Serological and cellular inflammatory signatures in end‐stage kidney disease and latent tuberculosis
Bibliographic record
Abstract
Abstract Objectives Tuberculosis comorbidity with chronic diseases including diabetes, HIV and chronic kidney disease is of rising concern. In particular, latent tuberculosis infection (LTBI) comorbidity with end‐stage kidney disease (ESKD) is associated with up to 52.5‐fold increased risk of TB reactivation to active tuberculosis infection (ATBI). The immunological mechanisms driving this significant rise in TB reactivation are poorly understood. To contribute to this understanding, we performed a comprehensive assessment of soluble and cellular immune features amongst a unique cohort of patients comorbid with ESKD and LTBI. Methods We assessed the plasma and cellular immune profiles from patients with and without ESKD and/or LTBI ( N = 40). We characterised antibody glycosylation, serum complement and cytokine levels. We also assessed classical and non‐classical monocytes and T cells with flow cytometry. Using a systems‐based approach, we identified key immunological features that discriminate between the different disease states. Results Individuals with ESKD exhibited a highly inflammatory plasma profile and an activated cellular state compared with those without ESKD, including higher levels of inflammatory antibody Fc glycosylation structures and activated CX3CR1 + monocytes that correlate with increased inflammatory plasma cytokines. Similar elevated inflammatory signatures were also observed in ESKD + /LTBI + compared with ESKD − /LTBI + , suggesting that ESKD induces an overwhelming inflammatory immune state. In contrast, no significant inflammatory differences were observed when comparing LTBI + and LTBI − individuals. Conclusion Our study highlights the highly inflammatory state induced by ESKD. We hypothesise that this inflammatory state could contribute to the increased risk of TB reactivation in ESKD patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".