Immune cell subsets in young kidney transplant recipients: Mechanistic and clinical perspectives
Bibliographic record
Abstract
Kidney transplantation provides the best survival advantage for children, adolescents, and young adults with end-stage kidney disease, yet this group paradoxically experiences the poorest long-term graft survival. Immune-mediated rejection is the predominant cause, but the cellular mechanisms that underpin this age-related disparity remain incompletely defined. This review synthesises current evidence on the impact of immune ageing across adaptive and innate compartments, focusing on T cells, B cells, and natural killer (NK) cells. In younger recipients, a large naïve T- and B-cell pool, robust thymic output, and efficient germinal centre activity confer heightened alloimmune reactivity, driving increased risk of acute cellular and antibody-mediated rejection. In contrast, older recipients exhibit features of immunosenescence, including loss of CD28 expression, accumulation of terminally differentiated effector subsets, impaired germinal centre responses, and attenuated NK cytotoxicity, resulting in diminished capacity to mount de novo responses but greater vulnerability to infection. These immune trajectories have direct clinical implications: younger recipients may require intensified, mechanism-targeted immunosuppression, whereas older recipients may be more amenable to minimisation or tolerance protocols. We further highlight emerging evidence for premature immunosenescence in paediatric dialysis populations, the contribution of age-associated B cells and NK subsets, and the role of immunophenotype-guided therapeutic strategies. Current uniform immunosuppression protocols inadequately account for developmental and age-related immune heterogeneity. We argue for an age- and immune phenotype-informed approach to therapy, integrating longitudinal immune profiling, biomarker development, and systems immunology to improve risk stratification, promote tolerance, and ultimately extend allograft survival across all age groups.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".