The Breathomics Profile of Volatile Sulfur Compounds in the Bipolar Spectrum, Does It Represent a Potential Tool for Early Diagnosis?
Bibliographic record
Abstract
Background/Objectives: Emerging laboratory technologies, such as breathomics, may enhance the early diagnosis of psychiatric disorders, including Bipolar Disorder (BD). This study investigates the detection of volatile sulfur compounds (VSCs) in exhaled breath as potential biomarkers for BD, comparing VSC levels between individuals with BD, healthy controls, and individuals with non-pathological hyperactivity. Methods: A matched case–control study was conducted involving 24 patients with BD and 95 healthy controls recruited at the University Hospital of Cagliari. Controls were selected using a matched-pair design based on age (±5 years) and sex through a block-matching technique to ensure comparability with cases. Participants underwent psychiatric interviews, completed the Mood Disorder Questionnaire (MDQ), and had their exhaled breaths analyzed for VSCs using a gas chromatograph (OralChroma™). Controls were selected and randomized for age and sex. Results: Patients with BD exhibited significantly higher levels of methyl mercaptan (CH3SH) compared to healthy controls (18.62 ± 5.04 vs. 9.45 ± 18.64 ppb, p = 0.022). Among individuals without BD, those with positive MDQ scores showed lower levels of CH3SH than those with negative scores (9.17 ± 5.42 vs. 15.05 ± 18.03); however, this difference did not reach statistical significance (p = 0.254), highlighting how the deep connection between some clinical and laboratory aspects needs to be investigated more thoroughly. Conclusions: The results suggest a correlation between oral dysbiosis and metabolic alterations in patients with BD, with CH3SH levels being higher in cases compared to controls. Further studies are needed to validate the use of VSCs as potential biomarkers for BD and to investigate their role in individuals with non-pathological hyperactivity.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".