American journal of respiratory and critical care medicine最新文献

Rationale: The global burden of sepsis is concentrated in sub-Saharan Africa, where inciting pathogens are diverse and HIV co-infection is a major driver of poor outcomes. Biological heterogeneity inherent to sepsis in this setting is poorly defined.

Objectives: To identify dominant pathobiological signatures of sepsis in sub-Saharan Africa and their relationship to clinical phenotypes, patient outcomes, and biological classifications of sepsis identified in high-income-countries (HICs).

Methods: We analyzed two prospective cohorts of adults hospitalized with sepsis (severe infection with qSOFA score≥1) at disparate settings in Uganda (discovery cohort [Entebbe,urban], N=242; validation cohort [Tororo,rural], N=253). To identify pathobiological signatures in the discovery cohort, we applied unsupervised clustering to 173 soluble proteins reflecting key domains of the host response to severe infection. A random forest-derived classifier was used to predict signature assignment in the validation cohort.

Measurements and main results: Two signatures (Uganda Sepsis Signature [USS]-1 and USS-2) were identified in the discovery cohort, distinguished by expression of proteins involved in myeloid cell and inflammasome activation, T cell co-stimulation and exhaustion, and endothelial barrier dysfunction. A five-protein classifier (AUROC 0.97) reproduced two signatures in the validation cohort with similar biological profiles. In both cohorts, USS-2 mapped to a more severe clinical phenotype associated with HIV and related immunosuppression, severe tuberculosis, and increased risk of 30-day mortality. Substantial biological overlap was observed between USS-2 and hyperinflammatory and reactive sepsis phenotypes identified in HICs.

Conclusions: We identified prognostically-enriched pathobiological signatures among sepsis patients with diverse infections and high HIV prevalence in Uganda. Globally inclusive investigations are needed to define generalizable and context-specific mechanisms of sepsis pathobiology, with the goal of improving access to precision medicine treatment strategies.

Rationale: Volatile organic compounds (VOCs) in asthmatic breath may be associated with sputum eosinophilia. We developed a volatile biomarker signature to predict sputum eosinophilia in asthma. Methods: VOCs emitted into the space above sputum samples (headspace) from patients with severe asthma (n = 36) were collected onto sorbent tubes and analyzed using thermal desorption gas chromatography-mass spectrometry (GC-MS). Elastic net regression identified stable VOCs associated with sputum eosinophilia ⩾ 3% and generated a volatile biomarker signature. This VOC signature was validated in breath samples from: 1) patients with acute asthma according to blood eosinophilia ⩾0.3 × 10⁹cells/L or sputum eosinophilia of ⩾3% in the UK EMBER (East Midlands Breathomics Pathology Node) consortium (n = 65) and 2) U-BIOPRED-IMI (Unbiased Biomarkers in Prediction of Respiratory Disease Outcomes Innovative Medicines Initiative) consortium (n = 42). Breath samples were collected onto sorbent tubes (EMBER) or Tedlar bags (U-BIOPRED) and analyzed by GC-MS (GC × GC-MS for EMBER or GC-MS for U-BIOPRED). Measurements and Main Results: The in vitro headspace identified 19 VOCs associated with sputum eosinophilia, and the derived VOC signature yielded good diagnostic accuracy for sputum eosinophilia ⩾3% in headspace (area under the receiver operating characteristic curve [AUROC] 0.90; 95% confidence interval [CI], 0.80-0.99; P < 0.0001), correlated inversely with sputum eosinophil percentage (r_s = -0.71; P < 0.0001), and outperformed fractional exhaled nitric oxide (AUROC 0.61; 95% CI, 0.35-0.86). Analysis of exhaled breath in replication cohorts yielded a VOC signature AUROC (95% CI) for acute asthma exacerbations of 0.89 (0.76-1.0) (EMBER cohort) with sputum eosinophilia and 0.90 (0.75-1.0) in U-BIOPRED, again outperforming fractional exhaled nitric oxide in U-BIOPRED (0.62 [0.33-0.90]). Conclusions: We have discovered and provided early-stage clinical validation of a volatile biomarker signature associated with eosinophilic airway inflammation. Further work is needed to translate our discovery using point-of-care clinical sensors.