Evidence map›Paper›PMID 29370177›Full record

ArticlePLoS medicine2018

Brain and blood metabolite signatures of pathology and progression in Alzheimer disease: A targeted metabolomics study.

Vijay R Varma, Anup M Oommen, Sudhir Varma, Ramon Casanova, Yang An, Ryan M Andrews, Richard O'Brien, Olga Pletnikova, Juan C Troncoso, Jon Toledo and 9 more

Open access · goldAbstract read
In one paragraph

Article in PLoS medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 290 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
290citing papers in PubMed, 5 pooled it
27.9field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

290 citing papers in PubMed, 5 syntheses or guidelines pooled it, 484 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Pooled it
  6. Trial
  7. Article
  8. Article
  9. Article
  10. Review
  11. Article
  12. Article
  13. Article
  14. Review
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article

230 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

19 authors at 9 institutions in 3 countries.

Vijay R VarmaClinical and Translational Neuroscience Unit, Laboratory of Behavioral Neuroscience, National Institute on Aging (NIA), National Institutes of Health (NIH), Baltimore, Maryland, United States of America.ORCID 0000-0001-7143-7726
Anup M OommenConsilience Research Advisors LLP, Bengaluru, Karnataka, India.
Sudhir VarmaHiThru Analytics, Laurel, Maryland, United States of America.
Ramon CasanovaDepartment of Biostatistical Science, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America.
Yang AnClinical and Translational Neuroscience Unit, Laboratory of Behavioral Neuroscience, National Institute on Aging (NIA), National Institutes of Health (NIH), Baltimore, Maryland, United States of America.
Ryan M AndrewsDepartment of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America.ORCID 0000-0002-7750-2397
Richard O'BrienDepartment of Neurology, Duke University School of Medicine, Durham, North Carolina, United States of America.
Olga PletnikovaDepartment of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.
Juan C TroncosoDepartment of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.ORCID 0000-0001-9553-6673
Jon ToledoDepartment of Neurology, Houston Methodist Hospital, Houston, Texas, United States of America.ORCID 0000-0003-4366-9268
Rebecca BaillieRosa & Co LLC, San Carlos, California, United States of America.ORCID 0000-0003-3966-6320
Matthias ArnoldInstitute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.ORCID 0000-0002-4666-0923
Gabi KastenmuellerInstitute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
Kwangsik NhoDepartment of Radiology and Imaging Sciences and the Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, Indiana, United States of America.ORCID 0000-0002-7624-3872
P Murali DoraiswamyDepartment of Psychiatry and Behavioral Sciences, Duke University, Durham, North Carolina, United States of America.
Andrew J SaykinDepartment of Radiology and Imaging Sciences and the Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, Indiana, United States of America.ORCID 0000-0002-1376-8532
Rima Kaddurah-DaoukDepartment of Psychiatry and Behavioral Sciences, Duke University, Durham, North Carolina, United States of America.
Cristina Legido-QuigleyIPS, Faculty of Life Sciences and Medicine, King's College London, London, United Kingdom.ORCID 0000-0002-4018-214X
Madhav ThambisettyClinical and Translational Neuroscience Unit, Laboratory of Behavioral Neuroscience, National Institute on Aging (NIA), National Institutes of Health (NIH), Baltimore, Maryland, United States of America.
Duke University · USJohns Hopkins University · USNational Institutes of Health · USHelmholtz Zentrum München · DEIndiana University School of MedicineKing's College London · GBMethodist Hospital · USRosa (United States) · USWake Forest University · US

Funding

Alzheimer's Disease Neuroimaging Initiative - SupplementU01AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE RES &EDUC · PI WEINER, MICHAEL W · 2004 to 2015
$121.0M
'T V ASSESSMENT - MEMORY DISORDERED PATIENTS'P50AG005146 · NIA · JOHNS HOPKINS UNIVERSITY · PI HUGANIR, RICHARD L · 1985 to 2019
$37.6M
Understanding mechanisms and seeking novel biomarkers in Alzheimer's diseaseZIAAG000200 · NIA · NATIONAL INSTITUTE ON AGING · PI RESNICK, SUSAN · 2013 to 2024
$12.2M
Research Training in Age-Related Cognitive DisordersT32AG027668 · NIA · JOHNS HOPKINS UNIVERSITY · PI MARILYN S. ALBERT · 2007 to 2026
$6.3M
Integrating Neuroimaging, Multi-omics, and Clinical Data in Complex DiseaseR01LM012535 · NLM · INDIANA UNIVERSITY INDIANAPOLIS · PI NHO, KWANGSIK TIMOTHY · 2017 to 2021
$1.7M
NIA NIH HHS P50 AG005146NIA NIH HHS T32 AG027668NIA NIH HHS U01 AG024904NLM NIH HHS R01 LM012535
6 · The paper itself

Abstract

backgroundThe metabolic basis of Alzheimer disease (AD) is poorly understood, and the relationships between systemic abnormalities in metabolism and AD pathogenesis are unclear. Understanding how global perturbations in metabolism are related to severity of AD neuropathology and the eventual expression of AD symptoms in at-risk individuals is critical to developing effective disease-modifying treatments. In this study, we undertook parallel metabolomics analyses in both the brain and blood to identify systemic correlates of neuropathology and their associations with prodromal and preclinical measures of AD progression. METHODS AND

findingsQuantitative and targeted metabolomics (Biocrates AbsoluteIDQ [identification and quantification] p180) assays were performed on brain tissue samples from the autopsy cohort of the Baltimore Longitudinal Study of Aging (BLSA) (N = 44, mean age = 81.33, % female = 36.36) from AD (N = 15), control (CN; N = 14), and "asymptomatic Alzheimer's disease" (ASYMAD, i.e., individuals with significant AD pathology but no cognitive impairment during life; N = 15) participants. Using machine-learning methods, we identified a panel of 26 metabolites from two main classes-sphingolipids and glycerophospholipids-that discriminated AD and CN samples with accuracy, sensitivity, and specificity of 83.33%, 86.67%, and 80%, respectively. We then assayed these 26 metabolites in serum samples from two well-characterized longitudinal cohorts representing prodromal (Alzheimer's Disease Neuroimaging Initiative [ADNI], N = 767, mean age = 75.19, % female = 42.63) and preclinical (BLSA) (N = 207, mean age = 78.68, % female = 42.63) AD, in which we tested their associations with magnetic resonance imaging (MRI) measures of AD-related brain atrophy, cerebrospinal fluid (CSF) biomarkers of AD pathology, risk of conversion to incident AD, and trajectories of cognitive performance. We developed an integrated blood and brain endophenotype score that summarized the relative importance of each metabolite to severity of AD pathology and disease progression (Endophenotype Association Score in Early Alzheimer's Disease [EASE-AD]). Finally, we mapped the main metabolite classes emerging from our analyses to key biological pathways implicated in AD pathogenesis. We found that distinct sphingolipid species including sphingomyelin (SM) with acyl residue sums C16:0, C18:1, and C16:1 (SM C16:0, SM C18:1, SM C16:1) and hydroxysphingomyelin with acyl residue sum C14:1 (SM (OH) C14:1) were consistently associated with severity of AD pathology at autopsy and AD progression across prodromal and preclinical stages. Higher log-transformed blood concentrations of all four sphingolipids in cognitively normal individuals were significantly associated with increased risk of future conversion to incident AD: SM C16:0 (hazard ratio [HR] = 4.430, 95% confidence interval [CI] = 1.703-11.520, p = 0.002), SM C16:1 (HR = 3.455, 95% CI = 1.516-7.873, p = 0.003), SM (OH) C14:1 (HR = 3.539, 95% CI = 1.373-9.122, p = 0.009), and SM C18:1 (HR = 2.255, 95% CI = 1.047-4.855, p = 0.038). The sphingolipid species identified map to several biologically relevant pathways implicated in AD, including tau phosphorylation, amyloid-β (Aβ) metabolism, calcium homeostasis, acetylcholine biosynthesis, and apoptosis. Our study has limitations: the relatively small number of brain tissue samples may have limited our power to detect significant associations, control for heterogeneity between groups, and replicate our findings in independent, autopsy-derived brain samples.

conclusionsWe present a novel framework to identify biologically relevant brain and blood metabolites associated with disease pathology and progression during the prodromal and preclinical stages of AD. Our results show that perturbations in sphingolipid metabolism are consistently associated with endophenotypes across preclinical and prodromal AD, as well as with AD pathology at autopsy. Sphingolipids may be biologically relevant biomarkers for the early detection of AD, and correcting perturbations in sphingolipid metabolism may be a plausible and novel therapeutic strategy in AD.

Indexed as

Disease ProgressionMetabolomeAgedAged, 80 and overAlzheimer DiseaseBaltimoreBiomarkersBloodBlood Chemical AnalysisBrainFemaleHumansLongitudinal StudiesMaleBiomarkers

Identifiers

PMID29370177
PMCPMC5784884
OpenAlexW2784429314

What OpenQuestion holds

Textmetadata
LicenceCC0
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.