Evidence map›Paper›PMID 40958907›Full record

ArticleFrontiers in endocrinology2025

Identification of potential biomarkers for diabetic nephropathy via UPLC-MS/MS-based metabolomics.

Hetao Chen, Peipei Du, Tao Jiang, Ying Li, Yuanyuan Li, Yalin Liu, Baotong Yang, Jingyi Kang, Jiajia Duan, Yujin Ma and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in endocrinology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Post‑translational modifications in diabetic kidney disease (Review).International journal of molecular medicine · 2026
    Review
  3. Article
  4. Review
  5. Review
  6. 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.

Hetao ChenDepartment of Clinical Laboratory, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China.
Peipei DuDepartment of Clinical Laboratory, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China.
Tao JiangDepartment of Clinical Laboratory, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China.
Ying LiDepartment of Pharmacy, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China.
Yuanyuan LiDepartment of Clinical Laboratory, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China.
Yalin LiuDepartment of Central Laboratory, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China.
Baotong YangDepartment of Clinical Laboratory, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China.
Jingyi KangDepartment of Central Laboratory, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China.
Jiajia DuanDepartment of Clinical Laboratory, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China.
Yujin MaHenan Key Laboratory of Rare Diseases, Endocrinology and Metabolism Center, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China.
Xiangmei ChenSenior Department of Nephrology, the First Medical Center of Chinese People's Liberation Army (PLA) General Hospital, Chinese People's Liberation Army (PLA) Institute of Nephrology, National Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China.
Hongwei JiangHenan Key Laboratory of Rare Diseases, Endocrinology and Metabolism Center, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Diabetes mellitus (DM) is a prevalent chronic disease, with diabetic nephropathy (DN) being a significant complication. Early detection of DN is critical for effective management. Current diagnostic methods, such as urinary albumin-to-creatinine ratio (uACR) and estimated glomerular filtration rate (eGFR), have limitations. Metabolomics offers a promising alternative by identifying specific metabolic signatures associated with DM and DN. This study aimed to identify potential metabolic biomarkers of DN using metabolomics. Methods: A total of 100 participants were recruited, including 20 healthy controls and 80 DM patients, who were classified into three groups based on uACR: normoalbuminuria (DM), microalbuminuria (DN-1), and macroalbuminuria (DN-2). Metabolomic profiles were analyzed using ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). Results: Results showed 74, 86, and 107 differentially expressed metabolites in the DM, DN-1, and DN-2 groups, respectively, compared to healthy controls. Compared to the DM group, DN-1 had 70 differential metabolites (55 upregulated, 15 downregulated), and DN-2 had 91 (81 upregulated, 10 downregulated). Between DN-1 and DN-2, 71 differential metabolites were identified (57 upregulated, 14 downregulated). Key metabolites such as lactate, L-ornithine, L-tryptophan, L-alanine, adenine, and cholecalciferol emerged as potential biomarkers and therapeutic targets. Venn diagram analysis identified 36 common differential metabolites across all groups. KEGG enrichment analysis highlighted significant involvement of amino acid biosynthesis and arginine and proline metabolism pathways in DN. Conclusion: In conclusion, this study provides valuable insights into potential metabolic markers and mechanisms for early identification and prediction of DN progression, which may aid in developing more accurate diagnostic tools and targeted therapies for DN.

Indexed as

BiomarkersDiabetic NephropathiesMetabolomicsAdultAgedAlbuminuriaCase-Control StudiesChromatography, High Pressure LiquidFemaleHumansLiquid Chromatography-Mass SpectrometryMaleMiddle AgedTandem Mass SpectrometryBiomarkersbiomarkersdiabetic nephropathymetabolitesserumUPLC-MS/MS

Identifiers

PMID40958907
PMCPMC12433848

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.