Systematic Evaluation of Plasma and Urine Metabolites to Predict the Risk of Adverse Kidney-related Outcomes in Chronic Kidney Disease: The GCKD Study∗

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Rationale & Objective Accurate risk prediction of adverse kidney-related outcomes in individuals with chronic kidney disease (CKD) is essential to guide personalized treatment. Plasma and urine metabolites may individually or jointly improve prediction beyond clinically established prognostic factors. Study Design Prospective German CKD cohort study. Setting & Participants 5,217 individuals with predominantly CKD stage G3 at baseline and 6.5-year follow-up data (IQR, 6.5-6.5). Exposure(s) or Predictor(s) Baseline metabolite levels measured using untargeted mass spectrometry: plasma (N=5,144; 1,096 metabolites) and urine (N=5,088; 1,129 metabolites). Outcome(s) (i) Kidney failure (KF): kidney replacement therapy or death by untreated KF; (ii) composite kidney endpoint (CKE): KF, ≥ 40% estimated glomerular filtration rate (eGFR) decline, or eGFR < 15 mL/min/1.73 m 2 . Analytical Approach Time-to-event analysis using subdistribution hazard models with component-wise boosting for metabolite selection. The predictive performance of metabolite models was compared to benchmark models, including established prognostic factors. Results Several individual metabolites improved KF risk prediction beyond established prognostic factors (age, sex, eGFR, and urinary albumin-to-creatinine ratio). For example, adding plasma pseudouridine increased the area under the receiver operating characteristic curve (AUC) for KF at year 6 by 0.012 (95% CI, 0.005-0.018). Multimetabolite models for KF (mean, 36 metabolites) showed good performance, declining for more distant time points: AUC values were ≥ 0.89 at year 2 and ≥ 0.85 at year 6. Some metabolites, such as plasma N2,N5-diacetylornithine and urine 1-palmitoyl-2-oleoyl-GPC (16:0/18:1), were selected more often than others. Overall, multimetabolite models demonstrated modest, partially significant improvements over clinical models, and were comparable to other suggested prognostic models of KF. Results for the CKE were similar. Limitations Single-point, semiquantitative metabolite measurements. Conclusions While certain metabolites improved the prediction of adverse kidney-related outcomes, added value was limited. However, prognostic metabolites may reflect relevant CKD-related metabolic pathways. Further research is warranted to refine prognostic models and explore the biological relevance of identified metabolites. Plain-Language Summary This study examined whether measuring small molecules (metabolites) in blood and urine can improve the prediction of kidney failure risk in more than 5,000 people with chronic kidney disease over 6 years. Some metabolites slightly improved risk prediction beyond common clinical factors such as sex, age, kidney function (the kidney's ability to remove waste and toxins), and kidney impairment (when protein leaks into the urine). We therefore evaluated whether models combining multiple metabolites provided additional benefits. As a result, these multimetabolite prognostic models provided reliable and clinically meaningful risk estimates of kidney failure, but their improvement over existing clinical models was small. However, metabolites selected into multimetabolite models may reflect important biological processes and could help guide future research.

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