Eloyan, Ani

Objective Early‐onset Alzheimer's disease (EOAD) and late‐onset Alzheimer's disease (LOAD) differ in many respects. Here, we address the issue of possible differences in fibrillar amyloid pathology as measured by positron emission tomography (PET), which remains unresolved due to the lack of large‐scale comparative studies. Methods Three hundred ninety‐nine cognitively impaired participants younger than 65 years of age from the multicenter Longitudinal Early‐onset Alzheimer's Disease Study (LEADS) and 450 cognitively impaired participants older than 65 years from the Alzheimer's Disease Neuroimaging Initiative (ADNI) underwent clinical assessment, brain magnetic resonance imaging (MRI), and amyloid PET and were included in this study. We compared amyloid PET outcomes (positivity rate based on visual read and quantified tracer uptake expressed as Centiloids [CLs]) between the 2 cohorts and studied their association with age, sex, APOE genotype, and cognition. Results The amyloid positivity rate was higher in LEADS (78%, 95% confidence interval [CI] = 74–82) than in ADNI (71%, 95% CI = 67–75, p = 0.02). Lower Mini‐Mental State Examination (MMSE) and APOE4 genotype increased the odds of amyloid positivity in both cohorts. Visually positive scans had higher CLs in LEADS (EOAD, mean = 95.3 ± 26.1) than in ADNI (LOAD, mean = 80.9 ± 36.8, p < 0.0001), predominantly in parietal cortex/precuneus, superior temporal, and frontal cortices. In amyloid‐positive patients, (1) CLs were higher in female patients in both cohorts; (2) APOE4 carriership was associated with lower CLs in EOAD, which was not observed in LOAD; and (3) correlations between CLs and MMSE scores were significantly stronger in EOAD than in LOAD. Interpretation Differences in the burden of amyloid pathology may contribute to differences in clinical and anatomic patterns in sporadic EOAD and LOAD, and have implications for optimizing therapeutic strategies in each group. ANN NEUROL 2025;98:236–248

Abstract INTRODUCTION Early‐onset and late‐onset Alzheimer's disease (EOAD and LOAD, respectively) have distinct clinical manifestations, with prior work based on small samples suggesting unique patterns of neurodegeneration. The current study performed a head‐to‐head comparison of cortical atrophy in EOAD and LOAD, using two large and well‐characterized cohorts (LEADS and ADNI). METHODS We analyzed brain structural magnetic resonance imaging (MRI) data acquired from 377 sporadic EOAD patients and 317 sporadicLOAD patients who were amyloid positive and had mild cognitive impairment (MCI) or mild dementia (i.e., early‐stage AD), along with cognitively unimpaired participants. RESULTS After controlling for the level of cognitive impairment, we found a double dissociation between AD clinical phenotype and localization/magnitude of atrophy, characterized by predominant neocortical involvement in EOAD and more focal anterior medial temporal involvement in LOAD. DISCUSSION Our findings point to the clinical utility of MRI‐based biomarkers of atrophy in differentiating between EOAD and LOAD, which may be useful for diagnosis, prognostication, and treatment. Highlights Early‐onset Alzheimer's disease (EOAD) and late‐onset AD (LOAD) patients showed distinct and overlapping cortical atrophy patterns. EOAD patients showed prominent atrophy in widespread neocortical regions. LOAD patients showed prominent atrophy in the anterior medial temporal lobe. Regional atrophy was correlated with the severity of global cognitive impairment. Results were comparable when the sample was stratified for mild cognitive impairment (MCI) and dementia.

AbstractBackgroundEarly‐onset Alzheimer’s disease (EOAD) occurs before age 65 and has more diverse disease presentations than late‐onset AD. To improve our understanding of phenotypic heterogeneity among EOAD individuals, we analyzed cognitive scores using data‐driven statistical analysis.MethodBaseline cognitive data from 286 sporadic EOAD individuals from the Longitudinal EOAD study (LEADS) were transformed to z‐scores using data from 95 cognitively normal (CN) individuals. Cognitive composites were generated for domains of memory, language, speed/attention, visuospatial, and executive function. Residuals from linear regression models on Z‐scores adjusted for age, sex, and education were obtained. Cluster analysis using the Ward method on the cognitive domain residuals was performed and scree plot using the pseudo T‐squared determined the optimal number of clusters for the EOAD sample. We also compared gray matter density (GMD) of each EOAD cluster to CN participants using voxel‐wise multiple linear regressions.ResultsThree clusters of cognitive performance were identified from the EOAD sample. Disease duration was not significantly different across clusters. Using a z‐score of ‐1.5 SD as the impairment threshold, all clusters were impaired across most domains (Table 1). Cluster‐3 was more impaired than cluster‐2 in all domains (Table 2; all p<.0001), and in all domains except episodic memory compared to cluster‐1 (all p<.01). Cluster‐1 (n = 71; 85.9% amnestic) was most impaired in executive function, visuospatial, and speed/attention. Cluster‐2 (n = 133; 88.7% amnestic) was most impaired in episodic memory. Cluster‐3 (n = 82; 69.5% amnestic) was most impaired in executive function, visuospatial, and speed/attention (Table 1). 3D‐comparisons showed all EOAD clusters had reduced GMD compared to CN. Cluster‐1 and cluster‐3 both showed widespread atrophy, with cluster‐3 being more severe. Cluster‐2 showed the most atrophy in the temporal and parietal lobes (Figure 1).ConclusionWe identified heterogeneity in cognitive patterns among sporadic EOAD individuals. Cluster‐3 appeared to reflect widespread impairment, and cluster‐2 represented an amnestic‐only presentation. Despite comparable disease duration, some EOAD patients progress faster, while some are more resilient. 3D‐comparisons showed neurodegenerative changes affecting brain regions responsible for respective impaired cognitive functions in each cluster (e.g., cluster‐2 is primarily amnestic‐impaired and has temporoparietal atrophy). Future work should explore amyloid‐PET and tau‐PET burden.

AbstractBackgroundDiagnosing sporadic early‐onset AD (EOAD, age‐at‐onset<65) is challenging: in the multi‐center Longitudinal Early‐onset Alzheimer’s Disease Study, ∼25% of patients with clinically diagnosed EOAD are amyloid‐PET‐negative. Here we used FDG‐PET to characterize the heterogeneity of hypometabolic profiles in these patients and better identify underlying etiologies.MethodSeventy‐four amyloid‐PET‐negative patients with clinical diagnosis of sporadic EOAD (MCI or mild dementia stage) underwent FDG‐PET. Patients were classified as having normal or hypometabolic FDG‐PET based on a data‐driven approach that compared each patient to a group of 61 age‐matched amyloid‐PET‐negative controls using 12 methodological combinations (3 reference regions, 2 voxel‐level thresholds, 2 outlier detection methods). We then assessed clinical and demographic differences between patients with normal versus hypometabolic FDG‐PET, and further compared groups using independent biomarkers of neurodegeneration (structural MRI and fluid biomarkers). Finally, we applied hierarchical clustering to hypometabolic FDG‐PET scans to identify patterns of hypometabolism.ResultThirty‐six amyloid‐negative patients (49%) had hypometabolic FDG‐PET scans. They were older and more severely impaired across most cognitive domains than patients with normal FDG‐PET (Table 1). They also had reduced hippocampal volumes and cortical thickness (Figure 1A), higher plasma and CSF neurofilament light chain (NfL) levels, and elevated plasma GFAP compared to patients with normal FDG‐PET (Figure 1B). In contrast, the latter, who had intermediate cognitive scores between hypometabolic patients and controls, had MRI and fluid biomarker levels in the range of controls (Figure 1). In hypometabolic patients, hierarchical clustering identified four profiles: i) anterior temporal extending to temporo‐parietal and frontal regions (n=5), ii) anterior temporal and orbitofrontal (n=11), iii) occipito‐parietal (n=6), and iv) lateral frontal and parietal (n=14) (Figure 2). Genetic testing identified two patients with Frontotemporal Lobar Degeneration (FTLD)‐associated pathogenic variants, both considered hypometabolic and assigned to the first (MAPT) and second (c9orf72) metabolic profiles.ConclusionFifty‐one percent of amyloid‐negative patients had normal FDG‐PET: they had milder clinical impairment, normal MRI measures, and normal NfL values, suggesting non‐neurodegenerative etiologies. Patients with abnormal FDG showed heterogeneous hypometabolic patterns suggestive of multiple etiologies including Lewy body disease, FTLD or corticobasal degeneration. Longitudinal follow‐up to autopsy will ultimately clarify the amyloid‐negative clinical mimics of sporadic EOAD.

AbstractBackgroundLarge‐scale studies comparing sporadic early‐onset AD (EOAD, age<65) and late‐onset AD (LOAD, age≥65) are lacking. We compared amyloid‐PET outcomes (positivity rate and amyloid burden) between patients clinically diagnosed with sporadic EOAD vs LOAD, leveraging data from the Longitudinal Early‐Onset AD Study (LEADS) and the Alzheimer’s Disease Neuroimaging Initiative 3 (ADNI3).Method731 patients meeting the 2011 NIA‐AA criteria for AD dementia or MCI were included (505 early‐onset from LEADS, 226 late‐onset from ADNI3, Table 1). All participants underwent amyloid‐PET with [18F]Florbetaben or [18F]Florbetapir. Amyloid positivity was centrally determined by a process involving a visual read by a trained expert and PET‐only quantification; in case of a discrepancy, a read from an independent physician acted as a tiebreaker. Logistic regressions in each cohort examined relations between amyloid positivity and age, sex, MMSE and APOE4 genotype. Amyloid burden was independently quantified in Centiloids using an MRI‐based pipeline. Mean Centiloids in LEADS and ADNI were compared with two‐way ANOVA, for visually positive and visually negative scans.ResultAmyloid positivity rate was higher in LEADS (76%) than ADNI (64%, p<0.001, Figure 1A). Lower MMSE and APOE4 genotype increased odds of amyloid positivity in both cohorts, although the APOE4 effect was stronger in ADNI than LEADS (OR=10.1 versus 2.4, p=0.007, Table 2). Amyloid positivity was more common in females across cohorts, but this effect was only statistically significant in LEADS (Table 2). Centiloids were bimodally distributed in both cohorts, although the separation between positive and negative scans was more prominent in LEADS (Figure 1B). Visually positive scans had significantly higher Centiloids in LEADS than in ADNI, whereas no cohort difference was observed for visually negative scans (Figure 1C). Sensitivity analyses showed that this effect was driven by patients with MCI (CDR≤0.5; Figure 1D‐E).ConclusionThe lower amyloid positivity rate in ADNI might be due to AD‐mimicking pathologies being more common at an older age. The higher amyloid burden in early‐onset, amyloid‐positive patients could reflect younger patients being diagnosed later in the disease course compared to typical, late‐onset patients. Alternatively, younger patients might tolerate higher neuropathology burden due to higher brain reserve or fewer co‐pathologies.

AbstractBackgroundLarge‐scale studies comparing sporadic early‐onset AD (EOAD, age<65) and late‐onset AD (LOAD, age = 65) are lacking. We compared amyloid‐PET outcomes (positivity rate and amyloid burden) between patients clinically diagnosed with sporadic EOAD vs LOAD, leveraging data from the Longitudinal Early‐Onset AD Study (LEADS) and the Alzheimer’s Disease Neuroimaging Initiative 3 (ADNI3).Method731 patients meeting the 2011 NIA‐AA criteria for AD dementia or MCI were included (505 early‐onset from LEADS, 226 late‐onset from ADNI3, Table 1). All participants underwent amyloid‐PET with [18F]Florbetaben or [18F]Florbetapir. Amyloid positivity was centrally determined by a process involving a visual read by a trained expert and PET‐only quantification; in case of a discrepancy, a read from an independent physician acted as a tiebreaker. Logistic regressions in each cohort examined relations between amyloid positivity and age, sex, MMSE and APOE4 genotype. Amyloid burden was independently quantified in Centiloids using an MRI‐based pipeline. Mean Centiloids in LEADS and ADNI were compared with two‐way ANOVA, for visually positive and visually negative scans.ResultAmyloid positivity rate was higher in LEADS (76%) than ADNI (64%, p<0.001, Figure 1A). Lower MMSE and APOE4 genotype increased odds of amyloid positivity in both cohorts, although the APOE4 effect was stronger in ADNI than LEADS (OR = 10.1 versus 2.4, p = 0.007, Table 2). Amyloid positivity was more common in females across cohorts, but this effect was only statistically significant in LEADS (Table 2). Centiloids were bimodally distributed in both cohorts, although the separation between positive and negative scans was more prominent in LEADS (Figure 1B). Visually positive scans had significantly higher Centiloids in LEADS than in ADNI, whereas no cohort difference was observed for visually negative scans (Figure 1C). Sensitivity analyses showed that this effect was driven by patients with MCI (CDR = 0.5; Figure 1D‐E).ConclusionThe lower amyloid positivity rate in ADNI might be due to AD‐mimicking pathologies being more common at an older age. The higher amyloid burden in early‐onset, amyloid‐positive patients could reflect younger patients being diagnosed later in the disease course compared to typical, late‐onset patients. Alternatively, younger patients might tolerate higher neuropathology burden due to higher brain reserve or fewer co‐pathologies.

AbstractBackgroundDiagnosing sporadic early‐onset AD (EOAD, age‐at‐onset<65) is challenging: in the multi‐center Longitudinal Early‐onset Alzheimer’s Disease Study, ∼25% of patients with clinically diagnosed EOAD are amyloid‐PET‐negative. Here we used FDG‐PET to characterize the heterogeneity of hypometabolic profiles in these patients and better identify underlying etiologies.MethodSeventy‐four amyloid‐PET‐negative patients with clinical diagnosis of sporadic EOAD (MCI or mild dementia stage) underwent FDG‐PET. Patients were classified as having normal or hypometabolic FDG‐PET based on a data‐driven approach that compared each patient to a group of 61 age‐matched amyloid‐PET‐negative controls using 12 methodological combinations (3 reference regions, 2 voxel‐level thresholds, 2 outlier detection methods). We then assessed clinical and demographic differences between patients with normal versus hypometabolic FDG‐PET, and further compared groups using independent biomarkers of neurodegeneration (structural MRI and fluid biomarkers). Finally, we applied hierarchical clustering to hypometabolic FDG‐PET scans to identify patterns of hypometabolism.ResultThirty‐six amyloid‐negative patients (49%) had hypometabolic FDG‐PET scans. They were older and more severely impaired across most cognitive domains than patients with normal FDG‐PET (Table 1). They also had reduced hippocampal volumes and cortical thickness (Figure 1A), higher plasma and CSF neurofilament light chain (NfL) levels, and elevated plasma GFAP compared to patients with normal FDG‐PET (Figure 1B). In contrast, the latter, who had intermediate cognitive scores between hypometabolic patients and controls, had MRI and fluid biomarker levels in the range of controls (Figure 1). In hypometabolic patients, hierarchical clustering identified four profiles: i) anterior temporal extending to temporo‐parietal and frontal regions (n = 5), ii) anterior temporal and orbitofrontal (n = 11), iii) occipito‐parietal (n = 6), and iv) lateral frontal and parietal (n = 14) (Figure 2). Genetic testing identified two patients with Frontotemporal Lobar Degeneration (FTLD)‐associated pathogenic variants, both considered hypometabolic and assigned to the first (MAPT) and second (c9orf72) metabolic profiles.ConclusionFifty‐one percent of amyloid‐negative patients had normal FDG‐PET: they had milder clinical impairment, normal MRI measures, and normal NfL values, suggesting non‐neurodegenerative etiologies. Patients with abnormal FDG showed heterogeneous hypometabolic patterns suggestive of multiple etiologies including Lewy body disease, FTLD or corticobasal degeneration. Longitudinal follow‐up to autopsy will ultimately clarify the amyloid‐negative clinical mimics of sporadic EOAD.

AbstractBackgroundThe Uniform Data Set (UDS) neuropsychological battery, administered across Alzheimer’s Disease Centers (ADC), includes memory tests but lacks a list‐learning paradigm. ADCs often supplement the UDS with their own preferred list‐learning task. Given the importance of list‐learning for characterizing memory, we aimed to develop a harmonized memory score that incorporates UDS memory tests while allowing centers to contribute differing list‐learning tasks.MethodWe applied item‐banking confirmatory factor analysis to develop a composite memory score in 5,287 participants (mean age 67.1; SD = 12.2) recruited through 18 ADCs and four consortia (DiverseVCID, MarkVCID, ALLFTD, LEADS) who completed UDS memory tasks (used as linking‐items) and one of five list‐learning tasks. All analyses used linear regression. We tested whether memory scores were affected by which list‐learning task was administered. To assess construct validity, we tested associations of memory scores with demographics, disease severity (CDR Box Score), an independent memory task (TabCAT Favorites, n = 675), and hippocampal volume (n = 811). We compared performances between cognitively unimpaired (n = 279), AD‐biomarker+ MCI (n = 26), and AD‐biomarker+ dementia (n = 98). In a subsample with amyloid‐ and tau‐PET (n = 49), we compared memory scores from participants with positive vs negative scans determined using established quantitative cutoffs.ResultModel fit indices were excellent (e.g., CFI = 0.998) and factor loadings were strong (0.43‐0.93). Differences in list‐learning task had a negligible effect on scores (average Cohen’s d = 0.11). Higher memory scores were significantly (p’s<.001) correlated with younger age (β = ‐0.18), lower CDR Box Scores (β = ‐0.63), female sex (β = 0.12), higher education (β = 0.19), larger hippocampal volume (β = 0.42), and an independent memory task (β = 0.71, p<0.001). The memory composite declined in a stepwise fashion by diagnosis (cognitively unimpaired>MCI>AD dementia, p<0.001). On average, amyloid‐PET positivity was associated with lower composite scores, but was not statistically significant (β = ‐0.34; p = 0.25; d = 0.40). Tau‐PET positivity was associated with worse performance, demonstrating a large effect size (β = ‐0.75; p<0.002; d = 0.91).ConclusionThe harmonized memory score developed in a large national sample was stable regardless of contributing list‐learning task and its validity for cross‐cohort ADRD research is supported by expected associations with demographics, clinical measures, and Alzheimer’s biomarkers. A processing script will be made available to enhance cross‐cohort ADRD research.

Abstract INTRODUCTION Early‐onset Alzheimer's disease (EOAD) manifests prior to the age of 65, and affects 4%–8% of patients with Alzheimer's disease (AD). The current analyses sought to examine longitudinal cognitive trajectories of participants with early‐onset dementia. METHODS Data from 307 cognitively normal (CN) volunteer participants and those with amyloid‐positive EOAD or amyloid‐negative cognitive impairment (EOnonAD) were compared. Cognitive trajectories across a comprehensive cognitive battery spanning 42 months were examined using mixed‐effects modeling. RESULTS The EOAD group displayed worse cognition at baseline relative to EOnonAD and CN groups, and more aggressive declines in cognition over time. The largest effects were observed on measures of executive functioning domains, while memory declines were blunted in EOAD. DISCUSSION EOAD declined 2–4× faster than EOnonAD, and EOAD pathology is not restricted to memory networks. Early identification of deficits is critical to ensure that individuals with sporadic EOAD can be considered for treatment using disease‐modifying medications. Highlights Represents the most comprehensive longitudinal characterization of sporadic EOAD to date. The trajectory of cognitive declines was steep for EOAD participants and worse than for other groups. Executive functioning measures exhibited the greatest declines over time in EOAD.

Abstract INTRODUCTION Early‐onset Alzheimer's disease (EOAD) and late‐onset Alzheimer's disease (LOAD) share similar amyloid etiology, but evidence from smaller‐scale studies suggests that they manifest differently clinically. Current analyses sought to contrast the cognitive profiles of EOAD and LOAD. METHODS Z‐ score cognitive‐domain composites for 311 amyloid‐positive sporadic EOAD and 314 amyloid‐positive LOAD participants were calculated from baseline data from age‐appropriate control cohorts. Z‐ score composites were compared between AD groups for each domain. RESULTS After controlling for cognitive status, EOAD displayed worse visuospatial, executive functioning, and processing speed/attention skills relative to LOAD, and LOAD displayed worse language, episodic immediate memory, and episodic delayed memory. DISCUSSION Sporadic EOAD possesses distinct cognitive profiles relative to LOAD. Clinicians should be alert for non‐amnestic impairments in younger patients to ensure proper identification and intervention using disease‐modifying treatments. Highlights Both early‐onset Alzheimer's disease (EOAD) and late‐onset Alzheimer's disease (LOAD) participants displayed widespread cognitive impairments relative to their same‐aged peers. Cognitive impairments were more severe for EOAD than for LOAD participants in visuospatial and executive domains. Memory and language impairments were more severe for LOAD than for EOAD participants Results were comparable after removing clinical phenotypes of posterior cortical atrophy (PCA), primary progressive aphasia (lv‐PPA), and frontal‐variant AD.