Introduction: This study aimed to develop and validate machine learning (ML) models based on serum Klotho for predicting end-stage kidney disease (ESKD) and cardiovascular disease (CVD) in patients with chronic kidney disease (CKD). Methods: Five different ML models were trained to predict the risk of ESKD and CVD at three different time points (3, 5, and 8 years) using a cohort of 400 non-dialysis CKD patients. The dataset was divided into a training set (70%) and an internal validation set (30%). These models were informed by data comprising 47 clinical features, including serum Klotho. The best-performing model was selected and used to identify risk factors for each outcome. Model performance was assessed using various metrics. Results: The findings showed that the least absolute shrinkage and selection operator regression model had the highest accuracy (C-index = 0.71) in predicting ESKD. The features mainly included in this model were estimated glomerular filtration rate, 24-h urinary microalbumin, serum albumin, phosphate, parathyroid hormone, and serum Klotho, which achieved the highest area under the curve (AUC) of 0.930 (95% CI: 0.897–0.962). In addition, for the CVD risk prediction, the random survival forest model with the highest accuracy (C-index = 0.66) was selected and achieved the highest AUC of 0.782 (95% CI: 0.633–0.930). The features mainly included in this model were age, history of primary hypertension, calcium, tumor necrosis factor-alpha, and serum Klotho. Conclusion: We successfully developed and validated Klotho-based ML risk prediction models for CVD and ESKD in CKD patients with good performance, indicating their high clinical utility.

Chronic kidney disease (CKD) has emerged as a significant global public health concern [1], with epidemiological surveys revealing a staggering prevalence of 11–13% worldwide [2], affecting over 850 million individuals [3]. In China, CKD affects 10.8% of the population, totaling approximately 120 million patients [4]. CKD is associated with three major adverse outcomes: end-stage kidney disease (ESKD), cardiovascular disease (CVD), and premature death [5]. The progression of CKD to ESKD, necessitating renal replacement therapy (RRT) such as dialysis or transplantation, imposes substantial economic burdens on individuals, families, and society while affecting the patient’s quality of life [6]. Alarmingly, CKD is projected to become the fifth leading cause of death globally by 2040 [7]. Furthermore, CVD is not only the most common complication but also the leading cause of death in CKD patients [8]. Thus, delaying CKD progression to ESKD and mitigating the risk of CVD events are of utmost importance.

Traditionally, risk factors for CKD progression to ESKD and the development of CVD have been associated with advanced age, hypertension, diabetes mellitus, obesity, and proteinuria [9, 10]. However, recent years have seen the emergence of new contributors to ESKD and CVD, including mineral and bone disorders, protein-energy wasting, overexpression of fibroblast growth factor 23 (FGF-23), and a deficiency of Klotho [11, 12]. The conventional kidney failure risk equation (KFRE) and the traditional Framingham equation have limitations in predicting ESKD and CVD [13]. Consequently, a multidimensional prognostic model and risk stratification system that encompasses a broader range of factors impacting CKD patient prognosis are needed.

α-Klotho (referred here as Klotho) was occasionally found in 1997 as an aging inhibitor gene [14]. The Klotho gene encodes a single transmembrane protein with a molecular weight of approximately 130 kDa expressed predominantly in renal distal tubules [15]. The Klotho protein exists in either membrane-bound or secreted forms. Soluble Klotho can enter the blood circulation as a hormone-like substance to exert specific effects. The results of our previous basic researches suggest that Klotho is positively correlated with estimated glomerular filtration rate (eGFR), suppresses the progression of uremic cardiomyopathy significantly, protects against atherosclerosis and thromboembolism complications, and is an important inhibitor of CKD vascular calcification [16‒18]. Moreover, in a mouse model of CKD, by inhibiting the FGF2 signaling, Klotho can reduce renal fibrosis and suppress CKD progression [19]. All of these studies suggest that Klotho plays an important protective role in reducing the progression of CKD and the occurrence of CVD. Meanwhile, our clinical research also suggests that lower serum Klotho levels are independently associated with CVD events and overall mortality in nondiabetic pre-dialysis CKD patients which show that Klotho can be an independent predictor of prognosis in CKD patients [20]. In summary, from basic to clinical translational studies, all have confirmed that Klotho plays a crucial role in the progression of CKD and the complication of CVD.

Machine learning (ML) is now widely used in the construction of clinical prediction models for specific tasks, ultimately yielding more accurate predictive models [21]. In nephrology, ML has found utility in identifying high-risk patients [22], managing transplant recipients [23], making prescribing decisions for hemodialysis patients [24], and more. Harnessing ML’s potential in kidney disease enhances clinicians’ ability to predict ESKD and CKD [25]. Although some ML models have been developed to predict ESKD in patients with diabetic kidney disease [26] and immunoglobulin A nephropathy [27, 28], as well as predict CKD or CKD progression [29, 30], few models are tailored to forecast ESKD. Moreover, existing ESKD models do not encompass non-dialysis CKD patients with stages 1–5 in the Chinese population, nor do they incorporate Klotho. Similarly, while numerous ML models exist for predicting CVD in the general population, there is a scarcity of models designed specifically for CKD patients [31]. Hence, the aim of this study was to utilize ML to establish risk prediction models, incorporating Klotho, for ESKD and CVD in non-dialysis CKD stages 1–5 patients within the Chinese population.

Study Population

This retrospective cohort study was conducted at a single center and included 456 participants diagnosed with CKD stages 1–5 who were hospitalized in the Department of Nephrology at Xinqiao Hospital, between February 7, 2012, and October 18, 2019. The follow-up period was extended until February 1, 2022. All data were analyzed anonymously.

Inclusion and Exclusion Criteria

The criteria for inclusion were as follows: (1) adults aged 18 or older; (2) individuals with CKD stages 1–5 of any cause, who were not undergoing RRT such as hemodialysis, peritoneal dialysis, or kidney transplantation; (3) availability of complete follow-up data and baseline data with less than 30% missing. The criteria for exclusion were: (1) acute renal failure; (2) pregnancy or breastfeeding; (3) history of organ transplantation requiring long-term immunosuppression; (4) past or current malignant tumor; (5) uncontrolled active infections; (6) lost to follow-up.

Clinical and Laboratory Variables

At the time of patient admission, the following clinical and laboratory variables were collected: demographic characteristics, smoking history, medical history, blood pressure, body mass index (height and weight), kidney function, urinary protein, liver function, blood lipid profile, complete blood count, T-cell subpopulation counts, complement levels, serum calcium, magnesium, phosphorus, parathyroid hormone (PTH) levels, and inflammation markers. The eGFR was calculated using the CKD-EPI equation [32], and CKD staging was based on the 2012 KDIGO clinical practice guideline.

Assay of Serum Klotho

Serum Klotho levels were measured using a Human Klotho ELISA kit provided by CUSABIO (Wuhan, China). Five milliliters of peripheral venous blood were collected from fasting patients in the morning, allowed to stand for 30 min, centrifuged at 3,000 r/min for 10 min, and the serum was stored at −80°C until analysis.

Definition and Follow-Up of Endpoints

The study endpoints included ESKD and CVD. ESKD was defined as the initiation of RRT, including hemodialysis, peritoneal dialysis, and kidney transplantation. Termination of follow-up occurred upon the start of RRT or when patients exhibited a persistent eGFR <15(mL/min/1.73 m2) without RRT. CVD was defined as the occurrence of new cardiovascular events, such as coronary heart disease (fatal or nonfatal myocardial infarction, angina pectoris), acute or chronic heart failure, serious cardiac arrhythmia, cerebrovascular disease (fatal or nonfatal stroke, transient ischemic attack), peripheral vascular disease (intermittent claudication, severe limb ischemia), aortic atherosclerosis, or aortic aneurysm. Termination of follow-up was determined by the occurrence of the first CVD event.

Statistical Analysis

To enhance data homogeneity, we initially performed log transformations on tumor necrosis factor-alpha (TNF-α) and Klotho [33]. Our statistical analysis was carried out using R version 3.4.3. Quatitative data were presented as mean ± standard deviation or the median (interquartile range), which depended on the data distribution. Comparisons were made by independent samples t test or Mann-Whitney U test. Quantitative data were presented as numbers and percentages, and comparisons were made by the χ2 test or the Fisher’s exact probability test. Our chosen significance level was α = 0.05.

Model Development

First, the multiple imputation of missing data was performed [34]. Second, the dataset was randomly split into two parts, with 70% of the samples assigned as a training set for model fitting and 30% of the samples designated as a validation set for model evaluation. Then, within the training set, a 10-fold cross-validation was conducted to compare the predictive accuracy of five models: Cox regression, gradient boosting machine (GBM), least absolute shrinkage and selection operator (LASSO), random survival forest (RSF), and support vector machine (SVM). For the ESKD model, the LASSO model with the highest prediction accuracy was selected. A 100-times 10 fold cross-validation was employed to determine the optimal λ, with lambda.min serving as the selection criterion. Similarly, for the CVD model, the RSF model with the highest prediction accuracy was chosen. Variables with importance scores exceeding 0.01 were retained. Next, stepwise Cox regression was used to identify predictors and construct both ESKD and CVD risk prediction models. Lastly, model performance was evaluated using various metrics, including receiver operating characteristic (ROC) curve analysis, calibration curves, decision curve analysis (DCA), and Kaplan-Meier (K-M) survival curves. Refer to online supplementary Figure 1 (for all online suppl. material, see https://doi.org/10.1159/000538510) for a detailed description of the model development process.

Baseline Characteristics of the Included Patients

We included 400 patients with non-dialysis CKD stages 1–5 for the final analysis. A total of 47 variables were included, and both ESKD and CVD outcomes were examined. During a median follow-up period of 6.55 years, 269 patients (67.25%) progressed to ESKD. The average age of individuals who developed ESKD was 47 years, with 49.1% being male. The demographic characteristics, complications, and baseline laboratory test results between the ESKD group and the control group are presented in Table 1. This comparison revealed statistically significant differences in 26 variables, including age, blood pressure, eGFR, 24-h urinary total protein, serum albumin, hemoglobin, calcium, phosphate, PTH, interleukin-6, TNF-α and serum Klotho, and more. Over a median follow-up period of 6.57 years, a total of 129 patients (32.25%) developed CVD. The average age of individuals who developed CVD was 49 years, with 53.5% being male. The demographic characteristics, complications, and baseline laboratory results for the enrolled participants are also summarized in Table 1. A comparison between the CVD group and the control group revealed that age, history of primary hypertension, absolute monocyte count, CD4/CD8 ratio, calcium, C-reactive protein, interleukin-6, TNF-α, and serum Klotho exhibited highly significant differences. Additionally, a history of diabetes, eGFR, cystatin-c, alkaline phosphatase, absolute eosinophil count, and PTH showed significant differences. The correlations among various variables and their relationships with ESKD and CVD are shown in Figure 1. The analysis revealed significant correlations between age, history of CVD, history of diabetes, serum albumin, calcium, and magnesium with CVD, and significant correlations between serum Klotho, TNF-α, systolic blood pressure, eGFR, calcium, phosphorus, PTH, and more with ESKD.

Table 1.

Baseline characteristics of the study population

CharacteristicNo ESKDESKDp value of ESKDNo CVDCVDp value of CVD
Number 131 269  271 129  
Age, years 49.00 [41.00, 63.00] 47.00 [37.00, 55.00] 0.006 46.00 [36.00, 55.00] 49.00 [41.00, 63.00] 0.001 
Sex, n (%) 
 Men 81 (61.8) 132 (49.1) 0.022 144 (53.1) 69 (53.5) 
 Women 50 (38.2) 137 (50.9)  127 (46.9) 60 (46.5)  
BMI, kg/m2 23.51 [21.56, 25.41] 23.19 [20.72, 25.35] 0.146 22.94 [20.67, 25.29] 23.88 [21.55, 25.39] 0.105 
Smoke, n (%) 
 Never 96 (73.3) 203 (75.5) 0.776 209 (77.1) 90 (69.8) 0.26 
 Previous 14 (10.7) 30 (11.2)  26 (9.6) 18 (14.0)  
 Current 21 (16.0) 36 (13.4)  36 (13.3) 21 (16.3)  
CVD history, n (%) 
 No 108 (82.4) 218 (81.0) 0.84 227 (83.8) 99 (76.7) 0.121 
 Yes 23 (17.6) 51 (19.0)  44 (16.2) 30 (23.3)  
DM history, n (%) 
 No 124 (94.7) 245 (91.1) 0.291 256 (94.5) 113 (87.6) 0.028 
 Yes 7 (5.3) 24 (8.9)  15 (5.5) 16 (12.4)  
HTN history, n (%) 
 No 93 (71.0) 195 (72.5) 0.846 209 (77.1) 79 (61.2) 0.001 
 Yes 38 (29.0) 74 (27.5)  62 (22.9) 50 (38.8)  
SBP, mm Hg 130.00 [119.00, 140.50] 141.00 [130.00, 158.00] <0.001 138.00 [126.00, 153.00] 139.00 [128.00, 150.00] 0.494 
DBP, mm Hg 83.00 [74.50, 90.00] 87.00 [79.00, 96.00] 0.004 86.00 [77.00, 95.00] 86.00 [78.00, 95.00] 0.787 
eGFR, mL/min·1.73 m2 53.73 [30.06, 74.52] 10.17 [6.28, 20.55] <0.001 19.15 [8.64, 46.11] 12.30 [6.49, 33.10] 0.042 
Urea, mmol/L 7.42 [5.73, 10.38] 16.81 [11.53, 24.82] <0.001 12.62 [7.83, 20.40] 13.57 [8.91, 24.21] 0.068 
UA, μmol/L 443.80 (107.45) 520.78 (122.19) <0.001 501.16 (126.08) 483.82 (115.45) 0.187 
Cys-c, mg/L 1.45 [1.15, 2.34] 4.08 [2.80, 5.10] <0.001 2.94 [1.63, 4.44] 3.66 [2.13, 4.77] 0.018 
RBP, mg/L 50.00 [38.50, 66.00] 86.00 [68.00, 107.00] <0.001 74.00 [54.00, 98.00] 71.00 [54.00, 99.00] 0.897 
UTP, g/24 h 0.76 [0.22, 2.21] 2.05 [1.17, 3.46] <0.001 1.52 [0.72, 3.08] 1.63 [0.80, 2.98] 0.82 
U-mAlb, mg/24 h 454.65 [67.40, 1,480.70] 1,296.00 [533.40, 2,586.30] <0.001 1,012.80 [343.45, 2,468.88] 1,100.40 [292.80, 2,004.00] 0.889 
U-mAlb/U-Cr, mg/mmol 174.80 [27.85, 703.55] 574.60 [202.10, 1,965.90] <0.001 395.70 [129.50, 1,379.40] 566.90 [122.40, 1,484.40] 0.527 
Fib, g/L 3.06 [2.54, 3.64] 3.32 [2.90, 3.75] 0.002 3.16 [2.64, 3.68] 3.39 [2.81, 4.00] 0.067 
ALP, U/L 61.00 [51.00, 75.45] 67.00 [54.00, 91.00] 0.004 63.00 [52.00, 82.00] 71.80 [57.00, 91.00] 0.019 
TC, mmol/L 4.58 [3.86, 5.82] 4.55 [3.71, 5.39] 0.169 4.59 [3.83, 5.49] 4.42 [3.57, 5.32] 0.192 
TG mmol/L 1.56 [1.11, 2.34] 1.49 [1.09, 2.05] 0.182 1.53 [1.09, 2.20] 1.48 [1.10, 2.09] 0.598 
HDL-C, mmol/L 1.08 [0.90, 1.30] 1.09 [0.91, 1.33] 0.929 1.09 [0.91, 1.31] 1.06 [0.89, 1.29] 0.569 
LDL-C, mmol/L 2.85 [2.26, 3.45] 2.76 [2.19, 3.34] 0.329 2.83 [2.22, 3.40] 2.70 [2.06, 3.34] 0.215 
Alb, g/L 40.30 [34.55, 44.40] 38.00 [34.00, 41.70] 0.007 38.90 [34.45, 42.45] 38.30 [33.40, 41.90] 0.286 
WBC, 109/L 6.27 [5.51, 7.54] 6.16 [5.00, 7.60] 0.191 6.17 [5.04, 7.48] 6.49 [5.32, 7.71] 0.318 
ANC, 109/L 4.05 [3.38, 5.36] 4.22 [3.35, 5.32] 0.766 4.11 [3.34, 5.30] 4.26 [3.37, 5.34] 0.601 
ALC, 109/L 1.48 [1.15, 1.86] 1.29 [0.91, 1.63] 0.001 1.35 [0.95, 1.73] 1.35 [1.04, 1.74] 0.708 
AEC, 109/L 0.11 [0.06, 0.21] 0.13 [0.07, 0.23] 0.125 0.12 [0.06, 0.22] 0.13 [0.09, 0.24] 0.031 
AMC, 109/L 0.38 [0.30, 0.49] 0.39 [0.28, 0.51] 0.939 0.36 [0.28, 0.49] 0.43 [0.31, 0.54] 0.004 
M:L 3.11 [2.26, 4.33] 3.57 [2.74, 4.98] 0.002 3.36 [2.58, 4.77] 3.49 [2.64, 4.78] 0.685 
HGB, g/L 125.00 [106.00, 143.50] 93.00 [74.00, 113.00] <0.001 105.00 [82.00, 126.00] 98.00 [80.00, 120.00] 0.175 
PLT, 109/L 187.00 [150.00, 243.00] 159.00 [119.00, 208.00] <0.001 170.00 [131.00, 222.00] 166.00 [132.00, 210.00] 0.64 
CD4 436.00 [348.00, 562.00] 456.00 [352.00, 608.00] 0.149 452.00 [352.00, 576.00] 464.00 [348.00, 660.00] 0.235 
CD8 404.00 [322.00, 524.00] 388.00 [284.00, 572.00] 0.795 412.00 [322.00, 574.00] 400.00 [272.00, 532.00] 0.096 
CD3 884.00 [728.00, 1,166.00] 888.00 [724.00, 1,272.00] 0.594 896.00 [750.00, 1,218.00] 888.00 [700.00, 1,256.00] 0.496 
CD4:CD8 1.10 [0.80, 1.30] 1.10 [0.90, 1.50] 0.028 1.06 [0.81, 1.35] 1.18 [0.96, 1.60] 0.001 
C3, g/L 0.80 [0.68, 0.89] 0.72 [0.62, 0.83] 0.001 0.75 [0.64, 0.86] 0.74 [0.64, 0.87] 0.874 
C4, mg/dL 18.90 [16.20, 24.05] 21.30 [17.30, 25.10] 0.015 20.30 [17.20, 24.80] 21.00 [17.00, 24.90] 0.434 
Ca, mmol/L 2.23 [2.13, 2.32] 2.16 [2.02, 2.25] <0.001 2.20 [2.09, 2.30] 2.14 [2.02, 2.24] <0.001 
Mg, mmol/L 0.83 [0.75, 0.89] 0.86 [0.78, 0.96] 0.001 0.85 [0.78, 0.91] 0.85 [0.76, 0.96] 0.551 
P, mmol/L 1.08 [0.91, 1.25] 1.39 [1.16, 1.75] <0.001 1.25 [1.06, 1.56] 1.34 [1.11, 1.61] 0.072 
PTH, pg/mL 51.30 [36.15, 81.25] 186.00 [86.70, 348.00] <0.001 97.20 [48.70, 262.00] 144.00 [76.90, 266.00] 0.018 
CRP, mg/L 2.70 [1.90, 4.90] 3.00 [1.90, 5.30] 0.356 2.60 [1.80, 4.95] 3.50 [2.10, 7.00] 0.009 
IL-6, pg/mL 2.70 [2.00, 3.90] 3.40 [2.20, 5.70] 0.003 2.95 [2.00, 4.65] 3.70 [2.70, 6.50] 0.001 
IL-8 pg/mL 6.47 [5.00, 10.65] 7.58 [5.06, 11.40] 0.049 7.20 [5.00, 11.00] 8.47 [5.00, 12.70] 0.063 
Lg (TNF-α), pg/mL 1.00 [0.88, 1.17] 1.12 [0.98, 1.23] <0.001 1.06 [0.93, 1.20] 1.13 [0.98, 1.25] 0.008 
Lg (Klotho), pg/mL 3.15 [2.91, 3.36] 2.81 [2.53, 2.97] <0.001 2.87 [2.56, 3.07] 2.77 [2.49, 2.92] 0.004 
CharacteristicNo ESKDESKDp value of ESKDNo CVDCVDp value of CVD
Number 131 269  271 129  
Age, years 49.00 [41.00, 63.00] 47.00 [37.00, 55.00] 0.006 46.00 [36.00, 55.00] 49.00 [41.00, 63.00] 0.001 
Sex, n (%) 
 Men 81 (61.8) 132 (49.1) 0.022 144 (53.1) 69 (53.5) 
 Women 50 (38.2) 137 (50.9)  127 (46.9) 60 (46.5)  
BMI, kg/m2 23.51 [21.56, 25.41] 23.19 [20.72, 25.35] 0.146 22.94 [20.67, 25.29] 23.88 [21.55, 25.39] 0.105 
Smoke, n (%) 
 Never 96 (73.3) 203 (75.5) 0.776 209 (77.1) 90 (69.8) 0.26 
 Previous 14 (10.7) 30 (11.2)  26 (9.6) 18 (14.0)  
 Current 21 (16.0) 36 (13.4)  36 (13.3) 21 (16.3)  
CVD history, n (%) 
 No 108 (82.4) 218 (81.0) 0.84 227 (83.8) 99 (76.7) 0.121 
 Yes 23 (17.6) 51 (19.0)  44 (16.2) 30 (23.3)  
DM history, n (%) 
 No 124 (94.7) 245 (91.1) 0.291 256 (94.5) 113 (87.6) 0.028 
 Yes 7 (5.3) 24 (8.9)  15 (5.5) 16 (12.4)  
HTN history, n (%) 
 No 93 (71.0) 195 (72.5) 0.846 209 (77.1) 79 (61.2) 0.001 
 Yes 38 (29.0) 74 (27.5)  62 (22.9) 50 (38.8)  
SBP, mm Hg 130.00 [119.00, 140.50] 141.00 [130.00, 158.00] <0.001 138.00 [126.00, 153.00] 139.00 [128.00, 150.00] 0.494 
DBP, mm Hg 83.00 [74.50, 90.00] 87.00 [79.00, 96.00] 0.004 86.00 [77.00, 95.00] 86.00 [78.00, 95.00] 0.787 
eGFR, mL/min·1.73 m2 53.73 [30.06, 74.52] 10.17 [6.28, 20.55] <0.001 19.15 [8.64, 46.11] 12.30 [6.49, 33.10] 0.042 
Urea, mmol/L 7.42 [5.73, 10.38] 16.81 [11.53, 24.82] <0.001 12.62 [7.83, 20.40] 13.57 [8.91, 24.21] 0.068 
UA, μmol/L 443.80 (107.45) 520.78 (122.19) <0.001 501.16 (126.08) 483.82 (115.45) 0.187 
Cys-c, mg/L 1.45 [1.15, 2.34] 4.08 [2.80, 5.10] <0.001 2.94 [1.63, 4.44] 3.66 [2.13, 4.77] 0.018 
RBP, mg/L 50.00 [38.50, 66.00] 86.00 [68.00, 107.00] <0.001 74.00 [54.00, 98.00] 71.00 [54.00, 99.00] 0.897 
UTP, g/24 h 0.76 [0.22, 2.21] 2.05 [1.17, 3.46] <0.001 1.52 [0.72, 3.08] 1.63 [0.80, 2.98] 0.82 
U-mAlb, mg/24 h 454.65 [67.40, 1,480.70] 1,296.00 [533.40, 2,586.30] <0.001 1,012.80 [343.45, 2,468.88] 1,100.40 [292.80, 2,004.00] 0.889 
U-mAlb/U-Cr, mg/mmol 174.80 [27.85, 703.55] 574.60 [202.10, 1,965.90] <0.001 395.70 [129.50, 1,379.40] 566.90 [122.40, 1,484.40] 0.527 
Fib, g/L 3.06 [2.54, 3.64] 3.32 [2.90, 3.75] 0.002 3.16 [2.64, 3.68] 3.39 [2.81, 4.00] 0.067 
ALP, U/L 61.00 [51.00, 75.45] 67.00 [54.00, 91.00] 0.004 63.00 [52.00, 82.00] 71.80 [57.00, 91.00] 0.019 
TC, mmol/L 4.58 [3.86, 5.82] 4.55 [3.71, 5.39] 0.169 4.59 [3.83, 5.49] 4.42 [3.57, 5.32] 0.192 
TG mmol/L 1.56 [1.11, 2.34] 1.49 [1.09, 2.05] 0.182 1.53 [1.09, 2.20] 1.48 [1.10, 2.09] 0.598 
HDL-C, mmol/L 1.08 [0.90, 1.30] 1.09 [0.91, 1.33] 0.929 1.09 [0.91, 1.31] 1.06 [0.89, 1.29] 0.569 
LDL-C, mmol/L 2.85 [2.26, 3.45] 2.76 [2.19, 3.34] 0.329 2.83 [2.22, 3.40] 2.70 [2.06, 3.34] 0.215 
Alb, g/L 40.30 [34.55, 44.40] 38.00 [34.00, 41.70] 0.007 38.90 [34.45, 42.45] 38.30 [33.40, 41.90] 0.286 
WBC, 109/L 6.27 [5.51, 7.54] 6.16 [5.00, 7.60] 0.191 6.17 [5.04, 7.48] 6.49 [5.32, 7.71] 0.318 
ANC, 109/L 4.05 [3.38, 5.36] 4.22 [3.35, 5.32] 0.766 4.11 [3.34, 5.30] 4.26 [3.37, 5.34] 0.601 
ALC, 109/L 1.48 [1.15, 1.86] 1.29 [0.91, 1.63] 0.001 1.35 [0.95, 1.73] 1.35 [1.04, 1.74] 0.708 
AEC, 109/L 0.11 [0.06, 0.21] 0.13 [0.07, 0.23] 0.125 0.12 [0.06, 0.22] 0.13 [0.09, 0.24] 0.031 
AMC, 109/L 0.38 [0.30, 0.49] 0.39 [0.28, 0.51] 0.939 0.36 [0.28, 0.49] 0.43 [0.31, 0.54] 0.004 
M:L 3.11 [2.26, 4.33] 3.57 [2.74, 4.98] 0.002 3.36 [2.58, 4.77] 3.49 [2.64, 4.78] 0.685 
HGB, g/L 125.00 [106.00, 143.50] 93.00 [74.00, 113.00] <0.001 105.00 [82.00, 126.00] 98.00 [80.00, 120.00] 0.175 
PLT, 109/L 187.00 [150.00, 243.00] 159.00 [119.00, 208.00] <0.001 170.00 [131.00, 222.00] 166.00 [132.00, 210.00] 0.64 
CD4 436.00 [348.00, 562.00] 456.00 [352.00, 608.00] 0.149 452.00 [352.00, 576.00] 464.00 [348.00, 660.00] 0.235 
CD8 404.00 [322.00, 524.00] 388.00 [284.00, 572.00] 0.795 412.00 [322.00, 574.00] 400.00 [272.00, 532.00] 0.096 
CD3 884.00 [728.00, 1,166.00] 888.00 [724.00, 1,272.00] 0.594 896.00 [750.00, 1,218.00] 888.00 [700.00, 1,256.00] 0.496 
CD4:CD8 1.10 [0.80, 1.30] 1.10 [0.90, 1.50] 0.028 1.06 [0.81, 1.35] 1.18 [0.96, 1.60] 0.001 
C3, g/L 0.80 [0.68, 0.89] 0.72 [0.62, 0.83] 0.001 0.75 [0.64, 0.86] 0.74 [0.64, 0.87] 0.874 
C4, mg/dL 18.90 [16.20, 24.05] 21.30 [17.30, 25.10] 0.015 20.30 [17.20, 24.80] 21.00 [17.00, 24.90] 0.434 
Ca, mmol/L 2.23 [2.13, 2.32] 2.16 [2.02, 2.25] <0.001 2.20 [2.09, 2.30] 2.14 [2.02, 2.24] <0.001 
Mg, mmol/L 0.83 [0.75, 0.89] 0.86 [0.78, 0.96] 0.001 0.85 [0.78, 0.91] 0.85 [0.76, 0.96] 0.551 
P, mmol/L 1.08 [0.91, 1.25] 1.39 [1.16, 1.75] <0.001 1.25 [1.06, 1.56] 1.34 [1.11, 1.61] 0.072 
PTH, pg/mL 51.30 [36.15, 81.25] 186.00 [86.70, 348.00] <0.001 97.20 [48.70, 262.00] 144.00 [76.90, 266.00] 0.018 
CRP, mg/L 2.70 [1.90, 4.90] 3.00 [1.90, 5.30] 0.356 2.60 [1.80, 4.95] 3.50 [2.10, 7.00] 0.009 
IL-6, pg/mL 2.70 [2.00, 3.90] 3.40 [2.20, 5.70] 0.003 2.95 [2.00, 4.65] 3.70 [2.70, 6.50] 0.001 
IL-8 pg/mL 6.47 [5.00, 10.65] 7.58 [5.06, 11.40] 0.049 7.20 [5.00, 11.00] 8.47 [5.00, 12.70] 0.063 
Lg (TNF-α), pg/mL 1.00 [0.88, 1.17] 1.12 [0.98, 1.23] <0.001 1.06 [0.93, 1.20] 1.13 [0.98, 1.25] 0.008 
Lg (Klotho), pg/mL 3.15 [2.91, 3.36] 2.81 [2.53, 2.97] <0.001 2.87 [2.56, 3.07] 2.77 [2.49, 2.92] 0.004 

BMI, body mass index = weight (in kg)/height2 (in m2); CVD history, history of cardiovascular disease; DM history, history of diabetes; HTN history, history of primary hypertension; SBP, systolic blood pressure; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; UA, uric acid; Cys-c, cystatin-c; RBP, retinol-binding protein; UTP, 24-h urinary total protein; U-mAlb, urinary microalbumin; U-mAlb/U-Cr, urinary microalbumin and creatinine ratio; Fib, fibrinogen; ALP, alkaline phosphatase; TC, total cholesterol; TG, triglyceride; HDL-C, high density lipoprotein cholesterol; LDL-C, low density lipoprotein cholesterol; Alb, serum albumin; WBC, white blood cell; ANC, absolute neutrophil count; ALC, absolute lymphocyte count; AEC, absolute eosinophil count; AMC, absolute monocyte count; M:L, myeloid to lymphoid ratio; HGB, hemoglobin; PLT, platelet count; CD4, CD4 lymphocyte count; CD8, CD8 lymphocyte count; CD3, CD3 lymphocyte count; CD4:CD8, CD4/CD8 ratio; C3, complement C3; C4, complement C4; Ca, calcium; Mg, magnesium; P, phosphate; PTH, parathyroid hormone; CRP, C-reactive protein; IL-6, interleukin-6; IL-8, interleukin-8; Lg (TNF-α), Log-transformed tumor necrosis factor-alpha; Lg (Klotho), Log-transformed Klotho.

Fig. 1.

Heatmap of correlation between variables and the Mantel test. The heatmap in the top right-hand corner shows the pairwise correlations between the 47 variables, with a color gradient denoting the Pearson correlation coefficient. The magnitude and direction of the correlation are reflected by the size (larger is stronger) and color (red is negative and blue is positive) of the circles, respectively. The Mantel test in the bottom left shows the correlation between the two endpoints (ESKD, CVD) and the other variables. The statistical significance or lack of significance of the correlation between the variables and the endpoints is reflected by the color of the network connecting line (gray represents no significance [Mantel p ≥ 0.05] and the rest of the colors are significant [Mantel p < 0.05]). The magnitude of the correlation between the variables and the endpoints is reflected by the width of the network connecting lines (the thicker the stronger). When p < 0.05, the greater the Mantel r, the greater the effect of the variables on ESKD or CVD.

Fig. 1.

Heatmap of correlation between variables and the Mantel test. The heatmap in the top right-hand corner shows the pairwise correlations between the 47 variables, with a color gradient denoting the Pearson correlation coefficient. The magnitude and direction of the correlation are reflected by the size (larger is stronger) and color (red is negative and blue is positive) of the circles, respectively. The Mantel test in the bottom left shows the correlation between the two endpoints (ESKD, CVD) and the other variables. The statistical significance or lack of significance of the correlation between the variables and the endpoints is reflected by the color of the network connecting line (gray represents no significance [Mantel p ≥ 0.05] and the rest of the colors are significant [Mantel p < 0.05]). The magnitude of the correlation between the variables and the endpoints is reflected by the width of the network connecting lines (the thicker the stronger). When p < 0.05, the greater the Mantel r, the greater the effect of the variables on ESKD or CVD.

Close modal

ML Model Selection and Construction for ESKD

First, we tested the rationality for random division and found no significant differences in various metrics between the training and validation sets (online suppl. Table 1). In the training set, we used 10-fold cross-validation to compare the accuracy of 5 ML models, and the LASSO regression model had the highest accuracy, with a mean C-index of 0.71 (Fig. 2a). Dimensionality reduction was carried out using LASSO regression analysis to select the most representative feature variables from the training set. A 100-times 10-fold cross-validation was performed to determine the optimal Lambda (λ = 0.085), with the selection criterion being lambda.min (Fig. 2b). The optimal LASSO model identified 12 indicators associated with ESKD prognosis. Subsequently, these indicators were subjected to stepwise Cox regression, resulting in the identification of eight independent factors significantly influencing ESKD (Table 2). In addition, we conducted a stepwise Cox regression on the 12 variables selected through LASSO regression. Among these, eight variables, categorized by their significance, were used to create a nomogram (Fig. 2c).

Fig. 2.

a Box-plot of the predictive accuracy of the five models for ESKD. b Risk factor screening for ESKD. The two dotted lines in the figure refer to the values of lambda.min and lambda.lse, respectively. c Risk prediction nomogram for ESKD. This figure is able to predict 3-, 5-, and 8-year renal survival in patients with CKD when combined scores for each risk variable. Each quantitative variable has a specific value that corresponds to a specific point. Each point extends a vertical line upward to the intersection of the “Points,” which corresponds to the value of the corresponding score and the total score of all variables can be found on the “Sum of all points” with corresponding coordinates. A vertical line is drawn from the “Sum of all points” coordinate and the value corresponding to its intersection with the “probability of 3-, 5-, and 8-year survival” coordinate is the probability of kidney survival.

Fig. 2.

a Box-plot of the predictive accuracy of the five models for ESKD. b Risk factor screening for ESKD. The two dotted lines in the figure refer to the values of lambda.min and lambda.lse, respectively. c Risk prediction nomogram for ESKD. This figure is able to predict 3-, 5-, and 8-year renal survival in patients with CKD when combined scores for each risk variable. Each quantitative variable has a specific value that corresponds to a specific point. Each point extends a vertical line upward to the intersection of the “Points,” which corresponds to the value of the corresponding score and the total score of all variables can be found on the “Sum of all points” with corresponding coordinates. A vertical line is drawn from the “Sum of all points” coordinate and the value corresponding to its intersection with the “probability of 3-, 5-, and 8-year survival” coordinate is the probability of kidney survival.

Close modal
Table 2.

Cox stepwise regression analysis of the risk of ESKD in CKD patients

VariablesHR (95% CI)p value
eGFR 0.286 (0.175, 0.467) 0.000 
Cys-c 1.844 (1.356, 2.507) 0.000 
RBP 1.287 (1.079, 1.535) 0.005 
U-mAlb 1.378 (1.143, 1.662) 0.001 
Alb 0.734 (0.588, 0.917) 0.007 
1.228 (1.007, 1.497) 0.042 
PTH 1.169 (1.018, 1.343) 0.027 
Lg (Klotho) 0.781 (0.652, 0.934) 0.007 
VariablesHR (95% CI)p value
eGFR 0.286 (0.175, 0.467) 0.000 
Cys-c 1.844 (1.356, 2.507) 0.000 
RBP 1.287 (1.079, 1.535) 0.005 
U-mAlb 1.378 (1.143, 1.662) 0.001 
Alb 0.734 (0.588, 0.917) 0.007 
1.228 (1.007, 1.497) 0.042 
PTH 1.169 (1.018, 1.343) 0.027 
Lg (Klotho) 0.781 (0.652, 0.934) 0.007 

HR (95% CI), hazard ratio (95% confidence intervals).

Validation of ESKD Risk Prediction Model

Finally, we evaluated the performance of the ESKD risk model in predicting renal survival at different time intervals. The ROC analysis showed an excellent AUC value predicting 3-, 5-, and 8-year renal survival in both the training set and validation set (Fig. 3a, d). The model’s sensitivity, specificity, and accuracy are detailed in online supplementary Table 3. The ROC curve demonstrated the model’s accurate prediction of ESKD, with the actual, calibration, and ideal curves closely aligned (Fig. 3b, e). Furthermore, the model showed positive net benefit across all thresholds in both the training and validation sets (Fig. 3c, f), indicating its high clinical utility. We also applied the model to predict the ESKD outcome. We classified high- and low-risk groups based on the median linear predictive value of risk, and the K-M curve results clearly distinguished between high-risk and low-risk populations in both the training and validation sets (online suppl. Fig. 2), confirming the accuracy of the prognostic model.

Fig. 3.

Predictive performance of the ESKD model. a ROC curve in the training set. b Calibration curve in the training set. c DCA curve in the training set. d ROC curve in the validation set. e Calibration curve in the validation set. f DCA curve in the validation set.

Fig. 3.

Predictive performance of the ESKD model. a ROC curve in the training set. b Calibration curve in the training set. c DCA curve in the training set. d ROC curve in the validation set. e Calibration curve in the validation set. f DCA curve in the validation set.

Close modal

ML Model Selection and Construction for CVD

First, this random data split was performed as the same before, no significant differences were found between the various metrics of the training and validation sets (online suppl. Table 2). In the training set, we used the same method for model comparison, and the RSF model had the highest accuracy, with a mean C-index of 0.66 (Fig. 4a). Variables with a significance level >0.01 (top 20 variables, including age, calcium, absolute monocyte count, serum Klotho, etc.) were selected for further analysis using Cox stepwise regression (Fig. 4c). Ultimately, eight of these variables were selected for constructing a nomogram (Fig. 4d). Out of these indicators, six were identified as independent influencers of CVD prognosis (Table 3).

Fig. 4.

a Box-plot of the predictive accuracy of 5 ML models for CVD. b Parameter testing for CVD. It illustrates the trend of the out-of-bag (OOB) error rate with the increasing number of trees. The error rate stabilized at approximately ntree = 600, indicating that further increases in the number of trees did not significantly improve model performance. c Risk factor screening for CVD. It provides insights into the importance of the variables considered. d Risk prediction nomogram for CVD. Risk scores are calculated in the same way as above.

Fig. 4.

a Box-plot of the predictive accuracy of 5 ML models for CVD. b Parameter testing for CVD. It illustrates the trend of the out-of-bag (OOB) error rate with the increasing number of trees. The error rate stabilized at approximately ntree = 600, indicating that further increases in the number of trees did not significantly improve model performance. c Risk factor screening for CVD. It provides insights into the importance of the variables considered. d Risk prediction nomogram for CVD. Risk scores are calculated in the same way as above.

Close modal
Table 3.

Cox stepwise regression analysis of the risk of CVD in CKD patients

VariablesHR (95% CI)p value
Age 1.021 (1.005, 1.036) 0.009 
HTN history 2.241 (1.412, 3.557) 0.001 
AMC 3.355 (1.353, 8.317) 0.009 
CD4:CD8 1.700 (1.088, 2.657) 0.020 
Ca 0.170 (0.068, 0.425) 0.000 
Mg 3.635 (0.868, 15.220) 0.077 
Lg (TNF-α) 2.302 (0.840, 6.310) 0.105 
Lg (Klotho) 0.523 (0.295, 0.925) 0.026 
VariablesHR (95% CI)p value
Age 1.021 (1.005, 1.036) 0.009 
HTN history 2.241 (1.412, 3.557) 0.001 
AMC 3.355 (1.353, 8.317) 0.009 
CD4:CD8 1.700 (1.088, 2.657) 0.020 
Ca 0.170 (0.068, 0.425) 0.000 
Mg 3.635 (0.868, 15.220) 0.077 
Lg (TNF-α) 2.302 (0.840, 6.310) 0.105 
Lg (Klotho) 0.523 (0.295, 0.925) 0.026 

HR (95% CI), hazard ratio (95% confidence intervals).

Validation of CVD Risk Prediction Model

To assess the predictive performance of the CVD risk model, we conducted ROC analysis. The results, as shown in Figure 5a and d, indicated an AUC of 0.750 (95% CI: 0.687, 0.813) for predicting the absence of CVD at 8 years in the training set. In the validation set, the AUC value was 0.782 (95% CI: 0.633, 0.930) at 5 years. The model’s sensitivity, specificity, and accuracy are detailed in online supplementary Table 4. These ROC curves demonstrated that the model exhibited robust predictive accuracy for CVD. Furthermore, the actual curve, calibration curve, and ideal curve were closely aligned (Fig. 5b, e), suggesting that the model possessed excellent stability. The model delivered positive benefits within a threshold value of 0.85 in the training set and within a threshold value of 0.50 in the validation set (Fig. 5c, f). These findings underscored the high clinical applicability and utility of the model for predicting CVD risk. Additionally, the K-M survival curves displayed clear stratification between high-risk and low-risk populations in both the training and validation sets (online suppl. Fig. 3), affirming the strong accuracy of the prognostic model.

Fig. 5.

Predictive performance of the CVD model. a ROC curve in the training set. b Calibration curve in the training set. c DCA curve in the training set. d ROC curve in the validation set. e Calibration curve in the validation set. f DCA curve in the validation set.

Fig. 5.

Predictive performance of the CVD model. a ROC curve in the training set. b Calibration curve in the training set. c DCA curve in the training set. d ROC curve in the validation set. e Calibration curve in the validation set. f DCA curve in the validation set.

Close modal

In this study, we developed an ML model using Klotho to predict ESKD and CVD through a retrospective analysis of a cohort study. We demonstrated a strong predictive capability of these models, and the development of these models will be valuable in predicting the unfavorable prognosis of CKD.

Currently, with the development of electronic health record system, AI is making significant inroads in healthcare [35]. About 10 ML prediction models for CKD prognosis (ESKD and CVD) exist in the literature. Four of these models primarily predict the risk of CKD progression to ESKD [36‒39], while six models encompass the prediction of CVD events [40‒45]. ESKD models have been developed for both North American and Asian populations, consistently exhibiting high predictive efficacy, with most achieving AUC values exceeding 0.8. Notably, most of these models incorporate eGFR, aligning with our ESKD risk prediction model. Importantly, they meet the clinical criteria for usefulness. In contrast, most CVD models focus on predicting specific cardiovascular events, such as heart failure, coronary atherosclerotic heart disease, or atrial fibrillation, and have been studied in North America and Europe. These models often integrate specific circulating proteins or serum markers to enhance predictive efficacy. While CVD models typically achieve AUCs of at least 0.7, they often exhibit limited clinical utility and share few overlapping risk factors with our model.

Despite CKD patients demonstrating higher cardiovascular risk [46], existing risk prediction tools for this population remain inadequate. For example, the Pool Cohort Equation (PCE), an endorsed CVD risk prediction model based on traditional risk factors like age, sex, and systolic blood pressure, achieves only moderate differentiation in CKD patients [47]. Efforts have been made to enhance predictive efficacy by incorporating CKD-specific factors, such as eGFR and proteinuria, into established models like PCE [43]. Additional research has explored improving prediction for heart failure by incorporating additional variables, like high-sensitive troponin T, echocardiographic measurements [44]. These endeavors have resulted in moderate improvements but still fall short of providing comprehensive risk assessment. Furthermore, studies have demonstrated the utility of assessing cardiovascular risk by examining specific circulating proteins, offering opportunities for personalized risk assessment and potential therapeutic targets [40, 41]. Importantly, one study has developed a CVD-related risk equation based on the circulating proteome, surpassing the efficacy of traditional clinical risk models, including eGFR [42].

KFRE is widely adopted for predicting ESKD risk worldwide, boasting excellent predictive performance. However, its external validation has not been confirmed in China, and it does not consider the influence of health behaviors on CKD progression [48]. Additionally, CKD has numerous etiologies, and different causes may have unique prognostic factors not included in the analysis [21]. Our study demonstrated that the ESKD risk prediction model, developed using ML techniques, delivered performance on par with or even superior to that of the KFRE. These models underscore the potential of ML in CKD prognosis prediction.

Several ESKD prediction models have been developed for CKD patients, demonstrating great efficacy in risk assessment. For instance, Segal et al. 2020 [36] developed an ESKD model (C-statistic: 0.93) tailored to US patients with CKD stages 1–4, utilizing the XGBoost algorithm. The model identified age as the most influential factor, followed by CKD stage and hypertension. Similarly, Hui et al. 2023 [38] designed an ESKD risk model for the Chinese CKD stages 1–4 population. Both the traditional Cox model and the XGBoost model showed comparable superiority, with the validation set C-index of 0.834 and 0.826, respectively. This model integrated variables such as age, eGFR, albumin, hemoglobin, hypertension, and so on. Although notable progress has been made in ML-based ESKD prediction models, there remains ample room for further development and refinement to address specific gaps in risk assessment for CKD patients.

Moreover, quite a few ML models have emerged to predict CKD or CKD progression based on complex multifactorial analyses in large datasets, consistently exhibiting superior predictive performance [29, 30, 49]. One model found that RSF outperformed other methods, achieving impressive AUCs for early and late CKD progression prediction [29]. Likewise, some ML models have focused on predicting ESKD events specifically in patients with diabetic nephropathy and IgA nephropathy, achieving AUCs between 0.8 and 0.9 [26‒28]. However, it is essential to note that some traditional models have demonstrated similar or superior predictive performance to some ML models, mainly due to data scarcity or insufficient positive events for ML models to reach optimal predictive accuracy [50].

Our current study has some significant strengths. First, the innovative use of serum Klotho level as a core predictor of CKD prognosis has not been reported at home and abroad. Second, adopting the risk factor integration method has established an AI prognostic prediction model by means of ML modeling, which in turn greatly improves the ability of CKD prognostic prediction. Third, we have expanded the definition of CVD events on this basis, and the vascular events have been extended from traditional coronary artery disease to systemic vascular disease, making the scope of application wider. Fourth, the risk indicators included in this study are comprehensive and have a long follow-up period, and the parameters are readily available in the clinic.

Our study has several limitations. First, our model came from a single-center sample size which will need to be externally validated in multiple centers in China. Second, additional predictor variables, such as gene, behavior, and so on may also affect outcome events but were not included in the analysis. Third, due to the sample size limitation, we did not make separate predictions for patients with each stage of CKD.

In conclusion, our study developed and validated risk prediction models for CVD and ESKD in patients with CKD. These models incorporate Klotho and use ML techniques. They provide a proper way to improve risk prognosis management for CKD patients, which could advance the clinical care of CKD patients.

This study was approved by the Xinqiao Hospital Ethics Committee (No. 2018-006-02). All participants volunteered for the study. Written informed consent was obtained from all study participants for participation in the study. The study protocol conformed to the Declaration of Helsinki.

The authors declare no conflict of interest.

This work was supported by Joint Funds of the National Natural Science Foundation of China (No. U22A20279), the National Key R and D Program of China (2022YFC2502501), the Natural Science Foundation of China (No. 81873605), key project of Chongqing technology development and application program (No. CSTB2023TIAD-KPX0060), and personal training program for Clinical Medicine Research of Army Medical University (No. 2018XLC1007).

Y.W. was responsible for drafting the manuscript. Y.S., T.X., X.B., and Q.H. collected and analyzed the data, while S.W. conducted the Klotho detection. The concept and design were conceived by J.X. and J.Z.

The data that support the findings of this study are not publicly available due to the fact that they contain sensitive information and pose a potential risk of harm to the pertinent information of the study participants but are available from the corresponding author (J.X. or J.Z.) upon reasonable request.

1.
Lozano
R
,
Naghavi
M
,
Foreman
K
,
Lim
S
,
Shibuya
K
,
Aboyans
V
, et al
.
Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010
.
Lancet
.
2012
;
380
(
9859
):
2095
128
.
2.
Hill
NR
,
Fatoba
ST
,
Oke
JL
,
Hirst
JA
,
O'Callaghan
CA
,
Lasserson
DS
, et al
.
Global prevalence of chronic kidney disease - a systematic review and meta-analysis
.
PLoS One
.
2016
;
11
(
7
):
e0158765
.
3.
Jager
KJ
,
Kovesdy
C
,
Langham
R
,
Rosenberg
M
,
Jha
V
,
Zoccali
C
.
A single number for advocacy and communication-worldwide more than 850 million individuals have kidney diseases
.
Nephrol Dial Transplant
.
2019
;
34
(
11
):
1803
5
.
4.
Zhang
L
,
Wang
F
,
Wang
L
,
Wang
W
,
Liu
B
,
Liu
J
, et al
.
Prevalence of chronic kidney disease in China: a cross-sectional survey
.
Lancet
.
2012
;
379
(
9818
):
815
22
.
5.
Levey
AS
,
Eckardt
KU
,
Tsukamoto
Y
,
Levin
A
,
Coresh
J
,
Rossert
J
, et al
.
Definition and classification of chronic kidney disease: a position statement from Kidney Disease: improving Global Outcomes (KDIGO)
.
Kidney Int
.
2005
;
67
(
6
):
2089
100
.
6.
Liyanage
T
,
Ninomiya
T
,
Jha
V
,
Neal
B
,
Patrice
HM
,
Okpechi
I
, et al
.
Worldwide access to treatment for end-stage kidney disease: a systematic review
.
Lancet
.
2015
;
385
(
9981
):
1975
82
.
7.
Foreman
KJ
,
Marquez
N
,
Dolgert
A
,
Fukutaki
K
,
Fullman
N
,
McGaughey
M
, et al
.
Forecasting life expectancy, years of life lost, and all-cause and cause-specific mortality for 250 causes of death: reference and alternative scenarios for 2016-40 for 195 countries and territories
.
Lancet
.
2018
;
392
(
10159
):
2052
90
.
8.
Streja
E
,
Norris
KC
,
Budoff
MJ
,
Hashemi
L
,
Akbilgic
O
,
Kalantar-Zadeh
K
.
The quest for cardiovascular disease risk prediction models in patients with nondialysis chronic kidney disease
.
Curr Opin Nephrol Hypertens
.
2021
;
30
(
1
):
38
46
.
9.
Taal
MW
,
Brenner
BM
.
Predicting initiation and progression of chronic kidney disease: developing renal risk scores
.
Kidney Int
.
2006
;
70
(
10
):
1694
705
.
10.
Gansevoort
RT
,
Correa-Rotter
R
,
Hemmelgarn
BR
,
Jafar
TH
,
Heerspink
HJ
,
Mann
JF
, et al
.
Chronic kidney disease and cardiovascular risk: epidemiology, mechanisms, and prevention
.
Lancet
.
2013
;
382
(
9889
):
339
52
.
11.
Lu
X
,
Hu
MC
.
Klotho/FGF23 Axis in chronic kidney disease and cardiovascular disease
.
Kidney Dis
.
2017
;
3
(
1
):
15
23
.
12.
Koppe
L
,
Fouque
D
,
Kalantar-Zadeh
K
.
Kidney cachexia or protein-energy wasting in chronic kidney disease: facts and numbers
.
J Cachexia Sarcopenia Muscle
.
2019
;
10
(
3
):
479
84
.
13.
Weiner
DE
,
Tighiouart
H
,
Elsayed
EF
,
Griffith
JL
,
Salem
DN
,
Levey
AS
, et al
.
The Framingham predictive instrument in chronic kidney disease
.
J Am Coll Cardiol
.
2007
;
50
(
3
):
217
24
.
14.
Kuro-o
M
,
Matsumura
Y
,
Aizawa
H
,
Kawaguchi
H
,
Suga
T
,
Utsugi
T
, et al
.
Mutation of the mouse klotho gene leads to a syndrome resembling ageing
.
Nature
.
1997
;
390
(
6655
):
45
51
.
15.
Neyra
JA
,
Hu
MC
,
Moe
OW
.
Klotho in clinical nephrology: diagnostic and therapeutic implications
.
Clin J Am Soc Nephrol
.
2020
;
16
(
1
):
162
76
.
16.
Yang
K
,
Wang
C
,
Nie
L
,
Zhao
X
,
Gu
J
,
Guan
X
, et al
.
Klotho protects against indoxyl sulphate-induced myocardial hypertrophy
.
J Am Soc Nephrol
.
2015
;
26
(
10
):
2434
46
.
17.
Liu
L
,
Liu
Y
,
Zhang
Y
,
Bi
X
,
Nie
L
,
Liu
C
, et al
.
High phosphate-induced downregulation of PPARγ contributes to CKD-associated vascular calcification
.
J Mol Cell Cardiol
.
2018
;
114
:
264
75
.
18.
Yang
K
,
Du
C
,
Wang
X
,
Li
F
,
Xu
Y
,
Wang
S
, et al
.
Indoxyl sulfate induces platelet hyperactivity and contributes to chronic kidney disease-associated thrombosis in mice
.
Blood
.
2017
;
129
(
19
):
2667
79
.
19.
Guan
X
,
Nie
L
,
He
T
,
Yang
K
,
Xiao
T
,
Wang
S
, et al
.
Klotho suppresses renal tubulo-interstitial fibrosis by controlling basic fibroblast growth factor-2 signalling
.
J Pathol
.
2014
;
234
(
4
):
560
72
.
20.
Yang
K
,
Yang
J
,
Bi
X
,
Yu
Z
,
Xiao
T
,
Huang
Y
, et al
.
Serum klotho, cardiovascular events, and mortality in nondiabetic chronic kidney disease
.
Cardiorenal Med
.
2020
;
10
(
3
):
175
87
.
21.
Thongprayoon
C
,
Kaewput
W
,
Choudhury
A
,
Hansrivijit
P
,
Mao
MA
,
Cheungpasitporn
W
.
Is it time for machine learning algorithms to predict the risk of kidney failure in patients with chronic kidney disease
.
J Clin Med
.
2021
;
10
(
5
):
1121
.
22.
Hong
D
,
Chang
H
,
He
X
,
Zhan
Y
,
Tong
R
,
Wu
X
, et al
.
Construction of an early alert system for intradialytic hypotension before initiating hemodialysis based on machine learning
.
Kidney Dis
.
2023
;
9
(
5
):
433
42
.
23.
Niel
O
,
Bastard
P
.
Artificial intelligence improves estimation of tacrolimus area under the concentration over time curve in renal transplant recipients
.
Transpl Int
.
2018
;
31
(
8
):
940
1
.
24.
Gabutti
L
,
Burnier
M
,
Mombelli
G
,
Malé
F
,
Pellegrini
L
,
Marone
C
.
Usefulness of artificial neural networks to predict follow-up dietary protein intake in hemodialysis patients
.
Kidney Int
.
2004
;
66
(
1
):
399
407
.
25.
Schena
FP
,
Anelli
VW
,
Abbrescia
DI
,
Di Noia
T
.
Prediction of chronic kidney disease and its progression by artificial intelligence algorithms
.
J Nephrol
.
2022
;
35
(
8
):
1953
71
.
26.
Belur Nagaraj
S
,
Pena
MJ
,
Ju
W
,
Heerspink
HL
;
BEAt-DKD Consortium
.
Machine-learning-based early prediction of end-stage renal disease in patients with diabetic kidney disease using clinical trials data
.
Diabetes Obes Metab
.
2020
;
22
(
12
):
2479
86
.
27.
Pesce
F
,
Diciolla
M
,
Binetti
G
,
Naso
D
,
Ostuni
VC
,
Di Noia
T
, et al
.
Clinical decision support system for end-stage kidney disease risk estimation in IgA nephropathy patients
.
Nephrol Dial Transplant
.
2016
;
31
(
1
):
80
6
.
28.
Schena
FP
,
Anelli
VW
,
Trotta
J
,
Di Noia
T
,
Manno
C
,
Tripepi
G
, et al
.
Development and testing of an artificial intelligence tool for predicting end-stage kidney disease in patients with immunoglobulin A nephropathy
.
Kidney Int
.
2021
;
99
(
5
):
1179
88
.
29.
Su
CT
,
Chang
YP
,
Ku
YT
,
Lin
CM
.
Machine learning models for the prediction of renal failure in chronic kidney disease: a retrospective cohort study
.
Diagnostics
.
2022
;
12
(
10
):
2454
.
30.
Xiao
J
,
Ding
R
,
Xu
X
,
Guan
H
,
Feng
X
,
Sun
T
, et al
.
Comparison and development of machine learning tools in the prediction of chronic kidney disease progression
.
J Transl Med
.
2019
;
17
(
1
):
119
.
31.
Burlacu
A
,
Iftene
A
,
Popa
IV
,
Crisan-Dabija
R
,
Brinza
C
,
Covic
A
.
Computational models used to predict cardiovascular complications in chronic kidney disease patients: a systematic review
.
Medicina
.
2021
;
57
(
6
):
538
.
32.
Levey
AS
,
Stevens
LA
,
Schmid
CH
,
Zhang
YL
,
Castro
AF
3rd
,
Feldman
HI
, et al
.
A new equation to estimate glomerular filtration rate
.
Ann Intern Med
.
2009
;
150
(
9
):
604
12
.
33.
Barrera-Gómez
J
,
Basagaña
X
.
Models with transformed variables: interpretation and software
.
Epidemiology
.
2015
;
26
(
2
):
e16
7
.
34.
Enders
CK
.
Multiple imputation as a flexible tool for missing data handling in clinical research
.
Behav Res Ther
.
2017
;
98
:
4
18
.
35.
Yu
KH
,
Beam
AL
,
Kohane
IS
.
Artificial intelligence in healthcare
.
Nat Biomed Eng
.
2018
;
2
(
10
):
719
31
.
36.
Segal
Z
,
Kalifa
D
,
Radinsky
K
,
Ehrenberg
B
,
Elad
G
,
Maor
G
, et al
.
Machine learning algorithm for early detection of end-stage renal disease
.
BMC Nephrol
.
2020
;
21
(
1
):
518
.
37.
Bai
Q
,
Su
C
,
Tang
W
,
Li
Y
.
Machine learning to predict end stage kidney disease in chronic kidney disease
.
Sci Rep
.
2022
;
12
(
1
):
8377
.
38.
Hui
M
,
Ma
J
,
Yang
H
,
Gao
B
,
Wang
F
,
Wang
J
, et al
.
ESKD risk prediction model in a multicenter chronic kidney disease cohort in China: a derivation, validation, and comparison study
.
J Clin Med
.
2023
;
12
(
4
):
1504
.
39.
Kanda
E
,
Epureanu
BI
,
Adachi
T
,
Kashihara
N
.
Machine-learning-based Web system for the prediction of chronic kidney disease progression and mortality
.
PLoS Digit Health
.
2023
;
2
(
1
):
e0000188
.
40.
Forné
C
,
Cambray
S
,
Bermudez-Lopez
M
,
Fernandez
E
,
Bozic
M
,
Valdivielso
JM
, et al
.
Machine learning analysis of serum biomarkers for cardiovascular risk assessment in chronic kidney disease
.
Clin Kidney J
.
2020
;
13
(
4
):
631
9
.
41.
Dubin
RF
,
Whooley
M
,
Pico
A
,
Ganz
P
,
Schiller
NB
,
Meyer
C
.
Proteomic analysis of heart failure hospitalization among patients with chronic kidney disease: the Heart and Soul Study
.
PLoS One
.
2018
;
13
(
12
):
e0208042
.
42.
Deo
R
,
Dubin
RF
,
Ren
Y
,
Murthy
AC
,
Wang
J
,
Zheng
H
, et al
.
Proteomic cardiovascular risk assessment in chronic kidney disease
.
Eur Heart J
.
2023
;
44
(
23
):
2095
110
.
43.
Matsushita
K
,
Jassal
SK
,
Sang
Y
,
Ballew
SH
,
Grams
ME
,
Surapaneni
A
, et al
.
Incorporating kidney disease measures into cardiovascular risk prediction: development and validation in 9 million adults from 72 datasets
.
EClinicalMedicine
.
2020
;
27
:
100552
.
44.
Zelnick
LR
,
Shlipak
MG
,
Soliman
EZ
,
Anderson
A
,
Christenson
R
,
Kansal
M
, et al
.
Prediction of incident heart failure in CKD: the CRIC study
.
Kidney Int Rep
.
2022
;
7
(
4
):
708
19
.
45.
Zelnick
LR
,
Shlipak
MG
,
Soliman
EZ
,
Anderson
A
,
Christenson
R
,
Lash
J
, et al
.
Prediction of incident atrial fibrillation in chronic kidney disease: the chronic renal insufficiency cohort study
.
Clin J Am Soc Nephrol
.
2021
;
16
(
7
):
1015
24
.
46.
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
.
47.
Avram
R
.
Revolutionizing cardiovascular risk prediction in patients with chronic kidney disease: machine learning and large-scale proteomic risk prediction model lead the way
.
Eur Heart J
.
2023
;
44
(
23
):
2111
3
.
48.
Lee
Y
,
Kwon
S
,
Moon
JJ
,
Han
K
,
Paik
NJ
,
Kim
WS
.
The effect of health-related behaviors on disease progression and mortality in early stages of chronic kidney disease: a Korean nationwide population-based study
.
J Clin Med
.
2019
;
8
(
8
):
1100
.
49.
Islam
MA
,
Majumder
MZH
,
Hussein
MA
.
Chronic kidney disease prediction based on machine learning algorithms
.
J Pathol Inform
.
2023
;
14
:
100189
.
50.
Nusinovici
S
,
Tham
YC
,
Chak Yan
MY
,
Wei Ting
DS
,
Li
J
,
Sabanayagam
C
, et al
.
Logistic regression was as good as machine learning for predicting major chronic diseases
.
J Clin Epidemiol
.
2020
;
122
:
56
69
.