Neurología Cognitiva.artículos

Regional effects of gantenerumab on neuroimaging biomarkers in the DIAN-TU-001 trial

2026-08-11T15:45:21Z

Regional effects of gantenerumab on neuroimaging biomarkers in the DIAN-TU-001 trial McCullough, Austin; Chen, Charles D.; Gordon, Brian A.; Joseph-Mathurin, Nelly; Jack Jr, Clifford R; Koeppe, Robert; Hornbeck, Russ; Koudelis, Deborah; McKay, Nicole S.; Hobbs, Diana A.; Flores, Shaney; Keefe, Sarah J.; Aggarwal, Neelum T.; Allegri, Ricardo Francisco; Berman, Sarah B.; Bird, Thomas; Black, Sandra E.; Brooks, William S.; Chhatwal, Jasmeer P.; Day, Gregory S.; DIAN‐TU Study Team Introduction: Monoclonal anti-amyloid therapies are now accessible, but how these treatments influence changes within the brain is still not clear. We investigated overall and regional change in amyloid removal, glucose metabolism, and atrophy in trial participants with dominantly inherited Alzheimer's disease (DIAD). Methods: In the DIAN-TU-001 trial, 92 carriers received gantenerumab or placebo and underwent serial neuroimaging assessments including [11C]-Pittsburgh compound-B (PiB) positron emission tomography (PET), [18F]-fluoro-2-deoxyglucose (FDG) PET, and magnetic resonance imaging (MRI). Results: Gantenerumab significantly reduced PiB-PET uptake overall and in most regions and showed no changes in FDG-PET or MRI measures. Drug effects were associated with baseline PiB-PET uptake, and the largest effects occurred in medial regions. Discussion: Treated DIAD participants, and especially those with higher amyloid burden, showed a decrease in PiB-PET uptake, which was more pronounced in the basal ganglia and medial frontal structures. These results may inform patient response and future drug trial design. Highlights: Gantenerumab unevenly decreased Aβ burden as measured by PiB-PET across brain regions. The strongest decrease in PiB-PET uptake was in basal ganglia and medial frontal structures. Variable drug effect on Aβ was partly due to the amount of burden present before treatment. There was no regional effect on FDG-PET metabolism or MRI volumetrics after 4 years.

Effect of Cognitive Reserve on Age at Symptom Onset and Cognitive Decline in Individuals With Dominantly Inherited Alzheimer Disease

2026-08-07T14:31:03Z

Effect of Cognitive Reserve on Age at Symptom Onset and Cognitive Decline in Individuals With Dominantly Inherited Alzheimer Disease Llibre Guerra, Jorge J.; Lu, Ruijin; Clarens, María Florencia; Liu, Ian; Renton, Alan E.; Ryan, Natalie S.; Goate, Alison M.; Aguillón, David; Allegri, Ricardo Francisco; Benzinger, Tammie L.S.; Berman, Sarah B.; Chhatwal, Jasmeer P.; Chrem Méndez, Patricio Alexis; Vigo, Gabriela; Cruchaga, Carlos; Day, Gregory S.; Farlow, Martin R.; Fox, Nick C.; Gordon, Brian A.; Hassenstab, Jason Background and objectives: Cognitive reserve has been shown to modulate the onset and progression of Alzheimer disease (AD) symptoms. Although its role in sporadic AD is well-studied, how cognitive reserve influences the timing and progression of symptoms in dominantly inherited AD (DIAD) remains unclear. This study aimed to quantify cognitive reserve in DIAD carriers and test whether higher cognitive reserve is associated with later symptom onset and slower functional decline. Methods: We analyzed data from the Dominantly Inherited Alzheimer's Network study. Cognitive reserve was modeled using a residual-based latent variable approach, decomposing cognitive performance into demographic (CogD), biomarker (CogB), and reserve or residual (CogR) components. Primary outcomes were age at clinical symptom onset (CDR >0) and longitudinal change in the Clinical Dementia Rating-Sum of Boxes (CDR-SBs). Data were analyzed using Cox proportional hazards models and linear mixed-effects models, adjusting for estimated years from onset (EYO). Result: A total of 710 Dominantly Inherited Alzheimer Network (DIAN) participants were included in the analysis, comprising 271 non-DIAD carriers (nMC), 284 asymptomatic DIAD carriers (aMC), and 155 symptomatic DIAD carriers. In asymptomatic carriers, using a zero-inflation model adjusted for EYO showed that a 1 SD increase in the reserve component (CogR) was associated with a 4.06-fold increase in the odds of being clinically unimpaired (CDR-SB = 0; 95% CI 1.84-8.95). Similarly, a 1 SD increase in the demographic (CogD) and biomarker (CogB) components increased the odds of being CDR-SB = 0 by 2.60 (95% CI 1.10-6.16) and 5.16 (95% CI 2.00-13.33), respectively. Among symptomatic carriers, only the reserve and the biomarker components were significant. A 1 SD increase in CogR was associated with a 0.81-fold reduction in baseline CDR-SB score (95% CI 0.72-0.92), and a 1 SD increase in CogB was associated with a 0.60-fold reduction in CDR-SB (95% CI 0.50-0.71). Discussion: Our findings indicate that higher cognitive reserve values are associated with delayed conversion to mild cognitive impairment and slower progression on clinical dementia rating scales. These findings suggest that cognitive reserve plays a protective role in modifying the clinical trajectory of genetically determined AD.

Characterizing Individuals Fulfilling Clinical Criteria for Limbic-Predominant Age-Related TDP-43 Encephalopathy in a Tertiary Memory Clinic

2026-08-12T14:34:40Z

Characterizing Individuals Fulfilling Clinical Criteria for Limbic-Predominant Age-Related TDP-43 Encephalopathy in a Tertiary Memory Clinic Groot, Colin; Calandri, Ismael Luis; Bader, Ilse; Bocancea, Diana I.; de Bruin, Hannah; Carrigan, Maria; Collij, Lyduine E.; Duits, Flora H.; Kamps, Suzie; de Koning, Lotte A.; Lemstra, Afina W.; Mastenbroek, Sophie E.; Rikken, Roos M.; van Tol, Bastiaan G. J.; Vermeiren, Marie R.; Wesseling, Alex; Xia, Ye; Teunissen, Charlotte E.; van de Giessen, Elsmarieke; Barkhof, Frederik Background and objectives: Limbic-predominant age-related TDP-43 encephalopathy (LATE) is characterized by an amnestic- and limbic-predominant phenotype, which can mimic Alzheimer disease (AD). In a memory clinic cohort, we tested whether clinical criteria for LATE can detect a clinical profile of LATE that is distinct from AD. Methods: In this retrospective examination of a longitudinal memory clinic cohort from the Alzheimer Center Amsterdam, we included individuals with mild cognitive impairment (MCI) and dementia (aged >50 years). We classified individuals based on baseline data on cognition, atrophy, amyloid-status, and tau-status into Probable- and Possible-LATE, co-occurring LATE and AD (LATE-AD), and AD (without LATE). Next, we compared these groups on demographics, clinical features, cognition, and atrophy. Results: Of 3,606 individuals (mean age at baseline 66 [SD 6], 49.2% female) available for classification, we classified 56 (1.6%) as Probable-LATE, 115 (3.2%) as Possible-LATE, 127 (3.5%) as LATE-AD, and 1,675 (46.5%) as AD. Individuals with Probable-LATE progressed slower than AD on mini-mental state examination (MMSE) (sβ [SE] = 0.12 [0.05], p = 0.02), memory (sβ [SE] = 0.11 [0.5], p = 0.01), attention (sβ [SE] = 0.12 [0.16], p = 0.05), executive functioning (sβ [SE] = 0.09 [0.04], p = 0.03), and visuospatial functioning (sβ [SE] = 0.10 [0.05], p = 0.05). Individuals with LATE-AD progressed faster than AD on MMSE (sβ [SE] = -0.12 [0.05], p = 0.01), attention (sβ [SE] = -0.13 [0.06], p = 0.04), and executive functioning (sβ [SE] = -0.10 [0.05], p = 0.03). Mortality risk, compared to AD, was lower in individuals with Probable-LATE (hazard ratio [HR] 0.70 [0.49-0.99], p = 0.04) and higher in Possible LATE-AD (HR 1.25 [1.01-1.53], p = 0.04). Compared to AD, at baseline, individuals with Probable-LATE and Possible-LATE had higher inferior temporal-to-hippocampus ratios (indicating limbic-predominant atrophy; sβ [SE] = 0.59 [0.16], p < 0.01; sβ [SE] = 0.40 [0.13], p < 0.01), and Probable-LATE, Possible-LATE, and LATE-AD all showed smaller amygdalar volumes at baseline than AD (sβ [SE] = -0.55 [0.15], p < 0.01; sβ [SE] = -0.43 [0.12], p < 0.01; sβ [SE] = -0.62 [0.11], p < 0.01). Individuals with LATE-AD had thinner cortex at baseline in an "AD-signature" composite region compared to AD (sβ [SE] = -0.73 [0.11], p < 0.01). Discussion: Using an operationalization of clinical criteria for LATE, 8.2% of participants with MCI or dementia from our tertiary memory clinic were classified as Possible-LATE, Probable-LATE, or LATE-AD. Probable-LATE was characterized by a milder disease course than AD, whereas LATE-AD was characterized by a more aggressive disease course. This underscores the value of the proposed clinical criteria in identifying individuals with suspected LATE, who have distinct clinical trajectories from AD. Our findings, therefore, support the use of these criteria to improve diagnostic and prognostic accuracy in the memory clinic.

The ICN-UN Battery: A Machine Learning-Optimized Tool for Expeditious Alzheimer's Disease Diagnosis

2026-08-04T12:55:42Z

The ICN-UN Battery: A Machine Learning-Optimized Tool for Expeditious Alzheimer's Disease Diagnosis Barceló, Ernesto; Romero, Duban; Allegri, Ricardo Francisco; Meza, Eliana; Mosquera-Heredia, María I.; Vidal, Oscar M.; Silvera-Redondo, Carlos; Arcos-Burgos, Mauricio; Garavito-Galofre, Pilar; Vélez, Jorge I. Background/Objectives: Alzheimer's disease (AD) accounts for ~70% of global dementia cases, with projections estimating 139 million affected individuals by 2050. This increasing burden highlights the urgent need for accessible, cost-effective diagnostic tools, particularly in low- and middle-income countries (LMICs). Traditional neuropsychological assessments, while effective, are resource-intensive and time-consuming. Methods: A total of 760 older adults (394 [51.8%] with AD) were recruited and neuropsychologically evaluated at the Instituto Colombiano de Neuropedagogía (ICN) in collaboration with Universidad del Norte (UN), Barranquilla. Machine learning (ML) algorithms were trained on a screening protocol incorporating demographic data and neuropsychological measures assessing memory, language, executive function, and praxis. Model performance was determined using 10-fold cross-validation. Variable importance analyses identified key predictors to develop optimized, abbreviated ML-based protocols. Metrics of compactness, cohesion, and separation further quantified diagnostic differentiation performance. Results: The eXtreme Gradient Boosting (xgbTree) algorithm achieved the highest diagnostic accuracy (91%) with the full protocol. Five ML-optimized screening protocols were also developed. The most efficient, the ICN-UN battery (including MMSE, Rey-Osterrieth Complex Figure recall, Rey Auditory Verbal Learning, Lawton & Brody Scale, and FAST), maintained strong diagnostic performance while reducing screening time from over four hours to under 25 min. Conclusions: The ML-optimized ICN-UN protocol offers a rapid, accurate, and scalable AD screening solution for LMICs. While promising for clinical adoption and earlier detection, further validation in diverse populations is recommended.