Human genetic associations of the airway microbiome in chronic obstructive pulmonary disease.

Little is known about the relationships between human genetics and the airway microbiome. Deeply sequenced airway metagenomics, by simultaneously characterizing the microbiome and host genetics, provide a unique opportunity to assess the microbiome-host genetic associations. Here we performed a co-profiling of microbiome and host genetics with the identification of over 5 million single nucleotide polymorphisms (SNPs) through deep metagenomic sequencing in sputum of 99 chronic obstructive pulmonary disease (COPD) and 36 healthy individuals. Host genetic variation was the most significant factor associated with the microbiome except for geography and disease status, with its top 5 principal components accounting for 12.11% of the microbiome variability. Within COPD individuals, 113 SNPs mapped to candidate genes reported as genetically associated with COPD exhibited associations with 29 microbial species and 48 functional modules (P < 1 × 10-5), where Streptococcus salivarius exhibits the strongest association to SNP rs6917641 in TBC1D32 (P = 9.54 × 10-8). Integration of concurrent host transcriptomic data identified correlations between the expression of host genes and their genetically-linked microbiome features, including NUDT1, MAD1L1 and Veillonella parvula, TTLL9 and Stenotrophomonas maltophilia, and LTA4H and Haemophilus influenzae. Mendelian randomization analyses revealed a potential causal link between PARK7 expression and microbial type III secretion system, and a genetically-mediated association between COPD and increased relative abundance of airway Streptococcus intermedius. These results suggest a previously underappreciated role of host genetics in shaping the airway microbiome and provide fresh hypotheses for genetic-based host-microbiome interactions in COPD.

Predicting Acute Exacerbation Phenotype in Chronic Obstructive Pulmonary Disease Patients Using VGG-16 Deep Learning.

INTRODUCTION: Exacerbations of chronic obstructive pulmonary disease (COPD) have a significant impact on hospitalizations, morbidity, and mortality of patients. This study aimed to develop a model for predicting acute exacerbation in COPD patients (AECOPD) based on deep-learning (DL) features. METHODS: We performed a retrospective study on 219 patients with COPD who underwent inspiratory and expiratory HRCT scans. By recording the acute respiratory events of the previous year, these patients were further divided into non-AECOPD group and AECOPD group according to the presence of acute exacerbation events. Sixty-nine quantitative CT (QCT) parameters of emphysema and airway were calculated by NeuLungCARE software, and 2,000 DL features were extracted by VGG-16 method. The logistic regression method was employed to identify AECOPD patients, and 29 patients of external validation cohort were used to access the robustness of the results. RESULTS: The model 3-B achieved an area under the receiver operating characteristic curve (AUC) of 0.933 and 0.865 in the testing cohort and external validation cohort, respectively. Model 3-I obtained AUC of 0.895 in the testing cohort and AUC of 0.774 in the external validation cohort. Model 7-B combined clinical characteristics, QCT parameters, and DL features achieved the best performance with an AUC of 0.979 in the testing cohort and demonstrating robust predictability with an AUC of 0.932 in the external validation cohort. Likewise, model 7-I achieved an AUC of 0.938 and 0.872 in the testing cohort and external validation cohort, respectively. CONCLUSIONS: DL features extracted from HRCT scans can effectively predict acute exacerbation phenotype in COPD patients.

Low human beta-defensin-2 levels in the sputum of COPD patients are associated with the risk of exacerbations.

RATIONALE: Chronic obstructive pulmonary disease (COPD) is a complicated chronic inflammatory disease. It is important to investigate the characteristics of acute exacerbation of COPD to develop new therapeutic strategies. OBJECTIVE: This study aimed to determine the relationship between the human beta-defensin-2 (hBD-2) levels and aggravation of COPD. METHODS: We detected the sputum hBD-2 level of 254 patients from Guangzhou, China, for 2 years. The study participants were categorized into the COPD group (n = 203, GOLD 0-4) and the control group (n = 51, 40-79 years old). At baseline, 12th month, and 24th month, we detected the sputum hBD-2 level and levels of cytokines, such as CXCL10, CXCL11, and IFN. RESULTS: At baseline, there were no significant differences in the sputum and serum hBD-2 levels between the patients and the controls. However, the sputum hBD-2 levels of patients who had at least one symptom aggravation over the next 2 years were significantly lower than those of patients without any exacerbations (1130.9 ± 858.4 pg/mL vs. 2103.7 ± 1294.2 pg/mL, respectively; p = 0.001). Nevertheless, there were no statistically significant differences in the sputum hBD-2 levels between patients (no aggravation history) and controls (2084.9 ± 1317.6 pg/mL vs. 2152.5 ± 1251.6 pg/mL, respectively; p = 0.626). We used a logistic regression model to assess the relationship between aggravation and sputum hBD-2 levels. Interestingly, we found that low hBD-2 level (< 1000 pg/mL) was significantly associated with exacerbations. Specifically, patients with low hBD-2 levels were more likely to experience exacerbations in the next 12 months (0.333 vs. 0.117; p = 0.001). Moreover, we compared the hBD-2 levels between controls and patients with GOLD 3-4 and found that participants with bacteria (+) and/or viruses (+) had an association between hBD-2 level and disease severity (p = 0.02). CONCLUSION: Patients at risk of exacerbations are more likely to have lower sputum hBD-2 levels. These results have important implications for future therapies for COPD.

Janus kinases inhibitors for coronavirus disease-2019: A pairwise and Bayesian network meta-analysis.

Background: JAK (Janus kinases) inhibitors have been proposed as a promising treatment option for the coronavirus disease-2019 (COVID-19). However, the benefits of JAK inhibitors and the optimum thereof for COVID-19 have not been adequately defined. Methods: Databases were searched from their inception dates to 17 June 2022. Eligible studies included randomized controlled trials and observational studies. Extracted data were analyzed by pairwise and network meta-analysis. The primary outcome was the coefficient of mortality. Results: Twenty-eight studies of 8,206 patients were included and assessed qualitatively (modified Jadad and Newcastle-Ottawa Scale scores). A pairwise meta-analysis revealed that JAK inhibitors effectively reduced the mortality (OR = 0.54; 95% CI: 0.46-0.63; P < 0.00001; I 2 = 32%) without increasing the risk of adverse events (OR = 1.02; 95% CI: 0.88-1.18; P = 0.79; I 2 = 12%). In a network meta-analysis, clinical efficacy benefits were seen among different types of JAK inhibitors (baricitinib, ruxolitinib, and tofacitinib) without the observation of a declined incidence of adverse events. The assessment of rank probabilities indicated that ruxolitinib presented the greatest likelihood of benefits regarding mortality and adverse events. Conclusion: JAK inhibitors appear to be a promising treatment for COVID-19 concerning reducing mortality, and they do not increase the risk of adverse events vs. standard of care. A network meta-analysis suggests that mortality benefits are associated with specific JAK inhibitors, and among these, ruxolitinib presents the greatest likelihood of having benefits for mortality and adverse events. Systematic review registration: [www.crd.york.ac.uk/prospero], identifier [CRD42022343338].

Efficacy of positive airway pressure therapy and high flow nasal cannula oxygen in acute cardiogenic pulmonary oedema: A protocol for systematic review and network meta-analysis.

Introduction: Positive airway pressure (PAP) therapy is currently the first-line respiratory support technique for acute respiratory failure (ARF) due to acute cardiogenic pulmonary edema (ACPE), but the accompanied adverse events and patient's intolerance with treatment in some cases limited its use in clinical practice. Some recent trials indicated that high-flow nasal cannula oxygen (HFNO) is a promising alternative to PAP therapy. In order to choose the optimum treatment for patients with ACPE, this network meta-analysis will firstly compares the efficacy of HFNO, PAP, and conventional oxygen therapy (COT). Methods and analysis: The Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols 2015 statement and its extension for network meta-analysis will be followed in the conduct of this investigation. We will examine these databases: PubMed, EMBASE, Cochrane Central Register of Controlled Trials and Web of Science. The ClinicalTrials.gov and World Health Organization International Clinical Trials Registry Platform Search Portal will be used to search ongoing trials. Only randomized controlled trials meeting the eligibility criteria will be included. Through the Cochrane Collaboration's tool, the included studies' risk of bias will be assessed. The pairwise meta-analysis will be performed with RevMan 5.4.1 software. A Bayesian network meta-analysis will use random-effects models to derive odds ratios for the treatment effects of all interventions compared to each other using R software (version 3.6.1), and the rjags and gemtc packages. The Q statistic and I2 index will be used for investigating the heterogeneity, and subgroup analysis or sensitivity analysis will be used to explore the source of heterogeneity. In addition, the Grading of Recommendations Assessment, Development and Evaluation system will be used to inspect the quality of evidence.