#5489 CROSS-TALK BETWEEN FRAILTY AND IMMUNOSENESCENCE IN PATIENTS WITH CHRONIC KIDNEY DISEASE
Bibliographic record
Abstract
Abstract Background and Aims Chronic kidney disease (CKD) has been proposed as a model of premature ageing. The immune system is an important factor in the ageing process and can modulate the rate of ageing. The uremic environment highly affects this system, deteriorating its functionality and increasing susceptibility to infections, cancer, and pathologies like cardiovascular disease. Also, CKD patients are highly predisposed to frailty, which increases the organism's vulnerability to disease. This premature ageing, partially caused by the immune disorder and increased frailty, is responsible for these patients' high morbidity and mortality. Understanding these processes and how they are affected by different treatments will help generate better nutritional, pharmacological and lifestyle strategies. For this, the aim of this study was to determine the immune and frailty status of patients with CKD and their therapies. Method We performed a cross-sectional study involving 18 healthy subjects (HS) and 156 patients from the Nephrology Department of the Hospital Universitario “12 de Octubre” (Madrid, Spain). The distribution of patients was as follows: 40 with end-stage renal disease (ESRD), 40 on haemodialysis (HD), 36 on peritoneal dialysis (PD) and 40 patients who had received initial kidney transplantation (KT). The frailty status of the patients was assessed by the Edmonton Frail Scale test. Lymphocyte populations (T lymphocytes, T-helper lymphocytes, T-cytotoxic lymphocytes, and B lymphocytes) and monocytes (classical, intermediate, non-classical, and the expression of the adhesion molecule ICAM-1 and co-stimulatory B7.2) were determined in peripheral blood samples. Results The patients were similar in age and sex. The number of frail individuals was higher in patients (ESRD p<0.001, PD p<0.001, KT p = 0.004) than in HS, particularly in HD (p<0.001) (Figure 1). Regarding immune phenotype (Figure 2), HD patients showed a lower number of T-cells (p<0.001), particularly T-helper cells (p<0.001), than the other groups. Also, DP patients presented fewer T and T-cytotoxic cells than HS (p = 0.029, p = 0.05). Also, HD showed lower T-cytotoxic and helper/cytotoxic ratios than HS (p = 0.022; p = 0.011) and ESRD (p = 0.017; p = 0.008). The proportion of classical monocytes decreased, and the proportion of intermediate and non-classical monocytes increased in HD with respect to the other groups (p<0.001). The expression of the costimulatory molecule B7.2 was increased in the patients with respect to HS in all monocyte subsets (Classical: ESRD p = 0.002, HD p<0.001, PD p<0.001, KT p<0.001; Intermediate: ESRD p = 0.014, HD p<0.001, PD p<0.001, KT p = 0.002; Non-classical: ESRD p = 0.006, HD p<0.001, PD p<0.001, KT p = 0.013), while adhesion molecules were only elevated in HD with respect to HS in all subsets (classical p<0.001, intermediate p<0.001, non-classical p = 0.023). Conclusion The CKD patients, regardless of the treatment, showed, in general, an alteration in the lymphocyte subsets. These alterations were more significant in dialysis patients, particularly in HD patients. This group also presented the most significant alterations in monocyte subsets and higher frailty. This may explain why haemodialysis patients show major adverse outcomes compared to other treatments. Determining immune profiles can help us to relate these alterations to adverse events to carry out preventive and personalised medicine.
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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 | 0.001 |
| 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) | 0.005 | 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".