether ambient black carbon particles can translocate to the brain and observe their biodistribution within the different brain regions.Design, Setting, and Participants In this case series a label-free and biocompatible detection technique of nonincandescence-related white light generation was used to screen different regions of biobanked brains of 4 individuals from Belgium with neuropathologically confirmed Alzheimer disease for the presence of black carbon particles. The selected biological specimens were acquired and subsequently stored in a biorepository between April 2013 and April 2017. Black carbon measurements and data analysis were conducted between June 2020 and December 2022.Main Outcomes and Measures The black carbon load was measured in various human brain regions. A Kruskal-Wallis test was used to compare black carbon loads across these regions, followed by Dunn multiple comparison tests.Results Black carbon particles were directly visualized in the human brain of 4 individuals (3 women [75%]; mean [SD] age, 86 [13] years). Screening of the postmortem brain regions showed a significantly higher median (IQR) number of black carbon particles present in the thalamus (433.6 [289.5-540.2] particles per mm3), the prefrontal cortex including the olfactory bulb (420.8 [306.6-486.8] particles per mm3), and the hippocampus (364.7 [342.0-448.7] particles per mm3) compared with the cingulate cortex (192.3 [164.2-277.5] particles per mm3), amygdala (217.5 [147.3-244.5] particles per mm3), and the superior temporal gyrus (204.9 [167.9-236.8] particles per mm3).Conclusions and Relevance This case

Published
2024

23. Amyloid-β peptide signature associated with cerebral amyloid angiopathy in familial Alzheimer's disease with APPdup and Down syndrome.

Author

Kasri, Amal, Camporesi, Elena, Gkanatsiou, Eleni, Boluda, Susana, Brinkmalm, Gunnar, Stimmer, Lev, Ge, Junyue, Hanrieder, Jörg, Villain, Nicolas, Duyckaerts, Charles, Vermeiren, Yannick, Pape, Sarah E., Nicolas, Gaël, Laquerrière, Annie, De Deyn, Peter Paul, Wallon, David, Blennow, Kaj, Strydom, Andre, Zetterberg, Henrik, and Potier, Marie-Claude

Subjects
CEREBRAL amyloid angiopathy, ALZHEIMER'S disease, PEPTIDES, DOWN syndrome, POLYNEUROPATHIES, TAU proteins
Abstract

Alzheimer's disease (AD) is characterized by extracellular amyloid plaques containing amyloid-β (Aβ) peptides, intraneuronal neurofibrillary tangles, extracellular neuropil threads, and dystrophic neurites surrounding plaques composed of hyperphosphorylated tau protein (pTau). Aβ can also deposit in blood vessel walls leading to cerebral amyloid angiopathy (CAA). While amyloid plaques in AD brains are constant, CAA varies among cases. The study focuses on differences observed between rare and poorly studied patient groups with APP duplications (APPdup) and Down syndrome (DS) reported to have higher frequencies of elevated CAA levels in comparison to sporadic AD (sAD), most of APP mutations, and controls. We compared Aβ and tau pathologies in postmortem brain tissues across cases and Aβ peptides using mass spectrometry (MS). We further characterized the spatial distribution of Aβ peptides with MS-brain imaging. While intraparenchymal Aβ deposits were numerous in sAD, DS with AD (DS-AD) and AD with APP mutations, these were less abundant in APPdup. On the contrary, Aβ deposits in the blood vessels were abundant in APPdup and DS-AD while only APPdup cases displayed high Aβ deposits in capillaries. Investigation of Aβ peptide profiles showed a specific increase in Aβx-37, Aβx-38 and Aβx-40 but not Aβx-42 in APPdup cases and to a lower extent in DS-AD cases. Interestingly, N-truncated Aβ2-x peptides were particularly increased in APPdup compared to all other groups. This result was confirmed by MS-imaging of leptomeningeal and parenchymal vessels from an APPdup case, suggesting that CAA is associated with accumulation of shorter Aβ peptides truncated both at N- and C-termini in blood vessels. Altogether, this study identified striking differences in the localization and composition of Aβ deposits between AD cases, particularly APPdup and DS-AD, both carrying three genomic copies of the APP gene. Detection of specific Aβ peptides in CSF or plasma of these patients could improve the diagnosis of CAA and their inclusion in anti-amyloid immunotherapy treatments. [ABSTRACT FROM AUTHOR]

Published
2024
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24. Antigen-specific age-related memory CD8 T cells induce and track Alzheimer's-like neurodegeneration.

Author

Panwar, Akanksha, Rentsendorj, Altan, Jhun, Michelle, Cohen, Robert M., Cordner, Ryan, Gull, Nicole, Pechnick, Robert N., Duvall, Gretchen, Mardiros, Armen, Golchian, David, Schubloom, Hannah, Lee-Way Jin, Van Dam, Debby, Vermeiren, Yannick, De Reu, Hans, De Deyn, Peter Paul, Raskatov, Jevgenij A., Black, Keith L., Irvin, Dwain K., and Williams, Brian A.

Subjects
ALZHEIMER'S disease, T cells, IMMUNOLOGIC memory, NEURODEGENERATION, BRAIN injuries
Abstract

Cerebral (Aβ) plaque and (pTau) tangle deposition are hallmarks of Alzheimer's disease (AD), yet are insufficient to confer complete AD-like neurodegeneration experimentally. Factors acting upstream of Aβ/pTau in AD remain unknown, but their identification could enable earlier diagnosis and more effective treatments. T cell abnormalities are emerging AD hallmarks, and CD8 T cells were recently found to mediate neurodegeneration downstream of tangle deposition in hereditary neurodegeneration models. The precise impact of T cells downstream of Aβ/pTau, however, appears to vary depending on the animal model. Our prior work suggested that antigen-specific memory CD8 T ("hiT") cells act upstream of Aβ/pTau after brain injury. Here, we examine whether hiT cells influence sporadic AD-like pathophysiology upstream of Aβ/pTau. Examining neuropathology, gene expression, and behavior in our hiT mouse model we show that CD8 T cells induce plaque and tangle-like deposition, modulate AD-related genes, and ultimately result in progressive neurodegeneration with both gross and fine features of sporadic human AD. T cells required Perforin to initiate this pathophysiology, and IFNγ for most gene expression changes and progression to more widespread neurodegenerative disease. Analogous antigen-specific memory CD8 T cells were significantly elevated in the brains of human AD patients, and their loss from blood corresponded to sporadic AD and related cognitive decline better than plasma pTau-217, a promising AD biomarker candidate. We identify an age-related factor acting upstream of Aβ/pTau to initiate AD-like pathophysiology, the mechanisms promoting its pathogenicity, and its relevance to human sporadic AD. [ABSTRACT FROM AUTHOR]

Published
2024
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25. Brain age as a biomarker for pathological versus healthy ageing – a REMEMBER study.

Author

Wittens, Mandy M.J., Denissen, Stijn, Sima, Diana M., Fransen, Erik, Niemantsverdriet, Ellis, Bastin, Christine, Benoit, Florence, Bergmans, Bruno, Bier, Jean-Christophe, de Deyn, Peter Paul, Deryck, Olivier, Hanseeuw, Bernard, Ivanoiu, Adrian, Picard, Gaëtane, Ribbens, Annemie, Salmon, Eric, Segers, Kurt, Sieben, Anne, Struyfs, Hanne, and Thiery, Evert

Subjects
AGE differences, AGE, MILD cognitive impairment, BIOMARKERS, BRAIN anatomy, COGNITION disorders
Abstract

Objectives: This study aimed to evaluate the potential clinical value of a new brain age prediction model as a single interpretable variable representing the condition of our brain. Among many clinical use cases, brain age could be a novel outcome measure to assess the preventive effect of life-style interventions. Methods: The REMEMBER study population (N = 742) consisted of cognitively healthy (HC,N = 91), subjective cognitive decline (SCD,N = 65), mild cognitive impairment (MCI,N = 319) and AD dementia (ADD,N = 267) subjects. Automated brain volumetry of global, cortical, and subcortical brain structures computed by the CE-labeled and FDA-cleared software icobrain dm (dementia) was retrospectively extracted from T1-weighted MRI sequences that were acquired during clinical routine at participating memory clinics from the Belgian Dementia Council. The volumetric features, along with sex, were combined into a weighted sum using a linear model, and were used to predict 'brain age' and 'brain predicted age difference' (BPAD = brain age–chronological age) for every subject. Results: MCI and ADD patients showed an increased brain age compared to their chronological age. Overall, brain age outperformed BPAD and chronological age in terms of classification accuracy across the AD spectrum. There was a weak-to-moderate correlation between total MMSE score and both brain age (r = -0.38,p <.001) and BPAD (r = -0.26,p <.001). Noticeable trends, but no significant correlations, were found between BPAD and incidence of conversion from MCI to ADD, nor between BPAD and conversion time from MCI to ADD. BPAD was increased in heavy alcohol drinkers compared to non-/sporadic (p =.014) and moderate (p =.040) drinkers. Conclusions: Brain age and associated BPAD have the potential to serve as indicators for, and to evaluate the impact of lifestyle modifications or interventions on, brain health. [ABSTRACT FROM AUTHOR]

Published
2024
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28. Antigen-specific age-related memory CD8 T cells induce and track Alzheimer’s-like neurodegeneration

Author

Panwar, Akanksha, primary, Rentsendorj, Altan, additional, Jhun, Michelle, additional, Cohen, Robert M., additional, Cordner, Ryan, additional, Gull, Nicole, additional, Pechnick, Robert N., additional, Duvall, Gretchen, additional, Mardiros, Armen, additional, Golchian, David, additional, Schubloom, Hannah, additional, Jin, Lee-Way, additional, Van Dam, Debby, additional, Vermeiren, Yannick, additional, De Reu, Hans, additional, De Deyn, Peter Paul, additional, Raskatov, Jevgenij A., additional, Black, Keith L., additional, Irvin, Dwain K., additional, Williams, Brian A., additional, and Wheeler, Christopher J., additional

Published
2024
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32. Cognitive functions as predictors of daily functioning among older adults

Author

Wardana, Nido Dipo, primary, Zhang, Zhenyu, additional, Lejko, Nena, additional, Opmeer, Esther, additional, De Deyn, Peter Paul, additional, Reesink, Fransje E, additional, Spikman, Jacoba M, additional, Herrmann, Christoph S, additional, Enriquez‐Geppert, Stefanie, additional, Aleman, André, additional, and Ćurčić‐Blake, Branislava, additional

Published
2023
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33. Late positive potential component and cognitive functioning in occipital area of Mild Cognitive Impairment

Author

Zhang, Zhenyu, primary, Aleman, André, additional, Opmeer, Esther, additional, De Deyn, Peter Paul, additional, Reesink, Fransje E, additional, Spikman, Jacoba M, additional, Herrmann, Christoph S, additional, Wardana, Nido Dipo, additional, Enriquez‐Geppert, Stefanie, additional, Lejko, Nena, additional, and Ćurčić‐Blake, Branislava, additional

Published
2023
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34. TREM2 Burden associates solely with Alzheimer’s Disease, and decreases the Chance to become a Centenarian

Author

Dijkstra, Janna I.R., primary, Vermunt, Lisa, additional, Biessels, Geert Jan, additional, Braber, Anouk, additional, Claassen, Jurgen A.H.R., additional, De Deyn, Peter Paul, additional, Ghanbari, Mohsen, additional, Huisman, Martijn, additional, Hulsman, Marc, additional, Ramakers, Inez H.G.B., additional, Reinders, Marcel J. T., additional, Seelaar, Harro, additional, Tesi, Niccoló, additional, Visser, Pieter Jelle, additional, van der Flier, Wiesje M., additional, Teunissen, Charlotte E., additional, Holstege, Henne, additional, and Van Der Lee, Sven J, additional

Published
2023
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35. Functional Brain Activation in Mild Cognitive Impairment With Defined Small Vessel Disease Burden

Author

Marcolini, Sofia, primary, Mondragon, Jaime, additional, Ramakers, Inez H.G.B., additional, Biessels, Geert Jan, additional, Claassen, Jurgen A.H.R., additional, Bron, Esther E, additional, Papma, Janne M., additional, van der Flier, Wiesje M., additional, van der Lugt, Aad, additional, Maurits, Natasha M., additional, and De Deyn, Peter Paul, additional

Published
2023
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37. Genome-wide association study of frontotemporal dementia identifies a C9ORF72 haplotype with a median of 12-G4C2 repeats that predisposes to pathological repeat expansions

Author

Reus, Lianne M., Jansen, Iris E., Mol, Merel O., van Ruissen, Fred, van Rooij, Jeroen, van Schoor, Natasja M., Tesi, Niccolò, Reinders, Marcel J. T., Huisman, Martijn A., Holstege, Henne, Visser, Pieter Jelle, de Boer, Sterre C. M., Hulsman, Marc, Ahmad, Shahzad, Amin, Najaf, Uitterlinden, Andre G., Ikram, Arfan, van Duijn, Cornelia M., Seelaar, Harro, Ramakers, Inez H. G. B., Verhey, Frans R. J., van der Lugt, Aad, Claassen, Jurgen A. H. R., Jan Biessels, Geert, De Deyn, Peter Paul, Scheltens, Philip, van der Flier, Wiesje M., van Swieten, John C., Pijnenburg, Yolande A. L., and van der Lee, Sven J.

Published
2021
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39. Subjective cognitive decline and rates of incident Alzheimer's disease and non–Alzheimer's disease dementia

Author

Boada, Mercè, de Deyn, Peter Paul, Jones, Roy, Frisoni, Giovanni, Spiru, Luiza, Nobili, Flavio, Freund-Levi, Yvonne, Soininen, Hilkka, Verhey, Frans, Wallin, Åsa K., Touchon, Jacques, Rikkert, Marcel Olde, Rigaud, Anne-Sophie, Bullock, Roger, Tsolaki, Magda, Vellas, Bruno, Wilcock, Gordon, Hampel, Harald, Froelich, Lutz, Bakardjian, Hovagim, Benali, Habib, Bertin, Hugo, Bonheur, Joel, Boukadida, Laurie, Boukerrou, Nadia, Cavedo, Enrica, Chiesa, Patrizia, Colliot, Olivier, Dubois, Bruno, Dubois, Marion, Epelbaum, Stéphane, Gagliardi, Geoffroy, Genthon, Remy, Habert, Marie-Odile, Houot, Marion, Kas, Aurélie, Lamari, Foudil, Levy, Marcel, Lista, Simone, Metzinger, Christiane, Mochel, Fanny, Nyasse, Francis, Poisson, Catherine, Potier, Marie-Claude, Revillon, Marie, Santos, Antonio, Andrade, Katia Santos, Sole, Marine, Surtee, Mohmed, Thiebaud de Schotten, Michel, Vergallo, Andrea, Younsi, Nadjia, Slot, Rosalinde E.R., Sikkes, Sietske A.M., Berkhof, Johannes, Brodaty, Henry, Buckley, Rachel, Dardiotis, Efthimios, Guillo-Benarous, Francoise, Kochan, Nicole A., Luck, Tobias, Maruff, Paul, Molinuevo, José Luis, Kornhuber, Johannes, Reisberg, Barry, Riedel-Heller, Steffi G., Risacher, Shannon L., Roehr, Susanne, Sachdev, Perminder S., Scarmeas, Nikolaos, Scheltens, Philip, Shulman, Melanie B., Saykin, Andrew J., Verfaillie, Sander C.J., Visser, Pieter Jelle, Vos, Stephanie J.B., Wagner, Michael, Wolfsgruber, Steffen, Jessen, Frank, and van der Flier, Wiesje M.

Published
2019
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45. Author Correction: Tau-targeting antisense oligonucleotide MAPTRx in mild Alzheimer’s disease: a phase 1b, randomized, placebo-controlled trial

Author

Mummery, Catherine J., primary, Börjesson-Hanson, Anne, additional, Blackburn, Daniel J., additional, Vijverberg, Everard G. B., additional, De Deyn, Peter Paul, additional, Ducharme, Simon, additional, Jonsson, Michael, additional, Schneider, Anja, additional, Rinne, Juha O., additional, Ludolph, Albert C., additional, Bodenschatz, Ralf, additional, Kordasiewicz, Holly, additional, Swayze, Eric E., additional, Fitzsimmons, Bethany, additional, Mignon, Laurence, additional, Moore, Katrina M., additional, Yun, Chris, additional, Baumann, Tiffany, additional, Li, Dan, additional, Norris, Daniel A., additional, Crean, Rebecca, additional, Graham, Danielle L., additional, Huang, Ellen, additional, Ratti, Elena, additional, Bennett, C. Frank, additional, Junge, Candice, additional, and Lane, Roger M., additional

Published
2023
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