Browsing by Author "Hammers, Dustin B."

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    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 Medicine
    Background: 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.
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    A simulative deep learning model of SNP interactions on chromosome 19 for predicting Alzheimer’s disease risk and rates of disease progression
    (Wiley, 2023) Bae, Jinhyeong; Logan, Paige E.; Acri, Dominic J.; Bharthur, Apoorva; Nho, Kwangsik; Saykin, Andrew J.; Risacher, Shannon L.; Nudelman, Kelly; Polsinelli, Angelina J.; Pentchev, Valentin; Kim, Jungsu; Hammers, Dustin B.; Apostolova, Liana G.; Alzheimer’s Disease Neuroimaging Initiative; Neurology, School of Medicine
    Background: Identifying genetic patterns that contribute to Alzheimer's disease (AD) is important not only for pre-symptomatic risk assessment but also for building personalized therapeutic strategies. Methods: We implemented a novel simulative deep learning model to chromosome 19 genetic data from the Alzheimer's Disease Neuroimaging Initiative and the Imaging and Genetic Biomarkers of Alzheimer's Disease datasets. The model quantified the contribution of each single nucleotide polymorphism (SNP) and their epistatic impact on the likelihood of AD using the occlusion method. The top 35 AD-risk SNPs in chromosome 19 were identified, and their ability to predict the rate of AD progression was analyzed. Results: Rs561311966 (APOC1) and rs2229918 (ERCC1/CD3EAP) were recognized as the most powerful factors influencing AD risk. The top 35 chromosome 19 AD-risk SNPs were significant predictors of AD progression. Discussion: The model successfully estimated the contribution of AD-risk SNPs that account for AD progression at the individual level. This can help in building preventive precision medicine.
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    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 Medicine
    Background: 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.
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    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 Medicine
    Introduction: 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.
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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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    Application of Different Standard Error Estimates in Reliable Change Methods
    (Oxford University Press, 2021) Hammers, Dustin B.; Duff, Kevin; Neurology, School of Medicine
    Objective: This study attempted to clarify the applicability of standard error (SE) terms in clinical research when examining the impact of short-term practice effects on cognitive performance via reliable change methodology. Method: This study compared McSweeney's SE of the estimate (SEest) to Crawford and Howell's SE for prediction of the regression (SEpred) using a developmental sample of 167 participants with either normal cognition or mild cognitive impairment (MCI) assessed twice over 1 week. One-week practice effects in older adults: Tools for assessing cognitive change. Using these SEs, previously published standardized regression-based (SRB) reliable change prediction equations were then applied to an independent sample of 143 participants with MCI. Results: This clinical developmental sample yielded nearly identical SE values (e.g., 3.697 vs. 3.719 for HVLT-R Total Recall SEest and SEpred, respectively), and the resultant SRB-based discrepancy z scores were comparable and strongly correlated (r = 1.0, p < .001). Consequently, observed follow-up scores for our sample with MCI were consistently below expectation compared to predictions based on Duff's SRB algorithms. Conclusions: These results appear to replicate and extend previous work showing that the calculation of the SEest and SEpred from a clinical sample of cognitively intact and MCI participants yields similar values and can be incorporated into SRB reliable change statistics with comparable results. As a result, neuropsychologists utilizing reliable change methods in research investigation (or clinical practice) should carefully balance mathematical accuracy and ease of use, among other factors, when determining which SE metric to use.
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    Assessing and validating reliable change across ADNI protocols
    (Taylor & Francis, 2022) Hammers, Dustin B.; Kostadinova, Ralitsa; Unverzagt, Frederick W.; Apostolova, Liana G.; Alzheimer’s Disease Neuroimaging Initiative; Neurology, School of Medicine
    Objective: Reliable change methods can aid in determining whether changes in cognitive performance over time are meaningful. The current study sought to develop and cross-validate 12-month standardized regression-based (SRB) equations for the neuropsychological measures commonly administered in the Alzheimer's Disease Neuroimaging Initiative (ADNI) longitudinal study. Method: Prediction algorithms were developed using baseline score, retest interval, the presence/absence of a 6-month evaluation, age, education, sex, and ethnicity in two different samples (n = 192 each) of robustly cognitively intact community-dwelling older adults from ADNI - matched for demographic and testing factors. The developed formulae for each sample were then applied to one of the samples to determine goodness-of-fit and appropriateness of combining samples for a single set of SRB equations. Results: Minimal differences were seen between Observed 12-month and Predicted 12-month scores on most neuropsychological tests from ADNI, and when compared across samples the resultant Predicted 12-month scores were highly correlated. As a result, samples were combined and SRB prediction equations were successfully developed for each of the measures. Conclusions: Establishing cross-validation for these SRB prediction equations provides initial support of their use to detect meaningful change in the ADNI sample, and provides the basis for future research with clinical samples to evaluate potential clinical utility. While some caution should be considered for measuring true cognitive change over time - particularly in clinical samples - when using these prediction equations given the relatively lower coefficients of stability observed, use of these SRBs reflects an improvement over current practice in ADNI.
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    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 Medicine
    Background: 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.
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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.