Evidence map›Paper›PMID 31785839›Full record

ArticleNeurobiology of aging2020

Brain and blood metabolome for Alzheimer's dementia: findings from a targeted metabolomics analysis.

Zhiguang Huo, Lei Yu, Jingyun Yang, Yun Zhu, David A Bennett, Jinying Zhao

Erratum issuedOpen access · greenAbstract read
In one paragraph

Article in Neurobiology of aging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 101 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
101citing papers in PubMed, 2 pooled it
5.0field-weighted citation impact, top 4% 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

101 citing papers in PubMed, 2 syntheses or guidelines pooled it, 159 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
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  12. Prediction of Episodic Memory With Multiomics Scores.Biological psychiatry global open science · 2026
    Article
  13. Article
  14. Article
  15. Article
  16. Metabolic dysregulation in Alzheimer's disease: A brain metabolomics approach.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025
    Article
  17. Article
  18. Article
  19. Article
  20. Article

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

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 1 country.

Zhiguang HuoDepartment of Biostatistics, University of Florida, Gainesville, FL, USA.
Lei YuRush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA.
Jingyun YangRush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA.
Yun ZhuDepartment of Epidemiology, University of Florida, Gainesville, FL, USA.
David A BennettRush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA.
Jinying ZhaoDepartment of Epidemiology, University of Florida, Gainesville, FL, USA. Electronic address: jzhao66@ufl.edu.
Rush University Medical Center · USUniversity of Florida · US

Funding

SUPPLEMENT TO RUSH ALZHEIMERS DISEASE CENTER COREP30AG010161 · NIA · RUSH UNIVERSITY MEDICAL CENTER · PI LEURGANS, SUE E. · 1991 to 2020
$49.1M
EPIDEMIOLOGY OF NEURAL RESERVE AND NEUROBIOLOGY IN AGINGR01AG017917 · NIA · RUSH UNIVERSITY MEDICAL CENTER · PI BENNETT, DAVID ALAN · 2001 to 2023
$43.3M
RISK FACTORS, PATHOLOGY, AND CLINICAL EXPRESSIONS OF ADR01AG015819 · NIA · RUSH UNIVERSITY MEDICAL CENTER · PI BENNETT, DAVID ALAN · 1998 to 2024
$21.4M
Pathway discovery, validation and compound identification for Alzheimer's disease - SupplementU01AG046152 · NIA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI BENNETT, DAVID ALAN, DE JAGER, PHILIP L · 2013 to 2017
$13.6M
Metabolomic Signatures for Disease Sub-classification and Target Prioritization in AMP-ADU01AG061359 · NIA · DUKE UNIVERSITY · PI KADDURAH-DAOUK, RIMA F, KASTENMULLER, GABI · 2018 to 2022
$10.0M
Metabolic Signatures Underlying Vascular Risk Factors for Alzheimer-type DementiasRF1AG051550 · NIA · DUKE UNIVERSITY · PI KADDURAH-DAOUK, RIMA F, KLING, MITCHEL ALLAN · 2015 to 2016
$6.3M
Metabolic Networks and Pathways Predictive of Sex Differences in AD Risk and Responsiveness to TreatmentRF1AG059093 · NIA · DUKE UNIVERSITY · PI BRINTON, ROBERTA EILEEN, CHANG, RUI · 2018 to 2018
$5.9M
Exploring the Role of the Brain Epigenome: Cognitive Decline and Life ExperiencesRF1AG036042 · NIA · RUSH UNIVERSITY MEDICAL CENTER · PI BENNETT, DAVID ALAN, DE JAGER, PHILIP L · 2016 to 2016
$5.5M
Metabolic Networks and Pathways in Alzheimer's DiseaseR01AG046171 · NIA · DUKE UNIVERSITY · PI KADDURAH-DAOUK, RIMA F · 2014 to 2017
$4.4M
Genome-wide Profiling of Brain DNA Hydroxymethylome in Alzheimer’s DiseaseRF1AG052476 · NIA · UNIVERSITY OF FLORIDA · PI BENNETT, DAVID ALAN, JIN, PENG · 2016 to 2019
$4.1M
Metabolic Network Analysis of Biochemical Trajectories in Alzheimer's DiseaseRF1AG057452 · NIA · DUKE UNIVERSITY · PI KADDURAH-DAOUK, RIMA F, KASTENMULLER, GABI · 2017 to 2017
$3.5M
Gut Liver Brain Biochemical Axis in Alzheimer's DiseaseRF1AG058942 · NIA · DUKE UNIVERSITY · PI KADDURAH-DAOUK, RIMA F, VAN DUIJN, CORNELIA MARJA · 2018 to 2018
$3.4M
NIA NIH HHS P30 AG010161NIA NIH HHS R01 AG015819NIA NIH HHS R01 AG017917NIA NIH HHS R01 AG036836NIA NIH HHS R01 AG042210NIA NIH HHS R01 AG046171NIA NIH HHS RF1 AG036042NIA NIH HHS RF1 AG051550NIA NIH HHS RF1 AG052476NIA NIH HHS RF1 AG057452NIA NIH HHS RF1 AG058942NIA NIH HHS RF1 AG059093NIA NIH HHS U01 AG046152NIA NIH HHS U01 AG061359NIDDK NIH HHS R01 DK107532
6 · The paper itself

Abstract

The development of Alzheimer's dementia (AD) accompanies both central and peripheral metabolic disturbance, but the metabolic basis underlying AD and metabolic markers predictive of AD risk remain to be determined. It is also unclear whether the metabolic changes in the peripheral blood and brain are overlapping in relation to AD. The present study addresses these questions by targeted metabolomics in both antemortem blood and postmortem brain samples in 2 community-based longitudinal cohorts of aging and dementia. We found that higher serum levels of 3 acylcarnitines, including decanoylcarnitine (C10), pimelylcarnitine (C7-DC), and tetradecadienylcarnitine (C14:2), significantly predict a lower risk of incident AD (composite hazard ratio = 0.368, 95% CI [0.207, 0.653]) after an average of 4.5-year follow-up, independent of age, sex, and education. In addition, baseline serum levels of ten glycerophospholipids, one amino acid, and 5 acylcarnitines predict the longitudinal change in cognitive functions. Moreover, 28 brain metabolites were associated with AD phenotypes. Of the putative metabolites identified in the serum and brain, 4 metabolites (3 glycerophospholipids [PC aa C30:0, PC ae C34:0, PC ae C36:1] and 1 acylcarnitine [C14:2]) were present in both the postmortem brain and antemortem blood, but only one metabolite (C14:2) was associated with AD in the same direction (i.e., protective). Partial correlation and network analyses suggest a potential tissue-specific regulation of metabolism, although other alternatives exist. Together, we identified significant associations of both central and peripheral metabolites with AD phenotypes, but there seems to be little overlap between the 2 tissues.

Indexed as

MetabolomeMetabolomicsAgedAged, 80 and overAlzheimer DiseaseBiomarkersBrainCarnitineCognitionCohort StudiesFemaleFollow-Up StudiesHumansLongitudinal StudiesMaleMiddle AgedacylcarnitineBiomarkersCarnitineAD neuropathologyAlzheimer's dementiaCognitive declinePostmortem brain metabolomicsTargeted metabolomics

Identifiers

PMID31785839
PMCPMC6995427
OpenAlexW2986788348

What OpenQuestion holds

Textmetadata
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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.