An Immuno-Fragile Profile Is Associated with Mortality Risk in Patients with Chronic Kidney Disease
Bibliographic record
Abstract
Background/Objectives: Patients with chronic kidney disease (CKD) face higher risks of infections, poor vaccine responses, and cardiovascular diseases, leading to increased morbidity and mortality due to immune dysfunction and frailty. This study aims to evaluate immune status and frailty in CKD patients across different treatments, examine the influence of frailty on immune status, and link these factors to mortality. Methods: A total of 174 participants were included (end-stage renal disease, ESRD n = 40; hemodialysis, HD n = 40; peritoneal dialysis, n = 36; kidney transplant patients, n = 40; healthy subjects n = 18). Immunophenotyping of lymphocyte and monocyte subpopulations was performed, and frailty was assessed using the Edmonton Frail Scale. Principal component analysis (PCA) integrated immune and frailty variables to define an “immuno-fragile profile,” and survival was monitored for up to six years. Results: CKD patients, especially those on HD, showed decreased lymphocyte counts and proinflammatory monocyte subpopulations with increased expression of costimulatory molecules (B7.2/CD86 and ICAM-1/CD54). Frailty was most prevalent in HD patients (53%), with notable sex differences. PCA identified three components—lymphocyte counts, monocyte co-stimulatory expression, and frailty—that together explained 70% of the variance. Survival analysis revealed that patients with lower lymphocyte counts and higher frailty scores had increased mortality risk, especially in the HD and ESRD groups. Cox regression confirmed that the immuno-fragile profile independently predicted mortality. Conclusions: The integration of immune alterations and frailty defines an immuno-fragile profile strongly associated with mortality in CKD patients, which may serve as a robust prognostic tool to improve risk stratification and guide personalized interventions in clinical practice.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".