Association between cardiovascular disease and chronic kidney disease and influencing factors of their comorbidity: Evidence from the China Health and Retirement Longitudinal Study (CHARLS)

doi:10.21203/rs.3.rs-4808514/v1

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Association between cardiovascular disease and chronic kidney disease and influencing factors of their comorbidity: Evidence from the China Health and Retirement Longitudinal Study (CHARLS)

https://doi.org/10.21203/rs.3.rs-4808514/v1

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Background and Hypothesis:

Chronic Kidney Disease (CKD) and Cardiovascular Disease (CVD) are significant public health concerns with high morbidity and mortality rates. the relationship between CKD and CVD comorbidities and their influencing factors in Chinese middle-aged and elderly people remains underexplored. We hypothesize that some socio-demographic factors, lifestyle factors, and physical condition factors significantly influence the prevalence and comorbidity of CKD and CVD.

Methods

Data from the China Health and Retirement Longitudinal Study (CHARLS) were used to perform logistic regression analyses on adults aged 45 and above. CKD stages were defined based on estimated glomerular filtration rate (eGFR), while CVD status was determined through self-reported diagnosis. Factors considered in the analysis included socio-demographic variables (age, gender, household registration, education, marital status), lifestyle behaviors (smoking, drinking), and physical conditions (body mass index (BMI), blood pressure, blood sugar, blood lipids, serum uric acid).

Results

CVD risk begins in early stages of CKD. Factors such as hypertension, diabetes, non-rural household registration, abnormal BMI, and lower education levels were significantly associated with CKD prevalence and its comorbidity with CVD. Low BMI increased CKD progression, while high BMI elevated the risk of CKD and CVD comorbidities. Specifically, in CKD patients, lower BMI was linked to reduced CVD risk, suggesting a complex interplay between physical health metrics, such as BMI, in influencing health outcomes..

Conclusion

Socio-demographic factors and physical conditions are crucial in managing and preventing CKD and its comorbidity with CVD. Targeted public health strategies are needed for non-rural and lower-educated populations. Further research should explore underlying mechanisms and develop effective interventions.

Chronic kidney disease

cardiovascular disease

CHARLS

Cardiovascular disease (CVD) and chronic kidney disease (CKD) represent two pervasive and interlinked health conditions globally, with their interplay posing substantial challenges to public health.[1] CVD, encompassing a range of heart and blood vessel disorders, remains the leading cause of mortality worldwide.[2] Concurrently, CKD, characterized by the gradual loss of kidney function, affects approximately 10% of the global population and is a significant contributor to morbidity and mortality.[3] The bidirectional relationship between CVD and CKD is well-documented, however, the specific factors that contribute to their comorbidity require further investigation, particularly in the context of an aging Chinese population.

The China Health and Retirement Longitudinal Study (CHARLS) provides a distinctive opportunity to investigate the association between CVD and CKD and their combined effect on mortality within an aging Chinese population. This longitudinal study, which tracks a nationally representative sample of Chinese residents aged 45 and older, offers comprehensive data on demographics, health status and medical history.[4] Previous studies have highlighted the individual burden of CVD and CKD; however, the CHARLS dataset allows for a nuanced analysis of their interplay and its implications for patient prognosis and healthcare strategies.

Emerging evidence indicates that the comorbidity of CVD and CKD significantly exacerbates health outcomes, leading to higher mortality rates than either condition alone.[5] This intersection is particularly critical in China, where rapid demographic shifts and increasing life expectancy are amplifying the prevalence of chronic diseases.[6] Understanding the factors influencing CVD and CKD comorbidity within this context is vital for developing targeted interventions and optimizing resource allocation in healthcare systems strained by the dual burden of non-communicable diseases.

This study aims to assess the risk of co-occurrence of CVD and CKD and its influencing factors using CHARLS data, thereby filling the gap in the literature and providing evidence for the complex relationship between CVD and CKD. By elucidating the magnitude of this risk, our findings will provide important insights for clinicians and policymakers to improve management strategies and ultimately improve patient outcomes in the aging population of China.

2.1 Study design and participants

A total of 7268 individuals aged 45 years and older from CHARLS 2011 were included. Figure 1 shows the flowchart of the recruitment process for this study.

2.2 CKD

CKD is defined as meeting one of the following diagnostic criteria:

i eGFR < 60 mL/min/1.73m² (calculated using the CKD Epidemiology Collaboration (CKD-EPI) equation). [7]

ii eGFR ≥ 60ml/min/1.73 m² but self-reported CKD.

The CKD-EPI equation was as follows:

eGFR (mL/min/1.73m²) = 135 × min(SCr/k, 1)^α × max(SCr/k, 1)^−0.601 × min(Cys/0.8, 1)^−0.375 × max(Cys/0.8, 1)^−0.711 × 0.995^age ×0.969[if female]

SCr refers to serum creatinine (mg/dL), Cys refers to serum cystatin C (mg/liter), age refers to the patient's age (years), α is − 0.248 for females and − 0.207 for males, k is 0.7 for females and 0.9 for males.

CKD was then divided into stages 1–2: eGFR ≥ 60ml/min/1.73 m², but previously diagnosed with CKD, stage 3a: 45 ≤ eGFR < 60ml/min/1.73 m², stage 3b: 30 ≤ eGFR < 45ml/min/1.73 m², stage 4: 15 ≤ eGFR < 30ml/min/1.73 m², and stage 5: eGFR < 15ml/min/1.73 m².

2.3 CVD

Participants who reported a heart attack or stroke were defined as having cardiovascular disease[8], and cardiovascular disease events were assessed with the following questions:

i "Have you ever been told by your doctor that you have been diagnosed with a heart attack, angina, coronary artery disease, heart failure, or other heart problem?"

ii "Have you ever been told by your doctor that you have been diagnosed with a stroke?

2.4 Covariates

Previous literature and clinical considerations have identified several covariates that may serve as potential confounders. These include demographic characteristics (age, gender, household registration, education, marital status)[9, 10], lifestyle factors (smoking, drinking)[11], physiological indicators (BMI, blood pressure, blood sugar, blood lipids, serum uric acid)[12, 13].

The baseline age was calculated by subtracting the date of birth from the year 2011, and then divided into three categories (Q1 [45,60), Q2 [60,70), Q3 [70,∞)) to demonstrate the prevalence of comorbidities, CKD alone, and CVD alone in different age groups. Household registration was divided into agricultual household registration and non-agricultual household registration. Education was classified as “below elementary school”, “elementary to middle school” and “high school or above”. Marital status was recorded as either "yes" (‘married with spouse present’/ married but not living with spouse temporarily) or "no" (‘widowed/divorced/separated/never married’)

Smoking was categorized as yes or no, and drinking was categorized as “drinking more than once a month”, “drinking less than once a month”, “none of the above”.

BMI was calculated using the following formula: BMI = Weight (kg)/[Height(m) ]². Then we divided BMI into 4 groups according to previous studies: [−∞,18.5) kg/m² (underweight), [18.5, 25) kg/m² (normal weight), [25, 30) kg/m² (overweight), and [30, ∞) kg/m² (obesity).[14] Hypertension was defined as systolic blood pressure ≥ 140mmHg or diastolic blood pressure ≥ 90mmHg or use of antihypertensive medication.[15] Diabetes was defined as a fasting blood glucose level of 126 mg/dL or higher (to convert to mmol/L, multiply by 0.0555 [i.e., ≥ 7.0mmol/L]), a glycated hemoglobin A1c level of 6.5% or higher (to convert to mmol/mol, multiply by 10.93 and subtract 23.5 [i.e., ≥ 48mmol/mol]), or the use of diabetes medications or insulin.[15] Hyperlipidemia was defined as total cholesterol above 200 mg/dL, or low-density lipoprotein cholesterol above 130 mg/dL, or high-density lipoprotein cholesterol below 40 mg/dL in men or below 50 mg/dL in women, or triglycerides above 150 mg/dL, or use of lipid-lowering medication.[16] Hyperuricemia is defined as a serum uric acid level > 7.0 mg/dL in men or > 6.0 mg/dL in women.[17]

2.6 Statistical analyses

The data are presented as the median (interquartile range (IQR)) for continuous variables and as number (percentage (%)) for categorical variables. The Kruskal-Wallis(K-W) rank sum test was employed for continuous variables, while the chi-square test was utilized for categorical variables. In the event that the data does not satisfy the assumptions of the chi-square test, the Fisher's Exact Test for Count Data with simulated p-value (Fisher's Exact Test (simulated)) is employed.

The prevalence of CVD in the general population was examined, and the data were stratified based on levels of CKD, demographic characteristics, lifestyle factors, and physiological indicators. To further investigate the association between levels of CKD and CVD, Logistic regression models were employed to compute the odds ratios (ORs) and 95% confidence intervals (95% CIs) in contrast with the non-CKD group. Trend testing was performed based on the median value of eGFR of each group.

In the above Logistic regressions, we created four models to eliminate the effects of various covariates. Specifically, the crude model made no adjustments, model 1 adjusted for demographic covariates, model 2 adjusted for lifestyle factors and demographic covariates, and model 3 adjusted for physiological indicator covariates, lifestyle factors and demographic covariates (unless otherwise specified). A restricted cubic spline analysis was employed to explore the dose–response relationship between eGFR and the specified outcomes, and linear associations were assessed using the sensitivity analysis.

Once the association between CKD and CVD had been established, a logistic regression model was employed to calculate the OR and 95% CI for each influencing factor in the only-CKD group in comparison with the normal group. The same method was then used to analyse the influencing factors of the comorbidity group and the only-CKD group.

All statistical analyses were conducted using the R software (R version 2024.04.1 + 748). Visualization was achieved with the "ggplot2", "crosstable" and "forestplot" packages. Statistically significant results were determined at a two-tailed p < 0.05.

3.1. The distribution of comorbidity, CKD and CVD

The prevalence of CKD, CVD, and comorbidities was 14.83%, 13.95%, and 3.18%, respectively, in the entire sample (Fig. 2). The subgroups with the highest prevalence of CKD only were hyperuricemic (28.4%), age > 70 (24.7%), and underweight (20.4%). The subgroups with the highest distribution of CVD only were obese (18.3%), non-agricultural household registration (15.6%), and hypertensive status (14.7%). Similarly, the subgroups with the highest distribution of comorbidity were hyperuricemic (9.1%), non-agricultural household registration (6.9%), and age > 70 (6.9%). Figure 3 shows the detailed distribution of each subgroup.

3.2 Demographic profiles in CKD

In the overall sample, except for hyperlipidemia status (n(%) = 5015 (69.00%)), all variables were statistically significant. The average age of the total population was 60.92 (0.12) years old, the average age of the non-CKD group was 59.77 (0.12) years old. The participation rate of women in the non-CKD group was 54.67%. The average BMI of the non-CKD group was 23.52 (0.05), while the average BMI of CKD stage 4 patients was 22.07 (0.64). In the subgroup classified by education level, as CKD advanced to stage 3b, the proportion of individuals with less than an elementary school education exhibited an upward trajectory: 50% in the CKD 1–2 group, 63.05% in the CKD 3a group, and 66.67% in the CKD 3b group. A similar trend was observed in the age group, the non-agricultural group, the non-marital status group, the non-drinkers, the diabetic group, the hypertensive group, and the hyperuricemic group. Table 1 shows the detailed summary results.

3.3 Demographic profiles in CVD

In the whole sample, 13.95% individuals were affected by CVD. All variables except education level and smoking status were statistically significant compared with non-CVD. The mean age of the CVD group was 63.89 (0.3) years and the mean BMI was 24.29 (0.13), both higher than the non-CVD group. The same trend was seen in the subgroups of female, non-Agricultural, non-married, non-drinking, obese and overweight, diabetes, hyperlipidemia, hypertension, and hyperuricemia. Table 2 shows the detailed summary results.

3.4 Association between levels of CKD and CVD

The relationship between the levels of CKD and CVD is illustrated in Fig. 4. In the crude model, the CKD1-2, CKD3a, CKD3b, and CKD4-5 groups exhibited a higher risk of CVD compared to the non-CKD group (OR (95% CI) = 2.04 (1.59, 2.60), 1.73 (1.38, 2.17), 1.73 (1.10, 2.72), and 3.63 (1.68, 7.84), respectively; p < 0.05). There was a significant trend (p for trend < 0.05). However, upon the addition of covariates to the model, the significance of the OR values for CKD stages 3a, 3b, and 4–5 gradually decreased. In the most adjusted model (model 3), the OR of the CKD1-2 group (95% CI) is the only significant value (p < 0.0001) when compared with non-CKD group. The OR is 2.17 (1.68, 2.80).

In the restricted cubic spline analysis conducted to investigate the dose–response relationships between the risk of CVD and eGFR (Fig. 5), we identified a linear negative association between CVD and eGFR. The results of the sensitivity testing indicated that when the participants with extreme 5% of eGFR were excluded, the elevated eGFR was found to be associated with a reduced risk of CVD.

3.5 The factors that influence the coexistence of CKD and CVD

In order to further elucidate the factors that influence the susceptibility of CKD patients to CVD, we constructed a logistic regression model(Fig. 6). A comparison of patients with CKD-only with normal individuals revealed that older age, non-agricultual household registration, smoking, and low body weight were risk factors (OR (95% CI) = 4.02 (3.27, 4.94), 1.33 (1.07, 1.66), 1.47 (1.19, 1.81), 1.44 (1.12, 1.85)), while higher education, blood pressure control, and uric acid control were protective factors (OR (95% CI) = 0.69 (0.49, 0.97), 0.72 (0.61, 0.85), 0.29 (0.22, 0.37))(P < 0.05). When comparing the comorbidity group with the CKD-only group, we found that non-agricultual living and obesity were risk factors (OR (95% CI) = 2.23 (1.54, 3.22), 2.12 (1.11, 4.03)), while non-diabetes was a protective factor (OR (95% CI) = 0.63 (0.45, 0.90))(P < 0.05).

Subsequently, in the interaction analysis, the interaction term between underweight and CKD was found to be statistically significant (OR (95% CI) = 0.43 (0.22, 0.85)), indicating that the impact of CKD on CVD is lower in underweight individuals. No significant interaction terms were identified in the other groups (p > 0.05). (Table 3)

4.1 Key Findings

Our study explored the association between CKD stages and CVD within an aging Chinese population using data from the CHARLS. We found that: 1) The overall prevalence of CKD, CVD, and their comorbidity were 14.83%, 13.95%, and 3.18%, respectively. Notably, subgroups with hyperuricemia, older age, and non-agricultural household registration exhibited higher rates of comorbidity of CKD and CVD. 2) A substantial correlation exists between the initial phases of CKD and the likelihood of developing CVD. This correlation persists even after adjusting for potential confounding variables in an optimized model. 3) Older age, non-agricultural household registration, smoking, and atypical weight were significant risk factors for the coexistence of CKD and CVD. Higher education, effective blood pressure control, uric acid control and blood sugar control emerged as protective factors. It is noteworthy that among all influencing factors, overweight is a risk factor for CVD and underweight is a risk factor for CKD. However, individuals with CKD who are underweight have a lower risk of developing CVD.

4.2 Comparison with Existing Literature

Our findings are consistent with previous studies that have highlighted the bidirectional relationship between CKD and CVD. For instance, Go et al. demonstrated that CKD is a significant risk factor for CVD events and mortality[1]. Our study contributes to this body of knowledge by providing specific insights into the Chinese aging population, emphasizing the compounded health burden posed by these conditions. Furthermore, our study demonstrated that the risk of CVD was inversely correlated with eGFR, with a turning point in CVD risk occurring at approximately eGFR = 85 ml/min/1.73 m². This suggests that risk factors influencing the onset of CVD are present in the early stages of CKD and intensify with declining eGFR.

With regard to the factors that contribute to the development of CKD and CVD comorbidity, previous research has primarily focused on traditional cardiovascular risk factors, including hypertension, diabetes, and dyslipidemia. For example, Park et al. highlighted the role of hypertension and diabetes as major contributors to CKD and CVD comorbidity in a Korean cohort[18]. The results of our cohort study corroborate and expand upon this conclusion, shedding new light on the impact of household registration, BMI, and education. Our finding that non-rural household registration is associated with higher comorbidity contrasts with studies conducted in Western populations, where rural residence is often linked to poorer health outcomes due to limited access to healthcare services[19]. This suggests that in China, non-rural residents may have lifestyle factors or environmental exposures that increase their risk of CKD and CVD comorbidity, such as higher levels of urban pollution, stress, and dietary patterns[20–22]. Additionally, prior research by Kimmel et al. indicated that lower educational levels correlate with higher rates of CKD and CVD independently[23]. Our study confirms and extends this relationship by demonstrating that lower education not only contributes to the individual diseases but also to their comorbidity, possibly due to reduced health literacy[24], lower access to preventive care[25], and poorer overall health management[26].

Besides, existing literature shows that in Western populations, higher BMI has been shown to increase the risk of CKD, with obesity (BMI ≥ 30 kg/m²) being significantly associated with the onset and progression of CKD[27–30]. In our study, however, we found a significant association between low body weight and the progression of CKD. Low body weight can indicate malnutrition or frailty, which are known to exacerbate CKD progression[31]. This finding indicates that in the Chinese elderly population, frailty resulting from low body weight exerts a more pronounced influence on the progression of CKD than the elevated metabolic burden associated with high body weight. Conversely, within the CKD population, higher BMI is an important risk factor for developing CVD. This dual relationship underscores the complex interplay between body weight, CKD, and CVD.

Interestingly, our study also observed that among CKD patients, those with lower body weight had a reduced risk of developing CVD. This finding may seem counterintuitive but can be explained by the specific metabolic and cardiovascular profiles of underweight individuals. While low BMI is associated with higher mortality in CKD patients, those who survive with lower body weight might have fewer traditional cardiovascular risk factors like hypertension and hyperlipidemia[32]. Additionally, the phenomenon known as the "obesity paradox," where overweight and mildly obese patients have better survival rates in chronic diseases, may partially explain this observation[33]

4.3 Potential Mechanisms

the existing literature on the molecular mechanisms underlying the comorbidity of CKD and CVD indicates that this phenomenon involves a complex interplay of interrelated factors, including hyperglycemia, insulin resistance, enhanced activity of the renin-angiotensin-aldosterone system (RAAS), production of advanced glycation end products, oxidative stress, lipotoxicity, endoplasmic reticulum stress, abnormal calcium handling, mitochondrial dysfunction, and impaired energy production.[34–36] Reduced kidney function can lead to hypertension, hyperlipidemia, and inflammation, all of which are established risk factors for CVD. Conversely, CVD can exacerbate CKD progression through mechanisms such as reduced renal perfusion and increased systemic vascular resistance.[1]

4.4 Clinical Implications

Our findings have significant clinical implications. Firstly, they underscore the importance of early detection and management of CKD to prevent subsequent CVD. Secondly, the identified risk factors such as smoking and under or overweight should be targeted in intervention strategies to mitigate the risk of comorbidity. Additionally, our results suggest that improving educational attainment and ensuring effective management of hypertension, hyperuricemia and diabetes could serve as protective measures against the dual burden of CKD and CVD.

4.5 Strengths and Limitations

This study's strengths include the use of a nationally representative sample and comprehensive data collection through CHARLS. However, there are limitations to consider. The cross-sectional design precludes the establishment of causality, and self-reported data may be subject to recall bias. Future longitudinal studies are needed to confirm our findings and elucidate the temporal relationship between CKD and CVD.

In conclusion, our study highlights the significant comorbidity of CKD and CVD in the aging Chinese population, driven by various demographic and clinical factors. These insights are crucial for developing targeted interventions to improve health outcomes and manage the dual burden of CKD and CVD effectively.

CKD

Chronic Kidney Disease

CVD

Cardiovascular Disease

CHARLS

China Health and Retirement Longitudinal Study

eGFR

estimated glomerular filtration rate

BMI

Body Mass Index

IQR

interquartile range

odds ratio

95% CI

95% confidence intervals

RAAS

the renin-angiotensin-aldosterone system

Ethics approval and consent to participate: Not applicable.

Consent for publication: Not applicable.

Availability of data and materials: The datasets generated and/or analysed during the current study are available in the CHARLS repository, https://charls.charlsdata.com

Competing interests: The authors declare that they have no competing interests.

Funding: This work was supported by the following grants: National Natural Science Foundation (no. 8217151785).

Authors' contributions: YD developed the overall design, methods, and objectives of the study, performed internal review of the article, checked the logic and language accuracy, and performed final proofreading. CG, WD and XZ collected, sorted and cleaned the data obtained from the database CHARLS. HL, DZ and ZW collected and summarized relevant literature to construct the research background and theoretical framework. ZF performed statistical analysis, data visualization, and interpreted the results to provide scientific explanation and significance, and was a major contributor in writing the manuscript. All authors read and approved the final manuscript.

Acknowledgements: Not applicable.

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Tables 1 to 3 are available in the Supplementary Files section.

No competing interests reported.

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Association between cardiovascular disease and chronic kidney disease and influencing factors of their comorbidity: Evidence from the China Health and Retirement Longitudinal Study (CHARLS)

Status:

Version 1

Abstract

Background and Hypothesis:

Methods

Results

Conclusion

Figures

1. Introduction

2. Materials and methods

2.1 Study design and participants

2.2 CKD

2.3 CVD

2.4 Covariates

2.6 Statistical analyses

3. Results

3.1. The distribution of comorbidity, CKD and CVD

3.2 Demographic profiles in CKD

3.3 Demographic profiles in CVD

3.4 Association between levels of CKD and CVD

3.5 The factors that influence the coexistence of CKD and CVD

4. Discussion

4.1 Key Findings

4.2 Comparison with Existing Literature

4.3 Potential Mechanisms

4.4 Clinical Implications

4.5 Strengths and Limitations

5. Conclusion

Abbreviations

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

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