Research ArticleSepsis

An Integrated Clinico-Metabolomic Model Improves Prediction of Death in Sepsis

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Science Translational Medicine  24 Jul 2013:
Vol. 5, Issue 195, pp. 195ra95
DOI: 10.1126/scitranslmed.3005893

Abstract

Sepsis is a common cause of death, but outcomes in individual patients are difficult to predict. Elucidating the molecular processes that differ between sepsis patients who survive and those who die may permit more appropriate treatments to be deployed. We examined the clinical features and the plasma metabolome and proteome of patients with and without community-acquired sepsis, upon their arrival at hospital emergency departments and 24 hours later. The metabolomes and proteomes of patients at hospital admittance who would ultimately die differed markedly from those of patients who would survive. The different profiles of proteins and metabolites clustered into the following groups: fatty acid transport and β-oxidation, gluconeogenesis, and the citric acid cycle. They differed consistently among several sets of patients, and diverged more as death approached. In contrast, the metabolomes and proteomes of surviving patients with mild sepsis did not differ from survivors with severe sepsis or septic shock. An algorithm derived from clinical features together with measurements of five metabolites predicted patient survival. This algorithm may help to guide the treatment of individual patients with sepsis.

Introduction

Sepsis is defined as infection resulting in systemic inflammatory response syndrome (SIRS; a combination of nonspecific clinical features of inflammation). Sepsis is the 10th leading cause of death in the United States (1, 2). Sepsis mortality has decreased over the past decade as a result of improved treatment protocols, such as potent antimicrobial drugs and early goal-directed therapy (EGDT) (36). Choice of treatment is based on the traditional concept of stepwise sepsis progression and corresponding clinical assessments, such as organ hypoperfusion (1, 7). Therapies that are optimized for individual patients and that target specific sepsis mechanisms have been hard to implement because of nonspecific clinical presentations, delayed diagnosis, cryptic severity, and a heterogeneous clinical course (8, 9). Patients may arrive at an emergency department with mild clinical manifestations yet rapidly progress to critical illness. Others have benign courses despite a similar onset of symptoms, suggesting that host factors play an important role in sepsis development and outcome. Given that infections account for more than 10 million emergency department visits per year, and sepsis treatment costs $16.7 billion in the United States (1), there exists an urgent need for more timely sepsis diagnosis, characterization, and prognosis, to inform personalized sepsis treatment of the appropriate intensity. Such information could include a choice of oral or intravenous antibiotics and whether to admit the patient to hospital or start EGDT (310). In addition to better sepsis outcomes, these decisions may decrease unnecessary patient stress and improve the efficiency of resource utilization.

Decades of clinical and molecular studies have identified numerous microbial and host perturbations associated with sepsis outcome. Age and comorbidity, as codified in the Acute Physiology and Chronic Health Evaluation II (APACHE II) score, for example, are determinants of sepsis outcome (11). Others include the severity of clinical signs at presentation and after initial therapy. Such clinical signs include the number of SIRS criteria met, lactic acid concentrations in the blood, and early development of shock (failure to maintain blood pressure despite adequate hydration) (1215). Clinical indices, such as APACHE II and the Sequential Organ Failure Assessment (SOFA), combine multiple clinical measurements in an attempt to aggregate the evidence of the heterogeneous organ dysfunctions that can precede poor outcomes (11, 16). A wide variety of host response biomarkers or biomarker panels have also been examined for utility in sepsis diagnosis and prognostic determination but, to date, have lacked the sensitivity and specificity to discriminate individual patient prognoses and outcomes (1722). This is believed to be due, in part, to the underlying heterogeneity of sepsis. In particular, mortality has been difficult to predict because there are many processes that are associated with death from sepsis, such as uncontrolled inflammation, oxidative stress, immune dysfunction, hemodynamic dysfunction, coagulopathy, metabolic dysfunction, and genetic predisposition (23).

Comprehensive, integrated analysis of molecular measurements (24) may allow unbiased identification and prioritization of sepsis outcome signals that may be obscured by false discovery cutoffs or overinterpreted by targeted hypothesis testing. In contrast, analyses of multiple clinicopathologic data sets should reveal multidimensional perturbations of causal networks and pathways. Here, we report the results of a prospective, integrated analysis of outcomes in community-acquired sepsis.

Results

Study design and clinical synopsis

One thousand one hundred fifty-two individuals with suspected, community-acquired sepsis (acute infection and ≥2 SIRS criteria) (15) were enrolled prospectively in the emergency departments at three urban, tertiary-care hospitals in the United States between 2005 and 2009 [Community Acquired Pneumonia and Sepsis Outcome Diagnostics (CAPSOD) study, ClinicalTrials.gov NCT00258869] (12, 17, 25). Patients with SIRS criteria but obvious noninfectious diseases were not enrolled (12). Medical history, physical examination, and acute illness scores (APACHE II and SOFA) (11, 16) were recorded at enrollment (t0) and 24 hours later (t24), and corresponding blood samples were obtained (Fig. 1A). t0 was the earliest sampling time available for community-acquired sepsis. Sampling at t0 and t24 allowed evaluation of the trajectory of changes after enrollment. Infection status and outcome through day 28 were independently adjudicated by a board-certified clinician, as described (12, 17, 25) (table S1). Survival/death was the primary outcome. Standard diagnostic tests were supplemented by tests for capillary lactic acid and urinary pneumococcal antigen and, for a subset of patients, polymerase chain reaction of blood for bacterial and fungal DNA (12, 17, 25). Sixty-three percent of the patients included in this analysis were African American. Twenty-eight–day mortality was low (4.9%) (12). Because CAPSOD was an observational study, clinical care was not standardized and was determined by individual providers.

Fig. 1 An integrative systems survey of sepsis survival and death.

(A) CONSORT flow chart of patient enrollment and selection. Patients presenting to emergency departments with suspected community-acquired sepsis (acute infection and ≥2 SIRS criteria) were grouped according to final adjudication (sepsis or SIRS, no infection), day 3 clinical course (septic shock, severe sepsis, and uncomplicated sepsis), and outcome at day 28 (survival or death). Groups were defined by the most severe stage of sepsis attained. A subset of cases were chosen for the derivation study based on planned number (n = 30) of patients per subgroup and enriched for etiologic agents and controlling for attributes defined by the sepsis nonsurvivor group. The validation group had limited number of sepsis nonsurvivors. 1One sepsis nonsurvivor initially refused phlebotomy at t0, yet later agreed at t24. The sample was used to maximize validation predictive modeling studies. No noninfected SIRS validation samples were selected because predictive modeling was not successful during derivation. (B) Experimental design. MS-based metabolome and proteome analysis was performed on plasma samples obtained at t0 and t24 from 150 matched derivation subjects. Validation of metabolome findings was sought by semiquantitative MS in an independent cohort comprising all remaining sepsis nonsurvivors and a matched group of sepsis survivors at t0 and t24 (n = 52). After molecular integration and analysis, predictive models were developed that were representative of the clinical and molecular findings. A top model using semiquantitative metabolomics clinical measures was trained at t0, and then tested against the derivation t24 group, validation groups (Vt0, Vt24), and an independent validation (RoCI) cohort. The utility of the predictive models was further tested by clinical measures and targeted, quantitative assays of butyroylcarnitine, 2-methylbutyroylcarnitine, hexanoylcarnitine, cis-4-decenoylcarnitine, 1-arachidonoyl-GPC, 1-linoleoyl-GPC, pseudouridine, 3-(4-hydroxyphenyl)lactate (HPLA), 4-methyl-2-oxopentanoate, 3-methoxytyrosine, and N-acetylthreonine of 382 samples; four samples were not included in a subset of metabolites because of limited serum volume. Tests included logistic regression of the top model derived by semiquantitative results and support vector machine (SVM) analysis of the top model.

The discovery set of 150 patients (13% of the total CAPSOD cohort) had five groups that reflected conventional concepts of sepsis progression as a pyramid (1, 4). The number of subjects was governed by power to test associations with survival/death. Infection status and infectious agent were adjudicated by a study physician before the generation of test data (12). Standard definitions of organ dysfunction and shock were used (12, 26). The five groups were as follows: day 28 sepsis survivors with uncomplicated courses (n = 27), sepsis survivors who developed severe sepsis or septic shock by day 3 (n = 25 and n = 38, respectively), sepsis nonsurvivors (by day 28; n = 31), and noninfected patients who exhibited SIRS criteria (SIRS-positive, “ill” controls, presumed septic at enrollment but later determined to have noninfectious reasons for SIRS; n = 29) (12). Because of the few deaths from sepsis in the CAPSOD study, that group defined the attributes of the patients selected for the other four groups (Table 1). The noninfected SIRS group had similar rates of clinical progression as did the sepsis groups (day 3 organ dysfunction and shock, and 28-day death), allowing distinction between the disease progression of sepsis and other SIRS-associated acute illnesses (Table 1). Patients within the sepsis groups were also chosen for infections with Streptococcus pneumoniae (n = 31), Escherichia coli (n = 16), and Staphylococcus aureus (n = 27), three common causes of community-acquired sepsis that often differ in the site of infection and rates of progression.

Table 1 Clinical variables and demographics.

Data are presented as means ± SD. B/W/O, black/white/other; MAP, mean arterial pressure; N/R, not reported; N/A, not applicable.

View this table:

The experimental design included two validation patient sets (Fig. 1A). First, a separate CAPSOD subset of 18 sepsis nonsurvivors and 34 matched sepsis survivors (at t0 [Vt0] and t24 [Vt24]). Few patients in the sepsis nonsurvivor group were available after selection of the discovery set because of a low death rate due to sepsis or phlebotomy refusal at t24. Therefore, the sepsis survivors chosen for inclusion in the validation set were matched to those of the available sepsis nonsurvivors based on age, race, sex, and enrollment site. The second validation set was from an independent sepsis study [the Brigham and Women’s Hospital Registry of Critical Illness (RoCI) cohort, approved by the Partners Human Research Committee, protocol #2008-P-000495] (27). This set had 29 noninfected patients with SIRS, 36 sepsis survivors, and 25 sepsis nonsurvivors.

Plasma metabolomics

Biochemicals in plasma with a mass-to-charge (m/z) ratio of 100 to 1000 daltons were measured using label-free liquid chromatography (LC) and gas chromatography (GC) and mass spectrometry (MS) (28) (Fig. 1B). Of ~4400 metabolites potentially detectable in human tissues (29), 439 were measured at either t0 or t24, and 332 were detected at both t0 and t24. Two hundred fourteen of the biochemicals detected at t0 and 224 detected at t24 were annotated metabolites (Fig. 2, A and B). The median relative SD of repeated MS measurements of standards was 10% after signal intensity normalization to batch medians. Clinical assays of serum creatinine, capillary lactate, and serum glucose correlated well with log-transformed normalized plasma MS values (Fig. 2, C to E), indicating that the MS assays of metabolite concentrations were semiquantitative.

Fig. 2 Metabolomic profiling of plasma in sepsis.

(A and B) Venn diagrams of overlap of biochemicals (A) and annotated metabolites (B) measured by MS in discovery plasma samples at t0 (n = 150) and t24 (n = 132) and 52 validation (V) patients at t0 and t24. One hundred sixty metabolites were removed from the analysis because they were detected in ≤50% of the patients. (C to E) Comparison of creatinine (C), lactate (D), and glucose (E) concentrations as determined in serum by clinical chemical analyzer and in plasma by MS in 149, 115, and 149 patients, respectively. Differences in n values were due to omissions in clinical values—a large group of patients did not require blood lactate values as part of their clinical care. MS values are normalized, log-transformed intensities. Clinical chemistry values (mg/dl) are log-transformed. (F) Z-score scatterplots of plasma metabolites from noninfected SIRS, uncomplicated sepsis, severe sepsis, septic shock, or sepsis nonsurvivor patients. Zero on the x axis represents the mean of the control group. Each data point is expressed as the number of SDs from the mean of the controls. The y axis shows all values for each biochemical on the same horizontal line. Z scores are SDs from the control mean, revealing changes relative to control. The boxed values are mScores, which are averages of the absolute values of z scores for all metabolites, calculated using nontruncated, nonimputed values. (G) The variance in plasma metabolite concentrations at the time of emergency department enrollment (t0) that was attributable to sepsis outcome decreased with increasing days to death (x axis).

Typically, metabolomics measurements in healthy populations exhibit a normal distribution of z scores. However, the distribution of z scores in the uninfected SIRS group was right-skewed (log-normal) (Fig. 2F). Patients with severe sepsis and those who died had larger z scores that were more skewed than the uninfected SIRS control group (Fig. 2F), indicative of greater metabolomic variance. Principal components analysis (PCA) and Bayesian factor analysis (with normalized factor score plots) were used to determine the main sources of interindividual variation in the plasma metabolome. The Bayesian factor analysis [cj = Byj + A(sj ° zj) + εj] correlated metabolite values (yj) to clinical parameters (cj) to define their relevance [where B was the relationship between MS data (yj) and a clinical parameter (cj), A was random or undefined effects, and ε was random noise]. Clinical parameters (cj) were normalized with zero-mean and SD and plotted on B-matrices. The strength of clinical parameter–metabolite associations increased from t0 to t24 (by PCA and Bayesian factor analysis; fig. S1), indicating that metabolomic perturbations were increasing at the time of enrollment. Furthermore, in sepsis nonsurvivors, the variance in the plasma metabolome that was explicable on the basis of sepsis outcomes increased as death approached (Fig. 2G), consistent with a causal association of metabolome changes with death from sepsis. Remaining variance in the plasma metabolome was largely explained by renal function (semiquantitative; four groups), liver function (binary), and immunosuppressants (binary) (figs. S1 and S2). Overlaid kernel densities and Mahalanobis distances of metabolome values revealed one septic shock patient to be an outlier, and this patient was therefore removed from subsequent metabolomics analyses.

Plasma metabolites that differed between groups were identified by analysis of variance (ANOVA) at t0 and t24. Variance unrelated to sepsis was controlled by inclusion of renal function and liver disease as fixed effects. Because acute renal dysfunction showed an association with sepsis nonsurvival, this may have resulted in underestimation of differences due to sepsis outcome (table S2). Remarkably, no metabolite differed significantly between sepsis survivor subgroups (uncomplicated sepsis, day 3 severe sepsis, day 3 septic shock) or between infectious etiologies (S. pneumoniae, S. aureus, or E. coli; fig. S3) at either t0 or t24. In contrast, plasma concentrations of 49 metabolites differed between the sepsis survivor groups and the uninfected SIRS-positive group at t0, whereas 42 metabolites differed at t24 [Fig. 3A; ANOVA with inclusion of renal and liver function as fixed effects and false discovery rate (FDR) 5%; sepsis survivor subgroups collapsed; table S3]. In all, 63 metabolites differed between sepsis survivors and uninfected patients at either time point. Of these, 60 had concordant direction of change at both time points, indicating a consistent early metabolic response in sepsis survivors (rather than multiphasic; fig. S4 and table S3). Sepsis survivors had lower plasma concentrations of citrate, malate, glycerol, glycerol 3-phosphate, phosphate, 21 amino acids and their catabolites, 12 glycerophosphocholine (GPC) and glycerophosphoethanolamine (GPE) esters, and 6 carnitine esters compared to uninfected patients (Fig. 3A, figs. S5 and S6, and table S3). Six acetaminophen catabolites and two androgenic steroids were increased. Notably, lactate, ketone bodies, and carnitine were relatively unchanged between sepsis survivors and uninfected patients.

Fig. 3 Comparisons of the plasma metabolome in community-acquired sepsis survivors and nonsurvivors.

(A) Comparison of annotated plasma metabolite concentrations at t24 in 132 discovery subjects (represented by columns). Individuals who died were ordered by days to death (decreasing from left to right as indicated by the red triangle). Rows show 82 host metabolites with statistically significant differences between groups (stratified ANOVA, P < 0.05). Colors indicate log-transformed standardized values. Highlighted are 13 acyl-GPCs and acyl-GPEs, which were decreased in sepsis survivors and further decreased in sepsis nonsurvivors (in comparison with controls), and 13 RNA catabolites and 14 acyl-carnitines, both of which were decreased in sepsis survivors and increased in sepsis nonsurvivors (in comparison with controls). Detailed images are given in fig. S5. (B to D) Three-dimensional scatterplots showing plasma acyl-carnitine and acyl-GPC concentrations in 382 samples, as measured by quantitative, targeted assays. (B and C) Acylcarnitine concentrations were generally increased in day 28 sepsis nonsurvivors (green contour ellipsoid) and decreased in sepsis survivors (blue ellipsoid) when compared with noninfected controls (red ellipsoid). Samples obtained from patients who died with sepsis within the 28-day follow-up period are indicated by green diamonds [n = 93; cis-4-decenoylcarnitine, 1825 ± 168 ng/ml; hexanoylcarnitine, 41.2 ± 3.5 ng/ml; butyroylcarnitine, 68.2 ± 11.7 ng/ml (mean ± SEM)], sepsis survivors by blue dots (n = 235; cis-4-decenoylcarnitine, 932 ± 50 ng/ml; hexanoylcarnitine, 20.3 ± 1.1 ng/ml; butyroylcarnitine, 31.9 ± 2.3 ng/ml), and noninfected controls by red dots (n = 54; cis-4-decenoylcarnitine, 1200 ± 115 ng/ml; hexanoylcarnitine, 24.6 ± 2.9 ng/ml; butyroylcarnitine, 35.0 ± 3.7 ng/ml). (D) Three-dimensional scatterplot showing similar trends in plasma values of two acyl-GPCs and an RNA catabolite in 378 samples. Acyl-GPCs generally were highest in noninfected (red contour ellipsoid), lower in sepsis survivors (blue contour ellipsoid), and lowest in day 28 sepsis nonsurvivors (green contour ellipsoid). Sepsis day 28 deaths are shown by green diamonds [n = 91; 1-arachidonoyl-GPC, 1.10 ± 0.09 μg/ml; 1-linoleoyl-GPC, 2.23 ± 0.21 mg/dl; pseudouridine, 954 ± 65 ng/ml (mean ± SEM)], sepsis survivors by blue dots (n = 234; 1-arachidonoyl-GPC, 1.38 ± 0.07 μg/ml; 1-linoleoyl-GPC, 3.40 ± 0.29 μg/ml; pseudouridine, 708 ± 43 μg/ml), and noninfected controls by red dots (n = 53; 1-arachidonoyl-GPC, 2.49 ± 0.13 μg/ml; 1-linoleoyl-GPC, 6.15 ± 0.52 μg/ml; pseudouridine, 628 ± 88 ng/ml). Ellipsoids encompass 90% of sample values. (E) Box-and-whisker plots of MS lactate values and targeted, quantitative values (red boxes) in 382 plasma samples (n = 378 in HPLA and 1-linoleoyl-GPC). Sample values are shown in black. Ranges are shown by black horizontal lines. Means are connected by blue lines.

Next, metabolite values in the collapsed sepsis survivor groups were compared with those in the sepsis nonsurvivor group. Seventy-six metabolites differed between the sepsis survivor and death groups at t0, and 128 metabolites at t24 (FDR, 5%; Fig. 3A, figs. S5 and S6, and table S3). The metabolic differences between the sepsis survivor and death groups were also temporally consistent. Thus, 84 metabolites at one time point that were significantly different between those who survived and those who died, and detected at the other time point, showed a concordant direction of change. However, interindividual variability in individual metabolite values was high. Nevertheless, the validity of the differences between survivors and nonsurvivors was supported by the finding that many members of biochemical families had the same direction of change: 17 amino acid catabolites, 16 carnitine esters, 11 nucleic acid catabolites, 5 glycolysis and citric acid cycle components (citrate, malate, pyruvate, dihydroxyacetone, and phosphate), and 4 free fatty acids were significantly increased in the sepsis nonsurvivor group (by ANOVA; fig. S5 and table S3). Seven GPC and GPE esters were decreased in the sepsis nonsurvivor group, in agreement with previous studies (23, 3032). Lactate, an established sepsis severity marker, was elevated in the sepsis nonsurvivor group. Carnitine and ketones were unchanged. Given the regulation of metabolism by steroids, it was notable that anabolic steroids were decreased in the sepsis nonsurvivor group, whereas cortisone was increased. These changes were consistent with increased exergonic metabolism in sepsis survivors. A clinical correlate of this conclusion was elevated core temperature in sepsis survivors (38.1°C), but not in the sepsis nonsurvivor group (37.4°C) (Table 1), as previously described (12).

Carnitine esters with medium- or short-chain fatty acids and branched-chain amino acids were the most pronounced biochemical groups that differed between the sepsis nonsurvivors and survivors. It was possible that these accumulated in blood because of renal dysfunction and not sepsis itself. To explore this hypothesis, we performed a Bayesian factor analysis with stratification by renal function at t0 [normal estimated glomerular filtration rate (eGFR) ≥75 ml/min, n = 44; 32 to 74 ml/min, n = 56] and binary primary groupings (noninfected, uncomplicated sepsis, severe sepsis, septic shock, and sepsis nonsurvivor), etiologic agents (S. aureus, S. pneumoniae, E. coli), gender, race, liver disease, hepatitis, alcohol abuse, and neoplastic disease. Metabolite factor scores ≥0.1 or ≤−0.1 were considered significant. Liver disease, hepatitis, and alcohol abuse had substantial overlap, which may reflect unity. Reassuringly, sepsis nonsurvival and liver disease remained the major contributors of metabolome variance (fig. S7). The metabolic changes associated with the sepsis nonsurvival factor also remained increased with time (fig. S7). Moreover, the association of carnitine esters with sepsis outcomes remained significant (tables S4 and S5). Thus, the changes in carnitine esters were not explained by renal function.

Validation of metabolomic findings

Confirmation of the veracity of differences was sought by metabolome profiling of a first validation set [all remaining sepsis nonsurvivors (validation t0, Vt0, n = 17; Vt24, n = 16) and matched sepsis survivors (Vt0, n = 34; Vt24, n = 33) (Fig. 1A)]. Samples from two sepsis nonsurvivors and one sepsis survivor were not available at t24; a sample was obtained from one sepsis nonsurvivor who had refused t0 phlebotomy. It should be noted that the median time to death of the validation group was greater than that of the discovery group (18.5 versus 10.7 days, respectively) because insufficient sepsis nonsurvivor samples were available for precise matching of discovery and validation sets. Not surprisingly, the metabolic variance attributable to sepsis outcome at Vt0 was less pronounced than that in the t0 set (fig. S2). Consequently, less stringent FDRs were applied in ANOVAs for Vt0 (25%) and Vt24 (15%). The differences were fewer and of smaller magnitude between sepsis survivors and nonsurvivors in the validation cohort (18 differences at t0 and 20 at t24; Fig. 3A, figs. S5 and S6, and table S3). Nevertheless, the major metabolite differences were recapitulated (elevated amino acid and RNA catabolites, citrate, malate, and fatty acids, and decreased anabolic steroids and GPC and GPE esters). The most consistently altered biochemical class in the validation set remained the carnitine esters, with significant increases in 19 of 21 compounds in the sepsis nonsurvivor group for at least one time point.

A second validation study was performed on an independently derived cohort from another institution with a different enrollment protocol (RoCI study). This validation set contained 29 noninfected subjects with SIRS, 36 sepsis survivors, and 25 sepsis nonsurvivors (Table 1). The demographics of RoCI differed from those of the CAPSOD study. A prominent difference was that the principal ethnicity in the RoCI study was Caucasian (78%). Neoplastic disease (75% RoCI versus ~23% CAPSOD) and administration of immunosuppressants (36% RoCI versus 6.5 to 15% CAPSOD) were much higher in the RoCI sepsis nonsurvivor category than in the sepsis nonsurvivor category for CAPSOD. The metabolome was profiled with identical methods in both studies. ANOVA of the metabolomic results from the RoCI cohort with a 5% FDR recapitulated the CAPSOD study results with regard to alterations in carnitine esters, GPC and GPE esters, amino acid derivatives, nucleic acid catabolites, and glycolysis and citric acid cycle components (representative results presented in fig. S8; full results to be published by the RoCI group). Furthermore, the direction of change of these analytes recapitulated those of the CAPSOD cohorts, providing strong evidence that these differences reflected sepsis outcomes rather than bias intrinsic to a single study or limited to a single ethnic group.

Further recapitulation of the major findings was sought for 11 representative metabolites by retesting 382 of the CAPSOD discovery and validation samples with targeted, quantitative assays (figs. S9 and S10 and tables S6 and S7); four samples were not re-assayed for 4-methyl-2-oxopentanoate, 1-linoleoyl-GPC, 1-arachidonoyl-GPC, HPLA, 3-methoxytyrosine, N-acetylthreonine, and pseudouridine because further aliquots were unavailable. The quantitative results correlated with the semiquantitative MS screening data (correlation coefficients ranged from +0.57 to +0.99) (fig. S11). Whereas interindividual variability of the concentrations of the 11 metabolites among subjects was considerable, the previously described differences between sepsis survivors, sepsis nonsurvivors, and uninfected SIRS patients were confirmed (Fig. 3, B to E, and fig. S12). The average differences in metabolite values between sepsis survivors and nonsurvivors using the quantitative assays were also examined as a function of time to death. The death survivor differences increased inversely with time to death, suggesting temporal correlations of the 11 metabolites with sepsis nonsurvival (fig. S13).

Plasma proteomics

A complementary survey of host response in sepsis survival and death was performed by proteome profiling of the 150 subjects in the CAPSOD discovery group (Fig. 1). Plasma proteins identified by MS with high confidence were quantified using two methods: log-transformed quantile-normalized areas under the curve (AUCs) of aligned chromatograms after background noise removal (33), and spectral counting. We note that the sensitivity of MS is too low to detect most changes in cytokines, and confidence in identities is low because typically only one peptide is detected (34).

After immunodepletion of abundant plasma proteins (33), 195 and 117 proteins identified with high confidence were measured by the two methods described above, respectively, of which 101 were detected by both methods (table S8). For proteins with spectral counts >10, measurements derived from the two methods correlated well (table S8). Clinical assays of serum C-reactive protein (CRP) and albumin correlated with log-transformed MS values in plasma (fig. S14), indicating MS to be at least semiquantitative. As observed for the metabolome, sepsis group membership explained part of the variation in the plasma proteome (fig. S15). Other categorical traits that explained variance were liver disease, immunosuppressant agents, and malignancy (fig. S15). As with the metabolome, only a single significant protein difference was found among sepsis survivor subgroups or between infectious etiologies (fig. S16). The concentrations of 16 plasma proteins differed between sepsis survivors and uninfected SIRS patients at t0, and 40 proteins differed at t24 (ANOVA with 5% FDR and with control of non–sepsis-related effects by inclusion of liver disease, immunosuppressants, and malignancy as fixed effects) (table S8). In agreement with previous reports, many inflammatory markers were elevated in sepsis (for example, CRP, lipopolysaccharide binding protein, leucine-rich α2 glycoprotein, serpin peptidase inhibitor 3, serum amyloid A1 and A3, and selenoprotein P) (table S8) (35, 36). Serpin peptidase inhibitor 1, which inhibits plasmin and thrombin, was increased in sepsis, consistent with previous reports (37, 38). Notably, several thrombolytic proteins (factor XII, plasminogen, kininogen 1, and fibronectin 1) were decreased in sepsis.

Like the metabolome, the plasma proteome disclosed a markedly different host response in sepsis survivors and nonsurvivors (with 56 and 27 significant protein differences at t0 and t24, respectively; table S9). There was strong concordance in protein differences at both time points: 44 of 59 plasma proteins with significant survivor-death differences had congruent changes at the other time point. Notable protein families exhibiting differences were complement components (22 of which were increased in the sepsis nonsurvivor group), thrombolytic proteins (8 of which were decreased and 3 increased in the sepsis nonsurvivor group), and fatty acid transport proteins (9 of which were increased in the sepsis nonsurvivor group: apolipoproteins AI, AII, AIV, L1, and CIV, transthyretin, hemopexin, afamin, and α-2-HS-glycoprotein; Fig. 4A and table S9).

Fig. 4 Integration of metabolomic and proteomic differences in sepsis nonsurvival.

(A) Changes in plasma proteins in the complement, coagulation, and fibrinolytic cascades in sepsis survivors and nonsurvivors. Adapted from KEGG (Kyoto Encyclopedia of Genes and Genomes). Red boxes indicate proteins that are decreased in sepsis nonsurvivors compared to survivors; green boxes are proteins that are increased in sepsis nonsurvivors. (B) Heatmap of hierarchical clustering of pairwise Pearson product-moment correlations of 332 log-transformed, annotated plasma metabolites in 132 subjects at t0 compared to matched subjects at t24. Positive correlations are red; inverse correlations are blue. Unannotated GC-MS identified biochemicals were excluded from the analysis. A detailed list of the metabolite clusters is given in fig. S17. (C) Heatmap of hierarchical clustering of pairwise Pearson product-moment correlations of 162 log-transformed annotated plasma proteins and 332 metabolites in 132 subjects at t0. Eighteen subjects at t0 were not included within this analysis because there was not a matched value at t24. Positive correlations are red; inverse correlations are blue. Excluded were metabolites or proteins detected in <50% of patients or that did not have a reported value at both t0 and t24. (D) Plasma metabolite correlations with succinate dehydrogenase complex, subunit D (SDHD) was increased 2.44-fold in sepsis nonsurvival compared with sepsis survival. Regulation of metabolite flow from the pyruvate dehydrogenase complex through the citric acid cycle is shown, along with associated reactions that replenish depleted cycle intermediates and entry into fatty acid β-oxidation. Correlation coefficients of plasma metabolite with plasma SDHD values are indicated by green integers. Plasma lactate, pyruvate, acetyl-carnitine, oxaloacetate, and α-ketoglutarate were higher in sepsis nonsurvivors than in sepsis survivors. Global cross-correlation analysis results determined from all relevant t0 metabolites (336 biochemicals) correlated with t0 proteins (165 proteins) in 150 derivation patient samples. The analysis included lower-confidence protein acyl-CoA synthetase ACSM6 and single–time point high-confidence proteins SDHD and FABP4.

Integration of proteomic and metabolomic data sets

We reasoned that true positive changes in the metabolome should be reflected by analogous changes in the proteome. In particular, this should be true for plasma proteomic and metabolomic measurements in the same biochemical pathway. For example, they should recapitulate known substrate-enzyme-product reaction models, and members of known biochemical families should cocluster. Further, we reasoned that it may be possible to impute the class membership of unknown metabolites, familial enzyme pathways, and novel enzymatic reaction models by integration of the proteomic and metabolomic data sets. To explore this, we performed a global cross-correlation and hierarchal clustering of matched metabolites (for example, t0 metabolome versus t24 metabolome) or proteins (for example, t0 proteome versus t24 proteome) for the 150 discovery subjects. Further, to assess recapitulation of known metabolome-proteome reaction models, we performed cross-correlation and clustering of metabolites with proteins at each time point (for example, t0 proteins versus t0 metabolites) in the same samples.

The metabolome-metabolome cross-correlation and hierarchal clustering did largely recapitulate known metabolite/biochemical class membership (Fig. 4B): For example, 7 carnitine esters were nearest neighbors at t0, as were 5 androgenic steroids, 11 GPC and GPE esters, 5 bile acids, 16 fatty acids, and 12 amino acid metabolites and energy metabolic derivatives (lactate, citrate, glycerol, pyruvate, oxaloacetate) (Fig. 4B and fig. S17). Furthermore, coclustering suggested class membership for several unannotated biochemicals. Several of these were confirmed by subsequent structural determination: Unannotated biochemicals X-11302, X-11245, and X-11445, which coclustered with dehydroepiandrosterone sulfate, androsterone sulfate, and epiandrosterone sulfate, were determined to be sulfated pregnenolone-related steroids (pregnen-steroid monosulfate, pregnen-diol disulfate, and 5α-pregnan-3β,20α-diol disulfate, respectively); unannotated biochemical X-11421 coclustered with 8 medium-chain acyl-carnitines and was determined to be cis-4-decenoylcarnitine; X-12465 coclustered with acetyl- and propionyl-carnitine and was determined to be 3-hydroxybutyrylcarnitine (Fig. 4B and fig. S17). Likewise, many functionally or structurally related proteins coclustered, such as 4 hemoglobin isoforms, 9 complement components, and 10 apolipoproteins (Fig. 4C).

In addition, plasma proteome-metabolome correlations recapitulated a number of known metabolic reaction models. Of 53,784 plasma protein–metabolite correlations, 4105 were concordant at t0 and t24 and statistically significant (Bonferroni-corrected log10 P < −6.03; table S10). These included known mass action kinetic models of catalysis or physicochemical complex assembly: Ribonuclease A1 correlated with 12 downstream products of its action (N6-carbamoylthreonyladenosine, N2,N2-dimethylguanosine, pseudouridine, arabitol, arabinose, erythritol, erythronate, gulono-1,4-lactone, allantoin, phosphate, xylonite, and xylose). Hemoglobin subunits α1, β, δ, and ζ correlated with the component heme, allosteric effector adenosine-5-monophosphate, and degradation product xanthine. Subunit D of succinate dehydrogenase (a high-confidence protein identification supported by a single peptide) correlated with three downstream citric acid cycle intermediates (l-malate, oxaloacetate, and citrate; Fig. 4D and table S11). Several carnitine esters and fatty acids correlated with plasma transporter fatty acid–binding proteins (FABP1 and FABP4; fig. S18 and table S11). Two fatty acid substrates correlated inversely with acyl–coenzyme A (CoA) synthetase mitochondrial-like 6 (ACSM6; another high-confidence protein identification supported by a single peptide), which catalyzes attachment of fatty acids to CoA for β-oxidation (fig. S19 and table S11).

We reasoned that cocluster hierarchies and correlations might suggest novel enzymatic reaction models. Thus, for example, subunit D of succinate dehydrogenase correlated with pyruvate, lactate, and acetyl-carnitine, and may suggest novel regulation of the citric acid cycle (Fig. 4D), which has animal model support (39). Another plausible model was suggested by correlations of ACSM6 with nine carnitine esters (fig. S18). ACSM6 acts upstream of carnitine esterification and mediates mitochondrial fatty acid import. Overall, these analyses served to validate the accuracy of the metabolomic and proteomic measurements.

Derivation and testing of outcome predictive biomarker panels

In light of the consistency of the metabolome and proteome changes between sepsis survivors and nonsurvivors, a biomarker panel was developed and assessed for utility in prediction of sepsis outcomes upon arrival at the emergency room (t0). Four clinical factors (age, mean arterial pressure, hematocrit, and temperature) and 12 metabolites (2-methylbutyroylcarnitine, cis-4-decenoylcarnitine, butyroylcarnitine, hexanoylcarnitine, 4-methyl-2-oxopentanoate, 1-arachidonoyl-GPC, 1-linoleoyl-GPC, HPLA, 3-methoxytyrosine, N-acetylthreonine, pseudouridine, and lactate) were nominated either by previous clinical analyses (12) or by selection of the most significantly different metabolomic differences in sepsis survivors and deaths by ANOVA and Bayesian factor analysis. These biomarkers were also selected for relevance to the molecular mechanisms suggested for sepsis survival and death. Proteomic biomarkers were not used in this analysis. These biomarkers were used to develop a sparse panel for prediction of sepsis outcomes with logistic regression. The number of biomarkers in the panel was reduced to seven by penalized predictor reduction (a statistical method that applies a penalty to the sum of squares of the coefficients to reduce the number of factors; we used a maximum of 10 effects, a log10 regularization parameter, and a maximum of five categories). These were cis-4-decenoylcarnitine, 2-methylbutyroylcarnitine, butyroylcarnitine, hexanoylcarnitine, lactate, age, and hematocrit. The resultant logistic regression model performed very well for prediction of sepsis outcomes at t0 in the discovery cohort (AUC of 0.847 and accuracy of 85.1%). The prognostic utility of the model was also good in the discovery t24 data set, and the validation Vt0 and Vt24 data sets (Table 2). Indeed, the model predicted sepsis nonsurvival or survival better than widely used clinical scores, such as SOFA (score ≥7), APACHE II (score ≥25), and capillary lactate (≥4.0 mg/dl) (Table 2). Because the discovery and validation studies used cohorts from the CAPSOD study, it was possible that the model was overfitted. Therefore, utility of the model was examined in an independently derived sepsis cohort from another institution and with separate metabolic measurements (RoCI) (27). ANOVA showed 9 of the 12 biomarker metabolites to have a statistically significant change in sepsis survivors versus nonsurvivors in the RoCI cohort, and all 12 followed the same trends as in the CAPSOD samples (FDR 5%, fig. S8). The biomarker panel also had strong predictive discrimination between sepsis survival and death in the RoCI cohort (Table 2).

Table 2 Predictive modeling of sepsis outcomes.

RMSE, root mean square error; PPV, positive predictive value (sepsis survivor prediction); NPV, negative predictive value (sepsis nonsurvivor prediction); QTA, quantitative targeted assay.

View this table:

The data generated in the global metabolomics studies were semiquantitative. To further examine the prognostic utility of the logistic regression model, we developed specific, quantitative MS assays for four of the biomarker metabolites (cis-4-decenoylcarnitine, 2-methylbutyroylcarnitine, butyroylcarnitine, and hexanoylcarnitine). The prognostic utility of the biomarker panel was then retested with quantitative clinical values (age, lactic acid, and hematocrit) and values from the specific metabolite assays in all samples from the CAPSOD discovery and validation cohorts (93 sepsis nonsurvivors and 235 sepsis survivors). Missing clinical measurements of lactate were imputed from the values obtained from semiquantitative metabolome methods. Predictive performance was similar to that with the semiquantitative assays (Table 2). Such recapitulation was important because quantitative, homogeneous assays would be used for a clinical prognostic test using these biomarkers.

SVM learning performs two-group classification that allows expansion of the solution vector on support vectors, extends the solution surfaces from linear to nonlinear, and allows for errors in the training set (40). SVM learning typically yields biomarker panels with superior performance to other methods. SVM was used to develop a weighted model for prediction of sepsis survival and death using quantitative measurements of the seven biomarkers. Data from 173 unique sepsis survivors and nonsurvivors were used. When values from the same person were available at both t0 and t24, one sample was randomly selected. This yielded 87 subjects for training and 86 for testing. Values were normalized by subtracting means and dividing by SDs. One hundred random partitions were performed for training and test data for each setting. The AUC of the SVM model in the test subjects was 0.74 and accuracy was 74.6% (55% for 28-day sepsis nonsurvival and 83.6% for sepsis survival; Table 2).

Discussion

This study sought to characterize and integrate the metabolome, proteome, and clinical variables in sepsis survival and death. Somewhat unexpectedly, this analysis delineated differences in host responses to sepsis in survivors and nonsurvivors that were robust and reproducible. As a consequence, the analytes and pathways that differentiate sepsis survival and death hold promise as potential prognostic biomarkers and may also be useful as targets for the development of new therapies for patients at higher risk of death. Prognostic markers of sepsis outcomes have been sought for decades. Previous candidate biomarker studies, although valuable, have had limited clinical prognostic utility, perhaps because of the heterogeneity and complexity of sepsis outcomes. The integrative approach described herein was based on three assumptions. First, a comprehensive, hypothesis-agnostic description of the molecular antecedents to sepsis survival and death would yield new, unbiased insights. Second, integration of clinical, metabolomic, and proteomic data might identify signals that were undetected or obscured by false discovery cutoffs in one-dimensional data sets. Third, analysis of the co-occurrence and correlations of molecular networks and pathways in complementary data sets would further identify and prioritize likely causal molecular mechanisms. Within the statistically significant group differences common to the discovery and replication cohorts, findings were further prioritized by (i) assembly into networks, pathways, or biochemical families; (ii) temporal correlations with clinical status; (iii) corroboration of bona fide networks and pathways by occurrence in complementary data sets; and (iv) cross-correlations, hierarchical coclustering, and assembly of mass action kinetic models of catalysis or physicochemical complexes. Finally, prognostic biomarker candidates were chosen to reflect potential underlying molecular mechanisms, rather than the ability to partition accurately.

The integrated, comprehensive analysis of host responses to sepsis revealed a complex, heterogeneous, and highly dynamic pathologic state and yielded new insights into molecular mechanisms of sepsis survival or death that may enable outcome prediction and individualized patient treatment. There were both negative and positive findings regarding the pathophysiology of sepsis. A major negative finding was that the plasma metabolome and proteome did not differ between sepsis survivors, severe sepsis survivors, and septic shock survivors. Another negative finding was that there were no major differences between patients with infections with S. pneumoniae, S. aureus, or E. coli. These negative findings may reflect heterogeneous patient responses, diverse comorbidities, sites of infection, or severity of infections within the 3-day window we focused on. It is also possible that changes were overwhelmed by a generalized septic response and therefore were difficult to detect. Instead, sepsis survivors appeared to represent a molecular continuum, irrespective of progression to severe sepsis or septic shock or class of infective agent. One caveat to this conclusion is that MS-based proteome analysis was insensitive for measurement of low-abundance proteins (34), such as cytokines, which are known to differ between etiologic agents (41). Our study did not support the popular concept that the clinical stages of sepsis progression (uncomplicated sepsis, severe sepsis, and septic shock) reflect host molecular progression (23). Instead, the homogeneity of the metabolome and proteome in the uncomplicated sepsis, severe sepsis, and septic shock groups was remarkable, challenging the traditional notion of a molecular pyramid of sepsis progression (16). The absence of substantive molecular differentiation of these clinical states, although surprising, does not negate the importance of early achievement of effective compartmental concentrations of appropriate antibiotics or the known differences in mortality between etiologic agents and sites of infection (3, 4, 42).

The major positive finding in this study was that most of the host molecular responses were altered antithetically in sepsis survivors and nonsurvivors, when compared to uninfected patients with SIRS criteria. This was evident at time of presentation, increased at t24, and became more pronounced as time to death decreased. It was observed in both the plasma metabolome and proteome. It was observed in comparisons of mean values of individual analytes, after inclusion of renal and hepatic diseases as fixed effects, and globally, as assessed by variance components and global cross-correlations. Divergent host responses were highly conserved temporally at the level of individual analyte classes, networks, and pathways. Thus, there exists a reproducible dichotomy in host molecular responses to sepsis, suggesting molecular allostasis in survivors and maladaptation in nonsurvivors.

Alterations in fatty acid metabolism were prominent components of the disparate metabolomic phenotype of sepsis survival and death. The plasma concentrations of six carnitine esters were decreased in sepsis survivors relative to controls. In addition, 16 carnitine esters and 4 fatty acids were elevated in sepsis nonsurvivors relative to controls. These findings were not explicable on the basis of unchanged ratios of free to acylated carnitine or free to protein-bound ratios of fatty acids. Thus, free carnitine concentrations were unchanged. Nine fatty acid transport proteins were decreased in sepsis nonsurvivors, whereas the plasma concentrations of two fatty acid–binding proteins were increased in sepsis nonsurvivors. Whereas some of these findings have been previously reported (43), together they suggest a profound defect in fatty acid β-oxidation in sepsis nonsurvivors that was absent in sepsis survivors. The rate-limiting step in β-oxidation is fatty acid transport from the cytoplasm into the mitochondrial matrix (44). Because the mitochondrial membrane is impermeable to acyl-CoA, the carnitine palmitoyltransferase (CPT; EC 2.3.1.21) enzyme system, in conjunction with acyl-CoA synthetase and carnitine/acylcarnitine translocase, is used to shuttle long-chain fatty acids across the mitochondrial membrane in the form of acyl-carnitines. CPT I is located in the mitochondrial outer membrane, whereas CPT II is in the inner mitochondrial membrane. Transport across the mitochondrial membrane is reversible. Thus, acyl-carnitines that are not used for energy production in fatty acid β-oxidation may be reverse-transported from mitochondria to the cytoplasm and then into the plasma, where they are excreted (44). Plasma values of acyl-carnitines of all fatty acid lengths were elevated in sepsis nonsurvivors, which was not explained by differences in renal function, suggesting that the metabolic defect in fatty acid β-oxidation occurs at the level of the carnitine shuttle.

Mitochondrial fatty acid β-oxidation in the mitochondrion is accomplished by several acyl-CoA dehydrogenases. Each acyl-CoA dehydrogenase acts on fatty acids of a particular chain length and with a specific degree of branching (44). Acyl-CoA dehydrogenase deficiencies are characterized by accumulation of fatty acids of the corresponding range of chain lengths. A potentially causal role for elevated carnitine esters in sepsis nonsurvival is suggested by the finding that micromolar amounts cause ventricular dysfunction (45). Furthermore, patients with mutations in medium-chain acyl-CoA dehydrogenase (MCAD) have high rates of sudden death (46). Animal models have shown that MCAD and CPT I are decreased in heart, liver, and kidney in sepsis, and are regulated by decreased expression of peroxisome proliferator–activated receptors (PPARs) α, β/δ (43, 4749). Sepsis survival in mouse models improved with PPAR agonist treatment (50, 51). In addition, PPARs regulate the expression of MCAD (52) and fatty acid β-oxidation (53). Furthermore, PPARα expression is decreased in septic shock and correlates with severity (54). Although clinically untested, these results suggest that treatment of selected patients with PPAR agonists may improve sepsis outcomes through increased β-oxidation in heart, liver, and kidney tissues. Because this study focused on patients with sepsis, it remains unclear whether elevations in carnitine esters are unique to sepsis nonsurvival or are a broad prognostic biomarker in critical illness. Hypoxia can also lead to increased plasma acyl-carnitines (55), suggesting that they may be a nonspecific signal of mitochondrial dysfunction. A prospective metabolomic study of critical illness outcomes in the absence of infection as well as animal/cell culture models of hypoxia and sepsis may provide a better understanding of the specificity of these biomarkers in death.

In stark contrast to increased carnitine esters and free fatty acids in sepsis nonsurvivors was a consistent decrease in GPC and GPE esters in sepsis survivors and nonsurvivors compared to noninfected patients with SIRS. The changes were consistent with published findings that GPC and GPE esters were predictive of sepsis mortality (32). Further, it has been suggested that these changes in lipid metabolism reflect decreases in PPARα (43, 49). Exogenous stearoyl-GPC improves outcomes in septic mice (56). Whereas free fatty acid supplementation has not proved effective in a clinical trial of acute lung injury (57), it is unknown whether outcomes would be improved by stearoyl-GPC supplementation.

Glycolysis, gluconeogenesis, and the citric acid cycle differed prominently between sepsis survivors and nonsurvivors. Plasma values of citrate, malate, glycerol, glycerol 3-phosphate, phosphate, and glucogenic and ketogenic amino acids were decreased in sepsis survivors relative to controls. In contrast, citrate, malate, pyruvate, dihydroxyacetone, lactate, phosphate, and gluconeogenic amino acids were increased in sepsis nonsurvivors. A corroborating proteomic change was found for succinate dehydrogenase, whose concentration correlated with downstream citric acid cycle metabolites malate, oxaloacetate, and citrate and with lactate, pyruvate, and acetyl-carnitine. A parsimonious explanation of these findings is that sepsis survivors mobilized various energetic substrates and used these completely in aerobic catabolism, resulting in decreased plasma concentrations, whereas sepsis patients who would ultimately die failed to use these fully, displaying elevated concentrations even at the earliest time points evaluated. Significantly lower core temperature in sepsis nonsurvivors versus survivors may be a correlate of poor aerobic catabolism in dying patients (12).

Several other lines of evidence support the hypothesis that mitochondrial function is a major determinant of sepsis outcome. Structural studies show mitochondrial derangements, decreased mitochondrial number, and reduced substrate utilization in sepsis nonsurvival, and a progressive drop in total body oxygen consumption occurs as sepsis severity increases (5865). Further, circulating mitochondrial damage–associated molecular patterns can activate the innate immune response, leading to neutrophil-mediated organ injury (66). Recent evidence indicates that increased succinate, a tricarboxylic acid cycle intermediate metabolite, is an inflammatory signal that can induce interleukin-1β (IL-1β) production in bone marrow–derived macrophages (67). Substantive literature demonstrates that an early indicator of sepsis outcomes is mitochondrial biogenesis (23, 30, 58, 59, 6872), another PPAR-regulated phenomenon (73). Finally, sepsis-induced multiple organ failure has been noted to occur despite minimal cell death, and patient recovery from organ failure is rapid in survivors, indicating that mitochondrial damage in sepsis survivors is reversible (23, 30, 46, 71, 74).

In summary, an integrated analysis revealed quite different host molecular responses to sepsis in patients who would survive and those who would die. In contrast, we found no metabolomic or proteomic differences between sepsis caused by S. pneumoniae, E. coli, or S. aureus. It will be interesting to ascertain whether the sepsis nonsurvival profile is recapitulated in other sepsis etiologies or in other SIRS-inducing conditions (60, 75, 76).

Finally, biomarker models were developed to aid in the prediction of sepsis outcomes that were based on these molecular findings. For ease of assay development for clinical utility, a homogeneous biomarker panel was developed, rather than heterogeneous combinations of protein and metabolite markers. In general, previous sepsis biomarker panels have shown disappointing external validation. Reasons may include data overfitting, reliance on cross-validation rather than independent validation, and recruitment at single sites. We sought to reduce the impact of these limitations by developing sparse panels, recruitment at three sites, selecting metabolites that had a high probability of representing molecular mechanisms, use of two metabolite measurement techniques, and validation both in a separate CAPSOD test set and in an independent cohort. A logistic regression model using carnitine esters and clinical variables consistently categorized survivors with greater than 85% accuracy, whereas sepsis nonsurvivors were accurately predicted with 45 to 55% accuracy in most of the test sets. This model performed better than capillary lactate, SOFA, or APACHE II scores. It should be noted that prognostic performance was evaluated in patients at time of presentation at an emergency department. The differences between survivors and nonsurvivors increased as time to death decreased. Thus, serial testing of sepsis patients may better differentiate those with poor outcomes. Thus, as with many current disease severity markers, this panel is likely to be especially useful when used serially in individual patients. Ideally, the panel would be deployed on a device that performs at point of care or hospital-based and with rapid time to result. The biomarkers presented here were the best-performing models but are by no means the only variables with such predictive utility. Independent replication studies are needed, as are finalization of markers, normalized time-to-death analysis, and additional assay development.

One concern for a model predicting survival or death is that subsequent clinical decision-making may be biased in a way that supports the prediction, resulting in considerable risk of harm. However, results in animal models targeting GPC esters and PPAR expression suggest that mechanisms can be reversed and outcomes improved by targeted treatments that improve β-oxidation and/or neutrophil-mediated bacterial killing (50, 51, 53, 56). Additionally, preliminary findings were that sepsis survivors after EGDT had higher levels of carnitine esters at presentation than sepsis survivors who did not receive EGDT, further suggesting that metabolic and mitochondrial dysfunction can be mitigated. Therapeutic targets that were nominated by this study include GPC and GPE esters, acetyl-carnitine supplementation, PPAR agonist treatment, inhibition of the γ-aminobutyric shunt, or enhancement of mitochondrial biogenesis (10, 39, 50, 51, 56, 67). Upon additional development, a sepsis prognosis panel may aid in the immense need for individualization of the intensity of sepsis treatment and, thereby, improvement in outcomes. Ideally, future studies will examine muscle tissue as well as blood to confirm the relevance of plasma changes.

As with any biomarker panel, there remains the possibility of overfitting. However, in the present study, reproducibility in internal and external validation sets, replication with targeted assays, and SVM analysis suggest that the sparse (seven-feature) panel has validity for prediction of sepsis-related mortality when applied at patient presentation in an emergency department setting. Our study has limitations. The biological sample chosen for analysis was peripheral blood. As such, we cannot draw conclusions about the effects of sepsis on other target tissues. Furthermore, blood samples were analyzed at only two time points. Additional collections would have allowed a temporal analysis of changes in sepsis, giving a more precise view of changes during convalescence or deterioration. The number of nonsurvivors tested was relatively small, and confirmatory studies are needed. The number of nonsepsis deaths also was small. As a result, we do not know whether the outcome predictive signature is specific for sepsis or may also differentiate other acutely ill patient groups.

Finally, global and temporal correlations of metabolome and proteome data from relevant biological fluids in well-phenotyped patient groups appear suitable for expanding our understanding of intermediary metabolism, particularly with respect to poorly annotated analytes, and for characterization of homogeneous subgroups in complex traits. Combinations of transcriptome, proteome, metabolome, and genetic data may establish multidimensional molecular models of complex diseases that can provide insights into network responses to perturbation.

Materials and Methods

Study design

Predefined study components. Metabolomic and proteomic analysis was predicted to require 30 samples per group (noninfected controls, uncomplicated sepsis, severe sepsis, septic shock, and sepsis nonsurvivors) for 80% power to detect differences. Enrollment was performed during daytime hours through a convenience sampling and continued until this goal was met. Inclusion criteria included adults in the emergency department with known or suspected acute infection and the presence of at least two SIRS criteria. Exclusions were as previously described (12, 17, 25). Outliers were identified using various techniques including overlaid kernel density estimates, univariate distribution results, Mahalanobis distances, and correlation coefficients.

Rationale and design. Sepsis is a leading cause of death in the United States, and there remain few therapeutic options. Understanding the pathobiology of sepsis outcomes can enable personalized patient management protocols and improve survival. Here, clinical care was not standardized but rather was determined by individual providers. We collected clinical data including infection likelihood, infection type, microbiological etiologies, sepsis severity, and 28-day mortality. Serum of enrolled patients was taken at presentation and 24 hours later. Metabolomics and proteomics were performed using MS techniques. Comprehensive, integrated analysis of serum metabolome and proteome data was performed to prioritize sepsis outcome signals. Logistic regression and SVM analysis were performed to predict patient outcomes.

Randomization. Patients were assigned to predefined clinical groups (noninfected controls, uncomplicated sepsis, severe sepsis, septic shock, and sepsis nonsurvivors) after retrospective clinical adjudications were performed. These assignments were made solely on the basis of information available in the medical record and were blind to any metabolomic or proteomic data, which had not yet been generated. Patients were matched for age, race, sex, and enrollment site, with the sepsis nonsurvivor group as the reference.

Replication. The clinical, metabolomic, and proteomic analyses were replicated in a separate CAPSOD subset of 18 sepsis nonsurvivors and 34 matched sepsis survivors (at t0 [Vt0] and t24 [Vt24]). A second validation set was performed in an independent sepsis study (the Brigham and Women’s Hospital RoCI cohort, approved by the Partners Human Research Committee, protocol #2008-P-000495) (27). This validation cohort had 29 noninfected patients with SIRS, 36 sepsis survivors, and 25 sepsis nonsurvivors. The study followed the Equator Network Library recommendation for biospecimens and conforms to BRISQ Tier 1 reporting (77). In addition, samples were stabilized in standard serum collection tubes. They were frozen for long-term preservation and then stored at −80°C until testing occurred, which was within 1 to 5 years. When necessary, samples were shipped on dry ice.

Patient enrollment

Patients presenting at emergency departments (Henry Ford Hospital, Duke University Hospital, and Durham Veterans Affairs Medical Center) with suspected sepsis (≥2 SIRS criteria and infection) were enrolled (12, 25). Approval was obtained by institutional ethics committees and filed at ClinicalTrials.gov (NCT00258869). Written informed consent was given by each patient or legal designate. Physical examination was performed, and venous plasma and whole blood were collected at enrollment (t0) and 24 hours later (t24); patients were followed for 28 days. Demographic and clinical data were anonymized and stored in compliance with HIPAA regulations (ProSanos Inc.). After independent audit of infection status and outcomes, 150 subjects were chosen for derivation studies. Patients were classified as noninfected SIRS, uncomplicated sepsis, severe sepsis, septic shock, or sepsis nonsurvivor. Fifty-two sepsis survivors and deaths at t0 and t24 were also used as an internal validation set. Recruitment for the Brigham and Women’s Hospital RoCI has been described in detail elsewhere (27). Briefly, demographic, clinical information, and blood specimens were collected from patients with critical illness in the medical intensive care unit (MICU) of Brigham and Women’s Hospital. Blood specimens were obtained within 2 days of ICU admission (day 1) and also at days 3 and 7. Informed consent was obtained directly from patients or, if not possible, their legal representatives. Four hundred subjects have been enrolled in RoCI from 2008 to 2012. Serum samples from 90 subjects on day 1 of enrollment were selected for metabolomic profiling. RoCI is approved by the Partners Human Research Committee under Institutional Review Board protocol #2008-P-000495.

Semiquantitative metabolomic analysis

Nontargeted ultra-performance liquid chromatography (UPLC)–MS/MS and GC-MS analyses were performed at Metabolon Inc. as described (7880). The UPLC-MS/MS platform used a Waters Acquity UPLC with Waters UPLC BEH C18 columns (2.1 × 100 mm, 1.7 μm) and a ThermoFisher LTQ mass spectrometer. GC-MS was performed on a Thermo-Finnigan Trace DSQ fast-scanning single-quadrupole MS. Metabolites were identified by automated comparison of the ion features in the experimental samples to a reference library of chemical standard entries that included retention time, molecular weight (m/z), preferred adducts, and in-source fragments as well as associated MS spectra and curated by visual inspection for quality control using software developed at Metabolon (81). Peaks were quantified using AUC. Raw area counts for each metabolite in each sample were normalized to correct for variation resulting from instrument interday tuning differences by the median value for each run-day, therefore setting the medians to 1.0 for each run. Missing values were imputed with the observed minimum after normalization. However, metabolites with missing values in >50% of the samples were excluded from analysis.

Quantitative metabolomics analysis

Fifty microliters of 382 human EDTA plasma samples, 48 quality control plasma aliquots, 6 calibration standards, and a blank internal standard (H2O) were treated (see Supplementary Materials and Methods) and injected onto a Waters Acquity UPLC/Thermo Quantum Ultra triple quadrupole LC-MS/MS with HESI (heated electrospray ionization) source equipped with a reversed-phase chromatographic column system to determine quantitative changes for 2-methylbutyroylcarnitine, cis-4-decenoylcarnitine, butyroylcarnitine, hexanoylcarnitine, 4-methyl-2-oxopentanoate, 1-arachidonoyl-GPC, 1-linoleoyl-GPC, HPLA, 3-methoxytyrosine, N-acetylthreonine, and pseudouridine. The peak areas of the respective product ions were measured against the peak areas of the corresponding internal standard product ions (fig. S9). Analyte concentrations are reported in the weight/volume format (μg/ml) and not in molar concentrations. Quantitation was performed using weighted linear least squares regression analysis generated from fortified calibration standards prepared immediately before each run (fig. S10). Correlation analysis of quantitative results to semiquantitative results was high (fig. S11).

Proteomic analysis

Plasma proteomic analysis was performed by Monarch Life Sciences Inc. as previously described (82). Briefly, tryptic digests (~20 μg) with the most abundant proteins removed (see Supplementary Materials and Methods) were analyzed using a Thermo-Fisher Scientific LTQ linear ion-trap mass spectrometer coupled with a Surveyor HPLC (high-performance liquid chromatography) system. Data were collected and analyzed as described (83, 84). Database searches against the IPI (International Protein Index) human database (v3.48) and the non-Redundant-Homo Sapiens database (update July 2009) were carried out using both the X!Tandem and SEQUEST algorithms (85, 86). The q value represented peptide false identification rate and was calculated by incorporating SEQUEST and X!Tandem results (83). Observed peptide MS/MS spectrum and theoretically derived spectra were used to assign quality scores (Xcorr in SEQUEST and e-Score in X!Tandem). Peptides with high confidence (>90%) and multiple unique sequences were used for analyses. Protein quantification was carried out as described (84). AUC for each individually aligned peak from each sample was measured and compared for relative abundance and was log2-transformed before quantile normalization (87). Raw LC-MS/MS data files were independently validated by the Duke Proteomics Core using spectral counting in the form of number of identified spectra per protein (see Supplementary Materials and Methods).

Statistical analysis

Overlaid kernel density estimates, univariate distribution results, Mahalanobis distances, correlation coefficients of pairwise sample comparisons, unsupervised principal components analysis (by Pearson product-moment correlation), and Ward hierarchal clustering of Pearson product-moment correlations were performed using log2-transformed data as described (88) with JMP Genomics 5.0 (SAS Institute). Decomposition of principal components of variance, including patient demographics, past medical history, and laboratory and clinical values, was performed to maximize sepsis group–related components of variance and minimize residual variance (88). Guided by these analyses, ANOVA was performed between sepsis groups, with 5 to 25% FDR correction (as noted in the text) and inclusion of substantive non-hypothesis components of variance as fixed effects (88). These included renal function, as determined by eGFR, hemodialysis, cirrhosis and liver disease, hepatitis, neoplastic disease, and immunosuppression. Predictive modeling was performed with JMP Genomics 5.0 using logistic regression. Data were presented as averages ± SEM. Bayesian clinical factor analysis [cj = Byj + A(sjzj) + εj] was performed to distinguish the effects of clinical outcomes (uninfected SIRS group, sepsis survivors, and sepsis nonsurvivors) and relevant clinical factors on the metabolome (see Supplementary Materials). The significant features were then plotted on B-matrix as well as plotted as normalized energy (referred to as factor scores within the article) of each clinical feature. Pairwise cross-correlations were performed using JMP Genomics 5.0 software to compare protein and metabolite values at t0 and t24 using Pearson product-moment correlation. Protein-metabolite correlations were considered significant if observed at t0 and t24 with P values <0.05 and <0.1 or at a single time point with Bonferroni correction. SVMs, both linear and with RBF (radial basis function) kernels, were used for binary classification of sepsis survivors and deaths. Performance was evaluated by test data scores for AUC and accuracy.

Supplementary Materials

www.sciencetranslationalmedicine.org/cgi/content/full/5/195/195ra95/DC1

Materials and Methods

Fig. S1. Sepsis group effects and clinical factors that lead to variation in the plasma metabolome.

Fig. S2. Variance decomposition (with Pearson correlation) of metabolomic changes in sepsis diagnosis and outcomes.

Fig. S3. Variance components attributable to sepsis survivor subgroups and etiologic agents.

Fig. S4. Venn diagrams of metabolites in sepsis diagnosis.

Fig. S5. Bar graphs of plasma metabolite levels at t0 and t24 and in validation patients at t0 and t24.

Fig. S6. Metabolomics cell plot.

Fig. S7. B-matrices of Bayesian factor analysis and the normalized factor scores.

Fig. S8. Bar graphs of plasma metabolite levels of the RoCI sepsis cohort.

Fig. S9. Representative chromatograms of quantitative LC-MS-MS measurement.

Fig. S10. Representative calibration curves of quantitative LC-MS-MS measurement.

Fig. S11. Correlation plots of semiquantitative screening data (x axis) and quantitative targeted data (y axis).

Fig. S12. Bar graphs of plasma levels by targeted, quantitative MS assays of butyroylcarnitine, 2-methylbutyroylcarnitine, hexanoylcarnitine, and cis-4-decenoylcarnitine.

Fig. S13. Plasma levels of 11 metabolites in all patients showing relationships between time to death and metabolite values.

Fig. S14. Comparison of CRP and albumin (ALB) levels by serum immunoassay [enzyme-linked immunosorbent assay (ELISA)] and plasma MS in 19 and 98 patients, respectively.

Fig. S15. Principal components of variance of plasma proteins in sepsis diagnosis and sepsis outcomes.

Fig. S16. Principal components and Volcano plots of plasma protein variation associated with etiologic agents.

Fig. S17. Metabolomic cross-correlation analysis with list of significant metabolite clusters.

Fig. S18. Plasma metabolite correlations with fatty acid–binding protein (FABP4, adipocyte), a plasma carrier protein for carnitine esters, and free fatty acids.

Fig. S19. Selected plasma metabolite correlations with acyl-CoA synthetase ACSM6.

Table S1. Clinical adjudication guidelines.

Table S2. Partial overlap of eGFR group and sepsis group membership.

Table S3. Plasma metabolite concentrations in noninfected SIRS, sepsis survivors, and sepsis nonsurvivors.

Table S4. Normalized factor scores of Bayesian factor analysis for normal renal function (eGFR ≥75 ml/min).

Table S5. Normalized factor scores of Bayesian factor analysis for poor renal function (eGFR 32 to 74 ml/min).

Table S6. Combined calibration spiking solution concentration [μg/ml] in acetonitrile/water.

Table S7. Monitored ion masses for quantitation.

Table S8. Plasma proteins detected with high confidence.

Table S9. Average, log-transformed, scaled plasma protein concentrations.

Table S10. Plasma protein-metabolite correlations.

Table S11. Protein-metabolite correlations of potential novel enzymatic pathways.

References and Notes

  1. Acknowledgments: We thank A. K. Jaehne for HFHS patient data management, T. Gagliano for graphic arts, J. M. Langley for motivation, and the study subjects. A Deo mirificatio, ab amicis auxilium—To God for creativity, to friends for help. Funding: Supported by grants from the NIH (U01AI066569, P20RR016480, HHSN266200400064C), Pfizer Inc., and Roche Diagnostics Inc. The RoCI cohort was supported by grants from NIH (HL112747 and HL05530). E.L.T. was supported by a National Research Service Award training grant provided by the Agency for Healthcare Research and Quality as well as a VA Career Development Award. Author contributions: S.F.K., R.J.L., R.P.M., E.P.R., R.M.O., V.G.F., C.W.W., C.B.C., L.C., M.A.M., M.W., B.T.E., D.R.N., B.G., and G.R.C. designed the experiments. S.F.K., V.G.F., E.P.R., C.W.W., R.M.O., A.M.K.C., G.S.G., and E.L.T. provided funding. R.J.L. performed metabolomic and proteomic analysis, predictive modeling, clinical diagnostic analysis, and cross-correlation analysis. R.P.M. performed metabolomic analysis. J.W. and J.Y. provided MS/MS technical expertise for the metabolomic and proteomic assays, respectively. J.C.v.V. performed clinical demographic analysis and helped with patient selection. S.R. served as a database manager. E.L.T., S.W.G., A.S., D.H.F., S.R., A.J.R., L.G., L.E.F., A.F.M., R.M.B., A.M.K.C., C.B.C., V.G.F., E.P.R., R.M.O., C.W.W., and S.F.K. performed clinical analysis and/or patient enrollment. R.J.L., S.F.K., R.P.M., B.J.R., J.C.v.V., C.W., B.C., R.P.M., M.W., J.Y., J.W.T., M.A.M., D.L.D., N.A.M., C.J.S., S.S.S., and A.J.R. performed experimental analysis. R.J.L., S.F.K., E.L.T., C.W.W., C.B.C., J.C.v.V., and G.S.G. wrote the manuscript. Competing interests: B.T.E. and C.B.C. are consultants for bioMerieux. V.G.F. has received honoraria in the last 3 years from Achaogen, Arpida, Astellas Pharma Inc., Cubist Pharmaceuticals, Durata, Inhibitex, Leo Pharma, Merck & Co. Inc., Pfizer, Targanta, Theravance Inc., and Ortho-McNeil. V.G.F. has been a paid consultant for Affinium, Astellas Pharma Inc., Biosynexus, Novartis, Cubist Pharmaceuticals, Inimex, Merck & Co. Inc., Galderma, Johnson & Johnson, Medicines Company, and NovaDigm. V.G.F. was chair of the Merck V710 vaccine for Staphylococcus aureus Scientific Advisory Committee and is on the Scientific Advisory Board for Affinium. B.J.R. owns Illumina stock. R.J.L., S.F.K., B.C., and L.C. have been awarded the following patent related to this work: Method for diagnosis of sepsis and risk of death, US 2010/0273207, 10.28, 2010. R.J.L., S.F.K., B.C., and L.C. have submitted U.S. patent application no. 12/766,882; R.J.L. and S.F.K. have submitted patent application no. 12/54951 related to this work. Data and materials availability: The raw proteomics data has been archived as a 7-zip document (www.7-zip.org) and is available for download at the following locations: Time zero data, https://discovery.genome.duke.edu/express/resources/2472/t_0.7z and Time 24 hr data, https://discovery.genome.duke.edu/express/resources/2472/t_24.7z. The CAPSOD and metabolomic data have been deposited at MetaboLights (http://www.ebi.ac.uk/metabolights/; accession number MTBLS50).
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