Amyloid β‐specific T cell response is enhanced in individuals with mild cognitive impairment

Decision letter: Stage-dependent differential influence of metabolic and structural networks on memory across Alzheimer’s disease continuum

A challenge in developing informative neuroimaging biomarkers for early diagnosis of Alzheimer's disease is the need to identify biomarkers that are evident before the onset of clinical symptoms, and which have sufficient sensitivity and specificity on an individual patient basis. Recent literature suggests that spatial patterns of brain atrophy discriminate amongst Alzheimer's disease, mild cognitive impairment (MCI) and cognitively normal (CN) older adults with high accuracy on an individual basis, thereby offering promise that subtle brain changes can be detected during prodromal Alzheimer's disease stages. Here, we investigate whether these spatial patterns of brain atrophy can be detected in CN and MCI individuals and whether they are associated with cognitive decline. Images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were used to construct a pattern classifier that recognizes spatial patterns of brain atrophy which best distinguish Alzheimer's disease patients from CN on an individual person basis. This classifier was subsequently applied to longitudinal magnetic resonance imaging scans of CN and MCI participants in the Baltimore Longitudinal Study of Aging (BLSA) neuroimaging study. The degree to which Alzheimer's disease-like patterns were present in CN and MCI subjects was evaluated longitudinally in relation to cognitive performance. The oldest BLSA CN individuals showed progressively increasing Alzheimer's disease-like patterns of atrophy, and individuals with these patterns had reduced cognitive performance. MCI was associated with steeper longitudinal increases of Alzheimer's disease-like patterns of atrophy, which separated them from CN (receiver operating characteristic area under the curve equal to 0.89). Our results suggest that imaging-based spatial patterns of brain atrophy of Alzheimer's disease, evaluated with sophisticated pattern analysis and recognition methods, may be useful in discriminating among CN individuals who are likely to be stable versus those who will show cognitive decline. Future prospective studies will elucidate the temporal dynamics of spatial atrophy patterns and the emergence of clinical symptoms.

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 < 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 < 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.

Alzheimer's disease (AD) is the main cause of gradual cognitive impairment in elderly individuals. This highlights the need of obtaining biomarkers to identify features that are different among mild cognitive impairment (MCI), AD and cognitively normal (CN) individuals. Ultra-performance liquid chromatography (UPLC)/mass spectrometry (MS) was employed to find the metabolic changes in plasma samples obtained from AD, MCI and CN individuals. Based on principal component analysis (PCA), the metabolic differences among AD, MCI and CN subjects were identified. The PCA of UPLC/MS spectra indicated metabolic differences among AD, MCI and CN subjects. The peak intensities of progesterone, lysophos- phatidylcholines (LPCs), tryptophan, L-phenylalanine, dihydrosphingosine and phytosphingosine in the plasma of the MCI and AD subjects were significantly different from the CN subjects. Furthermore, the peak intensities of tryptophan, LPCs, dihydrosphingosine in the plasma of the AD subjects were significantly lower compared to the MCI subjects. Our data provide a link between metabolite imbalance and AD, and suggest that metabolomics can be used to reveal the early disease mechanisms occurred in the progression from CN to MCI and AD.

Alzheimer's disease (AD) is the leading cause of Dementia, and mild cognitive impairment (MCI) is often considered a precursor to the development of AD dementia and other types of Dementia. Biomarkers such as amyloid beta are specific and sensitive in identifying AD and can identify individuals who have biological evidence of the disease but have no symptoms, but clinicians and researchers may not easily use them on a large scale. Ocular biomarkers, such as those obtained through eye tracking (ET) technology, have the potential as a diagnostic tool due to their accuracy, affordability, and ease of use. In this study, we show that eye movement (EM) metrics from an interleaved Pro/Anti-saccade (PS/AS) ET task can differentiate between cognitively normal (CN) and MCI subjects and that the presence of Aβ brain deposits, a biomarker of AD, significantly affects performance on these tasks. Individuals with Aβ deposits (Aβ+) performed worse than those without (Aβ-). Our findings suggest that eye-tracking measurements may be a valuable tool for detecting amyloid brain pathology and monitoring changes in cognitive function in CN and MCI individuals over time.Clinical Relevance- The PS/AS paradigm, which measures saccadic eye movements, can accurately detect subtle cognitive impairments and changes in the brain associated with Alzheimer's disease in CN and MCI individuals. This makes it a valuable tool for identifying individuals at risk for cognitive decline and tracking changes in cognitive function over time.

BackgroundFunctional connectivity (FC) biomarkers play a crucial role in the early diagnosis and mechanistic study of Alzheimer’s disease (AD). However, the identification of effective FC biomarkers remains challenging. In this study, we introduce a novel approach, the spatiotemporal graph convolutional network (ST-GCN) combined with the gradient-based class activation mapping (Grad-CAM) model (STGC-GCAM), to effectively identify FC biomarkers for AD.MethodsThis multi-center cross-racial retrospective study involved 2,272 participants, including 1,105 cognitively normal (CN) subjects, 790 mild cognitive impairment (MCI) individuals, and 377 AD patients. All participants underwent functional magnetic resonance imaging (fMRI) and T1-weighted MRI scans. In this study, firstly, we optimized the STGC-GCAM model to enhance classification accuracy. Secondly, we identified novel AD-associated biomarkers using the optimized model. Thirdly, we validated the imaging biomarkers using Kaplan–Meier analysis. Lastly, we performed correlation analysis and causal mediation analysis to confirm the physiological significance of the identified biomarkers.ResultsThe STGC-GCAM model demonstrated great classification performance (The average area under the curve (AUC) values for different categories were: CN vs MCI = 0.98, CN vs AD = 0.95, MCI vs AD = 0.96, stable MCI vs progressive MCI = 0.79). Notably, the model identified specific brain regions, including the sensorimotor network (SMN), visual network (VN), and default mode network (DMN), as key differentiators between patients and CN individuals. These brain regions exhibited significant associations with the severity of cognitive impairment (p < 0.05). Moreover, the topological features of important brain regions demonstrated excellent predictive capability for the conversion from MCI to AD (Hazard ratio = 3.885, p < 0.001). Additionally, our findings revealed that the topological features of these brain regions mediated the impact of amyloid beta (Aβ) deposition (bootstrapped average causal mediation effect: β = -0.01 [-0.025, 0.00], p < 0.001) and brain glucose metabolism (bootstrapped average causal mediation effect: β = -0.02 [-0.04, -0.001], p < 0.001) on cognitive status.ConclusionsThis study presents the STGC-GCAM framework, which identifies FC biomarkers using a large multi-site fMRI dataset.

Early diagnosis of Alzheimer's disease (AD) based on neuroimaging and fluid biomarker data has attracted a lot of interest in medical image analysis. Most existing studies have been focusing on two-class classification problems, e.g., distinguishing AD patients from cognitive normal (CN) elderly or distinguishing mild cognitive impairment (MCI) individuals from CN elderly. However, to achieve the goal of early diagnosis of AD, we need to identify individuals with AD and MCI, especially MCI individuals who will convert to AD, in a single setting, which essentially is a multi-class classification problem. In this paper, we propose an ordinal ranking based classification method for distinguishing CN, MCI non-converter (MCI-NC), MCI converter (MCI-C), and AD at an individual level, taking into account the inherent ordinal severity of brain damage caused by normal aging, MCI, and AD, rather than formulating the classification as a multi-class classification problem. Experiment results indicate that the proposed method can achieve a better performance than traditional multi-class classification techniques based on multimodal neuroimaging and CSF biomarker data of the ADNI.

Aligning biomarker evidence with clinical presentation in early Alzheimer disease (AD) is essential for improving diagnosis, prognosis, and interventions. This study evaluates the relationship between cognitive impairment, future decline, and phosphorylated tau levels in plasma and CSF in predementia AD. This longitudinal observational study included predementia cases and controls from 2 independent cohorts: the Norwegian Dementia Disease Initiation (DDI) and Canadian Pre-Symptomatic Evaluation of Experimental or Novel Treatments for Alzheimer's Disease (PREVENT-AD). In DDI, cognitively normal (CN) and mild cognitive impairment (MCI) cases were classified using CSF Aβ42/40 ratio (A) and p-tau181 (T), whereas classification in PREVENT-AD (A) was based on amyloid PET scans. In DDI, we assessed CSF-plasma correlations for p-tau181, p-tau217, and p-tau231. Diagnostic accuracies were evaluated through receiver operating characteristic analyses. Linear mixed models evaluated p-tau associations with future memory decline. Between-group differences in plasma p-tau217 were assessed in both cohorts. In DDI (n = 431), participants were classified as CN A-/T- (n = 169), A+/T- (CN = 26, MCI = 24), A+/T+ (CN = 40, MCI = 105), and A-/T+ (CN = 34, MCI = 33), with a mean age of 64.1 years and 55.9% female. In PREVENT-AD (n = 190), participants were categorized as CN A- (n = 118), CN A+ (n = 49), and MCI A+ (n = 21), with a mean age of 67.8 years and 72.6% female. In DDI, plasma p-tau217 showed high accuracy in identifying A+ participants (areas under the curve [AUC]: 0.85) and a moderate correlation with CSF p-tau217 (rho = 0.65, p < 0.001). Diagnostic accuracy of plasma p-tau217 was greater in MCI A+ (AUC: 0.89) than in CN A+ (AUC: 0.79, p < 0.05) and in A+/T+ (AUC: 0.88) vs A+/T- (AUC: 0.78, p < 0.05). p-Tau181 and p-tau231 had weaker CSF-plasma correlations (rho = 0.47 and rho = 0.32, p < 0.001) and were less associated with cognitive status in A+ individuals. Higher plasma p-tau217 in A+ MCI vs A+ CN individuals (p < 0.001) was confirmed in PREVENT-AD. All CSF p-tau markers, but only plasma p-tau217, were associated with future memory decline (β = 0.05, p < 0.05). Our findings suggest that, unlike p-tau181 and p-tau231, plasma p-tau217 consistently aligns with cognitive status in A+ individuals and better reflects CSF biomarker abnormalities, reducing discrepancies between clinical and biochemical findings. Its association with baseline and future memory decline highlights its diagnostic and prognostic value, particularly when CSF analysis or PET is unavailable.

Tau pathology mediated the plasma biomarkers and cognitive function in patients with mild cognitive impairment

A neuroinflammatory reaction in Alzheimer disease (AD) brains involves reactive astrocytes that overexpress monoamine oxidase-B (MAO-B). 18F-(S)-(2-methylpyrid-5-yl)-6-[(3-fluoro-2-hydroxy)propoxy]quinoline (18F-SMBT-1) is a novel 18F PET tracer highly selective for MAO-B. We characterized the clinical performance of 18F-SMBT-1 PET across the AD continuum as a potential surrogate marker of reactive astrogliosis. Methods: We assessed 18F-SMBT-1 PET regional binding in 77 volunteers (76 ± 5.5 y old; 41 women, 36 men) across the AD continuum: 57 who were cognitively normal (CN) (44 amyloid-β [Aβ]-negative [Aβ-] and 13 Aβ-positive [Aβ+]), 12 who had mild cognitive impairment (9 Aβ- and 3 Aβ+), and 8 who had AD dementia (6 Aβ+ and 2 Aβ-). All participants also underwent Aβ and tau PET imaging, 3-T MRI, and neuropsychologic evaluation. Tau imaging results were expressed in SUV ratios using the cerebellar cortex as a reference region, whereas Aβ burden was expressed in centiloids. 18F-SMBT-1 outcomes were expressed as SUV ratio using the subcortical white matter as a reference region. Results: 18F-SMBT-1 yielded high-contrast images at steady state (60-80 min after injection). When compared with the Aβ- CN group, there were no significant differences in 18F-SMBT-1 binding in the group with Aβ- mild cognitive impairment. Conversely, 18F-SMBT-1 binding was significantly higher in several cortical regions in the Aβ+ AD group but also was significantly lower in the mesial temporal lobe and basal ganglia. Most importantly, 18F-SMBT-1 binding was significantly higher in the same regions in the Aβ+ CN group as in the Aβ- CN group. When all clinical groups were considered together, 18F-SMBT-1 correlated strongly with Aβ burden and much less with tau burden. Although in most cortical regions 18F-SMBT-1 did not correlate with brain volumetrics, regions known for high MAO-B concentrations presented a direct association with hippocampal and gray matter volumes, whereas the occipital lobe was directly associated with white matter hyperintensity. 18F-SMBT-1 binding was inversely correlated with Mini Mental State Examination and the Australian Imaging Biomarkers and Lifestyle's Preclinical Alzheimer Cognitive Composite in some neocortical regions such as the frontal cortex, lateral temporal lobe, and supramarginal gyrus. Conclusion: Cross-sectional human PET studies with 18F-SMBT-1 showed that Aβ+ AD patients, but most importantly, Aβ+ CN individuals, had significantly higher regional 18F-SMBT-1 binding than Aβ- CN individuals. Moreover, in several regions in the brain, 18F-SMBT-1 retention was highly associated with Aβ load. These findings suggest that increased 18F-SMBT-1 binding is detectable at the preclinical stages of Aβ accumulation, providing strong support for its use as a surrogate marker of astrogliosis in the AD continuum.

Quantitative perfusion assessment with arterial spin labeling magnetic resonance imaging for predicting cognitive decline in Alzheimer's disease: A systematic review and meta-analysis.

BackgroundEarly prevention of Alzheimer’s disease (AD) is a feasible way to delay AD onset and progression. Information on AD prediction at the individual patient level will be useful in AD prevention. In this study, we aim to develop risk models for predicting AD onset at individual level using optimal set of predictors from multiple features.MethodsA total of 487 cognitively normal (CN) individuals and 796 mild cognitive impairment (MCI) patients were included from Alzheimer's Disease Neuroimaging Initiative. All the participants were assessed for clinical, cognitive, magnetic resonance imaging and cerebrospinal fluid (CSF) markers and followed for mean periods of 5.6 years for CN individuals and 4.6 years for MCI patients to ascertain progression from CN to incident prodromal stage of AD or from MCI to AD dementia. Least Absolute Shrinkage and Selection Operator Cox regression was applied for predictors selection and model construction.ResultsDuring the follow-up periods, 139 CN participants had progressed to prodromal AD (CDR ≥ 0.5) and 321 MCI patients had progressed to AD dementia. In the prediction of individual risk of incident prodromal stage of AD in CN individuals, the AUC of the final CN model was 0.81 within 5 years. The final MCI model predicted individual risk of AD dementia in MCI patients with an AUC of 0.92 within 5 years. The models were also associated with longitudinal change of Mini-Mental State Examination (p < 0.001 for CN and MCI models). An Alzheimer’s continuum model was developed which could predict the Alzheimer’s continuum for individuals with normal AD biomarkers within 3 years with high accuracy (AUC = 0.91).ConclusionsThe risk models were able to provide personalized risk for AD onset at each year after evaluation. The models may be useful for better prevention of AD.

Criteria for mild cognitive impairment (MCI) consider impairment in instrumental activities of daily living (IADL) as exclusionary, but cross-sectional studies suggest that some high-level functional deficits are present in MCI. This longitudinal study examines informant-rated IADL in MCI, compared with cognitively normal (CN) older individuals, and explores whether functional abilities, particularly those with high cognitive demand, are predictors of MCI and dementia over a 2-year period in individuals who were CN at baseline. A sample of 602 non-demented community dwelling individuals (375 CN and 227 with MCI) aged 70-90 years underwent baseline and 24-month assessments that included cognitive and medical assessments and an interview with a knowledgeable informant on functional abilities with the Bayer Activities of Daily Living Scale. Significantly more deficits in informant-reported IADL with high cognitive demand were present in MCI compared with CN individuals at baseline and 2-year follow-up. Functional ability in CN individuals at baseline, particularly in activities with high cognitive demand, predicted MCI and dementia at follow-up. Difficulties with highly cognitively demanding activities specifically predicted amnestic MCI but not non-amnestic MCI whereas those with low cognitive demand did not predict MCI or dementia. Age, depressive symptoms, cardiovascular risk factors and the sex of the informant did not contribute to the prediction. IADL are affected in individuals with MCI, and IADL with a high cognitive demand show impairment predating the diagnosis of MCI. Subtle cognitive impairment is therefore likely to be a major hidden burden in society.

BackgroundAltered innate immunity has been long associated with late‐onset Alzheimer’s disease (AD) and its associated blood‐based biomarkers are important for AD diagnosis and prognosis.MethodWe collected 38 participants from the AIBL study, including 22 cognitive normal (CN) individuals with negative amyloid burden (Centiloid [CL] &lt; 15), 5 CN individuals with positive amyloid burden (CL &gt; 15), and 11 mild cognitive impairment (MCI) and AD cases. A total of 17 leukocyte surface antigens were examined by flow cytometry immunophenotyping and compared between healthy controls (CN Ab ‐ve), pre‐clinical patients (CN Ab +ve), and clinical cases (MCI &amp; AD), including CD36, MerTK, Clec7a, RAGE, Tyro3, CR1 (CD35), CX3CR1, CCR2, Axl, LILRB2, LILRB3, LILRB4, PILRA, and P2×7.ResultWe identified leukocyte surface markers differentially expressed between HC, pre‐clinical patients, and clinical cases. Mean fluorescence intensities of CD85d and CD85k were significantly reduced in pre‐clinical patients and cases compared with HC, implicating CD85d and CD85k downregulation in AD pathogenesis. Significant upregulation of RAGE, Tyro3, CCR2, CD85a, and PILRA was also noted in cases compared with HC. Leukocyte surface expressions of RAGE, CCR2, PILRA, and CD85k were significantly associated with the Preclinical Alzheimer’s Cognitive Composite (PACC), which is one of the most accurate estimates of cognition in AD diagnosis.ConclusionOur preliminary investigations into these leukocyte markers demonstrated their dysregulations in pre‐clinical stage of AD, implicating early deficits in innate immunity, as supported by genomic studies of AD. This project will continue collecting more AIBL participants up to 200 to evaluate leukocyte surface markers more comprehensively, which would benefit AD screening and diagnosis.

BackgroundAutopsy‐based neuropathological studies have suggested the tau pathology observed in Alzheimer’s disease (AD) originates in brainstem nuclei, but no studies to date have quantified brainstem volumes in clinical populations with biomarker‐confirmed mild cognitive impairment (MCI) or dementia due to AD or determined the value of brainstem volumetrics in predicting dementia risk. The present study examined whether MRI‐based brainstem sub‐structural volumes differ between cognitively normal (CN) older adults and those with MCI or dementia due to AD, and whether early preclinical brainstem volumes predict future progression to dementia.MethodAlzheimer’s Disease Neuroimaging Initiative participants diagnosed as CN (n=822), MCI (n=543), or AD (n=300) underwent baseline T1‐weighted structural MRI scanning with variable clinical follow‐up (6‐120 months). A subset of participants completed baseline fasting lumbar puncture to quantify levels of amyloid‐beta 1‐42 and phospho‐tau. We employed region‐of‐interest and voxel‐based morphometric methods to assess differences between clinically‐diagnosed and biomarker‐defined groups in volumes of the whole brainstem, midbrain, pons, and locus coeruleus. Longitudinal Cox regression analyses determined value of baseline brainstem sub‐structure volumes in predicting progression to a future clinical diagnosis of AD dementia.ResultCompared with CN individuals, we observed smaller regional volumes in whole brainstem, midbrain, pons, and locus coeruleus regions in patients clinically diagnosed with MCI and AD, and specifically within the midbrain in biomarker‐confirmed cases. Brainstem‐masked voxel‐wise comparisons confirmed volume reduction of a dorsal rostral brainstem region corresponding to the locus coeruleus in MCI and AD relative to CN, as well as in CN who progressed to AD dementia versus those that did not progress to AD dementia. In a preclinical risk analysis, cognitively normal individuals who later progressed to AD dementia exhibited smaller baseline midbrain volumes than individuals who did not develop dementia, and longitudinal analyses indicated smaller midbrain and locus coeruleus volumes conferred greater risk of progression.ConclusionOur study indicated that brainstem MRI changes are detectible in preclinical and prodromal populations. Our findings are consistent with the neuropathological observation that AD‐related pathology occurs early in brainstem nuclei and further suggest the clinical relevance of brainstem sub‐structural volumes for preclinical and prodromal AD populations.