Evidence map›Paper›PMID 39934890›Full record

ReviewBiomarker research2025

Progress in multi-omics studies of osteoarthritis.

Yuanyuan Wei, He Qian, Xiaoyu Zhang, Jian Wang, Heguo Yan, Niqin Xiao, Sanjin Zeng, Bingbing Chen, Qianqian Yang, Hongting Lu and 4 more

Abstract readReview
In one paragraph

Review in Biomarker research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Review
  2. Review
  3. Decoding adolescent TMJ osteoarthritis with multimodal machine learning.Journal of oral & facial pain and headache · 2026
    Article
  4. Article
  5. Review
  6. Review
  7. Article
  8. Article
  9. Article
  10. Review
  11. 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

14 authors.

Yuanyuan Wei *First Clinical Medical College, Yunnan University of Chinese Medicine, Kunming, Yunnan, China.
He Qian *School of Basic Medical Sciences, Yunnan University of Chinese Medicine, Kunming, Yunnan, China.
Xiaoyu Zhang *First Clinical Medical College, Yunnan University of Chinese Medicine, Kunming, Yunnan, China.
Jian WangFirst Clinical Medical College, Yunnan University of Chinese Medicine, Kunming, Yunnan, China.
Heguo YanFirst Clinical Medical College, Yunnan University of Chinese Medicine, Kunming, Yunnan, China.
Niqin XiaoFirst Clinical Medical College, Yunnan University of Chinese Medicine, Kunming, Yunnan, China.
Sanjin ZengSchool of Basic Medical Sciences, Yunnan University of Chinese Medicine, Kunming, Yunnan, China.
Bingbing ChenSchool of Basic Medical Sciences, Yunnan University of Chinese Medicine, Kunming, Yunnan, China.
Qianqian YangFirst Clinical Medical College, Yunnan University of Chinese Medicine, Kunming, Yunnan, China.
Hongting LuFirst Clinical Medical College, Yunnan University of Chinese Medicine, Kunming, Yunnan, China.
Jing XieSchool of Basic Medical Sciences, Yunnan University of Chinese Medicine, Kunming, Yunnan, China.
Zhaohu XieFirst Clinical Medical College, Yunnan University of Chinese Medicine, Kunming, Yunnan, China. zhaohu1023@126.com.
Dongdong QinSchool of Basic Medical Sciences, Yunnan University of Chinese Medicine, Kunming, Yunnan, China. qindong108@163.com.
Zhaofu LiFirst Clinical Medical College, Yunnan University of Chinese Medicine, Kunming, Yunnan, China. lzf0817@126.com.

Funding

Innovative Scientific Research Project of "key support and characteristic discipline of Yunnan first-class discipline-.traditional Chinese Medicine ZYXYB202407Major Science and Technology Project of Yunnan Province in the Field of Biomedicine 202402AA310028National Natural Science Foundation of China 82374427Yunnan Key Laboratory of Integrated Traditional Chinese and Western Medicine for Chronic Disease in Prevention and Treatment CWCD2023-002Yunnan Key Laboratory of Integrated Traditional Chinese and Western Medicine for Chronic Disease in Prevention and Treatment CWCD2023-009Yunnan Province High-level Science and Technology Talents and Innovation Team Selection Special Project 202305AS350007
6 · The paper itself

Abstract

Osteoarthritis (OA), a ubiquitous degenerative joint disorder, is marked by pain and disability, profoundly impacting patients' quality of life. As the population ages, the global prevalence of OA is escalating. Omics technologies have become instrumental in investigating complex diseases like OA, offering comprehensive insights into its pathogenesis and progression by uncovering disease-specific alterations across genomics, transcriptomics, proteomics, and metabolomics levels. In this review, we systematically analyzed and summarized the application and recent achievements of omics technologies in OA research by scouring relevant literature in databases such as PubMed. These studies have shed light on new potential therapeutic targets and biomarkers, charting fresh avenues for OA diagnosis and treatment. Furthermore, in our discussion, we highlighted the immense potential of spatial omics technologies in unraveling the molecular mechanisms of OA and in the development of novel therapeutic strategies, proposing future research directions and challenges. Collectively, this study encapsulates the pivotal advances in current OA research and prospects for future investigation, providing invaluable references for a deeper understanding and treatment of OA. This review aims to synthesize the recent progress of omics technologies in the realm of OA, aspiring to furnish theoretical foundations and research orientations for more profound studies of OA in the future.

Indexed as

GenomicsOmicsOsteoarthritisProteomicsTranscriptomics

Identifiers

PMID39934890
PMCPMC11817798

What Socratic 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 Socratic graph.