• Stephanie A Schultz, Lei Liu, Aaron P Schultz, Colleen D Fitzpatrick, Raina Levin, Jean-Pierre Bellier, Zahra Shirzadi, Nelly Joseph-Mathurin, Charles D Chen, BenzingerTammie L STLSMallinckrodt Institute of Radiology, Washington University, St Louis, MO, USA., Gregory S Day, Martin R Farlow, Brian A Gordon, Jason J Hassenstab, Clifford R Jack, Mathias Jucker, Celeste M Karch, Jae-Hong Lee, Johannes Levin, Richard J Perrin, Peter R Schofield, Chengjie Xiong, Keith A Johnson, Eric McDade, Randall J Bateman, Reisa A Sperling, Dennis J Selkoe, Jasmeer P Chhatwal, and Dominantly Inherited Alzheimer Network.
• Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Department of Neurology, Medical School, Harvard University, Boston, MA, USA.
• Lancet Neurol. 2024 Sep 1; 23 (9): 913924913-924.

BackgroundGenetic variants that cause autosomal dominant Alzheimer's disease are highly penetrant but vary substantially regarding age at symptom onset (AAO), rates of cognitive decline, and biomarker changes. Most pathogenic variants that cause autosomal dominant Alzheimer's disease are in presenilin 1 (PSEN1), which encodes the catalytic core of γ-secretase, an enzyme complex that is crucial in production of amyloid β. We aimed to investigate whether the heterogeneity in AAO and biomarker trajectories in carriers of PSEN1 pathogenic variants could be predicted on the basis of the effects of individual PSEN1 variants on γ-secretase activity and amyloid β production.MethodsFor this cross-sectional and longitudinal analysis, we used data from participants enrolled in the Dominantly Inherited Alzheimer Network observational study (DIAN-OBS) via the DIAN-OBS data freeze version 15 (data collected between Feb 29, 2008, and June 30, 2020). The data freeze included data from 20 study sites in research institutions, universities, hospitals, and clinics across Europe, North and South America, Asia, and Oceania. We included individuals with PSEN1 pathogenic variants for whom relevant genetic, clinical, imaging, and CSF data were available. PSEN1 pathogenic variants were characterised via genetically modified PSEN1 and PSEN2 double-knockout human embryonic kidney 293T cells and immunoassays for Aβ37, Aβ38, Aβ40, Aβ42, and Aβ43. A summary measure of γ-secretase activity (γ-secretase composite [GSC]) was calculated for each variant and compared with clinical history-derived AAO using correlation analyses. We used linear mixed-effect models to assess associations between GSC scores and multimodal-biomarker and clinical data from DIAN-OBS. We used separate models to assess associations with Clinical Dementia Rating Sum of Boxes (CDR-SB), Mini-Mental State Examination (MMSE), and Wechsler Memory Scale-Revised (WMS-R) Logical Memory Delayed Recall, [11C]Pittsburgh compound B (PiB)-PET and brain glucose metabolism using [18F] fluorodeoxyglucose (FDG)-PET, CSF Aβ42-to-Aβ40 ratio (Aβ42/40), CSF log10 (phosphorylated tau 181), CSF log10 (phosphorylated tau 217), and MRI-based hippocampal volume.FindingsData were included from 190 people carrying PSEN1 pathogenic variants, among whom median age was 39·0 years (IQR 32·0 to 48·0) and AAO was 44·5 years (40·6 to 51·4). 109 (57%) of 190 carriers were female and 81 (43%) were male. Lower GSC values (ie, lower γ-secretase activity than wild-type PSEN1) were associated with earlier AAO (r=0·58; p<0·0001). GSC was associated with MMSE (β=0·08, SE 0·03; p=0·0043), CDR-SB (-0·05, 0·02; p=0·0027), and WMS-R Logical Memory Delayed Recall scores (0·09, 0·02; p=0·0006). Lower GSC values were associated with faster increase in PiB-PET signal (p=0·0054), more rapid decreases in hippocampal volume (4·19, 0·77; p<0·0001), MMSE (0·02, 0·01; p=0·0020), and WMS-R Logical Memory Delayed Recall (0·004, 0·001; p=0·0003).InterpretationOur findings suggest that clinical heterogeneity in people with autosomal dominant Alzheimer's disease can be at least partly explained by different effects of PSEN1 variants on γ-secretase activity and amyloid β production. They support targeting γ-secretase as a therapeutic approach and suggest that cell-based models could be used to improve prediction of symptom onset.FundingUS National Institute on Aging, Alzheimer's Association, German Center for Neurodegenerative Diseases, Raul Carrea Institute for Neurological Research, Japan Agency for Medical Research and Development, Korea Health Industry Development Institute, South Korean Ministry of Health and Welfare, South Korean Ministry of Science and ICT, and Spanish Institute of Health Carlos III.Copyright © 2024 Elsevier Ltd. All rights reserved, including those for text and data mining, AI training, and similar technologies.

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