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Abstract Background Perry syndrome (PS) is a rare and fatal hereditary autosomal dominant neurodegenerative disorder caused by mutations in dynactin ( DCTN1 ). PS brains accumulate inclusions positive for ubiquitin, transactive‐response DNA‐binding protein of 43 kDa (TDP‐43), and to a lesser extent dynactin. Objectives Little is known regarding the contributions of TDP‐43, an RNA binding protein that represses cryptic exon inclusion, in PS. Therefore, we sought to identify the degree of TDP‐43 dysfunction in two regions of PS brains. Methods We evaluated the levels of insoluble pTDP‐43 and TDP‐43‐regulated cryptic RNAs and protein in the caudate nucleus and substantia nigra of 7 PS cases, 12 cases of frontotemporal lobar degeneration (FTLD) with TDP‐43 pathology, and 11 cognitively healthy controls without TDP‐43 pathology. Results Insoluble pTDP‐43 protein levels were detected in PS brains to a similar extent in the caudate nucleus and substantia nigra but lower than those in FTLD brains. The caudate nucleus of PS showed accumulation of eight TDP‐43‐regulated cryptic RNAs ( ACTL6B , CAMK2B , STMN2 , UNC13A , KCNQ2 , ATG4B , GPSM2 , and HDGFL2 ) and cryptic protein (HDGFL2) characteristic of FTLD. Conversely, only one cryptic target, UNC13A , reached significance in the substantia nigra despite similar pTDP‐43 levels. Conclusion We detected TDP‐43 cryptic RNAs and protein in PS caudate nucleus. Given the importance of cryptic exon biology in the development of biomarkers, and the identification of novel targets for therapeutic intervention, it is imperative we understand the consequences of TDP‐43 dysfunction across different brain regions and determine the targets that are specific and common to TDP‐43 proteinopathies. © 2025 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

AbstractBackgroundDiagnosing 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.MethodSeventy‐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.ResultThirty‐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.ConclusionFifty‐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.

AbstractBackgroundAlzheimer's disease (AD) is the most prevalent neurodegenerative disease, yet our comprehension predominantly relies on studies within the non‐Hispanic White (NHW) population. To address this, Accelerating Medicines Partnership in AD (AMP‐AD) aimed to promote inclusivity in multi‐omics AD research, to unravel unique molecular signatures and pathways. The study aimed to provide comprehensive insights into the proteomic landscape of AD across diverse racial groups.MethodDorsolateral prefrontal cortex (DLPFC) and superior temporal gyrus (STG) brain tissues were donated from multiple centers (Mayo Clinic, Emory University, Rush University, Mt. Sinai School of Medicine) and were harmonized through neuropathological evaluation, specifically adhering to the Braak staging and CERAD criteria. Among 1105 DLPFC tissue samples (980 unique individuals), 322 were from African American donors, 214 from Latino Americans, 551 from NHW donors, and the rest were from a mixed or unknown racial background. Among 280 STG samples, 86 were African American, 76 Latino American, 116 NHW and the rest were mixed or unknown ethnicity. All tissues were uniformly homogenized and analyzed by tandem mass tag‐based mass spectrometry (TMT‐MS). Following quality control (QC), proteins with more than 50% missing values were removed and iterative principal component analysis was conducted to remove outliers within brain regions. Differential protein expression was conducted within and across races after adjusting for batch using regression‐analysis.ResultIn total, 9,180 proteins from the DLPFC, and 9,734 proteins from STG brain tissues were quantified by TMT‐MS. Further analysis identified approximately 3,000 differentially abundant proteins in AD brains compared to controls within race. The AD‐associated differentially abundant proteins were strongly correlated between DLPFC and STG regions and with CERAD and Braak. Protein levels of MAPT and APP demonstrated equivalent disease‐related elevations in both African American and NHW individuals with AD. Notably, after adjusting for AD, significant differences in protein levels were identified across race.ConclusionThe proteomics approach employed by AMP‐AD sheds light on the brain proteome in AD across diverse racial groups, emphasizing the importance of inclusive research in establishing robust and generalizable discoveries. The identified proteins here could underscore racial disparities beyond disease association, contributing to a more nuanced understanding of AD pathophysiology.

AbstractBackgroundNeuron‐derived extracellular vesicles (NDEVs) are a valuable resource for understanding brain conditions and discovering neurodegenerative diseases biomarkers, notably Alzheimer’s disease (AD). Recent interest focuses on capturing neuron‐specific EVs from patient‐derived samples, characterizing their contents as a pathological reflection of the central nervous system (CNS). Our recent study identified ATPase Na+/K+ Transporting Subunit Alpha 3 (ATP1A3) as a prevalent neuron‐specific EV marker specifically expressed in brains. This study systematically analyzes neuronal EVs to assess the specificity of ATP1A3 to NDEVs and its potential as a target for NDEV pulldown from accessible biofluids for disease monitoring. Additionally, ATP1A3 is compared with other proposed NDEV markers.MethodWe applied immunoelectron microscopy to detect ATP1A3, L1 cell adhesion molecule (L1CAM) and neural cell adhesion molecule 1 (NCAM1) in EVs isolated from iPSC‐derived excitatory neurons, brain tissue, cerebrospinal fluid (CSF) and plasma. Neuronal EV enrichment was achieved through immunoaffinity isolation, utilizing anti‐ATP1A3, L1CAM and NCAM1 antibodies separately. Evaluation involved quantitative mass‐spectrometry, immunoblotting and ELISA. To compare the enrichment of neuronal markers, EVs isolated from CSF and plasma samples were analyzed using ExoView and Nanoimager. Additionally, we investigated the potential of ATP1A3+ plasma EVs as AD biomarker by comparing the value of amyloid‐beta peptide (Aβ) in ATP1A3+ plasma EVs measured by Nanoimager and other plasma AD biomarkers as determined by SIMOA.ResultATP1A3, identified as a neuron‐specific protein, exhibits substantial enrichment in NDEVs isolated from induced human neurons, brain tissue, CSF, and plasma samples, surpassing NCAM1 or L1CAM. Both single‐ and bulk‐EV analysis consistently demonstrate a higher enrichment of ATP1A3 associated EVs in human samples. Immunoprecipitation of ATP1A3+ EVs from human brain tissues reveals superior neuronal cell‐type specificity over NCAM1+ and L1CAM+ EVs by label‐free mass spectrometry. Increased Aβ levels are observed in ATP1A3+ EVs derived from CSF and plasma of AD cases by SIMOA. Moreover, Aβ+ populations in ATP1A3+ EVs from plasma can distinguish AD from mild cognitive impairment and control cases via Nanoimager compared to the conventional quantification of AD markers.ConclusionOur findings demonstrate that ATP1A3 as a promising target for isolating NDEVs from biofluids, offering potential diagnostic advancements in neurological research.

AbstractBackgroundLarge‐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).Method731 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.ResultAmyloid 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).ConclusionThe 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.

AbstractBackgroundAlzheimer’s disease (AD) is a neurodegenerative disorder with significant environmental factors, including diet, that influence its onset and progression. While the ketogenic diet (KD) holds promise in reducing metabolic risks and potentially affecting AD progression, only a few studies have explored the KD’s molecular impact for markers of AD therapeutic potential. The BEAM diet study simultaneously profiled the KD’s effect on the lipidome, blood and cerebrospinal metabolome, and microbiome of both cognitively impaired and cognitively normal individuals. The findings summarized here assess the biological impact of a Modified Mediterranean KD in the context of Alzheimer’s disease treatment and prevention.MethodBEAM involved participants at risk for AD, either cognitively normal or with mild cognitive impairment. The participants consumed both a modified Mediterranean‐ketogenic diet (MMKD) and the American Heart Association diet (AHAD) for 6 weeks each, separated by a 6‐week washout period. We employed HPLC‐MS/MS lipidomics profiling in plasma, nuclear magnetic resonance (NMR)‐based metabolomics to profile serum and CSF, and metagenomics profiling on fecal samples before and after each diet to assess dietary‐induced changes.ResultThe MMKD led to significant alterations in the blood, CSF, and microbiome. These changes included a global elevation across all plasmanyl and plasmenyl ether lipid species, improved modifiable risk factors, like increased HDL‐C and reduced BMI, the reversal of serum metabolic disturbances linked to AD such as an increase in valine levels, and a reduction in systemic inflammation. Leveraging prior clinical studies on AD (n = 1,912), we found that MMKD was inversely associated with the peripheral lipidomic signature of prevalent and incident AD. In the CSF, the MMKD was linked to modified amino acid levels and the breakdown of branched‐chain amino acids (BCAAs). Importantly, we observed a strong correlation between metabolic changes in the CSF and serum, suggesting a systemic regulation of metabolism. In addition, participants with MCI on the MMKD had lower levels of GABA‐producing microbes and GABA, and higher levels of GABA‐regulating microbes.ConclusionOur findings highlight that MMKD can improve AD‐related risk factors, reverse some metabolic disturbances associated with AD, and align metabolic changes across the blood‐CSF barrier.Funding: Alzheimer’s Gut Microbiome Project, NIA U19AG063744

AbstractBackgroundLarge‐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).Method731 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.ResultAmyloid 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).ConclusionThe 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.

AbstractBackgroundDiagnosing 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.MethodSeventy‐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.ResultThirty‐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.ConclusionFifty‐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.

Background: Very-late-onset myasthenia gravis (VLOMG) refers to myasthenia gravis (MG) with onset at age 65 or older. Current research on VLOMG prognosis remains limited, especially regarding factors influencing outcomes. Objectives: To identify the clinical factors that affect the short- and long-term prognosis of MG patients with an onset age ⩾65 years. Design: This was a single-center, retrospective cohort study of AChR-ab positive VLOMG patients, classified into two subgroups based on age of onset: sub-very-late-onset MG (S-VLOMG, onset age ⩾65 and <75 years), and super-late-onset MG (SLOMG, onset age ⩾75 years). Methods: A total of 93 patients were included, including 75 in the S-VLOMG group and 18 in the SLOMG group. Clinical, therapeutic, and prognosis data were reviewed, and the Cox regression model was used to identify factors influencing short- and long-term prognosis. Results: Patient characteristics were well balanced between the groups. Overall, 49.5% of patients achieved minimal symptom expression (MSE) within 6 months and 86% within 24 months. There was no significant difference between the groups in the proportion achieving MSE at 6 months ( p = 0.635) or 24 months ( p = 0.714). The median time to achieve MSE was also comparable between the S-VLOMG and SLOMG groups (199.0 days vs 280.5 days, p = 0.463). Low baseline MG-ADL score and steroid therapy were associated with better short-term prognosis ( p = 0.007 and p = 0.002, respectively). For long-term prognosis, baseline bulbar and limb involvement, time to treatment initiation, and use of immunosuppressants were significant factors ( p = 0.025, p = 0.004, p = 0.025, and p < 0.0001, respectively). There were no significant differences in side effects or drug withdrawal rates between two groups. Conclusion: This study demonstrated that AChR-ab positive VLOMG patients have a favorable prognosis and responded well to medication, with age and comorbidities showing no significant impact.