Association between fibrosis-4 index and coronary heart disease: a population-based study

Abstract

Although the fibrosis-4 index (FIB‐4) was initially established as a liver fibrosis marker, recent studies have demonstrated its significant association with elevated risk of coronary artery disease(CHD). This study was conducted using data from five National Health and Nutrition Examination Surveys (NHANES) cycles between 2009 and 2018. Multivariable logistic regression analysis revealed a significant, positive relationship between FIB-4 and CHD.In sensitivity analyses, the highest FIB-4 quartile (Q4) showed a 4.22-fold increased CHD risk versus Q1 (OR = 4.22, 95% CI:1.93–9.24, P = 0.0005), with significant dose-response trends across quartiles (P < 0.05). Receiver operating characteristic (ROC) analysis revealed FIB-4 had good discriminative power for CHD (AUC = 0.80, 95%CI:0.78–0.81), with 75.9% sensitivity and 30.2% specificity at the optimal cutoff of 1.31. Restricted cubic splines (RCS) analysis revealed a nonlinear dose-response relationship between FIB-4 and CHD risk (P < 0.0001), with accelerated risk elevation beyond the inflection point (FIB-4 = 2.73). Subgroup analyses confirmed FIB-4’s robust association with CHD risk (overall OR = 1.66, 95%CI:1.57–1.76), with stronger effects in males (vs. females, Pinteraction = 0.042), non-diabetics (vs. diabetics, Pinteraction < 0.001), and racial minorities (highest OR = 2.36 in ‘Other Race’).These findings underscore the potential of FIB-4 as a novel biomarker for CHD risk assessment in clinical practice.

Introduction

As the predominant cause of global mortality and disability, cardiovascular disease (CVD) creates substantial public health and economic burdens across societies [1]. CVD accounts for around 32% of all deaths worldwide, with coronary heart disease (CHD) being a major contributor [2]. In the United States alone, CHD is responsible for nearly 25% of annual mortality, underscoring its critical role in shaping population health outcomes [3].

The Fibrosis-4 index (FIB-4) is a scoring system which has been clinically validated and is designed for the non-invasive assessment of the risk of liver fibrosis [4]. This simple yet powerful tool incorporates four easily obtainable parameters: age, aspartate aminotransferase (AST) and alanine aminotransferase (ALT) levels, and platelet count [5].Developed initially for assessing hepatic fibrosis among virally infected patients, this index has demonstrated remarkable diagnostic accuracy (AUC 0.82–0.91) across diverse liver pathologies, including non-alcoholic fatty liver disease (NAFLD) and metabolic dysfunction-associated steatotic liver disease (MASLD) [6]– [7]. FIB-4 is now recommended by the 2023 American Gastroenterological Association (AGA) guidelines as the primary metabolic liver fibrosis screening tool [8]. Growing evidence suggests that the FIB-4 index may have important implications beyond liver disease, particularly in cardiovascular health [9]. Studies show a link between high FIB-4 scores and a higher risk of heart disease and death [10]– [11]. These associations may be mediated through several interconnected pathways. First, elevated AST and low platelet counts in FIB-4 are linked to systemic inflammation and metabolic issues, both of which play a role in cardiovascular problems [12]– [13]. Second, Liver fibrosis and atherosclerosis exhibit shared pathogenic mechanisms, particularly chronic inflammation, oxidative stress, insulin resistance, and endothelial dysfunction [14–16]. Third, the FIB-4 index appears to reflect systemic metabolic dysfunction, demonstrating significant associations with visceral adiposity and coronary plaque characteristics in imaging studies [17]. Therefore, early monitoring of the FIB-4 index is imperative to avert a poor prognosis in coronary heart disease.

Current evidence regarding the relationship between FIB-4 index and coronary heart disease risk remains limited, particularly in large population-based cross-sectional studies. Hence, the aim of this study was to determine whether FIB-4 index levels can predict coronary heart disease risk.

Methods

Data sources and study population

National Health and Nutrition Examination Surveys (NHANES), a nationally representative cross-sectional survey conducted by the Centers for Disease Control and Prevention, systematically evaluates the health and nutritional status of civilian Americans through a multistage probability sampling framework. With biennial data releases and protocols approved by The National Center for Health Statistics Research Ethics Review Board, the survey collects demographic, dietary, laboratory, examination, and questionnaire data from consenting participants. Our analysis incorporated four consecutive cycles (2009–2018) of this ongoing surveillance system.

A total of 49,693 individuals were included in our analysis, drawn from four NHANES survey cycles (2009–2018). The study population was restricted to adults aged 20 years or older at baseline.Exclusions included missing data on: coronary heart disease (n = 20,968), PLT (n = 2,425), ALT (n = 492), and AST (n = 17). We further excluded 7,112 participants with incomplete covariate data (BMI, smoking, alcohol use, hypertension, diabetes, exercise, cholesterol). The final Analysis included 18,679 participants (Fig. 1).

Fig. 1.

Flow chart

Definitions of FIB-4 and coronary heart disease

The FIB-4 index represents a validated non-invasive biomarker panel for liver fibrosis risk stratification, calculated using the following formula:

CHD history was ascertained through interviewer-administered questionnaires, with participants asked whether a healthcare professional had ever diagnosed them with coronary heart disease (response options: yes/no). Individuals providing uncertain or missing responses were classified as absence. CHD status served as the primary outcome variable in our analysis.

Study variables

The following covariates were included: age, gender, race, education level, smoking status, drinking status, and Body Mass Index (BMI), waist circumference (WC) and cholesterol. Race categories: Mexican American, Hispanic, Non-Hispanic White, Non-Hispanic Black or Other.Educational level: Less than high school, High school, or above high school.Smoking status: Having smoked at least 100 cigarettes in lifetime. Drinking status: More than 12 drinks per year.Medical professionals confirmed diagnoses of hypertension and diabetes. Regular exercise: Self-reported moderate-to-vigorous intensity exercise within the last 30 days. Waist circumference and laboratory results: Collected through physical examinations and laboratory analyses.Detailed measurement protocols: Available in NHANES documentation (www.cdc.gov/nchs/nhanes/).

Statistical analysis

All statistical analyses used appropriate weights and accounted for the complex multistage cluster sampling design of NHANES, following CDC guidelines. Participants were grouped by FIB-4 index levels and CHD status. Mean ± sd is used for continuous variables, and frequencies with percentages for categorical data. Variance was evaluated using the chi-square, Kruskal-Wallis or analysis of variance tests. Multivariate regression analysis was used to identify a potential correlation between the FIB-4 index and coronary heart disease. This was done to find the odds ratio (OR) and 95% confidence interval (CI) in three models. The crude model was not adjusted for correlation. The minimally adjusted model was adjusted for age, gender, race and ethnicity, and education level. The fully adjusted model was adjusted for smoking status, drinking status, hypertension, diabetes, regular exercise, BMI, WC, and cholesterol. The model was calibrated to account for confounding variables such as age, gender, and race. Fib-4-CHD association subgroup analyses were performed across relevant strata defined by: (1) demographic variables (gender, age); (2)lifestyle behaviours (smoking, alcohol, regular exercise); (3) clinical/metabolic parameters (hypertension, diabetes, BMI, waist circumference, cholesterol), with interaction testing for each variable.The present study examined the relationship between the FIB-4 score and CHD risk. Restricted cubic spline (RCS) methods were used, and a threshold effect analysis was conducted to examine the relationship between the FIB-4 score and CHD. All analyses were conducted using R Studio (version 4.3.1) and DecisionLnnc software (version 1.1.5.8), adopting a two-sided significance threshold of p < 0.05.

Results

Baseline characteristics of participants

The study included 18,679 people, with a mean age of 49.89 Years, 54.31% were Male And 45.69% female. Ethnic composition Analysis revealed: 42.20% Non-Hispanic White, 20.11% Non-Hispanic Black, 15.11% Mexican American, 10.65% Other Hispanic, And 11.93% Other racial backgrounds.Coronary heart disease was identified in 3.88% of the study population. Table 1 presents the clinical characteristics distributed across FIB-4 quartiles, revealing significant inter-quartile differences (P < 0.05) for all examined variables. Comparative analysis showed that individuals in the upper FIB-4 quartile tended to be generally older, more likely to be male, non-Hispanic Whites, and had completed education beyond high school. This cohort also displayed elevated smoking rates, diminished levels of physical activity, and an augmented prevalence of chronic conditions, encompassing hypertension, diabetes mellitus, and coronary heart disease, along with increased waist circumference measurements.

Table 1.

Characteristics of the study population based on FIB-4

FIB-4	Quartiles 1 (0.18–0.66) (N = 4670)	Quartiles 2 (0.66-1.00) (N = 4670)	Quartiles 3 (1.00-1.50) (N = 4669)	Quartiles 4 (1.50-31.69) (N = 4670)	p-value
Age (years)	29.62 ± 8.11	42.32 ± 10.67	54.14 ± 11.31	66.27 ± 11.03	<0.001
Gender (%)					0.010
Male	54.04%	55.50%	58.10%	58.00%
Female	45.96%	44.50%	41.90%	42.00%
Race(%)					<0.001
Mexican American	13.04%	9.72%	6.50%	4.18%
Other Hispanic	7.91%	6.74%	4.57%	3.44%
Non-Hispanic White	58.73%	64.64%	72.53%	77.63%
Non-Hispanic Black	11.14%	10.83%	9.68%	9.24%
Other Races	9.18%	8.07%	6.72%	5.51%
Education level (%)					0.025
Less than high school	14.44%	14.18%	15.24%	17.09%
High school	22.21%	20.70%	21.32%	22.65%
More than high school	63.34%	65.12%	63.44%	60.26%
Smoking status (%)					<0.001
Yes	38.84%	43.80%	47.06%	49.93%
No	61.16%	56.20%	52.94%	50.07%
Drinking status (%)					<0.001
Yes	77.16%	78.64%	77.82%	72.53%
No	22.84%	21.36%	22.18%	27.47%
Regular exercise					<0.001
Yes	36.77%	31.09%	22.42%	14.90%
No	63.23%	68.91%	77.58%	85.10%
Hypertension (%)					<0.001
Yes	14.25%	24.65%	38.80%	53.40%
No	85.75%	75.35%	61.20%	46.60%
Diabetes (%)					<0.001
Yes	3.42%	7.05%	12.42%	17.97%
No	96.58%	92.95%	87.58%	82.03%
BMI	29.34 ± 7.51	29.19 ± 6.80	29.18 ± 6.46	28.38 ± 6.18	0.020
Waist circumference	97.54 ± 17.68	98.91 ± 16.13	100.72 ± 15.56	100.74 ± 15.35	<0.001
Cholesterol (mmol/L)	4.77 ± 0.97	5.09 ± 1.12	5.18 ± 1.05	4.97 ± 1.13	<0.001
Coronary heart disease (%)					<0.001
Yes	0.18%	1.45%	2.76%	9.74%
No	99.82%	98.55%	97.24%	90.26%

Mean ± SD for continuous variables the P value was calculated by the weighted linear regression model

(%) For categorical variables, the P value was calculated using the chi-square test

As demonstrated in Table 2, the clinical features exhibited by subjects are categorised according to whether they had CHD. The following factors were found to be significantly associated with the presence or absence of coronary heart disease: age, gender, race, educational level, smoking status, drinking status, hypertension, diabetes, regular exercise, BMI, WC, cholesterol level and the FIB-4.

Table 2.

Characteristics of the study population based on CHD

Characteristics	Total (N = 18679)	Non-CHD (N = 17954)	CHD (N = 725)	p-value
Age (years)	47.14 ± 16.89	46.48 ± 16.64	66.91 ± 11.47	< 0.001
Gender (%)
Male	56.31%	56.11%	62.43%	< 0.001
Female	43.69%	43.89%	37.57%
Race(%)				< 0.001
Mexican American	8.58%	8.74%	3.78%
Other Hispanic	5.79%	5.85%	3.87%
Non-Hispanic White	67.89%	67.48%	79.99%
Non-Hispanic Black	10.28%	10.41%	6.30%
Other Races	7.47%	7.52%	6.07%
Education level (%)				< 0.001
Less than high school	15.15%	14.90%	22.48%
High school	21.68%	21.56%	25.34%
More than high school	63.17%	63.54%	52.18%
Smoking status (%)				< 0.001
Yes	44.64%	43.95%	65.36%
No	55.36%	56.05%	34.64%
Drinking status (%)				0.578
Yes	76.72%	76.76%	75.61%
No	23.28%	23.24%	24.39%
Regular exercise				< 0.001
Yes	26.89%	27.49%	8.99%
No	73.11%	72.51%	91.01%
Hypertension (%)				< 0.001
Yes	31.71%	30.27%	74.61%
No	68.29%	69.73%	25.39%
Diabetes (%)				< 0.001
Yes	9.82%	8.96%	35.40%
No	90.18%	91.04%	64.60%
BMI	29.05 ± 6.80	29.01 ± 6.81	30.26 ± 6.38	< 0.001
Waist circumference	99.39 ± 16.31	99.15 ± 16.27	107.00 ± 15.66	< 0.001
Cholesterol (mmol/L)	5.00 ± 1.08	5.02 ± 1.07	4.48 ± 1.13	< 0.001
FIB-4	1.15 ± 0.87	1.12 ± 0.86	1.96 ± 1.00	< 0.001

Mean ± SD for continuous variables the P value was calculated by the weighted linear regression model

(%) For categorical variables, the P value was calculated using the chi-square test

CHD patients exhibited significantly greater age, male predominance, Non-Hispanic White ethnicity, higher education, smoking, alcohol consumption, no regular exercise, hypertension, and higher BMI, waist circumference, and FIB-4 levels versus non-CHD individuals (P < 0.05).

Relation between FIB-4 and CHD

The multivariate regression analysis of FIB-4 and CHD is displayed in Table 3.The fully adjusted model (Model 3) demonstrated a significant positive association between FIB-4 as a continuous variable and the risk of CHD (OR = 1.10; 95% CI: 1.02–1.19, P = 0.0188), indicating that each unit increase in FIB-4 was associated with a 10.0% higher odds of CHD. This association remained statistically significant despite comprehensive adjustment for covariates. The association was even more pronounced in the crude model (Model 1), where FIB-4 exhibited a stronger effect size (OR = 1.58; 95% CI: 1.28–1.95, P < 0.0001). After adjustment (Model 2), the effect reduced but remained (OR = 1.13; 95% CI: 1.04–1.23, P = 0.0064),indicating that confounding factors, including age, gender, race, and education level, partially mediated the relationship.

Table 3.

Association between FIB-4 and CHD

	Model 1			Model 2			Model 3
	OR (95% CI)	Pvalue		OR (95% CI)	Pvalue		OR (95% CI)	Pvalue
FIB-4	1.58(1.28,1.95)	< 0.0001		1.13(1.04,1.23)	0.0064		1.10(1.02,1.19)	0.0188
FIB-4 (quartile)
Q1	Reference			Reference			Reference
Q2	8.16(4.22,15.76)	< 0.0001		3.19(1.59,6.40)	0.0014		3.05(1.50,6.21)	0.0026
Q3	15.71(7.79,31.68)	< 0.0001		2.69(1.22,5.93)	0.0153		2.41(1.06,5.51)	0.0373
Q4	59.76(31.67,112.76)	< 0.0001		4.75(2.21,10.19)	0.0001		4.22(1.93,9.24)	0.0005

Model 1: no covariates were adjusted

Model 2: age, gender, race, and education level were adjusted

Model 3: age, gender, race, education level, smoking status, drinking status, hypertension, diabetes, regular exercise, BMI, WC, cholesterol level were adjusted

In sensitivity analyses, FIB-4 was categorized into quartiles (Q1-Q4). The fully adjusted model demonstrated that the highest quartile (Q4) had a 4.22-fold greater CHD risk versus the reference quartile (Q1) (OR = 4.22; 95% CI: 1.93–9.24, P = 0.0005). A significant dose-response relationship was identified across quartiles.In addition, all three models underwent trend tests, yielding P values of less than 0.05, thereby indicating statistical significance.

To assess the predictive performance of FIB-4 for CHD, receiver operating characteristics (ROC) curve was generated. Figure 2 displays the ROC curves. The ROC analysis demonstrated good discriminative ability for CHD (AUC = 0.80, 95% CI: 0.78–0.81). At the optimal threshold (1.31), sensitivity reached 75.9% with a specificity of 30.2%(Fig. 2).

Fig. 2.

ROC curves for FIB-4 to predict CHD. ROC, Receiver Operating Characteristic; AUC, area under the curve

In the fully adjusted models (Table 4), coronary heart disease risk was found to be substantially linked to age, race, education level, smoking status, hypertension, diabetes, BMI, WC, and cholesterol level.

Table 4.

Multivariate analysis of associations between various variables and CHD

Variable	OR (95% CI)	P value
Age (years)	1.06(1.04,1.07)	< 0.0001
Gender (%)
Female	Reference
Male	1.11(0.94,1.32)	0.2042
Race(%)
Mexican American	Reference
Other Hispanic	1.60(0.99,2.60)	0.0571
Non-Hispanic White	1.59(1.05,2.41)	0.0296
Non-Hispanic Black	0.89(0.58,1.37)	0.5874
Other Races	1.63(0.89,2.98)	0.1096
Education level (%)
Less than high school	Reference
High school	0.84(0.62,1.14)	0.2502
More than high school	0.72(0.53,0.99)	0.0407
Smoking status (%)
No	Reference
Yes	1.69(1.32,2.17)	0.0001
Drinking status (%)
No	Reference
Yes	1.24(0.96,1.62)	0.1048
Regular exercise
No	Reference
Yes	0.85(0.62,1.18)	0.3287
Hypertension (%)
No	Reference
Yes	2.30(1.77,3.00)	< 0.0001
Diabetes (%)
No	Reference
Yes	1.84(1.42,2.38)	< 0.0001
BMI	0.95(0.92,0.98)	0.0014
Waist circumference	1.03(1.02,1.05)	0.0001
Cholesterol (mmol/L)	0.68(0.58,0.80)	< 0.0001

Model 3: age, gender, race, education level, smoking status, drinking status, hypertension, diabetes, regular exercise, BMI, WC, cholesterol level were adjusted

Nonlinear relationship between the FIB-4 and CHD

In addition, dose–response relationship between the prevalence of CHD and FIB-4 scores was examined using RCS curves. We identifed a nonlinear relationship between FIB-4 and CHD (nonlinear P-value<0.0001). As FIB-4 increased, the OR curve for CHD exhibited a gradual initial rise, followed by a sharp upward trend beyond the threshold. The OR curve reached its turning point at an FIB-4 value of 2.73 ((Fig. 3).

Fig. 3.

RCS curves describing the dose–response relationship between FIB-4 and coronary heart disease

Analyses of subgroups and interactions

To confirm the robustness of FIB-4 in predicting coronary heart disease in various populations, we conducted additional subgroup analyses.The overall population exhibited a positive correlation between FIB-4 and CHD risk, as demonstrated in Fig. 4 (OR = 1.66; 95% CI: 1.57–1.76, P < 0.001). Stratified analyses demonstrated significant effect modification by demographic and clinical variables: males exhibited stronger FIB-4-associated risk than females (OR = 1.74, 95% CI: 1.62–1.88 vs. OR = 1.55, 95% CI: 1.42–1.69; Pinteraction = 0.042),while racial heterogeneity was particularly evident, with the highest risk observed in the “Other Race” subgroup (OR = 2.36, 95% CI: 1.85-3.00) compared to Non-Hispanic Whites (OR = 1.81, 95% CI: 1.67–1.97; Pinteraction < 0.001). Notably, the association was more pronounced in non-diabetic individuals (OR = 1.71, 95% CI: 1.59–1.84) relative to those with diabetes (OR = 1.32, 95% CI: 1.20–1.46; Pinteraction < 0.001), suggesting potential pathophysiological differences in FIB-4’s predictive value across metabolic statuses.

Fig. 4.

Subgroup analysis

Interaction tests revealed no significant effect modification by age (P = 0.859 for interaction test), drinking status (Pinteraction = 0.716),or hypertension status (P = 0.284 for interaction test), suggesting these factors did not alter the FIB-4–outcome relationship.However, significant interaction effects were observed for gender (P = 0.042 for interaction test), race (P < 0.001 for interaction test), educational level (P = 0.006 for interaction test), diabetes status(P < 0.001 for interaction test), smoking (P = 0.002 for interaction test), and exercise habits (P = 0.005 for interaction test), suggesting population-specific risk patterns that warrant further investigation (Fig. 4).

Discussion

The aim of this study is to assess the relationship between risk and FIB-4 in people with coronary heart disease. In this cross-sectional survey, an increased risk of coronary heart disease was found to be associated with higher continuous and categorical FIB-4 concentrations. The proportion of individuals at elevated risk of coronary heart disease exhibited a marked increase concomitant with a gradual rise in FIB-4. This relationship was not affected by other risk variables. The number of people who experienced this connection was in accordance with the results of the sub-group analyses and interaction assessments. A nonlinear dose-response relationship was observed between FIB-4 levels And CHD, with An inflection point of 2.73. In the highest FIB-4 quartile, the risk of CHD was 4.22 times higher than in Quartile 1. Our study provides new insight into the fact that FIB-4 is a risk stratification marker for CHD, which may be a key marker linking the hepatic and cardiovascular systems.

Evidence shows a link between liver problems and a higher risk of coronary artery disease(CAD). Long-term liver damage causes ongoing inflammation, with higher levels of IL-6 and TNF-α and NF-κB activation, which can lead to endothelial dysfunction and accelerate atherosclerosis [21]– [22].Progressive liver fibrosis, caused by activated stellate cells and excess extracellular matrix, is linked to metabolic problems like insulin resistance and dyslipidaemia [14].These metabolic derangements exacerbate systemic oxidative stress and lipid peroxidation, further contributing to plaque instability in coronary arteries [23].Experimental models demonstrate that P38 activation in hepatocytes exacerbates hepatic fibrosis while concurrently promoting myocardial inflammation and vascular stiffness [24].

FIB-4 is a validated biomarker of liver stiffness and an established prognostic indicator in chronic liver diseases, including both viral hepatitis and NAFLD [25]– [26]. Emerging evidence indicates that FIB-4-associated cardiovascular risk may be mediated through three key pathways: (1) hepatic insulin resistance driving atherogenic dyslipidemia (elevated small dense LDL, reduced HDL) [27]; (2) systemic inflammation via hepatokine release (fibrinogen, CRP) and oxidative stress, accelerating atherosclerosis [28–30]; And 3) gut-liver axis dysfunction promoting endotoxemia and elevated TMAO levels [31]. However, a bidirectional interplay exists between the cardiovascular system and the liver.Cardiovascular diseases (e.g., heart failure, ischemic heart disease) can influence hepatic function through multiple pathways, consequently altering levels of the liver fibrosis marker FIB-4. Acute cardiogenic shock or hypoperfusion may induce a dramatic elevation of hepatic enzymes (AST, ALT, LDH) accompanied by centrilobular hepatocyte necrosis [32]. Chronic right heart failure leads to increased hepatic venous pressure, resulting in sinusoidal dilation, hepatic fibrosis, and cholestasis [33].This bidirectional relationship suggests that FIB-4 is closely associated with cardiovascular diseases.

This study provides the first investigation into the association between FIB-4 and coronary heart disease risk, representing a novel contribution to the field. While prior studies have not directly examined this specific relationship, substantial evidence indicates that elevated FIB-4 levels in patients with chronic liver or kidney diseases correlate with adverse cardiovascular events. Maryam Namakchian et al. demonstrated that the FIB-4 index independently correlates with CAD in patients with MAFLD [34]. Elevated FIB-4 levels were significantly associated with increased risks of both all-cause mortality And cardiovascular mortality in the cohort of 1,192 CKD and CAD patients [35]. Fib-4 ≥ 2.67 is independently predicts concomitant CAD in patients with hepatic steatosis [36]. FIB-4 independently predicted MACE beyond conventional risk factors and liver disease status [37]. These studies exploring FIB-4 in specific populations. However, our study leverages a large, general-population sample from NHANES and employs nonlinear association modeling to position FIB-4 as a potential pan-population predictor for coronary heart disease.

Consequently, the present investigation examined the risk associations between FIB-4 and coronary heart disease. These results suggest that FIB-4 level is positively associated with coronary heart disease. In recent years, some studies have explored emerging biomarkers in heart failure and cardiovascular risk, including liver derived markers and inflammation related markers [38]– [39]. These novel biomarkers contribute to more precise patient phenotyping and individualized treatment strategies, demonstrating significant potential for improving diagnosis, risk stratification, and therapeutic monitoring.The results of our study indicate that the Fibrosis-4 (FIB-4) index is positively associated with coronary heart disease (CHD), supporting the rationale of the liver-heart axis theory. These findings reinforce the systemic and multi-organ nature of cardiovascular risk assessment. A nonlinear relationship was observed between FIB-4 levels And coronary artery disease, with An inflection point of 2.73. This finding holds significance for both primary prevention and risk reclassification of coronary heart disease. When FIB-4 exceeds this threshold, clinicians should consider more comprehensive CHD screening (e.g., coronary calcium scoring) and preventive interventions (e.g., lipid management, anti-inflammatory therapy). Secondly, the differential associations between FIB-4 and CHD risk across distinct populations hold significant implications for personalized medicine. For instance, the stronger correlation observed in non-diabetic individuals (OR = 1.71, 95% CI: 1.59–1.84; P < 0.001) suggests that this subgroup may require closer monitoring. Furthermore, our large-scale population-based analysis provides critical scientific evidence for informing CHD prevention strategies. This supports the clinical value of FIB-4 as a cost-effective tool and highlights the importance of hepatic assessment in cardiovascular prevention strategies.

Despite robust statistical adjustments minimizing confounding effects, this study has several limitations. The cross-sectional design precludes causal conclusions regarding FIB-4 and CHD, necessitating prospective cohort studies and mechanistic research for validation. Despite comprehensive covariate adjustment, residual confounding from unmeasured hepatic status, genetic predispositions, or environmental factors may persist. Future studies should incorporate stratified analyses by liver disease status for more robust evaluation. Additionally, reliance on a single baseline FIB-4 measurement and self-reported CHD status may lead to subjective bias and limit temporal assessment. More studies are needed to understand the biological pathways and to see if FIB-4 modulation can effectively reduce cardiovascular risk.

Conclusions

In conclusion, our investigation demonstrates a significant nonlinear association between FIB-4 levels And coronary heart disease risk in a nationally representative cohort, identifying 2.73 as a critical threshold value. Our findings provide a valuable and accessible indicator for the timely intervention of hepatic factors in individuals at elevated risk of coronary heart disease.

Abbreviations

FIB-4

Fibrosis-4

AST

Aspartate aminotransferase

ALT

Alanine aminotransferase

PLT

Platelet Count

CHD

Coronary heart disease

OR

Odds ratio

CI

Confidence interval

NHANES

National Health and Nutrition Examination Survey

Authors’ contributions

JP: Conceptualization, Formal analysis, Writing—Original draft preparation; MA: Conceptualization, Data curation, Writing—Original draft preparation; FG: Data curation, Writing—Original draft preparation; ZQ: Methodology, Software; SY: Data curation, Methodology; LQ: Visualization, Software; LM: Validation, Writing-review & editing; XZ: Conceptualization, Supervision; WY: Project administration, Supervision; Writing—review & editing; All authors read and approved the final manuscript.

Funding

This work was Sponsored by the National Science and Technology Major Project of the Ministry of Science and Technology of China (No.2023ZD0505703), Zhongyuan Leading Talents Program of Science and Technology Innovation (No. 244200510002), Joint Fund for Science and Technology Research and Development Program of Henan Province (No.232301420021 and No.242301420108), Natural Science Foundation of Henan Province (No. 242300420109), the Joint Major Research Project of National Traditional Chinese Medicine Inheritance and Innovation Center, Henan Provincial Health Commission (No. 2024ZXZX1015), and Science and Technology Research Project of Henan Province (No.242102310548).

Data availability

This study analysed publicly available datasets. This data can be found here: https://www.cdc.gov/nchs/nhanes/.

Declarations

Ethics approval and consent to participate

The NHANES study protocol received approval from the National Center for Health Statistics Institutional Review Board, with all participants providing written informed consent in compliance with institutional and regulatory requirements.

Consent for publication

Not applicable.

Competing interests

The authors have no relevant financial or non-financial competing interests to declare.