Trajectory-Informed Breathomics for Dynamic Mapping of Health and Disease: Toward a <i>Health Navigation Framework</i>
Bibliographic record
Abstract
ABSTRACT Background Most diagnostic frameworks treat disease as static, overlooking its inherently dynamic and continuous progression. Noninvasive biomarkers capable of capturing physiological trajectories are critically needed for proactive health management. Methods We developed a breath-based health navigation framework using exhaled volatile organic compounds (VOCs) as integrative, real-time indicators of systemic metabolism and immunity. Breath profiles were collected from healthy individuals and patients with nonalcoholic steatohepatitis (NASH, recently redefined as metabolic dysfunction–associated steatohepatitis, MASH), hepatocellular carcinoma (HCC), and periodontitis (PD). Multidimensional analysis and trajectory-informed mapping were applied to project individual health states into a low-dimensional physiological space. Results Ratio-normalized VOC profiles revealed disease-specific metabolic signatures across P450-derived and microbiota-derived compounds. Machine learning achieved high diagnostic accuracy for distinguishing health, NASH, HCC, and PD, while dimensionality reduction and topological analysis visualized a continuous progression from health to advanced liver disease. This approach captured preclinical shifts and transitional states often missed by static diagnostics. Conclusions Exhaled VOCs can serve as dynamic biomarkers for mapping health–disease transitions. By offering clinicians an intuitive map to locate patients within the health–disease continuum and anticipate their trajectories, this framework enables proactive decision-making and personalized intervention strategies. Collectively, our work points toward a paradigm shift in disease monitoring, with future integration into portable sensing technologies for noninvasive, continuous health-state assessment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".