Prognostic value of the fibrinogen-to-albumin ratio (FAR) in patients with chronic heart failure across the different ejection fraction spectrum

ABSTRACT

The ratio of fibrinogen to albumin (FAR) is considered a new inflammatory biomarker and a predictor of cardiovascular disease risk. However, its prognostic value for patients with chronic heart failure (CHF) with different ejection fractions (EFs) remains unclear. A total of 916 hospitalized patients with CHF from January 2017 to October 2021 in the First Affiliated Hospital of Kunming Medical University were included in the study. Death occurred in 417 (45.5%) patients out of 916 patients during a median follow-up time of 750 days. Among these patients, 381 patients suffered from HFrEF (LVEF <40%) and 535 patients suffered from HFpEF or HFmrEF (HFpEF plus HFmrEF, LVEF ≥ 40%). Patients were categorized into high-level FAR (FAR-H) and low-level FAR (FAR-L) groups based on the optimal cut-off value of FAR (9.06) obtained from receiver operating characteristic (ROC) curve analysis. Upon analysing the Kaplan – Meier plots, the incidence of death was significantly higher in all patients with FAR-H and patients in both HF subgroups (p < 0.001). The multivariate Cox proportional hazard analyses indicated that the FAR was an independent predictor of all-cause mortality, regardless of heart failure subtype. (HR 1.115, 95% CI 1.089–1.142, p < 0.001; HFpEF plus HFmrEF, HR 1.109, 95% CI 1.074–1.146, p < 0.0001; HFrEF, HR 1.138, 95% CI 1.094–1.183, p < 0.0001) The optimal cut-off value of FAR in predicting all-cause mortality was 9.06 with an area under the curve value of 0.720 (95% CI: 0.687–0.753, p < 0.001), a sensitivity of 68.8% and a specificity of 65.6%. After adjusting for the traditional indicators (LVEF, Lg BNP, etc.), the new model with the FAR had better prediction ability in patients with CHF. Elevated FAR is an independent predictor of death in CHF and is not related to the HF subtype.

KEYWORDS:

• Fibrinogen/Albumin ratio
• chronic heart failure
• mortality
• prognosis
• inflammation

1. Introduction

More than 2 decades after its designation as an emerging epidemic, heart failure (HF) remains a major clinical and public health problem [1]. With the increase in lifestyle-related risk factors such as coronary heart disease, hypertension, diabetes and the ageing population, the disease burden of HF may increase. Considering that China is the most populous country in the world, the total number of HF patients and new HF patients is enormous [2]. In China, in the CHS study, 40% of participants had heart failure with either reduced ejection fraction (HFrEF, i.e. left ventricular EF < 40%), 23% had heart failure with median ejection fraction (HFmrEF, i.e. left ventricular EF: 40–49%), and 36% had heart failure with preserved ejection fraction (HFpEF, i.e. left ventricular EF ≥ 50%) [3].

Chronic heart failure (CHF) is a complex clinical syndrome that represents the terminal stage of various heart diseases. CHF is currently considered a multifactorial, systemic disease involving the interaction among myocardial factors, systemic inflammation, renal dysfunction and neurohormone activation [4]. Previous studies have shown that CHF is related to the activation of coagulation and fibrinolysis, endothelial dysfunction and an increase in proinflammatory cytokines and adhesion molecules [5].

Fibrinogen is produced by the liver as an indicator of the procoagulant state, which plays a role in inflammatory reactions at various levels [6]. Albumin is the most abundant plasma protein and an essential protein in human plasma. The synthesis of albumin is inhibited by malnutrition and inflammation, so albumin is a common tool to reflect nutritional status [7,8]. In addition, it has been reported that the serum albumin concentration is related to inflammatory and haemostatic processes [9]. Therefore, the fibrinogen-to-albumin ratio (FAR), which comprises these two indicators above, indicates not only the status of inflammation and nutrition but also coagulation function. Recently, accumulating studies have shown that FAR, as a new inflammatory marker, is closely related to tumour progression and coronary artery disease (CAD) severity [10–14].

However, reports are lacking on the relationship of the FAR with the prognosis of CHF. Therefore, this retrospective study aimed to evaluate the relationship between the FAR and CHF with different ejection fractions.

2. Materials and methods

2.1. Study population

The clinical data of 1221 CHF patients enrolled from January 2017 to October 2021 in the First Affiliated Hospital of Kunming Medical University Yunnan Province, China, were collected. The inclusion criteria in the study were as follows: patients who were admitted with CHF (NYHA class III or IV) with either reduced ejection fraction (HFrEF) or preserved ejection fraction plus heart failure with median ejection fraction (HFpEF plus HFmrEF) and brain natriuretic peptide (BNP) level of ≥500 pg/mL. The current study analysed 916 patients with CHF. The exclusion criteria in the study were as follows: missing necessary data (e.g. routine blood tests or cardiac ultrasound data), had a combination of other serious diseases (e.g. malignant tumours, infectious diseases, blood disorders or severe renal or hepatic dysfunction), or lacking follow-up data. The diagnosis of CHF was based on the ESC Guidelines for the Management of Acute and Chronic Heart Failure 2021 [15].

2.2. Data collection and definitions

For each enrolled CHF patient, comprehensive information on demographics (age, sex, etc.), clinical data (body mass index (BMI), NYHA cardiac classification, electrocardiograms, etc.), laboratory data, complications, and medical history were obtained from hospital records or via interview. Routine haematology and biochemical parameters for baseline laboratory tests were drawn from the antecubital vein on admission before any therapy measures and after fasting for 12 hours, including routine blood tests, B-type natriuretic peptide (BNP), myoglobin, creatine phosphokinase isoenzyme (CK-MB), troponin, sodium, potassium, chloride, albumin (Alb), uric acid (UA), glomerular filtration rate (GFR), alanine aminotransferase (AST), aspartate transaminase (ALT), etc. Blood samples were collected in strict accordance with standard procedures and sent to the laboratory of the First Affiliated Hospital of Kunming Medical University for immediate testing according to standard techniques. We recorded the time and the data of the first readmission if the patient was readmitted due to aggravated heart failure.

Researchers collected survival data by telephone interviews with patients or their families. The primary endpoint was all-cause mortality.

The FAR was defined as the concentration ratio of fibrinogen (g/L) to albumin (g/L) multiplied by 100: (fibrinogen in g/L/albumin in g/L) × 100.

2.3. Ethics

This study was endorsed by the medical ethics committee of the First Affiliated Hospital of Kunming Medical University and complied with the Declaration of Helsinki. Informed written consent was obtained from all patients before the intervention.

2.4. Statistical analysis

Continuous variables were divided into normal and nonnormal distributions via a normality test. An independent sample t test was used to compare the differences in normally distributed continuous variables, and those in nonnormally distributed data were compared using the Mann‒Whitney U rank sum test. Continuous variables are expressed as the mean ± standard deviation (SD) or median [interquartile range (IQR)] according to whether they were normally distributed. Categorical variables are expressed as percentages. Between-group differences in categorical variables were compared using the χ2 test. Patients were categorized into two groups based on the optimal cut-off value of the FAR obtained from the receiver operating characteristic (ROC) curve. Namely, low FAR group (FAR-L): FAR < 9.06, and high FAR group (FAR-H): FAR ≥ 9.06.

The Kaplan‒Meier method and log-rank rank test were used to analyse the estimated survival value to investigate the survival curve. Multivariable Cox proportional hazards models were applied to determine the risk ratios for independently predicting all-cause mortality. The baseline variables with a p value of < 0.05 or clinically significant were included in the Cox proportional hazards models. The risk ratios (HRs) were calculated, and the results are reported as HRs and 95% confidence intervals (CIs). Receiver operating characteristic (ROC) analysis was used to estimate the predictive value of the FAR for the death risk of patients with CHF.

Data were analysed statistically using SPSS ver. 25.0. A double-sided p value < 0.05 was considered statistically significant.

3. Results

3.1. Baseline patient characteristics

We finally enrolled 916 patients (age: 66.9 ± 11.57 years; 36.7% female) with CHF in the present study. Of these patients, 381 patients had HFrEF, and 535 patients had HFpEF plus HFmrEF. Over a median follow-up of 750 days, 417 patients (45.5%) had all-cause mortality. Compared with the FAR-L group, patients in the FAR-H group had a higher average age, faster heart rate, higher fibrinogen, FPG, CRP, WBC, higher triglyceride levels, higher proportion of NYHA class IV, higher occurrence of coronary heart disease, diabetes, and hypertension and relatively lower DBP, Alb, haemoglobin, GFR, sodium, chlorine and ALT levels. There was no difference in BMI or AST levels between patients in the FAR-L group and those in the FAR-H group (Table 1).

Table 1. Baseline characteristics according to the FAR.

Characteristics	FAR-L(n = 456)	FAR-H(n = 460)	P value
Age, years	65.19 ± 12.80	68.94 ± 11.65	<0.001
Female, n (%)	177 (38.8)	159 (34.6)	0.182
BMI, kg/m2	23.23 ± 4.01	23.10 ± 3.64	0.601
NYHA, n (%)			<0.001
Class IV	147 (32.2)	196 (42.6)
Heart Rate (bpm)	83.07 ± 20.56	86.44 ± 21.69	0.016
SBP, mmHg	121.57 ± 22.03	123.60 ± 23.65	0.180
DBP, mmHg	77.42 ± 15.46	75.29 ± 14.67	0.032
Medical history, n (%)
Coronary heart disease	200 (43.9)	273 (59.3)	<0.001
Hypertension	234 (51.3)	286 (62.2)	0.001
Diabetes	108 (23.7)	151 (32.8)	0.002
Atrial fibrillation	166 (36.4)	148 (32.2)	0.178
Fibrinogen (g/L)	2.75 ± 0.57	4.28 ± 0.99	<0.001
ALB (g/L)	38.38 ± 4.02	35.37 ± 4.43	<0.001
Haemoglobin (g/L)	143.34 ± 22.65	133.70 ± 24.72	<0.001
Lg BNP	3.13 ± 0.28	3.17 ± 0.27	0.052
CRP (mg/L)	5.00 (2.20,12.78)	12.56 (5.2,30.38)	<0.001
FPG (mmol/L)	4.89 (4.16,6.08)	5.10 (4.23,6.94)	0.004
UA (μmol/L)	478.65 (3855.05,601.63)	477.30 (364.90,577.40)	0.160
Creatinine (μmol/L)	98.00 (81.80,123.63)	109.35 (85.3,141.60)	<0.001
GFR (ml/min)	47.23 (35.68,59.16)	41.95 (30.36,52.89)	<0.001
Sodium (mmol/L)	141.80 ± 4.19	140.52 ± 4.38	<0.001
Potassium (mmol/L)	3.93 ± 0.67	3.96 ± 0.62	0.386
Chlorine(mmol/L)	103.41 ± 4.46	102.45 ± 4.82	0.002
WBC (×109/L)	6.56 (5.32,8.06)	7.20 (5.69,9.67)	<0.001
Total cholesterol (mmol/L)	3.63 ± 0.94	3.61 ± 1.00	0.774
Triglyceride (mmol/L)	1.08 (0.83,1.46)	1.15 (0.88,1.54)	0.018
AST (U/L)	28.20 (20.13,40.00)	27.05 (19.65,45.18)	0.846
ALT (U/L)	26.55 (16.90,42.45)	22.95 (16.00,39.00)	0.034
Medications at admission, n (%)
SGLT2i	110 (24.1)	96 (20.8)	0.238
Beta-blocker	267 (58.5)	234 (50.9)	0.396
Aldosterone antagonist	360 (78.9)	364 (79.1)	0.946
ACEI/ARB/ARNI	255 (55.9)	203 (44.1)	0.752
Diuretics	307 (67.3)	408 (88.6)	0.970

BMI: body mass index; NYHA: New York Heart Association; SBP: systolic blood pressure; DBP: diastolic blood pressure; ALB: albumin; BNP: B-type natriuretic peptide; CRP: C-reactive protein; FPG: fasting plasma glucose; UA: uric acid; GFR: glomerular filtration rate; WBC: white blood cell; AST: alanine aminotransferase; ALT: aspartate transaminase; LVEF: left ventricular ejection fraction; SGLT2i: sodium-glucose cotransporter 2 inhibitor; ACEI angiotensin-converting enzyme inhibitor; ARB angiotensin receptor inhibitor; ARNI angiotensin receptor neprilysin inhibitor.

3.2. FAR and all-cause mortality

Kaplan – Meier analysis revealed that all CHF patients with high FARs had a significantly greater risk of all-cause mortality than those with low FARs (log-rank χ 100.716 p < 0.001). (Figure 1)

Figure 1. Kaplan‒Meier survival curves according to the optimal cut-off value of the FAR for all patients.

Among patients with HFpEF plus HFmrEF, the risk of all-cause mortality among the two groups was significantly different (log-rank χ 52.063, p < 0.001). (Figure 2(b)) Among patients with HFrEF, the differences between the groups were also statistically significant (log-rank χ 54.437, p < 0.001). (Figure 2(a)) Similarly, the cumulative incidence of all-cause death was significantly higher in CHF patients with high FARs than in those with low FARs, either in patients with HFpEF plus HFmrEF or in patients with HFrEF.

Figure 2. Kaplan‒Meier survival curves according to the optimal cut-off value of the FAR for patients with different subtypes of CHF. (HFrEF and HFpEF plus HFmrEF).

3.3. FAR as an independent predictor

In all CHF patients, univariate Cox proportional hazard analysis showed that the FAR was independently correlated with all-cause mortality. After adjustment for age, BMI, SBP, DBP, NYHA cardiac function classification, FPG, Lg BNP, serum sodium, chlorine, WBC, CRP, AST, ALT, creatinine, uric acid and GFR, the multivariate Cox proportional hazards analysis suggested that the FAR was still independently correlated with all-cause mortality (HR 1.115, 95% CI 1.089–1.142, p < 0.001) (Table 2).

Table 2. Univariable and multivariable analysis Cox proportional hazards models for all patients.

	Univariable		Multivariable
	HR (95% CI)	p	HR (95% CI)	p
Age	1.032 (1.024,1.041)	<0.001	1.020 (1.009,1.032)	<0.001
BMI	0.948 (0.923,0.973)	<0.001	0.969 (0.941,0.999)	0.040
SBP	0.995 (0.991,0.999)	0.025
DBP	0.986 (0.979,0.992)	<0.001
NYHA (reference: NYHA class IV)	0.415 (0.342,0.503)	<0.001	0.585 (0477,0.719)	<0.001
FPG	1.058 (1.033,1.085)	<0.001
Lg BNP	5.086 (3.551,7.283)	<0.001	2.686 (1.822,3.960)	<0.001
Sodium	0.930 (0.910,0.950)	<0.001
Chlorine	0.927 (0.908,0.946)	<0.001	0.958 (0.933,0.984)	0.001
WBC	1.051 (1.028,1.074)	<0.001
CRP	1.012 (1.011,1.014)	<0.001	1.009 (1.007,1.012)	<0.001
AST	1.005 (1.004,1.007)	<0.001
ALT	1.004 (1.002,1.005)	<0.001
Creatinine	1.003 (1.002,1.004)	<0.001
UA	1.001 (1.001,1.002)	<0.001
GFR	0.974 (0.968,0.980)	<0.001
FAR	1.132 (1.111,1.153)	<0.001	1.115 (1.089,1.142)	<0.001

BMI: Body mass index; SBP: Systolic blood pressure; DBP: Diastolic blood pressure; NYHA: New York Heart Association; LVEF: Left ventricular ejection fraction; FPG: Fasting plasma glucose; BNP: B-type natriuretic peptide; WBC: White blood cell; CRP: C-reactive protein; AST: Alanine aminotransferase; ALT: Aspartate transaminase; UA: Uric acid; GFR: Glomerular filtration rate; FAR: fibrinogen-to-albumin ratio * 100.

In both the HFrEF and HFpEF plus HFmrEF subgroups, the FAR was still independently related to all-cause mortality (HFpEF plus HFmrEF, HR 1.109, 95% CI 1.074–1.146, p < 0.001; HFrEF, HR 1.138, 95% CI 1.094–1.183, p < 0.001) (Table 3).

Table 3. Univariable and multivariable analysis Cox proportional hazards models for the patients with HFrEF and HFpEF plus HFmrEF.

	HFrEF				HFpEF plus HFmrEF
	Univariable		Multivariable		Univariable		Multivariable
	HR (95% CI)	p	HR (95% CI)	p	HR (95% CI)	p	HR (95% CI)	p
Age	1.029 (1.015,1.042)	<0.001			1.041 (1.028,1.054)	<0.001	1.031 (1.015,1.047)	<0.001
BMI	0.949 (0.912,0.989)	0.012			0.948 (0.915,0.982)	0.003
DBP	0.985 (0.975,0.995)	0.005			0.986 (0.977,0.994)	0.001
NYHA (reference: NYHA class IV)	0.384 (0.286,0.516)	<0.001	1.789 (1.296,2.469)	<0.001	0.440 (0.341,0.568)	<0.001	1.719 (1.305,2.264)	<0.001
FPG	1.036 (0.993,1.080)	0.100			1.074 (1.042,1.107)	<0.001
Lg BNP	6.734 (3.625,12.509)	<0.001	2.631 (1.317,5.259)	0.006	5.492 (3.397,8.879)	<0.001	2.538 (1.645,4.398)	0.001
Sodium	0.940 (0.909,0.972)	<0.001			0.923 (0.896,0.950)	<0.001
Chlorine	0.943 (0.914,0.973)	<0.001			0.916 (0.891,0.941)	<0.001	0.934 (0.902,0.966)	<0.001
WBC	1.056 (1.019,1.095)	0.003			1.050 (1.020,1.080)	0.001
CRP	1.017 (1.013,1.020)	<0.001	1.012 (1.008,1.016)	<0.001	1.011 (1.009,1.013)	<0.001	1.008 (1.005,1.011)	<0.001
AST	1.005 (1.003,1.007)	<0.001			1.006 (1.003,1.008)	<0.001
ALT	1.003 (1.002,1.005)	<0.001			1.004 (1.001,1.007)	0.004
Creatinine	1.004 (1.002,1.005)	<0.001	0.997 (0.994,1.000)	0.040	1.003 (1.001,1.004)	<0.001
UA	1.002 (1.001,1.003)	<0.001	1.002 (1.001,1.003)	0.001	1.000 (1.000,1.001)	0.307
GFR	0.966 (0.957,0.976)	<0.001	0.977 (0.960,0.993)	0.006	0.977 (0.969,0.985)	<0.001
FAR	1.129 (1.098,1.161)	<0.001	1.138 (1.094,1.183)	<0.001	1.137 (1.110,1.168)	<0.001	1.109 (1.074,1.146)	<0.001

BMI: Body mass index; DBP: Diastolic blood pressure; NYHA: New York Heart Association; FPG: Fasting plasma glucose; BNP: B-type natriuretic peptide; WBC: White blood cell; CRP: C-reactive protein; AST: Alanine aminotransferase; ALT: Aspartate transaminase; UA: Uric acid; GFR: Glomerular filtration rate; FAR: Fibrinogen-to-albumin ratio * 100.

3.4. Predictive ability of the FAR

We generated ROC curves and found the prognostic value of the FAR in all patients with CHF. The optimal cut-off value of the FAR in predicting death was 9.06, with an area under the curve of 0.720 (95% CI: 0.687–0.753, p < 0.0001), a sensitivity of 68.8% and a specificity of 65.6%. (Figure 3) Among patients with HFpEF plus HFmrEF, the area under the curve was 0.737. (Figure 4(a)) Among patients with HFrEF, the area under the curve was 0.718. (Figure 4(b))

Figure 3. The ROC curves according to different FARs for all patients.

Figure 4. The ROC curves according to different FARs for patients with different subtypes of CHF. (HFrEF and HFpEF plus HFmrEF).

To further investigate the predictive impact of the FAR, three basic models were built. Model 1 included age, female sex, BMI, WBC, CRP, NYHA class and LVEF and then Lg-BNP was added to Model 1 to form Model 2. In addition, a new model, Model 3, was formed by adding the FAR to Model 2. (M1: AUC = 0.787; HR, 0.037, p < 0.001; M2: AUC = 0.793; HR, 0.033, p < 0.001; M3: AUC = 0.826; HR, 0.032, p < 0.001) Model 3, with FAR added, had the highest area under receiver operating characteristic curve (AUC) for prediction of CHF compared with Model 1 and Model 2. We can conclude that the FAR had a good predictive impact for the prognosis of CHF. (Figure 5, Table 4)

Figure 5. The ROC curves according to the three models’ predictive value for all patients.

Model 1: Adjusted for Age, Female sex, BMI, WBC, CRP, NYHA, LVEF; Model 2: Adjusted for M1+Lg BNP; Model 3: Adjusted for M2+FAR.

Table 4. The predictive value of the FAR for all patients according to the three models.

	Roc curves			Cox proportional hazard analysis
	AUC	Sensitivity (%)	Specificity (%)	HR (95% CI)	p
Model 1	0.787	75.7	68.8	0.037 (0.025, 0.054)	<0.001
Model 2	0.793	76.7	69.6	0.033 (0.022, 0.049)	<0.001
Model 3	0.826	78.7	70.0	0.032 (0.022, 0.046)	<0.001

Model 1: Adjusted for Age, Female sex, BMI, WBC, CRP, NYHA, LVEF; Model 2: Adjusted for M1+Lg BNP; Model 3: Adjusted for M2+FAR.

4. Discussion

In this retrospective study, the prognostic role of the FAR in patients with different types of CHF was explored in a clinical setting. Our data demonstrated that CHF patients with a high FAR were associated with a significantly greater risk of all-cause mortality than those with a low FAR, which suggested that a more precise risk assessment should be performed in CHF patients with a higher FAR.

First, the cumulative incidence of all-cause death was significantly higher in CHF patients with high FARs than in those with low FARs, either in patients with HFpEF plus HFmrEF or in patients with HFrEF. HF is the terminal stage of all heart diseases, and numerous studies have reported that FAR is significantly and independently related to the prognosis of various cardiovascular diseases, regardless of other complications, which is consistent with our conclusion [16–19].

Second, the FAR can be used as an independent predictor of CHF in all CHF patients or in both the HFrEF and HFpEF plus HFmrEF subgroups. Even after multivariate adjustment for other factors, such as age, BMI, DBP, NYHA cardiac function classification, FPG, Lg BNP, serum sodium, chlorine, WBC, CRP, AST, ALT, creatinine, UA and GFR, the FAR was still independently related to mortality in different types of CHF. Among patients with HFrEF, the mortality increased by 13.8% for every unit of FAR. Among patients with HFpEF and HFmrEF, the mortality rate will increase by 10.9% for every unit of FAR.

Third, the FAR had a good predictive impact for the prognosis of CHF. Traditional indicators, such as CRP, NYHA, LVEF and Lg-BNP, have been well proven in predicting the prognosis of HF. Upon our analysis, the new model with the FAR added has a better prediction ability. (AUC = 0.826)

FIB, a serum glycoprotein synthesized by the liver with a dimeric molecular structure, plays a crucial role in the physiology and pathophysiology of coagulation and inflammation [20]. Fibrinogen biosynthesis increases rapidly during the acute phase of inflammation and is also involved in chronic, low-grade inflammatory processes, activation of platelets and upregulation of adhesion molecule expression [21]. In addition, many cardiovascular risk factors, such as age, diabetes, hypertension, obesity, dyslipidaemia, smoking and drinking status, can lead to an increase in plasma fibrinogen concentration [22]. Massimo Cugno et al. demonstrated that CHF leads to hypercoagulability and suggested that it is important in disease progression and thromboembolic complications [5]. In addition, we know that most clinically important cardiovascular events are mediated by acute arterial thrombosis or thromboembolism [20]. Serum albumin has many physiological characteristics, such as anti-inflammatory activity, antioxidation, anticoagulation, anti-platelet aggregation and maintaining the stability of the capillary membrane [23]. There is a negative correlation between serum albumin level and cardiovascular outcome in many studies [24,25]. In a cohort study of 299 patients with end-stage renal disease, hypoproteinaemia was used as an independent factor to predict new HF [26]. Uthamalingam et al. found that in a cohort of 438 patients with acute decompensated HF, hypoalbuminaemia (defined as a serum albumin ≤ 34 g/L) was predictive of outcome but mainly in those with reduced LVEF heart failure (systolic HF) [27]. Low albumin is one of the reasons for diuretic resistance in patients with heart failure. Most of the label diuretics and thiazide diuretics are transported to the glomeruli after binding to blood albumin in the plasma, and the decrease in blood albumin and the decrease in bound state diuretics in heart failure shift them to an inactivated state [28]. Meanwhile, in CHF, hypoalbuminaemia is associated with long-term malnutrition in protein [25].

Furthermore, the FAR has been proven to be more powerful than fibrinogen or albumin alone in predicting the prognosis of tumours and coronary artery disease. Weiyu Xu et al. indicated that the preoperative FAR could be used to predict the prognosis of gallbladder cancer [29]. Karahan et al. demonstrated that the FAR is significantly related to the SYNTAX score in predicting the severity of CAD in patients with STEMI [16]. Ming Kang Li et al. reported that a higher FAR combined with DM was associated with worse 5-year outcomes among patients with CAD undergoing PCI [30]. As described earlier, a high FAR can result from increased plasma fibrinogen levels and/or decreased albumin levels. The causes of heart failure fall into two main categories: myocardial pathology, such as myocardial ischaemia, and abnormal cardiac loading, such as high blood pressure [31]. Different causes lead to different types of heart failure. In addition, different types of heart failure may change from one to another as the disease progresses. So the mechanism of action of FAR may be different in patients with different types of heart failure.

HFpEF accounts for at least half of the patients diagnosed with heart failure [32]. There are many abnormalities in heart structure and function in HFpEF, including but not limited to diastolic dysfunction [33]. Additionally, HFpEF patients show many limitations in heart, blood vessels and peripheral functions [34]. Among them, the overactivation of the RAS system leads to the expansion of blood volume and the continuous increase in circulating blood volume through progressive sodium-water retention, which leads to a decrease in plasma albumin concentration.

HFrEF is secondary to a series of complex changes in the molecular and cellular composition of the heart, which eventually leads to a decrease in the pumping ability of the heart and then a decrease in cardiac output. To maintain circulatory homeostasis, the sympathetic nervous system (SNS) and renin-angiotensin-aldosterone system (RAAS) remain persistently activated in an ongoing attempt [35–37]. However, in this process, the overexpression of bioactive molecules has toxic effects on the heart and circulation, accompanied by the further activation of inflammatory signalling pathways [38]. Meanwhile, the synthesis of albumin is inhibited by malnutrition and inflammation [7]. As positive and negative inflammatory factors, FIB, AIB and AIB may increase and/or decrease accordingly.

Numerous studies have demonstrated that the FAR is associated with an adverse prognosis in many cancers and has been frequently studied in cardiovascular diseases (CVDs), particularly CAD [13,14,16,18,39,40]. However, there is no evidence that reducing fibrinogen or correcting hypoproteinaemia can improve the prognosis of CVD, including HF. Therefore, the potential mechanisms and targeted therapeutic strategies using the FAR in CHF should be further investigated.

5. Conclusion

FAR could be a potential inflammation marker and an independent predictor of long-term mortalities in patients with different types of HF (HFpEF plus HFmrEF and HFrEF). A higher FAR is significantly associated with higher all-cause mortality.

6. Limitations

This study had several limitations. First, due to the retrospective design, the FAR was not dynamically measured in patients over the follow-up period. Therefore, the relation between the dynamic changes in the FAR and outcomes could not be analysed. Second, we only included information about all-cause mortality but lacked data about cardiovascular mortality or HF hospitalization, which needs further exploration. Finally, our main investigation included patients with NYHA class III or IV and BNP levels ≥ 500 pg/mL, so these results may not apply to all patients with heart failure.

Author contributions

Sirui Yang, Jiangyuan Pi and Lixing Chen conceptualized and designed the survey, conducted the statistical analyses, drafted the first manuscript and approved the final manuscript as submitted. Sirui Yang and Lixing Chen performed the statistical analysis, was conducive to explaining the data and drafted the first manuscript. Wenfang Ma, Hongxing Zhang, Anyu Xu and Yanqing Liu have been involved in drafting the manuscript and conducted the statistical analyses. Tao Shi, Fazhi Yang, Wenyi Gu and Sirui Yang conducted the data collection and statistical analyses. All authors agreed to the submission of the final manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This study was supported by the Applied Basic Research Pro-gram of the Science and Technology Hall of Yunnan Province and Kunming Medical University (Project No.202301AY070001-130) and the National Natural Science Foundation of China (Project No. 82000337).