Introduction: Due to the cardiotoxicity of cancer treatment and traditional risk factors for cardiovascular disease (CVD) such as obesity, diabetes, dyslipidemia, and hypertension, cancer patients are at higher risk of developing CVD. However, limited research exists on the correlation between chronic kidney disease (CKD) and CVD risk in cancer patients. Methods: This cross-sectional study selected cancer patients aged ≥20 years from the National Health and Nutrition Examination Survey (NHANES) conducted from 2015 to 2020. Multivariable logistic regression was used to assess the association between CKD and CVD in cancer patients. Additionally, subgroup analyses were conducted to investigate the association among different groups of cancer patients. Results: We included 1,700 adult cancer patients (52.53% were females). After multivariable adjustment for covariates including traditional CVD factors, CKD was significantly associated with CVD, with an odds ratio (95% confidence interval) and p value of 1.61 (1.18, 2.19) and 0.004. Subgroup analyses after multivariable adjustment showed a significant correlation between CKD and increased CVD risk in the following cancer patients: age ≥60 years, males, white ethnicity, and individuals with or without traditional CVD factors (obesity, diabetes, dyslipidemia, and hypertension). Conclusions: CKD remains a significant factor in the higher risk of CVD among adult cancer patients in the United States, even after adjustment for traditional CVD risk factors. Therefore, to reduce the risk of CVD in cancer patients, it is important to treat CKD as a non-traditional risk factor for CVD and actively manage it.

Cardiovascular disease (CVD) and cancer are the leading causes of death and socioeconomic burden worldwide [1]. With advances in diagnostic and treatment strategies, the life expectancy of cancer patients continues to increase [2]. However, individuals with a history of cancer have a higher risk of CVD than the general population [3]. The rate of primary cardiovascular hospitalizations in cancer patients is increasing, and cardiovascular mortality in older cancer survivors exceeds that of primary cancer [4, 5], with heart failure being the most common cause of hospitalization [4]. Cardio-oncology, an emerging subspecialty within the field of cardiovascular medicine, encompasses the convergence of cardiology and oncology in both clinical practice and research. This means that CVD and cancer can coexist in the same individual. On one hand, cancer itself or cancer treatment can have harmful effects on the cardiovascular system [2], and on the other hand, several traditional cardiovascular risk factors such as smoking, obesity, diabetes, aging, and dyslipidemia are also associated with a higher risk of developing cancer [2, 6, 7].

Chronic kidney disease (CKD) is a chronic clinical state of renal structural and functional disorders affecting 15–20% of the adult population worldwide [8], making it a significant public health burden [9, 10]. It is worth noting that CKD may act as a non-traditional risk factor for CVD. Decreased estimated glomerular filtration rate (eGFR) can be used for stratification and prediction of major CVD events [11]. CKD is associated with various CVD outcomes, including coronary heart disease, stroke, peripheral artery disease, arrhythmias, heart failure, and venous thromboembolism [8], while CVD often also accelerates the progression of kidney damage [12]. After adjustment for traditional cardiovascular risk factors, CVD is significantly increased in people with CKD, with impaired kidney function and elevated urinary albumin concentrations increasing the risk of CVD by 2–4 times [13]. CKD and hemodialysis patients often have CVD. This may be related to anemia, inflammation, chronic volume overload, oxidative stress, changes in bone mineral metabolism, and uremic toxins caused by renal dysfunction [8, 14]. In addition, lower eGFR and CKD are strongly associated with a higher risk of CVD and overall mortality [15, 16].

Therefore, we speculate that CKD should be considered a higher non-traditional CVD risk for cancer patients. However, little research has focused on the correlation between CKD and CVD in cancer patients. It is currently unclear if CKD is an independent non-traditional risk factor for CVD in cancer patients. Therefore, this study aims to address this question through a population-based survey of cancer patients.

Study Design and Participants

The National Health and Nutrition Examination Survey (NHANES) is a cross-sectional survey implemented by the National Center for Health Statistics (NCHS) and administered by the United States Centers for Disease Control and Prevention (CDC). The survey uses a complex sampling design and multistage probability sampling to produce nationally representative data of the non-institutionalized resident population. The data are released in a 2-year cycle and include demographics, physical examination, health-related questionnaires, laboratory tests, and dietary information. The data of NHANES were collected through person-in-person household interviews and physical examinations conducted at the Mobile Examination Center (MEC). The investigation was approved by the research ethics review board of NCHS (https://www.cdc.gov/nchs/nhanes/irba98.htm), and written informed consent was obtained from each participant. Comprehensive information on NHANES study design, survey methods, and associated data can be accessed on the website (https://www.cdc.gov/nchs/nhanes/index.htm).

This cross-sectional study analyzed the association of CKD with CVD risk in cancer patients using data from three cycles of NHANES (2015–2020). A total of 34,785 individuals participated in the study. Cancer participants were established by answering “Yes” to the question “Ever been told by a doctor or other health professional that had cancer or a malignancy of any kind?” in the medical conditions questionnaire (MCQ). Exclusion criteria included individuals younger than 20 years, non-cancer patients, non-responders to the MCQ, and those without biochemical data on serum creatinine, hypersensitivity C-reactive protein (hs-CRP), glycated hemoglobin A1c (HbA1c), baseline data on body mass index (BMI), education, smoking, medical history data (including urine albumin/creatinine ratio to diagnose CKD). Ultimately, 1,700 cancer patients, including 570 with CKD and 1,130 without CKD, were included in the final analysis. The detailed data collection process is shown in Figure 1.

Fig. 1.

Flowchart of participation selection. MCQ, medical conditions questionnaire; CKD, chronic kidney disease. The 352 missing biochemical data included 334 for serum creatinine, 15 for hs-CRP, and 3 for glycated HbA1C. The 43 missing baseline data included 37 for BMI, 4 for education, and 2 for smoking. The 48 missing medical history data included 45 missing urinary albumin creatinine ratio (ACR) for diagnosis of CKD, 2 for hypertension, and 1 for diabetes.

Fig. 1.

Flowchart of participation selection. MCQ, medical conditions questionnaire; CKD, chronic kidney disease. The 352 missing biochemical data included 334 for serum creatinine, 15 for hs-CRP, and 3 for glycated HbA1C. The 43 missing baseline data included 37 for BMI, 4 for education, and 2 for smoking. The 48 missing medical history data included 45 missing urinary albumin creatinine ratio (ACR) for diagnosis of CKD, 2 for hypertension, and 1 for diabetes.

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Exposure Variable

Exposure variable for this study is CKD. CKD was defined as eGFR <60 mL/min/1.73 m2 or urine albumin/creatinine ratio (ACR) ≥30 mg/g, according to the Kidney Disease: Improving Global Outcomes (KDIGO) 2021 clinical practice guidelines [17].

We calculated eGFR based on the equation recommended by the recent ASN-NKF (National Kidney Foundation and the American Society of Nephrology) Task Force for removing race. The equation is eGFR = 142 × min(Scr/κ,1)α × max(Scr/κ,1)−1.200 × 0.9938age(year) × 1.012 (if female). The κ value was 0.9 for male and 0.7 for female, the α value was −0.302 for men and −0.241 for women [18]. The unit of eGFR was mL/min/1.73 m2, and Scr represented serum creatinine expressed in mg/dL.

The measurement of serum creatinine is helpful in the diagnosis and treatment of kidney disease, and it has been used as an important indicator to calculate eGFR and evaluate CKD [19]. Serum creatinine measurements were performed enzymatically by converting creatinine to creatine in the presence of creatinine enzymatic activity. The creatine then continues the reaction to finally produce a colored product measured at 546 nm (secondary wavelength = 700 nm), consistent with well-recognized high-performance liquid chromatography (HPLC) methods. Serum creatinine data were measured on a Beckman Coulter UniCel DXC 660i Synchrotron Access chemistry analyzer (DXC 660i) in 2015–2016 and the Roche cobas 6000 chemistry analyzer (cobas 6000) in 2017–2020. Further detailed laboratory process manuals are available as BIOPRO-creatinine documents on the official website.

Outcome Variable

The study outcome variable is CVD, which is defined by using structured questions in a standardized medical condition questionnaire (MCQ). Participants were asked the following questions, “Has your doctor or other health professionals ever told you that you have heart failure, coronary heart disease, angina, heart attack (or myocardial infarction), or stroke?” Participants who responded affirmatively to more than one of them were classified as having CVD [20].

Covariates

Covariates included age, sex, and ethnicity in demographic characteristics; lifestyle-related information like BMI, education, smoking, and physical activity; biochemical variables comprised of HbA1c and hs-CRP; and well-recognized traditional CVD risk factors in past medical history including diabetes, hypertension, and dyslipidemia [21].

Ethnicity included non-Hispanic white, non-Hispanic black, Mexican American, and other ethnicity; we reclassified it into white and non-white for ease of comparison. There were five categories of education level: less than 9th grade, 9–11th grade, high school, some college or AA grade, and college grade or about, and we reclassified it into high school or below and college or about. Smoking status was grouped into three categories, never, former, and now, according to the question “Have you smoked at least 100 cigarettes in the lifetime?” Physical activity was categorized into work activity and recreational activity by using a questionnaire on physical activity, and we categorized participants with moderate to vigorous activity into one category. Diabetes was confirmed by self-report, HbA1c ≥6.5%, or use of hypoglycemic drugs. Hypertension was documented as self-reported history of hypertension or antihypertension medication use and SBP (systolic blood pressure) greater than or equal to 140 mm Hg or/and DBP (diastolic blood pressure) greater than or equal to 90 mm Hg. Dyslipidemia was defined as participants with total cholesterol levels ≥240 mg/dL and the use of lipid-lowering medication.

Statistical Analysis

Since NHANES applied sample weighting, stratification, and cluster variables to account for complex survey designs such as oversampling and nonresponse, all statistical analyses in this study utilized the complex sampling design of the NHANES database to better represent the target population. Descriptive statistics for continuous variables including age, BMI, glycated hemoglobin, hs-CRP, serum creatinine, and eGFR are presented as weighted mean with standard error, and comparisons between groups were performed by using Student’s t test (eligible for normal distribution) or Mann-Whitney U test (not eligible for normal distribution). Categorical variables including gender, ethnicity, education level, smoking status, physical activity, stratification of eGFR, CKD, history of diabetes, hypertension, and dyslipidemia were expressed as number (n) and frequency (weighted %), and statistical differences between groups were analyzed by using the χ2 test.

We investigated the associations of serum creatinine, eGFR, eGFR levels, and CKD with CVD risk in cancer patients by logistic regression analysis. To elucidate the results, we built three adjusted models to provide statistical inferences. The crude model did not adjust for variables. Model I adjusted for age, sex, and ethnicity. Model II additionally adjusted for BMI, education, stroke, physical activity, hs-CRP, HbA1c, diabetes, hypertension, and hyperlipidemia on the basis of model I. To better understand the relationship between renal function and CVD in cancer patients, we classified eGFR levels into three categories based on sample size and CKD criteria.

To explore possible differences among different cancer populations, we performed subgroup analyses of the association between CKD and CVD under multivariable-adjusted model II. CKD with CVD risk under multivariable-adjusted model II. Stratification variables for subgroup analyses included age (<60 and ≥60 years old), sex (male and female), ethnicity (white and non-white), BMI (normal weight: <25, overweight: 25–30, obese: ≥30, kg/m2) and previous medical history of diabetes, dyslipidemia, and hypertension.

To make the baseline data of the study more clearly presented, we also performed demographic statistical analysis according to stratification by eGFR levels and CKD (online suppl. Tables 1, 2; for all online suppl. material, see https://doi.org/10.1159/000534182). For intergroup comparisons of variables classified as multiple (more than 2 groups), one-way analysis of variance or Kruskal-Wallis test for continuous variables was used. In addition, we also tested univariate and multivariate logistic regression analyses of baseline data (online suppl. Table 3). Similarly, subgroup analyses regarding eGFR and eGFR levels were also elaborated (online suppl. Table 4).

We considered sample weights for all statistical analyses and used R software (version 4.2.1) to perform the analyses. All results were considered statistically significant when the p value <0.05 (two-tailed).

Participants Characteristics

The weighted baseline characteristics for patients with cancer overall and by CVD groups are presented in Table 1. A total of 1,700 participants, of whom 52.53% were females and 47.47% were males. There were 415 patients with CVD among 1,700 cancer patients, accounting for about 25%. The mean age of the overall population was 63.61 (0.48). There were significant differences in age, sex, education, smoking, recreational physical activity, hs-CRP, HbA1c, serum creatinine, eGFR, ACR, eGFR levels, CKD, diabetes, hypertension, and dyslipidemia between the CVD and non-CVD groups.

Table 1.

Baseline characteristics of participants with cancer stratified by CVD in the NHANES 2015–2020

CharacteristicsOverallNon-CVDCVDp value
n = 1,700n = 1,285n = 415
Age 63.61 (0.48) 62.26 (0.54) 68.75 (0.67) <0.0001 
Sex    0.02 
 Female 893 (52.53) 721 (81.89) 172 (18.11)  
 Male 807 (47.47) 564 (75.39) 243 (24.61)  
Ethnicity    0.31 
 Non-white 678 (39.88) 545 (76.14) 133 (23.86)  
 White 1,022 (60.12) 740 (79.71) 282 (20.29)  
BMI, kg/m2 29.69 (0.19) 29.53 (0.23) 30.28 (0.43) 0.16 
Education    <0.001 
 High school or below 651 (38.29) 472 (71.90) 179 (28.10)  
 College or above 1,049 (61.71) 813 (82.23) 236 (17.77)  
Smoking    0.001 
 Never 825 (48.53) 678 (84.27) 147 (15.73)  
 Former 627 (36.88) 433 (74.97) 194 (25.03)  
 Now 248 (14.59) 174 (71.45) 74 (28.55)  
Physical activity (moderate to vigorous)    
 Work 760 (44.71) 593 (81.89) 167 (18.11) 0.1 
 Recreational 715 (42.06) 584 (85.30) 131 (14.70) <0.0001 
hs-CRP, mg/L 4.32 (0.22) 3.77 (0.20) 6.39 (0.87) 0.01 
HbA1c, % 5.85 (0.02) 5.76 (0.03) 6.20 (0.07) <0.0001 
Serum creatinine, mg/dL 0.94 (0.01) 0.90 (0.01) 1.09 (0.03) <0.0001 
eGFR, mg/min/1.73 m2 82.87 (0.65) 85.38 (0.65) 73.35 (1.67) <0.0001 
ACR, mg/g 53.39 (9.28) 38.26 (9.74) 110.68 (27.19) 0.02 
eGFR levels    <0.0001 
 eGFR ≥90 615 (36.18) 519 (86.33) 96 (13.67)  
 60≤ eGFR <90 740 (43.53) 558 (78.29) 182 (21.71)  
 eGFR <60 345 (20.29) 208 (60.05) 137 (39.95)  
ACR ≥30 365 (21.47) 237 (64.90) 128 (35.10)  
CKD 570 (33.53) 366 (64.53) 204 (35.47) <0.0001 
Diabetes 467 (27.47) 300 (61.41) 167 (38.59) <0.0001 
Dyslipidemia 1,367 (80.41) 991 (76.92) 376 (23.08) <0.001 
Hypertension 1,034 (60.82) 704 (69.67) 330 (30.33) <0.0001 
CharacteristicsOverallNon-CVDCVDp value
n = 1,700n = 1,285n = 415
Age 63.61 (0.48) 62.26 (0.54) 68.75 (0.67) <0.0001 
Sex    0.02 
 Female 893 (52.53) 721 (81.89) 172 (18.11)  
 Male 807 (47.47) 564 (75.39) 243 (24.61)  
Ethnicity    0.31 
 Non-white 678 (39.88) 545 (76.14) 133 (23.86)  
 White 1,022 (60.12) 740 (79.71) 282 (20.29)  
BMI, kg/m2 29.69 (0.19) 29.53 (0.23) 30.28 (0.43) 0.16 
Education    <0.001 
 High school or below 651 (38.29) 472 (71.90) 179 (28.10)  
 College or above 1,049 (61.71) 813 (82.23) 236 (17.77)  
Smoking    0.001 
 Never 825 (48.53) 678 (84.27) 147 (15.73)  
 Former 627 (36.88) 433 (74.97) 194 (25.03)  
 Now 248 (14.59) 174 (71.45) 74 (28.55)  
Physical activity (moderate to vigorous)    
 Work 760 (44.71) 593 (81.89) 167 (18.11) 0.1 
 Recreational 715 (42.06) 584 (85.30) 131 (14.70) <0.0001 
hs-CRP, mg/L 4.32 (0.22) 3.77 (0.20) 6.39 (0.87) 0.01 
HbA1c, % 5.85 (0.02) 5.76 (0.03) 6.20 (0.07) <0.0001 
Serum creatinine, mg/dL 0.94 (0.01) 0.90 (0.01) 1.09 (0.03) <0.0001 
eGFR, mg/min/1.73 m2 82.87 (0.65) 85.38 (0.65) 73.35 (1.67) <0.0001 
ACR, mg/g 53.39 (9.28) 38.26 (9.74) 110.68 (27.19) 0.02 
eGFR levels    <0.0001 
 eGFR ≥90 615 (36.18) 519 (86.33) 96 (13.67)  
 60≤ eGFR <90 740 (43.53) 558 (78.29) 182 (21.71)  
 eGFR <60 345 (20.29) 208 (60.05) 137 (39.95)  
ACR ≥30 365 (21.47) 237 (64.90) 128 (35.10)  
CKD 570 (33.53) 366 (64.53) 204 (35.47) <0.0001 
Diabetes 467 (27.47) 300 (61.41) 167 (38.59) <0.0001 
Dyslipidemia 1,367 (80.41) 991 (76.92) 376 (23.08) <0.001 
Hypertension 1,034 (60.82) 704 (69.67) 330 (30.33) <0.0001 

Continuous variables are given as mean (standard error) and categorical variables are given as n (weighted %). p values were calculated by the Student’s t-test or Mann-Whitney U test for continuous variables, and χ2 test for categorical variables, respectively.

CVD, cardiovascular disease; BMI, body mass index; hs-CRP, hypersensitive C-reactive protein; HbA1c, glycated hemoglobin A1c; eGFR, estimated glomerular filtration rate; ACR, urinary albumin creatinine ratio; CKD, chronic kidney disease.

The mean (standard error) serum creatinine and eGFR values of cancer patients were 0.94 (0.01) mg/dL and 82.87 (0.65) mg/min/1.73 m2, respectively, and there were 570 (33.53%) patients with CKD. In addition, according to the definition of CKD diagnosis, we found that ACR ≥30 and eGFR below 60 were 365 (21.47%) and 345 (20.29%), respectively. To visualize the frequency distribution of CKD and CVD in cancer patients, a ratio histogram was presented (Fig. 2a). Furthermore, to better know the associations between renal function and CVD and other covariates, we also performed baseline data as described by eGFR levels, and CKD (online suppl. Tables 1, 2).

Fig. 2.

a The prevalence of CVD based on ACR and eGFR levels in non-CKD and CKD cancer participants. b Associations of eGFR levels and CKD with CVD. Crude model was univariate logistic regression model. Model I adjusted for age, sex, and ethnicity. Model II adjusted for age, sex, ethnicity, BMI, education, smoking, physical activity, hs-CRP, HbA1c, diabetes, hypertension, and dyslipidemia.

Fig. 2.

a The prevalence of CVD based on ACR and eGFR levels in non-CKD and CKD cancer participants. b Associations of eGFR levels and CKD with CVD. Crude model was univariate logistic regression model. Model I adjusted for age, sex, and ethnicity. Model II adjusted for age, sex, ethnicity, BMI, education, smoking, physical activity, hs-CRP, HbA1c, diabetes, hypertension, and dyslipidemia.

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Association of CKD with CVD

The data was subjected to both univariate and multivariate logistic analyses, revealing that age, smoking (now), serum creatinine, eGFR, CKD, diabetes, and hypertension exhibited significant risk factors in the multivariate analysis for CVD among cancer patients (online suppl. Table 3). To elucidate the associations of serum creatinine, eGFR, eGFR levels, and CKD with CVD, we provided three models as presented in Table 2. As depicted in Table 2, the crude model was unadjusted; model I adjusted only for age, sex, and ethnicity, whereas model II was fully adjusted for multivariate analysis with other covariates on the basis of model I. Associations of serum creatinine, eGFR, eGFR <60, and CKD with CVD were significantly found in three models (all p values <0.05). In model II adjusted for all covariates from Table 1, the odds ratio (OR) of the association between serum creatinine and CVD was 1.85 (1.01, 3.39), p = 0.049, whereas the OR of the association between eGFR (per 10 units increase) and CVD was 0.88 (0.97, 0.99), p = 0.03. In other words, after multivariable adjustment, patients with cancer had an 85% increased risk of CVD for every 1 mg/dL increase in serum creatinine and a 12% decreased risk of CVD for every 10 mg/min/1.73 m2 increase in eGFR.

Table 2.

ORs (95% confidence intervals) for CVD associated with serum creatinine, eGFR, eGFR levels, and CKD in patients with cancer from NHANES 2015–2020

CharacteristicsCrude modelp valueModel Ip valueModel IIp value
OR (95% CI)OR (95% CI)OR (95% CI)
Serum creatinine 3.75 (1.69, 8.28) 0.002 2.40 (1.18, 4.87) 0.02 1.85 (1.01, 3.39) 0.049 
eGFR 0.76 (0.70, 0.82) <0.0001 0.81 (0.74, 0.88) <0.0001 0.88 (0.78, 0.99) 0.03 
eGFR levels 
 eGFR ≥90 Ref  Ref  Ref  
 60≤ eGFR <90 1.75 (1.14, 2.69) 0.01 1.24 (0.76, 2.03) 0.38 1.17 (0.69, 1.99) 0.56 
 eGFR <60 4.20 (2.76, 6.38) <0.0001 2.63 (1.58, 4.39) <0.001 2.07 (1.24, 3.44) 0.01 
CKD 
 No Ref  Ref  Ref  
 Yes 2.90 (2.17, 3.88) <0.0001 2.24 (1.63, 3.07) <0.0001 1.61 (1.18, 2.19) 0.004 
CharacteristicsCrude modelp valueModel Ip valueModel IIp value
OR (95% CI)OR (95% CI)OR (95% CI)
Serum creatinine 3.75 (1.69, 8.28) 0.002 2.40 (1.18, 4.87) 0.02 1.85 (1.01, 3.39) 0.049 
eGFR 0.76 (0.70, 0.82) <0.0001 0.81 (0.74, 0.88) <0.0001 0.88 (0.78, 0.99) 0.03 
eGFR levels 
 eGFR ≥90 Ref  Ref  Ref  
 60≤ eGFR <90 1.75 (1.14, 2.69) 0.01 1.24 (0.76, 2.03) 0.38 1.17 (0.69, 1.99) 0.56 
 eGFR <60 4.20 (2.76, 6.38) <0.0001 2.63 (1.58, 4.39) <0.001 2.07 (1.24, 3.44) 0.01 
CKD 
 No Ref  Ref  Ref  
 Yes 2.90 (2.17, 3.88) <0.0001 2.24 (1.63, 3.07) <0.0001 1.61 (1.18, 2.19) 0.004 

The association between eGFR and CVD is presented for per 10 units increase in eGFR.

Crude model was univariate logistic regression model.

Model I adjusted for age, sex, and ethnicity.

Model II adjusted for age, sex, ethnicity, BMI, education, smoking, physical activity, hs-CRP, HbA1c, diabetes, hypertension, and dyslipidemia.

CVD, cardiovascular disease; eGFR, estimated glomerular filtration rate (mg/min/1.73 m2); CKD, chronic kidney disease; OR, odds ratio; CI, confidence intervals.

Given that CKD stages can be classified by different eGFR levels, we compared the effects of eGFR levels and CKD on CVD in cancer patients. The ORs (95% CIs) of eGFR levels for cancer patients in the first (eGFR ≥90), second (60≤ eGFR <90), and third (eGFR <60) levels were 1 (Ref), 1.17 (0.69, 1.99), and 2.07 (1.24, 3.44), respectively, in multivariable-adjusted model II. Furthermore, we evaluated the association between CKD and CVD in cancer patients. The ORs (95% CIs) for the association between CKD and CVD were significant in all three models, presenting 2.90 (2.17, 3.88) in the crude model, 2.24 (1.63, 3.07) in model I, and 1.61 (1.18, 2.19) in model II, respectively. Among them, cancer patients with comorbid CKD had a 61% increased risk of CVD, as indicated by multivariable-adjusted model II. To visualize the results of Table 2, ORs (95% CIs) for the relationship between eGFR levels and CKD and CVD in patients with cancer were presented as error bar plots (Fig. 2b).

Subgroup Analyses

To further evaluate the association of CKD with CVD in cancer patients, subgroup analyses were performed. All analyses were adjusted for covariates in multivariable-adjusted model II, including age, sex, ethnicity, BMI, education, smoking, physical activity, hs-CRP, HbA1c, diabetes, hypertension, and dyslipidemia, except for the variable strata itself. The association between CKD and CVD risk was significant in those aged ≥60 years, male, white ethnicity, with normal weight or obesity, with or without diabetes and dyslipidemia, and hypertension (Fig. 3).

Fig. 3.

Multivariate adjusted ORs of CKD for CVD in subgroups. Adjustment covariates included age, sex, ethnicity, BMI, education, smoking, physical activity, hs-CRP, HbA1c, diabetes, hypertension, and dyslipidemia, except the stratification factor itself. CKD and CVD risk in cancer patients.

Fig. 3.

Multivariate adjusted ORs of CKD for CVD in subgroups. Adjustment covariates included age, sex, ethnicity, BMI, education, smoking, physical activity, hs-CRP, HbA1c, diabetes, hypertension, and dyslipidemia, except the stratification factor itself. CKD and CVD risk in cancer patients.

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In addition, subgroup analyses were conducted to further investigate the associations of eGFR and eGFR levels with CVD in different subgroups (online suppl. Table 4). The results indicated that an increase in eGFR by 10 units resulted in a significant reduction in CVD risk in the majority of subgroups. Conversely, eGFR <60 was found to significantly elevate the risk of CVD in most subgroups.

In this cross-sectional study of 1,700 adult cancer patients, we found that, after adjusting for covariates, the CVD risk in cancer patients was significantly associated with CKD and inversely associated with eGFR. Subgroup analyses based on multivariable adjustment revealed CKD was significantly associated with CVD risk in cancer patients that age ≥60 years, males, white ethnicity, with or without CVD traditional risk factors (obesity, diabetes, dyslipidemia, and hypertension).

The major cardiovascular risk factors in cancer patients, such as genetic factors, aging, smoking, obesity, dyslipidemia, and diabetes, are also closely associated with the development of cancer [22]. However, there is limited research reporting the relationship between CKD and CVD in cancer patients. Previous studies have focused on the relationship between renal function and CVD in people with diabetes [23, 24]. Gao et al. [23] found that eGFR ≤84.8 mL/min/1.73 m2 may increase the incidence and severity of CVD in patients with diabetes. Decreased eGFR is linked to a nearly twofold increase in the prevalence of CVD among individuals with diabetes, regardless of traditional cardiovascular risk factors and glycemic control [24]. Additionally, Go et al. [25] demonstrated an independent, graded, negative correlation between eGFR and the incidence of CVD events. A recent study showed that mild to moderate kidney dysfunction is causally related to cardiovascular prognosis in patients without apparent CVD or diabetes, and prevention methods for improving kidney function may have potential benefits for cardiovascular health [26]. The presence of CKD is often associated with CVD, and vice versa [12]. These findings are similar to our study of cancer patients, where CKD was significantly associated with CVD and eGFR was negatively associated with CVD.

CKD is a known risk factor for various CVDs, including coronary heart disease, stroke, peripheral arterial disease, arrhythmias, heart failure, and venous thrombosis [8]. In a large observational study of early CKD patients followed for 5.5 years, 24.9% of patients died before dialysis, and the majority of deaths were due to cardiovascular events [27]. Interestingly, cancer patients (all subtypes) have a higher risk of dying from CVD than the general population [28], with a cardiovascular mortality rate surpassing the mortality rate of cancer itself [3]. It is reasonable to assume that cancer patients with concurrent CKD may have an even greater risk of CVD and be susceptible to adverse cardiovascular events. Hence, to reduce CVD risk in cancer patients, screening for renal impairment should be considered, which, like other traditional CVD risk factors, needs to be positively managed.

CKD and CVD are closely interrelated and can mutually influence each other [8, 12, 29]. Similarly, cancer and CVD share common risk factors [7]. Therefore, it is reasonable to infer that cancer patients with concurrent CKD may have an elevated risk of CVD. Reduced kidney function is independently associated with CVD and risk factors for CVD, including hypertension, dyslipidemia, and BMI [30]. Smoking, a well-known risk factor for CVD, is also associated with the incidence of cancer and CKD [31]. Nicotine, the primary addictive component of cigarettes, exerts its effects by stimulating the postganglionic sympathetic nerve endings, resulting in an increase in circulating levels of adrenaline and noradrenaline. This leads to an elevation in blood pressure, activation of the renin-angiotensin-aldosterone system (RAAS), an increase in GFR, and ultimately an increase in intraglomerular pressure [32]. Additionally, there is a significant correlation between the severity of albuminuria and the amount of smoking [32]. Late-stage CKD patients are at increased risk of CVD, which may be partly attributed to the development of diffuse arteriosclerotic vascular disease and other cardiovascular comorbidities [30].

CKD-induced microvascular rarefaction can lead to microcirculatory dysfunction, reduced perfusion, shunting, and tissue hypoxia, with microvascular rarefaction being a major factor in CKD progression [33]. Microvascular dysfunction and tissue hypoxia, which are also observed in hypertension and diabetes, are key factors in the pathogenesis and progression of CVD [34]. Notably, hypoxia is a common occurrence in tumors and may be due to an insufficient microvascular system in the tumor microenvironment [35]. Consequently, the co-occurrence of cancer and CKD is more likely to result in hypoxia and microcirculatory dysfunction, thereby increasing the risk of CVD. In addition, given that both cancer and CKD may lead to microcirculation dysfunction, the relevant mechanisms include increased endothelin-1 [36], vascular endothelial dysfunction [37], and inflammation [8], which are important influencing factors of coronary microcirculation dysfunction in CVD [38, 39].

The significant impact of CKD on the cardiovascular system may stem from the involvement of multiple pathophysiological mechanisms that connect CKD to the development of CVD. These mechanisms include shared risk factors, such as diabetes and hypertension, alterations in bone mineral metabolism, the presence of anemia, volume overload, inflammation, and the existence of uremic toxins [8]. The toxic metabolites produced by uremia in CKD, as well as the conditions that alter the metabolism of chemical elements such as calcium and phosphorus, are recognized as non-traditional risk factors for CVD in CKD patients [29]. Additionally, the close association between CKD and CVD may be mediated by endothelial dysfunction, which has been shown to predict the incidence and mortality of CVD in both high-risk populations and low-risk populations [37].

Traditional CVD risk factors, such as diabetes, hypertension, and dyslipidemia, cannot fully explain the elevated cardiovascular risk in CKD patients, as even standard clinical interventions that successfully manage CVD in the general population do not reduce mortality rates in CKD patients [40]. Non-traditional factors related to mineral and vitamin D metabolism can explain the increased CVD risk in CKD from the perspective of vascular calcification [40]. CKD-related alterations contribute to increased inflammation and oxidative stress, promoting vascular changes such as atherosclerosis, arteriosclerosis, and calcification, as well as other abnormalities that increase the risk of CVD [8]. Therefore, cancer patients need to pay attention to non-traditional CVD risk factors, such as lower eGFR and CKD.

The traditional CVD risk factors, such as hypertension, dyslipidemia, and diabetes, fail to fully explain the heightened cardiovascular risk in patients with CKD, as even the standard clinical interventions that effectively manage CVD in the general population do not lower mortality rates in CKD patients [40]. Non-traditional factors linked to mineral and vitamin D metabolism can clarify the reason for the increased CVD risk in CKD, with vascular calcification as the underlying mechanism [40]. CKD-induced changes exacerbate inflammation and oxidative stress, leading to vascular alterations, including atherosclerosis, arterial stiffness, and calcification, as well as other abnormalities that raise the risk of CVD [8, 41]. Consequently, cancer patients should heed non-traditional CVD risk factors, such as low eGFR and CKD.

The present study has some strengths. A nationally representative, population-based sample was used, and all analyses were weighted, making the study representative. The study focused on cancer patients who are susceptible to CVD, highlighting the impact of non-traditional factors such as CKD on CVD risk. Even after adjusting for various traditional CVD risk factors such as smoking, obesity, hypertension, diabetes, and dyslipidemia, the results remained significant. Additionally, subgroup analyses supported the stability of the results. Of note, we used unified approach, without specification of race for assessing eGFR in the US population, as recently recommended by the NKF-ASN Task Force [18], by using this new approach to produce unbiased, accurate, and precise GFR measurements and estimation.

However, it should be acknowledged that this study also has some limitations. First, due to the nature of the cross-sectional study, it is hard to define the causal relationship behind the described associations. It is unknown whether there is a temporal relationship between CKD and the presence of CVD in cancer patients. Second, since the small number (only 29) of individuals with eGFR <30 and without data on hemodialysis, the study only stratified by eGFR for three groups and did not specifically stratify CKD stages 3–5, we cannot exclude the impact of end-stage renal disease or conduct further analysis of dialysis and non-dialysis patients. Moreover, although we adjusted for most of the relevant confounders, residual or unknown confounders cannot be ruled out (for example, cancer drug use or urinary system tumors). Finally, because cancer and CVD were obtained through self-report, self-reporting bias may exist. Additionally, further analysis on specific types of cancer and CVD was not conducted because of the limited sample size.

In summary, after taking covariates including traditional CVD risk factors into consideration, the risk of CVD in adult cancer patients was independently associated with CKD. Collectively, our results underscore the significance of considering CKD as a non-traditional risk factor for CVD in cancer patients. Future studies are needed to further explore the clinical and mechanistic relationship between CKD and CVD in cancer patients.

We thank all the efforts made by the healthcare workers in NCHS and CDC for the NHANES database. We would also like to thank Jing Zhang (Shanghai Tongren Hospital) for his support on nhanesR package and webpage, which makes it easier for us to explore the NHANES database.

The investigation was approved by the NCHS Research Ethics Review Board.

No disclosures were reported.

This work was supported by grants 2020ZX09201025 from Internationally Standardized Tumor Immunotherapy and Key Technology Platform Construction for Clinical Trials of Drug-Induced Heart Injury, 201805004 from Jinan Medical Science and Technology Innovation Project, and ZR2021MH019 from Natural Science Foundation of Shandong Province.

An-Bang Liu: statistical analyses, writing the original draft, and reviewing and editing. Dan Zhang: statistical analyses and writing the original draft. Ting-Ting Meng: data collection and methodology. Yu Zhang, Peng Tian, and Jian-Lin Chen: data curation and methodology. Yan Zheng: conceptualization, funding acquisition, and reviewing and editing. Guo-Hai Su: conceptualization, funding acquisition, reviewing and editing, and supervision.

Additional Information

An-Bang Liu and Dan Zhang contributed equally as first authors to this work.

The primary data generated or analyzed in this article can be provided upon reasonable request to the corresponding author.

1.
GBD 2016 Causes of Death Collaborators
.
Global, regional, and national age-sex specific mortality for 264 causes of death, 1980–2016: a systematic analysis for the Global Burden of Disease Study 2016
.
Lancet
.
2017
;
390
(
10100
):
1151
210
.
2.
Gilchrist
SC
,
Barac
A
,
Ades
PA
,
Alfano
CM
,
Franklin
BA
,
Jones
LW
.
Cardio-oncology rehabilitation to manage cardiovascular outcomes in cancer patients and survivors: a scientific statement from the American heart association
.
Circulation
.
2019
;
139
(
21
):
e997
1012
.
3.
Strongman
H
,
Gadd
S
,
Matthews
A
,
Mansfield
KE
,
Stanway
S
,
Lyon
AR
.
Medium and long-term risks of specific cardiovascular diseases in survivors of 20 adult cancers: a population-based cohort study using multiple linked UK electronic health records databases
.
Lancet
.
2019
;
394
(
10203
):
1041
54
.
4.
Kobo
O
,
Raisi-Estabragh
Z
,
Gevaert
S
,
Rana
JS
,
Van Spall
HGC
,
Roguin
A
.
Impact of cancer diagnosis on distribution and trends of cardiovascular hospitalizations in the USA between 2004 and 2017
.
Eur Heart J Qual Care Clin Outcomes
.
2022
;
8
(
7
):
787
97
.
5.
Strongman
H
,
Gadd
S
,
Matthews
AA
,
Mansfield
KE
,
Stanway
S
,
Lyon
AR
.
Does cardiovascular mortality overtake cancer mortality during cancer survivorship? an English retrospective cohort study
.
JACC CardioOncol
.
2022
;
4
(
1
):
113
23
.
6.
Moslehi
JJ
.
Cardiovascular toxic effects of targeted cancer therapies
.
N Engl J Med
.
2016
;
375
(
15
):
1457
67
.
7.
Meijers
WC
,
de Boer
RA
.
Common risk factors for heart failure and cancer
.
Cardiovasc Res
.
2019
;
115
(
5
):
844
53
.
8.
Matsushita
K
,
Ballew
SH
,
Wang
AY
,
Kalyesubula
R
,
Schaeffner
E
,
Agarwal
R
.
Epidemiology and risk of cardiovascular disease in populations with chronic kidney disease
.
Nat Rev Nephrol
.
2022
;
18
(
11
):
696
707
.
9.
GBD Chronic Kidney Disease Collaboration
.
Global, regional, and national burden of chronic kidney disease, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017
.
Lancet
.
2020
;
395
(
10225
):
709
33
.
10.
Levey
AS
,
Coresh
J
.
Chronic kidney disease
.
Lancet
.
2012
;
379
(
9811
):
165
80
.
11.
Xu
Y
,
Li
M
,
Qin
G
,
Lu
J
,
Yan
L
,
Xu
M
.
Cardiovascular risk based on ASCVD and KDIGO categories in Chinese adults: a nationwide, population-based, prospective cohort study
.
J Am Soc Nephrol
.
2021
;
32
(
4
):
927
37
.
12.
Hirata
Y
,
Kiyosue
A
,
Takahashi
M
,
Satonaka
H
,
Nagata
D
,
Sata
M
.
Progression of renal dysfunction in patients with cardiovascular disease
.
Curr Cardiol Rev
.
2008
;
4
(
3
):
198
202
.
13.
Gansevoort
RT
,
Correa-Rotter
R
,
Hemmelgarn
BR
,
Jafar
TH
,
Heerspink
HJ
,
Mann
JF
.
Chronic kidney disease and cardiovascular risk: epidemiology, mechanisms, and prevention
.
Lancet
.
2013
;
382
(
9889
):
339
52
.
14.
Cozzolino
M
,
Mangano
M
,
Stucchi
A
,
Ciceri
P
,
Conte
F
,
Galassi
A
.
Cardiovascular disease in dialysis patients
.
Nephrol Dial Transplant
.
2018
33
Suppl l_3
iii28
34
.
15.
Di Angelantonio
E
,
Chowdhury
R
,
Sarwar
N
,
Aspelund
T
,
Danesh
J
,
Gudnason
V
.
Chronic kidney disease and risk of major cardiovascular disease and non-vascular mortality: prospective population based cohort study
.
Bmj
.
2010
341
c4986
.
16.
Fox
CS
,
Matsushita
K
,
Woodward
M
,
Bilo
HJ
,
Chalmers
J
,
Heerspink
HJ
.
Associations of kidney disease measures with mortality and end-stage renal disease in individuals with and without diabetes: a meta-analysis
.
Lancet
.
2012
;
380
(
9854
):
1662
73
.
17.
Kidney Disease: Improving Global Outcomes KDIGO Glomerular Diseases Work Group
.
KDIGO 2021 clinical practice guideline for the management of glomerular diseases
.
Kidney Int
.
2021
100
4S
S1
s276
.
18.
Delgado
C
,
Baweja
M
,
Crews
DC
,
Eneanya
ND
,
Gadegbeku
CA
,
Inker
LA
.
A unifying approach for GFR estimation: recommendations of the NKF-ASN Task Force on reassessing the inclusion of race in diagnosing kidney disease
.
Am J Kidney Dis
.
2022
;
79
(
2
):
268
88.e1
.
19.
Levey
AS
,
Coresh
J
,
Greene
T
,
Marsh
J
,
Stevens
LA
,
Kusek
JW
.
Expressing the Modification of Diet in Renal Disease Study equation for estimating glomerular filtration rate with standardized serum creatinine values
.
Clin Chem
.
2007
;
53
(
4
):
766
72
.
20.
Pieters
M
,
Ferreira
M
,
de Maat
MPM
,
Ricci
C
.
Biomarker association with cardiovascular disease and mortality: the role of fibrinogen. A report from the NHANES study
.
Thromb Res
.
2021
;
198
:
182
9
.
21.
Teo
KK
,
Rafiq
T
.
Cardiovascular risk factors and prevention: a perspective from developing countries
.
Can J Cardiol
.
2021
;
37
(
5
):
733
43
.
22.
Vineis
P
,
Wild
CP
.
Global cancer patterns: causes and prevention
.
Lancet
.
2014
;
383
(
9916
):
549
57
.
23.
Gao
Z
,
Zhu
Y
,
Sun
X
,
Zhu
H
,
Jiang
W
,
Sun
M
.
Establishment and validation of the cut-off values of estimated glomerular filtration rate and urinary albumin-to-creatinine ratio for diabetic kidney disease: a multi-center, prospective cohort study
.
Front Endocrinol
.
2022
;
13
:
1064665
.
24.
Yokoyama
H
,
Oishi
M
,
Kawai
K
,
Sone
H
.
Reduced GFR and microalbuminuria are independently associated with prevalent cardiovascular disease in Type 2 diabetes: JDDM study 16
.
Diabet Med
.
2008
;
25
(
12
):
1426
32
.
25.
Go
AS
,
Chertow
GM
,
Fan
D
,
McCulloch
CE
,
Hsu
CY
.
Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization
.
N Engl J Med
.
2004
;
351
(
13
):
1296
305
.
26.
Gaziano
L
,
Sun
L
,
Arnold
M
,
Bell
S
,
Cho
K
,
Kaptoge
SK
.
Mild-to-Moderate kidney dysfunction and cardiovascular disease: observational and mendelian randomization analyses
.
Circulation
.
2022
;
146
(
20
):
1507
17
.
27.
Daly
C
.
Is early chronic kidney disease an important risk factor for cardiovascular disease? A background paper prepared for the UK Consensus Conference on early chronic kidney disease
.
Nephrol Dial Transplant
.
2007
22
Suppl 9
ix19
25
.
28.
Sturgeon
KM
,
Deng
L
,
Bluethmann
SM
,
Zhou
S
,
Trifiletti
DM
,
Jiang
C
.
A population-based study of cardiovascular disease mortality risk in US cancer patients
.
Eur Heart J
.
2019
;
40
(
48
):
3889
97
.
29.
Vallianou
NG
,
Mitesh
S
,
Gkogkou
A
,
Geladari
E
.
Chronic kidney disease and cardiovascular disease: is there any relationship
.
Curr Cardiol Rev
.
2019
;
15
(
1
):
55
63
.
30.
Tanaka
S
,
Nakano
T
,
Hiyamuta
H
,
Tsuruya
K
,
Kitazono
T
.
Association between multimorbidity and kidney function among patients with non-dialysis-dependent CKD: the fukuoka kidney disease registry study
.
J Atheroscler Thromb
.
2022
;
29
(
8
):
1249
64
.
31.
Nagasawa
Y
,
Kida
A
,
Nakanisihi
T
.
Effect of cigarette smoking cessation on CKD: is it a cancer-suppression-like effect or a CVD-suppression-like effect
.
Hypertens Res
.
2016
;
39
(
10
):
690
1
.
32.
Orth
SR
,
Hallan
SI
.
Smoking: a risk factor for progression of chronic kidney disease and for cardiovascular morbidity and mortality in renal patients--absence of evidence or evidence of absence
.
Clin J Am Soc Nephrol
.
2008
;
3
(
1
):
226
36
.
33.
Querfeld
U
,
Mak
RH
,
Pries
AR
.
Microvascular disease in chronic kidney disease: the base of the iceberg in cardiovascular comorbidity
.
Clin Sci
.
2020
;
134
(
12
):
1333
56
.
34.
Pieske
B
,
Wachter
R
.
Impact of diabetes and hypertension on the heart
.
Curr Opin Cardiol
.
2008
;
23
(
4
):
340
9
.
35.
Singleton
DC
,
Macann
A
,
Wilson
WR
.
Therapeutic targeting of the hypoxic tumour microenvironment
.
Nat Rev Clin Oncol
.
2021
;
18
(
12
):
751
72
.
36.
Asakura
H
,
Jokaji
H
,
Saito
M
,
Uotani
C
,
Kumabashiri
I
,
Morishita
E
.
Role of endothelin in disseminated intravascular coagulation
.
Am J Hematol
.
1992
;
41
(
2
):
71
5
.
37.
Moody
WE
,
Edwards
NC
,
Madhani
M
,
Chue
CD
,
Steeds
RP
,
Ferro
CJ
.
Endothelial dysfunction and cardiovascular disease in early-stage chronic kidney disease: cause or association
.
Atherosclerosis
.
2012
;
223
(
1
):
86
94
.
38.
Camici
PG
,
d’Amati
G
,
Rimoldi
O
.
Coronary microvascular dysfunction: mechanisms and functional assessment
.
Nat Rev Cardiol
.
2015
;
12
(
1
):
48
62
.
39.
Selthofer-Relatic
K
,
Mihalj
M
,
Kibel
A
,
Stupin
A
,
Stupin
M
,
Jukic
I
.
Coronary microcirculatory dysfunction in human cardiomyopathies: a pathologic and pathophysiologic review
.
Cardiol Rev
.
2017
;
25
(
4
):
165
78
.
40.
Liu
M
,
Li
XC
,
Lu
L
,
Cao
Y
,
Sun
RR
,
Chen
S
.
Cardiovascular disease and its relationship with chronic kidney disease
.
Eur Rev Med Pharmacol Sci
.
2014
;
18
(
19
):
2918
26
.
41.
Mok
Y
,
Ballew
SH
,
Matsushita
K
.
Chronic kidney disease measures for cardiovascular risk prediction
.
Atherosclerosis
.
2021
;
335
:
110
8
.