Objective: Amyloid-β (Aβ) positron emission tomography (PET) imaging is essential for diagnosing and monitoring Alzheimer’s disease (AD). The Centiloid (CL) scale standardizes Aβ quantification across centers and tracers, but its limitations include calibration requirements and the inability to capture regional A| heterogeneity. Methods: The ComBat-Centiloid method harmonizes A| PET data without calibration by combining a 11C-Pittsburgh Compound B standard reference database with the ComBat algorithm and CL framework to generate harmonized CL (HCL) and harmonized regional CL (HRCL) values. Pearson correlation analysis was used to evaluate the relationship between HCL and CL values within the same tracer/protocol combinations. Paired t -tests were used to assess differences in HCL and CL values between two same-subject scans using different tracers taken within 1 year. Multicenter analyses were performed with combined datasets with different tracer/protocols to compare the consistency of HRCL, regional CL (RCL), and SUVR for differentiating patients with AD from cognitively normal (CN) individuals. Results: HCL values strongly correlated with CL across all tracer/protocol combinations and effectively eliminated inter-tracer biases, showing no significant differences in paired tests. In multicenter analyses, HCL values outperformed SUVR and RCL, demonstrating superior consistency for distinguishing patients with AD from CN individuals. Conclusion: The ComBat-Centiloid method eliminates calibration requirements and supports robust harmonized assessments in multicenter multitracer studies.

ABSTRACTBackground and AimsAlzheimer's disease (AD) is a widespread neurodegenerative condition that has a growing impact on a global scale. This study aims to examine the relationship between cerebral blood flow (CBF) and the synaptic biomarker growth‐associated protein 43 (GAP‐43) through the utilization of arterial spin labeling (ASL). The research identified noteworthy correlations between cerebrospinal fluid (CSF) GAP‐43 levels, CBF, and cognitive composite scores, especially among participants with mild cognitive impairment (MCI) who possess the APOE‐ε4 gene.MethodsThe study examined 92 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, including 36 cognitively normal (CN) and 56 MCI. The cognitive status of 42 participants was evaluated using ADNI composite scores. Independent t‐tests and Mann‐Whitney tests were used for the comparison of continuous variables between groups, and multiple linear regression analysis with adjustments for confounding factors was used to assess the relationship between GAP‐43 and CBF values.ResultsSignificant positive correlations were observed between GAP‐43 levels and (A) the executive function composite score (ADNI_EF) in CN individuals, as well as (B) the language composite score (ADNI_LAN) in individuals with MCI. CSF biomarkers and ASL regions did not show statistical significance between diagnostic groups after correction for multiple comparisons. No significant differences in baseline characteristics were found between diagnostic groups. However, associations were observed between ROI CBF and Mini Mental State Examination in various subgroups.ConclusionThe findings indicate a potential function for ASL perfusion in identifying early AD‐related alterations and gaining insight into the pathophysiology of AD and mild cognitive impairment.The study revealed associations between CBF, cognitive scores, and APOE‐ε4 gene status. This study contributes to the comprehension of the correlation between CSF biomarkers, regional brain perfusion, and cognitive function in individuals with AD using ASL as a noninvasive approach.

Although there is some evidence of an association between Alzheimer’s disease polygenic risk score (AD PRS) and cognitive function, limited validations have been performed in large populations. We investigated the relationship between AD PRS and cognitive function in the UK Biobank in over 276,000 participants and further validated the association in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) sample. We developed the AD PRS (excluded the APOE variants) in the middle age UK Biobank participants (age ranged 39–72, mean age 57 years) of European ancestries by LDpred2. To validate the association of AD PRS and cognitive function internally in the UK Biobank, we linearly regressed standardized cognitive function on continuous standardized AD PRS with age at cognitive test, sex, genotyping array, first 10 principal components of genotyping, smoking, education in years, body mass index, and apolipoprotein E gene ε4 (APOE4) risk allele dosages. To validate the associations externally, we ran the linear mixed effects model in the ADNI sample free of dementia (age ranged 55–91, mean age 73), including similar covariates as fixed effects and participants’ IDs as the random effect. Stratification by age, APOE4 carrier status, and cognitive status (cognitively normal or mild cognitive impairment) was also investigated. Our study validated the associations of AD PRS and cognitive function in both midlife and late-life observational cohorts. Although not all of the cognitive measures were significantly associated with AD PRS, non-verbal fluid reasoning (matrix pattern completion, β = − 0.022, P = 0.003), processing speed (such as symbol digit substitution, β = − 0.017, P = 1.08E−05), short-term memory and attention (such as pairs matching, β = − 0.014, P = 1.66E−10), and reaction time (β = − 0.010, P = 1.19E−06) were inversely associated with increasing AD PRS in the UK Biobank. Higher likelihood of cognitive impairment was also associated with higher AD PRS in the ADNI cognitive normal individuals (AD assessment scale β = 0.079, P = 0.02). In summary, we confirmed that poorer cognitive function was associated with a higher polygenic AD risk, and suggested the potential utility of the AD PRS in identifying those who may be at risk for further cognitive decline.

BackgroundMild cognitive impairment (MCI) is an important prodromal stage of Alzheimer's disease (AD), affecting 69 million individuals worldwide. At present, there is a lack of a community-applicable tool for MCI screening. Exhaled breath volatile organic compounds (VOCs) have been used to distinguish MCI from cognitively normal (CN) individuals only in small sample size studies and the efficacy has not been compared with blood biomarkers.ObjectiveThis diagnostic study aimed to assess the feasibility of using exhaled breath VOCs detection by a portable micro-gas chromatography (μGC) device as a screening tool to discriminate MCI from CN individuals in a community population.MethodsA detection model was developed and optimized from five distinct machine learning algorithms based on the differential VOCs between 240 MCI and 241 CN individuals. Among these 481 participants, five plasma biomarkers were measured in 397 individuals (166 MCI and 231 CN).ResultsThe final model (481 individuals) incorporating eight differential VOCs showed good performance with an area under the receiver-operating characteristic curve (AUC) of 0.84 (95% confidence interval (95% CI): 0.83-0.85). The AUC of the VOC model (0.80, 95% CI: 0.69-0.90) was higher than that of the plasma model (0.77, 95% CI: 0.65-0.88) (397 individuals).ConclusionsThe detection of exhaled breath VOCs by a portable μGC device is feasible for MCI screening in community populations, potentially facilitating early detection and intervention strategies for individuals at high risk.

The role of the chemokine CX3CL1 in the processes of aging and Alzheimer's disease (AD) pathogenesis is well-established. This study aims to evaluate the diagnostic potential of urinary CX3CL1 levels in distinguishing between AD patients, those experiencing amnestic mild cognitive impairment (aMCI), and cognitively normal (CN) individuals. A cohort comprising 516 CN individuals across various age groups, 102 AD patients, and 65 subjects with aMCI was assembled, alongside 93 age- and sex-matched CN controls. Enzyme-linked immunosorbent assay (ELISA) was utilized to quantify urinary CX3CL1 levels. Urinary CX3CL1 concentrations exhibited an age-dependent increase and demonstrated a positive correlation with age. Comparatively, AD patients exhibited significantly elevated urinary CX3CL1 levels when contrasted with both the CN controls and the aMCI cohort. Conversely, aMCI patients displayed urinary CX3CL1 levels that were notably reduced in comparison to both the AD and CN groups. Urinary CX3CL1 levels correlate with the aging process and may serve as a potential diagnostic biomarker for both amnestic mild cognitive impairment (aMCI) and Alzheimer's disease (AD).

BackgroundBlood–brain barrier dysfunction is one characteristic of Alzheimer’s disease (AD) and is recognized as both a cause and consequence of the pathological cascade leading to cognitive decline. The goal of this study was to assess markers for barrier dysfunction in postmortem tissue samples from research participants who were either cognitively normal individuals (CNI) or diagnosed with AD at the time of autopsy and determine to what extent these markers are associated with AD neuropathologic changes (ADNC) and cognitive impairment.MethodsWe used postmortem brain tissue and plasma samples from 19 participants: 9 CNI and 10 AD dementia patients who had come to autopsy from the University of Kentucky AD Research Center (UK-ADRC) community-based cohort; all cases with dementia had confirmed severe ADNC. Plasma samples were obtained within 2 years of autopsy. Aβ40, Aβ42, and tau levels in brain tissue samples were quantified by ELISA. Cortical brain sections were cleared using the X-CLARITY™ system and immunostained for neurovascular unit-related proteins. Brain slices were then imaged using confocal microscopy and analyzed for microvascular diameters and immunoreactivity coverage using Fiji/ImageJ. Isolated human brain microvessels were assayed for tight-junction protein expression using the JESS™ automated Western blot system. S100 calcium-binding protein B (S100β), matrix metalloproteinase (MMP)-2, MMP-9, and neuron-specific enolase (NSE) levels in plasma were quantified by ELISA. All outcomes were assessed for linear associations with global cognitive function (MMSE, CDR) and cerebral atrophy scores by Pearson, polyserial, or polychoric correlation, as appropriate, along with generalized linear modeling or generalized linear mixed-level modeling.ResultsAs expected, we detected elevated Aβ and tau pathology in brain tissue sections from AD patients compared to CNI. However, we found no differences in microvascular diameters in cleared AD and CNI brain tissue sections. We also observed no differences in claudin-5 protein levels in capillaries isolated from AD and CNI tissue samples. Plasma biomarker analysis showed that AD patients had 12.4-fold higher S100β plasma levels, twofold lower NSE plasma levels, 2.4-fold higher MMP-9 plasma levels, and 1.2-fold lower MMP-2 plasma levels than CNI. Data analysis revealed that elevated S100β plasma levels were predictive of AD pathology and cognitive impairment.ConclusionOur data suggest that among different markers relevant to barrier dysfunction, plasma S100β is the most promising diagnostic biomarker for ADNC. Further investigation is necessary to assess how plasma S100β levels relate to these changes and whether they may predict clinical outcomes, particularly in the prodromal and early stages of AD.

ObjectiveTo investigate the associations between cerebral microhemorrhages (CMH) and cognitive decline across the Alzheimer’s dementia continuum.MethodsUsing the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, we studied 619 participants, categorized into 221 cognitively normal (CN) participants, 281 patients with mild cognitive impairment (MCI), and 117 patients with Alzheimer’s disease (AD). CMH prevalence and distribution were determined using T2-weighted magnetic resonance imaging (MRI), focusing on the frontal, occipital, and parietal subcortical regions of interest (ROIs).Clinical dementia rating scale sum of boxes (CDR-SB) and mini-mental state examination (MMSE) were used for diagnosis and composite cognitive scores regarding visuospatial abilities, language, memory, and executive functions were used as outcome variables. Age, gender, and APOE ε4 positivity status were used as covariates.ResultsThe AD group displayed significantly elevated tau and P-tau levels compared to MCI and CN groups (p < 0.001). APOE ε4 positivity was 67.5% in the AD group, surpassing the 50.2% in MCI and 29% in CN individuals (p < 0.001). Cognitive assessments revealed that the AD group’s CDR-SB score and MMSE both significantly differed from these scores in the MCI and CN groups (p < 0.001). Overall, CMH prevalence was 27.7%, with a predominant distribution in the frontal subcortical ROIs. MCI subjects with CMH showed notably diminished ADNI Visuospatial Composite Scores compared to those without CMH. Age significantly predicted CMH in CN and MCI (p < 0.05). In AD participants, APOE ε4 heterozygotes (p = 0.02) and homozygotes (p = 0.01) hadincreased CMH likelihood.ConclusionCMHs are significantly associated with cognitive decline in patients with MCI. This association is more prominent in regard to the decline in visuospatial abilities.

Validated computerized assessments for cognitive functioning are crucial for older individuals and those at risk of cognitive decline. The National Institutes of Health (NIH) Toolbox Cognition Battery (NIHTB-CB) exhibits good construct validity but requires validation in diverse populations and for adults aged 85+. This study uses data from the Assessing Reliable Measurement in Alzheimer's Disease and cognitive Aging study to explore differences in the factor structure of the NIHTB-CB for adults 85 and older, Black participants versus White participants, and those diagnosed as amnestic Mild Cognitive Impairment (aMCI) vs cognitively normal (CN). Subtests from the NACC UDS-3 and NIHTB-CB were administered to 503 community-dwelling Black and White adults ages 55-99 (367 CN; 136 aMCI). Confirmatory factor analyses were used to investigate the original factor structure of NIHTB-CB that forms the basis for NIHTB-CD Index factor scores. Factor analyses for all participants and some participant subsets (aMCI, White, 85+) substantiated the two anticipated factors (Fluid and Crystallized). However, while Black aMCI participants had the expected two-factor structure, for Black CN participants, the List Sorting Working Memory and Picture Sequence tests loaded on the Crystallized factor. Findings provide psychometric support for the NIHTB-CB. Differences in factor structure between Black CN individuals and Black aMCI individuals suggest potential instability across levels of cognitive impairment. Future research should explore changes in NIHTB-CB across diagnoses in different populations.

Abstract BackgroundPredicting brain age from neuroimaging data is an emerging field. The age gap (AG), the difference between chronological age (CA) and brain age (BA), is crucial for indicating individual neuroanatomical aging. Previous deep learning models faced challenges in generalizability and neuroanatomical interpretability. We incorporated patients with different dementia types, including dementia with Lewy bodies (DLB) and Alzheimer’s disease (AD), alongside mild cognitive impairment (MCI) and cognitive normal (CN) individuals. This inclusive strategy is essential for comprehensive mapping of neurocognitive trajectories and understanding distinct aging patterns across various cognitive conditions.MethodUtilizing T1‐weighted MRI images of n = 3,859 subjects (Table 1) from the CamCAN, NACC, and ADNI databases, this study aimed to predict brain age in four groups (CN, MCI, AD, and DLB). Structural MRI data were spatial normalized and skull‐striped. Then a 3D Convolutional Neural Network (CNN) based on the skull‐striped data was used for age prediction. The model’s architecture includes three convolutional layers with ReLU activation, max‐pooling, batch normalization, and dropout for regularization, ending with global average pooling and dense layers. The model was trained and validated on CN subjects. The trained model was used to predict age in MCI, DLB, and AD patients as well as the test set of CN subjects.ResultThe 3D CNN model accurately predicted brain age in the CN test set with an AG of 0.64 ± 2.74 years and an absolute AG of 1.86 ± 2.11 years (Figure 1 and Table 1). In DLB and AD patients, the average AG was 3.81 and 2.90 years, respectively, and significantly larger than 0 (P &lt; 10‐5), indicating accelerated aging patterns in these groups. The average AG of MCI was 0.09 years which was significantly smaller than that of both DLB and AD (P &lt; 10‐3), indicating the early stage of impairment in MCI patients.ConclusionOur 3D CNN model accurately predicted brain age in cognitively normal individuals and identified accelerated aging in DLB and AD patients. The model's precision highlights its potential for early detection and understanding of neurocognitive trajectories, contributing to advancements in neurological research and clinical diagnostics.

Abstract BackgroundThe medial temporal lobe's (MTL) early involvement in tau pathology makes it a key focus in the development of preclinical Alzheimer’s disease (AD) biomarkers. ROI analyses in prior studies reported significant MTL structural differences in cognitively normal individuals with and without ß‐amyloid (A+/‐CN). Pointwise analysis, offering spatial information of early neurodegeneration, has potential to pinpoint “signature regions” of pathological change, but has been underexplored in the MTL. This study employs a specialized pointwise analysis pipeline to examine the spatial pattern of MTL structural change in subgroups dichotomized by both ß‐amyloid and tau status in a large cohort of CN individuals.MethodsA dataset of 3036 CN (A‐/A+: 1270/1558, Table 1) individuals from ADNI, HABS, A4 and ABC were analyzed. We extracted MTL regional thickness maps from MRI using tailored pipelines, ASHS‐T1 and CRASHS. For participants with prospective longitudinal MRI (five years follow‐up), regional maps of longitudinal atrophy rate were extracted using SkelDBM. Subjects with cross‐sectional tau PET available (N=563) were further divided into A and T subgroups by tracer uptake. General linear modeling was performed on each surface point to investigate cross‐sectional and longitudinal MTL structural group differences (detailed in Figure 1) and their correlation with MTL tau burden in All/A+/A‐ CN. Age and sex were covariates and cluster‐level multiple comparison correction was performed.ResultsA+CN demonstrated a significantly faster atrophy rate than A‐CN across the whole MTL, primarily driven by A+T+CN individuals (Figure 1‐b). Notably, A‐T+CN showed significantly faster atrophy rate in the entorhinal cortex (ERC) and Brodmann area 35 (BA35), the earliest sites of tau pathology (Figure 1‐b, second column). Figure 2‐b displays an MTL‐wise significant correlation between atrophy rate and tau in All/A+/A‐ CN. In both analyses, cross‐sectional effects are consistently weaker than longitudinal ones, but have some significant clusters in ERC and BA35.ConclusionsPointwise analysis revealed extensive tau‐associated accelerated neurodegeneration in the MTL in preclinical AD. Furthermore, accelerated atrophy was observed in early Braak regions in A‐CN with evidence of tau pathology, potentially driven by primary age‐related tauopathy (PART). These pointwise longitudinal MTL measures provide sensitive measures that may allow for disease monitoring in preclinical AD.

Abstract BackgroundMounting evidence indicates that the accumulation of amyloid in AD starts 20 to 30 years before clinically detectable cognitive impairment is observed, suggesting the presence of a long period of asymptomatic AD. Although the specific onset of this preclinical period is difficult to target, it is potentially significant to identify subjects in this asymptomatic preclinical stage of the disease. This is because newly developed disease modifying treatments may have increased effectiveness if begun during this asymptomatic period. Currently, the preclinical phase of AD is identified in asymptomatic individuals as abnormal amyloid levels detected via neuroimaging or fluid biomarkers. However, this approach is not feasible as a routine screening tool in a clinical environment, therefore there is a critical need to develop blood‐based, cost‐effective screening tools to detect AD in asymptomatic patients. Because of this, the search for preclinical blood‐based biomarkers has become a major focus.Method200 Participants were randomly selected from the Australian Imaging, Biomarkers, and Lifestyle study (AIBL). A total of 34 leukocyte antigens were examined by flow cytometry immunophenotyping. Leukocyte markers were used in addition to age, ApoE4 status, years of education, and sex to predict the PET Aβ status. Data were analyzed by logistic regression and receiver operating characteristic (ROC) analyses.ResultWe identified 12 specific leukocyte markers that were differentially expressed in cognitively normal (CN) patients designated as amyloid negative compared with CN individuals who are amyloid positive (Amyloid PET Centiloid &gt;25). Combinations of these 12 markers produced AUC values as high as 0.94 in‐sample, and 0.90 out‐of‐sample. Specifically, markers CD11c, CD59, CD91, and CD163 had high performance when predicting amyloid positivity. These markers also showed strong predictive performance at other levels of Centiloid, maintaining AUCs greater than 0.85.ConclusionResults suggest that leukocyte surface biomarkers involved in Aβ transportation, innate phagocytosis and completement mediated clearance pathways are the first blood‐based biomarkers that can accurately predict amyloid load and detect amyloid at 25 centiloids. These biomarkers could have a major impact on clinical practice by allowing primary care physicians to identify individuals at high risk of having amyloid burden in their brains with a simple blood test.

Abstract BackgroundAlzheimer's Disease (AD) is a progressive neurodegenerative disorder characterized by memory loss and cognitive decline. Traditional diagnostic methods, mainly based on cognitive, memory, and behavioral tests, have limitations, particularly in the early detection of AD. Structural magnetic resonance imaging (sMRI) has emerged as a key tool in understanding the brain changes associated with AD, focusing particularly on alterations in gray matter (GM). However, the complexity of brain changes in AD requires sophisticated analysis methods. In recent years, machine learning (ML) models have shown great potential in interpreting complex neuroimaging data. These models can detect intricate patterns in neuroimaging data, making them invaluable in enhancing the diagnostic accuracy and early AD diagnosis. Therefore, combining the neuroimaging data with ML models presents a promising direction in improving the early‐diagnosis and understanding of AD.MethodWe propose a novel approach to diagnose AD using Vision Transformer (ViT) (Figure 1) [1], a cutting‐edge class of ML model, and GM of T1‐weighted sMRI data. The proposed approach leverages the power of deep‐learning (DL) to detect the GM changes that are indicative of AD. We used pretrained ViT model to extract features from the GM sagittal and coronal slices of sMRI data and classify AD from cognitively normal (CN). We employed ADNI dataset, focusing on subjects with T1‐weighed MPRAGE sMRI scans, including 70 AD patients and 85 CN individuals.ResultThe study achieved an average classification accuracy of 97.6% in sagittal slices and 97.7% in coronal slices (Figures 2 and 3). These results indicate a significantly higher accuracy in diagnosing AD using the proposed method compared to other state‐of‐the‐art models based on sMRI data. The high accuracy underscores the model's capability in effectively distinguishing between AD patients and CN individuals, demonstrating its potential utility in clinical settings.ConclusionThe proposed approach demonstrates a significant advancement in the accurate diagnosis of AD, which might be useful for early‐diagnosis. Our proposed ML model represents a considerable improvement over existing ML methods, offering a new avenue for research and application in the field of neurodegenerative diseases.

Abstract BackgroundPosterior Cortical Atrophy (PCA) is a syndrome characterized by a progressive decline in higher‐order visuospatial processing, leading to symptoms such as space perception deficit, simultanagnosia, and object perception impairment. While PCA is primarily known for its impact on visuospatial abilities, recent studies have documented language abnormalities in PCA patients. This study aims to delineate the nature and origin of language impairments in PCA, hypothesizing that language deficits reflect the visuospatial processing impairments of the disease.MethodWe compared the language samples of 25 patients with PCA with age‐matched cognitively normal (CN) individuals across two distinct tasks: a visually‐dependent picture description and a visually‐independent job description task. We extracted word frequency, word utterance latency, and spatial relational words for this comparison. We then conducted an in‐depth analysis of the language used in the picture description task to identify specific linguistic indicators that reflect the visuospatial processing deficits of PCA.ResultPatients with PCA showed significant language deficits in the visually‐dependent task, characterized by higher word frequency, prolonged utterance latency, and fewer spatial relational words, but not in the visually‐independent task. An in‐depth analysis of the picture description task further showed that PCA patients struggled to identify certain visual elements as well as the overall theme of the picture. A predictive model based on these language features distinguished PCA patients from CN individuals with high classification accuracy.ConclusionThe findings indicate that language is a sensitive behavioral construct to detect visuospatial processing abnormalities of PCA. These insights offer theoretical and clinical avenues for understanding and managing PCA, underscoring language as a crucial marker for the visuospatial deficits of this atypical variant of Alzheimer’s disease.

Abstract BackgroundAnosognosia, a hallmark of Alzheimer’s disease (AD), manifests as a gradual decline in disease awareness, yet its neural correlates remain unclear. This study investigates how amyloid accumulation, glucose hypometabolism and cortical atrophy relate to cognitive awareness across the AD continuum. Both the pathological processes and the brain regions involved were studied.MethodsWe included 263 cognitively‐normal (CN) individuals, 411 with mild cognitive impairment (MCI), and 111 diagnosed with AD from the ADNI cohort, with a mean±SD of 5.4±2.4 visits (Table 1). Awareness was assessed using the subject‐informant discrepancy on the ECog questionnaire, ranging from –3 to 3, with lower values indicating lower awareness. Neuroimaging measures included amyloid load assessed with 18F‐AV‐45‐PET, glucose metabolism with FDG‐PET, and cortical thickness with T1‐MRI, across the entire brain segmented into 86 regions of interest (ROIs) using FreeSurfer. In multivariate linear mixed models, we modeled the awareness index with baseline amyloid load, metabolism, and cortical thickness in each ROI. Models were adjusted for sex, age, education, and depression. We also factored in interactions between these variables and time, while correcting for multiple comparisons.ResultsResults are presented in Figure 1. In individuals with AD: awareness was lower at baseline than in the other groups (p&lt;0.001); it decreased over time (β±SE = ‐0.21±0.04; p&lt;0.001); and lower awareness was associated with lower education (β±SE = 0.07±0.02, p = 0.004) and reduced left posterior cingulate metabolism (standardized β±SE = ‐0.24±0.07, p = 0.042). In participants with MCI: awareness was lower at baseline than in CN participants and higher than in AD (all p&lt;0.001), and it decreased over time (β±SE = ‐0.07±0.01, p&lt;0.001); greater decline in awareness was associated, first, with higher baseline amyloid in all ROIs except the hippocampus, parahippocampus, right posterior cingulate and right amygdala (all p&lt;0.001); second, with decreased left posterior cingulate metabolism (standardized β±SE = ‐0.14±0.04, p = 0.006); third, with greater atrophy at baseline in the bilateral hippocampi, amygdala, and superior occipital gyri (all p&lt;0.001). In CN individuals, we observed no significant associations (all p&gt;0.05).ConclusionAll three processes were associated with cognitive awareness, exhibiting distinct patterns across different groups. Additionally, our findings revealed widespread involvement of multiple ROIs.

Abstract BackgroundThe emergence of blood‐based biomarkers offers a cost‐effective and less invasive alternative to established neuroimaging and cerebrospinal fluid biomarkers. Newly developed fluid biomarkers, including N‐terminal tau fragment (NT1), have shown promise for identifying individuals at risk for Alzheimer’s disease (AD). Evidence has shown NT1 may be more abundant than full‐length tau across the AD continuum and has high sensitivity and specificity to separate cognitively normal (CN) individuals from those with mild cognitive impaired (MCI) and AD in discovery and replication cohorts. Here we quantify plasma NT1 in a large, well‐characterized cohort and examine the association between plasma NT1 and cross‐sectional clinical and biomarkers measures.MethodsSeven hundred and seventeen individuals enrolled in the Alzheimer's Disease Neuroimaging Initiative (ADNI) who have plasma NT1, Aß‐PET, MRI, and clinical (Clinical Dementia Rating; CDR) measures were included in this study (Table 1). NT1 was assessed using Quanterix Simoa HD‐X platform. PET, MRI, clinical, and other plasma measures were derived using previously described procedures in ADNI. Linear regressions were performed to assess the cross‐sectional association of NT1 with clinical and biomarkers measures, after adjusting relevant covariates.ResultsNT1 levels were elevated in cognitively impaired (MCI/AD; CDR&gt;0) relative to CN (CDR=0) individuals (p=0.008, Figure 1A). Specifically, NT1 is elevated in the MCI group (CDR=0.5, MCI vs Aß‐ CN group: p=0.005), but not the AD group (CDR&gt;0.5, AD vs all other groups: p’s &gt;0.206, Figure 1B). NT1 was associated with plasma phosphorylated (p)Tau‐181 (p=1.27x10‐9, Figure 2A) and plasma neurofilament light chain (NfL; p=5.68x10‐6, Figure 2B) but not hippocampal volume (p=0.239).ConclusionPlasma NT1 differentiated CN from MCI/AD individuals and was elevated particularly in the early symptomatic phase of disease. Plasma NT1 was associated with plasma markers of tau and neurodegeneration. Together these results suggest that plasma NT1 may be a useful biomarker of AD‐related tau pathology and neurodegeneration.

Abstract BackgroundThe emergence of blood‐based biomarkers offers a cost‐effective and less invasive alternative to established neuroimaging and cerebrospinal fluid biomarkers. Newly developed fluid biomarkers, including N‐terminal tau fragment (NT1), have shown promise for identifying individuals at risk for Alzheimer’s disease (AD). Evidence has shown NT1 may be more abundant than full‐length tau across the AD continuum and has high sensitivity and specificity to separate cognitively normal (CN) individuals from those with mild cognitive impaired (MCI) and AD in discovery and replication cohorts. Here we quantify plasma NT1 in a large, well‐characterized cohort and examine the association between plasma NT1 and cross‐sectional clinical and biomarkers measures.MethodsSeven hundred and seventeen individuals enrolled in the Alzheimer's Disease Neuroimaging Initiative (ADNI) who have plasma NT1, Aβ‐PET, MRI, and clinical (Clinical Dementia Rating; CDR) measures were included in this study (Table 1). NT1 was assessed using Quanterix Simoa HD‐X platform. PET, MRI, clinical, and other plasma measures were derived using previously described procedures in ADNI. Linear regressions were performed to assess the cross‐sectional association of NT1 with clinical and biomarkers measures, after adjusting relevant covariates.ResultsNT1 levels were elevated in cognitively impaired (MCI/AD; CDR&gt;0) relative to CN (CDR=0) individuals (p=0.008, Figure 1A). Specifically, NT1 is elevated in the MCI group (CDR=0.5, MCI vs Aβ‐ CN group: p=0.005), but not the AD group (CDR&gt;0.5, AD vs all other groups: p’s &gt;0.206, Figure 1B). NT1 was associated with plasma phosphorylated (p)Tau‐181 (p=1.27x10‐9, Figure 2A) and plasma neurofilament light chain (NfL; p=5.68x10‐6, Figure 2B) but not hippocampal volume (p=0.239).ConclusionPlasma NT1 differentiated CN from MCI/AD individuals and was elevated particularly in the early symptomatic phase of disease. Plasma NT1 was associated with plasma markers of tau and neurodegeneration. Together these results suggest that plasma NT1 may be a useful biomarker of AD‐related tau pathology and neurodegeneration.

Abstract BackgroundWhile several non‐invasive plasma biomarkers are rapidly developing, they still have lower accuracy than the established CSF biomarkers. This study therefore comprehensively examined 6,905 plasma proteins in over 3,000 individuals to identify novel biomarkers for predicting clinical Alzheimer’s disease (AD) and monitoring disease progression.MethodTo identify and validate plasma biomarkers, we performed difference abundance analysis of 6,905 plasma proteins in a total of 3,366 cases and controls from three different datasets (Knight‐ADRC discovery, Knight‐ADRC replication and Stanford ADRC). Enrichment analysis with Gene Ontology and DisGeNet were performed to understand biological significance of the identified proteins. Machine learning model was used to create prediction models for AD risk, which were replicated on the Knight ADRC replication and Stanford datasets.ResultWe identified and replicated 257 plasma proteins (including NEFL, SMOC1, SPON1, and NEUROG1) associated with clinical AD status. Machine learning model identified 89 proteins with strong prediction power for AD (AUC=0.843 in Knight ADRC; AUC=0.771 in Stanford ADRC), but not for Parkinson's disease (AUC&lt; 0.6 in two independent datasets), frontotemporal dementia (AUC=0.726), and dementia with Lewy bodies (AUC=0.712), indicating AD specificity. This proteomic signature also predicted AD individuals with faster progression (clinical dementia rating sum‐of‐boxes changes per year; P=4.7∗10‐5). Furthermore, this model identified a subset of cognitive normal individuals at 201 times higher risk of developing AD individuals (hazard ratio =201.3; P=1.95∗10‐2). Pathway analysis highlighted proteins (APOE, SPP1, and PLTP) related to lipid transport pathways (FDR=2.7∗10‐2). DisGeNet indicated the relevance to senile cardiac amyloidosis (APOE, CRP, and NEFL; FDR=1.1∗10‐4), highlighting transthyretin, that was found to be potentially protective against amyloid beta deposition.ConclusionThis large‐scale plasma proteomic study identified proteins associated with AD, developed a robust prediction model that accurately predicted AD status, examined differences between predicted groups in progression and conversion, and revealed pathways and diseases relevant to AD. These findings from extensive analysis provides the potential of plasma proteins as biomarkers for routine clinical uses in early detection of AD and guiding AD treatment decisions. They provide valuable insights into the relevance of specific pathways and diseases in the context of AD.

Abstract BackgroundDiet has been associated with memory, emotion/stress regulation, structure and function of the hippocampus and amygdala and attenuation of cognitive aging. There is a well‐recognized lack of reliability in self‐reported dietary intake and great interest in objective metabolic readout of dietary patterns. In this study we constructed dietary profiles from untargeted metabolomics data using a novel metadata‐based source annotation method developed at the Dorrestein Lab, also referred to as “foodomics”. The aim of was to link these objective metabolic readouts of dietary intake to cognition and AD markers of disease.MethodUntargeted metabolomic profiling (LC‐MS/MS) was applied to blood samples from 872 individuals from the ADNI cohort (182 cognitive normal individuals, 449 MCI, 103 significant memory concern, and 139 AD); for whom brain imaging data was also collected. Principal component analysis (PCA) was to derive a dietary profile that explained the most variance in the dataset. Whole‐brain voxel‐wise analysis was applied to determine association between dietary intake profiles and brain amyloid‐β deposition (amyloid PET), gray matter density (brain atrophy; MRI), and glucose metabolism (FDG PET) using a multivariate regression analysis with SPM12. The general linear model was used to examining the associations of the dietary profile with cognition and AD markers. Covariates included age, education, sex, and BMI.ResultCorrelation between the first principal component (PC1) and the relative abundances of foods at level 3 of the food ontology showed that PC1 explained variance derived from food intake within poultry, dairy, meat, egg, vegetable, legume and seafood categories (Figure 1). Greater intake of poultry, dairy, and meat was significantly associated with 1) reduced glucose metabolism and brain gray matter atrophy in the frontal, parietal, and temporal (including the hippocampus; Figure 2,3) and 2) difficulties with executive functioning (p=0.03) and memory at baseline (p=0.002), longitudinal decreases in memory functioning over time (p=.02), and increases in baseline plasma levels of neurofilament light chain at baseline (p=.02).ConclusionDietary intake selectively influences glucose metabolism and gray matter atrophy of brain regions preferentially targeted in AD, cognition and neurofilament light chain. This proof‐of‐concept study supports the hypotheses that diet influences the progression of the disease. NIA U19 AG063744

Abstract BackgroundA current hypothesis of the etiology of Alzheimer’s disease (AD) suggests that soluble amyloid‐ß (Aß) oligomers (Aßo) play a more significant role than amyloid plaques. Among the numerous Aßo discovered in the brain, Aß*56 has been shown to be associated with aging and cognitive dysfunction in mice, dogs, and humans, and impair memory in rodents. Evidence from our recent study indicates that Aß*56 produced from Tg2576 mice modeling AD is a ∼56‐kDa, SDS‐stable, A11 (anti‐amyloid oligomer antibody)‐reactive, water‐soluble oligomer that impairs memory in healthy wild‐type mice; and that there exist at least two Aß*56 variants—Aß(40)*56 and Aß(42)*56—that contain canonical Aß(1‐40) and Aß(1‐42), respectively, in AD mouse models. In addition, Aß*56 appears prior to the formation of amyloid plaques in Tg2576 mice, suggesting that the biogenesis of Aß*56 is independent on plaques. Here, we extend the characterization of this non‐plaque‐dependent oligomer to human AD cases and investigate the relationship between Aß*56 and cognitive dysfunction in AD dementia.MethodWe analyzed AD‐affected, inferior temporal gyrus (ITG) postmortem specimens from de‐identified elderly individuals enrolled in the Religious Orders Study/Memory and Aging Project. This cohort consisted of cognitively normal (CN) individuals, individuals with mild cognitive impairment (MCI), and individuals with AD dementia (N = 19‐23 per group) that were age‐matched, sex‐balanced, and contained comparable amyloid plaque loads in the ITG. Using immunoprecipitation/western blotting coupled with densitometry‐based semi‐quantitative analysis, we measured levels of Aß(40)*56 and Aß(42)*56. We then compared levels of Aß*56 variants between different clinical diagnoses and correlated them to cognitive assessments.ResultWe showed that both levels of Aß(40)*56 and Aß(42)*56 are elevated in individuals with AD dementia compared to CN individuals and individuals with MCI. In addition, we showed that levels of Aß*56 variants are correlated to mini‐mental state examination scores and severity of impairment in global cognitive function, episodic memory, and semantic memory, but not working memory.ConclusionOur data suggest an association between Aß*56 to AD dementia in elderly human brains that is independent of amyloid pathology. Such findings support the use of Aß*56 as a molecular marker for AD diagnosis.

Abstract BackgroundExtensive research on the cerebral cortex and the hippocampus has improved our understanding of mild cognitive impairment (MCI) and Alzheimer’s disease (AD). Historically, however, the cerebellum’s role in these stages of cognitive decline has been traditionally neglected due to its associations with motor function. Recent research has demonstrated cerebellar structural and connectivity differences in MCI and AD. Further investigating is however needed. In cognitively normal (CN) older adults cerebello‐basal ganglia (CB‐BG) functional connectivity is lower compared to younger adults. However, it remains an open question how these networks differ across different stages of cognitive decline and whether they are associated with cognitive and/or task performance.MethodHere, we investigated CB‐BG resting state networks associated with motor and cognitive cortical circuits across CN, MCI, and AD populations. The Alzheimer’s Disease Neuroimaging Initiative (ADNI‐3) was used to obtain the data of 478 participants who completed a motor and cognitive battery. All participants also underwent resting state functional magnetic resonance imaging (fcMRI). Analysis was completed using the CONN toolbox.ResultWe found a higher FC between Crus I with globus pallidus pars externa and lower FC between lobule VIIIb and dorsal caudal putamen in AD compared to CN individuals. However, no significant results were found when comparing AD to MCI or MCI to CN, suggesting that this stage of cognitive decline does not affect cerebello‐basal ganglia networksConclusionFurther understanding of network dysfunction that includes the cerebellum can help inform future clinical work and elucidate the mechanisms of MCI and AD.