Validation of Plasma Proteomic Biomarkers Relating to Brain Amyloid Burden in the EMIF-Alzheimer's Disease Multimodal Biomarker Discovery Cohort.
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ABSTRACT:
SUBMITTER: Westwood S
PROVIDER: S-EPMC7175945 | biostudies-literature | 2020
REPOSITORIES: biostudies-literature
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Validation of Plasma Proteomic Biomarkers Relating to Brain Amyloid Burden in the EMIF-Alzheimer's Disease Multimodal Biomarker Discovery Cohort.
Westwood Sarah S Baird Alison L AL Anand Sneha N SN Nevado-Holgado Alejo J AJ Kormilitzin Andrey A Shi Liu L Hye Abdul A Ashton Nicholas J NJ Morgan Angharad R AR Bos Isabelle I Vos Stephanie J B SJB Baker Susan S Buckley Noel J NJ Ten Kate Mara M Scheltens Philip P Teunissen Charlotte E CE Vandenberghe Rik R Gabel Silvy S Meersmans Karen K Engelborghs Sebastiaan S De Roeck Ellen E EE Sleegers Kristel K Frisoni Giovanni B GB Frisoni Giovanni B GB Blin Olivier O Richardson Jill C JC Bordet Régis R Molinuevo José L JL Rami Lorena L Wallin Anders A Kettunen Petronella P Tsolaki Magda M Verhey Frans F Lléo Alberto A Sala Isabel I Popp Julius J Peyratout Gwendoline G Martinez-Lage Pablo P Tainta Mikel M Johannsen Peter P Freund-Levi Yvonne Y Frölich Lutz L Dobricic Valerija V Legido-Quigley Cristina C Bertram Lars L Barkhof Frederik F Zetterberg Henrik H Morgan B Paul BP Streffer Johannes J Visser Pieter Jelle PJ Lovestone Simon S
Journal of Alzheimer's disease : JAD 20200101 1
We have previously investigated, discovered, and replicated plasma protein biomarkers for use to triage potential trials participants for PET or cerebrospinal fluid measures of Alzheimer's disease (AD) pathology. This study sought to undertake validation of these candidate plasma biomarkers in a large, multi-center sample collection. Targeted plasma analyses of 34 proteins with prior evidence for prediction of in vivo pathology were conducted in up to 1,000 samples from cognitively healthy elder ...[more]
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Project description:Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder and the most common form of dementia in the elderly. Susceptibility to AD is considerably determined by genetic factors which hitherto were primarily identified using case-control designs. Elucidating the genetic architecture of additional AD-related phenotypic traits, ideally those linked to the underlying disease process, holds great promise in gaining deeper insights into the genetic basis of AD and in developing better clinical prediction models. To this end, we generated genome-wide single-nucleotide polymorphism (SNP) genotyping data in 931 participants of the European Medical Information Framework Alzheimer's Disease Multimodal Biomarker Discovery (EMIF-AD MBD) sample to search for novel genetic determinants of AD biomarker variability. Specifically, we performed genome-wide association study (GWAS) analyses on 16 traits, including 14 measures derived from quantifications of five separate amyloid-beta (A?) and tau-protein species in the cerebrospinal fluid (CSF). In addition to confirming the well-established effects of apolipoprotein E (APOE) on diagnostic outcome and phenotypes related to A?42, we detected novel potential signals in the zinc finger homeobox 3 (ZFHX3) for CSF-A?38 and CSF-A?40 levels, and confirmed the previously described sex-specific association between SNPs in geminin coiled-coil domain containing (GMNC) and CSF-tau. Utilizing the results from independent case-control AD GWAS to construct polygenic risk scores (PRS) revealed that AD risk variants only explain a small fraction of CSF biomarker variability. In conclusion, our study represents a detailed first account of GWAS analyses on CSF-A? and -tau-related traits in the EMIF-AD MBD dataset. In subsequent work, we will utilize the genomics data generated here in GWAS of other AD-relevant clinical outcomes ascertained in this unique dataset.
| S-EPMC7680793 | biostudies-literature
Project description:BACKGROUND:With the shift of research focus towards the pre-dementia stage of Alzheimer's disease (AD), there is an urgent need for reliable, non-invasive biomarkers to predict amyloid pathology. The aim of this study was to assess whether easily obtainable measures from structural MRI, combined with demographic data, cognitive data and apolipoprotein E (APOE) ?4 genotype, can be used to predict amyloid pathology using machine-learning classification. METHODS:We examined 810 subjects with structural MRI data and amyloid markers from the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery study, including subjects with normal cognition (CN, n?=?337, age 66.5?±?7.2, 50% female, 27% amyloid positive), mild cognitive impairment (MCI, n?=?375, age 69.1?±?7.5, 53% female, 63% amyloid positive) and AD dementia (n?=?98, age 67.0?±?7.7, 48% female, 97% amyloid positive). Structural MRI scans were visually assessed and Freesurfer was used to obtain subcortical volumes, cortical thickness and surface area measures. We first assessed univariate associations between MRI measures and amyloid pathology using mixed models. Next, we developed and tested an automated classifier using demographic, cognitive, MRI and APOE ?4 information to predict amyloid pathology. A support vector machine (SVM) with nested 10-fold cross-validation was applied to identify a set of markers best discriminating between amyloid positive and amyloid negative subjects. RESULTS:In univariate associations, amyloid pathology was associated with lower subcortical volumes and thinner cortex in AD-signature regions in CN and MCI. The multi-variable SVM classifier provided an area under the curve (AUC) of 0.81?±?0.07 in MCI and an AUC of 0.74?±?0.08 in CN. In CN, selected features for the classifier included APOE ?4, age, memory scores and several MRI measures such as hippocampus, amygdala and accumbens volumes and cortical thickness in temporal and parahippocampal regions. In MCI, the classifier including demographic and APOE ?4 information did not improve after additionally adding imaging measures. CONCLUSIONS:Amyloid pathology is associated with changes in structural MRI measures in CN and MCI. An automated classifier based on clinical, imaging and APOE ?4 data can identify the presence of amyloid pathology with a moderate level of accuracy. These results could be used in clinical trials to pre-screen subjects for anti-amyloid therapies.
| S-EPMC6161396 | biostudies-literature
Project description:IntroductionPlasma proteins have been widely studied as candidate biomarkers to predict brain amyloid deposition to increase recruitment efficiency in secondary prevention clinical trials for Alzheimer's disease. Most such biomarker studies are targeted to specific proteins or are biased toward high abundant proteins.Methods4001 plasma proteins were measured in two groups of participants (discovery group = 516, replication group = 365) selected from the European Medical Information Framework for Alzheimer's disease Multimodal Biomarker Discovery study, all of whom had measures of amyloid.ResultsA panel of proteins (n = 44), along with age and apolipoprotein E (APOE) ε4, predicted brain amyloid deposition with good performance in both the discovery group (area under the curve = 0.78) and the replication group (area under the curve = 0.68). Furthermore, a causal relationship between amyloid and tau was confirmed by Mendelian randomization.DiscussionThe results suggest that high-dimensional plasma protein testing could be a useful and reproducible approach for measuring brain amyloid deposition.
| S-EPMC6880298 | biostudies-literature
Project description:BackgroundThere is an urgent need for novel, noninvasive biomarkers to diagnose Alzheimer's disease (AD) in the predementia stages and to predict the rate of decline. Therefore, we set up the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery (EMIF-AD MBD) study. In this report we describe the design of the study, the methods used and the characteristics of the participants.MethodsParticipants were selected from existing prospective multicenter and single-center European studies. Inclusion criteria were having normal cognition (NC) or a diagnosis of mild cognitive impairment (MCI) or AD-type dementia at baseline, age above 50 years, known amyloid-beta (A?) status, availability of cognitive test results and at least two of the following materials: plasma, DNA, magnetic resonance imaging (MRI) or cerebrospinal fluid (CSF). Targeted and untargeted metabolomic and proteomic analyses were performed in plasma, and targeted and untargeted proteomics were performed in CSF. Genome-wide SNP genotyping, next-generation sequencing and methylation profiling were conducted in DNA. Visual rating and volumetric measures were assessed on MRI. Baseline characteristics were analyzed using ANOVA or chi-square, rate of decline analyzed by linear mixed modeling.ResultsWe included 1221 individuals (NC n?=?492, MCI n?=?527, AD-type dementia n?=?202) with a mean age of 67.9 (SD 8.3) years. The percentage A?+ was 26% in the NC, 58% in the MCI, and 87% in the AD-type dementia groups. Plasma samples were available for 1189 (97%) subjects, DNA samples for 929 (76%) subjects, MRI scans for 862 (71%) subjects and CSF samples for 767 (63%) subjects. For 759 (62%) individuals, clinical follow-up data were available. In each diagnostic group, the APOE ?4 allele was more frequent amongst A?+ individuals (p?<?0.001). Only in MCI was there a difference in baseline Mini Mental State Examination (MMSE) score between the A groups (p?<?0.001). A?+ had a faster rate of decline on the MMSE during follow-up in the NC (p?<?0.001) and MCI (p?<?0.001) groups.ConclusionsThe characteristics of this large cohort of elderly subjects at various cognitive stages confirm the central roles of A? and APOE ?4 in AD pathogenesis. The results of the multimodal analyses will provide new insights into underlying mechanisms and facilitate the discovery of new diagnostic and prognostic AD biomarkers. All researchers can apply for access to the EMIF-AD MBD data by submitting a research proposal via the EMIF-AD Catalog.
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Project description:Background and Objective: Plasma biomarkers for the diagnosis and stratification of Alzheimer's disease (AD) are intensively sought. However, no plasma markers are well established so far for AD diagnosis. Our group has identified and validated various blood-based proteomic biomarkers relating to AD pathology in multiple cohorts. The study aims to conduct a meta-analysis based on our own studies to systematically assess the diagnostic performance of our previously identified blood biomarkers. Methods: To do this, we included seven studies that our group has conducted during the last decade. These studies used either Luminex xMAP or ELISA to measure proteomic biomarkers. As proteins measured in these studies differed, we selected protein based on the criteria that it must be measured in at least four studies. We then examined biomarker performance using random-effect meta-analyses based on the mean difference between biomarker concentrations in AD and controls (CTL), AD and mild cognitive impairment (MCI), MCI, and CTL as well as MCI converted to dementia (MCIc) and non-converted (MCInc) individuals. Results: An overall of 2,879 subjects were retrieved for meta-analysis including 1,053 CTL, 895 MCI, 882 AD, and 49 frontotemporal dementia (FTD) patients. Six proteins were measured in at least four studies and were chosen for meta-analyses for AD diagnosis. Of them, three proteins had significant difference between AD and controls, among which alpha-2-macroglobulin (A2M) and ficolin-2 (FCN2) increased in AD while fibrinogen gamma chain (FGG) decreased in AD compared to CTL. Furthermore, FGG significantly increased in FTD compared to AD. None of the proteins passed the significance between AD and MCI, or MCI and CTL, or MCIc and MCInc, although complement component 4 (CC4) tended to increase in MCIc individuals compared to MCInc. Conclusions: The results suggest that A2M, FCN2, and FGG are promising biomarkers to discriminate AD patients from controls, which are worthy of further validation.
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