BREATHOMICS IN SYSTEMIC LUPUS ERYTHEMATOSUS: UNCOVERING NONINVASIVE MARKERS OF DISEASE ACTIVITY AND FATIGUE
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».