Serum and Urine Metabolites and Kidney Function

Abstract

Key Points:

1. We provide an atlas of cross-sectional and longitudinal serum and urine metabolite associations with eGFR and urine albumin-creatinine ratio in an older community-based cohort.

2. Metabolic profiling in serum and urine provides distinct and complementary insights into disease.

Background: Metabolites represent a read-out of cellular processes underlying states of health and disease.

Methods: We evaluated cross-sectional and longitudinal associations between 1255 serum and 1398 urine known and unknown (denoted with “X” in name) metabolites (Metabolon HD4, 721 detected in both biofluids) and kidney function in 1612 participants of the Atherosclerosis Risk in Communities study. All analyses were adjusted for clinical and demographic covariates, including for baseline eGFR and urine albumin-creatinine ratio (UACR) in longitudinal analyses.

Results: At visit 5 of the Atherosclerosis Risk in Communities study, the mean age of participants was 76 years (SD 6); 56% were women, mean eGFR was 62 ml/min per 1.73 m² (SD 20), and median UACR level was 13 mg/g (interquartile range, 25). In cross-sectional analysis, 675 serum and 542 urine metabolites were associated with eGFR (Bonferroni-corrected P < 4.0E-5 for serum analyses and P < 3.6E-5 for urine analyses), including 248 metabolites shared across biofluids. Fewer metabolites (75 serum and 91 urine metabolites, including seven metabolites shared across biofluids) were cross-sectionally associated with albuminuria. Guanidinosuccinate; N2,N2-dimethylguanosine; hydroxy-N6,N6,N6-trimethyllysine; X-13844; and X-25422 were significantly associated with both eGFR and albuminuria. Over a mean follow-up of 6.6 years, serum mannose (hazard ratio [HR], 2.3 [1.6–3.2], P = 2.7E-5) and urine X-12117 (HR, 1.7 [1.3–2.2], P = 1.9E-5) were risk factors of UACR doubling, whereas urine sebacate (HR, 0.86 [0.80–0.92], P = 1.9E-5) was inversely associated. Compared with clinical characteristics alone, including the top five endogenous metabolites in serum and urine associated with longitudinal outcomes improved the outcome prediction (area under the receiver operating characteristic curves for eGFR decline: clinical model=0.79, clinical+metabolites model=0.87, P = 8.1E-6; for UACR doubling: clinical model=0.66, clinical+metabolites model=0.73, P = 2.9E-5).

Conclusions: Metabolomic profiling in different biofluids provided distinct and potentially complementary insights into the biology and prognosis of kidney diseases.

Keywords: CKD; albuminuria.

Graphical abstract

Figure 1

Heat maps of the correlations and median correlations between every serum–urine metabolite pair, organized by superpathways. (A) Correlations between serum metabolites were more positive and generally stronger than correlations between urine metabolites or serum–urine metabolites. (B) Carbohydrates were most positively correlated with each other in serum (median correlation 0.2), urine (median correlation 0.06), and between biofluids (0.07). The strongest negative correlation was between serum carbohydrates and urine nucleotides (median correlation, 20.05). The superpathway abbreviations along the x axis and y axis are amino acid (AA), carbohydrate (Carb), cofactors and vitamins (Vit), energy (E), lipid (Lip), nucleotide (Nuc), partially characterized molecules (PCM), peptide (Pep), unknown (Un), and xenobiotics (Xen).

Figure 2

Histogram of correlations between serum–urine metabolite pairs and correlation summary statistics by superpathway. (A) The distribution of the correlations between serum–urine metabolite pairs was left skewed. (B) Xenobiotics (non-drugs) had the highest median correlation of 0.79, and peptides had the lowest median correlation of 0.21.

Figure 3

Volcano plots for cross-sectional analyses showing associations between metabolites (per doubling) and eGFR or log₂(UACR). The plots correspond to cross-sectional associations between (A) serum metabolites and eGFR, (B) urine metabolites and eGFR, (C) serum metabolites and albuminuria, and (D) urine metabolites and albuminuria. The top five metabolites for each analyses, as well as the five metabolites significant across all analyses (guanidinosuccinate; N2,N2-dimethylguanosine; hydroxy-N6,N6,N6-trimethyllysine; X-13844; and X-25422), are labeled. The horizontal red dotted line marks the Bonferroni-corrected significance threshold. UACR, urine albumin-creatinine ratio.

Figure 4

Bar plots showing proportion of significant metabolites for each superpathway, as well as over-represented pathways. The plots correspond to the cross-sectional associations between (A) serum metabolites and eGFR, (B) urine metabolites and eGFR, (C) serum metabolites and albuminuria, and (D) urine metabolites and albuminuria. The darker shaded portion of each superpathway bar represents the proportion of significant metabolites. The top percentage given above each bar is the proportion of metabolites in the superpathway within all metabolites, whereas the bottom percentage is the proportion of significant metabolites in the superpathway within all significant metabolites (not within each superpathway or within all metabolites). Asterisks represent over-represented superpathways (P < 0.05).

Figure 5

Volcano plots for longitudinal analyses showing the associations (hazard ratios) between metabolites (per doubling) and a 40% decline in eGFR or UACR doubling. The plots correspond to longitudinal associations between (A) serum metabolites and 40% eGFR decline, (B) urine metabolites and 40% eGFR decline, (C) serum metabolites and UACR doubling, and (D) urine metabolites and UACR doubling. The top five metabolites for each analyses are labeled. The horizontal red dotted line marks the Bonferroni-corrected significance threshold. HR, hazard ratio.

Figure 6

Pie charts showing proportion of significant metabolites within metabolites detected in each/both biofluids. The outer rings denominate the superpathway proportions within each significant category. The superpathway abbreviations are AA, Carb, Vit, E, Lip, Nuc, PCM, Pep, Un, and Xen.