Browsing by Author "Eloyan, Ani"
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Item A harmonized memory composite score for cross‐cohort Alzheimer’s disease and related dementia research: development and validation(Wiley, 2025-01-03) Sanderson-Cimino, Mark E.; Gross, Alden L.; Gaynor, Leslie S.; Paolillo, Emily W.; Casaletto, Kaitlin B.; Chatterjee, Ankita; Albert, Marilyn S.; Apostolova, Liana G.; Boersema, Brooke; Boxer, Adam L.; Boeve, Brad F.; Clark, Lindsay R.; La Joie, Renaud; Eloyan, Ani; Tomaszewski Farias, Sarah; Gonzales, Mitzi M.; Hammers, Dustin B.; Wise, Amy B.; Cobigo, Yann; Yballa, Claire; Schonhaut, Daniel R.; Hampstead, Benjamin M.; Mechanic-Hamilton, Dawn; Miller, Bruce L.; Rabinovici, Gil D.; Rascovsky, Katya; Ringman, John M.; Rosen, Howard J.; Ryman, Sephira; Salmon, David P.; Smith, Glenn E.; Decarli, Charles; Kramer, Joel H.; Staffaroni, Adam M.; Neurology, School of MedicineBackground: The 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. Method: We 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. Result: Model 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). Conclusion: The 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.Item Alzheimer’s Disease Polygenic Risk in the LEADS Cohort(Wiley, 2025-01-03) Nudelman, Kelly N.; Pentchev, Julian V.; Jackson, Trever; Eloyan, Ani; Dage, Jeffrey L.; Foroud, Tatiana M.; Hammers, Dustin B.; Carrillo, Maria C.; Dickerson, Bradford C.; Rabinovici, Gil D.; Apostolova, Liana G.; LEADS Consortium; Medical and Molecular Genetics, School of MedicineBackground: Currently, it is unclear to what extent late‐onset Alzheimer’s disease (AD) risk variants contribute to early‐onset AD (EOAD). One method to clarify the contribution of late‐onset AD genetic risk to EOAD is to investigate the association of AD polygenic risk scores (PRS) with EOAD. We hypothesize that in the Longitudinal Early‐Onset Alzheimer’s Disease Study (LEADS), EOAD participants will have greater PRS than early‐onset amyloid‐negative cognitively‐impaired participants (EOnonAD) and controls, and investigate the association of AD PRS with age of disease onset (AoO) and cognitive performance. Methods: GWAS data was generated for LEADS participants, including those with EOAD, EOnonAD, and controls, with the Illumina Global Screening Array. A PRS was calculated using the 31 SNPs and weights published previously by Desikan et al. (2017) for LEADS participants with imputed GWAS data (N = 369). Logistic regression models including age, sex, PRS, and genetic ancestry principal components were tested to identify predictors of EOAD (N = 210) vs. EOnonAD (N = 69) and controls (N = 89). ANCOVA models were used to assess group differences in PRS scores. Kaplan‐Meier regression was used to assess differences in EOAD AoO for tertile‐binned PRS groups. Within EOAD, pre‐calculated cognitive domain scores for speed and attention, working memory, episodic memory, language, and visuospatial performance were assessed for correlation with PRS. Results: The AD PRS was a predictor of EOAD (p = 0.014), with the model explaining 10.5% of variance (X2 = 40.971, p<0.001). EOAD participants had higher PRS scores (mean = 0.0012, standard deviation (SD) = 0.015) compared to EOnonAD and controls (mean = ‐0.0018, SD = 0.015) (F = 6.602, p = 0.011). Survival analysis indicated no significant differences in EOAD AoO between PRS groups (X2 = 3.396, p = 0.183). In the EOAD group, PRS was associated with cognitive scores for speed and attention (r = 0.204, p = 0.007), language (r = 0.230, p = 0.002), and visuospatial performance (r = 0.166, p = 0.037). Conclusions: In the LEADS cohort, AD PRS is a predictor for EOAD, and is associated with cognitive performance, but does not predict EOAD AoO. This suggests that while late onset AD‐associated genetic variants contribute to disease risk and processes, they do not account for a large portion of disease risk, and do not explain differences in disease AoO in the LEADS cohort.Item Amyloid and tau-PET in early-onset AD: Baseline data from the Longitudinal Early-onset Alzheimer's Disease Study (LEADS)(Wiley, 2023) Cho, Hanna; Mundada, Nidhi S.; Apostolova, Liana G.; Carrillo, Maria C.; Shankar, Ranjani; Amuiri, Alinda N.; Zeltzer, Ehud; Windon, Charles C.; Soleimani-Meigooni, David N.; Tanner, Jeremy A.; Heath, Courtney Lawhn; Lesman-Segev, Orit H.; Aisen, Paul; Eloyan, Ani; Lee, Hye Sun; Hammers, Dustin B.; Kirby, Kala; Dage, Jeffrey L.; Fagan, Anne; Foroud, Tatiana; Grinberg, Lea T.; Jack, Clifford R.; Kramer, Joel; Kukull, Walter A.; Murray, Melissa E.; Nudelman, Kelly; Toga, Arthur; Vemuri, Prashanthi; Atri, Alireza; Day, Gregory S.; Duara, Ranjan; Graff-Radford, Neill R.; Honig, Lawrence S.; Jones, David T.; Masdeu, Joseph; Mendez, Mario; Musiek, Erik; Onyike, Chiadi U.; Riddle, Meghan; Rogalski, Emily J.; Salloway, Stephen; Sha, Sharon; Turner, Raymond Scott; Wingo, Thomas S.; Wolk, David A.; Koeppe, Robert; Iaccarino, Leonardo; Dickerson, Bradford C.; La Joie, Renaud; Rabinovici, Gil D.; LEADS Consortium; Neurology, School of MedicineIntroduction: We aimed to describe baseline amyloid-beta (Aβ) and tau-positron emission tomograrphy (PET) from Longitudinal Early-onset Alzheimer's Disease Study (LEADS), a prospective multi-site observational study of sporadic early-onset Alzheimer's disease (EOAD). Methods: We analyzed baseline [18F]Florbetaben (Aβ) and [18F]Flortaucipir (tau)-PET from cognitively impaired participants with a clinical diagnosis of mild cognitive impairment (MCI) or AD dementia aged < 65 years. Florbetaben scans were used to distinguish cognitively impaired participants with EOAD (Aβ+) from EOnonAD (Aβ-) based on the combination of visual read by expert reader and image quantification. Results: 243/321 (75.7%) of participants were assigned to the EOAD group based on amyloid-PET; 231 (95.1%) of them were tau-PET positive (A+T+). Tau-PET signal was elevated across cortical regions with a parietal-predominant pattern, and higher burden was observed in younger and female EOAD participants. Discussion: LEADS data emphasizes the importance of biomarkers to enhance diagnostic accuracy in EOAD. The advanced tau-PET binding at baseline might have implications for therapeutic strategies in patients with EOAD. Highlights: 72% of patients with clinical EOAD were positive on both amyloid- and tau-PET. Amyloid-positive patients with EOAD had high tau-PET signal across cortical regions. In EOAD, tau-PET mediated the relationship between amyloid-PET and MMSE. Among EOAD patients, younger onset and female sex were associated with higher tau-PET.Item 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 MedicineBackground: 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.Item Association Between Age and Cognitive Severity in Early‐Onset AD: Extension of preliminary findings in the Longitudinal Early‐Onset Alzheimer’s Disease Study (LEADS)(Wiley, 2025-01-03) Hammers, Dustin B.; Eloyan, Ani; Taurone, Alexander; Thangarajah, Maryanne; Kirby, Kala; Wong, Bonnie; Dage, Jeffrey L.; Nudelman, Kelly N.; Carrillo, Maria C.; Rabinovici, Gil D.; Dickerson, Bradford C.; Apostolova, Liana G.; LEADS Consortium; Neurology, School of MedicineBackground: Widespread cognitive impairments have previously been documented in Early‐Onset Alzheimer’s Disease (EOAD) relative to cognitively normal (CN) same‐aged peers or those with cognitive impairment without amyloid pathology (Early‐Onset non‐Alzheimer’s Disease; EOnonAD; Hammers et al., 2023). Prior preliminary work has similarly observed worse cognitive performance being associated with earlier ages in EOAD participants enrolled in the Longitudinal Early‐Onset Alzheimer’s Disease Study (LEADS; Apostolova et al., 2019). It is unclear, however, if these age effects are seen across early‐onset conditions, and whether cognitive discrepancies among diagnostic groups are uniform across the age spectrum. The objective of the current study is to more‐extensively examine the impact of age‐at‐baseline on cognition within LEADS, with emphasis placed on the influence of diagnostic group on these associations. Method: Expanded cross‐sectional baseline cognitive data from 573 participants (CN, n = 97; EOAD, n = 364; EOnonAD, n = 112) enrolled in the LEADS study (aged 40‐64) were analyzed. Multiple linear regression analyses were conducted to investigate associations between age‐at‐baseline and cognition for each diagnostic group – and their interaction among diagnoses – controlling for gender, education, APOE ε4 status, and disease severity. Result: See Table 1 for demographic characteristics of our sample. Linear regression showed a significant interaction effect for the cognitive domain of Executive Functioning (p = .002). Specifically, while the EOAD group displayed a positive relationship between age‐at‐baseline and Executive Functioning performance (β = 0.08, p = .02; Figure 1), the CN group displayed a negative relationship (β = ‐0.04, p = .008) and the EOnonAD group displayed no relationship (β = ‐0.01, p = .50). A similar main‐effect for age was observed for the EOAD group when examining Visuospatial Skills (β = 0.12, p = .04), however no other age effects were evident across other diagnostic groups or cognitive domains (Episodic Memory, Language, or Speed/Attention; Table 2). Conclusion: Building off preliminary work, our results suggest that executive functioning may be disproportionately impacted earlier in the disease course in participants with EOAD relative to other diagnostic groups. This finding appears to be unique to executive functioning, as it was absent in other cognitive domains and remained after accounting for disease severity. This highlights the need for further investigation into executive dysfunction early in the course of EOAD.Item 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 MedicineIntroduction: 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.Item 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 MedicineBackground 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.Item Characterizing and validating 12-month reliable cognitive change in Early-Onset Alzheimer's Disease for use in clinical trials(Springer, 2025) Hammers, Dustin B.; Musema, Jane; Eloyan, Ani; Thangarajah, Maryanne; Taurone, Alexander; La Joie, Renaud; Touroutoglou, Alexandra; Vemuri, Prashanthi; Kramer, Joel; Aisen, Paul; Dage, Jeffrey L.; Nudelman, Kelly N.; Kirby, Kala; Atri, Alireza; Clark, David; Day, Gregory S.; Duara, Ranjan; Graff-Radford, Neill R.; Grant, Ian; Honig, Lawrence S.; Johnson, Erik C. B.; Jones, David T.; Masdeu, Joseph C.; Mendez, Mario F.; Womack, Kyle; Musiek, Erik; Onyike, Chiadi U.; Riddle, Meghan; Rogalski, Emily; Salloway, Steven; Sha, Sharon J.; Scott Turner, Raymond; Wingo, Thomas S.; Wolk, David A.; Carrillo, Maria C.; Rabinovici, Gil D.; Dickerson, Bradford C.; Apostolova, Liana G.; LEADS Consortium; Neurology, School of MedicineBackground: As literature suggests that Early-Onset Alzheimer's Disease (EOAD) and late-onset AD may differ in important ways, need exists for randomized clinical trials for treatments tailored to EOAD. Accurately measuring reliable cognitive change in individual patients with EOAD will have great value for these trials. Objectives: The current study sought to characterize and validate 12-month reliable change from the Longitudinal Early-Onset Alzheimer's Disease Study (LEADS) neuropsychological battery. Design: Standardized regression-based (SRB) prediction equations were developed from age-matched cognitively intact participants within LEADS, and applied to clinically impaired participants from LEADS. Setting: Participants were recruited from outpatient academic medical centers. Participants: Participants were enrolled in LEADS and diagnosed with amyloid-positive EOAD (n = 189) and amyloid-negative early-onset cognitive impairment not related to AD (EOnonAD; n = 43). Measurement: 12-month reliable change (Z-scores) was compared between groups across cognitive domain composites, and distributions of individual participant trajectories were examined. Prediction of Z-scores by common AD biomarkers was also considered. Results: Both EOAD and EOnonAD displayed significantly lower 12-month follow-up scores than were predicted based on SRB equations, with declines more pronounced for EOAD across several domains. AD biomarkers of cerebral β-amyloid, tau, and EOAD-specific atrophy were predictive of 12-month change scores. Conclusions: The current results support including EOAD patients in longitudinal clinical trials, and generate evidence of validation for using 12-month reliable cognitive change as a clinical outcome metric in clinical trials in EOAD cohorts like LEADS. Doing so will enhance the success of EOAD trials and permit a better understanding of individual responses to treatment.Item 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 MedicineBackground 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.Item Development and validation of a harmonized memory score for multicenter Alzheimer's disease and related dementia research(medRxiv, 2025-04-03) Sanderson-Cimino, Mark; Gross, Alden L.; Gaynor, Leslie S.; Paolillo, Emily W.; Saloner, Rowan; Albert, Marilyn S.; Apostolova, Liana G.; Boersema, Brooke; Boxer, Adam L.; Boeve, Bradley F.; Casaletto, Kaitlin B.; Hallgarth, Savannah R.; Diaz, Valentina E.; Clark, Lindsay R.; Maillard, Pauline; Eloyan, Ani; Tomaszewski Farias, Sarah; Gonzales, Mitzi M.; Hammers, Dustin B.; La Joie, Renaud; Cobigo, Yann; Wolf, Amy; Hampstead, Benjamin M.; Mechanic-Hamilton, Dawn; Miller, Bruce L.; Rabinovici, Gil D.; Ringman, John M.; Rosen, Howie J.; Ryman, Sephira G.; Prestopnik, Jillian L.; Salmon, David P.; Smith, Glenn E.; DeCarli, Charles; Rajan, Kumar B.; Jin, Lee-Way; Hinman, Jason; Johnson, David K.; Harvey, Danielle; Fornage, Myriam; Kramer, Joel H.; Staffaroni, Adam M.; Neurology, School of MedicineIntroduction: List-learning tasks are important for characterizing memory in ADRD research, but the Uniform Data Set neuropsychological battery (UDS-NB) lacks a list-learning paradigm; thus, sites administer a range of tests. We developed a harmonized memory composite that incorporates UDS memory tests and multiple list-learning tasks. Methods: Item-banking confirmatory factor analysis was applied to develop a memory composite in a diagnostically heterogenous sample (n=5943) who completed the UDS-NB and one of five list-learning tasks. Construct validity was evaluated through associations with demographics, disease severity, cognitive tasks, brain volume, and plasma phosphorylated tau (p-tau181 and p-tau217). Test-retest reliability was assessed. Analyses were replicated in a racially/ethnically diverse cohort (n=1058). Results: Fit indices, loadings, distributions, and test-retest reliability were adequate. Expected associations with demographics and clinical measures within development and validation cohorts supported validity. Discussion: This composite enables researchers to incorporate multiple list-learning tasks with other UDS measures to create a single metric.