Patient characteristics and influencing factors of CKD, CVD and their comorbidities in the middle-aged and elderly population in China

Patient characteristics and influencing factors of CKD, CVD and their comorbidities in the middle-aged and elderly population in China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Patient characteristics and influencing factors of CKD, CVD and their comorbidities in the middle-aged and elderly population in China Zhike Fu, Chuying Gui, Weitian Deng, Xiaoshan Zhou, Huijie Li, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5423032/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background and aims: Chronic Kidney Disease (CKD) and Cardiovascular Disease (CVD) are significant public health concerns with high morbidity and mortality. The relationship between CKD and CVD 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 We used data from the China Health and Retirement Longitudinal Study (CHARLS), focusing 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 in the Chinese population. Individuals with characteristics such as hypertension, diabetes, non-rural household registration, abnormal BMI, or low education levels are significantly more likely to develop CKD and its comorbidity with CVD. Both low and high BMI were associated with increased CKD and CVD risk, with low BMI linked to reduced CVD risk in CKD patients. Conclusion Socio-demographic and physical factors are critical in managing and preventing CKD and CVD comorbidities. Public health strategies should focus on non-rural and lower-educated populations in Chinese middle-aged and elderly people, and further research is needed to explore mechanisms and interventions. Chronic kidney disease cardiovascular disease CHARLS Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1.Introduction 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 affects approximately 10% of the global population and is a significant contributor to morbidity and mortality.[ 3 ] The bidirectional relationship between CKD and CVD is critical to understanding their interconnected impacts on health. Both conditions share common risk factors such as hypertension, diabetes, and dyslipidemia, which can lead to vascular damage.[ 4 – 6 ] Mechanistically, CKD contributes to the accumulation of uremic toxins[ 7 , 8 ], inflammation[ 9 ], and oxidative stress[ 10 ], promoting cardiovascular complications like left ventricular hypertrophy and vascular calcification. Conversely, CVD may result in renal hypoperfusion and heart failure, accelerating CKD progression.[ 5 ] Studies have shown that patients with CKD face a higher risk of cardiovascular events, while those with CVD often experience more rapid deterioration in kidney function.[ 11 ] However, the population characteristics of patients with comorbidities compared to those without, including demographic traits, lifestyle factors, and physiological indicators, have not been thoroughly examined. The China Health and Retirement Longitudinal Study (CHARLS) offers a unique opportunity to investigate the influencing factors and population characteristics of comorbidities between CVD and CKD specifically within the older adult population in China. 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.[ 12 ] Previous studies have highlighted the individual burden of CVD and CKD. However, the CHARLS dataset allows for a nuanced analysis of their interplay and healthcare strategies. This study aims to evaluate the risk of co-occurrence of CVD and CKD among the middle aged and elderly Chinese population and to identify the influencing factors. In doing so, it aims to fill a gap in the literature regarding the older adult population and provide evidence for the complex relationship between CVD and CKD in this demographic, thereby contributing to scientific health management. 2. Materials and methods 2.1 Study design and participants This study is based on the CHARLS, a publicly available dataset containing a nationally representative sample of middle-aged and elderly community dwellers in China. The study covered 450 villages and urban communities in 28 provinces in China. The CHARLS baseline survey was conducted from 2011. A total of 7268 individuals aged 45 years and older from June 2011 to March 2012 were included. Finally, CHARLS was approved by the Biomedical Ethics Review Committee of Peking University (IRB00001052-11015), and respondents were required to sign an informed consent form. Figure 1 a shows the flowchart of the recruitment process for this study. 2.2 Serum measurements The serum measurement procedure has been described in detail elsewhere[ 13 ]. Following an overnight fast of at least 8 hours, 8 ml fasting blood samples were collected at township hospitals or local Centers for Disease Control offices. Plasma was separated by centrifugation at 3200 rpm for 10–15 minutes within one hour of collection and was kept in the dark at room temperature. Whole blood and the centrifuged serum were stored at 4°C in a local laboratory and transported to the laboratory of Capital Medical University within two weeks at -80°C, where blood glucose, blood lipids, glycosylated hemoglobin, and serum creatinine levels were measured. 2.3 CKD CKD is defined as meeting one of the following diagnostic criteria: i eGFR < 60 mL/min/1.73m 2 (calculated using the CKD Epidemiology Collaboration (CKD-EPI) equation). [ 14 ] ii self-reported CKD diagnosed by a physician according to professional guidelines. The CKD-EPI equation was as follows: eGFR (mL/min/1.73m 2 ) = 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. 2.4 CVD Participants who reported a heart attack or stroke, as diagnosed by a physician, were defined as having cardiovascular disease. [ 15 ], 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? 2.5 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)[ 16 , 17 ], lifestyle factors (smoking, drinking)[ 18 ], physiological indicators (BMI, blood pressure, blood sugar, blood lipids, serum uric acid)[ 19 , 20 ]. 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: BMI < 18.5 kg/m 2 (underweight), 18.5 ≤ BMI < 25 kg/m 2 (normal weight), 25 ≤ BMI < 30 kg/m 2 (overweight), and BMI ≥ 30 kg/m 2 (obesity).[ 21 ] Hypertension was defined as systolic blood pressure ≥ 140mmHg or diastolic blood pressure ≥ 90mmHg or use of antihypertensive medication.[ 22 ] 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.[ 22 ] 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.[ 23 ] Hyperuricemia is defined as a serum uric acid level > 7.0 mg/dL in men or > 6.0 mg/dL in women.[ 24 ] 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. Results 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. 1 b). Figure 2 shows the detailed distribution of each subgroup. 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%). The prevalence of CKD increases with age, lower education level, reduced body weight, elevated blood pressure, and higher blood lipid levels. Additionally, the prevalence is higher in males than in females, whereas the prevalence of CVD shows the opposite trend. The proportion of patients with CVD increases with age, BMI, blood glucose, blood lipids, and blood pressure, and is higher among the non-agricultural population compared to the agricultural population. 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 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. 3 . 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. 4 ), we identified a linear negative association between CVD and eGFR. The sensitivity testing results indicated that excluding participants in the lowest and highest 5% of eGFR values revealed that elevated eGFR is associated with a reduced risk of CVD. This exclusion focuses the analysis on the central range of eGFR values, providing a clearer understanding of the relationship between eGFR and CVD risk without the influence of outliers. 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. 5 ). A comparison of patients with CKD-only with patients without CKD and CVD 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, normal blood pressure, and normal uric acid levels are 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. Discussion 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 in China 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 in China. 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[ 25 ]. 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​[ 26 ]. 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[ 27 – 29 ]. Additionally, prior research by Kimmel et al. indicated that lower educational levels correlate with higher rates of CKD and CVD independently​[ 30 ]. 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[ 31 ], lower access to preventive care[ 32 ], and poorer overall health management[ 33 ]. Besides, existing literature shows that in Western populations, higher BMI has been shown to increase the risk of CKD, with obesity (BMI ≥ 30 kg/m 2 ) being significantly associated with the onset and progression of CKD[ 34 – 37 ]. 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[ 38 ]. 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​[ 39 ]. 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[ 40 ] Lastly, although Fig. 4 shows an unusual decrease in the correlation between CKD exacerbation and CVD, it is important to note that the sample included only 29 patients in CKD stages 4–5, which may not accurately represent the broader real-world population of individuals with advanced CKD. 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.[ 10 , 41 , 42 ] 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. Finally, the higher incidence of CVD in the non-rural household registration population compared to the rural household registration population in China is also a question worth exploring. 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 sample size for CKD stages 4–5 is too small, which may introduce bias in the analysis of the association between CKD and CVD at these stages. Self-reported data may be prone to recall bias. Future longitudinal studies are necessary to validate our findings and clarify the temporal relationship between CKD and CVD. 5. Conclusion 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. Abbreviations 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 OR: odds ratio 95% CI: 95% confidence intervals RAAS: the renin-angiotensin-aldosterone system Declarations Ethics approval and consent to participate: CHARLS was approved by the Biomedical Ethics Review Committee of Peking University (IRB00001052-11015). 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. References Go AS, Chertow GM, Fan D, McCulloch CE, Hsu CY (2004) Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization. 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PLoS ONE 12(3):e0173515 Kovesdy CP, Furth SL, Zoccali C (2017) Obesity and kidney disease: hidden consequences of the epidemic. J Nephrol 30(1):1–10 Kjaergaard AD, Teumer A, Witte DR, Stanzick KJ, Winkler TW, Burgess S et al (2022) Obesity and Kidney Function: A Two-Sample Mendelian Randomization Study. Clin Chem 68(3):461–472 Nguyen A, Khafagy R, Gao Y, Meerasa A, Roshandel D, Anvari M et al (2023) Association Between Obesity and Chronic Kidney Disease: Multivariable Mendelian Randomization Analysis and Observational Data From a Bariatric Surgery Cohort. Diabetes 72(4):496–510 Carrero JJ, Aguilera A, Stenvinkel P, Gil F, Selgas R, Lindholm B (2008) Appetite disorders in uremia. J Ren Nutr 18(1):107–113 MacLaughlin HL, Pike M, Selby NM, Siew E, Chinchilli VM, Guide A et al (2021) Body mass index and chronic kidney disease outcomes after acute kidney injury: a prospective matched cohort study. BMC Nephrol 22(1):200 Kalantar-Zadeh K, Block G, Humphreys MH, Kopple JD (2003) Reverse epidemiology of cardiovascular risk factors in maintenance dialysis patients. Kidney Int 63(3):793–808 Marassi M, Fadini GP (2023) The cardio-renal-metabolic connection: a review of the evidence. Cardiovasc Diabetol 22(1):195 Sebastian SA, Padda I, Johal G (2024) Cardiovascular-Kidney-Metabolic (CKM) syndrome: A state-of-the-art review. Curr Probl Cardiol 49(2):102344 Tables Tables 1 to 3 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Tables123.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5423032","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":380801156,"identity":"d7a0e244-7856-4a1f-9f9a-99d32ea373b2","order_by":0,"name":"Zhike Fu","email":"","orcid":"","institution":"Shanghai University of Traditional Chinese Medicine, Longhua Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhike","middleName":"","lastName":"Fu","suffix":""},{"id":380801157,"identity":"1594c217-0c4e-4fad-8788-d8147ad428eb","order_by":1,"name":"Chuying Gui","email":"","orcid":"","institution":"Shanghai University of Traditional Chinese Medicine, Longhua Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chuying","middleName":"","lastName":"Gui","suffix":""},{"id":380801158,"identity":"06448eed-ddae-47c5-9d75-2ebb0171ffb5","order_by":2,"name":"Weitian Deng","email":"","orcid":"","institution":"Shanghai University of Traditional Chinese Medicine, Longhua Hospital","correspondingAuthor":false,"prefix":"","firstName":"Weitian","middleName":"","lastName":"Deng","suffix":""},{"id":380801159,"identity":"274ebc3a-316a-47ca-867f-8212ae0f54c8","order_by":3,"name":"Xiaoshan Zhou","email":"","orcid":"","institution":"Shanghai University of Traditional Chinese Medicine, Longhua Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaoshan","middleName":"","lastName":"Zhou","suffix":""},{"id":380801160,"identity":"c5895805-dab1-4f26-901c-346fa7adbd4d","order_by":4,"name":"Huijie Li","email":"","orcid":"","institution":"Shanghai University of Traditional Chinese Medicine, Longhua Hospital","correspondingAuthor":false,"prefix":"","firstName":"Huijie","middleName":"","lastName":"Li","suffix":""},{"id":380801161,"identity":"706f0c7d-7e4e-4b25-b78e-1374a41a5503","order_by":5,"name":"Di Zhang","email":"","orcid":"","institution":"Shanghai University of Traditional Chinese Medicine, Longhua Hospital","correspondingAuthor":false,"prefix":"","firstName":"Di","middleName":"","lastName":"Zhang","suffix":""},{"id":380801162,"identity":"697121fa-adc2-4bbc-8ce5-2b21ec463039","order_by":6,"name":"Zan Wang","email":"","orcid":"","institution":"Shanghai University of Traditional Chinese Medicine, Longhua Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zan","middleName":"","lastName":"Wang","suffix":""},{"id":380801163,"identity":"ecbbe49e-59c4-4130-ba60-4a1191bc9b13","order_by":7,"name":"Yueyi Deng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYDACCTCCgAMfDGzsSNNycEZBWjJpWph5PhxibCCkQ352A+ONjztqEzcc7zE8bGNwgJmB/fDRDfi0GNw5wGw588zxxA1nzhgczjG4w8fAk5Z2A68WiQQ2ad62Y4kbbuSAtDxjZpDgMcOrRX4GUMtfkJb7bwwOWxgcZmwgpIXhBlALY1sN0BYeg8MMxGgxuJHAbNnbdsB45pm0goM9BmnJbIT8AnQY442fbXWyfccPb/7w44+NHT/74WP4HcbA/wFIHHZsYOAwAPPZ8CuHgzp7Bgb2B0QqHgWjYBSMgpEGAG17Uu029/xmAAAAAElFTkSuQmCC","orcid":"","institution":"Shanghai University of Traditional Chinese Medicine, Longhua Hospital","correspondingAuthor":true,"prefix":"","firstName":"Yueyi","middleName":"","lastName":"Deng","suffix":""}],"badges":[],"createdAt":"2024-11-09 17:23:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5423032/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5423032/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71556828,"identity":"e40d9e52-c5df-4315-bf9b-3ba3ff1481e1","added_by":"auto","created_at":"2024-12-16 16:27:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":88994,"visible":true,"origin":"","legend":"\u003cp\u003ea, Flowchart of the asample selection process. b, prevalence of CKD, CVD and comorbidities. CKD only: patients with CKD without CVD, CVD only: patients with CVD without CKD, Both: patients with CVD and CKD, None: patients without CVD and CKD. CKD: Chronic kidney disease; CVD: Cardiovascular\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5423032/v1/4417c3f40d1e4da3382576d2.png"},{"id":71554944,"identity":"122f27ce-45dc-44f6-884d-8f4d9f559390","added_by":"auto","created_at":"2024-12-16 16:19:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":269218,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of CKD, CVD, and comorbidity in each subgroup. Comorbidity: patients with CVD and CKD\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5423032/v1/875028fd01f77bf20a82e4a5.png"},{"id":71553932,"identity":"506ca652-4032-41f1-8cb9-9b8466829ecf","added_by":"auto","created_at":"2024-12-16 16:11:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":91093,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between levels of CKD and comorbidity with CVD.\u003c/p\u003e\n\u003cp\u003eModels: crude model, no adjustment; model 1, adjusted for demographic characteristics (age, gender, household registration, education, marital status); model 2, adjusted for demographic characteristics and lifestyle factors (smoking, drinking); model 3 adjusted for covariates included in model 2 plus physiological indicators (BMI, blood pressure, blood sugar, blood lipids, serum uric acid). Median value of eGFR of each group was used in the trend test. The forest plot demonstrates the results of Logistic regression analysis that compared with non-CKD group.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5423032/v1/58bdffee72ce42b7a55e0351.png"},{"id":71553937,"identity":"02ece3b0-5e21-4333-a561-79a95c892b9f","added_by":"auto","created_at":"2024-12-16 16:11:49","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":241083,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline curve for the association between eGFR and the risk of CVD. a: association between eGFR and the risk of CVD in Model 3, knots=3. b: sensitivity testing for association between eGFR and the risk of CVD in Model 3 which excluded participants with extreme 5% of eGFR, knots=3. c: sensitivity testing for association between eGFR and the risk of CVD in Model 3 which excluded participants with extreme 5% of eGFR, knots=4. d: sensitivity testing for association between eGFR and the risk of CVD in Model 3 which excluded participants with extreme 5% of eGFR, knots=5. Model 3 was adjusted for covariates included demographic characteristics (age, gender, household registration, education, marital status), lifestyle factors (smoking, drinking) and physiological indicators (BMI, blood pressure, blood sugar, blood lipids, serum uric acid). eGFR: estimated glomerular filtration rate.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5423032/v1/df5b2a6e68dd7d66c1544503.png"},{"id":71553933,"identity":"593aa219-3fb8-4e9a-87b6-898c4e4008c3","added_by":"auto","created_at":"2024-12-16 16:11:49","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":161288,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of ORs of factors affecting the occurrence of CKD and the comorbidity of CKD and CVD. CKD vs nomal: Individuals with CKD but without CVD vs Individuals without CKD and CVD; comorbidity vs CKD: Individuals with CKD and CVD vs Individuals with CKD but without CVD.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5423032/v1/bfe1fc70800bcf79a90f652a.png"},{"id":75694326,"identity":"52089661-a1c8-46a6-bd18-66c7ac2040da","added_by":"auto","created_at":"2025-02-07 07:53:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1394181,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5423032/v1/50546df5-af64-4e18-83ee-d5025d79db1c.pdf"},{"id":71553934,"identity":"094995db-1df1-4bc6-8719-64d7a907608b","added_by":"auto","created_at":"2024-12-16 16:11:49","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1253139,"visible":true,"origin":"","legend":"","description":"","filename":"Tables123.docx","url":"https://assets-eu.researchsquare.com/files/rs-5423032/v1/ee0f31db73a556188dd59957.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Patient characteristics and influencing factors of CKD, CVD and their comorbidities in the middle-aged and elderly population in China","fulltext":[{"header":"1.Introduction","content":"\u003cp\u003eCardiovascular disease (CVD) and chronic kidney disease (CKD) represent two pervasive and interlinked health conditions globally, with their interplay posing substantial challenges to public health.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] CVD, encompassing a range of heart and blood vessel disorders, remains the leading cause of mortality worldwide.[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] Concurrently, CKD affects approximately 10% of the global population and is a significant contributor to morbidity and mortality.[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThe bidirectional relationship between CKD and CVD is critical to understanding their interconnected impacts on health. Both conditions share common risk factors such as hypertension, diabetes, and dyslipidemia, which can lead to vascular damage.[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] Mechanistically, CKD contributes to the accumulation of uremic toxins[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], inflammation[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], and oxidative stress[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], promoting cardiovascular complications like left ventricular hypertrophy and vascular calcification. Conversely, CVD may result in renal hypoperfusion and heart failure, accelerating CKD progression.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] Studies have shown that patients with CKD face a higher risk of cardiovascular events, while those with CVD often experience more rapid deterioration in kidney function.[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] However, the population characteristics of patients with comorbidities compared to those without, including demographic traits, lifestyle factors, and physiological indicators, have not been thoroughly examined.\u003c/p\u003e \u003cp\u003eThe China Health and Retirement Longitudinal Study (CHARLS) offers a unique opportunity to investigate the influencing factors and population characteristics of comorbidities between CVD and CKD specifically within the older adult population in China. 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.[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] Previous studies have highlighted the individual burden of CVD and CKD. However, the CHARLS dataset allows for a nuanced analysis of their interplay and healthcare strategies.\u003c/p\u003e \u003cp\u003eThis study aims to evaluate the risk of co-occurrence of CVD and CKD among the middle aged and elderly Chinese population and to identify the influencing factors. In doing so, it aims to fill a gap in the literature regarding the older adult population and provide evidence for the complex relationship between CVD and CKD in this demographic, thereby contributing to scientific health management.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design and participants\u003c/h2\u003e \u003cp\u003eThis study is based on the CHARLS, a publicly available dataset containing a nationally representative sample of middle-aged and elderly community dwellers in China. The study covered 450 villages and urban communities in 28 provinces in China. The CHARLS baseline survey was conducted from 2011. A total of 7268 individuals aged 45 years and older from June 2011 to March 2012 were included. Finally, CHARLS was approved by the Biomedical Ethics Review Committee of Peking University (IRB00001052-11015), and respondents were required to sign an informed consent form. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea shows the flowchart of the recruitment process for this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Serum measurements\u003c/h2\u003e \u003cp\u003eThe serum measurement procedure has been described in detail elsewhere[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Following an overnight fast of at least 8 hours, 8 ml fasting blood samples were collected at township hospitals or local Centers for Disease Control offices. Plasma was separated by centrifugation at 3200 rpm for 10\u0026ndash;15 minutes within one hour of collection and was kept in the dark at room temperature. Whole blood and the centrifuged serum were stored at 4\u0026deg;C in a local laboratory and transported to the laboratory of Capital Medical University within two weeks at -80\u0026deg;C, where blood glucose, blood lipids, glycosylated hemoglobin, and serum creatinine levels were measured.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 CKD\u003c/h2\u003e \u003cp\u003eCKD is defined as meeting one of the following diagnostic criteria:\u003c/p\u003e \u003cp\u003ei eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 mL/min/1.73m\u003csup\u003e2\u003c/sup\u003e (calculated using the CKD Epidemiology Collaboration (CKD-EPI) equation). [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/p\u003e \u003cp\u003e ii self-reported CKD diagnosed by a physician according to professional guidelines.\u003c/p\u003e \u003cp\u003eThe CKD-EPI equation was as follows:\u003c/p\u003e \u003cp\u003eeGFR (mL/min/1.73m\u003csup\u003e2\u003c/sup\u003e)\u0026thinsp;=\u0026thinsp;135 \u0026times; min(SCr/k, 1)\u003csup\u003eα\u003c/sup\u003e \u0026times; max(SCr/k, 1)\u003csup\u003e\u0026minus;0.601\u003c/sup\u003e \u0026times; min(Cys/0.8, 1)\u003csup\u003e\u0026minus;0.375\u003c/sup\u003e \u0026times; max(Cys/0.8, 1)\u003csup\u003e\u0026minus;0.711\u003c/sup\u003e \u0026times; 0.995\u003csup\u003eage\u003c/sup\u003e \u0026times;0.969[if female]\u003c/p\u003e \u003cp\u003eSCr refers to serum creatinine (mg/dL), Cys refers to serum cystatin C (mg/liter), age refers to the patient's age (years), α is \u0026minus;\u0026thinsp;0.248 for females and \u0026minus;\u0026thinsp;0.207 for males, k is 0.7 for females and 0.9 for males.\u003c/p\u003e \u003cp\u003eCKD was then divided into stages 1\u0026ndash;2: eGFR\u0026thinsp;\u0026ge;\u0026thinsp;60ml/min/1.73 m\u0026sup2;, but previously diagnosed with CKD, stage 3a: 45\u0026thinsp;\u0026le;\u0026thinsp;eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60ml/min/1.73 m\u0026sup2;, stage 3b: 30\u0026thinsp;\u0026le;\u0026thinsp;eGFR\u0026thinsp;\u0026lt;\u0026thinsp;45ml/min/1.73 m\u0026sup2;, stage 4: 15\u0026thinsp;\u0026le;\u0026thinsp;eGFR\u0026thinsp;\u0026lt;\u0026thinsp;30ml/min/1.73 m\u0026sup2;, and stage 5: eGFR\u0026thinsp;\u0026lt;\u0026thinsp;15ml/min/1.73 m\u0026sup2;.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 CVD\u003c/h2\u003e \u003cp\u003eParticipants who reported a heart attack or stroke, as diagnosed by a physician, were defined as having cardiovascular disease. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], and cardiovascular disease events were assessed with the following questions:\u003c/p\u003e \u003cp\u003ei \"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?\"\u003c/p\u003e \u003cp\u003eii \"Have you ever been told by your doctor that you have been diagnosed with a stroke?\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Covariates\u003c/h2\u003e \u003cp\u003ePrevious 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)[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], lifestyle factors (smoking, drinking)[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], physiological indicators (BMI, blood pressure, blood sugar, blood lipids, serum uric acid)[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe 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,\u0026infin;)) 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 \u0026ldquo;below elementary school\u0026rdquo;, \u0026ldquo;elementary to middle school\u0026rdquo; and \u0026ldquo;high school or above\u0026rdquo;. Marital status was recorded as either \"yes\" (\u0026lsquo;married with spouse present\u0026rsquo;/ married but not living with spouse temporarily) or \"no\" (\u0026lsquo;widowed/divorced/separated/never married\u0026rsquo;)\u003c/p\u003e \u003cp\u003eSmoking was categorized as yes or no, and drinking was categorized as \u0026ldquo;drinking more than once a month\u0026rdquo;, \u0026ldquo;drinking less than once a month\u0026rdquo;, \u0026ldquo;none of the above\u0026rdquo;.\u003c/p\u003e \u003cp\u003eBMI was calculated using the following formula: BMI\u0026thinsp;=\u0026thinsp;Weight (kg)/[Height(m) ]\u003csup\u003e2\u003c/sup\u003e. Then we divided BMI into 4 groups according to previous studies: BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e (underweight), 18.5\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e (normal weight), 25\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e (overweight), and BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e (obesity).[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] Hypertension was defined as systolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140mmHg or diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;90mmHg or use of antihypertensive medication.[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] 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., \u0026ge;\u0026thinsp;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., \u0026ge;\u0026thinsp;48mmol/mol]), or the use of diabetes medications or insulin.[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] 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.[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] Hyperuricemia is defined as a serum uric acid level\u0026thinsp;\u0026gt;\u0026thinsp;7.0 mg/dL in men or \u0026gt;\u0026thinsp;6.0 mg/dL in women.[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical analyses\u003c/h2\u003e \u003cp\u003eThe 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.\u003c/p\u003e \u003cp\u003eThe 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.\u003c/p\u003e \u003cp\u003eIn 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\u0026ndash;response relationship between eGFR and the specified outcomes, and linear associations were assessed using the sensitivity analysis.\u003c/p\u003e \u003cp\u003eOnce 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.\u003c/p\u003e \u003cp\u003eAll statistical analyses were conducted using the R software (R version 2024.04.1\u0026thinsp;+\u0026thinsp;748). Visualization was achieved with the \"ggplot2\", \"crosstable\" and \"forestplot\" packages. Statistically significant results were determined at a two-tailed p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1. The distribution of comorbidity, CKD and CVD\u003c/h2\u003e \u003cp\u003eThe prevalence of CKD, CVD, and comorbidities was 14.83%, 13.95%, and 3.18%, respectively, in the entire sample (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the detailed distribution of each subgroup. The subgroups with the highest prevalence of CKD only were hyperuricemic (28.4%), age\u0026thinsp;\u0026gt;\u0026thinsp;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\u0026thinsp;\u0026gt;\u0026thinsp;70 (6.9%). The prevalence of CKD increases with age, lower education level, reduced body weight, elevated blood pressure, and higher blood lipid levels. Additionally, the prevalence is higher in males than in females, whereas the prevalence of CVD shows the opposite trend. The proportion of patients with CVD increases with age, BMI, blood glucose, blood lipids, and blood pressure, and is higher among the non-agricultural population compared to the agricultural population.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Demographic profiles in CKD\u003c/h2\u003e \u003cp\u003eIn the overall sample, except for hyperlipidemia status (n(%)\u0026thinsp;=\u0026thinsp;5015 (69.00%)), all variables were statistically significant. The average age of the total population was 60.92 (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\u0026ndash;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\u0026nbsp;1 shows the detailed summary results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Demographic profiles in CVD\u003c/h2\u003e \u003cp\u003eIn 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\u0026nbsp;2 shows the detailed summary results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Association between levels of CKD and CVD\u003c/h2\u003e \u003cp\u003eThe relationship between the levels of CKD and CVD is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e. 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)\u0026thinsp;=\u0026thinsp;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\u0026thinsp;\u0026lt;\u0026thinsp;0.05). There was a significant trend (p for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, upon the addition of covariates to the model, the significance of the OR values ​​for CKD stages 3a, 3b, and 4\u0026ndash;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\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) when compared with non-CKD group. The OR is 2.17 (1.68, 2.80).\u003c/p\u003e \u003cp\u003eIn the restricted cubic spline analysis conducted to investigate the dose\u0026ndash;response relationships between the risk of CVD and eGFR (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e), we identified a linear negative association between CVD and eGFR. The sensitivity testing results indicated that excluding participants in the lowest and highest 5% of eGFR values revealed that elevated eGFR is associated with a reduced risk of CVD. This exclusion focuses the analysis on the central range of eGFR values, providing a clearer understanding of the relationship between eGFR and CVD risk without the influence of outliers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5 The factors that influence the coexistence of CKD and CVD\u003c/h2\u003e \u003cp\u003eIn order to further elucidate the factors that influence the susceptibility of CKD patients to CVD, we constructed a logistic regression model (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e5\u003c/span\u003e). A comparison of patients with CKD-only with patients without CKD and CVD revealed that older age, non-agricultual household registration, smoking, and low body weight were risk factors (OR (95% CI)\u0026thinsp;=\u0026thinsp;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, normal blood pressure, and normal uric acid levels are protective factors. (OR (95% CI)\u0026thinsp;=\u0026thinsp;0.69 (0.49, 0.97), 0.72 (0.61, 0.85), 0.29 (0.22, 0.37))(P\u0026thinsp;\u0026lt;\u0026thinsp;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)\u0026thinsp;=\u0026thinsp;2.23 (1.54, 3.22), 2.12 (1.11, 4.03)), while non-diabetes was a protective factor (OR (95% CI)\u0026thinsp;=\u0026thinsp;0.63 (0.45, 0.90))(P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eSubsequently, in the interaction analysis, the interaction term between underweight and CKD was found to be statistically significant (OR (95% CI)\u0026thinsp;=\u0026thinsp;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\u0026thinsp;\u0026gt;\u0026thinsp;0.05). (Table\u0026nbsp;3)\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Key Findings\u003c/h2\u003e \u003cp\u003eOur 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 in China 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 in China. 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.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Comparison with Existing Literature\u003c/h2\u003e \u003cp\u003eOur 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[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. 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\u0026thinsp;=\u0026thinsp;85 ml/min/1.73 m\u0026sup2;. This suggests that risk factors influencing the onset of CVD are present in the early stages of CKD and intensify with declining eGFR.\u003c/p\u003e \u003cp\u003eWith 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[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. 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​[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. 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[\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Additionally, prior research by Kimmel et al. indicated that lower educational levels correlate with higher rates of CKD and CVD independently​[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. 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[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], lower access to preventive care[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], and poorer overall health management[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBesides, existing literature shows that in Western populations, higher BMI has been shown to increase the risk of CKD, with obesity (BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e) being significantly associated with the onset and progression of CKD[\u003cspan additionalcitationids=\"CR35 CR36\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. 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[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. 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.\u003c/p\u003e \u003cp\u003eInterestingly, 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​[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. 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[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eLastly, although Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows an unusual decrease in the correlation between CKD exacerbation and CVD, it is important to note that the sample included only 29 patients in CKD stages 4\u0026ndash;5, which may not accurately represent the broader real-world population of individuals with advanced CKD.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Potential Mechanisms\u003c/h2\u003e \u003cp\u003ethe 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.[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] 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.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Clinical Implications\u003c/h2\u003e \u003cp\u003eOur 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. Finally, the higher incidence of CVD in the non-rural household registration population compared to the rural household registration population in China is also a question worth exploring.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Strengths and Limitations\u003c/h2\u003e \u003cp\u003eThis study's strengths include the use of a nationally representative sample and comprehensive data collection through CHARLS. However, there are limitations to consider. The sample size for CKD stages 4\u0026ndash;5 is too small, which may introduce bias in the analysis of the association between CKD and CVD at these stages. Self-reported data may be prone to recall bias. Future longitudinal studies are necessary to validate our findings and clarify the temporal relationship between CKD and CVD.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn 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.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCKD:\u0026nbsp;Chronic Kidney Disease\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCVD:\u0026nbsp;Cardiovascular Disease\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCHARLS: China Health and Retirement Longitudinal Study\u003c/p\u003e\n\u003cp\u003eeGFR: estimated glomerular filtration rate\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBMI: Body Mass Index\u003c/p\u003e\n\u003cp\u003eIQR: interquartile range\u003c/p\u003e\n\u003cp\u003eOR: odds ratio\u003c/p\u003e\n\u003cp\u003e95% CI: 95% confidence intervals\u003c/p\u003e\n\u003cp\u003eRAAS: the renin-angiotensin-aldosterone system\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e CHARLS was approved by the Biomedical Ethics Review Committee of Peking University (IRB00001052-11015).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials: \u003c/strong\u003eThe datasets generated and/or analysed during the current study are available in the CHARLS repository, https://charls.charlsdata.com\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: This work was supported by the following grants: National Natural Science Foundation (no. 8217151785).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u003c/strong\u003e 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e Not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGo AS, Chertow GM, Fan D, McCulloch CE, Hsu CY (2004) Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization. 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Clin Chem 68(3):461\u0026ndash;472\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNguyen A, Khafagy R, Gao Y, Meerasa A, Roshandel D, Anvari M et al (2023) Association Between Obesity and Chronic Kidney Disease: Multivariable Mendelian Randomization Analysis and Observational Data From a Bariatric Surgery Cohort. Diabetes 72(4):496\u0026ndash;510\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarrero JJ, Aguilera A, Stenvinkel P, Gil F, Selgas R, Lindholm B (2008) Appetite disorders in uremia. J Ren Nutr 18(1):107\u0026ndash;113\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMacLaughlin HL, Pike M, Selby NM, Siew E, Chinchilli VM, Guide A et al (2021) Body mass index and chronic kidney disease outcomes after acute kidney injury: a prospective matched cohort study. 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Curr Probl Cardiol 49(2):102344\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 3 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Chronic kidney disease, cardiovascular disease, CHARLS","lastPublishedDoi":"10.21203/rs.3.rs-5423032/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5423032/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground and aims:\u003c/h2\u003e \u003cp\u003eChronic Kidney Disease (CKD) and Cardiovascular Disease (CVD) are significant public health concerns with high morbidity and mortality. The relationship between CKD and CVD 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.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe used data from the China Health and Retirement Longitudinal Study (CHARLS), focusing 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).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eCVD risk begins in early stages of CKD in the Chinese population. Individuals with characteristics such as hypertension, diabetes, non-rural household registration, abnormal BMI, or low education levels are significantly more likely to develop CKD and its comorbidity with CVD. Both low and high BMI were associated with increased CKD and CVD risk, with low BMI linked to reduced CVD risk in CKD patients.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eSocio-demographic and physical factors are critical in managing and preventing CKD and CVD comorbidities. Public health strategies should focus on non-rural and lower-educated populations in Chinese middle-aged and elderly people, and further research is needed to explore mechanisms and interventions.\u003c/p\u003e","manuscriptTitle":"Patient characteristics and influencing factors of CKD, CVD and their comorbidities in the middle-aged and elderly population in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-16 16:11:45","doi":"10.21203/rs.3.rs-5423032/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"beea3c2a-2376-4b1e-8206-1b1d735e250f","owner":[],"postedDate":"December 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-07T07:53:17+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-16 16:11:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5423032","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5423032","identity":"rs-5423032","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}