The iterative process of fluid biomarker development and validation in Alzheimer's disease.
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ABSTRACT:
SUBMITTER: Bendlin BB
PROVIDER: S-EPMC9274795 | biostudies-literature | 2022
REPOSITORIES: biostudies-literature
Publications
The iterative process of fluid biomarker development and validation in Alzheimer's disease.
Bendlin Barbara B BB Zetterberg Henrik H
Alzheimer's & dementia (Amsterdam, Netherlands) 20220712 1
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Project description:There is significant interest in the development of methods to validate novel biomarkers for Alzheimer's disease (AD) diagnosis. Previously, a proteomic panel of cerebrospinal fluid (CSF) biomarker candidates that differentiated AD and non-AD CSF with accuracy higher than 90% was found; information about these CSF proteins can be used to develop multiple reaction monitoring (MRM) based analytical assays, which offer the possibility of quantifying protein expression level changes in samples, as well as, validation among multiple laboratories. Here we report an MRM assay that demonstrates good linearity (average R(2)=0.969) and reproducibility (average coefficient of variance of 6.93%) for the proposed AD CSF biomarkers. MRM quantification results of Aβ1-40, Aβ1-42, retinol-binding protein and cystatin C correlated well with those from ELISA (average R(2)=0.974). Analysis shows that 12 out of 16 selected targets exhibit the same trend in protein expression as that in literature.
| S-EPMC3710693 | biostudies-literature
Project description:In a large multicentre sample of cognitively normal subjects, as a function of age, gender and APOE genotype, we studied the frequency of abnormal cerebrospinal fluid levels of Alzheimer's disease biomarkers including: total tau, phosphorylated tau and amyloid-β1-42. Fifteen cohorts from 12 different centres with either enzyme-linked immunosorbent assays or Luminex® measurements were selected for this study. Each centre sent nine new cerebrospinal fluid aliquots that were used to measure total tau, phosphorylated tau and amyloid-β1-42 in the Gothenburg laboratory. Seven centres showed a high correlation with the new Gothenburg measurements; therefore, 10 cohorts from these centres are included in the analyses here (1233 healthy control subjects, 40-84 years old). Amyloid-β amyloid status (negative or positive) and neurodegeneration status (negative or positive) was established based on the pathological cerebrospinal fluid Alzheimer's disease cut-off values for cerebrospinal fluid amyloid-β1-42 and total tau, respectively. While gender did not affect these biomarker values, APOE genotype modified the age-associated changes in cerebrospinal fluid biomarkers such that APOE ε4 carriers showed stronger age-related changes in cerebrospinal fluid phosphorylated tau, total tau and amyloid-β1-42 values and APOE ε2 carriers showed the opposite effect. At 40 years of age, 76% of the subjects were classified as amyloid negative, neurodegeneration negative and their frequency decreased to 32% at 85 years. The amyloid-positive neurodegeneration-negative group remained stable. The amyloid-negative neurodegeneration-positive group frequency increased slowly from 1% at 44 years to 16% at 85 years, but its frequency was not affected by APOE genotype. The amyloid-positive neurodegeneration-positive frequency increased from 1% at 53 years to 28% at 85 years. Abnormally low cerebrospinal fluid amyloid-β1-42 levels were already frequent in midlife and APOE genotype strongly affects the levels of cerebrospinal fluid amyloid-β1-42, phosphorylated tau and total tau across the lifespan without influencing the frequency of subjects with suspected non-amyloid pathology.
| S-EPMC4643624 | biostudies-literature
Project description:IntroductionSynaptic dysfunction and degeneration is one of the earliest events in Alzheimer's disease (AD) and the best correlate of cognitive decline. Thus, identification and validation of biomarkers reflecting synaptic degeneration to be used as prognostic biomarkers are greatly needed.MethodSolid-phase extraction and parallel reaction monitoring mass spectrometry were used to quantify 17 synaptic proteins in CSF, in two cross-sectional studies including AD (n = 52) and controls (n = 37).ResultsIncreased concentrations of beta-synuclein, gamma-synuclein, neurogranin, phosphatidylethanolamine-binding protein 1, and 14-3-3 proteins were observed in AD patients compared to controls, while neuronal pentraxin-2 and neuronal pentraxin receptor were decreased.DiscussionWe have established a method with a novel panel of synaptic proteins as biomarkers of synaptic dysfunction. The results indicate that several of the proteins included in the panel may serve as synaptic biomarkers for AD.
| S-EPMC8087978 | biostudies-literature
Project description: Not available
| S-EPMC11606142 | biostudies-literature
Project description:IntroductionWhile Alzheimer's disease (AD) is defined by amyloid-β plaques and tau tangles in the brain, it is evident that many other pathophysiological processes such as inflammation, neurovascular dysfunction, oxidative stress, and metabolic derangements also contribute to the disease process and that varying contributions of these pathways may reflect the heterogeneity of AD. Here, we used a previously validated panel of cerebrospinal fluid (CSF) biomarkers to explore the degree to which different pathophysiological domains are dysregulated in AD and how they relate to each other.MethodsTwenty-five CSF biomarkers were analyzed in individuals with a clinical diagnosis of AD verified by positive CSF AD biomarkers (AD, n = 54) and cognitively unimpaired controls negative for CSF AD biomarkers (CU-N, n = 26) using commercial single- and multi-plex immunoassays.ResultsWe noted that while AD was associated with increased levels of only three biomarkers (MMP-10, FABP3, and 8OHdG) on a group level, half of all AD participants had increased levels of biomarkers belonging to at least two pathophysiological domains reflecting the diversity in AD. LASSO modeling showed that a panel of FABP3, 24OHC, MMP-10, MMP-2, and 8OHdG constituted the most relevant and minimally correlated set of variables differentiating AD from CU-N. Interestingly, factor analysis showed that two markers of metabolism and oxidative stress (24OHC and 8OHdG) contributed independent information separate from MMP-10 and FABP3 suggestive of two independent pathophysiological pathways in AD, one reflecting neurodegeneration and vascular pathology, and the other associated with metabolism and oxidative stress.DiscussionBetter understanding of the heterogeneity among individuals with AD and the different contributions of pathophysiological processes besides amyloid-β and tau will be crucial for optimizing personalized treatment strategies.HighlightsA panel of 25 highly validated biomarker assays were measured in CSF.MMP10, FABP3, and 8OHdG were increased in AD in univariate analysis.Many individuals with AD had increased levels of more than one biomarker.Markers of metabolism and oxidative stress contributed to an AD multianalyte profile.Assessing multiple biomarker domains is important to understand disease heterogeneity.
| S-EPMC10865489 | biostudies-literature
Project description:Disease-modifying therapies for Alzheimer's disease (AD) would be most effective during the preclinical stage (pathology present, cognition intact) before significant neuronal loss occurs. Therefore, biomarkers that detect AD pathology in its early stages and predict dementia onset and progression will be invaluable for patient care and efficient clinical trial design.AD-associated changes in cerebrospinal fluid (CSF) were measured using two-dimensional difference gel electrophoresis and liquid chromatography tandem mass spectrometry. Subsequently, CSF YKL-40 was measured by enzyme-linked immunosorbent assay in the discovery cohort (n = 47), validation cohort (n = 292) with paired plasma samples (n = 237), frontotemporal lobar degeneration (n=9) [corrected], and progressive supranuclear palsy (PSP; n = 6). Immunohistochemistry was performed to identify source(s) of YKL-40 in human AD brain.Discovery and validation cohorts, showed higher mean CSF YKL-40 in very mild and mild AD-type dementia (Clinical Dementia Rating [CDR] 0.5 and 1) versus control subjects (CDR 0) and PSP subjects. Importantly, CSF YKL-40/A?42 ratio predicted risk of developing cognitive impairment (CDR 0 to CDR > 0 conversion), as well as the best CSF biomarkers identified to date, tau/A?42 and p-tau 181/A?42. Mean plasma YKL-40 was higher in CDR 0.5 and 1 versus CDR 0, and correlated with CSF levels. YKL-40 immunoreactivity labeled astrocytes near a subset of amyloid plaques, implicating YKL-40 in the neuroinflammatory response to A? deposition.These data demonstrate that YKL-40, a putative indicator of neuroinflammation, is elevated in AD and, together with A?42, has potential prognostic utility as a biomarker for preclinical AD.
| S-EPMC3011944 | biostudies-literature
Project description:BackgroundWhile cutoffs for abnormal levels of the cerebrospinal fluid (CSF) biomarkers amyloid-β 1-42 (Aβ142), total tau (t-tau), phosphorylated tau (p-tau), and the ratios of t-tau/Aβ142 and p-tau/Aβ142, have been established in Alzheimer's disease (AD), biologically relevant cutoffs have not been studied extensively in Parkinson's disease (PD).ObjectiveAssess the suitability and diagnostic accuracy of established AD-derived CSF biomarker cutoffs in the PD population.MethodsBaseline and longitudinal data on CSF biomarkers, cognitive diagnoses, and PET amyloid imaging in 423 newly diagnosed patients with PD from the Parkinson's Progression Markers Initiative (PPMI) cohort were used to evaluate established AD biomarker cutoffs compared with optimal cutoffs derived from the PPMI cohort.ResultsUsing PET amyloid imaging as the gold standard for AD pathology, the optimal cutoff of Aβ142 was higher than the AD cutoff, the optimal cutoffs of t-tau/Aβ142 and p-tau/Aβ142 were lower than the AD cutoffs, and their confidence intervals (CIs) did not overlap with the AD cutoffs. Optimal cutoffs for t-tau and p-tau to predict cognitive impairment were significantly lower than the AD cutoffs, and their CIs did not overlap with the AD cutoffs.ConclusionOptimal cutoffs for the PPMI cohort for Aβ142, t-tau/Aβ142, and p-tau/Aβ142 to predict amyloid-PET positivity and for t-tau and p-tau to predict cognitive impairment differ significantly from cutoffs derived from AD populations. The presence of additional pathologies such as alpha-synuclein in PD may lead to disease-specific CSF biomarker characteristics.
| S-EPMC9934950 | biostudies-literature
Project description:We previously discovered microRNAs (miRNAs) in cerebrospinal fluid (CSF) that differentiate Alzheimer's disease (AD) patients from Controls. Here we examined the performance of 37 candidate AD miRNA biomarkers in a new and independent cohort of CSF from 47 AD patients and 71 Controls on custom TaqMan arrays. We employed a consensus ranking approach to provide an overall priority score for each miRNA, then used multimarker models to assess the relative contributions of the top-ranking miRNAs to differentiate AD from Controls. We assessed classification performance of the top-ranking miRNAs when combined with apolipoprotein E4 (APOE4) genotype status or CSF amyloid-β42 (Aβ42):total tau (T-tau) measures. We also assessed whether miRNAs that ranked higher as AD markers correlate with Mini-Mental State Examination (MMSE) scores. We show that of 37 miRNAs brought forth from the discovery study, 26 miRNAs remained viable as candidate biomarkers for AD in the validation study. We found that combinations of 6-7 miRNAs work better to identify AD than subsets of fewer miRNAs. Of 26 miRNAs that contribute most to the multimarker models, 14 have higher potential than the others to predict AD. Addition of these 14 miRNAs to APOE4 status or CSF Aβ42:T-tau measures significantly improved classification performance for AD. We further show that individual miRNAs that ranked higher as AD markers correlate more strongly with changes in MMSE scores. Our studies validate that a set of CSF miRNAs serve as biomarkers for AD, and support their advancement toward development as biomarkers in the clinical setting.
| S-EPMC6687305 | biostudies-literature
Project description:Alzheimer's disease biomarkers are crucial to understanding disease pathophysiology, aiding accurate diagnosis and identifying target treatments. Although the number of biomarkers continues to grow, the relative utility and uniqueness of each is poorly understood as prior work has typically calculated serial pairwise relationships on only a handful of markers at a time. The present study assessed the cross-sectional relationships among 27 Alzheimer's disease biomarkers simultaneously and determined their ability to predict meaningful clinical outcomes using machine learning. Data were obtained from 527 community-dwelling volunteers enrolled in studies at the Charles F. and Joanne Knight Alzheimer Disease Research Center at Washington University in St Louis. We used hierarchical clustering to group 27 imaging, CSF and plasma measures of amyloid beta, tau [phosphorylated tau (p-tau), total tau t-tau)], neuronal injury and inflammation drawn from MRI, PET, mass-spectrometry assays and immunoassays. Neuropsychological and genetic measures were also included. Random forest-based feature selection identified the strongest predictors of amyloid PET positivity across the entire cohort. Models also predicted cognitive impairment across the entire cohort and in amyloid PET-positive individuals. Four clusters emerged reflecting: core Alzheimer's disease pathology (amyloid and tau), neurodegeneration, AT8 antibody-associated phosphorylated tau sites and neuronal dysfunction. In the entire cohort, CSF p-tau181/Aβ40lumi and Aβ42/Aβ40lumi and mass spectrometry measurements for CSF pT217/T217, pT111/T111, pT231/T231 were the strongest predictors of amyloid PET status. Given their ability to denote individuals on an Alzheimer's disease pathological trajectory, these same markers (CSF pT217/T217, pT111/T111, p-tau/Aβ40lumi and t-tau/Aβ40lumi) were largely the best predictors of worse cognition in the entire cohort. When restricting analyses to amyloid-positive individuals, the strongest predictors of impaired cognition were tau PET, CSF t-tau/Aβ40lumi, p-tau181/Aβ40lumi, CSF pT217/217 and pT205/T205. Non-specific CSF measures of neuronal dysfunction and inflammation were poor predictors of amyloid PET and cognitive status. The current work utilized machine learning to understand the interrelationship structure and utility of a large number of biomarkers. The results demonstrate that, although the number of biomarkers has rapidly expanded, many are interrelated and few strongly predict clinical outcomes. Examining the entire corpus of available biomarkers simultaneously provides a meaningful framework to understand Alzheimer's disease pathobiological change as well as insight into which biomarkers may be most useful in Alzheimer's disease clinical practice and trials.
| S-EPMC10950051 | biostudies-literature
Project description:IntroductionThe relevance of the cerebrospinal fluid (CSF) biomarkers for the diagnosis of Alzheimer's disease (AD) and related disorders is clearly established. However, the question remains on how to use these data, which are often heterogeneous (not all biomarkers being pathologic). The objective of this study is to propose to physicians in memory clinics a biologic scale of probabilities that the patient with cognitive impairments has an Alzheimer's disease (AD) pathologic process.MethodsFor that purpose, we took advantage of the multicenter data of our Paris-North, Lille, and Montpellier (PLM) study, which has emerged through the initial sharing of information from these memory centers. Different models combining the CSF levels of amyloid-β 42, tau, and p-tau(181) were tested to generate categories of patients with very low (<10%), low (<25%), high (>75%), and very high predictive values (>90%) for positive AD. In total, 1,273 patients (646 AD and 627 non-AD) from six independent memory-clinic cohorts were included.ResultsA prediction model based on logistic regressions achieved a very good stratification of the population but had the disadvantages of needing mathematical optimization and being difficult to use in daily clinical practice. Remarkably, a simple and intuitive model based on the number (from zero to three) of three pathologic CSF biomarkers resulted in a very efficient predictive scale for AD in patients seen in memory clinics. The scale's overall predictive value for AD for the different categories were as follows: class 0, 9.6% (95% confidence interval (CI), 6.0% to 13.2%); class 1, 24.7% (95% CI, 18.0% to 31.3%); class 2, 77.2% (95% CI, 67.8% to 86.5%); and class 3, 94.2% (95% CI, 90.7% to 97.7%). In addition, with this scale, significantly more patients were correctly classified than with the logistic regression. Its superiority in model performance was validated by the computation of the net reclassification index (NRI). The model was also validated in an independent multicenter dataset of 408 patients (213 AD and 195 non-AD).ConclusionsIn conclusion, we defined a new scale that could be used to facilitate the interpretation and routine use of multivariate CSF data, as well as helping the stratification of patients in clinical research trials.
| S-EPMC4255520 | biostudies-literature