Evidence map›Paper›PMID 26890003›Full record

ArticlePloS one2016

The Potential Biomarkers to Identify the Development of Steatosis in Hyperuricemia.

Yong Tan, Xinru Liu, Ke Zhou, Xiaojuan He, Cheng Lu, Bing He, Xuyan Niu, Cheng Xiao, Gang Xu, Zhaoxiang Bian and 4 more

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
0.7field-weighted citation impact, top 29% 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, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.

  1. A metabolomic signatures in hyperuricemia: a systematic review.Metabolomics : Official journal of the Metabolomic Society · 2026
    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

14 authors at 6 institutions in 2 countries.

Yong TanInstitute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China.
Xinru LiuSchool of Pharmacy, Second Military Medical University, Shanghai, China.
Ke ZhouSecond Affiliated Hospital, Hunan University of Chinese Medicine, Changsha, China.
Xiaojuan HeInstitute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China.
Cheng LuInstitute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China.
Bing HeInstitute for Advancing Translational Medicine in Bone & Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China.
Xuyan NiuInstitute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China.
Cheng XiaoChina-Japan Friendship Hospital, Beijing, China.
Gang XuInstitute for Advancing Translational Medicine in Bone & Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China.
Zhaoxiang BianInstitute for Advancing Translational Medicine in Bone & Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China.
Xianpeng ZuSchool of Pharmacy, Second Military Medical University, Shanghai, China.
Ge ZhangInstitute for Advancing Translational Medicine in Bone & Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China.
Weidong ZhangSchool of Pharmacy, Second Military Medical University, Shanghai, China.
Aiping LuInstitute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China.
Chinese Academy of Medical Sciences & Peking Union Medical College · CNHong Kong Baptist University · HKSecond Military Medical University · CNChina-Japan Friendship Hospital · CNSecond Affiliated Hospital of Hunan University of Traditional Chinese Medicine · CNShanghai Municipal Education Commission · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hyperuricemia (HU) often progresses to combine with non-alcoholic fatty liver disease (NAFLD) in the clinical scenario, which further exacerbates metabolic disorders; early detection of biomarkers, if obtained during the HU progression, may be beneficial for preventing its combination with NAFLD. This study aimed to decipher the biomarkers and mechanisms of the development of steatosis in HU. Four groups of subjects undergoing health screening, including healthy subjects, subjects with HU, subjects with HU combined with NAFLD (HU+NAFLD) and subjects with HU initially and then with HU+NAFLD one year later (HU→HU+NAFLD), were recruited in this study. The metabolic profiles of all subjects' serum were analyzed by liquid chromatography quadruple time-of-flight mass spectrometry. The metabolomic data from subjects with HU and HU+NAFLD were compared, and the biomarkers for the progression from HU to HU+NAFLD were predicted. The metabolomic data from HU→HU+NAFLD subjects were collected for further verification. The results showed that the progression was associated with disturbances of phospholipase metabolism, purine nucleotide degradation and Liver X receptor/retinoic X receptor activation as characterized by up-regulated phosphatidic acid, cholesterol ester (18:0) and down-regulated inosine. These metabolic alterations may be at least partially responsible for the development of steatosis in HU. This study provides a new paradigm for better understanding and further prevention of disease progression.

Indexed as

AdultBiomarkersChinaChromatography, LiquidDisease ProgressionFemaleHumansHyperuricemiaMaleMetabolic Networks and PathwaysMetabolomeMetabolomicsMiddle AgedNon-alcoholic Fatty Liver DiseaseReproducibility of ResultsSpectrometry, Mass, Matrix-Assisted Laser Desorption-IonizationBiomarkers

Identifiers

PMID26890003
PMCPMC4758628
OpenAlexW2284937257

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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.