Evidence map›Paper›PMID 33552737›Full record

ArticlePeerJ2021

The longitudinal associations between bone mineral density and appendicular skeletal muscle mass in Chinese community-dwelling middle aged and elderly men.

Xuejuan Xu, Nuo Xu, Ying Wang, Jinsong Chen, Lushi Chen, Shengjian Zhang, Jingxian Chen, Hongwen Deng, Xiaojun Luan, Jie Shen

Open access · goldAbstract read
In one paragraph

Article in PeerJ, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
0.8field-weighted citation impact, top 29% 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

9 citing papers in PubMed, 10 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 5 institutions in 2 countries.

Xuejuan XuDepartment of Endocrinology, Southern Medical University, Guangzhou, Guangdong, China.
Nuo XuDepartment of Endocrinology, Affiliated Hospital of Jining Medical University, Jining Medical University, Jining, Shandong, China.
Ying WangDepartment of Nuclear Medicine, The First People's Hospital of Foshan, Foshan, Guangdong, China.
Jinsong ChenDepartment of Endocrinology, The First People's Hospital of Foshan, Foshan, Guangdong, China.
Lushi ChenDepartment of Health Care, The First People's Hospital of Foshan, Foshan, Guangdong, China.
Shengjian ZhangDepartment of Health Care, The First People's Hospital of Foshan, Foshan, Guangdong, China.
Jingxian ChenDepartment of Hematology, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong, China.
Hongwen DengCenter of Genomics and Bioinformatics, Tulane University, New Orleans, LA, United States of America.
Xiaojun LuanDepartment of Endocrinology, The First People's Hospital of Foshan, Foshan, Guangdong, China.
Jie ShenDepartment of Endocrinology, Southern Medical University, Guangzhou, Guangdong, China.
First People's Hospital of Foshan · CNAffiliated Hospital of Jining Medical University · CNEighth Affiliated Hospital of Sun Yat-sen UniversityThird Affiliated Hospital of Southern Medical University · CNTulane University · US

Funding

Trans-omics Integration of Multi-omics Studies for OsteoporosisU19AG055373 · NIA · TULANE UNIVERSITY OF LOUISIANA · PI YU-PING WANG · 2017 to 2026
$24.3M
Epigenomewide DNA Methylation Study for Osteoporosis RiskR01AR059781 · NIAMS · TULANE UNIVERSITY OF LOUISIANA · PI DENG, HONG-WEN · 2012 to 2016
$3.1M
Decoding Methylation Mediated Epigenomic Contributions to Male OsteoporosisR01AR069055 · NIAMS · TULANE UNIVERSITY OF LOUISIANA · PI DENG, HONG-WEN · 2017 to 2021
$2.9M
Identification of Proteins Important for Male OsteoporosisR01AR057049 · NIAMS · UNIVERSITY OF MISSOURI KANSAS CITY · PI DENG, HONG-WEN · 2009 to 2013
$2.4M
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
Integration of fMRI imaging, genomics, network and biological knowledgeR01MH107354 · NIMH · TULANE UNIVERSITY OF LOUISIANA · PI WANG, YU-PING · 2015 to 2018
$1.9M
Integration of multiscale genomic data for comprehensive analysis of complex diseR01GM109068 · NIGMS · TULANE UNIVERSITY OF LOUISIANA · PI WANG, YU-PING · 2014 to 2018
$1.6M
NIAMS NIH HHS R01 AR057049NIAMS NIH HHS R01 AR059781NIAMS NIH HHS R01 AR069055NIA NIH HHS U19 AG055373NIGMS NIH HHS R01 GM109068NIMH NIH HHS R01 MH104680NIMH NIH HHS R01 MH107354
6 · The paper itself

Abstract

backgroundThe present study aimed to investigate longitudinal associations between bone mineral densities (BMDs) and appendicular skeletal muscle (ASM) mass in different regions of the body using three different indicators, in Chinese community-dwelling middle-aged and elderly men.

methodsA total of 1,343 men aged ≥ 40 years from a Chinese community were assessed at baseline (2014-2016), one-year follow-up (2016-2017;

resultsThe incidence of low lean mass was 8.2% defined by ASMI, 16.3% defined by SMI, and 8.3% defined by ASM/BMI. There was a linear relationship between BMDs and ASM mass, and ASMI, ASM/BMI, and SMI gradually decreased with time. After adjusting for covariances, GAMM analysis determined longitudinal associations between BMDs and ASM mass by three indicators respectively: the skull BMD was negatively associated with ASM mass. For each unit increase in skull BMD, ASMI decreased by 0.28 kg/m

conclusionsCompared to ASMI and ASM/BMI, SMI was more sensitive to screen for the low lean mass. Skull BMD was negatively associated with ASM mass, while BMDs throughout the rest of the body were positively correlated with ASM mass among the middle-aged and elderly Chinese men.

Indexed as

Body compositionBone densityLongitudinal studiesOsteoporosisSarcopeniaSkeletal muscle

Identifiers

PMID33552737
PMCPMC7821753
OpenAlexW3124236198

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.