Evidence map›Paper›PMID 32719433›Full record

SynthesisInternational journal of obesity (2005)2020

Four pleiotropic loci associated with fat mass and lean mass.

Yu Liu, Shu Ran, Yong Lin, Yu-Xue Zhang, Xiao-Lin Yang, Xin-Tong Wei, Zi-Xuan Jiang, Xiao He, Hong Zhang, Gui-Juan Feng and 5 more

Abstract readMeta-Analysis
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in International journal of obesity (2005), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. Review
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

15 authors.

Yu Liu *Center for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Jiangsu, PR China.
Shu Ran *School of Medical Instruments and Food Engineering, University of Shanghai for Science and Technology, Shanghai, PR China.
Yong Lin *School of Medical Instruments and Food Engineering, University of Shanghai for Science and Technology, Shanghai, PR China.
Yu-Xue ZhangSchool of Medical Instruments and Food Engineering, University of Shanghai for Science and Technology, Shanghai, PR China.
Xiao-Lin YangCenter for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Jiangsu, PR China.
Xin-Tong WeiJiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Jiangsu, PR China.
Zi-Xuan JiangSchool of Medical Instruments and Food Engineering, University of Shanghai for Science and Technology, Shanghai, PR China.
Xiao HeSchool of Medical Instruments and Food Engineering, University of Shanghai for Science and Technology, Shanghai, PR China.
Hong ZhangCenter for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Jiangsu, PR China.
Gui-Juan FengJiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Jiangsu, PR China.
Hui ShenDepartment of Biostatistics and Bioinformatics, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA.ORCID http://orcid.org/0000-0003-0335-6064
Qing TianDepartment of Biostatistics and Bioinformatics, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA.
Hong-Wen DengDepartment of Biostatistics and Bioinformatics, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA. hdeng2@tulane.edu.ORCID http://orcid.org/0000-0002-0387-8818
Lei ZhangCenter for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Jiangsu, PR China. lzhang6@suda.edu.cn.ORCID http://orcid.org/0000-0002-1007-4834
Yu-Fang PeiJiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Jiangsu, PR China. ypei@suda.edu.cn.ORCID http://orcid.org/0000-0002-9157-0759

Funding

Tulane COBRE in Cardiometabolic Diseases Clinical Research CoreP20GM109036 · NIGMS · TULANE UNIVERSITY OF LOUISIANA · PI Katherine Teresa Mills · 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
RISK FACTORS FOR VERTEBRAL FRACTURE AND BONE LOSSR01AR041398 · NIAMS · RHODE ISLAND HOSPITAL (PROVIDENCE, RI) · PI KIEL, DOUGLAS P. · 1991 to 2025
$16.1M
Multivariate methods for identifying multitask/multimodal brain imaging biomarkersR01EB006841 · NIBIB · THE MIND RESEARCH NETWORK · PI VINCE D CALHOUN, Sergey Plis · 2007 to 2026
$9.8M
Male/Female differences in psychosis and mood disorders:Dynamic imaging-genomic models for characterizing and predicting psychosis and mood dR01MH118695 · NIMH · GEORGIA STATE UNIVERSITY · PI ADALI, TULAY, CALHOUN, VINCE D · 2019 to 2023
$3.8M
Epigenomewide DNA Methylation Study for Osteoporosis RiskR01AR059781 · NIAMS · TULANE UNIVERSITY OF LOUISIANA · PI DENG, HONG-WEN · 2012 to 2016
$3.1M
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
Unified multivariate data-driven solutions for static and dynamic brain connectivityR01EB020407 · NIBIB · THE MIND RESEARCH NETWORK · PI ADALI, TULAY, CALHOUN, VINCE D · 2015 to 2018
$2.7M
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
WOMENS HEALTH INITIATIVE-N01WH22110N01WH022110 · WH · FRED HUTCHINSON CANCER RESEARCH CENTER · PI ROSS, PRENTICE · 1992 to 2006
$1.2M
SLEEP AND ENTRAINMENT OF SCN FUNCTIONR01HL064278 · NHLBI · CASE WESTERN RESERVE UNIVERSITY · PI STROHL, KINGMAN PERKINS · 1999 to 2002
$871k
Medical Research Council MC_PC_17228Medical Research Council MC_QA137853NHLBI NIH HHS N01 HC025195NHLBI NIH HHS N02 HL064278NIAMS NIH HHS R01 AR041398NIAMS NIH HHS R01 AR059781NIAMS NIH HHS R01 AR069055NIA NIH HHS R01 AG061917NIA NIH HHS U19 AG055373NIBIB NIH HHS R01 EB006841NIBIB NIH HHS R01 EB020407NIGMS NIH HHS P20 GM109036NIMH NIH HHS R01 MH104680NIMH NIH HHS R01 MH118695WHI NIH HHS N01 WH022110
6 · The paper itself

Abstract

backgroundFat mass and lean mass are two biggest components of body mass. Both fat mass and lean mass are under strong genetic determinants and are correlated.

methodsWe performed a bivariate genome-wide association meta-analysis of (lean adjusted) leg fat mass and (fat adjusted) leg lean mass in 12,517 subjects from 6 samples, and followed by in silico replication in large-scale UK biobank cohort sample (N = 370 097).

resultsWe identified four loci that were significant at the genome-wide significance (GWS, α = 5.0 × 10

conclusionsOur findings further enhance the understanding of the genetic association between fat mass and lean mass and provide a new theoretical basis for their understanding.

Indexed as

Genetic PleiotropyGenome-Wide Association StudyAdiposityAdultAgedGenotypeHumansMiddle AgedPhenotypePolymorphism, Single NucleotideUnited Kingdom

Identifiers

PMID32719433
PMCPMC7912634

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.