Metabolic profiles in community-acquired pneumonia: developing assessment tools for disease severity

Multi-omics analysis reveals overactive inflammation and dysregulated metabolism in severe community-acquired pneumonia patients

BACKGROUND

Severe community-acquired pneumonia (S-CAP) is a public health threat, making it essential to identify novel biomarkers and investigate the underlying mechanisms of disease severity.

METHODS

Here, we profiled host responses to S-CAP through proteomics analysis of plasma samples from a cohort of S-CAP patients, non-severe (NS)-CAP patients, diseases controls (DCs), and healthy controls (HCs). Then, typical differentially expressed proteins were then validated by ELISA in an independent cohort. Metabolomics analysis was further performed on both the cohort 1 and cohort 2. Then, the proteomic and metabolomic signatures were compared between the adult and child cohorts to explore the characteristics of severe pneumonia patients.

RESULTS

There were clear differences between CAP patients and controls, as well as substantial differences between the S-CAP and NS-CAP. Pathway analysis of changes revealed excessive inflammation, suppressed immunity, and lipid metabolic disorders in S-CAP cases. Interestingly, comparing these signatures between the adult and child cohorts confirmed that overactive inflammation and dysregulated lipid metabolism were common features of S-CAP patients, independent of age. The change proportion of glycerophospholipids, glycerolipids, and sphingolipids were obviously different in the adult and child S-CAP cases.

CONCLUSION

The plasma multi-omics profiling revealed that excessive inflammation, suppressed humoral immunity, and disordered metabolism are involved in S-CAP pathogenesis.

Danger-associated metabolites trigger metaflammation: A crowbar in cardiometabolic diseases

Cardiometabolic diseases (CMDs) are characterized by a series of metabolic disorders and chronic low-grade inflammation. CMDs contribute to a high burden of mortality and morbidity worldwide. Host-microbial metabolic regulation that triggers metaflammation is an emerging field of study that promotes a new perspective for perceiving cardiovascular risks. The term metaflammation denotes the entire cascade of immune responses activated by a new class of metabolites known as "danger-associated metabolites" (DAMs). It is being proposed by the present review for the first time. We summarize current studies covering bench to bedside aspects of DAMs to better understand CMDs in the context of DAMs. We have focused on the involvement of DAMs in the pathophysiological development of CMDs, including the disruption of immune homeostasis and chronic inflammation-triggered damage leading to CMD-related adverse events, as well as emerging therapeutic approaches for targeting DAM metabolism in CMDs.

Recent advances in LC-MS-based metabolomics for clinical biomarker discovery

The employment of liquid chromatography-mass spectrometry (LC-MS) untargeted and targeted metabolomics has led to the discovery of novel biomarkers and improved the understanding of various disease mechanisms. Numerous strategies have been reported to expand the metabolite coverage in LC-MS-untargeted and targeted metabolomics. To improve the sensitivity of low-abundance or poor-ionized metabolites for reducing the amount of clinical sample, chemical derivatization methods are used to target different functional groups. Proper sample preparation is beneficial for reducing the matrix effect, maintaining the stability of the LC-MS system, and increasing the metabolite coverage. Machine learning has recently been integrated into the workflow of LC-MS metabolomics to accelerate metabolite identification and data-processing automation, and increase the accuracy of disease classification and clinical outcome prediction. Due to the rapidly growing utility of LC-MS metabolomics in discovering disease markers, this review will address the recent advances in the field and offer perspectives on various strategies for expanding metabolite coverage, chemical derivatization, sample preparation, clinical disease markers, and machining learning for disease modeling.

Study on the Common Molecular Mechanism of Metabolic Acidosis and Myocardial Damage Complicated by Neonatal Pneumonia

Pneumonia is a common clinical disease in the neonatal period and poses a serious risk to infant health. Therefore, the understanding of molecular mechanisms is of great importance for the development of methods for the rapid and accurate identification, classification and staging, and even disease diagnosis and therapy of pneumonia. In this study, a nontargeted metabonomic method was developed and applied for the analysis of serum samples collected from 20 cases in the pneumonia control group (PN) and 20 and 10 cases of pneumonia patients with metabolic acidosis (MA) and myocardial damage (MD), respectively, with the help of ultrahigh-performance liquid chromatography-high-resolution mass spectrometry (UPLC-HRMS). The results showed that compared with the pneumonia group, 23 and 21 differential metabolites were identified in pneumonia with two complications. They showed high sensitivity and specificity, with the area under the curve (ROC) of the receiver operating characteristic curve (ROC) larger than 0.7 for each differential molecule. There were 14 metabolites and three metabolic pathways of sphingolipid metabolism, porphyrin and chlorophyll metabolism, and glycerophospholipid metabolism existing in both groups of PN and MA, and PN and MD, all involving significant changes in pathways closely related to amino acid metabolism disorders, abnormal cell apoptosis, and inflammatory responses. These findings of molecular mechanisms should help a lot to fully understand and even treat the complications of pneumonia in infants.

Metabolomics Study Revealing Purines as Potential Diagnostic Biomarkers of Acute Respiratory Distress Syndrome in Patients with Community─Acquired Pneumonia

Community-acquired pneumonia (CAP) is a significant threat to human health and the leading cause of acute respiratory distress syndrome (ARDS). We aimed to reveal the metabolic profiling whether can be used for assessing CAP with or without ARDS (nARDS) and therapeutic effects on CAP patients after treatment. Urine samples were collected at the onset and recovery periods, and metabolomics was employed to identify robust biomarkers. 19 metabolites were significantly changed in the ARDS relative to nARDS, mainly involving purines and fatty acids. After treatment, 7 metabolites in the nARDS and 14 in the ARDS were found to be significantly dysregulated, including fatty acids and amino acids. In the validation cohort, we observed that the biomarker panel consisted of N2,N2-dimethylguanosine, 1-methyladenosine, 3-methylguanine, 1-methyladenosine, and uric acid exhibited better AUCs of 0.900 than pneumonia severity index and acute physiology and chronic health evaluation II (APACHE II) scores between the ARDS and nARDS. Combining L-phenylalanine, phytosphingosine, and N-acetylaspartylglutamate as biomarkers for discriminating the nARDS and ARDS patients after treatment exhibited good AUCs of 0.811 and 0.821, respectively. The metabolic pathway and defined biomarkers may serve as crucial indicators for predicting the development of ARDS in CAP patients and for assessing therapeutic effects.

Establishment of risk model for elderly CAP at different age stages: a single-center retrospective observational study

Community-acquired pneumonia (CAP) is one of the main reasons of mortality and morbidity in elderly population, causing substantial clinical and economic impacts. However, clinically available score systems have been shown to demonstrate poor prediction of mortality for patients aged over 65. Especially, no existing clinical model can predict morbidity and mortality for CAP patients among different age stages. Here, we aimed to understand the impact of age variable on the establishment of assessment model and explored prognostic factors and new biomarkers in predicting mortality. We retrospectively analyzed elderly patients with CAP in Minhang Hospital, Fudan University. We used univariate and multiple logistic regression analyses to study the prognostic factors of mortality in each age-based subgroup. The prediction accuracy of the prognostic factors was determined by the Receiver Operating Characteristic curves and the area under the curves. Combination models were established using several logistic regressions to save the predicted probabilities. Four factors with independently prognostic significance were shared among all the groups, namely Albumin, BUN, NLR and Pulse, using univariate analysis and multiple logistic regression analysis. Then we built a model with these 4 variables (as ABNP model) to predict the in-hospital mortality in all three groups. The AUC value of the ABNP model were 0.888 (95% CI 0.854-0.917, p < 0.000), 0.912 (95% CI 0.880-0.938, p < 0.000) and 0.872 (95% CI 0.833-0.905, p < 0.000) in group 1, 2 and 3, respectively. We established a predictive model for mortality based on an age variable -specific study of elderly patients with CAP, with higher AUC value than PSI, CURB-65 and qSOFA in predicting mortality in different age groups (66-75/ 76-85/ over 85 years).

Haemogram indices are as reliable as CURB-65 to assess 30-day mortality in Covid-19 pneumonia

Background Mortality due to Covid-19 and severe community-acquired pneumonia (CAP) remains high, despite progress in critical care management. We compared the precision of CURB-65 score with monocyte-to-lymphocyte ratio (MLR), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) in prediction of mortality among patients with Covid-19 and CAP presenting to the emergency department. Methods We retrospectively analysed two cohorts of patients admitted to the emergency department of Canakkale University Hospital, namely (i) Covid-19 patients with severe acute respiratory symptoms presenting between 23 March 2020 and 31 October 2020, and (ii) all patients with CAP either from bacterial or viral infection within the 36 months preceding the Covid-19 pandemic. Mortality was defined as in-hospital death or death occurring within 30 days after discharge. Results The first study group consisted of 324 Covid-19 patients and the second group of 257 CAP patients. The non-survivor Covid-19 group had significantly higher MLR, NLR and PLR values. In univariate analysis, in Covid-19 patients, a 1-unit increase in NLR and PLR was associated with increased mortality, and in multivariate analysis for Covid-19 patients, age and NLR remained significant in the final step of the model. According to this model, we found that in the Covid-19 group an increase in 1-unit in NLR would result in an increase by 5% and 7% in the probability of mortality, respectively. According to pairwise analysis, NLR and PLR are as reliable as CURB-65 in predicting mortality in Covid-19. Conclusions Our study indicates that NLR and PLR may serve as reliable predictive factors as CURB-65 in Covid-19 pneumonia, which could easily be used to triage and manage severe patients in the emergency department.

Serum-integrated omics reveal the host response landscape for severe pediatric community-acquired pneumonia

OBJECTIVE

Community-acquired pneumonia (CAP) is the primary cause of death for children under five years of age globally. Hence, it is essential to investigate new early biomarkers and potential mechanisms involved in disease severity.

METHODS

Proteomics combined with metabolomics was performed to identify biomarkers suitable for early diagnosis of severe CAP. In the training cohort, proteomics and metabolomics were performed on serum samples obtained from 20 severe CAPs (S-CAPs), 15 non-severe CAPs (NS-CAPs) and 15 healthy controls (CONs). In the verification cohort, selected biomarkers and their combinations were validated using ELISA and metabolomics in an independent cohort of 129 subjects. Finally, a combined proteomics and metabolomics analysis was performed to understand the major pathological features and reasons for severity of CAP.

RESULTS

The proteomic and metabolic signature was markedly different between S-CAPs, NS-CAPs and CONs. A new serum biomarker panel including 2 proteins [C-reactive protein (CRP), lipopolysaccharide (LBP)] and 3 metabolites [Fasciculol C, PE (14:0/16:1(19Z)), PS (20:0/22:6(4Z, 7Z, 10Z, 13Z, 16Z, 19Z))] was developed to identify CAP and to distinguish severe pneumonia. Pathway analysis of changes revealed activation of the cell death pathway, a dysregulated complement system, coagulation cascade and platelet function, and the inflammatory responses as contributors to tissue damage in children with CAP. Additionally, activation of glycolysis and higher levels of nucleotides led to imbalanced deoxyribonucleotide pools contributing to the development of severe CAP. Finally, dysregulated lipid metabolism was also identified as a potential pathological mechanism for severe progression of CAP.

CONCLUSION

The integrated analysis of the proteome and metabolome might open up new ways in diagnosing and uncovering the complexity of severity of CAP.

Association of admission lactate with mortality in adult patients with severe community-acquired pneumonia

PURPOSE

The present study was conducted to investigate the association of admission lactate with mortality in severe community-acquired pneumonia (SCAP).

METHODS

We performed a retrospective, observational, cohort study on adult SCAP patients admitted to intensive care unit (ICU) in West China Hospital of Sichuan University between December 2011 and December 2018. The primary outcome was hospital mortality. Univariate and then multivariate analysis were performed to identify independent risk factors for hospital mortality. The association of admission lactate categories with hospital mortality was examined in three logistic regression models and Kaplan-Meier plots. We also applied restricted cubic splines to estimate the potential non-linear associations.

RESULTS

In total, 2275 SCAP patients were included. Admission lactate remained a significant factor for mortality after multivariate regression (OR: 1.085; 95% CI: 1.033,1.141; by continuous variable). After lactate was categorized into quartiles and the confounders were fully adjusted, compared with the quartile 1, ORs (95% CIs) of hospital mortality for quartile 2, quartile 3 and quartile 4 were 1.001 (0.759-1.321), 1.153 (0.877-1.516) and 1.593 (1.202-2.109), respectively (P for trend =0.001). Survival curves indicated that elevated lactate was associated with poor prognosis (P < 0.001). Moreover, this association was non-linear, indicating that increased lactate has the most notable impact on mortality within the range of 1.5 to 4 mmol/L (P non-linear: 0.029 for hospital mortality; 0.004 for ICU mortality).

CONCLUSION

Elevated admission lactate has a significant, independent, and potentially non-linear association with increased mortality in SCAP patients.

Characterization of the Microbiome and Host's Metabolites of the Lower Respiratory Tract During Acute Community-Acquired Pneumonia Identifies Potential Novel Markers

Purpose

Community-acquired pneumonia (CAP) is one of the most frequently encountered infectious diseases worldwide. Few studies have explored the microbial composition of the lower respiratory tract (LRT) and host metabolites of CAP. We analyzed the microbial composition of the LRT and levels of host metabolites to explore new biomarkers for CAP.

Patients and Methods

Bronchoalveolar lavage fluid (BALF) was collected from 28 CAP patients and 20 healthy individuals. Following centrifugation, BALF pellets were used for amplicon sequencing of a variable region of the bacterial 16S rDNA gene to characterize the microbial composition. Non-targeted metabolomics was used to detect host's metabolites in the supernatant.

Results

Compared with healthy individuals, the bacterial alpha diversity in the LRT of CAP patients was significantly lower in CAP patients (p<0.05). On the bacterial genus level, over 20 genera were detected with lower relative abundance (p<0.05), while the relative abundance of Ruminiclostridium-6 was significantly higher in CAP patients. The levels of the host metabolites dimethyldisulfide, choline, pyrimidine, oleic acid and N-acetyl-neuraminic acid were all increased in BALF of CAP patients (p<0.05), while concentrations of lysophosphatidylcholines (LPC (12:0/0:0)) and phosphatidic acid (PA (20:4/2:0)) were decreased (p<0.05). Furthermore, the relative abundance of Parvimonas, Treponema-2, Moraxella, Aggregatibacter, Filifactor, Fusobacterium, Lautropia and Neisseria negatively correlated with concentrations of oleic acid (p<0.05). A negative correlation between the relative abundance of Treponema-2, Moraxella, Filifactor, Fusobacterium and dimethyldisulfide concentrations was also observed (p<0.05). In contrast, the relative abundance of Treponema-2, Moraxella, Filifactor, and Fusobacterium was found to be positively associated with concentrations of LPC (12:0/0:0) and PA (20:4/2:0) (p<0.05).

Conclusion

The composition of the LRT microbiome differed between healthy individuals and CAP patients. We propose that some respiratory microbial components and host metabolites are potentially novel diagnostic markers of CAP.

A Nomogram for Early Diagnosis of Community-Acquired Pneumonia Based on Bronchoalveolar Lavage Fluid Metabolomics

Purpose

There is a high disease burden associated with community-acquired pneumonia (CAP) around the world. A timely and correct diagnosis of CAP can facilitate early treatment and prevent illness progression. The present study aimed to find some novel biomarkers of CAP by metabolic analysis and construct a nomogram model for precise diagnosis and individualized treatment of CAP patients.

Patients and Methods

42 CAP patients and 20 controls were enrolled in this study. The metabolic profiles of bronchoalveolar lavage fluid (BALF) samples were identified by untargeted LC-MS/MS analysis. With a VIP score ≥ 1 in OPLS-DA analysis and P < 0.05, the significantly dysregulated metabolites were estimated as potential biomarkers of CAP, which were further included in the construction of the diagnostic prediction model along with laboratory inflammatory indexes via stepwise backward regression analysis. Discrimination, calibration, and clinical applicability of the nomogram were evaluated by the C-index, the calibration curve, and the decision curve analysis (DCA) estimated by bootstrap resampling.

Results

The metabolic profiles differed obviously between CAP patients and healthy controls, as shown by PCA and OPLS-DA plots. Seven metabolites significantly dysregulated in CAP were established: dimethyl disulfide, oleic acid (d5), N-acetyl-a-neuraminic acid, pyrimidine, choline, LPC (12:0/0:0) and PA (20:4/2:0). Multivariate logistic regression revealed that the expression levels of PA (20:4/2:0), N-acetyl-a-neuraminic acid, and CRP were associated with CAP. After being validated by bootstrap resampling, this model showed satisfactory diagnostic performance.

Conclusion

A novel nomogram prediction model containing metabolic potential biomarkers in BALF that was developed for the early diagnosis of CAP offers insights into the pathogenesis and host response in CAP.

Myristoyl lysophosphatidylcholine is a biomarker and potential therapeutic target for community-acquired pneumonia

There is no gold standard for evaluating the severity of community-acquired pneumonia (CAP), and it is still based on a score. This study aimed to use the metabolomics method to find promised biomarkers in assessing disease severity and potential therapeutic targets for CAP. The result found that the metabolites in the plasma samples of CAP patients had significantly different between the acute phase and the remission phase, especially lysophosphatidylcholine (LPCs) in glycerophospholipids, whose levels are negatively linked to the severity of the disease. Subsequently, the two key metabolites of myristoyl lysophosphatidylcholine (LPC 14:0) and LPC 16:1 were screened. We analyzed the predictive performance of the two metabolites using Spearman-related analysis and ROC curves, and LPC14:0 showed more satisfactory diagnostic performance than LPC16:1. Then we explored the protective role and mechanism of LPC 14:0 in animal and cell models. The results showed that LPC 14:0 could inhibit the LPS-induced secretion of IL-1β, IL-6, and TNF-α, lower the ROS and MDA levels, and decreased the depletion of SOD and GSH, thereby reducing lung tissue and cell damage, such as down-regulating the protein level in BALF, lung W/D ratio, MPO activity, and apoptosis. We found that LPC 14:0 inhibited LPS-induced inflammatory response and oxidative stress, and the above protection was achieved by inhibiting LPS-induced activation of the NLRP3 inflammasome. LPC 14:0 may serve as a novel biomarker for predicting the severity of CAP. In addition, our exploration of the role of LPC 14:0 in animal and cellular models has reinforced its promise as a therapeutic target to improve the clinical efficacy for CAP.

Exploration of prognostic factors for prediction of mortality in elderly CAP population using a nomogram model

Background

The incidence and mortality rate of community-acquired pneumonia (CAP) in elderly patients were higher than the younger population. The assessment tools including CURB-65 and qSOFA have been applied in early detection of high-risk patients with CAP. However, several disadvantages exist to limit the efficiency of these tools for accurate assessment in elderly CAP. Therefore, we aimed to explore a more comprehensive tool to predict mortality in elderly CAP population by establishing a nomogram model.

Methods

We retrospectively analyzed elderly patients with CAP in Minhang Hospital, Fudan University. The least absolute shrinkage and selection operator (LASSO) logistic regression combined with multivariate analyses were used to select independent predictive factors and established nomogram models via R software. Calibration plots, decision curve analysis (DCA) and receiver operating characteristic curve (ROC) were generated to assess predictive performance.

Results

LASSO and multiple logistic regression analyses showed the age, pulse, NLR, albumin, BUN, and D-dimer were independent risk predictors. A nomogram model (NB-DAPA model) was established for predicting mortality of CAP in elderly patients. In both training and validation set, the area under the curve (AUC) of the NB-DAPA model showed superiority than CURB-65 and qSOFA. Meanwhile, DCA revealed that the predictive model had significant net benefits for most threshold probabilities.

Conclusion

Our established NB-DAPA nomogram model is a simple and accurate tool for predicting in-hospital mortality of CAP, adapted for patients aged 65 years and above. The predictive performance of the NB-DAPA model was better than PSI, CURB-65 and qSOFA.

Metabolomics-based investigation of SARS-CoV-2 vaccination (Sinovac) reveals an immune-dependent metabolite biomarker

SARS-CoV-2 and its mutant strains continue to rapidly spread with high infection and fatality. Large-scale SARS-CoV-2 vaccination provides an important guarantee for effective resistance to existing or mutated SARS-CoV-2 virus infection. However, whether the host metabolite levels respond to SARS-CoV-2 vaccine-influenced host immunity remains unclear. To help delineate the serum metabolome profile of SARS-CoV-2 vaccinated volunteers and determine that the metabolites tightly respond to host immune antibodies and cytokines, in this study, a total of 59 sera samples were collected from 30 individuals before SARS-CoV-2 vaccination and from 29 COVID-19 vaccines 2 weeks after the two-dose vaccination. Next, untargeted metabolomics was performed and a distinct metabolic composition was revealed between the pre-vaccination (VB) group and two-dose vaccination (SV) group by partial least squares-discriminant and principal component analyses. Based on the criteria: FDR < 0.05, absolute log2 fold change greater than 0.25, and VIP >1, we found that L-glutamic acid, gamma-aminobutyric acid (GABA), succinic acid, and taurine showed increasing trends from SV to VB. Furthermore, SV-associated metabolites were mainly annotated to butanoate metabolism and glutamate metabolism pathways. Moreover, two metabolite biomarkers classified SV from VB individuals with an area under the curve (AUC) of 0.96. Correlation analysis identified a positive association between four metabolites enriched in glutamate metabolism and serum antibodies in relation to IgG, IgM, and IgA. These results suggest that the contents of gamma-aminobutyric acid and indole in serum could be applied as biomarkers in distinguishing vaccinated volunteers from the unvaccinated. What’s more, metabolites such as GABA and taurine may serve as a metabolic target for adjuvant vaccines to boost the ability of the individuals to improve immunity.

Reprogramming of Amino Acid Metabolism Differs between Community-Acquired Pneumonia and Infection-Associated Exacerbation of Chronic Obstructive Pulmonary Disease

Amino acids and their metabolites are key regulators of immune responses, and plasma levels may change profoundly during acute disease states. Using targeted metabolomics, we evaluated concentration changes in plasma amino acids and related metabolites in community-acquired pneumonia (CAP, n = 29; compared against healthy controls, n = 33) from presentation to hospital through convalescence. We further aimed to identify biomarkers for acute CAP vs. the clinically potentially similar infection-triggered COPD exacerbation (n = 13). Amino acid metabolism was globally dysregulated in both CAP and COPD. Levels of most amino acids were markedly depressed in acute CAP, and total amino acid concentrations on admission were an accurate biomarker for the differentiation from COPD (AUC = 0.93), as were reduced asparagine and threonine levels (both AUC = 0.92). Reduced tryptophan and histidine levels constituted the most accurate biomarkers for acute CAP vs. controls (AUC = 0.96, 0.94). Only kynurenine, symmetric dimethyl arginine, and phenylalanine levels were increased in acute CAP, and the kynurenine/tryptophan ratio correlated best with clinical recovery and resolution of inflammation. Several amino acids did not reach normal levels by the 6-week follow-up. Glutamate levels were reduced on admission but rose during convalescence to 1.7-fold above levels measured in healthy control. Our data suggest that dysregulated amino acid metabolism in CAP partially persists through clinical recovery and that amino acid metabolism constitutes a source of promising biomarkers for CAP. In particular, total amino acids, asparagine, and threonine may constitute plasma biomarker candidates for the differentiation between CAP and infection-triggered COPD exacerbation and, perhaps, the detection of pneumonia in COPD.

Metabolomic analyses reveal new stage-specific features of COVID-19

The current pandemic of coronavirus disease 2019 (COVID-19) has affected >160 million individuals to date, and has caused millions of deaths worldwide, at least in part due to the unclarified pathophysiology of this disease. Identifying the underlying molecular mechanisms of COVID-19 is critical to overcome this pandemic. Metabolites mirror the disease progression of an individual and can provide extensive insights into their pathophysiological significance at each stage of disease. We provide a comprehensive view of metabolic characterisation of sera from COVID-19 patients at all stages using untargeted and targeted metabolomic analysis. As compared with the healthy controls, we observed different alteration patterns of circulating metabolites from the mild, severe and recovery stages, in both the discovery cohort and the validation cohort, which suggests that metabolic reprogramming of glucose metabolism and the urea cycle are potential pathological mechanisms for COVID-19 progression. Our findings suggest that targeting glucose metabolism and the urea cycle may be a viable approach to fight COVID-19 at various stages along the disease course.

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.

Plasma Metabolites-Based Prediction in Cardiac Surgery-Associated Acute Kidney Injury

Background

Cardiac surgery–associated acute kidney injury (CSA‐AKI) is a common postoperative complication following cardiac surgery. Currently, there are no reliable methods for the early prediction of CSA‐AKI in hospitalized patients. This study developed and evaluated the diagnostic use of metabolomics‐based biomarkers in patients with CSA‐AKI.

Methods and Results

A total of 214 individuals (122 patients with acute kidney injury [AKI], 92 patients without AKI as controls) were enrolled in this study. Plasma samples were analyzed by liquid chromatography tandem mass spectrometry using untargeted and targeted metabolomic approaches. Time‐dependent effects of selected metabolites were investigated in an AKI swine model. Multiple machine learning algorithms were used to identify plasma metabolites positively associated with CSA‐AKI. Metabolomic analyses from plasma samples taken within 24 hours following cardiac surgery were useful for distinguishing patients with AKI from controls without AKI. Gluconic acid, fumaric acid, and pseudouridine were significantly upregulated in patients with AKI. A random forest model constructed with selected clinical parameters and metabolites exhibited excellent discriminative ability (area under curve, 0.939; 95% CI, 0.879–0.998). In the AKI swine model, plasma levels of the 3 discriminating metabolites increased in a time‐dependent manner (R², 0.480–0.945). Use of this AKI predictive model was then confirmed in the validation cohort (area under curve, 0.972; 95% CI, 0.947–0.996). The predictive model remained robust when tested in a subset of patients with early‐stage AKI in the validation cohort (area under curve, 0.943; 95% CI, 0.883–1.000).

Conclusions

High‐resolution metabolomics is sufficiently powerful for developing novel biomarkers. Plasma levels of 3 metabolites were useful for the early identification of CSA‐AKI.

Comparison of Different Scoring Systems for Prediction of Mortality and ICU Admission in Elderly CAP Population

Background

The incidence and mortality rate of community-acquired pneumonia (CAP) in elderly patients were higher than the younger population. Different scoring systems, including The quick Sequential Organ Function Assessment (qSOFA), Combination of Confusion, Urea, Respiratory Rate, Blood Pressure, and Age ≥65 (CURB-65), Modified Early Warning Score (MEWS) and National Early Warning Score (NEWS), were used widely for predicting mortality and ICU admission of patients with community-acquired pneumonia (CAP). This study aimed to identify the most suitable score system for better hospitalization.

Methods

We retrospectively analyzed elderly patients with CAP in Minhang Hospital, Fudan University from 1 January 2018 to 1 January 2020. We recorded information of the patients including age, gender, underlying disease, consciousness state, vital signs, physiological and laboratory variables and further calculated the qSOFA, CURB-65, MEWS, and NEWS scores. Receiver operating characteristic (ROC) curves were used to predict the mortality risk and ICU admission. Kaplan–Meier survival curves were used in survival rate.

Results

In total, 1044 patients were selected for analysis and divided into two groups, namely survivor groups (902 cases) and non-survivor groups (142 cases). Depending on ICU admission enrolled patients were classified into ICU admission (n = 102) and non-ICU admission (n = 942) groups. Mortality expressed as AUC values were 0.844 (p < 0.001), 0.868 (p < 0.001), 0.927 (p < 0.001) and 0.892 (p < 0.001) for qSOFA, CURB 65, MEWS and NEWS, respectively. There were clear differences in MEWS vs CURB-65 (p < 0.0001), MEWS vs NEWS (p < 0.001), MEWS vs qSOFA (p < 0.0001). For ICU-admission, the AUC values of qSOFA, CURB-65, MEWS and NEWS scores were 0.866 (p < 0.001), 0.854 (p < 0.001), 0.922 (p < 0.001), 0.976 (p < 0.001), respectively. There were significant differences in NEWS vs CURB-65 (p < 0.0001), NEWS vs MEWS (p < 0.001), NEWS vs qSOFA (p < 0.0001).

Conclusion

We explored the outcome prediction values of CURB65, qSOFA, MEWS and NEWS for patients aged 65-years and older with community-acquired pneumonia. We found that MEWS showed superiority over the other severity scores in predicting hospital mortality, and NEWS showed superiority over the other scores in predicting ICU admission.

Differentiating severe and non-severe lower respiratory tract illness in patients hospitalized with influenza: Development of the Influenza Disease Evaluation and Assessment of Severity (IDEAS) scale

BACKGROUND

Experimental studies have shown that vaccination can reduce viral replication to attenuate progression of influenza-associated lower respiratory tract illness (LRTI). However, clinical studies are conflicting, possibly due to use of non-specific outcomes reflecting a mix of large and small airway LRTI lacking specificity for acute lung or organ injury.

METHODS

We developed a global ordinal scale to differentiate large and small airway LRTI in hospitalized adults with influenza using physiologic features and interventions (PFIs): vital signs, laboratory and radiographic findings, and clinical interventions. We reviewed the literature to identify common PFIs across 9 existing scales of pneumonia and sepsis severity. To characterize patients using this scale, we applied the scale to an antiviral clinical trial dataset where these PFIs were measured through routine clinical care in adults hospitalized with influenza-associated LRTI during the 2010-2013 seasons.

RESULTS

We evaluated 12 clinical parameters among 1020 adults; 210 (21%) had laboratory-confirmed influenza, with a median severity score of 4.5 (interquartile range, 2-8). Among influenza cases, median age was 63 years, 20% were hospitalized in the prior 90 days, 50% had chronic obstructive pulmonary disease, and 22% had congestive heart failure. Primary influencers of higher score included pulmonary infiltrates on imaging (48.1%), heart rate ≥110 beats/minute (41.4%), oxygen saturation <93% (47.6%) and respiratory rate >24 breaths/minute (21.0%). Key PFIs distinguishing patients with severity < or ≥8 (upper quartile) included infiltrates (27.1% vs 90.0%), temperature ≥ 39.1°C or <36.0°C (7.1% vs 27.1%), respiratory rate >24 breaths/minute (7.9% vs 47.1%), heart rate ≥110 beats/minute (29.3% vs 65.7%), oxygen saturation <90% (14.3% vs 31.4%), white blood cell count >15,000 (5.0% vs 27.2%), and need for invasive or non-invasive mechanical ventilation (2.1% vs 15.7%).

CONCLUSION

We developed a scale in adults hospitalized with influenza-associated LRTI demonstrating a broad distribution of physiologic severity which may be useful for future studies evaluating the disease attenuating effects of influenza vaccination or other therapeutics.

Plasma amino acid levels in individuals with bacterial pneumonia and healthy controls

BACKGROUND & AIMS

Amino acids play an important role in immune responses and as neurotransmitters. During the course of a bacterial pneumonia episode, from the onset to the recovery phase, immune responses dramatically change, as does the metabolism of amino acids, a concept referred to as immuno-nutrition. We investigated the differences in plasma amino acid levels (PAA) between the acute and recovery phases in individuals with community-acquired pneumonia (CAP) and healthy controls.

METHODS

Two groups of participants were recruited: Healthy adults aged over 60 years and patients hospitalized with CAP. Samples were collected on Day 0 (the day of admission) and Day 7 (after 6-8 days treatment).

RESULTS

A total of 93 healthy adults and 60 patients with CAP participated in the study. Of those with CAP, 43 had their amino acids measured on Day 7. Patients with CAP had markedly decreased PAA of 12 amino acids on Day 0. Citrulline, histidine, and tryptophan remained low in male, while aspartic acid, asparagine, ornithine, proline, and threonine were higher on Day 7 in both males and females. Phenylalanine increased at Day 0 and Day7.

CONCLUSIONS

The findings suggest that the host response against bacterial infection changed the plasma amino acid levels. PAA on Day 7 (representing convalescence) continued to display an amino acid profile distinct from that observed in healthy individuals. Based on these findings, reconsideration for providing amino acids to patients with bacterial pneumonia should be needed depending on stage of the pneumonia from the perspective of immuno-nutrition.

Untargeted metabolomics and transcriptomics identified glutathione metabolism disturbance and PCS and TMAO as potential biomarkers for ER stress in lung

Endoplasmic reticulum (ER) stress is a cellular state that results from the overload of unfolded/misfolded protein in the ER that, if not resolved properly, can lead to cell death. Both acute lung infections and chronic lung diseases have been found related to ER stress. Yet no study has been presented integrating metabolomic and transcriptomic data from total lung in interpreting the pathogenic state of ER stress. Total mouse lungs were used to perform LC-MS and RNA sequencing in relevance to ER stress. Untargeted metabolomics revealed 16 metabolites of aberrant levels with statistical significance while transcriptomics revealed 1593 genes abnormally expressed. Enrichment results demonstrated the injury ER stress inflicted upon lung through the alteration of multiple critical pathways involving energy expenditure, signal transduction, and redox homeostasis. Ultimately, we have presented p-cresol sulfate (PCS) and trimethylamine N-oxide (TMAO) as two potential ER stress biomarkers. Glutathione metabolism stood out in both omics as a notably altered pathway that believed to take important roles in maintaining the redox homeostasis in the cells critical for the development and relief of ER stress, in consistence with the existing reports.

Evaluation of severity scoring systems in patients with severe community acquired pneumonia

BACKGROUND

The aim of this study was to evaluate the ability of severity scoring systems to predict 30-day mortality in patients with severe community-acquired pneumonia.

METHODS

The study included 98 patients aged ≥18 years with community acquired pneumonia hospitalized at the Intensive Care Unit of the University Clinic for Infectious Diseases in Skopje, Republic of North Macedonia, during a 3-year period. We recorded demographic, clinical and common biochemical parameters. Five severity scores were calculated at admission: CURB 65 (Confusion, Urea, Respiratory Rate, Blood pressure, Age ≥65 years), SCAP (Severe Community Acquired Pneumonia score), SAPS II (Simplified Acute Physiology Score), SOFA (Sequential Organ Failure Assessment Score) and MPM (Mortality Prediction Model). Primary outcome variable was 30-day in-hospital mortality.

RESULTS

The mean age of the patients was 59.08 ± 15.76 years, predominantly males (68%). The overall 30-day mortality was 52%. Charlson Comorbidity index was increased in non-survivors (3.72 ± 2.33) and was associated with the outcome. All severity indexes had higher values in patients who died, that showed statistical significance between the analysed groups. The areas under curve (AUC) values of the five scores for 30-day mortality were 0.670, 0.732, 0,726, 0.785 and 0.777, respectively.

CONCLUSION

Widely used severity scores accurately detected patients with pneumonia that had increased risk for poor outcome, but none of them individually demonstrated any advantage over the others.

Metabolomic profiling of microbial disease etiology in community-acquired pneumonia

Diagnosis of microbial disease etiology in community-acquired pneumonia (CAP) remains challenging. We undertook a large-scale metabolomics study of serum samples in hospitalized CAP patients to determine if host-response associated metabolites can enable diagnosis of microbial etiology, with a specific focus on discrimination between the major CAP pathogen groups S. pneumoniae, atypical bacteria, and respiratory viruses. Targeted metabolomic profiling of serum samples was performed for three groups of hospitalized CAP patients with confirmed microbial etiologies: S. pneumoniae (n = 48), atypical bacteria (n = 47), or viral infections (n = 30). A wide range of 347 metabolites was targeted, including amines, acylcarnitines, organic acids, and lipids. Single discriminating metabolites were selected using Student’s T-test and their predictive performance was analyzed using logistic regression. Elastic net regression models were employed to discover metabolite signatures with predictive value for discrimination between pathogen groups. Metabolites to discriminate S. pneumoniae or viral pathogens from the other groups showed poor predictive capability, whereas discrimination of atypical pathogens from the other groups was found to be possible. Classification of atypical pathogens using elastic net regression models was associated with a predictive performance of 61% sensitivity, 86% specificity, and an AUC of 0.81. Targeted profiling of the host metabolic response revealed metabolites that can support diagnosis of microbial etiology in CAP patients with atypical bacterial pathogens compared to patients with S. pneumoniae or viral infections.

Identification of Novel Biomarkers for Evaluating Disease Severity in House-Dust-Mite-Induced Allergic Rhinitis by Serum Metabolomics

The aim of this study was to identify differences in serum metabolomics profiles of house-dust-mite (HDM)-induced allergic rhinitis (AR) patients compared to controls and to explore novel biomarkers reflecting disease severity. Serum samples were collected from 29 healthy controls and HDM-induced 72 AR patients, including 30 mild patients (MAR) and 42 moderate to severe AR patients (MSAR). Metabolomics detection was performed, and orthogonal partial least square discriminate analysis was applied to assess the differences between AR patients and controls and for subgroups based on disease severity. These analysis results successfully revealed distinct metabolite signatures which distinguished MAR patients and MSAR patients from controls. MSAR patients also could be discriminated from MAR patients based on their metabolic fingerprints. Most observed metabolite changes were related to glycine, serine, and threonine metabolism, pyrimidine metabolism, sphingolipid metabolism, arginine and proline metabolism, and fatty acid metabolism. Levels of sarcosine, sphingosine-1-phosphate, cytidine, and linoleic acid significantly correlated with the total nasal symptom score and visual analogue scale in AR patients. These results suggest that metabolomics profiling may provide novel insights into the pathophysiological mechanisms of HDM-induced AR and contribute to its evaluation of disease severity.

Lipid profiles and differential lipids in serum related to severity of community-acquired pneumonia: A pilot study

This study aimed to characterize the lipidomic responses to community-acquired pneumonia (CAP) and provide new insight into the underlying mechanisms of pathogenesis and potential avenues for diagnostic and therapeutic treatments. This study was performed from January 2017 to October 2018. Lipidomic profiles were generated using ultra high-performance liquid chromatography with tandem mass spectrometry (UHPLC-MS/MS) platform. Spearman's rank correlation test and multiple linear regression analysis were applied to explore the correlation between changes in the relative abundance of lipids and clinical parameters. Kaplan-Meier methods were used to build 30-day survival curves. From the UHPLC-MS/MS results, a total of 509 and 195 lipid species were detected in the positive and negative ionization mode respectively. Positive ionization covered six lipid classes (glycerol-phospholipids, glycerolipids, sphingolipids, sterol-lipids, prenol-lipids, and fatty acid), whilst negative ionization covered three (glycerol-phospholipids, sphingolipids, fatty acid). Four lipids were selected as targets: PC (16:0_18:1), PC (18:2_20:4), PC (36:4), and PC (38:6). The relative increase of the areas under the curves for all four lipids were superior to the pneumonia severity index and CURB-65 (confusion, urea, respiratory rate, blood pressure, and age ≥65 years old) for discriminating severe CAP from CAP. Decreasing relative levels of PC (18:2_20:4), PC (38:6), and PC (36:4) were negatively related to fraction of inspiration O2; Changes in the relative abundance of PC (16:0_18:1) and PC (18:2_20:4) had significantly linear relationship with procalcitonin. Patients with an elevated level of PC (16:0_18:1) had significantly longer duration of hospital stays. As the relative abundance of PC (18:2_20:4), PC (36:4), and PC (38:6) decreased, the length of hospitalization days and 30-day mortality rate increased significantly (all log-rank p<0.05). Therefore, using the UHPLC-MS/MS platform's serum lipidomic approach can help reveal changes in lipid abundance during CAP and establish lipid profiles related to disease severity.

Admission lysophosphatidylethanolamine acyltransferase level predicts the severity and prognosis of community-acquired pneumonia

PURPOSE

Early diagnosis and prognosis of patients with community-acquired pneumonia (CAP) are still difficult clinical challenges. This study aimed to investigate the role of lysophosphatidylethanolamine acyltransferase (LPEAT) in CAP and to evaluate the effectiveness of this enzyme as an indicator of disease severity and risk of death in CAP.

METHODS

This retrospective, multi-center study was conducted in 2017. A total of 267 patients with CAP were included. Of these 267 patients, 175 patients had non-severe CAP (non-SCAP) and 92 patients had severe CAP (SCAP). In addition, we recruited 15 healthy volunteers and 42 hospitalized disease controls in our study. The demographic and clinical characteristics were recorded for all participants. Admission levels of LPEAT were determined by quantitative enzyme-linked immunosorbent assay.

RESULTS

Admission levels of LPEAT in patients with SCAP were significantly higher, particularly in non-survivors and were not affected by the causative etiology. Furthermore, when the patients were stratified according to PSI and CURB-65 scores, the patients with high severity scores had higher LPEAT levels upon admission than patients with low severity scores. LPEAT also performed well in predicting SCAP in patients with CAP. Moreover, LPEAT could predict the 30-day mortality rate of patients with CAP, and combining LPEAT with the clinical severity score further improved the accuracy of mortality prediction.

CONCLUSION

Elevated LPEAT levels can reliably predict the severity of illness in patients with CAP at the time of admission. Adding LPEAT to clinical scoring methods could improve prognostic accuracy. Trial registration ClinicalTrials.gov, NCT03093220. Registered on March 28th, 2017.

Sphingolipidomics in Translational Sepsis Research-Biomedical Considerations and Perspectives

Scientific Background: Sphingolipids are a highly diverse group of lipids with respect to physicochemical properties controlling either structure, distribution, or function, all of them regulating cellular response in health and disease. Mass spectrometry, on the other hand, is an analytical technique characterizing ionized molecules or fragments thereof by mass-to-charge ratios, which has been prosperingly developed for rapid and reliable qualitative and quantitative identification of lipid species. Parallel to best performance of in-depth chromatographical separation of lipid classes, preconditions of precise quantitation of unique molecular species by preprocessing of biological samples have to be fulfilled. As a consequence, “lipid profiles” across model systems and human individuals, esp. complex (clinical) samples, have become eminent over the last couple of years due to sensitivity, specificity, and discriminatory capability. Therefore, it is significance to consider the entire experimental strategy from sample collection and preparation, data acquisition, analysis, and interpretation.

Areas Covered: In this review, we outline considerations with clinical (i.e., human) samples with special emphasis on sample handling, specific physicochemical properties, target measurements, and resulting profiling of sphingolipids in biomedicine and translational research to maximize sensitivity and specificity as well as to provide robust and reproducible results. A brief commentary is also provided regarding new insights of “clinical sphingolipidomics” in translational sepsis research.

Expert Opinion: The role of mass spectrometry of sphingolipids and related species (“sphingolipidomics”) to investigate cellular and compartment-specific response to stress, e.g., in generalized infection and sepsis, is on the rise and the ability to integrate multiple datasets from diverse classes of biomolecules by mass spectrometry measurements and metabolomics will be crucial to fostering our understanding of human health as well as response to disease and treatment.

The Role of Serum Metabolomics in Distinguishing Chronic Rhinosinusitis With Nasal Polyp Phenotypes

Background: Chronic rhinosinusitis with nasal polyps (CRSwNP) is a heterogeneous disease characterized by different clinical features and treatment responsiveness. This study aimed to compare the serum metabolomics profiles between eosinophilic CRSwNP (eCRSwNP) and non-eosinophilic CRSwNP (neCRSwNP) and healthy controls (HC) and explore objective biomarkers for distinguishing eCRSwNP before surgery. Methods: Serum samples were collected from 33 neCRSwNP patients, 37 eCRSwNP patients, and 29 HC. Serum metabolomics profiles were investigated by ultra-high-performance liquid chromatography-mass spectrometry. Results: The analysis results revealed that neCRSwNP, eCRSwNP, and HC exhibited distinctive metabolite signatures. In addition, eCRSwNP could be distinguished from neCRSwNP referring to their serum metabolic profiles, and the top ten different metabolites were citrulline, choline, linoleic acid, adenosine, glycocholic acid, L-serine, triethanolamine, 4-guanidinobutyric acid, methylmalonic acid, and L-methionine, which were related to several most important pathways including arginine and proline metabolism; glycine, serine, and threonine metabolism; linoleic acid metabolism; and purine metabolism. Among these distinctive metabolites, citrulline, linoleic acid, adenosine, and 4-guanidinobutyric acid showed good predictabilities, and the serum levels of citrulline, linoleic acid, and adenosine were significantly correlated with tissue eosinophil (T-EOS) percentage and T-EOS count. Conclusion: eCRSwNP patients exhibited discriminative serum metabolic signatures in comparison with neCRSwNP patients and HC. These results suggested that metabolomics profiles contributed to understanding the pathophysiological mechanisms of CRSwNP and distinguishing its phenotypes.

Role of the inflammatory response in community-acquired pneumonia: clinical implications

INTRODUCTION

Despite adequate antibiotic coverage, community-acquired pneumonia (CAP) remains a leading cause of hospitalization and mortality worldwide. It induces both a local pulmonary and a systemic inflammatory response, particularly significant in severe cases. The intensity of the dysregulated host response varies from patient to patient and has a negative impact on survival and other outcomes.

AREAS COVERED

This comprehensive review summarizes the pathophysiological aspects of the inflammatory response in CAP, briefly discusses the usefulness of biomarkers, and assesses the clinical evidence for modulating the inflammatory pathways. We searched PubMed for the most relevant studies, reviews, and meta-analysis until August 2020.

EXPERT OPINION

Notable efforts have been made to identify biomarkers that can accurately differentiate between viral and bacterial etiology, and indeed, to enhance risk stratification in CAP. However, none has proven ideal and no recommended biomarker-guided algorithms exist. Biomarker signatures from proteomic and metabolomic studies could be more useful for such assessments. To date, most studies have produced contradictory results concerning the role of immunomodulatory agents (e.g. corticosteroids, macrolides, and statins) in CAP. Adequately identifying the population who may benefit most from effective modulation of the inflammatory response remains a challenge.

The utility of MEWS for predicting the mortality in the elderly adults with COVID-19: a retrospective cohort study with comparison to other predictive clinical scores

Background

Older adults have been reported to be a population with high-risk of death in the COVID-19 outbreak. Rapid detection of high-risk patients is crucial to reduce mortality in this population. The aim of this study was to evaluate the prognositc accuracy of the Modified Early Warning Score (MEWS) for in-hospital mortality in older adults with COVID-19.

Methods

A retrospective cohort study was conducted in Wuhan Hankou Hospital in China from 1 January 2020 to 29 February 2020. Receiver operating characteristic (ROC) analysis was used to evaluate the predictive value of MEWS, Acute Physiology and Chronic Health Evaluation II (APACHE II), Sequential Organ Function Assessment (SOFA), quick Sequential Organ Function Assessment (qSOFA), Pneumonia Severity Index (PSI), Combination of Confusion, Urea, Respiratory Rate, Blood Pressure, and Age ≥65 (CURB-65), and the Systemic Inflammatory Response Syndrome Criteria (SIRS) for in-hospital mortality. Logistic regression models were performed to detect the high-risk older adults with COVID-19.

Results

Among the 235 patients included in this study, 37 (15.74%) died and 131 (55.74%) were male, with an average age of 70.61 years (SD 8.02). ROC analysis suggested that the capacity of MEWS in predicting in-hospital mortality was as good as the APACHE II, SOFA, PSI and qSOFA (Difference in AUROC: MEWS vs. APACHE II, -0.025 (95% CI [-0.075 to 0.026]); MEWS vs. SOFA, -0.013 (95% CI [-0.049 to 0.024]); MEWS vs. PSI, -0.015 (95% CI [-0.065 to 0.035]); MEWS vs. qSOFA, 0.024 (95% CI [-0.029 to 0.076]), all P > 0.05), but was significantly higher than SIRS and CURB-65 (Difference in AUROC: MEWS vs. SIRS, 0.218 (95% CI [0.156-0.279]); MEWS vs. CURB-65, 0.064 (95% CI [0.002-0.125]), all P < 0.05). Logistic regression models implied that the male patients (≥75 years) had higher risk of death than the other older adults (estimated coefficients: 1.16, P = 0.044). Our analysis further suggests that the cut-off points of the MEWS score for the male patients (≥75 years) subpopulation and the other elderly patients should be 2.5 and 3.5, respectively.

Conclusions

MEWS is an efficient tool for rapid assessment of elderly COVID-19 patients. MEWS has promising performance in predicting in-hospital mortality and identifying the high-risk group in elderly patients with COVID-19.

Discovery and predictive modeling of urine microbiome, metabolite and cytokine biomarkers in hospitalized patients with community acquired pneumonia

Pneumonia is the leading cause of infectious related death costing 12 billion dollars annually in the United States alone. Despite improvements in clinical care, total mortality remains around 4%, with inpatient mortality reaching 5–10%. For unknown reasons, mortality risk remains high even after hospital discharge and there is a need to identify those patients most at risk. Also of importance, clinical symptoms alone do not distinguish viral from bacterial infection which may delay appropriate treatment and may contribute to short-term and long-term mortality. Biomarkers have the potential to provide point of care diagnosis, identify high-risk patients, and increase our understanding of the biology of disease. However, there have been mixed results on the diagnostic performance of many of the analytes tested to date. Urine represents a largely untapped source for biomarker discovery and is highly accessible. To test this hypothesis, we collected urine from hospitalized patients with community-acquired pneumonia (CAP) and performed a comprehensive screen for urinary tract microbiota signatures, metabolite, and cytokine profiles. CAP patients were diagnosed with influenza or bacterial (Streptococcus pneumoniae and Staphylococcus aureus) etiologies and compared with healthy volunteers. Microbiome signatures showed marked shifts in taxonomic levels in patients with bacterial etiology versus influenza and CAP versus normal. Predictive modeling of 291 microbial and metabolite values achieved a + 90% accuracy with LASSO in predicting specific pneumonia etiology. This study demonstrates that urine from patients hospitalized with pneumonia may serve as a reliable and accessible sample to evaluate biomarkers that may diagnose etiology and predict clinical outcomes.

Plasma lipid profiling for the prognosis of 90-day mortality, in-hospital mortality, ICU admission, and severity in bacterial community-acquired pneumonia (CAP)

Introduction

Pneumonia is the most common cause of mortality from infectious diseases, the second leading cause of nosocomial infection, and the leading cause of mortality among hospitalized adults. To improve clinical management, metabolomics has been increasingly applied to find specific metabolic biopatterns (profiling) for the diagnosis and prognosis of various infectious diseases, including pneumonia.

Methods

One hundred fifty bacterial community-acquired pneumonia (CAP) patients whose plasma samples were drawn within the first 24 h of hospital admission were enrolled in this study and separated into two age- and sex-matched cohorts: non-survivors (died ≤ 90 days) and survivors (survived > 90 days). Three analytical tools, ¹H-NMR spectroscopy, GC-MS, and targeted DI-MS/MS, were used to prognosticate non-survivors from survivors by means of metabolic profiles.

Results

We show that quantitative lipid profiling using DI-MS/MS can predict the 90-day mortality and in-hospital mortality among patients with bacterial CAP compared to ¹H-NMR- and GC-MS-based metabolomics. This study showed that the decreased lysophosphatidylcholines and increased acylcarnitines are significantly associated with increased mortality in bacterial CAP. Additionally, we found that decreased lysophosphatidylcholines and phosphatidylcholines (> 36 carbons) and increased acylcarnitines may be used to predict the prognosis of in-hospital mortality for bacterial CAP as well as the need for ICU admission and severity of bacterial CAP.

Discussion

This study demonstrates that lipid-based plasma metabolites can be used for the prognosis of 90-day mortality among patients with bacterial CAP. Moreover, lipid profiling can be utilized to identify patients with bacterial CAP who are at the highest risk of dying in hospital and who need ICU admission as well as the severity assessment of CAP.

Non-Targeted Metabolomic Profiling of Coronary Heart Disease Patients With Taohong Siwu Decoction Treatment

Traditional Chinese medicine is one of the complementary and alternative therapies to improve the prognosis of coronary heart disease (CHD). Taohong Siwu Decoction (THSWD), a classical traditional Chinese medication that promotes blood circulation, is clinically beneficial in CHD. However, the underlying mechanism of THSWD is still unclear. To comprehensively understand the material foundation of the “blood”, it is significantly important to study the differential metabolites involved in the treatment of CHD with Chinese medicinal herb promoting blood circulation in TCM theory. Hence, this study investigated the metabolic profiles of the serum in CHD patients to determine the differential metabolites between the THSWD group and the placebo group. Eleven CHD patients were recruited and divided into two groups randomly and double-blindly. Serum samples were determined by performing non-targeted ultra-performance liquid chromatography with tandem mass spectrometry-based metabolomics. Pearson’s correlation analysis was used to assess the association between identified metabolites and clinical serum indexes of CHD. Based on the result, a total of 513 metabolites were found in the serum of CHD patients, of which 27, involved in 29 metabolic pathways, were significantly different between the two groups. Among the differential metabolites, THSWD upregulated succinylcarnitine in fatty acid metabolism and 5′-methylthioadenosine in cysteine and methionine metabolism compared with the placebo group. However, THSWD downregulated pelargonic acid, involved in FA metabolism; succinate, involved in the tricarboxylic acid cycle; gluconic acid, gluconolactone, and d-glucose, involved in pentose phosphate pathway; glycerophosphocholine, involved in glycerophospholipid metabolism; 8,9-dihydroxyeicosatrienoic acid (8,9-DiHETrE), l-lysine, N-acetyl-l-aspartic acid, N-alpha-acetyl-l-asparagine, hippurate, indoxyl sulfate, and 3-ureidopropionate involved in amino acid metabolism compared with the placebo group. Moreover, succinylcarnitine, pelargonic acid, succinate, d-glucose, gluconic acid, l-lysine, N-alpha-acetyl-l-asparagine, 5′-methylthioadenosine, indoxyl sulfate, 8,9-DiHETrE, and 3-ureidopropionate were associated with total cholesterol or low-density lipoprotein. Succinylcarnitine, pelargonic acid, gluconolactone, N-acetyl-l-aspartic acid, N-alpha-acetyl-l-asparagine, hippurate, and 5′-methylthioadenosine were associated with activated partial thromboplastin time. Our findings indicated that glycerophosphocholine, 8,9-DiHETrE, 5′-methylthioadenosine, hippurate, indoxyl sulfate, and 3-ureidopropionate might constitute the partial material foundation of the “blood” in CHD patients treated with THSWD.

Physiopathological mechanisms of diaphragmatic dysfunction associated with mechanical ventilation

Ventilator-induced diaphragm dysfunction (VIDD) is the loss of diaphragmatic muscle strength'related to of mechanical ventilation, noticed during the first day or 48hours after initiating controlled mechanical ventilation. This alteration has been related to disruption on the insulin growth factor/phosphoinositol 3-kinase/kinase B protein pathway (IGF/PI3K/AKT), in addition to an overexpression of FOXO, expression of NF-kB signaling, increase function of muscular ubiquitin ligase and activation of caspasa-3. VIDD has a negative impact on quality of life, duration of mechanical ventilation, and hospitalization stance and cost. More studies are necessary to understated the process and impact of VIDD. This is a narrative review of non-systematic literature, aiming to explain the molecular pathways involved in VIDD.

Molecular Diagnostics in Pulmonary Infections

Infection of the lung parenchyma, or pneumonia, accounts for over four million deaths per year worldwide (Ferkol and Schraufnagel, Ann Am Thorac Soc 11:404–406, 2014). The condition is common, but also over-diagnosed, in part due to relatively poor laboratory and radiographic diagnostics. Indeed, we continue to rely on antiquated tools such as sputum culture and chest X-ray – the former of which lacks speed and sensitivity, and the latter specificity (Albaum et al. Chest 110:343–50, 1996). The resulting presumptive diagnoses of pneumonia lead to excessive use of empiric broad spectrum antibiotics; indeed, by some estimates, 30–70% of antibiotic prescriptions for lower respiratory tract infection are inappropriate (Kraus, PLoS One 12(3): e0174584, 2017). This approach begets microbial resistance, exposes patients to medication side effects, and puts patients at risk of potentially life-threatening complications including Clostridium difficile colitis.

To improve diagnostic certainty in patients with suspected pneumonia, we must begin to consider and implement emerging technologies for efficient and accurate characterization of host responses to infection and identification of pathogens. In this chapter, we will discuss precision diagnostics already in common practice and those poised to be, and how these tools may ultimately enable personalization in the diagnosis of pneumonia.

Metabolic Profiling Analysis of Patients With Coronary Heart Disease Undergoing Xuefu Zhuyu Decoction Treatment

Coronary heart disease (CHD) remains the leading cause of morbidity and mortality worldwide. Traditional Chinese medicine (TCM) is one of the effective complementary and alternative therapies used to improve the prognosis of CHD patients. Xuefu Zhuyu (XFZY) decoction, a classical traditional Chinese medication for regulating Qi and promoting blood circulation, has a clinical benefit in CHD; however, the underlying mechanism is not clear. Recently, it was found that the metabolites involved in amino acid metabolism and the tricarboxylic acid cycle were altered in CHD patients with Qi and Yin deficiency syndrome. To understand the material foundation of Qi, it is of great significance to study the differential metabolites involved in Qi during treatment of CHD with Qi-regulating and blood-promoting herbs. In this study, we investigated the metabolic profiles of serum in CHD patients by nontargeted metabolomics analysis to detect differential metabolites between the XFZY decoction group and placebo group. Ten CHD patients were enrolled and treated with placebo granules or XFZY decoction granules in a random and double-blind manner. Serum samples of all patients were evaluated by untargeted high-performance liquid chromatography with tandem mass spectrometry-based metabolomics. In total, 513 metabolites were detected in the serum of CHD patients, and six of these metabolites participating in seven metabolic pathways were significantly different between CHD patients treated with XFZY decoction and the placebo group. Among the six differential metabolites, FA (20:2)-H and tetracarboxylic acid (24:0), involved in fatty acid metabolism; cis-aconitic acid, which participates in the tricarboxylic acid cycle; 2-deoxy-D-glucose, involved in glucose metabolism; and N-acetylglycine, involved in amino acid metabolism, were decreased, whereas spermine, which participates in amino acid metabolism, was increased as compared with the placebo group. Our findings, combined with the perspective of biological functions, indicate that 2-deoxy-D-glucose and spermine might constitute the partial material foundation of Qi in CHD patients treated with XFZY decoction.

Inflammatory responses relate to distinct bronchoalveolar lavage lipidome in community-acquired pneumonia patients: a pilot study

Background

Community-acquired pneumonia (CAP) is a leading cause of morbidity and mortality worldwide. Antibiotics are losing their effectiveness due to the emerging infectious diseases, the scarcity of novel antibiotics, and the contributions of antibiotic misuse and overuse to resistance. Characterization of the lipidomic response to pneumonia and exploring the “lipidomic phenotype” can provide new insight into the underlying mechanisms of pathogenesis and potential avenues for diagnostic and therapeutic treatments.

Methods

Lipid profiles of bronchoalveolar lavage fluid (BALF) samples were generated through untargeted lipidomic profiling analysis using high-performance liquid chromatography with mass spectrometry (HPLC-MS). Principal component analysis (PCA) was applied to identify possible sources of variations among samples. Partitioning clustering analysis (k-means) was employed to evaluate the existence of distinct lipidomic clusters.

Results

PCA showed that BALF lipidomes differed significantly between CAP (n = 52) and controls (n = 68, including 35 healthy volunteers and 33 patients with non-infectious lung diseases); while no clear separation was found between severe CAP and non-severe CAP cases. Lactosylceramides were the most prominently elevated lipid constituent in CAP. Clustering analysis revealed three separate lipid profiles; subjects in each cluster exhibited significant differences in disease severity, incidence of hypoxemia, percentages of phagocytes in BALF, and serum concentrations of albumin and total cholesterol (all p < 0.05). In addition, SM (d34:1) was negatively related to macrophage (adjusted r = − 0.462, p < 0.0001) and PE (18:1p/20:4) was positively correlated with polymorphonuclear neutrophil (PMN) percentages of BALF (adjusted r = 0.541, p < 0.0001). The 30-day mortality did not differ amongst three clusters (p < 0.05).

Conclusions

Our data suggest that specific lower airway lipid composition is related to different intensities of host inflammatory responses, and may contribute to functionally relevant shifts in disease pathogenesis in CAP individuals. These findings argue for the need to tailor therapy based on specific lipid profiles and related inflammatory status.

Trial registration

ClinicalTrials.gov (NCT03093220). Registered on 28 March 2017 (retrospectively registered).

Electronic supplementary material

The online version of this article (10.1186/s12931-019-1028-8) contains supplementary material, which is available to authorized users.