Urinary metabolites predict mortality or need for renal replacement therapy after combat injury

Abstract

Background: Traditionally, patient risk scoring is done by evaluating vital signs and clinical severity scores with clinical intuition. Urinary biomarkers can add objectivity to these models to make risk prediction more accurate. We used metabolomics to identify prognostic urinary biomarkers of mortality or need for renal replacement therapy (RRT). Additionally, we assessed acute kidney injury (AKI) diagnosis, injury severity score (ISS), and AKI stage.

Methods: Urine samples (n = 82) from a previous study of combat casualties were evaluated using proton nuclear magnetic resonance (¹H-NMR) spectroscopy. Chenomx software was used to identify and quantify urinary metabolites. Metabolite concentrations were normalized by urine output, autoscaled, and log-transformed. Partial least squares discriminant analysis (PLS-DA) and statistical analysis were performed. Receiver operating characteristic (ROC) curves were used to assess prognostic utility of biomarkers for mortality and RRT.

Results: Eighty-four (84) metabolites were identified and quantified in each urine sample. Of these, 11 were identified as drugs or drug metabolites and excluded. The PLS-DA models for ISS and AKI diagnosis did not have acceptable model statistics. Therefore, only mortality/RRT and AKI stage were analyzed further. Of 73 analyzed metabolites, 9 were significantly associated with mortality/RRT (p < 0.05) and 11 were significantly associated with AKI stage (p < 0.05). 1-Methylnicotinamide was the only metabolite to be significantly associated (p < 0.05) with all outcomes and was significantly higher (p < 0.05) in patients with adverse outcomes. Elevated lactate and 1-methylnicotinamide levels were associated with higher AKI stage and mortality and RRT, whereas elevated glycine levels were associated with patients who survived and did not require RRT, or had less severe AKI. ROC curves for each of these metabolites and the combined panel had good predictive value (lactate AUC = 0.901, 1-methylnicotinamide AUC = 0.864, glycine AUC = 0.735, panel AUC = 0.858).

Conclusions: We identified urinary metabolites associated with AKI stage and the primary outcome of mortality or need for RRT. Lactate, 1-methylnicotinamide, and glycine may be used as a panel of predictive biomarkers for mortality and RRT. 1-Methylnicotinamide is a novel biomarker associated with adverse outcomes. Additional studies are necessary to determine how these metabolites can be utilized in clinically-relevant risk prediction models.

Keywords: Acute kidney injury; Biomarkers; Combat injury; Metabolites; Metabolomics; Renal replacement therapy; Risk prediction.

Fig. 1

Analysis for mortality and renal replacement therapy. a PLS-DA scores plot of urine samples collected from patients who reached the endpoint of mortality or RRT (mortality/RRT, square, n = 12) or who survived or did not need RRT (Survived/NoRRT, circle). Two (2) of the patients in the mortality/RRT group did not have or develop AKI. Each circle and square represents a urine sample. The ellipses represent the 95% confidence interval for the groups. b Loadings plot for mortality and RRT. Loadings show how metabolites contribute to separation seen in the scores plot. PC principal component, RRT renal replacement therapy

Fig. 2

Analysis for acute kidney injury (AKI) stage. a PLS-DA scores plot of urine samples collected from patients who were AKI stages 0–1 (none-mild, circle) or stages 2–3 (moderate-severe, square, n = 10). Each circle and square represents a urine sample. The ellipses represent the 95% confidence interval for the groups. b Loadings plot for AKI stage. Loadings show how metabolites contribute to separation seen in the scores plot. PC principal component

Fig. 3

Boxplots of 1-methylnicotinamide, lactate, and glycine and their relationship to outcomes. Metabolite concentrations were normalized by urine output, log-transformed and autoscaled. Boxplots were created using values for the median and interquartile range of each metabolite for each group. a Boxplots for 1-methylnicotinamide levels by mortality/RRT and AKI stage. b Boxplots for lactate levels by mortality/RRT and AKI stage. c Boxplots for glycine levels by mortality/RRT and AKI stage. ***p < 0.001; *p < 0.05; RRT renal replacement therapy

Fig. 4

Receiver operating characteristic (ROC) curves for mortality/RRT. a 1-methylnicotinamide (AUC = 0.864), b lactate (AUC = 0.901), c glycine (AUC = 0.735), and d all 3 (AUC = 0.858). AUC area under the curve

Fig. 5

Relationship of various metabolic pathways for AKI and combat injury. Metabolites included in the schematic were either a top 10 VIP metabolite or were statistically significant (p < 0.05). Metabolic pathways: glycolysis, TCA cycle, folate and methionine cycles, Cori and Cahill cycles, gut microbe metabolism, and NAD+ salvage pathway. Bolded metabolites were identified in the ¹H-NMR analysis. Metabolites in green were significantly higher in patients who reached the endpoint or mortality/RRT and/or had more severe AKI. Metabolites in red were significantly lower in patients who reached the endpoint or mortality/RRT and/or had more severe AKI

Fig. 6

Simplified pathway of lactic acid fermentation. This anaerobic metabolism occurs when there is not enough oxygen to run the TCA cycle and oxidative phosphorylation. Glycolysis converts glucose to pyruvate, producing ATP and NADH. Pyruvate makes lactate instead of going into the TCA cycle. This reaction produces NAD+ from NADH. Adapted from Reference [30]

Fig. 7

Simplified de novo NAD+ and NAD+ salvage pathways. De novo pathway (blue arrows) makes NAD+ from tryptophan and quinolinate. The salvage pathway (green arrows) makes NAD+ from nicotinamide and nicotinamide mononucleotide. 1-methylnicotinamide is a by-product of the salvage pathway. Adapted from reference [21]