Serum Magnesium and Risk of Arteriovenous Fistula Thrombosis in Hemodialysis: A Retrospective Cohort Study

Serum Magnesium and Risk of Arteriovenous Fistula Thrombosis in Hemodialysis: A Retrospective Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Serum Magnesium and Risk of Arteriovenous Fistula Thrombosis in Hemodialysis: A Retrospective Cohort Study Natalia Stepanova, Tetyana Ostapenko, Andriy Rysyev, Alina Holovanova, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7289523/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Dec, 2025 Read the published version in BMC Nephrology → Version 1 posted 13 You are reading this latest preprint version Abstract Background Arteriovenous fistula (AVF) thrombosis is a major cause of vascular access failure in patients undergoing maintenance hemodialysis (HD). Although magnesium has established vascular protective properties, its relationship with AVF thrombosis remains poorly characterized. This study aimed to examine the association between serum magnesium levels and the risk of AVF thrombosis in a retrospective cohort of HD patients. Methods This bi-center retrospective cohort study included 408 HD patients treated between January 2020 and May 2025. Baseline serum magnesium was categorized into tertiles: T1 (< 0.87 mmol/L), T2 (0.87–0.97 mmol/L), and T3 (≥ 0.98 mmol/L). The primary outcome was the first clinically confirmed episode of AVF thrombosis. Kaplan–Meier estimates and Cox proportional hazards models adjusted for demographic, clinical, and dialysis-related variables were used to assess the association. Sensitivity and subgroup analyses were performed to evaluate the robustness of findings. Results Over a median follow-up of 33.5 months, 83 patients (20.3%) experienced AVF thrombosis. Compared to T1, patients in T2 and T3 groups had significantly lower thrombotic risk: HR 0.57 (95% CI: 0.40–0.82, p = 0.002) and HR 0.46 (0.28–0.77, p = 0.003), corresponding to a 1.75- and 2.17-fold higher risk for T1 versus T2 and T3, respectively. Independent predictors of increased AVF thrombosis risk included older age (HR 1.05 per year, 95% CI: 1.02–1.09), higher serum phosphate (HR 1.11 per 0.1 mmol/L, 95% CI: 1.03–1.48), and calcium-based phosphate binder use (HR 1.02, 95% CI: 1.003–1.27). Protective factors included higher dialysis adequacy (spKt/V; HR 0.16, 95% CI: 0.11–0.35) and statin therapy (HR 0.35, 95% CI: 0.14–0.86). The association between low magnesium and AVF thrombosis remained robust in sensitivity and subgroup analyses. Conclusions Low serum magnesium (< 0.87 mmol/L) is independently associated with a twofold increased risk of AVF thrombosis in HD patients. Magnesium may represent a modifiable target for improving vascular access outcomes, warranting further prospective investigation. hemodialysis arteriovenous fistula magnesium thrombosis risk factors Figures Figure 1 Figure 2 Figure 3 Introduction Arteriovenous fistula (AVF) thrombosis is a major cause of vascular access failure among patients undergoing maintenance hemodialysis (HD), leading to increased morbidity, frequent hospitalizations, and higher healthcare costs [ 1 , 2 ]. Despite advances in surgical technique and access surveillance, the annual incidence of AVF thrombosis remains high, with reported rates of 0.2–0.5 episodes per 1000 patient days [ 3 ]. Identifying modifiable risk factors for AVF thrombosis is, therefore, critical to improving outcomes in this vulnerable population. Magnesium is a critical intracellular cation involved in various vascular functions, including endothelial protection, inhibition of vascular calcification, and suppression of platelet aggregation [ 4 – 6 ]. In patients with chronic kidney disease (CKD), and especially those on HD, disturbances in magnesium homeostasis are common due to impaired renal excretion, dietary restrictions, and variable dialysate magnesium concentrations [ 7 , 8 ]. Currently, clinical interest in magnesium has increased, as low serum magnesium has been associated with greater cardiovascular risk and mortality in patients undergoing HD [ 9 , 10 ]. Proposed mechanisms include its capacity to prevent calcium-phosphate precipitation, reduce oxidative stress and inflammation, and limit vascular smooth muscle cell transformation into pro-calcific phenotypes [ 11 – 13 ]. In this context, recent studies have begun to explore the association between serum magnesium and vascular access outcomes [ 14 – 16 ]. However, the prior investigations have focused on composite endpoints, combining thrombosis, stenosis, and access-related interventions, which may obscure the distinct pathophysiological mechanisms underlying each complication. Therefore, we aimed to investigate the association between serum magnesium levels and the risk of AVF thrombosis in a retrospective cohort of maintenance HD patients. We hypothesized that lower serum magnesium concentrations would be independently associated with a higher risk of AVF thrombosis and may represent a modifiable factor influencing AVF survival in this population. Methods Study design and setting This was a bicenter, retrospective observational cohort study analyzing data from patients undergoing HD between January 1, 2020, and May 31, 2025, at two affiliated dialysis units managed by Medical Center LLC “Nephrocenter” in Odesa and Zaporizhzhia, Ukraine. Both centers deliver HD care to patients with end-stage kidney disease following national and international guidelines. The study protocol received ethical approval from the Institutional Review Board of the Medical Center “Nephrocenter” (IRB No. 2/2025, June 16, 2025) and was carried out following the principles of the Declaration of Helsinki. Due to the retrospective design and use of de-identified data, the requirement for informed consent was waived. Study population All adult patients (aged ≥ 18 years) undergoing HD were screened for inclusion. Eligible patients met the following criteria: A functioning native AVF as the primary vascular access at baseline, A minimum of three months of continuous in-center HD, At least one recorded serum magnesium measurement during the baseline assessment period. Patients were excluded if they: Used a central venous catheter or arteriovenous graft as the primary access, Had a documented history of AVF thrombosis before the baseline magnesium measurement, Had missing or incomplete data for key variables, Had a follow-up duration of less than 30 days, were transplanted, or lost to follow-up within the same period. All patients received conventional high-volume hemodiafiltration using Fresenius 5008S dialysis machines (Fresenius Medical Care, Germany). High-flux synthetic dialyzers with polysulfone membranes were employed following standard clinical protocols. Dialysis treatment was prescribed as three sessions per week, each lasting approximately 4 hours. Standard parameters included a blood flow rate of 250–350 mL/min, a dialysate flow rate of 500 mL/min, and a dialysate composition containing magnesium at 0.5 mmol/L, sodium at 138–140 mmol/L, and bicarbonate at 32–35 mmol/L. Systemic anticoagulation was provided using unfractionated heparin, administered according to local protocol, with dosing individualized based on patient weight, bleeding risk, and prior clotting history. Exposure, outcome, and follow-up The primary exposure was serum magnesium concentration, assessed as part of routine laboratory monitoring. The baseline magnesium level was defined as the first available value within 30 days before the index date. For the main analysis, serum magnesium levels were divided into tertiles based on their distribution within the study population, with tertile 1 (T1) representing the lowest and tertile 3 (T3) the highest values. This approach was selected to explore potential non-linear associations and facilitate clinical interpretability. Importantly, no patients in the cohort received oral or intravenous magnesium supplementation during the observation period. The primary outcome was AVF thrombosis, defined as a documented clinical event requiring intervention for thrombotic occlusion, confirmed by physical examination, imaging, or surgical findings. The diagnosis was based on standard clinical evaluation (absence of bruit or thrill), confirmatory imaging (Doppler ultrasound), or operative findings. Only the first incident of AVF thrombosis after the index date was considered in the primary analysis. Patients were followed from the index date until the first occurrence of AVF thrombosis or until the end of follow-up (May 31, 2025). Those who developed AVF stenosis or infection requiring catheter conversion, underwent kidney transplantation, or died were not excluded from the analysis but were censored at the time of the respective event, as they were no longer considered at risk for AVF thrombosis. Covariates All data were retrospectively extracted from electronic medical records of the participating dialysis centers. Collected data included: demographics (age, sex), comorbidities (diabetes mellitus, hypertension, cardiovascular disease, hepatitis B and C status), dialysis-related factors (dialysis vintage, Kt/V, ultrafiltration volume), medications used, including antiplatelet agents and anticoagulants. AVF age was also recorded. Due to the small sample size of patients with AVF age < 6 months, a 1-year cutoff was used to categorize vascular access age for the analyses. Laboratory parameters included hematology (hemoglobin, hematocrit, platelet count), biochemistry (serum creatinine, urea, sodium, potassium, calcium, phosphate, magnesium), intact parathyroid hormone (iPTH), serum albumin, total protein, ferritin, transferrin saturation (TSAT), C-reactive protein (CRP), fasting glucose, and lipid profile markers such as total cholesterol, low-density lipoprotein (LDL), high-density lipoprotein (HDL), and triglycerides. All laboratory analyses were performed in ISO-certified external laboratories (Sinevo and/or Dila) using standardized and validated procedures. Hematologic parameters were measured using automated hematology analyzers based on flow cytometry and impedance methods. Serum creatinine, urea, sodium, potassium, calcium, phosphate, and magnesium were analyzed via photometric and ion-selective electrode methods on automated chemistry analyzers. Serum albumin, total protein, and CRP were determined using colorimetric or immunoturbidimetric assays. Lipid profile markers and fasting glucose were measured enzymatically using spectrophotometric methods. iPTH and ferritin were quantified via chemiluminescent immunoassays, and TSAT was calculated as the ratio of serum iron to total iron-binding capacity, both measured spectrophotometrically. The value closest to the index date within a 30-day window was used for each parameter. Bias minimization To minimize potential selection bias, all consecutive patients meeting the inclusion criteria during the study period were considered for inclusion. Exclusion criteria were predefined and applied uniformly across both centers. Exposure and outcome variables were derived from standardized, routinely collected clinical and laboratory data. The primary outcome was objectively confirmed through clinical examination, imaging, or operative findings. To address potential confounding, we applied multivariable adjustments and conducted sensitivity and subgroup analyses. Sample size justification Although no formal a priori sample size calculation was performed, a post hoc approximation was informed by the study by Yao et al. [ 15 ]. That study included 263 HD patients and reported 95 AVF dysfunction events (36%) over a median follow-up of 32 months, with a hazard ratio (HR) of 4.53 (95% CI: 2.63–7.81) for low serum magnesium. For our analysis, assuming a more conservative HR of 2.0 for AVF thrombosis between low and high magnesium tertiles, with a two-sided α of 0.05, 80% power, and an expected event rate of 20%, the estimated required sample size was approximately 360 patients with 72 events. Our final cohort included 408 patients and 83 thrombosis events, exceeding this threshold and thus providing adequate power to detect moderate associations. Statistical analysis Continuous variables were summarized as means with standard deviations (M ± SD) or medians with interquartile ranges [Me (Q25-Q75)], depending on the distribution assessed by the Shapiro–Wilk test. Categorical variables were summarized as frequencies and percentages. For comparison of baseline characteristics across serum magnesium quartiles, we used one-way ANOVA for normally distributed continuous variables, the Kruskal–Wallis test for non-normally distributed continuous variables, and the chi-squared (χ²) test for categorical variables. Correlation analyses were conducted using the Pearson or Spearman test based on the data distribution. Time-to-event was defined as the duration from baseline serum magnesium measurement to the first documented AVF thrombosis or censoring. Survival curves were estimated using the Kaplan–Meier method, with comparisons between tertiles made using the log-rank test. Univariable Cox proportional hazards regression was used to identify potential predictors of AVF thrombosis. Variables with p 5 indicated significant collinearity. Results are reported as HRs with 95% confidence intervals (CIs). To test the robustness of our findings, we performed both sensitivity and subgroup analyses. In sensitivity analyses, serum magnesium was also modeled as a continuous variable to assess the linearity of its association with AVF thrombosis in the Cox regression model. Additionally, we conducted a sensitivity analysis excluding patients who were censored due to AVF stenosis or infection, kidney transplantation, or death during the observation period. Subgroup analyses were stratified by age (< 70 vs. ≥70 years), diabetes status, serum albumin level (< 35 vs. ≥35 g/L), and vascular access age (< 1 year vs. ≥1 year), with separate Cox models fitted within each subgroup. Statistical analysis Continuous variables were summarized as means with standard deviations (M ± SD) or medians with interquartile ranges [Me (Q25-Q75)], depending on the distribution assessed by the Shapiro–Wilk test. Categorical variables were summarized as frequencies and percentages. For comparison of baseline characteristics across serum magnesium quartiles, we used one-way ANOVA for normally distributed continuous variables, the Kruskal–Wallis test for non-normally distributed continuous variables, and the chi-squared (χ²) test for categorical variables. Correlation analyses were conducted using the Pearson or Spearman test based on the data distribution. Time-to-event was defined as the duration from baseline serum magnesium measurement to the first documented AVF thrombosis or censoring. Survival curves were estimated using the Kaplan–Meier method, with comparisons between tertiles made using the log-rank test. Univariable Cox proportional hazards regression was used to identify potential predictors of AVF thrombosis. Variables with p 5 indicated significant collinearity. Results are reported as HRs with 95% confidence intervals (CIs). To test the robustness of our findings, we performed both sensitivity and subgroup analyses. In sensitivity analyses, serum magnesium was also modeled as a continuous variable to assess the linearity of its association with AVF thrombosis in the Cox regression model. Additionally, we conducted a sensitivity analysis excluding patients who were censored due to AVF stenosis or infection, kidney transplantation, or death during the observation period. Subgroup analyses were stratified by age (< 70 vs. ≥70 years), diabetes status, serum albumin level (< 35 vs. ≥35 g/L), and vascular access age (< 1 year vs. ≥1 year), with separate Cox models fitted within each subgroup. Results Study cohort A total of 480 patients undergoing HD were screened for eligibility. After applying exclusion criteria, 408 patients were included in the final analysis (Fig. 1 ). Serum magnesium levels ranged from 0.66 to 1.36 mmol/L, with a median of 0.93 (0.87–0.98) mmol/L. Baseline characteristics of the study population, stratified by serum magnesium tertiles, are summarized in Table 1 . Table 1 Baseline characteristics of the patients stratified by serum magnesium tertiles Variable All patients (n = 408) T1: < 0.87 mmol/L (n = 103) T2: 0.87–0.97 mmol/L (n = 214) T3: ≥ 0.98 mmol/L (n = 91) p -value Demographics Age, years 59 (49–67) 63 (51–69) T3 59 (49–67) T3 56 (47.2–61) T1,2 0.005 Male sex, n (%) 233 (57.1%) 64 (62.1%) 119 (55.6%) 50 (54.9%) 0.48 Smoking status, n (%) 52 (12.7%) 10 (9.7%) 30 (14.05%) 12 (13.2%) 0.58 Alcohol use, n (%) 22 (5.4%) 3 (2.9%) 11 (5.1%) 8 (8.8%) 0.19 Clinical characteristics Diabetes, n (%) 79 (19,4%) 22 (21.4%) 40 (18.7%) 17 (18.7%) 0.84 SBP (mmHg) 130 (125–140) 140 (130–145) T3 130 (125–140) T3 130 (120–140) T1,2 0.05 DBP (mmHg) 80 (80–90) 80 (80–90) 80 (80–90) 80 (70-83.7) 0.24 CVD history, n (%) 66 (16.2%) 21 (20.4%) 29 (13.6%) 16 (17.6%) 0.13 Positive HBV or HCV status, n (%) 55 (13.5%) 17 (16.5%) 22 (10.3%) 16 (17.6%) 0.34 Dialysis vintage (months) 65.0 (33.0–87.0) 73 (31,7-96.7) 57.5 (31.0–80.0) 63.0 (38.0–99.0) 0.09 Blood flow rate, mL/min 302.5 ± 17.4 300.6 ± 18.2 302.7 ± 17.1 304.5 ± 17.1 0.31 spKt/V 1.34 (1.24–1.45) 1.30 (1.19–1.43) 1.30 (1.25–1.40) 1.35 (1.25–1.48) 0.06 Total volume UF, mL 2500 (2000–3000) 2500 (2200–3000) 2800 (2250–2900) 2500 (1800–3000) 0.57 AVF age < 1 year, n (%) 42 (10.3%) 6 (5.8%) T2 30 (14.0%) T1,3 6 (6.6%) T2 0.03 Laboratory values Magnesium, mmol/L 0.93 (0.87–0.97) 0.83 (0.79–0.85) T2,3 0.93 (0.91–0.95) T1,3 1.03 (0.99–1.07) T12 < 0.0001 Creatinine, µmol/L 839 (695–972) 782 (594–891) T2,3 819 (673–941) T1,3 938 (869–1026) T1,2 < 0.0001 Urea, mmol/L 20.6 (17.1–25.2) 19.8 (15.8–24.2) T1,3 20/3 (16.9–24.4) T1,3 22.9 (19.4–27.4) T2,3 0.001 Hemoglobin, g/L 101 (92-110.5) 98 (90–108) T3 101 (92–111) T3 107 (96–118) T1,2 0.003 Ht, % 29.6 ± 4.8 28.4 ± 4.9 T3 29.4 ± 4.1 T3 31.2 ± 5.3 T1,2 0.001 PLT, ˟10 9 /L 179.5 (142.5–221.0) 179.0 (137.5–220) 174.5 (141.0-221.0) 184.0 (145.5-222.5) 0.79 TSAT, % 22.7 (15.6-31.02) 22.9 (14.7–28.8) 21.6 (14.9–31.2) 25.5 (15.0-14.3) 0.29 Ferritin, ng/mL 261.1 (103.0-205.5) 301.4 (88.2–529.0) 276 (104.0-505.5) 243 (98.9-487.7) 0.69 Total protein, g/L 67.6 ± 5.6 66.4 ± 6.8 T3 67.6 ± 4.9 68.8 ± 4.9 T1 0.002 Albumin, g/L 40 (38–42) 39 (36–42) T2,3 40 (39–42) T1 41 (39.2–42) T1 0.0002 CRP, mg/L 4.4 (3.3–9.5) 7.9 (4.5–11.6) 4.8 (2.7–6.9) 3.3 (2.9–13.9) 0.54 Glucose, mmol/L 4.9 (4.4–6.12) 4.8 (4.2–5.9) 4.9 (4.4–6.1) 4.9 (4.6–5.8) 0.78 Calcium, mmol/L 2.24 (2.15–2.33) 2.17 (2.11–2.29) T2,3 2.24 (2.16–2.34) T1 2.28 (2.17–2.37) T1 0.0001 Phosphate, mmol/L 1.63 (1.35–1.98) 1.37 (1.19–1.72) T2,3 1.67 (1.32–1.93) T1,3 1.95 (1.46–2.22) T1,2 < 0.0001 iPTH, pg/mL 302.6 (173.7-531.1) 321.8 (171.5-605.9) 294.8 (187.6-5015.7) 312.5 (130.1–55.4) 0.95 Total cholesterol, mmol/L 4.6 (4.0-5.5) 4.9 (4.3–5.7) 4.5 (3.9–5.4) 4.3 (3.9–5.2) 0.052 HDL, mmol/L 1.1 (0.32–1.32) 1.05 (0.8–1.3) 1.12 (0.9–1.35) 1.19 (1.0-1.35) 0.13 LDL, (mmol/L) 2.8 (2.2–3.5) 3.1 (2.6–3.5) 2.7 (2.2–3.3) 2.7 (2.1–3.3) 0.18 Triglycerides (mmol/L) 1.45 (1.1–2.2) 1.54 (1.14–2.22) 1.51 (1.04–2.33) 1.35 (1.11–2.08) 0.16 Medications Erythropoiesis-stimulating agents, n (%) 245 (52.7%) 65 (63.1%) 127 (59.3%) 56 (61.5%) 0.19 Iron therapy, n (%) 237 (58.1%) 65 (63.1%) 127 (58.9%) 45 (49.4%) 0.73 Statins, n (%) 124 (30.4%) 31 (30.1%) T3 75 (35.0%) T3 18 (19.8%) 0.04 Antihypertensives, n (%) 347 (85.1%) 95 (92.2%) T2 177 (82.7%) 76 (83.5%) 0.02 Anticoagulants, n (%) 41 (10.1%) 9 (8.7%) 20 (9.3%) 12 (132%) 0.75 Antiaggregants, n (%) 122 (29.9%) 25 (24.3%) 66 (30.8%) 31 (34.0%) 0.14 Calcium-based phosphate binders, n (%) 183 (44.8%) 59 (57.3%) T2,3 92 (43.0%) T1 32 (35.24%) T1 0.01 Notes: Values are presented as Me (Q25-Q75) or M ± SD as appropriate. Superscript letters denote statistically significant differences between tertiles based on pairwise comparisons using ANOVA or Kruskal–Wallis with post hoc testing. Abbreviations: CRP, C-reactive protein; CVD, cardiovascular disease; DBP, diastolic blood pressure; Hb, hemoglobin; HDL, high-density lipoprotein cholesterol; Ht, hematocrit; iPTH, intact parathyroid hormone; LDL, low-density lipoprotein cholesterol; PLT, platelet count; SBP, systolic blood pressure; spKt/V, single-pool Kt/V; TSAT, transferrin saturation; UF, ultrafiltration. As shown in Table 1 , patients in the lowest magnesium tertile (T1) exhibited several unfavorable clinical and biochemical characteristics. They were significantly older, had higher systolic blood pressure, and demonstrated lower hemoglobin and hematocrit levels compared to those in the highest tertile (T3). Serum calcium, phosphate, total protein, and albumin concentrations were also significantly lower in T1 than in the higher tertiles. Furthermore, serum creatinine and urea levels were significantly lower in T1, with a progressive increase observed across magnesium tertiles. A higher proportion of patients with AVF age < 1 year was observed in T2 compared to T1 and T3, although the clinical relevance of this distribution remains uncertain. The use of statins, antihypertensive agents, and calcium-based phosphate binders was more common in T1 than in the higher tertiles. Inflammatory and iron-related biomarkers, including CRP, ferritin, and TSAT, did not significantly differ among groups. However, there was a non-significant trend toward lower dialysis adequacy and a higher prevalence of CVD in the lowest magnesium tertile. Similarly, patients in T1 tended to exhibit a more atherogenic lipid profile, characterized by lower HDL cholesterol and higher triglyceride levels, though these differences did not reach statistical significance. Association between serum magnesium and AVF thrombosis During a median follow-up of 33.5 (16.2–55.6) months, 83 patients (20.3%) developed AVF thrombosis, corresponding to an incidence rate of 0.2 events per 1000 patient days. In addition, 10 (2.5%) patients developed AVF stenosis, and 7 (1.72%) experienced AVF infections requiring catheter conversion. Other censoring events included kidney transplantation (n = 13, 3.2%) and death (n = 32, 7.8%). Across serum magnesium tertiles, the proportion of patients who experienced AVF thrombosis differed significantly (T1: n = 42, 40.8%, T2: n = 29, 13.6%, T3: n = 12, 13.2%; χ² = 35.5, p < 0.0001), indicating an inverse relationship between baseline serum magnesium levels and thrombosis occurrence. Kaplan-Meier analysis demonstrated a significantly lower thrombosis-free survival in patients within the lowest serum magnesium tertile compared to higher tertiles (log-rank test 23.6, p < 0.0001) (Fig. 2 ). Univariable Cox regression analysis identified several factors significantly associated with AVF thrombosis (Fig. 3 , Supplementary Table S1 ). As illustrated in Fig. 3 , low serum magnesium (< 0.87 mmol/L) was associated with a twofold increased risk of AVF thrombosis. Similarly, advancing age, higher phosphate and calcium levels, use of calcium-based phosphate binders, and higher ultrafiltration volume also increased thrombotic risk. In contrast, higher serum magnesium levels, greater dialysis adequacy measured by spKt/V, higher albumin, shorter AVF age, and statin use were significantly associated with reduced risk of AVF thrombosis. Platelet count showed a borderline association with increased risk. We further assessed multicollinearity among variables considered for inclusion in the multivariable Cox regression model. All candidate variables demonstrated acceptable levels of collinearity, with VIF values ranging from 1.052 to 1.603 ( Supplementary Table S2 ). In light of the absence of significant collinearity and the relatively limited number of events (n = 83), we adopted a conservative variable selection strategy to preserve an events-per-variable ratio of at least 10. Variables were selected based on statistical significance in univariable analyses (p < 0.01). The final model included the primary exposure (serum magnesium tertiles), age, spKt/V, total ultrafiltration volume, serum albumin, serum phosphate, statin use, and calcium-based phosphate binder use. In the multivariable Cox regression model, low serum magnesium levels remained independently associated with the risk of AVF thrombosis after adjustment for relevant covariates. Compared to patients in the T1 group, those in the T2 and T3 groups had a 43% and 54% lower risk of AVF thrombosis, respectively. These results correspond to a 1.75-fold increased risk for T1 versus T2, and a 2.17-fold increased risk for T1 versus T3 (Table 2 ). Table 2 Multivariable Cox proportional hazards regression for risk of AVF thrombosis Variable HR (95% CI) p -value Serum magnesium T1: <0.87 mmol/L (reference) 1.00 — Serum magnesium T2 vs. T1: 0.87–0.97 mmol/L 0.57 (0.40–0.82) 0.002 Serum magnesium T3 vs. T1: ≥0.98 mmol/L 0.46 (0.28–0.77) 0.003 Age (per year) 1.05 (1.02–1.09) 0.02 spKt/V (per 0.1 increase) 0.16 (0.11–0.35) 0.002 Total volume UF (per 100 mL increase) 1.00 (0.97–1.02) 0.233 Albumin (per 1 g/L increase) 0.95 (0.88–1.05) 0.142 Phosphate (per 0.1 mmol/L increase) 1.11 (1.03–1.48) 0.004 Statins (yes vs. no) 0.35 (0.14–0.86) 0.022 Calcium-based phosphate binders (yes vs. no) 1.02 (1.003–1.27) 0.047 Abbreviations: HR (95% CI), Hazard ratio (95% Confidence Interval); spKt/V, single-pool Kt/V; T1, T2, T3, magnesium tertiles. Other independent predictors included higher age, higher serum phosphate, and calcium-based phosphate binder use, all associated with increased risk of AVF thrombosis. Conversely, greater dialysis adequacy and statin use were independently associated with reduced risk of AVF thrombosis. Total ultrafiltration volume and serum albumin did not retain independent significance after multivariable adjustment. Sensitivity and subgroup analyses To assess the robustness of our findings, we conducted two sensitivity analyses. First, serum magnesium was modeled as a continuous variable in the multivariable Cox regression model to evaluate the linearity of its association with AVF thrombosis. The inverse relationship remained statistically significant, with each 0.1 mmol/L increase in serum magnesium associated with a lower risk of AVF thrombosis (adjusted HR 0.36, 95% CI: 0.22–0.62, p = 0.021). Second, we repeated the multivariable Cox regression after excluding 62 patients who were censored due to competing events, including death, kidney transplantation, or catheter conversion due to AVF infection or stenosis. The resulting sensitivity cohort included 346 patients, among whom 83 developed AVF thrombosis during follow-up. In the adjusted model, which included the same covariates as the primary analysis, patients in the T1 group had a significantly increased risk of AVF thrombosis (HR 2.63, 95% CI: 1.82–3.80, p < 0.0001), while those in the T3 group had a significantly lower risk (HR 0.41, 95% CI: 0.22–0.75, p = 0.004). These results confirmed the robustness of the association between serum magnesium levels and AVF thrombosis risk. In subgroup analyses, the association between serum magnesium and AVF thrombosis was consistent. Patients in the lowest magnesium tertile showed significantly increased risk of thrombosis across all subgroups, with HRs ranging from 2.36 to 7.41 ( Table 3 ). Table 3. Subgroup analysis of the association between serum magnesium tertiles and AVF thrombosis (adjusted Cox regression) Subgroup Serum magnesium T1 (< 0.87 mmol/L) T3 (≥ 0.98 mmol/L) HR (95% CI_ p -value HR, 95% CI p -value Age ≥ 70 years 5.21 (1.44–8.18) 0.022 0.56 (0.11–5.62) 0.621 Age < 70 years 3.64 (1.44–8.18) <0.0001 0.43 (0.32–0.57) <0.0001 Diabetes – Yes 2.36 (1.02–5.47) 0.045 0.47 (0.13–1.74) 0.74 Diabetes – No 4.09 (2.77–6.02) <0.0001 0.37 (0.28–0.490 35 g/L 3.95 (2.68–5.82) <0.0001 0.43 (0.31–0.58) <0.0001 AVF age < 1 year 4.46 (1.43–7.32) 0.019 0.11 (0.02–1.00) 0.050 AVF age ≥ 1 year 2.47 (1.62–3.77) <0.0001 0.51 (0.26–0.96) 0.039 Notes: Multivariable Cox regressions were repeated within each subgroup, adjusting for age, spKt/V, phosphate, albumin, ultrafiltration volume, statin use, and calcium-phosphate binder use (excluding the stratifying variable). T2 (0.87–0.97 mmol/L) was used as the reference in all models. In contrast, the protective effect associated with the highest magnesium tertile was significant in younger patients, non-diabetics, well-nourished, and patients with AVF age ≥1 year. However, in older subjects (≥70 years), diabetics, and patients with hypoalbuminemia (≤35 g/L), the association with T3 was not statistically significant. Discussion This study is the first to investigate the association between serum magnesium levels and the risk of AVF thrombosis as an isolated clinical endpoint in patients undergoing HD. Unlike previous studies that examined AVF dysfunction using composite outcomes, we focused specifically on thrombotic events confirmed by clinical, imaging, or surgical criteria. This approach allowed for a more precise evaluation of risk factors specifically associated with thrombosis, rather than broader or overlapping vascular access complications. In our cohort of 408 HD patients, lower serum magnesium was independently associated with an increased risk of AVF thrombosis. Patients in the lowest tertile (<0.87 mmol/L) had a 1.75- to 2.17-fold higher risk of thrombosis compared to those in the middle and highest tertiles, even after adjustment for demographic, clinical, and dialysis-related covariates. This association remained consistent in sensitivity analyses, including one that excluded patients censored due to competing events. Subgroup analyses further supported the robustness of this relationship. The elevated thrombotic risk linked to low magnesium remained significant across all examined subgroups. However, the protective effect of higher magnesium was attenuated and no longer statistically significant in older individuals, patients with diabetes, and those with hypoalbuminemia, suggesting that magnesium’s vascular benefits may be less pronounced in patients with higher baseline cardiovascular or inflammatory risk. Our findings are consistent with and extend previous research examining the relationship between serum magnesium and vascular access outcomes. To date, only two studies have specifically addressed this topic, encompassing a combined cohort of 352 HD patients [15, 16]. In line with our results, Stolić et al. reported significantly lower serum magnesium concentrations in patients with AVF complications compared to those without [16]. Similarly, Yao et al. observed a 4.5-fold higher risk of AVF dysfunction in patients with serum magnesium levels below 0.88 mmol/L compared to those in the highest magnesium group [15]. However, both studies relied on composite endpoints, limiting the ability to isolate thrombosis-specific mechanisms. Several biological mechanisms may explain this relationship. Magnesium modulates vascular tone, inhibits platelet aggregation, stabilizes endothelial function, and reduces inflammation [4, 12]. Experimental studies have shown that low extracellular magnesium can promote vascular smooth muscle cell calcification and endothelial injury, both of which predispose to thrombosis [11, 17, 18]. Additionally, magnesium inhibits platelet activation by modulating intracellular calcium handling and interfering with thromboxane synthesis, both of which are critical steps in thrombus formation [19, 20]. These mechanisms provide a compelling rationale for the inverse association observed in our cohort. However, we also observed an attenuation of the protective effect of higher serum magnesium levels among older patients, individuals with diabetes, and those with hypoalbuminemia. In older patients, age-related alterations in magnesium handling, a higher prevalence of vascular calcification, and the burden of comorbidities may reduce the vascular and antithrombotic benefits typically conferred by elevated magnesium levels [21]. The presence of advanced vascular disease and frailty in this population may overshadow magnesium’s protective effects, including the inhibition of vascular smooth muscle cell calcification and the preservation of endothelial function. Among individuals with diabetes, persistent hyperglycemia and insulin resistance promote chronic vascular injury and inflammation, which may not be fully mitigated by elevated serum magnesium concentrations [22]. Additionally, diabetes is associated with increased magnesium loss and impaired cellular uptake [23, 24], potentially limiting the intracellular actions of magnesium that are critical for inhibiting platelet activation and maintaining endothelial health. In patients with hypoalbuminemia, the interpretation of serum magnesium is further complicated, as a significant portion of circulating magnesium is albumin-bound [25]. Low albumin levels may result in a falsely reassuring total serum magnesium despite a reduced physiologically active ionized fraction [25]. Moreover, hypoalbuminemia reflects underlying malnutrition and inflammation, both of which are strong risk factors for vascular complications [26] and may blunt the protective effects of magnesium. In line with this hypothesis, Streja et al. have demonstrated that HD patients with both low albumin and low magnesium had a 17% higher risk of death compared to those with low albumin and high magnesium [27]. However, when albumin levels were adequate, the relationship between magnesium and mortality was attenuated, suggesting that serum albumin may modify the clinical impact of magnesium levels [27]. Beyond magnesium, our study identified several other independent factors associated with the risk of AVF thrombosis, highlighting the multifactorial nature of vascular access failure in HD patients. Elevated serum phosphate levels were significantly associated with an increased risk of thrombosis, aligning with previous reports that have linked hyperphosphatemia to endothelial dysfunction, vascular smooth muscle cell calcification, and heightened platelet activation [28]. Excess phosphate can promote the transformation of vascular smooth muscle cells into osteoblast-like phenotypes, contributing to medial calcification and reduced vascular compliance, factors that may impair AVF integrity and predispose to thrombosis [29]. Additionally, the use of calcium-based phosphate binders was independently associated with a higher risk of AVF thrombosis. This finding is consistent with prior studies suggesting that calcium-based binders may contribute to arterial and arteriolar calcification through increased calcium loading and deposition in the vessel wall [30, 31]. In contrast, high dialysis adequacy, measured by spKt/V, and statin use were associated with a reduced risk of AVF thrombosis. It is well-proven that better uremic toxin clearance may improve endothelial function and reduce systemic inflammation, both of which are critical in maintaining vascular access patency [32]. Rodrigues et al. have also reported higher access failure rates in patients with low delivered dialysis doses [33], although studies are limited in this area. Statins use emerged as another protective factor in our analysis. Beyond their lipid-lowering properties, statins exert a wide range of pleiotropic effects, including enhancement of endothelial function, attenuation of oxidative stress, inhibition of pro-inflammatory cytokines, and suppression of thrombotic pathways such as tissue factor expression and platelet aggregation [34–36]. These mechanisms are particularly pertinent in the HD population, where chronic inflammation and a prothrombotic state are prevalent. Our findings are consistent with previous observational studies reporting a lower risk of AVF thrombosis among HD patients treated with statins [35, 36]. Although a meta-analysis by Wan et al. did not demonstrate a statistically significant association between statin use and AVF patency [37], our recent report has suggested a dose-dependent protective effect of statins on the risk of AVF thrombosis, highlighting the potential influence of treatment intensity [38]. Taken together, these findings underscore the multifactorial nature of AVF thrombosis and point toward several modifiable targets, including serum magnesium, phosphate balance, HD adequacy, and statin use, that may inform preventive strategies. Several limitations of this study should be acknowledged. First, the retrospective design precludes causal inference. Although we performed multivariable adjustments and sensitivity analyses, the possibility of residual confounding from unmeasured variables cannot be excluded. Second, serum magnesium was assessed at a single time point, which may not capture longitudinal variability or reflect cumulative exposure. Third, only total serum magnesium concentrations were available; ionized magnesium was not measured and may have provided a more accurate reflection of magnesium status, particularly in hypoalbuminemic patients. Fourth, although thrombotic AVF events were rigorously defined, we did not account for the timing of these events in relation to changes in medications or dialysis prescriptions, which could have influenced thrombosis risk. In addition, the relatively low number of outcome events constrained the number of covariates that could be included in the multivariable models. Finally, while subgroup analyses offered insights into potential effect modifiers, these analyses were exploratory in nature and not adequately powered for formal interaction testing. Conclusions The present study demonstrated that lower serum magnesium levels (<0.87 mmol/L) were independently associated with a twofold increased risk of AVF thrombosis in patients undergoing HD. This association remained robust across sensitivity analyses and was consistent across most clinically relevant subgroups. The findings underscore the potential importance of magnesium homeostasis in preserving vascular access patency and suggest that hypomagnesemia may represent a modifiable risk factor for AVF thrombosis. In addition to magnesium, other modifiable factors, such as elevated serum phosphate, use of calcium-based phosphate binders, suboptimal dialysis adequacy, and absence of statin therapy, were independently associated with increased risk of AVF thrombosis. Prospective studies are warranted to confirm these associations and to determine whether magnesium-targeted interventions can reduce AVF thrombosis and improve long-term vascular access outcomes in the HD population. List of abbreviations AVF – Arteriovenous fistula CRP – C-reactive protein CKD – Chronic kidney disease CVD – Cardiovascular disease DBP – Diastolic blood pressure ESA – Erythropoiesis-stimulating agents Hb – Hemoglobin HDL – High-density lipoprotein cholesterol HR – Hazard ratio Ht – Hematocrit HCV – Hepatitis C virus HBV – Hepatitis B virus HD – Hemodialysis iPTH – Intact parathyroid hormone LDL – Low-density lipoprotein cholesterol Mg – Magnesium Me – Median M ± SD – Mean ± standard deviation PLT – Platelet count SBP – Systolic blood pressure spKt/V – Single-pool Kt/V T1, T2, T3 – Tertiles of serum magnesium concentration TSAT – Transferrin saturation UF – Ultrafiltration Declarations Ethics approval and consent to participate The study was conducted following the principles of the Declaration of Helsinki. The protocol was approved by the Institutional Review Board of the Medical Center “Nephrocenter” (IRB No. 2/2025, June 16, 2025). Due to the retrospective design and use of de-identified data, the requirement for informed consent was waived. Clinical trial number: Not applicable. Consent for publication Not applicable. Data availability statement The data used in the study are available upon reasonable request to the corresponding author. Competing interests The authors declare no competing financial support or interests. Funding This study has not received Authors' contributions NS: Conceptualization, formal analysis, visualisation, review and editing; TO and AR: Original draft preparation; IP and AH: Formal analysis; AS, VM, and MD: Data curation. All the authors reviewed the manuscript and approved it for publication. Acknowledgments Not applicable. References Girerd S, Girerd N, Frimat L, Holdaas H, Jardine AG, Schmieder RE, et al. Arteriovenous fistula thrombosis is associated with increased all-cause and cardiovascular mortality in haemodialysis patients from the AURORA trial. 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16:30:10","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":190031,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-7289523/v1/c54f2666595c2ba486ad8555.png"},{"id":92736636,"identity":"661be004-6d15-44c2-8f40-fac3932ccd2f","added_by":"auto","created_at":"2025-10-03 16:38:10","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":164836,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-7289523/v1/c4729b78532cd65bfecbe488.png"},{"id":92735562,"identity":"dc4d04fa-0b36-438c-9ac9-03dec7a7f199","added_by":"auto","created_at":"2025-10-03 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16:38:10","extension":"html","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":160100,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7289523/v1/442b98b35624779dc94a03bd.html"},{"id":92736631,"identity":"52ddc16b-906b-4d29-8c97-357feeb5ac5a","added_by":"auto","created_at":"2025-10-03 16:38:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":954500,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of patient selection. \u003cem\u003eAbbreviations: AVF, arteriovenous fistula; Mg, magnesium.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-7289523/v1/3ca898328d48f22ddd83e596.png"},{"id":92735550,"identity":"c127c26d-7973-4919-bc62-57e73c69061c","added_by":"auto","created_at":"2025-10-03 16:30:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1077782,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier curves for AVF thrombosis-free survival stratified by serum magnesium tertiles in patients undergoing HD. \u003cem\u003eAbbreviations: T1, T2, T3, tertiles of serum magnesium concentration (T1 = lowest, T3 = highest).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-7289523/v1/a804affcf01a8950363ed57b.png"},{"id":92736629,"identity":"691a0cb0-9610-48b4-a4bc-e1bbe02d2b15","added_by":"auto","created_at":"2025-10-03 16:38:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1481505,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of HRs and 95% CIs for factors associated with AVF thrombosis in univariable Cox regression analysis. \u003cem\u003eAbbreviations: AVF, arteriovenous fistula; HDL, high-density lipoprotein cholesterol; HR (95% CI), Hazard ratio (95% Confidence Interval); PLT, platelet count; spKt/V, single-pool Kt/V; T1, T2, T3, tertiles of serum magnesium concentration (T1 = lowest, T3 = highest).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-7289523/v1/a9da9a23409b483c454d3407.png"},{"id":98244076,"identity":"15963e14-0bd7-459b-8b95-b925d2248113","added_by":"auto","created_at":"2025-12-15 16:12:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3592903,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7289523/v1/38411f38-6d49-4ec2-be70-65c778d7693c.pdf"},{"id":92735547,"identity":"2e7746db-1c90-498e-a0c9-13a539044012","added_by":"auto","created_at":"2025-10-03 16:30:10","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":35082,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile.docx","url":"https://assets-eu.researchsquare.com/files/rs-7289523/v1/43a17935726e5a258209341e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Serum Magnesium and Risk of Arteriovenous Fistula Thrombosis in Hemodialysis: A Retrospective Cohort Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eArteriovenous fistula (AVF) thrombosis is a major cause of vascular access failure among patients undergoing maintenance hemodialysis (HD), leading to increased morbidity, frequent hospitalizations, and higher healthcare costs [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Despite advances in surgical technique and access surveillance, the annual incidence of AVF thrombosis remains high, with reported rates of 0.2–0.5 episodes per 1000 patient days [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Identifying modifiable risk factors for AVF thrombosis is, therefore, critical to improving outcomes in this vulnerable population.\u003c/p\u003e\u003cp\u003eMagnesium is a critical intracellular cation involved in various vascular functions, including endothelial protection, inhibition of vascular calcification, and suppression of platelet aggregation [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e–\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In patients with chronic kidney disease (CKD), and especially those on HD, disturbances in magnesium homeostasis are common due to impaired renal excretion, dietary restrictions, and variable dialysate magnesium concentrations [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Currently, clinical interest in magnesium has increased, as low serum magnesium has been associated with greater cardiovascular risk and mortality in patients undergoing HD [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Proposed mechanisms include its capacity to prevent calcium-phosphate precipitation, reduce oxidative stress and inflammation, and limit vascular smooth muscle cell transformation into pro-calcific phenotypes [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e–\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn this context, recent studies have begun to explore the association between serum magnesium and vascular access outcomes [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e–\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, the prior investigations have focused on composite endpoints, combining thrombosis, stenosis, and access-related interventions, which may obscure the distinct pathophysiological mechanisms underlying each complication.\u003c/p\u003e\u003cp\u003eTherefore, we aimed to investigate the association between serum magnesium levels and the risk of AVF thrombosis in a retrospective cohort of maintenance HD patients. We hypothesized that lower serum magnesium concentrations would be independently associated with a higher risk of AVF thrombosis and may represent a modifiable factor influencing AVF survival in this population.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eStudy design and setting\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis was a bicenter, retrospective observational cohort study analyzing data from patients undergoing HD between January 1, 2020, and May 31, 2025, at two affiliated dialysis units managed by Medical Center LLC “Nephrocenter” in Odesa and Zaporizhzhia, Ukraine. Both centers deliver HD care to patients with end-stage kidney disease following national and international guidelines. The study protocol received ethical approval from the Institutional Review Board of the Medical Center “Nephrocenter” (IRB No. 2/2025, June 16, 2025) and was carried out following the principles of the Declaration of Helsinki. Due to the retrospective design and use of de-identified data, the requirement for informed consent was waived.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStudy population\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAll adult patients (aged ≥ 18 years) undergoing HD were screened for inclusion. Eligible patients met the following criteria:\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eA functioning native AVF as the primary vascular access at baseline,\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eA minimum of three months of continuous in-center HD,\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eAt least one recorded serum magnesium measurement during the baseline assessment period.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cp\u003ePatients were excluded if they:\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eUsed a central venous catheter or arteriovenous graft as the primary access,\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eHad a documented history of AVF thrombosis before the baseline magnesium measurement,\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eHad missing or incomplete data for key variables,\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eHad a follow-up duration of less than 30 days, were transplanted, or lost to follow-up within the same period.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cp\u003eAll patients received conventional high-volume hemodiafiltration using Fresenius 5008S dialysis machines (Fresenius Medical Care, Germany). High-flux synthetic dialyzers with polysulfone membranes were employed following standard clinical protocols. Dialysis treatment was prescribed as three sessions per week, each lasting approximately 4 hours. Standard parameters included a blood flow rate of 250–350 mL/min, a dialysate flow rate of 500 mL/min, and a dialysate composition containing magnesium at 0.5 mmol/L, sodium at 138–140 mmol/L, and bicarbonate at 32–35 mmol/L. Systemic anticoagulation was provided using unfractionated heparin, administered according to local protocol, with dosing individualized based on patient weight, bleeding risk, and prior clotting history.\u003c/p\u003e\u003cp\u003e\u003cb\u003eExposure, outcome, and follow-up\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe primary exposure was serum magnesium concentration, assessed as part of routine laboratory monitoring. The baseline magnesium level was defined as the first available value within 30 days before the index date. For the main analysis, serum magnesium levels were divided into tertiles based on their distribution within the study population, with tertile 1 (T1) representing the lowest and tertile 3 (T3) the highest values. This approach was selected to explore potential non-linear associations and facilitate clinical interpretability. Importantly, no patients in the cohort received oral or intravenous magnesium supplementation during the observation period.\u003c/p\u003e\u003cp\u003eThe primary outcome was AVF thrombosis, defined as a documented clinical event requiring intervention for thrombotic occlusion, confirmed by physical examination, imaging, or surgical findings. The diagnosis was based on standard clinical evaluation (absence of bruit or thrill), confirmatory imaging (Doppler ultrasound), or operative findings. Only the first incident of AVF thrombosis after the index date was considered in the primary analysis.\u003c/p\u003e\u003cp\u003ePatients were followed from the index date until the first occurrence of AVF thrombosis or until the end of follow-up (May 31, 2025). Those who developed AVF stenosis or infection requiring catheter conversion, underwent kidney transplantation, or died were not excluded from the analysis but were censored at the time of the respective event, as they were no longer considered at risk for AVF thrombosis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCovariates\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAll data were retrospectively extracted from electronic medical records of the participating dialysis centers. Collected data included: demographics (age, sex), comorbidities (diabetes mellitus, hypertension, cardiovascular disease, hepatitis B and C status), dialysis-related factors (dialysis vintage, Kt/V, ultrafiltration volume), medications used, including antiplatelet agents and anticoagulants. AVF age was also recorded. Due to the small sample size of patients with AVF age \u0026lt; 6 months, a 1-year cutoff was used to categorize vascular access age for the analyses.\u003c/p\u003e\u003cp\u003eLaboratory parameters included hematology (hemoglobin, hematocrit, platelet count), biochemistry (serum creatinine, urea, sodium, potassium, calcium, phosphate, magnesium), intact parathyroid hormone (iPTH), serum albumin, total protein, ferritin, transferrin saturation (TSAT), C-reactive protein (CRP), fasting glucose, and lipid profile markers such as total cholesterol, low-density lipoprotein (LDL), high-density lipoprotein (HDL), and triglycerides.\u003c/p\u003e\u003cp\u003eAll laboratory analyses were performed in ISO-certified external laboratories (Sinevo and/or Dila) using standardized and validated procedures. Hematologic parameters were measured using automated hematology analyzers based on flow cytometry and impedance methods. Serum creatinine, urea, sodium, potassium, calcium, phosphate, and magnesium were analyzed via photometric and ion-selective electrode methods on automated chemistry analyzers. Serum albumin, total protein, and CRP were determined using colorimetric or immunoturbidimetric assays.\u003c/p\u003e\u003cp\u003eLipid profile markers and fasting glucose were measured enzymatically using spectrophotometric methods. iPTH and ferritin were quantified via chemiluminescent immunoassays, and TSAT was calculated as the ratio of serum iron to total iron-binding capacity, both measured spectrophotometrically. The value closest to the index date within a 30-day window was used for each parameter.\u003c/p\u003e\u003cp\u003e\u003cb\u003eBias minimization\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo minimize potential selection bias, all consecutive patients meeting the inclusion criteria during the study period were considered for inclusion. Exclusion criteria were predefined and applied uniformly across both centers. Exposure and outcome variables were derived from standardized, routinely collected clinical and laboratory data. The primary outcome was objectively confirmed through clinical examination, imaging, or operative findings. To address potential confounding, we applied multivariable adjustments and conducted sensitivity and subgroup analyses.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSample size justification\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAlthough no formal a priori sample size calculation was performed, a post hoc approximation was informed by the study by Yao et al. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. That study included 263 HD patients and reported 95 AVF dysfunction events (36%) over a median follow-up of 32 months, with a hazard ratio (HR) of 4.53 (95% CI: 2.63–7.81) for low serum magnesium. For our analysis, assuming a more conservative HR of 2.0 for AVF thrombosis between low and high magnesium tertiles, with a two-sided α of 0.05, 80% power, and an expected event rate of 20%, the estimated required sample size was approximately 360 patients with 72 events. Our final cohort included 408 patients and 83 thrombosis events, exceeding this threshold and thus providing adequate power to detect moderate associations.\u003c/p\u003e\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eContinuous variables were summarized as means with standard deviations (M ± SD) or medians with interquartile ranges [Me (Q25-Q75)], depending on the distribution assessed by the Shapiro–Wilk test. Categorical variables were summarized as frequencies and percentages. For comparison of baseline characteristics across serum magnesium quartiles, we used one-way ANOVA for normally distributed continuous variables, the Kruskal–Wallis test for non-normally distributed continuous variables, and the chi-squared (χ²) test for categorical variables. Correlation analyses were conducted using the Pearson or Spearman test based on the data distribution.\u003c/p\u003e\u003cp\u003eTime-to-event was defined as the duration from baseline serum magnesium measurement to the first documented AVF thrombosis or censoring. Survival curves were estimated using the Kaplan–Meier method, with comparisons between tertiles made using the log-rank test. Univariable Cox proportional hazards regression was used to identify potential predictors of AVF thrombosis. Variables with p \u0026lt; 0.05 and clinical relevance were entered into the multivariable model. Multicollinearity among covariates was assessed using variance inflation factors (VIF); values \u0026gt; 5 indicated significant collinearity. Results are reported as HRs with 95% confidence intervals (CIs).\u003c/p\u003e\u003cp\u003eTo test the robustness of our findings, we performed both sensitivity and subgroup analyses. In sensitivity analyses, serum magnesium was also modeled as a continuous variable to assess the linearity of its association with AVF thrombosis in the Cox regression model. Additionally, we conducted a sensitivity analysis excluding patients who were censored due to AVF stenosis or infection, kidney transplantation, or death during the observation period. Subgroup analyses were stratified by age (\u0026lt; 70 vs. ≥70 years), diabetes status, serum albumin level (\u0026lt; 35 vs. ≥35 g/L), and vascular access age (\u0026lt; 1 year vs. ≥1 year), with separate Cox models fitted within each subgroup.\u003c/p\u003e\u003c/div\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eContinuous variables were summarized as means with standard deviations (M ± SD) or medians with interquartile ranges [Me (Q25-Q75)], depending on the distribution assessed by the Shapiro–Wilk test. Categorical variables were summarized as frequencies and percentages. For comparison of baseline characteristics across serum magnesium quartiles, we used one-way ANOVA for normally distributed continuous variables, the Kruskal–Wallis test for non-normally distributed continuous variables, and the chi-squared (χ²) test for categorical variables. Correlation analyses were conducted using the Pearson or Spearman test based on the data distribution.\u003c/p\u003e\u003cp\u003eTime-to-event was defined as the duration from baseline serum magnesium measurement to the first documented AVF thrombosis or censoring. Survival curves were estimated using the Kaplan–Meier method, with comparisons between tertiles made using the log-rank test. Univariable Cox proportional hazards regression was used to identify potential predictors of AVF thrombosis. Variables with p \u0026lt; 0.05 and clinical relevance were entered into the multivariable model. Multicollinearity among covariates was assessed using variance inflation factors (VIF); values \u0026gt; 5 indicated significant collinearity. Results are reported as HRs with 95% confidence intervals (CIs).\u003c/p\u003e\u003cp\u003eTo test the robustness of our findings, we performed both sensitivity and subgroup analyses. In sensitivity analyses, serum magnesium was also modeled as a continuous variable to assess the linearity of its association with AVF thrombosis in the Cox regression model. Additionally, we conducted a sensitivity analysis excluding patients who were censored due to AVF stenosis or infection, kidney transplantation, or death during the observation period. Subgroup analyses were stratified by age (\u0026lt; 70 vs. ≥70 years), diabetes status, serum albumin level (\u0026lt; 35 vs. ≥35 g/L), and vascular access age (\u0026lt; 1 year vs. ≥1 year), with separate Cox models fitted within each subgroup.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eStudy cohort\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA total of 480 patients undergoing HD were screened for eligibility. After applying exclusion criteria, 408 patients were included in the final analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSerum magnesium levels ranged from 0.66 to 1.36 mmol/L, with a median of 0.93 (0.87\u0026ndash;0.98) mmol/L. Baseline characteristics of the study population, stratified by serum magnesium tertiles, are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline characteristics of the patients stratified by serum magnesium tertiles\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAll patients\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;408)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eT1: \u0026lt; 0.87 mmol/L\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;103)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eT2: 0.87\u0026ndash;0.97 mmol/L\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;214)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eT3: \u0026ge; 0.98 mmol/L\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;91)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eDemographics\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e59 (49\u0026ndash;67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63 (51\u0026ndash;69)\u003csup\u003eT3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e59 (49\u0026ndash;67)\u003csup\u003eT3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e56 (47.2\u0026ndash;61)\u003csup\u003eT1,2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale sex, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e233 (57.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e64 (62.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e119 (55.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e50 (54.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking status, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52 (12.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (9.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30 (14.05%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12 (13.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlcohol use, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22 (5.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (2.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11 (5.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8 (8.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eClinical characteristics\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e79 (19,4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22 (21.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40 (18.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17 (18.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBP (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e130 (125\u0026ndash;140)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e140 (130\u0026ndash;145)\u003csup\u003eT3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e130 (125\u0026ndash;140)\u003csup\u003eT3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e130 (120\u0026ndash;140)\u003csup\u003eT1,2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.05\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDBP (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e80 (80\u0026ndash;90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e80 (80\u0026ndash;90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e80 (80\u0026ndash;90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e80 (70-83.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCVD history, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e66 (16.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21 (20.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29 (13.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16 (17.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePositive HBV or HCV status, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55 (13.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 (16.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22 (10.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16 (17.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDialysis vintage (months)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65.0 (33.0\u0026ndash;87.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e73 (31,7-96.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e57.5 (31.0\u0026ndash;80.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e63.0 (38.0\u0026ndash;99.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlood flow rate, mL/min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e302.5\u0026thinsp;\u0026plusmn;\u0026thinsp;17.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e300.6\u0026thinsp;\u0026plusmn;\u0026thinsp;18.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e302.7\u0026thinsp;\u0026plusmn;\u0026thinsp;17.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e304.5\u0026thinsp;\u0026plusmn;\u0026thinsp;17.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003espKt/V\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.34 (1.24\u0026ndash;1.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.30 (1.19\u0026ndash;1.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.30 (1.25\u0026ndash;1.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.35 (1.25\u0026ndash;1.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal volume UF, mL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2500 (2000\u0026ndash;3000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2500 (2200\u0026ndash;3000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2800 (2250\u0026ndash;2900)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2500 (1800\u0026ndash;3000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAVF age\u0026thinsp;\u0026lt;\u0026thinsp;1 year, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42 (10.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (5.8%)\u003csup\u003eT2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30 (14.0%)\u003csup\u003eT1,3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6 (6.6%)\u003csup\u003eT2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eLaboratory values\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMagnesium, mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.93 (0.87\u0026ndash;0.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.83 (0.79\u0026ndash;0.85)\u003csup\u003eT2,3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.93 (0.91\u0026ndash;0.95)\u003csup\u003eT1,3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.03 (0.99\u0026ndash;1.07)\u003csup\u003eT12\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCreatinine, \u0026micro;mol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e839 (695\u0026ndash;972)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e782 (594\u0026ndash;891)\u003csup\u003eT2,3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e819 (673\u0026ndash;941)\u003csup\u003eT1,3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e938 (869\u0026ndash;1026)\u003csup\u003eT1,2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrea, mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.6 (17.1\u0026ndash;25.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.8 (15.8\u0026ndash;24.2)\u003csup\u003eT1,3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20/3 (16.9\u0026ndash;24.4)\u003csup\u003eT1,3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22.9 (19.4\u0026ndash;27.4)\u003csup\u003eT2,3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin, g/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e101 (92-110.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e98 (90\u0026ndash;108)\u003csup\u003eT3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e101 (92\u0026ndash;111)\u003csup\u003eT3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e107 (96\u0026ndash;118)\u003csup\u003eT1,2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHt, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9\u003csup\u003eT3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003csup\u003eT3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e31.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003csup\u003eT1,2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLT, ˟10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e179.5 (142.5\u0026ndash;221.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e179.0 (137.5\u0026ndash;220)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e174.5 (141.0-221.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e184.0 (145.5-222.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTSAT, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22.7 (15.6-31.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.9 (14.7\u0026ndash;28.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21.6 (14.9\u0026ndash;31.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25.5 (15.0-14.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFerritin, ng/mL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e261.1 (103.0-205.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e301.4 (88.2\u0026ndash;529.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e276 (104.0-505.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e243 (98.9-487.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal protein, g/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e67.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8\u003csup\u003eT3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e67.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e68.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9\u003csup\u003eT1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlbumin, g/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40 (38\u0026ndash;42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39 (36\u0026ndash;42)\u003csup\u003eT2,3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40 (39\u0026ndash;42)\u003csup\u003eT1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e41 (39.2\u0026ndash;42)\u003csup\u003eT1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.0002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCRP, mg/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.4 (3.3\u0026ndash;9.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.9 (4.5\u0026ndash;11.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.8 (2.7\u0026ndash;6.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.3 (2.9\u0026ndash;13.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlucose, mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.9 (4.4\u0026ndash;6.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.8 (4.2\u0026ndash;5.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.9 (4.4\u0026ndash;6.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.9 (4.6\u0026ndash;5.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalcium, mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.24 (2.15\u0026ndash;2.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.17 (2.11\u0026ndash;2.29)\u003csup\u003eT2,3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.24 (2.16\u0026ndash;2.34)\u003csup\u003eT1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.28 (2.17\u0026ndash;2.37)\u003csup\u003eT1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.0001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhosphate, mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.63 (1.35\u0026ndash;1.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.37 (1.19\u0026ndash;1.72)\u003csup\u003eT2,3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.67 (1.32\u0026ndash;1.93)\u003csup\u003eT1,3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.95 (1.46\u0026ndash;2.22)\u003csup\u003eT1,2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eiPTH, pg/mL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e302.6 (173.7-531.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e321.8 (171.5-605.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e294.8 (187.6-5015.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e312.5 (130.1\u0026ndash;55.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal cholesterol, mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.6 (4.0-5.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.9 (4.3\u0026ndash;5.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.5 (3.9\u0026ndash;5.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.3 (3.9\u0026ndash;5.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL, mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.1 (0.32\u0026ndash;1.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.05 (0.8\u0026ndash;1.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.12 (0.9\u0026ndash;1.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.19 (1.0-1.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDL, (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.8 (2.2\u0026ndash;3.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.1 (2.6\u0026ndash;3.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.7 (2.2\u0026ndash;3.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.7 (2.1\u0026ndash;3.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTriglycerides (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.45 (1.1\u0026ndash;2.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.54 (1.14\u0026ndash;2.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.51 (1.04\u0026ndash;2.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.35 (1.11\u0026ndash;2.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eMedications\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eErythropoiesis-stimulating agents, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e245 (52.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65 (63.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e127 (59.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e56 (61.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIron therapy, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e237 (58.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65 (63.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e127 (58.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e45 (49.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStatins, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e124 (30.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31 (30.1%)\u003csup\u003eT3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e75 (35.0%)\u003csup\u003eT3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18 (19.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAntihypertensives, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e347 (85.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95 (92.2%)\u003csup\u003eT2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e177 (82.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e76 (83.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnticoagulants, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41 (10.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9 (8.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20 (9.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12 (132%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAntiaggregants, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e122 (29.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25 (24.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e66 (30.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e31 (34.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalcium-based phosphate binders, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e183 (44.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59 (57.3%)\u003csup\u003eT2,3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e92 (43.0%)\u003csup\u003eT1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e32 (35.24%)\u003csup\u003eT1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNotes: Values are presented as Me (Q25-Q75) or M\u0026thinsp;\u0026plusmn;\u0026thinsp;SD as appropriate. Superscript letters denote statistically significant differences between tertiles based on pairwise comparisons using ANOVA or Kruskal\u0026ndash;Wallis with post hoc testing.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eAbbreviations: CRP, C-reactive protein; CVD, cardiovascular disease; DBP, diastolic blood pressure; Hb, hemoglobin; HDL, high-density lipoprotein cholesterol; Ht, hematocrit; iPTH, intact parathyroid hormone; LDL, low-density lipoprotein cholesterol; PLT, platelet count; SBP, systolic blood pressure; spKt/V, single-pool Kt/V; TSAT, transferrin saturation; UF, ultrafiltration.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, patients in the lowest magnesium tertile (T1) exhibited several unfavorable clinical and biochemical characteristics. They were significantly older, had higher systolic blood pressure, and demonstrated lower hemoglobin and hematocrit levels compared to those in the highest tertile (T3). Serum calcium, phosphate, total protein, and albumin concentrations were also significantly lower in T1 than in the higher tertiles. Furthermore, serum creatinine and urea levels were significantly lower in T1, with a progressive increase observed across magnesium tertiles.\u003c/p\u003e\u003cp\u003eA higher proportion of patients with AVF age\u0026thinsp;\u0026lt;\u0026thinsp;1 year was observed in T2 compared to T1 and T3, although the clinical relevance of this distribution remains uncertain. The use of statins, antihypertensive agents, and calcium-based phosphate binders was more common in T1 than in the higher tertiles.\u003c/p\u003e\u003cp\u003eInflammatory and iron-related biomarkers, including CRP, ferritin, and TSAT, did not significantly differ among groups. However, there was a non-significant trend toward lower dialysis adequacy and a higher prevalence of CVD in the lowest magnesium tertile. Similarly, patients in T1 tended to exhibit a more atherogenic lipid profile, characterized by lower HDL cholesterol and higher triglyceride levels, though these differences did not reach statistical significance.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAssociation between serum magnesium and AVF thrombosis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDuring a median follow-up of 33.5 (16.2\u0026ndash;55.6) months, 83 patients (20.3%) developed AVF thrombosis, corresponding to an incidence rate of 0.2 events per 1000 patient days. In addition, 10 (2.5%) patients developed AVF stenosis, and 7 (1.72%) experienced AVF infections requiring catheter conversion. Other censoring events included kidney transplantation (n\u0026thinsp;=\u0026thinsp;13, 3.2%) and death (n\u0026thinsp;=\u0026thinsp;32, 7.8%).\u003c/p\u003e\u003cp\u003eAcross serum magnesium tertiles, the proportion of patients who experienced AVF thrombosis differed significantly (T1: n\u0026thinsp;=\u0026thinsp;42, 40.8%, T2: n\u0026thinsp;=\u0026thinsp;29, 13.6%, T3: n\u0026thinsp;=\u0026thinsp;12, 13.2%; χ\u0026sup2; = 35.5, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), indicating an inverse relationship between baseline serum magnesium levels and thrombosis occurrence.\u003c/p\u003e\u003cp\u003eKaplan-Meier analysis demonstrated a significantly lower thrombosis-free survival in patients within the lowest serum magnesium tertile compared to higher tertiles (log-rank test 23.6, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eUnivariable Cox regression analysis identified several factors significantly associated with AVF thrombosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAs illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, low serum magnesium (\u0026lt;\u0026thinsp;0.87 mmol/L) was associated with a twofold increased risk of AVF thrombosis. Similarly, advancing age, higher phosphate and calcium levels, use of calcium-based phosphate binders, and higher ultrafiltration volume also increased thrombotic risk. In contrast, higher serum magnesium levels, greater dialysis adequacy measured by spKt/V, higher albumin, shorter AVF age, and statin use were significantly associated with reduced risk of AVF thrombosis. Platelet count showed a borderline association with increased risk.\u003c/p\u003e\u003cp\u003eWe further assessed multicollinearity among variables considered for inclusion in the multivariable Cox regression model. All candidate variables demonstrated acceptable levels of collinearity, with VIF values ranging from 1.052 to 1.603 (\u003cb\u003eSupplementary Table S2\u003c/b\u003e). In light of the absence of significant collinearity and the relatively limited number of events (n\u0026thinsp;=\u0026thinsp;83), we adopted a conservative variable selection strategy to preserve an events-per-variable ratio of at least 10. Variables were selected based on statistical significance in univariable analyses (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The final model included the primary exposure (serum magnesium tertiles), age, spKt/V, total ultrafiltration volume, serum albumin, serum phosphate, statin use, and calcium-based phosphate binder use.\u003c/p\u003e\u003cp\u003eIn the multivariable Cox regression model, low serum magnesium levels remained independently associated with the risk of AVF thrombosis after adjustment for relevant covariates. Compared to patients in the T1 group, those in the T2 and T3 groups had a 43% and 54% lower risk of AVF thrombosis, respectively. These results correspond to a 1.75-fold increased risk for T1 versus T2, and a 2.17-fold increased risk for T1 versus T3 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariable Cox proportional hazards regression for risk of AVF thrombosis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum magnesium T1: \u0026lt;0.87 mmol/L (reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum magnesium T2 vs. T1: 0.87\u0026ndash;0.97 mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.57 (0.40\u0026ndash;0.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum magnesium T3 vs. T1: \u0026ge;0.98 mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.46 (0.28\u0026ndash;0.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (per year)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.05 (1.02\u0026ndash;1.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003espKt/V (per 0.1 increase)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.16 (0.11\u0026ndash;0.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal volume UF (per 100 mL increase)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00 (0.97\u0026ndash;1.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.233\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlbumin (per 1 g/L increase)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.95 (0.88\u0026ndash;1.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.142\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhosphate (per 0.1 mmol/L increase)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.11 (1.03\u0026ndash;1.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStatins (yes vs. no)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.35 (0.14\u0026ndash;0.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.022\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalcium-based phosphate binders (yes vs. no)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.02 (1.003\u0026ndash;1.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.047\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eAbbreviations: HR (95% CI), Hazard ratio (95% Confidence Interval); spKt/V, single-pool Kt/V; T1, T2, T3, magnesium tertiles.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eOther independent predictors included higher age, higher serum phosphate, and\u0026nbsp;calcium-based phosphate binder use, all associated with increased risk of AVF thrombosis. Conversely, greater dialysis adequacy and statin use were independently associated with reduced risk of AVF thrombosis. Total ultrafiltration volume and serum albumin did not retain independent significance after multivariable adjustment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSensitivity and subgroup analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess the robustness of our findings, we conducted two sensitivity analyses.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFirst, serum magnesium was modeled as a continuous variable in the multivariable Cox regression model to evaluate the linearity of its association with AVF thrombosis. The inverse relationship remained statistically significant, with each 0.1 mmol/L increase in serum magnesium associated with a lower risk of AVF thrombosis (adjusted HR 0.36, 95% CI: 0.22\u0026ndash;0.62, \u0026nbsp;p = 0.021).\u003c/p\u003e\n\u003cp\u003eSecond, we repeated the multivariable Cox regression after excluding 62 patients who were censored due to competing events, including death, kidney transplantation, or catheter conversion due to AVF infection or stenosis. The resulting sensitivity cohort included 346 patients, among whom 83 developed AVF thrombosis during follow-up. In the adjusted model, which included the same covariates as the primary analysis, patients in the T1 group had a significantly increased risk of AVF thrombosis (HR 2.63, 95% CI: 1.82\u0026ndash;3.80, p \u0026lt; 0.0001), while those in the T3 group had a significantly lower risk (HR 0.41, 95% CI: 0.22\u0026ndash;0.75, p = 0.004). These results confirmed the robustness of the association between serum magnesium levels and AVF thrombosis risk.\u003c/p\u003e\n\u003cp\u003eIn subgroup analyses, the association between serum magnesium and AVF thrombosis was consistent. Patients in the lowest magnesium tertile showed significantly increased risk of thrombosis across all subgroups, with HRs ranging from 2.36 to 7.41 (\u003cstrong\u003eTable 3\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Subgroup analysis of the association between serum magnesium tertiles and AVF thrombosis (adjusted Cox regression)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubgroup\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSerum magnesium\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 224px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT1 (\u0026lt; 0.87 mmol/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 241px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT3 (\u0026ge; 0.98 mmol/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR (95% CI_\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR, 95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge \u0026ge; 70 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.21 (1.44\u0026ndash;8.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.56 (0.11\u0026ndash;5.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.621\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge \u0026lt; 70 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.64 (1.44\u0026ndash;8.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.43 (0.32\u0026ndash;0.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDiabetes \u0026ndash; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.36 (1.02\u0026ndash;5.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.47 (0.13\u0026ndash;1.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDiabetes \u0026ndash; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.09 (2.77\u0026ndash;6.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.37 (0.28\u0026ndash;0.490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAlbumin \u0026le; 35 g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.41 (1.62\u0026ndash;11.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.58 (0.08\u0026ndash;4.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.582\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAlbumin \u0026gt; 35 g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.95 (2.68\u0026ndash;5.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.43 (0.31\u0026ndash;0.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAVF age \u0026lt; 1 year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.46 (1.43\u0026ndash;7.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.11 (0.02\u0026ndash;1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAVF age \u0026ge; 1 year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.47 (1.62\u0026ndash;3.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.51 (0.26\u0026ndash;0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNotes: Multivariable Cox regressions were repeated within each subgroup, adjusting for age, spKt/V, phosphate, albumin, ultrafiltration volume, statin use, and calcium-phosphate binder use (excluding the stratifying variable). T2 (0.87\u0026ndash;0.97 mmol/L) was used as the reference in all models.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn contrast, the protective effect associated with the highest magnesium tertile was significant in younger patients, non-diabetics, well-nourished, and patients with AVF age \u0026ge;1 year. However, in older subjects (\u0026ge;70 years), diabetics, and patients with hypoalbuminemia (\u0026le;35 g/L), the association with T3 was not statistically significant.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study is the first to investigate the association between serum magnesium levels and the risk of AVF thrombosis as an isolated clinical endpoint in patients undergoing HD. Unlike previous studies that examined AVF dysfunction using composite outcomes, we focused specifically on thrombotic events confirmed by clinical, imaging, or surgical criteria. This approach allowed for a more precise evaluation of risk factors specifically associated with thrombosis, rather than broader or overlapping vascular access complications.\u003c/p\u003e\n\u003cp\u003eIn our cohort of 408 HD patients, lower serum magnesium was independently associated with an increased risk of AVF thrombosis. Patients in the lowest tertile (\u0026lt;0.87 mmol/L) had a 1.75- to 2.17-fold higher risk of thrombosis compared to those in the middle and highest tertiles, even after adjustment for demographic, clinical, and dialysis-related covariates. This association remained consistent in sensitivity analyses, including one that excluded patients censored due to competing events.\u003c/p\u003e\n\u003cp\u003eSubgroup analyses further supported the robustness of this relationship. The elevated thrombotic risk linked to low magnesium remained significant across all examined subgroups. However, the protective effect of higher magnesium was attenuated and no longer statistically significant in older individuals, patients with diabetes, and those with hypoalbuminemia, suggesting that magnesium\u0026rsquo;s vascular benefits may be less pronounced in patients with higher baseline cardiovascular or inflammatory risk.\u003c/p\u003e\n\u003cp\u003eOur findings are consistent with and extend previous research examining the relationship between serum magnesium and vascular access outcomes. To date, only two studies have specifically addressed this topic, encompassing a combined cohort of 352 HD patients [15, 16]. In line with our results, Stolić et al. reported significantly lower serum magnesium concentrations in patients with AVF complications compared to those without [16]. Similarly, Yao et al. observed a 4.5-fold higher risk of AVF dysfunction in patients with serum magnesium levels below 0.88 mmol/L compared to those in the highest magnesium group [15].\u0026nbsp;However, both studies relied on composite endpoints, limiting the ability to isolate thrombosis-specific mechanisms.\u003c/p\u003e\n\u003cp\u003eSeveral biological mechanisms may explain this relationship. Magnesium modulates vascular tone, inhibits platelet aggregation, stabilizes endothelial function, and reduces inflammation [4, 12]. Experimental studies have shown that low extracellular magnesium can promote vascular smooth muscle cell calcification and endothelial injury, both of which predispose to thrombosis [11, 17, 18]. \u0026nbsp; Additionally, magnesium inhibits platelet activation by modulating intracellular calcium handling and interfering with thromboxane synthesis, both of which are critical steps in thrombus formation [19, 20].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThese mechanisms provide a compelling rationale for the inverse association observed in our cohort. However, we also observed an attenuation of the protective effect of higher serum magnesium levels among older patients, individuals with diabetes, and those with hypoalbuminemia. In older patients, age-related alterations in magnesium handling, a higher prevalence of vascular calcification, and the burden of comorbidities may reduce the vascular and antithrombotic benefits typically conferred by elevated magnesium levels [21]. The presence of advanced vascular disease and frailty in this population may overshadow magnesium\u0026rsquo;s protective effects, including the inhibition of vascular smooth muscle cell calcification and the preservation of endothelial function.\u003c/p\u003e\n\u003cp\u003eAmong individuals with diabetes, persistent hyperglycemia and insulin resistance promote chronic vascular injury and inflammation, which may not be fully mitigated by elevated serum magnesium concentrations [22]. Additionally, diabetes is associated with increased magnesium loss and impaired cellular uptake [23, 24], potentially limiting the intracellular actions of magnesium that are critical for inhibiting platelet activation and maintaining endothelial health.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn patients with hypoalbuminemia, the interpretation of serum magnesium is further complicated, as a significant portion of circulating magnesium is albumin-bound [25]. Low albumin levels may result in a falsely reassuring total serum magnesium despite a reduced physiologically active ionized fraction [25]. Moreover, hypoalbuminemia reflects underlying malnutrition and inflammation, both of which are strong risk factors for vascular complications [26]\u0026nbsp;and may blunt the protective effects of magnesium. In line with this hypothesis, Streja et al. have demonstrated that HD patients with both low albumin and low magnesium had a 17% higher risk of death compared to those with low albumin and high magnesium [27]. However, when albumin levels were adequate, the relationship between magnesium and mortality was attenuated, suggesting that serum albumin may modify the clinical impact of magnesium levels [27].\u003c/p\u003e\n\u003cp\u003eBeyond magnesium, our study identified several other independent factors associated with the risk of AVF thrombosis, highlighting the multifactorial nature of vascular access failure in HD patients. Elevated serum phosphate levels were significantly associated with an increased risk of thrombosis, aligning with previous reports that have linked hyperphosphatemia to endothelial dysfunction, vascular smooth muscle cell calcification, and heightened platelet activation [28]. Excess phosphate can promote the transformation of vascular smooth muscle cells into osteoblast-like phenotypes, contributing to medial calcification and reduced vascular compliance, factors that may impair AVF integrity and predispose to thrombosis [29].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdditionally, the use of calcium-based phosphate binders was independently associated with a higher risk of AVF thrombosis. This finding is consistent with prior studies suggesting that calcium-based binders may contribute to arterial and arteriolar calcification through increased calcium loading and deposition in the vessel wall [30, 31].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn contrast, high dialysis adequacy, measured by spKt/V, and statin use were associated with a reduced risk of AVF thrombosis. It is well-proven that better uremic toxin clearance may improve endothelial function and reduce systemic inflammation, both of which are critical in maintaining vascular access patency [32]. Rodrigues et al. have also reported higher access failure rates in patients with low delivered dialysis doses [33], although studies are limited in this area.\u003c/p\u003e\n\u003cp\u003eStatins use emerged as another protective factor in our analysis. Beyond their lipid-lowering properties, statins exert a wide range of pleiotropic effects, including enhancement of endothelial function, attenuation of oxidative stress, inhibition of pro-inflammatory cytokines, and suppression of thrombotic pathways such as tissue factor expression and platelet aggregation [34\u0026ndash;36]. These mechanisms are particularly pertinent in the HD population, where chronic inflammation and a prothrombotic state are prevalent. Our findings are consistent with previous observational studies reporting a lower risk of AVF thrombosis among HD patients treated with statins [35, 36]. Although a meta-analysis by Wan et al. did not demonstrate a statistically significant association between statin use and AVF patency [37], our recent report has suggested a dose-dependent protective effect of statins on the risk of AVF thrombosis, highlighting the potential influence of treatment intensity [38].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTaken together, these findings underscore the multifactorial nature of AVF thrombosis and point toward several modifiable targets, including serum magnesium, phosphate balance, HD adequacy, and statin use, that may inform preventive strategies.\u003c/p\u003e\n\u003cp\u003eSeveral limitations of this study should be acknowledged. First, the retrospective design precludes causal inference. Although we performed multivariable adjustments and sensitivity analyses, the possibility of residual confounding from unmeasured variables cannot be excluded. Second, serum magnesium was assessed at a single time point, which may not capture longitudinal variability or reflect cumulative exposure. Third, only total serum magnesium concentrations were available; ionized magnesium was not measured and may have provided a more accurate reflection of magnesium status, particularly in hypoalbuminemic patients. Fourth, although thrombotic AVF events were rigorously defined, we did not account for the timing of these events in relation to changes in medications or dialysis prescriptions, which could have influenced thrombosis risk. In addition, the relatively low number of outcome events constrained the number of covariates that could be included in the multivariable models. Finally, while subgroup analyses offered insights into potential effect modifiers, these analyses were exploratory in nature and not adequately powered for formal interaction testing.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe present study demonstrated that lower serum magnesium levels (\u0026lt;0.87 mmol/L) were independently associated with a twofold increased risk of AVF thrombosis in patients undergoing HD. This association remained robust across sensitivity analyses and was consistent across most clinically relevant subgroups. The findings underscore the potential importance of magnesium homeostasis in preserving vascular access patency and suggest that hypomagnesemia may represent a modifiable risk factor for AVF thrombosis. In addition to magnesium, other modifiable factors, such as elevated serum phosphate, use of calcium-based phosphate binders, suboptimal dialysis adequacy, and absence of statin therapy, were independently associated with increased risk of AVF thrombosis. Prospective studies are warranted to confirm these associations and to determine whether magnesium-targeted interventions can reduce AVF thrombosis and improve long-term vascular access outcomes in the HD population.\u003c/p\u003e"},{"header":"List of abbreviations","content":"\u003cp\u003eAVF \u0026ndash; Arteriovenous fistula\u003c/p\u003e\n\u003cp\u003eCRP \u0026ndash; C-reactive protein\u003c/p\u003e\n\u003cp\u003eCKD \u0026ndash; Chronic kidney disease\u003c/p\u003e\n\u003cp\u003eCVD \u0026ndash; Cardiovascular disease\u003c/p\u003e\n\u003cp\u003eDBP \u0026ndash; Diastolic blood pressure\u003c/p\u003e\n\u003cp\u003eESA \u0026ndash; Erythropoiesis-stimulating agents\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHb \u0026ndash; Hemoglobin\u003c/p\u003e\n\u003cp\u003eHDL \u0026ndash; High-density lipoprotein cholesterol\u003c/p\u003e\n\u003cp\u003eHR \u0026ndash; Hazard ratio\u003c/p\u003e\n\u003cp\u003eHt \u0026ndash; Hematocrit\u003c/p\u003e\n\u003cp\u003eHCV \u0026ndash; Hepatitis C virus\u003c/p\u003e\n\u003cp\u003eHBV \u0026ndash; Hepatitis B virus\u003c/p\u003e\n\u003cp\u003eHD \u0026ndash; Hemodialysis\u003c/p\u003e\n\u003cp\u003eiPTH \u0026ndash; Intact parathyroid hormone\u003c/p\u003e\n\u003cp\u003eLDL \u0026ndash; Low-density lipoprotein cholesterol\u003c/p\u003e\n\u003cp\u003eMg \u0026ndash; Magnesium\u003c/p\u003e\n\u003cp\u003eMe \u0026ndash; Median\u003c/p\u003e\n\u003cp\u003eM \u0026plusmn; SD \u0026ndash; Mean \u0026plusmn; standard deviation\u003c/p\u003e\n\u003cp\u003ePLT \u0026ndash; Platelet count\u003c/p\u003e\n\u003cp\u003eSBP \u0026ndash; Systolic blood pressure\u003c/p\u003e\n\u003cp\u003espKt/V \u0026ndash; Single-pool Kt/V\u003c/p\u003e\n\u003cp\u003eT1, T2, T3 \u0026ndash; Tertiles of serum magnesium concentration\u003c/p\u003e\n\u003cp\u003eTSAT \u0026ndash; Transferrin saturation\u003c/p\u003e\n\u003cp\u003eUF \u0026ndash; Ultrafiltration\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted following the principles of the Declaration of Helsinki. The protocol was approved by the Institutional Review Board of the Medical Center \u0026ldquo;Nephrocenter\u0026rdquo; (IRB No. 2/2025, June 16, 2025). Due to the retrospective design and use of de-identified data, the requirement for informed consent was waived.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number: \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in the study are available upon reasonable request to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing financial support or interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has not received \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNS: Conceptualization, formal analysis, visualisation, review and editing; TO and AR: Original draft preparation; IP and AH: Formal analysis; AS, VM, and MD: Data curation. All the authors reviewed the manuscript and approved it for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGirerd S, Girerd N, Frimat L, Holdaas H, Jardine AG, Schmieder RE, et al. Arteriovenous fistula thrombosis is associated with increased all-cause and cardiovascular mortality in haemodialysis patients from the AURORA trial. Clin Kidney J. 2019;13(1):116-122. doi: 10.1093/ckj/sfz048. \u003c/li\u003e\n\u003cli\u003eJim\u0026eacute;nez-Almonacid P, Gruss E, Lasala M, Del Riego S, L\u0026oacute;pez G, Rueda JA, et al. Economic repercussions of implementing a protocol for urgent surgical repair of thrombosed arteriovenous fistulae. Nefrologia. 2014;34(3):377-82. English, Spanish. doi: 10.3265/Nefrologia.pre2014.Feb.12347. \u003c/li\u003e\n\u003cli\u003eAl-Jaishi AA, Liu AR, Lok CE, Zhang JC, Moist LM. 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Magnesium, fibrinolysis and clotting interplay among children and adolescents with type 1 diabetes mellitus; potential mediators of diabetic microangiopathy. Nutr Diabetes. 2025;15(1):13. doi: 10.1038/s41387-025-00368-9. \u003c/li\u003e\n\u003cli\u003eBarbagallo M, Veronese N, Dominguez LJ. Magnesium in Aging, Health and Diseases. Nutrients. 2021;13(2):463. doi: 10.3390/nu13020463. \u003c/li\u003e\n\u003cli\u003eBarbagallo M, Dominguez LJ. Magnesium and type 2 diabetes. World J Diabetes. 2015;6(10):1152-1157. doi:10.4239/wjd.v6.i10.1152\u003c/li\u003e\n\u003cli\u003ePiuri G, Zocchi M, Della Porta M, Ficara V, Manoni M, Zuccotti GV, et al. Magnesium in Obesity, Metabolic Syndrome, and Type 2 Diabetes. Nutrients. 2021;13(2):320. doi: 10.3390/nu13020320. \u003c/li\u003e\n\u003cli\u003eXu EJ, Steele DJR, Fenves AZ. Hypomagnesemia With Metformin Use in Diabetes Mellitus: A Case and Narrative Review. Kidney Med. 2025;7(7):101030. doi: 10.1016/j.xkme.2025.101030. \u003c/li\u003e\n\u003cli\u003eScarpati G, Baldassarre D, Oliva F, Pascale G, Piazza O. Ionized or Total Magnesium levels, what should we measure in critical ill patients? Transl Med UniSa. 2020;23:68-76. doi: 10.37825/2239-9747.1015. \u003c/li\u003e\n\u003cli\u003eChoi SR, Lee YK, Cho AJ, Park HC, Han CH, Choi MJ, et al. Malnutrition, inflammation, progression of vascular calcification and survival: Inter-relationships in hemodialysis patients. PLoS One. 2019;14(5):e0216415. doi: 10.1371/journal.pone.0216415. \u003c/li\u003e\n\u003cli\u003eLi L, Streja E, Rhee CM, Mehrotra R, Soohoo M, Brunelli SM, et al. Hypomagnesemia and Mortality in Incident Hemodialysis Patients. Am J Kidney Dis. 2015;66(6):1047-55. doi: 10.1053/j.ajkd.2015.05.024. \u003c/li\u003e\n\u003cli\u003eJung J, Jeon-Slaughter H, Nguyen H, Patel J, Sambandam KK, Shastri S, Van Buren PN. Hyperphosphatemia and its relationship with blood pressure, vasoconstriction, and endothelial cell dysfunction in hypertensive hemodialysis patients. BMC Nephrol. 2022;23(1):291. doi: 10.1186/s12882-022-02918-0. \u003c/li\u003e\n\u003cli\u003eCozzolino M, Ciceri P, Galassi A, Mangano M, Carugo S, Capelli I, Cianciolo G. The Key Role of Phosphate on Vascular Calcification. Toxins (Basel). 2019;11(4):213. doi: 10.3390/toxins11040213. \u003c/li\u003e\n\u003cli\u003eSpoendlin J, Paik JM, Tsacogianis T, Kim SC, Schneeweiss S, Desai RJ. Cardiovascular Outcomes of Calcium-Free vs Calcium-Based Phosphate Binders in Patients 65 Years or Older With End-stage Renal Disease Requiring Hemodialysis. JAMA Intern Med. 2019;179(6):741-749. doi: 10.1001/jamainternmed.2019.0045. \u003c/li\u003e\n\u003cli\u003eTsai PH, Chung CH, Chien WC, Chu P. Effects of calcium-containing phosphate binders on cardiovascular events and mortality in predialysis CKD stage 5 patients. PLoS One. 2020;15(10):e0241435. doi: 10.1371/journal.pone.0241435. \u003c/li\u003e\n\u003cli\u003eCunha RSD, Santos AF, Barreto FC, Stinghen AEM. How do Uremic Toxins Affect the Endothelium? Toxins (Basel). 2020;12(6):412. doi: 10.3390/toxins12060412. \u003c/li\u003e\n\u003cli\u003eRodrigues N, Jorge A, Mendes P. 3179 Predictors Of Vascular Access Thrombosis In Maintenance Hemodialysis Patients \u0026ndash; QA, KT/V and Convective Volume. Nephrol Dialys Transplant. 2023;38(Suppl 1):gfad063c_3179. doi: 10.1093/ndt/gfad063c_3179.\u003c/li\u003e\n\u003cli\u003eWasim R, Ansari TM, Ahsan F, Siddiqui MH, Singh A, Shariq M, Parveen S. Pleiotropic Benefits of Statins in Cardiovascular Diseases. Drug Res (Stuttg). 2022;72(9):477-486. doi: 10.1055/a-1873-1978. \u003c/li\u003e\n\u003cli\u003eMartinez L, Duque JC, Escobar LA, Tabbara M, Asif A, Fayad F, Vazquez-Padron RI, Salman LH. Distinct impact of three different statins on arteriovenous fistula outcomes: a retrospective analysis. J Vasc Access. 2016;17(6):471-476. doi: 10.5301/jva.5000612. \u003c/li\u003e\n\u003cli\u003eSuh D, Amendola MF, Reeves M, Wolfe L, Posner M, Davis R. Statins Protect against Thrombosis of Cannulated Radiocephalic Fistulas in Diabetic Patients. Ann Vasc Surg. 2021;75:280-286. doi: 10.1016/j.avsg.2021.01.073. \u003c/li\u003e\n\u003cli\u003eWan Q, Li L, Yang S, Chu F. Impact of Statins on Arteriovenous Fistulas Outcomes: A Meta-Analysis. Ther Apher Dial. 2018;22(1):67-72. doi: 10.1111/1744-9987.12597. \u003c/li\u003e\n\u003cli\u003eStepanova N, Ostapenko T, Marchenko V, Holovanova A, Lysii M, Kucher T, et al. Retrospective analysis of statin use and arteriovenous fistula thrombosis in hemodialysis: Is there a dose-dependent effect? Ukr J Nephrol Dialys. 2025;2(86):24-34. doi: 10.31450/ukrjnd.2(86).2025.03.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-nephrology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnep","sideBox":"Learn more about [BMC Nephrology](http://bmcnephrol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bnep/default.aspx","title":"BMC Nephrology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"hemodialysis, arteriovenous fistula, magnesium, thrombosis, risk factors","lastPublishedDoi":"10.21203/rs.3.rs-7289523/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7289523/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eArteriovenous fistula (AVF) thrombosis is a major cause of vascular access failure in patients undergoing maintenance hemodialysis (HD). Although magnesium has established vascular protective properties, its relationship with AVF thrombosis remains poorly characterized. This study aimed to examine the association between serum magnesium levels and the risk of AVF thrombosis in a retrospective cohort of HD patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis bi-center retrospective cohort study included 408 HD patients treated between January 2020 and May 2025. Baseline serum magnesium was categorized into tertiles: T1 (\u0026lt;\u0026thinsp;0.87 mmol/L), T2 (0.87\u0026ndash;0.97 mmol/L), and T3 (\u0026ge;\u0026thinsp;0.98 mmol/L). The primary outcome was the first clinically confirmed episode of AVF thrombosis. Kaplan\u0026ndash;Meier estimates and Cox proportional hazards models adjusted for demographic, clinical, and dialysis-related variables were used to assess the association. Sensitivity and subgroup analyses were performed to evaluate the robustness of findings.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eOver a median follow-up of 33.5 months, 83 patients (20.3%) experienced AVF thrombosis. Compared to T1, patients in T2 and T3 groups had significantly lower thrombotic risk: HR 0.57 (95% CI: 0.40\u0026ndash;0.82, p\u0026thinsp;=\u0026thinsp;0.002) and HR 0.46 (0.28\u0026ndash;0.77, p\u0026thinsp;=\u0026thinsp;0.003), corresponding to a 1.75- and 2.17-fold higher risk for T1 versus T2 and T3, respectively. Independent predictors of increased AVF thrombosis risk included older age (HR 1.05 per year, 95% CI: 1.02\u0026ndash;1.09), higher serum phosphate (HR 1.11 per 0.1 mmol/L, 95% CI: 1.03\u0026ndash;1.48), and calcium-based phosphate binder use (HR 1.02, 95% CI: 1.003\u0026ndash;1.27). Protective factors included higher dialysis adequacy (spKt/V; HR 0.16, 95% CI: 0.11\u0026ndash;0.35) and statin therapy (HR 0.35, 95% CI: 0.14\u0026ndash;0.86). The association between low magnesium and AVF thrombosis remained robust in sensitivity and subgroup analyses.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eLow serum magnesium (\u0026lt;\u0026thinsp;0.87 mmol/L) is independently associated with a twofold increased risk of AVF thrombosis in HD patients. Magnesium may represent a modifiable target for improving vascular access outcomes, warranting further prospective investigation.\u003c/p\u003e","manuscriptTitle":"Serum Magnesium and Risk of Arteriovenous Fistula Thrombosis in Hemodialysis: A Retrospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-03 16:30:05","doi":"10.21203/rs.3.rs-7289523/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-04T16:36:54+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-21T05:57:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-11T03:19:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"210755359024702919708016527872741300252","date":"2025-10-10T04:02:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"301059452971551610968785526165158565053","date":"2025-10-10T01:57:24+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-23T09:12:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"53514944921793546095927641317077998308","date":"2025-09-23T08:57:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"154234516729570134985879985091726876274","date":"2025-09-22T14:55:29+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-22T09:32:30+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-08T13:00:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-06T12:43:20+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-06T12:42:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nephrology","date":"2025-08-04T09:31:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nephrology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnep","sideBox":"Learn more about [BMC Nephrology](http://bmcnephrol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bnep/default.aspx","title":"BMC Nephrology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d3ef7c91-e403-4695-8184-421337246f61","owner":[],"postedDate":"October 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-15T16:05:36+00:00","versionOfRecord":{"articleIdentity":"rs-7289523","link":"https://doi.org/10.1186/s12882-025-04679-y","journal":{"identity":"bmc-nephrology","isVorOnly":false,"title":"BMC Nephrology"},"publishedOn":"2025-12-10 15:59:21","publishedOnDateReadable":"December 10th, 2025"},"versionCreatedAt":"2025-10-03 16:30:05","video":"","vorDoi":"10.1186/s12882-025-04679-y","vorDoiUrl":"https://doi.org/10.1186/s12882-025-04679-y","workflowStages":[]},"version":"v1","identity":"rs-7289523","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7289523","identity":"rs-7289523","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}