Abstract
Objective
The fibrinogen/albumin ratio (FAR) is a novel inflammatory indicator, which has been associated with cardiovascular disease. However, the relationship between FAR and cardiovascular event (CVE) in patients with peritoneal dialysis (PD) remains unclear. This study aims to clarify the relationship between FAR and first-ever CVE in patients with PD.
Methods
A total of 278 patients were enrolled between January 2012 and June 2021. They were defined as the high FAR group and the low FAR group based on the median FAR value (0.107). The primary outcome was the occurrence of first-ever CVE. Kaplan–Meier’s curves and Cox regression analysis were used to analyse the relationship between FAR and first-ever CVE in patients with PD. Forest plots were employed to depict the relationship between FAR and first-ever CVE in each subgroup.
Results
The average follow-up period was 40.26 ± 28.27 months. A total of 101 (36.3%) patients developed first-ever CVE. Kaplan–Meier’s analysis showed that there was a higher risk of first-ever CVE (p = .002) in the high FAR group. Multivariate Cox regression analysis showed that FAR ≥ 0.107 and age were independently associated with the risk of first-ever CVE in patients with PD. Receiver operating characteristic (ROC) analysis showed that FAR had a greater predicting value on the first-ever CVE.
Conclusions
High levels of FAR are independently associated with an increased risk of first-ever CVE in patients with PD.
Keywords: Cardiovascular event, chronic kidney disease, fibrinogen/albumin ratio, inflammation, peritoneal dialysis
Introduction
Chronic kidney disease (CKD) is a major and serious public health problem in the world with a high incidence and many complications. As kidney function deteriorates, patients inevitably require long-term renal replacement therapy, imposing a huge economic burden and seriously impacting their quality of life. Peritoneal dialysis (PD) is a recognized and effective method of renal replacement therapy, which has the advantages of simple operation, independence from dialysis equipment, relatively small impact on haemodynamics, and better preservation of residual renal function [1]. However, even in patients with CKD who receive PD in time, prognosis can vary significantly among individuals. Cardiovascular events (CVEs) are an important and common complication of CKD. A meta-analysis of 28 articles which included retrospective cohort studies, prospective cohort studies and cross-sectional studies showed patients with PD had fewer CVE but higher cardiovascular mortality compared to patients with haemodialysis [2]. Similarly, an observational study investigating different dialysis modalities and their association with CVE reported an increased risk of emergency hospital admissions and CVE-related mortality in patients with PD [3]. Therefore, early detection and assessment of the risk of CVE is important for the management of patients with PD and the improvement of their prognosis.
The fibrinogen/albumin ratio (FAR) is a newly proposed index related to systemic inflammatory response, which has been confirmed to be used to predict the prognosis of patients with malignant tumours, cardiovascular disease (CVD), coronavirus infection 2019 (COVID-19) and other diseases [4–7]. A recent cross-sectional study reported that FAR was positively related to arterial stiffness, an early lesion of CVD, in men with type 2 diabetes [8]. Celebi et al. found in a study involving 356 patients with stable coronary artery disease (CAD) that FAR was correlated with the degree of CAD [9]. Other studies have found that FAR was significantly related to the severity of CAD in patients with ST-elevation myocardial infarction (STEMI) or non-STEMI [10,11]. However, these studies mainly focus on the general population without kidney disease.
The incidence rate of CVE in patients with dialysis is higher than that in the general population. In 2022, a retrospective study in our centre involving 218 patients with CKD found that the FAR was related to the degree of coronary artery calcification [12]. In addition, a study suggests that FAR may be used to predict all-cause and cardiovascular mortality in patients with PD [13]. However, it was a single-centre study limited to Guangzhou, China, and did not explore the potential relationship between FAR and the occurrence of CVE in patients with PD. As a clinically accessible indicator without additional examination, we aim to explore the greater value of FAR in clinical applications. Therefore, we further designed this study to clarify the relationship between FAR and the first-ever CVE in patients with PD, hoping to fill this gap and provide a simple, economical and feasible method to predict the risk of CVE in patients with PD. By applying FAR to the management of patients with PD, we hope to reduce the incidence of CVE and even cardiovascular mortality in patients with PD and improve their prognosis.
Methods
Participants
The inclusion criteria for this study were initiated PD at the Second Affiliated Hospital of Anhui Medical University between 1 January 2012 and 30 June 2021. The exclusion criteria included: (1) ages younger than 18 years; (2) duration of PD less than 3 months; (3) transferred from long-term haemodialysis; (4) active infection; (5) hematologic disorders which may cause abnormal fibrinogen (FIB) such as multiple myeloma and leukaemia; (6) lack of necessary data such as albumin (ALB), FIB and follow-up information. Finally, 278 patients were successfully enrolled in the study. All patients with PD used glucose peritoneal dialysate (1.5% or 2.5% dextrose) which was exchanged three or five times daily. This study was conducted in accordance with the declaration of Helsinki and was approved in advance by the Ethics Committee of the Second Affiliated Hospital of Anhui Medical University (No. KY201302). All participants signed an informed consent form.
Data collection
Baseline demographic data involving age, sex, body mass index (BMI), the primary cause of end-stage kidney disease (ESKD), history of diabetes, and history of CVD. Laboratory data including haemoglobin, leukocyte count, neutrophil count, lymphocyte count, platelet, serum ALB, serum creatinine, serum urea nitrogen, serum uric acid, serum calcium, serum phosphorus, triglyceride, total cholesterol, plasma FIB, ferritin, high-sensitivity C-reactive protein (hs-CRP). All laboratory data were measured using standard methods in the clinical laboratory. All baseline data were collected within 3 months of PD treatment initiation to explore the potential relationship between the baseline data and the first-ever CVE in patients with PD. To avoid introducing irrelevant variables or bringing potential bias, the data before PD were not assessed. Total Kt/V and residual renal function were obtained by 24-hour urine and dialysis fluid collected after 1 month of PD therapy. To obtain other data, the following formula was applied: BMI = weight/height [2]. Corrected calcium = serum calcium + 0.02 × (40 − serum ALB). FAR = fibrinogen/serum albumin. Platelet and lymphocyte ratio (PLR) = platelet/lymphocyte. Neutrophil and lymphocyte ratio (NLR) = neutrophil/lymphocyte. Patients were considered to have diabetes if they were currently using hypoglycaemic agents or insulin and/or had a medical record of a diagnosis of diabetes [14].
Study outcomes
All patients were followed up until CVE, death, transferred to haemodialysis therapy or renal transplantation, transferred to other centres, lost to follow-up, or censoring on 30 April 2023, whichever occurred first. CVE was defined as coronary heart disease, congestive heart failure, acute myocardial infarction, stroke and cardiac arrest [14]. The first-ever CVE refers to the first occurrence of CVE after PD initiation, regardless of whether there is a history of CVE.
Statistical analysis
Statistical analysis was performed with SPSS version 26.0 (SPSS Inc., Chicago, IL). Given the small total number of events, to ensure balanced grouping, we divided all participants into two groups based on the median value of FAR: low FAR group (FAR < 0.107), and high FAR group (FAR ≥ 0.107). For continuous variables, normally distributed data were expressed as the mean and standard deviation, and non-normally distributed data were represented by the median (interquartile range). t-test and Mann–Whitney’s U-test were applied for comparisons of continuous variables. For categorical variables, frequencies and percentages were used for description, and the Chi-square test was used for comparison. We used Spearman’s rank correlation analysis to explore the correlation between FAR and other inflammatory parameters. The incidence of the first-ever CVE was constructed based on Kaplan–Meier’s curves and compared using the log-rank test. The Cox proportional hazard models with 95% confidence intervals (CIs) were used to clarify the relationship between FAR and them. In multivariate Cox regression analysis, covariates with p < .2 in the univariate Cox regression analysis or considered clinically significant were included to avoid missing some possible relevant covariates, and the stepwise forward method was used to identify independent risk factors for first-ever CVE in patients with PD. Subsequently, receiver operating characteristic (ROC) curves were employed to assess the efficacy of FAR in predicting the first-ever CVE. In subgroup analyses, we performed a formal interaction test to investigate the interactions between subgroup variables including sex, age, BMI, history of diabetes, and history of CVD. Finally, we utilized forest plots to show the relationship between FAR and first-ever CVE in each subgroup. p < .05 was considered statistically significant.
Results
Patient characteristics
Following rigorous inclusion and exclusion criteria, a total of 278 patients with PD were successfully enrolled in this study (Figure 1), of which the average age was 50.54 ± 14.68 years, 138 (49.6%) were male, 64 (23.0%) had diabetes and 38 (13.7%) had CVD. Chronic glomerulonephritis (55.4%) was identified as the most common cause of ESKD, followed by diabetic nephropathy (20.1%) and hypertensive nephropathy (12.9%).
Figure 1.
Flowchart of the participants in the study cohort. PD: peritoneal dialysis; CVE: cardiovascular event.
Two groups were created based on the median value of FAR, the high FAR group and the low FAR group, with 138 and 140 participants, respectively. Table 1 illustrates that patients in the high FAR group exhibited advanced age and increased comorbidity rates of diabetes and CVD, as well as elevated levels of leukocyte, neutrophil, platelet, NLR, PLR, hs-CRP, creatinine, urea nitrogen, phosphorus, FIB, total cholesterol and ferritin, but lower levels of haemoglobin and ALB, in comparison to the low FAR group. In addition, the high FAR group demonstrated a higher prevalence of diabetic nephropathy. No significant differences were observed in terms of gender, BMI, total Kt/V or residual renal function between the two groups. In this study, the average follow-up period was 40.26 ± 28.27 months, 101 (36.3%) patients developed first-ever CVE, including 59 in the high FAR group and 42 in the low FAR group. Notably, patients in the high FAR group exhibited shorter follow-up periods and a higher proportion of first-ever CVE.
Table 1.
Baseline characteristics and outcomes of patients with PD according to the median FAR.
| Variables | Total (n = 278) | FAR < 0.107 (n = 138) | FAR ≥ 0.107 (n = 140) | p Value |
|---|---|---|---|---|
| Demographics | ||||
| Male, n (%) | 138 (49.6) | 66 (47.8) | 72 (51.4) | .548 |
| Age (years) | 50.54 ± 14.68 | 47.88 ± 14.05 | 53.15 ± 14.86 | .003 |
| Body mass index (kg/m2) | 22.26 ± 3.36 | 22.08 ± 3.39 | 22.43 ± 3.33 | .388 |
| Comorbidities | ||||
| Diabetes, n (%) | 64 (23.0) | 18 (13.0) | 46 (32.9) | <.001 |
| Cardiovascular disease, n (%) | 38 (13.7) | 10 (7.2) | 28 (20.0) | .002 |
| Primary kidney disease | .002 | |||
| Chronic glomerulonephritis, n (%) | 154 (55.4) | 88 (63.8) | 66 (47.1) | |
| Hypertensive nephropathy, n (%) | 36 (12.9) | 19 (13.8) | 17 (12.1) | |
| Diabetic nephropathy, n (%) | 56 (20.1) | 15 (10.9) | 41 (29.3) | |
| Other, n (%) | 32 (11.5) | 16 (11.6) | 16 (11.4) | |
| Laboratory variables | ||||
| Haemoglobin (g/L) | 76.8 ± 19.4 | 79.5 ± 19.3 | 74.2 ± 19.2 | .024 |
| Leukocyte (×109/L) | 6.29 ± 2.67 | 5.65 ± 2.03 | 6.93 ± 3.05 | <.001 |
| Neutrophil (×109/L) | 4.70 ± 2.57 | 4.05 ± 1.90 | 5.34 ± 2.97 | <.001 |
| Lymphocyte (×109/L) | 1.00 ± 0.46 | 1.03 ± 0.45 | 0.98 ± 0.47 | .281 |
| Platelet (×109/L) | 147.3 ± 62.3 | 134.4 ± 54.3 | 160.1 ± 67.1 | .001 |
| NLR | 6.43 ± 8.33 | 5.07 ± 4.76 | 7.77 ± 10.60 | .007 |
| PLR | 176.14 ± 120.58 | 154.94 ± 108.61 | 197.05 ± 128.30 | .003 |
| hs-CRP (mg/L) | 2.20 (1.02, 6.08) | 1.30 (0.66, 2.90) | 4.62 (1.23, 16.04) | <.001 |
| Serum creatinine (μmol/L) | 883.0 ± 342.3 | 838.3 ± 284.8 | 927.1 ± 387.2 | .030 |
| Serum urea nitrogen (mmol/L) | 31.5 ± 13.3 | 29.7 ± 12.2 | 33.1 ± 14.2 | .033 |
| Serum uric acid (μmol/L) | 512.6 ± 156.6 | 509.4 ± 155.9 | 515.8 ± 157.9 | .737 |
| Corrected serum calcium (mmol/L) | 2.04 ± 0.29 | 2.05 ± 0.26 | 2.02 ± 0.32 | .346 |
| Serum phosphorus (mmol/L) | 2.01 ± 0.68 | 1.87 ± 0.63 | 2.14 ± 0.71 | .001 |
| Fibrinogen (g/L) | 3.74 ± 1.11 | 2.92 ± 0.60 | 4.54 ± 0.90 | <.001 |
| Serum albumin (g/L) | 32.8 ± 5.7 | 35.6 ± 4.9 | 30.1 ± 5.2 | <.001 |
| Total cholesterol (mmol/L) | 4.13 ± 1.15 | 3.97 ± 1.02 | 4.30 ± 1.25 | .018 |
| Total triglycerides (mmol/L) | 1.36 ± 0.79 | 1.33 ± 0.75 | 1.39 ± 0.84 | .532 |
| Ferritin (μg/L) | 198.0 (76.9, 313.8) | 155.5 (52.0, 298.8) | 230.5 (108.3, 353.0) | .005 |
| Total Kt/V | 2.26 ± 0.74 | 2.32 ± 0.80 | 2.20 ± 0.67 | .242 |
| Residual renal function (mL/min/1.73 m2) | 2.62 (1.11, 4.44) | 2.97 (1.33, 5.26) | 2.54 (1.77, 4.29) | .554 |
| Outcomes | ||||
| First-ever CVE, n (%) | 101 (36.3) | 42 (30.4) | 59 (42.1) | .042 |
| Follow-up time (months) | 40.26 ± 28.27 | 46.07 ± 29.12 | 34.53 ± 26.26 | .001 |
PD: peritoneal dialysis; FAR: fibrinogen/albumin ratio; CVE: cardiovascular event; NLR: neutrophil and lymphocyte ratio; PLR: platelet and lymphocyte ratio; hs-CRP: high-sensitivity C-reactive protein. Bold values means p < 0.05.
FAR and inflammation parameters
As shown in Table 2, FAR was positively correlated with hs-CRP (r = 0.462, p < .001), NLR (r = 0.260, p < .001) and PLR (r = 0.293, p < .001).
Table 2.
Correlations between FAR and various parameters of inflammation.
FAR: fibrinogen/albumin ratio; hs-CRP: high-sensitivity C-reactive protein; NLR: neutrophil and lymphocyte ratio; PLR: platelet and lymphocyte ratio.
Correlation is significant at the .01 level (two-tailed).
Correlation is significant at the .05 level (two-tailed).
Risk factors for first-ever CVE
Table 3 presents the risk factors for first-ever CVE in patients with PD. The results demonstrated that older age, a history of diabetes, a history of CVD, and higher FAR levels were associated with an increased risk of first-ever CVE in patients with PD.
Table 3.
Risk factors for first-ever CVE.
| Risk factors | HR | p |
|---|---|---|
| Age (per 1 year increase) | 1.035 (1.019–1.051) | <.001 |
| Gender (male vs. female) | 1.028 (0.695–1.520) | .889 |
| History of diabetes (yes vs. no) | 1.890 (1.214–2.942) | .005 |
| History of cardiovascular disease (yes vs. no) | 2.380 (1.453–3.898) | .001 |
| FAR (≥0.107 vs. <0.107) | 1.867 (1.253–2.782) | .002 |
| Body mass index (per 1 kg/m2 increase) | 1.047 (0.989–1.108) | .117 |
| Haemoglobin (per 1 g/L increase) | 0.997 (0.987–1.008) | .598 |
| Leukocyte (per 1 × 109/L increase) | 1.035 (0.965–1.110) | .338 |
| NLR (per 1 unit increase) | 1.011 (0.987–1.035) | .381 |
| PLR (per 1 unit increase) | 1.001 (0.999–1.002) | .248 |
| hs-CRP (per 1 mg/L increase) | 1.009 (0.997–1.021) | .145 |
| Serum creatinine (per 1 μmol/L increase) | 1.000 (0.999–1.000) | .301 |
| Serum urea nitrogen (per 1 mmol/L increase) | 0.992 (0.976–1.008) | .320 |
| Corrected serum calcium (per 1 mmol/L increase) | 1.231 (0.615–2.466) | .557 |
| Serum phosphorus (per 1 mmol/L increase) | 0.975 (0.733–1.297) | .864 |
| Total cholesterol (per 1 mmol/L increase) | 1.116 (0.933–1.334) | .231 |
| Total triglycerides (per 1 mmol/L increase) | 1.101 (0.857–1.415) | .451 |
| Total Kt/V (per 1 unit increase) | 1.017 (0.797–1.297) | .894 |
| Residual renal function (per 1 mL/min/1.73 m2 increase) | 0.989 (0.948–1.033) | .627 |
CVE: cardiovascular event; FAR: fibrinogen/albumin ratio; NLR: neutrophil and lymphocyte ratio; PLR: platelet and lymphocyte ratio; hs-CRP: high-sensitivity C-reactive protein. Bold values means p < 0.05.
Relationship between FAR and first-ever CVE
Kaplan–Meier’s analysis showed that there was a higher risk of first-ever CVE (p = .002) in the high FAR group (Figure 2). Cox regression models were utilized to construct three models that adjusted for various covariables (Table S1). Regardless of the model employed, FAR ≥ 0.107 and age were associated with the risk of first-ever CVE in patients with PD (Table 4).
Figure 2.
Cumulative incidence curves for the first-ever CVE by classification the median of FAR (Kaplan–Meier’s analysis). CVE: cardiovascular event; FAR: fibrinogen/albumin ratio.
Table 4.
Relationship between FAR and first-ever CVE.
| FAR ≥ 0.107 (vs. FAR < 0.107) |
Age (per 1 year increase) |
|||
|---|---|---|---|---|
| HR (95%CI) | p Value | HR (95%CI) | p Value | |
| Unadjusted | 1.867 (1.253–2.782) | .002 | 1.035 (1.019–1.051) | <.001 |
| Model 1 | 1.611 (1.073–2.419) | .022 | 1.031 (1.016–1.047) | <.001 |
| Model 2 | 1.549 (1.028–2.333) | .036 | 1.028 (1.013–1.044) | <.001 |
| Model 3 | 1.676 (1.041–2.698) | .034 | 1.023 (1.004–1.042) | .016 |
FAR: fibrinogen/albumin ratio; CVE: cardiovascular event; HR: hazard ratio; 95%CI: 95% confidence interval.
Model 1: age, sex, body mass index; model 2: model 1 plus history of diabetes, and history of cardiovascular disease; model 3: model 2 plus haemoglobin, serum creatinine, corrected serum calcium, serum phosphorus, total Kt/V and residual renal function.
ROC curves of FAR
ROC analysis showed that the area under the curve (AUC) of FAR for predicting first-ever CVE in patients with PD was 0.572 (95%CI (0.500–0.643), p = .047), and the Youden index, sensitivity, specificity and optimal cut-off value were 0.179, 49.5%, 68.4% and 0.122, respectively. FAR had a higher AUC value in predicting first-ever CVE in patients with PD than NLR (0.484 (95%CI (0.412–0.556), p = .666)) and PLR (0.513 (95%CI (0.444–0.582), p = .720)). Furthermore, the AUC of combination of FAR and age was 0.657 (95%CI (0.591–0.722), p < .001) (Figure 3).
Figure 3.
ROC curves of FAR, NLR, PLR and combination of FAR and age in predicting the incidence of first-ever CVE. FAR: fibrinogen/albumin ratio; NLR: neutrophil and lymphocyte ratio; PLR: platelet and lymphocyte ratio.
Subgroup analysis
Subgroup analysis was conducted according to sex, age, BMI, history of diabetes, and history of CVD to explore the association between FAR levels and first-ever CVE in different subgroups, and the multiplicative interaction model was used to evaluate the interaction between the above subgroups and FAR. The forest plot details that there was no significant interaction between the subgroups, and FAR had better predictive value for first-ever CVE in patients with PD who were younger than 60 years, male, had a BMI of less than 24, had no history of diabetes, and had no history of CVD (Figure 4).
Figure 4.
Forest plots of relationship between FAR and first-ever CVE in different subgroups. FAR: fibrinogen/albumin ratio; BMI: body mass index; CVD: cardiovascular disease; HR: hazard ratio; 95%CI: 95% confidence interval; p1 value: p for each subgroup; p2 value: p for interaction.
Discussion
Through the longest follow-up of more than 10 years for 278 patients with PD in our centre, this retrospective study found that the risk of first-ever CVE was higher in the high FAR group, and the value of FAR in predicting its occurrence was better than that of NLR and PLR, which were also markers of systemic inflammatory response. Combined application of age and FAR showed better predictive value.
FIB is a glycoprotein with a symmetric dimer structure consisting of three different pairs of polypeptide chains, which is synthesized mainly in liver hepatic parenchymal cells [15]. FIB is an essential substrate for thrombus formation and is involved in the endogenous coagulation process. It is also a classic acute-phase reactant. When activated, as one of the most effective coagulation system proteins, it is involved in inflammatory response through many complex mechanisms and works in the acute phase response to tissue damage caused by various factors [16]. FIB is also a known risk marker for CVD. An investigation including 4487 participants from the United States shows that FIB was related not only to the incidence of CVD at baseline but also to CVD mortality during follow-up [17]. In Asia, recent study in Taiwan found that FIB may be a risk factor for CVD excluding stroke [18]. Even in patients with PD, FIB was also a reliable predictor of CVE [19]. The relationship between FIB and CVE can be explained partly by that as FIB levels increased, the balance between fibrinolysis and clotting is disrupted, making the body more prone to clotting. Previous animal experiments have shown that hyperfibrinogenemia promotes thrombosis and thrombolysis resistance by increasing the fibrin in the thrombus, promoting the faster formation of fibrin, and increasing the density, strength and stability of the fibrin network, which ultimately leads to an increased risk of CVE [20,21].
ALB is a protein synthesized in the liver, which can carry many endogenous and exogenous substances, maintain capillary membrane stability, and has anti-inflammatory properties. Previous studies found that ALB can effectively inhibit the inflammatory response, reduce inflammatory damage and play a significant protective role during inflammation [22]. There is a complex mechanism between ALB and inflammation. Hypoproteinaemia can aggravate oxidative stress and inflammation by reducing oxygen free radical clearance and weakening antioxidant capacity [23]. It has also been reported that inflammatory states can contribute to the escape of ALB by increasing capillary permeability, thus exacerbating hypoproteinaemia [24]. Studies have pointed out that lower ALB was strongly related to the occurrence of CVD, and may predict the prognosis of CVD patients [25,26]. In addition, low ALB levels suggest that patients may be complicated with malnutrition, which has been confirmed as an independent determinant of CVE [27].
In recent years, FAR has been gradually recognized as a reliable inflammatory marker. In this study, we found that FAR was positively correlated with other established inflammatory indicators involving NLR, PLR and hs-CRP, which was consistent with previous research results [23]. Previous studies have shown that NLR was associated with adverse cardiovascular prognosis [28], and PLR can be used to predict the risk of CVE in patients with PD [29]. This study found that FAR may be better than NLR and PLR in predicting CVE in patients with PD, and may have higher clinical practical value. The specific mechanism between FAR and CVE in patients with PD is still unclear. The reasons why patients with dialysis are more susceptible to CVE include traditional cardiovascular risk factors such as hypertension, diabetes and age, and non-traditional risk factors for instance inflammation, oxidative stress, endothelial dysfunction, anaemia, malnutrition and abnormal mineral metabolism. In addition, different from haemodialysis, patients with PD are more prone to metabolic abnormalities due to exposure of their peritoneum to glucose-based dialysis solution, which can induce the development of CVE [30]. We speculate that FAR may lead to CVE by mediating inflammation, oxidative stress and endothelial dysfunction. Systemic and local inflammation is ubiquitous in patients with PD, which is closely related to many adverse clinical outcomes including CVE and cardiovascular mortality [31,32]. Risk factors for inflammatory status in patients with PD include PD catheter implantation, exposure to peritoneal dialysate, PD-associated peritonitis, progressive decline in residual renal function, endotoxaemia and peritoneal dysfunction [31]. The inflammatory state induced by these risk factors ultimately leads to a susceptibility to CVE in patients with PD [33]. Previous studies have pointed out that inflammatory markers were risk factors for mortality in patients with PD [34], and Ridker et al. found that inflammation evaluated by hs-CRP had a stronger predictive value for the risk of CVE [35]. Moreover, FAR is calculated based on FIB and ALB, high FAR often means low ALB or high FIB levels, while low ALB levels are closely associated with protein-energy wasting (PEW). Previous studies have indicated that PEW and inflammation were closely related to endothelial dysfunction in patients with PD [36], which may be part of the significant reason for increased CVE in patients with PD.
We found that there were more patients with diabetes in the high FAR group, which may be due to the higher prevalence of malnutrition in patients with diabetes [37]. Patients with diabetes have higher permeability of the peritoneal membrane, which is also associated with hypoproteinaemia [38]. In addition, FIB production is increased due to hyperinsulinaemia in patients with diabetes [39]. We further performed subgroup analysis and found no interaction between diabetes and FAR. Additionally, we noted that patients with high levels of FAR were older and had lower haemoglobin, which was consistent with previous findings [8]. The association between anaemia and inflammation in patients with CKD has been observed by many studies, and the improvement of anaemia may be related to a decrease in the incidence of CVE by reducing inflammation [40]. Age was also an independent risk factor for CVE in this study, which has been widely confirmed in previous studies [41]. The mechanism is particularly complex, involving oxidative stress, inflammation, energy failure and so on.
Our study has several advantages. First, as a retrospective study, we have a long follow-up period, with some patients being followed up for more than 10 years. Second, to our knowledge, this is the first study to explore the relationship between FAR and the risk of first-ever CVE in patients with PD, filling the gap in this area. Third, we used FAR, a clinically accessible and relatively simple indicator, to predict the risk of first-ever CVE in patients with PD, providing a relatively simple and feasible means of early risk assessment for physicians. Fourth, FAR integrates FIB and ALB, which can more comprehensively reflect the systemic condition and inflammatory state of patients. By evaluating FAR levels in patients with PD, we can stratify CVE risk, personalize clinical decision-making and enhance patient management. Finally, we constructed multiple models for Cox regression analysis and designed subgroup analysis to further demonstrate the reliability of our results.
Inevitably, this study has some limitations. First, we cannot eliminate all confounding factors. Second, as this study was retrospective, the complete drug use of patients during the follow-up period could not be accurately traced. Therefore, the influence of drugs on the study results was not taken into account. Third, we did not consider the potential impact of liver diseases including hepatitis on our findings. Fourth, this study only considered the relationship between baseline FAR level and first-ever CVE, without longitudinal observation of changes in FAR level and inclusion in the analysis. This may lead to dynamic changes in the FAR not being fully considered and utilized, which is also the direction of our future research. Finally, these findings would need validation in a much larger PD population.
Conclusions
In summary, our study revealed that high levels of FAR are independently associated with an increased risk of first-ever CVE in patients with PD. Our findings highlight the potential significance of FAR as a convenient and easily accessible laboratory indicator for assessing the risk of CVE in this specific population. With its simplicity and widespread availability, FAR holds substantial clinical value in the evaluation and management of cardiovascular complications in patients with PD.
Supplementary Material
Acknowledgements
We thank all participants in this work.
Funding Statement
This work was supported by grants from the Natural Science Foundation of Anhui Province (2008085MH244) and the National Natural Incubation Program of the Second Affiliated Hospital of Anhui Medical University (2020GMFY04).
Ethical approval
This study was approved by the Ethics Committee of the Second Affiliated Hospital of Anhui Medical University (authorization number: KY201302).
Author contributions
Qiqi Yan designed research, performed analysis and drafted the manuscript. Deguang Wang designed and directed the study. Guiling Liu, Ruifeng Wang and Dandan Li collected data. All authors approved the final manuscript.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
The data underlying this article will be shared on reasonable request to the corresponding author.
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