Browsing by Author "Lagarde, Julien"

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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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    Characterization of the heterogeneity of amyloid‐PET‐negative patients with a clinical diagnosis of sporadic early‐onset AD: an FDG‐PET study in the LEADS cohort
    (Wiley, 2025-01-09) Lagarde, Julien; Schonhaut, Daniel R.; Maiti, Piyush; Zhang, Jiaxiuxiu; Soleimani-Meigooni, David N.; Zeltzer, Ehud; Windon, Charles; Hammers, Dustin B.; Dage, Jeffrey L.; Nudelman, Kelly N.; Eloyan, Ani; Koeppe, Robert A.; Carrillo, Maria C.; Touroutoglou, Alexandra; Vemuri, Prashanthi; Dickerson, Bradford C.; Apostolova, Liana G.; Rabinovici, Gil D.; La Joie, Renaud; Neurology, School of Medicine
    Background Diagnosing sporadic early‐onset AD (EOAD, age‐at‐onset<65) is challenging: in the multi‐center Longitudinal Early‐onset Alzheimer’s Disease Study, ∼25% of patients with clinically diagnosed EOAD are amyloid‐PET‐negative. Here we used FDG‐PET to characterize the heterogeneity of hypometabolic profiles in these patients and better identify underlying etiologies. Method Seventy‐four amyloid‐PET‐negative patients with clinical diagnosis of sporadic EOAD (MCI or mild dementia stage) underwent FDG‐PET. Patients were classified as having normal or hypometabolic FDG‐PET based on a data‐driven approach that compared each patient to a group of 61 age‐matched amyloid‐PET‐negative controls using 12 methodological combinations (3 reference regions, 2 voxel‐level thresholds, 2 outlier detection methods). We then assessed clinical and demographic differences between patients with normal versus hypometabolic FDG‐PET, and further compared groups using independent biomarkers of neurodegeneration (structural MRI and fluid biomarkers). Finally, we applied hierarchical clustering to hypometabolic FDG‐PET scans to identify patterns of hypometabolism. Result Thirty‐six amyloid‐negative patients (49%) had hypometabolic FDG‐PET scans. They were older and more severely impaired across most cognitive domains than patients with normal FDG‐PET (Table 1). They also had reduced hippocampal volumes and cortical thickness (Figure 1A), higher plasma and CSF neurofilament light chain (NfL) levels, and elevated plasma GFAP compared to patients with normal FDG‐PET (Figure 1B). In contrast, the latter, who had intermediate cognitive scores between hypometabolic patients and controls, had MRI and fluid biomarker levels in the range of controls (Figure 1). In hypometabolic patients, hierarchical clustering identified four profiles: i) anterior temporal extending to temporo‐parietal and frontal regions (n = 5), ii) anterior temporal and orbitofrontal (n = 11), iii) occipito‐parietal (n = 6), and iv) lateral frontal and parietal (n = 14) (Figure 2). Genetic testing identified two patients with Frontotemporal Lobar Degeneration (FTLD)‐associated pathogenic variants, both considered hypometabolic and assigned to the first (MAPT) and second (c9orf72) metabolic profiles. Conclusion Fifty‐one percent of amyloid‐negative patients had normal FDG‐PET: they had milder clinical impairment, normal MRI measures, and normal NfL values, suggesting non‐neurodegenerative etiologies. Patients with abnormal FDG showed heterogeneous hypometabolic patterns suggestive of multiple etiologies including Lewy body disease, FTLD or corticobasal degeneration. Longitudinal follow‐up to autopsy will ultimately clarify the amyloid‐negative clinical mimics of sporadic EOAD.
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    Differences in baseline cognitive performance between participants with early-onset and late-onset Alzheimer's disease: Comparison of LEADS and ADNI
    (Wiley, 2025) Hammers, Dustin B.; Eloyan, Ani; Thangarajah, Maryanne; Taurone, Alexander; Beckett, Laurel; Gao, Sujuan; Polsinelli, Angelina J.; Kirby, Kala; Dage, Jeffrey L.; Nudelman, Kelly; Aisen, Paul; Reman, Rema; La Joie, Renaud; Lagarde, Julien; Atri, Alireza; Clark, David; Day, Gregory S.; Duara, Ranjan; Graff-Radford, Neill R.; Honig, Lawrence S.; Jones, David T.; Masdeu, Joseph C.; Mendez, Mario F.; Womack, Kyle; Musiek, Erik; Onyike, Chiadi U.; Riddle, Meghan; Grant, Ian; Rogalski, Emily; Johnson, Erik C. B.; Salloway, Steven; Sha, Sharon J.; Turner, Raymond Scott; Wingo, Thomas S.; Wolk, David A.; Carrillo, Maria C.; Dickerson, Bradford C.; Rabinovici, Gil D.; Apostolova, Liana G.; LEADS Consortium 1 for the Alzheimer's Disease Neuroimaging Initiative; Neurology, School of Medicine
    Introduction: Early-onset Alzheimer's disease (EOAD) and late-onset Alzheimer's disease (LOAD) share similar amyloid etiology, but evidence from smaller-scale studies suggests that they manifest differently clinically. Current analyses sought to contrast the cognitive profiles of EOAD and LOAD. Methods: Z-score cognitive-domain composites for 311 amyloid-positive sporadic EOAD and 314 amyloid-positive LOAD participants were calculated from baseline data from age-appropriate control cohorts. Z-score composites were compared between AD groups for each domain. Results: After controlling for cognitive status, EOAD displayed worse visuospatial, executive functioning, and processing speed/attention skills relative to LOAD, and LOAD displayed worse language, episodic immediate memory, and episodic delayed memory. Discussion: Sporadic EOAD possesses distinct cognitive profiles relative to LOAD. Clinicians should be alert for non-amnestic impairments in younger patients to ensure proper identification and intervention using disease-modifying treatments. Highlights: Both early-onset Alzheimer's disease (EOAD) and late-onset Alzheimer's disease (LOAD) participants displayed widespread cognitive impairments relative to their same-aged peers. Cognitive impairments were more severe for EOAD than for LOAD participants in visuospatial and executive domains. Memory and language impairments were more severe for LOAD than for EOAD participants Results were comparable after removing clinical phenotypes of posterior cortical atrophy (PCA), primary progressive aphasia (lv-PPA), and frontal-variant AD.
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    Dissociable spatial topography of cortical atrophy in early‐onset and late‐onset Alzheimer's disease: A head‐to‐head comparison of the LEADS and ADNI cohorts
    (Wiley, 2025) Katsumi, Yuta; Touroutoglou, Alexandra; Brickhouse, Michael; Eloyan, Ani; Eckbo, Ryan; Zaitsev, Alexander; La Joie, Renaud; Lagarde, Julien; Schonhaut, Daniel; Thangarajah, Maryanne; Taurone, Alexander; Vemuri, Prashanthi; Jack, Clifford R., Jr.; Dage, Jeffrey L.; Nudelman, Kelly N. H.; Foroud, Tatiana; Hammers, Dustin B.; Ghetti, Bernardino; Murray, Melissa E.; Newell, Kathy L.; Polsinelli, Angelina J.; Aisen, Paul; Reman, Rema; Beckett, Laurel; Kramer, Joel H.; Atri, Alireza; Day, Gregory S.; Duara, Ranjan; Graff-Radford, Neill R.; Grant, Ian M.; Honig, Lawrence S.; Johnson, Erik C. B.; Jones, David T.; Masdeu, Joseph C.; Mendez, Mario F.; Musiek, Erik; Onyike, Chiadi U.; Riddle, Meghan; Rogalski, Emily; Salloway, Stephen; Sha, Sharon; Turner, R. Scott; Wingo, Thomas S.; Wolk, David A.; Womack, Kyle; Carrillo, Maria C.; Rabinovici, Gil D.; Apostolova, Liana G.; Dickerson, Bradford C.; LEADS Consortium for the Alzheimer's Disease Neuroimaging Initiative; Neurology, School of Medicine
    Introduction: Early-onset and late-onset Alzheimer's disease (EOAD and LOAD, respectively) have distinct clinical manifestations, with prior work based on small samples suggesting unique patterns of neurodegeneration. The current study performed a head-to-head comparison of cortical atrophy in EOAD and LOAD, using two large and well-characterized cohorts (LEADS and ADNI). Methods: We analyzed brain structural magnetic resonance imaging (MRI) data acquired from 377 sporadic EOAD patients and 317 sporadicLOAD patients who were amyloid positive and had mild cognitive impairment (MCI) or mild dementia (i.e., early-stage AD), along with cognitively unimpaired participants. Results: After controlling for the level of cognitive impairment, we found a double dissociation between AD clinical phenotype and localization/magnitude of atrophy, characterized by predominant neocortical involvement in EOAD and more focal anterior medial temporal involvement in LOAD. Discussion: Our findings point to the clinical utility of MRI-based biomarkers of atrophy in differentiating between EOAD and LOAD, which may be useful for diagnosis, prognostication, and treatment. Highlights: Early-onset Alzheimer's disease (EOAD) and late-onset AD (LOAD) patients showed distinct and overlapping cortical atrophy patterns. EOAD patients showed prominent atrophy in widespread neocortical regions. LOAD patients showed prominent atrophy in the anterior medial temporal lobe. Regional atrophy was correlated with the severity of global cognitive impairment. Results were comparable when the sample was stratified for mild cognitive impairment (MCI) and dementia.
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    Longitudinal cognitive performance of participants with sporadic early onset Alzheimer's disease from LEADS
    (Wiley, 2025) Hammers, Dustin B.; Eloyan, Ani; Taurone, Alexander; Thangarajah, Maryanne; Gao, Sujuan; Beckett, Laurel; Polsinelli, Angelina J.; Kirby, Kala; Dage, Jeffrey L.; Nudelman, Kelly; Aisen, Paul; Reman, Rema; La Joie, Renaud; Lagarde, Julien; 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.; Turner, Raymond Scott; Wingo, Thomas S.; Wolk, David A.; Carrillo, Maria C.; Rabinovici, Gil D.; Dickerson, Bradford C.; Apostolova, Liana G.; LEADS Consortium; Neurology, School of Medicine
    Introduction: Early-onset Alzheimer's disease (EOAD) manifests prior to the age of 65, and affects 4%-8% of patients with Alzheimer's disease (AD). The current analyses sought to examine longitudinal cognitive trajectories of participants with early-onset dementia. Methods: Data from 307 cognitively normal (CN) volunteer participants and those with amyloid-positive EOAD or amyloid-negative cognitive impairment (EOnonAD) were compared. Cognitive trajectories across a comprehensive cognitive battery spanning 42 months were examined using mixed-effects modeling. Results: The EOAD group displayed worse cognition at baseline relative to EOnonAD and CN groups, and more aggressive declines in cognition over time. The largest effects were observed on measures of executive functioning domains, while memory declines were blunted in EOAD. Discussion: EOAD declined 2-4× faster than EOnonAD, and EOAD pathology is not restricted to memory networks. Early identification of deficits is critical to ensure that individuals with sporadic EOAD can be considered for treatment using disease-modifying medications. Highlights: Represents the most comprehensive longitudinal characterization of sporadic EOAD to date. The trajectory of cognitive declines was steep for EOAD participants and worse than for other groups. Executive functioning measures exhibited the greatest declines over time in EOAD.
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    Reproducibility of Centiloid Values in Real‐World Amyloid PET Data: Comparison of the Imaging Dementia‐Evidence for Amyloid Scanning (IDEAS) to Four Large Research Datasets
    (Wiley, 2025-01-09) Blazhenets, Ganna; Zeltzer, Ehud; Lagarde, Julien; Landau, Susan M.; Koeppe, Robert A.; Carrillo, Maria C.; Dickerson, Bradford C.; Apostolova, Liana G.; Jagust, William J.; Rabinovici, Gil D.; La Joie, Renaud; Alzheimer’s Disease Neuroimaging Initiative (ADNI); Neurology, School of Medicine
    Background: The Centiloid framework was developed to harmonize amyloid‐PET quantification across radiotracers and processing pipelines to facilitate data sharing and merging; it is now widely used across research and clinical trials. As we just completed the quantification of 10,361 amyloid‐PET scans from the largest “real‐world” study of amyloid‐PET (IDEAS) and are about to release the data, we aimed to compare the distribution of IDEAS Centiloid values with other available datasets. Method: In IDEAS, amyloid scans were acquired across 343 facilities and centrally processed at UCSF using a PET‐only pipeline. We also had access to PET data from our own UCSF Alzheimer’s Disease Research Center and the LEADS study. Using the GAAIN platform, we identified two other cohorts with available Centiloids: ADNI and MCSA. For each cohort, we collected Centiloids, demographic, and basic clinical data. Gaussian mixture models (GMM) were fitted to Centiloid values for each cohort, and data‐driven Centiloid cutoffs were calculated as mean + 2SD of the first Gaussian. Finally, we compared Centiloids to PET visual reads (when available) and determined the Centiloid cutoff value maximizing correspondence between visual read and binarized Centiloids based on Cohen’s kappa. Result: The 5 cohorts were heterogeneous in terms of sample characteristics and radiotracers (Table 1). In all cohorts, a two‐Gaussian model was considered the best fit for the data based on the integrated completed likelihood criteria (Figure 1). The first Gaussian peaks were close to zero, with mild variability across studies (from ‐5 in IDEAS to 10 CL in MSCA). The second peak was more heterogeneous across cohorts (from 67 to 102 CL) with a rightward shift in cohorts enriched with clinically impaired patients. Mean Centiloid values in visually negative and positive scans generally matched well with results derived from GMM (Figure 2). Across all cohorts, GMM‐based Centiloid cutoffs tended to be slightly lower (18‐26) compared to those based on visual inspection (25‐31). Conclusion: The availability of Centiloids across cohorts enables a direct comparison of amyloid‐PET results in otherwise different studies. Despite some variability across cohorts and analysis methods, Centiloid cutoffs align well with thresholds from the existing literature.
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    The prevalence of tau‐PET positivity in aging and dementia
    (Wiley, 2025-01-09) Coomans, Emma M.; Groot, Colin; Rowe, Christopher C.; Dore, Vincent; Villemagne, Victor L.; van de Giessen, Elsmarieke; van der Flier, Wiesje M.; Pijnenburg, Yolande A. L.; Visser, Pieter Jelle; den Braber, Anouk; Pontecorvo, Michael; Shcherbinin, Sergey; Kennedy, Ian A.; Jagust, William J.; Baker, Suzanne L.; Harrison, Theresa M.; Gispert, Juan Domingo; Shekari, Mahnaz; Minguillon, Carolina; Smith, Ruben; Mattsson-Carlgren, Niklas; Palmqvist, Sebastian; Strandberg, Olof; Stomrud, Erik; Malpetti, Maura; O'Brien, John T.; Rowe, James B.; Jäger, Elena; Bischof, Gérard N.; Drzezga, Alexander; Garibotto, Valentina; Frisoni, Giovanni; Peretti, Débora Elisa; Schöll, Michael; Skoog, Ingmar; Kern, Silke; Sperling, Reisa A.; Johnson, Keith A.; Risacher, Shannon L.; Saykin, Andrew J.; Carrillo, Maria C.; Dickerson, Brad C.; Apostolova, Liana G.; Barthel, Henryk; Rullmann, Michael; Messerschmidt, Konstantin; Vandenberghe, Rik; Van Laere, Koen; Spruyt, Laure; Franzmeier, Nicolai; Brendel, Matthias; Gnörich, Johannes; Benzinger, Tammie L. S.; Lagarde, Julien; Sarazin, Marie; Bottlaender, Michel; Villeneuve, Sylvia; Poirier, Judes; Seo, Sang Won; Gu, Yuna; Kim, Jun Pyo; Mormino, Elizabeth; Young, Christina B.; Vossler, Hillary; Rosa-Neto, Pedro; Therriault, Joseph; Rahmouni, Nesrine; Coath, William; Cash, David M.; Schott, Jonathan M.; Rabinovici, Gil D.; La Joie, Renaud; Rosen, Howard J.; Johnson, Sterling C.; Christian, Bradley T.; Betthauser, Tobey J.; Hansson, Oskar; Ossenkoppele, Rik; Radiology and Imaging Sciences, School of Medicine
    Background Tau‐PET imaging allows in‐vivo detection of neurofibrillary tangles. One tau‐PET tracer (i.e., [18F]flortaucipir) has received FDA‐approval for clinical use, and multiple other tau‐PET tracers have been implemented into clinical trials for participant selection and/or as a primary or secondary outcome measure. To optimize future use of tau‐PET, it is essential to understand how demographic, clinical and genetic factors affect tau‐PET‐positivity rates. Method This large‐scale multi‐center study includes 9713 participants from 35 cohorts worldwide who underwent tau‐PET with [18F]flortaucipir (n = 6420), [18F]RO948 (n = 1999), [18F]MK6240 (n = 878) or [18F]PI2620 (n = 416) (Table‐1). We analyzed individual‐level tau‐PET SUVR data using a cerebellar reference region that were processed either centrally (n = 3855) or by each cohort (n = 5858). We computed cohort‐specific SUVR thresholds based on the mean + 2 standard deviations in a temporal meta‐region of amyloid‐negative cognitively normal (CN) individuals aged >50. Logistic generalized estimating equations were used to estimate tau‐PET‐positivity probabilities, using an exchangeable correlation structure to account for within‐cohort correlations. Analyses were performed with (interactions between) age, amyloid‐status, and APOE‐e4 carriership as independent variables, stratified for syndrome diagnosis. Result The study included 5962 CN participants (7.5% tau‐PET‐positive), 1683 participants with mild cognitive impairment (MCI, 33.8% tau‐PET‐positive) and 2068 participants with a clinical diagnosis of dementia (62.1% tau‐PET‐positive) (Figure‐1). From age 60 to 80 years, the estimated prevalence of tau‐PET‐positivity increased from 1.2% [95% CI: 0.9%‐1.5%] to 3.7% [2.3%‐5.1%] among CN amyloid‐negative participants; and from 16.4% [10.8%‐22.1%] to 20.5% [18.8%‐22.2%] among CN amyloid‐positive participants. Among amyloid‐negative participants with MCI and dementia, from age 60 to 80 years, the estimated prevalence of tau‐PET‐positivity increased from 3.5% [1.6%‐5.3%] to 11.8% [7.1%‐16.5%] and from 12.6% [4.5%‐20.7%] to 15.9% [6.7%‐25.1%] respectively. In contrast, among amyloid‐positive participants with MCI and dementia, from age 60 to 80 years, the estimated prevalence of tau‐PET‐positivity decreased from 66.5% [57.0%‐76.0%] to 48.3% [42.9%‐53.8%] and from 92.3% [88.7%‐95.9%] to 73.4% [67.5%‐79.3%] respectively. APOE‐e4 status primarily modulated the association of age with tau‐PET‐positivity estimates among CN and MCI amyloid‐positive participants (Figure‐2). Conclusion This large‐scale multi‐cohort study provides robust prevalence estimates of tau‐PET‐positivity, which can aid the interpretation of tau‐PET in the clinic and inform clinical trial designs.