Abstract
Background
Chronic liver disease (CLD) and heart disease (HD) are major contributors to global morbidity and mortality. Although increasing evidence supports close interactions between the liver and heart, population-based longitudinal data evaluating their bidirectional temporal relationship remain limited, particularly beyond metabolic dysfunction–associated steatotic liver disease (MASLD). This study aimed to investigate the bidirectional association between CLD, excluding steatotic liver disease, and HD, and to explore the potential mediating role of insulin resistance (IR) in a nationally representative cohort.
Methods
This longitudinal cohort study utilized data from five waves of the China Health and Retirement Longitudinal Study conducted between 2011 and 2020. Cox proportional hazards models were applied to examine the bidirectional associations between HD and CLD. Mediation analyses were performed to assess whether IR, measured by the triglyceride–glucose (TyG) index and the metabolic score for IR (METS-IR), mediated these associations. Multiple sensitivity and subgroup analyses were conducted to evaluate robustness.
Results
Among 6,230 participants free of HD at baseline, CLD was associated with a significantly increased risk of incident HD (HR:1.803, 95% CI 1.376–2.362). Conversely, among 6,917 participants without baseline CLD, HD was associated with a higher risk of developing CLD (HR: 2.303, 95% CI 1.813–2.924). These associations were consistent across predefined subgroups and sensitivity analyses. Mediation analyses indicated that the TyG index and the METS-IR partially mediated the association between HD and incident CLD but not the association between CLD and incident HD.
Conclusion
In this nationally representative longitudinal study, CLD and HD were bidirectionally associated among middle-aged and older adults. IR partially mediated the pathway from HD to CLD, suggesting asymmetric mechanisms underlying liver–heart interactions. These findings underscore the clinical relevance of the liver–heart axis and highlight the need for integrated strategies to prevent and manage liver and cardiovascular diseases in aging populations.
Graphical Abstract
Supplementary Information
The online version contains supplementary material available at 10.1186/s12933-026-03134-y.
Keywords: Chronic liver disease, Heart disease, Bidirectional association, Insulin resistance, Liver–heart axis
Introduction
Chronic liver disease (CLD) and cardiovascular disease (CVD) constitute two major global health burdens, contributing substantially to increasing morbidity and mortality and placing a sustained strain on public health systems [1–3]. CVD has emerged as a leading cause of non–liver-related mortality among patients with CLD, a relationship most clearly demonstrated in populations with metabolic dysfunction–associated steatotic liver disease (MASLD) [4–6]. MASLD affects more than 30% of adults and is associated with a two to three fold increased risk of cardiovascular events [7, 8]. Shared pathological mechanisms, including systemic inflammation, insulin resistance (IR), and dyslipidemia, are thought to collectively promote atherosclerotic progression and increase the risk of cardiac dysfunction [9, 10]. Conversely, heart disease (HD) may induce or exacerbate liver injury through multiple pathways, including chronic hepatic congestion, impaired hepatic perfusion, neurohormonal activation, and sustained exposure to cardiometabolic risk factors [11, 12]. Long‑term pharmacotherapy may also play a contributory role. Accumulating evidence highlights a complex interorgan interplay between the liver and heart, mediated through metabolic, inflammatory, hemodynamic, and neuroendocrine pathways—a concept now widely referred to as the liver–heart axis [11, 13, 14]. This cross-organ interplay is particularly relevant in middle-aged and older adults [15], in whom multimorbidity is increasingly prevalent and organ functional reserve progressively declines.
Despite rapid advances in mechanistic understanding, population-level epidemiological evidence remains relatively limited. Previous studies have predominantly focused on the impact of CLD on cardiovascular outcomes, whereas the reciprocal influence of HD on liver health has received comparatively less attention. Furthermore, studies employing cross-sectional designs or relying on specific clinical cohorts often struggle to establish temporal sequence and limit the generalizability of their findings. Importantly, CLD was notably heterogeneous. Compared with the relatively well‑studied MASLD, longitudinal population‑based evidence for the relationship between other chronic liver conditions, such as viral hepatitis, alcohol‑associated liver disease, and autoimmune liver diseases, and HD remains insufficient. Moreover, existing studies have largely focused on unidirectional associations, and high-quality longitudinal evidence systematically evaluating the bidirectional temporal relationship between the two conditions in the general middle-aged and older population remains limited.
To address these gaps, we explicitly excluded steatotic liver disease (SLD); thus, in the present study, CLD refers to non-SLD unless otherwise specified. Using longitudinal data from the nationally representative China Health and Retirement Longitudinal Study (CHARLS) across the 2011 to 2020 waves, we investigated the bidirectional temporal association between CLD and HD. We further explored the potential mediating role of IR in this relationship. This design enabled an examination of the dynamic interplay between CLD and HD over time and aims to provide population‑based evidence to inform the integrated management of chronic conditions in an aging population.
Methods
Study population
Data for this cohort study were derived from five waves of the CHARLS conducted between 2011 and 2020. CHARLS targets Chinese residents aged 45 years and older, with the primary objective of investigating population aging and monitoring longitudinal changes in health status among middle-aged and older adults. The present analysis employed de-identified, publicly available CHARLS data, which had received prior ethical approval from the Biomedical Ethics Review Committee of Peking University (IRB00001052–11015). Detailed information about the CHARLS study is available at https://charls.pku.edu.cn/index.htm. In accordance with prior studies [16, 17], participants with unknown or missing data were excluded from the analyses. For the two separate analyses, participants with HD at baseline were excluded from the analysis evaluating the risk of incident HD in relation to baseline CLD, whereas participants with CLD at baseline were excluded from the analysis evaluating the risk of incident CLD in relation to baseline HD. The final sample sizes for the two analyses were 6,230 and 6,917, respectively. (Additional File: Figure S1-S2).
Assessment of CLD and HD
Consistent with previous studies [18, 19], CLD and HD diagnoses were determined based on doctor-confirmed diagnosis via self-reported medical histories recorded during survey interviews. Specifically, incident CLD events were ascertained using the question:“Have you been diagnosed with chronic liver disease(except fatty liver, tumors, and cancer) by a doctor?” This included viral hepatitis, autoimmune hepatitis, primary biliary cirrhosis, and primary sclerosing cholangitis, but excludes SLD, tumors, and cancer [20, 21]. Incident HD events were identified using the following standardized question: “Have you been diagnosed with heart attack, coronary heart disease, angina, congestive heart failure, or other heart problems by a doctor?” To reduce potential recall bias, participants were additionally asked, “Are you currently receiving any treatment (e.g., traditional Chinese medicine, Western medicine, or other therapies) for CLD or HD or their complications?” CLD and HD status were subsequently classified as “yes” or “no” based on the combined responses to the two questions [22, 23].
Covariates
Covariates included in this study encompassed age, sex, place of residence (urban or rural), educational attainment (less than high school versus high school or higher), marital status (married versus other), body mass index (BMI), alcohol consumption status (current, former, or never), smoking status (current, former, or never), C-reactive protein (CRP), uric acid (UA), glycosylated hemoglobin A1c (HbA1c), fasting plasma glucose (FPG), triglyceride (TG), low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C), triglyceride-glucose (TyG) index, metabolic score for IR (METS-IR), hypertension and diabetes. BMI was calculated as weight in kilograms divided by the square of height in meters (kg/m²). The TyG index was calculated as: TyG index = Ln [TG (mg/dL) × FBG (mg/dL) / 2] [24]. The METS-IR was calculated as: METS-IR = Ln (2 × FBG [mg/dL] + TG [mg/dL]) × BMI/Ln (HDL-C [mg/dL]) [25]. Diabetes and hypertension were identified according to self-reported diagnosis or the use of related medications [26].
Statistical analysis
Continuous variables were summarized as mean ± standard deviation (SD) and differences between groups were assessed using t-tests or Mann-Whitney U test as appropriate. Categorical variables were presented as frequencies (percentages) and evaluated using the chi-square test. We examined whether death occurred prior to the outcome of interest to determine the need for competing-risk modeling. Because no deaths occurred before the onset of the outcome, death was not considered a competing event in this cohort. Accordingly, multivariable Cox proportional hazards models were employed to assess the bidirectional association between CLD and HD, with results expressed as hazard ratios (HRs) and 95% confidence intervals (CIs). Adjustments were sequentially applied across three models to account for potential confounding factors: Model 1 was unadjusted for covariates. Model 2 was adjusted for age and gender. Model 3 additionally adjusted for residence, educational attainment, marital status, drinking status, smoking status, and BMI. The proportional hazards assumption was assessed using Schoenfeld residuals, and no violations were detected. Subgroup and interaction analyses were performed to examine potential effect modification by various key covariates, such as gender, age, drinking status, smoking status, hypertension, and diabetes. Mediation analyses were conducted to examine whether the TyG index and the METS-IR, two widely acknowledged surrogate marker of IR, served as mediators in the association between CLD and HD. Because METS-IR incorporates BMI as a structural component of its calculation, BMI was not included as a covariate in mediation models involving METS-IR to avoid overadjustment and collinearity. To ensure the robustness of the results, a series of sensitivity analyses were conducted. Sensitivity analysis 1 was adjusted for Model 3 with additional adjustment for a history of hypertension and diabetes, both of which are common risk factors for multiple chronic diseases. Sensitivity analysis 2 was adjusted for Model 3 with additional adjustment for CRP and UA. CRP and UA are widely acknowledged surrogate indicates for systematic inflammation and oxidative stress levels, respectively [27, 28]. Sensitivity analysis 3 was adjusted for Model 3 with additional adjustment for HbA1c, TG, LDL-C, and HDL-C. Sensitivity analysis 4 was performed by excluding participants with obesity (BMI ≥ 28 kg/m²) at baseline to minimize the potential influence of undiagnosed concomitant SLD. Sensitivity analysis 5 was performed by excluding incident events occurring during the first follow-up wave to reduce the potential for reverse causation. All statistical analyses were performed using SPSS version 25.0 (IBM Corp., Armonk, NY, USA) and R version 4.5. Two-sided P values < 0.05 were considered statistically significant.
Results
Baseline characteristics
Baseline characteristics of the cohorts are presented in Tables 1 and 2. For the primary analysis assessing the association between CLD and incident HD, a total of 6,230 participants were included, with a mean (SD) age of 57.67 (8.48) years. For the primary analysis assessing the association between HD and incident CLD, 6,917 participants were included, with a mean (SD) age of 57.94 (8.47) years. Participants with baseline CLD had lower LDL than those without CLD, while no statistically significant differences were observed in baseline characteristics between participants with and without baseline CLD (P > 0.05). Participants with baseline HD were more likely to be older (60.32 vs. 57.66 years), past drinkers (7.69% vs. 5.37%), and past smokers (10.07% vs. 7.24%) than those without HD. They also exhibited a higher prevalence of hypertension (50.21% vs. 20.62%) and diabetes (12.59% vs. 5.32%).
Table 1.
Baseline characteristics of the study population in primary analysis of CLD and new-onset HD
| Parameters | CLD | |||
|---|---|---|---|---|
| Overall (n = 6230) |
No (n = 6011) |
Yes (n = 219) |
P | |
| Age, years | 57.67 ± 8.48 | 57.68 ± 8.49 | 57.48 ± 8.22 | 0.861 |
| Age category, n (%) | 0.685 | |||
| < 65 | 5113 (82.07) | 4931 (82.03) | 182 (83.11) | |
| ≥ 65 | 1117 (17.93) | 1080 (17.97) | 37 (16.89) | |
| Female, n (%) | 3386 (54.35) | 3274 (54.47) | 112 (51.14) | 0.332 |
| Residence, n (%) | 0.817 | |||
| Urban | 877 (14.08) | 845 (14.06) | 32 (14.61) | |
| Rural | 5353 (85.92) | 5166 (85.94) | 187 (85.39) | |
| Education, n (%) | 0.609 | |||
| Less than high school | 5627 (90.32) | 5427 (90.28) | 200 (91.32) | |
| High school and above | 603 (9.68) | 584 (9.72) | 19 (8.68) | |
| Marry, n (%) | 0.583 | |||
| Others | 615 (9.87) | 591 (9.83) | 24 (10.96) | |
| Married | 5615 (90.13) | 5420 (90.17) | 195 (89.04) | |
| BMI (kg/m2) | 23.47 ± 3.83 | 23.48 ± 3.85 | 23.30 ± 3.31 | 0.864 |
| CRP (mg/L) | 2.42 ± 6.58 | 2.42 ± 6.61 | 2.31 ± 5.54 | 0.729 |
| UA (mg/dL) | 4.41 ± 1.22 | 4.41 ± 1.22 | 4.51 ± 1.28 | 0.264 |
| FBG (mg/dL) | 108.44 ± 32.05 | 108.54 ± 32.34 | 105.91 ± 22.58 | 0.543 |
| HbA1c (%) | 5.24 ± 0.74 | 5.24 ± 0.75 | 5.22 ± 0.63 | 0.534 |
| TG (mg/dL) | 130.10 ± 96.64 | 130.29 ± 96.46 | 125.00 ± 101.50 | 0.188 |
| HDL (mg/dL) | 51.61 ± 15.18 | 51.64 ± 15.20 | 50.78 ± 14.67 | 0.521 |
| LDL (mg/dL) | 116.80 ± 34.35 | 117.00 ± 34.46 | 111.47 ± 30.76 | 0.015 |
| TyG index | 8.66 ± 0.65 | 8.66 ± 0.65 | 8.60 ± 0.62 | 0.195 |
| METS-IR | 35.39 ± 8.22 | 35.40 ± 8.24 | 35.12 ± 7.42 | 0.920 |
| Drinking status, n (%) | 0.068 | |||
| Never | 4301 (69.04) | 4150 (69.04) | 151 (68.95) | |
| Former | 337 (5.41) | 318 (5.29) | 19 (8.68) | |
| Now | 1592 (25.55) | 1543 (25.67) | 49 (22.37) | |
| Smoking status, n (%) | 0.210 | |||
| Never | 3872 (62.15) | 3741 (62.24) | 131 (59.82) | |
| Former | 463 (7.43) | 440 (7.32) | 23 (10.50) | |
| Now | 1895 (30.42) | 1830 (30.44) | 65 (29.68) | |
| Hypertension, n (%) | 1256 (20.16) | 1202 (20.00) | 54 (24.66) | 0.091 |
| Diabetes, n (%) | 263 (4.22) | 249 (4.14) | 14 (6.39) | 0.104 |
| New-onset HD, n (%) | 979 (15.71) | 923 (15.36) | 56 (25.57) | < 0.001 |
Continuous data are presented as mean ± standard deviation (SD)
Categorical data are presented as number (%)
CLD chronic liver disease, HD heart disease, BMI body mass index, CRP C-reactive protein, UA uric acid, HbA1c glycosylated hemoglobin A1c, FPG fasting plasma glucose, TG triglyceride, LDL-C low density lipoprotein cholesterol, HDL-C high density lipoprotein cholesterol, TyG triglyceride-glucose, METS-IR metabolic score for insulin resistance
Table 2.
Baseline characteristics of the study population in primary analysis of HD and new-onset CLD
| Parameters | HD | |||
|---|---|---|---|---|
| Overall (n = 6917) |
No (n = 6202) |
Yes (n = 715) |
P | |
| Age, years | 57.94 ± 8.47 | 57.66 ± 8.42 | 60.32 ± 8.53 | < 0.001 |
| Age category, n (%) | < 0.001 | |||
| < 65 | 5611 (81.12) | 5091 (82.09) | 520 (72.73) | |
| ≥ 65 | 1306 (18.88) | 1111 (17.91) | 195 (27.27) | |
| Female, n (%) | 3853 (55.70) | 3398 (54.79) | 455 (63.64) | < 0.001 |
| Residence, n (%) | < 0.001 | |||
| Urban | 1050 (15.18) | 872 (14.06) | 178 (24.90) | |
| Rural | 5867 (84.82) | 5330 (85.94) | 537 (75.10) | |
| Education, n (%) | 0.514 | |||
| Less than high school | 6239 (90.20) | 5599 (90.28) | 640 (89.51) | |
| High school and above | 678 (9.80) | 603 (9.72) | 75 (10.49) | |
| Marry, n (%) | < 0.001 | |||
| Others | 678 (9.80) | 603 (9.72) | 75 (10.49) | |
| Married | 6239 (90.20) | 5599 (90.28) | 640 (89.51) | |
| BMI (kg/m2) | 23.64 ± 3.94 | 23.54 ± 3.89 | 24.52 ± 4.25 | < 0.001 |
| CRP (mg/L) | 2.43 ± 6.51 | 2.42 ± 6.56 | 2.55 ± 6.03 | 0.001 |
| UA (mg/dL) | 4.40 ± 1.21 | 4.41 ± 1.21 | 4.33 ± 1.20 | 0.158 |
| FBG (mg/dL) | 109.09 ± 33.00 | 108.72 ± 32.40 | 112.30 ± 37.63 | 0.005 |
| HbA1c (%) | 5.25 ± 0.76 | 5.24 ± 0.75 | 5.36 ± 0.86 | < 0.001 |
| TG (mg/dL) | 131.35 ± 94.54 | 130.24 ± 95.23 | 140.94 ± 87.75 | < 0.001 |
| HDL (mg/dL) | 51.26 ± 15.09 | 51.56 ± 15.16 | 48.67 ± 14.21 | < 0.001 |
| LDL (mg/dL) | 117.43 ± 34.81 | 117.15 ± 34.56 | 119.86 ± 36.89 | 0.152 |
| TyG index | 8.68 ± 0.65 | 8.67 ± 0.65 | 8.79 ± 0.65 | < 0.001 |
| METS-IR | 35.76 ± 8.37 | 35.51 ± 8.23 | 37.91 ± 9.22 | < 0.001 |
| Drinking status, n (%) | < 0.001 | |||
| Never | 4839 (69.96) | 4293 (69.22) | 546 (76.36) | |
| Former | 388 (5.61) | 333 (5.37) | 55 (7.69) | |
| Now | 1690 (24.43) | 1576 (25.41) | 114 (15.94) | |
| Smoking status, n (%) | < 0.001 | |||
| Never | 4358 (63.00) | 3871 (62.42) | 487 (68.11) | |
| Former | 521 (7.53) | 449 (7.24) | 72 (10.07) | |
| Now | 2038 (29.46) | 1882 (30.35) | 156 (21.82) | |
| Hypertension, n (%) | 1638 (23.68) | 1279 (20.62) | 359 (50.21) | < 0.001 |
| Diabetes, n (%) | 345 (4.99) | 263 (4.24) | 82 (11.47) | < 0.001 |
| New-onset CLD, n (%) | 420 (6.07) | 330 (5.32) | 90 (12.59) | < 0.001 |
Continuous data are presented as mean ± standard deviation (SD)
Categorical data are presented as number (%)
CLD chronic liver disease, HD heart disease, BMI body mass index, CRP C-reactive protein, UA uric acid, HbA1c glycosylated hemoglobin A1c, FPG fasting plasma glucose, TG triglyceride, LDL-C low density lipoprotein cholesterol, HDL-C high density lipoprotein cholesterol, TyG triglyceride-glucose, METS-IR metabolic score for insulin resistance
Associations between CLD and new-onset HD
During a median 9-year follow-up, a total of 979 participants developed HD. Incidence rates of new-onset HD, stratified by baseline CLD status, are presented in Fig. 1. The incidence of HD was higher in the participants with CLD (25.57% vs. 15.36%) compared to those without CLD. Table 3; Fig. 2 present the association between CLD and incident HD. Participants with CLD exhibited a significantly higher risk of developing HD compared with those without CLD in both Model 1 (HR: 1.743, 95% CI 1.331–2.283) and Model 2 (HR: 1.787, 95% CI 1.364–2.340). After fully adjusting for covariates (Model 3), CLD remained markedly corresponded to an increased risk of HD (HR: 1.803, 95% CI 1.376–2.362). As illustrated in Fig. 3, the associations between CLD and the risk of new-onset HD were relatively consistent throughout the aforementioned subgroups. Interaction tests indicated that subgroups stratified by sex, age, drinking status, smoking status, hypertension, and diabetes did not significantly modify the association between CLD and the risk of new-onset HD (all P for interaction > 0.05). The robustness of the primary results was confirmed by the consistency of findings obtained via various validation methods (Table 4; Fig. 4).
Fig. 1.
The incidence of new-onset HD in participants with or without CLD. CLD chronic liver disease, HD heart disease
Table 3.
Longitudinal bidirectional association of CLD with HD
| Variables | HR (95%CI) | |||||
|---|---|---|---|---|---|---|
| Model 1 | P | Model 2 | P | Model 3 | P | |
| CLD and new-onset HD risk | ||||||
| CLD | ||||||
| No | Reference | Reference | Reference | |||
| Yes | 1.743 (1.331,2.283) | < 0.001 | 1.787 (1.364,2.340) | < 0.001 | 1.803 (1.376,2.362) | < 0.001 |
| HD and new-onset CLD risk | ||||||
| HD | ||||||
| No | Reference | Reference | Reference | |||
| Yes | 2.423 (1.920,3.060) | < 0.001 | 2.440 (1.928,3.090) | < 0.001 | 2.303 (1.813,2.924) | < 0.001 |
Model 1 was unadjusted for covariates. Model 2 was adjusted for age and gender. Model 3 additionally adjusted for residence, educational attainment, marital status, drinking status, smoking status, and BMI
HR hazard risk, CI confidence interval, CLD chronic liver disease, HD heart disease, BMI body mass index
Fig. 2.
Longitudinal bidirectional association of CLD with HD in different models. HR hazard risk, CI confidence interval, CLD chronic liver disease, HD heart disease
Fig. 3.
Subgroup analysis for the association between CLD and new-onset HD. HR hazard risk, CI confidence interval, CLD chronic liver disease, HD heart disease
Table 4.
Sensitivity analysis
| CLD and new-onset HD risk | HD and new-onset CLD risk | ||||
|---|---|---|---|---|---|
| Variables | HR (95%CI) | P | HR (95%CI) | P | |
| Sensitivity analysis 1 | |||||
| CLD | HD | ||||
| No | Reference | No | Reference | ||
| Yes | 1.750 (1.335,2.292) | < 0.001 | Yes | 2.173 (1.699,2.781) | < 0.001 |
| Sensitivity analysis 2 | |||||
| CLD | HD | ||||
| No | Reference | No | Reference | ||
| Yes | 1.805 (1.378,2.365) | < 0.001 | Yes | 2.303 (1.813,2.925) | < 0.001 |
| Sensitivity analysis 3 | |||||
| CLD | HD | ||||
| No | Reference | No | Reference | ||
| Yes | 1.825 (1.393,2.391) | < 0.001 | Yes | 2.312 (1.819,2.938) | < 0.001 |
| Sensitivity analysis 4 | |||||
| CLD | HD | ||||
| No | Reference | No | Reference | ||
| Yes | 1.858 (1.399,2467) | < 0.001 | Yes | 2.433 (1.861,3.182) | < 0.001 |
| Sensitivity analysis 5 | |||||
| CLD | HD | ||||
| No | Reference | No | Reference | ||
| Yes | 1.690 (1.247,2.290) | < 0.001 | Yes | 2.297 (1.763,2.993) | < 0.001 |
Sensitivity analysis 1 was adjusted for Model 3 plus hypertension and diabetes. Sensitivity analysis 2 was adjusted for Model 3 plus CRP and UA. Sensitivity analysis 3 was adjusted for Model 3 with additional adjustment for HbA1c, TG, LDL-C, and HDL-C. Sensitivity analysis 4 was conducted by excluding participants with obesity (BMI ≥ 28 kg/m²) at baseline. Sensitivity analysis 5 was conducted by excluding incident events that occurred during the first follow-up wave
HR hazard risk, CI confidence interval, CLD chronic liver disease, HD heart disease, CRP C-reactive protein, UA uric acid, HbA1c glycosylated hemoglobin A1c, FPG fasting plasma glucose, TG triglyceride, LDL-C low density lipoprotein cholesterol, HDL-C high density lipoprotein cholesterol, BMI body mass index
Fig. 4.
Longitudinal bidirectional association of CLD with HD in different sensitivity analyses. HR hazard risk, CI confidence interval, CLD chronic liver disease, HD heart disease
Associations between HD and new-onset CLD
During a median 9-year follow-up, a total of 420 participants developed CLD. Incidence rates of new-onset CLD stratified by baseline HD status are presented in Fig. 5. The incidence of CLD was higher among participants with HD (12.59% vs. 5.32%) than among those without HD. Table 3; Fig. 2 present the association between HD and incident CLD. Participants with HD exhibited a significantly higher risk of developing CLD than those without HD in both Model 1 (HR: 2.423, 95% CI 1.920–3.060) and Model 2 (HR: 2.440, 95% CI 1.928–3.090). After full adjustment for covariates (Model 3), HD remained significantly associated with an increased risk of CLD (HR: 2.303, 95% CI 1.813–2.924). As illustrated in Fig. 6, the associations between HD and the risk of new-onset CLD were generally consistent across the predefined subgroups. Interaction tests indicated that subgroups stratified by sex, age, drinking status, smoking status, hypertension, and diabetes did not significantly modify the association between HD and the risk of new-onset CLD (all P for interaction > 0.05). The robustness of the primary results was confirmed by the consistency of findings obtained via various validation methods (Table 4; Fig. 4).
Fig. 5.
The incidence of new-onset CLD in participants with or without HD. CLD chronic liver disease, HD heart disease
Fig. 6.
Subgroup analysis for the association between HD and new-onset CLD. HR hazard risk, CI confidence interval, CLD chronic liver disease, HD heart disease
Mediation analyses
As illustrated in Fig. 7, participants with HD exhibited higher TyG index and METS-IR levels than those without HD. No corresponding difference was observed between participants with and without CLD. Both the TyG index and METS-IR were found to partially mediate the association between HD and new-onset CLD; however, no such mediating effect was observed in the association between CLD and new-onset HD (Fig. 8 ).
Fig. 7.
The TyG index and METS-IR levels between different groups. TyG triglyceride-glucose, METS-IR metabolic score for insulin resistance, CLD chronic liver disease, HD heart disease
Fig. 8.
The mediating effect of TyG index and METS-IR on the relationship between CLD and HD. TyG triglyceride-glucose, METS-IR metabolic score for insulin resistance, CLD chronic liver disease, HD, heart disease
Discussion
In this nationally representative longitudinal cohort of middle-aged and older adults, we observed a robust bidirectional association between CLD and HD over a nine-year follow-up. Individuals with baseline CLD exhibited a significantly higher risk of incident HD, whereas those with baseline HD were also at a substantially increased risk of subsequent CLD. These associations remained consistent across multiple subgroups and were robust across a range of sensitivity analyses. In addition, mediation analyses suggested that IR, as reflected by the TyG index and METS-IR, partially mediated the association between HD and incident CLD, but not the reverse association.
Our findings extend existing epidemiological evidence by demonstrating the dynamic and reciprocal nature of the liver–heart relationship at the population level. Previous studies have predominantly focused on the cardiovascular consequences of chronic liver disease, particularly in the context of MASLD [29–31]. In contrast, longitudinal evidence addressing the reverse direction has remained limited. By explicitly excluding SLD and liver tumors or cancer, our study demonstrates that the bidirectional association between CLD and HD is not restricted to steatotic or malignant liver conditions but also extends to other CLDs that are frequently overlooked in population-based research.
The observed association between CLD and incident HD is biologically plausible and consistent with the current mechanistic understanding of the liver–heart axis [11]. CLD is characterized by persistent systemic inflammation, altered lipid and glucose metabolism, endothelial dysfunction, and dysregulation of coagulation pathways, all of which may contribute to accelerated atherosclerosis and adverse cardiac remodeling [9, 11, 32]. Advanced liver disease may further induce circulatory alterations, including hyperdynamic circulation and neurohormonal activation, thereby imposing additional stress on the cardiovascular system [11, 33, 34]. Importantly, these mechanisms are not exclusive to metabolic liver disease and may operate across diverse etiologies of chronic liver injury.
Conversely, the strong association between baseline HD and subsequent CLD underscores the frequently underappreciated impact of cardiac dysfunction on hepatic health. HD may promote liver injury through chronic hepatic congestion, impaired hepatic perfusion, elevated central venous pressure, and recurrent episodes of reduced cardiac output [11, 12]. Neurohormonal activation and systemic inflammation associated with HD may further exacerbate hepatic vulnerability [12, 35]. These pathways are consistent with emerging concepts of congestive hepatopathy and subclinical cardiac-induced liver injury [36, 37], which may precede overt biochemical abnormalities and remain clinically unrecognized for prolonged periods.
Notably, IR was identified as a partial mediator in the pathway from HD to incident CLD, but not in the reverse direction. This asymmetry suggests that metabolic dysregulation may play a more prominent role in cardiac-driven liver injury than in liver-driven cardiac disease in this population. HD is frequently accompanied by reduced physical activity, altered skeletal muscle metabolism, chronic inflammation, and neurohormonal disturbances [38–41], all of which may exacerbate systemic IR and subsequently contribute to hepatic metabolic stress and injury [42, 43]. In contrast, the impact of CLD on HD risk may be mediated through a broader spectrum of mechanisms beyond IR. The role of IR may be particularly pronounced in the association between MASLD and HD [44, 45].
From a clinical perspective, these findings have important implications. The presence of CLD should prompt heightened vigilance for future cardiovascular risk, even in the absence of SLD or overt metabolic liver pathology. Conversely, individuals with established HD may benefit from closer surveillance of liver health, particularly given the frequently silent and insidious progression of chronic liver injury. Collectively, the bidirectional nature of the observed associations supports an integrated, cross-disciplinary approach to risk stratification and long-term management, rather than traditional organ-specific disease models.
Using a large, nationally representative cohort with repeated follow-up enabled the assessment of bidirectional temporal associations. The exclusion of SLD and liver malignancies allowed for a more focused evaluation of other chronic liver diseases, thereby addressing an important gap in the existing literature. Nevertheless, several limitations of this study warrant consideration. First, although disease ascertainment relied on multidimensional questionnaires, it did not include laboratory tests, imaging, or medical record verification. This approach may have introduced recall bias and misclassification, particularly for asymptomatic or subclinical conditions, highlighting the need to integrate objective health monitoring data to improve diagnostic accuracy in future studies. Second, CLD was analyzed as a heterogeneous entity, without differentiation of etiologic subtypes. Different etiologies may confer distinct cardiovascular risk profiles. Similarly, HD was assessed as a composite outcome without stratification by specific diagnoses or disease severity. These limitations reduce the mechanistic specificity of the findings. Third, exclusion of SLD and liver malignancies was questionnaire-based and not clinically verified. Although a sensitivity analysis excluding participants with obesity was conducted, undiagnosed SLD may still have been present in the analytic cohort. Fourth, although adjustments were made for an extensive set of covariates, residual confounding such as background medication use, physical activity, and dietary habits cannot be entirely ruled out. Future studies incorporating more detailed clinical and lifestyle information are warranted to address these residual confounders. Fifth, metabolic parameters were measured only once at baseline, which may not capture temporal variability over the follow-up period. Future studies incorporating repeated metabolic assessments and time-varying causal mediation models are warranted to more precisely elucidate dynamic metabolic pathways underlying liver–heart interactions. Finally, as this study was conducted predominantly in a Chinese population, the generalizability of the findings requires validation in other ethnic and cultural contexts.
Conclusion
Our study provides robust evidence for a bidirectional association between CLD and HD. IR partially mediated the pathway from HD to CLD, suggesting the presence of asymmetric pathophysiological mechanisms underlying liver–heart interactions. Collectively, these findings highlight the clinical relevance of the liver–heart axis and underscore the need for integrated, system-level strategies to support the prevention and long-term management of liver and cardiovascular health in aging populations.
Supplementary Information
Acknowledgements
The authors express gratitude to all the staff and participants of the CHARLS study.
Abbreviations
- BMI
Body mass index
- CHARLS
China Health and Retirement Longitudinal Study
- CI
Confidence interval
- CLD
Chronic liver disease
- CRP
C-reactive protein
- CVD
Cardiovascular disease
- HD
Heart disease
- HR
Hazard ratio
- IR
Insulin resistance
- MASLD
Metabolic dysfunction-associated steatotic liver disease
- METS-IR
Metabolic score for IR
- SD
Standard deviation
- SLD
Steatotic liver disease
- TyG index
Triglyceride-glucose index
- UA
Uric acid
Author contributions
YZ: Writing – original draft, Formal analysis, Data curation. JW: Writing – original draft, Formal analysis. KXS: Formal analysis. ZHY: Writing – review & editing, Supervision, Methodology, Funding acquisition.
Funding
This study was supported by Project of Tianjin Union Medical Center (2025YJ018) and Tianjin Science and Technology Program (23JCYBJC01880).
Data availability
The data analyzed in the current study were publicly available and can be found at https://charls.pku.edu.cn/index.htm.
Declarations
Ethics approval and consent to participate
The present analysis employed de-identified, publicly available CHARLS data, which had received prior ethical approval from the Biomedical Ethics Review Committee of Peking University (IRB00001052–11015).
Consent for publication
All authors have consent for publication.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Yan Zhang and Jing Wang have contributed equally to this work.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data analyzed in the current study were publicly available and can be found at https://charls.pku.edu.cn/index.htm.