Wiltfang, Jens

[["dc.bibliographiccitation.artnumber","94"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Alzheimer's Research & Therapy"],["dc.bibliographiccitation.volume","13"],["dc.contributor.author","Aichholzer, Freyja"],["dc.contributor.author","Klafki, Hans-Wolfgang"],["dc.contributor.author","Ogorek, Isabella"],["dc.contributor.author","Vogelgsang, Jonathan"],["dc.contributor.author","Wiltfang, Jens"],["dc.contributor.author","Scherbaum, Norbert"],["dc.contributor.author","Weggen, Sascha"],["dc.contributor.author","Wirths, Oliver"],["dc.date.accessioned","2021-11-25T11:07:12Z"],["dc.date.accessioned","2022-08-18T12:39:08Z"],["dc.date.available","2021-11-25T11:07:12Z"],["dc.date.available","2022-08-18T12:39:08Z"],["dc.date.issued","2021-05-04"],["dc.date.updated","2022-07-29T12:17:48Z"],["dc.description.abstract","Abstract\r\n \r\n Background\r\n Alzheimer’s disease (AD) is a neurodegenerative disorder associated with extracellular amyloid-β peptide deposition and progressive neuron loss. Strong evidence supports that neuroinflammatory changes such as the activation of astrocytes and microglia cells are important in the disease process. Glycoprotein nonmetastatic melanoma protein B (GPNMB) is a transmembrane glycoprotein that has recently been associated with an emerging role in neuroinflammation, which has been reported to be increased in post-mortem brain samples from AD and Parkinson’s disease patients.\r\n \r\n \r\n Methods\r\n The present study describes the partial “fit for purpose” validation of a commercially available immunoassay for the determination of GPNMB levels in the cerebrospinal fluid (CSF). We further assessed the applicability of GPNMB as a potential biomarker for AD in two different cohorts that were defined by biomarker-supported clinical diagnosis or by neuroimaging with amyloid positron emission tomography, respectively.\r\n \r\n \r\n Results\r\n The results indicated that CSF GPNMB levels could not distinguish between AD or controls with other neurological diseases but correlated with other parameters such as aging and CSF pTau levels.\r\n \r\n \r\n Conclusions\r\n The findings of this study do not support GPNMB in CSF as a valuable neurochemical diagnostic biomarker of AD but warrant further studies employing healthy control individuals."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2021"],["dc.identifier.citation","Alzheimer's Research & Therapy. 2021 May 04;13(1):94"],["dc.identifier.doi","10.1186/s13195-021-00828-1"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/93530"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/112967"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-425"],["dc.publisher","BioMed Central"],["dc.relation.eissn","1758-9193"],["dc.rights","CC BY 4.0"],["dc.rights.holder","The Author(s)"],["dc.subject","Alzheimer’s disease"],["dc.subject","GPNMB"],["dc.subject","Cerebrospinal fluid"],["dc.subject","Biomarker"],["dc.subject","Inflammation"],["dc.subject","Immunoassay"],["dc.title","Evaluation of cerebrospinal fluid glycoprotein NMB (GPNMB) as a potential biomarker for Alzheimer’s disease"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","96"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Fluids and Barriers of the CNS"],["dc.bibliographiccitation.volume","19"],["dc.contributor.author","Klafki, Hans-Wolfgang"],["dc.contributor.author","Morgado, Barbara"],["dc.contributor.author","Wirths, Oliver"],["dc.contributor.author","Jahn, Olaf"],["dc.contributor.author","Bauer, Chris"],["dc.contributor.author","Esselmann, Hermann"],["dc.contributor.author","Schuchhardt, Johannes"],["dc.contributor.author","Wiltfang, Jens"],["dc.date.accessioned","2022-12-05T09:15:24Z"],["dc.date.available","2022-12-05T09:15:24Z"],["dc.date.issued","2022-12-03"],["dc.date.updated","2022-12-04T04:11:01Z"],["dc.description.abstract","Abstract\r\n \r\n Background\r\n A reduced amyloid-β (Aβ)42/40 peptide ratio in blood plasma represents a peripheral biomarker of the cerebral amyloid pathology observed in Alzheimer’s disease brains. The magnitude of the measurable effect in plasma is smaller than in cerebrospinal fluid, presumably due to dilution by Aβ peptides originating from peripheral sources. We hypothesized that the observable effect in plasma can be accentuated to some extent by specifically measuring Aβ1–42 and Aβ1–40 instead of AβX–42 and AβX–40.\r\n \r\n \r\n Methods\r\n We assessed the plasma AβX–42/X–40 and Aβ1–42/1–40 ratios in an idealized clinical sample by semi-automated Aβ immunoprecipitation followed by closely related sandwich immunoassays. The amyloid-positive and amyloid-negative groups (dichotomized according to Aβ42/40 in cerebrospinal fluid) were compared regarding the median difference, mean difference, standardized effect size (Cohen’s d) and receiver operating characteristic curves. For statistical evaluation, we applied bootstrapping.\r\n \r\n \r\n Results\r\n The median Aβ1–42/1–40 ratio was 20.86% lower in amyloid-positive subjects than in the amyloid-negative group, while the median AβX–42/X–40 ratio was only 15.56% lower. The relative mean difference between amyloid-positive and amyloid-negative subjects was −18.34% for plasma Aβ1–42/1–40 compared to −15.50% for AβX–42/X–40. Cohen’s d was 1.73 for Aβ1–42/1–40 and 1.48 for plasma AβX–42/X–40. Unadjusted p-values < 0.05 were obtained after .632 bootstrapping for all three parameters. Receiver operating characteristic analysis indicated very similar areas under the curves for plasma Aβ1–42/1–40 and AβX–42/X–40.\r\n \r\n \r\n Conclusions\r\n Our findings support the hypothesis that the relatively small difference in the plasma Aβ42/40 ratio between subjects with and without evidence of brain amyloidosis can be accentuated by specifically measuring Aβ1–42/1–40 instead of AβX–42/X–40. A simplified theoretical model explaining this observation is presented."],["dc.identifier.citation","Fluids and Barriers of the CNS. 2022 Dec 03;19(1):96"],["dc.identifier.doi","10.1186/s12987-022-00390-4"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/118429"],["dc.language.iso","en"],["dc.rights","CC BY 4.0"],["dc.rights.holder","The Author(s)"],["dc.subject","Alzheimer’s disease"],["dc.subject","Biomarker"],["dc.subject","Amyloid-β peptides"],["dc.subject","Blood plasma"],["dc.subject","Aβ42/40 ratio"],["dc.subject","Immunoassay"],["dc.title","Is plasma amyloid-β 1–42/1–40 a better biomarker for Alzheimer’s disease than AβX–42/X–40?"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","127"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Alzheimer's Research & Therapy"],["dc.bibliographiccitation.volume","14"],["dc.contributor.author","Klafki, Hans-W."],["dc.contributor.author","Vogelgsang, Jonathan"],["dc.contributor.author","Manuilova, Ekaterina"],["dc.contributor.author","Bauer, Chris"],["dc.contributor.author","Jethwa, Alexander"],["dc.contributor.author","Esselmann, Hermann"],["dc.contributor.author","Jahn-Brodmann, Anke"],["dc.contributor.author","Osterloh, Dirk"],["dc.contributor.author","Lachmann, Ingolf"],["dc.contributor.author","Breitling, Benedict"],["dc.contributor.author","Rauter, Carolin"],["dc.contributor.author","Hansen, Niels"],["dc.contributor.author","Bouter, Caroline"],["dc.contributor.author","Palme, Stefan"],["dc.contributor.author","Schuchhardt, Johannes"],["dc.contributor.author","Wiltfang, Jens"],["dc.date.accessioned","2022-09-12T07:56:44Z"],["dc.date.available","2022-09-12T07:56:44Z"],["dc.date.issued","2022-09-07"],["dc.date.updated","2022-09-11T03:10:27Z"],["dc.description.abstract","Abstract\r\n \r\n Background\r\n Measurements of the amyloid-β (Aβ) 42/40 ratio in blood plasma may support the early diagnosis of Alzheimer’s disease and aid in the selection of suitable participants in clinical trials. Here, we compared the diagnostic performance of fully automated prototype plasma Aβ42/40 assays with and without pre-analytical sample workup by immunoprecipitation.\r\n \r\n \r\n Methods\r\n A pre-selected clinical sample comprising 42 subjects with normal and 38 subjects with low cerebrospinal fluid (CSF) Aβ42/40 ratios was studied. The plasma Aβ42/40 ratios were determined with fully automated prototype Elecsys® immunoassays (Roche Diagnostics GmbH, Penzberg, Germany) by direct measurements in EDTA plasma or after pre-analytical Aβ immunoprecipitation. The diagnostic performance for the detection of abnormal CSF Aβ42/40 was analyzed by receiver operating characteristic (ROC) analysis. In an additional post hoc analysis, a biomarker-supported clinical diagnosis was used as a second endpoint.\r\n \r\n \r\n Results\r\n Pre-analytical immunoprecipitation resulted in a significant increase in the area under the ROC curve (AUC) from 0.73 to 0.88 (p = 0.01547) for identifying subjects with abnormal CSF Aβ42/40. A similar improvement in the diagnostic performance by pre-analytical immunoprecipitation was also observed when a biomarker-supported clinical diagnosis was used as a second endpoint (AUC increase from 0.77 to 0.92, p = 0.01576).\r\n \r\n \r\n Conclusions\r\n Our preliminary observations indicate that pre-analytical Aβ immunoprecipitation can improve the diagnostic performance of plasma Aβ assays for detecting brain amyloid pathology. The findings may aid in the further development of blood-based immunoassays for Alzheimer’s disease ultimately suitable for screening and routine use."],["dc.identifier.citation","Alzheimer's Research & Therapy. 2022 Sep 07;14(1):127"],["dc.identifier.doi","10.1186/s13195-022-01071-y"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/114202"],["dc.language.iso","en"],["dc.rights","CC BY 4.0"],["dc.rights.holder","The Author(s)"],["dc.subject","Alzheimer’s disease"],["dc.subject","Biomarker assay"],["dc.subject","Plasma Amyloid-β 42/40"],["dc.subject","Immunoprecipitation"],["dc.subject","Pre-analytical sample workup"],["dc.title","Diagnostic performance of automated plasma amyloid-β assays combined with pre-analytical immunoprecipitation"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","191"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Alzheimer's Research & Therapy"],["dc.bibliographiccitation.volume","13"],["dc.contributor.author","Dyrba, Martin"],["dc.contributor.author","Hanzig, Moritz"],["dc.contributor.author","Altenstein, Slawek"],["dc.contributor.author","Bader, Sebastian"],["dc.contributor.author","Ballarini, Tommaso"],["dc.contributor.author","Brosseron, Frederic"],["dc.contributor.author","Buerger, Katharina"],["dc.contributor.author","Cantré, Daniel"],["dc.contributor.author","Dechent, Peter"],["dc.contributor.author","Dobisch, Laura"],["dc.contributor.author","Düzel, Emrah"],["dc.contributor.author","Ewers, Michael"],["dc.contributor.author","Fliessbach, Klaus"],["dc.contributor.author","Glanz, Wenzel"],["dc.contributor.author","Haynes, John-Dylan"],["dc.contributor.author","Heneka, Michael T."],["dc.contributor.author","Janowitz, Daniel"],["dc.contributor.author","Keles, Deniz B."],["dc.contributor.author","Kilimann, Ingo"],["dc.contributor.author","Laske, Christoph"],["dc.contributor.author","Maier, Franziska"],["dc.contributor.author","Metzger, Coraline D."],["dc.contributor.author","Munk, Matthias H."],["dc.contributor.author","Perneczky, Robert"],["dc.contributor.author","Peters, Oliver"],["dc.contributor.author","Preis, Lukas"],["dc.contributor.author","Priller, Josef"],["dc.contributor.author","Rauchmann, Boris"],["dc.contributor.author","Roy, Nina"],["dc.contributor.author","Scheffler, Klaus"],["dc.contributor.author","Schneider, Anja"],["dc.contributor.author","Schott, Björn H."],["dc.contributor.author","Spottke, Annika"],["dc.contributor.author","Spruth, Eike J."],["dc.contributor.author","Weber, Marc-André"],["dc.contributor.author","Ertl-Wagner, Birgit"],["dc.contributor.author","Wagner, Michael"],["dc.contributor.author","Wiltfang, Jens"],["dc.contributor.author","Jessen, Frank"],["dc.contributor.author","Teipel, Stefan J."],["dc.contributor.authorgroup","for the ADNI, AIBL, DELCODE study groups"],["dc.date.accessioned","2021-12-01T09:23:04Z"],["dc.date.accessioned","2022-08-18T12:39:17Z"],["dc.date.available","2021-12-01T09:23:04Z"],["dc.date.available","2022-08-18T12:39:17Z"],["dc.date.issued","2021-11-23"],["dc.date.updated","2022-07-29T12:17:50Z"],["dc.description.abstract","Abstract\r\n \r\n Background\r\n Although convolutional neural networks (CNNs) achieve high diagnostic accuracy for detecting Alzheimer’s disease (AD) dementia based on magnetic resonance imaging (MRI) scans, they are not yet applied in clinical routine. One important reason for this is a lack of model comprehensibility. Recently developed visualization methods for deriving CNN relevance maps may help to fill this gap as they allow the visualization of key input image features that drive the decision of the model. We investigated whether models with higher accuracy also rely more on discriminative brain regions predefined by prior knowledge.\r\n \r\n \r\n Methods\r\n We trained a CNN for the detection of AD in N = 663 T1-weighted MRI scans of patients with dementia and amnestic mild cognitive impairment (MCI) and verified the accuracy of the models via cross-validation and in three independent samples including in total N = 1655 cases. We evaluated the association of relevance scores and hippocampus volume to validate the clinical utility of this approach. To improve model comprehensibility, we implemented an interactive visualization of 3D CNN relevance maps, thereby allowing intuitive model inspection.\r\n \r\n \r\n Results\r\n Across the three independent datasets, group separation showed high accuracy for AD dementia versus controls (AUC ≥ 0.91) and moderate accuracy for amnestic MCI versus controls (AUC ≈ 0.74). Relevance maps indicated that hippocampal atrophy was considered the most informative factor for AD detection, with additional contributions from atrophy in other cortical and subcortical regions. Relevance scores within the hippocampus were highly correlated with hippocampal volumes (Pearson’s r ≈ −0.86, p < 0.001).\r\n \r\n \r\n Conclusion\r\n The relevance maps highlighted atrophy in regions that we had hypothesized a priori. This strengthens the comprehensibility of the CNN models, which were trained in a purely data-driven manner based on the scans and diagnosis labels. The high hippocampus relevance scores as well as the high performance achieved in independent samples support the validity of the CNN models in the detection of AD-related MRI abnormalities. The presented data-driven and hypothesis-free CNN modeling approach might provide a useful tool to automatically derive discriminative features for complex diagnostic tasks where clear clinical criteria are still missing, for instance for the differential diagnosis between various types of dementia."],["dc.identifier.citation","Alzheimer's Research & Therapy. 2021 Nov 23;13(1):191"],["dc.identifier.doi","10.1186/s13195-021-00924-2"],["dc.identifier.pii","924"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/94551"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/112968"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-478"],["dc.publisher","BioMed Central"],["dc.relation.eissn","1758-9193"],["dc.rights.holder","The Author(s)"],["dc.subject","Alzheimer’s disease"],["dc.subject","Deep learning"],["dc.subject","Convolutional neural network"],["dc.subject","MRI"],["dc.subject","Layer-wise relevance propagation"],["dc.title","Improving 3D convolutional neural network comprehensibility via interactive visualization of relevance maps: evaluation in Alzheimer’s disease"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]