Accelerated immune aging was correlated with lupus‐associated brain fog in reproductive‐age systemic lupus erythematosus patients
Bibliographic record
Abstract
Abstract Aims Cognitive impairment is common in systemic lupus erythematosus (SLE) patients with substantial adverse effects on function and quality of life. One hypothesis to understand the mechanisms of cognitive impairment in SLE is accelerated immunosenescence. The aim of this study is to observe the correlation between immunosenescence with cognitive impairment in patients with SLE. Methods Sixty‐one female SLE patient were measured for CD4 and CD8 T cell‐associated senescence markers, including percentage of end‐stage differentiated T cells (CD4 and CD8 T cells expressing CD57 + or loss of CD28 expression), of naïve T cells (CD4 + CD45RA + and CD8 + CD45RA + ), memory T cells (CD4 + CD45RO + and CD8 + CD45RO + ), and antigen‐experienced T cells (CD4 + KLRG1 + and CD8 + KLRG1 + ) which were measured using flow cytometry. One hallmark of immunosenescence called immune risk profile (IRP) was defined by an inverted ratio of CD4 and CD8. Cognitive functions were measured by Mini‐Mental State Examination (MMSE) and Montréal Cognitive Assessment (MOCA) questionnaire. Results Thirty‐six (59.1%) SLE patients who had IRP develop significantly lower attention and recall from both MMSE ( P = .005 and P = .000) and MOCA ( P = .017 and P = .000) examinations. Decreased visuospatial ability was also found in patients with IRP measured by MOCA ( P = .046). There was a negative correlation between memory CD4 + CD45RO + T cells with recall and visuospatial domain (R = −0.204, P = .039 and R = −0.250, P = .033; respectively), and negative correlation between CD8 + CD28 ‐ T cells with recall and attention domain (R = −0.249, P = .027 and R = −0.145, P = .048, respectively). Conclusion Systemic lupus erythematosus patients develop an accelerated immunosenescence which contributes to cognitive dysfunction, especially in attention, recall, and visuospatial domains.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".