ArticleRenal failure2024
Identification of serum biomarkers for chronic kidney disease using serum metabolomics.
Article in Renal failure, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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.
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.
Who cites it
6 citing papers in PubMed.
- Article
- Serum metabolomics identifies gut-derived uremic toxins and bile acid dysregulation associated with chronic kidney disease severity.Scientific reports · 2026Article
- Integrating metabolomics and machine learning with in silico analysis to identify early biomarkers and molecular interactions in sepsis-associated acute kidney injury.Scientific reports · 2026Article
- Integrated serum metabolomics reveals severity-associated exploratory metabolic signatures in burn patients.Frontiers in physiology · 2026Article
- Advancements in the non-invasive diagnosis of renal fibrosis.Frontiers in medicine · 2025Review
- The Gut-Kidney Axis in Uric Acid Nephropathy: Microbiota, Metabolic Crosstalk, and Translational Prospects.Journal of multidisciplinary healthcare · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aimed to identify biomarkers for chronic kidney disease (CKD) by studying serum metabolomics. Serum samples were collected from 194 non-dialysis CKD patients and 317 healthy controls (HC). Using ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS), untargeted metabolomics analysis was conducted. A random forest model was developed and validated in separate sets of HC and CKD patients. The serum metabolomic profiles of patients with chronic kidney disease (CKD) exhibited significant differences compared to healthy controls (HC). A total of 314 metabolites were identified as significantly different, with 179 being upregulated and 135 being downregulated in CKD patients. KEGG enrichment analysis revealed several key pathways, including arginine biosynthesis, phenylalanine metabolism, linoleic acid metabolism, and purine metabolism. The diagnostic efficacy of the classifier was high, with an area under the curve of 1 in the training and validation sets and 0.9435 in the cross-validation set. This study provides comprehensive insights into serum metabolism in non-dialysis CKD patients, highlighting the potential involvement of abnormal biological metabolism in CKD pathogenesis. Exploring metabolites may offer new possibilities for the management of CKD.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.