BREATHOMICS IN SYSTEMIC LUPUS ERYTHEMATOSUS: UNCOVERING NONINVASIVE MARKERS OF DISEASE ACTIVITY AND FATIGUE
Bibliographic record
Abstract
PV024 / #545 Poster Topic: AS04 - Biomarkers Background/Purpose 3TR (taxonomy, treatment, targets and remission) aims to provide insights into the mechanisms of response and nonresponse to treatment in autoimmune diseases. The lupus arm of 3TR focuses on identifying reliable biomarkers that could serve as indicators of disease or disease severity, and molecular processes that determine patients’ response to medication. Volatile organic compounds (VOCs) can be generated by metabolic processes in the body being impacted by disease pathology. VOCs diffuse from their point of origin into the blood to be emitted through breath, providing a potential noninvasive method to assess whole-body metabolism. Methods Sixty patients (30 SLE and 30 age- and sex-matched healthy controls) were recruited to a single-site, case-control observational study. Breath VOC sampling was performed using the ReCIVA® breath sampler, linked to a clean air supply (CASPER®). Collected samples were analyzed by thermal desorption gas chromatography-mass spectrometry (TD-GC-MS) by Owlstone Medical. VOCs were chemically identified in alignment with the Metabolomics Standards Initiative (MSI) criteria, with blank air samples analyzed to discern VOCs genuinely present in patients’ breath. Univariate analyses were performed by linear regression modeling for categorical variables and by Spearman’s rank correlation coefficient for continuous variables, including physician/patient global assessments (PhGA/PGA) and FACIT-F scores. Results Patients had a median disease duration of 14 years (IQR: 6–21), with a mean (SD) SLEDAI-2K score of 3.6 (3.3). Twenty subjects (70%) were in LLDAS and 14 (46.7%) in DORIS remission. The mean PhGA and PGA scores were 19.7 (19) and 40.3 (33.7). Fourteen patients (46.7%) tested positive for anti-dsDNA. The mean serum C3 and C4 levels were 90 (20) and 8.6 (9.5) mg/dL, with 17 patients (56.7%) hypocomplementemic. The mean FACIT-F score was 37.9 (11.9). After quality control, 1,433 VOCs were observed. Of these, 539 were classified as “on-breath,” appearing at significantly higher levels than background. VOC identities were assigned based on pure analytical standards or matches to third-party databases, with on-breath statistically significant VOCs further interpreted for their biological relevance. Three main themes emerged from the analysis (Figure). First, a strong link was found between SLE and gut microbiome, with significant decreases in gut microbiome fermentation products (eg, 2-butanol and 1-propanol) in SLE. Additionally, elevated levels of 2,3-butanediol correlated with greater disease severity. Notably, differences in gut microbiome products were also observed in SLE according to complement levels. Second, there was a positive correlation between VOCs with potential links to oxidative stress and inflammation (ie, cyclopentene, 3-methyl-2-pentene, and 2-methyl-1-butene) and disease severity indicators, including SLEDAI-2K, LLDAS, DORIS remission and both PhGA and PGA. Third, there was evidence of a correlation between an altered gut microbiome and fatigue. Results pointed toward a decrease of sulfate-reducing bacteria that may eventually promote inflammation via a loss of degradation of cyclopentene, coupled with a syntropic compensatory production of butyrate. Figure. Conclusions These data demonstrate, for the first time, the potential of breath-based VOC analysis in detecting pathophysiological changes in SLE patients. They align with recent findings that highlight gut microbiome dysbiosis as central in SLE and suggest a potential link with complement levels. Our data demonstrate the functional nature of gut dysbiosis with significant correlation with fatigue. These data reveal possible markers of inflammation, which correlate with disease severity and patient’s perception of fatigue and offer an exciting prospect for noninvasive disease assessment. Future work should focus on validating these markers and their associations with additional inflammatory indicators.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".