Skip Navigation


NDT Advance Access originally published online on March 6, 2006
Nephrology Dialysis Transplantation 2006 21(6):1611-1617; doi:10.1093/ndt/gfl053
This Article
Right arrow Abstract Freely available
Right arrow FREE Full Text (PDF) Freely available
Right arrow Supplementary Data
Right arrow All Versions of this Article:
21/6/1611    most recent
gfl053v1
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 (6)
Right arrowRequest Permissions
Right arrow Disclaimer
Google Scholar
Right arrow Articles by Roberts, M. A.
Right arrow Articles by Ierino, F. L.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Roberts, M. A.
Right arrow Articles by Ierino, F. L.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author [2006]. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Original Articles: Dialysis and Transplantation

Low molecular weight advanced glycation end products predict mortality in asymptomatic patients receiving chronic haemodialysis

Matthew A. Roberts1, Merlin C. Thomas2, Dharsh Fernando3, Neil Macmillan4, David A. Power1 and Francesco L. Ierino1

1 Department of Nephrology, Austin Health, Heidelberg, and Department of Medicine, University of Melbourne, Victoria, 2 Division of Diabetic Complications, Baker Medical Research Institute, Melbourne, 3 Department of Cardiology and 4 Division of Laboratory Medicine, Austin Health, Heidelberg, Victoria, Australia

Correspondence and offprint requests to: Francesco L. Ierino, Department of Nephrology, Austin Health, PO Box 5555, Heidelberg 3084, Victoria, Australia. Email: Frank.IERINO{at}austin.org.au



   Abstract
Top
 Abstract
Introduction
 Methods
 Results
 Discussion
 References
 
Background. Advanced glycation end products (AGEs) have biological properties that may contribute to the premature cardiovascular mortality of haemodialysis patients. This study examines the hypothesis that low molecular weight forms of fluorescent AGEs (LMW fluorescence) predict mortality in haemodialysis patients.

Methods. The LMW fluorescence was measured in 85 patients treated with chronic haemodialysis and prospectively followed for 4 years. The primary outcome of all-cause mortality was assessed using Cox proportional hazards regression model.

Results. At the end of the follow-up period 37 (44%) patients died. The median LMW fluorescence level was 24.2 arbitrary units (range: 10.6–148.1 AU) and the receiver operator characteristic (ROC) curve cut-off for mortality was 37.0 AU. The LMW fluorescence predicted death both as a binary variable at the ROC cut-off, and as a continuous log-transformed variable when adjusted for age, albumin and C-reactive protein (CRP). Adjusted for age, albumin and CRP, the hazard ratio for mortality was 3.05 (1.41–6.60, P = 0.005) for LMW fluorescence as a binary variable and 2.71 per log unit (1.37–5.38, P = 0.004) as a continuous log-transformed variable.

Conclusion. The low molecular weight forms of AGEs predict mortality in patients receiving chronic haemodialysis, and may be important in the mechanisms leading to atherosclerosis and inflammation in such patients.

Keywords: advanced glycation end products; haemodialysis; mortality



   Introduction
Top
 Abstract
 Introduction
Methods
 Results
 Discussion
 References
 
Advanced glycation end products (AGEs) contribute to the development and progression of cardiovascular disease [1]. The AGEs are thought to act through receptor-independent and -dependent mechanisms to promote vascular damage, fibrosis and inflammation associated with accelerated atherogenesis [2]. The AGEs and their receptors have been detected within atherosclerotic lesions and correlate with the size and complexity of the lesions [1]. The AGE accumulation is associated with an increase in vascular [3] and cardiac [4] stiffness, and the circulating level of AGEs may be used as a biomarker of tissue AGE accumulation [5].

The production and accumulation of tissue AGEs are increased in patients with end stage renal disease (ESRD), potentially contributing to the observed high mortality in this population. Yet, despite the clear link between renal impairment and the accumulation of AGEs [6], studies in haemodialysis patients have failed to show any association between AGEs and clinically relevant outcomes [7–9]. This observation may be explained by the confounding effect of malnutrition [8]. Malnutrition in dialysis patients increases cardiovascular risk and also reduces circulating protein-bound AGEs via effects on endogenous protein turnover [10] and reduced exogenous AGEs from the diet [11].

Incomplete digestion of AGE-modified protein results in the formation of low molecular weight (LMW) degradation products incorporating AGE modifications. In addition to correlating with tissue AGE modification, LMW AGEs have a high toxic potential, being free to interact with AGE receptors at distant sites via the circulation. Several free AGEs have been identified, including pentosidine, N-(carboxymethyl) lysine (CML) and free-imidazole AGEs. In addition, fluorescence (370 nm [excitation]/440 nm [emission]) in the LMW phase of serum correlates with tissue fluorescence, an established marker for AGE modification [2,5,12]. We have previously demonstrated in patients with type 2 diabetes that LMW fluorescence is correlated with the presence of micro- and macro-vascular complications of disease [6]. This study examines the hypothesis that LMW fluorescence is associated with all-cause mortality in a cross-sectional cohort of patients on haemodialysis, prospectively followed for 4 years.



   Methods
Top
 Abstract
 Introduction
 Methods
Results
 Discussion
 References
 
Study population and design
Patients receiving chronic haemodialysis at the Department of Nephrology at Austin Health (Victoria, Australia) were approached to enter the study. Exclusion criteria were poor life expectancy (<6 months) and inability to give informed consent. Ninety-six patients gave written and informed consent and were enrolled into the study. These patients were then followed prospectively for 4 years, during which time they received routine clinical care by physicians blinded to the LMW AGE results. The study was undertaken in conformity with the Declaration of Helsinki, and approved by the Austin Health Human Research Ethics Committee.

Baseline characteristics
At baseline, age, gender, presence or absence of diabetes, history of hypertension, smoking history (any previous smoking) and dyslipidaemia (abnormal lipid profile or taking lipid-lowering agents) were recorded as cardiac risk factors. The use of HMGCoA Reductase Inhibitors (‘statins’) and previous cardiac disease (myocardial infarction, revascularization procedure, or positive diagnostic test for coronary artery disease or left ventricular dysfunction) were also recorded. Haemodialysis variables were obtained from patient records and included time on dialysis, dialysis hours per week and urea reduction ratio (URR) as an index of dialysis quality. Where data was available, adjusted Kt/V was calculated by the Daugirdas formula [13].

Biochemical assays
At baseline, serum samples were collected at the commencement of dialysis on mid-week dialysis days. Samples were stored at –80°C until analysed. Albumin and C-reactive protein (CRP) were measured on non-fasting samples by standard methods (Roche Hitachi 917). Apolipoproteins AI (Apo A) and B (Apo B) were measured by an immunoturbidometric method (Roche Hitachi 917, Mayne Health Dorevitch Pathology, Heidelberg, Victoria). Cardiac troponin I was measured using the AccuTnI assay (Beckman Coulter) on an Access 2 analyser and a level >0.06 µg/l was considered detectable.

The LMW fluorescence was assayed on serum stored at –80°C for 36 months using a previously described method [6], based on an original description by Wrobel et al. [12]. All samples were stored for the same length of time (within a week) and LMW fluorescence is stable in stored samples [14]. Briefly, 20 µl of serum was de-proteinated with 48 µl of trichloroacetic acid and mixed with 10 µl of chloroform. The resulting aqueous phase was injected directly into a carrier stream (deionized water) in which total LMW fluorescence (370/440 nm) was measured using a Waters 470 fluorescence spectrophotometer (Waters, Milford, Massachusetts, US). Injection volume was standardized using the absorbance at 280 nm by an UV detector placed in a series. Samples were processed in duplicate and analysed in triplicate. The content of LMW fluorescence was expressed as arbitrary units (AU) defined by the ratio of the area under the fluorescence curve divided by the area under the absorption curve, normalized to the values derived following exhaustive enzymatic hydrolysis of AGE-modified bovine serum albumin. Using this system, the inter-assay variability was 4% and the inter-day variability was 6%.

Data collection and end-points
After 4 years of follow-up, clinical data were collected by review of medical records and admissions listed on the hospital computer system. The primary endpoint was all-cause mortality. Cardiac death and non-cardiac death were analysed as secondary endpoints. Death was considered cardiac if it followed a myocardial infarction (MI) or witnessed cardiac arrest.

Statistical analysis
We used {chi}2 to analyse dichotomous variables (and Fisher's exact test where expected outcomes were few). A Student's t-test was used to compare means of continuous variables that were normally distributed and Wilcoxon rank sum test was used to compare means of non-parametric data. Normally distributed data are expressed as mean and standard deviation, and non-normally distributed data are expressed as median and inter-quartile range (IQR). A Cox proportional hazards model was used to assess the predictive value of LMW fluorescence. Patients were followed from the date of the serum sample until either death or end of follow-up. Patients were censored at the time of transplant.

Selection of co-variates
Data collected in the study included age and sex, cardiovascular risk factors, dialysis variables and biochemical indices. Because of its skewed distribution, LMW AGE fluorescence was natural log-transformed for the Cox model. In addition, receiver operator characteristic (ROC) analysis was performed to determine the optimum LMW fluorescence cut-point for predicting death. All variables were first analysed as single variables and those that were significant at the P<0.10 level were included in the multivariate analysis. Stepwise backward elimination was used to generate the final model. Each variable in the final model was then tested for interaction with the other variables. Results are expressed as hazard ratios (HRs) with 95% confidence intervals.

Because the sample size limited the number of co-variates that could be included in the model, models were also examined with different selection procedures to test if findings were consistent. Co-variates were also selected if the percentage change in the LMW AGE ß-coefficient was greater than 10% when that variable was added [15], and by the above two methods, but including age, gender, diabetes and previous cardiac disease (variables of clinical relevance to all-cause mortality). The proportional hazards assumption was assessed by examination of scaled Schoenfeld residuals over time and, for categorical variables, inspection of the plots of -ln[-ln(survival)] versus ln(analysis time). Statistical analysis was performed using STATA version 8.2 software (Statacorp, Texas).



   Results
Top
 Abstract
 Introduction
 Methods
 Results
Discussion
 References
 
Baseline characteristics
Ninety-six patients gave consent and four failed to have the baseline blood sample. Stored serum was unavailable in seven. This left 85 patients available for analysis. The baseline characteristics of the 85 haemodialysis patients are shown in Table 1. Seventy-five patients (88%) used Fresenius F7 dialysers (polysulfone), eight (9%) used Fresenius F8 dialysers, and two used high-flux dialysers. Seven patients underwent renal transplantation during the four-year follow-up period.


View this table:
[in this window]
[in a new window]
  		Table 1. Baseline characteristics of 85 participants. Characteristics are presented as mean±SD, median (inter-quartile range) or number (%)

 
Baseline LMW fluorescence levels
The median LMW fluorescence level was 24.2 AU (range: 10.6–148.1 AU) and the distribution was positively skewed. As previously described in patients with diabetes [5,16], the median LMW fluorescence was lower in men than in women (23.1 vs 29.2, P = 0.002). In addition, patients with hypertension had lower LMW fluorescence (23.7 vs 26.7 in patients without hypertension, P = 0.02). Whilst this may reflect the use of ACE inhibitors and angiotensin receptor blockers in this group, as these agents may independently modify LMW fluorescence [17], the LMW fluorescence was not different between users and non-users of these agents (23.7 vs 24.3 AU, respectively, P = 0.85). The LMW fluorescence was not significantly different between haemodialysis patients with and without diabetes (27.1 vs 24.0, P = 0.64), consistent with previous reports in patients on haemodialysis [16]. Correlation of log-transformed LMW fluorescence was assessed with all continuous variables and the only significant correlation was with apolipoprotein AI (r = 0.25, P = 0.02). Notably, log-transformed LMW fluorescence did not correlate with dialysis indices including URR (r = 0.1, P = 0.37), dialysis vintage (log-transformed, r = 0.12, P = 0.24) or Kt/V (r = 0.13, P = 0.31).

Mortality after four years
After four years, 37 out of 85 patients died (44%). Compared with the survivors, deceased patients were older (64.4±12.1 vs 55.6±16.2 years, P<0.01), and a greater proportion had diabetes (32.4% vs 14.6%, P = 0.05). In addition, they had lower serum albumin (33.2±3.6 g/l vs 36.0±4.0 g/l, P<0.01), higher CRP (14.7 mg/l, IQR 6.7–23.8, vs 5.4 mg/l, IQR 2.6–12.9, P<0.001) and were more likely to have detectable cardiac troponin I at baseline (35.1 vs 14.6%, P = 0.03). However, there was no difference in the proportion with a past history of cardiovascular disease (43.2 vs 41.7%) or in LMW fluorescence levels (24.2 vs 23.9, P = 0.18). The specific causes of death are listed in Table 2.


View this table:
[in this window]
[in a new window]
  		Table 2. Causes of death as a proportion of all deaths (n = 37)

 
The ROC curve for LMW AGE fluorescence and all-cause mortality is presented in Figure 1. The optimum cut-off was 37.0 AU. Four-year survival was 60% in the patients with LMW AGE fluorescence below the ROC cut-off and 17% in patients with LMW AGE fluorescence above the ROC cut-off (Figure 2). The univariate HRs for each variable suitable for inclusion in the final model (P<0.10) are presented in Table 3, including LMW AGE fluorescence as both the log-transformed continuous variable and dichotomized at the ROC curve determined cut-off value. (HRs for all variables are available as an on-line supplement.) LMW AGE fluorescence above 37 AU more than doubles the hazard of death, but LMW AGE fluorescence was not statistically significant as a log-transformed continuous variable.


Figure 1
View larger version (17K):
[in this window]
[in a new window]
  		Fig. 1. Receiver operator characteristic curve for LMW AGE fluorescence and all-cause mortality.

 

Figure 2
View larger version (14K):
[in this window]
[in a new window]
  		Fig. 2. Survival of patients according to the ROC curve determined cut-off of 37 AU for LMW AGE fluorescence.

 

View this table:
[in this window]
[in a new window]
  		Table 3. Variables with significant (P<0.10) HRs for all-cause mortality on univariate analysis

 
The multivariate models after backward stepwise elimination incorporating variables listed in Table 3 are presented in Table 4. As a continuous or dichotomous variable, LMW AGE fluorescence remained a strong predictor of mortality adjusted for age, albumin and CRP. The result of the backward stepwise procedure was the same if sex and previous cardiac disease were added to the model. Results were also similar if co-variates were selected based on the percentage change in the LMW AGE fluorescence ß-coefficient, except that age was not included and albumin was removed from Model A (see online supplementary data). Adjustment for use of antagonists of the renin-angiotensin system was also made, but did not alter the final models. Although albumin and CRP were significantly negatively correlated (Pearson's r for log-transformed CRP and albumin = –0.39, P<0.001), there was no interaction between these or other covariates in either model.


View this table:
[in this window]
[in a new window]
  		Table 4. Multivariate HRs for death using LMW fluorescence as a dichotomous variable in Model A, and a continuous, log-transformed variable in Model B. Both models were initially adjusted for diabetes status and cardiac troponin I, but these were removed in the stepwise elimination procedure

 
Secondary outcomes
Analysis of cardiac (n = 10) and non-cardiac (n = 27) deaths was undertaken. The LMW AGE fluorescence dichotomized at the ROC curve cut-off was a significant predictor of cardiac death on univariate analysis, with an HR of 4.9 (1.4–17.5, P = 0.014). However, there were too few events for a multivariate Cox regression analysis of these outcomes to be performed.



   Discussion
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
References
 
Advanced glycation end products and their receptors are viewed as having a pivotal role in the initiation and progression of cardiovascular disease [18]. This 4 year prospective study demonstrates that LMW fluorescence, a biomarker of tissue AGE accumulation [5], is associated with mortality in patients on haemodialysis. This finding is consistent with the potential role of AGEs as ‘middle molecule’ uraemic toxins in patients with ESRD and adds LMW fluorescence to the list of other biochemical markers of risk in ESRD such as the cardiac troponins [19] and CRP [20], both of which also predicted adverse outcome in this study.

These results are in contrast to previous studies in haemodialysis patients, which failed to show any association between AGEs and mortality [7–9]. An important difference between these studies and the present study is that the LMW fraction of AGEs in serum was analysed rather than the protein-linked high molecular weight AGEs measured in other studies. Fluorescence in the LMW fraction of serum is elevated in haemodialysis patients [16], and the current study confirms this, with 3-fold higher levels in patients on haemodialysis than healthy control patients with normal renal function using this same assay (previously reported as 7.0 AU) [6]. LMW AGEs form from the incomplete digestion of AGE-modified protein, with the LMW degradation products incorporating AGE modifications. These accumulate in the circulation of patients with ESRD because of both increased generation of glycoxidative intermediates and impaired renal clearance of exogenous and endogenous adducts.

Although the molecular identity of putative fluorophores remains to be established, there is strong evidence linking fluorescence measures to both AGE accumulation and its functional corollary. For example, collagen becomes proportionally more fluorescent and less soluble with age [21]. N{epsilon}-(carboxymethyl) lysine (CML) is a non-fluorescent AGE, and non-CML AGEs correlate better with complications in patients with type 1 diabetes [22]. Treatments that reduce the accumulation of tissue AGE fluorescence are also protective against the development of diabetic complications [1]. Therefore, LMW fluorescence should be considered as a biomarker of tissue amine modification linked to the presence of (unmeasured) toxic molecules with cross-linking potential or the ability to activate specific AGE receptors.

A number of mechanisms by which LMW AGE accumulation might contribute to complications of haemodialysis have been proposed. Indeed, LMW AGEs may have a higher toxic potential than those of larger AGE-modified proteins, being free to interact with AGE receptors at distant sites via the circulation [5]. Both in vitro and in vivo, AGE-adducts are able to activate endothelial cells and mononuclear phagocytes, triggering the production of pro-inflammatory and fibrogenic cytokines, chemokines and growth factors in the vessel wall [18]. Endothelial cells may also react with AGEs to promote cell adhesion, transendothelial migration, inflammation and the formation of blood clots [23]. The AGEs also have a number of actions independent of receptor activation, including direct actions on oxidative stress and the quenching of NO. In addition, AGE peptides isolated from serum can react covalently with other proteins, such as Apo B, resulting in significant changes in both structure and function [24].

In our study, the association between LMW fluorescence and mortality was significant for LMW AGE fluorescence dichotomized at the ROC cut-off but not as a log-transformed continuous variable. However, when adjusted for age, serum albumin and CRP, LMW AGE fluorescence became a strong independent predictor of death as a continuous or binary variable. Overall, serum albumin had the greatest influence on this relationship as demonstrated by the effect on the LMW fluorescence ß-coefficient (see online supplementary data). Several factors may affect metabolism of serum proteins in haemodialysis patients, including nutritional status, inflammation, metabolic acidosis and dialysis modality. These in turn may affect the measurement of circulating AGE-modified protein in this cohort, as these factors lead to increased protein turnover [10]. This association between AGEs and nutritional status is important because, the diet is a major source of AGEs in patients on dialysis and diets low in AGEs have been shown to reduce circulating AGE levels in these patients [11]. Reduced protein and calorie intake associated with uraemia may represent a protective mechanism in CKD, whereby a spontaneous reduction in protein intake acts to reduce exposure to dietary AGEs, and therein AGE-related toxicity.

The sample size of our study imposes limitations with regard to the number of variables we can include in the multivariate analysis. Because of a concern that the results may differ with different co-variates in the model, we evaluated variables based on two different criteria (univariate P<0.10 and >10% change in LMW AGE fluorescence ß-coefficient). In both cases, the LMW AGE fluorescence remained an important predictor. The lack of association of previous cardiovascular disease with death in this study probably reflects both the lower frequency of cardiac causes of death in this study (27%) and possible underestimation of the presence of cardiovascular disease at baseline.

Given the association between AGEs and outcomes, interventions to specifically reduce AGE accumulation in patients on haemodialysis may one day be considered as a component of standard clinical practice. Measures to reduce exposure of the peritoneum to excessive amounts of AGEs already form a useful part of peritoneal dialysis therapy. Reduction in circulating AGEs can be achieved through dietary modifications in patients on dialysis [11]. Dialysis itself is not sufficient to remove ‘middle molecule’ toxins such as AGEs, as suggested by the lack of association between LMW fluorescence and URR or Kt/V. Alternative dialytic strategies such as haemodialysis with a high-flux and super-flux dialysers and totally or partially convective treatments may also have particular utility [25]. The usefulness of drugs that specifically reduce AGE accumulation in patients on haemodialysis remains to be established. However, it should be noted that both ACE inhibitors and angiotensin receptor blockers have anti-AGE effects that may contribute to their particular efficacy in patients with ERSD [17,26].

In conclusion, this study demonstrates, for the first time, an association between LMW fluorescence and mortality in patients on haemodialysis, particularly when adjusted for serum albumin, CRP and patient age. Future studies testing the utility of interventions to specifically reduce AGE accumulation in patients on dialysis are keenly awaited.



   Acknowledgments
 
We thank the dialysis nursing staff for collecting multiple serum samples, in particular Cathy Chan and Steven Brown. Shorter follow-up of this work was presented in abstract form at the Australia and New Zealand Society of Nephrology, and American Society of Nephrology Annual Scientific Meetings in 2004. M.A.R. is the recipient of a National Health and Medical Research Council (NHMRC) Postgraduate Research Scholarship (310632) for 2004–2005, and a NHMRC Centre for Clinical Research Excellence in Renal Medicine Scholarship for the study of Clinical Epidemiology in 2004.

Conflict of interest statement. None declared.



   References
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 

  1. Forbes JM, Yee LT, Thallas V et al. Advanced glycation end product interventions reduce diabetes-accelerated atherosclerosis. Diabetes 2004; 53: 1813–1823[Abstract/Free Full Text]
  2. Yan SF, Ramasamy R, Naka Y, Schmidt AM. Glycation, inflammation, and RAGE: a scaffold for the macrovascular complications of diabetes and beyond. Circ Res 2003; 93: 1159–1169[Abstract/Free Full Text]
  3. Schram MT, Schalkwijk CG, Bootsma AH, Fuller JH, Chaturvedi N, Stehouwer CD. Advanced glycation end products are associated with pulse pressure in type 1 diabetes: the EURODIAB prospective complications study. Hypertension 2005; 46: 232–237.[Abstract/Free Full Text]
  4. Candido R, Forbes JM, Thomas MC et al. A breaker of advanced glycation end products attenuates diabetes-induced myocardial structural changes. Circ Res 2003; 92: 785–792[Abstract/Free Full Text]
  5. Thomas MC, Forbes JM, Macisaac R, Jerums G, Cooper ME. Low-molecular weight advanced glycation end products: markers of tissue AGE accumulation and more? Ann N Y Acad Sci 2005; 1043: 644–654[CrossRef][Web of Science][Medline]
  6. Thomas M, Tsalamandris C, MacIsaac R et al. Low-molecular-weight AGEs are associated with GFR and anemia in patients with type 2 diabetes. Kidney International 2004; 66: 1167–1172[CrossRef][Web of Science][Medline]
  7. Schwedler SB, Metzger T, Schinzel R, Wanner C. Advanced glycation end products and mortality in hemodialysis patients. Kidney Int 2002; 62: 301–310[CrossRef][Web of Science][Medline]
  8. Suliman ME, Heimburger O, Barany P et al. Plasma pentosidine is associated with inflammation and malnutrition in end-stage renal disease patients starting on dialysis therapy. J Am Soc Nephrol 2003; 14: 1614–1622[Abstract/Free Full Text]
  9. Busch M, Franke S, Muller A et al. Potential cardiovascular risk factors in chronic kidney disease: AGEs, total homocysteine and metabolites, and the C-reactive protein. Kidney Int 2004; 66: 338–347[CrossRef][Web of Science][Medline]
  10. Giordano M, De Feo P, Lucidi P et al. Increased albumin and fibrinogen synthesis in hemodialysis patients with normal nutritional status. J Am Soc Nephrol 2001; 12: 349–354[Abstract/Free Full Text]
  11. Uribarri J, Peppa M, Cai W et al. Restriction of dietary glycotoxins reduces excessive advanced glycation end products in renal failure patients. J Am Soc Nephrol 2003; 14: 728–731[Abstract/Free Full Text]
  12. Wrobel K, Garay-Sevilla ME, Nava LE, Malacara JM. Novel analytical approach to monitoring advanced glycosylation end products in human serum with on-line spectrophotometric and spectrofluorometric detection in a flow system. Clin Chem 1997; 43: 1563–1569[Abstract/Free Full Text]
  13. Daugirdas JT. Simplified equations for monitoring Kt/V, PCRn, eKt/V, and ePCRn. Adv Ren Replace Ther 1995; 2: 295–304[Medline]
  14. Januszewski AS, Thomas MC, Chung SJ et al. Plasma low-molecular weight fluorescence in type 1 diabetes mellitus. Ann NY Acad Sci 2005; 1043: 655–661[CrossRef][Medline]
  15. Vittinghoff E, Glidden D, Shiboski S, McCulloch C. Chapter 5 Predictor Selection, in Regression Methods in Biostatistics. Linear, Logistic, Survival and Repeated Measures Models, New York, Springer: 2005; 133–156
  16. Schalkwijk CG, Ter Wee PM, Stehouwer CD. Plasma levels of AGE peptides in type 1 diabetic patients are associated with serum creatinine and not with albumin excretion rate: possible role of AGE peptide-associated endothelial dysfunction. Ann NY Acad Sci 2005; 1043: 662–670[CrossRef][Medline]
  17. Forbes JM, Thorpe SR, Thallas-Bonke V et al. Modulation of soluble receptor for advanced glycation end products by angiotensin-converting enzyme-1 inhibition in diabetic nephropathy. J Am Soc Nephrol 2005; 16: 2363–2372[Abstract/Free Full Text]
  18. Thomas MC, Baynes JW, Thorpe SR, Cooper ME. The role of AGEs and AGE inhibitors in diabetic cardiovascular disease. Curr Drug Targets 2005; 6: 453–474[CrossRef][Web of Science][Medline]
  19. Roberts MA, Fernando D, Macmillan N et al. Single and serial measurements of cardiac troponin I in asymptomatic patients on chronic hemodialysis. Clin Nephrol 2004; 61: 40–46[Web of Science][Medline]
  20. Zimmermann J, Herrlinger S, Pruy A, Metzger T, Wanner C. Inflammation enhances cardiovascular risk and mortality in hemodialysis patients. Kidney Int 1999; 55: 648–658[CrossRef][Web of Science][Medline]
  21. Monnier VM, Elmets CA, Frank KE, Vishwanath V, Yamashita T. Age-related normalization of the browning rate of collagen in diabetic subjects without retinopathy. J Clin Invest 1986; 78: 832–835[CrossRef][Web of Science][Medline]
  22. Miura J, Yamagishi S, Uchigata Y et al. Serum levels of non-carboxymethyllysine advanced glycation endproducts are correlated to severity of microvascular complications in patients with type 1 diabetes. J Diabetes Complications 2003; 17: 16–21[Medline]
  23. Brownlee M, Vlassara H, Cerami A. Nonenzymatic glycosylation and the pathogenesis of diabetic complications. Ann Intern Med 1984; 101: 527–537[Abstract/Free Full Text]
  24. Bucala R, Makita Z, Vega G et al. Modification of low density lipoprotein by advanced glycation end products contributes to the dyslipidemia of diabetes and renal insufficiency. Proc Natl Acad Sci USA 1994; 91: 9441–9445[Abstract/Free Full Text]
  25. Tessitore N, Lapolla A, Arico NC et al. Effect of protein leaking BK-F PMMA-based hemodialysis on plasma pentosidine levels. J Nephrol 2004; 17: 707–714[Web of Science][Medline]
  26. Forbes JM, Thomas MC, Thorpe SR, Alderson NL, Cooper ME. The effects of valsartan on the accumulation of circulating and renal advanced glycation end products in experimental diabetes. Kidney International 2004; 66: S105–S107[CrossRef]
Received for publication: 19. 9.05
Accepted in revised form: 31. 1.06


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
 CJASNHome page
E. Linden, W. Cai, J. C. He, C. Xue, Z. Li, J. Winston, H. Vlassara, and J. Uribarri
Endothelial Dysfunction in Patients with Chronic Kidney Disease Results from Advanced Glycation End Products (AGE)-Mediated Inhibition of Endothelial Nitric Oxide Synthase through RAGE Activation
Clin. J. Am. Soc. Nephrol., May 1, 2008; 3(3): 691 - 698.
[Abstract] [Full Text] [PDF]

Home page
 Eur Heart JHome page
J. W.L. Hartog, A. A. Voors, C. G. Schalkwijk, J. Scheijen, T. D.J. Smilde, K. Damman, S. J.L. Bakker, A. J. Smit, and D. J. van Veldhuisen
Clinical and prognostic value of advanced glycation end-products in chronic heart failure
Eur. Heart J., December 1, 2007; 28(23): 2879 - 2885.
[Abstract] [Full Text] [PDF]

This Article
Right arrow Abstract Freely available
Right arrow FREE Full Text (PDF) Freely available
Right arrow Supplementary Data
Right arrow All Versions of this Article:
21/6/1611    most recent
gfl053v1
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 (6)
Right arrowRequest Permissions
Right arrow Disclaimer
Google Scholar
Right arrow Articles by Roberts, M. A.
Right arrow Articles by Ierino, F. L.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Roberts, M. A.
Right arrow Articles by Ierino, F. L.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?