Evidence map›Paper›PMID 33693744›Full record

ArticleThe Journal of clinical endocrinology and metabolism2021

Identification and Functional Characterization of Metabolites for Bone Mass in Peri- and Postmenopausal Chinese Women.

Rui Gong, Hong-Mei Xiao, Yin-Hua Zhang, Qi Zhao, Kuan-Jui Su, Xu Lin, Cheng-Lin Mo, Qiang Zhang, Ya-Ting Du, Feng-Ye Lyu and 20 more

Open access · bronzeAbstract read
In one paragraph

Article in The Journal of clinical endocrinology and metabolism, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 2 pooled it
1.2field-weighted citation impact, top 22% 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

16 citing papers in PubMed, 2 syntheses or guidelines pooled it, 20 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

30 authors at 7 institutions in 2 countries.

Rui GongDepartment of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China.ORCID 0000-0001-9942-5655
Hong-Mei XiaoCenter of System Biology, Data Information and Reproductive Health, School of Basic Medical Science, Central South University, Changsha, China.ORCID 0000-0002-8121-9498
Yin-Hua ZhangDepartment of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China.
Qi ZhaoDepartment of Preventive Medicine, College of Medicine, University of Tennessee Health Science Center, Memphis, TN, USA.
Kuan-Jui SuTulane Center for Biomedical Informatics and Genomics, School of Medicine, Tulane University, New Orleans, LA, USA.
Xu LinDepartment of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China.
Cheng-Lin MoBone-Muscle Research Center, College of Nursing and Health Innovation, The University of Texas-Arlington, Arlington, TX, USA.
Qiang ZhangTulane Center for Biomedical Informatics and Genomics, School of Medicine, Tulane University, New Orleans, LA, USA.
Ya-Ting DuBone-Muscle Research Center, College of Nursing and Health Innovation, The University of Texas-Arlington, Arlington, TX, USA.
Feng-Ye LyuLC-Bio Technologies (Hangzhou) CO.LTD, Hangzhou, China.
Yuan-Cheng ChenDepartment of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China.
Cheng PengDepartment of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China.
Hui-Min LiuCenter of System Biology, Data Information and Reproductive Health, School of Basic Medical Science, Central South University, Changsha, China.
Shi-Di HuDepartment of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China.
Dao-Yan PanDepartment of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China.
Zhi ChenDepartment of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China.
Zhang-Fang LiDepartment of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China.
Rou ZhouDepartment of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China.
Xia-Fang WangDepartment of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China.
Jun-Min LuDepartment of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China.
Zeng-Xin AoDepartment of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China.
Yu-Qian SongDepartment of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China.
Chan-Yan WengDepartment of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China.
Qing TianTulane Center for Biomedical Informatics and Genomics, School of Medicine, Tulane University, New Orleans, LA, USA.
Martin R SchillerNevada Institute of Personalized Medicine, University of Nevada Las Vegas, Las Vegas, NV, USA.
Christopher J PapasianDepartment of Biomedical Sciences, University of Missouri-Kansas City, School of Medicine, Kansas City, MO, USA.
Marco BrottoBone-Muscle Research Center, College of Nursing and Health Innovation, The University of Texas-Arlington, Arlington, TX, USA.
Hui ShenTulane Center for Biomedical Informatics and Genomics, School of Medicine, Tulane University, New Orleans, LA, USA.
Jie ShenDepartment of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China.ORCID 0000-0001-6952-9541
Hong-Wen DengTulane Center for Biomedical Informatics and Genomics, School of Medicine, Tulane University, New Orleans, LA, USA.ORCID 0000-0002-0387-8818
Third Affiliated Hospital of Southern Medical University · CNTulane University · USThe University of Texas at Arlington · USCentral South University · CNUniversity of Missouri–Kansas City · USUniversity of Nevada, Las Vegas · USUniversity of Tennessee Health Science Center · US

Funding

Transgenic and Mechanical Loading CoreP01AG039355 · NIA · UNIVERSITY OF MISSOURI KANSAS CITY · PI BONEWALD, LYNDA F · 2012 to 2024
$19.3M
The Genome Analysis and Sequencing Pipeline (GASP)P20GM121325 · NIGMS · UNIVERSITY OF NEVADA LAS VEGAS · PI CHEN, JINGCHUN · 2018 to 2022
$11.5M
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
Regulation of Store-Operated Calcium Entry During Muscle AgingR01AG056504 · NIA · OHIO STATE UNIVERSITY · PI BROTTO, MARCO, WEISLEDER, NOAH · 2018 to 2022
$2.6M
Integration of brain imaging with genomic and epigenomic dataR01MH104680 · NIMH · TULANE UNIVERSITY OF LOUISIANA · PI CALHOUN, VINCE D, DENG, HONG-WEN · 2014 to 2017
$2.1M
NIAMS NIH HHS R01 AR069055NIA NIH HHS P01 AG039355NIA NIH HHS PO1 AG039355NIA NIH HHS R01 AG056504NIA NIH HHS R01 AG061917NIGMS NIH HHS P20 GM121325NIH HHS U19AG05537301NIMH NIH HHS R01 MH104680
6 · The paper itself

Abstract

contextAlthough metabolic profiles appear to play an important role in menopausal bone loss, the functional mechanisms by which metabolites influence bone mineral density (BMD) during menopause are largely unknown.

objectiveWe aimed to systematically identify metabolites associated with BMD variation and their potential functional mechanisms in peri- and postmenopausal women. DESIGN AND

methodsWe performed serum metabolomic profiling and whole-genome sequencing for 517 perimenopausal (16%) and early postmenopausal (84%) women aged 41 to 64 years in this cross-sectional study. Partial least squares regression and general linear regression analysis were applied to identify BMD-associated metabolites, and weighted gene co-expression network analysis was performed to construct co-functional metabolite modules. Furthermore, we performed Mendelian randomization analysis to identify causal relationships between BMD-associated metabolites and BMD variation. Finally, we explored the effects of a novel prominent BMD-associated metabolite on bone metabolism through both in vivo/in vitro experiments.

resultsTwenty metabolites and a co-functional metabolite module (consisting of fatty acids) were significantly associated with BMD variation. We found dodecanoic acid (DA), within the identified module causally decreased total hip BMD. Subsequently, the in vivo experiments might support that dietary supplementation with DA could promote bone loss, as well as increase the osteoblast and osteoclast numbers in normal/ovariectomized mice. Dodecanoic acid treatment differentially promoted osteoblast and osteoclast differentiation, especially for osteoclast differentiation at higher concentrations in vitro (eg,10, 100 μM).

conclusionsThis study sheds light on metabolomic profiles associated with postmenopausal osteoporosis risk, highlighting the potential importance of fatty acids, as exemplified by DA, in regulating BMD.

Indexed as

Absorptiometry, PhotonAdultAnimalsBiomarkersBone DensityCell LineChinaCross-Sectional StudiesFemaleHumansLauric AcidsMetabolomeMiceMiddle AgedOsteogenesisOsteoporosis, PostmenopausalBiomarkerslauric acidLauric Acidsbone mineral densityfatty acidsmetabolitesmetabolomicspostmenopausal osteoporosiswhole-genome sequencing

Identifiers

PMID33693744
PMCPMC8277206
OpenAlexW3134851277

What Socratic 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 Socratic graph.