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Depression is common in patients with chronic kidney disease (CKD). Experimental studies suggest the role of urea toxicity in depression. We assessed both the incidence of antidepressant prescriptions and depressive symptoms (measured by CESD (Center for Epidemiologic Depression) scale) in 2505 patients with CKD (Stage 3-4) followed up over 5 years in the Chronic Kidney Disease Renal Epidemiology and Information Network (CKD-REIN) cohort. We used a joint model to assess the association between the serum urea level and incident antidepressant prescriptions, and mixed models for the association between the baseline serum urea level and CESD score over the 5-year follow-up. Among the 2505 patients, 2331 were not taking antidepressants at baseline. Of the latter, 87 started taking one during a median follow-up of 4.6 years. After adjustment for confounding factors, the hazard ratio for incident antidepressant prescription associated with the serum urea level (1.28 [95%CI, 0.94,1.73] per 5 mmol/L increment) was not significant. After adjustment, the serum urea level was associated with the mean change in the CESD score (β = 0.26, [95%CI, 0.11,0.41] per 5 mmol/L increment). Depressive symptoms burden was associated with serum urea level unlike depression events. Further studies are needed to draw firm conclusions and better understand the mechanisms of depression in CKD.

We previously reported associations between autoantibodies to the LG3 fragment of perlecan, anti-LG3, and a higher risk of delayed graft function (DGF) in kidney transplant recipients. Here, we aimed to determine whether some factors that modulate ischemia-reperfusion injury (IRI) can modify this association. We performed a retrospective cohort study in kidney transplant recipients in 2 university-affiliated centers. In 687 patients, we show that high pre-transplant anti-LG3 are associated with DGF when the kidney is transported on ice (odds ratio (OR): 1.75, 95% confidence interval 1.02-3.00), but not when placed on hypothermic perfusion pump (OR: 0.78, 95% CI 0.43-1.37). In patients with DGF, high pre-transplant anti-LG3 are associated with a higher risk of graft failure (subdistribution hazard ratio (SHR): 4.07, 95% CI: 1.80, 9.22), while this was not the case in patients with immediate graft function (SHR: 0.50, 95% CI 0.19, 1.29). High anti-LG3 levels are associated with a higher risk of DGF in kidneys exposed to cold storage, but not when hypothermic pump perfusion is used. High anti-LG3 are also associated with a higher risk of graft failure in patients who experience DGF, a clinical manifestation of severe IRI., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Mawad, Pinard, Medani, Chagnon, Boucquemont, Turgeon, Dieudé, Hamelin, Rimbaud, Belayachi, Yang, Collette, Sénécal, Foster, Hébert and Cardinal.)

Background: Although kidney transplantation (KT) is considered the best treatment for end-stage renal disease (ESRD), there are concerns about its benefit in the obese population because of the increased incidence of post-transplant adverse events. We compared patients who underwent KT versus patients awaiting KT on dialysis., Methods: We estimated the life expectancy [restricted mean survival time (RMST)] for a 10-year follow-up by matching on time-dependent propensity scores. The primary outcome was time to death., Results: In patients with a body mass index (BMI) ≥30 kg/m2 (n = 2155 patients per arm), the RMST was 8.23 years [95% confidence interval (CI) 8.05-8.40] in the KT group versus 8.00 years (95% CI 7.82-8.18) in the awaiting KT group, a difference of 2.71 months (95% CI -0.19-5.63). In patients with a BMI ≥35 kg/m2 (n = 212 patients per arm), we reported no significant difference [8.56 years (95% CI 7.96-9.08) versus 8.66 (95% CI 8.10-9.17)]. Hence we deduced that KT in patients with a BMI between 30 and 35 kg/m2 was beneficial in terms of life expectancy., Conclusion: Regarding the organ shortage, KT may be questionable for those with a BMI ≥35 kg/m2. These results do not mean that a BMI ≥35 kg/m2 should be a barrier to KT, but it should be accounted for in allocation systems to better assign grafts and maximize the overall life expectancy of ESRD patients., (© The Author(s) 2022. Published by Oxford University Press on behalf of the ERA.)

Background: The lack of a well-designed prospective study of the determinants of urgent dialysis start led us to investigate its individual- and provider-related factors in patients seeing nephrologists., Methods: The Chronic Kidney Disease Renal Epidemiology and Information Network (CKD-REIN) is a prospective cohort study that included 3033 patients with CKD [mean age 67 years, 65% men, mean estimated glomerular filtration rate (eGFR) 32 mL/min/1.73 m2] from 40 nationally representative nephrology clinics from 2013 to 2016 who were followed annually through 2020. Urgent-start dialysis was defined as that 'initiated imminently or <48 hours after presentation to correct life-threatening manifestations' according to the Kidney Disease: Improving Global Outcomes 2018 definition., Results: Over a 4-year (interquartile range 3.0-4.8) median follow-up, 541 patients initiated dialysis with a known start status and 86 (16%) were identified with urgent starts. The 5-year risks for the competing events of urgent and non-urgent dialysis start, pre-emptive transplantation and death were 4, 17, 3 and 15%, respectively. Fluid overload, electrolytic disorders, acute kidney injury and post-surgery kidney function worsening were the reasons most frequently reported for urgent-start dialysis. Adjusted odds ratios for urgent start were significantly higher in patients living alone {2.14 [95% confidence interval (CI) 1.08-4.25] or with low health literacy [2.22 (95% CI 1.28-3.84)], heart failure [2.60 (95% CI 1.47-4.57)] or hyperpolypharmacy [taking >10 drugs; 2.14 (95% CI 1.17-3.90)], but not with age or lower eGFR at initiation. They were lower in patients with planned dialysis modality [0.46 (95% CI 0.19-1.10)] and more nephrologist visits in the 12 months before dialysis [0.81 (95% CI 0.70-0.94)] for each visit., Conclusions: This study highlights several patient- and provider-level factors that are important to address to reduce the burden of urgent-start dialysis., (© The Author(s) 2021. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.)

Introduction: Chronic kidney disease (CKD) exposes to an increased incidence of fragility fractures. International guidelines recommend performing bone mineral density (BMD) if the results will impact treatment decisions. It remains unknown where bone loss occurs and what would preclude the longitudinal loss in patients with CKD. Here, we aimed to investigate factors influencing BMD and to analyze the longitudinal BMD changes., Methods: In the NephroTest cohort, we measured BMD at the femoral neck, total hip, lumbar spine, and proximal radius, together with circulating biomarkers and standardized measured glomerular filtration rate (mGFR) by 51 Cr-EDTA in a subset of patients with CKD stage 1 to 5 followed during 4.3 ± 2.0 years. A linear mixed model explored the longitudinal bone loss and the relationship of associated factors with BMD changes. A total of 858 patients (mean age 58.9 ± 15.2 years) had at least 1 and 477 had at least 2 BMD measures., Results: At baseline, cross-sectional analysis showed a significantly lower BMD at femoral neck and total hip and a significant higher serum parathyroid hormone (PTH) along with CKD stages. Baseline age, gender, tobacco, low body mass index (BMI), and high PTH levels were significantly associated with low BMD. Longitudinal analysis during the mean 4.3 years revealed a significant bone loss at the radius only. BMD changes at the femoral neck were associated with BMI, but not CKD stages or basal PTH levels., Conclusions: CKD is associated with low BMD and high PTH in the cross-sectional analysis. Longitudinal bone loss occurred at the proximal radius after 4.3 years., (© 2021 International Society of Nephrology. Published by Elsevier Inc.)

Background: There is extensive literature with comparisons between Anti-Thymocyte Globulin (ATG) and Basiliximab (BSX) as induction therapy in kidney transplant recipients. The purpose of our benchmarking study was to describe the consequences in terms of practices in 6 transplantation centers of a French prospective cohort., Methods: We included adult patients who received a first or second kidney graft between 2013 and 2019 (n = 4157). We used logistic regressions to identify characteristics associated with the use of ATG or BSX., Results: Use of ATG between the centers ranged from 41% to 75%. We observed different factors associated with the treatment decision. Compared to a first transplant, performing a second graft was the only factor significantly associated with the choice of ATG in all centers. The AUC ranged from 0.67 to 0.91, indicating that the centers seemed to define their own rules. As a result, for patients with the same low immunological risk, the probability of receiving ATG varied from 7% to 36%. We stratified the analyses according to two periods, from 2013 to 2015 and from 2016 to 2019. A similar heterogeneity was observed, and in some cases ATG indications between the centers were inverted., Conclusions: The heterogeneity of induction therapy practices did not decrease in France, even if the reated literature is prolific. This illustrates the necessity to improve the literature by using meta-analyses of recent studies stratified by graft and patient profiles., Competing Interests: Outside of the submitted work, Y. Foucher reported personal fees from Biogen and Sanofi-Genzyme; C. Legendre is a member of the advisory board for CSL Behring and Hansa Medical; E. Morelon reported personal fees from Astellas and Novartis; A. Sicard is membered of advisory Board for Novartis; M. Giral is membered of advisory board for Chiesi and reported personal fees from Astellas, Novartis and Sandoz. The DIVAT cohort is partially sponsored by Roche, Novartis, Chiesi, Astellas and Sanofi laboratories. This does not alter our adherence to PLOS ONE policies on sharing data and materials. All authors of this manuscript have no relevant conflicts of interest to disclose for this study.

Disruption of usual routines may hinder adherence, increasing the risk of rejection. We aimed to compare weekend versus weekday medication adherence among adolescent and young adult kidney transplant recipients, hypothesizing poorer adherence on weekends. We examined data from the Teen Adherence in Kidney transplant Effectiveness of Intervention Trial (TAKE-IT). We assessed the 3-month run-in period (no intervention) and the 12-month intervention interval, considering a potential interaction between weekend/weekday and treatment group. Adherence was monitored using electronic pillboxes in participants 11-24 years followed in eight transplant centers in Canada and the United States. We used logistic regression with generalized estimating equations to estimate the association between weekends/weekdays and each of perfect taking (100% of prescribed doses taken) and timing (100% of prescribed doses taken on time) adherence. Taking (OR = 0.72 [95% CI 0.65-0.79]) and timing (OR = 0.66 [95% CI 0.59-0.74]) adherence were poorer on weekends than weekdays in the run-in (136 participants) and the intervention interval (taking OR = 0.74 [0.67-0.81] and timing OR = 0.71 [95% CI 0.65-0.77]). There was no interaction by treatment group (64 intervention and 74 control participants). Weekends represent a disruption of regular routines, posing a threat to adherence. Patients and families should be encouraged to develop strategies to maintain adherence when routines are disrupted. TAKE-IT registration number: Clinicaltrials.gov registration: NCT01356277 (May 17, 2011)., (© 2019 The American Society of Transplantation and the American Society of Transplant Surgeons.)

Prior studies of sex differences in kidney graft survival showed conflicting results. We hypothesized that the association between recipient sex and kidney graft failure risk differs by recipient age and donor sex. We evaluated 159,417 patients recorded in the Scientific Registry of Transplant Recipients database who received a first deceased-donor kidney transplant (1995-2013). We used time-varying Cox models to estimate the association between recipient sex and death-censored graft failure. Models, stratified on donor sex and adjusted for potential confounders, included a recipient sex by current age interaction term. Among recipients of male donors, females of all ages had significantly higher graft failure risks than males (adjusted hazard ratios 0-14 years: 1.51 [95% confidence intervals 1.19 to 1.90]; 15-24 years: 1.37 [1.18 to 1.59]; 25-44 years: 1.14 [1.03 to 1.26]; 45 years: 1.05 [1.01 to 1.09]). Among recipients of female-donor grafts, only female recipients aged 15-24 years had a significantly higher graft failure risk than their male counterparts had (1.28 [1.06 to 1.53]). Indeed, female recipients aged ≥45 years had a significantly lower graft failure risk than their male counterparts had (0.95 [0.91 to 0.99]). These observations might be explained by the combined influence of several factors, including recognition of sex-determined minor histocompatibility antigens, influence of sex hormones on immune activation, sex- and age-related differences in medication adherence, and sex-related differences in body size. Additional studies should determine whether sex- and age-specific immunosuppression strategies are warranted for kidney graft recipients., (Copyright © 2017 by the American Society of Nephrology.)

Background: Renal function in patients with chronic kidney disease (CKD) may follow different trajectory profiles. The aim of this study was to evaluate and illustrate the ability of the latent class linear mixed model (LCMM) to identify clinically relevant subgroups of renal function trajectories within a multicenter hospital-based cohort of CKD patients., Methods: We analysed data from the NephroTest cohort including 1967 patients with all-stage CKD at baseline who had glomerular filtration rate (GFR) both measured by 51 Cr-EDTA renal clearance (mGFR) and estimated by the CKD-EPI equation (eGFR); 1103 patients had at least two measurements. The LCMM was used to identify subgroups of GFR trajectories, and patients' characteristics at baseline were compared between the subgroups identified., Results: Five classes of mGFR trajectories were identified. Three had a slow linear decline of mGFR over time at different levels. In the two others, patients had a high level of mGFR at baseline with either a strong nonlinear decline over time ( n  = 11) or a nonlinear improvement ( n  = 94) of mGFR. Higher levels of proteinuria and blood pressure at baseline were observed in classes with either severely decreased mGFR or strong mGFR decline over time. Using eGFR provided similar findings., Conclusion: The LCMM allowed us to identify in our cohort five clinically relevant subgroups of renal function trajectories. It could be used in other CKD cohorts to better characterize their different profiles of disease progression, as well as to investigate specific risk factors associated with each profile., (© The Author 2017. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.)

Background: The most commonly used methods to investigate risk factors associated with renal function trajectory over time include linear regression on individual glomerular filtration rate (GFR) slopes, linear mixed models and generalized estimating equations (GEEs). The objective of this study was to explain the principles of these three methods and to discuss their advantages and limitations in particular when renal function trajectories are not completely observable due to dropout., Methods: We generated data from a hypothetical cohort of 200 patients with chronic kidney disease at inclusion and seven subsequent annual measurements of GFR. The data were generated such that both baseline level and slope of GFR over time were associated with baseline albuminuria status. In a second version of the dataset, we assumed that patients systematically dropped out after a GFR measurement of <15 mL/min/1.73 m(2). Each dataset was analysed with the three methods., Results: The estimated effects of baseline albuminuria status on GFR slope were similar among the three methods when no patient dropped out. When 32.7% dropped out, standard GEE provided biased estimates of the mean GFR slope in normo-, micro- and macroalbuminuric patients. Linear regression on individual slopes and linear mixed models provided slope estimates of the same magnitude, likely because most patients had at least three GFR measurements. However, the linear mixed model was the only method to provide effect estimates on both slope and baseline level of GFR unaffected by dropout., Conclusion: This study illustrates that the linear mixed model is the preferred method to investigate risk factors associated with renal function trajectories in studies, where patients may dropout during the study period because of initiation of renal replacement therapy., (© The Author 2014. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.)

Background: In studies investigating risk factors of chronic kidney disease (CKD) progression, one may be interested in estimating factors effects on both a fall of glomerular filtration rate (GFR) below a specific level (i.e., a CKD stage) and death. Such studies have to account for the fact that GFR is measured at intermittent visit only, which implies that progression to the stage of interest is unknown for patients who die before being observed at that stage. Our objective was to compare the results of an illness-death model that handles this uncertainty, with frequently used survival models., Methods: This study included 1,519 patients from the NephroTest cohort with CKD stages 1-4 at baseline (69% males, 59±15 years, median protein/creatinine ratio [PCR] 27.4 mg/mmol) and subsequent annual measures of GFR (follow-up time 4.3±2.7 years). Each model was used to estimate the effects of sex, age, PCR, and GFR at baseline on the hazards of progression to CKD stage 5 (GFR<15 mL/min/1.73 m2, n = 282 observed) and death (n = 168)., Results: For progression to stage 5, there were only minor differences between results from the different models. The differences between results were higher for the hazard of death before or after progression. Our results also suggest that previous findings on the effect of age on end-stage renal disease are more likely due to a strong impact of age on death than to an effect on progression. The probabilities of progression were systematically under-estimated with the survival model as compared with the illness-death model., Conclusions: This study illustrates the advantages of the illness-death model for accurately estimating the effects of risk factors on the hazard of progression and death, and probabilities of progression. It avoids the need to choose arbitrary time-to-event and time-to-censoring, while accounting for both interval censoring and competition by death, using a single analytical model.

Regression models are being used to quantify the effect of an exposure on an outcome, while adjusting for potential confounders. While the type of regression model to be used is determined by the nature of the outcome variable, e.g. linear regression has to be applied for continuous outcome variables, all regression models can handle any kind of exposure variables. However, some fundamentals of representation of the exposure in a regression model and also some potential pitfalls have to be kept in mind in order to obtain meaningful interpretation of results. The objective of this educational paper was to illustrate these fundamentals and pitfalls, using various multiple regression models applied to data from a hypothetical cohort of 3000 patients with chronic kidney disease. In particular, we illustrate how to represent different types of exposure variables (binary, categorical with two or more categories and continuous), and how to interpret the regression coefficients in linear, logistic and Cox models. We also discuss the linearity assumption in these models, and show how wrongly assuming linearity may produce biased results and how flexible modelling using spline functions may provide better estimates., (© The Author 2013. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.)

Background: Chronic kidney disease (CKD) is a progressive and usually irreversible disease. Different types of outcomes are of interest in the course of CKD such as time-to-dialysis, transplantation or decline of the glomerular filtration rate (GFR). Statistical analyses aiming at investigating the association between these outcomes and risk factors raise a number of methodological issues. The objective of this study was to give an overview of these issues and to highlight some statistical methods that can address these topics., Methods: A literature review of statistical methods published between 2002 and 2012 to investigate risk factors of CKD outcomes was conducted within the Scopus database. The results of the review were used to identify important methodological issues as well as to discuss solutions for each type of CKD outcome., Results: Three hundred and four papers were selected. Time-to-event outcomes were more often investigated than quantitative outcome variables measuring kidney function over time. The most frequently investigated events in survival analyses were all-cause death, initiation of kidney replacement therapy, and progression to a specific value of GFR. While competing risks were commonly accounted for, interval censoring was rarely acknowledged when appropriate despite existing methods. When the outcome of interest was the quantitative decline of kidney function over time, standard linear models focussing on the slope of GFR over time were almost as often used as linear mixed models which allow various numbers of repeated measurements of kidney function per patient. Informative dropout was accounted for in some of these longitudinal analyses., Conclusions: This study provides a broad overview of the statistical methods used in the last ten years for investigating risk factors of CKD progression, as well as a discussion of their limitations. Some existing potential alternatives that have been proposed in the context of CKD or in other contexts are also highlighted.