This study provides robust evidence of the intricate and multifaceted relationship between liver health and CHD, highlighting liver stiffness and liver fat content as independent and significant predictors of cardiovascular risk. By utilizing data from the NHANES database, our findings underscore the systemic influence of liver-related pathophysiology on metabolic and vascular health. These results challenge the traditional view that CHD is driven solely by classical risk factors, such as hypertension, dyslipidemia, and smoking, advocating for an integrated approach to cardiovascular risk stratification that incorporates liver health markers. This paradigm shift carries significant implications for early detection, prevention strategies, and the development of targeted treatment protocols aimed at reducing the dual burden of liver and cardiovascular diseases.
Liver stiffness, measured through transient elastography, emerged as a reliable surrogate marker for liver fibrosis19. Liver fibrosis, characterized by excessive extracellular matrix deposition due to chronic liver injury, has traditionally been viewed as a condition confined to the liver20. However, growing evidence highlights its systemic implications, particularly its impact on cardiovascular health21. Our findings align with prior studies demonstrating a significant association between liver fibrosis and an increased risk of cardiovascular events. For instance, studies by Li et al. and zhang et al. observed that advanced fibrosis in patients with MASLD correlated strongly with elevated cardiovascular mortality, suggesting that liver fibrosis is not merely a hepatic complication but a systemic condition that contributes independently to cardiovascular disease (CVD) outcomes22,23.
The mechanisms underlying this relationship are complex and multifactorial, involving chronic inflammation, oxidative stress, endothelial dysfunction, and metabolic dysregulation. Chronic inflammation serves as a critical mediator linking liver stiffness to CHD. Persistent hepatic injury activates Kupffer cells and hepatic stellate cells, resulting in the release of pro-inflammatory cytokines such as tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), and C-reactive protein (CRP)24. These cytokines impair endothelial function by reducing nitric oxide bioavailability, increasing vascular permeability, and promoting the expression of adhesion molecules that facilitate leukocyte adhesion and infiltration into arterial walls25. This cascade accelerates atherosclerotic plaque formation and destabilization, significantly increasing the risk of acute cardiovascular events such as myocardial infarction and stroke. Additionally, systemic inflammation perpetuates a feedback loop that exacerbates both hepatic and vascular damage, as highlighted in studies by Wang et al., which emphasized the interplay between hepatic inflammation and systemic vascular injury26.
Chronic inflammation’s effects on cardiovascular health extend beyond endothelial damage. It also contributes to myocardial remodeling, hypertrophy, and fibrotic changes within the heart. This connection has been observed in advanced liver fibrosis cases where elevated pro-inflammatory markers, such as CRP and fibrinogen, were significantly correlated with left ventricular diastolic dysfunction, a precursor to heart failure27. These findings underscore the broader systemic impact of hepatic inflammation on cardiovascular structure and function.
Oxidative stress is another key pathway linking liver stiffness to CHD. Chronic liver disease disrupts the balance between reactive oxygen species (ROS) production and antioxidant defenses, leading to oxidative damage that extends beyond the liver28. ROS promotes lipid peroxidation, generating oxidized low-density lipoprotein (ox-LDL), a highly atherogenic molecule29. Ox-LDL facilitates foam cell formation and the progression of atherosclerotic plaques in arterial walls. Furthermore, oxidative stress reduces endothelial nitric oxide levels, impairing vascular compliance and promoting arterial rigidity. The dual impact of inflammation and oxidative stress creates a vicious cycle where hepatic and vascular damage mutually reinforce each other, amplifying CHD risk. The importance of these findings is supported by research from Liu et al., who demonstrated that oxidative stress markers were significantly elevated in patients with advanced liver fibrosis and correlated with subclinical atherosclerosis30.
Oxidative stress also directly impairs mitochondrial function, a key determinant of cellular energy production. Mitochondrial dysfunction has been implicated in both hepatic and cardiovascular conditions, with reduced mitochondrial efficiency linked to impaired myocardial contractility and increased cardiomyocyte apoptosis31. This cross-organ mitochondrial dysfunction reinforces the interconnected nature of liver and cardiovascular health.
Metabolic dysfunction further elucidates the relationship between liver stiffness and CHD. Insulin resistance, a hallmark of MASLD and metabolic syndrome, is a central driver of metabolic dysregulation that exacerbates cardiovascular risk32. Insulin resistance disrupts glucose and lipid metabolism, resulting in hyperglycemia and atherogenic dyslipidemia characterized by elevated triglycerides, reduced high-density lipoprotein (HDL) cholesterol, and increased levels of small, dense low-density lipoprotein (LDL) particles33. Hyperglycemia accelerates vascular damage through the formation of advanced glycation end-products (AGEs), which impair vascular elasticity and promote arterial stiffness. Additionally, insulin resistance exacerbates hepatic lipogenesis and fibrosis, further amplifying systemic metabolic dysfunction and cardiovascular risk. These findings suggest that addressing insulin resistance and metabolic dysfunction could have dual benefits for liver and cardiovascular health. Hou et al. further supported this relationship by demonstrating that insulin resistance significantly increased CHD risk, even in patients without overt diabetes, highlighting its central role in linking liver and cardiovascular disease34.
Liver fat content, a defining feature of MASLD, also emerged as a critical determinant of CHD risk in our study. MASLD is closely associated with obesity, type 2 diabetes, and metabolic syndrome, all of which are major risk factors for CHD35. However, our findings indicate that liver fat content contributes independently to CHD risk, beyond its association with traditional metabolic conditions. Hepatic steatosis exacerbates insulin resistance and disrupts normal glucose metabolism, resulting in hyperglycemia and endothelial dysfunction. These metabolic disturbances impair vascular homeostasis, promote endothelial dysfunction, and accelerate the progression of atherosclerosis. Moreover, hepatic fat accumulation triggers systemic inflammation by activating immune cells, including Kupffer cells, further compounding cardiovascular risk. These observations are consistent with the work of Ichikawa et al., who reported that hepatic steatosis was associated with an increased risk of subclinical atherosclerosis, independent of traditional cardiovascular risk factors36.
The non-linear relationship between liver stiffness, liver fat content, and CHD risk, as revealed through restricted cubic splines (RCS), provides critical clinical insights. Our findings suggest that CHD risk increases disproportionately beyond specific thresholds of liver stiffness or fat content, indicating a tipping point at which liver health markers exert a more pronounced effect on cardiovascular outcomes. This observation emphasizes the importance of early detection and intervention, as mild liver abnormalities may have a limited cardiovascular impact, whereas advanced liver disease significantly amplifies CHD risk. Identifying these thresholds could guide the development of targeted screening programs and therapeutic strategies tailored to individuals at highest risk.
Integrating liver health markers into established cardiovascular risk assessment frameworks has the potential to transform clinical practice. Existing tools, such as the Framingham Risk Score and ASCVD risk calculator, predominantly focus on traditional risk factors, which may underestimate cardiovascular risk in individuals with liver dysfunction37. Incorporating liver stiffness and fat content into these models could enhance their predictive accuracy, enabling earlier identification of high-risk individuals and more effective allocation of preventive resources. This approach is particularly relevant in populations with a high prevalence of MASLD and metabolic syndrome, where conventional models may fail to fully capture cardiovascular risk. The findings of Nebhinani et al. further underscore the necessity of incorporating liver health markers into risk assessment frameworks, particularly as the prevalence of MASLD continues to rise globally38.
Machine learning techniques provide additional opportunities to leverage liver health markers for CHD risk prediction. Advanced algorithms, such as decision trees, XGBoost classifiers, and neural networks, excel at identifying complex, non-linear relationships within high-dimensional datasets39. These models can generate personalized risk profiles that inform precision interventions, such as lifestyle modifications, pharmacological therapies, and monitoring strategies. Our study demonstrated the utility of these approaches in capturing the interplay between liver health markers and cardiovascular outcomes. This builds on findings from Dong et al., who explored the use of machine learning in risk stratification for patients with MASLD and CHD, revealing its potential to uncover interactions that traditional models might overlook40.
Despite its strengths, this study has several limitations that warrant consideration. Limitations of this study also include the method of CHD diagnosis, which relied on self-reported questionnaire data. This approach may introduce misclassification bias, as it lacks confirmatory clinical validation. However, previous validation studies using NHANES data have demonstrated a high concordance between self-reported cardiovascular conditions and both medication use and clinical outcomes. While we did not perform formal cross-validation in this study, the consistency of associations across multiple models and stratified analyses supports the robustness of our findings. Future studies incorporating objective diagnostic methods, such as medical records or symptom-based algorithms, are warranted to validate and refine these associations. The cross-sectional design of the NHANES dataset precludes causal inference, underscoring the need for longitudinal studies to validate these findings and establish temporal relationships. While transient elastography is a non-invasive and accessible measure of liver health, it may not fully capture the spectrum of liver pathology. Advanced imaging modalities, such as magnetic resonance elastography (MRE) or liver biopsy, could provide greater diagnostic precision and offer deeper insights into the relationship between liver and cardiovascular health. Furthermore, residual confounding may persist due to unmeasured variables, such as genetic predispositions, medication use, and lifestyle factors. Addressing these limitations in future research will be critical for refining risk prediction models and strengthening the evidence base.
From a public health perspective, our findings highlight the urgent need for comprehensive strategies to address the growing burden of liver and cardiovascular diseases. Screening programs that include liver stiffness and fat content as routine markers could facilitate earlier detection and intervention, mitigating risk before significant damage occurs. Lifestyle modifications, such as dietary changes, increased physical activity, and weight management, have demonstrated efficacy in improving both liver and cardiovascular health. Pharmacological therapies targeting hepatic fibrosis and fat metabolism, such as GLP-1 receptor agonists and novel anti-fibrotic agents, hold promise for reducing the dual burden of these conditions. These strategies align with recommendations from the American Association for the Study of Liver Diseases (AASLD), which advocates for an integrated approach to managing MASLD and its systemic complications.
Future research should prioritize longitudinal studies to elucidate causal pathways and assess the long-term impact of liver health interventions on cardiovascular outcomes. Advances in omics technologies, including metabolomics, proteomics, and genomics, offer exciting opportunities to uncover novel biomarkers and mechanisms linking liver and cardiovascular health. Additionally, randomized controlled trials are needed to evaluate the effectiveness of integrating liver health markers into cardiovascular risk prediction and management strategies. Such efforts could pave the way for precision medicine approaches that optimize prevention and treatment at both individual and population levels.
Another limitation of this study is that the diagnosis of CHD was based on self-reported questionnaire data, which may introduce potential misclassification bias. However, previous validation studies using NHANES data have shown that self-reported cardiovascular conditions are generally reliable and correlate well with medication use and clinical outcomes. Although we did not conduct a formal cross-validation analysis in this study, the consistency of our findings across multiple models and subgroups suggests that the associations observed are robust. Future studies incorporating medical record validation or symptom-based algorithms would further improve diagnostic accuracy.
In conclusion, this study highlights liver stiffness and fat content as critical and independent predictors of CHD risk. By integrating these markers into cardiovascular risk assessments, clinicians can enhance early detection, refine prevention strategies, and improve outcomes for patients with interconnected liver and cardiovascular conditions. As the global prevalence of MASLD and metabolic syndrome continues to rise, addressing these overlapping epidemics will be essential for advancing public health and reducing the global burden of disease.