Introduction

Liver fibrosis, recognized as a hallmark of chronic liver disease, has garnered increasing attention due to its broader implications beyond hepatic health. Recent studies have shed light on its association with cardiovascular disease (CVD), suggesting a systemic impact that extends beyond the liver1,2,3,4,5. This expanding understanding underscores the importance of comprehensive risk assessment strategies for hepatic and cardiovascular health.

Abdominal Aortic Aneurysm (AAA) presents a significant health concern characterized by the progressive dilation of the abdominal aorta, often remaining asymptomatic until a catastrophic rupture occurs, leading to severe internal bleeding and high mortality rates6. AAA larger than 5 cm in females and 5.5 cm in males carry a significantly higher risk of rupture and are therefore recommended for elective repair7. Epidemiologically, AAA exhibits a distinct prevalence pattern, with a notable increase in incidence among individuals over 60, particularly males8,9,10,11. The morbidity and mortality associated with AAA underscore the importance of identifying prognostic markers to guide clinical management effectively.

Recent research has revealed a correlation between metabolic dysfunction-associated steatotic liver disease (MASLD) and an increased risk of atherosclerosis12,13,14,15. This risk escalates proportionally with the severity of MASLD, particularly as fibrosis progresses16,17. Simultaneously, atherosclerosis emerges as a significant risk factor for the advancement of AAAs, thereby heightening the mortality risk among affected individuals18,19. Additionally, emerging evidence, such as the study conducted by Mohamid et al., highlights a heightened prevalence of MASLD in AAA patients and an increased risk of liver cirrhosis within this population20. This novel perspective underscores the need to thoroughly investigate the potential link between liver health and this insidious condition.

The FIB-4 index, initially designed for assessing liver fibrosis, has recently gained attention for its simplicity, cost-effectiveness, and reliance on routine clinical parameters21,22. Remarkably, this index has exhibited promise beyond its original purpose, suggesting a broader utility in evaluating vascular health and cardiovascular risk23. However, its potential as a predictor of AAA progression and mortality risk in AAA patients remains largely unexplored.

Hence, we designed this study to explore the clinical prognostic value of the FIB-4 index in patients with AAA. Specifically, we seek to determine whether the FIB-4 index is associated with increased mortality risk in AAA patients, identify its optimal cutoff value, and quantify its prognostic magnitude. Additionally, we investigate the relationship between the FIB-4 index and aneurysm size. By addressing these objectives, this study aspires to expand the utility of the FIB-4 index in risk stratification and surveillance of AAA patients, thereby informing both research arena and clinical practice.

Methods

Study design and data collection

This longitudinal, retrospective cohort study was conducted at Namazi Hospital, a prominent vascular surgery referral center in Shiraz, South Iran, from October 2016 to September 2021. The inclusion criteria included all patients who underwent open AAA repair following a preoperative diagnosis of AAA. Exclusion criteria were as follows: i) postoperative diagnosis other than AAA, ii) missing medical records, iii) missing data on AAA size, iv) incomplete laboratory results necessary for calculating the FIB-4 index and further adjustments, and v) no medical follow-up, as the last recorded contact with these patients occurred at the time of discharge. After applying these criteria, the final cohort consisted of 141 patients whose data were included in the analysis. A flowchart outlining the participant selection process is provided in Fig. 1. This study was approved by the Ethics Committee of Shiraz University of Medical Sciences (SUMS).

Fig. 1
figure 1

Flowchart of study exclusion criteria and patient inclusion.

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Demographic and medical history data were collected from patient records and hospital databases. Key laboratory parameters required for calculating the FIB-4 index and adjusting subsequent models were retrieved from the Health Information System (HIS) of Namazi Hospital. These parameters included platelet count (× 103/µL), alanine transaminase (ALT, U/L), aspartate transaminase (AST, U/L), blood urea nitrogen (BUN, mg/dL), creatinine (mg/dL), blood sugar (mg/dL), and serum albumin (g/dL). Blood sugar and creatinine levels were obtained from admission laboratory tests, while AST, ALT, albumin, and platelet count were sourced from follow-up tests after patient stabilization.

FIB-4 index

The FIB-4 index is a validated noninvasive biomarker to assess liver fibrosis severity. It is computed using the following formula:

FIB-4 index = (Age × AST) / (Platelet Count × √ALT).

The FIB-4 index incorporates patient age and serum levels of AST, ALT, and Platelet Count to derive a numerical indicator of liver fibrosis severity, with higher index values indicative of more advanced fibrosis21.

Outcomes

The primary outcome of this study was all-cause mortality in the cohort, with mortality data obtained from patients’ post-surgical follow-up records. The secondary outcome was an AAA size ≥ 8 cm, with aneurysm diameters recorded from operation notes and cross-referenced with radiological findings for accuracy. The association between the FIB-4 index and mortality was assessed longitudinally, while its association with aneurysm size was evaluated cross-sectionally.

Statistical analysis

Data analysis was conducted utilizing Python 3.11 programming language, harnessing an array of powerful libraries, including NumPy, Pandas, Scikit-learn (Sklearn), Statsmodels, Lifelines, and matplotlib.pyplot. Cox regression analysis was employed to explore the longitudinal association between the FIB-4 index and mortality. Logistic regression analysis was also utilized to evaluate the cross-sectional association between baseline characteristics and the FIB-4 index. Outcome measures were reported as hazard ratios with corresponding 95% confidence intervals (CI) for Cox regression analysis, while odds ratios with 95% CI were reported for logistic regression analysis. Kaplan–Meier plots were utilized for survival analysis.

In preprocessing the data, given the skewed distribution of Blood Sugar (BS) and Creatinine (CR) levels, a logarithmic transformation was applied to these variables to achieve a more symmetrical distribution. The logarithmic transformation of BS and CR was performed using the natural logarithm (log base e), denoted as log (BS) and log (CR), respectively. This transformation effectively reduces the skewness of the data, making it more suitable for subsequent statistical analysis.

Furthermore, to ensure that all continuous variables were on a comparable scale and to mitigate the impact of potential outliers, standard scaling was applied. Standard scaling, also known as Z-score normalization, transforms the data with a mean of zero and a standard deviation of one. This process involves subtracting the mean of each variable from its value and then dividing by the variable’s standard deviation.

By standardizing the continuous variables, the coefficients obtained from the regression models can be directly compared, and the model’s performance is enhanced. These preprocessing steps were crucial in preparing the data for subsequent analysis, ensuring that the statistical models accurately captured the outcomes.

To assess the impact of the FIB-4 index on mortality, hazard ratios were calculated with a range of FIB-4 index cut-offs from 1.5 to 3.25, with 0.01 steps. Additionally, hazard ratios with 95% confidence intervals were plotted for each cut-off of the FIB-4 index. This analysis was conducted at different adjustment levels to explore the robustness of the findings. Age, AST, ALT, and PLT were excluded from covariate adjustment, as they are inherent components of the FIB-4 index calculation.

Results

Baseline characteristics

A total of 141 patients (92% male), with a mean age of 70 years (SD: 11.5), were enrolled in the study and followed for a median duration of 35 months (IQR: 0.7 – 56.6). Throughout the follow-up period, 78 deaths occurred among the participants. Baseline characteristics of the patients are presented in Table 1.

Table 1 Baseline characteristics of the 141 patients.
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Among the cohort, 82 patients (58.16%) were admitted due to ruptured abdominal aortic aneurysms (AAA), while 59 patients (41.84%) underwent elective AAA repair. Seventy-six patients were classified as high risk for liver fibrosis (FIB-4 ≥ 2.67). Furthermore, 57 patients presented with AAA sizes equal to or greater than 8cm. Hypertension was identified as the most prevalent comorbidity, affecting 60% of the patients.

Primary outcome

Unadjusted hazard ratios revealed a higher mortality risk among AAA patients across most FIB-4 cut-off values (Fig. 2). However, after adjustment for sex, HTN, DM, smoking status, opium use, BS, Cr, Alb, and AAA size ≥ 8cm, only the FIB-4 cut-off range of 2.58 – 2.74 remained significantly associated with mortality risk. Specifically, the hazard ratio for all-cause mortality in AAA patients with a baseline FIB-4 ≥ 2.67 was 1.78 (95% CI: 1.06 – 3.00). Kaplan Meier’s plot further illustrated AAA patients’ survival and hazard trends over time (Fig. 3).

Fig. 2
figure 2

illustrates the relationship between Fib-4 cut-off values and hazard ratios. The x-axis displays each Fib-4 cut-off value, while the y-axis represents the corresponding hazard ratio. Confidence intervals around each Fib-4 cut-off are depicted to provide a measure of uncertainty. The first plot depicts unadjusted hazard ratios, while the second plot presents fully adjusted hazard ratios.

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Fig. 3
figure 3

illustrates Kaplan–Meier plots depicting cumulative survival and hazards stratified by Fib-4 levels (≥ 2.67).

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Secondary outcome

Multivariable logistic regression analysis revealed a significant association between baseline FIB-4 levels exceeding 2.67 and the baseline size of AAA, even after adjusting for various covariates (Table 2). In fully adjusted logistic regression models, the odds ratio (OR) for AAA size ≥ 8 cm in patients with a FIB-4 level ≥ 2.67 was 2.67 (95% CI: 1.17 – 6.09).

Table 2 Association between baseline FIB-4 ≥ 2.67 and AAA ≥ 8cm.
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Discussion

Main findings

In this longitudinal cohort study of 141 patients (92% male; mean age: 70 years) who underwent open AAA repair, 76 patients were classified as high risk for liver fibrosis (FIB-4 ≥ 2.67), and 57 patients presented with AAA ≥ 8 cm. During a median follow-up 35 months (IQR: 0.7–56.6), 78 deaths were recorded. A FIB-4 ≥ 2.67 was significantly associated with a larger aneurysm size (aOR: 2.67) and higher risk of mortality (aHR: 1.78), these findings underscore the potential clinical utility of the FIB-4 index in monitoring AAA patients, as it is linked to both increased mortality risk and larger aneurysm size.

Liver fibrosis and mortality risk in AAA patients

The analysis of unadjusted HR revealed a consistent trend of higher mortality risk among AAA patients across various FIB-4 index cut-off values, as shown in Fig. 2. However, after adjusting for several confounding variables, including sex, hypertension, diabetes mellitus, smoking status, opium use, blood sugar, creatinine, albumin, and AAA size ≥ 8cm, only a specific range of FIB-4 index cut-offs (2.58 – 2.74) remained significantly associated with mortality risk (Fig. 2). Specifically, a FIB-4 level ≥ 2.67 was linked to all-cause mortality (HR: 1.78, 95% CI: 1.06 – 3.00). Notably, previous studies have reported a similar association between a FIB-4 value ≥ 2.67 and cardiovascular disease (CVD) risk within the MASLD population24,25,26,27. For instance, Viera Barbosa et al. 27 found that over a median follow-up of 3 years, a FIB-4 ≥ 2.67 was the strongest predictor of major adverse cardiovascular events (adjusted HR: 1.80). Despite the diverse cut-offs for predicting advanced fibrosis in MASLD (optimal cut-off of 2.67) versus non-MASLD patients (optimal cut-off of 3.25)28, the similarity in predicting mortality between AAA and MASLD populations may suggest a shared risk profile between these two entities. These findings suggest that patients with AAA may also face an increased risk of MASLD, advanced fibrosis, and subsequent events associated with this risk.

Liver fibrosis and AAA size

Multivariable logistic regression analysis further elucidated the association between baseline Fib-4 levels and AAA size, revealing a significant correlation even after adjusting for various covariates. This discovery holds paramount clinical relevance, particularly when considering the alarming annual rupture risk associated with aneurysms ≥ 8cm, reported to be as high as 30–5029. These findings may elucidate why patients with more advanced fibrosis face a heightened risk of mortality. Furthermore, in addition to the bidirectional association between metabolic dysfunction and liver fibrosis30,31, the progression of fibrosis may exacerbate the likelihood of developing atherosclerosis16. These observations suggest a potential mechanistic link between liver fibrosis and higher AAA size, potentially mediated through the development of atherosclerosis. The presence of atherosclerotic plaques may contribute to the weakening and dilation of the abdominal aorta, ultimately leading to the enlargement of an AAA and an increased risk of rupture32,33.

Implications for clinical practice and future directions

The independent association observed in our adjusted analysis is not unique to patients undergoing open AAA repair. Previous studies have shown that liver fibrosis, as assessed by the FIB-4 index, is a prognostic marker for both all-cause mortality and cardiovascular outcomes across diverse populations34,35, suggesting that FIB-4 may serve as a surrogate marker for systemic vascular dysfunction. However, the optimal strategies for managing this high-risk group may vary depending on the clinical context.

In the context of AAA patients, our findings not only link liver fibrosis to increased mortality risk but also to larger aneurysm size. This highlights the potential utility of FIB-4 in identifying high-risk AAA patients who may benefit from closer surveillance. However, before incorporating FIB-4 into routine risk assessment and surveillance protocols for AAA patients, further research is needed to define its clinical implications. Specifically, studies are needed to determine whether FIB-4 is associated with AAA progression and whether it could be employed as a reliable tool for tracking disease progression over time.

Limitations and strengths

Several limitations should be considered when interpreting our study’s findings. First, the observational design of the study precludes us from establishing casualty. Second, the association between the FIB-4 index and aneurysm size was evaluated cross-sectionally, preventing us from assessing the impact of liver fibrosis on aneurysm progression over time. Thus, future longitudinal studies are needed to determine whether liver fibrosis contributes to aneurysm growth in the long term. it is important to acknowledge that additional variables not accounted for in our study may contribute to the observed association between FIB-4 levels and mortality risk. Lastly, the relatively modest sample size restricted our ability to perform sensitivity or subgroup analyses, limiting the generalizability of our findings. Therefore, validation in larger, multicenter cohorts is warranted to confirm and expand upon these results.

Despite limitations, our study stands out for several significant strengths. Initially, to the best of our knowledge, this is the first study to explore the association of liver fibrosis, as assessed by the FIB-4 index, with both mortality risk and aneurysm size in patients with AAA. This novel finding opens a new avenue for exploring the clinical relevance of FIB-4 in the context of AAA, potentially impacting risk assessment and management. Secondly, our study controlled for all available confounding factors in our statistical models. Notably, the strength of the association between FIB-4 levels and mortality remained significant even after this comprehensive adjustment, underscoring the robust, independent nature of this relationship. This adds a layer of validity and confidence to the observed correlation. Furthermore, our study introduces the concept of utilizing a non-invasive and cost-effective tool, the FIB-4 index, for risk stratification among AAA patients. This suggestion can potentially enhance the efficiency of risk assessment in a clinical setting, which can ultimately translate into more informed decision-making and improved patient care. Our research thus contributes to the ongoing effort to refine risk stratification and highlights the practical applicability of our findings in real-world clinical practice.

Conclusion

The findings of this study provide valuable insights into the prognostic factors affecting clinical outcomes in AAA patients undergoing repair surgery. Identifying the FIB-4 index as a potential predictor of mortality and higher AAA size underscores its utility as a non-invasive and readily accessible biomarker in clinical practice. However, further investigation is warranted to validate the observed association and establish an optimal cutoff for liver fibrosis. Specifically, more accurate fibrosis assessment methods, ideally through advanced imaging techniques, should be employed. Additionally, larger, multicenter studies are required to confirm generalizability and robustness of these associations across diverse cohorts. Future research should also explore whether liver fibrosis is linked to AAA progression over time. Such efforts are essential for refining risk stratification strategies and ultimately improving patient outcomes in the management of AAA.