Evidence map›Paper›PMID 33250761›Full record

ArticleFrontiers in aging neuroscience2020

Metabolic Profiling of Cognitive Aging in Midlife.

Zhiguang Huo, Brinda K Rana, Jeremy A Elman, Ruocheng Dong, Corinne D Engelman, Sterling C Johnson, Michael J Lyons, Carol E Franz, William S Kremen, Jinying Zhao

Open access · goldAbstract read
In one paragraph

Article in Frontiers in aging neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
1.8field-weighted citation impact, top 15% 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

13 citing papers in PubMed, 19 citations in OpenAlex.

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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

10 authors at 4 institutions in 1 country.

Zhiguang HuoDepartment of Biostatistics, University of Florida, Gainesville, FL, United States.
Brinda K RanaDepartment of Psychiatry, University of California, San Diego, San Diego, CA, United States.
Jeremy A ElmanDepartment of Psychiatry, University of California, San Diego, San Diego, CA, United States.
Ruocheng DongPopulation Health Sciences, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States.
Corinne D EngelmanPopulation Health Sciences, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States.
Sterling C JohnsonWisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States.
Michael J LyonsDepartment of Psychological & Brain Sciences, Boston University, Boston, MA, United States.
Carol E FranzDepartment of Psychiatry, University of California, San Diego, San Diego, CA, United States.
William S KremenDepartment of Psychiatry, University of California, San Diego, San Diego, CA, United States.
Jinying ZhaoDepartment of Epidemiology, University of Florida, Gainesville, FL, United States.
University of California, San Diego · USUniversity of Wisconsin–Madison · USUniversity of Florida · USBoston University · US

Funding

Wisconsin Registry for Alzheimer's Prevention: Sex Differences in DNA MethylationR01AG027161 · NIA · UNIVERSITY OF WISCONSIN-MADISON · PI Nathaniel Ark Chin, Sterling C Johnson · 2007 to 2026
$35.6M
Wisconsin Alzheimer's Disease Research CenterP30AG062715 · NIA · UNIVERSITY OF WISCONSIN-MADISON · PI Barbara Brigitta Bendlin · 2019 to 2026
$34.5M
The VETSA Longitudinal Twin Study of Cognition and Aging (VETSA 4)R01AG050595 · NIA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI ELMAN, JEREMY A, FRANZ, CAROL ELAINE · 2015 to 2024
$28.0M
The VETSA Longitudinal MRI Twin Study of AgingR01AG022381 · NIA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI KREMEN, WILLIAM S. · 2003 to 2019
$22.3M
Wisconsin Registry for Alzheimer's PreventionRF1AG027161 · NIA · UNIVERSITY OF WISCONSIN-MADISON · PI JOHNSON, STERLING C · 2018 to 2018
$18.8M
Scientific and Technical CoreP2CHD047873 · NICHD · UNIVERSITY OF WISCONSIN-MADISON · PI Katherine J. Curtis · 2014 to 2026
$5.1M
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
Genomic and Metabolomic Data Integration in a Longitudinal Cohort at Risk for Alzheimer's DiseaseRF1AG054047 · NIA · UNIVERSITY OF WISCONSIN-MADISON · PI ENGELMAN, CORINNE D. · 2022 to 2025
$3.8M
Genomic and Metabolomic Data Integration in a Longitudinal Cohort at Risk for Alzheimer's DiseaseR01AG054047 · NIA · UNIVERSITY OF WISCONSIN-MADISON · PI ENGELMAN, CORINNE D. · 2016 to 2020
$3.6M
Epigenetic Determinants of Major Depression: a Monozygotic Discordant TwinStudyR01MH097018 · NIMH · UNIVERSITY OF FLORIDA · PI ZHAO, JINYING · 2013 to 2017
$3.3M
Novel Metabolic Predictors of Diabetes in American IndiansR01DK107532 · NIDDK · UNIVERSITY OF FLORIDA · PI FIEHN, OLIVER, ZHAO, JINYING · 2016 to 2019
$3.0M
NIA NIH HHS P30 AG062715NIA NIH HHS R01 AG022381NIA NIH HHS R01 AG027161NIA NIH HHS R01 AG050595NIA NIH HHS R01 AG054047NIA NIH HHS RF1 AG027161NIA NIH HHS RF1 AG052476NIA NIH HHS RF1 AG054047NICHD NIH HHS P2C HD047873NIDDK NIH HHS R01 DK107532NIMH NIH HHS R01 MH097018
6 · The paper itself

Abstract

Alzheimer's dementia (AD) begins many years before its clinical symptoms. Metabolic dysfunction represents a core feature of AD and cognitive impairment, but few metabolomic studies have focused on cognitive aging in midlife. Using an untargeted metabolomics approach, we identified metabolic predictors of cognitive aging in midlife using fasting plasma sample from 30 middle-aged (mean age 57.2), male-male twin pairs enrolled in the Vietnam Era Twin Study of Aging (VETSA). For all twin pairs, one twin developed incident MCI, whereas his co-twin brother remained to be cognitively normal during an average 5.5-year follow-up. Linear mixed model was used to identify metabolites predictive of MCI conversion or cognitive change over time, adjusting for traditional risk factors. Results from twins were replicated in an independent cohort of middle-aged adults (mean age 59.1) in the Wisconsin Registry for Alzheimer's Prevention (WRAP). Results in twins showed that higher baseline levels of four plasma metabolites, including sphingomyelin (d18:1/20:1 and d18:2/20:0), sphingomyelin (d18:1/22:1, d18:2/22:0, and d16:1/24:1), DAG (18:2/20:4), and hydroxy-CMPF, were significantly associated with a slower decrease in one or more domains of cognitive function. The association of sphingomyelin (d18:1/20:1 and d18:2/20:0) was replicated in WRAP. Our results support that metabolic perturbation occurs many years before cognitive impairment and plasma metabolites may serve as early biomarkers for prediction or monitoring of cognitive aging and AD in midlife.

Indexed as

Alzheimer’s diseasecognitive changemiddle agetwin studiesuntargeted metabolomics

Identifiers

PMID33250761
PMCPMC7674168
OpenAlexW3094960702

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.