Evidence map›Paper›PMID 37943870›Full record

ArticlePloS one2023

Metabolomic profiles associated with physical activity in White and African American adult men.

Yan Du, Yuan-Yuan Li, Byeong Yeob Choi, Roman Fernadez, Kuan-Jui Su, Kumar Sharma, Lu Qi, Zenong Yin, Qi Zhao, Hui Shen and 6 more

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.5field-weighted citation impact, top 33% 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

6 citing papers in PubMed, 3 citations in OpenAlex.

  1. Trial
  2. Review
  3. Article
  4. Article
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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

16 authors at 5 institutions in 1 country.

Yan DuSchool of Nursing, University of Texas Health Science Center at San Antonio, San Antonio, Texas, United States of America.ORCID 0000-0003-0683-3989
Yuan-Yuan LiDepartment of Nutrition, Nutrition Research Institute, University of North Carolina at Chapel Hill School of Public Health, Kannapolis, North Carolina, United States of America.
Byeong Yeob ChoiDepartment of Population Health Sciences, University of Texas Health Science Center at San Antonio, San Antonio, Texas, United States of America.ORCID 0000-0002-4205-1442
Roman FernadezDepartment of Population Health Sciences, University of Texas Health Science Center at San Antonio, San Antonio, Texas, United States of America.
Kuan-Jui SuTulane Center for Biomedical Informatics and Genomics, School of Medicine, Tulane University; New Orleans, LA, United States of America.ORCID 0000-0002-5163-9774
Kumar SharmaCenter for Precision Medicine, School of Medicine, University of Texas Health Science Center at San Antonio, San Antonio, Texas, United States of America.
Lu QiDepartment of Epidemiology, School of Public Health and Tropical Medicine, Tulane University; New Orleans, LA, United States of America.
Zenong YinDepartment of Public Health, University of Texas at San Antonio, San Antonio, TX, United States of America.
Qi ZhaoDepartment of Preventive Medicine, University of Tennessee Health Science Center, Memphis, TN, United States of America.
Hui ShenTulane Center for Biomedical Informatics and Genomics, School of Medicine, Tulane University; New Orleans, LA, United States of America.
Chuan QiuTulane Center for Biomedical Informatics and Genomics, School of Medicine, Tulane University; New Orleans, LA, United States of America.
Lan-Juan ZhaoTulane Center for Biomedical Informatics and Genomics, School of Medicine, Tulane University; New Orleans, LA, United States of America.
Zhe LuoTulane Center for Biomedical Informatics and Genomics, School of Medicine, Tulane University; New Orleans, LA, United States of America.
Li WuTulane Center for Biomedical Informatics and Genomics, School of Medicine, Tulane University; New Orleans, LA, United States of America.
Qing TianTulane Center for Biomedical Informatics and Genomics, School of Medicine, Tulane University; New Orleans, LA, United States of America.
Hong-Wen DengTulane Center for Biomedical Informatics and Genomics, School of Medicine, Tulane University; New Orleans, LA, United States of America.
Tulane University · USThe University of Texas Health Science Center at San Antonio · USThe University of Texas at San Antonio · USUniversity of North Carolina at Chapel Hill · USUniversity of Tennessee Health Science Center · US

Funding

Tulane COBRE in Cardiometabolic Diseases Clinical Research CoreP20GM109036 · NIGMS · TULANE UNIVERSITY OF LOUISIANA · PI JING CHEN · 2016 to 2026
$25.3M
Trans-omics Integration of Multi-omics Studies for OsteoporosisU19AG055373 · NIA · TULANE UNIVERSITY OF LOUISIANA · PI HONG-WEN DENG · 2017 to 2026
$24.3M
Identification of Metabolomic Profiles for Sarcopenia Traits in Older Whites and BlacksR01AG061917 · NIA · UNIVERSITY OF TENNESSEE HEALTH SCI CTR · PI SHEN, HUI, ZHAO, QI · 2019 to 2023
$3.0M
Decoding Methylation Mediated Epigenomic Contributions to Male OsteoporosisR01AR069055 · NIAMS · TULANE UNIVERSITY OF LOUISIANA · PI DENG, HONG-WEN · 2017 to 2021
$2.9M
NIAMS NIH HHS R01 AR069055NIA NIH HHS R01 AG061917NIA NIH HHS U19 AG055373NIGMS NIH HHS P20 GM109036
6 · The paper itself

Abstract

backgroundPhysical activity (PA) is associated with various health benefits, especially in improving chronic health conditions. However, the metabolic changes in host metabolism in response to PA remain unclear, especially in racially/ethnically diverse populations.

objectiveThis study is to assess the metabolic profiles associated with the frequency of PA in White and African American (AA) men.

methodsUsing the untargeted metabolomics data collected from 698 White and AA participants (mean age: 38.0±8.0, age range: 20-50) from the Louisiana Osteoporosis Study (LOS), we conducted linear regression models to examine metabolites that are associated with PA levels (assessed by self-reported regular exercise frequency levels: 0, 1-2, and ≥3 times per week) in White and AA men, respectively, as well as in the pooled sample. Covariates considered for statistical adjustments included race (only for the pooled sample), age, BMI, waist circumstance, smoking status, and alcohol drinking.

resultsOf the 1133 untargeted compounds, we identified 7 metabolites associated with PA levels in the pooled sample after covariate adjustment with a false discovery rate of 0.15. Specifically, compared to participants who did not exercise, those who exercised at a frequency ≥3 times/week showed higher abundances in uracil, orotate, 1-(1-enyl-palmitoyl)-2-oleoyl-GPE (P-16:0/18:1) (GPE), threonate, and glycerate, but lower abundances in salicyluric glucuronide and adenine in the pooled sample. However, in Whites, salicyluric glucuronide and orotate were not significant. Adenine, GPE, and threonate were not significant in AAs. In addition, the seven metabolites were not significantly different between participants who exercised ≥3 times/week and 1-2 times/week, nor significantly different between participants with 1-2 times/week and 0/week in the pooled sample and respective White and AA groups.

conclusionsMetabolite responses to PA are dose sensitive and may differ between White and AA populations. The identified metabolites may help advance our knowledge of guiding precision PA interventions. Studies with rigorous study designs are warranted to elucidate the relationship between PA and metabolites.

Indexed as

Black or African AmericanExerciseMetabolomeWhiteAdenineAdultButyratesGlucuronidesHumansMaleMiddle AgedYoung AdultAdenineButyratesGlucuronidesthreonic acid

Identifiers

PMID37943870
PMCPMC10635561
OpenAlexW4388521275

What OpenQuestion holds

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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.