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Prediction of sepsis mortality using metabolite biomarkers in the blood: a meta-analysis of death-related pathways and prospective validation.

ABSTRACT: BACKGROUND:Sepsis is a leading cause of death in intensive care units (ICUs), but outcomes of individual patients are difficult to predict. The recently developed clinical metabolomics has been recognized as a promising tool in the clinical practice of critical illness. The objective of this study was to identify the unique metabolic biomarkers and their pathways in the blood of sepsis nonsurvivors and to assess the prognostic value of these pathways. METHODS:We searched PubMed, EMBASE, Cochrane, Web of Science, CNKI, Wangfang Data, and CQVIP from inception until July 2019. Eligible studies included the metabolomic analysis of blood samples from sepsis patients with the outcome. The metabolic pathway was assigned to each metabolite biomarker. The meta-analysis was performed using the pooled fold changes, area under the receiver operating characteristic curve (AUROC), and vote-counting of metabolic pathways. We also conducted a prospective cohort metabolomic study to validate the findings of our meta-analysis. RESULTS:The meta-analysis included 21 cohorts reported in 16 studies with 2509 metabolite comparisons in the blood of 1287 individuals. We found highly limited overlap of the reported metabolite biomarkers across studies. However, these metabolites were enriched in several death-related metabolic pathways (DRMPs) including amino acids, mitochondrial metabolism, eicosanoids, and lysophospholipids. Prediction of sepsis death using DRMPs yielded a pooled AUROC of 0.81 (95% CI 0.76-0.87), which was similar to the combined metabolite biomarkers with a merged AUROC of 0.82 (95% CI 0.78-0.86) (P?>?0.05). A prospective metabolomic analysis of 188 sepsis patients (134 survivors and 54 nonsurvivors) using the metabolites from DRMPs produced an AUROC of 0.88 (95% CI 0.78-0.97). The sensitivity and specificity for the prediction of sepsis death were 80.4% (95% CI 66.9-89.4%) and 78.8% (95% CI 62.3-89.3%), respectively. CONCLUSIONS:DRMP analysis minimizes the discrepancies of results obtained from different metabolomic methods and is more practical than blood metabolite biomarkers for sepsis mortality prediction. TRIAL REGISTRATION:The meta-analysis was registered on OSF Registries, and the prospective cohort study was registered on the Chinese Clinical Trial Registry (ChiCTR1800015321).

SUBMITTER: Wang J

PROVIDER: S-EPMC7157979 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Prediction of sepsis mortality using metabolite biomarkers in the blood: a meta-analysis of death-related pathways and prospective validation.

Wang Jing J Sun Yizhu Y Teng Shengnan S Li Kefeng K

BMC medicine 20200415 1

<h4>Background</h4>Sepsis is a leading cause of death in intensive care units (ICUs), but outcomes of individual patients are difficult to predict. The recently developed clinical metabolomics has been recognized as a promising tool in the clinical practice of critical illness. The objective of this study was to identify the unique metabolic biomarkers and their pathways in the blood of sepsis nonsurvivors and to assess the prognostic value of these pathways.<h4>Methods</h4>We searched PubMed, E ...[more]

PMID: 32290837

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Project description:Background: Alzheimer's disease (AD) is the major cause of dementia in population aged over 65 years, accounting up to 70% dementia cases. However, validated peripheral biomarkers for AD diagnosis are not available up to present. In this study, we adopted a new strategy of combination of computational prediction and experimental validation to identify blood protein biomarkers for AD. Methods: First, we collected tissue-based gene expression data of AD patients and healthy controls from GEO database. Second, we analyzed these data and identified differentially expressed genes for AD. Third, we applied a blood-secretory protein prediction program on these genes and predicted AD-related proteins in blood. Finally, we collected blood samples of AD patients and healthy controls to validate the potential AD biomarkers by using ELISA experiments and Western blot analyses. Results: A total of 2754 genes were identified to express differentially in brain tissues of AD, among which 296 genes were predicted to encode AD-related blood-secretory proteins. After careful analysis and literature survey on these predicted blood-secretory proteins, ten proteins were considered as potential AD biomarkers, five of which were experimentally verified with significant change in blood samples of AD vs. controls by ELISA, including GSN, BDNF, TIMP1, VLDLR, and APLP2. ROC analyses showed that VLDLR and TIMP1 had excellent performance in distinguishing AD patients from controls (area under the curve, AUC = 0.932 and 0.903, respectively). Further validation of VLDLR and TIMP1 by Western blot analyses has confirmed the results obtained in ELISA experiments. Conclusion: VLDLR and TIMP1 had better discriminative abilities between ADs and controls, and might serve as potential blood biomarkers for AD. To our knowledge, this is the first time to identify blood protein biomarkers for AD through combination of computational prediction and experimental validation. In addition, VLDLR was first reported here as potential blood protein biomarker for AD. Thus, our findings might provide important information for AD diagnosis and therapies.

Project description:BACKGROUND:Angiopoietin-1 (Ang-1) and 2 (Ang-2), high mobility group box 1 (HMGB1), soluble receptor for advanced glycation endproducts (sRAGE), soluble triggering receptor expressed on myeloid cells 1 (sTREM1), and soluble urokinase-type plasminogen activator receptor (suPAR) have shown promising results for predicting all-cause mortality in critical care patients. The aim of our systematic review and meta-analysis was to assess the prognostic value of these biomarkers for mortality in adult patients with sepsis. METHODS:A systematic literature search of the MEDLINE, PubMed, EMBASE, and Cochrane Library databases, for articles in English published from 01.01.1990 onwards, was conducted. The systematic review focused exclusively on observational studies of adult patients with sepsis, any randomized trials were excluded. For the meta-analysis, only studies which provide biomarker concentrations within 24 h of admission in sepsis survivors and nonsurvivors were included. Results are presented as pooled mean differences (MD) between nonsurvivors and survivors with 95% confidence interval for each of the six biomarkers. Studies not included in the quantitative analysis were narratively summarized. The risk of bias was assessed in all included studies using the Quality in Prognosis Studies (QUIPS) tool. RESULTS:The systematic literature search retrieved 2285 articles. In total, we included 44 studies in the qualitative analysis, of which 28 were included in the meta-analysis. The pooled mean differences in biomarker concentration (nonsurvivors - survivors), measured at onset of sepsis, are listed as follows: (1) Ang-1: -?2.9 ng/ml (95% CI -?4.1 to -?1.7, p?<?0.01); (2) Ang-2: 4.9 ng/ml (95% CI 2.6 to 7.1, p?<?0.01); (3) HMGB1: 1.2 ng/ml (95% CI 0.0 to 2.4, p?=?0.05); (4) sRAGE: 1003 pg/ml (95% CI 628 to 1377, p?<?0.01); (5) sTREM-1: 87 pg/ml (95% CI 2 to 171, p?=?0.04); (6) suPAR: 5.2 ng/ml (95% CI 4.5 to 6.0, p?<?0.01). CONCLUSIONS:Ang-1, Ang-2, and suPAR provide beneficial prognostic information about mortality in adult patients with sepsis. The further development of standardized assays and the assessment of their performance when included in panels with other biomarkers may be recommended. Trial registration This study was recorded on PROSPERO, prospective register of systematic reviews, under the registration ID: CRD42018081226.

Dataset Information

Prediction of sepsis mortality using metabolite biomarkers in the blood: a meta-analysis of death-related pathways and prospective validation.

Publications

Prediction of sepsis mortality using metabolite biomarkers in the blood: a meta-analysis of death-related pathways and prospective validation.

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