Abstract
Background and hypothesis
Arteriovenous fistula (AVF) is regarded as the most effective vascular access for hemodialysis in patients with end-stage renal disease (ESRD).However, research investigating the relationships between the systemic inflammation response index (SIRI), the triglyceride-glucose index combined with body mass index (TyG-BMI), and the high-sensitivity C-reactive protein to albumin ratio (HRR) with AVF maturation is still limited.
Methods
We included all ESRD patients undergoing first-time AVF creation between March and August 2024 at Haidian Hospital, Beijing (n = 249). To evaluate predictors of AVF maturation failure, we analyzed preoperative clinical/laboratory data and follow-up ultrasound assessments using restricted cubic spline models for dose-response relationships (SIRI, TyG-BMI, HRR). Multivariable Cox models identified independent risk factors, and a predictive nomogram was developed and validated through C-index and calibration plots.
Results
This study included 249 patients (73.5% AVF maturation rate). Restricted cubic spline analysis revealed linear associations between AVF maturation and SIRI (p-overall = 0.047), TyG-BMI (p-overall = 0.039), and HRR (p-overall = 0.026). Multivariable Cox regression identified three independent predictors: SIRI (HR = 1.69, 95%CI:1.45–1.96), TyG-BMI (HR = 2.68, 95%CI:1.62–4.41), and HRR (HR = 1.44, 95%CI:1.28–1.61) (all p < 0.001). The biomarker-based nomogram showed strong predictive accuracy, with calibration curves demonstrating excellent observed-versus-expected agreement.
Conclusion
Non-maturation of AVF was independently associated with three inflammation-based biomarkers, namely SIRI, TyG-BMI, and HRR. High predictive accuracy was demonstrated by the nomogram incorporating these markers, supporting its use for the early identification of high-risk patients.
Keywords: Arteriovenous fistula, Non-maturation, Restricted cubic spline, Predictive model, Inflammation biomarkers
Introduction
Establishing dependable vascular access is essential for effective hemodialysis treatment in end-stage renal disease (ESRD) patients. The high prevalence of non-maturation in arteriovenous fistulas (AVFs), the preferred permanent vascular access, can reach up to 60% [1]. This presents a significant clinical problem that impacts the effectiveness of dialysis treatment. Recent studies [2] suggest that chronic inflammation contributes to AVF maturation failure through multiple mechanisms; persistent inflammatory responses may cause endothelial injury, lead to neointimal hyperplasia, and impair vascular remodeling.
The systemic inflammation response index (SIRI) is a composite indicator that combines neutrophil, lymphocyte, and monocyte counts. It provides a more thorough representation of the systemic inflammatory milieu in this pathophysiological context. The high-sensitivity C-reactive protein to albumin ratio (HRR) simultaneously signifies the degree of inflammation and demonstrates the combined impact of inflammation and malnutrition. This “inflammation-malnutrition” vicious cycle may exacerbate vascular wall pathological changes.
Moreover, the triglyceride-glucose and body mass indexes (TyG-BMI) are metabolic biomarkers for insulin resistance. They can adversely impact AVF development by triggering endothelial dysfunction and abnormal vascular remodeling. Despite their potential significance, the dynamic roles and predictive value of these emerging inflammatory makers, SIRI, HRR, and TyG-BMI in AVF maturation, remain insufficiently studied.
This prospective cohort study aims to systematically analyze the relationships between SIRI, HRR, and TyG-BMI in the context of AVF maturation, emphasizing the interaction between inflammation and vascular remodeling. To improve AVF maturation outcomes, the study seeks to develop a multidimensional inflammation-metabolism evaluation model that will help identify patients at high risk for AVF non-maturation earlier and provide a theoretical basis for targeted anti-inflammatory interventions.
Materials and methods
Study design
This prospective, single-center cohort study was conducted at the Department of Nephrology at Haidian Hospital, Beijing. It enrolled patients undergoing new autologous arteriovenous fistula creation between March and August 2024. The following were the inclusion criteria: (1) ESRD patients or maintenance hemodialysis candidates who are scheduled for the creation of an AVF; (2) Patients without a previous AVF or requiring re-establishment of an autologous AVF; (3) Chinese male and female patients aged 18 to 80 years with an estimated clinical survival exceeding 9 months; (4) Patients who provided written informed consent to participate in the study; (5) Minimum arterial diameter of 1.5 mm and venous diameter of 2.0 mm, as assessed by preoperative imaging. Exclusion criteria were: (1) History of past AVF creation or revision on the ipsilateral side limb; (2) Patients undergoing hemodialysis and peritoneal dialysis simultaneously; (3) Patients with acute kidney injury from any cause; (4) Patients with severe comorbidities and an estimated life expectancy of less than 6 months; (5) Patients scheduled for kidney transplantation or a planned transition to peritoneal dialysis in the near future; (6) Incomplete data, inability to provide accurate information, or the presence of a psychiatric illness that prevents cooperation with follow-up.
Written informed consent was obtained from all participants before the procedure. Ethical approval was granted by the Ethics Committee of Haidian Hospital, Beijing (Approval No.: M202396), and the study was conducted in full compliance with the principles outlined in the Declaration of Helsinki.
Grouping of subjects
Patients were divided into two groups based on AVF maturation status: mature and non-mature groups. Maturation was defined using previously established criteria [3–5], as follows: (1) the AVF maturation was defined as a patent fistula capable of sustaining double-needle cannulation, with pump-controlled blood flow > 200 mL/min, allowing continuous hemodialysis for at least six sessions; (2) non-maturation referred to a fistula that failed to support hemodialysis due to intraoperative failure, thrombus or stenosis formation, or the inability to meet the aforementioned clinical requirements. AVF outcomes were determined based on follow-up records.
Data collection
Clinical and demographic data were systematically collected and comprised age, sex, etiology of renal disease, and comorbidities, including but not limited to hypertension, diabetes, and coronary artery disease. Relevant medication use (e.g., antiplatelet therapy, calcium channel blockers, ACE inhibitors, lipid-modifying agents) and history of tobacco use were also documented.
Clinical laboratory assessments included complete blood count, fasting plasma glucose, hepatic and renal function indices, serum lipid profile, electrolyte balance, and C-reactive protein levels to evaluate inflammatory status. Venous blood samples were obtained in a fasting state on the day preceding the vascular access procedure.
All patients underwent comprehensive vascular assessment before surgery, comprising both physical examination and ultrasonography:
(1) Physical examination: Performed by the operating surgeon, included bilateral arm blood pressure measurement (acceptable inter-arm difference < 20 mmHg), arterial pulsation assessment (radial, ulnar, and brachial arteries), vein patency testing, evaluation of outflow vein continuity and distensibility, upper limb edema, and history of central or peripheral venous catheterization; (2) Ultrasound examination: Conducted by experienced vascular access specialists in our department, included measurement of the anastomotic artery diameter (1 cm proximal to the anastomosis), anastomotic vein diameter (5 cm from the anastomosis at the intended puncture site), brachial artery diameter, and flow volume (measured approximately 5 cm above the elbow along the course of the brachial artery). Each measurement was repeated three times, and the average value was recorded.
Follow-up and study endpoint
The monitoring of patients started from the date of surgical creation of the AVF. Subsequent follow-up evaluations were performed at 1-month, 2-month, and 3-month intervals. Assessment of fistula maturation was performed at 3 months postoperatively, and classification into mature or non-mature groups was based on this outcome. The follow-up phase terminated on December 1, 2024, guaranteeing that each patient was observed for at least 12 weeks.
Statistical analysis
Depending on the data distribution, continuous variables were described as mean ± standard deviation (SD) or median with interquartile range (IQR), while categorical variables were summarized as frequencies with percentages. Between-group comparisons were performed using the student’s t-test, Wilcoxon rank-sum test, or chi-square test, depending on the nature and distribution of the variables. Missing data were addressed through multiple imputation techniques to minimize potential bias.
Survival analysis was conducted using Kaplan–Meier estimation. Univariate Cox proportional hazards regression was applied for each inflammation-related biomarker to calculate the hazard ratio (HR) and the 95% confidence interval (CI). Potential nonlinear dose-response associations between biomarker levels and AVF maturation were evaluated using restricted cubic spline (RCS) models.
Receiver operating characteristic (ROC) curves were generated to evaluate each biomarker’s predictive performance. A nomogram model based on multivariate Cox regression was developed to improve clinical applicability. Subgroup analyses were conducted, stratified by sex, age, diabetes, hypertension, atherosclerosis, and dialysis history, to explore the relationship between inflammatory markers and AVF maturation.
All statistical analyses were performed using R software (version 4.3.2; Vienna, Austria).
Results
General characteristics
The study initially enrolled 285 patients. During follow-up, four individuals either died or underwent kidney transplantation, 27 lacked complete preoperative or postoperative vascular ultrasound data, and five were undergoing peritoneal dialysis simultaneously. After these exclusions, 249 patients were included in the final analysis (Fig. 1).
Fig. 1.
Flowchart of patient selection for the study
Among these patients, the observed rate of unassisted AVF maturation was 73.5% (Fig. 2). No statistically significant differences were observed between patients with mature versus non-mature fistulas regarding age, underlying cause of renal failure, or duration of dialysis. However, three inflammatory markers, TyG-BMI, HRR, and SIRI, were significantly increased in the group that did not achieve AVF maturation compared to the AVF maturation group. Table 1 presents detailed baseline characteristics for the study population.
Fig. 2.
Kaplan–Meier Survival Curve for AVF Maturation
Table 1.
Baseline characteristics of the study population (n = 249)
| Characteristics | All (n = 249) |
Vascular access survival (n = 183) | Vascular access failure (n = 66) | p-Value |
|---|---|---|---|---|
| Demographic | ||||
| Age, Median (IQR) | 60 (52, 69) | 60 (52, 69) | 60 (49, 68) | 0.434 |
| BMI, n (%) | 0.986 | |||
| Normal | 138 (55.4) | 102 (55.7) | 36 (54.5) | |
| Overweight | 74 (29.7) | 54 (29.5) | 20 (30.3) | |
| Obesity | 37 (14.9) | 27 (14.8) | 10 (15.2) | |
| Clinical | ||||
| Diabetes, n (%) | 133 (53.4) | 100 (54.6) | 33 (50.0) | 0.517 |
| Hypertension, n (%) | 231 (92.8) | 170 (92.9) | 61 (92.4) | 0.899 |
| Atherosclerosis, n (%) | 55 (22.1) | 40 (21.9) | 15 (22.7) | 0.884 |
| Dialysis history, n (%) | 162 (65.1) | 115 (62.8) | 47 (71.2) | 0.221 |
| Lym, Median (IQR) | 1.08 (0.85, 1.39) | 1.10 (0.86, 1.39) | 1.03 (0.83, 1.40) | 0.619 |
| PLT, Median (IQR) | 174 (133, 214) | 174 (139, 215) | 176 (132, 208) | 0.865 |
| Neu, Median (IQR) | 4.48 (3.40, 5.33) | 4.48 (3.39, 5.35) | 4.49 (3.63, 5.15) | 0.754 |
| Mon, Median (IQR) | 0.40 (0.31, 0.47) | 0.39 (0.32, 0.47) | 0.41 (0.30, 0.47) | 0.898 |
| HGB, Median (IQR) | 115 (100, 129) | 115 (98, 127.5) | 120 (106, 133) | 0.092 |
| GLU, Median (IQR) | 7.12 (5.53, 11.23) | 7.16 (5.48, 12.01) | 6.53 (5.72, 9.16) | 0.634 |
| TG, Median (IQR) | 1.69 (1.12, 2.47) | 1.69 (1.12, 2.33) | 1.93 (1.19, 2.83) | 0.284 |
| Systemic immune-inflammation indices | ||||
| HRR, Median (IQR) | 8.03 (7.09, 9.27) | 7.86 (6.88, 8.96) | 8.56 (7.64, 9.68) | 0.001 |
| SIRI, Median (IQR) | 1.57 (1.15, 2.14) | 1.49 (1.01, 2.05) | 1.83 (1.50, 2.69) | < 0.001 |
| SII, Median (IQR) | 675 (483, 951) | 656 (471, 926) | 737 (536, 1020) | 0.201 |
| MLR, Median (IQR) | 0.36 (0.27, 0.47) | 0.35 (0.27, 0.46) | 0.36 (0.27, 0.52) | 0.839 |
| NLR, Median (IQR) | 4.01 (3.12, 5.27) | 3.99 (3.00, 5.16) | 4.27 (3.51, 5.36) | 0.199 |
| PLR, Median (IQR) | 163 (123, 201) | 163 (119, 199) | 161 (128, 211) | 0.757 |
| TyG-BMI, Median (IQR) | 0.05 (0.04, 0.06) | 0.05 (0.04, 0.06) | 0.06 (0.04, 0.07) | 0.031 |
Abbreviations: HGB, hemoglobulin; HRR, hemoglobin-to-red blood cell distribution width ratio; IQR, inter quartile range; MLR, monocyte-to-lymphocyte ratio; NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; PLT, platelet; SII, systemic immune-inflammation index; SIRI, systemic inflammation response index; TyG-BMI, Triglyceride-Glucose Body Mass Index
Dose-response relationships between novel inflammatory indices and AVF maturation
The association between novel inflammatory biomarkers and AVF non-maturation risk was investigated using RCS models. Adjusted analyses demonstrated that HRR (p-overall = 0.026, p-non-linear = 0.433), SIRI (p-overall = 0.047, p-non-linear = 0.188), and TyG-BMI (p-overall = 0.039, p-non-linear = 0.629) were linearly related to the risk of AVF non-maturation. Specifically, increasing values of these biomarkers correlated with a progressively elevated risk of AVF non-maturation. The other inflammatory indices—SII, MLR, NLR, and PLR—did not show significant linear or non-linear associations with AVF maturation (Fig. 3).
Fig. 3.
Dose-Response Relationship Between Novel Inflammatory Markers and AVF Maturation
Cox proportional hazards regression for AVF maturation
The independent risk variables for AVF non-maturation were identified by Cox regression analysis as, HRR (HR = 1.44, 95% CI: 1.28–1.61, P < 0.001), SIRI (HR = 1.69, 95% CI: 1.45–1.96, P < 0.001), and TyG-BMI (HR = 2.68, 95% CI: 1.62–4.41, P < 0.001). Furthermore, for each 1-unit rise in HRR, the risk of AVF non-maturation increased by 44%; for each 1-unit increase in SIRI, the risk increased by 69%; and for each 1-unit increase in TyG-BMI, the risk increased by 168% (Table 2).
Table 2.
Cox proportional hazard regression analysis of vascular access survival
| Characteristics | Coefficient | HR (95%CI) | p-Value |
|---|---|---|---|
| HRR | 0.363 | 1.44 (1.28, 1.61) | < 0.001 |
| SIRI | 0.524 | 1.69 (1.45, 1.96) | < 0.001 |
| SII | 0.003 | 1.00 (0.99, 1.01) | 0.123 |
| MLR | 0.147 | 1.16 (0.46, 2.89) | 0.753 |
| NLR | 0.040 | 1.04 (0.97, 1.11) | 0.237 |
| PLR | 0.002 | 1.00 (0.99, 1.02) | 0.797 |
| TyG-BMI | 0.984 | 2.68 (1.62, 4.41) | < 0.001 |
Prognostic analysis of AVF non-maturation
Analysis using ROC curves revealed that SIRI had the highest predictive capability for AVF non-maturation with an AUC of 0.668 (95% CI: 0.63–0.71), followed by HRR with an AUC of 0.651 (95% CI: 0.61–0.70), and TyG-BMI with an AUC of 0.592 (95% CI: 0.54–0.64). The optimal cutoff point for SIRI was 1.515, corresponding to a sensitivity of 51.8% and a specificity of 75.9%. This threshold indicates that a SIRI value above 1.515 is significantly associated with an increased likelihood of AVF non-maturation. Compared to HRR and TyG-BMI, SIRI showed the best predictive ability with an optimal cutoff point of 1.515, corresponding to a sensitivity of 51.8% and a specificity of 75.9%. This threshold indicates that a SIRI value above 1.515 is significantly associated with an increased likelihood of AVF non-maturation (Fig. 4; Table 3).
Fig. 4.
Predictive value of HRR, SIRI, SII, MLR, NLR, PLR, and TyG-BMI for AVF non-maturation (ROC curve)
Table 3.
Predictive value of novel inflammatory markers for AVF non-maturation
| Characteristics | Cutoff | Sensitivity | Specificity | Auc (95%CI) |
|---|---|---|---|---|
| HRR | 8.340 | 0.637 | 0.596 | 0.651 (0.61, 0.70) |
| SIRI | 1.515 | 0.518 | 0.759 | 0.668 (0.63, 0.71) |
| SII | 559 | 0.420 | 0.730 | 0.554 (0.51, 0.60) |
| MLR | 0.522 | 0.842 | 0.258 | 0.509 (0.46, 0.56) |
| NLR | 3.482 | 0.383 | 0.758 | 0.553 (0.51, 0.60) |
| PLR | 163 | 0.514 | 0.545 | 0.487 (0.44, 0.53) |
| TyG-BMI | 0.065 | 0.800 | 0.356 | 0.592 (0.54, 0.64) |
Subgroup analysis of serum HRR, SIRI, and TyG-BMI in relation to AVF non-maturation
Furthermore, the expression of the above markers was evaluated across various subgroups. The findings revealed the following:
Higher levels of HRR were observed in patients aged over 60 years (HR = 1.11, 95% CI: 1.01–1.23, P = 0.042), in females (HR = 1.14, 95% CI: 1.01–1.29, P = 0.035), and in individuals with hypertension (HR = 1.26, 95% CI: 1.02–1.55, P = 0.029). When compared to patients under 60 years of age, those over 60 exhibited an 11% increased risk of AVF non-maturation. Female patients demonstrated a 14% higher risk compared to males, while patients with hypertension had a 26% higher risk of AVF non-maturation relative to those without hypertension.
Higher SIRI values were significantly associated with several clinical subgroups, including patients diagnosed with diabetes (HR = 1.16, 95% CI: 1.01–1.34, P = 0.044), hypertension (HR = 1.14, 95% CI: 1.00–1.29, P = 0.044), coronary heart disease (HR = 1.19, 95% CI: 1.02–1.39, P = 0.030), and those receiving dialysis for a more extended period (HR = 1.16, 95% CI: 1.01–1.34, P = 0.041). Specifically, the presence of diabetes was associated with a 16% increased risk of AVF non-maturation, while hypertension conferred a 14% elevated risk. Moreover, coronary heart disease was associated with a 19% higher risk and prolonged dialysis duration was associated with a 16% increase in the risk of AVF non-maturation when compared with patients without these respective conditions.
Patients who had longer dialysis duration exhibited a significantly higher TyG-BMI (HR = 2.60, 95% CI: 1.54–4.39, P < 0.001), suggesting that their risk of AVF non-maturation was 160% higher than that of patients with shorter dialysis duration (Fig. 5).
Fig. 5.
Expression of HRR, SIRI, and TyG-BMI across different subgroups
Construction of a risk prediction model
Following the observed associations, a nomogram-based prediction model was constructed to quantify the risk of AVF non-maturation in patients undergoing hemodialysis. Each risk factor was assigned a score according to its weight in the model. The nomogram facilitates estimation by vertically projecting each variable onto its respective scoring axis, summing the scores, and aligning the total with a corresponding probability of AVF non-maturation. For instance, an HRR value of 7, a SIRI value of 2, and a TyG-BMI of 0.09 would yield scores of 20, 15, and 70, respectively. The cumulative score of 105 would indicate a 46% risk of AVF non-maturation (Fig. 5).
Subsequent assessment of this nomogram’s diagnostic efficacy demonstrated its ability to accurately predict the probability of AVF non-maturation. The calibration curve showed that the nomogram performed well (Figs. 6 and 7).
Fig. 6.
Risk prediction model for AVF non-maturation: Nomogram
Fig. 7.
Risk prediction model for AVF non-maturation: Calibration curve
Discussion
Recent investigations have demonstrated that systemic inflammation and metabolic dysregulation may be significant contributing factors in the pathophysiological mechanisms of AVF maturation failure [6]. The process of AVF maturation involves coordinated endothelial responses, inflammatory modulation, and adaptive hemodynamic changes. This study preliminarily identified three biomarkers—SIRI (systemic inflammatory response index), TyG-BMI (triglyceride glucose-body mass index), and HRR (high-density lipoprotein cholesterol to hematocrit ratio)—that may exhibit certain associations with AVF maturation status. These findings imply that chronic low-grade inflammation and metabolic abnormalities may compromise endothelial integrity and impair vascular remodeling, resulting in AVF maturation failure [7].Furthermore, These results show partial consistency with previous mechanistic studies on the metabolic-inflammatory axis and vascular remodeling [8], providing initial support for the hypothesis that systemic metabolic status and localized inflammatory responses may collectively influence the AVF maturation process.
Our analysis identified positive associations between novel inflammatory indicators (SIRI, HRR) and metabolic markers (TyG-BMI) with AVF non-maturation. Notably, TyG-BMI, a reliable indicator of insulin resistance, may contribute to impaired AVF maturation through several molecular and cellular mechanisms. Current evidence suggests Elevated TyG-BMI has been reported to suppress endothelial nitric oxide synthase (eNOS) activity and reduce nitric oxide (NO) production [9], a phenomenon also observed in diabetic vascular complicationsa process associated with diabetic vascular complications that might potentially impair adaptive venous dilatation, which is necessary for the development of AVF both of which are crucial for endothelial-mediated vasodilation [10]– [11]. Insulin resistance at the cellular level impairs the functionality of circulating endothelial progenitor cells, limiting postoperative endothelium repair and vascular remodeling [12]. Furthermore, some studies indicate that increased TyG-BMI upregulates endothelin-1 (ET-1), which affects AVF maturation through two different mechanisms: increasing vasoconstriction to restrict luminal expansion and promoting vascular smooth muscle cell (VSMC) migration and proliferation, which results in neointimal hyperplasia [13].These findings collectively suggest a potential association between insulin resistance, endothelial dysfunction, and aberrant vascular remodeling, offering a plausible pathophysiological framework that may help explain the observed adverse effects of metabolic disturbances on AVF maturation.
our study identified a significant association between SIRI and AVF non-maturation, suggesting that neutrophil-mediated inflammation may play an important role in AVF maturation failure. Neutrophils, essential effector cells in the innate immune system, can initiate complex inflammatory cascades when excessively activated. One mechanism involves the release of proteolytic enzymes such as elastase and myeloperoxidase [14], which may disrupt endothelial barrier integrity, induce apoptosis, and increase vascular permeability. Another involves the formation of neutrophil extracellular traps (NETs), which may intensify the inflammatory microenvironment and may serve as scaffolds for thrombosis [15]. Similarly, neutrophil–platelet interactions, mediated through adhesion molecules like P-selectin and PSGL-1, create a self-perpetuating cycle of “inflammation–thrombosis” [16], amplifying inflammatory signals and facilitating thrombus formation.These pathological alterations may contribute to compromised AVF development, potentially through promoting medial fibrosis and intimal hyperplasia.Our observations raise the possibility that interventions targeting neutrophil activation and related pathways could be explored as potential approaches to enhance AVF outcomes.
The observed correlation between HRR and AVF non-maturation suggests a potential interaction between inflammatory processes and nutritional status in vascular remodeling. As a composite biomarker, HRR may reflect two interrelated pathological aspects: first, Chronic inflammatory states may contribute to intimal hyperplasia through activation of pathways such as NF-κB, potentially promoting VSMC proliferation and extracellular matrix deposition. Additionally, malnutrition-related hypoalbuminemia could impair endothelial regeneration and may reduce production of vasoactive mediators including NO [17]. The combined effects of both disorders may severely reduce vascular plasticity; endothelial dysfunction associated with malnutrition impairs flow, while inflammation-induced vascular fibrosis decreases compliance-mediated dilatation, eventually resulting in insufficient postoperative shear stress conditions [18]. These findings are consistent with the hemodynamic investigations conducted by Robbin et al. [19], who reported that an AVF blood flow below 600 mL/min at four weeks following surgery is significantly associated with an increased risk of maturation failure.
Further, the present subgroup analysis revealed significant variations in the predictive strength of SIRI, HRR, and TyG-BMI depending on patient-specific clinical characteristics. Particularly, HRR was more strongly associated with AVF non-maturation in elderly patients (> 60 years), females, and hypertensive patients. These findings are likely due to specific pathophysiological traits in these groups, such as age-related vascular stiffness, sex-related inflammatory and nutritional status, and hypertension-induced endothelial dysfunction [20]. The inflammation–nutrition imbalance indicated by HRR may be particularly harmful in these populations. However, SIRI was a more powerful predictor in patients with metabolic conditions, such as diabetes, hypertension, and coronary artery disease, and in those with extended dialysis time. This suggests a fundamental role for sustained inflammation in impairing AVF maturation. The pro-inflammatory environment characteristic of diabetes and coronary artery disease may stimulate processes like neointimal hyperplasia and fibrosis [21]. Elevated SIRI levels in long-term dialysis patients likely reflect ongoing uremia-related inflammation and immune activation. In patients receiving long-term dialysis, TyG-BMI showed a strong predictive ability, suggesting that metabolic disorders and insulin resistance significantly contribute to vascular dysfunction in this population. Extended dialysis duration can worsen metabolic abnormalities by uremic toxin buildup, persistent microinflammation, and vascular calcification, eventually impairing AVF maturation. These findings possess significant clinical implications, emphasizing the necessity for improved surveillance of these biomarkers in high-risk populations (e.g., long-term dialysis patients, individuals with metabolic disorders, and elderly females) and indicating potential advantages from targeted interventions, including strict glycemic and blood pressure management, anti-inflammatory treatment, and nutritional assistance.
Furthermore, Our study developed a novel multidimensional nomogram model incorporating SIRI, HRR, and TyG-BMI, which may offer a clinically useful tool for evaluating the risk of AVF non-maturation. This approach attempts to translate complex biomarker data into a practical and visually interpretable scoring system, with the potential to enhance predictive accuracy and aid clinical decision-making.The model could help identify patients who may be at higher risk, possibly enabling more tailored surgical and perioperative management strategies to optimize AVF maturation and functional outcomes.
There are several limitations should be acknowledged, First: Limited Generalizability: The single-center design may introduce selection bias, restricting the applicability of findings to broader populations with different demographic, clinical, or regional characteristics. External validation in independent cohorts is necessary to confirm the robustness of our predictive model.Second: Sample Size Constraints: The relatively small sample size may reduce statistical power, particularly for subgroup analyses, and increase the risk of overfitting in predictive modeling. Larger, multicenter studies are needed to enhance reliability. Third: Lack of Mechanistic Confirmation: While circulating biomarkers (SIRI, HRR, TyG-BMI) were evaluated, direct assessment of local vascular tissue pathology was not performed. Future studies integrating tissue-based analyses could provide deeper mechanistic insights.Fouth.Short-Term Follow-Up: The study focused on AVF maturation within a defined postoperative period but did not assess.Fifth.Absence of External Validation: The nomogram model was derived and validated internally; independent validation in diverse clinical settings is essential before widespread adoption.
In conclusion, This study proposes that the SIRI, TyG-BMI, and HRR may serve as potential biomarkers for AVF maturation failure. Our findings provide preliminary evidence suggesting that systemic inflammation, metabolic imbalances, and nutritional status may collectively contribute to vascular remodeling processes.
In addition, the nomogram we have developed is not simply a tool for predicting risk; it establishes a full clinical pathway that includes ‘screening, risk stratification, and precision intervention.‘This model enables clinicians to: 1) classify patients into separate risk groups using personalized scoring, and 2) Develop tailored management strategies that enhance standard monitoring for patients at moderate or low risk, while starting early comprehensive interventions addressing various risk factors for high-risk individuals.This closed-loop management system indicates a possible move from passive prediction to active intervention in the field of clinical practice.
Author contributions
Experiment Design: Bin Zhao; Data Collection: Bin Zhao; Medical Record Screening and Enrollment: Shen Zhan, Lihong Zhang, Rui Cui, Shanshan Guo; Statistical Analysis: Bin Zhao, Jia Li, Xue Zhou; Writing: Bin Zhao; Review: Gang Fu, Yuzhu Wang.
Funding
This study was funded by the 2024 Haidian Hospital (Haidian District Branch of Peking University Third Hospital) important institutional fund project (KYZ2024001).
Data availability
Data is provided within the manuscript or supplementary information files.
Declarations
Competing interests
The authors declare no competing interests.
Conflict of interest
The authors declare no conflicts of interest.
Footnotes
Key learning points
What was known:
1. SIRI, TyG-BMI, and HRR can predict the maturation of arteriovenous fistulas.
2. Systemic inflammation, insulin resistance and abnormal lipid metabolism may be involved in the maturation process of AVF, but the specific molecular mechanism still needs to be further explored.
This study adds:
1. Long-term dialysis patients with high TyG-BMI—indicating metabolic dysfunction and insulin resistance—face greater vascular dysfunction risks.
2. SIRI was a stronger predictor of outcomes in long-term dialysis patients with diabetes, hypertension, or coronary artery disease.
3. HRR showed a stronger association with AVF non-maturation in patients over 60 years old, females, and those with hypertension.
Potential impact:
Our work establishes SIRI, TyG-BMI, and HRR as key predictors of AVF non-maturation and underscores the impact of systemic inflammation, metabolic dysfunction, and nutritional imbalance on vascular remodeling. The developed nomogram offers a practical tool for risk stratification in clinical practice.
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Bin Zhao and Gang Fu are contributed equally to this work.
References
- 1.Konner K, Nonnast-Daniel B, Ritz E (2003) The arteriovenous fistula. J Am Soc Nephrol 14(6):1669–1680 [DOI] [PubMed] [Google Scholar]
- 2.Wasse H, Huang R, Naqvi N, Smith E, Wang D, Husain A (2012) Inflammation, oxidation and venous neointimal hyperplasia precede vascular injury from AVF creation in CKD patients. J Vasc Access 13(2):168–174 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Gibson KD, Caps MT, Kohler TR et al (2001) Assessment of a policy to reduce placement of prosthetic Hemodialysis access. Kidney Int 59(6):2335–2345 [DOI] [PubMed] [Google Scholar]
- 4.Schinstock CA, Albright RC, Williams AW et al (2011) Outcomes of arteriovenous fistula creation after the fistula first initiative. Clin J Am Soc Nephrol 6(8):1996–2002 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Lok CE, Huber TS, Lee T et al (2020) National kidney foundation. KDOQI clinical practice guideline for vascular access: 2019 update. Am J Kidney Dis 75(4 Suppl 2):S1–S164 [DOI] [PubMed] [Google Scholar]
- 6.Singh M, Mahapatra HS, Pursnani L et al (2023) Study on prediction of arterio-venous fistula maturation by flow mediated dilatation and AVF blood flow. J Vasc Access 24(3):443–451 [DOI] [PubMed] [Google Scholar]
- 7.Remuzzi A, Bozzetto M, Brambilla P (2017) Is shear stress the key factor for AVF maturation? J Vasc Access 18(Suppl 1):10–14 [DOI] [PubMed] [Google Scholar]
- 8.Ohashi Y, Protack CD, Aoyagi Y et al (2024) Heterogeneous gene expression during early arteriovenous fistula remodeling suggests that downregulation of metabolism predicts adaptive venous remodeling. Sci Rep 14(1):13287 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Badal SS, Danesh FR (2012) Strategies to reverse endothelial dysfunction in diabetic nephropathy. Kidney Int 82(11):1151–1154 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Hoang HH, Padgham SV, Meininger CJ (2013) L-arginine, tetrahydrobiopterin, nitric oxide and diabetes. Curr Opin Clin Nutr Metab Care 16(1):76–82 [DOI] [PubMed] [Google Scholar]
- 11.Lee T, Qian J, Thamer M, Allon M (2019) Gender disparities in vascular access surgical outcomes in elderly Hemodialysis patients. Am J Nephrol 49(1):11–19 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Jenkins HN, Rivera-Gonzalez O, Gibert Y, Speed JS (2020) Endothelin-1 in the pathophysiology of obesity and insulin resistance. Obes Rev 21(12):e13086 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Haryono A, Ramadhiani R, Ryanto GRT, Emoto N (2022) Endothelin and the cardiovascular system: the long journey and where we are going. Biology (Basel) 11(5):759 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Grigorieva DV, Gorudko IV, Sokolov AV et al (2016) Myeloperoxidase stimulates neutrophil degranulation. Bull Exp Biol Med 161(4):495–500 [DOI] [PubMed] [Google Scholar]
- 15.Zhang H, Wang Y, Qu M et al (2023) Neutrophil, neutrophil extracellular traps and endothelial cell dysfunction in sepsis. Clin Transl Med 13(1):e1170 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Samra G, Rai V, Agrawal DK (2022) Innate and adaptive immune cells associate with arteriovenous fistula maturation and failure. Can J Physiol Pharmacol 100(8):716–727 [DOI] [PubMed] [Google Scholar]
- 17.Kaygin MA, Halici U, Aydin A et al (2013) The relationship between arteriovenous fistula success and inflammation. Ren Fail 35(8):1085–1088 [DOI] [PubMed] [Google Scholar]
- 18.Satish M, Gunasekar P, Agrawal DK (2019) Pro-inflammatory and pro-resolving mechanisms in the immunopathology of arteriovenous fistula maturation. Expert Rev Cardiovasc Ther 17(5):369–376 [DOI] [PubMed] [Google Scholar]
- 19.Robbin ML, Greene T, Allon M et al (2018) Hemodialysis fistula maturation study group. Prediction of arteriovenous fistula clinical maturation from postoperative ultrasound measurements: findings from the Hemodialysis fistula maturation study. J Am Soc Nephrol 29(11):2735–2744 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Harvey A, Montezano AC, Touyz RM (2015) Vascular biology of ageing-Implications in hypertension. J Mol Cell Cardiol 83:112–121 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Toubal A, Kiaf B, Beaudoin L et al (2020) Mucosal-associated invariant T cells promote inflammation and intestinal dysbiosis leading to metabolic dysfunction during obesity. Nat Commun 11(1):3755 [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Data is provided within the manuscript or supplementary information files.