Risk Factors for and a Preliminary Prediction Model of Vascular Calcification in Patients Beginning Hemodialysis

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Vascular calcification (VC) is an important risk factor for cardiovascular events in patients undergoing maintenance hemodialysis (MHD); however, there is limited data on VC-related factors in patients beginning hemodialysis. Thus, this study aimed to determine the risk factors of VC and to establish a prediction model for evaluating VC progression in new patients undergoing hemodialysis. Methods. This study selected 86 patients who initiated in-center MHD between March 2021 and November 2022. Demographic characteristics, medical history, and laboratory data were collected. Coronary artery calcification (CAC) was assessed based on the Agatston vascular score determined via computed tomography. Serum levels of the VC inhibitors fetuin-A was quantified via enzyme-linked immunosorbent assays. Univariate and multivariate regression analyses were conducted to determine the risk factors for VC, and a neural network-based approach was adopted to construct a VC prediction model. Results. The average age of the patients was 56.74 ± 12.79 years, and 65.1% were male. CAC was observed in 72.09% of patients. Age, body mass index, diabetes, the comorbidity index, and the number of coronary artery branches with calcification were positively correlated with the CAC score, whereas plasma fetuin-A levels was negatively correlated. The multivariate logistic regression analysis revealed that age[odds ratio (OR) 1.07, 95%CI: 1.00–1.14], the comorbidity index[OR 1.72, 95%CI: 1.16–2.57], diabetes[OR 3.97, 95%CI: 1.16–13.58] were independent risk factors for CAC; these factors were used to establish a simple scoring model to predict VC risk. Conclusion. Age, the comorbidity index, diabetes were identified as independent risk factors for CAC in patients beginning hemodialysis, and the new VC prediction model based on these factors may help identify VC in patients undergoing MHD, facilitating clinical interventions. Calcium-phosphorus metabolism fetuin-A hemodialysis vascular calcification Figures Figure 1 Figure 2 Introduction Chronic kidney disease (CKD) is a global health epidemic that can lead to anemia and bone disease, and it may increase the risk of infection, accelerate the development or progression of cardiovascular disease, and heighten the risk of death [ 1 ] . In patients with CKD, cardiovascular events are the primary factor responsible for high hospitalization and mortality rates in the later stages of the disease. In a cohort study published in 2011 [ 2 ] , vascular calcification (VC) was considered to be a marker of vascular injury as well as a strong predictor of cardiovascular events. Various domestic and international centers have reported a significantly higher incidence of VC in patients with CKD and in those undergoing maintenance hemodialysis (MHD) compared to that of healthy individuals of the same age, including coronary artery calcification (CAC) and cardiac valve calcification [ 3 ] . VC refers to the pathological deposition of minerals in blood vessels, including various forms of calcium and phosphate [ 4 ] , with endometrial calcification and medial calcification being two examples [ 5 ] ; CKD in particular is characterized by medial calcification. Over time, studies have confirmed that many molecular substances play a role in the development of VC, and an imbalance between factors that inhibit and promote calcification is considered to be one of the main mechanisms that drive VC development [ 6 ] . Fetuin-A, a negatively charged polymer protein produced by the liver, has a higher inhibitory effect on VC, which is mainly achieved through competitive inhibition of phosphate deposition, thereby reducing calcium ion levels, affecting the bone morphogenic protein (BMP) signaling pathway, reducing osteoblast generation [ 7 ] , and inhibiting VSMC apoptosis [ 8 ][ 9 ] . In patients with CKD, the synthesis and secretion of fetuin-A are inhibited, negatively impacting its ability to combat VC. In clinical practice, the early assessment and diagnosis of VC are important to prevent the progression of calcification. The coronary artery calcification score (CACS) is an important tool for assessing the risk of coronary artery disease, with the Agatston scoring system being the most widely used [ 10 ] . However, because of the adverse effects of radiation exposure from computed tomography (CT) examinations and its singular use in determining the degree of VC based on CT scores, many researchers are committed to identifying suitable circulating biomarkers that can be used to evaluate VC. The aims of the present study were to evaluate the degree of VC in patients beginning hemodialysis for the first time, to screen risk factors for VC, and to establish a preliminary prediction model of VC to develop useful tools for early prognostic assessment and clinical interventions in patients undergoing hemodialysis. Materials and Methods Study design and participants This study included patients with end-stage kidney disease who received treatment at the Blood Purification Center of Second Xiangya Hospital of Central South University between March 2021 and November 2022. All participants were at least 18 years of age and had been undergoing regular hemodialysis for less than 3 months. The reporting of this study conforms to Strengthening and Reporting of Observational Studies in Epidemiology (STROBE) guidelines, and the protocol was approved by the Ethics Committee of Second Xiangya Hospital, Central South University (2021C067). All patients provided written informed consent after obtaining detailed information about the study protocol. Definition of VC Patients underwent coronary artery multi-detector computed tomography (MDCT) imaging to determine the CACS at the time of initiation of regular hemodialysis. The Agatston score was calculated by two fixed radiologists. The scanned area of each calcification point was multiplied by the density factor to obtain a single calcification score, and the total CACS was calculated as the sum of all the calcification point scores. VC was defined as the presence of calcification in any coronary artery branch (CACS > 0). Risk factors and covariates of interest All risk factors and covariates were selected based on literature reviews and clinical knowledge. Patients’ baseline demographic and clinical characteristics, including age, sex, body mass index (BMI), blood pressure, smoking status, and history of diabetes were collected, as were laboratory values, including the estimated glomerular filtration rate (eGFR) and the levels of hemoglobin (Hb), serum albumin (ALB), serum creatinine (Scr), uric acid (UA), serum calcium (Ca), serum phosphorus (P), C-reactive protein (CRP), whole parathyroid hormone (iPTH), total cholesterol (TC), triglycerides (TG), low-density lipoprotein (LDL), and 25-hydroxyvitamin. Blood samples were collected to quantify the levels of fetuin-A via enzyme-linked immunosorbent assays (Mlbio, Shanghai, China). The aforementioned indicators were assessed before patients initiated regular dialysis treatment in our hospital. The total serum calcium level was adjusted if the concentration of serum ALB was < 4 g/dL to better reflect the free calcium level as follows: corrected total calcium (mmol/L) = total calcium (mmol/L) + 0.02 × [40-serum ALB (g/L)]. The Charlson Comorbidity Index (CCI) was calculated for each patient using the medical data recorded at the time of MHD initiation. Statistical analysis Measurement data with a normal distribution were expressed as the mean ± standard deviation (± s), and intergroup comparisons were performed using independent samples t-tests or multivariate analysis of variance (MANOVA). Measurement data with a skewed distribution were expressed as the median (M) and interquartile range, and intergroup comparisons were performed using the Kruskal-Wallis test or Mann-Whitney U test. Count data were expressed as the frequency or quantile, and the chi square test was used for intergroup comparisons. Spearman correlation analysis was conducted to assess correlations between relevant clinical indicators and the CACS. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the predictive value of the indicators. Univariate and multivariate logistic regression analyses were conducted to screen for independent risk factors. All statistical analyses were performed using SPSS software (version 25.0), and P values ≤ 0.05 indicated statistical significance. A prediction model was constructed using a neural network-based method, assessed using Python software. The precision and recall rate were used as indicators to assess the effectiveness of the model. Results Participant characteristics A total of 110 patients received regular hemodialysis at our blood purification center between March 2021 and November 2022. After excluding patients who had been receiving hemodialysis for more than 3 months, those under 18 years old, individuals with malignant tumors, and patients with incomplete data, 86 participants were enrolled in the study. Of these 86 patients, there were 56 males (65.1%) and 30 females (34.9%), with a sex ratio of 1.87:1. The average age was 56.74 ± 12.79 years. In terms of etiology, 35 patients (40.7%) had diabetic kidney disease (DKD), 31 (36.0%) had primary glomerular disease, 14 (16.3%) had hypertensive nephropathy, and six (7.0%) had other primary diseases. The median of CACS was 51.25 score. A summary of patients’ demographic and clinical characteristics is provided in Table 1. Table 1. Participant characteristics Classification Value（%/Q 1 ，Q 3 ） Age 56.74±12.79 Male（N，%） 56（65.1%） BMI（Kg/m 2 ） 22.77（20.72, 26.39） Diabetes（N，%） 35（40.7%） Smoke（N，%） 21（24.7%） CCI 4.67±1.98 Systolic pressure（mmHg） 148（135, 158.25） Diastolic pressure（mmHg） 85.76±15.02 Hb（g/L） 80（75.75, 92.25） ALB（g/L） 34.1（31.19, 37.65） Scr（umol/L） 842.5（633.4, 1150.98） eGFR（ml/min/1.73m 2 ） 8.08±2.83 UA（umol/L） 434.1（348.80, 510.00） Ca（mmol/L） 1.95±0.25 corrected Ca（mmol/L） 2.09（1.97, 2.22） P（mmol/L） 1.90±0.68 CRP（mg/L） 11.20±19.41 TC（mmol/L） 3.80±1.13 TG（mmol/L） 1.45±0.93 LDL（mmol/L） 2.25±0.98 iPTH（pg/mL） 298.56±242.85 25 hydroxyvitamin D（nmol/L） 43.72±23.89 Fetuin-A（pg/mL） 816.77（594.08, 949.36） CACS 51.25（0, 223.3） Number of CAC branches 1.76±1.42 Abbreviations:BMI, body mass index; CCI, Charlson Comorbidity Index ; Hb, hemoglobin ; ALB, serum albumin; Scr, serum creatinine; eGFR, estimated glomerular filtration rate; iPTH, intact parathyroid hormone; TC, total Cholesterol; TG, Triglyceride; LDL,low density lipoprotein-cholesterol; CRP, C-reactive protein; CACS, coronary artery calcification score. Comparison of patients with and without calcification Among the 86 patients, 62 developed CAC; thus, the calcification rate was 72.09%. According to the coronary MDCT results, the participants were divided into a non-calcification group (CACS=0) and a calcification group (CACS>0) (Table 2). The median of CACS in calcification group was 137.2 score. There were significant differences between the two groups in terms of the age, BMI, diabetes, CCI, and fetuin-A levels, and the number of branches exhibiting CAC ( P <0.05). There were no intergroup differences in terms of the sex, smoking status, blood pressure, eGFR, or the levels of Hb, ALB, Scr, UA, Ca, corrected Ca, P, CRP, TG, TC, LDL, iPTH, and 25 hydroxyvitamin D ( P >0.05). The imaging characteristics of the different degrees of CAC presented at the most prominent level are shown in Figure 1. Table 2 Comparison of patients with and without calcification Variable Non-calcification group (N=24) Calcification group (N=62) F/X 2 /Z P Age 47.88±15.61 60.18±11.67 -4.42 ＜0.001 Male（N，%） 12（50%） 44（71%） 3.35 0.081 BMI（Kg/m 2 ） 21.12（19.06, 23.52） 24.15±3.64 -2.74 0.006 Diabetes（N，%） 4（16.7%） 31（50%） 7.97 0.005 Smoke（N，%） 3（12.5%） 19（30.6%） 2.68 0.102 CCI 3（2, 4.75） 5（1,7） -3.88 ＜0.001 Systolic pressure（mmHg） 144.71±23.46 150.45±19.0 -1.18 0.243 Diastolic pressure（mmHg） 89.38±15.61 84.35±14.67 1.40 0.166 Hb（g/L） 78.54±15.92 83.06±16.2 -1.17 0.247 ALB（g/L） 34.08±5.49 34.0±4.52 0.07 0.946 Scr（umol/L） 971.84±288.44 846.67±336.02 1.61 0.111 eGFR（ml/min/1.73m 2 ） 7.25±1.51 7.47（6.21, 9.64） -1.29 0.197 UA（umol/L） 477.33±144.19 424.05±120.72 1.74 0.086 Ca（mmol/L） 2.02（1.85, 2.20） 1.96（1.86, 2.07） -1.14 0.256 Corrected Ca（mmol/L） 2.17（1.98, 2.25） 2.07（1.97,2.22） -1.26 0.207 P（mmol/L） 1.81±0.56 1.8（1.47, 2.21） -0.23 0.817 CRP（mg/L） 4.83（1.4, 7.9） 5.15（2.54, 10.67） -1.18 0.236 TC（mmol/L） 3.86±1.28 3.59（3.08, 4.34） -0.01 0.996 TG（mmol/L） 1.45±0.53 1.18（0.81,1.68） -1.22 0.223 LDL（mmol/L） 2.09（1.55, 2.93） 1.99（1.62, 2.51） -0.26 0.160 iPTH（pg/mL） 291.2（138.5,513.7） 216.3（91.6,366.5） -1.41 0.16 25 hydroxyvitamin D（nmol/L） 38.5（27, 50.75） 35.5（28, 57.5） -0.03 0.977 Fetuin-A（pg/mL） 953.0（923.9, 981.4） 661.0（528.4, 887.5） -5.05 ＜0.001 CACS 0 137.2（31.5, 451.35） -7.24 ＜0.001 Number of CAC branches 0 2（1, 3） -6.78 ＜0.001 Abbreviations:BMI, body mass index; CCI, Charlson Comorbidity Index ; Hb, hemoglobin ; ALB, serum albumin; Scr, serum creatinine; eGFR, estimated glomerular filtration rate; iPTH, intact parathyroid hormone; TC, total Cholesterol; TG, Triglyceride; LDL,low density lipoprotein-cholesterol; CRP, C-reactive protein; CACS, coronary artery calcification score. Analysis of risk factors affecting VC To screen for the risk factors related to VC, univariate and multivariate logistic regression analyses were conducted. The multivariate logistic analysis revealed that age (odds ratio (OR)=1.069, 95% confidence interval (CI): 1.001–1.142, P =0.006), diabetes (OR=3.972, 95% CI: 1.162–13.58, P =0.028), and the CCI (OR=1.722, 95% CI: 1.155–2.568, P =0.008) were independent risk factors for VC in patients beginning hemodialysis (Table 3). The multivariate logistic analysis was presented by adjusted the BMI, Scr, P, number of CAC branches and serum levels of Fetuin-A. Table 3. logistic regression analysis of VC in participant Variable Univariate logistic Multivariate logistic OR 95%CI P OR 95%CI P Age 1.09 1.04-1.14 ＜0.001 1.069 1.001-1.142 0.006 Diabetes 1.609 1.53-16.3 0.008 3.972 1.162-13.58 0.028 CCI 1.807 1.31-2.49 ＜0.001 1.722 1.155-2.568 0.008 Abbreviations: CCI, Charlson Comorbidity Index. Subgroup analysis of patients with DKD The aforementioned analysis confirmed that diabetes was an independent risk factor for VC. A subgroup analysis of patients with DKD was performed to further explore the effect of diabetes on VC (Table 4). Compared with the data from the non-diabetes group, those in the DKD group exhibited higher CACS, a greater number of branches affected by CAC, and lower levels of fetuin-A ( P <0.05). The eGFR of the patients with diabetes was higher than that of the patients without diabetes at the time of dialysis initiation, suggesting that the patients with DKD entered dialysis earlier. In addition, there were statistically significant differences between the two groups in terms of the age, BMI, CCI, systolic blood pressure, and ALB, Scr, and iPTH levels ( P <0.05). Table 4 Comparison of DKD and non-diabetes group Variable DKD group (N=35) Non- diabetes group (N=51) F/X 2 /Z P Age 60.37± 10.25 54.25± 13.82 2.20 0.028 BMI（Kg/m 2 ） 25. 18±3.73 21.5 (19.8, 25.7) -3.37 0.001 CCI 6.2± 1.35 3 (2,5) -5.97 ＜0.001 Systolic pressure（mmHg） 152 (143, 160) 144. 1± 19.94 -2.22 0.027 Diastolic pressure（mmHg） 82.91± 12.49 86 (76, 94) - 1. 19 0.24 Hb（g/L） 84.03± 16.21 80.27± 16. 1 1.06 0.293 ALB（g/L） 32.9±4.89 35.2 (32.9, 38. 1) -2.22 0.026 Scr（umol/L） 754.7±251.9 968.7±345.4 -3.33 0.001 eGFR（ml/min/1.73m 2 ） 8.53(7.07, 9.79) 6.9 (5.88, 8.3) -2.89 0.004 UA（umol/L） 443. 17± 124.89 435.99± 133.04 0.08 0.252 Ca（mmol/L） 1.94(1.86, 2.06) 1.98 (1.84, 2. 18) -0.85 0.39 Corrected Ca（mmol/L） 2.08±0.03 2. 10 (1.97, 2.22) - 1.07 0.86 P（mmol/L） 1.78±0.37 1.8 (1.49, 2.35) -0.76 0.45 Ca*P（mmol 2 /L 2 ） 3.38（2.78, 3.8） 3.75±1.34 -1.30 0.2 CRP（mg/L） 4.72 (2.37,7.33) 5.73 (2.05, 11.8) -0.95 0.34 TC（mmol/L） 3.72± 1.2 3.61 (3.08, 4.38) -0.63 0.53 TG（mmol/L） 1.31(0.78, 2. 14) 1. 15 (0.86, 1.67) -0.65 0.52 LDL（mmol/L） 1.89 (1.54, 2.6) 2.07 (1.68, 3.01) -0.74 0.46 iPTH（pg/mL） 163.8（101.6, 283.9） 297（170, 439.6） -2.37 0.018 25 hydroxyvitamin D（nmol/L） 34 (27, 52) 38 (29, 57) -0.72 0.47 Fetuin-A（pg/mL） 657.8±246.0 885.4 (690.5, 974.9) -3.02 0.002 CACS 155.3 (32.6, 496) 6 (0, 101.8) -3.53 ＜0.001 Number of CAC branches 3 (2, 3) 1 (0, 2) - 1.98 0.047 Abbreviations:BMI, body mass index; CCI, Charlson Comorbidity Index ; Hb, hemoglobin ; ALB, serum albumin; Scr, serum creatinine; eGFR, estimated glomerular filtration rate; iPTH, intact parathyroid hormone; TC, total Cholesterol; TG, Triglyceride; LDL,low density lipoprotein-cholesterol; CRP, C-reactive protein; CACS, coronary artery calcification score. Analysis of correlations between clinical indicators and CACS The Spearman correlation analysis (Table 5) of the CACS and clinical indicators revealed that the age, BMI, diabetes, CCI, and the number of branches affected by CAC were positively correlated with the CACS. The CACS was negatively correlated with fetuin-A(r=-0.876, P <0.001) levels, with the former exhibiting a stronger negative correlation with the CACS than the latter. Table 5. Correlation analysis between CACS and related clinical indicators Variable r P Age 0.381 ＜0.001 BMI（Kg/m 2 ） 0.274 0.011 CCI 0.368 ＜0.001 Diabetes 0.383 ＜0.001 Number of CAC branches 0.590 ＜0.001 Fetuin-A（pg/mL） -0.876 ＜0.001 Abbreviations: CCI, Charlson Comorbidity Index. Establishment of a preliminary prediction model for VC in patients beginning MHD The previous analyses revealed that the age, BMI, diabetes, CCI, and the levels of the calcification inhibitory factors fetuin-A significantly differed between the patients with and without VC. Those factors were selected as predictive factors for the model, as were iPTH and P levels based on clinical experience (Table 6). The 86 patients undergoing MHD were randomly divided into a derivation queue (51 cases, 60%) and a validation queue (35 cases, 40%) based on the neural network binary classification method. The corresponding integrals were assigned based on the contribution of each factor in the neural network; the integrals were adjusted based on clinical experience to generate the model (Table 6). Ta ble 6. Corresponding integrals of 8 factors in the prediction model Predictive factor Classification Point Age ≤45 0 45~65 1 ≥65 2 BMI（Kg/m 2 ） ＜24 0 ≥24 1 Diabetes Yes 2 CCI P（mmol/L） iPTH（pg/mL） Fetuin-A（pg/mL） No ＜5 ≥5 ＜1.78 ≥1.78 ＜600 ≥600 ＜700 ≥700 0 1 2 0 2 0 1 1 0 Risk model formula: calcification risk score=age+BMI+diabetes+CCI+P+iPTH+Fetuin-A The effectiveness of the prediction model As shown in Table 7, the area under the curve (AUC) (0.87,0.84) of the model was significant, with consistent sensitivity (85%, 84%) and specificity (78%, 78%) in both the derivation and validation queues, respectively. As shown in Figure 2, Through the precision, accuracy, and recall rate in the neural network indicated that the model's predictive performance was reliable. The cut-off point (6.5 points) was subsequently determined according to the best Youden's J statistic (0.62 points). Patients with a score below 6.5 points were considered to be at low risk of VC, whereas those with a score exceeding 6.5 points were considered to be at high risk of developing VC (Table 7). Table 7. Predictive performance of queues in neural networks( model 1) Jorden index Cut off point Sensitivity（%） Specificity（%） AUC Derivation queue(N=51） 0.62 6.5 85 78 0.87 Validation queue(N=35) 0.62 6.5 84 78 0.84 Due to the fact that fetuin-A levels are not routinely quantified in clinical practice, the scoring system was modified to exclude the fetuin-A levels, and the new model was retested. As shown in Table 8, the AUC (0.74,0.74) of the modified model remained significant, with stable sensitivity (72%, 83%) and specificity (70%, 64%). In the new model, the cut-off was 4.5 points according to the Youden's J statistic. The modified model is likely to be more convenient for clinical use. Table 8. Predictive performance of queues in new model excluding Fetuin-A(model 2) Jorden index Cut off point Sensitivity（%） Specificity（%） AUC Derivation queue(N=51） 0.48 4.5 72 70 0.74 Validation queue(N=35) 0.48 4.5 83 64 0.74 Discussion In the present study, the CAC rate among patients who had recently initiated MHD was 72.09%, which was higher than the 60% rate in patients with CKD that was reported in a meta-analysis of 47 studies [11] . That meta-analysis showed that as the calcification score increases, the incidence of cardiovascular events and all-cause hospitalization and mortality rates will increase by 2–4 times; in the present study, the levels of the VC inhibitory factors fetuin-A were significantly lower in the group with calcification than in the group without it, and they were negatively correlated with the CACS. The multivariate also revealed that age, the CCI, and diabetes were independent risk factors for VC in this patient population. Previous studies have shown that an imbalance between factors that promote and inhibit calcification is one of the predominant mechanisms driving VC [6] . In this study, fetuin-A levels also differed significantly between groups and were negatively correlated with the CACS. Fetuin-A is a serum glycoprotein that is mainly secreted by the liver. It is a biologically diverse substance that participates in many processes, including bone and lipid metabolism, and it is involved in central nervous system disorders. Fetuin-A can directly bind with calcium and phosphorus in the circulation, forming low-activity complexes that prevent calcification progression [12] . These substances are called fetal globulin mineral complexes or calcium protein particles, and they prevent premature and rapid ectopic calcification at the physiological level [13] . Fetuin-A binds to calcium ions to form stable colloidal calcium protein particles, but it can also bind to calcium phosphate crystals, thereby delaying mineral deposition [14] . Kettler et al. reported lower fetuin-A levels in patients receiving hemodialysis or peritoneal dialysis than in healthy people [15] . A multicenter, prospective, cohort study involving 987 patients undergoing dialysis demonstrated a link between fetuin-A levels and mortality [16] . However, another study of 93 patients receiving dialysis found no significant correlation between fetuin-A levels and CAC [17] . Thus, more experimental data is required to better assess whether fetuin-A can be used to predict VC in patients undergoing MHD. DKD has become the main cause of end-stage renal disease, and some studies have shown that in the past 20 years, diabetic angiopathy has increased the global mortality rate to 37.9% in patients with diabetes [18] . In this study, 40.7% patients had DKD, and the subgroup analysis revealed a higher incidence and greater severity of VC in patients with diabetes, with higher numbers of affected vascular branches, which was consistent with previous findings. VC in patients with DKD is a complex process with many causal factors, including advanced glycation end product accumulation [19-20] and an imbalance between nitric oxide bioavailability and reactive oxygen species accumulation, leading to endothelial dysfunction [21] . These processes are mediated by multiple regulatory factors and pathways, including the BMP2/SMADs, RAGE/JAK2, and Wnt/β-Catenin [22] . Diabetes is known to damage blood vessels; therefore, it is beneficial for patients undergoing hemodialysis to maintain stability and control disease progression. The age-corrected CCI is a scoring system proposed by Charlson et al. that considers age and multiple systemic comorbidities, and it is widely used to evaluate patients with tumors and those in the intensive care unit [23] . The CCI was shown to be correlated with and could consistently predict other survival analysis indicators such as the Kaplan-Feinstein Index, and its sensitivity has been repeatedly confirmed, facilitating a comprehensive assessment of the body's state before initiating disease treatment [24] . Because patients undergoing MHD patients always experience many complications, especially cardiac and cerebrovascular disease, which may be related to VC, it was included as a disease status indicator for patients beginning hemodialysis in this study. The CCI was one of the risk factors for CAC and could be used to predict in this patient population. Neural networks can predict the development and prognosis of diseases, helping doctors formulate more effective treatment plans [25] . Many scholars have utilized neural networks or improved neural network structures to enhance the predictive accuracy of classifications. For example, algorithms based on neural networks have been used to classify benign and malignant solitary pulmonary nodules. The present study adopted such an approach to construct and validate a VC prediction model. The precision, accuracy, and recall rate in the neural network indicated that the model's predictive performance was reliable. A score greater than 6.5 indicated a high risk of VC. Model 2 excluded fetuin-A levels, and scores greater than 4.5 indicated a high risk of VC. Model 2 will be more convenient for clinical use when fetuin-A levels cannot be quantified. It is worth mentioning that the level of iPTH in non-calcification group was higher than calcification group. However, iPTH was one of the important indicators for evaluating CKD-MBD in clinical practice.In response to this result, we considered that this study was based on real world research,so that iPTH was not significant in this research. Ultimately, this study showed that VC is affected by many factors in patients initiating MHD, including age, the CCI, diabetes. The prediction model established using neural networks in this study has the potential to become a risk stratification tool for VC in patients undergoing MHD; however, this scoring system must be validated in additional cohorts. Limitation This study has some limitations. First, it is a single-center study with a limited sample size, potential bias, and the results must be validated through multi-center, comprehensive studies with more participants. Then there were some factors that had not been considered, such as excessive calcium intake, dialysate calcium, hypomagnesemia, calcitriol, using antiplatelet drugs et al. Because of FGF 23 was a acknowledged factor related to VC, so that it wasn’t selected to the study. Finally, the predictive model may not have incorporated every useful indicators, affecting its accuracy. Declarations Acknowledgements We thank all patients and dialysis personnel who contributed to this study. Funding This work was supported by Grants from the National Natural Science Foundation of China (No. 81770730) and the Natural Science Fund of Changsha (No. kq2014235). Ethics This trial has been approved by the second xiangya hospital of central south university ethics committee, All participants have signed informed consent forms before entering this trial. The ethical code was LYF2022227. Data Availability Statement Data is not available due to ethical reasons. Further enquiries can be directed to the corresponding author. Author Contribution HX and FY designed the research.JS and YY collected the data.HX and FY analyzed the data and drafted the manuscript. CS and CW provided help during the research. All author have read and approved the manuscript. Conflict of interest statement None declared. The results presented in this paper have not been published previously in whole or part. Approval of the research protocol This trial has been approved by the second xiangya hospital of central south university ethics committee. Informed consent All participants have signed informed consent forms before entering this trial. The IRB number LYF2022227 Animal studies N/A References Musgrove J, Wolf M. Regulation and Effects of FGF23 in Chronic Kidney Disease[J]. Annual Review of Physiology, 2020, 82: 365–390. Russo D, Corrao S, Battagla Y, et al. Progression of coronary artery calcification and cardiac events in patients with chronic renal disease not receiving dialysis[J]. Kidney International, 2011, 80(1): 112–118. Bansal N. Evolution of Cardiovascular Disease During the Transition to End-Stage Renal Disease[J]. Seminars in Nephrology, 2017, 37(2): 120–131. Sigrist M K, Taal M W, Bungay P, et al. Progressive vascular calcification over 2 years is associated with arterial stiffening and increased mortality in patients with stages 4 and 5 chronic kidney disease[J]. Clinical journal of the American Society of Nephrology: CJASN, 2007, 2(6): 1241–1248. Vervloet M, Cozzolino M. Vascular calcification in chronic kidney disease: different bricks in the wall?[J]. Kidney International, 2017, 91(4): 808–817. Mizuiri S, Nishizawa Y, Yamashita K, et al. Relationship of matrix Gla protein and vitamin K with vascular calcification in hemodialysis patients[J]. Renal Fetuin-Ailure, 2019, 41(1): 770–777. Ombrellino M, Wang H, Yang H, et al. Fetuin, a negative acute phase protein, attenuates TNF synthesis and the innate inflammatory response to carrageenan[J]. Shock (Augusta, Ga.), 2001, 15(3): 181–185. Dautova Y, Kozlova D, Skepper J N, et al. Fetuin-A and albumin alter cytotoxic effects of calcium phosphate nanoparticles on human vascular smooth muscle cells[J]. PloS One, 2014, 9(5): e97565. Kapustin A N, Chatrou M L L, Drozdov I, et al. Vascular smooth muscle cell calcification is mediated by regulated exosome seScretion[J]. Circulation Research, 2015, 116(8): 1312–1323. Lehker A, Mukherjee D. Coronary Calcium Risk Score and Cardiovascular Risk[J]. Current Vascular Pharmacology, 2021, 19(3): 280–284. Wang X R, Zhang J J, Xu X X, et al. Prevalence of coronary artery calcification and its association with mortality, cardiovascular events in patients with chronic kidney disease: a systematic review and meta-analysis[J]. Renal Fetuin-Ailure, 2019, 41(1): 244–256. Heiss A, Duchesne A, Denecke B, et al. Structural basis of calcification inhibition by alpha 2-HS glycoprotein/fetuin-A. Formation of colloidal calciprotein particles[J]. The Journal of Biological Chemistry, 2003, 278(15): 13333–13341. Mori K, Emoto M, Inaba M. Fetuin-A and the cardiovascular system[J]. Advances in Clinical Chemistry, 2012, 56: 175–195. Cai M M X, Smithe R, Holt S G. The role of fetuin-A in mineral trafficking and deposition[J]. BoneKEy Reports, 2015, 4: 672. Ketteler M, Bongartz P, Westenfeld R, et al. Association of low fetuin-A (AHSG) concentrations in serum with cardiovascular mortality in patients on dialysis: a Scross-sectional study[J]. Lancet (London, England), 2003, 361(9360): 827–833. Hermans M M H, Brandenburg V, Ketteler M, et al. Association of serum fetuin-A levels with mortality in dialysis patients[J]. Kidney International, 2007, 72(2): 202–207. Ulutas O, Taskapan M C, Dogan A, et al. Vascular calcification is not related to serum fetuin-A and osteopontin levels in hemodialysis patients[J]. International Urology and Nephrology, 2018, 50(1): 137–142. Ling W, Huang Y, Huang Y M, et al. Global trend of diabetes mortality attributed to vascular complications, 2000–2016[J]. Cardiovascular Diabetology, 2020, 19(1): 182. Forbes J M, Cooper M E. Mechanisms of diabetic complications[J]. Physiological Reviews, 2013, 93(1): 137–188. Zhao J, Randive R, Stewart J A. Molecular mechanisms of AGE/RAGE-mediated fibrosis in the diabetic heart[J]. World Journal of Diabetes, 2014, 5(6): 860–867. Baktiroglu S, Yanar F, Ozata I H, et al. Arterial disease and vascular access in diabetic patients[J]. The Journal of Vascular Access, 2016, 17 Suppl 1: S69-71. Brodeur M R, Bouvet C, Bouchard S, et al. Reduction of advanced-glycation end products levels and inhibition of RAGE signaling deScreases rat vascular calcification induced by diabetes[J]. PloS One, 2014, 9(1): e85922. Charlson M E, Pompei P, Ales K L, et al. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation[J]. Journal of Chronic Diseases, 1987, 40(5): 373–383. Charlson M E, Carrozzino D, Guidi J, et al. Charlson Comorbidity Index: A Scritical Review of Clinimetric Properties[J]. Psychotherapy and Psychosomatics, 2022, 91(1): 8–35. Baxt W G. Application of artificial neural networks to clinical medicine[J]. Lancet (London, England), 1995, 346(8983): 1135–1138. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 07 Apr, 2025 Read the published version in European Journal of Medical Research → Version 1 posted Editorial decision: Revision requested 15 Feb, 2025 Reviews received at journal 15 Feb, 2025 Reviewers agreed at journal 07 Feb, 2025 Reviews received at journal 11 Jan, 2025 Reviewers agreed at journal 10 Jan, 2025 Reviewers invited by journal 08 Jan, 2025 Editor assigned by journal 06 Jan, 2025 Submission checks completed at journal 06 Jan, 2025 First submitted to journal 04 Jan, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5762835","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":398334094,"identity":"53f52d91-8df7-4516-a63d-5a81c7928bc1","order_by":0,"name":"Hao Xiong","email":"","orcid":"","institution":"the Second Xiangya Hospital of Central South University","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Xiong","suffix":""},{"id":398334095,"identity":"4ed50f2a-6a99-4ce8-ae7e-52e4689e1fa3","order_by":1,"name":"Cuifang Sun","email":"","orcid":"","institution":"the Second Xiangya Hospital of Central South University","correspondingAuthor":false,"prefix":"","firstName":"Cuifang","middleName":"","lastName":"Sun","suffix":""},{"id":398334096,"identity":"888a2e58-8eb5-4cf4-be5d-7a1d58251993","order_by":2,"name":"Jie Song","email":"","orcid":"","institution":"the Second Xiangya Hospital of Central South University","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Song","suffix":""},{"id":398334097,"identity":"8ba65089-edb0-4c87-a544-ac61273f1e5d","order_by":3,"name":"Yan Yu","email":"","orcid":"","institution":"the Second Xiangya Hospital of Central South University","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Yu","suffix":""},{"id":398334098,"identity":"a05cb785-9623-4db2-9037-fa8d4b5c5dee","order_by":4,"name":"Chang Wang","email":"","orcid":"","institution":"the Second Xiangya Hospital of Central South University","correspondingAuthor":false,"prefix":"","firstName":"Chang","middleName":"","lastName":"Wang","suffix":""},{"id":398334099,"identity":"520e99d8-4285-4195-8355-c10c6567ca11","order_by":5,"name":"Fang Yuan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYLCCCiDmhzCZidRyBoglG0jWYnCAWC0GN5KfPThQc8du8/HjzyQYKqwTG9jPHiCgJc3c4MCxZ8nbzuSYSTCcSU9s4MlLIKAlwUz6A9vhZLMbPGwSjG2HExskeAwIaEn/JnHg3+Fk4xnszyQY/xGlBeieg22H7QwkGMwkGBuI0CJ55k2ZxMG+wwkSZ3KMLRKOpRu38eTg18J3PH2bxIFvh+35248/vPGhxlq2n/0Mfi0KByB0YgOITABiNrzqgUC+AULbE1I4CkbBKBgFIxgAANT4SegHW0wkAAAAAElFTkSuQmCC","orcid":"","institution":"the Second Xiangya Hospital of Central South University","correspondingAuthor":true,"prefix":"","firstName":"Fang","middleName":"","lastName":"Yuan","suffix":""}],"badges":[],"createdAt":"2025-01-04 10:08:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5762835/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5762835/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40001-025-02523-5","type":"published","date":"2025-04-07T16:04:49+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":73316656,"identity":"2262393f-4ec9-4042-962c-b8bd799da37a","added_by":"auto","created_at":"2025-01-08 20:07:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":292326,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMDCT imaging characteristics of different degrees of CAC. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e: shows the CT image of a patient with CACS=0, indicating no coronary calcification; \u003cstrong\u003eB:\u003c/strong\u003e CACS=145.3 points, high-density calcification shadows can be observed; \u003cstrong\u003eC:\u003c/strong\u003eCACS=901.2 points, indicating calcification in multiple coronary arteries; \u003cstrong\u003eD:\u003c/strong\u003eCACS=2842.5 points, which was the highest CACS patient in this study. Large areas of coronary calcification are visible, indicating poor cardiovascular prognosis may occur in the later stage. Red arrow, imaging of calcified vascular)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5762835/v1/6a61867b66f81b250305acf7.png"},{"id":73316655,"identity":"6c044818-2abb-4564-9289-9cd34f5cd009","added_by":"auto","created_at":"2025-01-08 20:07:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":102030,"visible":true,"origin":"","legend":"\u003cp\u003ePrecision and Recall curves for derivation and validation queues\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5762835/v1/7bac24cf149be62b00e91fc5.png"},{"id":80558170,"identity":"3df28466-fb4c-4d39-97ca-1c6fd8a6430a","added_by":"auto","created_at":"2025-04-14 16:09:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1292244,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5762835/v1/0d3da114-f39e-4b98-8a11-e742b6d5bbd0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Risk Factors for and a Preliminary Prediction Model of Vascular Calcification in Patients Beginning Hemodialysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChronic kidney disease (CKD) is a global health epidemic that can lead to anemia and bone disease, and it may increase the risk of infection, accelerate the development or progression of cardiovascular disease, and heighten the risk of death\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. In patients with CKD, cardiovascular events are the primary factor responsible for high hospitalization and mortality rates in the later stages of the disease. In a cohort study published in 2011\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e, vascular calcification (VC) was considered to be a marker of vascular injury as well as a strong predictor of cardiovascular events. Various domestic and international centers have reported a significantly higher incidence of VC in patients with CKD and in those undergoing maintenance hemodialysis (MHD) compared to that of healthy individuals of the same age, including coronary artery calcification (CAC) and cardiac valve calcification\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eVC refers to the pathological deposition of minerals in blood vessels, including various forms of calcium and phosphate\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e, with endometrial calcification and medial calcification being two examples\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e; CKD in particular is characterized by medial calcification. Over time, studies have confirmed that many molecular substances play a role in the development of VC, and an imbalance between factors that inhibit and promote calcification is considered to be one of the main mechanisms that drive VC development\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFetuin-A, a negatively charged polymer protein produced by the liver, has a higher inhibitory effect on VC, which is mainly achieved through competitive inhibition of phosphate deposition, thereby reducing calcium ion levels, affecting the bone morphogenic protein (BMP) signaling pathway, reducing osteoblast generation\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e, and inhibiting VSMC apoptosis\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e][\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. In patients with CKD, the synthesis and secretion of fetuin-A are inhibited, negatively impacting its ability to combat VC.\u003c/p\u003e \u003cp\u003eIn clinical practice, the early assessment and diagnosis of VC are important to prevent the progression of calcification. The coronary artery calcification score (CACS) is an important tool for assessing the risk of coronary artery disease, with the Agatston scoring system being the most widely used\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. However, because of the adverse effects of radiation exposure from computed tomography (CT) examinations and its singular use in determining the degree of VC based on CT scores, many researchers are committed to identifying suitable circulating biomarkers that can be used to evaluate VC.\u003c/p\u003e \u003cp\u003eThe aims of the present study were to evaluate the degree of VC in patients beginning hemodialysis for the first time, to screen risk factors for VC, and to establish a preliminary prediction model of VC to develop useful tools for early prognostic assessment and clinical interventions in patients undergoing hemodialysis.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eThis study included patients with end-stage kidney disease who received treatment at the Blood Purification Center of Second Xiangya Hospital of Central South University between March 2021 and November 2022. All participants were at least 18 years of age and had been undergoing regular hemodialysis for less than 3 months. The reporting of this study conforms to Strengthening and Reporting of Observational Studies in Epidemiology (STROBE) guidelines, and the protocol was approved by the Ethics Committee of Second Xiangya Hospital, Central South University (2021C067). All patients provided written informed consent after obtaining detailed information about the study protocol.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDefinition of VC\u003c/h3\u003e\n\u003cp\u003ePatients underwent coronary artery multi-detector computed tomography (MDCT) imaging to determine the CACS at the time of initiation of regular hemodialysis. The Agatston score was calculated by two fixed radiologists. The scanned area of each calcification point was multiplied by the density factor to obtain a single calcification score, and the total CACS was calculated as the sum of all the calcification point scores. VC was defined as the presence of calcification in any coronary artery branch (CACS\u0026thinsp;\u0026gt;\u0026thinsp;0).\u003c/p\u003e\n\u003ch3\u003eRisk factors and covariates of interest\u003c/h3\u003e\n\u003cp\u003eAll risk factors and covariates were selected based on literature reviews and clinical knowledge. Patients\u0026rsquo; baseline demographic and clinical characteristics, including age, sex, body mass index (BMI), blood pressure, smoking status, and history of diabetes were collected, as were laboratory values, including the estimated glomerular filtration rate (eGFR) and the levels of hemoglobin (Hb), serum albumin (ALB), serum creatinine (Scr), uric acid (UA), serum calcium (Ca), serum phosphorus (P), C-reactive protein (CRP), whole parathyroid hormone (iPTH), total cholesterol (TC), triglycerides (TG), low-density lipoprotein (LDL), and 25-hydroxyvitamin.\u003c/p\u003e \u003cp\u003eBlood samples were collected to quantify the levels of fetuin-A via enzyme-linked immunosorbent assays (Mlbio, Shanghai, China). The aforementioned indicators were assessed before patients initiated regular dialysis treatment in our hospital. The total serum calcium level was adjusted if the concentration of serum ALB was \u0026lt;\u0026thinsp;4 g/dL to better reflect the free calcium level as follows: corrected total calcium (mmol/L)\u0026thinsp;=\u0026thinsp;total calcium (mmol/L)\u0026thinsp;+\u0026thinsp;0.02 \u0026times; [40-serum ALB (g/L)]. The Charlson Comorbidity Index (CCI) was calculated for each patient using the medical data recorded at the time of MHD initiation.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eMeasurement data with a normal distribution were expressed as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (\u0026plusmn;\u0026thinsp;s), and intergroup comparisons were performed using independent samples t-tests or multivariate analysis of variance (MANOVA). Measurement data with a skewed distribution were expressed as the median (M) and interquartile range, and intergroup comparisons were performed using the Kruskal-Wallis test or Mann-Whitney U test. Count data were expressed as the frequency or quantile, and the chi square test was used for intergroup comparisons. Spearman correlation analysis was conducted to assess correlations between relevant clinical indicators and the CACS. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the predictive value of the indicators. Univariate and multivariate logistic regression analyses were conducted to screen for independent risk factors.\u003c/p\u003e \u003cp\u003eAll statistical analyses were performed using SPSS software (version 25.0), and \u003cem\u003eP\u003c/em\u003e values\u0026thinsp;\u0026le;\u0026thinsp;0.05 indicated statistical significance. A prediction model was constructed using a neural network-based method, assessed using Python software. The precision and recall rate were used as indicators to assess the effectiveness of the model.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eParticipant characteristics\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA total of 110 patients received regular hemodialysis at our blood purification center between March 2021 and November 2022. After excluding patients who had been receiving hemodialysis for more than 3 months, those under 18 years old, individuals with malignant tumors, and patients with incomplete data, 86 participants were enrolled in the study. Of these 86 patients, there were 56 males (65.1%) and 30 females (34.9%), with a sex ratio of 1.87:1. The average age was 56.74 \u0026plusmn; 12.79 years. In terms of etiology, 35 patients (40.7%) had diabetic kidney disease (DKD), 31 (36.0%) had primary glomerular disease, 14 (16.3%) had hypertensive nephropathy, and six (7.0%) had other primary diseases. The median of CACS was 51.25 score. A summary of patients\u0026rsquo; demographic and clinical characteristics is provided in Table 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1. Participant characteristics\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"553\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eClassification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eValue（%/Q\u003csub\u003e1\u003c/sub\u003e，Q\u003csub\u003e3\u003c/sub\u003e）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e56.74\u0026plusmn;12.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eMale（N，%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e56（65.1%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eBMI（Kg/m\u003csup\u003e2\u003c/sup\u003e）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e22.77（20.72, 26.39）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eDiabetes（N，%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e35（40.7%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eSmoke（N，%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e21（24.7%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eCCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e4.67\u0026plusmn;1.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eSystolic pressure（mmHg）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e148（135, 158.25）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eDiastolic pressure（mmHg）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e85.76\u0026plusmn;15.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eHb（g/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e80（75.75, 92.25）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eALB（g/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e34.1（31.19, 37.65）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eScr（umol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e842.5（633.4, 1150.98）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eeGFR（ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e8.08\u0026plusmn;2.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eUA（umol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e434.1（348.80, 510.00）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eCa（mmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e1.95\u0026plusmn;0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003ecorrected Ca（mmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e2.09（1.97, 2.22）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eP（mmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e1.90\u0026plusmn;0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eCRP（mg/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e11.20\u0026plusmn;19.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eTC（mmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e3.80\u0026plusmn;1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eTG（mmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e1.45\u0026plusmn;0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eLDL（mmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e2.25\u0026plusmn;0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eiPTH（pg/mL）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e298.56\u0026plusmn;242.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e25 hydroxyvitamin D（nmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e43.72\u0026plusmn;23.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eFetuin-A（pg/mL）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e816.77（594.08, 949.36）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eCACS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e51.25（0, 223.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eNumber of CAC branches\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e1.76\u0026plusmn;1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAbbreviations:BMI, body mass index; CCI, Charlson Comorbidity Index ; Hb, hemoglobin ; ALB, serum albumin; Scr, serum creatinine; eGFR, estimated glomerular filtration rate; iPTH, intact parathyroid hormone; TC, total Cholesterol; TG, Triglyceride; LDL,low density lipoprotein-cholesterol; CRP, C-reactive protein; CACS, coronary artery calcification score.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eComparison of patients with and without calcification\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAmong the 86 patients, 62 developed CAC; thus, the calcification rate was 72.09%. According to the coronary MDCT results, the participants were divided into a non-calcification group (CACS=0) and a calcification group (CACS\u0026gt;0) (Table 2). The median of CACS in calcification group was 137.2 score. There were significant differences between the two groups in terms of the age, BMI, diabetes, CCI, and fetuin-A levels, and the number of branches exhibiting CAC (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). There were no intergroup differences in terms of the sex, smoking status, blood pressure, eGFR, or the levels of Hb, ALB, Scr, UA, Ca, corrected Ca, P, CRP, TG, TC, LDL, iPTH, and 25 hydroxyvitamin D (\u003cem\u003eP\u003c/em\u003e\u0026gt;0.05). The imaging characteristics of the different degrees of CAC presented at the most prominent level are shown in Figure 1.\u003c/p\u003e\n\u003cp\u003eTable 2 Comparison of patients with and without calcification\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"591\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.2724%;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8426%;\"\u003e\n \u003cp\u003eNon-calcification group (N=24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.5347%;\"\u003e\n \u003cp\u003eCalcification group (N=62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3519%;\"\u003e\n \u003cp\u003eF/X\u003csup\u003e2\u003c/sup\u003e/Z\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.9983%;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.2724%;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8426%;\"\u003e\n \u003cp\u003e47.88\u0026plusmn;15.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.5347%;\"\u003e\n \u003cp\u003e60.18\u0026plusmn;11.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3519%;\"\u003e\n \u003cp\u003e-4.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.9983%;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.2724%;\"\u003e\n \u003cp\u003eMale（N，%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8426%;\"\u003e\n \u003cp\u003e12（50%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.5347%;\"\u003e\n \u003cp\u003e44（71%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3519%;\"\u003e\n \u003cp\u003e3.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.9983%;\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.2724%;\"\u003e\n \u003cp\u003eBMI（Kg/m\u003csup\u003e2\u003c/sup\u003e）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8426%;\"\u003e\n \u003cp\u003e21.12（19.06, 23.52）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.5347%;\"\u003e\n \u003cp\u003e24.15\u0026plusmn;3.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3519%;\"\u003e\n \u003cp\u003e-2.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.9983%;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.2724%;\"\u003e\n \u003cp\u003eDiabetes（N，%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8426%;\"\u003e\n \u003cp\u003e4（16.7%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.5347%;\"\u003e\n \u003cp\u003e31（50%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3519%;\"\u003e\n \u003cp\u003e7.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.9983%;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.2724%;\"\u003e\n \u003cp\u003eSmoke（N，%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8426%;\"\u003e\n \u003cp\u003e3（12.5%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.5347%;\"\u003e\n \u003cp\u003e19（30.6%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3519%;\"\u003e\n \u003cp\u003e2.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.9983%;\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.2724%;\"\u003e\n \u003cp\u003eCCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8426%;\"\u003e\n \u003cp\u003e3（2, 4.75）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.5347%;\"\u003e\n \u003cp\u003e5（1,7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3519%;\"\u003e\n \u003cp\u003e-3.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.9983%;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.2724%;\"\u003e\n \u003cp\u003eSystolic pressure（mmHg）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8426%;\"\u003e\n \u003cp\u003e144.71\u0026plusmn;23.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.5347%;\"\u003e\n \u003cp\u003e150.45\u0026plusmn;19.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3519%;\"\u003e\n \u003cp\u003e-1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.9983%;\"\u003e\n \u003cp\u003e0.243\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.2724%;\"\u003e\n \u003cp\u003eDiastolic pressure（mmHg）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8426%;\"\u003e\n \u003cp\u003e89.38\u0026plusmn;15.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.5347%;\"\u003e\n \u003cp\u003e84.35\u0026plusmn;14.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3519%;\"\u003e\n \u003cp\u003e1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.9983%;\"\u003e\n \u003cp\u003e0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.2724%;\"\u003e\n \u003cp\u003eHb（g/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8426%;\"\u003e\n \u003cp\u003e78.54\u0026plusmn;15.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.5347%;\"\u003e\n \u003cp\u003e83.06\u0026plusmn;16.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3519%;\"\u003e\n \u003cp\u003e-1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.9983%;\"\u003e\n \u003cp\u003e0.247\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.2724%;\"\u003e\n \u003cp\u003eALB（g/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8426%;\"\u003e\n \u003cp\u003e34.08\u0026plusmn;5.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.5347%;\"\u003e\n \u003cp\u003e34.0\u0026plusmn;4.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3519%;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.9983%;\"\u003e\n \u003cp\u003e0.946\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.2724%;\"\u003e\n \u003cp\u003eScr（umol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8426%;\"\u003e\n \u003cp\u003e971.84\u0026plusmn;288.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.5347%;\"\u003e\n \u003cp\u003e846.67\u0026plusmn;336.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3519%;\"\u003e\n \u003cp\u003e1.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.9983%;\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.2724%;\"\u003e\n \u003cp\u003eeGFR（ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8426%;\"\u003e\n \u003cp\u003e7.25\u0026plusmn;1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.5347%;\"\u003e\n \u003cp\u003e7.47（6.21, 9.64）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3519%;\"\u003e\n \u003cp\u003e-1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.9983%;\"\u003e\n \u003cp\u003e0.197\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.2724%;\"\u003e\n \u003cp\u003eUA（umol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8426%;\"\u003e\n \u003cp\u003e477.33\u0026plusmn;144.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.5347%;\"\u003e\n \u003cp\u003e424.05\u0026plusmn;120.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3519%;\"\u003e\n \u003cp\u003e1.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.9983%;\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.2724%;\"\u003e\n \u003cp\u003eCa（mmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8426%;\"\u003e\n \u003cp\u003e2.02（1.85, 2.20）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.5347%;\"\u003e\n \u003cp\u003e1.96（1.86, 2.07）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3519%;\"\u003e\n \u003cp\u003e-1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.9983%;\"\u003e\n \u003cp\u003e0.256\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.2724%;\"\u003e\n \u003cp\u003eCorrected Ca（mmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8426%;\"\u003e\n \u003cp\u003e2.17（1.98, 2.25）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.5347%;\"\u003e\n \u003cp\u003e2.07（1.97,2.22）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3519%;\"\u003e\n \u003cp\u003e-1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.9983%;\"\u003e\n \u003cp\u003e0.207\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.2724%;\"\u003e\n \u003cp\u003eP（mmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8426%;\"\u003e\n \u003cp\u003e1.81\u0026plusmn;0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.5347%;\"\u003e\n \u003cp\u003e1.8（1.47, 2.21）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3519%;\"\u003e\n \u003cp\u003e-0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.9983%;\"\u003e\n \u003cp\u003e0.817\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.2724%;\"\u003e\n \u003cp\u003eCRP（mg/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8426%;\"\u003e\n \u003cp\u003e4.83（1.4, 7.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.5347%;\"\u003e\n \u003cp\u003e5.15（2.54, 10.67）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3519%;\"\u003e\n \u003cp\u003e-1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.9983%;\"\u003e\n \u003cp\u003e0.236\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.2724%;\"\u003e\n \u003cp\u003eTC（mmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8426%;\"\u003e\n \u003cp\u003e3.86\u0026plusmn;1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.5347%;\"\u003e\n \u003cp\u003e3.59（3.08, 4.34）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3519%;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.9983%;\"\u003e\n \u003cp\u003e0.996\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.2724%;\"\u003e\n \u003cp\u003eTG（mmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8426%;\"\u003e\n \u003cp\u003e1.45\u0026plusmn;0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.5347%;\"\u003e\n \u003cp\u003e1.18（0.81,1.68）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3519%;\"\u003e\n \u003cp\u003e-1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.9983%;\"\u003e\n \u003cp\u003e0.223\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.2724%;\"\u003e\n \u003cp\u003eLDL（mmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8426%;\"\u003e\n \u003cp\u003e2.09（1.55, 2.93）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.5347%;\"\u003e\n \u003cp\u003e1.99（1.62, 2.51）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3519%;\"\u003e\n \u003cp\u003e-0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.9983%;\"\u003e\n \u003cp\u003e0.160\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.2724%;\"\u003e\n \u003cp\u003eiPTH（pg/mL）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8426%;\"\u003e\n \u003cp\u003e291.2（138.5,513.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.5347%;\"\u003e\n \u003cp\u003e216.3（91.6,366.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3519%;\"\u003e\n \u003cp\u003e-1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.9983%;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.2724%;\"\u003e\n \u003cp\u003e25 hydroxyvitamin D（nmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8426%;\"\u003e\n \u003cp\u003e38.5（27, 50.75）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.5347%;\"\u003e\n \u003cp\u003e35.5（28, 57.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3519%;\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.9983%;\"\u003e\n \u003cp\u003e0.977\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.2724%;\"\u003e\n \u003cp\u003eFetuin-A（pg/mL）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8426%;\"\u003e\n \u003cp\u003e953.0（923.9, 981.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.5347%;\"\u003e\n \u003cp\u003e661.0（528.4, 887.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3519%;\"\u003e\n \u003cp\u003e-5.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.9983%;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.2724%;\"\u003e\n \u003cp\u003eCACS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8426%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.5347%;\"\u003e\n \u003cp\u003e137.2（31.5, 451.35）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3519%;\"\u003e\n \u003cp\u003e-7.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.9983%;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.2724%;\"\u003e\n \u003cp\u003eNumber of CAC branches\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8426%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.5347%;\"\u003e\n \u003cp\u003e2（1, 3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3519%;\"\u003e\n \u003cp\u003e-6.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.9983%;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAbbreviations:BMI, body mass index; CCI, Charlson Comorbidity Index ; Hb, hemoglobin ; ALB, serum albumin; Scr, serum creatinine; eGFR, estimated glomerular filtration rate; iPTH, intact parathyroid hormone; TC, total Cholesterol; TG, Triglyceride; LDL,low density lipoprotein-cholesterol; CRP, C-reactive protein; CACS, coronary artery calcification score.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAnalysis of risk factors affecting VC\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo screen for the risk factors related to VC, univariate and multivariate logistic regression analyses were conducted. The multivariate logistic analysis revealed that age (odds ratio (OR)=1.069, 95% confidence interval (CI): 1.001\u0026ndash;1.142, \u003cem\u003eP\u003c/em\u003e=0.006), diabetes (OR=3.972, 95% CI: 1.162\u0026ndash;13.58, \u003cem\u003eP\u003c/em\u003e=0.028), and the CCI (OR=1.722, 95% CI: 1.155\u0026ndash;2.568, \u003cem\u003eP\u003c/em\u003e=0.008) were independent risk factors for VC in patients beginning hemodialysis (Table 3). The multivariate logistic analysis was presented by adjusted the BMI, Scr, P, number of CAC branches and serum levels of Fetuin-A.\u003c/p\u003e\n\u003cp\u003eTable 3. logistic regression analysis of VC in participant\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"602\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 101px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eUnivariate logistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eMultivariate logistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1.04-1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e1.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.001-1.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e1.609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1.53-16.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e3.972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.162-13.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003eCCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e1.807\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1.31-2.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e1.722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.155-2.568\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAbbreviations: CCI, Charlson Comorbidity Index.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSubgroup analysis of patients with DKD\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe aforementioned analysis confirmed that diabetes was an independent risk factor for VC. A subgroup analysis of patients with DKD was performed to further explore the effect of diabetes on VC (Table 4). Compared with the data from the non-diabetes group, those in the DKD group exhibited higher CACS, a greater number of branches affected by CAC, and lower levels of fetuin-A (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). The eGFR of the patients with diabetes was higher than that of the patients without diabetes at the time of dialysis initiation, suggesting that the patients with DKD entered dialysis earlier. In addition, there were statistically significant differences between the two groups in terms of the age, BMI, CCI, systolic blood pressure, and ALB, Scr, and iPTH levels (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05).\u003c/p\u003e\n\u003cp\u003eTable 4 \u0026nbsp; Comparison of DKD and non-diabetes group\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"591\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eDKD group\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(N=35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eNon-\u0026nbsp;diabetes group (N=51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003eF/X\u003csup\u003e2\u003c/sup\u003e/Z\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e60.37\u0026plusmn;\u0026nbsp;10.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e54.25\u0026plusmn; 13.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e2.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eBMI（Kg/m\u003csup\u003e2\u003c/sup\u003e）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e25. 18\u0026plusmn;3.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e21.5\u0026nbsp;(19.8, 25.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e-3.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eCCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e6.2\u0026plusmn; 1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e3\u0026nbsp;(2,5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e-5.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eSystolic pressure（mmHg）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e152\u0026nbsp;(143, 160)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e144. 1\u0026plusmn; 19.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e-2.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eDiastolic pressure（mmHg）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e82.91\u0026plusmn; 12.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e86\u0026nbsp;(76, 94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e-\u0026nbsp;1.\u0026nbsp;19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eHb（g/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e84.03\u0026plusmn; 16.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e80.27\u0026plusmn; 16.\u0026nbsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.293\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eALB（g/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e32.9\u0026plusmn;4.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e35.2\u0026nbsp;(32.9, 38. 1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e-2.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eScr（umol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e754.7\u0026plusmn;251.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e968.7\u0026plusmn;345.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e-3.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eeGFR（ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e8.53(7.07, 9.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e6.9\u0026nbsp;(5.88, 8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e-2.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eUA（umol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e443. 17\u0026plusmn; 124.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e435.99\u0026plusmn;\u0026nbsp;133.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.252\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eCa（mmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e1.94(1.86, 2.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e1.98\u0026nbsp;(1.84, 2. 18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e-0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eCorrected Ca（mmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e2.08\u0026plusmn;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e2. 10\u0026nbsp;(1.97, 2.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e-\u0026nbsp;1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eP（mmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e1.78\u0026plusmn;0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e1.8\u0026nbsp;(1.49, 2.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e-0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eCa*P（mmol\u003csup\u003e2\u003c/sup\u003e/L\u003csup\u003e2\u003c/sup\u003e）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e3.38（2.78, 3.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e3.75\u0026plusmn;1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e-1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eCRP（mg/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e4.72\u0026nbsp;(2.37,7.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e5.73\u0026nbsp;(2.05, 11.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e-0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eTC（mmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e3.72\u0026plusmn; 1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e3.61\u0026nbsp;(3.08, 4.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e-0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eTG（mmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e1.31(0.78, 2.\u0026nbsp;14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e1. 15\u0026nbsp;(0.86, 1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e-0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eLDL（mmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e1.89\u0026nbsp;(1.54, 2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e2.07\u0026nbsp;(1.68, 3.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e-0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eiPTH（pg/mL）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e163.8（101.6, 283.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e297（170, 439.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e-2.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e25 hydroxyvitamin D（nmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e34\u0026nbsp;(27, 52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e38\u0026nbsp;(29, 57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e-0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eFetuin-A（pg/mL）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e657.8\u0026plusmn;246.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e885.4\u0026nbsp;(690.5, 974.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e-3.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eCACS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e155.3\u0026nbsp;(32.6,\u0026nbsp;496)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e6\u0026nbsp;(0, 101.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e-3.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eNumber of CAC branches\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e3\u0026nbsp;(2, 3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e1\u0026nbsp;(0, 2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e-\u0026nbsp;1.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAbbreviations:BMI, body mass index; CCI, Charlson Comorbidity Index ; Hb, hemoglobin ; ALB, serum albumin; Scr, serum creatinine; eGFR, estimated glomerular filtration rate; iPTH, intact parathyroid hormone; TC, total Cholesterol; TG, Triglyceride; LDL,low density lipoprotein-cholesterol; CRP, C-reactive protein; CACS, coronary artery calcification score.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAnalysis of correlations between clinical indicators and CACS\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe Spearman correlation analysis (Table 5) of the CACS and clinical indicators revealed that the age, BMI, diabetes, CCI, and the number of branches affected by CAC were positively correlated with the CACS. The CACS was negatively correlated with fetuin-A(r=-0.876, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001) levels, with the former exhibiting a stronger negative correlation with the CACS than the latter.\u003c/p\u003e\n\u003cp\u003eTable 5. Correlation analysis between CACS and related clinical indicators\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"553\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e0.381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eBMI（Kg/m\u003csup\u003e2\u003c/sup\u003e）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e0.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eCCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e0.368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e0.383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eNumber of\u0026nbsp;CAC branches\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e0.590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eFetuin-A（pg/mL）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e-0.876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAbbreviations: CCI, Charlson Comorbidity Index.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEstablishment of a preliminary prediction model for VC in patients beginning MHD\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe previous analyses revealed that the age, BMI, diabetes, CCI, and the levels of the calcification inhibitory factors fetuin-A significantly differed between the patients with and without VC. Those factors were selected as predictive factors for the model, as were iPTH and P levels based on clinical experience (Table 6).\u003c/p\u003e\n\u003cp\u003eThe 86 patients undergoing MHD were randomly divided into a derivation queue (51 cases, 60%) and a validation queue (35 cases, 40%) based on the neural network binary classification method. The corresponding integrals were assigned based on the contribution of each factor in the neural network; the integrals were adjusted based on clinical experience to generate the model (Table 6).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTa\u003c/strong\u003e\u003cstrong\u003eble 6. Corresponding integrals of 8 factors in the\u003c/strong\u003e \u003cstrong\u003eprediction model\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"553\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003ePredictive factor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eClassification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003ePoint\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e\u0026le;45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e45~65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e\u0026ge;65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eBMI（Kg/m\u003csup\u003e2\u003c/sup\u003e）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e＜24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e\u0026ge;24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eCCI\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP（mmol/L）\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eiPTH（pg/mL）\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eFetuin-A（pg/mL）\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003e＜5\u003c/p\u003e\n \u003cp\u003e\u0026ge;5\u003c/p\u003e\n \u003cp\u003e＜1.78\u003c/p\u003e\n \u003cp\u003e\u0026ge;1.78\u003c/p\u003e\n \u003cp\u003e＜600\u003c/p\u003e\n \u003cp\u003e\u0026ge;600\u003c/p\u003e\n \u003cp\u003e＜700\u003c/p\u003e\n \u003cp\u003e\u0026ge;700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eRisk model formula:\u003c/strong\u003e calcification risk score=age+BMI+diabetes+CCI+P+iPTH+Fetuin-A\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe effectiveness of the prediction model\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Table 7, the area under the curve (AUC) (0.87,0.84) of the model was significant, with consistent sensitivity (85%, 84%) and specificity (78%, 78%) in both the derivation and validation queues, respectively. As shown in Figure 2, Through the precision, accuracy, and recall rate in the neural network indicated that the model\u0026apos;s predictive performance was reliable. The cut-off point (6.5 points) was subsequently determined according to the best Youden\u0026apos;s J statistic (0.62 points). Patients with a score below 6.5 points were considered to be at low risk of VC, whereas those with a score exceeding 6.5 points were considered to be at high risk of developing VC (Table 7).\u003c/p\u003e\n\u003cp\u003eTable 7. Predictive performance of queues in neural networks( model 1)\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"577\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003eJorden index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003eCut off point\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003eSensitivity（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003eSpecificity（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eDerivation queue(N=51）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eValidation queue(N=35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDue to the fact that fetuin-A levels are not routinely quantified in clinical practice, the scoring system was modified to exclude the fetuin-A levels, and the new model was retested. As shown in Table 8, the AUC (0.74,0.74) of the modified model remained significant, with stable sensitivity (72%, 83%) and specificity (70%, 64%). In the new model, the cut-off was 4.5 points according to the Youden\u0026apos;s J statistic. The modified model is likely to be more convenient for clinical use.\u003c/p\u003e\n\u003cp\u003eTable 8. Predictive performance of queues in new model excluding Fetuin-A(model 2)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"576\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003eJorden index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003eCut off point\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003eSensitivity（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003eSpecificity（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eDerivation queue(N=51）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eValidation queue(N=35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the present study, the CAC rate among patients who had recently initiated MHD was 72.09%, which was higher than the 60% rate in patients with CKD that was reported in a meta-analysis of 47 studies\u003csup\u003e[11]\u003c/sup\u003e. That meta-analysis showed that as the calcification score increases, the incidence of cardiovascular events and all-cause hospitalization and mortality rates will increase by 2\u0026ndash;4 times; in the present study, the levels of the VC inhibitory factors fetuin-A were significantly lower in the group with calcification than in the group without it, and they were negatively correlated with the CACS. The multivariate also revealed that age, the CCI, and diabetes were independent risk factors for VC in this patient population.\u003c/p\u003e\n\u003cp\u003ePrevious studies have shown that an imbalance between factors that promote and inhibit calcification is one of the predominant mechanisms driving VC\u003csup\u003e[6]\u003c/sup\u003e. In this study, fetuin-A levels also differed significantly between groups and were negatively correlated with the CACS. Fetuin-A is a serum glycoprotein that is mainly secreted by the liver. It is a biologically diverse substance that participates in many processes, including bone and lipid metabolism, and it is involved in central nervous system disorders. Fetuin-A can directly bind with calcium and phosphorus in the circulation, forming low-activity complexes that prevent calcification progression\u003csup\u003e[12]\u003c/sup\u003e. These substances are called fetal globulin mineral complexes or calcium protein particles, and they prevent premature and rapid ectopic calcification at the physiological level\u003csup\u003e[13]\u003c/sup\u003e. Fetuin-A binds to calcium ions to form stable colloidal calcium protein particles, but it can also bind to calcium phosphate crystals, thereby delaying mineral deposition\u003csup\u003e[14]\u003c/sup\u003e. Kettler et al. reported lower fetuin-A levels in patients receiving hemodialysis or peritoneal dialysis than in healthy people\u003csup\u003e[15]\u003c/sup\u003e. A multicenter, prospective, cohort study involving 987 patients undergoing dialysis demonstrated a link between fetuin-A levels and mortality\u003csup\u003e[16]\u003c/sup\u003e. However, another study of 93 patients receiving dialysis found no significant correlation between fetuin-A levels and CAC\u003csup\u003e[17]\u003c/sup\u003e. Thus, more experimental data is required to better assess whether fetuin-A can be used to predict VC in patients undergoing MHD.\u003c/p\u003e\n\u003cp\u003eDKD has become the main cause of end-stage renal disease, and some studies have shown that in the past 20 years, diabetic angiopathy has increased the global mortality rate to 37.9% in patients with diabetes\u003csup\u003e[18]\u003c/sup\u003e. In this study, 40.7% patients had DKD, and the subgroup analysis revealed a higher incidence and greater severity of VC in patients with diabetes, with higher numbers of affected vascular branches, which was consistent with previous findings. VC in patients with DKD is a complex process with many causal factors, including advanced glycation end product accumulation\u003csup\u003e[19-20]\u003c/sup\u003e and an imbalance between nitric oxide bioavailability and reactive oxygen species accumulation, leading to endothelial dysfunction\u003csup\u003e[21]\u003c/sup\u003e. These processes are mediated by multiple regulatory factors and pathways, including the BMP2/SMADs, RAGE/JAK2, and Wnt/\u0026beta;-Catenin\u003csup\u003e[22]\u003c/sup\u003e. Diabetes is known to damage blood vessels; therefore, it is beneficial for patients undergoing hemodialysis to maintain stability and control disease progression.\u003c/p\u003e\n\u003cp\u003eThe age-corrected CCI is a scoring system proposed by Charlson et al. that considers age and multiple systemic comorbidities, and it is widely used to evaluate patients with tumors and those in the intensive care unit\u003csup\u003e[23]\u003c/sup\u003e. The CCI was shown to be correlated with and could consistently predict other survival analysis indicators such as the Kaplan-Feinstein Index, and its sensitivity has been repeatedly confirmed, facilitating a comprehensive assessment of the body\u0026apos;s state before initiating disease treatment\u003csup\u003e[24]\u003c/sup\u003e. Because patients undergoing MHD patients always experience many complications, especially cardiac and cerebrovascular disease, which may be related to VC, it was included as a disease status indicator for patients beginning hemodialysis in this study. The CCI was one of the risk factors for CAC and could be used to predict in this patient population.\u003c/p\u003e\n\u003cp\u003eNeural networks can predict the development and prognosis of diseases, helping doctors formulate more effective treatment plans\u003csup\u003e[25]\u003c/sup\u003e. Many scholars have utilized neural networks or improved neural network structures to enhance the predictive accuracy of classifications. For example, algorithms based on neural networks have been used to classify benign and malignant solitary pulmonary nodules. The present study adopted such an approach to construct and validate a VC prediction model. The precision, accuracy, and recall rate in the neural network indicated that the model\u0026apos;s predictive performance was reliable. A score greater than 6.5 indicated a high risk of VC. Model 2 excluded fetuin-A levels, and scores greater than 4.5 indicated a high risk of VC. Model 2 will be more convenient for clinical use when fetuin-A levels cannot be quantified.\u003c/p\u003e\n\u003cp\u003eIt is worth mentioning that the level of iPTH in non-calcification group was higher than calcification group. However, iPTH was one of the important indicators for evaluating CKD-MBD in clinical practice.In response to this result, we considered that this study was based on real world research,so that iPTH was not significant in this research.\u003c/p\u003e\n\u003cp\u003eUltimately, this study showed that VC is affected by many factors in patients initiating MHD, including age, the CCI, diabetes. The prediction model established using neural networks in this study has the potential to become a risk stratification tool for VC in patients undergoing MHD; however, this scoring system must be validated in additional cohorts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitation\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has some limitations. First, it is a single-center study with a limited sample size, potential bias, and the results must be validated through multi-center, comprehensive studies with more participants. Then there were some factors that had not been considered, such as excessive calcium intake, dialysate calcium, hypomagnesemia, calcitriol, using antiplatelet drugs et al. Because of \u0026nbsp;FGF 23 was a acknowledged factor related to VC, so that it wasn\u0026rsquo;t selected to the study. Finally, \u0026nbsp;the predictive model may not have incorporated every useful indicators, affecting its accuracy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all patients and dialysis personnel who contributed to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Grants from the National Natural Science Foundation of China (No. 81770730) and the Natural Science Fund of Changsha (No. kq2014235).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis trial has been approved by the second xiangya hospital of central south university ethics committee, All participants have signed informed consent forms before entering this trial. The ethical code was LYF2022227.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData is not available due to ethical reasons. Further enquiries can be directed to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHX and FY designed the research.JS and YY collected the data.HX and FY analyzed the data and drafted the manuscript. CS and CW provided help during the research. All author have read and approved the manuscript. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone declared.\u003c/p\u003e\n\u003cp\u003eThe results presented in this paper have not been published previously in whole or part.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eApproval of the research protocol\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis trial has been approved by the second xiangya hospital of central south university ethics committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants have signed informed consent forms before entering this trial.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe IRB number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLYF2022227\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnimal studies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eN/A\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMusgrove J, Wolf M. Regulation and Effects of FGF23 in Chronic Kidney Disease[J]. Annual Review of Physiology, 2020, 82: 365\u0026ndash;390.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRusso D, Corrao S, Battagla Y, et al. Progression of coronary artery calcification and cardiac events in patients with chronic renal disease not receiving dialysis[J]. Kidney International, 2011, 80(1): 112\u0026ndash;118.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBansal N. Evolution of Cardiovascular Disease During the Transition to End-Stage Renal Disease[J]. Seminars in Nephrology, 2017, 37(2): 120\u0026ndash;131.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSigrist M K, Taal M W, Bungay P, et al. Progressive vascular calcification over 2 years is associated with arterial stiffening and increased mortality in patients with stages 4 and 5 chronic kidney disease[J]. Clinical journal of the American Society of Nephrology: CJASN, 2007, 2(6): 1241\u0026ndash;1248.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVervloet M, Cozzolino M. Vascular calcification in chronic kidney disease: different bricks in the wall?[J]. Kidney International, 2017, 91(4): 808\u0026ndash;817.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMizuiri S, Nishizawa Y, Yamashita K, et al. Relationship of matrix Gla protein and vitamin K with vascular calcification in hemodialysis patients[J]. Renal Fetuin-Ailure, 2019, 41(1): 770\u0026ndash;777.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOmbrellino M, Wang H, Yang H, et al. Fetuin, a negative acute phase protein, attenuates TNF synthesis and the innate inflammatory response to carrageenan[J]. Shock (Augusta, Ga.), 2001, 15(3): 181\u0026ndash;185.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDautova Y, Kozlova D, Skepper J N, et al. Fetuin-A and albumin alter cytotoxic effects of calcium phosphate nanoparticles on human vascular smooth muscle cells[J]. PloS One, 2014, 9(5): e97565.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKapustin A N, Chatrou M L L, Drozdov I, et al. Vascular smooth muscle cell calcification is mediated by regulated exosome seScretion[J]. Circulation Research, 2015, 116(8): 1312\u0026ndash;1323.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLehker A, Mukherjee D. Coronary Calcium Risk Score and Cardiovascular Risk[J]. Current Vascular Pharmacology, 2021, 19(3): 280\u0026ndash;284.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang X R, Zhang J J, Xu X X, et al. Prevalence of coronary artery calcification and its association with mortality, cardiovascular events in patients with chronic kidney disease: a systematic review and meta-analysis[J]. Renal Fetuin-Ailure, 2019, 41(1): 244\u0026ndash;256.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeiss A, Duchesne A, Denecke B, et al. Structural basis of calcification inhibition by alpha 2-HS glycoprotein/fetuin-A. Formation of colloidal calciprotein particles[J]. The Journal of Biological Chemistry, 2003, 278(15): 13333\u0026ndash;13341.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMori K, Emoto M, Inaba M. Fetuin-A and the cardiovascular system[J]. Advances in Clinical Chemistry, 2012, 56: 175\u0026ndash;195.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCai M M X, Smithe R, Holt S G. The role of fetuin-A in mineral trafficking and deposition[J]. BoneKEy Reports, 2015, 4: 672.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKetteler M, Bongartz P, Westenfeld R, et al. Association of low fetuin-A (AHSG) concentrations in serum with cardiovascular mortality in patients on dialysis: a Scross-sectional study[J]. Lancet (London, England), 2003, 361(9360): 827\u0026ndash;833.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHermans M M H, Brandenburg V, Ketteler M, et al. Association of serum fetuin-A levels with mortality in dialysis patients[J]. Kidney International, 2007, 72(2): 202\u0026ndash;207.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUlutas O, Taskapan M C, Dogan A, et al. Vascular calcification is not related to serum fetuin-A and osteopontin levels in hemodialysis patients[J]. International Urology and Nephrology, 2018, 50(1): 137\u0026ndash;142.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLing W, Huang Y, Huang Y M, et al. Global trend of diabetes mortality attributed to vascular complications, 2000\u0026ndash;2016[J]. Cardiovascular Diabetology, 2020, 19(1): 182.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eForbes J M, Cooper M E. Mechanisms of diabetic complications[J]. Physiological Reviews, 2013, 93(1): 137\u0026ndash;188.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao J, Randive R, Stewart J A. Molecular mechanisms of AGE/RAGE-mediated fibrosis in the diabetic heart[J]. World Journal of Diabetes, 2014, 5(6): 860\u0026ndash;867.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaktiroglu S, Yanar F, Ozata I H, et al. Arterial disease and vascular access in diabetic patients[J]. The Journal of Vascular Access, 2016, 17 Suppl 1: S69-71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrodeur M R, Bouvet C, Bouchard S, et al. Reduction of advanced-glycation end products levels and inhibition of RAGE signaling deScreases rat vascular calcification induced by diabetes[J]. PloS One, 2014, 9(1): e85922.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCharlson M E, Pompei P, Ales K L, et al. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation[J]. Journal of Chronic Diseases, 1987, 40(5): 373\u0026ndash;383.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCharlson M E, Carrozzino D, Guidi J, et al. Charlson Comorbidity Index: A Scritical Review of Clinimetric Properties[J]. Psychotherapy and Psychosomatics, 2022, 91(1): 8\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaxt W G. Application of artificial neural networks to clinical medicine[J]. Lancet (London, England), 1995, 346(8983): 1135\u0026ndash;1138.\u003c/span\u003e\u003c/li\u003e\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":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Calcium-phosphorus metabolism, fetuin-A, hemodialysis, vascular calcification","lastPublishedDoi":"10.21203/rs.3.rs-5762835/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5762835/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground and hypothesis.\u003c/h2\u003e \u003cp\u003eVascular calcification (VC) is an important risk factor for cardiovascular events in patients undergoing maintenance hemodialysis (MHD); however, there is limited data on VC-related factors in patients beginning hemodialysis. Thus, this study aimed to determine the risk factors of VC and to establish a prediction model for evaluating VC progression in new patients undergoing hemodialysis.\u003c/p\u003e\u003ch2\u003eMethods.\u003c/h2\u003e \u003cp\u003eThis study selected 86 patients who initiated in-center MHD between March 2021 and November 2022. Demographic characteristics, medical history, and laboratory data were collected. Coronary artery calcification (CAC) was assessed based on the Agatston vascular score determined via computed tomography. Serum levels of the VC inhibitors fetuin-A was quantified via enzyme-linked immunosorbent assays. Univariate and multivariate regression analyses were conducted to determine the risk factors for VC, and a neural network-based approach was adopted to construct a VC prediction model.\u003c/p\u003e\u003ch2\u003eResults.\u003c/h2\u003e \u003cp\u003eThe average age of the patients was 56.74\u0026thinsp;\u0026plusmn;\u0026thinsp;12.79 years, and 65.1% were male. CAC was observed in 72.09% of patients. Age, body mass index, diabetes, the comorbidity index, and the number of coronary artery branches with calcification were positively correlated with the CAC score, whereas plasma fetuin-A levels was negatively correlated. The multivariate logistic regression analysis revealed that age[odds ratio (OR) 1.07, 95%CI: 1.00\u0026ndash;1.14], the comorbidity index[OR 1.72, 95%CI: 1.16\u0026ndash;2.57], diabetes[OR 3.97, 95%CI: 1.16\u0026ndash;13.58] were independent risk factors for CAC; these factors were used to establish a simple scoring model to predict VC risk.\u003c/p\u003e\u003ch2\u003eConclusion.\u003c/h2\u003e \u003cp\u003eAge, the comorbidity index, diabetes were identified as independent risk factors for CAC in patients beginning hemodialysis, and the new VC prediction model based on these factors may help identify VC in patients undergoing MHD, facilitating clinical interventions.\u003c/p\u003e","manuscriptTitle":"Risk Factors for and a Preliminary Prediction Model of Vascular Calcification in Patients Beginning Hemodialysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-08 20:07:42","doi":"10.21203/rs.3.rs-5762835/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-02-15T19:30:53+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-15T08:41:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"26949749225168168201308422834761151193","date":"2025-02-07T08:36:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-11T06:31:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"158945362375034929690136306247446236104","date":"2025-01-10T08:01:03+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-01-08T17:32:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-01-06T13:58:55+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-01-06T11:59:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Medical Research","date":"2025-01-04T09:52:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ad5e6652-c43d-497a-8e65-a7139b1f5ca2","owner":[],"postedDate":"January 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-04-14T16:06:17+00:00","versionOfRecord":{"articleIdentity":"rs-5762835","link":"https://doi.org/10.1186/s40001-025-02523-5","journal":{"identity":"european-journal-of-medical-research","isVorOnly":false,"title":"European Journal of Medical Research"},"publishedOn":"2025-04-07 16:04:49","publishedOnDateReadable":"April 7th, 2025"},"versionCreatedAt":"2025-01-08 20:07:42","video":"","vorDoi":"10.1186/s40001-025-02523-5","vorDoiUrl":"https://doi.org/10.1186/s40001-025-02523-5","workflowStages":[]},"version":"v1","identity":"rs-5762835","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5762835","identity":"rs-5762835","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}