Background About one third of amyloid positive, cognitively normal individuals develop mild cognitive impairment or clinical Alzheimer dementia (AD) over 5 years of follow-up. Sensitive cognitive measures, in addition to biomarkers of amyloid pathology, add to the efficiency of secondary prevention trials by identifying cognitively normal individuals at greatest risk of clinical progression. The Stages of Objective Memory Impairment (SOMI) system, based on the picture version of the Free and Cued Selective Reminding Test with immediate recall (pFCSRT+IR), predicted clinical progression in two observational studies. Objective Our objective was to extend SOMI’s findings to clinical trials using participants from the Anti-Amyloid Treatment in Asymptomatic Alzheimer’s(A4) study. Methods Eligible participants were cognitively normal, had a Clinical Dementia Rating (CDR) =0, an elevated amyloid level, the pFCSRT+IR, pTau217, and longitudinal data on the CDR. Cox proportional hazards model was used to assess the association of baseline SOMI stage for clinical progression defined by time to the first of 2 consecutive CDRs > 0 or CDR>0 at last assessment. The sample was censored at 4.5 years of follow-up. Results Of the 911 eligible participants, mean age was 72 years, 59% were female, 62% were APOE ε4 carriers, and 37% progressed over 4.5 years. Hazard ratios (HR) for progression were estimated with follow-up time as the timescale and the SOMI 0 group as the reference. The HRs for progression across SOMI stage increased from 1.48(1.15–1.92 p=.003) for SOMI-1, to 1.83 (1.32–2.54, p ≤ 0.001) for SOMI-2, and to 3.04 (1.97–4.68, p ≤ 0.001) for SOMI 3/4. SOMI remained an independent and significant predictor when pTau217 was added to the model. Conclusion SOMI’s risk profile in A4 was similar to prior findings in observational cohorts. SOMI provides a low-cost, non-invasive enrichment tool for identifying individuals at risk for early cognitive decline in secondary prevention trials.
Priyanka Kumari, R. Lipton, A. Aschenbrenner et al.· The journal of prevention of...· 0 citations
Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by heterogeneous pathophysiological changes that begin years before symptoms emerge. Existing biomarkers like Aβ and pTau capture only fragments of this complexity, limiting diagnosis and therapeutic development. Leveraging high-resolution cerebrospinal fluid (CSF) proteomics, quantifying 2,492 proteins using tandem-mass-tag mass spectrometry (TMT-MS), in 1,104 ADNI participants, we identified pathways reflecting AD pathogenesis and stage-specific molecular events in-vivo. In biomarker-positive MCI (due-to-AD) and AD Dementia, beyond well-established metabolic and mitochondrial dysfunction, we observed upregulated neuropeptide signaling, G-protein-coupled receptors activity, and synaptic remodeling, highlighting underrecognized synaptic and signaling alterations. Asymptomatic AD showed significant alterations in mitochondrial metabolism, RNA processing, and extracellular matrix pathways. Across the continuum from asymptomatic AD to MCI (due-to-AD) and AD Dementia, 92 proteins were differentially abundant, revealing a stage-specific progression, with early disruptions in neurodevelopmental and extracellular vesicle-related pathways in asymptomatic and MCI (due-to-AD) participants, transitioning to impairments in intracellular signaling, synaptic architecture, and cytoskeletal integrity in AD Dementia. This progressive dysregulation supports a continuum model where early compensatory mechanisms gradually give way to widespread neuronal degeneration. Using machine learning, we derived CSF proteomic panels capable of accurately distinguishing disease stages (asymptomatic AD vs. MCI (due-to-AD): AUC = 0.92; MCI (due-to-AD) vs. AD Dementia: AUC = 0.87). In parallel, we developed machine learning models to estimate pathological burden (Aβ-PET, tau-PET), which substantially outperformed conventional biomarkers. These findings uncover protein signatures that reflect underlying AD biology and provide a foundation for stage-specific biomarkers and therapeutic targeting, with important implications for patient stratification and personalized intervention strategies.
Saima Rathore, E. Dammer, Anantharaman Shantaraman et al.· Molecular Neurodegeneration· 0 citations
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