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...
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| Format: | Article |
| Language: | English |
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BMC
2021-03-01
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| Series: | Critical Care |
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| Online Access: | https://doi.org/10.1186/s13054-021-03544-2 |
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| author | Sarah Gisewhite Ian J. Stewart Greg Beilman Elizabeth Lusczek |
| author_facet | Sarah Gisewhite Ian J. Stewart Greg Beilman Elizabeth Lusczek |
| author_sort | Sarah Gisewhite |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-14T11:37:01Z |
| format | Article |
| id | doaj.art-6a5f3698fe26493e9cc09f267b3d0e1c |
| institution | Directory Open Access Journal |
| issn | 1364-8535 |
| language | English |
| last_indexed | 2024-12-14T11:37:01Z |
| publishDate | 2021-03-01 |
| publisher | BMC |
| record_format | Article |
| series | Critical Care |
| spelling | doaj.art-6a5f3698fe26493e9cc09f267b3d0e1c2022-12-21T23:02:59ZengBMCCritical Care1364-85352021-03-0125111410.1186/s13054-021-03544-2Urinary metabolites predict mortality or need for renal replacement therapy after combat injurySarah Gisewhite0Ian J. Stewart1Greg Beilman2Elizabeth Lusczek3Department of Surgery, University of MinnesotaDepartment of Medicine, Uniformed Services UniversityDepartment of Surgery, University of MinnesotaDepartment of Surgery, University of MinnesotaAbstract 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.https://doi.org/10.1186/s13054-021-03544-2Acute kidney injuryBiomarkersMetabolitesCombat injuryRisk predictionMetabolomics |
| spellingShingle | Sarah Gisewhite Ian J. Stewart Greg Beilman Elizabeth Lusczek Urinary metabolites predict mortality or need for renal replacement therapy after combat injury Critical Care Acute kidney injury Biomarkers Metabolites Combat injury Risk prediction Metabolomics |
| title | Urinary metabolites predict mortality or need for renal replacement therapy after combat injury |
| title_full | Urinary metabolites predict mortality or need for renal replacement therapy after combat injury |
| title_fullStr | Urinary metabolites predict mortality or need for renal replacement therapy after combat injury |
| title_full_unstemmed | Urinary metabolites predict mortality or need for renal replacement therapy after combat injury |
| title_short | Urinary metabolites predict mortality or need for renal replacement therapy after combat injury |
| title_sort | urinary metabolites predict mortality or need for renal replacement therapy after combat injury |
| topic | Acute kidney injury Biomarkers Metabolites Combat injury Risk prediction Metabolomics |
| url | https://doi.org/10.1186/s13054-021-03544-2 |
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