Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.4field-weighted citation impact, top 34% 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
2 citing papers in PubMed, 3 citations in OpenAlex.
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
11 authors at 4 institutions in 2 countries.
Shu Ran *School of Medical Instruments and Food Engineering, University of Shanghai for Science and Technology, Shanghai, PR China.
Yu-Xue Zhang *School of Medical Instruments and Food Engineering, University of Shanghai for Science and Technology, Shanghai, PR China.ORCID http://orcid.org/0000-0001-5053-6100
Lu Liu *Kunshan Hospital of Traditional Chinese Medicine, 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.
Yu LiuSchool of Medical Instruments and Food Engineering, University of Shanghai for Science and Technology, Shanghai, PR China.
Hui ShenDepartment of Biostatistics and Bioinformatics, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, 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, Louisiana, USA.
Yu-Fang PeiDepartment of Epidemiology and Statistics, School of Public Health, Soochow University, Jiangsu, PR China.
Hong-Wen DengDepartment of Biostatistics and Bioinformatics, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, USA.ORCID http://orcid.org/0000-0002-0387-8818
Lei ZhangJiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Soochow University, Jiangsu, PR China. lzhang6@suda.edu.cn.
University of Shanghai for Science and Technology · CNTulane University · USSoochow University · CNShanghai University of Traditional Chinese Medicine · CN
Funding
Tulane COBRE in Cardiometabolic Diseases Clinical Research CoreP20GM109036 · NIGMS · TULANE UNIVERSITY OF LOUISIANA · PI Tony Y. Hu · 2016 to 2026
$25.3M
Trans-omics Integration of Multi-omics Studies for OsteoporosisU19AG055373 · NIA · TULANE UNIVERSITY OF LOUISIANA · PI Qi Zhao · 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
Nutrigenetics and Nutrigenomics for Precision Weight-Loss Diet InterventionsR01DK115679 · NIDDK · TULANE UNIVERSITY OF LOUISIANA · PI Lu Qi · 2018 to 2026
$5.3M
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
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
THE FRAMINGHAM HEART STUDY-N01HC25195-268025195-268025195N01HC025195 · HC · TRUSTEES OF BOSTON UNIVERSITY · PI WOLF, PHILIP A · 2002 to 2006
Whole body lean mass (WBLM) is a heritable trait predicting sarcopenia. To identify genomic locus underlying WBLM, we performed a genome-wide association study of fat-adjusted WBLM in the Framingham Heart Study (FHS, N = 6,004), and replicated in the Kansas City Osteoporosis Study (KCOS, N = 2,207). We identified a novel locus 3p27.1 that was associated with WBLM (lead SNP rs3732593 P = 7.19 × 10
Indexed as
Body CompositionPolymorphism, Single NucleotideChromosomes, Human, Pair 3FemaleGenetic LociGenome-Wide Association StudyHumansMaleMiddle AgedPhenotypeSarcopeniaThinness
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.
Association of 3p27.1 Variants with Whole Body Lean Mass Identified by a Genome-wide Association Study. · full record | OpenQuestion