Acute allograft rejection (AR) is a major complication that impairs long-term graft function and patient survival in kidney transplant (KT) recipients [1–3]. Early allograft dysfunction can also arise from delayed graft function (DGF) or other causes, such as infection, nephrotoxicity, or surgical complications, which may necessitate an urgent graft needle biopsy to confirm AR [3]. However, in KT recipients who exhibit satisfactory early graft function, subclinical rejection may go undetected unless identified through an early protocol biopsy [3,4]. The use of a protocol biopsy program is not universally adopted, and among transplant centers that do implement this protocol, the timing of the biopsy can vary significantly [3,5]. Although major complications from graft biopsies are rare when performed by experienced operators under imaging guidance, serious complications can still occur, including hematoma, bladder obstruction, the need for blood transfusions or surgical intervention, graft loss, and even death [6,7]. Additionally, certain medical conditions may contraindicate the procedure. Other limitations, such as interobserver variability and sampling errors, also compromise its accuracy [8–10]. Therefore, various noninvasive alternatives have been developed recently, including serum biomarkers, with a particular focus on blood cell count-derived ratios such as the neutrophil-to-lymphocyte ratio (NLR) and the platelet-to-lymphocyte ratio (PLR).

In this meta-analysis, we systematically reviewed studies that investigated the roles of NLR and PLR in predicting AR and DGF in KT recipients. Our findings show that the NLR levels were significantly higher in KT recipients with AR than in controls. Additionally, preoperative NLR levels were also significantly higher in KT recipients with DGF compared to controls after excluding outliers. However, no justification for this exclusion was provided; thus, we conclude that the increase in NLR is not significant in DGF cases. PLR levels were found to be insignificantly higher in KT recipients with AR compared to controls. Furthermore, there was no significant difference in PLR levels between KT recipients with DGF and controls.

The exact etiology underlying AR and/or DGF in KT remains unclear, although recent studies have identified inflammation as a potential cause. Systemic inflammation is known to disrupt hematologic cell lines, specifically causing neutrophilia, thrombocytosis, and lymphopenia. Additionally, KT is considered an inflammatory process [12]. NLR values have been shown to correlate with inflammatory cytokines in end-stage renal disease (ESRD) patients [18]. KT recipients exhibit higher NLR values compared to healthy individuals, likely due to ongoing inflammation [19].

All included studies on NLR and AR conducted either indication-based or for-cause biopsies (when there is a clinical suspicion of acute rejection, particularly a rise in creatinine levels), except for one study by Kolonko et al. [3], which performed protocolar or surveillance biopsies on the 8th and 10th days posttransplant. Ergin et al. [13] and Sayilar et al. [14] measured NLR on the day of clinical presentation for the acute rejection group and during the last outpatient clinic visit for the control group. Ercan Emreol et al. [12] recorded NLR during acute rejection episodes, comparing these measurements with those taken just before an acute rejection event as a control. Kolonko et al. [3] conducted NLR measurements 6 and 3 days prior to the biopsy and on the day of the biopsy itself. Improvements in NLR values were observed in the first year after transplantation but did not reach the levels found in healthy controls, suggesting possible permanent chronic inflammation in KT patients [20]. NLR was highest in the first month post-KT, with values stabilizing and decreasing to basal levels within 3 months. However, a substantial increase was reported in patients who subsequently developed malignant diseases [12]. These observations might assist in determining the optimal timing for NLR screening and predicting differential diagnoses, although no definitive screening time has yet been established. Nevertheless, protocolar biopsies at set intervals are recommended for future studies to detect rejection earlier and to align with NLR measurements. This approach reduces reliance on symptom-based timing, which may vary among patients, and could provide insights into the most optimal timing for NLR screening.

After KT, allograft antigens, including intact cells, extracellular vesicles such as exosomes, and shed antigens, migrate to the lymph nodes and lymphoid organs of the recipient through lymphatic vessels. This migration represents the primary event in allorecognition. These alloantigens may be incorporated onto recipient APCs through a process known as "cross-dressing," which can lead to either allograft tolerance or rejection [21]. Proinflammatory biomarkers, such as NLR, may indicate the direction of the immune response and contribute to the outcome [21].

The NLR and PLR were significantly higher in KT recipients experiencing AR compared to controls, suggesting these markers may reflect systemic inflammation and indicate ongoing inflammation in the graft [13,14,22]. Additionally, NLR and PLR levels were found to be higher during AR episodes than in attack-free periods in the same patients [12]. The initial increase in NLR was significantly more pronounced in cases of antibody-mediated rejection than in those involving vascular rejection or T cell-mediated rejection (TCMR) [3]. Although this study primarily focused on the occurrence of AR in general, most of the included studies pertained to the TCMR subtype. Subgroup and meta-regression analysis showed that differences in AR subtypes did not contribute to the heterogeneity; however, this finding should be interpreted with caution due to the small number of studies included.

DGF has been identified as a significant risk factor for AR, based on a 12-year cohort study involving 645 KT patients. This underscores the critical importance of screening for and implementing interventions to mitigate the risk of DGF [23]. Elevated preoperative NLR has also been linked to DGF following KT [20]. Hogendorf et al. [24] increased the predictive accuracy for early poor graft function by applying the natural logarithm (ln) to NLR, PLR, and lymphocyte counts. They discovered that a combination of lnLymphocytes, lnNLR, or lnPLR could improve detection of patients at risk [24].

While some studies have shown a correlation between high NLR and the development of DGF and/or AR [13,15,25], others have reported contrary findings [16,24,26]. Preoperative low NLR levels were partially linked to a relatively higher lymphocyte count in peripheral blood, which aligns with the pathogenesis of DGF [16,26].

The NLR and PLR were positively correlated with C-reactive protein, suggesting their potential to identify inflammation in nondialysis ESRD patients, although the NLR demonstrated a stronger correlation [27]. The preoperative NLR also correlated with postoperative serum creatinine levels, indicating its utility as a predictive factor for postoperative KT complications [28]. In contrast, other studies have indicated that the PLR may have a superior predictive value for inflammation compared to the NLR [17,29]. In this study, the PLR was found to be insignificantly higher among KT recipients with AR than in controls. Similar to the NLR, PLR is a marker of systemic inflammation. Acute rejection, characterized by immune activation and inflammation, can lead to an elevated PLR. In KT recipients, a higher PLR may be associated with increased inflammatory activity, potentially indicating immune responses against the graft. Sayilar et al. [14] observed that both the NLR and PLR were significantly higher in transplant patients experiencing AR, suggesting that these ratios could serve as noninvasive, cost-effective markers for early detection of rejection events. Kolonko et al. [3] noted discrepancies, with higher ratios primarily in cases of antibody-mediated rejection rather than cellular rejection. This distinction suggests that PLR may be more predictive of specific types of immune responses. Naranjo et al. [26] reported an inverse trend, with nonrejection cases exhibiting higher NLR and PLR, possibly due to other systemic factors affecting these markers. This observation implies that an elevated PLR may not exclusively indicate rejection but could also reflect other inflammatory or physiological conditions. The discrepancies in the results can be attributed to several factors: (1) variations in patient population and study design: different studies involve diverse patient demographics, immunosuppressive regimens, and types of rejection, all of which could influence PLR values. For instance, pediatric and adult transplant recipients may show different inflammatory responses. (2) Type of acute rejection: PLR may be more relevant in cases of antibody-mediated rejection, where higher inflammatory activity could result in a more pronounced PLR response compared to cellular rejection, as noted by Kolonko et al. [3]. (3) Timing and context of measurement: acute events such as infections or recent surgeries can also independently elevate the PLR, potentially confounding results if these factors are not controlled for in study designs. Further research on PLR is necessary to clarify its predictive value across different rejection types and patient populations.

There are some important limitations to this study. First, all the included studies were retrospectively designed; thus, due to their retrospective nature, certain potential biases should be considered: (1) selection bias: relying solely on medical records rather than a standardized, prospective protocol may result in an unrepresentative sample, as only patients with complete and accessible records are included. (2) Information bias: relying on historical data, which may not have been collected with the same precision and consistency across cases, can impact the comparability of results across the included studies. (3) Temporal bias: a retrospective design may be subject to temporal differences in treatment protocols or clinical practices over time, potentially affecting outcome measures. For example, studies conducted over an extended period, such as Ergin et al. [13], which spanned over two decades, may include patients who received different immunosuppressive regimens and follow-up care. This is supported by the results of the meta-regression analysis, which suggests that the publication year may be a source of heterogeneity. Second, we decided to include grey literature in the form of conference poster data to anticipate overestimation of effect size, although current empirical research indicates that overestimation occurs only in a minority of reviews [30]. Nevertheless, the impact of including grey literature data remains unclear, and further research is needed to determine which reviews might benefit most from this inclusion [30]. Third, there was a presence of high statistical heterogeneity. Subgroup and meta-regression analyses were conducted to explore possible sources of heterogeneity, which identified publication year and donor type as potential sources of the marked heterogeneity. However, these findings must be interpreted with caution due to the limited number of studies included. Despite these limitations, more large multicenter prospective studies are needed to further clarify the predictive ability of NLR in AR and DGF.

We suggest several strategies to control confounding factors. First, conducting more prospective studies would allow real-time monitoring of NLR, thereby reducing recall bias and ensuring consistent timing of NLR measurements in relation to rejection events and biopsies. Second, standardizing NLR measurement protocols, including uniform timing for NLR testing and conducting scheduled biopsies at set intervals, could facilitate earlier detection of rejection and better synchronization with NLR measurements, minimizing the variability introduced by symptom-based timing that may differ among patients. Third, stratifying rejection types (e.g., antibody-mediated vs. cellular) would help clarify the effectiveness of NLR in specific immune responses. Lastly, performing propensity score calculations, similar to those done by Kolonko et al. [3], can help create well-matched groups and improve control of confounding variables.

This meta-analysis showed a significantly higher NLR in KT recipients who experienced AR, thus suggesting that the NLR may represent a simple, inexpensive, and noninvasive predictive marker for the early detection of AR. For future research, we recommend conducting larger, multicenter cohort studies to increase statistical power, improve generalizability, and determine the optimal timing and cutoff values for NLR testing in a clinical setting. Additionally, exploring the utility of combining NLR with other noninvasive biomarkers, such as PLR, could offer valuable insights.