{"doi":"10.1681/asn.0000000602","title":"Biomarker Panels for Discriminating Risk of CKD Progression in Children","abstract":"Key Points Plasma biomarkers (kidney injury molecule-1, KIM-1), urine biomarkers (EGF/creatinine and urine albumin-creatinine ratio), and eGFR identified four prognostic groups in children with CKD progression. A panel of biomarkers may better capture the complexity of kidney disease in children and may allow for a broader assessment of kidney health. Background We have previously studied biomarkers of tubular health (EGF), injury (kidney injury molecule-1 [KIM-1]), dysfunction ( α -1 microglobulin), and inflammation (TNF receptor-1, TNF receptor-2, monocyte chemoattractant protein-1, YKL-40, and soluble urokinase plasminogen activator receptor) and demonstrated that plasma KIM-1, TNF receptor-1, TNF receptor-2, urine KIM-1, EGF, monocyte chemoattractant protein-1, and urine α -1 microglobulin are each independently associated with CKD progression in children. In this study, we used bootstrapped survival trees to identify a combination of biomarkers to predict CKD progression in children. Methods The Chronic Kidney Disease in Children (CKiD) Cohort Study prospectively enrolled children aged 6 months to 16 years with an eGFR of 30–90 ml/min per 1.73 m 2 . We measured biomarkers in stored plasma and urine collected 5 months after study enrollment. The primary outcome of CKD progression was a composite of 50% eGFR decline or kidney failure. We constructed a regression tree–based model for predicting the time to the composite event, using a panel of clinically relevant biomarkers with empirically derived thresholds, in addition to conventional risk factors. Results Of the 599 children included, the median age was 12 years (interquartile range [IQR], 8–15), 371 (62%) were male, baseline urine protein-creatinine ratio was 0.33 (IQR, 0.12–0.95) mg/mg, and baseline eGFR was 53 (IQR, 40–66) ml/min per 1.73 m 2 . Overall, 205 children (34%) reached the primary outcome of CKD progression. A single regression tree–based model using the most informative predictors with data-driven biomarker thresholds suggested a final set of four prognosis groups. In the final model, urine albumin/creatinine was the variable with the highest importance and along with urine EGF/creatinine identified the highest risk group of 24 children, 100% of whom developed CKD progression at a median time of 1.3 years (95% confidence interval [CI], 1.0 to 1.7). When the regression tree–derived risk group classifications were added to prediction models including the clinical risk factors, the C-statistic increased from 0.76 (95% CI, 0.71 to 0.80) to 0.85 (95% CI, 0.81 to 0.88). Conclusions Using regression tree–based methods, we identified a biomarker panel of urine albumin/creatinine, urine EGF/creatinine, plasma KIM-1, and eGFR, which significantly improved discrimination for CKD progression.","journal":"Journal of the American Society of Nephrology","year":2025,"id":516888,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":8,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9528,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":303931,"name":"Alison G. Abraham","orcid":"0000-0002-6863-0565","position":1,"is_corresponding":false},{"id":312220,"name":"Yunwen Xu","orcid":"0000-0002-5946-2272","position":2,"is_corresponding":false},{"id":261910,"name":"Jeffrey R. Schelling","orcid":"0000-0003-3503-2208","position":3,"is_corresponding":false},{"id":241218,"name":"Steven G. Coca","orcid":"0000-0002-0928-9168","position":4,"is_corresponding":false},{"id":228813,"name":"Sarah J. Schrauben","orcid":"0000-0003-2557-5161","position":5,"is_corresponding":false},{"id":234941,"name":"F. Perry Wilson","orcid":"0000-0002-2633-2412","position":6,"is_corresponding":false},{"id":34473,"name":"Sushrut S. Waikar","orcid":"0000-0003-4004-326X","position":7,"is_corresponding":false},{"id":24661,"name":"Ramachandran S. Vasan","orcid":"0000-0001-7357-5970","position":8,"is_corresponding":false},{"id":261906,"name":"Orlando M. Gutiérrez","orcid":"0000-0001-6593-3571","position":9,"is_corresponding":false},{"id":19396,"name":"Michael G. Shlipak","orcid":"0000-0002-9559-204X","position":10,"is_corresponding":false},{"id":261907,"name":"Joachim H. Ix","orcid":"0000-0002-8084-9869","position":11,"is_corresponding":false},{"id":283027,"name":"Bradley A. Warady","orcid":"0000-0003-1812-6587","position":12,"is_corresponding":false},{"id":214519,"name":"Paul L. Kimmel","orcid":null,"position":13,"is_corresponding":false},{"id":34474,"name":"Joseph V. Bonventre","orcid":"0000-0001-7144-386X","position":14,"is_corresponding":false},{"id":241229,"name":"Chirag R. Parikh","orcid":"0000-0001-9051-7385","position":15,"is_corresponding":false},{"id":312222,"name":"Michelle Denburg","orcid":"0000-0002-0704-3607","position":16,"is_corresponding":false},{"id":261904,"name":"Susan L. Furth","orcid":"0000-0003-2081-5242","position":17,"is_corresponding":false},{"id":261905,"name":"Jason H. Greenberg","orcid":"0000-0001-5874-1109","position":0,"is_corresponding":true}],"reference_count":21,"raw_metadata":null,"created_at":"2026-07-19T02:48:54.768083Z","pmid":"39820177","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}