Asthma affects an estimated 363 million people worldwide and caused 442,000 deaths in 2023 alone, according to the World Health Organization's asthma fact sheet. Despite decades of research, clinicians still lack a reliable, non-invasive way to determine which type of asthma a patient has — a gap that exhaled breath analysis is increasingly positioned to fill. In a recent episode of the Concentrating on Chromatography podcast, Dr. Pierre-Hugues Stefanuto, an analytical chemist and co-organizer of the Multidimensional Chromatography Workshop (MDCW), discussed how his research group is using comprehensive two-dimensional gas chromatography–mass spectrometry (GCxGC-MS) to build molecular "breath prints" that could one day guide asthma treatment decisions in real time.
This article breaks down the science behind breath VOC biomarker discovery, why GCxGC-MS has distinct advantages for this application, and where rigorous, reproducible sample preparation — including nitrogen blowdown evaporation — fits into the workflow.
Watch the full podcast with Dr. Pierre-Hugues Stefanuto:
Asthma is not a single disease. It is a heterogeneous condition with distinct inflammatory phenotypes — most notably eosinophilic asthma, which tends to emerge earlier in life, and neutrophilic asthma, which is more often environmentally or genetically driven and appears later. Each phenotype responds differently to treatment, yet clinicians currently assign patients to a phenotype using largely empirical criteria, balancing symptoms, spirometry, and blood markers without a definitive molecular test.
Exhaled nitric oxide (FeNO) has served as a proxy biomarker for eosinophilic inflammation for years, but Dr. Stefanuto's work builds on the idea that if the body produces inorganic markers like nitric oxide during inflammation, it likely also produces a broader panel of organic volatile metabolites that could provide a more complete inflammatory fingerprint. This concept — often called "breathomics" or breath printing — treats the hundreds of volatile organic compounds (VOCs) in a single exhaled breath as a composite biomarker panel rather than searching for one silver-bullet molecule.
The clinical evidence is accumulating. A 2019 systematic review in the European Respiratory Journal found that exhaled VOCs are consistently sensitive to underlying airway inflammation, even though methodological standardization across studies remains a challenge. A 521-patient case-control study published in the American Journal of Respiratory and Critical Care Medicine found that a panel of exhaled VOCs — including hexane, 2-hexanone, and nonanal — could distinguish eosinophilic from neutrophilic asthma with classification performance comparable to, and in some cases exceeding, FeNO and blood eosinophil counts. Owlstone Medical's literature review of breath VOC biomarkers for asthma has identified nearly 200 distinct VOCs associated with asthma across the literature, with alkanes, aldehydes, and cyclic hydrocarbons pointing to lipid peroxidation as a shared underlying mechanism.
One of the more technical — and practically important — points Dr. Stefanuto raised is why gas chromatography, rather than liquid chromatography, is the preferred platform for breath VOC analysis. GC-MS remains the standard method for characterizing volatile components of the human metabolome, including breath VOCs.
The advantage comes down to separation physics and library depth. In GC, separation is driven primarily by a compound's boiling point: a C14 hydrocarbon will never elute before a C4 hydrocarbon, regardless of which stationary phase is used. This predictability, combined with electron ionization mass spectrometry and access to commercial spectral libraries containing 500,000 to 800,000 reference compounds (NIST and Wiley), gives GC-MS a structurally consistent framework for compound annotation. LC-MS, by contrast, must contend with positive and negative ionization modes, ion-source variability between vendors, and fragmentation differences that make library-building far more difficult.
GCxGC adds a second retention dimension, effectively creating a two-dimensional chemical map where a compound's position indicates polarity and approximate carbon number. Layering accurate mass data on top narrows the pool of candidate identities further — a molecule with an unusually high carbon number, for instance, can immediately be flagged as unlikely to originate from a GC-amenable sample. The tradeoff is a narrower molecular weight range compared to LC-MS, but a meaningfully higher confidence of annotation within that range — a critical distinction for a field where reliable compound identification, not just detection, is what ultimately matters for clinical translation.
Before any breath sample reaches a GC-MS instrument, it must be collected, extracted, and concentrated — and this stage is where analytical rigor either builds or erodes clinical trust in the resulting biomarker signature. Exhaled breath condensate (EBC) and VOC extracts are dilute, and low-abundance, labile compounds are easily lost or degraded if evaporation is too aggressive or uncontrolled.
This is precisely the challenge that controlled nitrogen blowdown evaporation is designed to solve in metabolomics sample preparation workflows more broadly. A gentle, steady stream of nitrogen gas passed over the sample surface accelerates solvent removal while protecting thermally sensitive analytes from degradation — a method already validated for preserving cellular metabolome integrity during sample concentration. The same principle extends naturally to breath-derived extracts: samples need to be dried down and reconstituted for injection without disturbing the very low-molecular-weight, semi-volatile compounds that make up the breath print. Platforms such as N-EVAP and MICROVAP nitrogen evaporators are built around this need — providing controlled temperature and gas flow so that concentration happens without cooking off or unevenly stripping the analytes researchers are trying to measure.
Perhaps the most pointed message from Dr. Stefanuto's interview is a critique of how the breath analysis field has historically operated. Many early studies were small "proof-of-concept" cohorts — often 10 patients versus 10 controls — that demonstrated a signal existed but were never followed up with larger, externally validated studies. This pattern has real consequences: multiple systematic reviews of exhaled VOC research report that studies suffer from a lack of methodological standardization and insufficient external validation, undermining clinical confidence even when the underlying biology is sound.
A meta-analysis published in Allergy pooling exhaled VOC asthma diagnosis studies calculated a pooled area under the curve of 0.94 — an excellent discriminative result — yet noted that only three of eighteen reviewed studies performed external validation using an independent dataset. Dr. Stefanuto's group is deliberately working against this trend, scaling cohorts from the historical 10-versus-10 design toward populations in the hundreds specifically to withstand scientific scrutiny and build a diagnostic tool clinicians can actually trust. That kind of rigor depends on reproducible, well-documented sample handling at every step — including standardized evaporation and concentration protocols that eliminate batch-to-batch variability as a confounding variable.
The long-term vision described in the interview goes well beyond a one-time clinical breath test. Dr. Stefanuto draws a direct comparison to continuous glucose monitoring in diabetes, where a small wearable sensor gives patients real-time feedback and can even trigger automated insulin delivery. He envisions a similar future for asthma: a patient blows into a smartphone-connected device each morning and receives guidance informed by their inflammation markers, local pollen counts, ozone levels, and other environmental data, layered with AI-driven prediction models.
This isn't purely speculative. Recent research published in PMC has already begun integrating exhaled VOC profiles with clinical biomarkers such as FeNO, blood eosinophils, and total IgE to build predictive models for treatment response and bronchodilator responsiveness, reporting AUC values as high as 0.92 for distinguishing conditions like COPD from asthma. A 2025 cross-sectional study indexed on PubMed using portable micro-GC devices demonstrated that VOC panel-based classification could rapidly discriminate between COPD, asthma, and pre-COPD populations — a meaningful step toward the point-of-care vision Dr. Stefanuto described.
Breath VOC biomarker discovery sits at the intersection of two disciplines Organomation customers already know well: rigorous GC-MS method development and disciplined sample preparation. As the field moves past isolated proof-of-concept studies toward validated, clinically translatable breath diagnostics — building on the clinical foundation reviewed in Respiratory Research's systematic review of exhaled VOCs in pulmonary disease — the labs generating that evidence will need concentration workflows that protect volatile and semi-volatile analytes through every step, from breath condensate collection to final GC-MS injection. Nitrogen blowdown evaporation, long established in metabolomics and lipidomics sample prep, is a natural fit for this next frontier in non-invasive diagnostics.
Interview source: Dr. Pierre-Hugues Stefanuto, guest on the Concentrating on Chromatography podcast.