Dataset Information

Primary fatty amides in plasma associated with brain amyloid burden, hippocampal volume, and memory in the European Medical Information Framework for Alzheimer's Disease biomarker discovery cohort.

ABSTRACT:

Introduction

A critical and as-yet unmet need in Alzheimer's disease (AD) is the discovery of peripheral small molecule biomarkers. Given that brain pathology precedes clinical symptom onset, we set out to test whether metabolites in blood associated with pathology as indexed by cerebrospinal fluid (CSF) AD biomarkers.

Methods

This study analyzed 593 plasma samples selected from the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery study, of individuals who were cognitively healthy (n = 242), had mild cognitive impairment (n = 236), or had AD-type dementia (n = 115). Logistic regressions were carried out between plasma metabolites (n = 883) and CSF markers, magnetic resonance imaging, cognition, and clinical diagnosis.

Results

Eight metabolites were associated with amyloid β and one with t-tau in CSF, these were primary fatty acid amides (PFAMs), lipokines, and amino acids. From these, PFAMs, glutamate, and aspartate also associated with hippocampal volume and memory.

Discussion

PFAMs have been found increased and associated with amyloid β burden in CSF and clinical measures.

SUBMITTER: Kim M

PROVIDER: S-EPMC6849698 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Publications

Primary fatty amides in plasma associated with brain amyloid burden, hippocampal volume, and memory in the European Medical Information Framework for Alzheimer's Disease biomarker discovery cohort.

Kim Min M Snowden Stuart S Suvitaival Tommi T Ali Ashfaq A Merkler David J DJ Ahmad Tahmina T Westwood Sarah S Baird Alison A Proitsi Petroula P Nevado-Holgado Alejo A Hye Abdul A Bos Isabelle I Vos Stephanie S Vandenberghe Rik R Teunissen Charlotte C Ten Kate Mara M Scheltens Philip P Gabel Silvy S Meersmans Karen K Blin Olivier O Richardson Jill J De Roeck Ellen E Sleegers Kristel K Bordet Régis R Rami Lorena L Kettunen Petronella P Tsolaki Magda M Verhey Frans F Sala Isabel I Lléo Alberto A Peyratout Gwendoline G Tainta Mikel M Johannsen Peter P Freund-Levi Yvonne Y Frölich Lutz L Dobricic Valerija V Engelborghs Sebastiaan S Frisoni Giovanni B GB Frisoni Giovanni B GB Molinuevo José L JL Wallin Anders A Popp Julius J Martinez-Lage Pablo P Bertram Lars L Barkhof Frederik F Ashton Nicholas N Blennow Kaj K Zetterberg Henrik H Streffer Johannes J Visser Pieter J PJ Lovestone Simon S Legido-Quigley Cristina C

Alzheimer's & dementia : the journal of the Alzheimer's Association 20190508 6

<h4>Introduction</h4>A critical and as-yet unmet need in Alzheimer's disease (AD) is the discovery of peripheral small molecule biomarkers. Given that brain pathology precedes clinical symptom onset, we set out to test whether metabolites in blood associated with pathology as indexed by cerebrospinal fluid (CSF) AD biomarkers.<h4>Methods</h4>This study analyzed 593 plasma samples selected from the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery study ...[more]

PMID: 31078433

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Project description:Alzheimer's disease (AD) is the most frequent neurodegenerative disease with an increasing prevalence in industrialized, aging populations. AD susceptibility has an established genetic basis which has been the focus of a large number of genome-wide association studies (GWAS) published over the last decade. Most of these GWAS used dichotomized clinical diagnostic status, i.e., case vs. control classification, as outcome phenotypes, without the use of biomarkers. An alternative and potentially more powerful study design is afforded by using quantitative AD-related phenotypes as GWAS outcome traits, an analysis paradigm that we followed in this work. Specifically, we utilized genotype and phenotype data from n = 931 individuals collected under the auspices of the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery (EMIF-AD MBD) study to perform a total of 19 separate GWAS analyses. As outcomes we used five magnetic resonance imaging (MRI) traits and seven cognitive performance traits. For the latter, longitudinal data from at least two timepoints were available in addition to cross-sectional assessments at baseline. Our GWAS analyses revealed several genome-wide significant associations for the neuropsychological performance measures, in particular those assayed longitudinally. Among the most noteworthy signals were associations in or near EHBP1 (EH domain binding protein 1; on chromosome 2p15) and CEP112 (centrosomal protein 112; 17q24.1) with delayed recall as well as SMOC2 (SPARC related modular calcium binding 2; 6p27) with immediate recall in a memory performance test. On the X chromosome, which is often excluded in other GWAS, we identified a genome-wide significant signal near IL1RAPL1 (interleukin 1 receptor accessory protein like 1; Xp21.3). While polygenic score (PGS) analyses showed the expected strong associations with SNPs highlighted in relevant previous GWAS on hippocampal volume and cognitive function, they did not show noteworthy associations with recent AD risk GWAS findings. In summary, our study highlights the power of using quantitative endophenotypes as outcome traits in AD-related GWAS analyses and nominates several new loci not previously implicated in cognitive decline.

Project description:IntroductionAnxiety is the most common manifestation of psychopathology in youth, negatively affecting academic, social, and adaptive functioning and increasing risk for mental health problems into adulthood. Anxiety disorders are diagnosed only after clinical symptoms emerge, potentially missing opportunities to intervene during critical early prodromal periods. In this study, we used a new empirical approach to extracting nonlinear features of the electroencephalogram (EEG), with the goal of discovering differences in brain electrodynamics that distinguish children with anxiety disorders from healthy children. Additionally, we examined whether this approach could distinguish children with externalizing disorders from healthy children and children with anxiety.MethodsWe used a novel supervised tensor factorization method to extract latent factors from repeated multifrequency nonlinear EEG measures in a longitudinal sample of children assessed in infancy and at ages 3, 5, and 7 years of age. We first examined the validity of this method by showing that calendar age is highly correlated with latent EEG complexity factors (r = 0.77). We then computed latent factors separately for distinguishing children with anxiety disorders from healthy controls using a 5-fold cross validation scheme and similarly for distinguishing children with externalizing disorders from healthy controls.ResultsWe found that latent factors derived from EEG recordings at age 7 years were required to distinguish children with an anxiety disorder from healthy controls; recordings from infancy, 3 years, or 5 years alone were insufficient. However, recordings from two (5, 7 years) or three (3, 5, 7 years) recordings gave much better results than 7 year recordings alone. Externalizing disorders could be detected using 3- and 5 years EEG data, also giving better results with two or three recordings than any single snapshot. Further, sex assigned at birth was an important covariate that improved accuracy for both disorder groups, and birthweight as a covariate modestly improved accuracy for externalizing disorders. Recordings from infant EEG did not contribute to the classification accuracy for either anxiety or externalizing disorders.ConclusionThis study suggests that latent factors extracted from EEG recordings in childhood are promising candidate biomarkers for anxiety and for externalizing disorders if chosen at appropriate ages.

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Dataset Information

Primary fatty amides in plasma associated with brain amyloid burden, hippocampal volume, and memory in the European Medical Information Framework for Alzheimer's Disease biomarker discovery cohort.

Introduction

Methods

Results

Discussion

Publications

Primary fatty amides in plasma associated with brain amyloid burden, hippocampal volume, and memory in the European Medical Information Framework for Alzheimer's Disease biomarker discovery cohort.

Similar Datasets

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