Glucose Concentration in the Dialysate does not Contribute to Lipid Profiles in Patients Undergoing CAPD

Abstract

The aim of this study was to investigate lipid profiles in patients with end-stage renal disease receiving hemodialysis (HD), continuous ambulatory peritoneal dialysis (CAPD), or no dialysis (nondialytic treatment group, NT), and to analyze the association between dyslipidemia in CAPD patients with glucose-containing dialysate dosages. Lipid profiles were determined in 64 NT patients, 62 HD patients, and 180 CAPD patients at a single time point. NT patients' samples were collected following hospitalization due to renal failure. HD and CAPD patients' samples were collected after 3 months of dialysis. The association between lipid profiles of 180 CAPD patients and glucose-containing dialysate was analyzed using Pearson methods; 76.56% of NT patients, 66.13% of HD patients, and 72.22% of CAPD patients had dyslipidemia. Compared with NT patients, CAPD patients had significantly altered levels of cholesterol, triglycerides, high-density lipoprotein, apolipoproein (Apo)-A1, and Apo-E (p < 0.05), but unchanged levels of low-density lipoprotein or Apo-B. There was no correlation between the three different concentrations of glucose in the dialysate with the lipid profile of CAPD patients. We concluded that patients on CAPD exhibit dyslipidemia, and that different concentrations of glucose in the dialysate do not affect lipid profiles in these patients.

• continuous ambulatory peritoneal dialysis (CAPD)
• dyslipidemia
• hemodialysis
• lipid profile

INTRODUCTION

Dyslipidemia is a common metabolic abnormality associated with end-stage renal disease (ESRD). ESRD patients on hemodialysis (HD) have normal or nearly normal levels of total cholesterol (TCHOL) and low-density lipid cholesterol (LDL-C).¹ Approximately 20–40% of HD patients have increased triglycerides (TG) and reduced high-density lipid cholesterol (HDL-C).^2,3 Patients on continuous ambulatory peritoneal dialysis (CAPD) have more abnormal lipid profiles than HD patients; 20–40% of peritoneal dialysis patients have increased TCHOL and LDL-C; and 25–50% of patients have increased TG and Apo-B along with lowered HDL-C levels.^4–6 Dyslipidemia is due to abnormal metabolism of lipoproteins and is an established cardiovascular disease (CVD) risk factor in the general population. Increased blood levels of cholesterol, LDL‐C, TG, and Apo-B, and decreased levels of HDL-C and Apo-A are associated with higher risks of developing CVD.^7–13 The ratio of Apo-B and Apo-AI is the best predictor for CVD risk,¹⁴ whereas changes in blood levels of lipoprotein(a) [LP(a)] are also a risk factor for CVD.^15,16 Studies indicate that patients with chronic kidney disease and maintenance dialysis (MD) are in a higher risk group of developing CVD.¹⁷ Almost half of the maintenance hemodialysis (MHD) patients died of CVD.¹⁸ At our center, the prevalence of dyslipidemia among ESRD patients receiving HD, CAPD, or no dialysis (NT group) is not known. This study was designed to determine lipid profiles among ESRD patients at our center.

CAPD is one of the successful therapies for ESRD. As the abdominal membrane can absorb glucose from glucose dialysate, excessive glucose absorption may increase the energy burden and contribute to hyperlipidemia. There is no study to date that has investigated the effect of glucose dialysate on the lipid profiles of CAPD patients. Therefore, we analyzed the lipid profiles in HD, CAPD, and NT patients and assessed the association between the lipid profile of CAPD patients and the different concentrations of glucose in dialysate.

SUBJECTS AND METHODS

Subjects

This study was approved by the First Affiliated Hospital, Sun Yat-Sen University, Nephrology Department committee. We enrolled 50 healthy subjects in the control group (32 males and 18 females) with a mean age of 45.5 ± 8.11 years. The ESRD patients enrolled in our study were selected from the First Affiliated Hospital, Sun Yat-Sen University. There were three groups of ESRD patients: Group I: 64 patients on NT (40 males and 24 females) with a mean age of 50.0 ± 16.2 years; Group II: 62 patients on MHD (33 males and 29 females) with a mean age of 53.32 ± 13.01 years. Four-hour HD was performed for more than 3 months (2–3 times/week); and Group III: 180 CAPD patients (CAPD; 99 males and 81 females) with a mean age of 53.32 ± 13.01 years. CAPD was carried out for more than 3 months. Peritoneal dialysis equipment including Tenckhoff straight vessels, pigtail vessels, and double bag systems were purchased from Baxter China Ltd. (Guangzhou, PR China). During CAPD, dialysate was retained in the abdominal cavity for several hours (4–6 h in daytime CAPD and 8–12 h in nighttime CAPD). For subtotal fluid exchange, 1.5% glucose lactate glucose dialysate (Baxter China Ltd.) was used. To remove even more fluid, 2.5 and 4.25% glucose dialysate were used. Two thousand milliliters of dialysate was used 3–4 times/day. MD patients with diabetic mellitus were given insulin by hypodermic injection to control blood glucose. MD patients were injected with erythropoietin (EPO) regularly. Sixty-four ESRD patients on NT were not injected with EPO as they were newly diagnosed with renal failure. Patients with innate or malignant tumors, chronic liver disease, liver cirrhosis, tuberculosis, thyroid hyperfunction or degradation, and acute infection were excluded from this study as these diseases could affect lipid metabolism.

For primary renal disease code, we coded 1 as chronic glomerulonephritis, 2 as diabetic nephropathy, 3 as renal arteriosclerosis, 4 as gouty nephropathy, 5 as lupus nephritis, 6 as polycystic kidney, 7 as obstructive nephropathy, 8 as medicine kidney lesion, 9 as chronic renal insufficiency, 10 as acute renal failure, and 11 as renal arteriosclerosis and diabetes mellitus.

For the peritoneal equilibrium test (PET) code, we coded 1 as low transport type of peritoneum, 2 as low-average transport type of peritoneum, 3 as high-average transport type of peritoneum, and 4 as high transport type of peritoneum.

Urine, blood, and dialysate samples were collected from each patient at a set time. NT patients' samples were collected following hospitalization due to renal failure. HD and CAPD patients' samples were collected after 3 months on dialysis. The samples were collected only once and were used for lipid profile analysis as described below.

Methods

We determined eight different parameters of lipid profile index of CAPD patients whose dialysis lasted for more than 3 months. We drew 5 mL of blood in heparin-coated tubes from fasted subjects. The blood was centrifuged and supernatant was used for the detection of lipid levels using complete automatic biochemistry analyzer HITACHI 7170A (Tokyo, Japan). In the plasma, TCHOL levels were detected by cholesterol oxidase-cholesterol esterase-peroxidase, 4-aminoantipyrine, phenol (COD-CE-PAP) method and total TG levels were detected by TG esterase reagent assay kits (HUMAN company, Germany). Serum HDL-C was detected by chemical modification enzymic method kit and serum LDL-C was detected by chemistry selectivity inhibitory enzyme assay kit (provided by the first chemicals strain society in Japan). Apo-A1, Apo-B, Apo-E, and LP(a) were detected by immunoturbidimetry assay kits (Shanghai Kehua Bio-Engineering Co., Ltd., China).

Lipid profiles in plasma, urine, and dialysate were analyzed at one time point only. Data were presented as mean ± standard deviation. We estimated glomerular filtration rate eGFR in CAPD and NT patients using the following formula:

eGFR = (residual urea clearance rate + residual creatinine clearance rate)/2, where, residual urea clearance rate = (urine urea concentration/serum urea concentration) × urine volume (mL)/1440; residual creatinine clearance rate = (urine creatinine concentration/serum creatinine concentration) × urine volume (mL)/1440.

Nutritional status of patients was determined by using the subjective global assessment (SGA) scores¹⁹ and modified quantitative SGA scores.²⁰ We assessed patients and categorized them based on the following four parameters: up-to-date body weight changes, decreased food appetite, subcutaneous tissue, and muscle volume. Scoring of 5–7 was good nutrition, 3–5 was bad nutrition, and 1–3 was severe malnutrition. Modified quantitative SGA scores were composed of five subjective evaluations and two medical examinations. Subjective evaluations were done for body weight change during the recent 6 months, food intake, gastrointestinal symptoms, function and locomotor activity correlated to nutrition, and accompanying diseases. Medical examinations were performed to determine the extent of reduction in subcutaneous fat and skeletal muscle. The modified quantitative SGA scores of 1–10 indicated normal nutrition and scores exceeding 10 indicated malnutrition. The entire spectrum of lipid profiles was examined. We choose HD and CAPD patients who were on dialysis for more than 3 months.

An important index of general health status is to evaluate performance status. Commonly scored Eastern Cooperative Oncology Group (ECOG) activity index and Karnofsky score were used. ECOG instituted a simplified scoring table for activity status. They divided activity status of patients into six grades (0–5). Physical work capacity state (ECOG activity index) was scored as score 0 – normal locomotor activity; 1 – gentle physical work, such as common household duties and office work, without heavy physical work; 2 – free walking and self-care, but loss of work ability; 3 – lying in bed or sitting in a wheelchair more than half of the daytime; 4 – lying in bed continuously; and 5 – death.

The Karnofsky score was used to evaluate quality of life. Subjects with a score of 100 are normal with the absence of signs and symptoms. Subjects with a score of 90 can do normal activity with gentle signs and symptoms. Subjects with a score of 80 may do normal activity reluctantly with some signs and symptoms. Subjects with a score of 70 can take care of themselves but cannot live or work normally. Subjects with a score of 60 can do perform most self-care, but need help occasionally. Subjects with a score of 50 require frequent care. Subjects with a score of 40 cannot take care of themselves and need special care. Subjects with a score of 30 require much more care. Subjects with a score of 20 are dangerously ill and need hospitalization and supportive treatment. A score of 10 is impending death, and 0 is death.

Statistical Analyses

Data were analyzed using the statistical program SPSS, version 13.0. Analysis of variance was used to compare lipid profile differences among the three groups of ESRD patients; NT, HD, and CAPD, the significant groups were compared by Bonferroni post hoc analysis. The differences were considered statistically significant if p < 0.05. We applied Pearson's product–moment correlation coefficient to determine correlations among variables. First, we screened univariate significant results by applying monofactorial regression analysis, then to bring univariate significant results into pattern by multiple linear regression for screening target variables.

RESULTS

Nutritional Atate, Body Weight, and BMI

Comparison among the three groups of patients (i.e., ESRD patients on NT, HD, or CAPD) in age, body weight, BMI, and nutritional status showed that HD and CAPD patients had a better nutritional status than NT patients at the time when lipid levels were analyzed (Table 1). There was no significant difference in the age among the three groups; however, body weights were significantly lower (p < 0.05) in HD compared with NT or CAPD patients. Similarly, the BMI of HD patients was also significantly (p < 0.05) lower than those of NT or CAPD patients.

Table 1. Nutritional status and renal function parameters of ESRD patients

	NT (n = 64)	HD (n = 62)	CAPD (n = 180)	P (NT vs. HD)	P (NT vs. CAPD)	P (HD vs. CAPD)
Age (years)	50.30 ± 15.99	53.32 ± 13.01	51.70 ± 16.70	0.246	0.552	0.434
Body height (m)	1.64 ± 0.08	1.62 ± 0.08	1.63 ± 0.08	0.334	0.470	0.636
Body weight (kg)	60.47 ± 11.75	55.01 ± 11.25	59.08 ± 10.07	0.009**	0.402	0.015
BMI (kg/m^2)	22.46 ± 3.54	20.98 ± 3.67	22.20 ± 3.16	0.030	0.598	0.033
Male (%)	62.50	53.23	55.00	0.296	0.296	0.811
Proportion of diabetics (%)	26.60	11.29	20.56	0.014	0.392	0.020
Hemoglobin (g/L)	79.77 ± 20.36	106.02 ± 29.88	108.33 ± 23.00	0.000**	0.000**	0.590
Serum albumin (g/L)	36.34 ± 6.32	41.64 ± 5.76	37.58 ± 6.20	0.000**	0.174	0.000**
Serum urea (mmol/L)	27.93 ± 9.90	16.30 ± 11.82	17.75 ± 5.90	0.000**	0.000**	0.391
Serum creatinine (µmol/L)	856.78 ± 322.52	572.29 ± 466.98	870.34 ± 336.50	0.000**	0.777	0.000**
Dialysis age (months)	–	80.71 ± 63.76	18.64 ± 19.25	–	–	0.000**
Urine volume (mL)	1326.09 ± 585.45	178.30 ± 299.28	517.83 ± 487.45	0.000**	0.000**	0.000**
eGFR (mL/min)	7.99 ± 3.20	–	2.30 ± 2.68	–	0.000**	–

Notes: ESRD, end-stage renal disease; NT, ESRD patients on non-dialytic treatment; HD, hemodialysis patients; CAPD, continuous ambulatory peritoneal dialysis patients.

* and ** denote p < 0.05 and p < 0.01, respectively.

Diabetes and Renal Function

Renal function-related parameters and the proportion of diabetic patients among ESRD patients are given in Table 1. Most importantly, we observed that among HD patients the incidence of diabetes mellitus was significantly lower than in NT and CAPD patients. There was no significant difference in the incidence of diabetes between the NT and CAPD groups.

Lipid Profile

Table 2 shows the lipid profile in the three groups of patients. NT patients' samples were collected after hospitalizing because of renal failure. HD and CAPD patients' samples were collected after 3 months of dialysis.

Table 2. Lipid profiles among the three groups of ESRD patients

	Healthy person (n = 50)	NT (n = 64)	HD (n = 62)	CAPD (n = 180)	P (NT vs. HD)	P (NT vs. CAPD)	P (HD vs. CAPD)
TCHOL (mmol/L)	4.94 ± 0.25	4.64 ± 2.15	4.66 ± 1.89	5.26 ± 1.58	0.956	0.032	0.003**
TG (mmol/L)	1.29 ± 0.51	1.45 ± 0.53	1.69 ± 0.90	1.91 ± 1.00	0.253	0.011	0.371
HDL-C (mmol/L)	1.41 ± 0.13	1.03 ± 0.13	1.24 ± 0.15	1.28 ± 0.16	0.003**	0.000**	0.419
LDL-C (mmol/L)	3.02 ± 0.25	3.04 ± 1.86	2.75 ± 1.25	3.26 ± 1.01	0.290	0.369	0.002**
Apo-A1 (mmol/L)	1.33 ± 0.06	1.28 ± 0.11	1.20 ± 0.16	1.43 ± 0.15	0.210	0.003**	0.000**
Apo-B (mmol/L)	0.79 ± 0.01	0.88 ± 0.14	0.86 ± 0.08	0.96 ± 0.05	0.812	0.098	0.018
Apo-A1/apo-B	1.73 ± 0.22	1.60 ± 0.32	1.56 ± 0.44	1.73 ± 0.33	0.692	0.153	0.092
Apo-E (mmol/L)	40.20 ± 0.00	41.05 ± 19.32	50.03 ± 25.80	53.49 ± 18.40	0.035	0.000**	0.362
LP(a)	–	488.75 ± 652.60	282.16 ± 239.00	421.93 ± 312.00	0.025	0.440	0.004**

Notes: ESRD, end-stage renal disease; NT, ESRD patients on non-dialytic treatment; HD, hemodialysis patients; CAPD, continuous ambulatory peritoneal dialysis patients; TCHOL, total cholesterol; TG, triglycerides; HDL-C, high-density lipoprotein; LDL-C, low-density lipoprotein; Apo-A1, apolipoprotein-A1; Apo-B, apolipoprotein-B; Apo-A1/Apo-B, apolipoprotein-A1/apolipoprotein-B; Apo-E, apolipoprotein-E; LP(a), lipoprotein(a); –, no data.

* and ** denote p < 0.05 and p < 0.01, respectively.

CAPD patients were dyslipidemic. Accordingly, TCHOL, TG, HDL-C, Apo-A1, and Apo-E levels in these patients were significantly higher than in NT patients. On the contrary, the levels of TCHOL and TG were not significantly different between HD and NT patients. Surprisingly, we found that the levels of HDL-C were significantly higher in HD and CAPD patients relative to NT patients.

Oral administration of lipid-regulating agents in NT patients was 1.56% (1/64), and in NT patients with CVD was 10.94% (7/64). None of the HD patients required lipid-regulating agents, whereas 9.68% (6/62) of HD patients with CVD did. The oral administration proportion of lipid-regulating agents in CAPD patients was 18.89% (34/180) and CAPD with CVD was 7.22% (13/180). Table 3 shows the proportion of normal and hyperlipidemic patients among the different groups. Chi-square criterion indicated that the percentage of cases of hyperlipoidemia in the three groups of patients were not statistically significant (χ² = 1.72, p = 0.423).

Table 3. Percentage of hyperlipidemia among the three groups of patients

	NT (n = 64)	HD (n = 62)	CAPD (n = 180)
Normal lipid	23.44 (15)	33.87 (21)	27.78 (50)
Hyperlipoidemia	76.56 (49)	66.13 (41)	72.22 (130)

Notes: Chi-square criterion indicated that the percentage of three groups of hyperlipoidemia patients was not statistically significant, χ ² = 1.72, p = 0.423. Values in parenthesis denote the number of patients. ESRD, end-stage renal disease; NT, ESRD patients on non-dialytic treatment; HD, hemodialysis patients; CAPD, continuous ambulatory peritoneal dialysis patients.

Association of Physiological and Biochemical Parameters with Lipid Profile in CAPD Patients

No significant correlation was observed between the lipid profiles of CAPD patients with physiological parameters. TCHOL, TG, HDL-C, LDL-C, Apo-A1, Apo-B, Apo-E, and PL(a) were not associated with parameters of SGA scores or improvement in SGA scores. Apo-B was positively correlated with primary kidney disease (r = 0.169, p < 0.05), whereas HDL-C was negatively correlated with body weight (r = –0.288, p < 0.01). Apo-B was positively correlated with ECOG activity index (r = 0.202, p < 0.05). TC, Apo-B, and Apo-E were negatively correlated with Karnofsky scores (r = –0.213, p < 0.05; r = –0.245, p < 0.05; r = –0.245, p < 0.05, respectively).

No correlation was found between blood albumin and TCHOL, TG, HDL-C, LDL-C, Apo-A1, Apo-B, Apo-E, or PL(a). TCHOL was correlated with blood glucose and serum creatinine (r = 0.185, p < 0.05; r = –0.188, p < 0.05). TG was positively correlated with blood glucose (r = 0.278, p < 0.01). HDL-C was correlated with hemoglobin and blood glucose (r = 0.152, p < 0.05; r = –0.224, p < 0.01). LDL-C was not associated with parameters of hemoglobin, blood glucose, blood albumin, blood BUN, or blood Cr. Apo-A1 was correlated with hemoglobin, blood albumin, and blood Cr (r = 0.245, p < 0.01; r = 0.172, p < 0.05; r = –0.147, p < 0.05, respectively). Apo-B was correlated with the Karnofsky score, blood glucose, blood Cr, urinary albumin, and deprived amounts of urinary albumin (r = –0.245, p < 0.05; r = 0.213, p < 0.01; r = –0.153, p < 0.05; r = 0.288, p < 0.01; r = 0.272, p < 0.01, respectively) (Table 4).

Table 4. Association between blood lipid profiles of CAPD patients and physiological and biochemical parameters, and the glucose concentration of the dialysate

Independent variables	Mean ± SD	Correlation coefficient (r) between blood lipid and independent variables
		TC	TG	HDL-C	LDL-C	Apo-A1	Apo-B	Apo-E	PL(a)
Primary renal disease code^a	1.91 ± 1.76	0.088	0.192	–0.096	–0.011	–0.010	0.169	0.084	–0.047
Body weight (kg)	59.08 ± 10.07	–0.145	0.002	–0.288**	–0.086	–0.144	–0.140	–0.129	–0.048
BMI(kg/m^2)	22.20 ± 3.16	–0.025	0.121	–0.239**	–0.063	–0.078	–0.041	0.045	–0.031
SGA score	6.23 ± 0.77	–0.152	0.068	–0.09	–0.115	0.066	–0.163	–0.065	0.051
Improvement SGA score	9.36 ± 2.72	0.069	0.137	–0.138	–0.062	–0.171	0.044	0.181	–0.127
ECOG activity index	0.39 ± 0.68	0.129	0.184	–0.030	0.141	0.161	0.202	0.110	0.090
Karnofsky score	90.47 ± 12.37	–0.213	–0.123	0.046	–0.122	–0.121	–0.245	–0.245	–0.063
Hb (g/L)	108.33 ± 23.65	0.103	0.045	0.152	0.122	0.245**	0.027	–0.088	0.041
Serum glucose (mmol/L)	5.55 ± 2.63	0.185	0.278**	–0.224**	0.089	–0.048	0.213**	–0.190	–0.128
Serum albumin (g/L)	37.58 ± 5.99	–0.053	0.056	–0.036	0.005	0.172	–0.092	–0.091	–0.011
Serum urea (mmol/L)	17.75 ± 5.65	–0.095	–0.127	–0.009	–0.003	0.036	–0.125	–0.279**	0.095
Serum creatinine (µmol/L)	870.34 ± 343.86	–0.188	–0.102	–0.126	–0.080	–0.147	–0.153	–0.193	0.045
PET code^b	2.73 ± 0.68	–0.027	–0.113	0.150	–0.006	0.119	–0.061	–0.014	0.004
1.5% dextrose dialysate (L/24 h)	6.31 ± 2.03	–0.076	–0.075	0.138	–0.013	0.151	–0.125	–0.064	0.167
2.5% dextrose dialysate (L/24 h)	1.66 ± 1.96	0.010	0.109	–0.207**	–0.015	–0.188	0.067	0.063	0.181
4.25% dextrose dialysate (L/24 h)	0.04 ± 0.36	0.104	–0.017	–0.028	0.149	–0.020	0.125	–0.070	–0.007
24-h mixing dialysate glucose (mmol/L)	31.84 ± 10.75	–0.037	0.159	–0.166	–0.006	0.025	–0.010	0.008	–0.195
Amount of glucose absorbed in 24 h from dialysate (mmol)	471.93 ± 148.50	0.053	0.021	–0.048	0.025	–0.133	0.079	–0.019	–0.033
24-h mixing dialysate albumin (mg/L)	408.38 ± 161.77	0.050	–0.103	0.036	0.118	0.042	0.048	–0.023	0.055
Concentration of urinary albumin (mg/L)	1056.24 ± 2997.81	0.267	0.047	–0.016	0.302**	0.037	0.288**	0.072	–0.113
Urinary albumin excretion (mg/24 h)	409.11 ± 1064.46	0.270	0.027	–0.006	0.305**	0.052	0.272**	0.083	–0.109
Urine volume (mL/24 h)	517.83 ± 487.45	0.057	0.029	0.043	0.042	0.080	–0.063	0.026	0.016
RRF (mL/min)	2.30 ± 2.68	0.097	0.023	0.028	0.070	0.054	0.048	0.100	–0.026
NPCR (g/kg/day)	0.90 ± 0.23	–0.036	–0.103	0.203**	–0.011	0.142	–0.089	–0.163	0.105
Fluid removed (L/24 h)	1.08 ± 0.48	0.037	0.051	0.012	0.019	0.023	0.030	0.022	–0.029
Kt/V	2.13 ± 0.57	0.144	0.114	0.130	0.047	0.165	0.102	0.226	–0.003
Ccl (L/week/1.73 m^2 body surface area)	71.91 ± 28.11	0.131	0.079	0.097	0.046	0.120	0.072	0.170	–0.020

Notes: NPCR, normalized protein catabolic rate, [g/(kg × 24-h)]; SGA, subjective global assessment; TCHOL, total cholesterol; TG, triglycerides; HDL-C, high-density lipoprotein; LDL-C, low-density lipoprotein; Apo-A1, apolipoprotein-A1; Apo-B, apolipoprotein-B; Apo-A1/Apo-B, apolipoprotein-A1/apolipoprotein-B; Apo-E, apolipoprotein-E; LP(a), lipoprotein(a).

^aPrimary renal disease code – renal disease diagnosis was categorized into following codes: chronic glomerulonephritis (code 1), diabetic nephropathy (code 2), renal arteriosclerosis (code 3), gouty nephropathy (code 4), lupus nephritis (code 5), polycystic kidney (code 6), obstructive nephropathy (code 7), medicine kidney lesion (code 8), chronic renal insufficiency (code 9), acute renal failure (code 10), and renal arteriosclerosis and diabetes mellitus (code 11).

^bPET code – low transport type of peritoneum (code 1), low-average transport type of peritoneum (code 2), high-average transport type of peritoneum (code 3), and high transport type of peritoneum (code 4).

* and ** denote p < 0.05 and p < 0.01, respectively.

Association Between Glucose Concentration in Dialysate and the Lipid Profile in CAPD Patients

Lipid profiles of CAPD patients were not correlated with glucose concentrations in the dialysate. Furthermore, we found that the levels of TCHOL, Apo-B, and Apo-E were not correlated with glucose dialysate dosages and glucose concentrations (i.e., 1.5, 2.5, or 4.25%). TG was positively correlated with 24-h mixing of the dialysate glucose (r = 0.159, p < 0.05). HDL-C was negatively correlated with 2.5% concentration of glucose dialysate dosages and glucose concentration of 24-h mixing of the dialysate (r = –0.207, p < 0.01; r = –0.166, p < 0.05). LDL-C was positively correlated with 4.25% concentration of glucose dialysate dosages (r = 0.149, p < 0.05). Apo-A1 was correlated with 1.5% and 2.5% concentration glucose dialysate dosages (r = 0.151, p < 0.05; r = –0.188, p < 0.05, respectively). PL(a) was correlated with a 2.5% concentration of glucose dialysate dosages and glucose concentration of 24-h mixing the dialysate (r = 0.181, p < 0.05; r = –0.195, p < 0.05). Thus, glucose-containing dialysate may not contribute to dyslipidemia in CAPD patients (Table 4). The concentrations of 24-h mixing dialysate albumin were not correlated with TCHOL, TG, HDL-C, LDL-C, Apo-A1, Apo-B, Apo-E, and PL(a). Concentrations of urinary albumin and deprived amounts of urinary albumin were intensely correlated with TCHOL(r = 0.267, p < 0.05; r = 0.270, p < 0.05), LDL-C(r = 0.302, p < 0.01; r = 0.305, p < 0.01), and Apo-B (r = 0.288, p< 0.01; r = 0.272, p < 0.01) respectively. The parameters of PET code, urine volume, residual renal function (RRF), fluid removed, total solute clearance (Kt/V), and creatinine clearance (Ccl) were not correlated with TCHOL, TG, HDL-C, LDL-C, Apo-A1, Apo-B, Apo-E, and PL(a). HDL-C was positively correlated with normalized protein catabolic rate (NPCR; r = 0.203, p < 0.01). Apo-A1 and Apo-E were positively correlated with Kt/V (r = 0.165, p < 0.05; r = 0.226, p < 0.05, respectively) (Table 4).

DISCUSSION

At our center, analysis of lipid profiles in ESRD patients (with or without dialysis treatments) revealed that 76.56% of NT, 66.13% of HD, and 72.22% of CAPD patients developed dyslipidemia. Furthermore, CAPD patients had more atherogenic lipid profiles relative to HD or NT patients. However, to our surprise, we found that patients on dialysis (both CAPD and HD patients) had higher HDL-C levels relative to NT patients. In addition, we did not find any associations between concentrations of glucose in the dialysate and lipid levels in CAPD patients.

Several longitudinal and cross-sectional studies have been performed to determine the associations between dyslipidemia and cardiovascular risk in patients undergoing dialysis.^21–235 Results from these studies have been nicely summarized in the review article by Kwan BD et al.,²⁴ which suggested that there is no correlation between dyslipidemia and CVD risk in dialysis patients. In agreement with our finding, Burrell et al.²⁵ revealed that ethnic black renal patients tended to have higher HDL-C after HD and CAPD. However, Tan et al.²¹ found lower HDL-C levels in Caucasian men and diabetic patients following CAPD treatment. Similarly, a Turkish group also showed lower HDL-C levels in HD patients of Turkish race.²⁶

It has been reported that CAPD patients have more atherogenic lipid profiles than HD patients.²⁷ We also found more atherogenic lipid profiles in CAPD patients relative to HD. The high prevalence of diabetic mellitus found in CAPD patients relative to HD may play a role in worsening hyperlipidemia in CAPD patients. Tumer et al.,²⁸ in their prospective study, reported five baseline risk factors for CVD in type 2 diabetes patients; hyperglycemia, high LDL, low HDL, high blood pressure, and smoking. Furthermore, in a Canadian primary care setting, Harris et al²⁹ found that 55% of type 2 diabetes mellitus patients were diagnosed with hyperlipidemia during a 2-year follow-up period and 66% during a 15-year follow-up period. Thus, hyperglycemia in CAPD patients in our study may play a role in worsening lipid profiles in these patients.

In our study, the blood glucose levels of HD and CAPD patients with diabetes mellitus were monitored during dialysis. These patients were using insulin subcutaneously for treating their diabetes. Although the fasting blood glucoses of HD and CAPD patients were similar (5.31 ± 1.84 mmol/L and 5.55 ± 2.63 mmol/L, respectively), hyperlipidemia was worse in CAPD patients relative to HD patients. It could be that the peritoneal protein loss or the glucose absorption from the dialysate or both may contribute to these alterations. Furthermore, it has been shown that long-term exposure to high-gluc ose dialysate reduced perlecan synthesis in peritoneal dialysis patients.³⁰ Thus, long-term exposure to high glucose concentrations has the potential to induce structural and functional alterations in the peritoneal membrane of CAPD patients, which may affect glucose absorption and hence their lipid profile. Therefore, we decided to determine whether there was any relationship between the increasing concentrations of glucose in the dialysate and the lipid profiles in CAPD patients. We used dialysate with different concentrations of glucose (1.5–4.25%). The percentage of absorption of glucose was determined by permeability of the peritoneum to be 40–88%. Our correlation analysis showed that concentrations of glucose in the dialysate did not associate with lipid levels in CAPD patients. Race and gender have independent influence on lipid levels in HD and CAPD patients.²⁵ Nutrition status, as defined by serum albumin level, is associated with hyperlipidemia in CAPD patients.²⁹

To determine whether atherogenic lipid profiles in CAPD patients are the reflection of higher nutritional status of these patients, we analyzed their nutritional status (by SGA scores), body weight, and serum protein levels. CAPD patients had significantly higher body weight than HD, although both HD and CAPD patients had similar nutritional status, as measured by SGA scores. However, serum albumin levels in CAPD patients were significantly lower than in HD patients. Thus, the probable cause for worsening lipid profiles in CAPD patients cannot be determined from our results; however, loss of protein or higher nutritional status may play a role. Further studies are warranted to determine the development of atherogenic lipid profiles in CAPD patients. However, based on the correlation analysis of data collected during follow-up visits of CAPD patients, we found a significant positive correlation between TCHOL, LDL-C, and Apo-B with urinary albumin. Also, Apo-B was positively correlated with blood urea nitrogen, suggesting that altered renal function may play a role. In this regard, we observed lower serum albumin and higher serum creatinine in CAPD patients relative to HD patients.

In conclusion, we confirm the previous findings that suggest that CAPD patients have more atherogenic lipid profiles. Higher exposure to glucose may not affect lipid profiles in these patients. Further studies are warranted to determine the cause for worsening lipid profiles in these patients. Higher nutritional status and/or reduced renal function may play a role. Early detection of hyperlipidemia and dietary therapy could be beneficial.

Declaration of interest: The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.