Skip to content

Author

A. den Braber

2 papers indexed here

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Open access Sep 2026

CSF proteomic quantitative trait loci mapping reveals genetic insights into Alzheimer’s disease

Despite the identification of numerous genetic risk variants for Alzheimer's disease (AD), mechanisms through which these variants act remain unclear. Identifying specific proteins levels affected by genetic variation can provide valuable insights into the underlying biological pathways implicated in AD. To gain more insight into effects of genetic variation on AD-related processes, we conducted a genome-wide protein pQTL study using untargeted TMT mass spectrometry in cerebrospinal fluid (CSF) of 2,215 proteins across 487 individuals. Replication was assessed in the independent EMIF-AD MBD cohort of 242 individuals. We identified 399 independent CSF pQTL signals (PBonferroni < 2.26 × 10⁻11) associated with 222 proteins, 69% of which were novel. Findings included gene-protein links such as RPS23P10/HSPA6 with CSF FCGR2A, BIN2 with CSF GALNT6, APOE with CSF HS3ST1, and the HLA-region with CSF HLA-DPB1 and PLXDC2. We replicated 230 of 270 gene-protein associations. A proteome-wide association study identified genetically predicted CSF protein levels to be associated with AD, including SIRPA, PLXDC2, and GALNT6. Many AD pQTLs in CSF were enriched in neuroimmune activation, suggesting a genetic basis for neuroimmune dysregulation in AD. This study highlights how genetic variation shapes protein expression in the central nervous system, offering mechanistic insight into AD.

L. Reus, Chen-Yang Jiang, N. Vilor-Tejedor et al. · 0 citations
Open access Aug 2026

AMYPAD-PNHS: A Pan-European, Multi-Site & Multimodal MRI Dataset of Older Adults Without Dementia

The Amyloid Imaging to Prevent Alzheimer’s Disease Prognostic and Natural History Study (AMYPAD-PNHS) multimodal magnetic resonance imaging (MRI) dataset provides open-access longitudinal MRI data of 2759 cognitively normal or mild cognitive impairment individuals, encompassing (micro-)structural, physiological, and functional MRI sequences from 10 European parent cohorts. Processed and raw images, and image-derived (endo-)phenotypes, are organized in Brain Imaging Data Structure (BIDS) standards and accessible upon request, to enhance generalizability and comparability between future neuroimaging studies. This dataset supports preclinical Alzheimer’s disease (AD) and aging-related research by enabling robust multimodal analyses of neurodegeneration, microvascular pathology, and structural and functional connectivity changes, facilitating advanced investigations into preclinical AD mechanisms, informing early intervention strategies and allowing reproducible and centralized neuroimaging (endo-)phenotyping.

L. Pieperhoff, M. Tranfa, Prithvi Arunachalam et al. · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.