Risk prediction of postoperative renal dysfunction based on preoperative lipid profiles in renal transplant recipients: A retrospective cohort study

Risk prediction of postoperative renal dysfunction based on preoperative lipid profiles in renal transplant recipients: A retrospective cohort study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Risk prediction of postoperative renal dysfunction based on preoperative lipid profiles in renal transplant recipients: A retrospective cohort study Hong Zhang, Haoxiang Zhang, Ronghua Li, Lin Zhuo, Ling Liu, Ling Tan, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5823279/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 Background Renal dysfunction is a frequent complication after kidney transplantation, leading to poor prognosis and increased mortality. Abnormal blood lipids are closely related to renal dysfunction, yet their associations and mechanisms among renal transplant recipients remain unclear. This study aimed to establish an effective risk prediction model for renal dysfunction among RTRs based on abnormal lipid profiles using machine learning. Methods This retrospective cohort study recruited a cohort of 345 RTRs and followed up for one year after renal transplantation. Patients' demographic and clinical characteristics, including blood lipids, were retrieved from the electronic medical record system and analyzed using machine learning. Renal dysfunction was defined as estimated glomerular filtration rate (eGFR) < 60 mL/min /1.73 m 2 . The cohort was randomly split into training (n = 276) and validation (n = 69) groups at a 4:1 ratio. Predictors of renal dysfunction were determined using three ML models: RandomForest, XGBoost, and LightGBM. Results During the one-year follow-up, 193 (55.9%) patients had renal dysfunction. A total of 20 demographic and clinical variables were selected to screen for significant predictors of renal dysfunction, and five were retained, including age, gender, HDL-C, non-HDL-C, and LDL-C, based on which a nomogram was developed. The nomogram showed good diagnostic performance with an area under the curve (AUC) of 0.87 in the training group and 0.81 in the validation group. Conclusions Our study showed that preoperative lipid profiles predicted postoperative renal function among RTRs, based on which we developed a risk prediction model. The model can quickly identify high-risk RTRs with renal dysfunction, which is crucial for optimizing patient management and improving the prognosis. kidney transplantation renal dysfunction eGFR risk prediction blood lipid levels machine learning nomogram Figures Figure 1 Figure 2 Figure 3 1 Introduction Kidney transplantation is the best treatment option for end-stage renal disease [ 1 ] . Despite the well-demonstrated benefits of kidney transplantation in improving the quality of life and prolonging life expectancy, renal dysfunction is a frequent complication after kidney transplantation, leading to poor prognosis and increased mortality [ 2 ] . Recent estimates show that the rate of functional graft death is 12%, graft loss is 41%, and the cumulative mortality rate is as high as 37% 10 years after kidney transplantation [ 3 ] . Identifying possible risk factors for renal dysfunction among renal transplant recipients is crucial in initiating early preventive interventions to prevent renal dysfunction and improve survival. Increasing evidence has consistently shown that abnormal blood lipids are closely related to renal dysfunction [ 4 , 5 ] . Abnormal blood lipids can lead to reduced estimated glomerular filtration rate (eGFR) and increased risk of renal dysfunction, which may further cause graft failure, affecting long-term survival and increasing all-cause mortality in RTRs [ 6 , 7 ] . Previous research has identified multiple common blood lipids that are associated with renal dysfunction, including low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and non-high-density lipoprotein cholesterol (non-HDL-C). Although the international guidelines recommend controlling LDL-C levels as the primary target for lipid-lowering treatment, its efficacy in preventing postoperative complications among RTRs remains uncertain [ 8 – 10 ] . In addition, renal transplant recipients generally have low HDL-C and high non-HDL-C, significantly increasing the risk of renal dysfunction [ 11 – 13 ] . Although increasing HDL-C and reducing non-HDL-C have been shown to improve renal outcomes, the mechanism by which the two components affect renal function is still unclear. A reliable and cost-effective risk prediction model is thus needed to establish the associations between various lipid profiles and renal dysfunction among RTRs to inform further prevention and intervention efforts. Machine learning is an efficient data analytic method that enables the machines to learn by themselves without being explicitly programmed [ 14 ] . It has been widely used in the medical field to predict health outcomes based on clinical information extracted from electronic medical record data. Machine learning can discover the relationships and interactions between variables, therefore accurately determining the risk threshold for disease occurrence [ 15 ] . Some machine learning-based predictive models have been developed to evaluate the renal prognosis of various renal diseases [ 16 , 17 ] . However, no studies have used machine learning to explore the associations between blood lipids and renal dysfunction among RTRs. To address the research gap, we conducted the current study to establish a risk prediction model for renal dysfunction among RTRs based on abnormal lipid profiles using machine learning. Specifically, this study employed a retrospective cohort design and retrieved demographic and clinical data, including preoperative blood lipid and renal dysfunction data, from the hospital information systems. We then examined the relationship between preoperative blood lipid levels and postoperative renal dysfunction, based on which we established the optimal prediction model for renal dysfunction and verified it. The nomogram can quickly identify high-risk patients with eGFR abnormality, which is crucial for optimizing patient management and delaying the progression of renal dysfunction. 2. Methods 2.1 Study design and participants The retrospective cohort study was conducted at Xiangya Hospital of Central South University in Changsha, China, from January 2015 to December 2021. A cohort of 442 patients who underwent kidney transplantation were consecutively recruited and followed up every 3 months till the 12th month after transplantation. The inclusion criteria were as follows: (1) age ≥ 18 years old, (2) completed kidney transplant with functional transplanted kidney, (3) with complete data on key variables such as lipid levels and eGFR, (4) completed one-year follow-up. Patients with the following conditions were excluded: (1) age < 18 years old (n = 3), (2) comorbid with other organ transplantation or renal re-transplantation (n = 5), (3) renal failure within 3 months after surgery (n = 7), (4) lost to follow-up (n = 45), and (5) missing data on preoperative blood lipids (n = 37). Finally, 345 patients were included in the analysis. Figure 1 shows the flowchart of patient enrollment. The research protocol was approved by the Ethics Committee of Xiangya Hospital, Central South University (Protocol No. 202308173). Informed consent was waived due to the retrospective nature of the study. All patient information was collected anonymously and kept confidential. 2.2 Data collection We collected patients' general information and laboratory test data from their electronic medical records in the hospital health information system. General information included gender, age, marital status, medical payment method, preoperative comorbidities (diabetes, HBV), family history (hypertension, diabetes), smoking history, drinking history, dialysis method, systolic blood pressure (SBP), and diastolic blood pressure (DBP) at admission. Laboratory test data included preoperative total cholesterol (TC), triglycerides (TG), LDL-C, HDL-C, non-HDL-C, albumin (ALB), and fasting plasma glucose (FPG), as well as creatinine at 12 months after transplantation. The non-HDL-C value was calculated by subtracting HDL-C from TC [ 18 ] . Renal function was assessed by calculating the eGFR based on creatinine levels using the CKD-EPI formula [ 19 ] . All patients received postoperative triple immunosuppressive therapy with glucocorticoids, calcineurin inhibitors, and mycophenolate mofetil. All patients underwent regular postoperative follow-up according to clinical guidelines. 2.3 Definition of key indicators According to the 2009 CKD model and previous studies, renal dysfunction was defined as eGFR < 60 mL/min / 1.73 m 2 [ 7 , 20 , 21 ] . eGFR was calculated using the CKD-EPI equation developed by the Chronic Nephropathy Epidemiology Collaborative Group in 2009 as follows: eGFR = 141×min(Scr/79.6, 1) −0.411 ×max(Scr/79.6, 1) −1.209 ×0.993 Age (Male); eGFR = 141×min(Scr/61.9, 1) −0.329 ×max(Scr/61.9, 1) −1.209 ×0.993 Age ×1.018 (Female). The median creatinine value κ, Mg/dL * 88.4 was converted to µ Mol/L. Scr was creatinine, min indicated the minimum of Scr/κ or 1, and max indicated the maximum of Scr/κ or 1 [ 19 ] . In this study, renal dysfunction was defined as eGFR < 60 mL/min /1.73 m 2 , dyslipidemia was defined as TC ≥ 5.18mmol/L or TG ≥ 1.7mmol/L or LDL-C ≥ 3.37mmol/L or HDL-C<1.04mmol/L or Non-HDL-C ≥ 4.1mmol/L [ 18 , 22 ] . 2.4 Statistical analysis Statistical analyses were performed using R (University of Auckland, New Zealand, Version 3.6.2) and SPSS 26.0 (IBM Corp., Armonk, NY, USA). The continuous data were expressed as means ± standard deviations (‾x ± s), and the differences between groups were compared using the student t-test. Categorical data were expressed as numbers (percentages), and the differences between groups were compared using the χ 2 test. A two-sided P < 0.05 was considered statistically significant. The risk prediction model for renal dysfunction was built as follows: Dataset construction: A total of 345 patients were included in this study, among whom 55.9% had renal dysfunction. To improve the stability of the prediction model, each continuous variable was standardized into a Z-score. After preprocessing the raw data, the patients were randomly assigned to the training (n = 276) and validation set (n = 69) in a 4:1 ratio. The training set was used for model selection and hyperparameter adjustment, and the validation set was used to evaluate the final established model. Predictor screening: predictors of renal dysfunction were screened by calculating the weighted importance of each variable using three common ML models: RandomForest, XGBoost, and LightGBM. All three ML models were developed and validated using open-source Python packages (Scikit learn, XGBoost) and LightGBM. We used correlation tests to screen out the first batch of predictors. This study used 5-fold cross-validation for model training by dividing the training set into five mutually exclusive parts (4 for model training and 1 for internal validation). This process was repeated 5 times to generate five different but overlapping training data and five independent validation data. During the training process, a grid search was used to optimize the model hyperparameters, and the area under the curve (AUC) of the receiver operating (ROC) curve was used as the evaluation criterion for screening the final model, which was independently evaluated on the validation set. A logistic regression model was used to draw an alignment diagram and evaluate the probability of renal dysfunction occurring 12 months after transplantation. Calibration curves were used to assess the accuracy of the prediction model. Decision Curve Analysis (DCA) was used to evaluate the net return of the prediction model. DCA could visually demonstrate the performance of different strategies in balancing treatment and missed diagnosis. To investigate whether there was a significant linear trend in the screening of essential eigenvalues and eGFR at 12 months after surgery, we used trend tests (p for trend) and interaction tests (P for interaction). 3 Results 3.1 Sample characteristics Table 1 shows the baseline sample characteristics and comparison by renal dysfunction. Among the 345 patients in the cohort, 228 (66.1%) were males and 117 (33.9%) were females, with an average age of 42.0 ± 10.7 (18.0–70.0) years. In addition, 26 (7.5%) patients had diabetes, 40 (11.6%) had HBV, 34(9.9%) had a family history of hypertension, 14(4.1%) had a family history of diabetes, 73(21.2%) had smoking history, 40(11.6%) had an alcohol use history. Most patients received dialysis treatment before transplantation (92.8%). The prevalence of preoperative TC, TG, LDL-C, HDL-C, and non-HDL-C abnormality were 136(39.4%), 183(53.0%), 111(32.2%), 146(42.3%), 122(35.4%), respectively. No significant differences were observed in the sample characteristics between the training and validation sets. Table 1 Comparison of general characteristics of patients with different levels of eGFR (n = 345) Variable eGFR < 60 (n = 193,%) eGFR ≥ 60 (n = 152,%) P value Gender <0.0001 Male 108(47.4) 120(52.6) Female 85(72.6) 32(27.4) Age, year 0.010 18–30, year 20(38.5) 32(61.5) 31–45, year 86(54.1) 73(45.9) 46–59, year 77(65.8) 40(34.2) ≥ 60, year 10(58.8) 7(41.2) Marital status 0.084 Unmarried 20(43.5) 26(56.5) Married 168(57.1) 125(42.9) Divorced 5(83.3) 1(16.7) Medical payment Method 0.423 At public expense 2(40.0) 3(60.0) Municipal medical insurance 43(50.0) 43(50.0) Provincial health insurance 15(65.2) 8(34.8) Other medical insurance 133(57.6) 98(42.4) T2DM 0.550 Yes 16(61.5) 10(38.5) No 177(55.5) 142(44.5) HBV 0.898 Yes 22(55.0) 18(45.0) No 171(56.1) 134(43.9) Family history of hypertension 0.070 Yes 24(70.6) 10(29.4) No 169(54.3) 142(45.7) Family history of diabetes 0.233 Yes 10(71.4) 4(29.6) No 183(55.3) 148(44.7) Smoking history 0.824 Yes 40(54.8) 33(45.2) No 153(56.3) 119(43.7) History of drinking 0.641 Yes 21(52.5) 19(47.5) No 172(56.4) 133(43.6) Dialysis type 0.458 Hemodialysis 145(57.1) 109(42.9) Peritoneal dialysis 28(50.9) 27(49.1) Both 8(72.7) 3(27.3) Neither 12(48.0) 13(52.0) TC, mmol/L 5.15 ± 1.32 4.84 ± 1.31 0.028 * TG, mmol/L 2.17 ± 1.20 1.79 ± 1.08 0.002 * HDL-C, mmol/L 1.11 ± 0.41 1.26 ± 0.42 0.001 * LDL-C, mmol/L 3.19 ± 1.07 2.92 ± 0.94 0.014 * non-HDL-C, mmol/L 4.04 ± 1.17 3.58 ± 1.13 0.0002 * ALB, g/L 42.38 ± 5.60 43.72 ± 5.50 0.026 * FPG, mmol/L, median 6.56 ± 2.94 5.98 ± 1.92 0.037 * SBP,mmHg ≥140 149(56.0) 117(44.0) 0960 <140 44(55.7) 35(44.3) DBP,mmHg 0.292 ≥90 115(53.7) 99(46.3) <90 78(59.5) 53(40.5) Abbreviations: TC, total cholesterol; TG, triglyceride;HDL-C, high-density lipoproteincholesterol; LDL-C, low-density lipoprotein cholesterol; non-HDL-C,non-highdensity lipoprotein cholesterol; ALB,Albumin ;FPG, fasting plasma glucose;T2DM,diabetes mellitus type 2. The P value was calculated by the Chi-square test. *The P value was calculated by the student t-test. At the 12-month follow-up, the cohort had a mean eGFR of 57.14 ± 19.61mL/min/1.73 m 2 , with 193(55.9%) patients having renal dysfunction with eGFR abnormality. Comparison of baseline characteristics between patients with and without renal dysfunction showed significant differences in the following nine variables: gender, age, TC, TG, LDL-C, non-HDL-C, FPG, HDL-C, and ALB. Specifically, patients with renal dysfunction were more likely to be female, aged < 60, and had higher levels of TC, TG, LDL-C, non-HDL-C, and FPG, as well as lower levels of HDL-C and ALB (Table 1 ). 3.2 Screening of candidate predictors All baseline sample characteristics (20 variables) were selected as candidate predictors as the input, and renal dysfunction was set as the outcome in the training set and validation set. We used Random Forest, XGBoost, and LightGBM to calculate the feature importance (Table S1), weighted feature importance (Table S2), and Pearson values (Table S3). Based on importance and correlation p-value, the following nine variables were selected: preoperative TC, TG, LDL-C, HDL-C, non-HDL-C, ALB, FPG, gender, and age. HDL-C, non-HDL-C, gender, and age showed a high coefficient in the SVM model (Table 2 ). Table S1 Importance of all features RF XGBoost LightGBM prenonHDL-C 0.0928 0.0565 0.1048 preHDL-C 0.0914 0.0541 0.1264 age 0.0866 0.0580 0.1003 preLDL-C 0.0758 0.0411 0.0441 gender 0.0406 0.0643 0.0700 preTG 0.1116 0.0622 0.1183 preTC 0.0766 0.0429 0.0910 preFPG 0.0881 0.0430 0.0872 DBP 0.0676 0.0369 0.0540 SBP 0.0646 0.0306 0.0642 dialysis modality_1 0.0156 0.0616 0.0326 Abbreviations: prenonHDL-C, preHDL-C, preLDL-C, preTG, preTC and preFPG are preoperative non HDL-C, HDL-C, LDL-C, TG, TC and FPG. Table 2 Results of Gaussian linear model fitting coef preHDL-C -3.260845 prenon-HDL-C 1.800374 age 1.528826 gender -1.153818 preALB -0.932429 preTG 0.885750 preFPG 0.532901 preTC 0.525701 preLDL-C -0.182000 Table S2 Weighted importance of all features RF XGBoost LightGBM Weighted preTG 0.1116 0.0622 0.1183 0.096587 medical payment methods 0.0098 0.2610 0.0001 0.094190 prenon-HDL-C 0.0928 0.0565 0.1048 0.084106 age 0.0866 0.0580 0.1003 0.081152 preFPG 0.0881 0.0430 0.0872 0.072076 preTC 0.0766 0.0429 0.0910 0.069605 preALB 0.0810 0.0427 0.0753 0.065762 preHDL-C 0.0914 0.0541 0.1264 0.063549 gender 0.0406 0.0642 0.0700 0.05840 preLDL-C 0.0758 0.0411 0.0441 0.053230 Table S3 Importance of all features Pearson Value p gender 0.241078 0.000006 age 0.195275 0.000263 prenon-HDL-C 0.195064 0.000267 preHDL-C 0.172942 0.001260 preTG 0.164450 0.002182 preLDL-C 0.132041 0.014112 preALB 0.119484 0.026472 preTC 0.118324 0.027984 preFPG 0.112582 0.036601 Table S4 Trend Analysis Q1 Q2 Q3 Q4 P Value for trend preHDL-C 1.00 0.68 0.59 0.44 0.008 prenon-HDL-C 1.00 1.23 2.18 2.82 0.0002 preLDL-C 1.00 1.23 2.49 1.58 0.036 preTC 1.00 1.03 1.42 1.22 0.055 PreTG 1.00 1.56 1.42 1.26 0.056 preALB 1.00 0.71 0.59 0.59 0.085 preFPG 1.00 1.73 2.06 1.65 0.098 Table S5 Trend Analysis of Calibration Model Q1 (n = 90) Q2 (95%CI, n = 83) Q3 (95%CI, n = 86) Q4 (95%CI, n = 86) P Value for trend Crude Model1 1.00 1.16(0.64–2.12) 2.11(1.15–3.86) 2.73(1.47–5.06) 0.0003 0.621 0.015 0.001 Adjusted Model 2 1.00 1.09(0.583–2.03) 1.93(1.03–3.64) 2.38(1.26–4.55) 0.0026 0.793 0.041 0.008 Adjusted Model 3 1.00 1.16(0.606–2.210) 2.57(1.33–5.05) 3.17(1.59–6.28) 0.00016 0.656 0.005 0.001 Preoperative non HDL-C was divided into four groups based on quartiles (Q1, Q2, Q3, Q4), where n represents the sample size and the default confidence level is 95%. Single factor logistic regression was used as the Crude model1, and the model Adjusted model2 was calibrated using "sex" and "age". The model Adjusted model3 was calibrated using "age", "sex", "non HDL-C", "HDL-C", and "LDL-C". 3.3 Establishment of the risk prediction model We found low correlations for the nine variables and performed trending testing for each variable multiples, which identified three suitable variables showing a linear trend with renal dysfunction: non-HDL-C, HDL-C, and LDL-C (Tables S4 & S5). Higher non-HDL-C, higher LDL-C, and lower HDL-C were associated with a higher risk of renal dysfunction. HDL and non-HDL showed no interaction (P = 0.257). In contrast, TC, TG, FPG, and ALB showed no apparent trend. Therefore, the final predictive model was constructed based on five preoperative variables: non-HDL-C, HDL-C, LDL-C, age, and gender, with the ROC curves plotted. The AUC of the three models in the training set were 1.00, 0.93, and 1.00, respectively, and in the validation set were 0.80, 0.75, and 0.80, respectively (Figure S1). 3.4 Evaluating the diagnosis performance of the model A nomogram was plotted with the final five selected predictive factors using logistic regression to predict the individualized risk of renal dysfunction among RTRs after transplantation. The nomogram indicated that older age, female gender, lower preoperative HDL-C, higher preoperative non-HDL-C, and higher preoperative LDL-C were associated with a higher risk of renal dysfunction (Fig. 2 ). The nomogram showed good diagnostic performance with an area under the curve (AUC) of 0.87 in the training group and 0.81 in the validation group (Figure S2), which was consistent with the calibrations (Figure S3). DCA was employed to assess the clinical utility of the diagnostic nomogram, which showed sufficient robustness in both the training and validation sets. Moreover, a threshold probability of 0.56 provided more benefits in predicting potential patients with renal dysfunction (Fig. 3 A). The model's clinical utility was further tested using the clinical impact curve (CIC), with a threshold probability of 0.78 showing the best-predicting performance (Fig. 3 B). 4 Discussion In this retrospective cohort study, we used three machine learning methods to explore the predicting effects of preoperative lipid profiles on postoperative renal dysfunction among RTRs. Our results showed that older age, female gender, lower preoperative HDL-C, higher preoperative non-HDL-C, and higher preoperative LDL-C were associated with a higher risk of renal dysfunction. We further established a nomogram based on the five predictors, which showed good diagnostic performance and clinical utility in both the training set and the validation set. In our study, more than half of RTRs experienced renal dysfunction with eGFR abnormality at 12 months of follow-up. In addition, the prevalence of preoperative dyslipidemia was also high, with 42.3% having HDL-C abnormality, 35.4% having a non-HDL-C abnormality, and 32.2% having LDL-C abnormality, which was highly correlated with postoperative renal dysfunction. Although kidney transplantation has improved the long-term outcomes of patients with end-stage renal disease, postoperative renal dysfunction remains a considerable challenge. Our findings suggest that before kidney transplantation, the risk of renal dysfunction should be carefully evaluated, and evidence-based intervention strategies should be established, such as routine screening of non-HDL-C, HDL-C, LDL-C levels, rational use of lipid-lowering drugs, and lifestyle interventions. Our study showed that lower preoperative HDL-C was associated with a higher risk of postoperative renal dysfunction, which is consistent with the results of a large cross-sectional study based on 4753 older adults [23] . The mechanism by which low HDL-C, as a part of lipid abnormality before kidney transplantation, impairs renal function has not been fully elucidated. HDL-C is a highly heterogeneous particle that carries various substances, including lipids, proteins, hormones, etc. It enhances the reverse transport of cholesterol in macrophages, promotes the production of nitric oxide (NO) by endothelial cells (ECs), and has antioxidant, anti-inflammatory, and anti-apoptotic properties. Low HDL-C levels may impair the antioxidant and anti-inflammatory properties and reduce cholesterol reverse transport, leading to the occurrence and progression of renal damage [ 24 , 25 ] . Therefore, it is suggested that controlling HDL-C at a normal level before kidney transplantation may be beneficial for preventing postoperative renal dysfunction and prolonging graft survival. In addition, our study showed that higher preoperative non-HDL-C was associated with a higher risk of postoperative renal dysfunction. Non-HDL-C refers to the total cholesterol in ApoB lipoprotein particles in the blood after excluding HDL-C, which has a potential atherogenic effect. High levels of non-HDL-C will lead to the accumulation of arterial plaque, thus increasing the risk of CVD [11]. Non-HDL-C is suggested as a lipid-lowering target by multiple guidelines and literature reviews [ 9 , 26 , 27 ] . Although there is no conclusive evidence on the mechanism of non-HDL-C affecting renal function, high levels of non-HDL-C may promote the progression of renal dysfunction by increasing the risk of CVD. A prospective cohort study evaluating 3909 participants found a significant correlation between non-HDL-C levels and early progression of renal injury [ 28 ] . Furthermore, high TC or low HDL-C levels may cause high non-HDL-C, and the accumulation of cholesterol cells can lead to lipotoxicity, ultimately causing renal dysfunction [ 29 ] . The damage caused by high preoperative non-HDL-C on renal function may be attributed to lipid toxicity-induced arterial plaque accumulation or high TC levels associated with inhibition of renal tubular epithelial cell proliferation [ 30 ] . Furthermore, this study found that higher preoperative LDL-C was associated with a higher risk of postoperative renal dysfunction, though it did not contribute as much as non-HDL-C and HDL-C. Our finding was consistent with Tsai et al.'s [ 31 ] prospective cohort study of 46 278 community participants, which found that eGFR showed a significant downward trend with increasing LDL-C levels. Higher preoperative LDL-C indicates the loss of antioxidants and the accumulation of oxidative products, which can lead to excessive oxidative stress OS and oxidation of LDL in the arterial wall, thus accelerating the deterioration of renal function [ 32 , 33 ] . Studies showed that patients with low LDL-C had better renal prognosis than those with high LDL-C [ 34 ] . Our findings suggest that LDL-C is an independent risk factor for renal dysfunction that warrants special attention in transplant physicians. Routine postoperative screening and postoperative monitoring of LDL-C levels are highly recommended for RTRs. Patients with high levels of LDL-C should be given statin therapy and adopt a healthy lifestyle to control LDL-C at the target level to prevent renal dysfunction. In this study, we developed a risk prediction model for renal dysfunction based on five factors: non-HDL-C, HDL-C, LDL-C, age, and gender, which showed good diagnostic performance and clinical utility through ROC and DCA. Although the effects of lipids on renal function are not specific, these indicators are readily obtainable from preoperative blood testing in clinical practice, making the model simple and practical. Evaluation of preoperative lipid profiles can predict the function of allografts, thus benefiting RTRs. Therefore, this nomogram has excellent potential to be widely applied in clinical practice to predict renal dysfunction in RTRs. Our study has several limitations. First, our study used a retrospective design, and all data were retrieved from medical records, which may lead to bias and missing information. Second, the one-year follow-up period was relatively short and may not reflect the long-term changes in the association between lipid profiles and renal dysfunction. Third, the small sample size may affect the stability of the model. Future studies should consider using larger sample sizes, longer follow-up duration, and trajectory analysis methods to test our model and analyze the impact of lipid profiles on renal dysfunction among RTRs. 5 Conclusion This study indicated that age, gender, and preoperative levels of non-HDL-C, HDL-C, and LDL-C were independent predictors of postoperative renal dysfunction in RTRs. Furthermore, we constructed a nomogram composed of the five factors, which showed good predictive performance. This model can help physicians quickly identify RTRs at high risk of renal dysfunction and provide a clinical decision-making basis for promoting the rational use of lipid-lowering drugs and personalized lifestyle interventions. Therefore, it is recommended that RTR's blood lipid levels should be screened regularly, with active medication and lifestyle interventions provided to those with dyslipidemia to prevent renal dysfunction and improve prognosis. In the future, more external validation cohorts are needed to confirm the effectiveness of our model for further clinical applications. Declarations Data availability statement The raw data supporting the conclusions of this article will be made available by the authors without undue reservation. Ethics statement The study involving human participants was reviewed and approved by the Ethics Committee of Xiangya Hospital, Central South University. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements. Author contributions Concept and design: Hong Zhang, Sai Zhang. Data collection and compilation: Lin Zhuo, Ling Liu, Ling Tan, Rongrong Li, Haoxiang Zhang. Data analysis and interpretation: Hong Zhang and Sai Zhang. The manuscript writing and final approval of the manuscript: all authors. Funding This study was supported by grants from the Hunan Provincial Natural Science Foundation of China (No. 2022JJ70083) ,and the Innovative Province Construction Science Popularization Special Project of Hunan Province(No. 2023ZK4058). Acknowledgments We thank Dr. Wen Zheng for his assistance with the statistical analysis of the data. Conflict of interest statement. None of the authors has any conflict of interest. References Abecassis M, Bartlett ST, Collins AJ et al. Kidney transplantation as primary therapy for end-stage renal disease: a National Kidney Foundation/Kidney Disease Outcomes Quality Initiative (NKF/KDOQITM) conference. Clin J Am Soc Nephrol . 2008;3(2):471–480. Fellström B, Jardine AG, Soveri I, et al. Renal dysfunction is a strong and independent risk factor for mortality and cardiovascular complications in renal transplantation. Am J Transpl. 2005;5(8):1986–91. Miller G, Ankerst DP, Kattan MW, et al. Kidney Transplantation Outcome Predictions (KTOP): A Risk Prediction Tool for Kidney Transplants from Brain-dead Deceased Donors Based on a Large European Cohort. Eur Urol. 2023;83(2):173–9. Noels H, Lehrke M, Vanholder R, Jankowski J. Lipoproteins and fatty acids in chronic kidney disease: molecular and metabolic alterations. Nat Rev Nephrol. 2021;17(8):528–42. Baek J, He C, Afshinnia F, Michailidis G, Pennathur S. Lipidomic approaches to dissect dysregulated lipid metabolism in kidney disease. Nat Rev Nephrol. 2022;18(1):38–55. Cohen E, Korah M, Callender G, Belfort de Aguiar R, Haakinson D. Metabolic Disorders with Kidney Transplant. Clin J Am Soc Nephrol. 2020;15(5):732–42. Wang X, Wang H, Li J, et al. Combined Effects of Dyslipidemia and High Adiposity on the Estimated Glomerular Filtration Rate in a Middle-Aged Chinese Population. Diabetes Metab Syndr Obes. 2021;14:4513–22. Published 2021 Nov 10. Bae S, Ahn JB, Joseph C, et al. Statins in Kidney Transplant Recipients: Usage, All-Cause Mortality, and Interactions with Maintenance Immunosuppressive Agents. J Am Soc Nephrol. 2023;34(6):1069–77. Authors/Task Force Members; ESC Committee for Practice Guidelines (CPG); ESC National Cardiac Societies. 2019 ESC/EAS guidelines for the management of dyslipidaemias: Lipid modification to reduce cardiovascular risk [published correction appears in Atherosclerosis. 2020;292:160–162. 10.1016/j.atherosclerosis. 2019.11.020] [published correction appears in Atherosclerosis. 2020;294:80–82. doi: 10.1016/j.atherosclerosis.2019.12.004]. Atherosclerosis . 2019;290:140–205. Wanner C, Tonelli M, Kidney Disease: Improving Global Outcomes Lipid Guideline Development Work Group Members. 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. Suh SH, Oh TR, Choi HS, et al. Non-High-Density Lipoprotein Cholesterol and Progression of Chronic Kidney Disease: Results from the KNOW-CKD Study. Nutrients. 2022;14(21):4704. Published 2022 Nov 7. Jankowski J, Floege J, Fliser D, Böhm M, Marx N. Cardiovascular Disease in Chronic Kidney Disease: Pathophysiological Insights and Therapeutic Options. Circulation. 2021;143(11):1157–72. Kon V, Yang HC, Smith LE, Vickers KC, Linton MF. High-Density Lipoproteins in Kidney Disease. Int J Mol Sci. 2021;22(15):8201. Published 2021 Jul 30. Binson VA, Thomas S, Subramoniam M, Arun J, Naveen S, Madhu S. A Review of Machine Learning Algorithms for Biomedical Applications. Ann Biomed Eng. 2024;52(5):1159–83. Wong J, Horwitz MM, Zhou L, Toh S. Using machine learning to identify health outcomes from electronic health record data. Curr Epidemiol Rep. 2018;5(4):331–42. Sun F, Wang H, Zhang D, Han F, Ye S. One-year renal outcome in lupus nephritis patients with acute kidney injury: a nomogram model. Rheumatology (Oxford). 2022;61(7):2886–93. Martini A, Cumarasamy S, Beksac AT, et al. A Nomogram to Predict Significant Estimated Glomerular Filtration Rate Reduction After Robotic Partial Nephrectomy. Eur Urol. 2018;74(6):833–9. Li JJ, Zhao SP, Zhao D, et al. 2023 China Guidelines for Lipid Management. J Geriatr Cardiol. 2023;20(9):621–63. Levey AS, Stevens LA, Schmid CH et al. A new equation to estimate glomerular filtration rate [published correction appears in Ann Intern Med. 2011;155(6):408]. Ann Intern Med . 2009;150(9):604–612. Levey AS, Stevens LA, Coresh J. Conceptual model of CKD: applications and implications. Am J Kidney Dis. 2009;53(3 Suppl 3):S4–16. Yu Z, Grams ME, Ndumele CE, et al. Association Between Midlife Obesity and Kidney Function Trajectories: The Atherosclerosis Risk in Communities (ARIC) Study. Am J Kidney Dis. 2021;77(3):376–85. 2019;10(2):101–111. 23.You Branch of Organ Transplantation of Chinese Medical A. Management specification for blood lipid on recipients with solid organ transplantation in China (2019 edition). ORGAN TRANSPLANTATION, Li A, Tomlinson Y. B, Association Between Renal Dysfunction and Low HDL Cholesterol Among the Elderly in China. Front Cardiovasc Med . 2021;8:644208. Published 2021 May 12. Rysz J, Gluba-Brzózka A, Rysz-Górzyńska M, Franczyk B. The Role and Function of HDL in Patients with Chronic Kidney Disease and the Risk of Cardiovascular Disease. Int J Mol Sci. 2020;21(2):601. Published 2020 Jan 17. Julve J, Escolà-Gil JC. High-Density Lipoproteins and Cardiovascular Disease: The Good, the Bad and the Future. Biomedicines. 2021;9(8):857. Pearson GJ, Thanassoulis G, Anderson TJ, et al. 2021 Canadian Cardiovascular Society Guidelines for the Management of Dyslipidemia for the Prevention of Cardiovascular Disease in Adults. Can J Cardiol. 2021;37(8):1129–50. Hodkinson A, Tsimpida D, Kontopantelis E, Rutter MK, Mamas MA, Panagioti M. Comparative effectiveness of statins on non-high density lipoprotein cholesterol in people with diabetes and at risk of cardiovascular disease: systematic review and network meta-analysis. BMJ. 2022;376:e067731. Published 2022 Mar 24. Zhai Q, Dou J, Wen J, et al. Association between changes in lipid indexes and early progression of kidney dysfunction in participants with normal estimated glomerular filtration rate: a prospective cohort study. Endocrine. 2022;76(2):312–23. Pan X. Cholesterol Metabolism in Chronic Kidney Disease: Physiology, Pathologic Mechanisms, and Treatment. Adv Exp Med Biol. 2022;1372:119–43. Honzumi S, Takeuchi M, Kurihara M, et al. The effect of cholesterol overload on mouse kidney and kidney-derived cells. Ren Fail. 2018;40(1):43–50. Tsai MH, Lin MY, Hsu CY, et al. Factors associated with renal function state transitions: A population-based community survey in Taiwan. Front Public Health. 2022;10:930798. Published 2022 Sep 8. Liakopoulos V, Roumeliotis S, Gorny X, Dounousi E, Mertens PR. Oxidative Stress in Hemodialysis Patients: A Review of the Literature. Oxid Med Cell Longev. 2017;2017:3081856. Roumeliotis S, Tavridou A, Panagoutsos S, SP378PLASMA OXIDIZED LDL LEVELS ARE ASSOCIATED WITH HYPERTENSION AND RENAL FUNCTION DETERIORATION BUT NOT SURVIVAL IN DIABETIC NEPHROPATHY, et al. Nephrol Dialysis Transplantation. 2018;33(suppl1):i473–473. Ozsoy RC, van der Steeg WA, Kastelein JJ, Arisz L, Koopman MG. Dyslipidaemia as predictor of progressive renal failure and the impact of treatment with atorvastatin. Nephrol Dial Transpl. 2007;22(6):1578–86. Additional Declarations No competing interests reported. Supplementary Files tableS1.docx tableS2.docx tableS3.docx tableS4.docx tableS5.docx FigureS1.tif FigureS2.tif FigureS3.tif 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5823279","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":401920355,"identity":"4475921c-004d-4201-9c06-a5d9e02c6913","order_by":0,"name":"Hong Zhang","email":"","orcid":"","institution":"Teaching and Research Section of Clinical Nursing, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China","correspondingAuthor":false,"prefix":"","firstName":"Hong","middleName":"","lastName":"Zhang","suffix":""},{"id":401920356,"identity":"c5db0e90-0e2e-4eb3-80f7-6df4bd74795d","order_by":1,"name":"Haoxiang Zhang","email":"","orcid":"","institution":"Xiangya School of Medicine, Central South University, Changsha 410013, Hunan, China","correspondingAuthor":false,"prefix":"","firstName":"Haoxiang","middleName":"","lastName":"Zhang","suffix":""},{"id":401920357,"identity":"596864c8-a2b5-4db7-867c-98ead3f749f8","order_by":2,"name":"Ronghua Li","email":"","orcid":"","institution":"Nuclear Medicine Department, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China","correspondingAuthor":false,"prefix":"","firstName":"Ronghua","middleName":"","lastName":"Li","suffix":""},{"id":401920358,"identity":"17bee408-1670-4bbf-b56e-a94f7efacd13","order_by":3,"name":"Lin Zhuo","email":"","orcid":"","institution":"Organ Transplant Center, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Zhuo","suffix":""},{"id":401920359,"identity":"2ed66ad4-1f49-48cd-b534-dcc81a21d610","order_by":4,"name":"Ling Liu","email":"","orcid":"","institution":"Organ Transplant Center, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China","correspondingAuthor":false,"prefix":"","firstName":"Ling","middleName":"","lastName":"Liu","suffix":""},{"id":401920360,"identity":"42520300-22d0-4d81-a52e-4bcd37c31967","order_by":5,"name":"Ling Tan","email":"","orcid":"","institution":"Organ Transplant Center, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China","correspondingAuthor":false,"prefix":"","firstName":"Ling","middleName":"","lastName":"Tan","suffix":""},{"id":401920361,"identity":"cc7f85bd-c9d7-4784-9905-9798799c70de","order_by":6,"name":"Rongrong Li","email":"","orcid":"","institution":"Organ Transplant Center, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China","correspondingAuthor":false,"prefix":"","firstName":"Rongrong","middleName":"","lastName":"Li","suffix":""},{"id":401920362,"identity":"9821053b-79b7-4121-9c61-b17b6e00c57c","order_by":7,"name":"Sai Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYDACCQjFw8/M2HDgA4RjQJwWyfbmgw9nkKKFweDMsWRjHmK0yM9ufvbwyx87GYYbOWbSNjV1iQ3szdskGGru4NTCOOeYubFsWzIP4wyglpxjhxMbeI6VSTAce4ZTC7NEgpm0ZAMzD7MEUEtuw4HEBiBDgrHhME4tbBLp36Ql/tTzsIG0WDYAHSb/Br8WHqBKyQ9sh3l4eIDeZ2xgBtrCg1+LhEROmTRj23EeCXZgIPccO2zcxpNWbJFwDLcW+Rnp2yR//Km2tz8MjMofNXWy/eyHN974UINbCzgIeFB8ByIS8GoABvQPAgpGwSgYBaNghAMASaBPqZRSflkAAAAASUVORK5CYII=","orcid":"","institution":"National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China","correspondingAuthor":true,"prefix":"","firstName":"Sai","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-01-14 02:08:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5823279/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5823279/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73868406,"identity":"0a083f6f-5986-445a-9139-fe79550d3499","added_by":"auto","created_at":"2025-01-15 12:07:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":173001,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow diagram of the study participants.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5823279/v1/1be9732eb956bc39c6856085.png"},{"id":73870234,"identity":"2d59535a-75d5-425c-9b2a-2189acbf0deb","added_by":"auto","created_at":"2025-01-15 12:15:32","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":42887,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNomogram for identifying eGFR abnormalities. \u003c/strong\u003eThe value of each variable was scored on a point scale from 0 to 100, after which the scores for each variable were added together. That sum is located on the total points axis, which enables us to predict the probability of eGFR abnormality risk. For gender categories, 0= male, 1= female.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5823279/v1/b8057b0cbdcfbe737fe40e91.jpg"},{"id":73868412,"identity":"eed15964-090d-470c-bc00-ad06563eec8a","added_by":"auto","created_at":"2025-01-15 12:07:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1098178,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe clinical value of the prediction model for renal function. \u003c/strong\u003eA. Decision curves. The y-axis denotes the net benefit. Red line: the prediction model; Grey line: all recipients were eGFR abnormalities (assumption); Black line: none recipients were eGFR abnormalities (assumption). B.\u003c/p\u003e\n\u003cp\u003eClinical impact curves. Solid line: people judged as high risk; Dotted line: people judged as high risk and actually experienced an outcome event.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5823279/v1/f0199d3ec7b57324d61b78b4.png"},{"id":74256793,"identity":"686a1a21-acce-4463-8df6-85acc53a8666","added_by":"auto","created_at":"2025-01-20 11:32:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2293806,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5823279/v1/8aab4e72-e50c-4b37-9136-bcff903253c8.pdf"},{"id":73870232,"identity":"295bcbd4-0263-452d-81f7-77ab2f36fada","added_by":"auto","created_at":"2025-01-15 12:15:32","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16507,"visible":true,"origin":"","legend":"","description":"","filename":"tableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5823279/v1/f3dec727de81f1f15cc210f0.docx"},{"id":73868391,"identity":"b60aa725-a638-4f62-9513-b7c52bfce894","added_by":"auto","created_at":"2025-01-15 12:07:32","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":16440,"visible":true,"origin":"","legend":"","description":"","filename":"tableS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-5823279/v1/1c2fa5c3ed9ab3f4d13ff734.docx"},{"id":73868392,"identity":"7fdd5642-b1c7-4dc7-b9a5-40bae81bceed","added_by":"auto","created_at":"2025-01-15 12:07:32","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":15692,"visible":true,"origin":"","legend":"","description":"","filename":"tableS3.docx","url":"https://assets-eu.researchsquare.com/files/rs-5823279/v1/325af61604c54bc5f5b990e2.docx"},{"id":73868411,"identity":"112026ed-54a3-4f17-92a0-47f4339b9cd7","added_by":"auto","created_at":"2025-01-15 12:07:33","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":16101,"visible":true,"origin":"","legend":"","description":"","filename":"tableS4.docx","url":"https://assets-eu.researchsquare.com/files/rs-5823279/v1/d3fa3349175f4cc73a5195fe.docx"},{"id":73868402,"identity":"6a44cc00-a403-49a9-964d-fcaa93245053","added_by":"auto","created_at":"2025-01-15 12:07:32","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":16704,"visible":true,"origin":"","legend":"","description":"","filename":"tableS5.docx","url":"https://assets-eu.researchsquare.com/files/rs-5823279/v1/06b612e24922e45c5e7fd8d9.docx"},{"id":73870230,"identity":"c52aff9f-0819-4d35-a070-882c03f0a266","added_by":"auto","created_at":"2025-01-15 12:15:32","extension":"tif","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":644488,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1.tif","url":"https://assets-eu.researchsquare.com/files/rs-5823279/v1/cafa67af930dab9fab79ee4e.tif"},{"id":73868398,"identity":"bfc8ee64-370c-4b6d-b131-cc317a7ac76d","added_by":"auto","created_at":"2025-01-15 12:07:32","extension":"tif","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":178005,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS2.tif","url":"https://assets-eu.researchsquare.com/files/rs-5823279/v1/5bfffdda058ff5d8250c0178.tif"},{"id":73868420,"identity":"1d7ec7a7-0295-456f-ab07-2a680f3dc5e4","added_by":"auto","created_at":"2025-01-15 12:07:33","extension":"tif","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":144487,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS3.tif","url":"https://assets-eu.researchsquare.com/files/rs-5823279/v1/223c53fe2111a04c0df5623a.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"Risk prediction of postoperative renal dysfunction based on preoperative lipid profiles in renal transplant recipients: A retrospective cohort study","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eKidney transplantation is the best treatment option for end-stage renal disease \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Despite the well-demonstrated benefits of kidney transplantation in improving the quality of life and prolonging life expectancy, renal dysfunction is a frequent complication after kidney transplantation, leading to poor prognosis and increased mortality \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Recent estimates show that the rate of functional graft death is 12%, graft loss is 41%, and the cumulative mortality rate is as high as 37% 10 years after kidney transplantation \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Identifying possible risk factors for renal dysfunction among renal transplant recipients is crucial in initiating early preventive interventions to prevent renal dysfunction and improve survival.\u003c/p\u003e \u003cp\u003eIncreasing evidence has consistently shown that abnormal blood lipids are closely related to renal dysfunction\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Abnormal blood lipids can lead to reduced estimated glomerular filtration rate (eGFR) and increased risk of renal dysfunction, which may further cause graft failure, affecting long-term survival and increasing all-cause mortality in RTRs \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Previous research has identified multiple common blood lipids that are associated with renal dysfunction, including low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and non-high-density lipoprotein cholesterol (non-HDL-C). Although the international guidelines recommend controlling LDL-C levels as the primary target for lipid-lowering treatment, its efficacy in preventing postoperative complications among RTRs remains uncertain \u003csup\u003e[\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. In addition, renal transplant recipients generally have low HDL-C and high non-HDL-C, significantly increasing the risk of renal dysfunction \u003csup\u003e[\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Although increasing HDL-C and reducing non-HDL-C have been shown to improve renal outcomes, the mechanism by which the two components affect renal function is still unclear. A reliable and cost-effective risk prediction model is thus needed to establish the associations between various lipid profiles and renal dysfunction among RTRs to inform further prevention and intervention efforts.\u003c/p\u003e \u003cp\u003eMachine learning is an efficient data analytic method that enables the machines to learn by themselves without being explicitly programmed \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. It has been widely used in the medical field to predict health outcomes based on clinical information extracted from electronic medical record data. Machine learning can discover the relationships and interactions between variables, therefore accurately determining the risk threshold for disease occurrence \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Some machine learning-based predictive models have been developed to evaluate the renal prognosis of various renal diseases \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. However, no studies have used machine learning to explore the associations between blood lipids and renal dysfunction among RTRs.\u003c/p\u003e \u003cp\u003eTo address the research gap, we conducted the current study to establish a risk prediction model for renal dysfunction among RTRs based on abnormal lipid profiles using machine learning. Specifically, this study employed a retrospective cohort design and retrieved demographic and clinical data, including preoperative blood lipid and renal dysfunction data, from the hospital information systems. We then examined the relationship between preoperative blood lipid levels and postoperative renal dysfunction, based on which we established the optimal prediction model for renal dysfunction and verified it. The nomogram can quickly identify high-risk patients with eGFR abnormality, which is crucial for optimizing patient management and delaying the progression of renal dysfunction.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design and participants\u003c/h2\u003e \u003cp\u003eThe retrospective cohort study was conducted at Xiangya Hospital of Central South University in Changsha, China, from January 2015 to December 2021. A cohort of 442 patients who underwent kidney transplantation were consecutively recruited and followed up every 3 months till the 12th month after transplantation. The inclusion criteria were as follows: (1) age\u0026thinsp;\u0026ge;\u0026thinsp;18 years old, (2) completed kidney transplant with functional transplanted kidney, (3) with complete data on key variables such as lipid levels and eGFR, (4) completed one-year follow-up. Patients with the following conditions were excluded: (1) age\u0026thinsp;\u0026lt;\u0026thinsp;18 years old (n\u0026thinsp;=\u0026thinsp;3), (2) comorbid with other organ transplantation or renal re-transplantation (n\u0026thinsp;=\u0026thinsp;5), (3) renal failure within 3 months after surgery (n\u0026thinsp;=\u0026thinsp;7), (4) lost to follow-up (n\u0026thinsp;=\u0026thinsp;45), and (5) missing data on preoperative blood lipids (n\u0026thinsp;=\u0026thinsp;37). Finally, 345 patients were included in the analysis. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the flowchart of patient enrollment. The research protocol was approved by the Ethics Committee of Xiangya Hospital, Central South University (Protocol No. 202308173). Informed consent was waived due to the retrospective nature of the study. All patient information was collected anonymously and kept confidential.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data collection\u003c/h2\u003e \u003cp\u003eWe collected patients' general information and laboratory test data from their electronic medical records in the hospital health information system. General information included gender, age, marital status, medical payment method, preoperative comorbidities (diabetes, HBV), family history (hypertension, diabetes), smoking history, drinking history, dialysis method, systolic blood pressure (SBP), and diastolic blood pressure (DBP) at admission. Laboratory test data included preoperative total cholesterol (TC), triglycerides (TG), LDL-C, HDL-C, non-HDL-C, albumin (ALB), and fasting plasma glucose (FPG), as well as creatinine at 12 months after transplantation. The non-HDL-C value was calculated by subtracting HDL-C from TC \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Renal function was assessed by calculating the eGFR based on creatinine levels using the CKD-EPI formula \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. All patients received postoperative triple immunosuppressive therapy with glucocorticoids, calcineurin inhibitors, and mycophenolate mofetil. All patients underwent regular postoperative follow-up according to clinical guidelines.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Definition of key indicators\u003c/h2\u003e \u003cp\u003eAccording to the 2009 CKD model and previous studies, renal dysfunction was defined as eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 mL/min / 1.73 m\u003csup\u003e2 [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. eGFR was calculated using the CKD-EPI equation developed by the Chronic Nephropathy Epidemiology Collaborative Group in 2009 as follows: eGFR\u0026thinsp;=\u0026thinsp;141\u0026times;min(Scr/79.6, 1)\u003csup\u003e\u0026minus;0.411\u003c/sup\u003e\u0026times;max(Scr/79.6, 1)\u003csup\u003e\u0026minus;1.209\u003c/sup\u003e\u0026times;0.993 \u003csup\u003eAge\u003c/sup\u003e (Male); eGFR\u0026thinsp;=\u0026thinsp;141\u0026times;min(Scr/61.9, 1)\u003csup\u003e\u0026minus;0.329\u003c/sup\u003e\u0026times;max(Scr/61.9, 1)\u003csup\u003e\u0026minus;1.209\u003c/sup\u003e\u0026times;0.993\u003csup\u003eAge\u003c/sup\u003e\u0026times;1.018 (Female). The median creatinine value κ, Mg/dL * 88.4 was converted to \u0026micro; Mol/L. Scr was creatinine, min indicated the minimum of Scr/κ or 1, and max indicated the maximum of Scr/κ or 1 \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. In this study, renal dysfunction was defined as eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 mL/min /1.73 m\u003csup\u003e2\u003c/sup\u003e, dyslipidemia was defined as TC\u0026thinsp;\u0026ge;\u0026thinsp;5.18mmol/L or TG\u0026thinsp;\u0026ge;\u0026thinsp;1.7mmol/L or LDL-C\u0026thinsp;\u0026ge;\u0026thinsp;3.37mmol/L or HDL-C\u0026lt;1.04mmol/L or Non-HDL-C\u0026thinsp;\u0026ge;\u0026thinsp;4.1mmol/L \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using R (University of Auckland, New Zealand, Version 3.6.2) and SPSS 26.0 (IBM Corp., Armonk, NY, USA). The continuous data were expressed as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations (\u0026oline;x\u0026thinsp;\u0026plusmn;\u0026thinsp;s), and the differences between groups were compared using the student t-test. Categorical data were expressed as numbers (percentages), and the differences between groups were compared using the χ\u003csup\u003e2\u003c/sup\u003e test. A two-sided P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. The risk prediction model for renal dysfunction was built as follows:\u003c/p\u003e \u003cp\u003eDataset construction: A total of 345 patients were included in this study, among whom 55.9% had renal dysfunction. To improve the stability of the prediction model, each continuous variable was standardized into a Z-score. After preprocessing the raw data, the patients were randomly assigned to the training (n\u0026thinsp;=\u0026thinsp;276) and validation set (n\u0026thinsp;=\u0026thinsp;69) in a 4:1 ratio. The training set was used for model selection and hyperparameter adjustment, and the validation set was used to evaluate the final established model.\u003c/p\u003e \u003cp\u003ePredictor screening: predictors of renal dysfunction were screened by calculating the weighted importance of each variable using three common ML models: RandomForest, XGBoost, and LightGBM. All three ML models were developed and validated using open-source Python packages (Scikit learn, XGBoost) and LightGBM. We used correlation tests to screen out the first batch of predictors. This study used 5-fold cross-validation for model training by dividing the training set into five mutually exclusive parts (4 for model training and 1 for internal validation). This process was repeated 5 times to generate five different but overlapping training data and five independent validation data. During the training process, a grid search was used to optimize the model hyperparameters, and the area under the curve (AUC) of the receiver operating (ROC) curve was used as the evaluation criterion for screening the final model, which was independently evaluated on the validation set.\u003c/p\u003e \u003cp\u003eA logistic regression model was used to draw an alignment diagram and evaluate the probability of renal dysfunction occurring 12 months after transplantation. Calibration curves were used to assess the accuracy of the prediction model. Decision Curve Analysis (DCA) was used to evaluate the net return of the prediction model. DCA could visually demonstrate the performance of different strategies in balancing treatment and missed diagnosis. To investigate whether there was a significant linear trend in the screening of essential eigenvalues and eGFR at 12 months after surgery, we used trend tests (p for trend) and interaction tests (P for interaction).\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Sample characteristics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the baseline sample characteristics and comparison by renal dysfunction. Among the 345 patients in the cohort, 228 (66.1%) were males and 117 (33.9%) were females, with an average age of 42.0\u0026thinsp;\u0026plusmn;\u0026thinsp;10.7 (18.0\u0026ndash;70.0) years. In addition, 26 (7.5%) patients had diabetes, 40 (11.6%) had HBV, 34(9.9%) had a family history of hypertension, 14(4.1%) had a family history of diabetes, 73(21.2%) had smoking history, 40(11.6%) had an alcohol use history. Most patients received dialysis treatment before transplantation (92.8%). The prevalence of preoperative TC, TG, LDL-C, HDL-C, and non-HDL-C abnormality were 136(39.4%), 183(53.0%), 111(32.2%), 146(42.3%), 122(35.4%), respectively. No significant differences were observed in the sample characteristics between the training and validation sets.\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\u003eComparison of general characteristics of patients with different levels of eGFR (n\u0026thinsp;=\u0026thinsp;345)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eeGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 (n\u0026thinsp;=\u0026thinsp;193,%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eeGFR\u0026thinsp;\u0026ge;\u0026thinsp;60\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;152,%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\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\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e108(47.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e120(52.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e85(72.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32(27.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;30, year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20(38.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32(61.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31\u0026ndash;45, year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e86(54.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73(45.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e46\u0026ndash;59, year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e77(65.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40(34.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60, year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10(58.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7(41.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20(43.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26(56.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e168(57.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e125(42.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5(83.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1(16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical payment Method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.423\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAt public expense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2(40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3(60.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMunicipal medical insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43(50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43(50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProvincial health insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15(65.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8(34.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther medical insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e133(57.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e98(42.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.550\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16(61.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10(38.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e177(55.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e142(44.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHBV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.898\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22(55.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18(45.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e171(56.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e134(43.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily history of hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24(70.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10(29.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e169(54.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e142(45.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily history of diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.233\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10(71.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4(29.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e183(55.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e148(44.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.824\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40(54.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33(45.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e153(56.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e119(43.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of drinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.641\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21(52.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19(47.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e172(56.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e133(43.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDialysis type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.458\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemodialysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e145(57.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e109(42.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeritoneal dialysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28(50.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27(49.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8(72.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3(27.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeither\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12(48.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13(52.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.15\u0026thinsp;\u0026plusmn;\u0026thinsp;1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.84\u0026thinsp;\u0026plusmn;\u0026thinsp;1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.028\u003csup\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.17\u0026thinsp;\u0026plusmn;\u0026thinsp;1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.79\u0026thinsp;\u0026plusmn;\u0026thinsp;1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.19\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enon-HDL-C, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.04\u0026thinsp;\u0026plusmn;\u0026thinsp;1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.58\u0026thinsp;\u0026plusmn;\u0026thinsp;1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0002\u003csup\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42.38\u0026thinsp;\u0026plusmn;\u0026thinsp;5.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43.72\u0026thinsp;\u0026plusmn;\u0026thinsp;5.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.026\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPG, mmol/L, median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.56\u0026thinsp;\u0026plusmn;\u0026thinsp;2.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.98\u0026thinsp;\u0026plusmn;\u0026thinsp;1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.037\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP,mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e149(56.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e117(44.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0960\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44(55.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35(44.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP,mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.292\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e115(53.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e99(46.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e78(59.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53(40.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eAbbreviations: TC, total cholesterol; TG, triglyceride;HDL-C, high-density lipoproteincholesterol; LDL-C, low-density lipoprotein cholesterol; non-HDL-C,non-highdensity lipoprotein cholesterol; ALB,Albumin ;FPG, fasting plasma glucose;T2DM,diabetes mellitus type 2. The \u003cem\u003eP\u003c/em\u003e value was calculated by the Chi-square test. *The \u003cem\u003eP\u003c/em\u003e value was calculated by the student t-test.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAt the 12-month follow-up, the cohort had a mean eGFR of 57.14\u0026thinsp;\u0026plusmn;\u0026thinsp;19.61mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e, with 193(55.9%) patients having renal dysfunction with eGFR abnormality. Comparison of baseline characteristics between patients with and without renal dysfunction showed significant differences in the following nine variables: gender, age, TC, TG, LDL-C, non-HDL-C, FPG, HDL-C, and ALB. Specifically, patients with renal dysfunction were more likely to be female, aged\u0026thinsp;\u0026lt;\u0026thinsp;60, and had higher levels of TC, TG, LDL-C, non-HDL-C, and FPG, as well as lower levels of HDL-C and ALB (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Screening of candidate predictors\u003c/h2\u003e \u003cp\u003eAll baseline sample characteristics (20 variables) were selected as candidate predictors as the input, and renal dysfunction was set as the outcome in the training set and validation set. We used Random Forest, XGBoost, and LightGBM to calculate the feature importance (Table S1), weighted feature importance (Table S2), and Pearson values (Table S3). Based on importance and correlation p-value, the following nine variables were selected: preoperative TC, TG, LDL-C, HDL-C, non-HDL-C, ALB, FPG, gender, and age. HDL-C, non-HDL-C, gender, and age showed a high coefficient in the SVM model (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable S1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eImportance of all features\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eXGBoost\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLightGBM\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eprenonHDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epreHDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1264\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epreLDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0441\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0700\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epreTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.1116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1183\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epreTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0910\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epreFPG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0872\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0540\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0642\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edialysis modality_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0326\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eAbbreviations: prenonHDL-C, preHDL-C, preLDL-C, preTG, preTC and preFPG are preoperative non HDL-C, HDL-C, LDL-C, TG, TC and FPG.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \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 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of Gaussian linear model fitting\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecoef\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epreHDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-3.260845\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eprenon-HDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.800374\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.528826\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.153818\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epreALB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.932429\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epreTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.885750\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epreFPG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.532901\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epreTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.525701\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epreLDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.182000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable S2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eWeighted importance of all features\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\u003eRF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eXGBoost\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLightGBM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWeighted\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epreTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.1116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.096587\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emedical payment methods\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.094190\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eprenon-HDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.084106\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.081152\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epreFPG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.072076\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epreTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.069605\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epreALB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.065762\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epreHDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.063549\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.05840\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epreLDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.053230\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable S3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eImportance of all features\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\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\u003ePearson Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.241078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.195275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000263\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eprenon-HDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.195064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000267\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epreHDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.172942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001260\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epreTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.164450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002182\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epreLDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.132041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.014112\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epreALB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.119484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.026472\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epreTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.118324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.027984\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epreFPG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.112582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.036601\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable S4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTrend Analysis\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=\"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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\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\u003eQ1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP Value for trend\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epreHDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eprenon-HDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epreLDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epreTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epreALB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epreFPG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable S5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTrend Analysis of Calibration Model\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=\"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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\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\u003eQ1\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;90)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003cp\u003e(95%CI, n\u0026thinsp;=\u0026thinsp;83)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003cp\u003e(95%CI, n\u0026thinsp;=\u0026thinsp;86)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003cp\u003e(95%CI, n\u0026thinsp;=\u0026thinsp;86)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP Value for trend\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrude Model1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.16(0.64\u0026ndash;2.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.11(1.15\u0026ndash;3.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.73(1.47\u0026ndash;5.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted Model 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.09(0.583\u0026ndash;2.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.93(1.03\u0026ndash;3.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.38(1.26\u0026ndash;4.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted Model 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.16(0.606\u0026ndash;2.210)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.57(1.33\u0026ndash;5.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.17(1.59\u0026ndash;6.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003ePreoperative non HDL-C was divided into four groups based on quartiles (Q1, Q2, Q3, Q4), where n represents the sample size and the default confidence level is 95%. Single factor logistic regression was used as the Crude model1, and the model Adjusted model2 was calibrated using \"sex\" and \"age\". The model Adjusted model3 was calibrated using \"age\", \"sex\", \"non HDL-C\", \"HDL-C\", and \"LDL-C\".\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Establishment of the risk prediction model\u003c/h2\u003e \u003cp\u003eWe found low correlations for the nine variables and performed trending testing for each variable multiples, which identified three suitable variables showing a linear trend with renal dysfunction: non-HDL-C, HDL-C, and LDL-C (Tables S4 \u0026amp; S5). Higher non-HDL-C, higher LDL-C, and lower HDL-C were associated with a higher risk of renal dysfunction. HDL and non-HDL showed no interaction (P\u0026thinsp;=\u0026thinsp;0.257). In contrast, TC, TG, FPG, and ALB showed no apparent trend.\u003c/p\u003e \u003cp\u003eTherefore, the final predictive model was constructed based on five preoperative variables: non-HDL-C, HDL-C, LDL-C, age, and gender, with the ROC curves plotted. The AUC of the three models in the training set were 1.00, 0.93, and 1.00, respectively, and in the validation set were 0.80, 0.75, and 0.80, respectively (Figure S1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Evaluating the diagnosis performance of the model\u003c/h2\u003e \u003cp\u003eA nomogram was plotted with the final five selected predictive factors using logistic regression to predict the individualized risk of renal dysfunction among RTRs after transplantation. The nomogram indicated that older age, female gender, lower preoperative HDL-C, higher preoperative non-HDL-C, and higher preoperative LDL-C were associated with a higher risk of renal dysfunction (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The nomogram showed good diagnostic performance with an area under the curve (AUC) of 0.87 in the training group and 0.81 in the validation group (Figure S2), which was consistent with the calibrations (Figure S3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDCA was employed to assess the clinical utility of the diagnostic nomogram, which showed sufficient robustness in both the training and validation sets. Moreover, a threshold probability of 0.56 provided more benefits in predicting potential patients with renal dysfunction (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The model's clinical utility was further tested using the clinical impact curve (CIC), with a threshold probability of 0.78 showing the best-predicting performance (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eIn this retrospective cohort study, we used three machine learning methods to explore the predicting effects of preoperative lipid profiles on postoperative renal dysfunction among RTRs. Our results showed that older age, female gender, lower preoperative HDL-C, higher preoperative non-HDL-C, and higher preoperative LDL-C were associated with a higher risk of renal dysfunction. We further established a nomogram based on the five predictors, which showed good diagnostic performance and clinical utility in both the training set and the validation set.\u003c/p\u003e \u003cp\u003eIn our study, more than half of RTRs experienced renal dysfunction with eGFR abnormality at 12 months of follow-up. In addition, the prevalence of preoperative dyslipidemia was also high, with 42.3% having HDL-C abnormality, 35.4% having a non-HDL-C abnormality, and 32.2% having LDL-C abnormality, which was highly correlated with postoperative renal dysfunction. Although kidney transplantation has improved the long-term outcomes of patients with end-stage renal disease, postoperative renal dysfunction remains a considerable challenge. Our findings suggest that before kidney transplantation, the risk of renal dysfunction should be carefully evaluated, and evidence-based intervention strategies should be established, such as routine screening of non-HDL-C, HDL-C, LDL-C levels, rational use of lipid-lowering drugs, and lifestyle interventions.\u003c/p\u003e \u003cp\u003eOur study showed that lower preoperative HDL-C was associated with a higher risk of postoperative renal dysfunction, which is consistent with the results of a large cross-sectional study based on 4753 older adults \u003csup\u003e[23]\u003c/sup\u003e. The mechanism by which low HDL-C, as a part of lipid abnormality before kidney transplantation, impairs renal function has not been fully elucidated. HDL-C is a highly heterogeneous particle that carries various substances, including lipids, proteins, hormones, etc. It enhances the reverse transport of cholesterol in macrophages, promotes the production of nitric oxide (NO) by endothelial cells (ECs), and has antioxidant, anti-inflammatory, and anti-apoptotic properties. Low HDL-C levels may impair the antioxidant and anti-inflammatory properties and reduce cholesterol reverse transport, leading to the occurrence and progression of renal damage \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Therefore, it is suggested that controlling HDL-C at a normal level before kidney transplantation may be beneficial for preventing postoperative renal dysfunction and prolonging graft survival.\u003c/p\u003e \u003cp\u003eIn addition, our study showed that higher preoperative non-HDL-C was associated with a higher risk of postoperative renal dysfunction. Non-HDL-C refers to the total cholesterol in ApoB lipoprotein particles in the blood after excluding HDL-C, which has a potential atherogenic effect. High levels of non-HDL-C will lead to the accumulation of arterial plaque, thus increasing the risk of CVD [11]. Non-HDL-C is suggested as a lipid-lowering target by multiple guidelines and literature reviews \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Although there is no conclusive evidence on the mechanism of non-HDL-C affecting renal function, high levels of non-HDL-C may promote the progression of renal dysfunction by increasing the risk of CVD. A prospective cohort study evaluating 3909 participants found a significant correlation between non-HDL-C levels and early progression of renal injury \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Furthermore, high TC or low HDL-C levels may cause high non-HDL-C, and the accumulation of cholesterol cells can lead to lipotoxicity, ultimately causing renal dysfunction \u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. The damage caused by high preoperative non-HDL-C on renal function may be attributed to lipid toxicity-induced arterial plaque accumulation or high TC levels associated with inhibition of renal tubular epithelial cell proliferation \u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFurthermore, this study found that higher preoperative LDL-C was associated with a higher risk of postoperative renal dysfunction, though it did not contribute as much as non-HDL-C and HDL-C. Our finding was consistent with Tsai et al.'s \u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e prospective cohort study of 46 278 community participants, which found that eGFR showed a significant downward trend with increasing LDL-C levels. Higher preoperative LDL-C indicates the loss of antioxidants and the accumulation of oxidative products, which can lead to excessive oxidative stress OS and oxidation of LDL in the arterial wall, thus accelerating the deterioration of renal function \u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. Studies showed that patients with low LDL-C had better renal prognosis than those with high LDL-C \u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. Our findings suggest that LDL-C is an independent risk factor for renal dysfunction that warrants special attention in transplant physicians. Routine postoperative screening and postoperative monitoring of LDL-C levels are highly recommended for RTRs. Patients with high levels of LDL-C should be given statin therapy and adopt a healthy lifestyle to control LDL-C at the target level to prevent renal dysfunction.\u003c/p\u003e \u003cp\u003eIn this study, we developed a risk prediction model for renal dysfunction based on five factors: non-HDL-C, HDL-C, LDL-C, age, and gender, which showed good diagnostic performance and clinical utility through ROC and DCA. Although the effects of lipids on renal function are not specific, these indicators are readily obtainable from preoperative blood testing in clinical practice, making the model simple and practical. Evaluation of preoperative lipid profiles can predict the function of allografts, thus benefiting RTRs. Therefore, this nomogram has excellent potential to be widely applied in clinical practice to predict renal dysfunction in RTRs.\u003c/p\u003e \u003cp\u003eOur study has several limitations. First, our study used a retrospective design, and all data were retrieved from medical records, which may lead to bias and missing information. Second, the one-year follow-up period was relatively short and may not reflect the long-term changes in the association between lipid profiles and renal dysfunction. Third, the small sample size may affect the stability of the model. Future studies should consider using larger sample sizes, longer follow-up duration, and trajectory analysis methods to test our model and analyze the impact of lipid profiles on renal dysfunction among RTRs.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThis study indicated that age, gender, and preoperative levels of non-HDL-C, HDL-C, and LDL-C were independent predictors of postoperative renal dysfunction in RTRs. Furthermore, we constructed a nomogram composed of the five factors, which showed good predictive performance. This model can help physicians quickly identify RTRs at high risk of renal dysfunction and provide a clinical decision-making basis for promoting the rational use of lipid-lowering drugs and personalized lifestyle interventions. Therefore, it is recommended that RTR's blood lipid levels should be screened regularly, with active medication and lifestyle interventions provided to those with dyslipidemia to prevent renal dysfunction and improve prognosis. In the future, more external validation cohorts are needed to confirm the effectiveness of our model for further clinical applications.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eData availability statement\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe raw data supporting the conclusions of this article will be made available by the authors without undue reservation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEthics statement\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe study involving human participants was reviewed and approved by the Ethics Committee of Xiangya Hospital, Central South University. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAuthor contributions \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConcept and design: Hong Zhang, Sai Zhang. Data collection and compilation: Lin Zhuo, Ling Liu, Ling Tan, Rongrong Li, Haoxiang Zhang. Data analysis and interpretation: Hong Zhang and Sai Zhang. The manuscript writing and final approval of the manuscript: all authors.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis study was supported by grants from the Hunan Provincial Natural Science Foundation of China (No. 2022JJ70083) ,and the Innovative Province Construction Science Popularization Special Project of Hunan Province(No. 2023ZK4058).\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe thank Dr. Wen Zheng for his assistance with the statistical analysis of the data.\u003c/p\u003e\n\u003cp\u003eConflict of interest statement.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNone of the authors has any conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbecassis M, Bartlett ST, Collins AJ et al. Kidney transplantation as primary therapy for end-stage renal disease: a National Kidney Foundation/Kidney Disease Outcomes Quality Initiative (NKF/KDOQITM) conference. \u003cem\u003eClin J Am Soc Nephrol\u003c/em\u003e. 2008;3(2):471\u0026ndash;480.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFellstr\u0026ouml;m B, Jardine AG, Soveri I, et al. Renal dysfunction is a strong and independent risk factor for mortality and cardiovascular complications in renal transplantation. Am J Transpl. 2005;5(8):1986\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiller G, Ankerst DP, Kattan MW, et al. Kidney Transplantation Outcome Predictions (KTOP): A Risk Prediction Tool for Kidney Transplants from Brain-dead Deceased Donors Based on a Large European Cohort. Eur Urol. 2023;83(2):173\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNoels H, Lehrke M, Vanholder R, Jankowski J. Lipoproteins and fatty acids in chronic kidney disease: molecular and metabolic alterations. Nat Rev Nephrol. 2021;17(8):528\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaek J, He C, Afshinnia F, Michailidis G, Pennathur S. Lipidomic approaches to dissect dysregulated lipid metabolism in kidney disease. Nat Rev Nephrol. 2022;18(1):38\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCohen E, Korah M, Callender G, Belfort de Aguiar R, Haakinson D. Metabolic Disorders with Kidney Transplant. Clin J Am Soc Nephrol. 2020;15(5):732\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang X, Wang H, Li J, et al. Combined Effects of Dyslipidemia and High Adiposity on the Estimated Glomerular Filtration Rate in a Middle-Aged Chinese Population. Diabetes Metab Syndr Obes. 2021;14:4513\u0026ndash;22. Published 2021 Nov 10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBae S, Ahn JB, Joseph C, et al. Statins in Kidney Transplant Recipients: Usage, All-Cause Mortality, and Interactions with Maintenance Immunosuppressive Agents. J Am Soc Nephrol. 2023;34(6):1069\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAuthors/Task Force Members; ESC Committee for Practice Guidelines (CPG); ESC National Cardiac Societies. 2019 ESC/EAS guidelines for the management of dyslipidaemias: Lipid modification to reduce cardiovascular risk [published correction appears in Atherosclerosis. 2020;292:160\u0026ndash;162. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.atherosclerosis.\u003c/span\u003e\u003cspan address=\"10.1016/j.atherosclerosis.\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e2019.11.020] [published correction appears in Atherosclerosis. 2020;294:80\u0026ndash;82. doi: 10.1016/j.atherosclerosis.2019.12.004]. \u003cem\u003eAtherosclerosis\u003c/em\u003e. 2019;290:140\u0026ndash;205.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWanner C, Tonelli M, Kidney Disease: Improving Global Outcomes Lipid Guideline Development Work Group Members. 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 \u003cli\u003e\u003cspan\u003eSuh SH, Oh TR, Choi HS, et al. Non-High-Density Lipoprotein Cholesterol and Progression of Chronic Kidney Disease: Results from the KNOW-CKD Study. Nutrients. 2022;14(21):4704. Published 2022 Nov 7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJankowski J, Floege J, Fliser D, B\u0026ouml;hm M, Marx N. 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\u003eKon V, Yang HC, Smith LE, Vickers KC, Linton MF. High-Density Lipoproteins in Kidney Disease. Int J Mol Sci. 2021;22(15):8201. Published 2021 Jul 30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBinson VA, Thomas S, Subramoniam M, Arun J, Naveen S, Madhu S. A Review of Machine Learning Algorithms for Biomedical Applications. Ann Biomed Eng. 2024;52(5):1159\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWong J, Horwitz MM, Zhou L, Toh S. Using machine learning to identify health outcomes from electronic health record data. Curr Epidemiol Rep. 2018;5(4):331\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun F, Wang H, Zhang D, Han F, Ye S. One-year renal outcome in lupus nephritis patients with acute kidney injury: a nomogram model. Rheumatology (Oxford). 2022;61(7):2886\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartini A, Cumarasamy S, Beksac AT, et al. A Nomogram to Predict Significant Estimated Glomerular Filtration Rate Reduction After Robotic Partial Nephrectomy. Eur Urol. 2018;74(6):833\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi JJ, Zhao SP, Zhao D, et al. 2023 China Guidelines for Lipid Management. J Geriatr Cardiol. 2023;20(9):621\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLevey AS, Stevens LA, Schmid CH et al. A new equation to estimate glomerular filtration rate [published correction appears in Ann Intern Med. 2011;155(6):408]. \u003cem\u003eAnn Intern Med\u003c/em\u003e. 2009;150(9):604\u0026ndash;612.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLevey AS, Stevens LA, Coresh J. Conceptual model of CKD: applications and implications. Am J Kidney Dis. 2009;53(3 Suppl 3):S4\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu Z, Grams ME, Ndumele CE, et al. Association Between Midlife Obesity and Kidney Function Trajectories: The Atherosclerosis Risk in Communities (ARIC) Study. Am J Kidney Dis. 2021;77(3):376\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e2019;10(2):101\u0026ndash;111. 23.You Branch of Organ Transplantation of Chinese Medical A. Management specification for blood lipid on recipients with solid organ transplantation in China (2019 edition). ORGAN TRANSPLANTATION, Li A, Tomlinson Y. B, Association Between Renal Dysfunction and Low HDL Cholesterol Among the Elderly in China. \u003cem\u003eFront Cardiovasc Med\u003c/em\u003e. 2021;8:644208. Published 2021 May 12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRysz J, Gluba-Brz\u0026oacute;zka A, Rysz-G\u0026oacute;rzyńska M, Franczyk B. The Role and Function of HDL in Patients with Chronic Kidney Disease and the Risk of Cardiovascular Disease. Int J Mol Sci. 2020;21(2):601. Published 2020 Jan 17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJulve J, Escol\u0026agrave;-Gil JC. High-Density Lipoproteins and Cardiovascular Disease: The Good, the Bad and the Future. Biomedicines. 2021;9(8):857.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePearson GJ, Thanassoulis G, Anderson TJ, et al. 2021 Canadian Cardiovascular Society Guidelines for the Management of Dyslipidemia for the Prevention of Cardiovascular Disease in Adults. Can J Cardiol. 2021;37(8):1129\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHodkinson A, Tsimpida D, Kontopantelis E, Rutter MK, Mamas MA, Panagioti M. Comparative effectiveness of statins on non-high density lipoprotein cholesterol in people with diabetes and at risk of cardiovascular disease: systematic review and network meta-analysis. BMJ. 2022;376:e067731. Published 2022 Mar 24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhai Q, Dou J, Wen J, et al. Association between changes in lipid indexes and early progression of kidney dysfunction in participants with normal estimated glomerular filtration rate: a prospective cohort study. Endocrine. 2022;76(2):312\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePan X. Cholesterol Metabolism in Chronic Kidney Disease: Physiology, Pathologic Mechanisms, and Treatment. Adv Exp Med Biol. 2022;1372:119\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHonzumi S, Takeuchi M, Kurihara M, et al. The effect of cholesterol overload on mouse kidney and kidney-derived cells. Ren Fail. 2018;40(1):43\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsai MH, Lin MY, Hsu CY, et al. Factors associated with renal function state transitions: A population-based community survey in Taiwan. Front Public Health. 2022;10:930798. Published 2022 Sep 8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiakopoulos V, Roumeliotis S, Gorny X, Dounousi E, Mertens PR. Oxidative Stress in Hemodialysis Patients: A Review of the Literature. Oxid Med Cell Longev. 2017;2017:3081856.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoumeliotis S, Tavridou A, Panagoutsos S, SP378PLASMA OXIDIZED LDL LEVELS ARE ASSOCIATED WITH HYPERTENSION AND RENAL FUNCTION DETERIORATION BUT NOT SURVIVAL IN DIABETIC NEPHROPATHY, et al. Nephrol Dialysis Transplantation. 2018;33(suppl1):i473\u0026ndash;473.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOzsoy RC, van der Steeg WA, Kastelein JJ, Arisz L, Koopman MG. Dyslipidaemia as predictor of progressive renal failure and the impact of treatment with atorvastatin. Nephrol Dial Transpl. 2007;22(6):1578\u0026ndash;86.\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":"kidney transplantation, renal dysfunction, eGFR, risk prediction, blood lipid levels, machine learning, nomogram","lastPublishedDoi":"10.21203/rs.3.rs-5823279/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5823279/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eRenal dysfunction is a frequent complication after kidney transplantation, leading to poor prognosis and increased mortality. Abnormal blood lipids are closely related to renal dysfunction, yet their associations and mechanisms among renal transplant recipients remain unclear. This study aimed to establish an effective risk prediction model for renal dysfunction among RTRs based on abnormal lipid profiles using machine learning.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis retrospective cohort study recruited a cohort of 345 RTRs and followed up for one year after renal transplantation. Patients' demographic and clinical characteristics, including blood lipids, were retrieved from the electronic medical record system and analyzed using machine learning. Renal dysfunction was defined as estimated glomerular filtration rate (eGFR)\u0026thinsp;\u0026lt;\u0026thinsp;60 mL/min /1.73 m\u003csup\u003e2\u003c/sup\u003e. The cohort was randomly split into training (n\u0026thinsp;=\u0026thinsp;276) and validation (n\u0026thinsp;=\u0026thinsp;69) groups at a 4:1 ratio. Predictors of renal dysfunction were determined using three ML models: RandomForest, XGBoost, and LightGBM.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eDuring the one-year follow-up, 193 (55.9%) patients had renal dysfunction. A total of 20 demographic and clinical variables were selected to screen for significant predictors of renal dysfunction, and five were retained, including age, gender, HDL-C, non-HDL-C, and LDL-C, based on which a nomogram was developed. The nomogram showed good diagnostic performance with an area under the curve (AUC) of 0.87 in the training group and 0.81 in the validation group.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur study showed that preoperative lipid profiles predicted postoperative renal function among RTRs, based on which we developed a risk prediction model. The model can quickly identify high-risk RTRs with renal dysfunction, which is crucial for optimizing patient management and improving the prognosis.\u003c/p\u003e","manuscriptTitle":"Risk prediction of postoperative renal dysfunction based on preoperative lipid profiles in renal transplant recipients: A retrospective cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-15 12:07:27","doi":"10.21203/rs.3.rs-5823279/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":"0eb24964-ace5-479c-ae9b-f5892c5a3a1d","owner":[],"postedDate":"January 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-27T10:53:40+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-15 12:07:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5823279","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5823279","identity":"rs-5823279","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}