Inflammation in the early phase after kidney transplantation is associated with increased long-term all-cause mortality
2022; Elsevier BV; Volume: 22; Issue: 8 Linguagem: Inglês
10.1111/ajt.17047
ISSN1600-6143
AutoresTorbjørn F. Heldal, Anders Åsberg, Thor Ueland, Anna Varberg Reisæter, Søren Erik Pischke, Tom Eirik Mollnes, Pål Aukrust, Anders Hartmann, Kristian Heldal, Trond Jenssen,
Tópico(s)Renal Transplantation Outcomes and Treatments
ResumoAmerican Journal of TransplantationEarly View ORIGINAL ARTICLEOpen Access Inflammation in the early phase after kidney transplantation is associated with increased long-term all-cause mortality Torbjørn Fossum Heldal, Corresponding Author Torbjørn Fossum Heldal torbjorn.heldal@hotmail.com toheld@sthf.no orcid.org/0000-0002-3049-4705 Department of Internal Medicine, Telemark Hospital Trust, Skien, Norway Institute of Clinical Medicine, University of Oslo, Oslo, Norway Department of Transplantation Medicine, Oslo University Hospital – Rikshospitalet, Oslo, Norway Correspondence Torbjørn Fossum Heldal, Department of Internal Medicine, Telemark Hospital Trust, Skien, Norway. Email: torbjorn.heldal@hotmail.com, toheld@sthf.noSearch for more papers by this authorAnders Åsberg, Anders Åsberg orcid.org/0000-0002-0628-1769 Department of Transplantation Medicine, Oslo University Hospital – Rikshospitalet, Oslo, Norway Norwegian Renal Registry, Oslo University Hospital – Rikshospitalet, Oslo, Norway Department of Pharmacy, University of Oslo, Oslo, NorwaySearch for more papers by this authorThor Ueland, Thor Ueland Institute of Clinical Medicine, University of Oslo, Oslo, Norway K.G. Jebsen Thrombosis Research and Expertise Center, University of Tromsø, Tromsø, Norway Research Institute of Internal Medicine, Oslo University Hospital – Rikshospitalet, Oslo, NorwaySearch for more papers by this authorAnna Varberg Reisæter, Anna Varberg Reisæter Department of Transplantation Medicine, Oslo University Hospital – Rikshospitalet, Oslo, Norway Norwegian Renal Registry, Oslo University Hospital – Rikshospitalet, Oslo, NorwaySearch for more papers by this authorSøren E. Pischke, Søren E. Pischke orcid.org/0000-0003-2543-3251 Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway Department of Anesthesiology, Division of Emergencies and Critical Care, Oslo University Hospital, Oslo, NorwaySearch for more papers by this authorTom Eirik Mollnes, Tom Eirik Mollnes orcid.org/0000-0002-5785-802X K.G. Jebsen Thrombosis Research and Expertise Center, University of Tromsø, Tromsø, Norway Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway Research Laboratory, Nordland Hospital Bodø, Bodø, Norway Center of Molecular Inflammation Research, Norwegian University of Science and Technology, Trondheim, NorwaySearch for more papers by this authorPål Aukrust, Pål Aukrust K.G. Jebsen Thrombosis Research and Expertise Center, University of Tromsø, Tromsø, Norway Research Institute of Internal Medicine, Oslo University Hospital – Rikshospitalet, Oslo, Norway Section of Clinical Immunology and Infectious Diseases, Oslo University Hospital – Rikshospitalet, Oslo, NorwaySearch for more papers by this authorAnders Hartmann, Anders Hartmann Institute of Clinical Medicine, University of Oslo, Oslo, Norway Department of Transplantation Medicine, Oslo University Hospital – Rikshospitalet, Oslo, NorwaySearch for more papers by this authorKristian Heldal, Kristian Heldal orcid.org/0000-0003-4427-9528 Department of Internal Medicine, Telemark Hospital Trust, Skien, Norway Department of Transplantation Medicine, Oslo University Hospital – Rikshospitalet, Oslo, NorwaySearch for more papers by this authorTrond Jenssen, Trond Jenssen orcid.org/0000-0001-6602-8274 Institute of Clinical Medicine, University of Oslo, Oslo, Norway Department of Transplantation Medicine, Oslo University Hospital – Rikshospitalet, Oslo, NorwaySearch for more papers by this author Torbjørn Fossum Heldal, Corresponding Author Torbjørn Fossum Heldal torbjorn.heldal@hotmail.com toheld@sthf.no orcid.org/0000-0002-3049-4705 Department of Internal Medicine, Telemark Hospital Trust, Skien, Norway Institute of Clinical Medicine, University of Oslo, Oslo, Norway Department of Transplantation Medicine, Oslo University Hospital – Rikshospitalet, Oslo, Norway Correspondence Torbjørn Fossum Heldal, Department of Internal Medicine, Telemark Hospital Trust, Skien, Norway. Email: torbjorn.heldal@hotmail.com, toheld@sthf.noSearch for more papers by this authorAnders Åsberg, Anders Åsberg orcid.org/0000-0002-0628-1769 Department of Transplantation Medicine, Oslo University Hospital – Rikshospitalet, Oslo, Norway Norwegian Renal Registry, Oslo University Hospital – Rikshospitalet, Oslo, Norway Department of Pharmacy, University of Oslo, Oslo, NorwaySearch for more papers by this authorThor Ueland, Thor Ueland Institute of Clinical Medicine, University of Oslo, Oslo, Norway K.G. Jebsen Thrombosis Research and Expertise Center, University of Tromsø, Tromsø, Norway Research Institute of Internal Medicine, Oslo University Hospital – Rikshospitalet, Oslo, NorwaySearch for more papers by this authorAnna Varberg Reisæter, Anna Varberg Reisæter Department of Transplantation Medicine, Oslo University Hospital – Rikshospitalet, Oslo, Norway Norwegian Renal Registry, Oslo University Hospital – Rikshospitalet, Oslo, NorwaySearch for more papers by this authorSøren E. Pischke, Søren E. Pischke orcid.org/0000-0003-2543-3251 Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway Department of Anesthesiology, Division of Emergencies and Critical Care, Oslo University Hospital, Oslo, NorwaySearch for more papers by this authorTom Eirik Mollnes, Tom Eirik Mollnes orcid.org/0000-0002-5785-802X K.G. Jebsen Thrombosis Research and Expertise Center, University of Tromsø, Tromsø, Norway Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway Research Laboratory, Nordland Hospital Bodø, Bodø, Norway Center of Molecular Inflammation Research, Norwegian University of Science and Technology, Trondheim, NorwaySearch for more papers by this authorPål Aukrust, Pål Aukrust K.G. Jebsen Thrombosis Research and Expertise Center, University of Tromsø, Tromsø, Norway Research Institute of Internal Medicine, Oslo University Hospital – Rikshospitalet, Oslo, Norway Section of Clinical Immunology and Infectious Diseases, Oslo University Hospital – Rikshospitalet, Oslo, NorwaySearch for more papers by this authorAnders Hartmann, Anders Hartmann Institute of Clinical Medicine, University of Oslo, Oslo, Norway Department of Transplantation Medicine, Oslo University Hospital – Rikshospitalet, Oslo, NorwaySearch for more papers by this authorKristian Heldal, Kristian Heldal orcid.org/0000-0003-4427-9528 Department of Internal Medicine, Telemark Hospital Trust, Skien, Norway Department of Transplantation Medicine, Oslo University Hospital – Rikshospitalet, Oslo, NorwaySearch for more papers by this authorTrond Jenssen, Trond Jenssen orcid.org/0000-0001-6602-8274 Institute of Clinical Medicine, University of Oslo, Oslo, Norway Department of Transplantation Medicine, Oslo University Hospital – Rikshospitalet, Oslo, NorwaySearch for more papers by this author First published: 30 March 2022 https://doi.org/10.1111/ajt.17047AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract In the general population, low-grade inflammation has been established as a risk factor for all-cause mortality. We hypothesized that an inflammatory milieu beyond the time of recovery from the surgical trauma could be associated with increased long-term mortality in kidney transplant recipients (KTRs). This cohort study included 1044 KTRs. Median follow-up time post-engraftment was 10.3 years. Inflammation was assessed 10 weeks after transplantation by different composite inflammation scores based on 21 biomarkers. We constructed an overall inflammation score and five pathway-specific inflammation scores (fibrogenesis, vascular inflammation, metabolic inflammation, growth/angiogenesis, leukocyte activation). Mortality was assessed with Cox regression models adjusted for traditional risk factors. A total of 312 (29.9%) patients died during the follow-up period. The hazard ratio (HR) for death was 4.71 (95% CI: 2.85–7.81, p < .001) for patients in the highest quartile of the overall inflammation score and HRs 2.35–2.54 (95% CI: 1.40–3.96, 1.52–4.22, p = .001) for patients in the intermediate groups. The results were persistent when the score was analyzed as a continuous variable (HR 1.046, 95% CI: 1.033–1.056, p < .001). All pathway-specific analyses showed the same pattern with HRs ranging from 1.19 to 2.70. In conclusion, we found a strong and consistent association between low-grade systemic inflammation 10 weeks after kidney transplantation and long-term mortality. Abbreviations AXL6 receptor tyrosine kinase 6 CatS Cathepsin S CNI calcineurin inhibitor CRP C-reactive protein CsA cyclosporine CVD cardiovascular disease DGF delayed graft function DM diabetes mellitus ECM extracellular matrix EGFR estimated glomerular filtration rate EPCR endothelial protein C receptor ESRD end-stage renal disease FGF fibroblast growth factor GAS6 growth arrest specific gene 6 GDF-15 growth differentiation factor 15 HR hazard ratio IGF insulin-like growth factor IGFBP insulin-like growth factor binding protein IL interleukin IPTW inverse probability of treatment weighing IQR interquartile range KT kidney transplantation KTRs kidney transplant recipients MIF macrophage inhibitory factor NGAL neutrophil gelatinase-associated lipocalin OPN osteopontin OR odds ratio PRA panel reactive antibody PTDM post-transplant diabetes mellitus PTX3 pentraxin 3 sTNFR1 soluble tumor necrosis factor receptor 1 TCC terminal C5b-9 complement complex TNF tumor necrosis factor YKL40 tyrosine (Y), lysine (K), leucine (L)-40 1 INTRODUCTION Low-grade systemic inflammation is established as a risk factor for all-cause mortality in the general population1 and is associated with increased risk and severity of several diseases, including cardiovascular disease (CVD) and cancer.2-5 In the general population, activity in the innate immune system, as measured by elevated levels of tumor necrosis factor (TNF), interleukin (IL)-6, and high-sensitivity c-reactive protein (CRP), has been associated with increased morbidity and mortality,1, 6, 7 and inflammation is considered an important pathway in the development of atherosclerosis. Other inflammatory pathways such as endothelial dysfunction and vascular inflammation have also been associated with increased mortality.8, 9 It is well known that kidney transplant recipients (KTRs) have increased overall mortality, in particular caused by CVD, malignancy, and infectious diseases.10 Inflammation seems to be of particular importance in the pathogenesis for development of CVD in KTRs.11-13 Increased IL-6 and CRP have been associated with cardiovascular events and all-cause mortality in KTRs.14 A study performed on the same population as in this study, showed an association between elevated terminal C5b-9 complement complex (TCC) early after kidney transplantation (KT) and mortality and reduced graft survival.15 However, data that include several inflammatory pathways describing the complex network of inflammation and the relation to all-cause mortality in KTRs are scarce. In end-stage kidney disease the levels of general inflammatory markers (e.g., TNF, IL-6, CRP) are increased. It has been reported that some inflammatory biomarkers regress and eventually normalize within the first two months after KT.16 The surgical procedure per se induces a short transient inflammatory response. It has however been shown that a low-grade systemic inflammation can persist in a proportion of KTRs.16, 17 Such low-grade inflammation 10 weeks after transplantation has previously been postulated to play a role in the pathogenesis of post-transplant diabetes mellitus (PTDM)18-20 which is as an important risk factor for overall mortality after KT.21, 22 It has also been suggested that an inflammatory milieu plays a role in vascular calcification by stimulating fibrogenesis23, 24 being risk factors for death in chronic kidney disease patients.25 The main goal of the study was to examine the effect of general systemic inflammation measured 10 weeks after KT on long-term mortality, and for this purpose we analyzed a wide spectrum of markers reflecting different but overlapping inflammatory pathways. We also constructed composite inflammation scores representing different pathways of subclinical inflammation and investigated their associations with long-term post-transplant patient survival. 2 MATERIALS AND METHODS 2.1 Study population and design The study population has previously been described by Witczak et al.15 Patients who received a kidney transplant at the Norwegian national transplant center at Oslo University Hospital, Rikshospitalet between April 2007 and October 2012 were eligible for inclusion. In this period, a total of 1349 adult patients (18 years and older) underwent a kidney transplantation. After transplantation all patients were to perform a 10-weeks surveillance investigation unless they had an ongoing infection, or a recent or ongoing acute rejection. Patients with early graft loss (n = 22) or other clinical reasons for not attending the 10-weeks investigation (n = 136), as well as 147 patients who were not examined due to reduced lab accessibility in 2011 were excluded from analysis. The remaining 1044 (77.3%) KTRs had valid measurements for most of the specific inflammatory biomarkers and were included in the study, and among these 1001 patients had measures of all the biomarkers included in the overall inflammation score. Survival data were retrieved from the Norwegian Renal Registry on December 23rd, 2020. The study was approved by the Regional Ethics Committee in Norway and was performed in accordance with the Helsinki Declaration. 2.2 Immunosuppressive protocol For standard immunological risk recipients, immunosuppressive therapy during the study period consisted of induction treatment with methylprednisolone and IL-2 receptor antibody (basiliximab), and maintenance treatment with glucocorticoids, the cell proliferation inhibitor mycophenolate, and a calcineurin inhibitor (CNI).15 Patient with known donor specific antibodies at time of transplantation or receiving an ABO-incompatible transplant were classified as immunological high risk and received intravenous human immunoglobulin and rituximab, in addition to methylprednisolone and basiliximab, as induction therapy. Panel reactive antibody (PRA) positive (>20%) recipients were classified as immunological intermediate risk and received methylprednisolone and anti-thymocyte globulin as induction treatment. At the beginning of the study period, tacrolimus was preferred to younger patients ( 50 years) and to patients with BMI above 30 kg/m2 or with preoperative impaired glucose tolerance. During the study period, the standard regimen was revised, and from January 2011, tacrolimus was preferred for all patients with the exception of patients with impaired glucose tolerance. CNI trough concentrations for immunological standard-risk recipients were tacrolimus 3–7 µg/L from day of engraftment, while for CsA the target was initially 200–300 µg/L and then gradually reduced to 75–125 µg/L after 6 months. For high and intermediate immunological risk recipients, initial trough levels were higher and tapered to 6–10 t µg/L (tacrolimus) and 150–250 µg/L (CsA) by 10 weeks after transplantation and throughout the first year. Mycophenolate mofetil 750 mg (540 mg mycophenolate sodium) twice daily was used in combination with tacrolimus and 1000 mg (720 mg) in combination with CsA. Prednisolone was tapered to 10 mg/day by week 5 in standard – risk recipients and by week 6–7 in high or intermediate risk recipients. 2.3 Measurement of inflammatory biomarkers Blood samples were taken at the follow-up visit 10 weeks after KT and were stored in a biobank at minus 80°C for research purposes. All analyses in this study were performed on these samples by measurement in plasma or serum by enzyme immunoassays (EIAs) from R&D Systems (Stillwater, MN). Intra- and inter-assay coefficients of variation were <10% for all assays. The inflammatory biomarkers were chosen due to their relevance in post-transplant complications, in particular metabolic disturbances, and morbidity and mortality in the general population (Table 1). We selected a wide specter of inflammatory biomarkers reflecting several different pathways including fibrogenesis, vascular inflammation and endothelial dysfunction, metabolic inflammation, angiogenesis, and leukocyte activity. The aim was to explore the subtle variation in the inflammatory response in order to identify if any pathways were more relevant in post-transplant mortality. TABLE 1. Overview over the inflammation scores and their corresponding inflammatory and related biomarkers Inflammatory biomarkers Description and functional aspects Overall inflammation score Growth differentiation factor 15 (GDF−15) GDF−15 is part of TGF-beta-family, and it plays a role in the extracellular matrix regulation. It is expressed in a broad variety of tissues. The molecule is associated with increased cardiovascular stress and inflammation, and it predicts risk of several diseases, including cardiovascular disease and development of CKD. Produced by macrophages and is associated with an inflammatory environment31-33 CXC chemokine ligand 16 (CXCL16) CXCL16 also plays a role in the chemotaxis with recruitment of leukocytes to locations with inflammation, in particularly vascular inflammation. Mediator of the atherogenesis development27 Soluble tumor necrosis factor receptor 1 (sTNFR1) General marker representing TNF activity. Increases during inflammation. Involved in innate immunity Macrophage inhibitory factor (MIF) Important regulator of innate immunity and is classified as an inflammatory cytokine. Regulated by several cytokines including TNF and sustains macrophage inflammatory functions. Associated with vascular dysfunction and graft loss34 Pentraxin 3 (PTX3) PTX3 is an acute phase protein, it is a marker of activity of the innate immune system and is also in particular related to vascular inflammation. Produced and released by many cell types in response to primary inflammatory signals such as IL−1 and TNF35 Tyrosine (Y), lysine (K), leucine (L)−40 (YKL40) YKL40 is associated with inflammation and endothelial dysfunction among patients after kidney transplantation. Connected with overall and cardiovascular mortality in the non-transplant population9, 36 Granulysin Granulysin is expressed in CD8+ T-lymphocytes, NK-cells, and to a lesser extent in CD4+ T-lymphocytes. It has to main functions; 1) a cytotoxic antimicrobial effect against bacteria, parasites and fungi, and 2) involvement in the migration of immune cells as it is released from damaged immune cells and thus leads to chemotaxis37, 38 Insulin-like growth factor binding protein 1 (IGFBP1) Regulates IGF−1 bioactivity, glucose homeostasis, and tissue regeneration. Increases during inflammation Periostin Periostin is associated with ECM remodeling. Associated with renal fibrosis and chronic inflammatory diseases such as asthma, atopic dermatitis etc.39 Neutrophil gelatinase-associated lipocalin (NGAL) Is possibly related to an inflammatory response involved in CVD. It is also used as a biomarker of acute kidney injury40 Terminal 5b−9complement complex (TCC) Biomarker for complement system activity. Important part of the innate immune system. Associated with a proinflammatory environment Fibrogenesis GDF−15 Described above Syndecan Involved in fibrosis processing and is associated with renal function. There are reports connecting the molecule to an inflammatory state41 Osteopontin (OPN) Involved in calcification and inflammatory processes. Related to several metabolic and vascular outcomes42 Cathepsin S (CatS) Associated with IMT in CKD and could be associated with several vascular and metabolic outcomes. Induces CCL2-expression. Produced in response to inflammatory stimuli43 Periostin Described above General/vascular inflammation CXCL16 Described above sTNFR1 Described above PTX3 Described above TCC Described above Metabolic inflammation IGFBP1 Described above Resistin Adipokine, could be related to vascular and metabolic outcomes, and is believed to play a regulatory role in several inflammatory diseases44 Insulin-like growth factor 1 (IGF−1) Reduced by inflammation. Insulin sensitivity and vasculoprotective factor Chemerin Linked to renal function, obesity, glucose tolerance and hyperlipidemia. Have been shown to correlate with an inflammatory state45 Growth and angiogenesis Growth arrest-specific gene 6 (GAS6) Receptor tyrosine kinase 6 (AXL6) Relationship between GAS6 and TAM receptors. Involved in vascular inflammation and several kidney diseases. Can have both pro- and anti-inflammatory effects46 Endothelial protein C receptor (EPCR) Enhances anticoagulation by accelerating the activation of protein C to activated protein C and mediates anti-inflammatory effects47 Leukocyte activity MIF Described above Granulysin Described above NGAL Described above YKL−40 Described above Predictive post-hoc inflammation score CXCL16 Described above GDF−15 Described above Granulysin Described above IGFBP1 Described above Biomarkers only tested individually Insulin-like growth factor binding protein 3 (IGFBP3) IGFBP3 is involved in ECM regulation, and is induced by inflammatory cytokines. In patients with RA, it has been shown that IGFBP3 suppresses the production of proinflammatory cytokines by reducing the NF-kappa-B activity. Regulator of IGF-signaling48, 49 Note CKD, chronic kidney disease; CVD, cardiovascular disease; ECM, extracellular matrix remodeling; IMT, interna-media thickness; IL, interleukin; NK, natural killer; RA, rheumatoid arthritis; TAM, Tyro3, Axl and MER tyrosine receptor kinases; TGF, transforming growth factor; TNF, tumor necrosis factor. 2.4 Inflammation scores Detailed information about the biomarkers included for each score is presented in Table 1. Seven composite inflammation scores were constructed based on the statistical principles used by Bonaccio et al. for the INFLA-score in the Moli-sani study.1 Deciles were generated for each biomarker. Values within the four highest deciles scored 1 to 4, while the four lowest deciles scored −4 to −1. The fifth and sixth deciles scored 0 points. The scores for each inflammatory biomarker were summed up, and the total score was then divided into quartiles for survival analyses. In addition to an overall inflammation score including 11 biomarkers, we studied five pathway-specific inflammatory scores representing increased fibrogenesis, general/vascular inflammation, metabolic inflammation, growth/angiogenesis, and leukocyte activation. The inflammation scores were assessed as both categorical and continuous variables. One biomarker, IGFBP3, was only tested as an individual biomarker and was not included in any of the composite scores to avoid covariance bias in the pathway analyses due to its close relationship with IGF-1. Based on the independent biomarker analyses, we constructed a post-hoc inflammation score including the biomarkers with the largest effect estimates to test its predictive ability (IGFBP1, CXCL16, GDF-15, and Granulysin). 2.5 Statistical analyses All statistical analyses were performed by using IBM SPSS Statistics 27, except of Kaplan-Meier Plots that were made using StataCorp Stata/SE 16.0. Differences in baseline characteristics between the inflammatory groups were tested by One-Way ANOVA for continuous data, and Pearsons Chi-square tests were used for categorical data. Kaplan-Meier plots were created for the different inflammation scores, and Log-Rank tests were used to test differences in survival rates. Cox proportional hazard regression analyses were performed to examine the independent association for each inflammatory score with all-cause mortality. The models were adjusted for age, body mass index, estimated glomerular filtration rate (eGFR), sex, pretransplant diabetes mellitus (DM), PTDM, smoking status, type of CNI, dialysis vintage, deceased/living donor, and immunological risk. Time zero in the survival analyses was date of transplant. The inflammation scores were tested both as continuous and categorical variables, and in the analyses with the categorical variable the group with lowest inflammation score (first quartile) was used as reference value. We also performed analyses on 5-year mortality. The proportional-hazards assumption was tested by PH-Tests. Additional stratified Cox regression analyses were performed for KTRs below and above 65 years of age, for patients with or without DM (both pretransplant DM and PTDM), and for patients with autoimmune/inflammatory vs. non-inflammatory causes of end-stage renal disease (ESRD). We also performed Cox models adjusted for delayed graft function (DGF) (only on patients transplanted between 2009–2012). All of the inflammatory biomarkers were tested both independently and all together in the same multivariable model, and the values were included in separate models both as their true and standardized value. The values were standardized by dividing the true value by the standard deviation. We constructed a post-hoc inflammation score consisting of the four biomarkers with the highest HRs per standard deviation in the analysis involving all biomarkers and tested it in the same Cox regression model. Predictive factors for being in the upper overall inflammation score group (fourth quartile) were explored using multivariable logistic regression models. Inverse probability of treatment weighing (IPTW) analysis, with logarithmic treatment model, was performed to examine the effect of cyclosporine and tacrolimus on the probability of being in the upper quartile of the overall inflammation score. 3 RESULTS 3.1 Study population Descriptive baseline characteristics are presented in Table 2. The median follow-up time was 10.3 years (interquartile range [IQR] 8.5 to 11.8 years), and a total of 312 (29.9%) patients died during the study period. Median time to death was 6.4 years (IQR 3.9 to 8.9 years). The sum of the overall inflammation score varied from −39 to 38 (see Methods for score definition). CVD was the cause of death in 100 (32.1%) patients, 82 (26.3%) patients died of infections, and 130 (41.7%) died of malignancy and other causes. The relative frequencies of death cause per inflammation score percentiles are presented in Table 2. During the study period we experienced 409 (39.2%) overall graft losses. Of these, 144 (35.2%) were death-censored graft loss. TABLE 2. Demographic and baseline data according to quartiles of the overall inflammation score Quartiles of the overall inflammation score All patients First quartile (−39 to −11) Second quartile (−10 to −1) Third quartile (0 to 9) Fourth quartile (10 to 38) p-value Number (%) 1044 240 (24.0%) 250 (25.0%) 258 (25.8%) 253 (25.3%) – Age (years) 52.2 (14.4) 43.8 (14.3) 50.7 (13.5) 54.4 (13.0) 59.6 (11.9) <.001 Sex (male) 718 (68.8%) 149 (62.0%) 171 (68.4%) 186 (72.1%) 180 (71.4%) .068 BMI (kg/height2) 25.4 (6.8) 24.5 (3.6) 26.1 (11.5) 25.6 (4.2) 25.5 (4.5) .068 Weight (kg) 77.0 (15.7) 73.8 (14.6) 77.8 (16.0) 78.2 (15.1) 78.0 (16.7) .03 Current smoker (%) 217 (20.8%) 40 (16.7%) 53 (21.3%) 63 (24.4%) 49 (19.4%) .353 Dialysis vintage (months) 13.3 (15.2) 10.0 (13.9) 10.7 (13.7) 14.2 (15.4) 18.3 (16.3) <.001 Deceased donor (%) 706 (67.6%) 128 (53.3%) 151 (60.4%) 194 (75.2%) 204 (80.1%) < .001 Immunological high riska a Immunological high risk was defined as one of either: PRA >20%, ABO-incompatible transplantation, or more than two prior kidney transplants. 79 (7.6%) 15 (20.5%) 21 (28.8%) 21 (28.8%) 16 (21.9%) .686 Delayed graft functionb b Structured data on delayed graft function was only available from 2009 (704 patients). 59 (8.4%) 6 (10.2%) 6 (10.2%) 15 (25.4%) 32 (54.2%) <.001 Prednisolon dose (mg) 11.2 (4.4) 11.0 (4.2) 11.6 (4.7) 11.3 (4.6) 11.1 (3.8) .377 CNI: <.001 Tacrolimus (%) 567 (54.3%) 175 (73.5%) 140 (56.0%) 121 (47.5%) 106 (42.1%) Cyclosporine (%) 444 (42.5%) 57 (23.9%) 105 (42.0%) 125 (49.0%) 140 (55.6%) Other (%) 27 (2.6%) 6 (2.5%) 5 (2.0%) 9 (3.5%) 6 (2.4%) Cyclosporine conc (μg/l) 153.2 (57.8) 130.1 (47.5) 152.2 (56.5) 153.5 (59.3) 163.3 (59.1) .005 Tacrolimus conc (μg/l) 7.0 (2.2) 7.0 (2.3) 6.9 (2.0) 7.1 (2.0) 6.8 (2.4) .737 Type 1 DM (%) 99 (9.5%) 14 (5.8%) 17 (6.8%) 37 (14.3%) 27 (10.7%) <.001 Type 2 DM (%) 120 (11.5%) 16 (6.7%) 25 (10.0%) 28 (10.8%) 47 (18.6%) <.001 PTDM (%) 72 (6.9%) 17 (7.1%) 8 (3.2%) 21 (8.1%) 24 (9.5%) <.001 Creatinine (umol/l) 120.5 (39.8) 101.7 (25.0) 112.0 (28.7) 123.0 (36.0) 144.6 (50.7) <.001 eGFR (ml/min/1.73m2) 61.3 (21.2) 73.9 (18.3) 64.7 (18.7) 58.2 (19.5) 48.6 (20.2) <.001 Graft loss (%) 409 (39.2%) 40 (10.2%) 79 (20.2%) 106 (27.0
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