Skip Navigation


NDT Advance Access originally published online on February 27, 2008
Nephrology Dialysis Transplantation 2008 23(7):2337-2343; doi:10.1093/ndt/gfm951
This Article
Right arrow Abstract Freely available
Right arrow FREE Full Text (PDF) Freely available
Right arrow All Versions of this Article:
23/7/2337    most recent
gfm951v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrow Search for citing articles in:
ISI Web of Science (4)
Right arrowRequest Permissions
Right arrow Disclaimer
Google Scholar
Right arrow Articles by Panichi, V.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Panichi, V.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author [2008]. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.For Permissions, please e-mail: journals.permissions@oxfordjournals.org


Chronic inflammation and mortality in haemodialysis: effect of different renal replacement therapies. Results from the RISCAVID study

Vincenzo Panichi1, Giovanni M. Rizza2, Sabrina Paoletti1, Roberto Bigazzi3, Mauro Aloisi4, Giuliano Barsotti1, Paolo Rindi5, Giacli' Donati2, Alessandro Antonelli6, Erica Panicucci1, Gianni Tripepi7, Ciro Tetta8, Roberto Palla9,10 on behalf of the RISCAVID Study Group

1 Internal Medicine Department, University of Pisa 2 Renal Unit Pontedera 3 Renal Unit Livorno 4 Renal Unit Versilia 5 Nephrology Department, Hospital of Pisa 6 Renal Unit Lucca 7 CNR Reggio Calabria, Italy 8 Research Extracorporeal Therapy, Fresenius Medical Care, Bad Homburg, Germany 9 Renal Unit Massa, Italy

Correspondence and offprint requests to: Vincenzo Panichi, Dipartimento Medicina Interna, Via Roma 67, 56100 Pisa, Italy. Tel: +39-050-992887; Fax: +39-050-553414; E-mail: vpanichi{at}med.unipi.it



   Abstract
Top
 Abstract
Short summary
 Introduction
 Patients and methods
 Results
 Discussion
 References
 
Background. The ‘RISchio CArdiovascolare nei pazienti afferenti all’ Area Vasta In Dialisi' (RISCAVID) study is an observational and prospective trial including the whole chronic haemodialysis (HD) population in the northwest part of Tuscany (1.235 million people). The aim of the study was to elucidate the relevance of traditional and non-traditional risk factors of mortality and morbidity in HD patients as well as the impact of different HD modalities.

Methods. A total of 757 HD patients (mean age 66 ± 14 years, mean dialytic age 70 ± 76 months, diabetes 19%) were prospectively followed up for 30 months and all-cause mortality, cardiovascular (CV) mortality and non-fatal CV events (acute myocardial infarction and stroke) were registered. At the time of the enrolment, demographic, clinical and laboratory data of the whole population were entered into a centralized database. Serum albumin, high-sensitive C-reactive protein (CRP), interleukin-6 (IL-6) and interleukin-8 (IL-8) were centrally determined at the start of the study. Patients were stratified into three groups according to the HD modality: standard bicarbonate HD (BHD) (n = 424), haemodiafiltration (HDF) with sterile bags (n = 204) and online HDF (n = 129). The Cox proportional hazards regression assessed adjusted differences in CV morbidity and mortality risk; a multivariate analysis was also performed.

Results. All-cause and CV mortality was 12.9%/year and 5.9%/year, respectively. Patients with combined high levels of CRP and pro-inflammatory cytokines showed an increased risk for CV (RR 1.9, P < 0.001) and all-cause mortality (RR 2.57, P < 0.001). Multivariate analysis adjusted for comorbidity and demographic showed CRP as the most powerful mortality predictor (P < 0.001) followed by IL-6. The Cox proportional hazards regression assessed that online HDF and HDF patients had a significantly increased adjusted cumulative survival than BHD (P < 0.01).

Conclusions. Data at 30 months from this study showed the synergic effect of CRP and pro-inflammatory cytokines as the strong predictors of all-cause and CV mortality. HDF was associated with an improved cumulative survival independent of the dialysis dose.

Keywords: cardiovascular mortality; chronic inflammation; C-reactive protein; haemodiafiltration; IL-6



   Short summary
Top
 Abstract
 Short summary
Introduction
 Patients and methods
 Results
 Discussion
 References
 
RISCAVID, a prospective, observational study on 757 prevalent patients with a 30-month follow-up, showed a synergistic effect of single, basal determinations of CRP and pro-inflammatory cytokines in predicting all-cause and CV mortality. HDF was associated with an improved cumulative survival independent of the dialysis dose.



   Introduction
Top
 Abstract
 Short summary
 Introduction
Patients and methods
 Results
 Discussion
 References
 
Cardiovascular (CV) disease is the primary cause of morbidity in the western world, and the clinical impact of atherosclerosis is even more dramatically present in chronic uraemic patients. Despite continuous technical improvement and better global patient-care management, the annual mortality rate of patients with end-stage renal disease (ESRD) undergoing haemodialysis (HD) remains unacceptably high (10–22%) [1,2]. Factors affecting mortality include advanced age and comorbid conditions at the start of dialysis [3], the efficacy and quality of renal replacement therapy [4], practice patterns that may vary from region to region [5] and the intensity of chronic systemic inflammation. Chronic inflammation may play an important role in early morbidity and mortality in HD patients [6]. Several studies have attempted to address the question as to whether the type of the dialysis membrane, the quality of the dialysate and the uraemic state may be responsible for the induction of a chronic inflammatory state [7–11].

Acute-phase reactants represent a class of proteins-–mainly C-reactive protein (CRP) and serum amyloid A-–that are secreted primarily by hepatocytes [12] under different appropriate stimuli such as interleukin 6 (IL-6), a strong stimulatory agent [13]. The IL-6 production is enhanced in long-term HD patients [14]. Both IL-6 and CRP have been negatively related to low serum albumin levels [15]. Furthermore, CRP plasma values have been shown to be increased in patients on HD, but not in those on peritoneal dialysis [16], suggesting the role of monocyte activation and IL-6 production in the increased CRP generation due to backfiltered dialysate contaminants [17] or blood–membrane interaction.

In June 2004, a prospective, observational study (RISCAVID, ‘RISchio CArdiovascolare nei pazienti afferenti all’Area Vasta In Dialisi’) was started with the aim of investigating the link between traditional and non-traditional risk factors of mortality and morbidity in a large and homogenous HD population in the northwest part of Tuscany. Seven hundred and fifty-seven patients representing the whole HD population of 1 235 062 inhabitants were included. Each of the 15 dialysis facilities of this area provided, at the start of the study, blood samples from all patients for the determination of inflammatory markers, and at the start and every 6 months, data on patients’ demographic characteristics, renal history, laboratory values, comorbid disease, dialysis techniques, vascular access prescriptions and outcomes.

This paper describes the role of chronic inflammation and the impact of different HD modalities on morbidity and mortality rates in this large population of ESRD patients during a follow-up of 30 months.



   Patients and methods
Top
 Abstract
 Short summary
 Introduction
 Patients and methods
Results
 Discussion
 References
 
Protocol
The protocol was in conformity to the ethical guidelines of our institutions, and informed consent was obtained from each participant. This was a prospective observational study registered in the Cochrane Renal Group registry for ‘Cardiovascular risk in dialysis: RISCAVID study’ (#CRG040700112). All studies were performed during a midweek dialysis day.

Study cohort
Seven hundred and fifty-seven patients with ESRD (61% males) who had been on regular HD for at least 3 months and with a dialytic vintage of 70 ± 76 months were enrolled in the study. These patients represented the entire HD population from 15 HD facilities of the northwest part of Tuscany. Renal diuresis at baseline was present in a small proportion of patients, namely 27% on BHD, 22% on HDF with bags and 17% on online HDF, with no significant difference between HD modalities. All patients were on three times a week HD and no further selection was performed in patients. Patients’ dry weight was targeted to achieve a normotensive oedema-free state. Six hundred and twenty-two patients had arteriovenous fistula, 73 had a subcutaneous polytetrafluoroethylene graft (PTFE) and 60 had a semi-permanent transcutaneous access. Chronic renal failure was caused by primary glomerulonephritis (GN) (n = 109), secondary GN (n = 68), congenital or hereditary kidney disease (n = 136), diabetic nephropathy (n = 89), chronic pyelonephritis (n = 50), vascular disease (n = 123), interstitial nephritis (n = 25) and in the remaining, chronic renal failure was caused by uncertain aetiology (n = 157). The methodologies used to categorize CV disease (CVD) were described elsewhere [18]. Two physicians independent of the study were responsible for the clinical ascertainment. Diabetes was defined by the use of insulin or oral hypoglycaemic agents. Furthermore, data regarding smoking status, body mass index (BMI), blood pressure and the use of medications were recorded.

HD modalities
Standard low- or high-flux bicarbonate dialysis (BHD) was performed in 424 patients using either synthetic low-flux (from 1.4 to 1.6 m2) or high-flux polysulphone membranes (from 1.4 to 1.8 m2, Fresenius Medical Care, Bad Homburg, Germany and Bellco, Mirandola, Italy) polyamide (Gambro, Lund, Sweden), modified cellulosic membranes (from 1.2 to 1.6 m2, Bellco) and polymethacrylate (1.6 m2, Toray, Tokyo, Japan). Low-flux BHD was used in 403 patients and high-flux BHD in 21 (4.9%). Modified cellulose membranes were used in 23 patients of the low-flux BHD group (5.4% of the BHD group and 3.0% of the entire study population).

HDF with reinfusion bags was performed using either high-flux polysulphone (from 1.8 to 2.0 m2, Fresenius Medical Care) or polyamide (from 1.7 to 2.1 m2, Gambro) using commercially available sterile bicarbonate bags (10– 15 L/session) in 130 patients or performed as acetate-free biofiltration (AFB) in 74 patients using sterile acetate-free dialysate (Safebag 93G, Hospal, Medolla, Italy) with the AN69 dialyzers (from 1.65 to 2.1 m2, Hospal). Online HDF was performed in 129 patients by the online production of ultrapure bicarbonate-buffered dialysate (22–25 L/session) using high-flux polysulphone membranes (from 1.8 to 2.0 m2, Fresenius Medical Care), polyamide (2.1 m2, Gambro) or as haemodiafiltration (HDF) with online regeneration of ultrafiltrate (Bellco). In Table 2, the demographic factors, comorbidities and laboratory data at baseline are described according to the type of treatment modality. In all centres, analysis of the dialysis water system was performed every 3–6 months and revealed the absence of bacteria (<100 colony-forming units/mL) or bacteriological contaminant products (endotoxin levels <0.025 endotoxin units).


View this table:
[in this window]
[in a new window]

  		Table 2 Demographic characteristics of the study population according to renal replacement therapies

 
Follow-up
The occurrence of morbid or fatal events was recorded every 6 months from June 2004 until December 2006 (30 months of follow-up) by five nephrologists who periodically visited each centre. Deaths and major non-fatal events were classified as due to CVD (myocardial infarction, congestive heart failure, stroke, sudden death) or non-CVD (infection, malignancy, unknown causes). During the first visit, a questionnaire addressed ESRD history, medical and psychosocial history, dialysis prescription, laboratory data, and prescribed medications at the time of study enrolment of all prevalent patients. In the following visits, all-cause and CVD mortality, major non-fatal CVD events, dialysis prescription and medication were recorded.

Laboratory measurements
Blood sampling was performed starting the study on a midweek dialysis day after an overnight fast between 7:00 a.m. and 9:00 a.m. and after 20–30 min of quiet resting in a semirecumbent position. Serum lipids, calcium, phosphate, haemoglobin and other routine parameters were measured using standard laboratory methods. Serum albumin, CRP, IL-6 and IL-8 were centrally determined at the Immunopathology Laboratory of the Internal Medicine Department, University of Pisa. Samples were taken into chilled EDTA vacutainers, placed immediately in ice and centrifuged within 30 min at –4°C, and the plasma was stored at –80°C before assay. Serum albumin was measured with a nephelometric technique (Dade Behring GmbH, Marburg, Germany) with an intra- and inter-assay variability of 4.3% and 4.4%, respectively. CRP was measured by a modified high-sensitivity laser nephelometry technique (Berhing Diagnostics, GmbH, Rarburg, Germany). The CRP assay was standardized according to the WHO First International Reference Standard with a sensitivity of 0.1 mcg/mL and a standard reference range of 0.1– 0.4 mg/L.

IL-6 (EIA, R&D Systems, Minneapolis, MN, USA) and IL-8 (EIA, Biosource International, CA, USA) were measured by quantitative sandwich enzyme immunoassay techniques. Samples were assayed in duplicate and the intra- and inter-assay coefficient of variation for IL-6 was <5.3% and <7.2%, respectively, with a detection limit <5 pg/mL. The intra- and inter-assay coefficient of variation for IL-8 was <4.7% and <6.9%, respectively, with a detection limit <3 pg/mL. No cross-reactivity or interference was found with other factors related to or associated with IL-6 and IL-8.

Statistical analysis
The {chi}2 test and the Mann–Whitney test were used to compare proportions and means, respectively. The Spearman's correlation coefficient was used to explore the relationship between quantitative variables. The cumulative probability of survival from the entry in the study (defined as the time of June 2004) to the terminal event (stated as all-cause mortality) was estimated by the product-limit (Kaplan–Meier) method. The log-rank test was used to compare the homogeneity of survival functions across strata defined by binary transformation of prognostic variables. A Cox's proportional hazard model, with all-cause mortality outcome, was used to jointly test the effect of predictors of survival times, adjusted for age, gender, diabetes, dialytic vintage, albumin levels, haemoglobin levels, use of epoetin, blood pressure, use of antihypertensive medications and dialytic efficiency. The backward elimination was used in identifying the most important prognostic factors. In the regression analysis, the explanatory variables were recorded to binary variables. Cox's proportional hazard model was also used to compute multivariate-adjusted relative risk estimates and 95% confidence intervals (CI). In Cox's model, computations were performed on log-transformed values of quantitative variables. The independent prognostic power for all-cause and CV mortality of each inflammation marker was calculated as reported by Tripepi et al. [19]. Briefly, in this analysis to construct a basic multivariate Cox model for all-cause and CV mortality, we preliminarily identified a set of variables that were associated (with P < 0.10) with these outcomes in the univariate Cox regression analysis. These variables were then used to construct two basic models: the first for all-cause mortality and the second for CV mortality. We then compared the prediction power attributable to CRP and IL-6 by using the –2 log likelihood (–2 log L) test. This test compares different Cox's models fitted to the same set of data, and the smaller the –2 log L value, the stronger the agreement between the model and the observed data. The difference between the –2 log L values of the models gives a statistical estimate as to which of them provides a better fit to the data. A 3.841 difference in the –2 log L values coincides with a significance level of 0.05 in a chi-square distribution with 1 degree of freedom and indicates a better prediction of risk estimate provided by the method leading to the lowest –2 log L value. The gain in the explained variation (R2) in all-cause mortality attributable to serum CRP and IL-6 was calculated by using the standard approach [20]. Hazard ratios and their 95% CI were calculated with the use of the estimated regression coefficients and their standard errors in the Cox regression analysis. All calculations were made using a standard statistical package (SPSS for Windows version 9.0.1, Chicago, IL, USA, 11 March 1999). Results were expressed as means and side deviation. All tests were considered significant with P <0.05.



   Results
Top
 Abstract
 Short summary
 Introduction
 Patients and methods
 Results
Discussion
 References
 
Study cohort and follow-up
Age, sex distribution, bodyweight, BMI and other demographic and clinical characteristics are reported in Table 1.


View this table:
[in this window]
[in a new window]

  		Table 1 Demographic characteristics of the study population

 
During the follow-up period, 244 deaths occurred, 111 due to CV events and 133 due to non-CV events; furthermore, 67 major CV non-fatal events were recorded. Gross mortality was 12.9% per year with a CV mortality of 5.9%.

Impact of chronic inflammation on fatal and non-fatal events
CRP plasma levels at baseline >5 mg/L (48% of the entire population) were associated with an enhanced risk for all-cause mortality (CV mortality 7.2%/year versus 3.8%/year RR 1.88, CI 1.08–3.28, P < 0.001; all-cause mortality 17%/year versus 8%/year with RR 2.18, CI 1.53–3.09, P < 0.001). As shown in Table 3, patients in the highest tertiles of CRP showed an increased RR for all-cause mortality with respect to the lower tertile. The associated risk of all-cause mortality, CV mortality and non-fatal events for IL-6 and IL-8 highest tertiles with respect to the lower are shown in Table 2.


View this table:
[in this window]
[in a new window]

  		Table 3 Relative risks (95% confidence interval) for all-cause mortality, cardiovascular death and non-fatal events according to tertiles of CRP, IL-6 and IL-8. The relative risk of all-cause mortality, CV mortality and non-fatal events for highest tertiles (II and III) of CRP, IL-6 and IL-8, with respect to the lower (I) tertiles, defined as 1.0 are shown

 
Patients with combined high levels of CRP and pro-inflammatory cytokines (CRP > 5 mg/dL, IL-6 > 3.2 pg/ mL, IL-8 > 1 pg/mL) showed an increased risk for CV (RR 1.9; P < 0.001) and all-cause mortality (RR 2.5; P < 0.001). When albumin values were added to the analysis, patients with high levels of CRP and pro-inflammatory cytokines and low albumin levels (<3.5 g/dL) reached the highest levels of risk for a CV fatal event (RR 2.1; P < 0.001) and all-cause mortality of 2.7 (P < 0.001). The stepwise regression analysis showed CRP as the strongest independent predictor of all-cause and CV events (P = 0.0025; F-value 9.839) even after adjustment for age, dialytic age, diabetes, comorbidity and BMI followed by IL-6 (P < 0.01).

Furthermore, the independent prognostic power for all-cause and CV mortality of CRP and IL-6 was calculated by using the –2 log L test. Cox's basic model showed an R2 of 32% with a –2 log L test value ranging from 703.661 to 628.370. A significant increase of risk was reached when adding CRP to this model (R2 33.4%, P = 0.0164, –2 log L test value from 703.661 to 623.817) or IL-6 (R2 33%, P = 0.0175, –2 log L test value from 703.661 to 623.873).

Impact of dialysis techniques on inflammatory state and patient survival
In Figure 1, the evaluation of the inflammatory state in patients categorized according to the different HD modalities is reported. IL-6 but not CRP was statistically reduced in online HDF versus BHD and HDF (P < 0.01).


Figure 1
View larger version (20K):
[in this window]
[in a new window]
[Download PowerPoint slide]
  		Fig. 1 Inflammatory markers and dialytic techniques. The evaluation of the inflammatory state in patients categorized according to the different HD modalities is reported. CRP is expressed as mg/L and IL-6 as pg/mL (*P < 0.01).

 
No significant differences were observed in received single-pool Kt/V (1.40 versus 1.43 versus 1.40; P = NS) among BHD, HDF with sterile bags and online HDF, respectively.

HDF with sterile bags and online HDF had lower all-cause mortality rates than standard HD even after adjustment for age, gender, diabetes, dialytic vintage, albumin levels, haemoglobin levels, use of epoetin, blood pressure, use of antihypertensive medications and dialytic efficiency (RR 0.78, P = 0.01) (Figure 2 as shown by the Kaplan–Meier survival curve).


Figure 2
View larger version (14K):
[in this window]
[in a new window]
[Download PowerPoint slide]
  		Fig. 2 Cumulative survival and dialytic techniques. Cumulative survival curves estimated from the Cox model according to dialytic techniques. Haemodiafiltration with bags (n= 204) and online HDF (n = 129) had lower crude mortality rates than standard HD patients (n = 424) even after adjustment for comorbidity after 30 months of follow-up.

 
Online HDF had lower CV mortality rates than standard HD and HDF with bags even after adjustment for age, gender, diabetes, dialytic vintage, albumin levels, haemoglobin levels, use of epoetin, blood pressure, use of antihypertensive medications and dialytic efficiency (RR 0.78, P = 0.01) (Figure 3 as shown by the Kaplan–Meier survival curve).


Figure 3
View larger version (16K):
[in this window]
[in a new window]
[Download PowerPoint slide]
  		Fig. 3 Cardiovascular survival and dialytic techniques. Cardiovascular curves of cardiovascular survival estimated from the Cox model according to dialytic techniques. Online HDF (n= 129) had lower CV mortality rates than standard HD (n = 424) and HDF with bags (n= 204) even after adjustment for comorbidity after 30 months of follow-up.

 


   Discussion
Top
 Abstract
 Short summary
 Introduction
 Patients and methods
 Results
 Discussion
References
 
The first most important and new result of RISCAVID, a prospective, observational study, concerned with the synergistic effect of single, basal measurements of CRP, IL-6, IL-8 and albumin in predicting CV and all-cause mortality over a 30-month follow-up.

High plasma levels of CRP (>5 mg/dL) and of pro-inflammatory cytokines (IL-6 > 3.2 pg/mL, IL-8 > 1 pg/ mL) showed an increased risk for CV (RR 1.9; P < 0.001) and all-cause mortality (RR 2.5 P < 0.001). The highest levels of risk for CV (RR 2.1; P < 0.001) and all-cause mortality (RR 2.7; P < 0.001) were reached in patients with low albumin plasma values (<3.5 g/dL). By the stepwise regression analysis and the –2 log L test, CRP was the strongest predictor of all-cause and CV mortality even after adjustment for age, dialytic age, diabetes, comorbidities and BMI followed by IL-6.

Several studies performed in HD patients have shown that serum albumin, CRP, IL-6, fetuin and adhesion molecules are associated with all-cause and CV mortality [18,19,21–25]. Of interest, our study was performed on a prevalent population relatively homogenous for race, geography, medical care and HD management. We suggest that our results may have practical relevance. Routine determination of plasma values of CRP using a high-sensitivity assay might be recommended to evaluate an increased risk of all-cause mortality. However, different laboratory methods to detect CRP may show a large variability [26] and in clinical trials, at least centralization of CRP measurement is recommended.

Our study has a number of limitations. First, our patients could be a selected group of survivors since we studied prevalent patients on HD for about 6 years.

Second, multiple rather than single measurements of CRP and cytokines would have been valuable to improve predictability on all-cause and CV mortality. Furthermore, using multiple measurements, the effects of different HD modalities on inflammatory markers (see below) could be evaluated. However, when the study protocol was established and agreed on by the participating centres, we had followed the indication given at that time by Yeun et al. [27], suggesting the relevance of single measurements of CRP in predicting mortality over a long-term (34 months) follow-up.

The second relevant aspect was the association of different treatment modalities (low- and high-flux BHD and HDF) with chronic inflammation and consequently with cumulative survival.

In the RISCAVID study, the enhanced cumulative survival of patients receiving HDF in comparison to BHD (Figures 2 and 3) confirmed the results by Canaud et al. [28], who showed that the mortality risk in high-efficiency HDF was significantly lower (35%) than that in BHD when adjusting for differences in the comorbidity profile in a large patient cohort prospectively followed for 3 years in the Dialysis Outcomes and Practice Patterns Study (DOPPS) [3,29]. However, in the DOPPS, only surrogate markers of inflammation were measured, i.e. albumin and ferritin, since the measurements of CRP and cytokines were not included in the data collection. In our study, IL-6, a strong predictor for CV mortality [18,23], was significantly decreased in online HDF. With respect to the DOPPS [28], the RISCAVID population differed for the high incidence of mixed convective–diffusive techniques (HDF, 44% of the entire population). This equalled to 333 patients being followed for 30 months with respect to the 89 of 443 Italian patients of the DOPPS [29]. Of interest, in our study, the difference in cumulative survival was already significant after the 15-month follow-up after adjustment for age, dialysis vintage and comorbidities (Figure 2). At variance with the DOPPS [28], we could not establish a relationship between mortality and volume exchange. Despite this limitation, HDF using sterile bags is customarily prescribed using 10–15 L/session of reinfusion fluid while online HDF is performed with at least 22–25 L/session.

We could not elucidate the exact mechanism by which HDF could be associated with lower mortality than BHD. However, several explanations may be proposed: the removal of a wider spectrum of uraemic solutes [30], an improved intradialytic haemodynamic stability, which facilitates treatment in elderly and high-risk patients [31] and finally the combination of high-flux synthetic, biocompatible membranes with ultrapure dialysis fluid [31–33]. Furthermore, as recently shown by Carracedo et al. [34], online HDF may reduce the potential to produce pro-inflammatory cytokines by modulating the circulating levels of CD14+ CD16+ mononuclear cells.

The ‘anti-inflammatory’ effect of HDF may render it and is particularly indicated in patients who have elevated plasma levels of CRP and an increased risk for CV and all-cause mortality.

In conclusion, CRP and pro-inflammatory cytokines are independently associated with all-cause and CV mortality in dialysis patients. The combined determination of CRP and IL-6 seems to be the best option for risk stratification in dialysis patients, particularly in the context of clinical studies. Future randomized clinical trials will be needed to better discern whether single or repeated measurements of biomarkers of chronic inflammation, namely CRP, are able to predict all-cause and CV morbidity and mortality. New, large studies should aim at whether and to what extent the use of these biomarkers might be informative not only for risk stratification in incident patients, but also to better evaluate the potential to reverse the effect on the all-cause and CV mortality by combined anti-inflammatory pharmacologic and haemodialytic strategies.

Conflict of interest statement. C. Tetta is a full-time employee of Fresenius Medical Care, GmbH Deutschland.



   Notes
 
10 Data collection. Emanuela Mantuano, Sara Beati and Valentina Marchetti (Pisa). Laboratory analysis. Cristina Consani, Cristina Filippi and Maria Rita Metelli (Pisa). Study participants. Marina Barattini (Carrara), Giancarlo Betti (Massa), Massimiliano Migliori, Paolo Ferrandello, Stefano De Pietro (Versilia), Riccardo Giusti (Lucca), Giacomina Fontana (Castelnuovo Garfagnana), Franco Saloi (Barga), Adamasco Cupisti, Alberto Lippi, Raffaele Caprioli (Pisa), Giovanni Grazi (Pontedera), Gabriella Sibilia (Volterra), Valentina Batini, Daniela Guzzo (Livorno), Piero Paparatto (Cecina), Renato Cominotto, Alessandro Baronti, Roberto Menicucci (Piombino) and Giuseppe Pratesi (Portoferraio). Back



   References
Top
 Abstract
 Short summary
 Introduction
 Patients and methods
 Results
 Discussion
 References
 

  1. Rayner HC, Pisoni RL, Bommer J, et al. Mortality and hospitalization in haemodialysis patients in five European countries: results from the Dialysis Outcomes and Practice Patterns Study (DOPPS). Nephrol Dial Transplant (2004) 19:108–120.[Abstract/Free Full Text]
  2. Ganesh SK, Hulbert-Shearon T, et al. Mortality differences by dialysis modality among incident ESRD patients with and without coronary artery disease. J Am Soc Nephrol (2003) 14:415–424.[Abstract/Free Full Text]
  3. Goodkin DA, Bragg-Gresham JL, Koenig KG, et al. Association of comorbid conditions and mortality in hemodialysis patients in Europe, Japan, and the United States: the Dialysis Outcomes and Practice Patterns Study (DOPPS). J Am Soc Nephrol (2003) 14:3270–3277.[Abstract/Free Full Text]
  4. Wolfe RA, Hulbert-Shearon TE, Ashby VB, et al. Improvements in dialysis patient mortality are associated with improvements in urea reduction ratio and hematocrit, 1999 to 2002. Am J Kidney Dis (2005) 45:127–135.[CrossRef][Medline]
  5. Goodkin DA, Mapes DL, Held PJ. The dialysis outcomes and practice patterns study (DOPPS): how can we improve the care of hemodialysis patients? Semin Dial (2001) 14:157–159.[CrossRef][Web of Science][Medline]
  6. Arici M, Walls J. End-stage renal disease, atherosclerosis, and cardiovascular mortality: is C-reactive protein the missing link? Kidney Int (2001) 59:407–414.[CrossRef][Web of Science][Medline]
  7. Schouten WEM, Grooteman MPC, van Houte AJ, et al. Effects of dialysers and dialysate on the acute phase reaction in clinical bicarbonate dialysis. Nephrol Dial Transplant (2000) 15:379–384.[Abstract/Free Full Text]
  8. Honkanen E, Gronhagen-Riska C, Teppo AM, et al. Acute-phase proteins during hemodialysis: correlations with serum interleukin-1 levels and different dialysis membranes. Nephron (1991) 57:283–287.[Medline]
  9. Tsirpanlis G, Bagos P, Ioannou D, et al. The variability and accurate assessment of microinflammation in haemodialysis patients. Nephrol Dial Transplant (2004) 19:150–157.[Abstract/Free Full Text]
  10. Kerr PG, Sutherland WH, de Jong S, et al. The impact of standard high-flux polysulfone versus novel high-flux polysulfone dialysis membranes on inflammatory markers: a randomized, single-blinded, controlled clinical trial. Am J Kidney Dis (2007) 49:533–539.[CrossRef][Medline]
  11. Lagrand WK, Visser CA, Hermens WT, et al. C-reactive protein as a cardiovascular risk factor: more than an epiphenomenon? Circulation (1999) 100:96–102.[Abstract/Free Full Text]
  12. Ballou SP, Kushner I. C-reactive protein and the acute phase response. Adv Int Med (1992) 37:313–336.[Medline]
  13. Lu Y, Berthier-Schaad Y, Fallin MD, et al. IL-6 haplotypes, inflammation, and risk for cardiovascular disease in a multiethnic dialysis cohort. J Am Soc Nephrol (2006) 17:863–870.[Abstract/Free Full Text]
  14. Bologa RM, Levine DM, Parker TS, et al. Interleukin-6 predicts hypoalbuminemia, hypocholesterolemia, and mortality in hemodialysis patients. Am J Kidney Dis (1998) 32:107–114.[Web of Science][Medline]
  15. Kaysen GA, Dublin JA, Muller HG, et al. The acute phase response varies with time and predicts serum albumin levels in hemodialysis. Kidney Int (2000) 58:346–352.[CrossRef][Web of Science][Medline]
  16. Haubitz M, Brunkhorst R, Wrengen E, et al. Chronic induction of C-reactive protein by hemodialysis, but not by peritoneal dialysis. Perit Dial (1996) 16:158–162.
  17. Panichi V, De Pietro S, Andreini B, et al. Cytokine production in haemodiafiltration: a multicentre study. Nephrol Dial Transplant (1998) 13:1737–1744.[Abstract/Free Full Text]
  18. Panichi V, Maggiore U, Taccola D, et al. Interleukin-6 is a stronger predictor of total and cardiovascular mortality than C-reactive protein in hemodialysis patients. Nephrol Dial Transplant (2004) 19:1154–1160.[Abstract/Free Full Text]
  19. Tripepi G, Mallamaci F, Zoccali C. Inflammation markers, adhesion molecules, and all-cause and cardiovascular mortality in patients with ESRD: searching for the best risk marker by multivariate modeling. J Am Soc Nephrol (2005) 16(Suppl_1):S83–S88.[Abstract/Free Full Text]
  20. Collet D. Modelling Survival Data in Medical Research (1994) London, UK: Chapmann & Hall. 72–85.
  21. Zimmermann J, Herrlinger S, Pruy A, et al. Inflammation enhances cardiovascular risk and mortality in hemodialysis patients. Kidney Int (1999) 55:648–658.[CrossRef][Web of Science][Medline]
  22. Wanner C, Zimmermann J, Schwedler S, et al. Inflammation and cardiovascular risk in dialysis patients. Kidney Int (2002) (S80):99–102.
  23. Pecoits Filho R, Barany P, Lindholm B, et al. Interleukin 6 is an independent predictor of mortality in patients starting dialysis treatment. Nephrol Dial Transplant (2002) 17:1684–1688.[Abstract/Free Full Text]
  24. Stenvinkel P, Heinburger O, Paultre F, et al. Strong associations between malnutrition, inflammation and atherosclerosis in chronic renal failure. Kidney Int (1999) 55:1899–1911.[CrossRef][Web of Science][Medline]
  25. Honda H, Qureshi AR, Heimburger O, et al. Serum albumin, C-reactive protein, interleukin 6, and fetuin a as predictors of malnutrition, cardiovascular disease, and mortality in patients with ESRD. Am J Kidney Dis (2006) 47:139–148.[CrossRef][Web of Science][Medline]
  26. Maggiore U, Cristol JP, Canaud B, et al. Comparison of 3 automated assays for C-reactive protein in end-stage renal disease: clinical and epidemiological implications. J Lab Clin Med (2005) 145:305–308.[CrossRef][Web of Science][Medline]
  27. Yeun JY, Levine RA, Mantadilok V, et al. C-reactive protein predicts all-cause and cardiovascular mortality in hemodialysis patients. Am J Kidney Dis (2000) 35:469–476.[Web of Science][Medline]
  28. Canaud B, Bragg-Gresham JL, Marshall MR, et al. Mortality risk for patients receiving hemodiafiltration versus hemodialysis: European results from the DOPPS. Kidney Int (2006) 69:2087–2093.[CrossRef][Web of Science][Medline]
  29. Pontoriero G, Santoro D, Messina A, et al. The Dialysis Outcomes and Practice Patterns Study (DOPPS): results of the Italian cohort. G Ital Nefrol (2005) 22:494–502.[Medline]
  30. Lornoy W, Becaus I, Billiouw JM, et al. On-line haemodiafiltration. Remarkable removal of beta2-microglobulin. Long-term clinical observations. Nephrol Dial Transplant (2000) 15(Suppl 1):49–54.[Abstract/Free Full Text]
  31. Wizemann V, Lotz C, Techert F, et al. On-line haemodiafiltration versus low-flux haemodialysis. A prospective randomized study. Nephrol Dial Transplant (2000) 15(Suppl 1):43–48.[Abstract/Free Full Text]
  32. Guth HJ, Gruska S, Kraatz G. On-line production of ultrapure substitution fluid reduces TNF-alpha- and IL-6 release in patients on hemodiafiltration therapy. Int J Artif Organs (2003) 26:181–187.[Web of Science][Medline]
  33. Locatelli F, Marcelli D, Conte F, et al. Comparison of mortality in ESRD patients on convective and diffusive extracorporeal treatments. The registro lombardo dialisi e trapianto. Kidney Int (1999) 55:286–293.[CrossRef][Web of Science][Medline]
  34. Carracedo J, Merino A, Nogueras S, et al. On-line hemodiafiltration reduces the proinflammatory CD14+CD16+ monocyte-derived dendritic cells: a prospective, crossover study. J Am Soc Nephrol (2006) 17:2315–2321.[Abstract/Free Full Text]
Received for publication: 9. 6.07
Accepted in revised form: 21.12.07


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 Nephrol Dial TransplantHome page
E. L. Penne, N. C. van der Weerd, M. L. Bots, M. A. van den Dorpel, M. P. C. Grooteman, R. Levesque, M. J. Nube, P. M. ter Wee, P. J. Blankestijn, and On behalf of the CONTRAST investigators
Patient- and treatment-related determinants of convective volume in post-dilution haemodiafiltration in clinical practice
Nephrol. Dial. Transplant., November 1, 2009; 24(11): 3493 - 3499.
[Abstract] [Full Text] [PDF]

Home page
 Eur Heart JHome page
C. Etter, Y. Straub, M. Hersberger, H. R. Raz, T. Kistler, D. Kiss, R. P. Wuthrich, H.-J. Gloor, D. Aerne, P. Wahl, et al.
Pregnancy-associated plasma protein-A is an independent short-time predictor of mortality in patients on maintenance haemodialysis
Eur. Heart J., October 21, 2009; (2009) ehp429v1.
[Abstract] [Full Text] [PDF]

Home page
 CJASNHome page
J. F. Navarro-Gonzalez, C. Mora-Fernandez, M. Muros, H. Herrera, and J. Garcia
Mineral Metabolism and Inflammation in Chronic Kidney Disease Patients: A Cross-Sectional Study
Clin. J. Am. Soc. Nephrol., October 1, 2009; 4(10): 1646 - 1654.
[Abstract] [Full Text] [PDF]

Home page
 CirculationHome page
T. B. Horwich and G. C. Fonarow
Measures of Obesity and Outcomes After Myocardial Infarction
Circulation, July 29, 2008; 118(5): 469 - 471.
[Full Text] [PDF]

This Article
Right arrow Abstract Freely available
Right arrow FREE Full Text (PDF) Freely available
Right arrow All Versions of this Article:
23/7/2337    most recent
gfm951v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrow Search for citing articles in:
ISI Web of Science (4)
Right arrowRequest Permissions
Right arrow Disclaimer
Google Scholar
Right arrow Articles by Panichi, V.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Panichi, V.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?