Abstract
Background:
Chronic kidney disease (CKD) increases the risk of adverse cardiovascular outcomes. However, the causal relationships between renal function and cardiovascular diseases (CVD) remain incompletely understood. This study aimed to determine the causal relationships between genetic susceptibility to impaired renal function and the risk of CVD endpoints, as well as cardiac structure and function detectable by cardiac magnetic resonance imaging (CMR).
Methods:
Bidirectional Mendelian randomization (MR) analyses were conducted using summary-level data from genome-wide association studies. The exposures were blood urea nitrogen (BUN), estimated glomerular filtration rate (eGFR), urine albumin-to-creatinine ratio (UACR), and CKD. The outcomes included atrial fibrillation, coronary artery disease (CAD), myocardial infarction, heart failure, stroke, and various CMR parameters. Sensitivity analyses, multivariable MR adjusting for cardiometabolic traits, and replication in the FinnGen cohort were performed.
Results:
Elevated BUN levels (OR 1.505; 95% CI 1.077 to 2.103; P = 0.017) were causally associated with increased CAD risk, but this relationship was attenuated after adjusting for cardiometabolic traits. Increased UACR was causally linked to higher risks of CAD (OR 1.260; 95% CI 1.042 to 1.523; P = 0.017), myocardial infarction (OR 1.424; 95% CI 1.137 to 1.783; P = 0.002), and stroke (OR 1.182; 95% CI 1.012 to 1.379; P = 0.035), with the association for stroke remaining significant after multivariable adjustment. Reduced eGFR was causally related to decreases in ascending aorta diameter, proximal pulmonary artery diameter, right atrial size, left ventricular stroke volume, and right ventricular volumes, even after accounting for potential confounders. CKD was causally associated with a reduced pulmonary artery-to-aorta ratio and proximal pulmonary artery diameter.
Conclusions:
This comprehensive MR study establishes causal roles of genetic susceptibility to impaired renal function influencing cardiovascular outcomes and cardiac structure.
Keywords: Mendelian randomization, Coronary artery disease, Stroke, Chronic kidney disease, Estimated glomerular filtration rate
What was known: CKD increases cardiovascular disease risk, but causal links were uncertain. There is limited research on causal effects of renal function on cardiac structure.
This study adds: Elevated BUN causally increases coronary artery disease risk. Albuminuria is an independent causal risk factor for stroke. Reduced eGFR/CKD causally contributes to pulmonary vascular alterations.
Potential impact: Supports albuminuria screening for cerebrovascular risk mitigation in CKD. Monitoring of pulmonary pressures/right heart in advanced CKD is warranted. Provides insights into kidney-heart crosstalk for future studies.
Introduction
Chronic Kidney Disease (CKD), affecting approximately 10 to 15% of the global adult population, poses a significant public health challenge (1). CKD is characterized by long-term, progressive damage to the kidneys, resulting in a gradual loss of renal function over time. As CKD progresses, the kidneys’ ability to efficiently filter blood weakens. This deterioration in renal function leads to an accumulation of toxins and metabolic dysfunction, ultimately resulting in uremia. There is substantial evidence that CKD significantly increases the risk of adverse cardiovascular outcomes, including heart failure (HF), stroke, myocardial infarction (MI), and cardiovascular mortality (2). Critically, there exists a bidirectional causality in this relationship. Conditions that induce circulatory strain, notably hypertension, are known to exacerbate renal damage due to reduced renal perfusion and ischemia, thus reciprocally influencing the risk and progression of CKD and cardiovascular diseases (CVD) (3).
While randomized controlled trials (RCTs) are the gold standard for establishing causality, their application in the context of CKD and cardiovascular outcomes is often impeded by ethical, practical, or financial constraints. This limitation necessitates reliance on observational studies, which, despite their extensive utility, encounter challenges in definitively determining causal relationships due to inherent residual confounding. Furthermore, studies exploring the causal relationship between renal function and pathological cardiac remodeling (measured via cardiac magnetic resonance (CMR) phenotypes) are limited by sample size and financial barriers. To date, this issue has been predominantly explored through case-control studies (4).
Mendelian randomization (MR) leverages naturally occurring genetic variations, specifically single nucleotide polymorphisms (SNPs), as instrumental variables (IVs), providing a method akin to a natural experiment for strengthening causal inferences in observational data (5). This approach mirrors the methodology of RCTs by using the random segregation and independent assortment of alleles, as dictated by Mendel’s laws during meiosis, effectively creating a ‘natural randomized trial.’ SNPs, which have established associations with specific risk factors, biomarkers, or health outcomes, serve as proxies (5). They enable the simulation of treatment and control groups in traditional RCTs, thereby allowing for an unbiased estimation of the causal effects of various exposures. In the realm of cardiovascular research, MR has been increasingly utilized to unravel the intricate causal relationships between various risk factors and CVD, offering insights that surpass the limitations of conventional observational studies and addressing the challenges of RCTs, such as their often prohibitive cost and scale (6).
This study, through a bidirectional MR approach, aims to determine the causal relationships between genetic susceptibility to impaired renal function and the risk of CVD endpoints, as well as imaging parameters of cardiac structure and function detectable by CMR. This research could lay a theoretical foundation for the prevention and treatment of CKD and unveil novel perspectives.
Methods
Study design
In our study, we conducted bidirectional MR analyses to investigate potential causal relationships between renal function and cardiovascular outcomes, as well as their influence on cardiac structure and function. To ensure the robustness of our findings, we implemented various MR methods and carried out reverse MR to evaluate possible reverse causality. To minimize the impact of confounding factors, we used multivariable MR to adjust for related cardiometabolic traits. For external validation of these cardiovascular endpoints, data from the FinnGen database were utilized. The sources of our data are detailed in Table S1. Additionally, our research follows the STROBE-MR Guidelines. Figure 1 provides a visual representation of our study’s procedural outline, which was created using BioRender.com.
Figure 1.
Schematic representation of MR analysis. MR, Mendelian randomization.
Data sources for Renal function
Our study utilized summary data from Genome-wide association studies (GWAS) for blood urea nitrogen (BUN), estimated glomerular filtration rate (eGFR), urine albumin-to-creatinine ratio (UACR), and CKD, obtained from the CKDGen Consortium. This data originated from the consortium’s cross-ethnic GWAS meta-analyses. We specifically focused on GWAS data for individuals of European ancestry. The meta-analysis for CKD GWAS encompassed 64,164 cases and 561,055 controls, all of European ancestry. CKD was defined as an eGFR below 60 ml/min/1.73 m2.
Data sources for cardiac outcomes
The cardiovascular disease outcomes investigated in our study encompassed atrial fibrillation (AF), coronary artery disease (CAD), MI, HF, and stroke. We sourced the GWAS summary statistics for these endpoints from comprehensive meta-analyses conducted by leading genetic consortia. To externally validate these cardiovascular outcomes, we used summary data from a GWAS meta-analysis by the FinnGen consortium (R9 data release). For comprehensive details about these data sources, please refer to Table S1.
Data sources for cardiac structure and function
We obtained data on key CMR structure and functional parameters from GWAS conducted among 36,000–45,000 European ancestry participants of the UK Biobank Imaging Study. These traits encompassed left atrial maximum volume (LA Max), left atrial total ejection fraction (LATEF), left ventricular mass (LV Mass), left ventricular end-diastolic volume (LVEDV), left ventricular end-systolic volume (LVESV), left ventricular stroke volume (LVSV), left ventricular ejection fraction (LVEF), degree of myocardial interstitial fibrosis, ascending aorta diameter (Asc Aorta Diam), pulmonary artery-to-aorta diameter ratio (PA Aorta ratio), proximal pulmonary artery diameter (Prox PA Diam), right atrial fractional area change (RA FAC), right atrial maximum area (RA Max), right atrial minimum area (RA Min), right ventricular ejection fraction (RVEF), right ventricular end-diastolic volume (RVEDV), right ventricular end-systolic volume (RVESV), right ventricular stroke volume (RVSV), and the ratio of right ventricular end-systolic to left ventricular end-systolic volume (RVESV LVESV ratio). To ensure comparability across different body sizes, all cardiac imaging measures were indexed to body surface area, except for dimensionless metrics like total ejection fraction and fractional area change.
Data sources for Confounding Traits
To address potential confounding factors in our evaluation of causal relationships, we sourced GWAS summary statistics from the UK Biobank.
These statistics included data on low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, triglycerides (TG), and Apolipoprotein A1 and Apolipoprotein B, as well as systolic and diastolic blood pressure and fasting glucose. For Body Mass Index (BMI) data, our source was a comprehensive meta-analysis focused on individuals of European ancestry.
Instrumental variable selection
We carefully selected independent SNPs meeting stringent criteria to serve as IVs for MR analyses (5). IVs were required to have genome-wide significant associations with the exposure (P < 5e-8), reside in loci exhibiting no significant between-study heterogeneity, and be independent SNPs in linkage equilibrium (r2 < 0.001). Only SNPs with minor allele frequencies > 0.01 were included. For reverse MR examining SNPs associated with cardiac MRI structure and function, a P-value threshold of < 5e-7 was employed when the number of SNPs was limited (less than three for some traits). The F-statistics of chosen IVs were confirmed to be > 10, indicating sufficient strength as IVs (7). These rigorous criteria minimized the possibility of violating core MR assumptions. Details of the SNPs selected as IVs are provided in Table S2.
Main MR analyses
In our study, we utilized a two-sample MR framework based on summary-level data. The primary method of analysis was inverse variance weighted (IVW) regression, operating under a random-effects model (8). This approach involved pooling Wald ratio estimates for each SNP via meta-analysis, using the inverse of their variance as weights to derive causal effect estimates.
We adjusted the significance threshold using Bonferroni correction to address multiple testing. This correction was applied only to the exposures, with the adjusted P-value threshold set at 0.0125 (0.05/4), considering the four exposures in our study. This approach to setting the P-value threshold aimed to reduce the risk of Type I errors while simultaneously mitigating Type II errors. Furthermore, if statistical significance was observed in both the primary and validation cohorts, we opted not to use the Bonferroni correction and maintained the P-value threshold at 0.05.
Sensitivity analyses
To verify the validity of significant causal associations, we conducted a series of sensitivity analyses using complementary MR methods. We implemented several additional MR methods in sensitivity analyses, including MR-Egger regression, Weighted Median estimator, Simple Mode and Weighted Mode, to evaluate consistency and robustness. We evaluated the presence of horizontal pleiotropy via MR-Egger intercept tests (9). Heterogeneity was inspected using Cochran’s Q statistic, necessitating the adoption of random-effects models where significant (P < 0.05) (10). Global test P-values < 0.05 suggested the presence of outliers distorting causal estimates (11). If outliers were detected, we examined whether Outlier-Corrected Estimates significantly altered the results. We also performed reverse MR analysis, swapping the exposure and outcome, to explore any potential reverse causality.
Multivariable MR analysis
We utilized multivariable MR to mitigate the impact of confounding factors on the causal relationships between exposure and outcomes. By adjusting for cardiovascular risk factors such as blood pressure, glucose, and lipid levels, we aimed to obtain robust outcomes and address potential confounding influences.
Replication analysis
In our replication analysis for cardiovascular outcomes, we used data from the FinnGen cohort. Successful replication required statistical significance (P < 0.05) in the IVW method and consistent effect estimates across all methods.
Analysis software
All analyses were performed in R version 4.2.3 (R Foundation for Statistical Computing, Vienna, Austria). We utilized the TwosampleMR (version 0.5.7) and MR-PRESSO (version 1.0) (11) packages for conducting main and sensitivity MR tests.
Results
Renal function and Cardiovascular Outcomes
Our MR analysis revealed a causal relationship between increased BUN levels and a higher risk of CAD in the primary cohort (odds ratio (OR) 1.505; 95% CI 1.077 to 2.103; P = 0.017). These findings were further corroborated in the FinnGen cohort for CAD (OR 1.840; 95% CI 1.148 to 2.948; P = 0.011). However, in the primary cohort, MR-Egger results did not reach statistical significance, and there was an inconsistency in the direction of the effect compared to the IVW method.
Moreover, an increased genetic predisposition for a higher UACR was associated with greater odds of CAD (OR 1.260; 95% CI 1.042 to 1.523; P = 0.017), MI (OR 1.424; 95% CI 1.137 to 1.783; P = 0.002), and stroke (OR 1.182; 95% CI 1.012 to 1.379; P = 0.035). In the five MR analysis methods, the MR-Egger method did not show statistical significance for CAD and MI results, and the direction of the OR was inconsistent with the IVW method. The relationships between UACR and CAD, MI, and stroke were validated in FinnGen (CAD: OR 1.475; 95% CI 1.167 to 1.864; P = 0.001; MI: OR 1.416; 95% CI 1.057 to 1.897; P = 0.020; Stroke: OR 1.285; 95% CI 1.050 to 1.572; P = 0.015) (Figures 2, 3A, 3B, and Table S3 for details).
Figure 2.
Causal effects of renal function on cardiovascular diseases.
IVW, Inverse variance weighted; SM, Simple Mode; WME, Weighted Median Estimator; WMo, Weighted Mode.
Figure 3.
A. Causal effects of BUN on cardiac outcomes. B. Causal effects of UACR on cardiac outcomes. C. Causal effects of cardiac outcomes on CKD. D. Multivariable MR analysis of the causal effects between renal function and Cardiac Outcomes after adjusting for risk factors.
AF, Atrial Fibrillation; BUN, Blood Urea Nitrogen; CKD, Chronic Kidney Disease; eGFR, Estimated Glomerular Filtration Rate; UACR, Urinary Albumin-to-Creatinine Ratio.
We detected heterogeneity (P < 0.05), which led us to implement a random-effects model. The MR-PRESSO global test identified outliers (P < 0.05); however, the causal estimates retained their significance and directional consistency even after adjusting for these outliers. Furthermore, MR-Egger regression revealed no evidence of horizontal pleiotropy (P > 0.05) (Table S4).
No causal links were found between CKD or eGFR and the cardiovascular outcomes. Similarly, no causal associations were detected between renal function traits and the risk of AF or HF.
In reverse-direction MR tests, the presence of stroke showed a causal relationship with increased BUN levels (OR 1.014; 95% CI 1.001 to 1.027; P = 0.032). However, this association was not replicated in the FinnGen cohort and became non-significant after applying the Bonferroni correction. Notably, AF (OR 1.049; 95% CI 1.018 to 1.082; P = 0.002) and stroke (OR 1.181; 95% CI 1.014 to 1.376; P = 0.032) were found to have causal impacts on elevating CKD risk, with these relationships being validated in the FinnGen cohort (details in Figure 3C, Figure 4, Figure S1, and Table S5). No reverse causal links were detected between the cardiovascular outcomes and either eGFR or UACR. Notably, the causal effects of stroke in the primary cohort and AF in the FinnGen cohort on CKD exhibited heterogeneity, but no horizontal pleiotropy was detected (Table S6).
Figure 4.
Causal effects of cardiovascular diseases on renal function using reverse MR.
BUN, Blood Urea Nitrogen; CKD, Chronic Kidney Disease; eGFR, Estimated Glomerular Filtration Rate; UACR, Urinary Albumin-to-Creatinine Ratio.
In the multivariable MR analysis, after adjusting for cardiometabolic traits, the causal relationship between BUN levels and CAD was no longer significant. Additionally, after these adjustments, the impact of an elevated UACR on the risks of CAD and MI was also found to be non-significant. However, the causal link between increased UACR and the risk of stroke remained significant, even when considering potential cardiovascular risk factors. In a similar vein, after adjusting for cardiometabolic traits in the multivariable MR analysis, neither AF nor stroke demonstrated a significant association with an increased risk of CKD (Figure 3D and Table S7).
Renal function and cardiac structure/function
Our analysis revealed that elevated BUN levels were causally associated with a decrease in LVEF (β = –0.400; 95% CI –0.692 to –0.108; P = 0.007) and Prox PA Diam Indexed (β = –0.381; 95% CI –0.679 to –0.084; P = 0.012). Additionally, CKD showed a causal link to a reduced PA Aorta ratio (β = –0.086; 95% CI –0.130 to –0.042; P = 1.4e-4) and a decrease in Prox PA Diam Indexed (β = –0.053; 95% CI –0.094 to –0.012; P = 0.011). No causal relationships were observed between the UACR and the cardiac parameters assessed (Figure 5, Figures S2–S3, Figure S4A, and Table S8).
Figure 5.
Causal effects of renal function on cardiac structure and function.
Asc Aorta Diam, Ascending aorta diameter; BUN, Blood Urea Nitrogen; CKD, Chronic Kidney Disease; eGFR, Estimated Glomerular Filtration Rate; LA Max, Left atrial maximum volume; LATEF, Left atrial total ejection fraction; LVEDV, Left ventricular end diastolic volume; LVEF, Left ventricular ejection fraction; LVESV, Left ventricular end systolic volume; LV Mass, Left ventricular mass; LVSV, Left ventricular stroke volume; Prox PA Diam, Proximal pulmonary artery diameter; RA FAC, Right atrial fractional area change; RA Max, Right atrial maximum area; RA Min, Right atrial minimum area; RVEDV, Right ventricular end diastolic volume; RVEF, Right ventricular ejection fraction; RVESV, Right ventricular end systolic volume; RVSV, Right ventricular systolic volume; UACR, Urinary Albumin-to-Creatinine Ratio.
Crucially, a decline in the eGFR has been causally linked to reductions in a range of key cardiac parameters. These included the Asc Aorta Diam Indexed (β = 0.716; 95% CI 0.276 to 1.157; P = 0.001), Prox PA Diam Indexed (β = 0.961; 95% CI 0.538 to 1.385; P = 8.62e-6), RA Max Indexed (β = 0.714; 95% CI 0.312 to 1.116; P = 5e-4), RA Min Indexed (β = 0.783; 95% CI 0.351 to 1.216; P = 3.8e-4), LVSV Indexed (β = 0.676; 95% CI 0.256 to 1.096; P = 0.002), RVEDV Indexed (β = 0.625; 95% CI 0.189 to 1.061; P = 0.005), and RVSV Indexed (β = 0.593; 95% CI 0.130 to 1.055; P = 0.012). Our sensitivity analyses indicated the presence of heterogeneity. However, there was no evidence of horizontal pleiotropy (Table S4).
In the multivariable MR analysis, after adjusting for cardiometabolic traits, Prox PA Diam Indexed was found to have a significant causal relationship with both eGFR and CKD. Additionally, significant causal associations were also observed with eGFR for Asc Aorta Diam Indexed, LVSV Indexed, RA Max Indexed, RA Min Indexed, and RVSV Indexed (Figure S4 B and Table S7).
Discussion
In this comprehensive MR study, we robustly establish the causal relationships within the kidney-cardiac axis, thereby integrating key findings that bridge genetic susceptibilities impacting renal function with cardiovascular disease risks.
Renal function and Cardiac Outcomes
In our primary analysis, we found that increased levels of BUN were genetically associated with a higher risk of CAD. Similarly, elevated UACR was linked to an increased risk of CAD, MI, and stroke. These findings were consistently validated in the FinnGen cohort. However, results from the MR-Egger regression for BUN and CAD, as well as UACR and CAD/MI, lacked statistical significance, with OR directions inconsistent with those from the IVW method. This indicates that potential pleiotropy and confounding factors may have influenced our results. Consequently, interpretations of BUN’s impact on CAD and UACR’s influence on CAD/MI require cautious consideration.
The detection of causal relationships of increased BUN and UACR with greater CAD risk accords with prior observational studies demonstrating the heightened burden of atherosclerotic cardiovascular disease, including CAD, MI, and cardiovascular mortality, among individuals with CKD (12,13,14). Even after adjustment for conventional cardiovascular risk factors like hypertension and diabetes, CKD remained an independent risk factor for adverse coronary events in early reports (13,15,16).
Contrastingly, our multivariable MR analysis, after adjusting for cardiometabolic traits, revealed that the causal relationship between BUN and UACR with the risk of CAD and MI was eliminated. This suggests that the adverse effects of increased BUN and urinary albumin excretion on the likelihood of coronary disease are largely mediated through associated disturbances in blood pressure, glucose, and lipids. Therefore, focusing on lifestyle modifications and pharmaceutical treatments that target these traditional, modifiable risk factors may reduce the additional risk of CAD/MI, even in the presence of impaired renal function.
However, a key finding from our multivariable MR analysis is the sustained association between UACR and stroke, even after adjusting for cardiometabolic traits. This emphasizes UACR’s role as an independent risk factor for stroke in CKD. The association between UACR and stroke may be explained by the underlying mechanism that albuminuria is a marker of endothelial dysfunction and reflects a more generalized vascular pathology such as atherosclerosis. The above pathological changes involve the hardening and narrowing of arteries, reducing blood flow to the brain (17,18).
Emerging evidence has also unveiled albuminuria’s involvement in small-vessel cerebral disease detectable on neuroimaging (19). A higher UACR is associated with stroke, and the potential mechanisms for this association may include microvascular damage, disruption of the blood-brain barrier, and impaired cerebral perfusion (18,19). This expanded insight into albuminuria’s multifactorial impacts on cerebrovascular health could underpin its causal elevation of stroke risk independent of traditional risk factors.
The strength of albuminuria’s causal stroke risk association surviving adjustment for common risk factors highlights the need to specifically monitor UACR, even in patients without markedly reduced eGFR. Irrespective of a CKD diagnosis, UACR screening should be conducted, as the risk assessment it provides exceeds that offered by eGFR alone. Therefore, our study spotlights albuminuria as a prognostic indicator warranting close monitoring and aggressive control of accompanying vascular risk factors to mitigate cerebrovascular risk in CKD. Therapies directly targeting endothelial dysfunction and inflammatory pathways activated by albuminuria merit exploration as adjunct stroke prevention strategies.
Furthermore, our reverse-direction MR analysis initially indicated a potential causal link between stroke, AF, and an increased risk of CKD. Understanding the intricate connections between the heart, brain, and kidneys is critical. Cerebral ischemia can lead to acute kidney injury via neurohormonal pathways, which encompass sympathetic overactivity, inflammation, and oxidative stress (20). Additionally, cerebral infarcts may disrupt the central nervous system’s ability to regulate salt and water balance, negatively impacting renal function (21). These causal effects, once adjusted for cardiometabolic traits in multivariable MR analysis, are no longer significant. Therefore, meticulous control of accompanying vascular risk factors is imperative regardless of the initial organ affected to disrupt this vicious cycle and avert adverse sequelae.
Renal function and Cardiac Remodeling
Of the cardiac parameters assessed, our multivariable analysis, even after adjusting for cardiometabolic traits, consistently showed that lower eGFR and CKD are significantly associated with a reduced index diameter of the proximal pulmonary artery. Constriction of the proximal pulmonary vasculature can reflect the presence of pulmonary arterial hypertension (PAH) and often foreshadows a dismal prognosis (22). Our findings support existing evidence that CKD is an independent risk factor for PAH, potentially through mechanisms such as endothelial dysfunction, an imbalance of vasoactive compounds, inflammation, and metabolic disorders (23,24). The pulmonary circulation is exceptionally vulnerable to these systemic effects accompanying CKD. Hence, our study suggests that declining renal function may play a causal role in predisposing individuals to pulmonary arterial hypertension and deleterious right chamber alterations.
Additionally, we found a significant correlation between reduced eGFR and decreased right heart dimensions, further highlighting eGFR’s role in cardiopulmonary damage. Our study suggests that impaired renal function may play a causal role in predisposing individuals to pulmonary arterial hypertension and deleterious right ventricular alterations. The secondary pulmonary hypertension pathophysiology in CKD patients is still complex and not completely clear. This structural remodeling of pulmonary vasculature offers clues to the underpinnings of elevated pulmonary pressures attending renal dysfunction. Future studies should focus on the genetic and epigenetic interplay between renal and cardiopulmonary pathways to identify potential therapeutic targets.
Furthermore, our study confirms a causal relationship between increased BUN levels and reduced LVEF. BUN, a key biomarker for renal excretory function, has been associated with higher rates of cardiovascular complications and mortality across multiple cohorts (25,26,27). The potential mechanisms for this relationship may involve myocardial energetic deficiencies as a result of the accumulation of nitrogenous wastes. Ammonia, a key nitrogenous waste, can cross cell membranes and disrupt tricarboxylic acid cycle enzymes at high concentrations, leading to ATP depletion (28). Given that cardiomyocytes heavily depend on mitochondrial oxidative metabolism for their high energy needs, a decrease in ATP can significantly impair contractile function (29). Despite these theoretical mechanisms and previous observational studies indicating a link, our multivariable MR analysis revealed that after adjusting for factors like lipid levels, blood pressure, and blood sugar, there is no causal association between elevated BUN and decreased LVEF.
Limitation
Our study has several limitations. First, our participants were exclusively of European descent, limiting the applicability of our findings to other ethnicities. This underscores the need for further research to validate our results in diverse ethnic populations. Additionally, while genetic variants were selected through stringent criteria, the potential for pleiotropy and residual confounding cannot be completely excluded.
Conclusion
Overall, this analysis deepens our understanding of the complex interplay between kidney disease and cardiovascular health, suggesting differential impacts on cardiac outcomes and structure from distinct facets of impaired renal function. It highlights avenues for reducing CAD/MI risk via modifying traditional risk factors even after kidney injury, while underscoring the need for tightened monitoring and management of cerebrovascular risk with albuminuria. Our comprehensive MR study, applying summary-level data from European ancestry cohorts, establishes causal roles of genetic susceptibility to impaired renal function in influencing cardiovascular outcomes and inducing cardiac remodeling.
Data Accessibility Statement
Publicly available GWAS summary data were utilized for the analyses. These are available to download at cited sources.
Additional Files
The additional files for this article can be found as follows:
Causal effects of cardiovascular diseases on renal function.
Causal effects of renal function on cardiac structure and function. A. Causal effects of eGFR on cardiac structure/function. B. Causal effects of BUN on cardiac structure/function.
Causal effects of renal function on cardiac structure and function. A. Causal effects of CKD on cardiac structure/function. B. Causal effects of UACR on cardiac structure/function.
A. Causal effects of renal function on cardiac structure and function. B. Multivariable MR analysis on renal function and cardiac structure/function after adjusting for risk factors.
BUN, Blood Urea Nitrogen; CKD, Chronic Kidney Disease; eGFR, Estimated Glomerular Filtration Rate; UACR, Urinary Albumin-to-Creatinine Ratio.
Summary of GWAS data sources used in the study.
Instrumental variables of renal function used for MR analysis.
Effect estimates of the renal function on cardiovascular diseases.
Test results for pleiotropy and heterogeneity – causal effects of the renal function on cardiovascular diseases, structure and function.
Effect estimates of the cardiovascular diseases, structure and function on renal function using reverse MR.
Test results for pleiotropy and heterogeneity – causal effects of the cardiovascular diseases on renal function.
Multivariable MR analysis of the causal effects after adjusting for risk factors.
Effect estimates of the renal function on cardiovascular magnetic resonance imaging parameters of cardiac structure and function.
Acknowledgements
We extend our gratitude to the committed participants, the researchers who generously shared their insights, the GWAS data platforms, and the prominent research consortia, all of which have paved the way for further explorations and discoveries in subsequent studies.
Nonstandard Abbreviations and Acronyms
| AF | Atrial Fibrillation |
| Asc Aorta Diam | Ascending Aorta Diameter |
| BMI | Body Mass Index |
| BUN | Blood Urea Nitrogen |
| CAD | Coronary Artery Disease |
| CKD | Chronic Kidney Disease |
| CMR | Cardiac Magnetic Resonance |
| CVD | Cardiovascular Disease |
| DBP | Diastolic Blood Pressure |
| eGFR | Estimated Glomerular Filtration Rate |
| GWAS | Genome-Wide Association Studies |
| HDL | High Density Lipoprotein |
| HF | Heart Failure |
| IVs | Instrumental Variables |
| IVW | Inverse Variance Weighted |
| LA Max | Left Atrial Maximum Volume |
| LATEF | Left Atrial Total Ejection Fraction |
| LDL | Low Density Lipoprotein |
| LV Mass | Left Ventricular Mass |
| LVEDV | Left Ventricular End-Diastolic Volume |
| LVESV | Left Ventricular End-Systolic Volume |
| LVEF | Left Ventricular Ejection Fraction |
| LVSV | Left Ventricular Stroke Volume |
| MI | Myocardial Infarction |
| MR | Mendelian Randomization |
| OR | Odds Ratio |
| PA Aorta ratio | Pulmonary Artery-to-Aorta Diameter Ratio |
| Prox PA Diam | Proximal Pulmonary Artery Diameter |
| RA FAC | Right Atrial Fractional Area Change |
| RA Max | Right Atrial Maximum Area |
| RA Min | Right Atrial Minimum Area |
| RCT | Randomized Controlled Trials |
| RVEDV | Right Ventricular End-Diastolic Volume |
| RVESV | Right Ventricular End-Systolic Volume |
| RVESV LVESV ratio | Ratio of Right Ventricular End-Systolic to Left Ventricular End-Systolic Volume |
| RVEF | Right Ventricular Ejection Fraction |
| RVSV | Right Ventricular Stroke Volume |
| SBP | Systolic Blood Pressure |
| SM | Simple Mode |
| SNPs | Single Nucleotide Polymorphisms |
| TG | Triglycerides |
| UACR | Urinary Albumin-to-Creatinine Ratio |
Competing Interests
The authors have no competing interests to declare.
Author contributions
Study design and conception: XQZ, WQR, LJZ, KL, LJ; data collection and validation data: HZL, TW, GYZ; statistical analysis: XQZ, WQR; interpretation of the data and findings: XQZ, WQR, LJZ, KL; writing-original draft preparation: XQZ, WQR; project supervision: XQZ, KL, LJ. Each author contributed important intellectual content during manuscript drafting or revision and accepts accountability for the overall work by ensuring that questions pertaining to the accuracy or integrity of any portion of the work are appropriately investigated and resolved.
References
- 1.Jager KJ, Fraser S. The ascending rank of chronic kidney disease in the global burden of disease study. Nephrology Dialysis Transplantation. 2017; 32(suppl_2):ii121–ii128. DOI: 10.1093/ndt/gfw330 [DOI] [PubMed] [Google Scholar]
- 2.Tonelli M, Muntner P, Lloyd A, Manns BJ, Klarenbach S, Pannu N, et al. Risk of coronary events in people with chronic kidney disease compared with those with diabetes: A population-level cohort study. Lancet. 2012; 380(9844):807–814. DOI: 10.1016/S0140-6736(12)60572-8 [DOI] [PubMed] [Google Scholar]
- 3.Pedrinelli R, Giampietro O, Carmassi F, Melillo E, Dell’Omo G, Catapano G, et al. Microalbuminuria and endothelial dysfunction in essential hypertension. Lancet. 1994; 344(8914):14–18. DOI: 10.1016/S0140-6736(94)91047-2 [DOI] [PubMed] [Google Scholar]
- 4.Paoletti E, De Nicola L, Gabbai FB, Chiodini P, Ravera M, Pieracci L, et al. Associations of left ventricular hypertrophy and geometry with adverse outcomes in patients with CKD and hypertension. Clinical Journal of the American Society of Nephrology. 2016; 11(2):271–279. DOI: 10.2215/CJN.06980615 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Emdin CA, Khera AV, Kathiresan S. Mendelian randomization. JAMA. 2017; 318(19):1925–1926. DOI: 10.1001/jama.2017.17219 [DOI] [PubMed] [Google Scholar]
- 6.Levin MG, Burgess S. Mendelian randomization as a tool for cardiovascular research: A review. JAMA Cardiology. 2024; 9(1):79–89. DOI: 10.1001/jamacardio.2023.4115 [DOI] [PubMed] [Google Scholar]
- 7.Burgess S, Thompson SG. Avoiding bias from weak instruments in Mendelian randomization studies. International Journal of Epidemiology. 2011; 40(3):755–764. DOI: 10.1093/ije/dyr036 [DOI] [PubMed] [Google Scholar]
- 8.Bowden J, Del GMF, Minelli C, Davey SG, Sheehan N, Thompson J. A framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization. Statistics in Medicine. 2017; 36(11):1783–1802. DOI: 10.1002/sim.7221 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Bowden J, Davey SG, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. International Journal of Epidemiology. 2015; 44(2):512–525. DOI: 10.1093/ije/dyv080 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Bowden J, Holmes MV. Meta-analysis and Mendelian randomization: A review. Research Synthesis Methods. 2019; 10(4):486–496. DOI: 10.1002/jrsm.1346 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Verbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nature Genetics. 2018; 50:693–698. DOI: 10.1038/s41588-018-0099-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Nakano T, Ninomiya T, Sumiyoshi S, Fujii H, Doi Y, Hirakata H, et al. Association of kidney function with coronary atherosclerosis and calcification in autopsy samples from Japanese elders: The Hisayama study. American Journal of Kidney Diseases. 2010; 55(1):21–30. DOI: 10.1053/j.ajkd.2009.06.034 [DOI] [PubMed] [Google Scholar]
- 13.Sarnak MJ, Levey AS, Schoolwerth AC, Coresh J, Culleton B, Hamm LL, et al. Kidney disease as a risk factor for development of cardiovascular disease: a statement from the American Heart Association Councils on Kidney in Cardiovascular Disease, High Blood Pressure Research, Clinical Cardiology, and Epidemiology and Prevention. Hypertension. 2003; 42(5):1050–1065. DOI: 10.1161/01.HYP.0000102971.85504.7c [DOI] [PubMed] [Google Scholar]
- 14.Matsushita K, van der Velde M, Astor BC, Woodward M, Levey AS, de Jong PE, et al. Association of estimated glomerular filtration rate and albuminuria with all-cause and cardiovascular mortality in general population cohorts: a collaborative meta-analysis. Lancet. 2010; 375(9731):2073–2081. DOI: 10.1016/S0140-6736(10)60674-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Cai Q, Mukku VK, Ahmad M. Coronary artery disease in patients with chronic kidney disease: A clinical update. Current Cardiology Reviews. 2013; 9(4):331–339. DOI: 10.2174/1573403X10666140214122234 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Lin CC, Chen TY, Li CI, Liu CS, Lin CH, Wang MC, et al. Renal markers and risks of all cause and cardiovascular mortality from the Taichung community based cohort study. Scientific Reports. 2021; 11:14143. DOI: 10.1038/s41598-021-93627-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Masson P, Webster AC, Hong M, Turner R, Lindley RI, Craig JC. Chronic kidney disease and the risk of stroke: A systematic review and meta-analysis. Nephrology Dialysis Transplantation. 2015; 30(7):1162–1169. DOI: 10.1093/ndt/gfv009 [DOI] [PubMed] [Google Scholar]
- 18.Ghoshal S, Freedman BI. Mechanisms of stroke in patients with chronic kidney disease. American Journal of Nephrology. 2019; 50(4):229–239. DOI: 10.1159/000502446 [DOI] [PubMed] [Google Scholar]
- 19.Xiao L, Lan W, Sun W, Dai Q, Xiong Y, Li L, et al. Chronic kidney disease in patients with lacunar stroke: Association with enlarged perivascular spaces and total magnetic resonance imaging burden of cerebral small vessel disease. Stroke. 2015; 46(8):2081–2086. DOI: 10.1161/STROKEAHA.114.008155 [DOI] [PubMed] [Google Scholar]
- 20.Zhao Q, Yan T, Chopp M, Venkat P, Chen J. Brain-kidney interaction: Renal dysfunction following ischemic stroke. Journal of Cerebral Blood Flow & Metabolism. 2020; 40(2):246–262. DOI: 10.1177/0271678X19890931 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Tisdall M, Crocker M, Watkiss J, Smith M. Disturbances of sodium in critically ill adult neurologic patients: A clinical review. Journal of Neurosurgical Anesthesiology. 2006; 18(1):57–63. DOI: 10.1097/01.ana.0000191280.05170.0f [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Hoeper MM, Humbert M, Souza R, Idrees M, Kawut SM, Sliwa-Hahnle K, et al. A global view of pulmonary hypertension. Lancet Respiratory Medicine. 2016; 4(4):306–322. DOI: 10.1016/S2213-2600(15)00543-3 [DOI] [PubMed] [Google Scholar]
- 23.Sise ME, Courtwright AM, Channick RN. Pulmonary hypertension in patients with chronic and end-stage kidney disease. Kidney International. 2013; 84(4):682–692. DOI: 10.1038/ki.2013.186 [DOI] [PubMed] [Google Scholar]
- 24.Bolignano D, Rastelli S, Agarwal R, Fliser D, Massy Z, Ortiz A, et al. Pulmonary hypertension in CKD. American Journal of Kidney Diseases. 2013; 61(4):612–622. DOI: 10.1053/j.ajkd.2012.07.029 [DOI] [PubMed] [Google Scholar]
- 25.Matsushita K, Kwak L, Sang Y, Ballew SH, Skali H, Shah AM, et al. Kidney disease measures and left ventricular structure and function: The Atherosclerosis Risk in Communities study. Journal of American Heart Association. 2017; 6(9):e006259. DOI: 10.1161/JAHA.117.006259 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.van der Velde M, Matsushita K, Coresh J, Astor BC, Woodward M, Levey A, et al. Lower estimated glomerular filtration rate and higher albuminuria are associated with all-cause and cardiovascular mortality. A collaborative meta-analysis of high-risk population cohorts. Kidney International. 2011; 79(12):1341–1352. DOI: 10.1038/ki.2010.536 [DOI] [PubMed] [Google Scholar]
- 27.Matsushita K, Selvin E, Bash LD, Astor BC, Coresh J. Risk implications of the new CKD Epidemiology Collaboration (CKD-EPI) equation compared with the MDRD study equation for estimated GFR: The Atherosclerosis Risk in Communities (ARIC) study. American Journal of Kidney Diseases. 2010; 55(4):648–659. DOI: 10.1053/j.ajkd.2009.12.016 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Felipo V, Butterworth RF. Neurobiology of ammonia. Progress in Neurobiology. 2002; 67(4):259–279. DOI: 10.1016/S0301-0082(02)00019-9 [DOI] [PubMed] [Google Scholar]
- 29.Kohlhaas M, Nickel AG, Maack C. Mitochondrial energetics and calcium coupling in the heart. The Journal of Physiology. 2017; 595(12):3753–3763. DOI: 10.1113/JP273609 [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Causal effects of cardiovascular diseases on renal function.
Causal effects of renal function on cardiac structure and function. A. Causal effects of eGFR on cardiac structure/function. B. Causal effects of BUN on cardiac structure/function.
Causal effects of renal function on cardiac structure and function. A. Causal effects of CKD on cardiac structure/function. B. Causal effects of UACR on cardiac structure/function.
A. Causal effects of renal function on cardiac structure and function. B. Multivariable MR analysis on renal function and cardiac structure/function after adjusting for risk factors.
BUN, Blood Urea Nitrogen; CKD, Chronic Kidney Disease; eGFR, Estimated Glomerular Filtration Rate; UACR, Urinary Albumin-to-Creatinine Ratio.
Summary of GWAS data sources used in the study.
Instrumental variables of renal function used for MR analysis.
Effect estimates of the renal function on cardiovascular diseases.
Test results for pleiotropy and heterogeneity – causal effects of the renal function on cardiovascular diseases, structure and function.
Effect estimates of the cardiovascular diseases, structure and function on renal function using reverse MR.
Test results for pleiotropy and heterogeneity – causal effects of the cardiovascular diseases on renal function.
Multivariable MR analysis of the causal effects after adjusting for risk factors.
Effect estimates of the renal function on cardiovascular magnetic resonance imaging parameters of cardiac structure and function.
Data Availability Statement
Publicly available GWAS summary data were utilized for the analyses. These are available to download at cited sources.