The role of metabolomic markers for patients with infectious diseases: implications for risk stratification and therapeutic modulation

Enhancing Influenza Detection through Integrative Machine Learning and Nasopharyngeal Metabolomic Profiling: A Comprehensive Study

Background/Objectives: Nasal and nasopharyngeal swabs are commonly used for detecting respiratory viruses, including influenza, which significantly alters host cell metabolites. This study aimed to develop a machine learning model to identify biomarkers that differentiate between influenza-positive and -negative cases using clinical metabolomics data. Method: A publicly available dataset of 236 nasopharyngeal samples screened via liquid chromatography-quadrupole time-of-flight (LC/Q-TOF) mass spectrometry was used. Among these, 118 samples tested positive for influenza (40 A H1N1, 39 A H3N2, 39 Influenza B), while 118 were negative controls. A stacking-based model was proposed using the top 20 selected features. Thirteen machine learning models were initially trained, and the top three were combined using predicted probabilities to form a stacking classifier. Results: The ExtraTrees stacking model outperformed other models, achieving 97.08% accuracy. External validation on a prospective cohort of 96 symptomatic individuals (48 positive and 48 negatives for influenza) showed 100% accuracy. SHAP values were used to enhance model explainability. Metabolites such as Pyroglutamic Acid (retention time: 0.81 min, m/z: 84.0447) and its in-source fragment ion (retention time: 0.81 min, m/z: 130.0507) showed minimal impact on influenza-positive cases. On the other hand, metabolites with a retention time of 10.34 min and m/z 106.0865, and a retention time of 8.65 min and m/z 211.1376, demonstrated significant positive contributions. Conclusions: This study highlights the effectiveness of integrating metabolomics data with machine learning for accurate influenza diagnosis. The stacking-based model, combined with SHAP analysis, provided robust performance and insights into key metabolites influencing predictions.

Multi-omics association analysis reveals interactions between the oropharyngeal microbiome and the metabolome in pediatric patients with influenza A virus pneumonia

Children are at high risk for influenza A virus (IAV) infections, which can develop into severe illnesses. However, little is known about interactions between the microbiome and respiratory tract metabolites and their impact on the development of IAV pneumonia in children. Using a combination of liquid chromatography tandem mass spectrometry (LC-MS/MS) and 16S rRNA gene sequencing, we analyzed the composition and metabolic profile of the oropharyngeal microbiota in 49 pediatric patients with IAV pneumonia and 42 age-matched healthy children. The results indicate that compared to healthy children, children with IAV pneumonia exhibited significant changes in the oropharyngeal macrobiotic structure (p = 0.001), and significantly lower microbial abundance and diversity (p < 0.05). These changes came with significant disturbances in the levels of oropharyngeal metabolites. Intergroup differences were observed in 204 metabolites mapped to 36 metabolic pathways. Significantly higher levels of sphingolipid (sphinganine and phytosphingosine) and propanoate (propionic acid and succinic acid) metabolism were observed in patients with IAV pneumonia than in healthy controls. Using Spearman’s rank-correlation analysis, correlations between IAV pneumonia-associated discriminatory microbial genera and metabolites were evaluated. The results indicate significant correlations and consistency in variation trends between Streptococcus and three sphingolipid metabolites (phytosphingosine, sphinganine, and sphingosine). Besides these three sphingolipid metabolites, the sphinganine-to-sphingosine ratio and the joint analysis of the three metabolites indicated remarkable diagnostic efficacy in children with IAV pneumonia. This study confirmed significant changes in the characteristics and metabolic profile of the oropharyngeal microbiome in pediatric patients with IAV pneumonia, with high synergy between the two factors. Oropharyngeal sphingolipid metabolites may serve as potential diagnostic biomarkers of IAV pneumonia in children.

The role of proteomics and metabolomics in severe infections

PURPOSE OF REVIEW

Severe infections are a common cause of ICU admission, with a high morbidity and mortality. Omics, namely proteomics and metabolomics, aim to identify, characterize, and quantify biological molecules to achieve a systems-level understanding of disease. The aim of this review is to provide a clear overview of the current evidence of the role of proteomics and metabolomics in severe infections.

RECENT FINDINGS

Proteomics and metabolomics are technologies that are being used to explore new markers of diagnosis and prognosis, clarify mechanisms of disease, and consequently discover potential targets of therapy and finally of a better disease phenotyping. These technologies are starting to be used but not yet in clinical use.

SUMMARY

Our traditional way of approaching the disease as sepsis is believing that a process can be broken into its parts and that the whole can be explained by the sum of each part. This approach is highly reductionist and does not take the system complexity nor the nonlinear dynamics of the processes. Proteomics and metabolomics allow the analysis of several proteins and metabolites simultaneously, thereby generating diagnostic and prognostic signatures. An exciting future prospect for proteomics and metabolomics is their employment towards precision medicine.

Infection Biomarkers Based on Metabolomics

Current infection biomarkers are highly limited since they have low capability to predict infection in the presence of confounding processes such as in non-infectious inflammatory processes, low capability to predict disease outcomes and have limited applications to guide and evaluate therapeutic regimes. Therefore, it is critical to discover and develop new and effective clinical infection biomarkers, especially applicable in patients at risk of developing severe illness and critically ill patients. Ideal biomarkers would effectively help physicians with better patient management, leading to a decrease of severe outcomes, personalize therapies, minimize antibiotics overuse and hospitalization time, and significantly improve patient survival. Metabolomics, by providing a direct insight into the functional metabolic outcome of an organism, presents a highly appealing strategy to discover these biomarkers. The present work reviews the desired main characteristics of infection biomarkers, the main metabolomics strategies to discover these biomarkers and the next steps for developing the area towards effective clinical biomarkers.

Past Experiences for Future Applications of Metabolomics in Critically Ill Patients with Sepsis and Septic Shocks

A disruption of several metabolic pathways in critically ill patients with sepsis indicates that metabolomics might be used as a more precise tool for sepsis and septic shock when compared with the conventional biomarkers. This article provides information regarding metabolomics studies in sepsis and septic shock patients. It has been shown that a variety of metabolomic pathways are altered in sepsis and septic shock, including amino acid metabolism, fatty acid oxidation, phospholipid metabolism, glycolysis, and tricarboxylic acid cycle. Based upon this comprehensive review, here, we demonstrate that metabolomics is about to change the world of sepsis biomarkers, not only for its utilization in sepsis diagnosis, but also for prognosticating and monitoring the therapeutic response. Additionally, the future direction regarding the establishment of studies integrating metabolomics with other molecular modalities and studies identifying the relationships between metabolomic profiles and clinical characteristics to address clinical application are discussed in this article. All of the information from this review indicates the important impact of metabolomics as a tool for diagnosis, monitoring therapeutic response, and prognostic assessment of sepsis and septic shock. These findings also encourage further clinical investigations to warrant its use in routine clinical settings.

SARS-CoV2 Infection Alters Tryptophan Catabolism and Phospholipid Metabolism

Coronavirus disease 2019 (COVID-19) has so far infected hundreds of million individuals, with several million deaths worldwide. The lack of understanding of the disease pathophysiology and the host’s immune response has resulted in this rapid spread of the disease on a global scale. In this respect, we employed UPLC-MS to compare the metabolites in the serum from COVID-19-positive patients and COVID-19-recovered subjects to determine the metabolic changes responsible for an infection. Our investigations revealed significant increase in the levels of serum phospholipids including sphingomyelins, phosphatidylcholines and arachidonic acid in the serum of COVID-19-positive patients as compared to COVID-19-recovered individuals. We further show increased levels of tryptophan and its metabolites in the serum of COVID-19-positive patients thus emphasizing the role of tryptophan metabolism in the disease pathogenesis of COVID-19. Future studies are required to determine the changes in the lipid and tryptophan metabolism at various stages of COVID-19 disease development, progression and recovery to better understand the host–pathogen interaction and the long-term effects of a severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) infection in humans.

Prevalence and mortality of COVID-19 are associated with the L55M functional polymorphism of Paraoxonase 1

Introduction

Accumulating evidence recommends that infectious diseases including coronavirus disease 2019 (COVID-19) are often associated with oxidative stress and inflammation. Paraoxonase 1 (PON1, OMIM: 168,820), a member of the paraoxonase gene family, has antioxidant properties. Enzyme activity of paraoxonase depends on a variety of influencing factors such as polymorphisms of PON1, ethnicity, gender, age, and a number of environmental variables. The PON1 has two common functional polymorphisms, namely, Q192R (rs662) and L55M (rs854560). The R192 and M55 alleles are associated with increase and decrease in enzyme activity, respectively.

Objective

The present study was conducted to investigate the possible association of rs662 and rs854560 polymorphisms with morbidity and mortality of COVID-19.

Methods

Data for the prevalence, mortality, and amount of accomplished diagnostic test (per 10⁶ people) on 25 November 2020 from 48 countries were included in the present study. The Human Development Index (HDI) was used as a potential confounding variable.

Results

The frequency of M55 was positively correlated with the prevalence (partial r = 0.487, df = 36, p = 0.002) and mortality of COVID-19 (partial r = 0.551, df = 36, p < 0.001), after adjustments for HDI and amount of the accomplished diagnostic test as possible confounders.

Conclusions

This means that countries with higher M55 frequency have higher prevalence and mortality of COVID-19.

Nasopharyngeal metabolomics and machine learning approach for the diagnosis of influenza

Background

Respiratory virus infections are significant causes of morbidity and mortality, and may induce host metabolite alterations by infecting respiratory epithelial cells. We investigated the use of liquid chromatography quadrupole time-of-flight mass spectrometry (LC/Q-TOF) combined with machine learning for the diagnosis of influenza infection.

Methods

We analyzed nasopharyngeal swab samples by LC/Q-TOF to identify distinct metabolic signatures for diagnosis of acute illness. Machine learning models were performed for classification, followed by Shapley additive explanation (SHAP) analysis to analyze feature importance and for biomarker discovery.

Findings

A total of 236 samples were tested in the discovery phase by LC/Q-TOF, including 118 positive samples (40 influenza A 2009 H1N1, 39 influenza H3 and 39 influenza B) as well as 118 age and sex-matched negative controls with acute respiratory illness. Analysis showed an area under the receiver operating characteristic curve (AUC) of 1.00 (95% confidence interval [95% CI] 0.99, 1.00), sensitivity of 1.00 (95% CI 0.86, 1.00) and specificity of 0.96 (95% CI 0.81, 0.99). The metabolite most strongly associated with differential classification was pyroglutamic acid. Independent validation of a biomarker signature based on the top 20 differentiating ion features was performed in a prospective cohort of 96 symptomatic individuals including 48 positive samples (24 influenza A 2009 H1N1, 5 influenza H3 and 19 influenza B) and 48 negative samples. Testing performed using a clinically-applicable targeted approach, liquid chromatography triple quadrupole mass spectrometry, showed an AUC of 1.00 (95% CI 0.998, 1.00), sensitivity of 0.94 (95% CI 0.83, 0.98), and specificity of 1.00 (95% CI 0.93, 1.00). Limitations include lack of sample suitability assessment, and need to validate these findings in additional patient populations.

Interpretation

This metabolomic approach has potential for diagnostic applications in infectious diseases testing, including other respiratory viruses, and may eventually be adapted for point-of-care testing.

Typhoid and Enteric Fevers in Intensive Care Unit

Enteric fever (typhoid and paratyphoid)is caused by Salmonella typhi and Salmonella paratyphi. It is spread by fecal-oral route, largely through contamination of water and foodstuff. Developing countries are the worst affected. It takes 7 – 21 days from ingestion of the organism to manifestation of symptoms which are generally Fever, relative bradycardia, and pain abdomen. Hepatosplenomegaly, intestinal bleeding, and perforation are the features at various stages of the disease. The bacteria invade the submucous layer and proliferate in the Payer's patches. Blood culture is the gold standard for diagnosis but it is only rarely positive. Fluroquinolones, cephalosporins, and azithromycin are antibiotics of choice. There is increasing evidence of the development of resistance to all antibiotics. Salmonella sepsis, though uncommon, can occur. Intestinal perforation, peritonitis, and secondary sepsis are complications that may require intensive care unit management.

How to cite this article: Ray B, Raha A. Typhoid and Enteric Fevers in Intensive Care Unit. Indian J Crit Care Med 2021;25(Suppl 2):S144–S149.

Integrative Metabolomics to Identify Molecular Signatures of Responses to Vaccines and Infections

Approaches to the identification of metabolites have progressed from early biochemical pathway evaluation to modern high-dimensional metabolomics, a powerful tool to identify and characterize biomarkers of health and disease. In addition to its relevance to classic metabolic diseases, metabolomics has been key to the emergence of immunometabolism, an important area of study, as leukocytes generate and are impacted by key metabolites important to innate and adaptive immunity. Herein, we discuss the metabolomic signatures and pathways perturbed by the activation of the human immune system during infection and vaccination. For example, infection induces changes in lipid (e.g., free fatty acids, sphingolipids, and lysophosphatidylcholines) and amino acid pathways (e.g., tryptophan, serine, and threonine), while vaccination can trigger changes in carbohydrate and bile acid pathways. Amino acid, carbohydrate, lipid, and nucleotide metabolism is relevant to immunity and is perturbed by both infections and vaccinations. Metabolomics holds substantial promise to provide fresh insight into the molecular mechanisms underlying the host immune response. Its integration with other systems biology platforms will enhance studies of human health and disease.

Metabolomics in chronic pain research

BACKGROUND AND OBJECTIVE

Metabolomics deals with the identification and quantification of small molecules (metabolites) in biological samples. As metabolite levels can reflect normal or altered metabolic pathways, their measurement provides information to improve the understanding, diagnosis and management of diseases. Despite its immense potential, metabolomics applications to pain research have been sparse. This paper describes current metabolomics techniques, reviews published human metabolomics pain research and compares successful metabolomics research in other areas of medicine with the goal of highlighting opportunities offered by metabolomics to advance pain medicine.

DATABASES AND DATA TREATMENT

Non-systematic review.

RESULTS

Our search identified 19 studies that adopted a metabolomics approach in: fibromyalgia (7), chronic widespread pain (4), other musculoskeletal pain conditions (5), neuropathic pain (1), complex regional pain syndrome (1) and pelvic pain (1). The studies used either mass spectrometry or nuclear magnetic resonance. Most are characterized by small sample sizes. Some consistency has been found for alterations in glutamate and testosterone metabolism, and metabolic imbalances caused by the gut microbiome.

CONCLUSIONS

Metabolomics research in chronic pain is in its infancy. Most studies are at the pilot stage. Metabolomics research has been successful in other areas of medicine. These achievements should motivate investigators to expand metabolomics research to improve the understanding of the basic mechanisms of human pain, as well as provide tools to diagnose, predict and monitor chronic pain conditions. Metabolomics research can lead to the identification of biomarkers to support the development and testing of treatments, thereby facilitating personalized pain medicine.

The Current Status of Enteric Fever Diagnostics and Implications for Disease Control

Enteric (typhoid) fever remains a problem in low- and middle-income countries that lack the infrastructure to maintain sanitation and where inadequate diagnostic methods have restricted our ability to identify and control the disease more effectively. As we move into a period of potential disease elimination through the introduction of typhoid conjugate vaccine (TCV), we again need to reconsider the role of typhoid diagnostics in how they can aid in facilitating disease control. Recent technological advances, including serology, transcriptomics, and metabolomics, have provided new insights into how we can detect signatures of invasive Salmonella organisms interacting with the host during infection. Many of these new techniques exhibit potential that could be further explored with the aim of creating a new enteric fever diagnostic to work in conjunction with TCV. We need a sustained effort within the enteric fever field to accelerate, validate, and ultimately introduce 1 (or more) of these methods to facilitate the disease control initiative. The window of opportunity is still open, but we need to recognize the need for communication with other research areas and commercial organizations to assist in the progression of these diagnostic approaches. The elimination of enteric fever is now becoming a real possibility, but new diagnostics need to be part of the equation and factored into future calculations for disease control.

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

BACKGROUND

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

METHODS

We searched PubMed, EMBASE, Cochrane, Web of Science, CNKI, Wangfang Data, and CQVIP from inception until July 2019. Eligible studies included the metabolomic analysis of blood samples from sepsis patients with the outcome. The metabolic pathway was assigned to each metabolite biomarker. The meta-analysis was performed using the pooled fold changes, area under the receiver operating characteristic curve (AUROC), and vote-counting of metabolic pathways. We also conducted a prospective cohort metabolomic study to validate the findings of our meta-analysis.

RESULTS

The meta-analysis included 21 cohorts reported in 16 studies with 2509 metabolite comparisons in the blood of 1287 individuals. We found highly limited overlap of the reported metabolite biomarkers across studies. However, these metabolites were enriched in several death-related metabolic pathways (DRMPs) including amino acids, mitochondrial metabolism, eicosanoids, and lysophospholipids. Prediction of sepsis death using DRMPs yielded a pooled AUROC of 0.81 (95% CI 0.76-0.87), which was similar to the combined metabolite biomarkers with a merged AUROC of 0.82 (95% CI 0.78-0.86) (P > 0.05). A prospective metabolomic analysis of 188 sepsis patients (134 survivors and 54 nonsurvivors) using the metabolites from DRMPs produced an AUROC of 0.88 (95% CI 0.78-0.97). The sensitivity and specificity for the prediction of sepsis death were 80.4% (95% CI 66.9-89.4%) and 78.8% (95% CI 62.3-89.3%), respectively.

CONCLUSIONS

DRMP analysis minimizes the discrepancies of results obtained from different metabolomic methods and is more practical than blood metabolite biomarkers for sepsis mortality prediction.

TRIAL REGISTRATION

The meta-analysis was registered on OSF Registries, and the prospective cohort study was registered on the Chinese Clinical Trial Registry (ChiCTR1800015321).

Association of acyl carnitines and mortality in out-of-hospital-cardiac-arrest patients: Results of a prospective observational study

PURPOSE

Out-of-hospital cardiac arrest (OHCA) is a leading cause of mortality, yet the prediction of its outcome remains challenging. Serum Acyl Carnitines (ACs), a biomarker of beta-oxidation, have been associated with cardiovascular events. We evaluated the association of different AC species with mortality and neurological outcome in a cohort of OHCA patients.

MATERIAL AND METHODS

We consecutively included OHCA patients in this prospective observational study upon admission to the intensive care unit. We studied the association of thirty-nine different ACs measured at admission and 30-day mortality (primary endpoint), as well as neurological outcome at hospital discharge (secondary endpoint) using the Cerebral Performance Category scale. Multivariate models were adjusted for age, gender, comorbidities and shock markers.

RESULTS

Of 281 included patients, 137 (48.8%) died within 30 days and of the 144 survivors (51.2%), 15 (10.4%) had poor neurological outcome. While several ACs were associated with mortality, AC C2 had the highest prognostic value for mortality (fully-adjusted odds ratio 4.85 (95%CI 1.8 to 13.06, p < .01), area under curve (AUC) 0.65) and neurological outcome (fully-adjusted odds ratio 3.96 (95%CI 1.47 to 10.66, p < .01), AUC 0.63).

CONCLUSIONS

ACs are interesting surrogate biomarkers that are associated with mortality and poor neurological outcome in patients after OHCA and may help to improve the understanding of pathophysiological mechanisms and risk stratification.

Low Plasma Sphingomyelin Levels Show a Weak Association with Poor Neurological Outcome in Cardiac Arrest Patients: Results from the Prospective, Observational COMMUNICATE Trial

There is interest in novel blood markers to improve risk stratification in patients presenting with cardiac arrest. We assessed associations of different plasma sphingomyelin concentrations and neurological outcome in patients with cardiac arrest. In this prospective observational study, adult patients with cardiac arrest were included upon admission to the intensive care unit (ICU). We studied associations of admission plasma levels of 15 different sphingomyelin species with neurological outcome at hospital discharge (primary endpoint) defined by the modified Rankin Scale by the calculation of univariable and multivariable logistic regression models adjusted for age, gender, and clinical shock markers. We included 290 patients (72% males, median age 65 years) with 162 (56%) having poor neurological outcome at hospital discharge. The three sphingomyelin species SM C24:0, SM(OH) C22:1, and SM(OH) C24:1 were significantly lower in patients with poor neurological outcome compared to patients with favorable outcome with areas under the curve (AUC) of 0.58, 0.59, and 0.59. SM(OH) C24:1 was independently associated with poor neurological outcome in a fully-adjusted regression model (adjusted odds ratio per log-transformed unit increase in SM(OH) C24:1 blood level 0.18, 95% CI 0.04 to 0.87, p = 0.033). Results were similar for 1-year mortality. Low admission sphingomyelin levels showed a weak association with poor neurological outcome in patients after cardiac arrest. If validated in future studies, a better understanding of biological sphingomyelin function during cardiac arrest may help to further advance the therapeutic approach and risk stratification in this vulnerable patient group.

Biomarkers of Infection: Are They Useful in the ICU?

Biomarkers are increasingly used in patients with serious infections in the critical care setting to complement clinical judgment and interpretation of other diagnostic and prognostic tests. The main purposes of such blood markers are (1) to improve infection diagnosis (i.e., differentiation between bacterial vs. viral vs. fungal vs. noninfectious), (2) to help in the early risk stratification and thus provide prognostic information regarding the risk for mortality and other adverse outcomes, and (3) to optimize antibiotic tailoring to individual needs of patients ("antibiotic stewardship").Especially in critically ill patients, in whom sepsis is a major cause of morbidity and mortality, rapid diagnosis is desirable to start timely and specific treatment.Besides some biomarkers, such as procalcitonin, which is well established and has shown positive effects in regard to utilization of antimicrobials and clinical outcomes, there is a growing number of novel markers from different pathophysiological pathways, where the final proof of an added value to clinical judgment and ultimately clinical benefit to patients is still lacking.Without a doubt, the addition of blood biomarkers to clinical medicine has had a strong impact on the way we care for patients today. Recent trials show that as an adjunct to other clinical and laboratory parameters these markers provide important information about risks for bacterial infection and resolution of infection. Moreover, biomarkers can help to optimize management of patients with serious illness in the intensive care unit, thereby offering more individualized treatment courses with overall improvements in clinical outcomes.