Offline Two-Dimensional Liquid Chromatography-Mass Spectrometry for Deep Annotation of the Fecal Metabolome Following Fecal Microbiota Transplantation.

Biological interpretation of untargeted LC-MS-based metabolomics data depends on accurate compound identification, but current techniques fall short of identifying most features that can be detected. The human fecal metabolome is complex, variable, incompletely annotated, and serves as an ideal matrix to evaluate novel compound identification methods. We devised an experimental strategy for compound annotation using multidimensional chromatography and semiautomated feature alignment and applied these methods to study the fecal metabolome in the context of fecal microbiota transplantation (FMT) for recurrent C. difficile infection. Pooled fecal samples were fractionated using semipreparative liquid chromatography and analyzed by an orthogonal LC-MS/MS method. The resulting spectra were searched against commercial, public, and local spectral libraries, and annotations were vetted using retention time alignment and prediction. Multidimensional chromatography yielded more than a 2-fold improvement in identified compounds compared to conventional LC-MS/MS and successfully identified several rare and previously unreported compounds, including novel fatty-acid conjugated bile acid species. Using an automated software-based feature alignment strategy, most metabolites identified by the new approach could be matched to features that were detected but not identified in single-dimensional LC-MS/MS data. Overall, our approach represents a powerful strategy to enhance compound identification and biological insight from untargeted metabolomics data.

metabCombiner 2.0: Disparate Multi-Dataset Feature Alignment for LC-MS Metabolomics.

Liquid chromatography-high-resolution mass spectrometry (LC-HRMS), as applied to untargeted metabolomics, enables the simultaneous detection of thousands of small molecules, generating complex datasets. Alignment is a crucial step in data processing pipelines, whereby LC-MS features derived from common ions are assembled into a unified matrix amenable to further analysis. Variability in the analytical factors that influence liquid chromatography separations complicates data alignment. This is prominent when aligning data acquired in different laboratories, generated using non-identical instruments, or between batches from large-scale studies. Previously, we developed metabCombiner for aligning disparately acquired LC-MS metabolomics datasets. Here, we report significant upgrades to metabCombiner that enable the stepwise alignment of multiple untargeted LC-MS metabolomics datasets, facilitating inter-laboratory reproducibility studies. To accomplish this, a "primary" feature list is used as a template for matching compounds in "target" feature lists. We demonstrate this workflow by aligning four lipidomics datasets from core laboratories generated using each institution's in-house LC-MS instrumentation and methods. We also introduce batchCombine, an application of the metabCombiner framework for aligning experiments composed of multiple batches. metabCombiner is available as an R package on Github and Bioconductor, along with a new online version implemented as an R Shiny App.

Sex modulates the diet-induced changes to the plasma lipidome in a rat model of cardiorespiratory fitness.

OBJECTIVE: Individuals with higher intrinsic cardiorespiratory fitness (CRF) experience decreased rates of cardiometabolic disease and mortality, and high CRF is associated with increased utilization of fatty acids (FAs) for energy. Studies suggest a complex relationship between CRF, diet, and sex with health outcomes, but this interaction is understudied. We hypothesized that FA utilization differences by fitness and sex could be detected in the plasma metabolome when rats or humans were fed a high carbohydrate (HC) or high fat (HF) diet. METHODS: Male and female rats selectively bred for low (LCR) and high (HCR) CRF were fed a chow diet or a sucrose-free HF (45 % fat) or HC (10 % fat) diet. Plasma samples were collected at days 0, 3, and 14. Human plasma data was collected from male and female participants who were randomized into a HC or HF diet for 21 days. Samples were analyzed using liquid chromatography-mass spectrometry and regression statistics were used to quantify the effect of diet, CRF, and sex on the lipidome. RESULTS: In rats, the baseline lipidome is more significantly influenced by sex than by CRF, especially as elevated diglycerides, triglycerides, phosphatidylcholines, and lysophosphatidylcholines in males. A dynamic response to diet was observed 3 days after diet, but after 14 days of either diet, the lipidome was modulated by sex with a larger effect size than by diet. Data from the human study also suggests a sex-dependent response to diet with opposite directionality of affect compared to rats, highlighting species-dependent responses to dietary intervention.

Offline Two-dimensional Liquid Chromatography-Mass Spectrometry for Deep Annotation of the Fecal Metabolome.

Compound identification is an essential task in the workflow of untargeted metabolomics since the interpretation of the data in a biological context depends on the correct assignment of chemical identities to the features it contains. Current techniques fall short of identifying all or even most observable features in untargeted metabolomics data, even after rigorous data cleaning approaches to remove degenerate features are applied. Hence, new strategies are required to annotate the metabolome more deeply and accurately. The human fecal metabolome, which is the focus of substantial biomedical interest, is a more complex, more variable, yet lesser-investigated sample matrix compared to widely studied sample types like human plasma. This manuscript describes a novel experimental strategy using multidimensional chromatography to facilitate compound identification in untargeted metabolomics. Pooled fecal metabolite extract samples were fractionated using offline semi-preparative liquid chromatography. The resulting fractions were analyzed by an orthogonal LC-MS/MS method, and the data were searched against commercial, public, and local spectral libraries. Multidimensional chromatography yielded more than a 3-fold improvement in identified compounds compared to the typical single-dimensional LC-MS/MS approach and successfully identified several rare and novel compounds, including atypical conjugated bile acid species. Most features identified by the new approach could be matched to features that were detectable but not identifiable in the original single-dimension LC-MS data. Overall, our approach represents a powerful strategy for deeper annotation of the metabolome that can be implemented with commercially-available instrumentation, and should apply to any dataset requiring deeper annotation of the metabolome.

Architecture of androgen receptor pathways amplifying glucagon-like peptide-1 insulinotropic action in male pancreatic ß cells.

Male mice lacking the androgen receptor (AR) in pancreatic ß cells exhibit blunted glucose-stimulated insulin secretion (GSIS), leading to hyperglycemia. Testosterone activates an extranuclear AR in ß cells to amplify glucagon-like peptide-1 (GLP-1) insulinotropic action. Here, we examined the architecture of AR targets that regulate GLP-1 insulinotropic action in male ß cells. Testosterone cooperates with GLP-1 to enhance cAMP production at the plasma membrane and endosomes via: (1) increased mitochondrial production of CO2, activating the HCO3--sensitive soluble adenylate cyclase; and (2) increased Gαs recruitment to GLP-1 receptor and AR complexes, activating transmembrane adenylate cyclase. Additionally, testosterone enhances GSIS in human islets via a focal adhesion kinase/SRC/phosphatidylinositol 3-kinase/mammalian target of rapamycin complex 2 actin remodeling cascade. We describe the testosterone-stimulated AR interactome, transcriptome, proteome, and metabolome that contribute to these effects. This study identifies AR genomic and non-genomic actions that enhance GLP-1-stimulated insulin exocytosis in male ß cells.

Nutrient-dependent regulation of ß-cell proinsulin content.

Insulin is made from proinsulin, but the extent to which fasting/feeding controls the homeostatically regulated proinsulin pool in pancreatic ß-cells remains largely unknown. Here, we first examined ß-cell lines (INS1E and Min6, which proliferate slowly and are routinely fed fresh medium every 2-3 days) and found that the proinsulin pool size responds to each feeding within 1 to 2 h, affected both by the quantity of fresh nutrients and the frequency with which they are provided. We observed no effect of nutrient feeding on the overall rate of proinsulin turnover as quantified from cycloheximide-chase experiments. We show that nutrient feeding is primarily linked to rapid dephosphorylation of translation initiation factor eIF2α, presaging increased proinsulin levels (and thereafter, insulin levels), followed by its rephosphorylation during the ensuing hours that correspond to a fall in proinsulin levels. The decline of proinsulin levels is blunted by the integrated stress response inhibitor, ISRIB, or by inhibition of eIF2α rephosphorylation with a general control nonderepressible 2 (not PERK) kinase inhibitor. In addition, we demonstrate that amino acids contribute importantly to the proinsulin pool; mass spectrometry shows that ß-cells avidly consume extracellular glutamine, serine, and cysteine. Finally, we show that in both rodent and human pancreatic islets, fresh nutrient availability dynamically increases preproinsulin, which can be quantified without pulse-labeling. Thus, the proinsulin available for insulin biosynthesis is rhythmically controlled by fasting/feeding cycles.

Sustained Perturbation of Metabolism and Metabolic Subphenotypes Are Associated With Mortality and Protein Markers of the Host Response.

Perturbed host metabolism is increasingly recognized as a pillar of sepsis pathogenesis, yet the dynamic alterations in metabolism and its relationship to other components of the host response remain incompletely understood. We sought to identify the early host-metabolic response in patients with septic shock and to explore biophysiological phenotyping and differences in clinical outcomes among metabolic subgroups. DESIGN: We measured serum metabolites and proteins reflective of the host-immune and endothelial response in patients with septic shock. SETTING: We considered patients from the placebo arm of a completed phase II, randomized controlled trial conducted at 16 U.S. medical centers. Serum was collected at baseline (within 24 hr of the identification of septic shock), 24-hour, and 48-hour postenrollment. Linear mixed models were built to assess the early trajectory of protein analytes and metabolites stratified by 28-day mortality status. Unsupervised clustering of baseline metabolomics data was conducted to identify subgroups of patients. PATIENTS: Patients with vasopressor-dependent septic shock and moderate organ dysfunction that were enrolled in the placebo arm of a clinical trial. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Fifty-one metabolites and 10 protein analytes were measured longitudinally in 72 patients with septic shock. In the 30 patients (41.7%) who died prior to 28 days, systemic concentrations of acylcarnitines and interleukin (IL)-8 were elevated at baseline and persisted at T24 and T48 throughout early resuscitation. Concentrations of pyruvate, IL-6, tumor necrosis factor-α, and angiopoietin-2 decreased at a slower rate in patients who died. Two groups emerged from clustering of baseline metabolites. Group 1 was characterized by higher levels of acylcarnitines, greater organ dysfunction at baseline and postresuscitation (p < 0.05), and greater mortality over 1 year (p < 0.001). CONCLUSIONS: Among patients with septic shock, nonsurvivors exhibited a more profound and persistent dysregulation in protein analytes attributable to neutrophil activation and disruption of mitochondrial-related metabolism than survivors.

The unknown lipids project: harmonized methods improve compound identification and data reproducibility in an inter-laboratory untargeted lipidomics study.

Untargeted lipidomics allows analysis of a broader range of lipids than targeted methods and permits discovery of unknown compounds. Previous ring trials have evaluated the reproducibility of targeted lipidomics methods, but inter-laboratory comparison of compound identification and unknown feature detection in untargeted lipidomics has not been attempted. To address this gap, five laboratories analyzed a set of mammalian tissue and biofluid reference samples using both their own untargeted lipidomics procedures and a common chromatographic and data analysis method. While both methods yielded informative data, the common method improved chromatographic reproducibility and resulted in detection of more shared features between labs. Spectral search against the LipidBlast in silico library enabled identification of over 2,000 unique lipids. Further examination of LC-MS/MS and ion mobility data, aided by hybrid search and spectral networking analysis, revealed spectral and chromatographic patterns useful for classification of unknown features, a subset of which were highly reproducible between labs. Overall, our method offers enhanced compound identification performance compared to targeted lipidomics, demonstrates the potential of harmonized methods to improve inter-site reproducibility for quantitation and feature alignment, and can serve as a reference to aid future annotation of untargeted lipidomics data.

FMO rewires metabolism to promote longevity through tryptophan and one carbon metabolism in C. elegans.

Flavin containing monooxygenases (FMOs) are promiscuous enzymes known for metabolizing a wide range of exogenous compounds. In C. elegans, fmo-2 expression increases lifespan and healthspan downstream of multiple longevity-promoting pathways through an unknown mechanism. Here, we report that, beyond its classification as a xenobiotic enzyme, fmo-2 expression leads to rewiring of endogenous metabolism principally through changes in one carbon metabolism (OCM). These changes are likely relevant, as we find that genetically modifying OCM enzyme expression leads to alterations in longevity that interact with fmo-2 expression. Using computer modeling, we identify decreased methylation as the major OCM flux modified by FMO-2 that is sufficient to recapitulate its longevity benefits. We further find that tryptophan is decreased in multiple mammalian FMO overexpression models and is a validated substrate for FMO-2. Our resulting model connects a single enzyme to two previously unconnected key metabolic pathways and provides a framework for the metabolic interconnectivity of longevity-promoting pathways such as dietary restriction. FMOs are well-conserved enzymes that are also induced by lifespan-extending interventions in mice, supporting a conserved and important role in promoting health and longevity through metabolic remodeling.

Compound Identification Strategies in Mass Spectrometry-Based Metabolomics and Pharmacometabolomics.

The metabolome is composed of a vast array of molecules, including endogenous metabolites and lipids, diet- and microbiome-derived substances, pharmaceuticals and supplements, and exposome chemicals. Correct identification of compounds from this diversity of classes is essential to derive biologically relevant insights from metabolomics data. In this chapter, we aim to provide a practical overview of compound identification strategies for mass spectrometry-based metabolomics, with a particular eye toward pharmacologically-relevant studies. First, we describe routine compound identification strategies applicable to targeted metabolomics. Next, we discuss both experimental (data acquisition-focused) and computational (software-focused) strategies used to identify unknown compounds in untargeted metabolomics data. We then discuss the importance of, and methods for, assessing and reporting the level of confidence of compound identifications. Throughout the chapter, we discuss how these steps can be implemented using today's technology, but also highlight research underway to further improve accuracy and certainty of compound identification. For readers interested in interpreting metabolomics data already collected, this chapter will supply important context regarding the origin of the metabolite names assigned to features in the data and help them assess the certainty of the identifications. For those planning new data acquisition, the chapter supplies guidance for designing experiments and selecting analysis methods to enable accurate compound identification, and it will point the reader toward best-practice data analysis and reporting strategies to allow sound biological and pharmacological interpretation.

Modifying Chromatography Conditions for Improved Unknown Feature Identification in Untargeted Metabolomics.

Untargeted metabolomics is an essential component of systems biology research, but it is plagued by a high proportion of detectable features not identified with a chemical structure. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) experiments produce spectra that can be searched against databases to help identify or classify these unknowns, but many features do not generate spectra of sufficient quality to enable successful annotation. Here, we explore alterations to gradient length, mass loading, and rolling precursor ion exclusion parameters for reversed phase liquid chromatography (RPLC) and hydrophilic interaction liquid chromatography (HILIC) that improve compound identification performance for human plasma samples. A manual review of spectral matches from the HILIC data set was used to determine reasonable thresholds for search score and other metrics to enable semi-automated MS/MS data analysis. Compared to typical LC-MS/MS conditions, methods adapted for compound identification increased the total number of unique metabolites that could be matched to a spectral database from 214 to 2052. Following data alignment, 68.0% of newly identified features from the modified conditions could be detected and quantitated using a routine 20-min LC-MS run. Finally, a localized machine learning model was developed to classify the remaining unknowns and select a subset that shared spectral characteristics with successfully identified features. A total of 576 and 749 unidentified features in the HILIC and RPLC data sets were classified by the model as high-priority unknowns or higher-importance targets for follow-up analysis. Overall, our study presents a simple strategy to more deeply annotate untargeted metabolomics data for a modest additional investment of time and sample.

Pharmacometabolomics identifies candidate predictor metabolites of an L-carnitine treatment mortality benefit in septic shock.

Sepsis-induced metabolic dysfunction contributes to organ failure and death. L-carnitine has shown promise for septic shock, but a recent phase II study of patients with vasopressor-dependent septic shock demonstrated a non-significant reduction in mortality. We undertook a pharmacometabolomics study of these patients (n = 250) to identify metabolic profiles predictive of a 90-day mortality benefit from L-carnitine. The independent predictive value of each pretreatment metabolite concentration, adjusted for L-carnitine dose, on 90-day mortality was determined by logistic regression. A grid-search analysis maximizing the Z-statistic from a binomial proportion test identified specific metabolite threshold levels that discriminated L-carnitine responsive patients. Threshold concentrations were further assessed by hazard ratio and Kaplan-Meier estimate. Accounting for L-carnitine treatment and dose, 11 1 H-NMR metabolites and 12 acylcarnitines were independent predictors of 90-day mortality. Based on the grid-search analysis numerous acylcarnitines and valine were identified as candidate metabolites of drug response. Acetylcarnitine emerged as highly viable for the prediction of an L-carnitine mortality benefit due to its abundance and biological relevance. Using its most statistically significant threshold concentration, patients with pretreatment acetylcarnitine greater than or equal to 35 µM were less likely to die at 90 days if treated with L-carnitine (18 g) versus placebo (p = 0.01 by log rank test). Metabolomics also identified independent predictors of 90-day sepsis mortality. Our proof-of-concept approach shows how pharmacometabolomics could be useful for tackling the heterogeneity of sepsis and informing clinical trial design. In addition, metabolomics can help understand mechanisms of sepsis heterogeneity and variable drug response, because sepsis induces alterations in numerous metabolite concentrations.

The molecular and metabolic program by which white adipocytes adapt to cool physiologic temperatures.

Although visceral adipocytes located within the body's central core are maintained at approximately 37°C, adipocytes within bone marrow, subcutaneous, and dermal depots are found primarily within the peripheral shell and generally exist at cooler temperatures. Responses of brown and beige/brite adipocytes to cold stress are well studied; however, comparatively little is known about mechanisms by which white adipocytes adapt to temperatures below 37°C. Here, we report that adaptation of cultured adipocytes to 31°C, the temperature at which distal marrow adipose tissues and subcutaneous adipose tissues often reside, increases anabolic and catabolic lipid metabolism, and elevates oxygen consumption. Cool adipocytes rely less on glucose and more on pyruvate, glutamine, and, especially, fatty acids as energy sources. Exposure of cultured adipocytes and gluteal white adipose tissue (WAT) to cool temperatures activates a shared program of gene expression. Cool temperatures induce stearoyl-CoA desaturase-1 (SCD1) expression and monounsaturated lipid levels in cultured adipocytes and distal bone marrow adipose tissues (BMATs), and SCD1 activity is required for acquisition of maximal oxygen consumption at 31°C.

Flavin-Containing Monooxygenases Are Conserved Regulators of Stress Resistance and Metabolism.

Flavin-Containing Monooxygenases are conserved xenobiotic-detoxifying enzymes. Recent studies have revealed endogenous functions of FMOs in regulating longevity in Caenorhabditis elegans and in regulating aspects of metabolism in mice. To explore the cellular mechanisms of FMO's endogenous function, here we demonstrate that all five functional mammalian FMOs may play similar endogenous roles to improve resistance to a wide range of toxic stresses in both kidney and liver cells. We further find that stress-activated c-Jun N-terminal kinase activity is enhanced in FMO-overexpressing cells, which may lead to increased survival under stress. Furthermore, FMO expression modulates cellular metabolic activity as measured by mitochondrial respiration, glycolysis, and metabolomics analyses. FMO expression augments mitochondrial respiration and significantly changes central carbon metabolism, including amino acid and energy metabolism pathways. Together, our findings demonstrate an important endogenous role for the FMO family in regulation of cellular stress resistance and major cellular metabolic activities including central carbon metabolism.

metabCombiner: Paired Untargeted LC-HRMS Metabolomics Feature Matching and Concatenation of Disparately Acquired Data Sets.

LC-HRMS experiments detect thousands of compounds, with only a small fraction of them identified in most studies. Traditional data processing pipelines contain an alignment step to assemble the measurements of overlapping features across samples into a unified table. However, data sets acquired under nonidentical conditions are not amenable to this process, mostly due to significant alterations in chromatographic retention times. Alignment of features between disparately acquired LC-MS metabolomics data could aid collaborative compound identification efforts and enable meta-analyses of expanded data sets. Here, we describe metabCombiner, a new computational pipeline for matching known and unknown features in a pair of untargeted LC-MS data sets and concatenating their abundances into a combined table of intersecting feature measurements. metabCombiner groups features by mass-to-charge (m/z) values to generate a search space of possible feature pair alignments, fits a spline through a set of selected retention time ordered pairs, and ranks alignments by m/z, mapped retention time, and relative abundance similarity. We evaluated this workflow on a pair of plasma metabolomics data sets acquired with different gradient elution methods, achieving a mean absolute retention time prediction error of roughly 0.06 min and a weighted per-compound matching accuracy of approximately 90%. We further demonstrate the utility of this method by comprehensively mapping features in urine and muscle metabolomics data sets acquired from different laboratories. metabCombiner has the potential to bridge the gap between otherwise incompatible metabolomics data sets and is available as an R package at https://github.com/hhabra/metabCombiner and Bioconductor.

An alternative food source for metabolism and longevity studies in Caenorhabditis elegans.

Caenorhabditis elegans is an instrumental research model used to advance our knowledge in areas including development, metabolism, and aging. However, research on metabolism and/or other measures of health/aging are confounded by the nematode's food source in the lab, live E. coli bacteria. Commonly used treatments, including ultraviolet irradiation and antibiotics, are successful in preventing bacterial replication, but the bacteria can remain metabolically active. The purpose of this study is to develop a metabolically inactive food source for the worms that will allow us to minimize the confounding effects of bacterial metabolism on worm metabolism and aging. Our strategy is to use a paraformaldehyde (PFA) treated E. coli food source and to determine its effects on worm health, metabolism and longevity. We initially determine the lowest possible concentrations of PFA necessary to rapidly and reproducibly kill bacteria. We then measure various aspects of worm behavior, healthspan and longevity, including growth rate, food attraction, brood size, lifespan and metabolic assessments, such as oxygen consumption and metabolomics. Our resulting data show that worms eat and grow well on these bacteria and support the use of 0.5% PFA-killed bacteria as a nematode food source for metabolic, drug, and longevity experiments.

Using l-Carnitine as a Pharmacologic Probe of the Interpatient and Metabolic Variability of Sepsis.

OBJECTIVE: The objective of this review is to discuss the therapeutic use and differential treatment response to Levo-carnitine (l-carnitine) treatment in septic shock, and to demonstrate common lessons learned that are important to the advancement of precision medicine approaches to sepsis. We propose that significant interpatient variability in the metabolic response to l-carnitine and clinical outcomes can be used to elucidate the mechanistic underpinnings that contribute to sepsis heterogeneity. METHODS: A narrative review was conducted that focused on explaining interpatient variability in l-carnitine treatment response. Relevant biological and patient-level characteristics considered include genetic, metabolic, and morphomic phenotypes; potential drug interactions; and pharmacokinetics (PKs). MAIN RESULTS: Despite promising results in a phase I study, a recent phase II clinical trial of l-carnitine treatment in septic shock showed a nonsignificant reduction in mortality. However, l-carnitine treatment induces significant interpatient variability in l-carnitine and acylcarnitine concentrations over time. In particular, administration of l-carnitine induces a broad, dynamic range of serum concentrations and measured peak concentrations are associated with mortality. Applied systems pharmacology may explain variability in drug responsiveness by using patient characteristics to identify pretreatment phenotypes most likely to derive benefit from l-carnitine. Moreover, provocation of sepsis metabolism with l-carnitine offers a unique opportunity to identify metabolic response signatures associated with patient outcomes. These approaches can unmask latent metabolic pathways deranged in the sepsis syndrome and offer insight into the pathophysiology, progression, and heterogeneity of the disease. CONCLUSIONS: The compiled evidence suggests there are several potential explanations for the variability in carnitine concentrations and clinical response to l-carnitine in septic shock. These serve as important confounders that should be considered in interpretation of l-carnitine clinical studies and broadly holds lessons for future clinical trial design in sepsis. Consideration of these factors is needed if precision medicine in sepsis is to be achieved.

Untargeted Metabolomics Differentiates l-Carnitine Treated Septic Shock 1-Year Survivors and Nonsurvivors.

l-Carnitine is a candidate therapeutic for the treatment of septic shock, a condition that carries a ≥40% mortality. Responsiveness to l-carnitine may hinge on unique metabolic profiles that are not evident from the clinical phenotype. To define these profiles, we performed an untargeted metabolomic analysis of serum from 21 male sepsis patients enrolled in a placebo-controlled l-carnitine clinical trial. Although treatment with l-carnitine is known to induce changes in the sepsis metabolome, we found a distinct set of metabolites that differentiated 1-year survivors from nonsurvivors. Following feature alignment, we employed a new and innovative data reduction strategy followed by false discovery correction, and identified 63 metabolites that differentiated carnitine-treated 1-year survivors versus nonsurvivors. Following identification by MS/MS and database search, several metabolite markers of vascular inflammation were determined to be prominently elevated in the carnitine-treated nonsurvivor cohort, including fibrinopeptide A, allysine, and histamine. While preliminary, these results corroborate that metabolic profiles may be useful to differentiate l-carnitine treatment responsiveness. Furthermore, these data show that the metabolic signature of l-carnitine-treated nonsurvivors is associated with a severity of illness (e.g., vascular inflammation) that is not routinely clinically detected.