Breath Biomarkers of Influenza Infection

Abstract

Background: Volatile organic compounds (VOCs) are produced systemically due to varied physiological states such as oxidative stress and are excreted through the lungs. Benchtop and preliminary clinical data suggest that breath testing may be a useful diagnostic modality for viral respiratory tract infections.

Methods: Patients with influenza-like illness (ILI) presenting to a single clinic in San Antonio, Texas, from 3/2017 to 3/2019 submitted a 2-minute breath sample in addition to a nasopharyngeal swab collected for polymerase chain reaction (PCR) assay for respiratory pathogens. VOCs were assayed with gas chromatography-mass spectrometry (GC-MS), and data were analyzed to identify breath VOC biomarkers that discriminated between ILI patients with and without a polymerase chain reaction (PCR) assay that was positive for influenza.

Results: Demographic, clinical, PCR, and breath data were available for 237 episodes of ILI, among which 32 episodes (13.5%) were PCR positive for influenza. Twenty candidate VOCs identified patients with influenza with greater than random accuracy. A predictive algorithm using 4 candidate biomarkers identified this group with 78% accuracy (74% sensitivity, 70% specificity). Based on their mass spectra, most of these biomarkers were n-alkane derivatives, consistent with products of oxidative stress.

Conclusions: A breath test for VOC biomarkers accurately identified ILI patients with PCR-proven influenza. These findings bolster those of others that a rapid, accurate, universal point-of-care influenza diagnostic test based on assay of exhaled-breath VOCs may be feasible. The next step will be a study of patients with ILI using a simplified method of breath collection that would facilitate translation for use in clinical practice.

Keywords: breath test; diagnosis; influenza; polymerase chain reaction PCR; respiratory tract infection; volatile organic compound VOC.

Figure 1.

ILI with influenza infection vs ILI without influenza infection. A, Monte Carlo analysis of breath VOC biomarkers. Multiple Monte Carlo simulations were employed to identify chromatogram segments that distinguished participants with influenza from those without with greater than random accuracy. The average random behavior of each chromatogram segment was determined by randomly assigning subjects to the “influenza” or “no influenza” group and performing 40 estimates of the C-statistic (AUC of the ROC curve). For any given value of the C-statistic, it was then possible to identify the chromatogram segments that exhibited greater diagnostic accuracy with correct assignment than with multiple random assignments. This figure shows that the random assignment curve fell to 0 where the C-statistic ∼0.7. However, about 20 chromatogram segments exhibited greater than random diagnostic accuracy, thereby fulfilling the requirements of true biomarkers. These segments were entered into the multivariate predictive algorithm. B, Effect of number of VOCs in the algorithm on predictive accuracy. This graph displays variation in AUC of the ROC curve as a function of the number of biomarkers in the algorithm. The algorithm achieved maximal predictive accuracy (∼78%) with 4 biomarkers (chromatogram segments) in the model, and addition of more biomarkers to the algorithm did not significantly improve its performance. C, ROC curve. This curve displays the sensitivity (true-positives) vs 1-specificity (true-negatives) of the breath test utilizing the algorithm with 4 biomarkers. Test accuracy was 78% (ie, the C-statistic, or AUC of the ROC curve). With a cutoff point at the “shoulder” of the ROC curve, the test had ∼74% sensitivity and ∼70% specificity. Abbreviations: AUC, area under the curve; ILI, influenza-like illness; ROC, receiver operating characteristic; VOCs, volatile organic compounds.

Figure 2.

ROC curves for ILI with influenza infection vs ILI with no virus detected and ILI with influenza infection vs ILI with any other virus detected. Abbreviations: AUC, area under the curve; ILI, influenza-like illness; ROC, receiver operating characteristic.