Evidence map›Paper›PMID 39934788›Full record

ArticleBMC medicine2025

Metabolomics- and proteomics-based multi-omics integration reveals early metabolite alterations in sepsis-associated acute kidney injury.

Pengfei Huang, Yanqi Liu, Yue Li, Yu Xin, Chuanchuan Nan, Yinghao Luo, Yating Feng, Nana Jin, Yahui Peng, Dawei Wang and 17 more

Erratum issuedAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing 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

28 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Review
  12. Review
  13. Article
  14. Review
  15. Review
  16. Review
  17. Review
  18. Article
  19. Review
  20. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

27 authors.

Pengfei Huang *Department of Critical Care Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, 150001, China.
Yanqi Liu *Department of Critical Care Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, 150001, China.
Yue Li *Department of Critical Care Medicine, Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, 150086, China.
Yu Xin *Department of Critical Care Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, 150001, China.
Chuanchuan Nan *Department of Critical Care Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, 150001, China.
Yinghao LuoDepartment of Critical Care Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, 150001, China.
Yating FengDepartment of Critical Care Medicine, First Affiliated Hospital of Southern, Shenzhen People's Hospital, University of Science and Technology, Shenzhen, 518020, China.
Nana JinDepartment of Critical Care Medicine, First Affiliated Hospital of Southern, Shenzhen People's Hospital, University of Science and Technology, Shenzhen, 518020, China.
Yahui PengDepartment of Critical Care Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, 150001, China.
Dawei WangDepartment of Critical Care Medicine, Harbin Medical University Cancer Hospital, Harbin, 150081, China.
Yang ZhouDepartment of Critical Care Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, 150001, China.
Feiyu LuanDepartment of Critical Care Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, 150001, China.
Xinran WangDepartment of Critical Care Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, 150001, China.
Xibo WangDepartment of Critical Care Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, 150001, China.
Hongxu LiDepartment of Critical Care Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, 150001, China.
Yuxin ZhouDepartment of Critical Care Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, 150001, China.
Weiting ZhangDepartment of Critical Care Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, 150001, China.
Yuhan LiuDepartment of Critical Care Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, 150001, China.
Mengyao YuanDepartment of Critical Care Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, 150001, China.
Yuxin ZhangDepartment of Critical Care Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, 150001, China.
Yuchen SongDepartment of Critical Care Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, 150001, China.
Yu XiaoDepartment of Critical Care Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, 150001, China.
Lifeng ShenDepartment of Critical Care Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, 150001, China.
Kaijiang YuDepartment of Critical Care Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, 150001, China. drkaijiang@163.com.
Mingyan ZhaoDepartment of Critical Care Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, 150001, China. mingyan1970@126.com.
Lixin ChengDepartment of Critical Care Medicine, First Affiliated Hospital of Southern, Shenzhen People's Hospital, University of Science and Technology, Shenzhen, 518020, China. easonlcheng@gmail.com.
Changsong WangDepartment of Critical Care Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, 150001, China. changsongwangicu@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSepsis-associated acute kidney injury (SA-AKI) is a frequent complication in patients with sepsis and is associated with high mortality. Therefore, early recognition of SA-AKI is essential for administering supportive treatment and preventing further damage. This study aimed to identify and validate metabolite biomarkers of SA-AKI to assist in early clinical diagnosis.

methodsUntargeted renal proteomic and metabolomic analyses were performed on the renal tissues of LPS-induced SA-AKI and sepsis mice. Glomerular filtration rate (GFR) monitoring technology was used to evaluate real-time renal function in mice. To elucidate the distinctive characteristics of SA-AKI, a multi-omics Spearman correlation network was constructed integrating core metabolites, proteins, and renal function. Subsequently, metabolomics analysis was used to explore the dynamic changes of core metabolites in the serum of SA-AKI mice at 0, 8, and 24 h. Finally, a clinical cohort (28 patients with SA-AKI vs. 28 patients with sepsis) serum quantitative metabolomic analysis was carried out to build a diagnostic model for SA-AKI via logistic regression (LR).

resultsThirteen differential renal metabolites and 112 differential renal proteins were identified through a multi-omics study of SA-AKI mice. Subsequently, a multi-omics correlation network was constructed to highlight five core metabolites, i.e., 3-hydroxybutyric acid, 3-hydroxymethylglutaric acid, creatine, myristic acid, and inosine, the early changes of which were then observed via serum time series experiments of SA-AKI mice. The levels of 3-hydroxybutyric acid, 3-hydroxymethylglutaric acid, and creatine increased significantly at 24 h, myristic acid increased at 8 h, while inosine decreased at 8 h. Ultimately, based on the identified core metabolites, we recruited 56 patients and constructed a diagnostic model named IC3, using inosine, creatine, and 3-hydroxybutyric acid, to early identify SA-AKI (AUC = 0.90).

conclusionsWe proposed a blood metabolite model consisting of inosine, creatine, and 3-hydroxybutyric acid for the early screening of SA-AKI. Future studies will observe the performance of these metabolites in other clinical populations to evaluate their diagnostic role.

Indexed as

Acute Kidney InjuryMetabolomicsProteomicsSepsisAgedAnimalsBiomarkersEarly DiagnosisFemaleHumansMaleMiceMice, Inbred C57BLMiddle AgedMultiomicsBiomarkersDiagnostic biomarkersMetabolomeMulti-omics analysisProteomeSepsisSepsis-associated acute kidney injury

Identifiers

PMID39934788
PMCPMC11818193

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.