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 ? 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 ? burden in CSF and clinical measures.

SUBMITTER: Kim M

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

REPOSITORIES: biostudies-literature

ACCESS DATA

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 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

Similar Datasets

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.

Project description:White matter hyperintensities of presumed vascular origin (WMH) are a prevalent form of cerebral small-vessel disease and an important risk factor for post-stroke cognitive dysfunction. Despite this prevalence, it is not well understood how WMH contributes to post-stroke cognitive dysfunction. Preliminary findings suggest that increasing WMH volume is associated with total hippocampal volume in chronic stroke patients. The hippocampus, however, is a complex structure with distinct subfields that have varying roles in the function of the hippocampal circuitry and unique anatomical projections to different brain regions. For these reasons, an investigation into the relationship between WMH and hippocampal subfield volume may further delineate how WMH predispose to post-stroke cognitive dysfunction. In a prospective study of acute ischemic stroke patients with moderate/severe WMH burden, we assessed the relationship between quantitative WMH burden and hippocampal subfield volumes. Patients underwent a 3T MRI brain within 2-5 days of stroke onset. Total WMH volume was calculated in a semi-automated manner. Mean cortical thickness and hippocampal volumes were measured in the contralesional hemisphere. Total and subfield hippocampal volumes were measured using an automated, high-resolution, ex vivo computational atlas. Linear regression analyses were performed for predictors of total and subfield hippocampal volumes. Forty patients with acute ischemic stroke and moderate/severe white matter hyperintensity burden were included in this analysis. Median WMH volume was 9.0 cm3. Adjusting for intracranial volume and stroke laterality, age (β = -3.7, P < 0.001), hypertension (β = -44.7, P = 0.04), WMH volume (β = -0.89, P = 0.049), and mean cortical thickness (β = 286.2, P = 0.006) were associated with total hippocampal volume. In multivariable analysis, age (β = -3.3, P < 0.001) and cortical thickness (β = 205.2, P = 0.028) remained independently associated with total hippocampal volume. In linear regression for predictors of hippocampal subfield volume, increasing WMH volume was associated with decreased hippocampal-amygdala transition area volume (β = -0.04, P = 0.001). These finding suggest that in ischemic stroke patients, increased WMH burden is associated with selective hippocampal subfield degeneration in the hippocampal-amygdala transition area.

Project description:Background:Many research papers claim that patients with specific psychiatric disorders (major depressive disorder, posttraumatic stress disorder, borderline personality disorder, alcohol use disorder, and others) have smaller hippocampi, but most of those reports compared patients to healthy controls. We hypothesized that if psychiatrically matched controls (psychiatric control, matched for demographics and psychiatric comorbidities) were used, much of the biomarker literature in psychiatric research would not replicate. We used hippocampus and amygdala volume only as examples, as these are very commonly replicated results in psychiatry biomarker research. We propose that psychiatry biomarker research could benefit from using psychiatric controls, as the use of healthy controls results in data that are not disorder-specific. Method:Hippocampus/amygdala volumes were compared between major depressive disorder, sex-/age-/race-matched healthy control, and psychiatric control (N?=?126/group). Similar comparisons were performed for posttraumatic stress disorder (N?=?67), borderline personality disorder (N?=?111), and alcohol use disorder (N?=?136). Results:Major depressive disorder patients had smaller left (p?=?8.79?×?10-3) and right (p?=?3.13?×?10-3) hippocampal volumes than healthy control. Posttraumatic stress disorder had smaller left (p?=?0.018) and right (p?=?8.64?×?10-4) hippocampi than healthy control. Borderline personality disorder had smaller right hippocampus (p?=?7.90?×?10-3) and amygdala (p?=?1.49?×?10-3) than healthy control. Alcohol use disorder had smaller right hippocampus (p?=?0.034) and amygdala (p?=?.024) than healthy control. No differences were found between any of the four diagnostic groups and psychiatric control. Conclusion:When psychiatric controls were used, there was no difference in hippocampal or amygdalar volume between any of the diagnoses studied and controls. This strategy (keeping all possible relevant variables matched between experimental groups) has been used to advance science for hundreds of years, and we propose should also be used in biomarker psychiatry research.

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.

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

OmicsDI is part of the ELIXIR infrastructure

Tweets