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Background There is an urgent need for accurately predicting amyloid‐beta (Aβ) pathology in Alzheimer’s disease (AD) using non‐invasive and readily available methods, particularly in the early stages of the disease. While blood‐based biomarkers have shown promising consistency with brain Aβ pathology, there remains a scarcity of data predicting Aβ pathology using these biomarkers across different clinical settings in the Chinese population. Method We retrospectively selected 278 participants from five hospitals across China, comprising 127 patients with mild cognitive impairment (MCI) and 151 with dementia. All participants underwent amyloid PET scanning, cerebrospinal fluid (CSF) tests, and plasma analysis for Aβ42/40 ratios, phosphorylated tau181 (p‐tau181), neurofilament light chain (NfL), and APOE ε4 genotype, alongside cognitive assessments using the MMSE and MoCA. Patients were categorized as Aβ positivity based on amyloid PET or CSF Aβ42/40 ratio results, with 171 classified as Aβ positivity. Predictive models were constructed using the Random Forest algorithm for feature selection and model building. Participants were divided into a training set (n = 223) and a testing set (n = 55) for model development and validation, respectively. Result Two models were developed to differentiate Aβ positive from Aβ negative patients. The high‐accuracy model, incorporating plasma p‐tau181, plasma Aβ42/40 ratios, APOE ε4 status, MMSE, and MoCA, demonstrated excellent predictive capability with an area under the receiver‐operating characteristic curve (AUC) of 0.93 (95% CI 0.86–0.99) (Figure 1). A more parsimonious model including only plasma p‐tau181, MMSE, and MoCA also showed high accuracy (AUC: 0.86 [95% CI 0.77–0.96]) (Figure 2) in identifying patients with Aβ positivity. Both models were validated across participating centers and outperformed individual plasma biomarkers. Additionally, these models exhibited similar performance among participants with or without comorbidities or populations that included cognitively normal individuals. Conclusion The developed models, utilizing plasma biomarkers and clinically accessible parameters, provide a non‐invasive, cost‐effective approach for predicting brain amyloid positivity in the early stages of AD. This approach has potential to enhance early screening, clinical diagnosis, and the initiation of disease‐modifying therapies within the Chinese context.

Background Mnemonic discrimination tasks (MDTs) hold potential for early detection of memory changes in Alzheimer’s disease (AD). Object and scene processing tasks differently tap into memory networks vulnerable to early tau and amyloid pathology, respectively. We used an object and scene MDT to assess longitudinal effects of AD on distinct functional memory networks and investigate their potential as markers for different disease stages. Method 202 participants from the DELCODE study completed an object and scene MDT of highly similar objects and scenes during fMRI scanning each year from baseline to 36‐month follow‐up. Participants were classified as cognitively unimpaired (CU; Table 1) or having subjective cognitive decline (SCD), mild cognitive impairment (MCI), or dementia of the Alzheimer’s type (DAT). Result The combined MCI/DAT group showed object and scene mnemonic discrimination impairment. SCDs only differed from CUs on scenes, but trended towards object discrimination decline over follow‐up. Cross‐sectionally, amygdala, BA35/36, anterior hippocampus, and parahippocampal cortex showed increased activity during successful discrimination of both scenes and objects. Entorhinal cortex, posterior hippocampus, and precuneus did so only for scenes, dovetailing with the framework of partially dissociable memory networks. Longitudinally, CUs showed increasing brain activity during object but not scene memory throughout the medial temporal lobe. SCDs showed a pattern of longitudinally increasing activity during scene memory coupled with decreasing activity during object memory in BA35, BA36, and hippocampus. Strikingly, the opposite was true for BA35 and entorhinal cortex in the MCI/DAT group. Conclusion These results provide a first glimpse at longitudinal changes in brain activity during mnemonic discrimination throughout the AD continuum. Both neural and behavioural indices of the MDT may be sensitive to longitudinal disease effects. Future analyses will assess the complex relationship between disease effects on neurodegeneration, neural activity, and memory to better understand how early AD erodes memory and brain systems.

Background Alzheimer’s disease (AD) is a neurodegenerative condition characterized by a gradual decline in mental function, progressing to death. Evidence suggests the apolipoprotein E (ApoE) gene influences Alzheimer’s risk. Allele E2 is neuroprotective, E3 is neutral, and E4 is associated with a higher genetic risk for AD. Our objective is to study the gut microbiome of Puerto Ricans with AD compared to unimpaired cognitive controls and its association with ApoE allele variants. Methods With IRB # 2290033626, we recruited 98 participants, 50 with AD and 48 controls, who underwent clinical and cognitive assessments. Fecal samples were collected for genomic DNA extractions, followed by 16S rRNA genes (V4 region) amplification. ApoE genotyping was done at the PR‐INBRE CRI genomics core, using real‐time PCR TaqMan‐BHQ probes. Results Analyses showed no significant differences in bacterial diversity and richness when comparing ApoE genotypes. However, participants with at least one E2 allele showed higher levels of Firmicutes, such as CAG:352 and NK4A214 group, while those with at least one E4 allele had higher levels of Euryarchaeota and UBA1819. When comparing the ApoE genotypes of the AD participants, we found a significant difference in microbial richness (Faith PD pairwise p value = 0.018) between E2E3 and E2E4. Additionally, AD participants with the E2E3 genotype had higher levels of Fusobacteriota and Desulfobacterota than those with at least one E4 allele. Moreover, we found no significant differences in bacterial diversity and richness when comparing the control participants based on their ApoE genotype. Nevertheless, controls with the E3E4 genotype had higher levels of Odoribacter and Oscillibacter, while those with E2E4 had a higher density of Anaerotruncus, Desulfovibrio, and Faecalibaculum. Conclusion Our study, the first of its kind in Puerto Rico, combines a chronic disease in an aging population with high‐resolution gut microbiome analyses. While overall bacterial diversity and richness did not differ significantly across ApoE genotypes, distinct microbial compositions were observed. This pioneering work in a developing study area may open the possibility for preventive microbiome‐based therapies that could result in a clinical benefit for patients with/without AD.

Background There is a growing interest in investigating cerebrovascular dysfunction related to early Alzheimer disease (AD) pathology. The objective of this study is to evaluate changes in brain hemodynamic properties in relation to excess of β‐amyloid (Aβ) deposition using the quantitative Gradient Recalled Echo (qGRE) MRI technique. Additionally, we aim to assess brain Aβ status by combining output metrics of qGRE and quantitative susceptibility maps (QSM). Method 43 amyloid positive (Aβ+, mean age 74.28), and 62 amyloid negative (Aβ−, mean age 72.02) participants were recruited by Knight ADRC. qGRE and QSM images were obtained from the same 3D multi‐gradient echo sequence using 3T Siemens MRI scanners. R2' metric of qGRE signal defines regional concentration of deoxyhemoglobin (Ulrich&Yablonskiy, MRM‐2016). Combining qGRE and QSM data allows separate evaluation of deoxyhemoglobin and tissue contributions to QSM metric (Yablonskiy, et al, Neuroimage‐2021). The principal component analysis (PCA) for the whole grey matter and lobe‐wise analysis (medial temporal (MTL), temporal, parietal, occipital, and frontal lobes) were used to present data. Result Statistical analysis for the whole grey matter shows increased deoxyhemoglobin concentration (p = 0.0003) in Aβ+ as compared to Aβ− groups. This increased deoxyhemoglobin was detected in all lobes: MTL (p = 0.001), temporal (p = 6.5E‐5), parietal (p = 0.006), occipital (p = 0.02), and frontal (p = 0.008). Data also showed brain‐wide decreased in tissue susceptibility (p = 0.01) in Aβ+ as compared to Aβ− groups, though lobe‐wise difference reached statistical significance only in temporal (p = 0.01) and frontal (p = 0.04) lobes. Conclusion Our results indicate that participants with excessive amyloid deposition exhibit an increased concentration of deoxygenated blood. Importantly, the odds ratio of being amyloid positive in the presence of higher concentration of deoxyhemoglobin is increased by a factor about five. Detail analysis of qGRE data shows that increased concentration of deoxyhemoglobin is mostly caused by increased of the deoxyhemoglobin‐carrying blood vessel network (venous blood and pre‐venous part of the capillary bed). In MTL and temporal lobes qGRE also detected significant increases in oxygen extraction fraction (OEF). Additionally, discovered reduced tissue magnetic susceptibility in Aβ+ participants is indicative of increased Aβ deposition, thus providing new MRI‐based tool for identifying brain tissue Aβ status.

Background As blood‐based biomarkers become critical to Alzheimer’s Disease (AD) clinical testing, establishment of less invasive plasma collection methods are key. We compared the Tasso+ and the TAPII capillary whole blood collection devices to traditional venipuncture for safety and efficacy in our clinic and measured AD biomarkers neurofilament‐light (NFL), glial fibrillary acidic protein (GFAP), phosphorylated‐tau 217 (ptau217), and amyloid‐beta 42/40 ratio (Aβ42/40) in cerebrospinal fluid (CSF), capillary, and venous plasma to assess potential age and cognition related group differences. Method Patients seen at the Johns Hopkins Center for CSF Disorders (N = 193) underwent diagnostic lumbar punctures and/or extended lumbar drainage procedures. After CSF and venous plasma was collected, warming packs were held to the patient’s upper arm site for 5 minutes before capillary blood was collected via TAPII or Tasso+ device for 5 minutes. Post processing, CSF, venous, and capillary plasma was analyzed using Quanterix SIMOA immunoassays. Clinical Dementia Ratings and Montreal Cognitive Assessment scores were also collected. Result 86 TAPII and 121 Tasso+ plasma samples were collected from patients (78 M, 115 F) with mean age 61 years ± 18.3 (range 16‐91 years). The mean volume for Tasso+ samples was 294.1uL±127.2 and 266.94uL±18.71 for TAPII though this difference was not statistically significant. Tasso+ devices were 90.3% successful in collecting >50uL whole blood, and TAPII devices were 86% successful. NFL, GFAP, and Aβ42/40 ratio in CSF demonstrated moderate positive correlations with both venous and capillary plasma, although TASSO+ samples showed stronger correlation with venous plasma than TAPII (NFL Tasso r = 0.93, p<0.001, TAP r = 0.85, p<0.001; GFAP Tasso r = 0.9, p<0.001, TAP r = 0.85, p<0.001; Aβ42/40 Tasso r = 0.72, p<0.001, TAP r = 0.86, p<0.001). Ptau217 CSF measurements showed a weaker positive correlation with venous and capillary samples (Venous r = 0.34, p<0.001, Tasso r = 0.19, p = 0.048, TAP 0.05, p = 0.623). Conclusion Neurodegenerative biomarkers measured in capillary plasma as collected by the Tasso+ and TAPII correlated well with similar measures obtained from venous plasma. Tasso+ and TAPII devices performed similarly regarding success rate and volume collected.

Background Nearly everyone (>90%) with Down Syndrome (DS) develops Alzheimer’s Disease (AD), and neuropathological development is studied extensively in this group. However, there is a gap in our understanding of AD age onset variation in DS, from 40 to 70 years. Cardiometabolic conditions (CC) are known dementia risk‐factors in the general population, and cohort‐studies document that cardiometabolic multimorbidity (CM) synergistically increases MCI, dementia, and AD. CC are highly prevalent in DS. Meanwhile, amyloid burden is associated with AD and elevated in DS across lifespan. However, there are no cohort studies on the association between CM and amyloid burden in DS. We tested the association between CM and amyloid burden in DS. Method We studied baseline data from 240 participants, 25‐65 years (198 cognitively stable and 42 with MCI or dementia), from the Alzheimer’s Biomarker Consortium‐Down Syndrome (ABC‐DS) longitudinal study. Participants were grouped in 0‐1 or >1 CC (hyperlipidemia, hypothyroidism, hypertension, cardiovascular conditions, stroke). Participants completed an amyloid positron emission tomography (PET) scan with [¹¹C]‐Pittsburgh compound B ([¹¹C]‐PiB) or [¹⁸F]‐AV45 (florbetapir). Regional standard uptake value ratios (SUVRs) were calculated relative to the cerebellar cortex and transformed to Centiloid global amyloid burden scores for further analysis. We tested the association between CM and amyloid burden scores using hierarchical linear regression, including age and apolipoprotein E (ApoE) status as covariates. Result The regression model revealed that CM was significantly associated with greater amyloid burden after accounting for age and ApoE status [F (3,194) = 57.752, p<0.001, ß = 0.228, 95% CI [0.007 ‐ 0.449], p = 0.043]. Centiloid values were higher for those with two or more CC (Figure 1). We did not find a significant association among participants with either MCI or dementia, possibly due to small samples within these categories. Conclusion Our results suggest that incidence of two or more CC synergistically affect amyloid burden in cognitively stable adults with DS. These findings can help us understand the prodromal role of CM in accelerating AD in DS and provide insights to preventative strategies of relevance to the general population. We plan future studies examining CM and related biomarkers, eg., chronic peripheral inflammation, influence AD in DS.

Background The early detection of dementia is paramount for clinical study design and early treatment. Smartwatches allow for passive data collection of behavior, making it an accessible and scalable data source. Previous studies have demonstrated the efficacy of smartwatch data in identifying incident Parkinson’s disease. This study seeks to explore the potential of smartwatch‐derived data in predicting incident dementia. Method Using UK Biobank, we investigated the predictive value of accelerometry in identifying incident (N = 201) all cause dementia in the general population (N = 33009), including healthy controls and other diseases). We further examined performance in identification from age and sex matched healthy control (N = 201) and from all healthy control (N = 24987). We compared the performance with models based on genetics, lifestyle, blood biochemistry or prodromal symptoms data. Feature analysis identified the most stable contributors to the risk prediction. Result A machine learning model trained using accelerometry data achieved higher test performance (Figure 1) for identifying incident dementia in the general population (AUPRC 0.04±0.01, prevalence: 0.006) than models based on genetics (p‐value = 0.008) and lifestyle (p‐value = 0.02). It performed on‐par with models based on blood biochemisty (AUPRC 0.03±0.005) and prodromal symptoms (AUPRC 0.04±0.02). A combined model achieved highest performance (AUPRC 0.08±0.03) with identified important and stable features (Figure 2) including age (mean coefficient and standard error: 0.93±0.08), polygenic risk score for Alzheimer’s disease (0.53±0.06), mean acceleration during light physical activity (‐0.29±0.04), and maximum time spent continuously asleep (‐0.36±0.03). Conclusion Accelerometry with features describing activity and sleep did not outperform other modalities in the identification of incident dementia. Further features derived from accelerometry need to be investigated for their potential use in early screening for dementia.

Background Identification of useful fluid biomarkers is expected to advance frontotemporal dementia spectrum disorders (FTD) care and therapeutic development. The clinical utility of emerging plasma Amyloid, Tau, and Neurodegeneration (ATN) biomarkers in FTD remains unexplored. This study analyzed ATN patterns by sporadic FTD phenotype, based on amyloid beta1‐42 (Aβ1‐42), amyloid beta1‐40 (Aβ1‐40), phospho‐tau217 (p‐tau217), and neurofilament (NfL) plasma concentrations. Method The present study included 625 participants enrolled in ALLFTD (46% female, median age 61 ± 12 years) for whom biomarker data were available. Plasma Aβ1‐42/Aβ1‐40 measures were determined by immunoprecipitation‐mass spectrometry whereas plasma p‐tau217 and NfL concentrations were respectively determined by electrochemiluminescence‐ and fluorescence‐based immunoassays. Biomarker concentrations were compared by phenotype and disease severity with ANOVA. Their diagnostic performance was tested with Receiver Operating Characteristic (ROC) curves, and biomarker relationships with measures of clinical severity were tested through linear regressions controlling for age, sex, and APOE genotype. Result Higher plasma p‐tau217 and p‐tau217/Aβ1‐42, levels were observed in logopenic primary progressive aphasia (lvPPA) and amnestic dementia (AmD) cohorts, compared to other FTD groups. These biomarkers were not associated with disease severity but were affected by APOE4 genotype. High plasma NfL level was observed in fully symptomatic patients compared to controls, and as a function of disease severity. Plasma Aβ1‐42/Aβ1‐40 level was not different between phenotypes and was not affected by clinical severity. P‐tau217 level, but not Aβ1‐42/Aβ1‐40 or NfL levels showed high discrimination of lvPPA and AmD from other phenotypes (AUC = 0.87 (95% CI 0.77 – 0.96, p < 0.0001, 73% sensitivity, 92% specificity). The p‐tau217/Aβ1‐42 ratio improved this discrimination (AUC = 0.9, 95% CI 0.83 – 1, p < 0.0001, 77% sensitivity, 94% specificity). Plasma Aβ1‐42/Aβ1‐40, p‐tau217, NfL, p‐tau217/Aβ1‐42, p‐tau217/NfL, and (Aβ1‐42 x p‐tau217)/NfL were associated with cognitive (MoCA, CVLT), motor (UPDRS), and social behavior (RSMS) performances at baseline. Conclusion Plasma p‐tau217/Aβ1‐42 identifies, with high specificity, two phenotypes associated with Alzheimer’s disease: lvPPA and AmD. Pending molecular neuroimaging and neuropathological confirmation, this biomarker could be of value to identify primary Alzheimer’s disease pathology or co‐pathology associated with Alzheimer’s disease when sporadic FTD is suspected.

Background XPro1595 (XProTM) is a brain‐penetrant, recombinant protein variant of human tumor necrosis factor (TNF) rationally designed to selectively neutralize only the soluble, pro‐inflammatory form of the cytokine (solTNF). An unbiased proteomic analysis of CSF samples from an open‐label, phase‐1b study (NCT03943264) in patients with Alzheimer’s disease (AD) was conducted to assess for pharmacodynamic activity and disease‐specific target engagement. Method Patients with AD (n = 20) were treated for 12‐weeks with one of three doses of XProTM: 0.3 (n = 5), 0.6 (n = 6) or 1.0 mg/kg/wk (n = 9). Tandem‐mass tag mass spectrometry (TMT‐MS) proteomics was performed on CSF, with lysates from three postmortem AD brains (Braak Stage IV‐VI) as control samples. Protein levels were recorded as Log2 ratios (sample/reference), with two‐sided t‐tests for determination of significant change from baseline. Differential abundance (Metascape and SynGO) and proteomic pathway (STRING) analyses were used to identify gene ontology (GO) biological processes (GOBPs) and interrelated clusters dose‐dependently affected by treatment. Due to the small sample size and exploratory nature of the study, the threshold for determination of informative values was set at P<0.05 (nominal), with no correction for multiple comparisons. Result CSF samples from fourteen patients were available for analysis: 0.3 (n = 4), 0.6 (n = 5), and 1.0 mg/kg/wk (n = 5). In total, 29,607 peptides associated with 3,632 distinct protein groups were quantified in all samples. CSF levels of 221 proteins significantly increased (n = 111) or decreased (n = 116) after treatment with the 1.0 mg/kg/wk dose of XProTM (Figure 1). GOBP and STRING pathway analyses showed high enrichment for synaptic proteins (24%) and identified a protein‐protein interaction (PPI) network of 41 proteins affected by treatment in a dose‐related manner (Figure 2). Analysis of baseline levels by PPI group (decreased or increased) showed positive within‐group intercorrelations (r > 0.535, P<0.05), with similarly negative correlations across the two groups, thus confirming interrelatedness in this sample (Figure 3A). Thirty‐two (78%) of the PPI proteins were also found to be differentially expressed in AD (Figure 3B). Conclusion Synaptic dysfunction is associated with both the clinical symptoms and core pathologies of AD. These findings provide further evidence of disease‐specific and dose‐related target engagement for XProTM in AD.