Leveraging Large‐scale CSF Proteomics to identify Disease Progression Biomarkers in Autosomal Dominant Frontotemporal Lobar Degeneration

Background The pathophysiological mechanisms driving frontotemporal lobar degeneration (FTLD) disease progression and corresponding biomarkers that capture these changes are not fully understood. We leveraged high‐throughput proteomics to identify networks of co‐expressed CSF proteins and candidate...

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Veröffentlicht in:Alzheimer's & dementia 2023-12, Vol.19 (S14), p.n/a
Hauptverfasser: Saloner, Rowan, Staffaroni, Adam M., Dammer, Eric B., Johnson, Erik C.B., Paolillo, Emily W., Heuer, Hilary W., Wise, Amy B., Forsberg, Leah K., Kramer, Joel H., Miller, Bruce L, Li, Jingyao, Loureiro, Joseph, Sivasankaran, Rajeev, Worringer, Katie, Boeve, Brad F., Rosen, Howard J., Boxer, Adam L., Rojas, Julio C., Casaletto, Kaitlin B.
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Sprache:eng
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Zusammenfassung:Background The pathophysiological mechanisms driving frontotemporal lobar degeneration (FTLD) disease progression and corresponding biomarkers that capture these changes are not fully understood. We leveraged high‐throughput proteomics to identify networks of co‐expressed CSF proteins and candidate biomarkers for prediction of disease progression in adults with autosomal dominant FTLD mutations. Method 113 adults carrying autosomal dominant FTLD mutations (C9orf72/GRN/MAPT) and 38 noncarrier controls from the ALLFTD consortium completed baseline lumbar puncture and longitudinal neuropsychological testing. CSF was assayed for SOMAmer proteomics on SOMAscan® v3.0 (4,140 proteins). Network analysis identified weighted protein co‐expression modules, which were annotated via gene ontology and cell‐type enrichment analysis. Disease age estimates that capture proximity to symptom onset were derived from a Bayesian disease progression model incorporating genotype‐specific clinical (cognition, CDR®+NACC FTLD) and biomarker (brain volume, plasma NfL) information. At baseline, local‐weighted regression examined module z‐scores (based on mean/SD of module levels [first principal component] in noncarriers) in relation to disease age. Longitudinal linear mixed‐effects models examined global cognitive trajectories in relation to baseline module z‐scores, as well as individual proteins within prognostic modules. Result Network analysis identified 32 CSF protein co‐expression modules (37 to 223 proteins/module). 16 modules showed mutation‐related deviations from noncarriers. Deviations in ion transport, neurodevelopmental, metabolic, and transcriptional modules occurred several decades before estimated symptom onset (disease age: ‐30 to ‐20 years) and persisted across disease age. Deviations in neuroinflammatory and ubiquitination/autophagy modules occurred ∼10 years before estimated symptom onset. Sharp deviations in RNA binding, protein phosphorylation, neurogenesis, synaptic function, and lysosomal modules occurred near symptom onset (disease age: ‐1 to 4 years). Baseline synaptic and neurogenesis modules were the strongest prognosticators of cognitive decline (module x time: bs>.10, ps
ISSN:1552-5260
1552-5279
DOI:10.1002/alz.073417