METABOLITES ARE ASSOCIATED WITH FLARE REMISSION AND DNA METHYLATION CHANGES IN SYSTEMIC LUPUS ERYTHEMATOSUS
Bibliographic record
Abstract
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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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".