Prediction of Acute COPD Exacerbation in the Swiss Multicenter COPD Cohort Study (TOPDOCS) by Clinical Parameters, Medication Use, and Immunological Biomarkers.

BACKGROUND AND OBJECTIVE: Whether immunological biomarkers combined with clinical characteristics measured during an exacerbation-free period are predictive of acute exacerbation of chronic obstructive pulmonary disease (AECOPD) frequency and severity is unknown. METHOD: We measured immunological biomarkers and clinical characteristics in 271 stable chronic obstructive pulmonary disease (COPD) patients (67% male, mean age 63 years) from "The Obstructive Pulmonary Disease Outcomes Cohort of Switzerland" cohort on a single occasion. One-year follow-up data were available for 178 patients. Variables independently associated with AECOPD frequency and severity were identified by multivariable regression analyses. Receiver operating characteristic analysis was used to obtain optimal cutoff levels and measure the area under the curve (AUC) in order to assess if baseline data can be used to predict future AECOPD. RESULTS: Higher number of COPD medications (adjusted incident rate ratio [aIRR] 1.17) and platelet count (aIRR 1.03), and lower FEV1% predicted (aIRR 0.84) and IgG2 (aIRR 0.84) were independently associated with AECOPD frequency in the year before baseline. Optimal cutoff levels for experiencing frequent (>1) AECOPD were ≥3 COPD medications (AUC = 0.72), FEV1 ≤40% predicted (AUC = 0.72), and IgG2 ≤2.6 g/L (AUC = 0.64). The performance of a model using clinical and biomarker parameters to predict future, frequent AECOPD events in the same patients was fair (AUC = 0.78) but not superior to a model using only clinical parameters (AUC = 0.79). The IFN-lambda rs8099917GG-genotype was more prevalent in patients who had severe AECOPD. CONCLUSIONS: Clinical and biomarker parameters assessed at a single point in time correlated with the frequency of AECOPD events during the year before and the year after assessment. However, only clinical parameters had fair discriminatory power in identifying patients likely to experience frequent AECOPD.

Real-Time Monitoring of Metabolism during Exercise by Exhaled Breath.

Continuous monitoring of metabolites in exhaled breath has recently been introduced as an advanced method to allow non-invasive real-time monitoring of metabolite shifts during rest and acute exercise bouts. The purpose of this study was to continuously measure metabolites in exhaled breath samples during a graded cycle ergometry cardiopulmonary exercise test (CPET), using secondary electrospray high resolution mass spectrometry (SESI-HRMS). We also sought to advance the research area of exercise metabolomics by comparing metabolite shifts in exhaled breath samples with recently published data on plasma metabolite shifts during CPET. We measured exhaled metabolites using SESI-HRMS during spiroergometry (ramp protocol) on a bicycle ergometer. Real-time monitoring through gas analysis enabled us to collect high-resolution data on metabolite shifts from rest to voluntary exhaustion. Thirteen subjects participated in this study (7 female). Median age was 30 years and median peak oxygen uptake (VO2max) was 50 mL·/min/kg. Significant changes in metabolites (n = 33) from several metabolic pathways occurred during the incremental exercise bout. Decreases in exhaled breath metabolites were measured in glyoxylate and dicarboxylate, tricarboxylic acid cycle (TCA), and tryptophan metabolic pathways during graded exercise. This exploratory study showed that selected metabolite shifts could be monitored continuously and non-invasively through exhaled breath, using SESI-HRMS. Future studies should focus on the best types of metabolites to monitor from exhaled breath during exercise and related sources and underlying mechanisms.