ArticleJournal of the American Society of Nephrology : JASN2022
Using Machine Learning to Identify Metabolomic Signatures of Pediatric Chronic Kidney Disease Etiology.
Article in Journal of the American Society of Nephrology : JASN, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT00327860 (Chronic Kidney Disease in Children Prospective Cohort Study), which is not on this map. Cited by 39 papers.
What it found
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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.
Chronic Kidney Disease in Children Prospective Cohort Study (CKiD)
Who cites it
39 citing papers in PubMed, 52 citations in OpenAlex.
- GADD45A may be a potential biomarker associated with endoplasmic reticulum stress in focal segmental glomerulosclerosis.Biochemistry and biophysics reports · 2026Article
- Developmental origins and environmental determinants of cardiovascular-kidney-metabolic syndrome: A pediatric precision prevention perspective.Biomedical journal · 2026Review
- Inflammation-guided timing of thoracoscopic repair for recurrent tracheoesophageal fistula: predictive value of preoperative chest CT and a transfer learning model.BMC pediatrics · 2026Article
- Artificial Intelligence in Nephrology-State of the Art on Theoretical Background, Molecular Applications, and Clinical Interpretation.International journal of molecular sciences · 2026Review
- Blood metabolic panels for identifying significant fibrosis and inflammation in patients with MASLD.Cell reports. Medicine · 2026Article
- Therapeutic remodeling of the ceramide backbone prevents kidney injury.Cell metabolism · 2026Article
- The role of gut dysbiosis in endocrine and metabolic derangements of chronic kidney disease: mechanisms, controversies, and future perspectives.Frontiers in endocrinology · 2026Review
- Observational
- Application of machine learning in constructing a diagnostic model for neonatal biliary atresia.Pediatric investigation · 2025Article
- Mapping the plasma metabolome to human health and disease in 274,241 adults.Nature metabolism · 2025Article
- Microbiota and kidney disease: the road ahead.Nature reviews. Nephrology · 2025Review
- Metabolomic profiling for predicting breast cancer treatment toxicities.Translational cancer research · 2025Article
- Identification and validation of an explainable early-stage chronic kidney disease prediction model: a multicenter retrospective study.EClinicalMedicine · 2025Article
- Identifying serum lipidomic signatures related to prognosis in first-episode schizophrenia.BMC psychiatry · 2025Article
- Gut-derived metabolites as treatment targets in chronic kidney disease-an avenue toward personalized medicine.Pediatric nephrology (Berlin, Germany) · 2025Article
- Microdose Cocktail Study Reveals the Activity and Key Influencing Factors of OATP1B, P-Gp, BCRP, and CYP3A in End-Stage Renal Disease Patients.Clinical pharmacology and therapeutics · 2025Article
- Investigation of a targeted panel of gut microbiome-derived toxins in children with chronic kidney disease.Pediatric nephrology (Berlin, Germany) · 2025Article
- Development and validation of an insulin resistance prediction model in children and adolescents using machine learning algorithms.Translational pediatrics · 2025Article
- Leveraging complementary multi-omics data integration methods for mechanistic insights in kidney diseases.JCI insight · 2025Article
- A unified framework harnessing multi-scale feature ensemble and attention mechanism for gastric polyp and protrusion identification in gastroscope imaging.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
16 authors at 8 institutions in 1 country.
Funding
Abstract
backgroundUntargeted plasma metabolomic profiling combined with machine learning (ML) may lead to discovery of metabolic profiles that inform our understanding of pediatric CKD causes. We sought to identify metabolomic signatures in pediatric CKD based on diagnosis: FSGS, obstructive uropathy (OU), aplasia/dysplasia/hypoplasia (A/D/H), and reflux nephropathy (RN).
methodsUntargeted metabolomic quantification (GC-MS/LC-MS, Metabolon) was performed on plasma from 702 Chronic Kidney Disease in Children study participants (
resultsML models were evaluated on holdout subsets with receiver-operator and precision-recall area-under-the-curve, F1 score, and Matthews correlation coefficient. ML models outperformed no-skill prediction. Metabolomic profiles were identified based on cause. FSGS was associated with the sphingomyelin-ceramide axis. FSGS was also associated with individual plasmalogen metabolites and the subpathway. OU was associated with gut microbiome-derived histidine metabolites.
conclusionML models identified metabolomic signatures based on CKD cause. Using ML techniques in conjunction with traditional biostatistics, we demonstrated that sphingomyelin-ceramide and plasmalogen dysmetabolism are associated with FSGS and that gut microbiome-derived histidine metabolites are associated with OU.
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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.