Evidence map›Paper›PMID 35942381›Full record

ArticleEvidence-based complementary and alternative medicine : eCAM2022

Serum Biomarkers for Chronic Renal Failure Screening and Mechanistic Understanding: A Global LC-MS-Based Metabolomics Research.

Xiaojuan Su, Ruru Ren, Lingling Yang, Chao Su, Yingli Wang, Jun Lu, Jing Liu, Rong Zong, Fangfang Lu, Gidion Wilson and 2 more

Open access · hybridAbstract read
In one paragraph

Article in Evidence-based complementary and alternative medicine : eCAM, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.2field-weighted citation impact, top 52% 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

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. 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

12 authors at 2 institutions in 1 country.

Xiaojuan SuDepartment of Pharmaceutical Analysis, School of Pharmacy, Ningxia Medical University, 1160 Shenli Street, Yinchuan 750004, China.ORCID https://orcid.org/0000-0001-7067-0067
Ruru RenDepartment of Pharmaceutical Analysis, School of Pharmacy, Ningxia Medical University, 1160 Shenli Street, Yinchuan 750004, China.ORCID https://orcid.org/0000-0001-6013-8368
Lingling YangDepartment of Pharmaceutical Analysis, School of Pharmacy, Ningxia Medical University, 1160 Shenli Street, Yinchuan 750004, China.ORCID https://orcid.org/0000-0003-1149-3237
Chao SuDepartment of Pharmaceutical Analysis, School of Pharmacy, Ningxia Medical University, 1160 Shenli Street, Yinchuan 750004, China.ORCID https://orcid.org/0000-0003-3310-7819
Yingli WangDepartment of Pharmaceutical Analysis, School of Pharmacy, Ningxia Medical University, 1160 Shenli Street, Yinchuan 750004, China.ORCID https://orcid.org/0000-0003-1031-1673
Jun LuYinchuan Weikang Nephrology Hospital, Intersection of Lijing North Street and Agricultural Materials Lane, Yinchuan 750004, China.ORCID https://orcid.org/0000-0001-6392-4882
Jing LiuSchool of Clinical Medicine, Ningxia Medical University, 692 Shenli Street, Yinchuan 750004, China.ORCID https://orcid.org/0000-0002-4872-9867
Rong ZongDepartment of Pharmaceutical Analysis, School of Pharmacy, Ningxia Medical University, 1160 Shenli Street, Yinchuan 750004, China.ORCID https://orcid.org/0000-0002-3370-8246
Fangfang LuDepartment of Pharmaceutical Analysis, School of Pharmacy, Ningxia Medical University, 1160 Shenli Street, Yinchuan 750004, China.ORCID https://orcid.org/0000-0002-2002-7898
Gidion WilsonDepartment of Pharmaceutical Analysis, School of Pharmacy, Ningxia Medical University, 1160 Shenli Street, Yinchuan 750004, China.ORCID https://orcid.org/0000-0002-5231-8884
Shuqin DingSchool of Clinical Medicine, Ningxia Medical University, 692 Shenli Street, Yinchuan 750004, China.ORCID https://orcid.org/0000-0002-5105-1030
Xueqin MaDepartment of Pharmaceutical Analysis, School of Pharmacy, Ningxia Medical University, 1160 Shenli Street, Yinchuan 750004, China.ORCID https://orcid.org/0000-0003-4818-5967
Ningxia Medical University · CNYinchuan First People's Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Chronic kidney disease, including renal failure (RF), is a global public health problem. The clinical diagnosis mainly depends on the change of estimated glomerular filtration rate, which usually lags behind disease progression and likely has limited clinical utility for the early detection of this health problem. Now, we employed Q-Exactive HFX Orbitrap LC-MS/MS based metabolomics to reveal the metabolic profile and potential biomarkers for RF screening. 27 RF patients and 27 healthy controls were included as the testing groups, and comparative analysis of results using different techniques, such as multivariate pattern recognition and univariate statistical analysis, was applied to screen and elucidate the differential metabolites. The dot plots and receiver operating characteristics curves of identified different metabolites were established to discover the potential biomarkers of RF. The results exhibited a clear separation between the two groups, and a total of 216 different metabolites corresponding to 13 metabolic pathways were discovered to be associated with RF; and 44 metabolites showed high levels of sensitivity and specificity under curve values of close to 1, thus might be used as serum biomarkers for RF. In summary, for the first time, our untargeted metabolomics study revealed the distinct metabolic profile of RF, and 44 metabolites with high sensitivity and specificity were discovered, 3 of which have been reported and were consistent with our observations. The other metabolites were first reported by us. Our findings might provide a feasible diagnostic tool for identifying populations at risk for RF through detection of serum metabolites.

Identifiers

PMID35942381
PMCPMC9356786
OpenAlexW4288843726

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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.