Abstract
Background
Left ventricular hypertrophy (LVH) is common among patients undergoing dialysis. However, the dynamic structural changes of LV are rarely discussed. The study aimed to investigate the prognostic significance of left ventricular mass index (LVMI)-progression in incident peritoneal dialysis (PD) patients, and explore risks factors for LVMI-progression.
Methods
Incident PD patients between February 2008 and July 2018 were recruited. Echocardiography was performed yearly to collect LVMI and evaluate its changes. Participants were divided into three subgroups: group with LVMI-regression, group with LVMI stable and group with LVMI-progression. The end points include all-cause mortality, cardiovascular mortality and cardiovascular events. Cox regression models were performed to identify the associations between LVMI-progression and these endpoints. Multivariate logistic regression was conducted to identify risk factors for LVMI-progression.
Results
A total of 216 PD patients (130 men,60.2%) with a mean age of 54.3 ± 16.8 years were recruited. LVMI-progression was identified in 72 patients (33.3%) after PD initiation. The cohort was followed for a median duration of 65.9 months. Multivariable Cox regression analysis revealed that LVMI-progression was an independent predictor of all-cause mortality (HR, 1.419; 95% CI, 1.016–1.982; p = 0.040), cardiovascular mortality (HR, 1.836; 95%CI, 1.084–3.108; p = 0.024), and cardiovascular events (HR, 1.494; 95%CI, 1.063–2.099; p = 0.021). Multivariable logistic regression showed that hemoglobin, ferritin, blood pressure and fibrinogen were significantly associated with LVMI-progression.
Conclusion
Early LVMI-progression was independently associated with all-cause mortality and cardiovascular outcomes in PD patients. The dynamic monitoring of LVMI might therefore help identify high-risk patients.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12882-022-02831-6.
Keywords: Peritoneal dialysis, Left ventricular hypertrophy, Cardiovascular risks, Survival
Introduction
Cardiovascular mortality in dialysis patients is 10 to 20 times higher than in the general population [1]. Given the far greater cardiovascular risks among dialysis patients, it is important to assess the role of traditional and nontraditional risk factors for cardiovascular disease. Alterations in left ventricular (LV) mass and structure are common in patients with chronic kidney disease (CKD) [2]. Among patients treated by hemodialysis or peritoneal dialysis (PD), it is reported that the prevalence of left ventricular hypertrophy (LVH) is approximately 75% [3].
Echocardiogram is one of the most common imaging tests to evaluate the LV geometry. And high left ventricular mass index (LVMI) is widely used as a quantitative tool to assess LVH [4]. Previous reports have shown that LVH in CKD contributes to high cardiovascular mortality [5–7], and increased LVMI predicts higher mortality in PD patients [8, 9]. LVMI thus has been widely accepted as an indicator of prognosis in dialysis patients. Although many studies have investigated the results of a single echocardiogram in PD patients, very few studies reviewed serial echocardiograms dynamically. As early as in 1992 [10], Hüting J, et al. have found that LVH could be changeable during the long-term CAPD treatment. However, the structural change of LV over time and its impact on PD patients are rarely discussed.
In the present study, we regularly followed up the LVMI of PD patients through echocardiogram. The study aimed to investigate the prognostic significance of LVMI-progression in patients on PD, and explore risks factors for LVMI-progression.
Materials and methods
Population and study design
It was designed as a prospective observational study. From February 2008 to July 2018, adult incident PD patients from Huashan Hospital, Fudan University, China were recruited. Patients were excluded if they were transferred from hemodialysis or failed renal transplantation. Patients exhibited an ongoing infection, neoplasia, or unstable cardiovascular disease with a life expectancy of less than 6 months were also excluded. Participants were followed from enrollment until death, a switch to hemodialysis (HD), renal transplantation, a transfer, or March 2020. The study was approved by the Ethics Committee of Huashan Hospital, Fudan University and all participants provided written informed consent.
LVMI acquirement
In each patient, standard transthoracic echocardiogram was performed at PD initiation and followed up yearly. All measurement data were read and confirmed by professional cardiac sonographer according to the recommendations of the American Society of Echocardiography (ASE) [11]. Left ventricular end-diastolic diameter (LVDd), interventricular septum thickness (IVST), posterior wall thickness (PWT) during diastole, left ventricular ejection fraction (LVEF) and E/A ratio were evaluated and recorded. LVMI was calculated using the following equations [4]:
Definition of LVMI changes and the grouping method
LVMIs in the first 2 years after PD initiation were collected. ∆LVMI was calculated to define LVMI changes (LVMI0 = the baseline LVMI, LVMI2 = LVMI in the second year, ∆LVMI = LVMI2-LVMI0). Participants were divided into 3 subgroups according to ∆LVMI:
Group 1: participants with LVMI-regression: ∆LVMI ≤ -5%LVMI0;
Group 2: participants with LVMI stable: -5%LVMI0 < ∆LVMI < 5%LVMI0;
Group 3: participants with LVMI-progression: ∆LVMI ≥ 5%LVMI0.
Clinical and laboratory data
Demographic characteristics including sex, age, history of diabetes, smoking status and medication status (use of statins, anti-hypertensive drugs and renin–angiotensin–aldosterone system [RAAS] antagonists) were obtained at the time of enrollment. Height, weight, BSA, body mass index (BMI), blood pressure, laboratory parameters and dialysis-related parameters were also recorded at baseline and collected semiannually. The laboratory data include hemoglobin, albumin, iron metabolism parameters, calcium-phosphate metabolism parameters, lipid metabolism parameters, plasma fibrinogen (FIB), N-terminal pro brain natriuretic peptide (NT-proBNP) and high-sensitivity C-reactive protein (hsCRP). The PD-related data include urinary output, total Kt/V, residual Kt/V, total clearance of creatinine (Ccr), residual Ccr, normalized protein catabolic rate (nPCR), glucose concentration in dialysate and volume of dialysate. Dialysis adequacy was evaluated using Baxter PD Adequest 2.0 software (Baxter Healthcare Corporation, Deerfield, IL, USA). The average values of all these indexes in the first two years were calculated.
End points
The primary endpoint of the study was all-cause mortality. The secondary endpoints were cardiovascular events (CVEs) and cardiovascular mortality. CVEs were defined when patients exhibited myocardial infarction, acute coronary syndrome, non-hemorrhagic or hemorrhagic stroke, heart failure, arterial embolization, diabetic foot or non-traumatic amputation. Mortality caused by CVEs was defined as cardiovascular death.
Statistical analysis
Statistical analyses were carried out with SPSS 22.0 (SPSS, Inc., Chicago, IL, USA). Numeric variables were presented as mean values ± standard deviation or as median with quartile ranges. Categorical variables were presented as percentages. One‐way analysis of variance (ANOVA), and two‐way ANOVA analysis were performed in continuous variables to make comparisons while Chi-square test was performed in categorical variables as appropriate. Univariate and multivariate Cox regression models were used to explore risk factors for all-cause mortality, CVEs and cardiovascular mortality and to clarify the associations between LVMI changes and these endpoints. Binary logistic regressions were conducted to identify the risk factors for LVMI-progression.
All statistical tests were 2-sided and a p value < 0.05 was considered statistically significant.
Results
Characteristics of study population
The study population included 216 patients (130 men; mean age of 54.3 ± 16.8 years) with a mean BMI of 22.4 ± 3.3 kg/m2. LVMI-regression was identified in 113 patients (52.3%) while 72 patients (33.3%) were detected to have LVMI-progression. Clinical data changes in the first 2 years after PD initiation were displayed in Table 1. And Table 2 showed the average values of all these clinical indexes among 3 groups. Compared to patients with LVMI-regression, patients with LVMI-progression had higher blood pressure, NT-proBNP, hsCRP and FIB but lower level of hemoglobin, ferritin and iron saturation. Besides, HDL and ApoA1 were lower in the group with LVMI-progression. No difference was observed in medication status and dialysis-related variables among three groups.
Table 1.
Clinical characteristics of participants in the first 2 years after PD initiation
| Baseline | 1st year | 2nd year | |
|---|---|---|---|
| Age | 54.3 ± 16.8 | / | / |
| Male, n(%) | 130(60.2%) | / | / |
| CAPD,n(%) | 202(93.5%) | / | / |
| BMI (kg/m2) | 22.4 ± 3.3 | 23.3 ± 3.6* | 23.6 ± 3.8 |
| SBP (mmHg) | 140(130,155) | 130(117,145) | 131(120,141) |
| DBP (mmHg) | 80(73,90) | 80(70,90) | 80(72,90) |
| MAP | 100.7(93.3,110.0) | 96.7(89.7,106.7)* | 98.0(90.0,106.7)* |
| LVDd (cm) | 5.0(4.6,5.4) | 4.9(4.5,5.3)* | 4.9(4.4, 5.2)* |
| PWT (cm) | 1.1(0.9,1.2) | 1.0(0.9,1.1)* | 1.0(0.9,1.2)* |
| IVS (cm) | 1.1(1.0,1.3) | 1.1(1.0,1.3) | 1.1(1.0,1.3) |
| LVMI (g/m2) | 128.9(107.7, 154.9) | 115.9(98.1,136.6)* | 116.6(93.3,139.6)* |
| LVEF (%) | 66.0(62.0,71.0) | 67.0(64.0,71.0) | 67.0(63.0,72.0) |
| E/A ratio | 0.79(0.63,1.00) | 0.73(0.61,0.90) | 0.71(0.59,0.87) |
| Dialysis-related variables | |||
| nPCR (g/kg/24 h) | 0.91 ± 0.19 | 0.95 ± 0.20 | 0.92 ± 0.22 |
| Urinary output(L/24 h) | 1.10(0.63,1,50) | 0.80(0.40,1.33)* | 0.55(0.08,1.10)*# |
| Total Ccr (L/week) | 78.4(62.4,96.7) | 64.9(52.5,80.2)* | 54.5(47.5,72.1)*# |
| Residual renal Ccr (L/week) | 43.0(27.4,64.2) | 30.7(13.6,46.7)* | 17.1(0.83,38.2)* |
| Total Kt/V | 2.04(1.76,2.42) | 1.86(1.62,2.14) | 1.79(1.58,2.08)* |
| Residual renal Kt/V | 0.84(0.50,1.21) | 0.63(0.24,0.93)* | 0.33(0.01, 0.72)*# |
| Volume of dialysate (per 1000 mL/24 h) | 5.98(5.51,6.37) | 6.21(5.75,7.89)* | 6.60(6.00,8.70)*# |
| Proportion of 1.5%-glucose dialysate(%) | 100(100,100) | 70.8(66.7,100) | 100(66.7,100) |
| Biochemical parameters | |||
| Hemoglobin (g/L) | 86.7 ± 19.0 | 106.1 ± 16.7* | 107.0 ± 16.9* |
| Ferritin (ng/ml) | 192.1(86.4,370.5) | 212.6(140.7,344.5) | 216.9(131.3,305.5) |
| Serum iron (umol/L) | 10.3(7.7,14.1) | 12.6(9.9,17.4)* | 12.8(9.9,16.6)* |
| Iron saturation (%) | 27.0(20.0,37.0) | 33.0(25.0, 43.0)* | 32.0(24.0,41.0)* |
| Albumin (g/L) | 35.3 ± 5.1 | 35.9 ± 5.0 | 36.4 ± 4.8* |
| Calcium (mmol/L) | 2.05(1.89, 2.19) | 2.18(2.05, 2.34)* | 2.24(2.09,2.37)* |
| Phosphate (mmol/L) | 1.62(1.30,1.97) | 1.49(1.25,1.72)* | 1.49(1.24,1.79)* |
| iPTH (pg/ml) | 270.5(158.4,440.0) | 261.2(156.5,408.6) | 255.5(164.0,389.5) |
| CHO (mmol/L) | 4.13(3.54,5.11) | 4.24(3.65,4.93) | 4.18(3.61,5.02) |
| TG (mmol/L) | 1.39(1.05,1.98) | 1.68(1.15,2.47)* | 1.72(1.15,2.56)* |
| LDL (mmol/L) | 2.33(1.87,2.93) | 2.17(1.74,2.66)* | 2.19(1.72,2.71)* |
| HDL (mmol/L) | 0.95(0.81,1.18) | 0.91(0.77,1.13) | 0.90(0.75,1.12)* |
| ApoA1 (g/L) | 0.97(0.84,1.10) | 1.00(0.88,1.15)* | 0.99(0.87,1.17) |
| ApoB (g/L) | 0.65(0.55,0.81) | 0.66(0.56,0.78) | 0.66(0.57.0.77) |
| NT-proBNP (per 1000 pg/ml) | 1.81(0.83,6.45) | 1.31(0.57,3.08)* | 1.89(0.70,6.41)# |
| hsCRP (mg/L) | 2.11(0.47,4.46) | 0.86(0.33,2.79)* | 0.85(0.41,2.67)* |
| FIB (g/L) | 3.48(2.95,4.32) | 3.80(3.39,4.40)* | 3.82(3.32,4.45)* |
CAPD continuous ambulatory peritoneal dialysis, BMI body mass index, SBP systolic blood pressure, DBP diastolic blood pressure, MAP mean arterial pressure, nPCR normalized protein catabolic rate, Ccr creatinine clearance, Kt/V urea clearance, CHO cholesterol, TG triglyceride, LDL low-density lipoprotein, HDL high-density lipoprotein, NT-proBNP N-terminal pro brain natriuretic peptide, hsCRP high-sensitivity C-reactive protein, FIB fibrinogen
*p < 0.05 vs. baseline; #p < 0.05 vs. 1st year
Table 2.
Clinical characteristics of participants with different LVMI changes
| Total | LVMI-regression N = 113 | Stable N = 31 | LVMI-progression N = 72 | P value | |
|---|---|---|---|---|---|
| Age | 55.3 ± 16.8 | 54.2 ± 16.8 | 60.1 ± 16.4 | 55. 2 ± 17.0 | 0.785 |
| Male, n(%) | 130(60.2%) | 65(57.5%) | 18(58.1%) | 42(64.6%) | 0.557 |
| BMI (kg/m2) | 23.1 ± 3.5 | 22.6 ± 3.3 | 24.2 ± 3.1 | 23.7 ± 3.6 | 0.047 |
| SBP (mmHg) | 135 ± 14 | 134 ± 14 | 135 ± 15 | 138 ± 15 | 0.124 |
| DBP (mmHg) | 82 ± 8 | 81 ± 9 | 80 ± 8 | 84 ± 10 | 0.021 |
| MAP | 99.6 ± 9.2 | 98.3 ± 8.8 | 98.7 ± 8.3 | 102.1 ± 9.7 | 0.018 |
| History of smoking | 45(20.8%) | 23(20.4%) | 8(25.8%) | 13(20.0%) | 0.754 |
| History of diabetes | 53(24.5%) | 29(25.7%) | 4(12.9%) | 20(30.8%) | 0.253 |
| Use of statins | 42(19.4%) | 21(18.6%) | 6(19.4%) | 15(23.1%) | 0.931 |
| Use of anti-hypertensive drugs | 200(92.6%) | 103(91.2%) | 28(90.3%) | 69(65.8%) | 0.436 |
| Use of RAAS antagonists | 93(43.1%) | 51(45.1%) | 11(35.5%) | 30(46.2%) | 0.130 |
| Dialysis-related variables | |||||
| CAPD, n(%) | 202(93.5%) | 106(93.8%) | 27(87.1%) | 69(95.8%) | 0.251 |
| nPCR (g/kg/24 h) | 0.93 ± 0.18 | 0.94 ± 0.17 | 0.91 ± 0.15 | 0.92 ± 0.18 | 0.647 |
| Urinary output(L/24 h) | 0.86(0.50,1.28) | 0.83(0.46,1.28) | 0.83(0.67, 1.14) | 0.92(0.57,1.29) | 0.858 |
| Total Ccr (L/week) | 67.5(57.1, 81.8) | 67.7(57.5,84.0) | 68.2(54.6, 88.7) | 66.8(57.4,79.5) | 0.733 |
| Residual renal Ccr (L/week) | 31.3(18.9, 46.5) | 32.4(20.1,49.6) | 30.5(15.5,52.7) | 31.2(18.8,42.9) | 0.609 |
| Total Kt/V | 1.93(1.72,2.22) | 1.94(1.71,2.26) | 1.98(1.70,2.16) | 1.88(1.73,2.15) | 0.539 |
| Residual renal Kt/V | 0.61(0.36, 0.88) | 0.61(0.35,0.92) | 0.61(0.35,0.88) | 0.62(0.42,0.87) | 0.984 |
| Proportion of 1.5%-glucose dialysate(%) | 88.9(69.4,100) | 88.9(67.8,100) | 80.6(66.7,100) | 91.7(75.0,100) | 0.770 |
| Volume of dialysate(L/24 h) | 6.27(5.82, 7.59) | 6.19(5.83,7.53) | 6.40(5.92,7.44) | 6.30(5.73,7.68) | 0.917 |
| Biochemical parameters | |||||
| Hemoglobin (g/L) | 100.0 ± 13.8 | 101.8 ± 13.7 | 99.9 ± 13.7 | 96.6 ± 13.8 | 0.055 |
| Ferritin (ng/ml) | 233.1(153.8, 327.3) | 256.0(163.7,343.2) | 207.1(157.0,267.7) | 205.8(138.7,307.3) | 0.033 |
| Serum iron (umol/L) | 12.3(10.1, 15.1) | 13.3(10.6,15.3) | 12.1(10.4,14.5) | 11.6(9.2,14.3) | 0.129 |
| Iron saturation (%) | 31.7(26.0, 38.0) | 32.7(27.3, 39.7) | 31.7(27.7, 36.0) | 28.3(24.0,34.3) | 0.059 |
| Albumin (g/L) | 35.8 ± 4.4 | 36.3 ± 4.7 | 34.8 ± 4.2 | 35.3 ± 4.0 | 0.184 |
| Calcium (mmol/L) | 2.15 ± 0.17 | 2.15 ± 0.18 | 2.16 ± 0.15 | 2.14 ± 0.14 | 0.812 |
| Phosphate (mmol/L) | 1.53(1.32, 1.79) | 1.56 ± 0.32 | 1.55 ± 0.36 | 1.61 ± 0.40 | 0.513 |
| iPTH (pg/mL) | 284.1(186.6, 393.7) | 302.3(203.7,423.3) | 208.5(156.4,332.3) | 283.8(180.8,388.4) | 0.139 |
| CHO (mmol/L) | 4.40 ± 0.91 | 4.46 ± 0.99 | 4.22 ± 0.80 | 4.40 ± 0.83 | 0.439 |
| TG (mmol/L) | 1.62(1.25, 2.28) | 1.64(1.23, 2.57) | 1.54(1.32, 2.33) | 1.64(1.22, 2.29) | 0.994 |
| LDL (mmol/L) | 2.29(1.92, 2.71) | 2.25(1.88,2.73) | 2.29(1.74,2.59) | 2.32(2.08,2.70) | 0.311 |
| HDL (mmol/L) | 0.94(0.80, 1.12) | 0.97(0.84,1.15) | 0.84(0.76,1.03) | 0.90(0.77,1.09) | 0.036 |
| ApoA1 (g/L) | 0.99(0.89, 1.12) | 1.01(0.94, 1.15) | 0.93(0.86, 1.09) | 0.96(0.86, 1.06) | 0.009 |
| ApoB (g/L) | 0.69 ± 0.15 | 0.69 ± 0.16 | 0.68 ± 0.14 | 0.70 ± 0.14 | 0.822 |
| NT-proBNP (per 1000 pg/ml) | 2.26(0.93, 7.47) | 1.95(0.80,7.58) | 2.28(1.46,5.23) | 2.50(1.15,8.93) | 0.428 |
| hsCRP (mg/L) | 1.65(0.79, 4.12) | 1.33(0.61,3.73) | 2.11(1.12,4.40) | 1.96(1.12,5.40) | 0.032 |
| FIB (g/L) | 3.72(3.26, 4.31) | 3.64(3.13,4.13) | 3.85(3.34,4.36) | 3.88(3.45, 4.59) | 0.014 |
The duration of the observational period was 65.9 (31.9–87.4) months for all patients. Overall, 53 patients experienced CVEs, 56 patients died with 22 of them died from CVEs. More all-cause mortality, cardiovascular morbidity and mortality occurred in the group with LVMI-progression. During the follow-up, 2 patients transferred to other centers, 17 switched to HD and 15 received renal transplantation.
Association of LVMI changes with all-cause mortality and cardiovascular outcomes
In the group with LVMI-regression, a total of 23 (20.4%) patients experienced CVEs; 20 (17.7%) patients died and 8 (7.1%) died from CVEs. Compared to the LVMI-regression group, more all-cause mortality (28,38.9%; p = 0.020) and cardiovascular events (22, 30.6%; p = 0.037) occurred in the group with LVMI-progression.
In univariable Cox regression models, LVMI-progression was a risk factor for both all-cause mortality and cardiovascular events while not for cardiovascular mortality (Table 3). In multivariable Cox regression analysis after adjustment, LVMI-progression turned out to be an independent predictor for all-cause mortality (HR, 1.419; 95% CI, 1.016–1.982; p = 0.040), cardiovascular events (HR, 1.494; 95%CI, 1.063–2.099; p = 0.021) and cardiovascular mortality (HR, 1.836; 95%CI, 1.084–3.108; p = 0.024) (Table 4). In addition, a negative correlation was observed between LVMI and LVEF (Supplementary Fig. 1). The correlation coefficient was -0.287 with p < 0.001, verifying the adverse role of LVH progression in cardiac function.
Table 3.
Univariable cox regression analysis for possible predictors of all-cause mortality, cardiovascular events and cardiovascular mortality
| All-cause mortality | Cardiovascular events | Cardiovascular mortality | |||||||
|---|---|---|---|---|---|---|---|---|---|
| HR | 95.0% CI | p | HR | 95.0% CI | p | HR | 95%CI | p | |
| Age | 1.054 | 1.032–1.076 | < 0.001 | 1.037 | 1.017–1.057 | < 0.001 | 1.036 | 1.006–1.066 | 0.017 |
| Sex | 0.792 | 0.445–1.412 | 0.430 | 0.546 | 0.300–0.914 | 0.048 | 0.657 | 0.268–1.612 | 0.359 |
| History of diabetes | 2.547 | 1.451–4.470 | 0.001 | 2.684 | 1.556–4.629 | < 0.001 | 2.650 | 1.142–6.151 | 0.023 |
| BMI (kg/m2) | 1.135 | 1.083–1.217 | < 0.001 | 1.119 | 1.032–1.214 | 0.006 | 1.069 | 0.945–1.209 | 0.287 |
| MAP | 0.998 | 0.967–1.030 | 0.904 | 1.021 | 0.991–1.052 | 0.181 | 1.022 | 0.976–1.071 | 0.353 |
| Use of statins | 1.263 | 0.553–2.882 | 0.579 | 1.639 | 0.875–3.069 | 0.123 | 1.978 | 0.773–5.509 | 0.155 |
| Use of RAAS antagonists | 0.577 | 0.317–1.049 | 0.072 | 0.575 | 0.323–1.024 | 0.060 | 0.389 | 0.143–1.055 | 0.064 |
| Use of anti-hypertensive drugs | 0.683 | 0.271–1.723 | 0.419 | 0.895 | 0.322–2.489 | 0.832 | 2.451 | 0.103–3.256 | 0.404 |
| LVMI (g/m2) | 1.010 | 1.001–1.020 | 0.027 | 1.017 | 1.008–1.025 | < 0.001 | 1.012 | 0.999–1.026 | 0.073 |
| LVMI -progression | 1.417 | 1.049–1.915 | 0.023 | 1.359 | 1.015–1.820 | 0.039 | 1.475 | 0.935–2.326 | 0.095 |
| Hemoglobin (g/L) | 0.990 | 0.970–1.010 | 0.319 | 0.996 | 0.977–1.016 | 0.717 | 1.005 | 0.974–1.038 | 0.735 |
| Albumin (g/L) | 0.954 | 0.890–1.023 | 0.187 | 1.011 | 0.940–1.088 | 0.761 | 1.046 | 0.938–1.165 | 0.418 |
| CHO (mmol/L) | 1.028 | 0.748–1.412 | 0.866 | 0.971 | 0.705–1.337 | 0.857 | 1.022 | 0.637–1.640 | 0.928 |
| LDL (mmol/L) | 1.239 | 0.820–1.874 | 0.309 | 1.187 | 0.790–1.783 | 0.408 | 1.243 | 0.676–2.286 | 0.484 |
| TG (mmol/L) | 0.883 | 0.659–1.182 | 0.403 | 0.977 | 0.759–1.257 | 0.856 | 0.531 | 0.273–1.036 | 0.063 |
| HDL (mmol/L) | 1.157 | 0.438–3.057 | 0.768 | 0.354 | 0.115–1.091 | 0.071 | 1.867 | 0.481–7.244 | 0.367 |
| ApoA (g/L) | 1.262 | 1.108–1.437 | < 0.001 | 1.127 | 1.103–1.463 | 0.001 | 1.315 | 1.140–1.517 | < 0.001 |
| ApoB (g/L) | 2.495 | 0.411–3.309 | 0.772 | 1.953 | 0.293–12.992 | 0.489 | 0.682 | 0.109–29.868 | 0.682 |
| hsCRP (mg/L) | 1.001 | 0.967–1.037 | 0.936 | 1.018 | 0.992–1.044 | 0.178 | 1.004 | 0.955–1.055 | 0.884 |
| nPCR (g/kg/24 h) | 0.040 | 0.006–0.256 | 0.001 | 0.107 | 0.017–0.653 | 0.016 | 0.222 | 0.016–3.123 | 0.265 |
| Urinary output(L/24 h) | 0.542 | 0.309–0.952 | 0.033 | 0.856 | 0.499–1.467 | 0.571 | 0.771 | 0.325–1.830 | 0.556 |
| Total Ccr (L/week) | 0.997 | 0.985–1.009 | 0.646 | 0.998 | 0.987–1.010 | 0.786 | 1.001 | 0.984–1.019 | 0.880 |
| Residual renal Ccr (L/week) | 0.997 | 0.986–1.008 | 0.575 | 0.998 | 0.987–1.008 | 0.650 | 0.999 | 0.989–1.009 | 0.872 |
| Total Kt/V | 1.075 | 0.931–1.241 | 0.326 | 1.081 | 0.931–1.254 | 0.305 | 1.148 | 0.997–1.323 | 0.055 |
| Residual renal Kt/V | 0.814 | 0.438–1.511 | 0.514 | 0.834 | 0.460–1.511 | 0.549 | 0.995 | 0.403–2.459 | 0.992 |
| Proportion of 1.5%-glucose dialysate(%) | 1.452 | 0.263–8.014 | 0.669 | 1.057 | 0.172–6.479 | 0.952 | 2.080 | 0.178–24.236 | 0.559 |
| Volume of dialysate (per1000mL/24 h) | 1.319 | 1.096–1.586 | 0.003 | 1.294 | 1.075–1.557 | 0.006 | 1.193 | 0.898–1.583 | 0.223 |
Table 4.
Proportional hazard ratios for all-cause mortality, cardiovascular events and cardiovascular mortality for LVMI-progression
| All-cause mortality | Cardiovascular events | Cardiovascular mortality | ||||
|---|---|---|---|---|---|---|
| HR (95%CI) | P-value | HR (95%CI) | P-value | HR (95%CI) | P-value | |
| Model 1 | 1.417(1.049–1.915) | 0.023 | 1.359(1.015–1.820) | 0.039 | 1.475(0.935–2.326) | 0.095 |
| Model 2 | 1.405(1.035–1.905) | 0.029 | 1.0381(1.018–1.059) | 0.047 | 1.438(0.905–2.285) | 0.124 |
| Model 3 | 1.424(1.042–1.947) | 0.027 | 1.391(1.012–1.912) | 0.042 | 1.643(1.020–2.645) | 0.042 |
| Model 4 | 1.419(1.016–1.982) | 0.040 | 1.494(1.063–2.099) | 0.021 | 1.836(1.084–3.108) | 0.024 |
Model 1: without adjustment
Model 2: adjusted for age, gender, BMI
Model 3: adjusted for age, gender, BMI, albumin
Model 4: adjusted for age, gender, BMI, albumin, history of diabetes, use of hypotensive drugs, glucose dialysate concentration, LVMI, ApoA1, FIB and NT-proBNP
Risk factors for LVMI-progression
To explore risk factors for LVMI-progression, participants with LVMI-regression and LVMI-stable were merged as a new group without LVMI-progression. Age, sex, BMI,use of anti-hypertensive drugs, glucose concentration in dialysate, MAP, hemoglobin, ferritin, iron saturation, ApoA1, NT-proBNP and FIB were added to binary logistic regressions. The multivariate analysis showed that MAP, hemoglobin, ferritin and FIB were independent risk factors for LVMI-progression (Table 5).
Table 5.
Odds ratios for LVMI-progression in incident PD patients based on logistic regression analysis
| Univariate | Multivariate | |||
|---|---|---|---|---|
| OR (95%CI) | P-value | OR (95%CI) | P-value | |
| Age | 1.000 (0.982–1.017) | 0.974 | - | - |
| Sex | 0.765(0.419–1.397) | 0.383 | - | - |
| BMI (kg/m2) | 1.085(0.995–1.182) | 0.064 | - | - |
| MAP | 1.061(1.025–1.099) | 0.001 | 1.074(1.029–1.121) | 0.001 |
| Use of anti-hypertensive drugs | 3.219(0.710–14.591) | 0.129 | - | - |
| Hemoglobin (g/L) | 0.974(0.953–0.995) | 0.017 | 0.970(0.942–0.998) | 0.037 |
| Ferritin (ng/ml) | 0.998(0.995–1.000) | 0.071 | 0.995(0.992–0.999) | 0.006 |
| ApoA1(g/L) | 0.051(0.006–0.402) | 0.005 | - | - |
| hsCRP (mg/L) | 1.043(1.003–1.085) | 0.036 | - | - |
| Fib (g/L) | 1.745(1.241–2.453) | 0.001 | 1.800(1.181–2.745) | 0.006 |
| NT-proBNP (per 1000 pg/ml) | 1.032(0.989–1.076) | 0.147 | - | - |
| Proportion of 1.5%-glucose dialysate(%) | 0.852(0.132–5.507) | 0.866 | - | - |
Discussion
In this study, we followed up the patients’ LVMI changes after PD initiation and identified early LVMI-progression was an independent predictor for all-cause mortality, cardiovascular events, and cardiovascular mortality. We also explored the risk factors related to LVMI-progression. It revealed that MAP, hemoglobin, ferritin and fibrinogen were significantly associated with the process. The findings indicated that dynamic follow-up of LVMI after PD initiation might be useful for the early stratification of patients with a high risk of cardiovascular disease and mortality.
Before our research, several observational studies have confirmed the association between LVMI and high cardiovascular mortality in dialysis patients [5, 12, 13]. However, most of them were cross-sectional and performed in prevalent dialysis patients. Only Zoccali C. et al. have monitored LVMI among hemodialysis (HD) patients and verified that changes in LVMI had an independent prognostic value for cardiovascular events in HD patients [14]. Recently, Tamara's group have found that concentric LVH at PD initiation was independently associated with survival in PD patients [15]. Similarly, they advocated early evaluation of LV geometry at PD initiation. Although the group didn’t evaluate changes in LV geometry over time, they suggested that monitoring of LV geometry over time might provide more accurate predictions regarding patients’ prognosis. Our study to some extent could compensate the limitation of their exploration. To our knowledge, it’s the first study that focuses on the relationship between early-stage LVH changes after PD initiation and the prognosis of patients.
According to the suggestion made by the 2007 ESH/ESC guidelines [16], LVH was defined as LVMI more than 125 g/m2 in men and more than 110 g/m2 in women. Previous reports showed that at the initiation of maintenance dialysis, 70–80% of patients had evidence of LVH [17–19]. Consistent with the data, 73.6% patients in our study was diagnosed with LVH at baseline. It is noteworthy that more than half of the participants (52.3%) in our study got LVMI reduced after 2-year PD treatment. Like our data, Rebic D et al. have observed a significant reduction in LVMI and a significant improvement of LV function after 1-year PD treatment [20]. It is explainable. LVMI is a modifiable factor. For patients with end-stage renal disease (ESRD), PD offers the advantage of continuous dialysis, which is useful in preventing disequilibrium syndrome such as hypertension, poor extracellular volume control, and anemia. As displayed in Table 1, the median LVMI of all participants was 128 g/m2 at baseline and it decreased to 115.9 g/m2 one year later. Concomitantly, parameters such as hemoglobin, blood pressure, NT-proBNP and CRP were all improved one year after PD initiation.
In the univariate Cox regression analysis, we found that both baseline LVMI and LVMI-progression were risk factors for all-cause mortality and cardiovascular events (Table 3). However, when they were put into the multivariate models together, the predictive role of LVMI was diluted while LVMI-progression was still strongly linked to all these endpoints. Therefore, LVMI-progression was related to mortality and cardiovascular risks independently of LVMI and of a large series of traditional risk factors. Compared to a single cross-sectional LVMI, LVMI-changes might be a more powerful indicator for PD patients’ prognosis.
In PD patients, the most prominent hemodynamic parameters include hypertension, volume overload and anemia [21]. Anemia, together with hypertension, has been reported to be one of the main etiological factors for the development of LVH in dialysis patients [22, 23]. Similarly, our results showed that MAP, hemoglobin and ferritin were independently associated with LVMI-progression in PD patients. Fibrinogen is considered as an established predictor of cardiovascular events [24]. In our data, its adverse role in LVMI-progression was further verified. Volume overload is also thought to be a major factor in the development of LVH [25, 26]. In the present study, the association of NT-proBNP with LVMI-progression could not be demonstrated in the logistic regression models. Firstly, NT-proBNP isn’t the gold standard for volume overload. Secondly, this may be due to the short-term of PD duration. Earlier studies have suggested that patients on short-term CAPD were less volume overload than those on long-term CAPD [27]. We only followed up the first 2-year clinical data after PD initiation. Therefore, the role of volume overload might have been attenuated. In addition, antihypertensive treatment didn’t affect LVH progression in our results. It was reasonable. In our study, over 90% of participants used anti-hypertensive drugs. Interindividual variations of anti-hypertensive therapy might be too small to distinguish statistical differences. Nevertheless, antihypertensive treatment can never be neglected in clinic. Our results showed MAP was an independent risk factor for LVMI-progression, illustrating the importance of blood pressure control.
Several limitations remained in this study. First, the study was single-center based with a relatively small sample size. Further multicenter research with more participants is required to confirm our conclusions. Second, number of events especially the cardiovascular death in our study was small and it may affect the statistical power. Some potential risk factors could be missed. Third, the observational nature of this study didn’t not allow us to examine whether therapeutic intervention on anemia etc. could slow LVMI-progression, improving the survival of PD patients. Further studies were needed to explore the causal relationship between them. Finally, we only collected the clinical data in the first 2 years after PD initiation and didn’t further evaluate changes of LVMI over time. It is reported that long-term CAPD is disadvantageous for volume clearance, so it is associated with higher LVMI compared to short-term CAPD [28, 29]. Although a long-time monitoring of left ventricular structure might provide more accurate predictions for patients’ prognosis, our results showed that the long-term outcomes of PD patients could be predicted by LVMI-progression at an early point of PD initiation.
Conclusion
In conclusion, early LVMI-progression after PD initiation was a predictor for all-cause mortality, cardiovascular events and cardiovascular in PD patients. The dynamic monitoring of LVMI after PD initiation might therefore help identify patients with a high risk of cardiovascular disease and mortality. In addition, the study found that hypertension, anemia, iron deficiency and high level of fibrinogen were independent risk factors for LVMI-progression. These factors could be the possible point for interventions. Further investigations are needed to clarify whether early treatment for factors such as anemia could improve outcomes in PD patients.
Supplementary Information
Additional file 1: Supplementary Figure 1. Relationship between LVEF and LVMI.
Acknowledgements
Not applicable.
Abbreviations
- ASE
Society of Echocardiography
- ANOVA
Analysis of variance
- BMI
Body mass index
- CAPD
Continuous ambulatory peritoneal dialysis
- CKD
Chronic kidney disease
- CVEs
Cardiovascular events
- Ccr
Creatinine clearance
- CHO
Cholesterol
- DBP
Diastolic blood pressure
- ESRD
End-stage renal disease
- FGF-23
Fibroblast growth factor-23
- FIB
Fibrinogen
- HDL
High-density lipoprotein
- HD
Hemodialysis
- hsCRP
High-sensitivity C-reactive protein
- IVST
Interventricular septum thickness
- Kt/V
Urea clearance
- LV
Left ventricular
- LVH
Left ventricular hypertrophy
- LVDd
Left ventricular end-diastolic diameter
- LVEF
Left ventricular ejection fraction
- LVMI
Left ventricular mass index
- LDL
Low-density lipoprotein
- MAP
Mean arterial pressure
- NT-proBNP
N-terminal pro brain natriuretic peptide
- nPCR
Normalized protein catabolic rate
- PD
Peritoneal dialysis
- PWT
Posterior wall thickness
- RAAS
Renin-angiotensin-aldosterone system
- SBP
Systolic blood pressure
- TG
Triglyceride
Authors’ contributions
Tongying Zhu designed the study. Yun Chen and Shuqi Dai are major contributors in writing the manuscript and Chuanming Hao reviewed it. Xiaolin Ge and Da Shang interpreted the patient data and Qionghong Xie made analysis. All authors read and approved the final manuscript.
Funding
This study was supported by National Natural Science Foundation of China (grant number 81670692,81930120 and 81520108006).
Availability of data and materials
All data generated or analysed during this study are included in this published article.
Declarations
Ethics approval and consent to participate
The study was approved by the Ethics Committee of Huashan Hospital, Fudan University and all participants provided written informed consent. And all methods were performed in accordance with Declaration of Helsinki.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Yun Chen and Shuqi Dai share co-first authorship.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Additional file 1: Supplementary Figure 1. Relationship between LVEF and LVMI.
Data Availability Statement
All data generated or analysed during this study are included in this published article.