IU Indianapolis ScholarWorks :: Browsing by Author "Nudelman, Kelly N."

Browsing by Author "Nudelman, Kelly N."

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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.
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.
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.
Association of Alzheimer’s disease polygenic risk score with concussion severity and recovery metrics
(Wiley, 2025-01-09) Dybing, Kaitlyn M.; McAllister, Thomas W.; Wu, Yu-Chien; McDonald, Brenna C.; McCrea, Michael A.; Broglio, Steven P.; Pasquina, Paul F.; Brooks, M. Alison; Mihalik, Jason P.; Guskiewicz, Kevin M.; Giza, Christopher C.; Goldman, Joshua; Duma, Stefan; Rowson, Steve; Svoboda, Steven; Cameron, Kenneth L.; Houston, Megan N.; Campbell, Darren E.; McGinty, Gerald; Jackson, Jonathan; Risacher, Shannon L.; Saykin, Andrew J.; Nudelman, Kelly N.; Radiology and Imaging Sciences, School of Medicine
Background: Shared genetic risk between Alzheimer’s disease (AD) and concussion may help explain the association between concussion and elevated risk for dementia. However, there has been little investigation into whether AD risk genes also associate with concussion severity/recovery, and the limited findings are mixed. We used AD polygenic risk scores (PRS) and APOE genotypes to investigate associations between AD genetic risk and concussion severity/recovery in the NCAA‐DoD Grand Alliance CARE Consortium (CARE) dataset. Method: There were 1,917 injuries in the dataset upon project initiation. After removing repeated injuries, related participants, and those without genetic/outcome data, we had 931 participants. Outcomes were number of days to return to play (RTP) as a recovery measure, and four severity measures (scores on SAC and BESS, SCAT symptom severity and total number of symptoms). We calculated PRS using a published score (de Rojas et al., 2021) and performed a linear regression (MLR) of RTP by PRS in normal (<24 days) and long (>24 days) RTP subgroups. We then compared severity measures by PRS using MLR. Next, we used t‐tests to examine outcomes by APOE genotype in military and civilian subgroups. We also performed chi‐squared tests of RTP category (normal vs. long) by APOE genotype. Finally, we analyzed outcomes by PRS in European or African genetic ancestry subgroups using MLR. Result: Higher PRS was associated with longer injury to RTP interval in the normal RTP (<24 days) subgroup (estimate = 0.0412, SE = 0.182, p = 0.0237). 1 SD increase in PRS resulted in a 0.412 day (9.89 hours) increase to the interval. This may be clinically meaningful in the collegiate athlete environment. We did not identify any other significant differences. Conclusion: Our preliminary results provide limited evidence for an impact of AD PRS on concussion recovery, though the pattern was inconsistent and its clinical significance is uncertain. Future studies should attempt to replicate these findings in larger samples with longer follow‐up using PRS calculated from multiple/diverse populations, which will be especially relevant for diverse datasets like CARE.
Author Correction: Genetic factors affecting dopaminergic deterioration during the premotor stage of Parkinson disease
(Springer Nature, 2022-03-09) Lee, Myung Jun; Pak, Kyoungjune; Kim, Han-Kyeol; Nudelman, Kelly N.; Kim, Jong Hun; Kim, Yun Hak; Kang, Junho; Baek, Min Seok; Lyoo, Chul Hyoung; Medical and Molecular Genetics, School of Medicine
Erratum for: Genetic factors affecting dopaminergic deterioration during the premotor stage of Parkinson disease. Lee MJ, Pak K, Kim HK, Nudelman KN, Kim JH, Kim YH, Kang J, Baek MS, Lyoo CH. NPJ Parkinsons Dis. 2021 Nov 26;7(1):104. doi: 10.1038/s41531-021-00250-2. PMID: 34836969
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.
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.
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.
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.
Effects of BDNF and COMT variants on cognitive decline in Early‐Onset Alzheimer’s Disease
(Wiley, 2025-01-03) Hammers, Dustin B.; Foroud, Tatiana M.; Kim, Hee Jin; Musema, Jane; Dage, Jeffrey L.; Eloyan, Ani; Carrillo, Maria C.; Dickerson, Bradford C.; Rabinovici, Gil D.; Apostolova, Liana G.; Nudelman, Kelly N.; LEADS Consortium; Medical and Molecular Genetics, School of Medicine
Background: Early‐Onset Alzheimer’s Disease (EOAD) is a rare condition that affects only 5% of patients with Alzheimer’s Disease (AD). At present, only basic information is known about the impact of AD risk variants on EOAD, and the effects of more subtle genetic contributions to cognitive decline have yet to be investigated. Genetic variants for brain derived neurotrophic factor (BDNF) and catechol‐O‐methyltransferase (COMT) have both been implicated in cognitive change (Fiocco et al., 2010; Ferrer et al., 2019), consequently the aim of the current study was to examine the role of these genetic variants on cognitive decline in EOAD. Method: Data from 88 amyloid‐positive EOAD participants enrolled in the Longitudinal Early Onset Alzheimer’s Disease Study (LEADS; aged 40‐64) were analyzed. Exploratory multivariate analyses of covariance (MANCOVA) were conducted to investigate differences in 12‐month cognitive decline as a function of BDNF rs6265 (p.V66M) and COMT rs4680 (p.V158M) variants using dominant genetic models (Val/Val versus Val/Met or Met/Met). Cox Regression analyses were also conducted to consider the effect of genetic variants on age of onset. Result: See Table 1 for demographic characteristics of our sample. MANCOVA, controlling for age, education, sex, and race/ethnicity, showed significant effects for BDNF p.V66M on domains of Memory (p<0.001) and Executive Functioning (p = 0.04; Table 2). Specifically, greater 12‐month cognitive decline was observed for the CRAFT Immediate and Delayed Story Memory, with worse performance associated with BDNF minor alleles (ps. = 0.007 to 0.02). Conversely, worse decline was observed for the reference group for RAVLT Immediate Memory (p<0.006) and Digit Span Backwards (p<0.02). No significant effects were evident for domains of Language, Speed/Attention, or Visuospatial skills (ps = 0.34‐0.97), nor for any analyses of COMT carrier status (ps = 0.26‐0.87). Cox Regression analyses, controlling for race and ethnicity, were not significant for BDNF or COMT carrier status (ps = 0.59‐0.64; Figure 1). Conclusion: Results suggest subtle effects of BDNF p.V66M carrier status on memory decline in EOAD participants, which was not observed for disease progression/age‐of‐onset. No effects for COMT p.V158M carrier status were observed. Future investigation will replicate these effects in larger samples, permitting stratification of additional covariates including APOE genotype.

Browsing by Author "Nudelman, Kelly N."

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