Respiratory viruses are associated with serum metabolome among infants hospitalized for bronchiolitis: A multicenter study

Asthma Inception: Epidemiologic Risk Factors and Natural History Across the Life Course

Asthma is a descriptive label for an obstructive inflammatory disease in the lower airways manifesting with symptoms including breathlessness, cough, difficulty in breathing, and wheezing. From a clinician's point of view, asthma symptoms can commence at any age, although most patients with asthma-regardless of their age of onset-seem to have had some form of airway problems during childhood. Asthma inception and related pathophysiologic processes are therefore very likely to occur early in life, further evidenced by recent lung physiologic and mechanistic research. Herein, we present state-of-the-art updates on the role of genetics and epigenetics, early viral and bacterial infections, immune response, and pathophysiology, as well as lifestyle and environmental exposures, in asthma across the life course. We conclude that early environmental insults in genetically vulnerable individuals inducing abnormal, pre-asthmatic airway responses are key events in asthma inception, and we highlight disease heterogeneity across ages and the potential shortsightedness of treating all patients with asthma using the same treatments. Although there are no interventions that, at present, can modify long-term outcomes, a precision-medicine approach should be implemented to optimize treatment and tailor follow-up for all patients with asthma.

Metabolomics in pediatric lower respiratory tract infections and sepsis: a literature review

Lower respiratory tract infections (LRTIs) are a leading cause of morbidity and mortality in children. The ability of healthcare providers to diagnose and prognose LRTIs in the pediatric population remains a challenge, as children can present with similar clinical features regardless of the underlying pathogen or ultimate severity. Metabolomics, the large-scale analysis of metabolites and metabolic pathways offers new tools and insights that may aid in diagnosing and predicting the outcomes of LRTIs in children. This review highlights the latest literature on the clinical utility of metabolomics in providing care for children with bronchiolitis, pneumonia, COVID-19, and sepsis. IMPACT: This article summarizes current metabolomics approaches to diagnosing and predicting the course of pediatric lower respiratory infections. This article highlights the limitations to current metabolomics research and highlights future directions for the field.

Combining Semi-Targeted Metabolomics and Machine Learning to Identify Metabolic Alterations in the Serum and Urine of Hospitalized Patients with COVID-19

Viral infections cause metabolic dysregulation in the infected organism. The present study used metabolomics techniques and machine learning algorithms to retrospectively analyze the alterations of a broad panel of metabolites in the serum and urine of a cohort of 126 patients hospitalized with COVID-19. Results were compared with those of 50 healthy subjects and 45 COVID-19-negative patients but with bacterial infectious diseases. Metabolites were analyzed by gas chromatography coupled to quadrupole time-of-flight mass spectrometry. The main metabolites altered in the sera of COVID-19 patients were those of pentose glucuronate interconversion, ascorbate and fructose metabolism, nucleotide sugars, and nucleotide and amino acid metabolism. Alterations in serum maltose, mannonic acid, xylitol, or glyceric acid metabolites segregated positive patients from the control group with high diagnostic accuracy, while succinic acid segregated positive patients from those with other disparate infectious diseases. Increased lauric acid concentrations were associated with the severity of infection and death. Urine analyses could not discriminate between groups. Targeted metabolomics and machine learning algorithms facilitated the exploration of the metabolic alterations underlying COVID-19 infection, and to identify the potential biomarkers for the diagnosis and prognosis of the disease.

Nasopharyngeal lipidomic endotypes of infants with bronchiolitis and risk of childhood asthma: a multicentre prospective study

BACKGROUND

Bronchiolitis is the leading cause of hospitalisation of US infants and an important risk factor for childhood asthma. Recent evidence suggests that bronchiolitis is clinically heterogeneous. We sought to derive bronchiolitis endotypes by integrating clinical, virus and lipidomics data and to examine their relationship with subsequent asthma risk.

METHODS

This is a multicentre prospective cohort study of infants (age <12 months) hospitalised for bronchiolitis. We identified endotypes by applying clustering approaches to clinical, virus and nasopharyngeal airway lipidomic data measured at hospitalisation. We then determined their longitudinal association with the risk for developing asthma by age 6 years by fitting a mixed-effects logistic regression model. To account for multiple comparisons of the lipidomics data, we computed the false discovery rate (FDR). To understand the underlying biological mechanism of the endotypes, we also applied pathway analyses to the lipidomics data.

RESULTS

Of 917 infants with bronchiolitis (median age, 3 months), we identified clinically and biologically meaningful lipidomic endotypes: (A) cinicalclassiclipidmixed (n=263), (B) clinicalseverelipidsphingolipids-high (n=281), (C) clinicalmoderatelipidphospholipids-high (n=212) and (D) clinicalatopiclipidsphingolipids-low (n=161). Endotype A infants were characterised by 'classic' clinical presentation of bronchiolitis. Profile D infants were characterised by a higher proportion of parental asthma, IgE sensitisation and rhinovirus infection and low sphingolipids (eg, sphingomyelins, ceramides). Compared with endotype A, profile D infants had a significantly higher risk of asthma (22% vs 50%; unadjusted OR, 3.60; 95% CI 2.31 to 5.62; p<0.001). Additionally, endotype D had a significantly lower abundance of polyunsaturated fatty acids (eg, docosahexaenoic acid; FDR=0.01). The pathway analysis revealed that sphingolipid metabolism pathway was differentially expressed in endotype D (FDR=0.048).

CONCLUSIONS

In this multicentre prospective cohort study of infants with bronchiolitis, integrated clustering of clinical, virus and lipidomic data identified clinically and biologically distinct endotypes that have a significantly differential risk for developing asthma.Delete.

Proteomics endotyping of infants with severe bronchiolitis and risk of childhood asthma

BACKGROUND

Bronchiolitis is the leading cause of hospitalization in U.S. infants and a major risk factor for childhood asthma. Growing evidence supports clinical heterogeneity within bronchiolitis. We aimed to identify endotypes of infant bronchiolitis by integrating clinical, virus, and serum proteome data, and examine their relationships with asthma development.

METHODS

This is a multicenter prospective cohort study of infants hospitalized for physician-diagnosis of bronchiolitis. We identified bronchiolitis endotypes by applying unsupervised machine learning (clustering) approaches to integrated clinical, virus (respiratory syncytial virus [RSV], rhinovirus [RV]), and serum proteome data measured at hospitalization. We then examined their longitudinal association with the risk for developing asthma by age 6 years.

RESULTS

In 140 infants hospitalized with bronchiolitis, we identified three endotypes: (1) clinicalatopic virusRV proteomeNFκB-dysregulated , (2) clinicalnon-atopic virusRSV/RV proteomeTNF-dysregulated , and (3) clinicalclassic virusRSV proteomeNFκB/TNF-regulated endotypes. Endotype 1 infants were characterized by high proportion of IgE sensitization and RV infection. These endotype 1 infants also had dysregulated NFκB pathways (FDR < 0.001) and significantly higher risks for developing asthma (53% vs. 22%; adjOR 4.04; 95% CI, 1.49-11.0; p = 0.006), compared with endotype 3 (clinically resembling "classic" bronchiolitis). Likewise, endotype 2 infants were characterized by low proportion of IgE sensitization and high proportion of RSV or RV infection. These endotype 2 infants had dysregulated tumor necrosis factor (TNF)-mediated signaling pathway (FDR <0.001) and significantly higher risks for developing asthma (44% vs. 22%; adjOR 2.71; 95% CI, 1.03-7.11, p = 0.04).

CONCLUSION

In this multicenter cohort, integrated clustering of clinical, virus, and proteome data identified biologically distinct endotypes of bronchiolitis that have differential risks of asthma development.

Integrated relationship of nasopharyngeal airway host response and microbiome associates with bronchiolitis severity

Bronchiolitis is a leading cause of infant hospitalizations but its immunopathology remains poorly understood. Here we present data from 244 infants hospitalized with bronchiolitis in a multicenter prospective study, assessing the host response (transcriptome), microbial composition, and microbial function (metatranscriptome) in the nasopharyngeal airway, and associate them with disease severity. We investigate individual associations with disease severity identify host response, microbial taxonomical, and microbial functional modules by network analyses. We also determine the integrated relationship of these modules with severity. Several modules are significantly associated with risks of positive pressure ventilation use, including the host-type I interferon, neutrophil/interleukin-1, T cell regulation, microbial-branched-chain amino acid metabolism, and nicotinamide adenine dinucleotide hydrogen modules. Taken together, we show complex interplays between host and microbiome, and their contribution to disease severity.

Association of Nasopharyngeal and Serum Glutathione Metabolism with Bronchiolitis Severity and Asthma Risk: A Prospective Multicenter Cohort Study

Infants hospitalized for bronchiolitis are at high risk for asthma. Glutathione-related metabolites may antagonize oxidative stress, which induces airway injuries in respiratory infection and subsequent airway remodeling. However, little is known about the relationship of glutathione-related metabolites with bronchiolitis severity and the risk of asthma. In a multicenter prospective observational cohort study of infants hospitalized for bronchiolitis, we measured nasopharyngeal and serum glutathione-related metabolites by using liquid chromatography−tandem mass spectrometry. We then examined their association with bronchiolitis severity (defined by positive pressure ventilation (PPV) use). We also identified severity-related glutathione-related metabolite signatures and examined their association with asthma at age 6 years. In 1013 infants, we identified 12 nasopharyngeal and 10 serum glutathione-related metabolites. In the multivariable models, lower relative abundances of seven metabolites, e.g., substrates of glutathione, including cysteine (adjOR 0.21, 95%CI 0.06−0.76), glycine (adjOR 0.25, 95%CI 0.07−0.85), and glutamate (adjOR 0.25, 95%CI 0.07−0.88), were significantly associated with PPV use (all FDR < 0.05). These associations were consistent with serum glutathione-related metabolites. The nasopharyngeal glutathione-related metabolite signature was also associated with a significantly higher risk of asthma (adjOR 0.90, 95%CI 0.82−0.99, p = 0.04). In infants hospitalized for bronchiolitis, glutathione-related metabolites were associated with bronchiolitis severity and asthma risk.

Persistence of Metabolomic Changes in Patients during Post-COVID Phase: A Prospective, Observational Study

Several relatively recently published studies have shown changes in plasma metabolites in various viral diseases such as Zika, Dengue, RSV or SARS-CoV-1. The aim of this study was to analyze the metabolome profile of patients during acute COVID-19 approximately one month after the acute infection and to compare these results with healthy (SARS-CoV-2-negative) controls. The metabolome analysis was performed by NMR spectroscopy from the peripheral blood of patients and controls. The blood samples were collected on 3 different occasions (at admission, during hospitalization and on control visit after discharge from the hospital). When comparing sample groups (based on the date of acquisition) to controls, there is an indicative shift in metabolomics features based on the time passed after the first sample was taken towards controls. Based on the random forest algorithm, there is a strong discriminatory predictive value between controls and different sample groups (AUC equals 1 for controls versus samples taken at admission, Mathew correlation coefficient equals 1). Significant metabolomic changes persist in patients more than a month after acute SARS-CoV-2 infection. The random forest algorithm shows very strong discrimination (almost ideal) when comparing metabolite levels of patients in two various stages of disease and during the recovery period compared to SARS-CoV-2-negative controls.

Human Serum Metabolites as Potential Mediators from Type 2 Diabetes and Obesity to COVID-19 Severity and Susceptibility: Evidence from Mendelian Randomization Study

Obesity, type 2 diabetes (T2D), and severe coronavirus disease 2019 (COVID-19) are closely associated. The aim of this study was to elucidate the casual and mediating relationships of human serum metabolites on the pathways from obesity/T2D to COVID-19 using Mendelian randomization (MR) techniques. We performed two-sample MR to study the causal effects of 309 metabolites on COVID-19 severity and susceptibility, based on summary statistics from genome-wide association studies (GWAS) of metabolites (n = 7824), COVID-19 phenotypes (n = 2,586,691), and obesity (n = 322,154)/T2D traits (n = 898,130). We conducted two-sample network MR analysis to determine the mediating metabolites on the causal path from obesity/T2D to COVID-19 phenotypes. We used multivariable MR analysis (MVMR) to discover causal metabolites independent of body mass index (BMI). Our MR analysis yielded four causal metabolites that increased the risk of severe COVID-19, including 2-stearoylglycerophosphocholine (OR 2.15; 95% CI 1.48–3.11), decanoylcarnitine (OR 1.32; 95% CI 1.17–1.50), thymol sulfate (OR 1.20; 95% CI 1.10–1.30), and bradykinin-des-arg(9) (OR 1.09; 95% CI 1.05–1.13). One significant mediator, gamma-glutamyltyrosine, lay on the causal path from T2D/obesity to severe COVID-19, with 16.67% (0.64%, 32.70%) and 6.32% (1.76%, 10.87%) increased risk, respectively, per one-standard deviation increment of genetically predicted T2D and BMI. Our comprehensive MR analyses identified credible causative metabolites, mediators of T2D and obesity, and obesity-independent causative metabolites for severe COVID-19. These biomarkers provide a novel basis for mechanistic studies for risk assessment, prognostication, and therapeutic purposes in COVID-19.

Identifying and predicting severe bronchiolitis profiles at high risk for developing asthma: Analysis of three prospective cohorts

BACKGROUND

Bronchiolitis is the leading cause of infants hospitalization in the U.S. and Europe. Additionally, bronchiolitis is a major risk factor for the development of childhood asthma. Growing evidence suggests heterogeneity within bronchiolitis. We sought to identify distinct, reproducible bronchiolitis subgroups (profiles) and to develop a decision rule accurately predicting the profile at the highest risk for developing asthma.

METHODS

In three multicenter prospective cohorts of infants (age < 12 months) hospitalized for bronchiolitis in the U.S. and Finland (combined n = 3081) in 2007-2014, we identified clinically distinct bronchiolitis profiles by using latent class analysis. We examined the association of the profiles with the risk for developing asthma by age 6-7 years. By performing recursive partitioning analyses, we developed a decision rule predicting the profile at highest risk for asthma, and measured its predictive performance in two separate cohorts.

FINDINGS

We identified four bronchiolitis profiles (profiles A-D). Profile A (n = 388; 13%) was characterized by a history of breathing problems/eczema and non-respiratory syncytial virus (non-RSV) infection. In contrast, profile B (n = 1064; 34%) resembled classic RSV-induced bronchiolitis. Profile C (n = 993; 32%) was comprised of the most severely ill group. Profile D (n = 636; 21%) was the least-ill group. Profile A infants had a significantly higher risk for asthma, compared to profile B infants (38% vs. 23%, adjusted odds ratio [adjOR] 2⋅57, 95%confidence interval [CI] 1⋅63-4⋅06). The derived 4-predictor (RSV infection, history of breathing problems, history of eczema, and parental history of asthma) decision rule strongly predicted profile A-e.g., area under the curve [AUC] of 0⋅98 (95%CI 0⋅97-0⋅99), sensitivity of 1⋅00 (95%CI 0⋅96-1⋅00), and specificity of 0⋅90 (95%CI 0⋅89-0⋅93) in a validation cohort.

INTERPRETATION

In three prospective cohorts of infants with bronchiolitis, we identified clinically distinct profiles and their longitudinal relationship with asthma risk. We also derived and validated an accurate prediction rule to determine the profile at highest risk. The current results should advance research into the development of profile-specific preventive strategies for asthma.

Plasma Metabolomic Profiles in Recovered COVID-19 Patients without Previous Underlying Diseases 3 Months After Discharge

BACKGROUND

It remains unclear whether discharged COVID-19 patients have fully recovered from severe complications, including the differences in the post-infection metabolomic profiles of patients with different disease severities.

METHODS

COVID-19-recovered patients, who had no previous underlying diseases and were discharged from Wuhan Union Hospital for 3 months, and matched healthy controls (HCs) were recruited in this prospective cohort study. We examined the blood biochemical indicators, cytokines, lung computed tomography scans, including 39 HCs, 18 recovered asymptomatic (RAs), 34 recovered moderate (RMs), and 44 recovered severe/ critical patients (RCs). A liquid chromatography-mass spectrometry-based metabolomics approach was employed to profile the global metabolites of fasting plasma of these participants.

RESULTS

Clinical data and metabolomic profiles suggested that RAs recovered well, but some clinical indicators and plasma metabolites in RMs and RCs were still abnormal as compared with HCs, such as decreased taurine, succinic acid, hippuric acid, some indoles, and lipid species. The disturbed metabolic pathway mainly involved the tricarboxylic cycle, purine, and glycerophospholipid metabolism. Moreover, metabolite alterations differ between RMs and RCs when compared with HCs. Correlation analysis revealed that many differential metabolites were closely associated with inflammation and the renal, pulmonary, heart, hepatic, and coagulation system functions.

CONCLUSION

We uncovered metabolite clusters pathologically relevant to the recovery state in discharged COVID-19 patients which may provide new insights into the pathogenesis of potential organ damage in recovered patients.

Integrated associations of nasopharyngeal and serum metabolome with bronchiolitis severity and asthma: A multicenter prospective cohort study

BACKGROUND

While infant bronchiolitis contributes to substantial acute (eg, severity) and chronic (eg, asthma development) morbidities, its pathobiology remains uncertain. We examined the integrated relationships of local (nasopharyngeal) and systemic (serum) responses with bronchiolitis morbidities.

METHODS

In a multicenter prospective cohort study of infants hospitalized for bronchiolitis, we applied a network analysis approach to identify distinct networks (modules)-clusters of densely interconnected metabolites-of the nasopharyngeal and serum metabolome. We examined their individual and integrated relationships with acute severity (defined by positive pressure ventilation [PPV] use) and asthma development by age 5 years.

RESULTS

In 140 infants, we identified 285 nasopharyngeal and 639 serum metabolites. Network analysis revealed 7 nasopharyngeal and 8 serum modules. At the individual module level, nasopharyngeal-amino acid, tricarboxylic acid (TCA) cycle, and carnitine modules were associated with higher risk of PPV use (r > .20; P < .001), while serum-carnitine, amino acid, and glycerophosphorylcholine (GPC)/glycerophosphorylethanolamine (GPE) modules were associated with lower risk (all r < -.20; P < .05). The integrated analysis for PPV use revealed consistent findings-for example, nasopharyngeal-TCA (adjOR: 2.87, 95% CI: 1.68-12.2) and serum-GPC/GPE (adjOR: 0.54, 95% CI: 0.38-0.80) modules-and an additional module-serum-glucose-alanine cycle module (adjOR: 0.69, 95% CI: 0.56-0.86). With asthma risk, there were no individual associations, but there were integrated associations (eg, nasopharyngeal-carnitine module; adjOR: 1.48, 95% CI: 1.11-1.99).

CONCLUSION

In infants with bronchiolitis, we found integrated relationships of local and systemic metabolome networks with acute and chronic morbidity. Our findings advance research into the complex interplay among respiratory viruses, local and systemic response, and disease pathobiology in infants with bronchiolitis.

Predicting the development of adverse cardiac events in patients with hypertrophic cardiomyopathy using machine learning

BACKGROUND

Only a subset of patients with hypertrophic cardiomyopathy (HCM) develop adverse cardiac events - e.g., end-stage heart failure, cardiovascular death. Current risk stratification methods are imperfect, limiting identification of high-risk patients with HCM. Our aim was to improve the prediction of adverse cardiac events in patients with HCM using machine learning methods.

METHODS

We applied modern machine learning methods to a prospective cohort of adults with HCM. The outcome was a composite of death due to heart failure, heart transplant, and sudden death. As the reference model, we constructed logistic regression model using known predictors. We determined 20 predictive characteristics based on random forest classification and a priori knowledge, and developed 4 machine learning models. Results Of 183 patients in the cohort, the mean age was 53 (SD = 17) years and 45% were female. During the median follow-up of 2.2 years (interquartile range, 0.6-3.8), 33 subjects (18%) developed an outcome event, the majority of which (85%) was heart transplant. The predictive accuracy of the reference model was 73% (sensitivity 76%, specificity 72%) while that of the machine learning model was 85% (e.g., sensitivity 88%, specificity 84% with elastic net regression). All 4 machine learning models significantly outperformed the reference model - e.g., area under the receiver-operating-characteristic curve 0.79 with the reference model vs. 0.93 with elastic net regression (p < 0.001).

CONCLUSIONS

Compared with conventional risk stratification, the machine learning models demonstrated a superior ability to predict adverse cardiac events. These modern machine learning methods may enhance identification of high-risk HCM subpopulations.