Confounding Factors in Breath Analysis

Confounding Factors in Breath Analysis: Noise, Reality, and the Path to Robust Diagnostics

Breath is a remarkably rich biological matrix. Every exhalation carries a complex mixture of volatile organic compounds (VOCs), reflecting metabolic processes throughout the body. That richness is precisely what makes breath analysis so valuable—and at the same time, so challenging. One of the central challenges, familiar to anyone working in this field, is the presence of confounding factors.

A targeted approach: controlling variability during discovery

In many breath research studies, particularly those using mass spectrometry (MS) techniques, the aim is to identify disease-associated VOCs while maintaining biological relevance and interpretability. To support this goal, study designs often seek to limit variability by carefully controlling known confounding factors.

Patients may be asked to fast, certain medications may be excluded, and factors such as smoking, recent infections, diet, and time of day are often standardised. These design choices are especially important during early discovery phases, where understanding which VOCs are associated with disease—and why—requires careful management of confounding. In this context, controlling variability is not a limitation, but a deliberate methodological choice aligned with molecular discovery.

Breath in the real world is never “clean”

In routine clinical practice, however, patients do not arrive fasting, medication-free, and metabolically identical. They present with comorbidities, varying lifestyles, different diets, and diverse environmental exposures. These factors do not merely introduce noise; they are part of biological reality.

From a diagnostic perspective, the key question therefore shifts. The challenge is no longer only whether disease-associated signals can be detected under highly controlled conditions, but whether diagnostic models can withstand the full complexity of real patients and real clinical settings.

Complementary perspectives: representing clinical complexity

In addition to approaches that control confounding factors, other study designs aim to explicitly represent clinical complexity. In such studies, breath data are collected in real-world settings across broad patient populations, using pragmatic inclusion criteria.

As a result, factors such as medication use, comorbidities, lifestyle, and environmental exposure are naturally embedded in the data. Rather than being treated as exceptions, these sources of variability become part of the reference against which diagnostic models are developed and evaluated.

The underlying premise is demanding but straightforward: if disease-related signals can be identified and validated within this level of biological and environmental variability, they are more likely to be robust, clinically meaningful, and transferable across settings.

From individual molecules to composite patterns

Many breath analysis approaches — both MS-based and sensor-based — use multivariate models that combine information from multiple VOCs into aggregated scores or latent variables. Differences between approaches therefore lie not in the use of multivariate modelling itself, but in the level at which signals are ultimately represented and interpreted.

In our approach, interpretation is centred on breath profiles as composite patterns rather than on individual, chemically identified VOCs. These profiles capture disease-related information as it emerges from the interaction of multiple compounds, alongside the background of everyday life.

Identifying individual VOCs can support biological hypotheses and mechanistic insight. At the same time, such identification does not automatically equate to clinical interpretability. Individual VOCs may be linked to multiple physiological pathways, influenced by non-disease-related factors, or reflect downstream effects rather than disease-specific mechanisms.

Composite breath profiles place a stronger emphasis on diagnostic performance and robustness under real-world conditions. Although they are less directly interpretable at the level of single molecules, they enable models to be developed and validated in the presence of overlapping sources of biological and environmental variability—conditions that closely resemble clinical reality.

The ultimate test: external validation at scale

Developing a model in complex, real-world data is only the first step. The decisive test lies in external validation at scale. When diagnostic models continue to perform well across different hospitals, patient populations, and devices, robustness becomes observable rather than assumed.

At that point, models are no longer tightly coupled to a single cohort, site, or experimental setup. They become transferable, which is a prerequisite for breath analysis to move beyond research settings and towards meaningful clinical implementation.

Rethinking robustness in breath diagnostics

Confounding factors are often framed as obstacles—sources of noise that must be removed. A broader perspective recognises that robustness in breath analysis benefits from considering confounding factors not only as sources of variability, but also as reflections of clinical reality.

Isolating biology from its context can be valuable for certain research questions. For diagnostic applications intended for real patients, however, robustness emerges from confronting that context directly. Breath is inherently complex. Patients are heterogeneous. And it is within that complexity that durable diagnostic value must be demonstrated.

From real-world data to robust diagnostics

Working with real-world breath data inevitably raises an important question: how can reliable diagnostic models be developed when breath profiles are influenced by many sources of biological and environmental variability at the same time?

The answer does not lie in a single methodological choice, but in aligning study design, modelling strategy, and data scale with the intended purpose of the analysis. Across breath research, different approaches address different questions — ranging from VOC identification to diagnostic robustness — and each comes with its own methodological emphasis.

Scale and structure: enabling robust pattern detection

Regardless of the analytical platform used, data volume is a critical factor. Variables such as medication use, smoking status, comorbidities, age, sex, and environmental exposure all contribute to variability in breath profiles. In small datasets, this variability can obscure disease-related information. In larger datasets, however, these factors become statistically represented, allowing disease-associated patterns to be identified within the broader biological background.

Multivariate modelling plays an important role here. By learning from relationships between many signals simultaneously, such models capture disease-related information as distributed patterns rather than as isolated variables. This reduces sensitivity to single confounding factors and supports generalisation across populations.

Discovery in pattern-based breath analysis

The way confounding factors are handled during discovery depends on the focus of the study. In research aimed at identifying individual VOCs, careful management of confounders is essential to support chemical attribution and biological interpretation. In other contexts, discovery focuses on identifying reproducible patterns that can support diagnostic decision-making under real-world conditions.

In pattern-based breath analysis using an electronic nose, discovery is centred on identifying stable disease-associated patterns within breath profiles. These patterns arise from the combined behaviour of multiple VOCs and their interactions, rather than from individual compounds considered in isolation.

To support this, heterogeneous datasets are used that reflect clinical reality, including variability in medication use, comorbidities, lifestyle, and environmental exposure. Rather than excluding such variability, candidate models are evaluated across subgroups, clinical settings, and independent cohorts to assess whether observed patterns remain consistent across contexts.

Patterns that depend strongly on specific conditions or subpopulations tend to lose performance during this process and are not taken forward. Patterns that remain stable across diverse settings are prioritised for further development. In this way, variability becomes an integral part of model evaluation, rather than a factor addressed only through exclusion.

Importantly, this approach does not imply that confounding factors are ignored. Instead, they are addressed at the level most relevant to the intended outcome: the development of diagnostic models that can be applied reliably across real-world patient populations.

From discovery to clinical application

Ultimately, the relevance of any diagnostic approach is determined by its performance beyond the original study context. External validation across centres, populations, and devices remains essential, irrespective of the underlying technology. When diagnostic models maintain performance under these conditions, robustness becomes an empirical observation rather than an assumption.

Taken together, large-scale data, pattern-oriented discovery, and rigorous external validation enable breath-based diagnostic models to be developed that are both scientifically grounded and clinically applicable, even in the presence of substantial biological complexity.