Renal transplantation is life-changing in many aspects. This includes changes to the gut microbiome likely due to exposure to immunosuppressive drugs and antibiotics. As a consequence, renal transplant recipients (RTRs) might suffer from intestinal dysbiosis. We aimed to investigate the gut microbiome of RTRs and compare it with healthy controls and to identify determinants of the gut microbiome of RTRs. Therefore, RTRs and healthy controls participating in the TransplantLines Biobank and Cohort Study (NCT03272841) were included. We analyzed the gut microbiome using 16S rRNA sequencing and compared the composition of the gut microbiome of RTRs to healthy controls using multivariate association with linear models (MaAsLin). Fecal samples of 139 RTRs (50% male, mean age: 58.3 ± 12.8 years) and 105 healthy controls (57% male, mean age: 59.2 ± 10.6 years) were collected. Median time after transplantation of RTRs was 6.0 (1.5–12.5)years. The microbiome composition of RTRs was significantly different from that of healthy controls, and RTRs had a lower diversity of the gut microbiome (p < 0.01). Proton-pump inhibitors, mycophenolate mofetil, and estimated glomerular filtration rate (eGFR) are significant determinants of the gut microbiome of RTRs (p < 0.05). Use of mycophenolate mofetil correlated to a lower diversity (p < 0.01). Moreover, significant alterations were found in multiple bacterial taxa between RTRs and healthy controls. The gut microbiome of RTRs contained more Proteobacteria and less Actinobacteria, and there was a loss of butyrate-producing bacteria in the gut microbiome of RTRs. By comparing the gut microbiome of RTRs to healthy controls we have shown that RTRs suffer from dysbiosis, a disruption in the balance of the gut microbiome.

International consensus supports the development of standardized protocols for measured glomerular filtration rate (mGFR) to facilitate the integration of mGFR testing in both clinical and research settings. To this end, the European Kidney Function Consortium convened an international group of experts with relevant experience in mGFR. The working group performed an extensive literature search to inform the development of recommendations for mGFR determination using 1-compartment plasma clearance models and iohexol as the exogenous filtration marker. Iohexol was selected as it is non-radio labeled, inexpensive, and safe, can be assayed at a central laboratory, and the other commonly used non-radio-labeled tracers have been (inulin) or are soon to be (iothalamate) discontinued. A plasma clearance model was selected over urine clearance as it requires no urine collection. A 1 compartment was preferred to 2 compartments as it requires fewer samples. The recommendations are based on published evidence complemented by expert opinion. The consensus paper covers practical advice for patients and health professionals, preparation, administration, and safety aspects of iohexol, laboratory analysis, blood sample collection and sampling times using both multiple and single-sample protocols, description of the mGFR mathematical calculations, as well as implementation strategies. Supplementary materials include patient and provider information sheets, standard operating procedures, a study protocol template, and support for mGFR calculation.

Single-kidney glomerular filtration rate (GFR) increases after living kidney donation due to compensatory hyperfiltration and structural changes. The implications of inter-individual variability in this increase in single-kidney GFR are unknown. Here, we aimed to identify determinants of the increase in single-kidney GFR at three-month postdonation, and to investigate its relationship with long-term kidney function. In a cohort study in 1024 donors, we found considerable inter-individual variability of the early increase in remaining single-kidney estimated GFR (eGFR) (median [25th-75th percentile]) 12 [8-18] mL/min/1.73m². Predonation eGFR, age, and cortical kidney volume measured by CT were the main determinants of the early postdonation increase in single-kidney eGFR. Individuals with a stronger early increase in single-kidney eGFR had a significantly higher five-year postdonation eGFR, independent of predonation eGFR and age. Addition of the postdonation increase in single-kidney eGFR to a model including predonation eGFR and age significantly improved prediction of a five-year postdonation eGFR under 50 mL/min/1.73m². Results at ten-year follow-up were comparable, while accounting for left-right differences in kidney volume did not materially change the results. Internal validation using ¹²⁵I-iothalamate-based measured GFR in 529 donors and external validation using eGFR data in 647 donors yielded highly similar results. Thus, individuals with a more pronounced increase in single-kidney GFR had better long-term kidney function, independent of predonation GFR and age. Hence, the early postdonation increase in single-kidney GFR, considered indicative for kidney reserve capacity, may have additional value to eGFR and age to personalize follow-up intensity after living kidney donation.

Compensatory gomerular filtration rate (GFR) increase after kidney donation results in a GFR above 50% of the predonation value. The renal functional reserve (RFR) assessed by the renal response to dopamine infusion (RFR_dopa) is considered to reflect functional reserve capacity and is thought to be a tool for living donor screening. However, it is unknown if the RFR_dopapredicts long-term kidney function. Between 1984 and 2017, we prospectively measured GFR (¹²⁵I-iothalamate) and RFR by dopamine infusion in 937 living kidney donors. We performed linear regression analysis of predonation RFR_dopaand postdonation GFR. In donors with 5-yr follow-up after donation we assessed the association with long-term GFR. Mean donor age was 52 yr (SD 11); 52% were female. Mean predonation GFR was 114 ml/min (SD 22), GFR_dopawas 124 ml/min (SD 24), resulting in an RFR of 9 ml/min (SD 10). Three months postdonation, GFR was 72 ml/min (SD 15) and GFR_dopawas 75 ml/min (SD 15), indicating that donors still had RFR_dopa[3 ml/min (SD 6), P < 0.001]. Predonation RFR_dopawas not associated with predonation GFR [standardized (st.) β −0.009, P = 0.77] but was positively associated with GFR 3 mo after donation (st. β 0.12, P < 0.001). In the subgroup of donors with 5-yr follow-up data ( n = 383), RFR_dopawas not associated with GFR at 5 yr postdonation (st. β 0.05, P = 0.35). In conclusion, RFR_dopais a predictor of short-term GFR after living kidney donation but not of long-term kidney function. Therefore, measurement of the RFR_dopais not a useful tool for donor screening. Studies investigating long-term renal adaptation are warranted to study the effects of living kidney donation and improve donor screening.

Fatigue and impaired health-related quality of life (HRQoL) are common among kidney transplant recipients (KTR). We hypothesized that both may partially be attributable to poor sleep. Cross-sectional and longitudinal data of KTR enrolled in the TransplantLines Biobank and Cohort Study were used. Sleep quality was assessed using the Pittsburgh Sleep Quality Index questionnaire. Individual strength (i.e. a composite of fatigue, concentration, motivation and physical activity), societal participation and HRQoL were assessed using validated questionnaires. We included 872 KTR (39% female, age 56 ± 13 years) and 335 healthy controls. In total, 33% of male KTR and 49% of female KTR reported poor sleep quality, which was higher compared with male and female healthy controls (19% and 28%, respectively, P < .001 for both). In logistic regression analyses, female sex, anxiety, active smoking, low protein intake, physically inactive lifestyle, low plasma magnesium concentration, using calcineurin inhibitors, not using mTOR inhibitors and using benzodiazepine agonists were associated with poor sleep quality. In adjusted linear regression analyses, poor sleep was strongly and independently associated with lower individual strength [standardized β (st.β) = 0.59, 95% confidence interval (CI) 0.45 to 0.74, P < .001], poorer societal participation (frequency: st.β = −0.17, 95% CI −0.32 to −0.01, P = .04; restrictions: st.β = −0.36, 95% CI −0.51 to −0.21, P < .001; satisfaction: st.β = −0.44, 95% CI −0.59 to −0.28, P < .001) and lower HRQoL (physical: st.β = −0.53, 95% CI −0.68 to −0.38, P < .001; mental: st.β = −0.64, 95% CI −0.78 to −0.50, P < .001). The associations with poorer societal participation and lower HRQoL were strongly mediated by individual strength (P < .001 for all), yet the suggested direct effects of poor sleep quality on HRQoL remained significant (P_physical = .03, P_mental = .002). Longitudinal data of 292 KTR showed that sleep quality improves after kidney transplantation in males (P < .001), but not in females (P = .9). Poor sleep quality is common among KTR, and may be a potential target to improve fatigue, societal participation and HRQoL among KTR.

Living kidney donor safety requires reliable long-term follow-up of renal function after donation. The current study aimed to define the precision and accuracy of post-donation estimated glomerular filtration rate (eGFR) slopes compared with measured GFR (mGFR) slopes. In 349 donors (age 51 ± 10, 54% female), we analysed eGFR according to the 2009 Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation, Modification of Diet in Renal Disease (MDRD) and Cockcroft–Gault/body surface area (CG/BSA), creatinine clearance (CrCl) and mGFR (¹²⁵I-iothalamate) changes from 3 months until 5 years post-donation. Donors had a pre-donation mGFR of 116 ± 23 mL/min, at 3 months post-donation mGFR was 73 ± 14 mL/min and at 5 years it was 79 ± 16 mL/min. Between 3 months and 5 years post-donation, 28% of donors had a declining mGFR (−0.82 ± 0.79 mL/min/year), 47% were stable and 25% had an increasing mGFR. Overall, eGFR equations showed good slope estimates (bias eGFR_CKD-EPI 0.13 ± 2.16 mL/min/year, eGFR_MDRD 0.19 ± 2.10 mL/min/year, eGFR_CG/BSA −0.08 ± 2.06 mL/min/year, CrCl −0.12 ± 4.75 mL/min/year), but in donors with a decreasing mGFR the slope was underestimated (bias eGFR_CKD-EPI 1.41 ± 2.03 mL/min/year, eGFR_MDRD 1.51 ± 1.96 mL/min/year, eGFR_CG/BSA 1.20 ± 1.87 mL/min/year). The CrCl had a high imprecision [bias interquartile range −1.51–3.41 mL/min/year]. All eGFR equations underestimated GFR slopes in donors with a declining GFR between 3 months and 5 years post-donation. This study underlines the value of mGFR in the follow-up of donors with risk of progressive GFR loss.