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121 citation(s) found for "Salloway, Stephen".

Association ofBDNFVal66Met With Tau Hyperphosphorylation and Cognition in Dominantly Inherited Alzheimer Disease
Barthélemy, Nicolas R. , Goate, Alison , Hassenstab, Jason , Fagan, Anne M. , Xiong, Chengjie , Cruchaga, Carlos , Farlow, Martin R. , Graff‐Radford, Neill R. , Laske, Christoph , Masters, Colin L. , Salloway, Stephen , Morris, John C. , Bateman, Randall J. , Network, Dominantly Inherited Alzheimer, Chhatwal, Jasmeer P. , Fitzpatrick, Colleen , Bodge, Courtney, Cruz, Chrismary De La, Mejia, Arlene, Neimeyer, Katie, Gardener, Samantha L. , Taddei, Kevin , Ping, Lingyan, Seyfried, Nick , Gräber‐Sultan, Susanne, Häsler, Lisa M., Käser, Stephan, Kuder-Buletta, Elke, Preische, Oliver, Diffenbacher, Anna, Igor, Yakushev, Levin, Johannes, Vöglein, Jonathan , Obermüller, Ulricke, Esposito, Bianca, Goate, Alison, Buck, Jill , Farlow, Marty, Chrem, Patricio, Egido, Noelia , Haass, Christian , Nuscher, Brigitte , Day, Gregory S. , Graham, Morgan, Stephens, Sochenda, Bechara, Jacob , Araki, Aki, Ishii, Kenji , Senda, Michio , Ihara, Ryoko , Nagamatsu, Akemi, Fox, Nick C. , Mummery, Cath, Goldberg, Sarah B. , Ikonomović, Snežana, Mountz, James M. , Nadkarni, Neelesh K. , Patira, Riddhi , Smith, Lori, Snitz, Beth E. , Thompson, Sarah , Weamer, Elise A., Mason, Neal Scott, Chui, Helena, Ringman, John M. , Adams, Sarah , Brandon, Susan E. , Buckles, Virginia, Cash, Lisa, Chen, Charlie, Chua, Jasmin, Cruchaga, Carlos, Denner, Darcy, Dincer, Aylin , Donahue, Tamara, Feldman, Becca , Franklin, Erin , Friedrichsen, Nelly, Gonzalez, Alyssa, Gray, Julia, Gremminger, Emily, Groves, Alex, Hassenstab, Jason, Hellm, Cortaiga, Herries, Elizabeth, Hoechst-Swisher, Laura, Hornbeck, Russ C. , Jerome, Gina, Keefe, Sarah , Koudelis, Deb, Martinez, Rita, Mawuenyega, Kwasi G., McCullough, Austin , McDade, Eric, Norton, Joanne, Perrin, Richard J. , Shady, Kristine, Sigurdson, Wendy, Xu, Jinbin , Cruchaga, Carlos , Day, Gregory S , Bateman, Randall J , Masters, Colin L , Vöglein, Jonathan , Xiong, Chengjie , Fox, Nick C , Hassenstab, Jason , Morris, John C , Goate, Alison M , Lim, Yen Ying , Maruff, Paul , Sato, Chihiro , Benzinger, Tammie L.S. , Xiong, Chengjie , Schofield, Peter R. , McDade, Eric , Network, Dominantly Inherited Alzheimer, Fitzpatrick, Colleen, Goldman, Jill , Noble, James M. , Martins, Ralph N. , Sohrabi, Hamid R. , Carter, Kathleen, Duong, Duc M , Johnson, Erik C. B. , Levey, Allan I. , Ping, Lingyan, Hofmann, Anna, Jucker, Mathias , Levin, Johannes, Goate, Alison, Renton, Alan , Brosch, Jared R. , Buck, Jill, Ghetti, Bernardino , Allegri, Ricardo , Morenas‐Rodríguez, Estrella , Jack, Clifford R. , Brooks, William S. , Ikeuchi, Takeshi , Kasuga, Kensaku , Fujii, Hisako , Shimada, Hiroyuki , Niimi, Yoshiki , Douglas, Jane, Grilo, Miguel, O’Connor, Antoinette , Koeppe, Robert A. , Berman, Sarah , Klunk, William E. , López, Oscar L. , Brandon, Susan, Cruchaga, Carlos, Feldman, Becca, Flores, Shaney , Gordon, Brian A. , Hassenstab, Jason, Holtzman, David M. , Karch, Celeste M. , Li, Yan , Marsh, Jacob, McDade, Eric, Smith, Jennifer A. , Wang, Peter , Wang, Qing , Xu, Xiong

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JAMA Neurology | January 31, 2022 | Volume: 79 | Issue: 3 | Pages: 261

10.1001/jamaneurol.2021.5181 | PMID 35099506 Get It

Cognitive, neuropsychiatric and imaging comparisons between early‐onset and late‐onset Alzheimer’s disease participants from LEADS and ADNI3
Apostolova, Liana G. , Eloyan, Ani , Gao, Sujuan , Touroutoglou, Alexandra , Aisen, Paul , Borowski, Bret, Fagan, Anne M. , Gatsonis, Constantine , Kramer, Joel H. , Graff‐Radford, Neill R. , Masdeu, Joseph C. , Mendez, Mario F. , Onyike, Chiadi U. , Rogalskı, Emily , Salloway, Stephen , Dickerson, Brad C., Iaccarino, Leonardo , Beckett, Laurel , Donohue, Michael , Foroud, Tatiana , Jack, Clifford R. , Koeppe, Robert A. , Saykin, Andrew J. , Toga, Arthur W. , Vemuri, Prashanthi , Day, Gregory S. , Honig, Lawrence S. , Jones, David T. , Masdeu, Joseph C , Wolk, David A. , Wingo, Thomas S , Carrillo, María C. , Rabinovici, Gil D.

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Alzheimer's & Dementia | December 31, 2021 | Volume: 17 | Issue: S6 | Pages: No page available

10.1002/alz.056676 Get It

AbstractBackgroundThe overarching goal of the Longitudinal Early‐onset Alzheimer Disease study (LEADS) is to optimally characterize early‐onset AD (EOAD) and establish an EOAD clinical trials network. Here we report the baseline demographic and imaging biomarker comparisons of the LEADS cohort to late‐onset AD (LOAD) subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI3).Method123 amyloid‐positive EOAD, 47 amyloid‐negative EOnonAD, 60 cognitively normal young controls were compared to 130 amyloid‐positive LOAD, 110 amyloid‐negative LOnonAD and 286 amyloid‐negative cognitively normal older controls. To account for the effect of cognitive aging between EO and LO populations, each cognitive measure was Z‐transformed. Cortical and hippocampal atrophy were quantified using W‐scores adjusted for age, sex and total intracranial volume. Z‐scores and W‐scores were compared using t‐test or Wilcoxson rank test as appropriate. All p‐values were corrected for multiple comparisons using the false discovery rate correction.ResultEOAD showed greater pathology burden and greater cortical atrophy (AD signature) relative to LOAD. EOAD also showed greater cognitive impairment across all cognitive tests. EOAD showed greater functional impairment, more depression but less neuropsychiatric behaviors overall compared to LOAD (Table 1 and Figure 1, all ps<0.05). Repeating the analyses stratified by cognitive stage (MCI/dementia) or CDR global rating (0.5/1) did not result in any major differences.EOnonAD differed from LOnonAD by also showing greater impairment on all cognitive and functional measures There were no significant differences in amyloid and tau burden, or atrophy W‐scores between these groups. EOnonAD were more depressed and showed more functional impairment compared to LOnonAD (Table 2 and Figure 2, all ps<0.05). Repeating the analyses split by cognitive stage (MCI/dementia) or CDR global rating (0.5/1) did not result in any major differences.ConclusionConsistent with our preexisting hypotheses, EOAD and EOnonAD perform much worse relative to their LO counterparts. EOAD also show greater pathological burden as expected. The reported analyses were done in chronological rather than disease time (time since disease onset). Benchmarking individuals along the disease spectrum might prove to be a better strategy especially when conducting analyses on rate of disease progression.
Characterization of amyloid‐related imaging abnormality in the DIAN‐TU‐001 trial
Hofmann, Carsten, Wang, Guoqiao , Chen, Charles D., Gauthier, Serge , Holdridge, Karen C. , Klein, Gregory , Mummery, Catherine J. , Preboske, Gregory M., Santacruz, Anna , Yaari, R. , Salloway, Stephen , Clifford, David B., Unit, Dominantly Inherited Alzheimer Network Trials, Joseph‐Mathurin, Nelly , Llibre‐Guerra, Jorge J. , McCullough, Austin , Li, Yan , Wojtowicz, Jakub , Wang, Qing , Gordon, Brian A. , Brooks, William S. , Hsiung, Ging‐Yuek Robin , Jack, Clifford R. , Masellis, Mario , Wallon, David , Xiong, Chengjie , McDade, Eric , Bateman, Randall J. , Benzinger, Tammie L.S. , Unit, Dominantly Inherited Alzheimer Network Trials

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Alzheimer's & Dementia | December 31, 2021 | Volume: 17 | Issue: S1 | Pages: No page available

10.1002/alz.056393 Get It

AbstractBackgroundAmyloid‐related imaging abnormalities (ARIA), edema (E) or hemorrhagic (H) type, have been reported in trials of anti‐β‐amyloid passive immunotherapies in sporadic and dominantly inherited Alzheimer Disease (DIAD). However, beyond APOE‐ɛ4 the risk factors and clinical implications of ARIA are not well understood, especially in DIAD populations. Here we characterize ARIA, focusing on ARIA‐E in the DIAN‐TU‐001 trial evaluating gantenerumab and solanezumab in DIAD.MethodThe DIAN‐TU‐001 trial enrolled 194 participants, including 144 DIAD mutation‐carriers receiving gantenerumab (n=52), solanezumab (n=52), or a placebo (n=40). Clinical assessments included the Clinical Dementia Rating (CDR). Imaging assessments included PiB‐PET and safety MR, including T2‐FLAIR and T2*‐GRE. Treatment dosage and APOE‐ɛ4 status were also reported.ResultEleven participants developed a total of 14 ARIA‐E episodes (discovered on scheduled safety MR) and three had associated mild symptoms reported in retrospect (e.g. headache, imbalance disorder). Though one ARIA‐E case was observed in the solanezumab arm, the following results focus on the gantenerumab arm to prevent unblinding. Recipients of gantenerumab were more likely to develop ARIA‐E compared to placebo (odds ratio (OR)=9.29, 95% confidence interval (CI)=[1.1,75.9], p‐value<0.05). ARIA‐E participants were PiB‐PET+ and 60% were CDR>0. APOE‐ɛ4 tends to be associated with risk for developing ARIA‐E (OR=5.0, 95%CI=[1.0,30.4], p‐value=0.055). ARIA‐E were not observed at initial 225mg dose and were first observed after a titration step (within 4‐24 weeks, after 1‐6 injections, Table 1). Time‐to‐resolution of ARIA‐E was 10.4±6.2weeks. Seven of ten ARIA‐E patients paused/reduced dose escalation and three discontinued treatment. Overall, developing ARIA‐E did not increase the odds of trial discontinuation (OR=1.57, 95%CI=[0.34,7.33], p‐value=0.57). ARIA‐E occurred primarily in the occipital lobe (71%) with associated incident ARIA‐H (microhemorrhages or superficial siderosis) in 60% of ARIA‐E participants. ARIA‐E size was associated with microhemorrhage count (Spearman’s rho=0.72, p‐value<0.05), rate of change (rho=0.78, p‐value<0.01), and baseline PiB‐PET (rho=0.68, p‐value<0.05). Normalizing by baseline, PiB‐PET decreased similarly in ARIA‐E+ (‐0.21±0.20) and ARIA‐E‐ (‐0.16±0.20) participants.ConclusionIn DIAD, gantenerumab dose over 225mg increased ARIA‐E risk, possibly more for APOE‐ɛ4 carriers. ARIA‐E was reversible, generally asymptomatic, and without increased odds of trial discontinuation. These aspects of drug response give insights for managing ARIA‐E occurrence in future trials.
Different rates of cognitive decline in autosomal dominant and late‐onset Alzheimer disease
Buckles, Virginia, Xiong, Chengjie , Bateman, Randall J. , Hassenstab, Jason , Berman, Sarah , Chhatwal, Jasmeer P. , Fagan, Anne M. , Goate, Alison , Graff‐Radford, Neill R. , Marcus, Daniel S., Masters, Colin L. , McCue, Lena , Mori, Hiroshi , Moulder, Krista L., Noble, James M. , Paumier, Katrina L. , Preische, Oliver, Ringman, John M. , Fox, Nick C. , Salloway, Stephen , Vöglein, Jonathan , Morris, John C. , Network, for the Dominantly Inherited Alzheimer's, Bateman, Randall J , Masters, Colin L , Vöglein, Jonathan , Xiong, Chengjie , Fox, Nick C , Hassenstab, Jason , Morris, John C , Goate, Alison M , Berman, Sarah , Xiong, Chengjie , Allegri, Ricardo , Danek, Adrian , Ghetti, Bernardino , Jucker, Mathias , McDade, Eric , Schofield, Peter R. , Martins, Ralph N. , Network, for the Dominantly Inherited Alzheimer's, Noble, James M

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Alzheimer's & Dementia | December 2, 2021 | Volume: 18 | Issue: 10 | Pages: 1754-1764

10.1002/alz.12505 | PMID 34854530 Get It

AbstractAs prevention trials advance with autosomal dominant Alzheimer disease (ADAD) participants, understanding the similarities and differences between ADAD and “sporadic” late‐onset AD (LOAD) is critical to determine generalizability of findings between these cohorts. Cognitive trajectories of ADAD mutation carriers (MCs) and autopsy‐confirmed LOAD individuals were compared to address this question. Longitudinal rates of change on cognitive measures were compared in ADAD MCs (n = 310) and autopsy‐confirmed LOAD participants (n = 163) before and after symptom onset (estimated/observed). LOAD participants declined more rapidly in the presymptomatic (preclinical) period and performed more poorly at symptom onset than ADAD participants on a cognitive composite. After symptom onset, however, the younger ADAD MCs declined more rapidly. The similar but not identical cognitive trajectories (declining but at different rates) for ADAD and LOAD suggest common AD pathologies but with some differences.
Pattern and degree of individual brain atrophy predicts dementia onset in dominantly inherited Alzheimer's disease
Keret, Ophir , Staffaroni, Adam M. , Ringman, John M. , Goh, Sheng‐Yang M., Wolf, Amy , Allen, Isabel Elaine , Salloway, Stephen , Chhatwal, Jasmeer P. , Reyes‐Dumeyer, Dolly , Bateman, Randall J. , Morris, John C. , Joseph‐Mathurin, Nelly , Perrin, Richard J. , Gordon, Brian A. , Xiong, Chengjie , Vöglein, Jonathan , Fougère, Christian la , Martins, Ralph N. , Sohrabi, Hamid R. , Taddei, Kevin , Villemagne, Victor L. , Fulham, Michael , Masters, Colin L. , Saykin, Andrew J. , Graff‐Radford, Neill R. , Weiner, Michael W. , Cash, David M. , Chrem, Patricio, Rabinovici, Gil D. , Rosen, Howard J. , Network, Dominantly Inherited Alzheimer, Gordon, Brian A , Saykin, Andrew J , Bateman, Randall J , Masters, Colin L , Vöglein, Jonathan , Joseph-Mathurin, Nelly , Xiong, Chengjie , Morris, John C , Cash, David M , Rabinovici, Gil D , Cobigo, Yann , Brickman, Adam M. , Benzinger, Tammie L.S. , Ances, Beau M. , Jucker, Mathias , Schofield, Peter R. , Brooks, William S. , Ghetti, Bernardino , Jack, Clifford R. , Allegri, Ricardo , Su, Yi , Miller, Bruce L. , Network, Dominantly Inherited Alzheimer

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Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring | July 5, 2021 | Volume: 13 | Issue: 1 | Pages: No page available

10.1002/dad2.12197 | PMID 34258377 Get It

Abstract Introduction Asymptomatic and mildly symptomatic dominantly inherited Alzheimer's disease mutation carriers (DIAD‐MC) are ideal candidates for preventative treatment trials aimed at delaying or preventing dementia onset. Brain atrophy is an early feature of DIAD‐MC and could help predict risk for dementia during trial enrollment. Methods We created a dementia risk score by entering standardized gray‐matter volumes from 231 DIAD‐MC into a logistic regression to classify participants with and without dementia. The score's predictive utility was assessed using Cox models and receiver operating curves on a separate group of 65 DIAD‐MC followed longitudinally. Results Our risk score separated asymptomatic versus demented DIAD‐MC with 96.4% (standard error = 0.02) and predicted conversion to dementia at next visit (hazard ratio = 1.32, 95% confidence interval [CI: 1.15, 1.49]) and within 2 years (area under the curve = 90.3%, 95% CI [82.3%–98.2%]) and improved prediction beyond established methods based on familial age of onset. Discussion Individualized risk scores based on brain atrophy could be useful for establishing enrollment criteria and stratifying DIAD‐MC participants for prevention trials.
Segregation of functional networks is associated with cognitive resilience in Alzheimer’s disease
Ewers, Michael , Luan, Ying , Frontzkowski, Lukas , Rubinski, Anna , Dichgans, Martin , Hassenstab, Jason , Gordon, Brian A. , Chhatwal, Jasmeer P. , Xiong, Chengjie , Morris, John C. , Goate, Alison , Fagan, Anne M. , Berman, Sarah , Chui, Helena C., Cruchaga, Carlos , Farlow, Marty, Graff‐Radford, Neill R. , Martins, Ralph N. , Mori, Hiroshi , Perrin, Richard J. , Xiong, Chengjie , Fox, Nick C. , O’Connor, Antoinette , Salloway, Stephen , Danek, Adrian , Büerger, Katharina , Bateman, Randall J. , Habeck, Christian , Network, for the Alzheimer’s Disease Neuroimaging Initiative and the Dominantly Inherited Alzheimer, Vöglein, Jonathan , Gordon, Brian A , O'Connor, Antoinette , Stern, Yaakov , Franzmeier, Nicolai , Dichgans, Martin , Cruchaga, Carlos , Bateman, Randall J , Vöglein, Jonathan , Xiong, Chengjie , Fox, Nick C , Hassenstab, Jason , Morris, John C , Goate, Alison M , Berman, Sarah , Neitzel, Julia , Schofield, Peter R. , Benzinger, Tammie L.S. , Karch, Celeste M. , McDade, Eric , Allegri, Ricardo , Chui, Helena C. , Jucker, Mathias , Lee, Jae‐Hong , Rossor, Martin N. , Stern, Yaakov , Franzmeier, Nicolai , Network, for the Alzheimer’s Disease Neuroimaging Initiative and the Dominantly Inherited Alzheimer

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Brain | March 12, 2021 | Volume: 144 | Issue: 7 | Pages: 2176-2185

10.1093/brain/awab112 | PMID 33725114 Get It

Abstract Cognitive resilience is an important modulating factor of cognitive decline in Alzheimer’s disease, but the functional brain mechanisms that support cognitive resilience remain elusive. Given previous findings in normal ageing, we tested the hypothesis that higher segregation of the brain’s connectome into distinct functional networks represents a functional mechanism underlying cognitive resilience in Alzheimer’s disease. Using resting-state functional MRI, we assessed both resting-state functional MRI global system segregation, i.e. the balance of between-network to within-network connectivity, and the alternate index of modularity Q as predictors of cognitive resilience. We performed all analyses in two independent samples for validation: (i) 108 individuals with autosomal dominantly inherited Alzheimer’s disease and 71 non-carrier controls; and (ii) 156 amyloid-PET-positive subjects across the spectrum of sporadic Alzheimer’s disease and 184 amyloid-negative controls. In the autosomal dominant Alzheimer’s disease sample, disease severity was assessed by estimated years from symptom onset. In the sporadic Alzheimer’s sample, disease stage was assessed by temporal lobe tau-PET (i.e. composite across Braak stage I and III regions). In both samples, we tested whether the effect of disease severity on cognition was attenuated at higher levels of functional network segregation. For autosomal dominant Alzheimer’s disease, we found higher functional MRI-assessed system segregation to be associated with an attenuated effect of estimated years from symptom onset on global cognition (P = 0.007). Similarly, for patients with sporadic Alzheimer’s disease, higher functional MRI-assessed system segregation was associated with less decrement in global cognition (P = 0.001) and episodic memory (P = 0.004) per unit increase of temporal lobe tau-PET. Confirmatory analyses using the alternate index of modularity Q revealed consistent results. In conclusion, higher segregation of functional connections into distinct large-scale networks supports cognitive resilience in Alzheimer’s disease.
Modeling autosomal dominant Alzheimer's disease with machine learning
Luckett, Patrick H. , McCullough, Austin , Gordon, Brian A. , Strain, Jeremy F. , Flores, Shaney , Dincer, Aylin , Kuffner, Todd A. , Stern, Ari, Meeker, Karin L. , Berman, Sarah , Chhatwal, Jasmeer P. , Cruchaga, Carlos , Fagan, Anne M. , Farlow, Martin R. , Fox, Nick C. , Xiong, Chengjie , Masters, Colin L. , Mori, Hiroshi , Noble, James M. , Salloway, Stephen , Cash, David M. , Fulham, Michael , Jack, Clifford R. , Vöglein, Jonathan , Klunk, William E. , Oh, Hwamee , Swisher, Laura, Marcus, Daniel S. , Koudelis, Deborah, Joseph‐Mathurin, Nelly , Cash, Lisa, Hornbeck, Russ C. , Perrin, Richard J. , Hassenstab, Jason , Morris, John C. , Bateman, Randall J. , (DIAN), for the Dominantly Inherited Alzheimer Network, Wang, Qing , Cruchaga, Carlos , Jack, Clifford R , Gordon, Brian A , Bateman, Randall J , Masters, Colin L , Vöglein, Jonathan , Joseph-Mathurin, Nelly , Xiong, Chengjie , Fox, Nick C , Hassenstab, Jason , Morris, John C , Cash, David M , Berman, Sarah , McCarthy, John Edward , Jucker, Mathias , Schofield, Peter R. , Brickman, Adam M. , Brooks, William S. , Ghetti, Bernardino , Koeppe, Robert A. , Su, Yi , Weiner, Michael W. , Wáng, Qīng , Xiong, Chengjie , Karch, Celeste M. , McDade, Eric , Benzinger, Tammie L.S. , Ances, Beau M. , (DIAN), for the Dominantly Inherited Alzheimer Network, Noble, James M

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Alzheimer's & Dementia | January 21, 2021 | Volume: 17 | Issue: 6 | Pages: 1005-1016

10.1002/alz.12259 | PMID 33480178 Get It

AbstractIntroductionMachine learning models were used to discover novel disease trajectories for autosomal dominant Alzheimer's disease.MethodsLongitudinal structural magnetic resonance imaging, amyloid positron emission tomography (PET), and fluorodeoxyglucose PET were acquired in 131 mutation carriers and 74 non‐carriers from the Dominantly Inherited Alzheimer Network; the groups were matched for age, education, sex, and apolipoprotein ε4 (APOE ε4). A deep neural network was trained to predict disease progression for each modality. Relief algorithms identified the strongest predictors of mutation status.ResultsThe Relief algorithm identified the caudate, cingulate, and precuneus as the strongest predictors among all modalities. The model yielded accurate results for predicting future Pittsburgh compound B (R2 = 0.95), fluorodeoxyglucose (R2 = 0.93), and atrophy (R2 = 0.95) in mutation carriers compared to non‐carriers.DiscussionResults suggest a sigmoidal trajectory for amyloid, a biphasic response for metabolism, and a gradual decrease in volume, with disease progression primarily in subcortical, middle frontal, and posterior parietal regions.
Neurodegeneration in the Longitudinal Evaluation of Early Onset Alzheimer’s Disease Study (LEADS) sample: Results from the MRI core
Collins, Jessica A. , Ezzo, Rania , Borowski, Bret, Bourakova, Viktoriya, Eloyan, Ani , Aisen, Paul , Fagan, Anne M. , Gatsonis, Constantine , Kramer, Joel H. , Graff‐Radford, Neill R., Masdeu, Joseph C. , Mendez, Mario F. , Onyike, Chiadi U. , Rogalskı, Emily , Salloway, Stephen , Apostolova, Liana G. , Dickerson, Brad C., Vemuri, Prashanthi , Iaccarino, Leonardo , Joie, Renaud La , Lesman‐Segev, Orit H. , Foroud, Tatiana , Jack, Clifford R. , Koeppe, Robert A. , Saykin, Andrew J. , Toga, Arthur W. , Day, Gregory S. , Graff‐Radford, Neill R., Honig, Lawrence S. , Jones, David T. , Masdeu, Joseph C , Wolk, David A. , Wingo, Thomas S , Carrillo, María C. , Rabinovici, Gil D.

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Alzheimer's & Dementia | December 7, 2020 | Volume: 16 | Issue: S5 | Pages: No page available

10.1002/alz.046338 Get It

AbstractBackgroundApproximately 5% of the 5.6 million (∼280,000) Americans with Alzheimer’s disease (AD) develop symptoms at age 65 or younger and are classified as having early‐onset AD (EOAD). Although EOAD and late‐onset AD (LOAD) share the same pathologic substrate, there are notable differences in their clinical and biological phenotypes. The Longitudinal Early‐onset AD study (LEADS) is a multi‐site, observational clinical and biomarker study of EOAD. In the present study we utilized the currently available LEADS imaging data to explore the spatial extent and magnitude of neurodegeneration EOADMethod95 amyloid positive patients with EOAD, 37 amyloid negative cognitively impaired patients (EOnonAD) , and 55 cognitively unimpaired controls (CNs) were included in the analysis. The magnitude of cortical thinning in the EOAD and EOnonAD groups was estimated using whole‐brain GLMs compared to the CN group. In addition, w‐scores were derived for each EOAD patient for these ROIs: average cortical thickness, hippocampal volume, AD‐signature cortical thickness (typically used in LOAD), and precuneus cortical thickness controlling for age, sex, and acquisition site. The utility of each ROI to differentiate EOAD patients from CNs was assessed by calculating the percentage of patients falling below a W‐threshold of ‐1.5.ResultIn EOAD, a distributed pattern of cortical thinning was observed in lateral temporal, parietal, and frontal cortex (p<.01), and was most prominent in the precuneus (Cohen’s d’ = 1.5). In the EOnonAD group, cortical thinning was observed but was highly variable across individuals. The w‐score analysis in the EOAD group yielded the following results: Mean Cortical thickness: Mean = ‐3.34, SD = 2.97, Classification = 72%.; Hippocampal volume: Mean = ‐2.96, SD = 2.39, Classification = 74%; AD Signature: Mean = ‐4.63, SD = 3.07, Classification = 80%; Precuneus: Mean = ‐5.98, SD = 4.1, Classification = 90%.ConclusionOur results are consistent with previous findings demonstrating cortical thinning in lateral parietal, precuneus, and lateral temporal lobes in EOAD with relatively less involvement of the medial temporal lobes. Our results also suggest that the precuneus may be especially vulnerable to neurodegeneration in patients with EOAD and may serve as a useful biomarker for patient classification.
Increased white matter MRI T1 hypointensity volume in young‐onset Alzheimer’s disease patients is not accounted for by age or cardiovascular risk factors
Flaherty, Ryn , Ezzo, Rania , Collins, Jessica A. , Krivensky, Samantha, Eckbo, Ryan, Borowski, Bret, Bourakova, Viktoriya, Eloyan, Ani , Aisen, Paul, Fagan, Anne M. , Gatsonis, Constantine , Kramer, Joel H. , Toga, Arthur W. , Graff‐Radford, Neill R. , Masdeu, Joseph C. , Mendez, Mario F. , Onyike, Chiadi U. , Rogalskı, Emily , Salloway, Stephen , Apostolova, Liana G. , Dickerson, Brad C., Vemuri, Prashanthi , Iaccarino, Leonardo , Joie, Renaud La , Lesman‐Segev, Orit H. , Foroud, Tatiana , Jack, Clifford R. , Koeppe, Robert A. , Saykin, Andrew J. , Day, Gregory S. , Honig, Lawrence S. , Jones, David T. , Masdeu, Joseph C , Wolk, David A. , Wingo, Thomas S , Carrillo, María C. , Rabinovici, Gil D.

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Alzheimer's & Dementia | December 7, 2020 | Volume: 16 | Issue: S4 | Pages: No page available

10.1002/alz.045577 Get It

AbstractBackgroundWhite matter (WM) hypointensity volume in T1 MRI correlates strongly with T2 hyperintensities and both are typically associated with advancing age and vascular brain injury (VBI). Recent research has suggested that Alzheimer’s Disease (AD) neuropathologic changes (ADNC) also increases WM hypointensity volume. Sporadic early onset AD (EOAD) patients provide a unique model to examine the relationship between ADNC biomarkers and WM hypointensity volume in the absence of advanced age, cardiovascular injury, or autosomal dominant genetic mutations.MethodIncluded in the analysis were sporadic EOAD patients (n = 75) and cognitively normal controls (n = 39) from the Longitudinal Alzheimer’s Disease Study (LEADS) for whom full demographic and health information were available. WM hypointensity volume in T1 MRI was segmented by Freesurfer and corrected for total white matter volume. Multiple‐factor ANOVA models with logarithmic transformations were used to determine the effects of age, cohort, and presence of cardiovascular conditions on WM hypointensity volume. Further multiple‐factor ANOVA models assessed whether the addition of covariates improved the model fit.ResultIn a reproduction of prior research, WM hypointensity volume was correlated with age for controls (p < 0.05), but not for EOAD patients. We additionally found an effect of cohort, such that EOAD patients had a higher mean WM hypointensity volume than controls when accounting for age (p < 0.05). No difference in the presence or number of cardiovascular conditions was found between groups. Additionally, no correlation was found between WM hypointensity volume and the presence or number of cardiovascular conditions. Including cardiovascular condition as a covariate failed to improve the model fit, even when accounting for interaction effects.ConclusionThese results support the hypothesis that patients with ADNC have higher WM hypointensity volume beyond what would be expected based on aging or cardiovascular risk factors. Further research is needed to assess the co‐localization of WM hypointensities with cortical degradation, amyloid pathology, and tau pathology.
Amyloid and tau PET in sporadic early‐onset Alzheimer’s disease: Preliminary results from LEADS
Soleimani‐Meigooni, David N. , Collins, Jessica A. , Aisen, Paul , Borowski, Bret, Eloyan, Ani , Fagan, Anne M. , Gatsonis, Constantine , Kramer, Joel H. , Graff-Radford, N. R., Masdeu, Joseph C. , Mendez, Mario F. , Onyike, Chiadi U. , Rogalskı, Emily , Salloway, Stephen , Dickerson, Brad C., Apostolova, Liana G. , Rabinovici, Gil D. , Iaccarino, Leonardo , Joie, Renaud La , Lesman‐Segev, Orit H. , Provost, Karine , Foroud, Tatiana , Jack, Clifford R. , Saykin, Andrew J. , Toga, Arthur W. , Vemuri, Prashanthi , Day, Gregory S. , Honig, Lawrence S. , Jones, David T. , Masdeu, Joseph C , Wolk, David A. , Wingo, Thomas S , Koeppe, Robert A. , Carrillo, María C.

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Alzheimer's & Dementia | December 7, 2020 | Volume: 16 | Issue: S4 | Pages: No page available

10.1002/alz.041613 Get It

AbstractBackgroundPrevious studies have reported that age modifies the distribution and burden of tau (and, to a lesser extent, amyloid) pathology in sporadic Alzheimer’s disease (AD). Here we present preliminary baseline amyloid and tau PET results from the Longitudinal Early‐Onset Alzheimer’s Disease Study (LEADS), a multi‐site longitudinal study of sporadic early‐onset AD.Method135 patients meeting clinical criteria for MCI or probable AD and 50 cognitively normal controls (all age<65 at enrollment) were enrolled at 12 US centers between August 2018 and December 2019 (Table 1). 18F‐Florbetaben amyloid‐PET (FBB) was used to assign patients to EOAD (amyloid‐positive) or EOnonAD (amyloid‐negative) subgroups based on visual rating and semi‐quantification. 130 patients and all controls had 18F‐Flortaucipir tau‐PET (FTP). Regional Standardized Uptake Value Ratios (SUVR) for FBB (whole cerebellum reference) and FTP (inferior cerebellar gray reference) were extracted using co‐registered 3T‐MRI.Result98 patients (72.6%) were amyloid PET‐positive (EOAD) and 37 (27.4%) were amyloid PET‐negative (EOnonAD). Compared to EOAD, EOnonAD patients had higher MMSE, MOCA and CDR sum‐of‐boxes (CDR‐SB) and were more frequently male (Table 1). Patients with EOAD showed elevated FBB and FTP SUVR in temporoparietal and frontal cortex compared to CN and EOnonAD (Figures 1‐2). In EOAD, MMSE, MOCA and CDR‐SB were significantly correlated with FTP SUVR (Figure 3), while no significant correlations were found with FBB SUVR. EOnonAD patients showed variable FTP binding ranging from negative to mildly elevated binding in anterior temporal and frontal cortex and underlying white matter. Two EOnonAD cases showed intense FTP binding comparable to typical EOAD cases, despite visually and quantitatively negative FBB scans.ConclusionPatients with clinically mild, sporadic EOAD typically show an extensive distribution and burden of tau pathology in the setting of positive amyloid PET. Global clinical measures correlate with tau but not amyloid PET. Over 25% of patients meeting clinical criteria for early‐onset MCI/probable AD have negative amyloid PET, suggesting alternative etiologies for cognitive decline. These findings will inform future design of drug trials in this important and under‐studied population.