Alzheimer's Disease Biomarkers Research Articles

CSF proteomic profiles of neurodegeneration biomarkers in Alzheimer's disease.

We aimed to unravel the underlying pathophysiology of the neurodegeneration (N) markers neurogranin (Ng), neurofilament light (NfL), and hippocampal volume (HCV), in Alzheimer's disease (AD) using cerebrospinal fluid (CSF) proteomics. Individuals without dementia were classified as A+ (CSF amyloid beta [Aβ]42), T+ (CSF phosphorylated tau181), and N+ or N- based on Ng, NfL, or HCV separately. CSF proteomics were generated and compared between groups using analysis of covariance. Only a few individuals were A+T+Ng-. A+T+Ng+ and A+T+NfL+ showed different proteomic profiles compared to A+T+Ng- and A+T+NfL-, respectively. Both Ng+ and NfL+ were associated with neuroplasticity, though in opposite directions. Compared to A+T+HCV-, A+T+HCV+ showed few proteomic changes, associated with oxidative stress. Different N markers are associated with distinct neurodegenerative processes and should not be equated. N markers may differentially complement disease staging beyond amyloid and tau. Our findings suggest that Ng may not be an optimal N marker, given its low incongruency with tau pathophysiology. In Alzheimer's disease, neurogranin (Ng)+, neurofilament light (NfL)+, and hippocampal volume (HCV)+ showed differential protein expression in cerebrospinal fluid. Ng+ and NfL+ were associated with neuroplasticity, although in opposite directions. HCV+ showed few proteomic changes, related to oxidative stress. Neurodegeneration (N) markers may differentially refine disease staging beyond amyloid and tau. Ng might not be an optimal N marker, as it relates more closely to tau.

Proteome-wide analysis identifies plasma immune regulators of amyloid-beta progression

While immune function is known to play a mechanistic role in Alzheimer’s disease (AD), whether immune proteins in peripheral circulation influence the rate of amyloid-β (Aβ) progression – a central feature of AD – remains unknown. In the Baltimore Longitudinal Study of Aging, we quantified 942 immunological proteins in plasma and identified 32 (including CAT [catalase], CD36 [CD36 antigen], and KRT19 [keratin 19]) associated with rates of cortical Aβ accumulation measured with positron emission tomography (PET). Longitudinal changes in a subset of candidate proteins also predicted Aβ progression, and the mid- to late-life (20-year) trajectory of one protein, CAT, was associated with late-life Aβ-positive status in the Atherosclerosis Risk in Communities (ARIC) study. Genetic variation that influenced plasma levels of CAT, CD36 and KRT19 predicted rates of Aβ accumulation, including causal relationships with Aβ PET levels identified with two-sample Mendelian randomization. In addition to associations with tau PET and plasma AD biomarker changes, as well as expression patterns in human microglia subtypes and neurovascular cells in AD brain tissue, we showed that 31 % of candidate proteins were related to mid-life (20-year) or late-life (8-year) dementia risk in ARIC. Our findings reveal plasma proteins associated with longitudinal Aβ accumulation, and identify specific peripheral immune mediators that may contribute to the progression of AD pathophysiology.

Longitudinal plasma phosphorylated-tau217 and other related biomarkers in a non-demented Alzheimer's risk-enhanced sample.

Understanding longitudinal change in key plasma biomarkers will aid in detecting presymptomatic Alzheimer's disease (AD). Serial plasma samples from 424 Wisconsin Registry for Alzheimer's Prevention participants were analyzed for phosphorylated-tau217 (p-tau217; ALZpath) and other AD biomarkers, to study longitudinal trajectories in relation to disease, health factors, and cognitive decline. Of the participants, 18.6% with known amyloid status were amyloid positive (A+); 97.2% were cognitively unimpaired (CU). In the CU, amyloid-negative (A-) subset, plasma p-tau217 levels increased modestly with age but were unaffected by body mass index and kidney function. In the whole sample, average p-tau217 change rates were higher in those who were A+ (e.g., simple slopes(se) for A+ and A- at age 60 were 0.232(0.028) and 0.038(0.013))). High baseline p-tau217 levels predicted faster preclinical cognitive decline. p-tau217 stands out among markers for its strong association with disease and cognitive decline, indicating its potential for early AD detection and monitoring progression. Phosphorylated-tau217 (p-tau217) trajectories were significantly different in people who were known to be amyloid positive. Subtle age-related trajectories were seen for all the plasma markers in amyloid-negative cognitively unimpaired. Kidney function and body mass index were not associated with plasma p-tau217 trajectories. Higher plasma p-tau217 was associated with faster preclinical cognitive decline.

Novel measures of cognition and function for the AD spectrum in the Novel Measures for Alzheimer's Disease Prevention Trials (NoMAD) project: Psychometric properties, convergent validation, and contrasts with established measures.

This study derived composite scores for two novel cognitive measures, the No Practice Effect (NPE) battery and the Miami Computerized Functional Skills Assessment and Training system for use in early-stage Alzheimer's disease (AD) clinical trials. Their psychometric properties and associations with AD risk markers were compared to those of well-established measures. For 291 older adults with healthy cognition or early mild cognitive impairment, Exploratory factor analyses were used to identify the factor structure of the NPE. Factor and total scores were examined for their psychometric properties and associations with AD risk biomarkers. Composite scores from the novel cognitive and functional measures demonstrated better psychometric properties (distribution and test-retest reliability) and stronger associations with AD-related demographic, genetic, and brain risk markers than well-established measures, DISCUSSION: These novel measures have potential for use as primary cognitive and functional outcomes in early-stage AD clinical trials. Well-established cognitive tests may not accurately detect subtle cognitive changes. No Practice Effect (NPE) and Computerized Functional Skills Assessment and Training are novel measures designed to have improved psychometric properties. NPE had Executive Function, Cognitive Control/Speed, and Episodic Memory domains. Novel measures had better psychometric properties compared to established measures. Significant associations with Alzheimer's disease biomarkers were found with novel measures.

Time-encoded ASL reveals lower cerebral blood flow in the early AD continuum.

Cerebral blood flow (CBF) is reduced in cognitively impaired (CI) Alzheimer's disease (AD) patients. We checked the sensitivity of time-encoded arterial spin labeling (te-ASL) in measuring CBF alterations in individuals with positive AD biomarkers and associations with relevant biomarkers in cognitively unimpaired (CU) individuals. We compared te-ASL with single-postlabel delay (PLD) ASL in measuring CBF in 59 adults across the AD continuum, classified as CU amyloid beta (Aβ) negative (-), CU Aβ positive (+), and CI Aβ+. We sought associations of CBF with biomarkers of AD, cerebrovascular disease, synaptic dysfunction, neurodegeneration, and cognition in CU participants. te-ASL was more sensitive at detecting CBF reduction in the CU Aβ+ and CI Aβ+ groups. In CU participants, lower CBF was associated with altered biomarkers of Aβ, tau, synaptic dysfunction, and neurodegeneration. CBF reduction occurs early in the AD continuum. te-ASL is more sensitive than single-PLD ASL at detecting CBF changes in AD. Lower CBF can be detected in CU subjects in the early AD continuum. te-ASL is more sensitive than single-PLD ASL at detecting CBF alterations in AD. CBF is linked to biomarkers of AD, synaptic dysfunction, and neurodegeneration.

Neuroimaging biomarkers and CSF sTREM2 levels in Alzheimer’s disease: a longitudinal study

Understanding the exact pathophysiological mechanisms underlying the involvement of triggering receptor expressed on myeloid cells 2 (TREM2) related microglia activation is crucial for the development of clinical trials targeting microglia activation at different stages of Alzheimer’s disease (AD). Given the contradictory findings in the literature, it is imperative to investigate the longitudinal alterations in cerebrospinal fluid (CSF) soluble TREM2 (sTREM2) levels as a marker for microglia activation, and its potential association with AD biomarkers, in order to address the current knowledge gap. In this study, we aimed to assess the longitudinal changes in CSF sTREM2 levels within the framework of the A/T/N classification system for AD biomarkers and to explore potential associations with AD pathological features, including the presence of amyloid-beta (Aβ) plaques and tau aggregates. The baseline and longitudinal (any available follow-up visit) CSF sTREM2 levels and processed tau-PET and Aβ-PET data of 1001 subjects were recruited from the ADNI database. The participants were classified into four groups based on the A/T/N framework: A+ /TN+ , A+ /TN− , A− /TN+ , and A− /TN− . Linear regression analyses were conducted to assess the relationship between CSF sTREM2 with cognitive performance, tau and Aβ-PET adjusting for age, gender, education, and APOE ε4 status. Based on our analysis there was a significant difference in baseline and rate of change of CSF sTREM2 between ATN groups. While there was no association between baseline CSF sTREM2 and cognitive performance (ADNI-mem), we found that the rate of change of CSF sTREM2 is significantly associated with cognitive performance in the entire cohort but not the ATN groups. We found that the baseline CSF sTREM2 is significantly associated with baseline tau-PET and Aβ-PET rate of change only in the A+ /TN+ group. A significant association was found between the rate of change of CSF sTREM2 and the tau- and Aβ-PET rate of change only in the A+ /TN− group. Our study suggests that the TREM2-related microglia activation and their relations with AD markers and cognitive performance vary the in presence or absence of Aβ and tau pathology. Furthermore, our findings revealed that a faster increase in the level of CSF sTREM2 might attenuate future Aβ plaque formation and tau aggregate accumulation only in the presence of Aβ pathology.

The role of the Alzheimer's Disease Neuroimaging Initiative in establishing the Dominantly Inherited Alzheimer Network.

The Dominantly Inherited Alzheimer Network (DIAN) initially was funded by the National Institute on Aging (NIA) in 2008 and thus was able to adopt and incorporate the protocols developed by the Alzheimer's Disease Neuroimaging Initiative (ADNI) that had been established by the NIA in 2004. The use of ADNI protocols for DIAN neuroimaging studies and assays of biological fluids for Alzheimer disease (AD) biomarkers permitted examination of the hypothesis that autosomal dominant AD (ADAD), studied by DIAN, and "sporadic" late-onset AD (LOAD), studied by ADNI, shared the same pathobiological construct. In a collaborative effort, the longitudinal DIAN and ADNI databases were compared and the findings supported the conclusion that ADAD and LOAD share a similar pathophysiology. The importance of the DIAN study thus is amplified by its relevance to LOAD, as characterized by the "parent" ADNI program.

Nanoscale flow cytometry-based quantification of blood-based extracellular vesicle biomarkers distinguishes MCI and Alzheimer's disease.

Accurate testing for Alzheimer's disease (AD) represents a crucial step for therapeutic advancement. Currently, tests are expensive and require invasive sampling or radiation exposure. We developed a nanoscale flow cytometry (nFC)-based assay of extracellular vesicles (EVs) to screen biomarkers in plasma from mild cognitive impairment (MCI), AD, or controls. Circulating amyloid beta (Aβ), tau, phosphorylated tau (p-tau)181, p-tau231, p-tau217, p-tauS235, ubiquitin, and lysosomal-associated membrane protein 1-positive EVs distinguished AD samples. p-tau181, p-tau217, p-tauS235, and ubiquitin-positive EVs distinguished MCI samples. The most sensitive marker for AD distinction was p-tau231, with an area under the receiver operating characteristic curve (AUC) of 0.96 (sensitivity 0.95/specificity 1.0) improving to an AUC of 0.989 when combined with p-tauS235. This nFC-based assay accurately distinguishes MCI and AD plasma without EV isolation, offering a rapid approach requiring minute sample volumes. Incorporating nFC-based measurements in larger populations and comparison to "gold standard" biomarkers is an exciting next step for developing AD diagnostic tools. Extracellular vesicles represent promising biomarkers of Alzheimer's disease (AD) that can be measured in the peripheral circulation. This study demonstrates the utility of nanoscale flow cytometry for the measurement of circulating extracellular vesicles (EVs) in AD blood samples. Multiple markers including amyloid beta, tau, phosphorylated tau (p-tau)181, p-tau231, p-tau217, and p-tauS235 accurately distinguished AD samples from healthy controls. Future studies should expand blood and cerebrospinal fluid-based EV biomarker development using nanoflow cytometry approaches.

Diffusion model enables quantitative CBF analysis of Alzheimer's Disease.

Cerebral blood flow (CBF) measured by arterial spin labeling (ASL) is a promising biomarker for Alzheimer's Disease (AD). ASL data from multiple vendors were included in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. However, the M0 images were missing in Siemens ASL data, prohibiting CBF quantification. Here, we utilized a generative diffusion model to impute the missing M0 and validated generated CBF data with acquired data from GE. A conditional latent diffusion model was trained to generate the M0 image and validate it on an in-house dataset (N=55) based on image similarity metrics, accuracy of CBF quantification, and consistency with the physical model. This model was then applied to the ADNI dataset (Siemens: N=211) to impute the missing M0 for CBF calculation. We further compared the imputed data (Siemens) and acquired data (GE) regarding regional CBF differences by AD stages, their classification accuracy for AD prediction, and CBF trajectory slopes estimated by a mixed effect model. The trained diffusion model generated the M0 image with high fidelity (Structural similarity index, SSIM=0.924±0.019; peak signal-to-noise ratio, PSNR=33.348±1.831) and caused minimal bias in CBF values (mean difference in whole brain is 1.07±2.12ml/100g/min). Both generated and acquired CBF data showed similar differentiation patterns by AD stages, similar classification performance, and decreasing slopes with AD progression in specific AD-related regions. Generated CBF data also improved accuracy in classifying AD stages compared to qualitative perfusion data. This study shows the potential of diffusion models for imputing missing modalities for large-scale studies of CBF variation with AD.

Data-driven retrieval of population-level EEG features and their role in neurodegenerative diseases.

Electrophysiologic disturbances due to neurodegenerative disorders such as Alzheimer's disease and Lewy Body disease are detectable by scalp EEG and can serve as a functional measure of disease severity. Traditional quantitative methods of EEG analysis often require an a-priori selection of clinically meaningful EEG features and are susceptible to bias, limiting the clinical utility of routine EEGs in the diagnosis and management of neurodegenerative disorders. We present a data-driven tensor decomposition approach to extract the top 6 spectral and spatial features representing commonly known sources of EEG activity during eyes-closed wakefulness. As part of their neurologic evaluation at Mayo Clinic, 11 001 patients underwent 12 176 routine, standard 10-20 scalp EEG studies. From these raw EEGs, we developed an algorithm based on posterior alpha activity and eye movement to automatically select awake-eyes-closed epochs and estimated average spectral power density (SPD) between 1 and 45 Hz for each channel. We then created a three-dimensional (3D) tensor (record × channel × frequency) and applied a canonical polyadic decomposition to extract the top six factors. We further identified an independent cohort of patients meeting consensus criteria for mild cognitive impairment (30) or dementia (39) due to Alzheimer's disease and dementia with Lewy Bodies (31) and similarly aged cognitively normal controls (36). We evaluated the ability of the six factors in differentiating these subgroups using a Naïve Bayes classification approach and assessed for linear associations between factor loadings and Kokmen short test of mental status scores, fluorodeoxyglucose (FDG) PET uptake ratios and CSF Alzheimer's Disease biomarker measures. Factors represented biologically meaningful brain activities including posterior alpha rhythm, anterior delta/theta rhythms and centroparietal beta, which correlated with patient age and EEG dysrhythmia grade. These factors were also able to distinguish patients from controls with a moderate to high degree of accuracy (Area Under the Curve (AUC) 0.59-0.91) and Alzheimer's disease dementia from dementia with Lewy Bodies (AUC 0.61). Furthermore, relevant EEG features correlated with cognitive test performance, PET metabolism and CSF AB42 measures in the Alzheimer's subgroup. This study demonstrates that data-driven approaches can extract biologically meaningful features from population-level clinical EEGs without artefact rejection or a-priori selection of channels or frequency bands. With continued development, such data-driven methods may improve the clinical utility of EEG in memory care by assisting in early identification of mild cognitive impairment and differentiating between different neurodegenerative causes of cognitive impairment.

Baseline levels and longitudinal changes in plasma Aβ42/40 among Black and white individuals

Blood-based biomarkers of Alzheimer disease (AD) may facilitate testing of historically under-represented groups. The Study of Race to Understand Alzheimer Biomarkers (SORTOUT-AB) is a multi-center longitudinal study to compare AD biomarkers in participants who identify their race as either Black or white. Plasma samples from 324 Black and 1,547 white participants underwent analysis with C2N Diagnostics’ PrecivityAD test for Aβ42 and Aβ40. Compared to white individuals, Black individuals had higher average plasma Aβ42/40 levels at baseline, consistent with a lower average level of amyloid pathology. Interestingly, this difference resulted from lower average levels of plasma Aβ40 in Black participants. Despite the differences, Black and white individuals had similar longitudinal rates of change in Aβ42/40, consistent with a similar rate of amyloid accumulation. Our results agree with multiple recent studies demonstrating a lower prevalence of amyloid pathology in Black individuals, and additionally suggest that amyloid accumulates consistently across both groups.

Changes in Alzheimer's disease blood biomarkers in kidney failure before and after kidney transplant.

Alzheimer's disease (AD) blood biomarkers show promise for clinical diagnosis but their reliability in chronic kidney disease (CKD) is debated. This study investigates the impact of kidney transplant (KT) on AD biomarkers in CKD. We assessed AD biomarkers in 46 CKD patients pre-KT, at 12weeks and 12 months post-KT, with baseline measures from 13 non-CKD controls. Using linear mixed models, we examined associations with participant groups, estimated glomerular filtration rate (eGFR) and cognition. CKD patients showed elevated levels of neurofilament light (117±72vs. 11±5 pg/mL), phosphorylated tau 181 (75±42vs. 13±8 pg/mL), glial fibrillary acidic protein (193±127vs. 94±39 pg/mL), amyloid β 42 (17±5vs. 5±1 pg/mL), and amyloid β 40 (259±96vs. 72±17 pg/mL) compared to controls. Post-KT, biomarker levels approached normal with improved eGFR, paralleled by enhanced cognitive function. AD blood biomarker elevations in CKD are reversible with improved kidney function through KT. AD biomarker levels are extremely high in severe CKD.AD biomarker levels are higher in patients with kidney failure on dialysis when compared to CKD patients not on dialysis.These elevations in AD biomarker levels in kidney failure are reversable and decrease dramatically after kidney transplantation.The change in biomarker levels after transplantation align with changes in kidney function.The change in biomarker levels after transplantation align with changes in cognitive function.

Polygenic hazard score predicts synaptic and axonal degeneration and cognitive decline in Alzheimer's disease continuum

BackgroundGrowth associated protein-43 (GAP-43) and neurofilaments light (NFL) are biomarkers of synaptic and axonal injury, and are associated with cognitive decline in Alzheimer's disease (AD) contiuum. We investigated whether Polygenic Hazard Score (PHS) is associated with specific biomarkers and cognitive measures, and if it can predict the relationship between GAP-43, NFL, and cognitive decline in AD. MethodWe enrolled 646 subjects: 93 with AD, 350 with mild cognitive impairment (MCI), and 203 cognitively normal controls. Variables included GAP-43, plasma NFL, and PHS. A PHS of 0.21 or higher was considered high risk while a PHS below this threshold was considered low risk. A subsample of 190 patients with MCI with four years of follow-up cognitive assessments were selected for longitudinal analysis . We assessed the association of the PHS with AD biomarkers and cognitive measures, as well as the predictive power of PHS on cognitive decline and the conversion of MCI to AD. ResultsPHS showed high diagnostic accuracy in distinguishing AD, MCI, and controls. At each follow-up point, high risk MCI patients showed higher level of cognitive impairment compared to the low risk group. GAP-43 correlated with all follow-up cognitive tests in high risk MCI patients which was not detected in low risk MCI patients. Moreover, high risk MCI patients progressed to dementia more rapidly compared to low risk patients. ConclusionPHS can predict cognitive decline and impacts the relationship between neurodegenerative biomarkers and cognitive impairment in AD contiuum. Categorizing patients based on PHS can improve the prediction of cognitive outcomes and disease progression.

Advances in Blood Biomarkers for Alzheimer's Disease: Ultra-Sensitive Detection Technologies and Impact on Clinical Diagnosis.

Alzheimer's disease has escalated into a critical public health concern, marked by its neurodegenerative nature that progressively diminishes cognitive abilities. Recognized as a continuously advancing and presently incurable condition, AD underscores the necessity for early-stage diagnosis and interventions aimed at delaying the decline in mental function. Despite the proven efficacy of cerebrospinal fluid and positron emission tomography in diagnosing AD, their broader utility is constrained by significant costs and the invasive nature of these procedures. Consequently, the innovation of blood biomarkers such as Amyloid-beta, phosphorylated-tau, total-tau et al, distinguished by their high sensitivity, minimal invasiveness, accessibility, and cost-efficiency, emerges as a promising avenue for AD diagnosis. The advent of ultra-sensitive detection methodologies, including single-molecule enzyme-linked immunosorbent assay and immunoprecipitation-mass spectrometry, has revolutionized the detection of AD plasma biomarkers, supplanting previous low-sensitivity techniques. This rapid advancement in detection technology facilitates the more accurate quantification of pathological brain proteins and AD-associated biomarkers in the bloodstream. This manuscript meticulously reviews the landscape of current research on immunological markers for AD, anchored in the National Institute on Aging-Alzheimer's Association AT(N) research framework. It highlights a selection of forefront ultra-sensitive detection technologies now integral to assessing AD blood immunological markers. Additionally, this review examines the crucial pre-analytical processing steps for AD blood samples that significantly impact research outcomes and addresses the practical challenges faced during clinical testing. These discussions are crucial for enhancing our comprehension and refining the diagnostic precision of AD using blood-based biomarkers. The review aims to shed light on potential avenues for innovation and improvement in the techniques employed for detecting and investigating AD, thereby contributing to the broader field of neurodegenerative disease research.

Novel diagnostic and prognostic approach for rapidly progressive dementias: Indicators based on amyloid/tau/neurodegeneration (ATN) framework.

Apply established cerebrospinal fluid (CSF) and serum biomarkers and novel combined indicators based on the amyloid/tau/neurodegeneration (ATN) framework to improve diagnostic and prognostic power in patients with rapidly progressive dementias (RPDs). CSF and serum biomarkers of Alzheimer's disease (AD) common neuropathology including Aβ42, Aβ40, p-Tau, and t-Tau were measured in cognitively normal (CN) controls (n = 33) and three RPD groups with rapidly progressive AD (rpAD, n = 23), autoimmune encephalitis (AE, n = 25), and Creutzfeldt-Jakob disease (CJD, n = 28). Logistic regression and multiple linear regression were used for producing combined indicators and prognostic assessment, respectively, including A&T, A&N, T&N, A&T&N, etc. RESULTS: Combined diagnostic indicator with A&T&N had the potential for differentiating AE from other types of RPDs, identifying 62.51% and 75% of AE subjects based on CSF and serum samples, respectively, compared to 39.13% and 37.5% when using autoantibodies. CSF t-Tau was associated with survival in the CJD group (adjusted R-Square = 0.16, p = 0.02), and its prognosis value improved when using combined predictors based on the ATN framework (adjusted R-Square = 0.273, p = 0.014). Combined indicators based on the ATN framework provide a novel perspective for establishing biomarkers for early recognition of RPDs due to treatment-responsive causes.

Associations between IL-1β, IL-6, and TNFα polymorphisms and longitudinal trajectories of cognitive function in non-demented older adults

Inflammation is implicated in Alzheimer's disease (AD), and specific single nucleotide polymorphisms (SNPs) in inflammatory cytokine genes are associated with increased AD risk. Whether the same polymorphisms also predict domain-specific cognitive change in cognitively healthy older adults is unclear. Specific SNPs in three cytokine genes, IL-1β (rs16944), IL-6 (rs1800795), and TNFα (rs1800629) were assessed for association with longitudinal trajectories spanning up to 16 years of global cognitive function, episodic memory, attention and working memory, and executive function in a sample of 324 non-demented older adults. Only rs1800629 (TNFα) was associated with significant change in global cognitive function over time [γ = 5.22; 95% CI: 0.61, 9.83; p = 0.027]. Despite an association with AD risk, rs16944 and rs1800795 may not predict cognitive decline in cognitively healthy older adults. The presence of an A at rs1800629 (TNFα) may have broad, protective effects on cognitive function, over time. More validation studies are needed to determine whether specific cytokine SNPs are associated with respective serum levels to further understanding of AD biomarkers that may also serve as markers of cognitive decline.

Assessing amyloid PET positivity and cognitive function in Down syndrome to guide clinical trials targeting amyloid.

Trisomy 21, or Down syndrome (DS), predisposes individuals to early-onset Alzheimer's disease (AD). While monoclonal antibodies (mAbs) targeting amyloid are approved for older AD patients, their efficacy in DS remains unexplored. This study examines amyloid positron emission tomography (PET) positivity (A+), memory function, and clinical status across ages in DS to guide mAb trial designs. Cross-sectional data from the Alzheimer Biomarker Consortium-Down Syndrome (ABC-DS) was analyzed. PET amyloid beta in Centiloids classified amyloid status using various cutoffs. Episodic memory was assessed using the modified Cued Recall Test, and clinical status was determined through consensus processes. Four hundred nine DS adults (mean age=44.83 years) were evaluated. A+ rates increased with age, with mean amyloid load rising significantly. Memory decline and cognitive impairment are also correlated with age. These findings emphasize the necessity of tailoring mAb trials for DS, considering age-related AD characteristics. There is rapid increase in prevalence of amyloid beta (Aβ) positron emission tomography (PET) positivity in Down syndrome (DS) after the age of 40 years. Aβ PET positivity thresholds have significant impact on prevalence rates in DS. There is a significant lag between Aβ PET positivity and clinical symptom onset in DS.

Integration of single-cell sequencing with machine learning and Mendelian randomization analysis identifies the NAP1L1 gene as a predictive biomarker for Alzheimer's disease.

The most effective approach to managing Alzheimer's disease (AD) lies in identifying reliable biomarkers for AD to forecast the disease in advance, followed by timely early intervention for patients. Transcriptomic data on peripheral blood mononuclear cells (PBMCs) from patients with AD and the control group were collected, and preliminary data processing was completed using standardized analytical methods. PBMCs were initially segmented into distinct subpopulations, and the divisions were progressively refined until the most significantly altered cell populations were identified. A combination of high-dimensional weighted gene co-expression analysis (hdWGCNA), cellular communication, pseudotime analysis, and single-cell regulatory network inference and clustering (SCENIC) analysis was used to conduct single-cell transcriptomics analysis and identify key gene modules from them. Genes were screened using machine learning (ML) in the key gene modules, and internal and external dataset validations were performed using multiple ML methods to test predictive performance. Finally, bidirectional Mendelian randomization (MR) analysis, regional linkage analysis, and the Steiger test were employed to analyze the key gene. A significant decrease in non-classical monocytes was detected in PMBC of AD patients. Subsequent analyses revealed the inherent connection of non-classical monocytes to AD, and the NAP1L1 gene identified within its gene module appeared to exhibit some association with AD as well. The NAP1L1 gene is a potential predictive biomarker for AD.

Predicting Alzheimer's disease CSF core biomarkers: a multimodal Machine Learning approach.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder. Current core cerebrospinal fluid (CSF) AD biomarkers, widely employed for diagnosis, require a lumbar puncture to be performed, making them impractical as screening tools. Considering the role of sleep disturbances in AD, recent research suggests quantitative sleep electroencephalography features as potential non-invasive biomarkers of AD pathology. However, quantitative analysis of comprehensive polysomnography (PSG) signals remains relatively understudied. PSG is a non-invasive test enabling qualitative and quantitative analysis of a wide range of parameters, offering additional insights alongside other biomarkers. Machine Learning (ML) gained interest for its ability to discern intricate patterns within complex datasets, offering promise in AD neuropathology detection. Therefore, this study aims to evaluate the effectiveness of a multimodal ML approach in predicting core AD CSF biomarkers. Mild-moderate AD patients were prospectively recruited for PSG, followed by testing of CSF and blood samples for biomarkers. PSG signals underwent preprocessing to extract non-linear, time domain and frequency domain statistics quantitative features. Multiple ML algorithms were trained using four subsets of input features: clinical variables (CLINVAR), conventional PSG parameters (SLEEPVAR), quantitative PSG signal features (PSGVAR) and a combination of all subsets (ALL). Cross-validation techniques were employed to evaluate model performance and ensure generalizability. Regression models were developed to determine the most effective variable combinations for explaining variance in the biomarkers. On 49 subjects, Gradient Boosting Regressors achieved the best results in estimating biomarkers levels, using different loss functions for each biomarker: least absolute deviation (LAD) for the Aβ42, least squares (LS) for p-tau and Huber for t-tau. The ALL subset demonstrated the lowest training errors for all three biomarkers, albeit with varying test performance. Specifically, the SLEEPVAR subset yielded the best test performance in predicting Aβ42, while the ALL subset most accurately predicted p-tau and t-tau due to the lowest test errors. Multimodal ML can help predict the outcome of CSF biomarkers in early AD by utilizing non-invasive and economically feasible variables. The integration of computational models into medical practice offers a promising tool for the screening of patients at risk of AD, potentially guiding clinical decisions.

A New Perspective on Alzheimer's Disease Biomarkers in the IVD Field

A New Perspective on Alzheimer's Disease Biomarkers in the IVD Field