Browsing by Subject "Early‐onset Alzheimer’s disease (EOAD)"

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    Cognitive clusters in sporadic early‐onset Alzheimer’s disease patients from the LEADS study
    (Wiley, 2025-01-09) Logan, Paige E.; Lane, Kathleen A.; Gao, Sujuan; Eloyan, Ani; Taurone, Alexander; Thangarajah, Maryanne; Touroutoglou, Alexandra; Vemuri, Prashanthi; Dage, Jeffrey L.; Nudelman, Kelly N.; Carrillo, Maria C.; Dickerson, Bradford C.; Rabinovici, Gil D.; Apostolova, Liana G.; Hammers, Dustin B.; LEADS Consortium; Neurology, School of Medicine
    Background Early‐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. Method Baseline 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. Results Three 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). Conclusion We 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.
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    Dissociable spatial topography of neurodegeneration in Early‐onset and Late‐onset Alzheimer’s Disease: A head‐to‐head comparison of MRI‐derived atrophy measures between the LEADS and ADNI cohorts
    (Wiley, 2025-01-09) Katsumi, Yuta; Touroutoglou, Alexandra; Brickhouse, Michael; Eckbo, Ryan; La Joie, Renaud; Eloyan, Ani; Nudelman, Kelly N.; Foroud, Tatiana M.; Dage, Jeffrey L.; Carrillo, Maria C.; Rabinovici, Gil D.; Apostolova, Liana G.; Dickerson, Bradford C.; LEADS Consortium; Neurology, School of Medicine
    Background: Understanding how early‐onset Alzheimer’s disease (EOAD) differs from typical late‐onset AD (LOAD) is an important goal of AD research that may help increase the sensitivity of unique biomarkers for each phenotype. Building upon prior work based on small samples, here we leveraged two large, well‐characterized natural history study cohorts of AD patients (LEADS and ADNI3) to test the hypothesis that EOAD patients would show more prominent lateral and medial parietal and lateral temporal cortical atrophy sparing the medial temporal lobe (MTL), whereas LOAD patients would show prominent MTL atrophy. Method: We investigated differences in the spatial topography of cortical atrophy between EOAD and LOAD patients by analyzing structural MRI data collected from 211 patients with sporadic EOAD and 88 cognitively unimpaired (CU) participants from the LEADS cohort as well as 144 patients with LOAD and 365 CU participants from the ADNI3 cohort. MRI data were processed via FreeSurfer v6.0 to estimate cortical thickness for each participant. A direct comparison of cortical thickness was performed between EOAD and LOAD patients based on W‐scores (i.e., Z‐scores adjusted for age and sex relative to CU participants within each cohort) while controlling for MMSE total scores. All patients underwent amyloid PET with 18F‐Florbetaben or 18F‐Florbetapir and amyloid positivity was centrally determined by quantification‐supported visual read. Result: As expected, a direct comparison of cortical thickness between patients with EOAD and LOAD revealed a double dissociation between AD clinical phenotype and localization of cortical atrophy: EOAD patients showed greater atrophy in widespread cortical areas including the inferior parietal lobule (EOAD marginal mean W‐score ± SEM = ‐1.33±0.08 vs. LOAD = ‐0.52±0.09, p<.001, η2=.097), precuneus (‐1.66±0.09 vs. ‐0.59±0.10, p<.001, η2=.13), and caudal middle frontal gyrus (‐1.65±0.08 vs. ‐0.90±0.10, p<.001, η2=.074), whereas LOAD patients showed greater atrophy in the entorhinal/perirhinal cortex and temporal pole (‐1.00±0.09 vs. ‐1.41±0.11, p<.008, η2=.019). Conclusion: These findings demonstrate a clearly dissociable spatial pattern of neurodegeneration between EOAD and LOAD, supporting our previously developed LOAD and EOAD signatures of cortical atrophy, which underlies the distinct episodic memory and other cognitive characteristics of these AD clinical phenotypes.
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    Heterogeneous clinical phenotypes of sporadic Early‐onset Alzheimer’s disease: A data‐driven approach
    (Wiley, 2025-01-03) Putcha, Deepti; Katsumi, Yuta; Touroutoglou, Alexandra; Dage, Jeffrey L.; Eloyan, Ani; Nudelman, Kelly N.; Carrillo, Maria C.; Rabinovici, Gil D.; Apostolova, Liana G.; Dickerson, Bradford C.; Hammers, Dustin B.; LEADS Consortium; Neurology, School of Medicine
    Background: Early‐onset Alzheimer’s disease (EOAD) manifests prior to the age of 65. Clinical presentation of EOAD is distinct from that of late‐onset Alzheimer’s disease, and is characterized as having a more aggressive disease course with greater heterogeneity. Recent publications from the Longitudinal Early‐Onset Alzheimer’s Disease Study (LEADS) described their sample as predominantly amnestic, though this phenotypic description was based solely on clinical judgment. To better understand the range of EOAD presentation, we applied a neuropsychological data‐driven method to phenotypic subtyping within the LEADS cohort. Method: Data from 169 amyloid‐positive EOAD participants with composite data in all cognitive domains (Episodic Memory, Executive Functioning, Speed/Attention, Language, and Visuospatial) were analyzed. Our approach consisted of comparing the relative levels of baseline impairment in each cognitive domain. Education‐corrected normative comparisons were made using a sample of 98 aged‐matched cognitively normal participants. A cut‐off of 1 SD below all other composite domain scores was applied to indicate a phenotype of “predominant” impairment in a given cognitive domain. Individuals were otherwise considered to have a phenotype best characterized by multidomain impairment. Result: We identified 6 phenotypic subtypes of EOAD (Table 1): Dysexecutive‐predominant (22% of sample), Amnestic‐predominant (11%), Language‐predominant (11%), Visuospatial‐predominant (15%), Mixed Amnestic/Dysexecutive‐predominant (11%), and Multidomain (30%). These subtypes did not differ on age, age‐at‐symptom‐onset, sex, or overall clinical severity (p>0.05). Groups differed on global cognitive functioning (MMSE) such that the Amnestic‐predominant group performed better than other domain‐predominant subtypes of EOAD (p>0.05). In contrast to the heterogeneity observed from our data‐driven approach, diagnostic classifications for this same sample based solely on clinical judgment indicated that 82% of individuals were amnestic‐predominant, 9% were non‐amnestic, 4% were visuospatial‐predominant, and 5% were language‐predominant. Conclusion: Applying a neuropsychological data‐driven method of phenotyping EOAD individuals uncovered a more detailed understanding of the diversity of presenting heterogeneity in this atypical AD group compared to clinical judgment alone. These results suggest that clinicians and patients may over‐prioritize memory dysfunction during subjective reporting at the expense of non‐memory symptoms, which has important implications for diagnostic accuracy and treatment considerations. We plan to investigate the patterns of cortical atrophy and network dysfunction subserving this heterogeneity.
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    Longitudinal neurodegeneration in Early‐Onset Alzheimer’s Disease: A summary of MRI‐derived atrophy in LEADS
    (Wiley, 2025-01-09) Touroutoglou, Alexandra; Katsumi, Yuta; Eckbo, Ryan; Brickhouse, Michael; Eloyan, Ani; Nudelman, Kelly N.; Foroud, Tatiana M.; Dage, Jeffrey L.; Carrillo, Maria C.; Rabinovici, Gil D.; Apostolova, Liana G.; Dickerson, Bradford C.; LEADS Consortium; Neurology, School of Medicine
    Background: Prior work has advanced our understanding of cortical atrophy in early‐onset Alzheimer’s disease (EOAD), but longitudinal data are sparse. Current longitudinal MRI studies point to progressive atrophy in cerebral cortex exhibiting a posterior‐to‐anterior gradient, but these studies include small samples with mostly amnestic EOAD. Here, we analyzed a large sample of sporadic EOAD patients from the Longitudinal Early‐Onset Alzheimer's Disease Study (LEADS) to test the central hypothesis that areas in our recently described EOAD signature (Touroutoglou et al., 2023) affected at baseline in the posterior lateral temporal cortex, inferior parietal lobule, and PCC/precuneus will continue to degenerate and additional longitudinal atrophy will be found in the medial temporal lobe and frontal regions as cognitive decline progresses over time in multiple domains. Method: We investigated longitudinal changes in cortical thickness by analyzing structural MRI data collected from 367 patients with EOAD and 99 cognitively unimpaired (CN) older adults, totaling 839 MRI scans across the cohorts with up to 4 years of follow‐up. MRI data were longitudinally processed in FreeSurfer 6.0. Linear mixed effects models were constructed to estimate the rate of cortical atrophy with random intercepts and slopes for individual participants while controlling for baseline age and sex. Result: EOAD patients exhibited cortical atrophy at a faster rate than controls in widespread areas of the cerebral cortex. As expected, the regions exhibiting accelerated longitudinal atrophy included not only the EOAD signature regions as a whole (EOAD: ‐0.052±0.002 mm/year vs. CN: 0.0001±0.002 mm/year; Dslopes = ‐0.052, p<.001), but also those that were minimally atrophied at baseline, such as superior frontal gyrus (EOAD: ‐0.052+/‐0.004 vs. CN: ‐0.001+/‐0.004, Dslopes = ‐ 0.051, p<.001) and medial temporal lobe (EOAD: ‐0.083±0.005 mm/year vs. CN: 0.001±0.006 mm/year; Dslopes = ‐0.082, p<.001). We observed no difference in the rate of atrophy in the calcarine fissure (a control region not expected to change; Dslopes = ‐0.002, p£.69). Conclusion: Our findings show that neurodegeneration in EOAD accelerates over time in the EOAD signature regions and spreads to additional areas within large‐scale brain networks (consistent with those observed in late‐onset AD) contributing to the worsening of symptoms over time.
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    Recurring genetic variant in LEADS
    (Wiley, 2025-01-03) Rumbaugh, Malia C.; Nudelman, Kelly N.; Dage, Jeffrey L.; Eloyan, Ani; Hammers, Dustin B.; Carrillo, Maria C.; Dickerson, Bradford C.; Rabinovici, Gil D.; Apostolova, Liana G.; Foroud, Tatiana M.; DIAN/DIAN-TU Clinical Genetics Committee; LEADS Consortium; Medical and Molecular Genetics, School of Medicine
    Background: The Longitudinal Early‐onset Alzheimer’s Disease Study (LEADS) is analyzing the genetic etiology of early onset (40‐64 years) cognitive impairment, including amyloid‐positive early‐onset Alzheimer’s disease (EOAD) and amyloid‐negative early‐onset Alzheimer’s disease (EOnonAD). One goal of this investigation is to identify novel or under‐characterized genetic variants. Methods: Cognitively impaired (CI) LEADS participants, including amyloid‐positive and amyloid‐negative early‐onset cases, were whole exome or genome sequenced (N = 361). Genetic and copy number variants in APP, PSEN1, PSEN2, GRN, MAPT and C9ORF72 were assessed. Variants in these genes were annotated with Annovar and assessed for potential pathogenicity with criteria including 1,000 genomes population frequency, computational functional prediction, and reported disease occurrence in databases including ClinVar and the Human Gene Mutation Database. Variants were reviewed manually as well as with VarSome and evaluated for pathogenicity using the American College of Medical Genetics criteria. Patient information for carriers of likely pathogenic variants not reported as pathogenic in ClinVar or included in the Dominantly Inherited Alzheimer Network Trial Unit (DIAN‐TU) Trial Eligible list were reviewed. Results: Three CI participants were found to carry the p.I227L variant in PSEN1 (3/361, 1%), with no other pathogenic variants identified in these individuals in the six genes analyzed. This variant was previously described in a single individual. It is classified as likely pathogenic by Varsome based on In‐Silico predictors; however, it is listed as “Pathogenicity Not Classified” in AlzForum and as a Variant of Unknown Significance in ClinVar due to the lack of reported cases. The variant is not included in the DIAN‐TU Trial Eligible List. We describe the family history, demographic and clinical data for these three participants. Conclusions: The PSEN1 variant p.I227L was identified in three LEADS CI participants and warrants further investigation as a potentially pathogenic variant.
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    Utility of CSF biomarkers in assessing neurodegeneration in Early‐Onset Alzheimer’s disease
    (Wiley, 2025-01-09) Eldaief, Mark C.; Touroutoglou, Alexandra; Brickhouse, Michael; Katsumi, Yuta; Eloyan, Ani; Nudelman, Kelly N.; Foroud, Tatiana M.; Carrillo, Maria C.; Rabinovici, Gil D.; Apostolova, Liana G.; Schindler, Suzanne E.; Herries, Elizabeth M.; Henson, Rachel L.; Dage, Jeffrey L.; Dickerson, Bradford C.; Medical and Molecular Genetics, School of Medicine
    Background: There is a significant need for biomarkers of neurodegenerative burden in Early‐onset Alzheimer’s disease (EOAD). Evidence suggests that levels of specific CSF biomarkers (e.g., Neurofilament light (NfL), Synaptosomal‐Associated Protein (SNAP‐25), neurogranin, Visinin‐like protein 1 (VILIP‐1), Aß 42/40, phospho‐tau and total tau) index the extent of neurodegeneration in dementing illnesses. However, it remains unclear whether these biomarkers correlate to cortical atrophy patterns in EOAD. Based on prior work demonstrating correlations between NfL, SNAP‐25, neurogranin and Aß 42/40 CSF levels and cognitive impairment in EOAD (Dage et al. 2023), we hypothesized that these biomarkers (and not VILIP‐1, phospho‐tau or total tau) would variably predict cortical atrophy within our recently described EOAD signature (Touroutoglou et al. 2023). Method: We recruited 92 EOAD patients. In each patient, atrophy within the EOAD cortical signature were calculated as W‐scores (i.e., Z‐scores adjusted for age and sex relative to a sample of healthy controls). We first ran a simple regression analysis of each of the 7 CSF biomarkers and W‐scores in the EOAD signature across EOAD patients. We then entered the biomarkers with significant correlations to the EOAD signature into a stepwise regression analysis with backward elimination to ascertain the most parsimonious model predicting atrophy in the EOAD signature. As a control region not expected to be related to these biomarkers, we assessed correlations to calcarine fissure cortical atrophy. Result: As predicted, we observed a significant correlation between CSF levels of NfL, SNAP‐25, neurogranin and Aß 42/40 and atrophy within the EOAD signature. After entering these four biomarkers into the stepwise regression analysis, the most parsimonious model identified complementary contributions of NfL, SNAP‐25, and Aß 42/40, in predicting atrophy in the EOAD signature. There were no correlations between any biomarker and calcarine atrophy. Conclusion: Selected CSF biomarkers in EOAD patients predict the degree of atrophy within the EOAD cortical signature. Ongoing work includes correlating these biomarkers with topographical atrophy patterns on the whole‐brain voxel‐wise level. Our results suggest that certain CSF biomarkers could assess neurodegenerative burden within EOAD individuals. This would provide valuable information regarding disease progression for clinical care and clinical trials involving disease‐modifying therapies.