The fibrosis-4 index (FIB-4), originally developed to estimate liver fibrosis, is a non-invasive score based on age and laboratory parameters: platelet count and the liver enzymes aspartate aminotransferase (AST) and alanine aminotransferase (ALT) [1]. This index has recently been assessed in cardiovascular (CV) settings, where it has been associated with an increased risk of adverse outcomes (e.g. myocardial infarction, stroke, heart failure hospitalization, and mortality) in individuals at higher CV risk and in CV patients [2]. In particular, FIB-4 was independently associated with presence of coronary artery disease (CAD) and adverse outcomes in a general population from the Athens Cardiometabolic Cohort [3]. Moreover, FIB-4 was associated with CV risk (estimated by the SCORE or Framingham risk scoring systems) in patients with viral hepatitis (HCV/HBV) or non-alcoholic fatty liver disease [4]. Subsequently, FIB-4 (≥ 2.67) was found to independently predict major adverse CV events (MACE; myocardial infarction, heart failure hospitalization, and coronary revascularization) in a large study enrolling patients with nonalcoholic fatty liver disease or nonalcoholic steatohepatitis (NAFLD/NASH; n = 81,000) [5]. These findings were further supported by a recent meta-analysis (19 cohorts, 1,481,875 adults, mean age 55 years, 50% male), which found an association between FIB-4 and an increased risk of CV events in patients with and without NAFLD, thus independently of the presence of NAFLD [6].

Among patients with CV disease, FIB-4 was found to be associated with an increased risk of adverse CV events in a meta-analysis (25,252 patients) [7]. Another meta-analysis (11 studies, 36,696 patients), which evaluated the association between the FIB-4 score and all-cause and CV mortality in CAD patients, found that FIB-4 was independently associated with increased all-cause and CV mortality among CAD patients [8]. In another recent study conducted on patients with acute coronary syndrome, FIB-4 (≥ 3.25) showed better predictive accuracy than traditional clinical scores (SYNTAX and GRACE) [9]. Similar results were obtained in patients with acute coronary infarction, where FIB-4 (> 3.25) independently predicted mortality and MACE [10]. In the heart failure (HF) context, FIB-4 has been associated with adverse prognosis in ambulatory patients, in patients with heart failure with reduced ejection fraction (HFrEF) but especially in those with heart failure with preserved ejection fraction (HFpEF) [11].

In the present issue, two manuscripts further address this issue, adding insights into the value of this multimarker index in the prediction of CV adverse events. Ren et al. [12] evaluated the association between FIB-4 and 30-day all-cause mortality in acute HF patients (1,535 patients from the MIMIC-IV database). Results from this study indicated that FIB-4 was an independent predictor of 30-day mortality in this patient population, although its predictive discrimination was only moderate. Gao et al. [13] further extended these findings in a meta-analysis focused on the predictive power of FIB-4 for mortality in coronary artery disease. Twelve studies (31,817 CAD patients) were included in the final analysis. The results showed that FIB-4 (when considered as a continuous parameter) retains a significant positive association with all-cause as well as CV death, although caution is required when FIB-4 is used as a categorical variable, due to significant between-study heterogeneity.

As the authors discussed, FIB-4 may account for important aspects underlying liver fibrosis and is involved in key interconnected biological processes causing the onset and progression of CV disease, such as inflammatory processes, oxidative stress, and metabolic dysfunction. In this context, it would be useful to consider that liver fibrosis is not an isolated event, but a component within the broader network of liver-CV-metabolic syndrome; thus, FIB-4 may be useful as a reliable surrogate biomarker of overall cardiometabolic health [14]. For these reasons, FIB-4 thus emerges as a possible additive costless and easily available dynamic biomarker for risk stratification in HF and CAD, and a target in the cardiometabolic and hepatic system for emerging therapies (e.g. GLP-1 receptor agonists and SGLT2 inhibitors) potentially beneficial for the cardio-hepatic-metabolic axis [11, 15]. In this context, FIB-4, although not incorporated in standard CV risk calculators (e.g. SCORE2, ASCVD, or Framingham), might help stratification of risk in selected patient groups (e.g., subjects with type 2 diabetes, obesity, chronic kidney disease, and coronary artery disease) and improve prediction of adverse prognosis beyond traditional clinical risk factors in the CV setting.

In general, increasing data further support the concept that integrated biomarker panels and multimarker scores may improve CV risk prediction beyond traditional clinical variables [16]. Effectively, biomarkers reflecting inflammation, oxidative stress, and metabolic dysregulation in a single score may provide a more comprehensive assessment of key biological events driving CV risk. Increasing recognition of the complex and interdependent nature of the number of pathophysiological pathways associated with the onset and progression of CV disease has led to the identification of a number of biomarkers involved in different culprit underlying pathophysiological pathways and the opportunity to develop multi-marker scores able to capture the complexity and heterogeneity of cardiometabolic conditions [17]. It is therefore plausible that the combination of some blood established biomarkers in a new comprehensive score reflecting different, yet related, pathophysiological aspects grouped together may offer additive prognostic information for adverse outcomes. Accordingly, multiple data have evidenced that combining multiple markers allows better accuracy, discrimination, and risk reclassification when compared with traditional clinical risk models [16]. Multimarker scores may promote personalized medicine by identifying patients at higher risk who could benefit more from intensive monitoring, earlier interventions, or tailored therapies. Clearly, the use of an “independent-pathway” approach is preferable, thus including biomarkers that are weakly or not at all related to each other, reflecting different pathophysiological events. From this perspective, the opportunity to combine not only blood-derived biomarkers, but also imaging biomarkers should also be considered. Indeed, this approach has shown promise, integrating the biological with the anatomical risk for an adverse event, facilitating decision-making in primary and secondary prevention clinical contexts. Nonetheless, it is more opportune, economical and, in some cases, safer to repeat a blood draw than an imaging test.

However, there are a number of points to be considered because they represent critical barriers to routine clinical use, and are also valid for application of FIB-4 before its routine assessment (Table I). A biomarker may show biological and analytical variability, being influenced by age, sex, renal function, comorbidities, and drugs, all factors that may decrease the generalizability of prediction models. Moreover, there is a need to establish shared standardized cut-off, clarify multiorgan effects in response to therapy, and assess real cost-effectiveness. In conclusion, what should guide us is the recognition that it is not necessary to consider a multitude of unrelated or redundant biomarkers in an expensive panel when, collectively, they do not give significant incremental prognostic value, but instead increase the possibility of overfitting and decreasing fitness in external population cohorts or distinct healthcare settings (even different CV conditions). Rather, it is important to identify a few biologically relevant, clinically meaningful, and modifiable parameters which could perhaps be incorporated into a single score that can contribute to risk assessment in a particular clinical context.