Abstract
Background
In therapeutic drug monitoring (TDM) of tacrolimus, trough concentration is the most commonly used monitoring indicator, which has limited association with clinical outcomes. Intrapatient variability (IPV), as a novel monitoring marker for describing the extent of fluctuation in blood concentration, holds promise as an additional monitoring tool. The effect of tacrolimus IPV on Chinese kidney transplantation outcomes is unclear, and the aim of this study is to explore the association between IPV and poorer outcomes of Chinese kidney transplantation recipients and to discuss the influence of genotype.
Methods
A total of 152 patients were enrolled, whose kidney transplantation operations were carried out from January 2015 to February 2022 in Peking University Third Hospital. IPV was calculated by coefficient of variation (CV) of tacrolimus whole blood concentrations during 6 and 12 months after transplantation. Clinical outcomes were analyzed between patients with high IPV and low IPV, including graft loss, acute rejection (AR), elevated serum creatinine (Scr), infection, hemogram abnormality and electrolyte disturbance. Moreover, the associations were compared according to different genotypes.
Results
High IPV was associated with AR [hazard ratio (HR) =3.420; 95% confidence interval (CI): 1.142–10.245], hyperuricemia, and hypocalcemia (P<0.05). In cytochrome P450 (CYP)3A5 nonexpressers, high IPV was associated with graft loss, elevated Scr, hyperuricemia, hypocalcemia, hypomagnesemia and leukopenia (P<0.05), whereas no significant association was detected in CYP3A5 expressers (P>0.05).
Conclusions
IPV is associated with clinical outcomes such as AR in Chinese kidney transplantation recipients. More attention should be paid to the association between high IPV and adverse outcomes in CYP3A5 nonexpresser recipients.
Keywords: Kidney transplantation, tacrolimus, therapeutic drug monitoring (TDM), intrapatient variability (IPV)
Highlight box.
Key findings
• A significant association was detected between intrapatient variability (IPV) and clinical outcomes in Chinese kidney transplant recipients. The association between IPV and negative outcomes was stronger in recipients classified as CYP3A5 nonexpressers.
What is known and what is new?
• Although it is well-established that tacrolimus IPV affects renal function in Chinese kidney transplant recipients, the impact on clinical outcomes remains unclear.
• This study not only confirms this relationship but also delves into the influence of genotype.
What is the implication, and what should change now?
• The findings indicate that managing tacrolimus IPV may improve outcomes for Chinese kidney transplant recipients, particularly for CYP3A5 nonexpressers. Clinical practices should incorporate IPV monitoring to optimize immunosuppressive regimens.
Introduction
Tacrolimus, a cornerstone of immunosuppressive therapy following kidney transplantation (1-3), demands meticulous individualized dosing due to its narrow therapeutic index, interactions with foods, herbal products and co-medications, and genetic polymorphisms (4-7). Therapeutic drug monitoring (TDM), which often relies on trough concentrations, is essential for guiding this dosing (8). While trough concentration is widely used for TDM in tacrolimus therapy, relying solely on a single time-point measurement offers an incomplete picture of drug exposure. This limited snapshot falls short as a reliable surrogate for both overall drug exposure over time and therapeutic outcomes (9,10). Recent research has highlighted the potential of intrapatient variability (IPV), defined as the variation that occurs in the blood concentration of a drug in a particular person over a specific time period (11), as a promising, complementary monitoring indicator. Studies have shown that IPV holds significant prognostic value for clinical outcomes (11,12). Nevertheless, its link to the clinical outcomes of Chinese kidney transplant recipients remains to be fully elucidated.
Given this context, our study explored the association between IPV and clinical outcomes of Chinese kidney transplant recipients using real-world data, aiming to provide real-world evidence from the Chinese population. We present this article in accordance with the STROBE reporting checklist (available at https://tau.amegroups.com/article/view/10.21037/tau-2025-456/rc).
Methods
Study population
The participants of this cohort study were 374 recipients who underwent kidney transplantation at Peking University Third Hospital between January 2015 and February 2022. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Peking University Third Hospital Medical Science Research Ethics Committee (approval No. M2024069). Informed consent was waived in this retrospective study. Patients were excluded if they were <18 years old, underwent kidney autotransplantation, had a history of other organ transplant, had a follow-up or death-censored graft survival period of less than one year, or had fewer than three measures of tacrolimus trough concentration during the 6–12-month period posttransplantation.
Data collection
Information was retrieved from medical records, including basic characteristics (age, sex, height, weight), clinical information [cytochrome P450 3A5 (CYP3A5) genotype, transplantation history, transplantation time, smoking history, drinking history, primary disease for transplantation, concomitant disease such as hypertension and diabetes, induction therapy, maintenance therapy] and laboratory indicators [tacrolimus blood concentration, serum creatinine (Scr) concentration, uric acid concentration, electrolyte concentration and hemogram results]. Data were collected from the posttransplantation period to the last follow-up or nephrectomy.
IPV calculation
IPV was measured as coefficient of variation (CV) using all the blood concentration obtained during the 6–12-month period posttransplantation, a period in which patients attended the outpatient clinic monthly for routine follow-up and drug-concentration monitoring. Blood concentration was tested via the enzyme-multiplied immunoassay technique (EMIT), in samples drawn 30 min before the morning dose. To guarantee an accurate CV calculation, at least three trough concentrations were required.
Genetic testing
Genetic testing was conducted for 93 recipients to develop individualized tacrolimus dosing regimens via fluorescence in situ hybridization. All recipients were classified into two categories by genotype: CYP3A5 expressers (CYP3A5*1/*1, CYP3A5*1/*3) and CYP3A5 nonexpressers (CYP3A5*3/*3).
Immunosuppression regimen
The induction therapy included interleukin-2 receptor antagonist (IL-2 RA) basiliximab and/or anti-lymphocyte immunoglobulin (rabbit or porcine anti-human T lymphocyte porcine immunoglobulin). Maintenance immunosuppressive therapy consisted of tacrolimus, mycophenolic acid (MPA) agents, and glucocorticoid. Tacrolimus administration was initiated at 0.1–0.2 mg/kg b.i.d., genotype-guided, with blood concentrations monitored regularly to maintain 8–15 ng/mL during months 0–3, 6–12 ng/mL during months 4–6, 5–10 ng/mL during months 7–12, and 5–9 ng/mL thereafter (13). The MPA agents were mycophenolate mofetil (MMF) administered at a dosage of 0.5–1 g b.i.d., and enteric-coated mycophenolate sodium (EC-MPS) administered at 360–720 mg b.i.d. Intraoperatively and for 3 days postoperatively, methylprednisolone was intravenously administered at a dose of 500 mg/day, followed by oral prednisone administration at an initial dose of 20 mg/day with a gradual taper.
Outcomes
The primary clinical outcomes were acute rejection (AR) and graft loss, with other outcomes including infection, elevated Scr concentration (>133 µmol/L), hyperuricemia, blood electrolyte disturbances (i.e., potassium, calcium, phosphorus, sodium, and magnesium), and hemogram abnormalities (i.e., abnormal level of white blood cells, platelets, and hemoglobin). Graft loss was defined as the occurrence of any of the following conditions: a consistent estimated glomerular filtration rate (eGFR) <15 mL/min/1.73 m2, re-establishment of long-term dialysis, kidney transplant nephrectomy or re-transplantation, excluding patient death with a functioning graft. The diagnostic criterion for AR was a significant increase in Scr concentration by 50% or more within 3 days without a reasonable explanation by other factors such as infection or renal artery stenosis. Recipients were also considered as having developed AR if they intravenous high-dose glucocorticoid pulse therapy for three days or more, or were treated with polyclonal antibodies. A biopsy was conducted if necessary. Infection was diagnosed based on clinical symptoms like fever and cough, as well as clear infection indicators such as abnormal procalcitonin (PCT) levels, which was monitored at each follow-up. Elevated Scr concentration was defined as >133 µmol/L because previous reports revealed that early renal function was linked to long-term death-censored graft survival and that Scr >133 µmol/L at 6 months or 1 year predicted lower 3-year survival (14,15). According to hospital laboratory and general consensus, normal uric acid upper limits were 420 µmol/L (men) and 360 µmol/L (women). Serum potassium normal range was 3.5–5 mmol/L; serum sodium, 135–145 mmol/L; serum magnesium, 0.7–1 mmol/L; serum calcium, 2.1–2.55 mmol/L; serum phosphorus, 0.8–1.61 mmol/L; white blood cells, (4–10)×109/L; platelets, (100–300)×109/L; hemoglobin, 120–170 g/L for women and 130–170 g/L for men. These laboratory indictors were analyzed across three distinct timeframes due to their rapid fluctuations.
Statistical analysis
Categorical variables were compared using the χ2-test or Fisher’s exact test, while continuous variables were compared using Student’s t-test or nonparametric tests. Time to graft loss, AR, and infection was analyzed using the Kaplan-Meier method, with between-group differences assessed using the log-rank test. Cox proportional hazards regression was employed to identify predictors of AR and graft loss. Variables with P<0.15 in the univariable analysis were included in the multivariable analysis. Statistical analysis was performed using SPSS 27.0 and P<0.05 was considered indicative of statistical significance.
Results
From January 2015 to February 2022, 374 patients received kidney transplantation in Peking University Third Hospital. A total of 222 patients were excluded for at least one of the following reasons: kidney autotransplantation (n=2); a history of other organ transplantation (n=1); death immediately following transplantation (n=2); use of non-tacrolimus calcineurin inhibitor (CNI) (n=5); follow-up or death-censored graft survival period of less than 1 year (n=83); graft loss within 1 year (n=6); and fewer than three measures of trough concentration during the 6–12-month period posttransplantation (n=133). Patients were divided into a high IPV group and a low IPV group based on a cut-off value of 27%. Baseline characteristics were comparable between the groups (Table S1).
Impact of IPV on graft loss, AR, infection and Scr concentration
The Kaplan-Meier curve demonstrated no significant difference in the risk of graft loss between the low IPV group and the high IPV group (P=0.06; Figure 1). Five patients experienced AR in the low IPV group (5.32%), compared to 9 in the high IPV group (15.5%). Kaplan-Meier analysis indicated that the low IPV group had significantly better rejection-free survival than the high IPV group (P=0.03; Figure 2). Univariable analysis showed that high IPV was a risk factor for AR (Table 1), which confirmed by multivariable analysis [hazard ratio (HR) =3.420; 95% confidence interval (CI): 1.142–10.245; P=0.03]. Kaplan-Meier analysis showed no significant difference in infection rate between the groups (P=0.86; Figure 3). The rate of elevated Scr concentration posttransplantation was not significant between groups (Table S2).
Figure 1.
Kaplan-Meier curves of graft survival (P=0.06). IPV, intrapatient variability.
Figure 2.
Kaplan-Meier curves of rejection-free survival (P=0.02). IPV, intrapatient variability.
Table 1. Univariate and multivariate analysis of risk factors for AR.
| Variables | Univariate | Multivariate | |||
|---|---|---|---|---|---|
| HR (95% CI) | P | HR (95% CI) | P | ||
| High IPV | 3.324 (1.110–9.953) | 0.03 | 3.420 (1.142–10.245) | 0.03 | |
| Male | 1.256 (0.393–4.017) | 0.70 | – | – | |
| Age | 1.014 (0.969–1.014) | 0.55 | – | – | |
| Height | 1.006 (0.941–1.075) | 0.87 | – | – | |
| Weight | 1.019 (0.981–1.060) | 0.33 | – | – | |
| BMI | 1.080 (0.942–1.238) | 0.27 | – | – | |
| Genotype | |||||
| CYP3A5 nonexpressers | Reference | – | – | ||
| CYP3A5 expressers | 1.030 (0.231–4.604) | 0.97 | – | – | |
| Primary disease for transplantation | |||||
| Non-biopsy-proven kidney diseases | Reference | – | – | ||
| Biopsy-proven kidney diseases | 0 (0–)† | 0.98 | – | – | |
| Other primary diseases | 1.253 (0.164–9.603) | 0.83 | – | – | |
| Hypertension history | 0.417 (0.131–1.330) | 0.14 | NS | >0.05 | |
| Diabetes history | 1.677 (0.375–7.506) | 0.50 | – | – | |
| Smoking history | 1.372 (0.307–6.131) | 0.97 | – | – | |
| Drinking history | 2.151 (0.600–7.721) | 0.24 | – | – | |
| Dialysis technique | |||||
| Peritoneal dialysis | Reference | – | – | ||
| Hemodialysis | 2.390 (0.640–8.924) | 0.20 | – | – | |
| Other | 0 (0–)† | 0.98 | – | – | |
| No dialysis | 2.773 (0.662–11.612) | 0.16 | – | – | |
| Duration of dialysis | 0.987 (0.961–1.013) | 0.33 | – | – | |
| Duration of cold ischemia | 1.018 (0.880–1.177) | 0.81 | – | – | |
| Duration of warm ischemia | 1.051 (0.654–1.687) | 0.84 | – | – | |
| Induction therapy | |||||
| IL-2 RA + other agents | Reference | – | – | ||
| ALG | 0.035 (0–84.738) | 0.40 | – | – | |
| IL-2 RA | 0.035 (0–165.584) | 0.44 | – | – | |
| Maintenance therapy | |||||
| MMF | Reference | – | – | ||
| EC-MPS | 0.438 (0.121–1.585) | 0.21 | – | – | |
| Mean tacrolimus concentration | 1.068 (0.920–1.240) | 0.39 | – | – | |
†, the upper limit of HR value was not obtained. ALG, anti-lymphocyte immunoglobulin; AR, acute rejection; BMI, body mass index; CI, confidence interval; CYP, cytochrome P450; EC-MPS, enteric-coated mycophenolate sodium; HR, hazard ratio; IL-2 RA, interleukin-2 receptor antagonist; IPV, intrapatient variability; MMF, mycophenolate mofetil; NS, not significant.
Figure 3.
Kaplan-Meier curves of infection-free survival (P=0.86). IPV, intrapatient variability.
Impact of IPV on uric acid concentration, electrolyte concentration, and hemogram
Compared to the high IPV group, the rate of hyperuricemia in the low IPV group was significantly lower during the 1–2-year period (P=0.03). Additionally, the low IPV group had a significantly lower rate of hypokalemia during the 2–3-year period (P=0.01). The rate of hypocalcemia in the low IPV group was also significantly decreased during the 6–12-month period, the 1–2-year period, and the 2–3-year period posttransplantation (P=0.003, P<0.001, and P=0.041, respectively), while the risk of hypercalemia was significantly higher during the 2–3-year period posttransplantation (P=0.02). No significant differences were observed in other laboratory indicators (P>0.05; Table S2).
Influence of genotype
Ninety-three patients underwent genetic testing, with 40 classified as CYP3A5 expressers and 53 as nonexpressers. No significant difference in IPV value was observed between CYP3A5 expressers and nonexpressers (27.5% vs. 23.4%, P=0.20). The CYP3A5 nonexpressers exhibited a significantly higher incidence of graft loss in the high IPV group (P=0.03), with a significant increase in the rate of elevated Scr concentration in the high IPV group during the 1–2-year period posttransplantation (P=0.01). Also, the CYP3A5 nonexpressers exhibited significant association of IPV with hyperuricemia, hypocalcemia, hypomagnesemia and leukopenia during the 1–2-year period posttransplantation (P=0.04). In the CYP3A5 expressers, no such association was observed. For more detailed results, please refer to Table S3.
Discussion
Association between IPV and clinical outcomes
This study confirmed the association between IPV, as measured by CV, and adverse outcomes including AR, hyperuricemia and electrolyte disturbances such as hypokalemia, hypocalemia, and hypercalemia in Chinese kidney transplant recipients. Even after adjusting for confounding factors, high IPV continued to be an independent risk factor for AR, a finding that aligns with previous reports (16,17). However, the present cohort study observed no significant association between IPV and graft loss, elevated Scr concentration posttransplantation, possibly due to the limited sample size, different influencing factors and short follow-up period. A Korean study (18) found that high IPV predicted graft failure only among recipients already classified as high immunological risk, whereas death-censored survival in the low-risk stratum did not differ by IPV. The predominance of stable, low-risk patients in our cohort may likewise have blunted any observable effect of IPV on clinical outcome. Besides, infection was insignificant too, possibly due to the strict inclusion and exclusion criteria in this study, which resulted in an underestimation of the incidence.
IPV was significantly associated with electrolyte disturbances such as hypokalemia, hypocalcemia, and hypercalcemia. Electrolyte concentration of kidney transplant recipients could change rapidly due to various factors (19,20), influenced by the frequency of follow-up. There was a considerable interval between the occurrence of hypokalemia and hypercalemia and the time period selected for IPV calculation, whereas the association between IPV and hypocalcemia appeared somewhat stronger. These findings should, however, be interpreted cautiously given the limited sample size.
In Korean adult and pediatric kidney transplantation recipients, high IPV has been linked to adverse events such as rejection among CYP3A5 expressers, but not among nonexpressers (21,22). By contrast, in our cohort high IPV was significantly associated with clinical outcomes such as graft loss, elevated Scr concentration, electrolyte disturbances and hemogram abnormalities, as well as hypocalcemia, hypomagnesemia, and leukopenia in CYP3A5 nonexpressers; no such association was detected in CYP3A5 expressers. This suggests that greater caution should be exercised when IPV is elevated in CYP3A5 nonexpressers, as it may predict worse clinical outcomes. The probable explanation is that tacrolimus is metabolised mainly via CYP3A4 in these patients, increasing the likelihood of drug-drug interactions. Nevertheless, the small sample size precludes definitive conclusions, and larger studies are needed to validate this finding.
Key considerations in IPV monitoring for effective clinical practice
Several key points in IPV monitoring require discussion for more effective clinical application.
Firstly, there is no clear requirement for trough concentration quantity in IPV calculation. Too few trough concentrations lead to large calculation errors and weak association with clinical outcomes; too many make it hard to meet calculation requirement, reduce indicator accessibility, prolong monitoring, and hinder early identification of high IPV recipients. When limiting trough concentration quantity, consider the time period for IPV calculation and follow-up frequency within that period. Current studies show great heterogeneity in trough concentration quantity limits and time period selection, making it hard to give a perfect quantity requirement. At present, it is generally recommended to exclude recipients with less than three trough concentrations (23,24), while Schumacher et al. (25) suggest at least 1 trough concentration per month. While there is no defined upper limit for trough concentration quantity, Kuypers et al. (24) indicate that 4 to 6 trough concentrations within 6 months are advisable. They note that more than six measurements may signal clinical events that could influence IPV, as frequent monitoring often implies such occurrences. In this study, many recipients had only three trough concentration measurements. Yet, a significant association between CV and clinical outcomes was observed. This shows that CV is a reliable monitoring index. With just a few trough concentrations, it can facilitate the early identification of high risk recipients.
Secondly, existing studies do not recommend including inpatient blood concentrations in IPV calculation (24,25). Inpatient measurements increase trough concentration quantity, potentially lowering IPV, but physiological changes and drug interactions during hospitalization may raise IPV. So, the impact of inpatient trough concentrations on IPV is unclear (21), and they are often excluded from IPV calculation. However, trough concentrations during hospitalization may not fluctuate significantly but could influence clinical outcomes. Also, IPV is a useful tool for identifying risks like nonadherence, drug interactions, and gastrointestinal diseases. A clinical study comparing IPV values of 220 kidney transplant recipients calculated from outpatient-only and both outpatient and inpatient trough concentrations found no significant difference and a strong correlation (coefficient r =0.872) (26), indicating limited impact of inpatient trough concentrations on IPV calculation. Thus, there is no need to overly focus on the measurement period when calculating IPV.
Thirdly, there is doubt over whether to dose-correct trough concentrations. IPV was originally defined as tacrolimus blood concentration fluctuation during a specific period with an unchanged tacrolimus dose. But in practice, keeping the dose constant in a long term is hard, so dose-corrected trough concentrations has become a useful solution. Gonzales et al. (23) nevertheless caution that dose-corrected trough concentrations could assess tacrolimus absorption and clearance rates but not directly reflect drug exposure. High IPV recipients often have exposure beyond or below the therapeutic range, leading to adverse outcomes. But dose-corrected trough concentrations lack a defined therapeutic range. Also, there is no theoretical support for the correlation between absorption or clearance variability and clinical outcomes; fluctuations may not directly affect outcomes. Moreover, dose-correcting each trough concentration increases calculation workload and reduces the index’s convenience. In summary, dose-corrected trough concentrations is not sufficiently necessary. A study (26) shows that the CV calculated from trough concentrations and dose-corrected ones are significantly correlated (r=0.745). When kidney transplant recipients were divided into three groups by tertiles of the two CVs respectively, 75.7% were in the same CV group, and 24.3% were in different groups. This indicates consistency and some differences in the CVs from the two calculation methods.
Influencing factors and interventions
Based on previous studies (27,28) and our findings, when IPV of recipients increases, possible causes include poor adherence, formulation differences, concomitant medications, hypoproteinemia, anemia, and gastrointestinal diseases. Therefore, to reduce IPV, it is essential to enhance monitoring and provide patient education, particularly for non-adherent individuals, as adherence is a significant determinant of IPV. Additionally, switching to the extended-release formulation and simplifying the treatment regimen may be beneficial. A thorough medication review is recommended to avoid potential drug interactions. Furthermore, hypoproteinemia, anemia, and gastrointestinal diseases can affect the distribution and absorption of tacrolimus, leading to fluctuations in blood concentrations, necessitating timely preventive and therapeutic measures. Multifaceted pharmacist-led interventions, including medication review, patient education, and regimen simplification have been demonstrated as effective strategies to address these issues.
Limitations
This study presents several limitations. Firstly, as a single-center retrospective cohort study, confounding factors cannot be avoided. It can only explore the association between IPV and clinical outcomes, not establish causality. Secondly, Peking University Third Hospital monitored both trough and peak concentrations, relying solely on the analysis of trough concentration and its variability does not encompass all clinical interventions. Thirdly, the sample size is relatively small, especially concerning the genotype analysis part. Additionally, as an invasive procedure, biopsy was not routinely performed; thus, most of the diagnoses of AR lacked evidence support from biopsy. Finally, this study focuses on IPV during the 6–12-month period posttransplantation, which has not established a comprehensive time-segmented IPV management system, requiring further research.
Conclusions
Despite these limitations, a significant association was detected between IPV and clinical outcomes such as AR, hyperuricemia, and electrolyte disturbances in Chinese kidney transplant recipients. The association between IPV and negative outcomes was stronger in recipients classified as CYP3A5 nonexpressers than in CYP3A5 expressers.
Supplementary
The article’s supplementary files as
Acknowledgments
None.
Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Peking University Third Hospital Medical Science Research Ethics Committee (approval No. M2024069). Informed consent was waived in this retrospective study.
Footnotes
Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://tau.amegroups.com/article/view/10.21037/tau-2025-456/rc
Funding: This work was supported by the National Natural Science Foundation of China (NSFC) (No. 72474013), and Beijing Municipal Health Commission Scientific and Technological Achievements and Appropriate Technologies Promotion Program (No. BHTPP2024007).
Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tau.amegroups.com/article/view/10.21037/tau-2025-456/coif). The authors have no conflicts of interest to declare.
Data Sharing Statement
Available at https://tau.amegroups.com/article/view/10.21037/tau-2025-456/dss
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