Daily Respiratory Research Analysis
Three studies advance respiratory science and practice: (1) COPD research links low serum IgG to reduced lung bacterial diversity and delineates B-cell memory phenotypes; (2) an interrupted time series shows COVID-19 control measures suppressed influenza positivity with resurgence and altered seasonality after lifting; (3) a prospective primary care cohort finds no symptomatic benefit from systemic corticosteroids, benzonatate, or albuterol for lower respiratory tract infections.
Summary
Three studies advance respiratory science and practice: (1) COPD research links low serum IgG to reduced lung bacterial diversity and delineates B-cell memory phenotypes; (2) an interrupted time series shows COVID-19 control measures suppressed influenza positivity with resurgence and altered seasonality after lifting; (3) a prospective primary care cohort finds no symptomatic benefit from systemic corticosteroids, benzonatate, or albuterol for lower respiratory tract infections.
Research Themes
- Airway immunology and lung microbiome in COPD
- Population respiratory epidemiology and non-pharmaceutical interventions
- Pragmatic therapeutics and stewardship in primary care respiratory infections
Selected Articles
1. Associations of serum and bronchoalveolar immunoglobulins with lung microbiota diversity, B-cell memory phenotypes, and COPD morbidity and exacerbations.
In a SPIROMICS sub-cohort, lower serum IgG was associated with reduced lower-airway bacterial diversity, whereas serum IgA correlated with higher switched-memory and lower double-negative B-cell proportions in blood. BAL Ig levels were not associated with lung function or exacerbations, underscoring compartment-specific immunologic relationships.
Impact: This study links systemic immunoglobulin levels to the lung microbiome and B-cell phenotypes in COPD, offering mechanistic clues to exacerbation risk and informing vaccine or immunomodulatory strategies.
Clinical Implications: Serum IgG may serve as a surrogate for lower-airway microbial diversity and exacerbation vulnerability, suggesting potential use in risk stratification and in guiding compartment-aware vaccination approaches.
Key Findings
- Lower serum IgG was associated with reduced lung bacterial diversity (dysbiosis) in BAL microbiome data (n=107).
- Serum IgA correlated positively with switched-memory (IgD−/CD27+) B cells (β=6.06, p=0.01) and inversely with double-negative (IgD−/CD27−) B cells (β=−9.96, p=0.02).
- BAL albumin-corrected IgG and IgA levels were not associated with lung function or exacerbations.
- Mean serum IgG and IgA were 1,486.1±510.6 mg/dL and 237.7±131.6 mg/dL, respectively; albumin-corrected BAL IgG and IgA were 0.03±0.02 and 0.01±0.01 mg/dL.
Methodological Strengths
- Multicomponent assessment combining serum, BAL immunoglobulins, B-cell phenotyping, and 16S rRNA microbiome profiling.
- Use of regression models adjusting for compartment-specific Ig (albumin-corrected) and multiple diversity metrics (Faith PD, inverse Simpson, richness).
Limitations
- Observational design limits causal inference between immunoglobulins and microbiome changes.
- Subgroup analyses with varying sample sizes (e.g., serum Ig n=66, BAL microbiome n=107) may limit power and generalizability.
Future Directions: Prospective interventional studies should test whether augmenting humoral immunity (e.g., targeted vaccination) restores airway microbial diversity and reduces COPD exacerbations, and delineate compartment-specific immune targets.
RATIONALE: Immunoglobulins (Ig) protect against pathogens frequently implicated in COPD exacerbations. We previously demonstrated an association of low-normal serum IgA and IgG concentrations with prospective exacerbation risk, but responsible mechanisms are undefined. Here, we examined associations of lower respiratory tract bacterial diversity to Ig levels in serum and bronchoalveolar lavage (BAL) and to the memory phenotypes of blood and BAL B cells. METHODS: We analyzed data from phase I of SPIROMICS, an observational cohort study of smoking-related COPD. A subset of participants completed comprehensive research bronchoscopies, including analysis of BAL bacterial microbiota by 16 S rRNA gene (V4 region) sequencing and of blood and BAL B-cells by 12-color flow cytometry. In some participants, we also analyzed serum and BAL Ig levels by ELISA. We constructed linear regression models including either serum or BAL (albumin-corrected) Ig measurements as the independent variable and separate dependent variables, including B-cell subsets, BAL bacterial diversity metrics (Faith phylogenetic diversity, inverse Simpson, and richness indices), and clinical measures (FEV RESULTS: Serum IgG and IgA (n = 66 participants) were 1,486.1 ± 510.6 mg/dL [mean ± standard deviation (SD)] and 237.7 ± 131.6 mg/dL, respectively. Albumin-corrected BAL IgG and IgA (n = 117) were 0.03 ± 0.02 mg/dL and 0.01 ± 0.01 mg/dL, respectively. B-cells (n = 82) comprised 3.5 ± 3.0% of blood leukocytes. Serum IgA was associated with higher blood switched memory (IgD- CD27+) B-cell percentages (β 6.06, p = 0.01) and inversely associated with blood double-negative (IgD-CD27-) B-cell percentages (β - 9.96, p = 0.02). Available BAL microbiome data (n = 107) showed that reduced lung bacterial diversity associated with lower serum IgG, but not with serum IgA, BAL IgA, or BAL IgG concentrations. Neither BAL IgG nor IgA were associated with lung function or exacerbations. CONCLUSIONS: These results demonstrate an association of low serum IgG with reduced lung bacterial diversity, a feature of dysbiosis that may predispose to exacerbation. Defining the role of Ig in specific anatomic compartments is relevant to designing vaccine strategies.
2. Impact of COVID-19 control measures on influenza positivity among patients with acute respiratory infections, 2018-2023: an interrupted time series analysis.
In 98,244 ARI cases across two sentinel hospitals (2018–2023), COVID-19 NPIs produced an immediate reduction in influenza positivity (β = −1.75, p=0.003). After measures were lifted, influenza activity resurged with an atypical dual-peak pattern, and trends increased significantly in GAM analyses.
Impact: The study quantifies how NPIs reshape influenza transmission dynamics and seasonality, informing post-pandemic surveillance and control strategies.
Clinical Implications: Health systems should anticipate intensified and shifted seasonal influenza activity after lifting NPIs, reinforcing vaccination timing, school-based strategies for 6–17-year-olds, and flexible surge capacity planning.
Key Findings
- Total 98,244 ARI cases with an overall influenza positivity of 39.34%.
- Immediate significant decrease in positivity following NPIs (β = −1.75, p=0.003).
- Post-lifting, an unusual dual-peak seasonal pattern emerged with significant increasing trend in GAM (edf=7.00, p<0.001).
- Higher positivity in females and in the 6–17-year age group.
Methodological Strengths
- Interrupted time series with dual-model approach (beta regression and GAM) and cross-validation.
- Six-year surveillance across pre-, during-, and post-pandemic periods enabling robust temporal comparisons.
Limitations
- Data from only two sentinel hospitals may limit generalizability to other regions.
- Ecological/time-series design cannot account for all confounders (e.g., testing behaviors, co-circulating viruses).
Future Directions: Integrate viral genomics and mobility/contact data into time-series models to refine attribution, and simulate optimal timing/intensity of NPIs with vaccination in school-aged populations.
BACKGROUND: After experiencing the global COVID-19 pandemic, whether there have been new changes in the epidemiological characteristics of influenza has become a topic of great concern. This study aims to investigate the impact of implementation and lifting of COVID-19 control measures on influenza positivity among patients with acute respiratory infections (ARI) from 2018 to 2023. METHODS: The data were collected from January 2018 to December 2023 in two designated sentinel hospitals in Jinhua. We performed an interrupted time series analysis (ITSA) using a beta regression model and a generalized additive model (GAM), adopting a two-model cross-validation strategy to assess the effect of two major interventions on influenza positivity: the COVID-19 control measures implemented in early 2020 and lifted at the end of 2022. We also analyzed influenza epidemiological characteristics and seasonality before, during, and after the pandemic. RESULTS: A total of 98,244 cases were included in this study, and the overall influenza positivity rate was 39.34%. Females and the 6-17-year age group had higher positivity rates. Before the pandemic, influenza primarily showed a winter peak pattern, whereas during the pandemic, the positivity rate declined significantly with no distinct peak. After the pandemic ended, an unusual dual-peak pattern emerged. The interrupted time series analysis revealed that, following the implementation of non-pharmaceutical interventions (NPIs) in early 2020, influenza positivity immediately decreased significantly in the beta regression model (β = -1.75, p = 0.003). After the lifting of measures in late 2022, a marginally lagged increasing trend was observed in the beta regression model (β = 0.14, p = 0.096) and a significant increasing trend was found in the GAM model (edf = 7.00, p < 0.001). Seasonal effects differed between the models: the beta regression model exhibited significant annual seasonal fluctuations (sin12 = 0.67, p < 0.001), while the GAM model did not exhibit a significant association independent of the time trend. CONCLUSION: COVID-19 and its control measures substantially reduced influenza positivity rates; however, once these measures were lifted, influenza activity resurged, and its seasonal epidemic pattern changed. The intensity of influenza appeared to exceed pre-pandemic levels, underscoring the importance of NPIs in controlling respiratory infectious diseases. Strengthened surveillance and optimized strategies remain necessary to mitigate the threat of influenza in the post-pandemic era.
3. Corticosteroids, Antitussives, and Inhalers for Lower Respiratory Tract Infections in US Primary Care: A Prospective Cohort Study.
Among 718 adult outpatients with LRTI, benzonatate (23.2%), albuterol inhalers (19.1%), and systemic corticosteroids (18.6%) were widely prescribed. Propensity-matched analyses showed no reduction in cough duration or severity or unscheduled visits; systemic corticosteroids were associated with higher odds of subsequent antibiotic prescribing.
Impact: Provides real-world evidence that commonly used symptomatic therapies for LRTI lack measurable benefit and may promote antibiotic use, informing de-implementation and stewardship.
Clinical Implications: Avoid routine use of systemic corticosteroids, benzonatate, or albuterol inhalers for uncomplicated LRTI; emphasize supportive care and safety-netting while reserving antibiotics for clear bacterial indications.
Key Findings
- Benzonatate (23.2%), albuterol inhalers (19.1%), and systemic corticosteroids (18.6%) were commonly prescribed for LRTI.
- Propensity score–matched analyses found no association between these medications and reduced cough duration, reduced cough severity, or fewer unscheduled visits.
- Systemic corticosteroid use increased the likelihood of antibiotic prescribing (adjusted OR 1.37).
- Univariate data suggested that medication receipt tracked with higher baseline cough severity, indicating confounding by indication.
Methodological Strengths
- Prospective cohort design with patient-reported symptom diaries and baseline severity assessment.
- Propensity score matching to mitigate confounding by indication across treatment groups.
Limitations
- Nonrandomized design leaves residual confounding possible despite matching.
- Missing data for symptom diaries (duration/severity) in a subset may bias estimates.
Future Directions: Conduct pragmatic, adequately powered RCTs in primary care to test de-implementation strategies and evaluate patient-centered outcomes and downstream antibiotic use.
BACKGROUND: Physicians often prescribe medications other than antibiotics to address symptoms in patients with lower respiratory tract infection (LRTI), but their frequency and effectiveness have been poorly studied. OBJECTIVE: To describe use of non-antibiotic medications in adult outpatients with LRTI, and whether their use is associated with reduced duration or severity of illness. DESIGN: Prospective cohort study. PARTICIPANTS: Adult outpatients 18 to 75 years with acute cough and at least one lower or systemic respiratory symptom suspected of having a LRTI. MAIN MEASURES: The percentage of patients given non-antibiotic medications and propensity score matched analysis of association of systemic corticosteroids, benzonatate, and albuterol inhaler on duration of cough, severity of cough, receipt of an antibiotic, and need for an unscheduled follow-up visit. KEY RESULTS: Of 718 patients, 690 had baseline severity data, 517 had diary data for duration, and 499 had data for severity. Benzonatate (23.2%), an albuterol inhaler (19.1%), and a systemic corticosteroid (18.6%) were commonly prescribed. In univariate analysis, receipt of benzonatate, an albuterol inhaler, or any antitussive was associated with greater baseline severity of cough. Receipt of benzonatate was also associated with greater overall duration and severity of cough. Receipt of a systemic corticosteroid was associated with a greater likelihood of receiving an antibiotic (54.7% vs. 24.2%, p < 0.001). In the propensity score matched analysis, there was no association between receiving a systemic corticosteroid, an albuterol inhaler, or benzonatate and the duration of cough, severity of cough, or need for an unscheduled follow-up visit. Receiving a systemic corticosteroid was associated with an increased likelihood of also getting an antibiotic (adjusted odds ratio 1.37). CONCLUSIONS: Despite having no evidence of benefit and well-known harms, systemic corticosteroids and benzonatate were commonly prescribed. Adequately powered pragmatic trials in primary care settings are badly needed to provide appropriate data to guide practice.