Altered Balance of Pro-Inflammatory Immune Cells to T Regulatory Cells Differentiates Symptomatic From Asymptomatic Individuals With Anti-Nuclear Antibodies
Bibliographic record
Abstract
Systemic Autoimmune Rheumatic Diseases (SARDs) are characterized by the production of anti-nuclear antibodies (ANAs). ANAs are also seen in healthy individuals and can be detected years before disease onset in SARD. Both the immunological changes that promote development of clinical symptoms in SARD and those that prevent autoimmunity in asymptomatic ANA+ individuals (ANA+ NS) remain largely unexplored. To address this question, we used flow cytometry to examine peripheral blood immune populations in ANA+ individuals, with and without SARD, including 20 individuals who subsequently demonstrated symptom progression. Several immune populations were expanded in ANA+ individuals with and without SARD, as compared with ANA- healthy controls, particularly follicular and peripheral T helper, and antibody-producing B cell subsets. In ANA+ NS individuals, there were significant increases in T regulatory subsets and TGF-ß1 that normalized in SARD patients, whereas in SARD patients there were increases in Th2 and Th17 helper cell levels as compared with ANA+ NS individuals, resulting in a shift in the balance between inflammatory and regulatory T cell subsets. Patients with SARD also had increases in the proportion of pro-inflammatory innate immune cell populations, such as CD14+ myeloid dendritic cells, and intermediate and non-classical monocytes, as compared to ANA+ NS individuals. When comparing ANA+ individuals without SARD who progressed clinically over the subsequent 2 years with those who did not, we found that progressors had significantly increased T and B cell activation, as well as increased levels of LAG3+ T regulatory cells and TGF-ß1. Collectively, our findings suggest that active immunoregulation prevents clinical autoimmunity in ANA+ NS and that this becomes impaired in patients who progress to SARD, resulting in an imbalance favoring inflammation.
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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 | 0.000 |
| 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".