Increased platelet–monocyte aggregates and cardiovascular disease in end-stage renal failure patients

Neil Ashman1, Marion G. Macey2, Stanley L. Fan1, Urooj Azam2 and Muhammad M. Yaqoob1

1Department of Renal Medicine and Transplantation and 2Department of Haematology, St Bartholomew’s and the Royal London Hospitals, London, UK

Correspondence and offprint requests to: Dr Marion G. Macey, Department of Haematology, The Royal London Hospital, Whitechapel, London E1 1BB, UK. Email: marion.macey{at}bartsandthelondon.nhs.uk



   Abstract
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 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 References
 
Background. Atherosclerotic cardiovascular disease is a major cause of morbidity and mortality in patients with end-stage renal disease. This excess morbidity cannot be entirely explained by well-recognized conventional and novel risk factors alone, and occurs irrespective of dialysis modality. Recent evidence suggests that the activation of platelets and their interaction with circulating cells are important independent risk factors for atherosclerosis in non-uraemic patients. We therefore studied platelet activation and circulating platelet–leucocyte aggregates in stable patients without evidence of cardiovascular disease on continuous ambulatory peritoneal dialysis (CAPD) and haemodialysis and investigated an association with cardiovascular events.

Methods. Immunofluorescent flow cytometry was used to measure the percentage of P-selectin- (CD62P) positive platelets, the percentage of platelet–neutrophil and platelet–monocyte aggregates, and the expression of the P-selectin ligand, P-selectin glycoprotein ligand-1 (PSGL-1, CD162) on leucocytes in haemodialysis and CAPD patients and normal controls. The platelet count and the mean platelet component (MPC, a measure of platelet activation) were determined on the ADVIATM 120 Haematology System (Bayer, NY).

Results. Platelet activation as assessed by MPC or CD62P expression was significantly increased in haemodialysis but not CAPD patients compared with controls. Circulating platelet–monocyte aggregates were significantly increased in parallel with a significant reduction in PSGL-1 expression on monocytes in both patient groups compared with normal controls. The presence of higher platelet–monocyte aggregates in dialysis patients was associated with increased cardiovascular events.

Conclusion. We describe increased platelet–monocyte aggregates with reduced leucocyte PSGL-1 expression in patients with end-stage renal disease irrespective of dialysis modality, associated with an increased risk of cardiovascular disease. These findings may suggest a novel mechanism by which accelerated atherosclerosis occurs in uraemic patients.

Keywords: CD62P; dialysis; platelet–monocyte aggregates; platelets; PSGL-1



   Introduction
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 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 References
 
Atherosclerotic cardiovascular disease is a major cause of morbidity and mortality in patients with end-stage renal disease (ESRD) undergoing renal replacement therapy [1]. Despite significant progress in dialysis technology and in the prevention and treatment of renal and coronary artery disease, the prevalence of cardiovascular disease has remained static during the last decade, and is only partly explained by conventional risk factors [2].

In the non-uraemic state, vascular inflammation plays an essential role in advancing endothelial injury and atherogenesis [3], as well as in the formation and propagation of platelet-dependent thrombi in acute coronary syndromes [3,4]. Long recognized as having a role in inflammation, platelets and platelet–leucocyte aggregates are now known to contribute to ongoing injury at atheromatous sites, and in plaque disruption [5]. Platelet P-selectin (CD62P) interacts with its natural ligand on neutrophils and monocytes, P-selectin glycoprotein ligand-1 (PSGL-1), to allow formation of heterotypic aggregates, thus providing an anchoring source for inflammatory cells on activated platelets [6]. These bioactive platelet–monocyte aggregates have been shown to be important in evolving coronary syndromes [4,7] in humans. As they are involved in ongoing vascular inflammation and thrombosis in potentially unstable plaques, they may provide a more useful marker of cardiovascular disease than markers of downstream myocyte injury [6].

Uraemia and dialysis induce a pro-inflammatory state, with widespread microvascular and circulatory changes [8]. This is reflected in elevated acute phase proteins and inflammatory molecules, and implicated in the high incidence of cardiovascular disease in ESRD [9]. Investigation into platelet dysfunction in uraemia has focused largely on prolonged bleeding time, and the role of nitric oxide in platelets of chronic and end-stage renal failure (ESRF) patients, and on surface receptor abnormalities on haemodialysis [1013]. Platelet activation and platelet–leucocyte interactions have been studied in haemodialysis patients as a parameter of membrane bioincompatibility, but these abnormalities have not been investigated in patients on chronic ambulatory peritoneal dialysis (CAPD).

The cardiovascular mortality in patients on CAPD is similar to that on haemodialysis. In light of the recent reported association between platelet activation, platelet–leucocyte aggregates and cardiovascular morbidity in non-uraemic patients, we investigated circulating platelet–leucocyte interactions in patients on CAPD and haemodialysis as a potential cardiovascular risk factor.



   Subjects and methods
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 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 References
 
Patient population
Patients (48) with ESRF who were treated on the out-patient maintenance renal replacement therapy programme at St Bartholomew’s and the Royal London hospitals were recruited into the study in 2001. Of these patients, 23 had been maintained on haemodialysis and 25 on CAPD. Blood samples were obtained for measurement on the ADVIATM120 haematology system as detailed below. A group of 10 patients from each dialysis group also had samples analysed for platelet–leucocyte aggregates and leucocyte PSGL-1 expression by flow cytometry. A control group of 40 individuals with normal renal function and no medical history of vascular disease was also enrolled: within this group, 20 were analysed for platelet–leucocyte aggregates and PSGL-1 expression. Patient characteristics are shown in Table 1. Because the normal controls were younger than the dialysis cohort, the latter were stratified into two age-related groups, a young group (aged 29.6 ± 3.2 years) and an older group (aged 55.5 ± 4.0 years, mean ± SEM). Analysis of these two groups showed no difference for any experimental result. For example, percentage platelet–monocyte aggregates in the younger group was 25.8 ± 6.5%, whilst in the older group it was 16.8 ± 3.6% (mean ± SEM, P = NS). Both age groups were significantly different from normal controls (P < 0.001). Amongst those on dialysis, the cause of renal failure varied, and included glomerulonephritis, adult polycystic kidney disease, reflux nephropathy, chronic pyelonephritis and unknown cause (scarred and shrunken kidneys on ultrasound at presentation). Patients with ESRF due to diabetes mellitus, renovascular disease or active vasculitis were excluded, as were all patients on medication for ischaemic heart disease, or with history of angina or coronary artery bypass grafting, or known left ventricular hypertrophy. No subject had been treated with aspirin, clopidogrel, HMG-co-A reductase inhibitors or any non-steroidal anti-inflammatory agent for at least 1 month prior to the study. All subjects were on a stable erythropoeitin dose for the preceding 3 months. All subjects were well when included in the study, with no concurrent infection within 3 months of the study.


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Table 1. Baseline characteristics of the three cohorts

 
All haemodialysis patients were dialysed three times per week with a HemophanTM modified cellulose hollow fibre dialysis membrane delivering a Kt/V according to UK Renal Association guidelines (Kt/V of >1.2). All were stable and not prone to intradialytic events such as hypotension or cardiovascular instability. Venepuncture for samples was performed before any given haemodialysis session after the short interdialytic interval. All samples were obtained prior to any heparin bolus. All CAPD patients used a BaxterTM Mini-solo system and performed four 2–2.5 l exchanges per day, again delivering adequate dialysis according to UK Renal Association guidelines (weekly Kt/V of >2.0). Informed consent was obtained from all patients as per local ethical protocols in accordance with the current (October, 2000) revision of the Declaration of Helsinki. Blood was collected into vacutainers containing EDTA and sodium citrate in all cases and analysed immediately.

Materials
Tyrodes salt solution (TS; CaCl2c2H2O 0.265 g/l, MgCl2c6H2O 0.214 g/l, KCl 0.2 g/l, NaH2CO3 1.0 g/l, NaCl 8.0 g/l, Na2HPO4 0.05 g/l, glucose 1.0 g/l) was from Sigma (Poole, Dorset, UK). K3EDTA and sodium citrate in Vacutainers were from BD Biosciences (Cowley, Oxford, UK).

Antisera
Fluorescein isothiocyanate (FITC)-conjugated mouse IgG1, FITC–CD62P (CLBThromb/6), FITC–CD42a (SZ1) and phycoerythrin (PE)-conjugated CD45 were from Immunotech (Luton, Bedfordshire, UK). PE-conjugated CD162 (KPL-1 clone) was from BD (Oxford, UK).

Assessment of platelet count and platelet activation on the ADVIATM 120
Whole blood samples were taken into Vacutainers that contained K3EDTA. Samples were held at ambient temperature, and analysed at 30 min after venesection. The platelet count (PLT), platelet crit and mean platelet component (MPC) concentration were determined using the ADVIATM120 Haematology system (Bayer Corporation, Tarrytown, NY). The system was calibrated and standardized prior to use with ADVIATM-SETpoint Haematology Control and ADVIATMOPTIpoint, respectively (Bayer Corporation). The ADVIATM120 system has a laser optical assembly that consists of a laser diode, a flow cell and detector assemblies, and is essentially a flow cytometer. A laser diode is used to produce monochromatic light at 675 nm. The light from the laser diode is focused onto the flow cell. The sample/sheath stream in the flow cell contains platelets and red cells that are iso-volumetrically sphered with sodium dodecyl sulfate (SDS) using a procedure first described by Kim and Ornstein [14]. Platelets passing through the flow cell scatter light. The scattered light in the forward direction at low and high angles is detected by photodiodes and generates two signals. Using the Mie theory of light scattering for homogeneous spheres, the high angle light scatter measurement is converted into refractive index or as presented on the ADVIATM120 system as the MPC. The high angle light scatter is analogous to the side scatter detected by flow cytometers, and so a decrease in high angle light scatter may be associated with a decrease in granularity. This led us to consider whether the ADVIATM120 system could be used to measure platelet activation based upon changes in refractive index. Since platelet activation is associated with degranulation, we speculated that the increase in CD62P cell surface expression would correlate with a fall in refractive index (RI). We have shown that the ADVIATM120 system may be used to measure changes in light scatter due to changes in RI in activated platelets. In vitro stimulation of normal platelets in whole blood by bovine thrombin resulted in activation leading to increased platelet CD62P expression and a concomitant decrease in RI. This response was dose and time dependent and could be inhibited by ridogrel, a specific inhibitor of thromboxane synthesis, at levels known to be occur in blood (10–7 M) and tissues (10–5 M) [15].

Measurement of the percentage of platelets expressing CD62P and of the percentage of leucocytes that had platelets attached (platelet–leucocyte aggregates)
Sodium citrate anticoagulated blood (5 µl) was labelled at ambient temperature with 5 µl of one of the following: (i) FITC–isotype control; (ii) FITC–CD62P; (iii) PE–CD45 and FITC–isotype control; or (iv) PE–CD45 and FITC–CD42a, in 90 µl of TS for 5 min. Previous studies have shown that antibody binding is complete within this time. Samples were diluted to 1 ml with TS and analysed immediately by flow cytometry.

Measurement of leucocytes expressing CD162
Sodium citrate-anticoagulated blood (5 µl) was labelled at ambient temperature with PE–CD162 (5 µl) in 90 µl of TS for 5 min. Samples were then diluted to 1 ml with TS and analysed immediately by flow cytometry.

Flow cytometry
Blood cells were analysed on a FACScan (BD Biosciences) equipped with CellQuest® software. The flow cytometer was calibrated and standardized prior to use with fluorochrome-labelled beads (Fluorospheres; Dako, Ely, Cambridgeshire, UK). For the analysis of CD62P expression, data were acquired in real time with a primary gate set on a dual parameter histogram of forward light scatter (FLS) logarithmic scale (abscissa) and side light scatter (SLS) logarithmic scale (ordinate). This facilitated identification of the platelets within the blood and was confirmed by the analysis of CD42a expression. Background fluorescence was assessed with platelets labelled with the FITC-conjugated isotype control antibody. Cursors were set in a single parameter histogram of frequency (ordinate) and green fluorescence intensity (abscissa), so that <1% of the platelets stained positively with the control antibody. Changes in CD62P expression (green fluorescence logarithmic scale), together with FLS and SLS, were then recorded on the gated platelets.

For the analysis of CD162 expression, data were acquired in real time with a primary gate set on a dual parameter histogram of orange fluorescence (FLS) logarithmic scale (abscissa) and SLS linear scale (ordinate); this allowed identification of CD162-positive leucocytes within the blood based upon their PE fluorescence and granularity. The leucocytes were then gated to a dual parameter histogram of FLS linear scale (abscissa) and SLS linear scale (ordinate). This facilitated identification of the granulocytes within the blood. CD162 expression (orange fluorescence logarithmic scale), together with those of FLS and SLS, was then recorded on the gated granulocytes.

For the analysis of platelet–leucocyte aggregates, cells were analysed first in a histogram of side scatter (logarithmic scale ordinate) and orange fluorescence (logarithmic scale abscissa). Leucocytes identified by their positive staining with PE CD45 were gated to a dot plot of green fluorescence (logarithmic scale ordinate) and orange fluorescence (logarithmic scale abscissa). Events that were both green and orange were considered to be platelet–leucocyte aggregates and recorded as a percentage of a total of 10 000 gated leucocytes. Platelet–leucocyte aggregates could then be gated to a histogram of side scatter (logarithmic scale ordinate) and orange fluorescence (logarithmic scale abscissa) to identify, by their characteristic SLS, which leucocytes were forming platelet–leucocyte aggregates.

Statistical analysis
Results from the flow cytometer and the ADVIATM120 Haematology system were compared using analysis of variance (ANOVA) with appropriate post hoc tests to allow for the confounding potential of multiple comparisons. Data for these analyses are expressed as mean ± SEM. Biochemical parameters in the CAPD and haemodialysis cohorts were compared using the unpaired t-test, two-tailed for independent variables, to test for significant differences between the patient groups and the control group when normally distributed. Non-parametric data were analysed using the Mann–Whitney U-test. Correlation was sought using Pearson rank correlation. Significance was considered at P < 0.05.



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 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 References
 
Blood counts
PLT (mean ± SEM; n = 40) for the normal controls was 264 ± 7 x 109/l, that for the CAPD patients was 277 ± 20 x 109/l (n = 25) whilst that for the haemodialysis patients was 216 ± 14 x 109/l (n = 23). The PLT for the haemodialysis patients was significantly lower (P < 0.01) than in the normal controls (Figure 1).



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Fig. 1. The analysis of platelet–leucocyte aggregates and leucocyte expression of CD162 in whole blood. Blood was stained with PE-conjugated CD45 and FITC-conjugated CD42a. Leucocytes were identified by their positive staining with PE–CD45 in a plot of side scatter (logarithmic scale, ordinate) vs PE fluorescence (logarithmic scale, abscissa) (dot plots A and E). Back-gating these events to a plot of side scatter (logarithmic scale, ordinate) vs forward scatter (logarithmic scale, abscissa) (dot plots B and F) showed the typical light scatter characteristics of neutrophils, monocytes and lymphocytes, regions R1, R2 and R3, respectively. Gated events were displayed in a plot of FITC–CD42a fluorescence (logarithmic scale, ordinate) and PE–CD45 fluorescence (logarithmic scale, abscissa) (dot plots C and G). Events that were both CD42a and CD45 positive (upper right quadrant, C and G) were considered to be platelet–leucocyte aggregates. Histograms (D) and (H) show the cell count (logarithmic scale, ordinate) vs CD162 PE (logarithmic scale, abscissa) and illustrate the analysis of the expression of CD162 on gated leukocytes. An example of the analysis performed on blood from a haemodialysis patient analysed within 30 min after venesection is illustrated. Plots (C) and (D) were obtained from an aliquot of a peripheral blood sample that had been analysed gating on granulocytes (region R1), and plots (G) and (H) from the same aliquot that had been analysed gating on monocytes (region R2).

 
MPC
The MPC, a measure of platelet activation, for the normal controls was (mean ± SEM; n = 40) 27.5 ± 0.17 g/dl, that for the CAPD patients was 26.8 ± 0.33g/dl (n = 25) whilst that for the haemodialysis patients was 26.4 ± 0.26 g/dl (n = 23). The MPC for the haemodialysis patients was significantly reduced compared with the controls (P < 0.05), but no statistically significant difference was found between controls and CAPD patients (P = 0.045, NS with post hoc adjustment for multiple comparisons).

Expression of CD62P on platelets
Shortly after venesection, there was a low percentage of platelets that expressed CD62P. The mean ± SEM for the normal controls (n = 40) was 0.985 ± 0.19% CD62P-expressing platelets, that for the CAPD patients was 1.53 ± 0.32% (n = 25) whilst that for the haemodialysis patients was 2.12 ± 0.34% (n = 23). The percentage CD62P-positive platelets in blood from the haemodialysis patients was significantly higher (P < 0.01) than in the normal controls. There was no difference in percentage CD62P-positive platelets between normal controls and CAPD patients (P = 0.08, NS with post hoc adjustment for multiple comparisons), nor a difference between the haemodialysis and CAPD groups. The increased platelet P-selectin expression and decrease in MPC, both markers of platelet activation, showed a significant inverse correlation in haemodialysis patients (r = –0.51, P = 0.019).

Expression of CD162 on neutrophils
All neutrophils (100%) in the blood samples from both the control and patient groups expressed CD162. However, the median fluorescence intensity (measured as channel fluorescence units) was significantly lower in the CAPD patients (597 ± 11.2; n = 10; P < 0.05) and haemodialysis patients (605 ± 10.3; n = 10; P < 0.05) than in the control group (631 ± 6; n = 20) (Figure 2).



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Fig. 2. P-selectin glycoprotein ligand-1 (PSGL-1, CD162) expression on leucocytes. PSGL-1 was significantly reduced in both CAPD patients (P < 0.05 for neutrophils, P < 0.01 for monocytes) and HD patients (P < 0.05 for neutrophils, P < 0.05 for monocytes) against normal controls. Results are expressed as mean ± SEM. Statistical analysis using ANOVA with post hoc Bonferroni test, * = significance achieved.

 
Expression of CD162 on monocytes
Similarly, all monocytes in the blood samples from both the control and patient groups expressed CD162. However, the median fluorescence intensity was lower in the CAPD patients (639 ± 13; n = 10; P < 0.01) and haemodialysis patients (647 ± 8; n = 10; P < 0.05) than in the control group (678 ± 6; n = 20) (Figure 2).

Platelet–neutrophil aggregate formation
Immediately after venesection, a small percentage of neutrophils associated with platelets were found in blood from all controls and patients. The percentage of platelet–neutrophil aggregates (mean ± SEM; n = 20) for the normal controls was 7.4 ± 0.8%, that for the CAPD patients was 13.5 ± 5.8% (n = 10) and that for the haemodialysis patients was 8.3 ± 1.9% (n = 10). There were no statistically significant differences between CAPD patients and the controls. In the haemodialysis cohort, platelet–neutrophil aggregates were significantly increased (P < 0.05) compared with controls, but not compared with CAPD patients (Figure 3).



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Fig. 3. Platelet–leucocyte aggregates. Platelet–monocyte aggregates were significantly increased in both cohorts (P < 0.05 for CAPD patients, P < 0.001 for HD patients). Platelet–neutrophil aggregates were significantly increased in patients on HD (P < 0.05). Although platelet–neutrophil aggregates were increased against normal controls in CAPD patients, this difference was non-significant. Results are expressed as mean ± SEM. Statistical analysis using ANOVA with post hoc Bonferroni test, * = significance achieved.

 
Platelet–monocyte aggregate formation
Immediately after venesection, a small percentage of monocytes were to be found associated with platelets in blood from all patient samples, but were found in only a few normal controls. The percentage platelet–monocyte aggregates (mean ± SEM; n = 20) for the normal controls was 3.72 ± 1.39%, that for the CAPD patients was 17.4 ± 4.7% (n = 10) and that for the haemodialysis patients was 22.2 ± 4.7% (n = 10). The percentage platelet–monocyte aggregates found in the CAPD and haemodialysis patients was significantly greater (P < 0.05 and P < 0.001 respectively) than in the normal control group (Figure 3).

Effect of parathyroid hormone on platelets and aggregate formation
We have shown that platelet calcium is influenced by ambient parathyroid hormone (PTH) concentration in ESRD [16]: this may then affect platelet reactivity. We thus analysed two dialysis groups by PTH concentration by arbitrarily examining a 10-fold change in PTH. The low PTH group (n = 8) had a mean PTH of 9.4 pmol/l, whilst the high PTH group (n = 12) had a mean of 90.4 pmol/l. In the two groups, the percentage of platelets expressing P-selectin was similar (low PTH group, 1.65 ± 0.6%; high PTH group 1.39 ± 0.3%; mean ± SEM, P = 0.68), as was mean platelet component (low PTH, 26.3 ± 0.45 g/dl; high PTH, 26.6 ± 0.44 g/dl; mean ± SEM, P = 0.62). No difference was found for any other parameter. In the low PTH group, the percentage of platelet–monocyte aggregates was 20.8 ± 4.5%, whilst in the high PTH group it was 16.0 ± 4.7 (mean ± SEM, P = 0.52).

Platelet–monocyte aggregates and cardiovascular events
In non-uraemic subjects, platelet–monocyte aggregates have been associated with the presence of cardiovascular disease. Given the excess of these aggregates in both dialysis cohorts, we sought clinical evidence of cardiovascular disease after a mean of 16.2 months (range 12–22) of follow-up. The group had been selected on the basis of not having clinically apparent cardiovascular disease at recruitment. The composite end points were cardiac death, a vascular event (myocardial infarct, the development of angina pectoris) or left ventricular hypertrophy (LVH) on echocardiogram or electrocardiogram. The 20 dialysis patients were thus stratified into a group with lower platelet–monocyte aggregates (6.5 ± 0.8%; n = 9, range 2.9–10%), and a higher group (31.7 ± 2.5%; n = 11, range 16.7–47%). The lower group was defined as falling within the normal range (mean ± 2 SD for normal controls, or <15.5%). After at least 1 year’s follow-up, eight patients of 11 within the higher group had evidence of cardiovascular disease or had died a cardiac death (Figure 4). Two patients had died of myocardial infarction, one had a non-fatal infarct and one had a cerebrovascular accident (with documented LVH), and a further four patients had evidence of LVH. In the lower platelet–monocyte aggregates group, one patient had had non-transmural infarcts and developed an ischaemic cardiomyopathy. A further patient had died of overwhelming sepsis. The latter patient, and the remaining seven had no evidence of LVH, nor any history of angina. This achieved significance at P = 0.02 (Yates-corrected {chi}2 test).



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Fig. 4. Cardiovascular disease and platelet–monocyte aggregates (PMA). Amongst ESRF patients, higher platelet–monocyte aggregates (31.7 ± 2.5%; n = 11) are associated with an increased incidence of cardiovascular events after at least 1 year follow-up as defined by cardiac death, proven vascular disease or de novo left ventricular hypertrophy against patients with lower platelet–monocyte aggregates (6.5 ± 0.8%; n = 9). Open bars, number of dialysing patients free of cardiovascular disease; shaded bars, number with cardiovascular disease, P = 0.02, Yates-corrected {chi}2 test.

 


   Discussion
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 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 References
 
We selected patients with ESRF and no evidence of cardiovascular disease on haemodialysis or CAPD to examine the activation of platelets and the formation of platelet–leucocyte aggregates. We selected a group free of apparent atherosclerosis to allow prospective follow-up, and to discover whether circulating cell changes preceded clinical vascular syndromes in uraemia. Platelet interactions with other circulating cells and with the endothelium play a key part in the pathogenesis of atherosclerosis and infarction in non-uraemic models. As accelerated vascular disease is found in excess regardless of dialysis modality in ESRF patients, we examined platelet surface changes in CAPD and haemodialysis patients.

P-selectin is translocated to the surface of activated platelets, where it contributes to platelet-assisted enhancement of thrombosis at sites of endothelial injury. Platelet–platelet aggregates are stabilized on subendothelial matrix, and then leucocyte ‘rolling’ and recruitment on this damaged, platelet-rich surface is enabled by efficient binding to leucocyte PSGL-1—this leads to tighter platelet–leucocyte binding, increased local fibrin deposition [17] and supports more stable interactions between platelets and leucocytes. Cross-linking of PSGL-1 by recruited monocytes may then amplify production of pro-coagulant tissue factor, tumour necrosis factor-{alpha}, monocyte chemoattractant protein-1 and chemokines. In flow conditions, platelet P-selectin also binds PSGL-1 on the surface of monocytes and neutrophils to form circulating mixed cell aggregates which are stable over many hours, in contrast to the transient rolling interactions. PSGL-1 on leucocytes may also bind endothelial P-selectin in early engagement to enable tethering then contributing to platelet sequestration into injured tissue with an intensification of the local inflammatory response [4,18,19].

Platelet P-selectin expression is increased during haemodialysis in association with increased platelet–leucocyte aggregation, an effect replicated in vitro by stimulating platelet activation with ADP [1012]. This aggregate formation has since been reported to occur via an interaction between platelet P-selectin and leucocyte sialyl-Lewis x (CD15s) in haemodialysis [11,13]. We confirm platelet activation as measured by CD62P expression or reduced MPC to be significantly increased in haemodialysis. In CAPD patients, although a similar trend is apparent, platelet activation is not statistically significantly increased. This may reflect the regular exposure of platelets to relatively bioincompatible membranes in haemodialysis patients. It is also worth acknowledging that degranulated platelets rapidly lose their surface P-selectin, but continue to circulate and function, capable of cell–cell interaction.

Leucocyte PSGL-1 is the natural ligand for platelet P-selectin: expression is reduced on both monocytes and neutrophils in both dialysis cohorts. Again, this may represent PSGL-1 redistribution or shedding in activated monocytes or neutrophils. Importantly, surface expression does not necessarily correlate with function [20]. We find that despite this reduction in expression, and the lack of significant platelet activation in CAPD patients, platelet–monocyte aggregation is enhanced in both CAPD and haemodialysis. Although most aggregation occurs through P-selectin–PSGL-1 binding, a recent report has identified significant platelet–monocyte binding through other mechanisms [21]: this may explain the findings in our study.

In patients with stable coronary artery disease, platelet–monocyte aggregates are significantly increased compared with normal controls [22]. Furthermore, high aggregates discriminate patients with myocardial infarction from other causes in those presenting with chest pain [7,23]: this was not the case with platelet P-selectin alone. Furman showed that when patients had 15.3 ± 3.0% platelet-positive monocytes, they were proven subsequently to have a myocardial infarct [7]; dialysis patients with no history of coronary artery disease, or risk factors for ischaemic heart disease other than uraemia had 17.4 ± 4.7% (CAPD) and 22.2 ± 4.7% (haemodialysis) platelet-positive monocytes.

Recently, other ligands potentially involved in this cell–cell interaction have been implicated in cardiovascular risk in non-uraemic patients [24], and a potential mechanism by which platelet–monocyte aggregates might be implicated in atherogenesis described in animals [25]. It is clear that bioactive platelet–monocyte aggregates are found in ESRF patients on both haemodialysis and CAPD, where they are associated with cardiovascular events, as occurs in the non-uraemic setting. They may contribute to local thrombotic changes in acute plaque erosion and in the evolution of atherosclerosis, but may yet prove an epiphenomenon reflecting widespread inflammatory changes in the microcirculation associated with uraemia. Larger scale prospective studies are needed to assess the predictive value of these novel parameters in uraemic cardiovascular morbidity and mortality.

Conflict of interest statement. None declared.



   References
 Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 References
 

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Received for publication: 12.11.02
Accepted in revised form: 5. 5.03