An accurate fully automated panel of plasma biomarkers for Alzheimer's disease.
Ontology highlight
ABSTRACT: Introduction
There is a great need for fully automated plasma assays that can measure amyloid beta (Aβ) pathology and predict future Alzheimer's disease (AD) dementia.Methods
Two cohorts (n = 920) were examined: Panel A+ (n = 32 cognitively unimpaired [CU], n = 106 mild cognitive impairment [MCI], and n = 89 AD) and BioFINDER-1 (n = 461 CU, n = 232 MCI). Plasma Aβ42/Aβ40, phosphorylated tau (p-tau)181, two p-tau217 variants, ApoE4 protein, neurofilament light, and GFAP were measured using Elecsys prototype immunoassays.Results
The best biomarker for discriminating Aβ-positive versus Aβ-negative participants was Aβ42/Aβ40 (are under the curve [AUC] 0.83-0.87). Combining Aβ42/Aβ40, p-tau181, and ApoE4 improved the AUCs significantly (0.90 to 0.93; P< 0.01). Adding additional biomarkers had marginal effects (ΔAUC ≤0.01). In BioFINDER, p-tau181, p-tau217, and ApoE4 predicted AD dementia within 6 years in CU (AUC 0.88) and p-tau181, p-tau217, and Aβ42/Aβ40 in MCI (AUC 0.87).Discussion
The high accuracies for Aβ pathology and future AD dementia using fully automated instruments are promising for implementing plasma biomarkers in clinical trials and clinical routine.
SUBMITTER: Palmqvist S
PROVIDER: S-EPMC9918613 | biostudies-literature | 2023 Apr
REPOSITORIES: biostudies-literature
Publications
An accurate fully automated panel of plasma biomarkers for Alzheimer's disease.
Palmqvist Sebastian S Stomrud Erik E Cullen Nicholas N Janelidze Shorena S Manuilova Ekaterina E Jethwa Alexander A Bittner Tobias T Eichenlaub Udo U Suridjan Ivonne I Kollmorgen Gwendlyn G Riepe Matthias M von Arnim Christine A F CAF Tumani Hayrettin H Hager Klaus K Heidenreich Fedor F Mattsson-Carlgren Niklas N Zetterberg Henrik H Blennow Kaj K Hansson Oskar O
Alzheimer's & dementia : the journal of the Alzheimer's Association 20220811 4
<h4>Introduction</h4>There is a great need for fully automated plasma assays that can measure amyloid beta (Aβ) pathology and predict future Alzheimer's disease (AD) dementia.<h4>Methods</h4>Two cohorts (n = 920) were examined: Panel A+ (n = 32 cognitively unimpaired [CU], n = 106 mild cognitive impairment [MCI], and n = 89 AD) and BioFINDER-1 (n = 461 CU, n = 232 MCI). Plasma Aβ42/Aβ40, phosphorylated tau (p-tau)181, two p-tau217 variants, ApoE4 protein, neurofilament light, and GFAP were measu ...[more]
Similar Datasets
Project description:BackgroundThe recent promise of disease-modifying therapies for Alzheimer's disease (AD) has reinforced the need for accurate biomarkers for early disease detection, diagnosis and treatment monitoring. Advances in the development of novel blood-based biomarkers for AD have revealed that plasma levels of tau phosphorylated at various residues are specific and sensitive to AD dementia. However, the currently available tests have shortcomings in access, throughput, and scalability that limit widespread implementation.MethodsWe evaluated the diagnostic and prognostic performance of a high-throughput and fully-automated Lumipulse plasma p-tau181 assay for the detection of AD. Plasma from older clinically unimpaired individuals (CU, n = 463) and patients with mild cognitive impairment (MCI, n = 107) or AD dementia (n = 78) were obtained from the longitudinal Stanford University Alzheimer's Disease Research Center (ADRC) and the Stanford Aging and Memory Study (SAMS) cohorts. We evaluated the discriminative accuracy of plasma p-tau181 for clinical AD diagnosis, association with amyloid β peptides and p-tau181 concentrations in CSF, association with amyloid positron emission tomography (PET), and ability to predict longitudinal cognitive and functional change.ResultsThe assay showed robust performance in differentiating AD from control participants (AUC 0.959, CI: 0.912 to 0.990), and was strongly associated with CSF p-tau181, CSF Aβ42/Aβ40 ratio, and amyloid-PET global SUVRs. Associations between plasma p-tau181 with CSF biomarkers were significant when examined separately in Aβ+ and Aβ- groups. Plasma p-tau181 significantly increased over time in CU and AD diagnostic groups. After controlling for clinical diagnosis, age, sex, and education, baseline plasma p-tau181 predicted change in MoCA overall and change in CDR Sum of Boxes in the AD group over follow-up of up to 5 years.ConclusionsThis fully-automated and available blood-based biomarker assay therefore may be useful for early detection, diagnosis, prognosis, and treatment monitoring of AD.
| S-EPMC9652927 | biostudies-literature
Project description:BackgroundLUMIPULSE G-automated immunoassays represent a widely used method for the quantification of Alzheimer's disease (AD) biomarkers in the cerebrospinal fluid (CSF). Less invasive blood-based markers confer a promising tool for AD diagnosis at prodromal stages (mild cognitive impairment (MCI)). Highly sensitive assays for the quantification of amyloid-beta (Aβ) and phosphorylated Tau-181 (p-Tau181) in the blood are showing promising results. In this study, we evaluated the clinical performance of the recently available fully automated LUMIPULSE plasma marker assays for detecting brain AD pathology and for predicting progression from MCI to AD dementia stage.MethodsA retrospective exploratory cohort of 138 individuals (22 neurological controls [NC], 72 MCI, and 44 AD dementia patients) was included. Data regarding baseline CSF concentrations of Aβ42, Aβ40, t-Tau, and p-Tau181 was available and used to establish the presence of AD brain pathology. Baseline Aβ42, Aβ40, and p-Tau181 concentrations were determined in stored plasma samples using high-throughput fully automated LUMIPULSE assays. Progression from MCI to AD dementia was evaluated during follow-up (mean 6.4 ± 2.5 years). Moreover, a prospective validation cohort of 72 individuals with memory complaints underwent AD biomarker quantification, closely mirroring typical clinical practice. This cohort aimed to confirm the study's main findings.ResultsIn the exploratory cohort, correlations between CSF and plasma were moderate for p-Tau181 (ρ = 0.61, p < 0.001) and weak for Aβ42/Aβ40 ratio (ρ = 0.39, p < 0.001). Plasma p-Tau181 and p-Tau181/Aβ42 concentrations were significantly increased while Aβ42/Aβ40 was significantly decreased (p < 0.001) in patients with AD dementia and prodromal AD, as well as in individuals with CSF abnormal amyloid concentrations (A +). Plasma p-Tau181 showed a robust performance in differentiating patients clinically diagnosed as AD (AUC = 0.89; 95% CI 0.83-0.94); A + vs. A - (AUC = 0.84, 95% CI 0.77-0.91) and also in predicting conversion to AD dementia in MCI patients (AUC = 0.89, 95% CI 0.81-0.96). When tested in the validation cohort, plasma p-Tau181 displayed 83.3% of the overall percentage of agreement according to amyloid status.ConclusionsOur results show that the measurement of p-Tau181 in plasma has great potential as a non-invasive prognostic screening tool for implementation in a clinical setting.
| S-EPMC10918996 | biostudies-literature
Project description:To study cerebrospinal fluid (CSF) biomarkers of Alzheimer's disease (AD) analyzed by fully automated Elecsys immunoassays in comparison to neuropathologic gold standards, and compare their accuracy to plasma phosphorylated tau (p-tau181) measured using a novel Simoa method. We studied ante-mortem Elecsys-derived CSF biomarkers in 45 individuals who underwent standardized post-mortem assessments of AD and non-AD neuropathologic changes at autopsy. In a subset of 26 participants, we also analysed ante-mortem levels of plasma p-tau181 and neurofilament light (NfL). Reference biomarker values were obtained from 146 amyloid-PET-negative healthy controls (HC). All CSF biomarkers clearly distinguished pathology-confirmed AD dementia (N=27) from HC (AUCs=0.86-1.00). CSF total-tau (t-tau), p-tau181, and their ratios with Aβ1-42, also accurately distinguished pathology-confirmed AD from non-AD dementia (N=8; AUCs=0.94-0.97). In pathology-specific analyses, intermediate-to-high Thal amyloid phases were best detected by CSF Aβ1-42 (AUC[95% CI]=0.91[0.81-1]), while intermediate-to-high CERAD neuritic plaques and Braak tau stages were best detected by CSF p-tau181 (AUC=0.89[0.79-0.99] and 0.88[0.77-0.99], respectively). Optimal Elecsys biomarker cut-offs were derived at 1097/229/19 pg/ml for Aβ1-42, t-tau, and p-tau181. In the plasma subsample, both plasma p-tau181 (AUC=0.91[0.86-0.96]) and NfL (AUC=0.93[0.87-0.99]) accurately distinguished pathology-confirmed AD (N=14) from HC. However, only p-tau181 distinguished AD from non-AD dementia cases (N=4; AUC=0.96[0.88-1.00]), and showed a similar, though weaker, pathologic specificity for neuritic plaques (AUC=0.75[0.52-0.98]) and Braak stage (AUC=0.71[0.44-0.98]) as CSF p-tau181. Elecsys-derived CSF biomarkers detect AD neuropathologic changes with very high discriminative accuracy in-vivo. Preliminary findings support the use of plasma p-tau181 as an easily accessible and scalable biomarker of AD pathology. This study provides Class II evidence that fully-automated CSF t-tau and p-tau181measurements discriminate between autopsy-confirmed Alzheimer's disease and other dementias.
| S-EPMC8480485 | biostudies-literature
Project description:Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder, affecting one in ten people aged over 65 years. Despite the severity of the disease, early diagnosis of AD is still challenging due to the low accuracy or high cost of neuropsychological tests and neuroimaging. Here we report clinically accurate and ultrasensitive detection of multiple AD core biomarkers (t-tau, p-tau181, Aβ42, and Aβ40) in human plasma using densely aligned carbon nanotubes (CNTs). The closely packed and unidirectionally aligned CNT sensor array exhibits high precision, sensitivity, and accuracy, evidenced by a low coefficient of variation (<6%), a femtomolar-level limit of detection, and a high degree of recovery (>93.0%). By measuring the levels of t-tau/Aβ42, p-tau181/Aβ42, and Aβ42/Aβ40 in clinical blood samples, the sensor array successfully discriminates the clinically diagnosed AD patients from healthy controls with an average sensitivity of 90.0%, a selectivity of 90.0%, and an average accuracy of 88.6%.
| S-EPMC6949261 | biostudies-literature
Project description:Many diseases cause significant changes to the concentrations of small molecules (a.k.a. metabolites) that appear in a person's biofluids, which means such diseases can often be readily detected from a person's "metabolic profile"-i.e., the list of concentrations of those metabolites. This information can be extracted from a biofluids Nuclear Magnetic Resonance (NMR) spectrum. However, due to its complexity, NMR spectral profiling has remained manual, resulting in slow, expensive and error-prone procedures that have hindered clinical and industrial adoption of metabolomics via NMR. This paper presents a system, BAYESIL, which can quickly, accurately, and autonomously produce a person's metabolic profile. Given a 1D 1H NMR spectrum of a complex biofluid (specifically serum or cerebrospinal fluid), BAYESIL can automatically determine the metabolic profile. This requires first performing several spectral processing steps, then matching the resulting spectrum against a reference compound library, which contains the "signatures" of each relevant metabolite. BAYESIL views spectral matching as an inference problem within a probabilistic graphical model that rapidly approximates the most probable metabolic profile. Our extensive studies on a diverse set of complex mixtures including real biological samples (serum and CSF), defined mixtures and realistic computer generated spectra; involving > 50 compounds, show that BAYESIL can autonomously find the concentration of NMR-detectable metabolites accurately (~ 90% correct identification and ~ 10% quantification error), in less than 5 minutes on a single CPU. These results demonstrate that BAYESIL is the first fully-automatic publicly-accessible system that provides quantitative NMR spectral profiling effectively-with an accuracy on these biofluids that meets or exceeds the performance of trained experts. We anticipate this tool will usher in high-throughput metabolomics and enable a wealth of new applications of NMR in clinical settings. BAYESIL is accessible at http://www.bayesil.ca.
| S-EPMC4446368 | biostudies-literature
Project description:Peripheral biomarkers of Alzheimer's disease (AD) reflecting early neuropathological change are critical to the development of treatments for this condition. The most widely used indicator of AD pathology in life at present is neuroimaging evidence of brain atrophy. We therefore performed a proteomic analysis of plasma to derive biomarkers associated with brain atrophy in AD. Using gel based proteomics we previously identified seven plasma proteins that were significantly associated with hippocampal volume in a combined cohort of subjects with AD (N = 27) and MCI (N = 17). In the current report, we validated this finding in a large independent cohort of AD (N = 79), MCI (N = 88) and control (N = 95) subjects using alternative complementary methods-quantitative immunoassays for protein concentrations and estimation of pathology by whole brain volume. We confirmed that plasma concentrations of five proteins, together with age and sex, explained more than 35% of variance in whole brain volume in AD patients. These proteins are complement components C3 and C3a, complement factor-I, γ-fibrinogen and alpha-1-microglobulin. Our findings suggest that these plasma proteins are strong predictors of in vivo AD pathology. Moreover, these proteins are involved in complement activation and coagulation, providing further evidence for an intrinsic role of these pathways in AD pathogenesis.
| S-EPMC3244409 | biostudies-literature
Project description:Background/objectiveThe aim of this study was to determine if plasma concentrations of 5 surrogate markers of Alzheimer's disease (AD) pathology and neuroinflammation are associated with disease status in African Americans.MethodsWe evaluated 321 African Americans (159 AD, 162 controls) from the Florida Consortium for African-American Alzheimer's Disease Studies (FCA3DS). Five plasma proteins reflecting AD neuropathology or inflammation (A?42, tau, IL6, IL10, TNF?) were tested for associations with AD, age, sex, APOE and MAPT genotypes, and for pairwise correlations.ResultsPlasma tau levels were higher in AD when adjusted for biological and technical covariates. APOE?4 was associated with lower plasma A?42 and tau levels. Older age was associated with higher plasma A?42, tau, and TNF?. Females had lower IL10 levels. Inflammatory proteins had strong pairwise correlations amongst themselves and with A?42.ConclusionWe identified effects of demographic and genetic variants on five potential plasma biomarkers in African Americans. Plasma inflammatory biomarkers and A?42 may reflect correlated pathologies and elevated plasma tau may be a biomarker of AD in this population.
| S-EPMC7902984 | biostudies-literature
Project description:Alzheimer's disease (AD) is the most common form of neurodegenerative dementia and its timely diagnosis remains a major challenge in biomarker discovery. In the present study, we analyzed publicly available high-throughput low-sample -omics datasets from studies in AD blood, by the AutoML technology Just Add Data Bio (JADBIO), to construct accurate predictive models for use as diagnostic biosignatures. Considering data from AD patients and age-sex matched cognitively healthy individuals, we produced three best performing diagnostic biosignatures specific for the presence of AD: A. A 506-feature transcriptomic dataset from 48 AD and 22 controls led to a miRNA-based biosignature via Support Vector Machines with three miRNA predictors (AUC 0.975 (0.906, 1.000)), B. A 38,327-feature transcriptomic dataset from 134 AD and 100 controls led to six mRNA-based statistically equivalent signatures via Classification Random Forests with 25 mRNA predictors (AUC 0.846 (0.778, 0.905)) and C. A 9483-feature proteomic dataset from 25 AD and 37 controls led to a protein-based biosignature via Ridge Logistic Regression with seven protein predictors (AUC 0.921 (0.849, 0.972)). These performance metrics were also validated through the JADBIO pipeline confirming stability. In conclusion, using the automated machine learning tool JADBIO, we produced accurate predictive biosignatures extrapolating available low sample -omics data. These results offer options for minimally invasive blood-based diagnostic tests for AD, awaiting clinical validation based on respective laboratory assays. They also highlight the value of AutoML in biomarker discovery.
| S-EPMC7563988 | biostudies-literature
Project description:Over the last few years, intense research efforts have been made to anticipate or improve the diagnosis of Alzheimer's disease by detecting blood biomarkers. However, the most promising blood biomarkers identified to date have some limitations, most of them related to the techniques required for their detection. Hence, new blood biomarkers should be identified to improve the diagnosis of AD, better discriminate between AD and mild cognitive impairment (MCI) and identify cognitively unimpaired (CU) older individuals at risk for progression to AD. Our previous studies demonstrated that both the purinergic receptor P2X7 and the tissue-nonspecific alkaline phosphatase ectoenzyme (TNAP) are upregulated in the brains of AD patients. Since both proteins are also present in plasma, we investigated whether plasma P2X7R and TNAP are altered in MCI and AD patients and, if so, their potential role as AD biomarkers. We found that AD but not MCI patients present increased plasma P2X7R levels. Nevertheless, TNAP plasma activity was increased in MCI patients and decreased in the AD group. ROC curve analysis indicated that measuring both parameters has a reasonable discriminating capability to diagnose MCI and AD conditions. In addition to confirming that individuals progressing to MCI have increased TNAP activity in plasma, longitudinal studies also revealed that CU individuals have lower plasma TNAP activity than stable controls. Thus, we propose that P2X7 and TNAP could serve as new plasma biomarkers for MCI and AD.
| S-EPMC10342008 | biostudies-literature
Project description:While plasma biomarkers for Alzheimer's disease (AD) are increasingly being evaluated for clinical diagnosis and prognosis, few population-based autopsy studies have evaluated their utility in the context of predicting neuropathological changes. Our goal was to investigate the utility of clinically available plasma markers in predicting Braak staging, neuritic plaque score, Thal phase, and overall AD neuropathological change (ADNC).We utilized a population-based prospective study of 350 participants with autopsy and antemortem plasma biomarker testing using clinically available antibody assay (Quanterix) consisting of Aβ42/40 ratio, p-tau181, GFAP, and NfL. We utilized a variable selection procedure in cross-validated (CV) logistic regression models to identify the best set of plasma predictors along with demographic variables, and a subset of neuropsychological tests comprising the Mayo Clinic Preclinical Alzheimer Cognitive Composite (Mayo-PACC). ADNC was best predicted with plasma GFAP, NfL, p-tau181 biomarkers along with APOE ε4 carrier status and Mayo-PACC cognitive score (CV AUC = 0.798). Braak staging was best predicted using plasma GFAP, p-tau181, and cognitive scores (CV AUC = 0.774). Neuritic plaque score was best predicted using plasma Aβ42/40 ratio, p-tau181, GFAP, and NfL biomarkers (CV AUC = 0.770). Thal phase was best predicted using GFAP, NfL, p-tau181, APOE ε4 carrier status and Mayo-PACC cognitive score (CV AUC = 0.754). We found that GFAP and p-tau provided non-overlapping information on both neuritic plaque and Braak stage scores whereas Aβ42/40 and NfL were mainly useful for prediction of neuritic plaque scores. Separating participants by cognitive status improved predictive performance, particularly when plasma biomarkers were included. Plasma biomarkers can differentially inform about overall ADNC pathology, Braak staging, and neuritic plaque score when combined with demographics and cognitive variables and have significant utility for earlier detection of AD.
| S-EPMC10478071 | biostudies-literature