Metabolomic profiling to improve glomerular filtration rate estimation: a proof-of-concept study

Abstract

Background: Estimation of glomerular filtration rate (GFR) using estimated glomerular filtration rate creatinine (eGFRcr) is central to clinical practice but has limitations. We tested the hypothesis that serum metabolomic profiling can identify novel markers that in combination can provide more accurate GFR estimates.

Methods: We performed a cross-sectional study of 200 African American Study of Kidney Disease and Hypertension (AASK) and 265 Multi-Ethnic Study of Atherosclerosis (MESA) participants with measured GFR (mGFR). Untargeted gas chromatography/dual mass spectrometry- and liquid chromatography/dual mass spectrometry-based quantification was followed by the development of targeted assays for 15 metabolites. On the log scale, GFR was estimated from single- and multiple-metabolite panels and compared with eGFR using the Chronic Kidney Disease Epidemiology equations with creatinine and/or cystatin C using established metrics, including the proportion of errors >30% of mGFR (1-P30), before and after bias correction.

Results: Of untargeted metabolites in the AASK and MESA, 283 of 780 (36%) and 387 of 1447 (27%), respectively, were significantly correlated (P ≤ 0.001) with mGFR. A targeted metabolite panel eGFR developed in the AASK and validated in the MESA was more accurate (1-P30 3.7 and 1.9%, respectively) than eGFRcr [11.2 and 18.5%, respectively (P < 0.001 for both)] and estimating GFR using cystatin C (eGFRcys) [10.6% (P = 0.02) and 9.1% (P < 0.05), respectively] but was not consistently better than eGFR using both creatinine and cystatin C [3.7% (P > 0.05) and 9.1% (P < 0.05), respectively]. A panel excluding creatinine and demographics still performed well [1-P30 6.4% (P = 0.11) and 3.4% (P < 0.001) in the AASK and MESA] versus eGFRcr.

Conclusions: Multimetabolite panels can enable accurate GFR estimation. Metabolomic equations, preferably excluding creatinine and demographic characteristics, should be tested for robustness and generalizability as a potential confirmatory test when eGFRcr is unreliable.

Keywords: GFR; creatinine; estimating equations; kidney function; metabolomics.

FIGURE 1

Correlations of untargeted metabolite screen with mGFR in the (A) AASK and (B) MESA and (C) a comparison of the correlations for overlapping metabolites in the two studies. In (A) and (B), dashed lines show the null hypothesis expected distribution of correlations. In (C), red indicates correlations significant at P < 0.001 and dots indicate correlations stronger than the untargeted serum creatinine.

FIGURE 2

Percent difference between eGFR and mGFR in validation MESA. Panels show estimates calibrated to zero bias for CKD-EPI (A) eGFRcr, (B) eGFRcys, (C) eGFRcr-cys and the AASK metabolite including (D) demographics and excluding (E) creatinine and (F) creatinine and demographics. Supplementary figures show the data without calibration as well as for the AASK and MESA population combined.