Breath and Blood Metabolomics: A Comparative Study Using SESI-HRMS/MS and UHPLC-ESI-HRMS/MS
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Breath metabolomics enables noninvasive and rapid acquisition of metabolic information by detecting volatile organic compounds (VOCs) in exhaled breath. Secondary electrospray ionization high-resolution tandem mass spectrometry (SESI-HRMS/MS) offers the highest coverage for detecting breath metabolites among current real-time breath analysis techniques. Although it has been generally recognized that metabolites in breath originate from the blood, a molecular-level understanding of the characteristics of metabolites in both breath and blood remains insufficient. In this study, nontargeted analyses of breath and blood samples from 11 healthy volunteers were performed using SESI-HRMS/MS and ultrahigh performance liquid chromatography electrospray ionization high-resolution tandem mass spectrometry (UHPLC-ESI-HRMS/MS), respectively. Tandem mass spectrometry was employed for metabolite annotation. Twenty-six breath-unique metabolites and 73 blood-unique metabolites were identified. Besides, seven metabolites were found in both breath and blood, including 7-oxabicyclo [2.2.1] heptane, levulinic acid, indole, pyroglutamic acid, malic acid, glutamic acid, and histidine. Intriguingly, the correlation of these metabolites between breath and blood was low ( r < 0.4 or p > 0.05). Among all the confirmed metabolites, breath metabolites exhibit higher volatility according to their water–gas partition coefficient (log P w/g ) compared to blood metabolites. In addition, gender-derived differences in breath were significantly smaller than blood. In summary, this study indicates that breath metabolites are likely to offer complementary information on blood metabolites. When combined with blood metabolomics, this would be advantageous for the appropriate application of breath metabolomics in life sciences, such as in biomarker discovery.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".