Evidence map›Paper›PMID 33219452›Full record

ReviewClinical rheumatology2021

A review of applications of metabolomics in osteoarthritis.

Jie-Ting Li, Ni Zeng, Zhi-Peng Yan, Tao Liao, Guo-Xin Ni

Abstract readReview
PubMed Publisher
In one paragraph

Review in Clinical rheumatology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 1 pooled it
–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

22 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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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

5 authors.

Jie-Ting LiDepartment of Rehabilitation Medicine, The First Affiliated Hospital of Fujian Medical University, Fuzhou, 350005, People's Republic of China.
Ni ZengDepartment of Rehabilitation Medicine, The First Affiliated Hospital of Fujian Medical University, Fuzhou, 350005, People's Republic of China.
Zhi-Peng YanDepartment of Rehabilitation Medicine, The First Affiliated Hospital of Fujian Medical University, Fuzhou, 350005, People's Republic of China.
Tao LiaoDepartment of Rehabilitation Medicine, The First Affiliated Hospital of Fujian Medical University, Fuzhou, 350005, People's Republic of China.
Guo-Xin NiSchool of Sport Medicine and Rehabilitation, Beijing Sport University, Beijing, 100084, People's Republic of China. niguoxin@bsu.edu.cn.ORCID http://orcid.org/0000-0001-9181-8155

Funding

National Natural Science Foundation of China 81871848
6 · The paper itself

Abstract

Osteoarthritis (OA) represents the most prevalent and disabling arthritis worldwide due to its heterogeneous and progressive articular degradation. However, effective and timely diagnosis and fundamental treatment for this disorder are lacking. Metabolomics, a growing field in life science research in recent years, has the potential to detect many metabolites and thus explains the underlying pathophysiological processes. Hence, new specific metabolic markers and related metabolic pathways can be identified for OA. In this review, we aimed to provide an overview of studies related to the metabolomics of OA in animal models and humans to describe the metabolic changes and related pathways for OA. The present metabolomics studies reveal that the pathogenesis of OA may be significantly related to perturbations of amino acid metabolism. These altered amino acids (e.g., branched-chain amino acids, arginine, and alanine), as well as phospholipids, were identified as potential biomarkers to distinguish patients with OA from healthy individuals.

Indexed as

MetabolomicsOsteoarthritisAnimalsArginineBiomarkersHumansArginineBiomarkersBiomarkersMetabolic pathwaysMetabolomicsOsteoarthritis

Identifiers

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.