205 Longitudinal immune changes during and after recent flares in lupus
Bibliographic record
Abstract
Background Previous studies suggest substantial immunologic heterogeneity in lupus. However, the majority of these studies were cross-sectional in nature. Here we followed flaring and quiescent patients longitudinally to determine how their immunologic profile changes over time. Methods Forty-seven SLE patients with a recent flare (change in clinical SLEDAI ≥ 2 in the past month that prompted a change in therapy), 25 quiescent SLE patients (clinical SLEDAI = 0 for ≥ 1 year with no increase in immunosuppressive treatment , ≤ 10 mg prednisone, matched for disease duration) and 16 healthy controls (HC) were recruited. The peripheral blood immunologic profile at baseline and follow-up (every 6 months for 1 year, COVID permitting) was examined by multi-parameter flow cytometry. Expression of interferon (IFN)-induced proteins that correlated with gene expression was examined in immune populations of interest using CyTOF. Results Using unsupervised clustering, incorporating all subjects and visits, four distinct immunologic profiles were seen: Cluster 1, with increased levels of activated B cells and age- associated B cells (ABCs); Cluster 2, with Tfh and Tph expansion; Cluster 3, with reduced levels of innate, naïve B, and Tfh cells; and Cluster 4 with expansion of Th1 and innate immune cells relative to other clusters. Although patients with new-onset flares were found in all clusters, Cluster 1 had the highest number of these patients, whereas Cluster 4 has the highest number of patients who were inactive at baseline, as well as HC. Patients moved between clusters over time and/or in response to treatment. A substantial proportion of flaring patients in Cluster 3 transitioned to Cluster 1 on follow-up, suggesting that B cell changes accumulate post-flare. Similar findings were seen for myeloid populations in a smaller subset of patients that transitioned from Cluster 3 to 4. In general, patients in Cluster 1, 2, or 4 at baseline tended to remain in the same cluster subsequently, with a notable exception being patients with early disease (< 6 months duration), where switching between clusters was frequent. Patients in Cluster 1 at follow-up were more likely to remain active or flare than those in Cluster 4. Analysis of IFN-induced protein expression, revealed considerable variability in the levels of these proteins between immune populations in the same patient and between patients, with significantly higher levels in flaring than in quiescent patients in most immune populations. Cluster 1 visits tended to have higher levels of IFN-induced proteins than Cluster 4 visits, particularly within B cell populations and the T helper cell populations that support their activation. Conclusion Accumulation of activated B cells and ABCs can occur during or after flare, is associated with high levels of IFN-induced proteins in these populations, and defines patients who are more likely to have ongoing disease activity or subsequent flares.
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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 | 0.000 |
| 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) | 0.001 | 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".