Background and Objectives Early-onset Alzheimer disease (EOAD) is associated with substantial variability in clinical progression, and reliable biomarkers to predict the transition from mild cognitive impairment (MCI) to dementia remain limited. Structural MRI measures have demonstrated prognostic value in late-onset Alzheimer disease, but their utility for predicting progression in EOAD is less well understood. The goal was to examine whether baseline cortical atrophy predicts progression to dementia in patients with MCI because of EOAD. Methods This study included a well-characterized cohort of patients with EOAD enrolled in the large multisite natural history Longitudinal Early-Onset Alzheimer's Disease Study. Participants underwent standardized clinical assessments and structural MRI at baseline. Participants were aged between 40 and 64 years with biomarker-supported sporadic EOAD at the MCI stage. Cortical atrophy was measured within the EOAD-signature, a set of predominantly parieto-temporal regions showing greater atrophy in EOAD than in controls. Clinical severity was measured with the global Clinical Dementia Rating. Cox proportional hazards models estimated the association between baseline EOAD-signature atrophy burden and the hazard of progression to dementia over time. We evaluated whether EOAD-signature atrophy improved prognostic performance beyond baseline clinical severity using likelihood ratio tests, Akaike Information Criterion (AIC), and Harrell concordance index. Results A total of 130 patients with MCI due to EOAD (mean age 59.6 ± 4.1 years; 49% female) and 97 cognitively normal controls (mean age 56.9 ± 6.0 years; 64% female) were included. Greater baseline atrophy within the EOAD-signature predicted faster progression to dementia (hazard ratio [HR] = 1.24 per 1-SD increase in atrophy; 95% CI 1.13–1.37; p < 0.002). Adding EOAD-signature atrophy burden to a model including baseline clinical severity significantly improved model fit (ΔAIC = −4.5; likelihood ratio test p = 0.011). Discussion Baseline cortical atrophy within the EOAD-signature predicts progression from MCI to dementia in EOAD and provides prognostic information beyond baseline clinical severity. These findings support the potential value of EOAD-signature atrophy as an MRI-based biomarker for individualized prognostication and clinical trial stratification.
T. Paranhos, Yuta Katsumi, M. Brickhouse et al.· Neurology· 0 citations
Type 2 diabetes mellitus is associated with cognitive impairment and greater Alzheimer's disease risk, with cross-sectional studies suggesting that this is driven by neurodegeneration and vascular changes. Longitudinal studies, however, report similar rates of total brain atrophy over time in diabetic and non-diabetic adults. We recently showed that diabetes-related neurodegeneration in cognitively unimpaired adults is regional and may not be evident in longitudinal studies of global brain atrophy. The mechanisms driving diabetes-related neurodegeneration are unknown; glycemic control may play an important role but the relative influence of Alzheimer's and cerebrovascular pathology is unclear. Here, we longitudinally examined regional patterns of cortical thinning associated with diabetes and glycemic control over nearly 3 years. We controlled for Alzheimer's disease neuropathology and white matter hyperintensity burden. We also examined whether subsequent changes in hemoglobin A1c (HbA1c) levels correlated with rates of cortical thinning in diabetes-associated regions and tested whether faster cortical thinning in diabetes-relevant regions mediated a relationship between diabetes and decline in cognitive performance. Among 1,298 cognitively unimpaired participants (mean age=65.0 years, 305 diabetic, 869 female) from the Health and Aging Brain Study-Health Disparities cohort who completed baseline and follow-up MRI scans, we used linear mixed-effects models to examine the relationship between diabetes and cortical thickness changes across 34 brain regions, controlling for socioeconomic factors and comorbidities. We further adjusted for amyloid-PET, tau-PET, white matter hyperintensities, and APOE ε4 carrier status. We also examined associations between baseline and longitudinal HbA1c levels and cortical thinning rates in diabetes-related regions in the whole sample and separately in diabetic and non-diabetic participants. P-values were corrected using the false discovery rate method. Path analysis tested whether diabetes-related cortical thinning mediated the relationship between diabetes and decline in cognitive performance. Diabetic participants exhibited faster cortical thinning in seven frontal, parietal, and occipital regions (-0.060≤βs≤-0.046, corrected Ps<0.017). Associations remained unchanged after accounting for socioeconomic factors, comorbidities, amyloid, tau, white matter hyperintensities, and APOE ε4. Higher baseline HbA1c predicted faster thinning in diabetes-vulnerable regions (corrected Ps<0.032), independent of subsequent HbA1c changes. Diabetes was associated with faster decline in processing speed (β=-0.022, P=0.033), with cortical thinning in diabetes-related regions mediating approximately 13% of this effect (indirect effect β=-0.034, P=0.019). Diabetes-related cortical thinning in cognitively unimpaired older adults follows a regional pattern independent of Alzheimer's and cerebrovascular pathology and partially mediates accelerated decline in processing speed. Chronic hyperglycemia may contribute to diabetes-related neurodegeneration, regardless of subsequent short-term changes in glycemic control.
A. Tsiknia, Victoria R. Tennant, Marylan Davison et al.· Brain : a journal of neurolo...· 1 citation
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
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
