Risk factors for and predictors of intracranial artery stenosis in patients with end-stage kidney disease

Risk factors for and predictors of intracranial artery stenosis in patients with end-stage kidney disease | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Risk factors for and predictors of intracranial artery stenosis in patients with end-stage kidney disease Yui Zheng, Yingying Zhang, Madiya Madeniyet, Jing Xiao, Zhibin Ye This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6778528/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose To explore the risk factors for and predictors of intracranial artery stenosis (ICAS) in patients with end-stage kidney disease (ESKD). Methods A cross-sectional study of 178 patients who were hospitalized in Department of Nephrology, Huadong Hospital, Fudan University, Shanghai, China between June 2017 and March 2021 and underwent brain MRA during hospitalization was conducted. ICAS was defined as intracranial artery stenosis exceeding 50%. We included 45 patients as the ICAS group and 26 patients without ICAS as the control group. Univariate analysis was used to explore different indicators. Binary logistic regression analysis further explored whether the different factors were independent risk factors, the ROC curve was used to explore the predictive value of various indicators for ICAS in patients with ESKD. Results In this analysis, the average age of patients was 67.61 ± 11.62 years, and the eGFR was 7.00 ± 2.95 ml/min/1.73 m 2 . Binary logistic regression analysis showed that total cholesterol (TC) (OR=3.372, 95% CI: 1.497-7.593) and low-density lipoprotein (LDL) (OR=3.795, 95% CI: 1.550-9.289) were independently associated with ICAS. ROC analysis demonstrated that TC (71.4%, 72%) with a cutoff value of 3.870 mmol/l and LDL (75.6%, 69.6%) with a cutoff value of 2.025 mmol/l had strong predictive value for ICAS in patients with ESKD. Conclusion In patients with ESKD, TC and LDL were associated with ICAS after adjusting for covariates and have strong predictive value for ICAS. TC LDL ESKD Intracranial artery stenosis Figures Figure 1 Figure 2 Introduction Chronic kidney disease (CKD) is an independent risk factor for cardiovascular and cerebrovascular diseases[ 1 ]. Among all stages of CKD, CKD5, or end-stage renal disease (ESKD), is associated with the greatest risk of cardiovascular and cerebrovascular disease[ 2 ]. Both albuminuria and eGFR, two key parameters for CKD stratification, have been shown to be associated with stroke risk in a dose‒response fashion, such as a 10% increase in stroke risk for every 25 mg/mmol increase in albuminuria and a 7% increase in stroke risk for every 10 min/ml/1.73 m 2 decrease in eGFR[ 3 ]. The risk of stroke among patients with ESKD is approximately 30 times higher than that of the general population[ 4 ]. Although the incidence of hemorrhagic stroke has increased substantially among patients with ESKD, ischemic stroke remains the majority of all stroke types[ 5 ]. According to TOAST criteria, large artery atherosclerosis (carotid artery and/or intracranial artery) is a major cause of ischemic stroke[ 6 ]. Moreover, intracranial artery stenosis (ICAS) is considered to be a noteworthy risk factor for stroke in Asian populations compared with non-Asian populations[ 7 ]. However, data on the comorbidity of ICAS in Chinese patients with ESKD are scarce and warrant exploration. Digital subtraction angiography (DSA) is the gold standard for the diagnosis of ICAS, but its application in the screening of ICAS is limited due to the high radiation exposure risk and the need for a large dose of iodine contrast agent, which is a contraindication in CKD[ 8 ]. Recently, magnetic resonance angiography (MRA) has become a commonly used method for ICAS detection[ 9 ]. Several studies have confirmed that, compared with DSA, the sensitivity of MRA detection for ICAS is 78–92%, and the specificity is 91–95%[ 10 ]. Therefore, MRA is a rational choice for ICAS detection in patients with CKD for whom the usage of iodine contrast agent should be avoided. In this study, we focused on ICAS comorbidity in CKD patients and confirmed the risk factors and their cutoff values as predictors for ICAS detection by MRA in patients with ESKD, providing insights for stroke prevention in this specific patient group. Methods Study participantsh For the present study, a retrospective design was employed. We recruited 178 patients who were hospitalized in our unit (Nephrology, Huadong Hospital Affiliated to Fudan University, Shanghai, China) between June 2017 and March 2021 and underwent brain MRA during hospitalization. Exclusion criteria mainly included congenital cerebrovascular disease, severe cardiac, pulmonary and liver dysfunction, and other diseases affecting calcium and phosphorus metabolism, such as malignant tumor, multiple myeloma, primary parathyroid disease, recent fracture ( within the last 3 months), and acute stroke during hospitalization. Based on the intracranial MRA, ICAS was defined as intracranial artery stenosis exceeding 50% in any artery. We included 45 patients in the ICAS group and 26 patients without ICAS as the control group. Informed consent was obtained from all patients, and the study protocol was approved by the Ethics Committee of Huadong Hospital Affiliated to Fudan University, Shanghai. Clinical data collection and laboratory tests Regarding the collection of clinical information, previous history, medication history, family history, history of smoking, alcohol consumption, dietary habits, other information, BMI index, and blood pressure, all nurses received unified training. Hypertension was defined as systolic blood pressure ≥ 140 mmHg, diastolic blood pressure ≥ 90 mmHg, or the use of antihypertensive medication. The diagnosis of diabetes mellitus (DM) was based on the 2016 American Diabetes Association (ADA) diagnostic criteria. After 8 hours of overnight fasting, blood samples were collected for laboratory measurements. Lab tests included: routine blood test, coagulation function parameters, and C-reactive protein (CRP), albumin (ALB), total cholesterol (TC), triglycerides (TG), low-density lipoprotein (LDL), high-density lipoprotein (HDL), fasting blood glucose (FBG), urea nitrogen (BUN), serum creatinine (SCR), serum uric acid (SUA), serum calcium (SCA), serum phosphorus (SP), serum potassium (SK), serum sodium (SNA), serum chlorine (SCL), parathyroid hormone (PTH), 25 hydroxyl vitamin D [25(OH)-Vit D], osteocalcin, etc. Estimated glomerular filtration rate (eGFR milliliters per minute per 1.73 m 2 ) was calculated using the Chronic Kidney Disease-EPI formula. Assessment of ICAS MRI was performed on a 3 Tesla Siemens Magnetom Prisma scanner using a 32-channel head coil at the Imaging Department of Huadong Hospital. The three-dimensional time-of-flight MRA images (3D TOF MRA) were acquired with a repetition time of 21 ms, time to echo of 3.42 ms, flip angle of 18°, 220*176 mm field of view, 218 × 256 acquisition matrix, slice thickness of 0.60 mm, distance factor − 25%, and an acquisition time of 4 min and 54 s. Intracranial MRA was performed on the 3rd to 4th day of hospitalization on patients who complained of dizziness, and ICAS was diagnosed by experienced radiologists. ICAS was defined as localized stenosis greater than 50% in any artery assessed, including the vertebral artery, basilar artery, internal carotid artery, posterior cerebral artery, middle cerebral artery, and anterior cerebral artery. Statistical analysis Patients were divided into two groups according to the quantiles of TC: TC-I (TC ≤ 3.990 mmol/l) and TC-II (TC > 3.990 mmol/l) or according to the quantiles of LDL: LDL-I (LDL ≤ 2.175 mmol/l) and LDL-II (LDL > 2.175 mmol/l). ICAS proportions of each group were described, and the chi-square test was used to compare the differences in ICAS proportions of each group. The association of TC and LDL with ICAS was determined initially using a logistic regression model with odds ratios (ORs) and 95% confidence intervals (CIs). The confounding factors for ICAS including age, sex, BMI, hypertension, DM, FBG and eGFR. Finally, a corresponding ROC curve was plotted, and the best threshold is calculated. All statistical tests were two-tailed, and the results were considered significant at P < 0.05. SPSS 26 was used for statistical analysis. Results Comparison of general clinical characteristics between ICAS and NICAS in ESKD patients The general clinical characteristics of each group are listed in Table 1 . The average age of patients in all participants was 67.61 ± 11.62 years, and the eGFR was 7.00 ± 2.95 ml/min/1.73 m2. Significant difference in TC, LDL and osteocalcin were observed between groups. The levels of TC and LDL were significantly higher in the ICAS group than in the NICAS group, while the level of osteocalcin in the ICAS group was significantly lower than that in the NICAS group. (The ICAS group had significantly higher TC and LDL levels, while osteocalcin levels were lower compared to the NICAS group.) Other parameters were not significantly different between the two groups. Table 1 The baseline characteristics of ESKD patients with ICAS or NICAS parameters Total (n = 71) ICAS (n = 45) NICAS (n = 26) P value Age (year), 67.61 ± 11.62 67.33 ± 11.65 68.12 ± 11.80 0.788 Female, n (%) 25(35.2%) 15(33.3%) 10(38.5%) 0.663 Hypertension(%) 66(93.0%) 41(91.1%) 25(96.2%) 0.750 DM(%) 43(60.6%) 25(55.6%) 18(69.2%) 0.256 BMI (kg/m 2 ) 23.43 ± 4.38 23.72 ± 4.92 22.91 ± 3.28 0.465 Neutrophil count(%) 70.08 ± 9.36 69.46 ± 9.16 71.19 ± 9.79 0.464 Lymphocyte count(%) 18.78 ± 7.60 18,78 ± 7.27 18.76 ± 8.34 0.992 Monocyte count(%) 7.11 ± 2.58 7.07 ± 2.77 7.20 ± 2.26 0.838 Platelet count(10 9 ) 199.01 ± 68.58 209.07 ± 65.98 181.62 ± 70.77 0.105 Fibrinogen(g/l) 4.44 ± 1.32 4.53 ± 1.38 4.28 ± 1.23 0.440 HB(g/l) 94.42 ± 22.11 97.2 ± 17.93 89.61 ± 27.65 0.165 CRP(mg/l) 25.45 ± 37.27 23.10 ± 36.77 29.93 ± 38.69 0.491 SNa(mmol/l) 140.38 ± 5.61 140.52 ± 6.10 140.14 ± 4.70 0.789 Sca(mmol/l) 2.15 ± 0.23 2.17 ± 0.22 2.12 ± 0.24 0.351 SK(mmol/l) 4.25 ± 0.62 4.19 ± 0.62 4.34 ± 0.60 0.331 eGFR (ml/min/1.73 m 2 ) 7.00 ± 2.95 7.10 ± 2.97 6.80 ± 2.94 0.681 TC (mmol/l) 4.12 ± 1.19 4.49 ± 1.24 3.51 ± 0.79 0.001 ** TG (mmol/l) 1.40(0.90–2.10) 1.63(1.15–2.20) 1.20(0.75–1.70) 0.055 LDL (mmol/l) 2.34 ± 1.06 2.66 ± 1.08 1.76 ± 0.77 0.001 ** HDL (mmol/l) 1.10 ± 0.30 1.07 ± 0.30 1.16 ± 0.30 0.251 PTH (pg/ml) 256.11 ± 188.54 231.39 ± 171.08 301.43 ± 213.37 0.145 FBG (mmol/l) 4.90(4.40–5.50) 4.90(4.35–5.54) 4.70(4.48–5.18) 0.591 Osteocalcin (ng/ml) 95.33 ± 69.84 81.94 ± 61.03 121.49 ± 79.57 0.030 * ALB (g/l) 35.47 ± 5.94 35.17 ± 6.09 36.01 ± 5.75 0.574 BUN(mmol/l) 21.73 ± 8.31 21.88 ± 8.47 21.47 ± 8.19 0.842 SP(mmol/l) 1.72 ± 0.54 1.68 ± 0.46 1.79 ± 0.67 0.444 Data are presented as mean ± SD, median ± interquartile range and percentage, where appropriate. Abbreviations：BMI：body mass index;DM: Diabetes mellitus, ALB: albumin, TC:cholesterol, TG:triglycerides, LDL: low-density lipoprotein, HDL:high-density lipoprotein, FBG:fasting blood glucose, BUN:urea nitrogen, HB: hemoglobin, CRP: C-reactive protein, SP:serum phosphorus, eGFR: estimated glomerular filtration rate. The association of TC, LDL and osteocalcin with ICAS in ESKD patients To explore whether the above parameters with significant differences between the two groups were independent factors for ICAS, a binary logistic regression model was employed. The results are shown in Table 2 . In the unadjusted model, TC (OR = 2.815, 95% CI: 1.455–5.466, P = 0.002), LDL (OR = 3.221, 95% CI: 1.508–6.883, P = 0.003), and osteocalcin (OR = 0.992, 95% CI: 0.984-1.000, P = 0.038) were associated with ICAS. After adjusting for potential confounding factors such as age, sex, BMI, hypertension, DM, FBG and eGFR, TC (OR = 3.372, 95% CI: 1.497–7.593, P = 0.003) and LDL (OR = 3.795, 95% CI: 1.550–9.289, P = 0.004) remained associated with ICAS, while osteocalcin (OR = 0.992, 95% CI: 0.983–1.001, P = 0.065) was no longer linked to ICAS. Table 2 The association of TC, LDL and Osteocalcin with ICAS in ESKD patients parameters ModelⅠ(OR 95%CI) P value ModelⅡ(OR 95%CI) P value TC(mmol/l) 2.815(1.455–5.446) 0.002** 3.372(1.497–7.593) 0.003** LDL(mmol/l) 3.221(1.508–6.883) 0.003** 3.795(1.550–9.289) 0.004** Osteocalcin(ng/l) 0.992(0.984-1.000) 0.038* 0.992(0.983–1.001) 0.065 Notes: ModelⅠ:crude model; Model Ⅱ:adjustment for age, gender, BMI, hypertension, DM, FBG, eGFR. The proportion of ICAS in TC and LDL quantiles TC and LDL were divided into two groups according to their means, and the proportions of ICAS in different quantiles are shown in Fig. 1 . For TC, the chi-square test indicated that the proportion of ICAS in the second quantile was significantly higher than that in the first quantile (81.8% vs. 44.1%, χ2 = 10.176, P = 0.001, Fig. 1 A). For LDL, the chi-square test showed that the proportion of ICAS in the second quantile was significantly higher than that in the first quantile (81.3% vs. 46.9%, χ2 = 8.212, P = 0.004, Fig. 1 B). The association of TC and LDL quantiles with ICAS Next, we explored the association of TC and LDL quantiles with ICAS in ESKD patients. The results of binary logistic regression are shown in Table 3 . In model I, the risk for ICAS in the second quantiles of TC (OR = 5.70, 95% CI: 1.871–17.364, P = 0.002) and LDL (OR = 4.91, 95% CI: 1.591–15.157, P = 0.006) was significantly increased from their respective first quantiles. After adjustment for confounding factors such as age, sex, BMI, hypertension, DM, FBG and eGFR in model II, the risk for ICAS in the second quantiles of TC (OR = 8.842, 95% CI: 2.223–35.178, P = 0.002) and LDL (OR = 6.795, 95% CI: 1.657–27.859, P = 0.008) was still significantly increased from their first quantiles. Table 3 the association of TC and LDL with ICAS according to their quantiles in ESKD patients modelⅠ(OR 95%CI) P value modelⅡ(OR 95% CI) P value TC(mmol/l) ≤ 3.990 1.00(Ref.) 1.00(Ref.) >3.990 5.70(1.871–17.364) 0.002** 8.842(2.223–35.178) 0.002** LDL(mmol/l) ≤ 2.175 1.00(Ref.) 1.00(Ref.) > 2.175 4.91(1.5911–15.157) 0.006** 6.795(1.657–27.859) 0.008** Notes: ModelⅠ:crude model; Model Ⅱ:adjustment for age, gender, BMI, hypertension, DM, FBG, eGFR. The predictive value of TC and LDL for ICAS in patients with ESKD ROC analysis was used to determine the predictive value of the above variables for ICAS. The outcomes are shown in Fig. 2 and Table 4 . The AUC of TC was 0.750, with a sensitivity of 71.4% and specificity of 72.0%, and the AUC of LDL was 0.756, with a sensitivity of 75.6% and specificity of 69.6%. Table 4 the outcome of ROC of the following variables predicting ICAS in ESKD patients variables AUC Cut-off point Sensitivity(%) Specificity(%) 95%CI TC 0.750 3.870mmol/ 71.400 72.000 0.630–0.870 LDL 0.756 2.025mmol/l 75.600 69.600 0.630–0.882 Discussion In this study, we found that TC and LDL were associated with ICAS in patients with ERSD after controlling confounding factors. They both exhibited predictive value for ICAS in patients with ESKD. TC and LDL were associated with the presence of ICAS[ 11 ]. However, in these studies, the association of TC and LDL with ICAS was not adjusted for eGFR, as renal function is also a very important factor affecting ICAS and stroke. The present study may further provide support for TC and LDL as independent risk factors for ICAS in ESKD population. Different from other studies, this study not only found that TC and LDL were independent risk factors for ICAS but also could predict the occurrence of ICAS in patients with ESKD, especially when TC was greater than 3.870 mmol/l or LDL was greater than 2.025 mmol/l. Dyslipidemia is common in CKD patients, especially ESKD patients[ 12 ]. However, the patterns of lipid disorders vary in different categories or stages of CKD[ 13 ]. There are many reasons for the complex lipid profile, such as inflammation, malnutrition, complications (secondary hyperparathyroidism), drugs (cyclosporine, mTOR inhibitor, glucocorticoid), insulin resistance and dialysis-related factors (dialysis membrane, heparin)[ 14 ].It is well documented that high levels of TC and LDL are related to cardiovascular diseases in the general population. However, it seems that this relationship does not exist in CKD patients, especially in ESKD patients on dialysis[ 14 ]. There is a contradictory conclusion; that is, when LDL-C is below the average level, LDL-C levels are negatively related to all-cause mortality, while when LDL-C is above the average level, there is no relationship or only a weak positive correlation between the two[ 15 ]. This phenomenon is called “reverse causality”. At present, there have been many explanations. The most recognized reason is inflammation and malnutrition in ESKD patients undergoing dialysis[ 16 ]. In addition, some factors that cause nonatherosclerotic cardiovascular events also increase in such patients, such as uremic toxins, anemia, hypertension, hypervolemia, abnormal calcium and phosphorus metabolism, mineral and bone abnormalities, electrolyte disorders, and comorbid conditions such as diabetes[ 17 ]. These reasons together lead to the “reverse causality” relationship between lipids and cardiovascular all-cause mortality in CKD patients. To date, three large randomized controlled clinical trials (RCTs) have tested the effect of lipid lowering on cardiovascular mortality in CKD patients, including the Die Deutsche Diabetes Disease Study (4D)[ 18 ], An Assessment of Survival and Cardiovascular Events (AURORA)[ 19 ] and Study of Heart and Renal Protection (SHARP) trials[ 20 ]. The 4D and AURORA trials focused on dialysis patients (1255 and 2776 dialysis patients, respectively), while the SHARP trial enrolled 9270 CKD patients (3023 on dialysis). Both the 4D study and the AURORA study showed that lipid-lowering therapy plays no role in decreasing cardiovascular events in ESKD patients undergoing dialysis. In addition, the 4D study also showed that fatal stroke incidence was significantly higher in the atorvastatin arm. Contrary to the 4D study and AURORA study, the SHARP trial suggested that the combination of simvastatin and ezetimibe can significantly reduce 17% of major atherosclerosis events and 15% of major cardiovascular events in the entire cohort, including subgroup dialysis patients. Furthermore, the post hoc analysis of the 4D trial showed that for hemodialysis patients with baseline LDL-C levels above 3.76 mmol/l, lipid-lowering therapy could significantly reduce cardiovascular events[ 21 ]. However, for the results of the subgroup analysis of the 4D study and SHARP study, we should hold caution due to reduced statistical power and insufficient testing between subgroup patients. In addition, a large meta-analysis of 35 studies involving 8289 dialysis patients also showed that lipid-lowering therapy had no benefit in reducing cardiovascular events[ 22 ]. In general, for pre-ESKD patients, the cardiovascular protective effect of lipid-lowering treatment is the same as that of other non-CKD patients[ 23 ], but it is still unclear whether ESKD patients on dialysis need active lipid-lowering treatment. Based on the above RCTs, observational experiments and meta-analysis results, the 2013 Kidney Disease: Improving Global Prognosis (KDIGO) organization proposed corresponding guidelines on lipid-lowering management for CKD patients[ 24 ]. This guideline emphasizes that lipid-lowering therapy should focus on CKD patients (not on dialysis), while for dialysis patients, lipid-lowering therapy should only be applied in patients who have received statins or statin/ezetimibe combination therapy at the initiation of dialysis, but the evidence level is 2C[ 24 ]. The KDIGO organization holds a relatively conservative attitude toward lipid-lowering therapy for dialysis patients. In the present study, we found that TC and LDL were associated with ICAS in ESKD patients even after adjustment for confounders. As mentioned above, ESKD patients have unique stroke-prone statuses, such as anticoagulation, anemia, platelet function, and mineral and bone metabolism abnormalities. However, in this study, we did not find a difference between fibrinogen, anemia, calcium and phosphorus metabolism, and PTH in the two groups (some are not shown), which indicates that for ESKD patients, ICAS is related to abnormal lipids, not caused by vascular calcification. It seems that our study is more inclined to support lipid-lowering therapy, but only for ESKD patients whose LDL level exceeds 2.025 mmol/l or TC exceeds 3.870 mmol/l. Study strengths and limitations To our knowledge, this is the first study to explore risk factors and predictive factors for ICAS in patients with ESKD, which can provide insight into stroke prevention for ESKD patients. The potential limitations of this study need to be stated. First, our study is a small single-center study, and the results cannot be extrapolated. Second, there was a lack of data on some confounders, such as smoking, alcohol consumption and CVD history. Third, the diagnosis of ICAS is not the gold standard. Fourth, selection bias may exist in this study. Last but not least, this is a cross-sectional study, so it is necessary to further carry out a randomized controlled study with a larger population to confirm the conclusions of this study. Conclusions Our study identifies total cholesterol (TC) and low-density lipoprotein (LDL) as significant predictors of intracranial artery stenosis (ICAS) in patients with end-stage renal disease (ESKD), even after adjusting for relevant confounding factors. Elevated levels of TC and LDL are independently associated with an increased risk of ICAS, suggesting that lipid profile assessment may play an important role in identifying high-risk ESKD patients for early ICAS detection. These findings underscore the potential value of lipid-lowering therapies targeted at ESKD patients with higher LDL and TC levels to reduce cerebrovascular complications. However, further large-scale, randomized controlled studies are needed to confirm the efficacy and safety of lipid management in this unique population. Declarations Acknowledgments This manuscript has been seen and approved by all authors and is not under consideration for publication elsewhere in a similar form in any language. Author contributions ZBY and JX designed this study. XZ and YX collected the data. YYZ, YQZ and DPW , MM analyzed the data and prepared the paper. ZBY and JX revised and approved the final version and take responsibility for the data analysis. All authors have read the paper and agreed to publication. Funding Excellence Programme of Fudan University to Dr. Jing Xiao (Grant Number JIF163001) and Shanghai Municipal Key Clinical Specialty to Huadong hospital (Grant Number shslczdzk02801). And this work was supported by the Key Specialized Disease Construction Plan of Huadong Hospital (Grant Number ZDZB2223). Ethics Approval Number: 20190034 Clinical trial number: not applicable Conflict of interest None. References Jankowski J, et al. Cardiovascular Disease in Chronic Kidney Disease: Pathophysiological Insights and Therapeutic Options. Circulation. 2021;143(11):1157–72. de Jager DJ, et al. Cardiovascular and noncardiovascular mortality among patients starting dialysis. JAMA. 2009;302(16):1782–9. Masson P, et al. Chronic kidney disease and the risk of stroke: a systematic review and meta-analysis. Nephrol Dial Transpl. 2015;30(7):1162–9. De La Mata NL, et al. 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HMG CoA reductase inhibitors (statins) for dialysis patients. Cochrane Database Syst Rev, 2013(9): p. Cd004289. Yen CL, et al. Association of Low-Density Lipoprotein Cholesterol Levels During Statin Treatment With Cardiovascular and Renal Outcomes in Patients With Moderate Chronic Kidney Disease. J Am Heart Assoc. 2022;11(19):e027516. Wanner C, Tonelli M. KDIGO Clinical Practice Guideline for Lipid Management in CKD: summary of recommendation statements and clinical approach to the patient. Kidney Int. 2014;85(6):1303–9. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-6778528","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":475743784,"identity":"77e795a2-b4a5-448b-b685-c6549541124b","order_by":0,"name":"Yui Zheng","email":"","orcid":"","institution":"Huadong Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yui","middleName":"","lastName":"Zheng","suffix":""},{"id":475743785,"identity":"8f53785a-b9ac-482c-ac58-57978862a36a","order_by":1,"name":"Yingying Zhang","email":"","orcid":"","institution":"Huadong Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yingying","middleName":"","lastName":"Zhang","suffix":""},{"id":475743786,"identity":"a4993a33-2c95-4a3d-9c4a-eba2887258b9","order_by":2,"name":"Madiya Madeniyet","email":"","orcid":"","institution":"Huadong Hospital","correspondingAuthor":false,"prefix":"","firstName":"Madiya","middleName":"","lastName":"Madeniyet","suffix":""},{"id":475743787,"identity":"f4f5ef28-a188-4bcb-84c7-0473c36a2c84","order_by":3,"name":"Jing Xiao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYBACxmYQacDAwMbffOADAxsDlEuMFj6JY4kziNICB3IMOYbEaWFu5z38mqfATo6N4czHZp4yuzwG9uZtEgw1d/A4jC/NcoZBsjEbc+/GZp5zycUMPMfKJBiOPcOjhcfM4IMBc2Ibw9ntj3nbmBMbJHLMJBgbDuPXkmBQD9SS87CZt60+sUH+DUEtxg8+GBwGaWEEajkMtIWHsC2MMwyOG7NJHDNsnHPueGIbT1qxRcIx3FoM+88Yf+b5Uy0n39/8sOFNWXViP/vhjTc+1ODR0sDAJoEiAo6aBJwaGBjkgVHzAY/8KBgFo2AUjAIGBgCtE1AjCNhJvgAAAABJRU5ErkJggg==","orcid":"","institution":"Huadong Hospital","correspondingAuthor":true,"prefix":"","firstName":"Jing","middleName":"","lastName":"Xiao","suffix":""},{"id":475743788,"identity":"a0e9dfa9-606c-4aaa-a34e-9dc54de5a6d6","order_by":4,"name":"Zhibin Ye","email":"","orcid":"","institution":"Huadong Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhibin","middleName":"","lastName":"Ye","suffix":""}],"badges":[],"createdAt":"2025-05-29 17:08:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6778528/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6778528/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85647434,"identity":"0c291936-f88f-4dd1-b8bc-8feec9674eaf","added_by":"auto","created_at":"2025-06-30 08:48:31","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":570961,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe proportion of ICAS in patients with ESKD according to TC and LDL quantiles.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A)The proportion of ICAS in patients with ESKD according to TC quantiles. (B) The proportion of ICAS in patients with ESKD according to LDL quantiles.\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6778528/v1/14b4035a9ddc54762fba572a.jpg"},{"id":85647435,"identity":"cdd68c3e-8278-42aa-b32d-a1833b7d114d","added_by":"auto","created_at":"2025-06-30 08:48:31","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":67980,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe outcome of the receiver operating curve of TC and LDL for predicting ICAS in patients with ESKD.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A)The results of ROC of TC for predicting ICAS. (B) The ROC curve of LDL for predicting ICAS.\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6778528/v1/6491e678db55ff4f7600e09f.jpg"},{"id":93708771,"identity":"f641e7af-f4e7-469f-81b3-99835ac02b0d","added_by":"auto","created_at":"2025-10-16 17:16:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1550134,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6778528/v1/77a92da1-3f4d-487c-9dc2-5102d28a2d33.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Risk factors for and predictors of intracranial artery stenosis in patients with end-stage kidney disease","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChronic kidney disease (CKD) is an independent risk factor for cardiovascular and cerebrovascular diseases[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Among all stages of CKD, CKD5, or end-stage renal disease (ESKD), is associated with the greatest risk of cardiovascular and cerebrovascular disease[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Both albuminuria and eGFR, two key parameters for CKD stratification, have been shown to be associated with stroke risk in a dose‒response fashion, such as a 10% increase in stroke risk for every 25 mg/mmol increase in albuminuria and a 7% increase in stroke risk for every 10 min/ml/1.73 m\u003csup\u003e2\u003c/sup\u003e decrease in eGFR[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe risk of stroke among patients with ESKD is approximately 30 times higher than that of the general population[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Although the incidence of hemorrhagic stroke has increased substantially among patients with ESKD, ischemic stroke remains the majority of all stroke types[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. According to TOAST criteria, large artery atherosclerosis (carotid artery and/or intracranial artery) is a major cause of ischemic stroke[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Moreover, intracranial artery stenosis (ICAS) is considered to be a noteworthy risk factor for stroke in Asian populations compared with non-Asian populations[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, data on the comorbidity of ICAS in Chinese patients with ESKD are scarce and warrant exploration.\u003c/p\u003e \u003cp\u003eDigital subtraction angiography (DSA) is the gold standard for the diagnosis of ICAS, but its application in the screening of ICAS is limited due to the high radiation exposure risk and the need for a large dose of iodine contrast agent, which is a contraindication in CKD[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Recently, magnetic resonance angiography (MRA) has become a commonly used method for ICAS detection[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Several studies have confirmed that, compared with DSA, the sensitivity of MRA detection for ICAS is 78\u0026ndash;92%, and the specificity is 91\u0026ndash;95%[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Therefore, MRA is a rational choice for ICAS detection in patients with CKD for whom the usage of iodine contrast agent should be avoided.\u003c/p\u003e \u003cp\u003eIn this study, we focused on ICAS comorbidity in CKD patients and confirmed the risk factors and their cutoff values as predictors for ICAS detection by MRA in patients with ESKD, providing insights for stroke prevention in this specific patient group.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy participantsh\u003c/h2\u003e\n \u003cp\u003eFor the present study, a retrospective design was employed. We recruited 178 patients who were hospitalized in our unit (Nephrology, Huadong Hospital Affiliated to Fudan University, Shanghai, China) between June 2017 and March 2021 and underwent brain MRA during hospitalization. Exclusion criteria mainly included congenital cerebrovascular disease, severe cardiac, pulmonary and liver dysfunction, and other diseases affecting calcium and phosphorus metabolism, such as malignant tumor, multiple myeloma, primary parathyroid disease, recent fracture ( within the last 3 months), and acute stroke during hospitalization. Based on the intracranial MRA, ICAS was defined as intracranial artery stenosis exceeding 50% in any artery. We included 45 patients in the ICAS group and 26 patients without ICAS as the control group.\u003c/p\u003e\n \u003cp\u003eInformed consent\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ewas obtained from all patients, and the study protocol was approved by the Ethics Committee of Huadong Hospital Affiliated to Fudan University, Shanghai.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eClinical data collection and laboratory tests\u003c/h3\u003e\n\u003cp\u003eRegarding the collection of clinical information, previous history, medication history, family history, history of smoking, alcohol consumption, dietary habits, other information, BMI index, and blood pressure, all nurses received unified training. Hypertension was defined as systolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg, diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg, or the use of antihypertensive medication. The diagnosis of diabetes mellitus (DM) was based on the 2016 American Diabetes Association (ADA) diagnostic criteria.\u003c/p\u003e\n\u003cp\u003eAfter 8 hours of overnight fasting, blood samples were collected for laboratory measurements. Lab tests included: routine blood test, coagulation function parameters, and C-reactive protein (CRP), albumin (ALB), total cholesterol (TC), triglycerides (TG), low-density lipoprotein (LDL), high-density lipoprotein (HDL), fasting blood glucose (FBG), urea nitrogen (BUN), serum creatinine (SCR), serum uric acid (SUA), serum calcium (SCA), serum phosphorus (SP), serum potassium (SK), serum sodium (SNA), serum chlorine (SCL), parathyroid hormone (PTH), 25 hydroxyl vitamin D [25(OH)-Vit D], osteocalcin, etc. Estimated glomerular filtration rate (eGFR milliliters per minute per 1.73 m\u003csup\u003e2\u003c/sup\u003e) was calculated using the Chronic Kidney Disease-EPI formula.\u003c/p\u003e\n\u003ch3\u003eAssessment of ICAS\u003c/h3\u003e\n\u003cp\u003eMRI was performed on a 3 Tesla Siemens Magnetom Prisma scanner using a 32-channel head coil at the Imaging Department of Huadong Hospital. The three-dimensional time-of-flight MRA images (3D TOF MRA) were acquired with a repetition time of 21 ms, time to echo of 3.42 ms, flip angle of 18\u0026deg;, 220*176 mm field of view, 218 \u0026times; 256 acquisition matrix, slice thickness of 0.60 mm, distance factor \u0026minus;\u0026thinsp;25%, and an acquisition time of 4 min and 54 s. Intracranial MRA was performed on the 3rd to 4th day of hospitalization on patients who complained of dizziness, and ICAS was diagnosed by experienced radiologists. ICAS was defined as localized stenosis greater than 50% in any artery assessed, including the vertebral artery, basilar artery, internal carotid artery, posterior cerebral artery, middle cerebral artery, and anterior cerebral artery.\u003c/p\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003ePatients were divided into two groups according to the quantiles of TC: TC-I (TC\u0026thinsp;\u0026le;\u0026thinsp;3.990 mmol/l) and TC-II (TC\u0026thinsp;\u0026gt;\u0026thinsp;3.990 mmol/l) or according to the quantiles of LDL: LDL-I (LDL\u0026thinsp;\u0026le;\u0026thinsp;2.175 mmol/l) and LDL-II (LDL\u0026thinsp;\u0026gt;\u0026thinsp;2.175 mmol/l). ICAS proportions of each group were described, and the chi-square test was used to compare the differences in ICAS proportions of each group. The association of TC and LDL with ICAS was determined initially using a logistic regression model with odds ratios (ORs) and 95% confidence intervals (CIs). The confounding factors for ICAS including age, sex, BMI, hypertension, DM, FBG and eGFR. Finally, a corresponding ROC curve was plotted, and the best threshold is calculated. All statistical tests were two-tailed, and the results were considered significant at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. SPSS 26 was used for statistical analysis.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eComparison of general clinical characteristics between ICAS and NICAS in ESKD patients\u003c/h2\u003e \u003cp\u003eThe general clinical characteristics of each group are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The average age of patients in all participants was 67.61\u0026thinsp;\u0026plusmn;\u0026thinsp;11.62 years, and the eGFR was 7.00\u0026thinsp;\u0026plusmn;\u0026thinsp;2.95 ml/min/1.73 m2. Significant difference in TC, LDL and osteocalcin were observed between groups. The levels of TC and LDL were significantly higher in the ICAS group than in the NICAS group, while the level of osteocalcin in the ICAS group was significantly lower than that in the NICAS group. (The ICAS group had significantly higher TC and LDL levels, while osteocalcin levels were lower compared to the NICAS group.) Other parameters were not significantly different between the two groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe baseline characteristics of ESKD patients with ICAS or NICAS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eparameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;71)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eICAS (n\u0026thinsp;=\u0026thinsp;45)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNICAS (n\u0026thinsp;=\u0026thinsp;26)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (year),\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67.61\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;11.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.33\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;11.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68.12\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;11.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.788\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25(35.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15(33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10(38.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.663\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66(93.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41(91.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25(96.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.750\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43(60.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25(55.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18(69.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.43\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;4.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.72\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;4.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.91\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;3.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.465\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophil count(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.08\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;9.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.46\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;9.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71.19\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;9.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.464\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocyte count(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.78\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;7.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18,78\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;7.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.76\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;8.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonocyte count(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.11\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;2.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.07\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;2.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.20\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;2.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.838\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet count(10\u003csup\u003e9\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e199.01\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;68.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e209.07\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;65.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e181.62\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;70.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFibrinogen(g/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.44\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.53\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.28\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.440\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHB(g/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94.42\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;22.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97.2\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;17.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89.61\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;27.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP(mg/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.45\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;37.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.10\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;36.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.93\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;38.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.491\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSNa(mmol/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e140.38\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;5.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e140.52\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;6.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e140.14\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;4.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.789\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSca(mmol/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.15\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.17\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.12\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.351\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSK(mmol/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.25\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.19\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.34\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.331\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR (ml/min/1.73 m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.00\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;2.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.10\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;2.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.80\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;2.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.681\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC (mmol/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.12\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.49\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.51\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mmol/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.40(0.90\u0026ndash;2.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.63(1.15\u0026ndash;2.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.20(0.75\u0026ndash;1.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL (mmol/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.34\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.66\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.76\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL (mmol/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.10\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.07\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.16\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.251\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTH (pg/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e256.11\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;188.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e231.39\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;171.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e301.43\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;213.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFBG (mmol/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.90(4.40\u0026ndash;5.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.90(4.35\u0026ndash;5.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.70(4.48\u0026ndash;5.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.591\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOsteocalcin (ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95.33\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;69.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81.94\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;61.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e121.49\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;79.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.030\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALB (g/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.47\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;5.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.17\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;6.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.01\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;5.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.574\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN(mmol/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.73\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;8.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.88\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;8.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.47\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;8.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.842\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSP(mmol/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.72\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.68\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.79\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.444\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eData are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, median\u0026thinsp;\u0026plusmn;\u0026thinsp;interquartile range and percentage, where appropriate.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\u003cp\u003eAbbreviations：BMI：body mass index;DM: Diabetes mellitus, ALB: albumin, TC:cholesterol, TG:triglycerides, LDL: low-density lipoprotein, HDL:high-density lipoprotein, FBG:fasting blood glucose, BUN:urea nitrogen, HB: hemoglobin, CRP: C-reactive protein, SP:serum phosphorus, eGFR: estimated glomerular filtration rate.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ch3\u003eThe association of TC, LDL and osteocalcin with ICAS in ESKD patients\u003c/h3\u003e\n\u003cp\u003eTo explore whether the above parameters with significant differences between the two groups were independent factors for ICAS, a binary logistic regression model was employed. The results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In the unadjusted model, TC (OR\u0026thinsp;=\u0026thinsp;2.815, 95% CI: 1.455\u0026ndash;5.466, P\u0026thinsp;=\u0026thinsp;0.002), LDL (OR\u0026thinsp;=\u0026thinsp;3.221, 95% CI: 1.508\u0026ndash;6.883, P\u0026thinsp;=\u0026thinsp;0.003), and osteocalcin (OR\u0026thinsp;=\u0026thinsp;0.992, 95% CI: 0.984-1.000, P\u0026thinsp;=\u0026thinsp;0.038) were associated with ICAS. After adjusting for potential confounding factors such as age, sex, BMI, hypertension, DM, FBG and eGFR, TC (OR\u0026thinsp;=\u0026thinsp;3.372, 95% CI: 1.497\u0026ndash;7.593, P\u0026thinsp;=\u0026thinsp;0.003) and LDL (OR\u0026thinsp;=\u0026thinsp;3.795, 95% CI: 1.550\u0026ndash;9.289, P\u0026thinsp;=\u0026thinsp;0.004) remained associated with ICAS, while osteocalcin (OR\u0026thinsp;=\u0026thinsp;0.992, 95% CI: 0.983\u0026ndash;1.001, P\u0026thinsp;=\u0026thinsp;0.065) was no longer linked to ICAS.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe association of TC, LDL and Osteocalcin with ICAS in ESKD patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eparameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModelⅠ(OR 95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModelⅡ(OR 95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC(mmol/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.815(1.455\u0026ndash;5.446)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.002**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.372(1.497\u0026ndash;7.593)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.003**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL(mmol/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.221(1.508\u0026ndash;6.883)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.003**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.795(1.550\u0026ndash;9.289)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.004**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOsteocalcin(ng/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.992(0.984-1.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.038*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.992(0.983\u0026ndash;1.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNotes: ModelⅠ:crude model; Model Ⅱ:adjustment for age, gender, BMI, hypertension, DM, FBG, eGFR.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eThe proportion of ICAS in TC and LDL quantiles\u003c/h3\u003e\n\u003cp\u003eTC and LDL were divided into two groups according to their means, and the proportions of ICAS in different quantiles are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. For TC, the chi-square test indicated that the proportion of ICAS in the second quantile was significantly higher than that in the first quantile (81.8% vs. 44.1%, χ2\u0026thinsp;=\u0026thinsp;10.176, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). For LDL, the chi-square test showed that the proportion of ICAS in the second quantile was significantly higher than that in the first quantile (81.3% vs. 46.9%, χ2\u0026thinsp;=\u0026thinsp;8.212, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eThe association of TC and LDL quantiles with ICAS\u003c/h2\u003e \u003cp\u003eNext, we explored the association of TC and LDL quantiles with ICAS in ESKD patients. The results of binary logistic regression are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. In model I, the risk for ICAS in the second quantiles of TC (OR\u0026thinsp;=\u0026thinsp;5.70, 95% CI: 1.871\u0026ndash;17.364, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002) and LDL (OR\u0026thinsp;=\u0026thinsp;4.91, 95% CI: 1.591\u0026ndash;15.157, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006) was significantly increased from their respective first quantiles. After adjustment for confounding factors such as age, sex, BMI, hypertension, DM, FBG and eGFR in model II, the risk for ICAS in the second quantiles of TC (OR\u0026thinsp;=\u0026thinsp;8.842, 95% CI: 2.223\u0026ndash;35.178, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002) and LDL (OR\u0026thinsp;=\u0026thinsp;6.795, 95% CI: 1.657\u0026ndash;27.859, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008) was still significantly increased from their first quantiles.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ethe association of TC and LDL with ICAS according to their quantiles in ESKD patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003emodelⅠ(OR 95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003emodelⅡ(OR 95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC(mmol/l)\u003c/p\u003e \u003cp\u003e\u0026le;\u0026thinsp;3.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00(Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00(Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;3.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.70(1.871\u0026ndash;17.364)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.002**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.842(2.223\u0026ndash;35.178)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.002**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL(mmol/l)\u003c/p\u003e \u003cp\u003e\u0026le;\u0026thinsp;2.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00(Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00(Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;2.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.91(1.5911\u0026ndash;15.157)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.006**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.795(1.657\u0026ndash;27.859)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.008**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNotes: ModelⅠ:crude model; Model Ⅱ:adjustment for age, gender, BMI, hypertension, DM, FBG, eGFR.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eThe predictive value of TC and LDL for ICAS in patients with ESKD\u003c/h2\u003e \u003cp\u003eROC analysis was used to determine the predictive value of the above variables for ICAS. The outcomes are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The AUC of TC was 0.750, with a sensitivity of 71.4% and specificity of 72.0%, and the AUC of LDL was 0.756, with a sensitivity of 75.6% and specificity of 69.6%.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ethe outcome of ROC of the following variables predicting ICAS in ESKD patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003evariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCut-off point\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSensitivity(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSpecificity(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.870mmol/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71.400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e72.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.630\u0026ndash;0.870\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.025mmol/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e75.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e69.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.630\u0026ndash;0.882\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we found that TC and LDL were associated with ICAS in patients with ERSD after controlling confounding factors. They both exhibited predictive value for ICAS in patients with ESKD.\u003c/p\u003e \u003cp\u003eTC and LDL were associated with the presence of ICAS[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, in these studies, the association of TC and LDL with ICAS was not adjusted for eGFR, as renal function is also a very important factor affecting ICAS and stroke. The present study may further provide support for TC and LDL as independent risk factors for ICAS in ESKD population. Different from other studies, this study not only found that TC and LDL were independent risk factors for ICAS but also could predict the occurrence of ICAS in patients with ESKD, especially when TC was greater than 3.870 mmol/l or LDL was greater than 2.025 mmol/l.\u003c/p\u003e \u003cp\u003eDyslipidemia is common in CKD patients, especially ESKD patients[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, the patterns of lipid disorders vary in different categories or stages of CKD[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. There are many reasons for the complex lipid profile, such as inflammation, malnutrition, complications (secondary hyperparathyroidism), drugs (cyclosporine, mTOR inhibitor, glucocorticoid), insulin resistance and dialysis-related factors (dialysis membrane, heparin)[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].It is well documented that high levels of TC and LDL are related to cardiovascular diseases in the general population. However, it seems that this relationship does not exist in CKD patients, especially in ESKD patients on dialysis[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. There is a contradictory conclusion; that is, when LDL-C is below the average level, LDL-C levels are negatively related to all-cause mortality, while when LDL-C is above the average level, there is no relationship or only a weak positive correlation between the two[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. This phenomenon is called \u0026ldquo;reverse causality\u0026rdquo;. At present, there have been many explanations. The most recognized reason is inflammation and malnutrition in ESKD patients undergoing dialysis[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In addition, some factors that cause nonatherosclerotic cardiovascular events also increase in such patients, such as uremic toxins, anemia, hypertension, hypervolemia, abnormal calcium and phosphorus metabolism, mineral and bone abnormalities, electrolyte disorders, and comorbid conditions such as diabetes[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. These reasons together lead to the \u0026ldquo;reverse causality\u0026rdquo; relationship between lipids and cardiovascular all-cause mortality in CKD patients.\u003c/p\u003e \u003cp\u003eTo date, three large randomized controlled clinical trials (RCTs) have tested the effect of lipid lowering on cardiovascular mortality in CKD patients, including the Die Deutsche Diabetes Disease Study (4D)[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], An Assessment of Survival and Cardiovascular Events (AURORA)[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] and Study of Heart and Renal Protection (SHARP) trials[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The 4D and AURORA trials focused on dialysis patients (1255 and 2776 dialysis patients, respectively), while the SHARP trial enrolled 9270 CKD patients (3023 on dialysis). Both the 4D study and the AURORA study showed that lipid-lowering therapy plays no role in decreasing cardiovascular events in ESKD patients undergoing dialysis. In addition, the 4D study also showed that fatal stroke incidence was significantly higher in the atorvastatin arm. Contrary to the 4D study and AURORA study, the SHARP trial suggested that the combination of simvastatin and ezetimibe can significantly reduce 17% of major atherosclerosis events and 15% of major cardiovascular events in the entire cohort, including subgroup dialysis patients. Furthermore, the post hoc analysis of the 4D trial showed that for hemodialysis patients with baseline LDL-C levels above 3.76 mmol/l, lipid-lowering therapy could significantly reduce cardiovascular events[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. However, for the results of the subgroup analysis of the 4D study and SHARP study, we should hold caution due to reduced statistical power and insufficient testing between subgroup patients. In addition, a large meta-analysis of 35 studies involving 8289 dialysis patients also showed that lipid-lowering therapy had no benefit in reducing cardiovascular events[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In general, for pre-ESKD patients, the cardiovascular protective effect of lipid-lowering treatment is the same as that of other non-CKD patients[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], but it is still unclear whether ESKD patients on dialysis need active lipid-lowering treatment.\u003c/p\u003e \u003cp\u003eBased on the above RCTs, observational experiments and meta-analysis results, the 2013 Kidney Disease: Improving Global Prognosis (KDIGO) organization proposed corresponding guidelines on lipid-lowering management for CKD patients[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This guideline emphasizes that lipid-lowering therapy should focus on CKD patients (not on dialysis), while for dialysis patients, lipid-lowering therapy should only be applied in patients who have received statins or statin/ezetimibe combination therapy at the initiation of dialysis, but the evidence level is 2C[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The KDIGO organization holds a relatively conservative attitude toward lipid-lowering therapy for dialysis patients. In the present study, we found that TC and LDL were associated with ICAS in ESKD patients even after adjustment for confounders. As mentioned above, ESKD patients have unique stroke-prone statuses, such as anticoagulation, anemia, platelet function, and mineral and bone metabolism abnormalities. However, in this study, we did not find a difference between fibrinogen, anemia, calcium and phosphorus metabolism, and PTH in the two groups (some are not shown), which indicates that for ESKD patients, ICAS is related to abnormal lipids, not caused by vascular calcification. It seems that our study is more inclined to support lipid-lowering therapy, but only for ESKD patients whose LDL level exceeds 2.025 mmol/l or TC exceeds 3.870 mmol/l.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStudy strengths and limitations\u003c/h2\u003e \u003cp\u003eTo our knowledge, this is the first study to explore risk factors and predictive factors for ICAS in patients with ESKD, which can provide insight into stroke prevention for ESKD patients. The potential limitations of this study need to be stated. First, our study is a small single-center study, and the results cannot be extrapolated. Second, there was a lack of data on some confounders, such as smoking, alcohol consumption and CVD history. Third, the diagnosis of ICAS is not the gold standard. Fourth, selection bias may exist in this study. Last but not least, this is a cross-sectional study, so it is necessary to further carry out a randomized controlled study with a larger population to confirm the conclusions of this study.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur study identifies total cholesterol (TC) and low-density lipoprotein (LDL) as significant predictors of intracranial artery stenosis (ICAS) in patients with end-stage renal disease (ESKD), even after adjusting for relevant confounding factors. Elevated levels of TC and LDL are independently associated with an increased risk of ICAS, suggesting that lipid profile assessment may play an important role in identifying high-risk ESKD patients for early ICAS detection. These findings underscore the potential value of lipid-lowering therapies targeted at ESKD patients with higher LDL and TC levels to reduce cerebrovascular complications. However, further large-scale, randomized controlled studies are needed to confirm the efficacy and safety of lipid management in this unique population.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis manuscript has been seen and approved by all authors and is not under consideration for publication elsewhere in a similar form in any language.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003eZBY and JX designed this study. XZ and YX collected the data. YYZ, YQZ and DPW , MM analyzed the data and prepared the paper. ZBY and JX revised and approved the final version and take responsibility for the data analysis. All authors have read the paper and agreed to publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e Excellence Programme of Fudan University to Dr. Jing Xiao (Grant Number JIF163001) and Shanghai Municipal Key Clinical Specialty to Huadong hospital (Grant Number shslczdzk02801). And this work was supported by the Key Specialized Disease Construction Plan of Huadong Hospital (Grant Number ZDZB2223).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval Number:\u0026nbsp;\u003c/strong\u003e20190034\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e None.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJankowski J, et al. Cardiovascular Disease in Chronic Kidney Disease: Pathophysiological Insights and Therapeutic Options. Circulation. 2021;143(11):1157\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Jager DJ, et al. Cardiovascular and noncardiovascular mortality among patients starting dialysis. JAMA. 2009;302(16):1782\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMasson P, et al. Chronic kidney disease and the risk of stroke: a systematic review and meta-analysis. Nephrol Dial Transpl. 2015;30(7):1162\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe La Mata NL, et al. Absolute risk and risk factors for stroke mortality in patients with end-stage kidney disease (ESKD): population-based cohort study using data linkage. BMJ Open. 2019;9(2):e026263.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe La Mata NL, et al. Death From Stroke in End-Stage Kidney Disease. Stroke. 2019;50(2):487\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilterdink JL, et al. Effect of prior aspirin use on stroke severity in the trial of Org 10172 in acute stroke treatment (TOAST). Stroke. 2001;32(12):2836\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKirkpatrick J. Benefit of carotid endarterectomy in patients with symptomatic moderate or severe stenosis. J Insur Med. 1998;30(4):274\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWillinsky RA, et al. Neurologic complications of cerebral angiography: prospective analysis of 2,899 procedures and review of the literature. Radiology. 2003;227(2):522\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen H, et al. Relationship between visible branch arteries distal to the stenosis on magnetic resonance angiography and stroke recurrence in patients with severe middle cerebral artery trunk stenosis: a one-year follow up study. BMC Neurol. 2015;15:167.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoi CG, et al. Detection of intracranial atherosclerotic steno-occlusive disease with 3D time-of-flight magnetic resonance angiography with sensitivity encoding at 3T. AJNR Am J Neuroradiol. 2007;28(3):439\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang WS, et al. Importance of lipid ratios for predicting intracranial atherosclerotic stenosis. Lipids Health Dis. 2020;19(1):160.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePandya V, Rao A, Chaudhary K. Lipid abnormalities in kidney disease and management strategies. World J Nephrol. 2015;4(1):83\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMassy ZA, de Zeeuw D. LDL cholesterol in CKD\u0026ndash;to treat or not to treat? Kidney Int. 2013;84(3):451\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFerro CJ, et al. Lipid management in patients with chronic kidney disease. Nat Rev Nephrol. 2018;14(12):727\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaigent C, Landray MJ, Wheeler DC. Misleading associations between cholesterol and vascular outcomes in dialysis patients: the need for randomized trials. Semin Dial. 2007;20(6):498\u0026ndash;503.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsirpanlis G, et al. Low cholesterol along with inflammation predicts morbidity and mortality in hemodialysis patients. Hemodial Int. 2009;13(2):197\u0026ndash;204.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSarnak MJ, et al. Chronic Kidney Disease and Coronary Artery Disease: JACC State-of-the-Art Review. J Am Coll Cardiol. 2019;74(14):1823\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWanner C, et al. Atorvastatin in patients with type 2 diabetes mellitus undergoing hemodialysis. N Engl J Med. 2005;353(3):238\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFellstr\u0026ouml;m BC, et al. Rosuvastatin and cardiovascular events in patients undergoing hemodialysis. N Engl J Med. 2009;360(14):1395\u0026ndash;407.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaigent C, et al. The effects of lowering LDL cholesterol with simvastatin plus ezetimibe in patients with chronic kidney disease (Study of Heart and Renal Protection): a randomised placebo-controlled trial. Lancet. 2011;377(9784):2181\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM\u0026auml;rz W, et al. Atorvastatin and low-density lipoprotein cholesterol in type 2 diabetes mellitus patients on hemodialysis. Clin J Am Soc Nephrol. 2011;6(6):1316\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePalmer SC et al. HMG CoA reductase inhibitors (statins) for dialysis patients. Cochrane Database Syst Rev, 2013(9): p. Cd004289.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYen CL, et al. Association of Low-Density Lipoprotein Cholesterol Levels During Statin Treatment With Cardiovascular and Renal Outcomes in Patients With Moderate Chronic Kidney Disease. J Am Heart Assoc. 2022;11(19):e027516.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWanner C, Tonelli M. KDIGO Clinical Practice Guideline for Lipid Management in CKD: summary of recommendation statements and clinical approach to the patient. Kidney Int. 2014;85(6):1303\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"TC, LDL, ESKD Intracranial artery stenosis","lastPublishedDoi":"10.21203/rs.3.rs-6778528/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6778528/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose \u003c/strong\u003eTo explore the risk factors for and predictors of intracranial artery stenosis (ICAS) in patients with end-stage kidney disease (ESKD).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003eA cross-sectional study of 178 patients who were hospitalized in Department of Nephrology, Huadong Hospital, Fudan University, Shanghai, China between June 2017 and March 2021 and underwent brain MRA during hospitalization was conducted. ICAS was defined as intracranial artery stenosis exceeding 50%. We included 45 patients as the ICAS group and 26 patients without ICAS as the control group. Univariate analysis was used to explore different indicators. Binary logistic regression analysis further explored whether the different factors were independent risk factors, the ROC curve was used to explore the predictive value of various indicators for ICAS in patients with ESKD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eIn this analysis,\u003cstrong\u003e \u003c/strong\u003ethe average age of patients was 67.61 ± 11.62 years, and the eGFR was 7.00 ± 2.95 ml/min/1.73 m\u003csup\u003e2\u003c/sup\u003e. Binary logistic regression analysis showed that total cholesterol (TC) (OR=3.372, 95% CI: 1.497-7.593) and low-density lipoprotein (LDL) (OR=3.795, 95% CI: 1.550-9.289) were independently associated with ICAS. ROC analysis demonstrated that TC (71.4%, 72%) with a cutoff value of 3.870 mmol/l and LDL (75.6%, 69.6%) with a cutoff value of 2.025 mmol/l had strong predictive value for ICAS in patients with ESKD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion \u003c/strong\u003eIn patients with ESKD, TC and LDL were associated with ICAS after adjusting for covariates and have strong predictive value for ICAS.\u003c/p\u003e","manuscriptTitle":"Risk factors for and predictors of intracranial artery stenosis in patients with end-stage kidney disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-30 08:48:27","doi":"10.21203/rs.3.rs-6778528/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a1bf1ced-508a-477e-9909-86897f3608dc","owner":[],"postedDate":"June 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-16T17:08:36+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-30 08:48:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6778528","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6778528","identity":"rs-6778528","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}