†These authors contributed equally.
Background: Abnormal glucose metabolism is present in most patients
with coronary artery disease (CAD). Inflammation is considered to be a common
risk factor for CAD and diabetes. Fibrinogen-to-albumin ratio (FAR), a novel
inflammation biomarker, has been proposed as a predictor for cardiovascular
disease. However, the relationship between the level of FAR and long-term
mortality including all-cause, cardiovascular and cancer mortality, remains
unknown in CAD patients, especially those with prediabetes. Methods: We
enrolled 66,761 CAD patients from 2007 to 2020 from a multi-center registry
cohort study. The primary outcomes were the all-cause, cardiovascular and cancer
mortality. FAR was calculated using the following formula: Fibrinogen
(g/L)/Albumin (g/L). Patients were divided into three groups by FAR tertile (low
FAR (FAR-L), median FAR (FAR-M), high FAR (FAR-H)), and further categorized into
9 groups according to FAR and glucose metabolism status (normal glucose
regulation (NGR), prediabetes mellitus (PreDM), diabetes mellitus (DM)). Cox
regression models and competing risk models were used to examine the
relationships between FAR and clinical outcomes. Results: 66,761
Coronary artery disease (CAD) is identified as a major cause of death with more than 9 million deaths in 2019, accounting for 16% of all-cause deaths worldwide . Despite the significant advancements in medical treatment, patients with CAD still face a substantial risk of death, which imposes immense health and economic burdens worldwide .
Abnormal glucose metabolism, including diabetes mellitus (DM) and prediabetes mellitus (PreDM), is present in most patients with CAD . The 2019 ESC Guidelines on diabetes, prediabetes, and cardiovascular diseases indicate that about 20–30% of patients with CAD have DM, and up to 70% of the remaining patients will be newly diagnosed with DM or PreDM . Multiple epidemiological studies have confirmed that DM is one of the risk factors for CAD patients [5, 6]. In addition, PreDM also increases the risk of adverse prognosis in CAD patients [7, 8]. As a result, it is critical to pay closer attention to the glucose metabolism status of CAD patients in order to stratify their risk and better manage these risk factors for patients who are at high risk.
Inflammation is considered to be a common risk factor for CAD and diabetes [9, 10]. A number of studies have shown that inflammatory markers have increased sensitivity for predicting the prognosis of both diabetes and CAD [11, 12, 13, 14]. Fibrinogen (FIB), an inflammatory marker synthesized by the liver, as well as a coagulation factor involved in the formation of thrombosis and the progression of atherosclerosis, has been found to be closely related to the poor prognosis of CAD patients [15, 16]. Albumin (ALB) is the most abundant plasma protein and has anti-inflammatory, antioxidant, and antithrombotic properties. Previous studies have indicated that low serum albumin concentration is associated with an increased risk of adverse cardiovascular events in CAD patients [17, 18]. Fibrinogen-to-albumin ratio (FAR) is an innovative inflammatory biomarker that combines the above two indicators and has been widely used to predict adverse prognosis among patients with various cancers [19, 20]. Recently, several studies have also confirmed that FAR is strongly linked to the severity of coronary lesions and poor clinical outcomes in patients with ST-segment elevation myocardial infarction (STEMI) and multivessel disease [21, 22, 23]. However, the relationship between FAR, all-cause and cause-specific mortality is not fully understood in CAD patients with different glucose metabolism, especially those with prediabetes.
The purpose of this study was to investigate the potential relationship between FAR and long-term mortality among CAD patients, including all-cause, cardiovascular and cancer mortality, and further determine whether the association between FAR levels and clinical outcomes varies according to the glucose metabolism status, especially in those patients with prediabetes.
This multi-center cohort study was based on the Cardiorenal Improvement II (CIN-II) study, which included patients recruited from 5 large tertiary hospitals in China between January 2007 and December 2020 (Cardiorenal Improvement II, ClinicalTrials.gov NCT05050877). We enrolled 66,761 CAD patients who underwent coronary angiography (CAG) at the time of initial admission. The exclusion criteria were as follows: (1) patients younger than eighteen years; (2) patients missing survival information; (3) patients without baseline plasma fibrinogen and plasma albumin data; (4) patients without baseline fasting blood glucose (FBG) and hemoglobin A1c (HbA1c) (Fig. 1). This research was approved by the Ethics Committee of the Guangdong Provincial People’s Hospital (No. GDREC2019-555H-2). To protect the privacy of patients, we removed all traceable personal identifiers from the database, and each participating site obtained institutional review board permission from the local ethics committees. Since this research involved retrospective cases, no additional intervention was necessary. Furthermore, the data we used has been desensitized and patient informed consent was not required. The study complied with the Declaration of Helsinki.
Flow of patients through the study. FAR, fibrinogen-to-albumin ratio; FAR-L, low FAR; FAR-M, median FAR; FAR-H, high FAR.
The baseline information including demographic characteristics, complications, procedures, laboratory examinations, and medications were obtained from the electronic clinical management system (ECMS). On initial admission, biochemistry data such as plasma fibrinogen, plasma albumin and HbA1c were obtained. Survival information was derived from a cause-specific surveillance dataset at the regional Center for Disease Control and Prevention (CDC).
The outcomes of this study were all-cause, cardiovascular and cancer mortality.
FAR was defined as the ratio of fibrinogen (g/L) to albumin (g/L). According to
the American Diabetes Association , DM was identified by FBG
According to the baseline FAR, patients were categorized into three groups:
FAR-L, FAR-M, FAR-H, and further divided into nine groups according to FAR and
glucose metabolism status (NGR, PreDM, DM). For the baseline characteristics,
continuous variables were presented as mean with standard deviation (SD) or
median with interquartile range (IQR) as appropriate. Categorical variables were
described as counts and percentages. One-way ANOVA or the Kruskal–Wallis test
was used for continuous variables. The
A total of 66,761 patients with CAD (the mean age 63.1
|n = 66,761||n = 22,335||n =22,466||n = 21,960|
|Age, years||63.1 (11.0)||61.4 (11.2)||63.7 (10.5)||64.5 (10.9)|
||40,666 (60.8)||12,361 (54.2)||14,437 (63.3)||13,823 (65.4)|
|Female, n (%)||16,514 (24.7)||5086 (22.3)||6239 (27.4)||5170 (24.4)|
|Glucose metabolism status|
|NGR, n (%)||21,982 (32.9)||9439 (42.3)||6825 (30.4)||5715 (26.0)|
|PreDM, n (%)||20,723 (31.0)||6117 (27.4)||7632 (34.0)||6970 (31.8)|
|DM, n (%)||24,056 (36.0)||6779 (30.4)||8009 (35.6)||9262 (42.2)|
|AMI, n (%)||15,788 (23.6)||4512 (19.8)||3809 (16.7)||7420 (35.1)|
|HT, n (%)||37,614 (56.3)||12,101 (53.1)||13,148 (57.6)||12,324 (58.3)|
|CHF, n (%)||9933 (14.9)||2423 (10.6)||2654 (11.6)||4835 (22.9)|
|CKD, n (%)||13,734 (20.5)||2757 (12.1)||4276 (18.7)||6689 (31.6)|
|AF, n (%)||3005 (4.5)||1060 (4.6)||967 (4.2)||975 (4.6)||0.068|
|Stroke, n (%)||4141 (6.2)||1240 (5.4)||1366 (6.0)||1531 (7.2)|
|Hyperlipemia, n (%)||43,135 (64.5)||13,524 (59.3)||14,483 (63.5)||15,078 (71.3)|
|PCI, n (%)||48,050 (71.9)||14,544 (63.8)||16,533 (72.5)||16,919 (80.0)|
|CABG, n (%)||104 (0.2)||24 (0.1)||38 (0.2)||42 (0.2)||0.0407|
|DES, n (%)||45,842 (68.6)||13,855 (60.8)||15,772 (69.2)||16,164 (76.4)|
|BMS, n (%)||1048 (1.6)||267 (1.2)||359 (1.6)||422 (2.0)|
|eGFR, mL/min/1.73 m
||78.5 (25.8)||84.2 (22.6)||79.3 (24.9)||71.6 (28.4)|
|HGB, g/L||133.5 (17.4)||138.0 (15.9)||134.4 (16.2)||127.7 (18.6)|
|SCr, mg/dL||1.0 [0.8, 1.1]||0.9 [0.8, 1.1]||0.9 [0.8, 1.1]||1.0 [0.8, 1.3]|
|LDLC, mmol/L||2.9 (1.0)||2.9 (1.1)||2.9 (1.0)||2.9 (1.0)||0.2722|
|HDLC, mmol/L||1.0 (0.3)||1.1 (0.3)||1.0 (0.3)||0.9 (0.3)|
|HbA1c, %||6.6 (1.4)||6.4 (1.3)||6.5 (1.3)||6.8 (1.6)|
|FIB, g/L||3.9 (1.3)||2.8 (0.4)||3.7 (0.4)||5.3 (1.1)|
|ALB, g/L||37.6 (4.5)||40.5 (3.7)||37.8 (3.3)||34.2 (4.1)|
|FAR||0.108 (0.045)||0.070 (0.010)||0.098 (0.008)||0.158 (0.044)|
|Beta blocker, n (%)||51,171 (80.0)||16,657 (77.7)||17,722 (80.4)||16,733 (82.1)|
|Statins, n (%)||61,237 (95.8)||20,586 (96.0)||21,126 (95.9)||19,450 (95.4)||0.0047|
|CCB, n (%)||14,745 (23.1)||5395 (25.2)||5065 (23.0)||4261 (20.9)|
|ACEI/ARB, n (%)||44,850 (70.1)||14,386 (67.1)||15,763 (71.5)||14,651 (71.9)|
|Diuretics, n (%)||11,602 (18.1)||3112 (14.5)||3547 (16.1)||4929 (24.2)|
|Antiplatelet, n (%)||62,188 (97.3)||20,788 (97.0)||21,444 (97.3)||19,885 (97.5)||0.0016|
|All-cause mortality, n (%)||10,534 (15.8)||2090 (9.4)||3237 (14.4)||5204 (23.7)|
|Cardiovascular mortality, n (%)||4991 (7.5)||833 (3.7)||1378 (6.1)||2779 (12.7)|
|Cancer mortality, n (%)||1092 (1.6)||193 (0.9)||365 (1.6)||533 (2.4)|
During a median follow-up of 4.68 years, 10,534 (15.8%) participants died, of which, 4991 (7.5%) were cardiovascular-specific deaths, and 1092 (1.6%) were cancer-specific deaths. For all-cause death or cause-specific death, the proportion of patients with a higher FAR was significantly higher than the FAR-L group (Table 1). A similar trend was also observed in patients with different glucose metabolism status (Table 2).
|FAR||All-cause mortality||Cardiovascular mortality||Cancer mortality|
|Events/subjects||HR (95% CI)||Events/subjects||HR (95% CI)||Events/subjects||HR (95% CI)|
|FAR per-SD increase||1.12 (1.09–1.16) **||1.19 (1.14–1.25) **||1.22 (1.11–1.34) **|
|FAR-M||988/6825||1.18 (1.08–1.29) **||405/6825||1.15 (1.00–1.33)||111/6825||1.69 (1.25–2.28) **|
|FAR-H||1283/5715||1.35 (1.24–1.48) **||640/5715||1.47 (1.28–1.68) **||138/5715||2.02 (1.50–2.73) **|
|FAR per-SD increase||1.18 (1.14–1.21) **||1.23 (1.18–1.29) **||1.25 (1.14–1.36) **|
|FAR-L||552/6117||0.86 (0.78–0.96) *||179/6117||0.68 (0.57–0.81) **||62/6117||1.21 (0.87–1.70)|
|FAR-M||998/7632||0.99 (0.90–1.08)||381/7632||0.88 (0.76–1.02)||132/7632||1.61 (1.20–2.16) *|
|FAR-H||1476/6970||1.32 (1.21–1.44) **||678/6970||1.34 (1.17–1.53) **||192/6970||2.27 (1.70–3.02) **|
|FAR per-SD increase||1.21 (1.18–1.25) **||1.28 (1.24–1.33) **||1.25 (1.14–1.37) **|
|FAR-L||705/6029||1.01 (0.91–1.11)||314/6029||1.07 (0.92–1.25)||66/6029||1.24 (0.88–1.74)|
|FAR-M||1283/8167||1.21 (1.11–1.32) **||610/8167||1.29 (1.13–1.47) **||127/8167||1.59 (1.18–2.15) *|
|FAR-H||2395/8959||1.71 (1.58–1.86) **||1434/8959||2.11 (1.86–2.38) **||198/8959||2.20 (1.66–2.92) **|
FAR, fibrinogen-to-albumin ratio; NGR, normal glucose regulation; PreDM, prediabetes mellitus; DM, diabetes mellitus; FAR-L, low FAR; FAR-M, median FAR; FAR-H, high FAR; HR, hazard ratio; CI, confidence interval; SD, standard deviation.
RCS analysis demonstrated that the risk of all-cause or cause-specific death
increased as FAR increased in CAD patients, but the relationship was not linear
(all nonlinear p
Restricted spline curve for the association between FAR and all-cause, cardiovascular, and cancer mortality based on multivariate Cox regression models. The multivariate Cox regression model includes adjustment for age, gender, hypertension, acute myocardial infarction, stroke, chronic kidney disease, hyperlipemia, and congestive heart failure. FAR, fibrinogen-to-albumin ratio; HR, hazard ratio.
Kaplan–Meier curves for all-cause, cardiovascular, and cancer mortality according to different FAR levels based on univariate Cox regression models. FAR, fibrinogen-to-albumin ratio; FAR-L, low FAR; FAR-M, median FAR; FAR-H, high FAR.
Kaplan–Meier curves for all-cause, cardiovascular, and cancer mortality according to states of both FAR levels and glucose metabolism based on univariate Cox regression models. FAR, fibrinogen-to-albumin ratio; FAR-L, low FAR; FAR-M, median FAR; FAR-H, high FAR; NGR, normal glucose regulation; PreDM, prediabetes mellitus; DM, diabetes mellitus.
Table 2 shows three Cox regression models to further investigate the association
among FAR, different glucose metabolism states and clinical outcomes. After
adjusting for age, gender, HT, CKD, stroke, AMI, hyperlipemia, CHF, the higher
FAR group had a higher risk for all-cause and cause-specific mortality in NGR
patients; similar results were found in the PreDM and DM groups. The risks of
all-cause mortality (NGR, PreDM, DM: HR (95% CI) = 1.12 (1.09–1.16), 1.18
(1.14–1.21), 1.21 (1.18–1.25), respectively; all p values
In order to determine whether FAR has an additional predictive value compared
with the original model, we calculated the C-indexes with and without FAR. The
original model included age, gender, HT, CHF, AMI, CKD, stroke and hyperlipemia.
Regardless of all-cause or cause-specific mortality, the addition of FAR to the
model resulted in a significant improvement in C-indexes for NGR, PreDM and DM
patients (all p value
|Model||All-cause mortality||Cardiovascular mortality||Cancer mortality|
|NGR original model||0.682 (0.670–0.694)||0.720 (0.704–0.736)||0.720 (0.693–0.747)|
|NGR original model + FAR||0.687 (0.675–0.699)||0.724 (0.708–0.74)||0.733 (0.706–0.760)|
|PreDM original model||0.673 (0.661–0.685)||0.748 (0.732–0.764)||0.701 (0.676–0.726)|
|PreDM original model + FAR||0.683 (0.671–0.695)||0.758 (0.742–0.774)||0.721 (0.697–0.745)|
|DM original model||0.691 (0.681–0.701)||0.734 (0.722–0.746)||0.685 (0.656–0.714)|
|DM original model + FAR||0.701 (0.693–0.709)||0.748 (0.736–0.760)||0.700 (0.673–0.727)|
FAR, fibrinogen-to-albumin ratio; NGR, normal glucose regulation; PreDM, prediabetes mellitus; DM, diabetes mellitus.
FAR showed a significantly positive correlation with glucose metabolism indexes
including HbA1c (r = 0.14, p
In this retrospective study, we demonstrated that elevated FAR was significantly related to a higher risk of all-cause, cardiovascular, and cancer mortality in CAD patients with different glucose metabolism status. Furthermore, abnormal glucose metabolism increases this association. In addition, adding FAR to the original model enhanced the predictive power for long-term mortality. Our results indicate that FAR is an effective risk stratification tool for CAD patients with NGR, PreDM and DM.
Abnormal glucose metabolism is common in patients with CAD . In a diabetes and heart survey involving 110 centers in Europe, up to 31% of patients with CAD were already known to have diabetes and 40% had newly discovered glucose abnormalities . Hyperglycemia caused by insulin resistance induces mitochondrial dysfunction and endoplasmic reticulum stress, leading to endothelial dysfunction, ROS accumulation and inflammation, which ultimately contributes to the development of CAD [27, 28]. Several epidemiological studies have also demonstrated that abnormal glucose metabolism was related to higher risk of adverse cardiovascular events and mortality among CAD patients. A large cohort study, including 3276 postinfarction patients, indicated that the occurrence of sudden cardiac death was higher in DM patients compared to non-DM patients . Lenzen et al.  found that CAD patients with previously recognized and newly detected DM was associated with a 1.4- and 1-fold elevated risk of 1-year mortality, respectively, compared to those with NGR. The first Diabetes Mellitus Insulin Glucose Infusion in Acute Myocardial Infarction study also demonstrated an apparent beneficial effect of enhanced insulin-based glucometabolic control after AMI, which prolonged survival by 2.3 years . Therefore, it is critical to monitor the glucose metabolism of CAD patients and intervene as soon as possible to improve the long-term prognosis of high-risk populations.
CAD and DM involve complex physiological processes, and chronic inflammation has
been widely accepted as the pathological mechanism for these two diseases .
CAD is essentially an inflammatory disease, risk factors such as dyslipidemia,
hyperglycemia, smoking, and hypertension induce endothelial cell damage,
stimulate immune cell proliferation, resulting in the upregulate of the
expression of inflammatory and procoagulant cytokines such as IL-1
Several studies have found a strong connection between diabetes and cancer. Diabetes may stimulate the proliferation and metastasis of cancer cells and influence the development of tumors through various biological mechanisms, such as hyperinsulinemia, hyperglycemia or chronic inflammation . Moreover, patients with diabetes have a higher cancer mortality compared with non-diabetic patients [46, 47]. Therefore, this study also evaluated the predictive value of FAR for cancer death in CAD patients with different glucose metabolism states. Consistently, our research showed that PreDM and DM patients with FAR-H were significantly linked to higher risks of cancer mortality. However, the connection between FAR and cancer mortality in CAD patients classified by different glucose metabolism states is not fully understood. Therefore, additional research is required to clarify these potential connections.
In our study, elevated FAR was associated with increased long-term mortality in patients with CAD, and correlated with different glucose metabolism states. Abnormal glucose metabolism amplifies the relationship between FAR and death. Moreover, to the best of our knowledge, this study revealed the predictive effect of FAR on mortality risk in CAD patients with prediabetes, for the first time. On the basis of the correlation between FAR and mortality risk, monitoring FAR levels and controlling inflammation with several anti-inflammatory therapy may play an important role in decreasing mortality in high-risk groups. Further investigations will be necessary to prospectively verify the prediction power of FAR for all-cause, cardiovascular and cancer mortality among CAD patients with different glucose metabolism status.
This research has several limitations. First, this research was a retrospective observational analysis, which didn’t reflect direct causation. Second, due to data limitations, the effect of the severity of coronary artery disease on the relationship between FAR and mortality was not analyzed. Third, we only assessed FAR levels at the time of admission. The dynamic changes of FAR were absent during follow-up. Fourth, despite the adjustment for potential confounding factors, possible confounders could not be fully adjusted. Fifth, this study only included CAD patients in China, and whether the findings of this study can be generalized to other populations remains unclear.
In conclusion, FAR is a valuable tool for risk stratification of CAD patients with NGR, PreDM, and DM. Increased levels of FAR were significantly associated with higher risks of all-cause and cause-specific mortality. Moreover, abnormal glucose metabolism amplifies the relationship between FAR and mortality. FAR levels can provide more precise risk stratification for high-risk CAD patients and provide essential information for improving the long-term prognosis of these patients.
CAD, coronary artery disease; DM, diabetes mellitus; PreDM, prediabetes mellitus; NGR, normal glucose regulation; AMI, acute myocardial infarction; HT, hypertension; CHF, congestive heart failure; CKD, chronic kidney disease; AF, atrial fibrillation; PCI, percutaneous coronary intervention; CABG, coronary artery bypass grafting; DES, drug eluting stent; BMS, bare metal stent; eGFR, estimated glomerular filtration rate; HGB, hemoglobin; SCr, serum creatinine; LDLC, low density lipoprotein cholesterol; HDLC, high density lipoprotein cholesterol; HbA1c, Hemoglobin A1c; FIB, fibrinogen; ALB, albumin; FAR, fibrinogen-to-albumin ratio; CCB, calcium channel blocker; ACEI/ARB, angiotensin-converting enzyme inhibitor/angiotensin receptor blocker; FAR-L, low FAR; FAR-M, median FAR; FAR-H, high FAR.
The datasets generated and analyzed during the current study are not publicly available due to the institution policy but are available from the corresponding author on reasonable request.
The authors’ responsibilities were as follows: (I) Research idea and study design: YX, XYX, DMW, YL. (II) Data acquisition: YX, XYX, WGL, JL, XMY. (III) Data analysis/interpretation: YX, YZ, HYL, SQC, JYX, XYH. (IV) Statistical analysis: YX, XYX, DMW, YK. (V) Supervision and mentorship: JL, SQC and JYX. (VI) Writing guidance: XMY, XYH and YL. All authors contributed to editorial changes in the manuscript. All authors read and approved the final manuscript. All authors have participated sufficiently in the work and agreed to be accountable for all aspects of the work.
The Ethics Committee of the Guangdong Provincial People’s Hospital approved the study (No. GDREC2019-555H-2). All traceable personal identifiers were removed from the analytic dataset to protect patients’ privacy and all participating sites received institutional review board approval from their own ethics committees. Our database is not open to the public since the privacy of the participants should be protected. Since our research included retrospective cases, there was no additional intervention, and information of all patients was desensitized, and no informed consent was required. It was conducted in accordance with the principles of the Declaration of Helsinki.
This research was funded by Guangdong Provincial science and technology project (2020B1111170011, DFJH2020026, DFJH201919); Guangdong Provincial science and technology project (KJ022021049); The National Science Foundation for Young Scientist of China (grant no.82070360). The funders had no role in the study design, data collection, and analysis, decision to publish, or preparation of the manuscript; the work was not funded by any industry sponsors.
The authors declare no conflict of interest.
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