Evidence map›Paper›PMID 37049535›Full record

ArticleNutrients2023

Integration of Metabolomics and Transcriptomics to Reveal the Metabolic Characteristics of Exercise-Improved Bone Mass.

Jin-Li Hou, Wan-Yu Yang, Qiong Zhang, Hao Feng, Xiao-Bao Wang, Hui Li, Sheng Zhou, Su-Mei Xiao

Open access · goldAbstract read
In one paragraph

Article in Nutrients, 2023. 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
1.4field-weighted citation impact, top 19% 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, 9 citations in OpenAlex.

  1. Review
  2. Article
  3. Gut microbes reports · 2026
    Article
  4. Article
  5. Article
  6. Article
  7. Modulation of the gut-bone axis:Frontiers in immunology · 2025
    Article
  8. Article
  9. Article
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

8 authors at 2 institutions in 1 country.

Jin-Li HouDepartment of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.
Wan-Yu YangDepartment of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.
Qiong ZhangDepartment of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.
Hao FengDepartment of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.
Xiao-Bao WangDepartment of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.
Hui LiDepartment of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.
Sheng ZhouCollege of Marine Sciences, South China Agricultural University, Guangzhou 510642, China.
Su-Mei XiaoDepartment of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.ORCID 0000-0003-4823-1601
Sun Yat-sen University · CNSouth China Agricultural University · CN

Funding

National Natural Science Foundation of China No. 81400857Natural Science Foundation of Guangdong Province No. 2021A1515011527Pearl River S&T Nova Program of Guangzhou No. 201610010093
6 · The paper itself

Abstract

(1) Background: Exercise is effective in promoting and maintaining bone mass. The aim of this study was to detect the exercise-induced metabolic changes in bone tissue of zebrafish. (2) Methods: Thirty-eight zebrafish (Danio rerio, six months old) were analyzed. The exercise group (

Indexed as

TranscriptomeZebrafishAlanineAnimalsAspartic Acidbeta-AlanineGlutamatesMetabolomicsPyrimidinesAlanineAspartic Acidbeta-AlanineGlutamatesPyrimidinesbone massmetabolomicsswimming trainingtranscriptomicszebrafish

Identifiers

PMID37049535
PMCPMC10097349
OpenAlexW4362520067

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.