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Войти в исследования Kidney medicine · 2026
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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Butz Elena, Steinbrenner Inga, Schultheiss Ulla T, Behning Charlotte, Binder Harald, Hansmann Helena, Gronwald Wolfram, Oefner Peter J, Schaeffner Elke, Eckardt Kai-Uwe, Köttgen Anna, Sekula Peggy, GCKD Investigators
Аннотация
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).
EXPOSURES OR PREDICTORS: Baseline metabolite levels measured using untargeted mass spectrometry: plasma (N=5,144; 1,096 metabolites) and urine (N=5,088; 1,129 metabolites).
OUTCOMES: (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 m2.
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.
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).
EXPOSURES OR PREDICTORS: Baseline metabolite levels measured using untargeted mass spectrometry: plasma (N=5,144; 1,096 metabolites) and urine (N=5,088; 1,129 metabolites).
OUTCOMES: (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 m2.
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.