No abstract

Recent studies of the microbiome proposed that resident microbes play a beneficial role in maintaining human health. Although lower respiratory tract disease is a leading cause of sickness and mortality, how the lung microbiome interacts with human health remains largely unknown. Here we assessed the association between the lung microbiome and host gene expression, cytokine concentration, and over 20 clinical features. Intriguingly, we found a stratified structure of the active lung microbiome which was significantly associated with bacterial biomass, lymphocyte proportion, human Th17 immune response, and COPD exacerbation frequency. These observations suggest that the microbiome plays a significant role in lung homeostasis. Not only microbial composition but also active functional elements and host immunity characteristics differed among different individuals. Such diversity may partially account for the variation in susceptibility to particular diseases.

BackgroundThis study aimed to determine whether community-acquired pneumonia (CAP) had a metabolic profile and whether this profile can be used for disease severity assessment.MethodsA total of 175 individuals including 119 CAP patients and 56 controls were enrolled and divided into two cohorts. Serum samples from a discovery cohort (n = 102, including 38 non-severe CAP, 30 severe CAP, and 34 age and sex-matched controls) were determined by untargeted ultra-high-performance liquid chromatography with tandem mass spectrometry (LC-MS/MS)-based metabolomics. Selected differential metabolites between CAP patients versus controls, and between the severe CAP group versus non-severe CAP group, were confirmed by targeted mass spectrometry assays in a validation cohort (n = 73, including 32 non-severe CAP, 19 severe CAP and 22 controls). Pearson’s correlation analysis was performed to assess relationships between the identified metabolites and clinical severity of CAP. The area under the curve (AUC), sensitivity and specificity of the metabolites for predicting the severity of CAP were also investigated.ResultsThe metabolic signature was markedly different between CAP patients and controls. Fifteen metabolites were found to be significantly dysregulated in CAP patients, which were mainly mapped to the metabolic pathways of sphingolipid, arginine, pyruvate and inositol phosphate. The alternation trends of five metabolites among the three groups including sphinganine, p-Cresol sulfate, dehydroepiandrosterone sulfate (DHEA-S), lactate and l-arginine in the validation cohort were consistent with those in the discovery cohort. Significantly lower concentrations of sphinganine, p-Cresol sulfate and DHEA-S were observed in CAP patients than in controls (p < 0.05). Serum lactate and sphinganine levels were positively correlated with confusion, urea level, respiratory rate, blood pressure, and age > 65 years (CURB-65), pneumonia severity index (PSI) and Acute Physiology and Chronic Health Evaluation II (APACHE II) scores, while DHEA-S inversely correlated with the three scoring systems. Combining lactate, sphinganine and DHEA-S as a metabolite panel for discriminating severe CAP from non-severe CAP exhibited a better AUC of 0.911 (95% confidence interval 0.825–0.998) than CURB-65, PSI and APACHE II scores.ConclusionsThis study demonstrates that serum metabolomics approaches based on the LC-MS/MS platform can be applied as a tool to reveal metabolic changes during CAP and establish a metabolite signature related to disease severity.Trial registrationClinicalTrials.gov, NCT03093220. Registered retrospectively on 28 March 2017.Electronic supplementary materialThe online version of this article (10.1186/s13054-018-2049-2) contains supplementary material, which is available to authorized users.

Study Objectives: Clinical practice guidelines recommend polysomnography (PSG) for diagnosis of obstructive sleep apnea (OSA) in patients with major comorbidities. We evaluated home sleep apnea testing (HSAT) using a type 3 portable monitor (PM, Nox-T3, Nox Medical, Reykjavik, Iceland) to diagnose OSA in adults with chronic obstructive pulmonary disease (COPD). Methods: Ninety adults with COPD (89.0% males, mean ± standard deviation age 66.5 ± 7.8 years, body mass index 27.5 ± 5.8 kg/m 2 , forced expiratory volume in the first second/forced vital capacity [FEV1/ FVC] 53.5 ± 12.4%, FEV1 54.0 ± 18.4% predicted) underwent unattended HSAT followed by an in-laboratory PSG with simultaneous PM recording. Results: Scoring hypopneas with a ≥ 4% oxygen desaturation, the apnea-hypopnea index (AHI) was 16.7 ± 20.6 events/h on HSAT, 20.0 ± 23.3 events/h on in-laboratory PM, and 21.2 ± 26.2 events/h on PSG (P < .0001). Bland-Altman analysis of AHI on HSAT versus PSG showed a mean difference (95% confidence interval) of −5.08 (−7.73, −2.42) events/h (P = .0003) and limits of agreement (± 2 standard deviations) of −30.00 to 19.85 events/h; HSAT underestimated AHI to a greater extent for more severe values (rho = −.529, P < .0001). Using an AHI ≥ 5 events/h to diagnose OSA, HSAT had 95% sensitivity, 78% specificity, 88% positive predictive value, and 89% negative predictive value compared to PSG. Mean oxygen saturation was 93.2 ± 3.7% on PSG, 91.0 ± 3.3% on in-laboratory PM, and 90.8 ± 4.0% on HSAT (P < .0001). Percentage time oxygen saturation ≤ 88% was 17.9 ± 26.4% on HSAT, 17.4 ± 25.5% on in-laboratory PM, and 10.0 ± 21.1% on PSG (P < .0001). Conclusions:The Nox-T3 PM can be used to diagnose OSA in patients with COPD but, most likely due to differences among pulse oximeters, a greater number of patients with COPD and without OSA qualified for nocturnal oxygen treatment using this PM than PSG.

Common variable immunodeficiency (CVID) belongs to the primary immunodeficiency disorders (PIDs), presenting a profound heterogeneity in phenotype and genotype, with monogenic or complex causes. Recurrent respiratory infections are the most common clinical manifestations. CVID patients can also develop various autoimmune and lymphoproliferative complications. Genetic testing such as whole exome sequencing (WES) can be utilized to investigate likely genetic defects, helping for better clinical management. We described the clinical phenotypes of three sporadic cases of CVID, who developed recurrent respiratory infections with different autoimmune and lymphoproliferative complications. WES was applied to screen disease-causing or disease-associated mutations. Two patients were identified to have monogenic disorders, with compound heterozygous mutations in LRBA for one patient and a frameshift insertion in NFKB1 for another. The third patient was identified to be a complex form of CVID. Two novel mutations were identified, respectively, in LRBA and NFKB1. A combination of clinical and genetic diagnosis can be more extensively utilized in the clinical practice due to the complexity and heterogeneity of CVID.