ArticleBMC nephrology2026
Explainable machine learning integrating biochemical and metabolomic biomarkers with conventional clinical factors improves chronic kidney disease prediction and risk stratification.
Article in BMC nephrology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
backgroundChronic kidney disease (CKD) is a leading cause of morbidity and mortality worldwide, yet existing risk models have limited ability to identify individuals at high long-term risk. Whether integrating circulating biochemical and metabolomic biomarkers can improve CKD prediction and risk stratification remains unclear.
methodsWe included 233,589 UK Biobank participants without CKD at baseline. Biomarkers were screened using multiple feature selection strategies. Predictive performance and effect sizes were evaluated using Cox proportional hazards models. CatBoost and SHAP were applied to identify key predictors, derive interpretable binary thresholds, and construct a simplified biomarker risk score (BRS). Relative and absolute CKD risks were assessed across tertiles of the BRS. Model discrimination and calibration were evaluated in an England development cohort and a geographically independent validation cohort from Scotland and Wales.
resultsA combined biochemical–metabolomic signature (BioMet) showed good discrimination for incident CKD and CKD-related mortality and consistently outperformed conventional risk models in both cohorts. Key risk-elevating biomarkers included cystatin C, HbA1c, CRP, and urea, whereas higher eGFR, M-VLDL-CE, histidine, and IGF-1 were inversely associated with CKD risk. A SHAP-derived Top10 BRS (Top10BRS) effectively stratified individuals into distinct risk groups. Compared with the lowest tertile, participants in the highest tertile had a substantially higher risk of incident CKD (HR: 3.73) and CKD-related mortality (HR: 10.40). Discrimination improved after adding Top10BRS to conventional models, while calibration and prediction error remained stable. Similar patterns were observed in the validation cohort.
conclusionIntegrating biochemical and metabolomic biomarkers with conventional clinical predictors improves long-term prediction and risk stratification for CKD. An interpretable SHAP-derived BRS enables robust identification of individuals at elevated risk and may support earlier risk assessment and personalized prevention strategies for CKD.
Indexed as
Identifiers
What Socratic 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 Socratic graph.