Biomedical subjects

Frank Jessen

Publications and source records attributed to Frank Jessen.

4 recordsLinked to original sources

PubMed2026

APOE-stratified genome-wide association analyses provide insights into the genetic etiology of Alzheimers's disease.

Among the more than 90 identified genetic risk loci for late-onset Alzheimer's disease (AD) and related dementias, the apolipoprotein E (APOE) gene ɛ2/ɛ3/ɛ4 polymorphisms remain the longstanding benchmark for genetic disease risk with a consistently large effect across studies1-10. Despite this massive signal, the exact mechanisms by which ɛ4 increases and ɛ2 decreases dementia risk remain poorly understood. Notably, recent trials of anti-amyloid therapies suggest less efficacy and higher risks of severe side effects in ε4 carriers11-13, hampering the treatment of those with the highest unmet need. To improve our understanding of the genetic architecture of AD in the context of its main genetic driver, we performed genome-wide association studies (GWASs) stratified by ε4 and ε2 carrier status. HP1BP3, SLC50A1, PTPRC, NPAS3, DDHD1, CHST9, SMYD2, PRAMEF1 and GFRA1 emerged as new genomic signals for AD risk, appearing only when stratified by APOE carrier status. DDHD1 appeared especially promising, showing protective effects in ε4 carriers, being identified as an expression quantitative trait locus and being involved in rare neuronal diseases. Such APOE-stratified insights may help understand and overcome side effects, inform clinical trial enrollment strategies, and create the scientific basis for targeted, mechanism-driven therapies in neurodegenerative diseases.

Journal Article↗

PubMed2026

Subjective cognition trajectories, Alzheimer biomarkers, and incident mild cognitive impairment.

BACKGROUND: Subjective cognitive decline is common in older adults and may represent an early clinical signal along the Alzheimer's disease continuum. The clinical relevance of longitudinal changes in subjective cognitive decline remains unclear. OBJECTIVES: To determine whether trajectories of self- or study partner-reported cognitive decline predict progression to mild cognitive impairment and reflect Alzheimer's disease-specific biological patterns. DESIGN, SETTING, PARTICIPANTS: Data were pooled from two observational cohorts. Cognitively unimpaired participants with baseline amyloid status, repeated assessments of subjective cognitive decline, and clinical follow-up were included. The study included 770 participants with a median follow-up of 5.0 years (interquartile range 4.0-7.0). MEASUREMENTS: Subjective cognitive decline was assessed using the Everyday Cognition questionnaire completed by participants and study partners. Linear mixed-effects models examined associations with amyloid status and progression to mild cognitive impairment. Cox proportional hazards models tested whether one-year changes predicted progression. RESULTS: Amyloid-positive participants and those who progressed to mild cognitive impairment showed steeper increases in self- and study partner-reported cognitive difficulties over time. Among amyloid-positive participants, only increases in study partner-report differentiated progressors from non-progressors. One-year increases in study partner-report predicted a higher risk of mild cognitive impairment compared with unchanged scores (hazard ratio 3.24; 95% confidence interval 1.73-6.07]), with effects confined to amyloid-positive participants. CONCLUSIONS: Short-term increases in study partner-reported cognitive difficulties identify amyloid-positive cognitively unimpaired older adults at increased risk of near-term progression to mild cognitive impairment. Longitudinal monitoring using study partner reports may provide a low-burden and clinically relevant approach for early risk stratification and surveillance in aging populations.

Humans↗

PubMed2025

A computational ontology framework for the synthesis of multi-level pathology reports from brain MRI scans.

BackgroundConvolutional neural network (CNN) based volumetry of MRI data can help differentiate Alzheimer's disease (AD) and the behavioral variant of frontotemporal dementia (bvFTD) as causes of cognitive decline and dementia. However, existing CNN-based MRI volumetry tools lack a structured hierarchical representation of brain anatomy, which would allow for aggregating regional pathological information and automated computational inference.ObjectiveDevelop a computational ontology pipeline for quantifying hierarchical pathological abnormalities and visualize summary charts for brain atrophy findings, aiding differential diagnosis.MethodsUsing FastSurfer, we segmented brain regions and measured volume and cortical thickness from MRI scans pooled across multiple cohorts (N = 3433; ADNI, AIBL, DELCODE, DESCRIBE, EDSD, and NIFD), including healthy controls, prodromal and clinical AD cases, and bvFTD cases. Employing the Web Ontology Language (OWL), we built a semantic model encoding hierarchical anatomical information. Additionally, we created summary visualizations based on sunburst plots for visual inspection of the information stored in the ontology.ResultsOur computational framework dynamically estimated and aggregated regional pathological deviations across different levels of neuroanatomy abstraction. The disease similarity index derived from the volumetric and cortical thickness deviations achieved an AUC of 0.88 for separating AD and bvFTD, which was also reflected by distinct atrophy profile visualizations.ConclusionsThe proposed automated pipeline facilitates visual comparison of atrophy profiles across various disease types and stages. It provides a generalizable computational framework for summarizing pathologic findings, potentially enhancing the physicians' ability to evaluate brain pathologies robustly and interpretably.

Humans↗

PubMed2011

Common variants at ABCA7, MS4A6A/MS4A4E, EPHA1, CD33 and CD2AP are associated with Alzheimer's disease.

We sought to identify new susceptibility loci for Alzheimer's disease through a staged association study (GERAD+) and by testing suggestive loci reported by the Alzheimer's Disease Genetic Consortium (ADGC) in a companion paper. We undertook a combined analysis of four genome-wide association datasets (stage 1) and identified ten newly associated variants with P ≤ 1 × 10(-5). We tested these variants for association in an independent sample (stage 2). Three SNPs at two loci replicated and showed evidence for association in a further sample (stage 3). Meta-analyses of all data provided compelling evidence that ABCA7 (rs3764650, meta P = 4.5 × 10(-17); including ADGC data, meta P = 5.0 × 10(-21)) and the MS4A gene cluster (rs610932, meta P = 1.8 × 10(-14); including ADGC data, meta P = 1.2 × 10(-16)) are new Alzheimer's disease susceptibility loci. We also found independent evidence for association for three loci reported by the ADGC, which, when combined, showed genome-wide significance: CD2AP (GERAD+, P = 8.0 × 10(-4); including ADGC data, meta P = 8.6 × 10(-9)), CD33 (GERAD+, P = 2.2 × 10(-4); including ADGC data, meta P = 1.6 × 10(-9)) and EPHA1 (GERAD+, P = 3.4 × 10(-4); including ADGC data, meta P = 6.0 × 10(-10)).

ATP-Binding Cassette Transporters↗