Browsing by Author "Touroutoglou, Alexandra"

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    Amyloid‐PET in patients with a clinical diagnosis of sporadic early‐ versus late‐onset AD: comparison of the LEADS and ADNI cohorts
    (Wiley, 2025-01-09) Lagarde, Julien; Maiti, Piyush; Schonhaut, Daniel R.; Zhang, Jiaxiuxiu; Soleimani-meigooni, David N.; Zeltzer, Ehud; Windon, Charles; Raya, Maison Abu; Vrillon, Agathe; Hammers, Dustin B.; Dage, Jeffrey L.; Nudelman, Kelly N.; Eloyan, Ani; Koeppe, Robert A.; Landau, Susan M.; Carrillo, Maria C.; Touroutoglou, Alexandra; Vemuri, Prashanthi; Dickerson, Bradford C.; Apostolova, Liana G.; Rabinovici, Gil D.; La Joie, Renaud; LEADS Consortium, Alzheimer’s Disease Neuroimaging Initiative; Neurology, School of Medicine
    Background: Large‐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). Method: 731 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. Result: Amyloid 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). Conclusion: The 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.
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    Baseline neuropsychiatric symptoms and psychotropic medication use midway through data collection of the Longitudinal Early-Onset Alzheimer's Disease Study (LEADS) cohort
    (Wiley, 2023) Polsinelli, Angelina J.; Wonderlin, Ryan J.; Hammers, Dustin B.; Pena Garcia, Alex; Eloyan, Anii; Taurone, Alexander; Thangarajah, Maryanne; Beckett, Laurel; Gao, Sujuan; Wang, Sophia; Kirby, Kala; Logan, Paige E.; Aisen, Paul; Dage, Jeffrey L.; Foroud, Tatiana; Griffin, Percy; Iaccarino, Leonardo; Kramer, Joel H.; Koeppe, Robert; Kukull, Walter A.; La Joie, Renaud; Mundada, Nidhi S.; Murray, Melissa E.; Nudelman, Kelly; Soleimani-Meigooni, David N.; Rumbaugh, Malia; Toga, Arthur W.; Touroutoglou, Alexandra; Vemuri, Prashanthi; Atri, Alireza; Day, Gregory S.; Duara, Ranjan; Graff-Radford, Neill R.; Honig, Lawrence S.; Jones, David T.; Masdeu, Joseph; Mendez, Mario F.; Womack, Kyle; Musiek, Erik; Onyike, Chiadi U.; Riddle, Meghan; Rogalski, Emily; Salloway, Steven; Sha, Sharon J.; Turner, Raymond S.; Wingo, Thomas S.; Wolk, David A.; Carrillo, Maria C.; Dickerson, Bradford C.; Rabinovici, Gil D.; Apostolova, Liana G.; LEADS Consortium; Neurology, School of Medicine
    Introduction: We examined neuropsychiatric symptoms (NPS) and psychotropic medication use in a large sample of individuals with early-onset Alzheimer's disease (EOAD; onset 40-64 years) at the midway point of data collection for the Longitudinal Early-onset Alzheimer's Disease Study (LEADS). Methods: Baseline NPS (Neuropsychiatric Inventory - Questionnaire; Geriatric Depression Scale) and psychotropic medication use from 282 participants enrolled in LEADS were compared across diagnostic groups - amyloid-positive EOAD (n = 212) and amyloid negative early-onset non-Alzheimer's disease (EOnonAD; n = 70). Results: Affective behaviors were the most common NPS in EOAD at similar frequencies to EOnonAD. Tension and impulse control behaviors were more common in EOnonAD. A minority of participants were using psychotropic medications, and use was higher in EOnonAD. Discussion: Overall NPS burden and psychotropic medication use were higher in EOnonAD than EOAD participants. Future research will investigate moderators and etiological drivers of NPS, and NPS differences in EOAD versus late-onset AD. Keywords: early-onset Alzheimer's disease; early-onset dementia; mild cognitive impairment; neuropharmacology; neuropsychiatric symptoms; psychotropic medications.
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    Cerebrospinal fluid biomarkers in the Longitudinal Early-onset Alzheimer's Disease Study
    (Wiley, 2023) Dage, Jeffrey L.; Eloyan, Ani; Thangarajah, Maryanne; Hammers, Dustin B.; Fagan, Anne M.; Gray, Julia D.; Schindler, Suzanne E.; Snoddy, Casey; Nudelman, Kelly N. H.; Faber, Kelley M.; Foroud, Tatiana; Aisen, Paul; Griffin, Percy; Grinberg, Lea T.; Iaccarino, Leonardo; Kirby, Kala; Kramer, Joel; Koeppe, Robert; Kukull, Walter A.; La Joie, Renaud; Mundada, Nidhi S.; Murray, Melissa E.; Rumbaugh, Malia; Soleimani-Meigooni, David N.; Toga, Arthur W.; Touroutoglou, Alexandra; Vemuri, Prashanthi; Atri, Alireza; Beckett, Laurel A.; Day, Gregory S.; Graff-Radford, Neill R.; Duara, Ranjan; Honig, Lawrence S.; Jones, David T.; Masdeu, Joseph C.; Mendez, Mario F.; Musiek, Erik; Onyike, Chiadi U.; Riddle, Meghan; Rogalski, Emily; Salloway, Stephen; Sha, Sharon J.; Turner, Raymond S.; Wingo, Thomas S.; Wolk, David A.; Womack, Kyle B.; Carrillo, Maria C.; Dickerson, Bradford C.; Rabinovici, Gil D.; Apostolova, Liana G.; LEADS Consortium; Neurology, School of Medicine
    Introduction: One goal of the Longitudinal Early Onset Alzheimer's Disease Study (LEADS) is to define the fluid biomarker characteristics of early-onset Alzheimer's disease (EOAD). Methods: Cerebrospinal fluid (CSF) concentrations of Aβ1-40, Aβ1-42, total tau (tTau), pTau181, VILIP-1, SNAP-25, neurogranin (Ng), neurofilament light chain (NfL), and YKL-40 were measured by immunoassay in 165 LEADS participants. The associations of biomarker concentrations with diagnostic group and standard cognitive tests were evaluated. Results: Biomarkers were correlated with one another. Levels of CSF Aβ42/40, pTau181, tTau, SNAP-25, and Ng in EOAD differed significantly from cognitively normal and early-onset non-AD dementia; NfL, YKL-40, and VILIP-1 did not. Across groups, all biomarkers except SNAP-25 were correlated with cognition. Within the EOAD group, Aβ42/40, NfL, Ng, and SNAP-25 were correlated with at least one cognitive measure. Discussion: This study provides a comprehensive analysis of CSF biomarkers in sporadic EOAD that can inform EOAD clinical trial design.
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    Characterization of the heterogeneity of amyloid‐PET‐negative patients with a clinical diagnosis of sporadic early‐onset AD: an FDG‐PET study in the LEADS cohort
    (Wiley, 2025-01-09) Lagarde, Julien; Schonhaut, Daniel R.; Maiti, Piyush; Zhang, Jiaxiuxiu; Soleimani-Meigooni, David N.; Zeltzer, Ehud; Windon, Charles; Hammers, Dustin B.; Dage, Jeffrey L.; Nudelman, Kelly N.; Eloyan, Ani; Koeppe, Robert A.; Carrillo, Maria C.; Touroutoglou, Alexandra; Vemuri, Prashanthi; Dickerson, Bradford C.; Apostolova, Liana G.; Rabinovici, Gil D.; La Joie, Renaud; Neurology, School of Medicine
    Background Diagnosing 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. Method Seventy‐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. Result Thirty‐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. Conclusion Fifty‐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.
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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 cortical atrophy in early‐onset and late‐onset Alzheimer's disease: A head‐to‐head comparison of the LEADS and ADNI cohorts
    (Wiley, 2025) Katsumi, Yuta; Touroutoglou, Alexandra; Brickhouse, Michael; Eloyan, Ani; Eckbo, Ryan; Zaitsev, Alexander; La Joie, Renaud; Lagarde, Julien; Schonhaut, Daniel; Thangarajah, Maryanne; Taurone, Alexander; Vemuri, Prashanthi; Jack, Clifford R., Jr.; Dage, Jeffrey L.; Nudelman, Kelly N. H.; Foroud, Tatiana; Hammers, Dustin B.; Ghetti, Bernardino; Murray, Melissa E.; Newell, Kathy L.; Polsinelli, Angelina J.; Aisen, Paul; Reman, Rema; Beckett, Laurel; Kramer, Joel H.; Atri, Alireza; Day, Gregory S.; Duara, Ranjan; Graff-Radford, Neill R.; Grant, Ian M.; Honig, Lawrence S.; Johnson, Erik C. B.; Jones, David T.; Masdeu, Joseph C.; Mendez, Mario F.; Musiek, Erik; Onyike, Chiadi U.; Riddle, Meghan; Rogalski, Emily; Salloway, Stephen; Sha, Sharon; Turner, R. Scott; Wingo, Thomas S.; Wolk, David A.; Womack, Kyle; Carrillo, Maria C.; Rabinovici, Gil D.; Apostolova, Liana G.; Dickerson, Bradford C.; LEADS Consortium for the Alzheimer's Disease Neuroimaging Initiative; Neurology, School of Medicine
    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.
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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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    Effects of APOE genotype on cortical atrophy in early onset Alzheimer’s disease
    (Wiley, 2025-01-09) Chan, Diane; Brickhouse, Michael; Zaitsev, Alexander; Wong, Bonnie; Hammers, Dustin B.; Dage, Jeffrey L.; Foroud, Tatiana M.; Eloyan, Ani; Nudelman, Kelly N.; Nemes, Sára; Carrillo, Maria C.; Rabinovici, Gil D.; Apostolova, Liana G.; Dickerson, Bradford C.; Touroutoglou, Alexandra; LEADS Consortium; Medical and Molecular Genetics, School of Medicine
    Background: APOE‐ɛ4 is a major risk factor for Alzheimer’s disease (AD); its effects have been examined in late‐onset AD (LOAD) but less so in early‐onset AD (EOAD). In LOAD, APOE genotype has strong effects on episodic memory and medial temporal lobe (MTL) atrophy (Wolk & Dickerson, 2010). However, EOAD often presents with more cognitive impairments in executive function, language, and visuospatial abilities than memory. These differences reflect more prominent atrophy in posterior lateral temporal and inferior parietal cortex that mainly constitute the EOAD‐signature of atrophy. Based on the cognitive and neuroanatomical profile of EOAD, we hypothesized that EOAD ɛ4 carriers will have relatively more atrophy in MTL regions subserving episodic memory, whereas non carriers would express more atrophy in cortical regions of the EOAD‐signature involved in executive function, language, and visuospatial abilities including inferior parietal and posterior temporal regions. We also expected worse performance on episodic memory tests in ɛ4 carriers with EOAD. Methods: We examined the effects of APOE genotype on cortical atrophy and episodic memory of 144 ɛ4 carriers and 117 ɛ4 non‐carriers with EOAD from the Longitudinal Early‐Onset Alzheimer’s Disease Study (LEADS). Between‐group comparisons using independent T‐tests were made for morphometric measures of cortical atrophy in MTL and hippocampus localized in LOAD as well as in cortical regions within our newly developed EOAD‐Signature tool (Touroutoglou et al., 2023). ANCOVA with Bonferonni’s correction was used to evaluate for effects of age on significant differences between groups. Results: As predicted, ɛ4 carriers with EOAD had more atrophy in the MTL and bilateral hippocampi, whereas non‐carriers had more atrophy in regions of the EOAD‐signature including bilateral caudal temporal, parietal lobule, middle frontal gyrus, mid temporal, posterior cingulate cortex, precuneus, superior frontal gyrus, superior parietal lobule. Post hoc vertex wise cortical maps further confirmed the specificity of the results. In addition, ɛ4 carriers had worse performance on episodic memory testing (AVLT delayed recall). These results were not explained by a difference in age between the groups. Conclusions: These results are consistent with prior work (Nemes et al. 2023) and support the hypothesis that the ɛ4 genotype modulates distinct neuroanatomic phenotypes of AD in EOAD patients.
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    Heterogeneous clinical phenotypes of sporadic early-onset Alzheimer's disease: a neuropsychological data-driven approach
    (Springer Nature, 2025-02-06) Putcha, Deepti; Katsumi, Yuta; Touroutoglou, Alexandra; Eloyan, Ani; Taurone, Alexander; Thangarajah, Maryanne; Aisen, Paul; Dage, Jeffrey L.; Foroud, Tatiana; Jack, Clifford R., Jr.; Kramer, Joel H.; Nudelman, Kelly N. H.; Raman, Rema; Vemuri, Prashanthi; Atri, Alireza; Day, Gregory S.; Duara, Ranjan; Graff-Radford, Neill R.; Grant, Ian M.; Honig, Lawrence S.; Johnson, Erik C. B.; Jones, David T.; Masdeu, Joseph C.; Mendez, Mario F.; Musiek, Erik; Onyike, Chiadi U.; Riddle, Meghan; Rogalski, Emily; Salloway, Stephen; Sha, Sharon; Turner, R. Scott; Wingo, Thomas S.; Wolk, David A.; Womack, Kyle; Carrillo, Maria C.; Rabinovici, Gil D.; Dickerson, Bradford C.; Apostolova, Liana G.; Hammers, Dustin B.; LEADS Consortium; Neurology, School of Medicine
    Background: The clinical presentations of early-onset Alzheimer's disease (EOAD) and late-onset Alzheimer's disease are distinct, with EOAD having a more aggressive disease course with greater heterogeneity. Recent publications from the Longitudinal Early-Onset Alzheimer's Disease Study (LEADS) described EOAD as predominantly amnestic, though this phenotypic description was based solely on clinical judgment. To better understand the phenotypic range of EOAD presentation, we applied a neuropsychological data-driven method to subtype the LEADS cohort. Methods: Neuropsychological test performance from 169 amyloid-positive EOAD participants were analyzed. Education-corrected normative comparisons were made using a sample of 98 cognitively normal participants. Comparing the relative levels of impairment between each cognitive domain, we applied a cut-off of 1 SD below all other domain scores to indicate a phenotype of "predominant" impairment in a given cognitive domain. Individuals were otherwise considered to have multidomain impairment. Whole-cortex general linear modeling of cortical atrophy was applied as an MRI-based validation of these distinct clinical phenotypes. Results: We identified 6 phenotypic subtypes of EOAD: Dysexecutive Predominant (22% of sample), Amnestic Predominant (11%), Language Predominant (11%), Visuospatial Predominant (15%), Mixed Amnestic/Dysexecutive Predominant (11%), and Multidomain (30%). These phenotypes did not differ by age, sex, or years of education. The APOE ɛ4 genotype was enriched in the Amnestic Predominant group, who were also rated as least impaired. Cortical thickness analysis validated these clinical phenotypes with dissociations in atrophy patterns observed between the Dysexecutive and Amnestic Predominant groups. In contrast to the heterogeneity observed from our neuropsychological 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% met criteria for Posterior Cortical Atrophy, and 5% met criteria for Primary Progressive Aphasia. Conclusion: A neuropsychological data-driven method to phenotype EOAD individuals uncovered a more detailed understanding of the presenting heterogeneity in this atypical AD sample compared to clinical judgment alone. Clinicians and patients may over-report memory dysfunction at the expense of non-memory symptoms. These findings have important implications for diagnostic accuracy and treatment considerations.
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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.