METABOLITES ARE ASSOCIATED WITH FLARE REMISSION AND DNA METHYLATION CHANGES IN SYSTEMIC LUPUS ERYTHEMATOSUS
Notice bibliographique
Résumé
O049 / #570 Topic: AS04 - Biomarkers ABSTRACT CONCURRENT SESSION 08: RECENT ADVANCES IN LUPUS BIOMARKERS 23-05-2025 1:40 PM - 2:40 PM Background/Purpose Recently, interest has increased in the role of metabolites and metabolic pathways in autoimmunity and SLE. Evidence suggests that immune cells are influenced by metabolic programs. Several studies identified metabolites with different levels in SLE cases compared to controls. It is unknown whether metabolite levels are associated with SLE disease activity or are correlated with other biomarkers of SLE disease activity such as DNA methylation. Metabolites, and their correlations with other markers, might serve as indicators of immune cell function and improve our ability to successfully treat patients. Using a cohort of SLE patients recruited during a flare and followed up over time, we aimed to identify whether changes in metabolites were associated with flare remission and whether these changes were correlated with changes in DNA methylation. Methods Forty multiethnic SLE patients were recruited during a rheumatologist-confirmed flare and returned to the clinic approximately 3 months later. At both visits, we obtained whole blood and generated untargeted metabolomics data from plasma (LC-QTOF) and DNA methylation profiles (Illumina EPIC array). Clinical data, including SLEDAI SELENA and medications, were collected at each visit. Remission was defined as SLEDAI=0 at the follow-up visit. We identified metabolites whose changes over time were associated with remission status, after adjusting for follow-up time and medications, using linear regression models. Previously in this study sample and using a similar statistical approach, we identified 291 DNA methylation sites whose changes over time were associated with remission. Significant metabolite changes (FDR q<0.05) were tested for their association with DNA methylation changes at these 291 sites using correlation coefficients. Results Sixteen SLE patients were in remission at the follow-up visit. Remitters and nonremitters did not differ significantly by race, ethnicity, age, disease activity or symptoms at the baseline flare, or medications. We identified 9 metabolite changes associated with remission status (Figure 1). These included oleic acid (P= 5.49×10^−8) and 2 isomers of adenine (P= 3.77×10^−5, each). For nonremitters, these metabolite levels changed very little between visits. For remitters, 4 metabolites increased while 5 decreased between visits. We identified 57 significantly correlated metabolite-DNA methylation pairs. This included a strong correlation between oleic acid and a DNA methylation site within the body of EBF1, an interferon response gene and key transcription factor of B cell specification (correlation = -0.79, p=1.1×10^−7). We also identified a strong correlation between adenine and a DNA methylation site within the body of IL12B, another interferon response gene which encodes a cytokine that acts on T and natural killer cells (correlation = 0.70, p=1.30×10^−6). Figure 1. Nine metabolites had levels that changed between flare and follow-up visits differently by remission status (FDR q<0.05). Colors represented patient’s remission status. Bold line represented mean change in metabolite by remission status. Conclusions Current treatments do not adequately prevent SLE flares or disease-related organ damage. Understanding the biological markers and pathways associated with remission after a flare might improve our ability to successfully treat patients. Our results showed that changes in several metabolites, including oleic acid and adenine, were associated with remission status and were correlated with changes in DNA methylation at SLE-relevant loci. These might be promising targets for future therapeutics and help us understand the underlying biology of SLE. Acknowledgments: This work was funded in part by U01DP005120 CDC.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 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) | 0,002 | 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 source (Gemma direct ou Codex distillé), 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 ».