Skip to main content
Download PDF

Prognostic value of fibrinogen in patients with coronary artery disease and prediabetes or diabetes following percutaneous coronary intervention: 5-year findings from a large cohort study

Cardiovascular Diabetology volume 20, Article number: 143 (2021) Cite this article

Abstract

Background

Fibrinogen (FIB) is an independent risk factor for mortality and cardiovascular events in the general population. However, the relationship between FIB and long-term mortality among CAD patients undergoing PCI remains unclear, especially in individuals complicated with diabetes mellitus (DM) or prediabetes (Pre-DM).

Methods

6,140 patients with CAD undergoing PCI were included in the study and subsequently divided into three groups according to FIB levels (FIB-L, FIB-M, FIB-H). These patients were further grouped by glycemic status [normoglycemia (NG), Pre-DM, DM]. The primary endpoint was all-cause mortality. The secondary endpoint was cardiac mortality.

Results

FIB was positively associated with hemoglobin A1c (HbA1c) and fasting blood glucose (FBG) in CAD patients with and without DM (P < 0.001). During a median follow-up of 5.1 years (interquartile range 5.0–5.2 years), elevated FIB was significantly associated with long-term all-cause mortality (adjusted HR: 1.86; 95% CI 1.28–2.69; P = 0.001) and cardiac mortality (adjusted HR: 1.82; 95% CI 1.15–2.89; P = 0.011). Similarly, patients with DM, but not Pre-DM, had increased risk of all-cause and cardiac mortality compared with NG group (all P < 0.05). When grouped by both FIB levels and glycemic status, diabetic patients with medium and high FIB levels had higher risk of mortality [(adjusted HR: 2.57; 95% CI 1.12–5.89), (adjusted HR: 3.04; 95% CI 1.35–6.82), all P < 0.05]. Notably, prediabetic patients with high FIB also had higher mortality risk (adjusted HR: 2.27; 95% CI 1.01–5.12).

Conclusions

FIB was independently associated with long-term all-cause and cardiac mortality among CAD patients undergoing PCI, especially in those with DM and Pre-DM. FIB test may help to identify high-risk individuals in this specific population.

Background

Despite advances in revascularization strategies over recent decades, the clinical outcomes remain unfavorable in patients with coronary artery disease (CAD), especially when complicated with diabetes mellitus (DM)1. Regarded as a pivotal component of coagulation as well as a biomarker of inflammation, fibrinogen (FIB) plays a crucial role in the pathophysiological process of thrombosis and atherosclerosis2,3,4,5. Previous evidences suggested that FIB was an independent risk factor of CAD development and cardiovascular events in the general population6, 7. Similar results on the prognostic value of FIB were also observed in patients with CAD8,9,10.

Glycemic metabolism abnormality, including DM and prediabetes (pre-DM), is increasingly prevalent on a global scale. By 2045, over 600 million individuals are projected to develop DM, with about the same number developing pre-DM11. The cardiovascular disease (CVD) risk, disability and mortality brought by glycemic metabolism abnormality is undisputedly a serious public health concern. Interestingly, FIB level was found to be higher in diabetic and prediabetic patients, and was involved in glycemic metabolism abnormality and insulin resistance12, 13. Moreover, recent studies reported that FIB was positively related with the glycemic metabolism (hemoglobin A1c [HbA1c] and fasting blood glucose [FBG]) in patients with acute coronary syndrome (ACS) or stable CAD9, 10. However, few data are available examining the correlation between FIB and glycemic metabolism in CAD patients undergoing percutaneous coronary intervention (PCI). Furthermore, the association of FIB with long-term outcomes in this population was far less investigated, particularly in those with impaired glycemic metabolism.

In light of the above, we aimed to evaluate the relationship between FIB and glycemic metabolism, and further determine the combined effect of FIB and impaired glycemic metabolism on long-term all-cause and cardiac mortality in CAD patients undergoing PCI.

Methods

Study population

This study was based on a prospective, observational, single-center cohort. From January 2013 to December 2013, 10,724 CAD patients were consecutively enrolled undergoing PCI at Fuwai Hospital, Chinese Academy of Medical Sciences (Beijing, China) (Fig. 1). Patients with missing FIB (n = 4431) and Low-density lipoprotein cholesterol (n = 153) values were excluded. A total of 6,140 patients were ultimately included in the analysis. The study protocol was approved by the Institutional Review Board of Fuwai Hospital and complied with the Declaration of Helsinki. All patients provided written informed consent before the intervention. Regular follow-up assessment of patients was performed at five time points (1-month, 6-month, 12-month, 2-year, and 5-year after the discharge). Follow-up data were collected through medical records and telephone interview. The primary endpoint was all-cause mortality. The secondary outcome was cardiac mortality. Mortality that could not be attributed to a noncardiac etiology was considered cardiac mortality. All endpoints were adjudicated centrally by 2 independent cardiologists, and disagreement was resolved by consensus.

Fig. 1
figure1

Study flowchart. FIB, fibrinogen, LDL-C, low-density lipoprotein cholesterol, PCI, percutaneous coronary intervention

Full size image

Procedure and medications

Before the procedure, patients receiving selective PCI were treated with aspirin (300 mg) and ticagrelor (loading dose, 180 mg) or clopidogrel (loading dose, 300 mg), except for patients already on dual antiplatelet therapy; for patients with ACS receiving emergency PCI, the same dose of aspirin and ticagrelor or clopidogrel (loading dose, 300–600 mg) were administered as soon as possible. All patients were administered with unfractionated heparin (100 U/kg), and interventional cardiologist decided whether to use glycoprotein IIb/IIIa antagonist according to the clinical conditions and coronary lesions during the procedure. After the procedure, the dual antiplatelet therapy including aspirin (100 mg daily), ticagrelor (90 mg, twice daily) or clopidogrel (75 mg, daily) were recommended for at least 1 year. The choice of equipment and techniques during PCI was at the discretion of the physicians.

Definition of clinical status

Glycemic categories were based on the guideline recommendations of American Diabetes Association14. Diabetes mellitus (DM) was defined by HbA1c levels ≥ 6.5%, or fasting blood glucose (FBG) ≥ 7.0 mmol/L, or 2-h blood glucose levels of oral glucose tolerance test ≥ 11.1 mmol/L), or current use of hypoglycemic medications. Prediabetes (Pre-DM) was defined as nondiabetic patients with FBG ranging from 5.6 to 6.9 mmol/L, HbA1c levels ranging from 5.7% to 6.4%. Patients without Pre-DM or DM were defined as normoglycemia (NG). Hypertension was defined as systolic blood pressure ≥ 140 mmHg and/or diastolic blood pressure ≥ 90 mmHg and/or current use of antihypertensive drugs. Low-density lipoprotein cholesterol ≥ 3.4 mmol/L, fasting total cholesterol ≥ 5.2 mmol/L, triglyceride ≥ 1.7 mmol/L, high-density lipoprotein cholesterol < 1.0 mmol/L and/or chronic use of lipid-lowering drugs were considered criteria for dyslipidemia. Left main disease was defined as stenosis of ≥ 50% in left main coronary artery, and three-vessel disease was defined as stenosis of ≥ 50% in all three main coronary arteries (right coronary artery, left circumflex artery and left anterior descending artery) confirmed by coronary angiography.

Laboratory analysis

Fasting blood samples were drawn from all patients within 24 h after admission. Enzymatic hexokinase method was used to measure the concentrations of blood glucose. Tosoh Automated Glycohemoglobin Analyzer (HLC-723G8) was used to measure the HbA1c levels. Stago autoanalyzer with the STA fibrinogen kit (Diagnostica Stago, Taverny, France) was used to measure the concentrations of FIB. All other laboratory measurements were conducted at the biochemistry center of Fu Wai Hospital by standard biochemical techniques.

Statistical analysis

Continuous variables were presented as mean ± standard deviation, while categorical variables were presented as frequency and percentage. Differences of continuous and categorical variables were analyzed by analysis of variance or Kruskal–Wallis test and χ2 test or Fisher's exact test, as appropriate. Pearson correlation and linear regression analysis were performed to evaluate the correlation between FIB and glycemic metabolism (HbA1c and FBG). In survival analysis, the association between FIB and clinical endpoint was initially examined using restricted cubic splines. The FIB was subsequently analyzed as both a continuous and a categorical variable. For categorical analysis, patients were grouped according to tertiles of the distribution [FIB-L(< 2.98 mg/dL), FIB-M(2.98–3.59 mg/dL), FIB-H(< 3.59 mg/dL)]. Survival distributions were presented by Kaplan–Meier curves and compared by log-rank test. Cox regression analyses were performed to calculate the hazard ratios (HRs) and 95% confidence interval (CI). Proportional hazards assumption was verified by Schoenfeld residuals. In multivariate Cox analyses, covariates including age, sex, body mass index (BMI), hypertension, family history of CAD, prior PCI/CABG, LVEF, LDL-C, creatine, DES implantation, clopidogrel, ACEI/ARB were adjusted because of their statistical significance in univariate analysis or clinical importance. The prognostic impact of glycemic metabolism status (NG, Pre-DM and DM) for all-cause mortality was also assessed by using the model mentioned above. Patients were further divided into 9 groups by both FIB levels and glycemic metabolism status to calculate HRs for all-cause mortality using FIB-L plus NG as reference. Statistical analyses were conducted with SPSS version 25.0 (IBM Corp., Armonk, N.Y., USA), R Programming Language version 4.0.0 (R Core Team, 2014), and GraphPad Prism version 7.0.0 for windows (GraphPad Software, San Diego, California USA). A two-tailed P value < 0.05 was considered statistically significant.

Results

Baseline characteristics of patients with different FIB levels

Among the 6,140 patients, the mean age was 58.4 ± 10.4 years, and 4771(77.7%) were male. The baseline characteristics of patients according to the tertiles of FIB are summarized in Table 1. Patients with higher FIB levels were older and less likely to be male (all P < 0.05). In addition, they had higher proportion of diabetes, hypertension, prior stroke, ACS, and left main or three-vessel disease (all P < 0.05). Moreover, patients with elevated FIB levels had higher FBG, HbA1c, hs-CRP, D-dimer, TC, LDL cholesterol, creatinine, lesion vessels, SYNTAX score, but lower LVEF and lower rate of complete revascularization (all P < 0.05). No significant differences were noted regarding dyslipidemia, family history of CAD, smoking, HDL cholesterol, number of stents and DES implantation among these groups (all P > 0.05).

Table 1 Baseline characteristics for patients with different FIB levels
Full size table

Comparison of clinical data among groups with different glycemic metabolism status

In Table 2, Patients were divided into three subgroups based on different glycemic metabolism status. In general, the DM and pre-DM group had a less favorable cardiovascular risk profile. Patients with DM or pre-DM tended to be older and female, with a larger burden of concomitant diseases, such as hypertension, dyslipidemia and prior stroke compared with those in NG group (all P < 0.05). Additionally, the prevalence of prior PCI/CABG and left main or three-vessel disease was higher in the DM and pre-DM group (all P < 0.05). Meanwhile, there were also higher BMI, FBG, HbA1c, TG, number of diseased vessels, SYNTAX score, and lower LVEF, HDL cholesterol, complete revascularization, DES implantation in the DM group (all P < 0.05). Notably, FIB levels were significantly elevated from NG to DM group (P < 0.001).

Table 2 Baseline characteristics for patients with different glycemic metabolism status
Full size table

Relationship between HbA1c/FBG and FIB

Linear regression analysis was used to assess the correlation between glycemic metabolism and FIB (Table 3). The results showed that both admission HbA1c (R2 = 0.018, Standard β = 0.133, P < 0.001) and FBG (R2 = 0.012, Standard β = 0.111,P < 0.001) were positively associated with FIB in the whole cohort. In DM patients, HbA1c (R2 = 0.026, Standard β = 0.163, P < 0.001) and FBG (R2 = 0.012, Standard β = 0.111,P < 0.001) were also positively associated with FIB. Furthermore, this positive relationship between HbA1c (R2 = 0.012, Standard β = 0.111, P < 0.001) and FBG (R2 = 0.009, Standard β = 0.096, P < 0.001) with FIB remained significant in the non-DM patients (Fig. 2). Of note, the correlation coefficients between HbA1c/FBG and FIB were relatively weak and may not be able to provide sufficient clinical value despite of statistically significant correlation.

Table 3 Correlation analysis between glycemic metabolism and FIB in patients with DM, without DM and whole
Full size table
Fig. 2
figure2

Correlation analysis of the relationship between glycemic metabolism and FIB. a Linear regression analysis of the relationship between glycemic metabolism (HbA1c and FBG) and FIB in whole patients. b Correlation analysis of the relationship between glycemic metabolism (HbA1c and FBG) and FIB in patients with DM. c Correlation analysis of the relationship between glycemic metabolism (HbA1c and FBG) and FIB in patients without DM. DM, diabetes mellitus, FBG, fasting blood glucose, FIB, fibrinogen, HbA1c, hemoglobin A1c

Full size image

Predictive value of FIB on all-cause mortality and cardiac mortality

The median follow-up time was 5.1 years (interquartile range 5.0–5.2 years), and the response rate was 91.2% (Fig. 1). During follow-up, 214 (3.5%) patients died, with 127 (59.3%) of whom from cardiac causes. Myocardial infarction (37.8%) was the most frequently reported cause of cardiac mortality, while malignancy (13.1%) was the most common cause of non-cardiac mortality (Additional file 1: Table S1).

The incidence of all-cause mortality in FIB-L, FIB-M and FIB-H group was 2.1%, 3.7% and 4.7%, respectively. Restricted cubic splines visualized a positive relation between FIB on a continuous scale with long-term risk of all-cause mortality and cardiac mortality (all P for non-linearity > 0.05) (Additional file 1: Figure S1). The Kaplan–Meier survival curve revealed that patients with higher FIB levels had significantly increased risk of all-cause mortality and cardiac mortality (all log-rank P < 0.001) (Fig. 3a, Additional file 1: Figure S2a). The univariate Cox analysis showed a strong relation between continuous FIB with all-cause mortality (HR: 1.36; 95% CI 1.20–1.55 per 1 unit increase in FIB; P < 0.001) and cardiac mortality (HR: 1.46; 95% CI 1.25–1.71 per 1 unit increase in FIB; P < 0.001). On multivariate analysis, the relationship between continuous as well as tertiles of FIB with all-cause mortality (adjusted HR: 1.23; 95% CI 1.07–1.42 per 1 unit increase in FIB; P = 0.004) and cardiac mortality (adjusted HR: 1.31; 95% CI 1.10–1.55 per 1 unit increase in FIB; P = 0.003) remained statistically significant after adjustment for potential confounders (Table 4).

Fig. 3
figure3

Kaplan–Meier analysis for all-cause death according to different FIB levels (a), glycemic metabolism status (b), and status of both FIB levels and glycemic metabolism (c)

Full size image
Table 4 Predictive value of the FIB level for all-cause death and cardiac death in univariate and multivariate analysis
Full size table

Glycemic metabolism, FIB levels and occurrence of all-cause mortality

The prevalence of all-cause mortality in NG, Pre-DM and DM group was 2.4%, 3.3% and 4.7%, respectively. The Kaplan–Meier curve demonstrated that patients with DM had significantly increased risk of all-cause mortality and cardiac mortality among the three groups (all log-rank P < 0.05) (Fig. 3b, Additional file 1: Figure S2b). Univariate Cox analysis revealed that DM group had 1.91-fold higher risk of all-cause mortality (HR: 1.91; 95% CI 1.28–2.84; P = 0.001) and 2.18-fold higher risk of cardiac mortality (HR: 2.18; 95% CI 1.28–3.37; P = 0.004) when compared with NG group. And this significant association remained unchanged after adjustment for other covariates. However, Pre-DM group did not increase the risk of all-cause mortality and cardiac mortality compared with NG group (Fig. 4, Additional file 1: Figure S3).

Fig. 4
figure4

Relations of different glycemic metabolism status and all-cause death in univariate and multivariate survival analysis. Model adjusted for age, sex, BMI, hypertension, family history of CAD, prior PCI/CABG, LVEF, LDL-C, creatine, DES implantation, clopidogrel, ACEI/ARB. CI, confidence interval; NG, normoglycemia, Pre-DM, prediabetes; DM, diabetes mellitus

Full size image

When patients were evaluated according to both glycemic metabolism and FIB levels, the Kaplan–Meier curve showed that those with DM and FIB-H levels had significantly highest risk of all-cause mortality compared with the reference group (NG plus FIB-L group, log rank P < 0.001). Furthermore, NG plus FIB-H, Pre-DM plus FIB-M, Pre-DM plus FIB-H and DM plus FIB-M groups also had significantly increased risk of all-cause mortality than the reference group (NG plus FIB-L group, all log rank P < 0.05) (Fig. 3c). The further univariate Cox analysis revealed similar results. Multivariate Cox analysis according to both glycemic metabolism and FIB levels indicated that patients in Pre-DM plus FIB-H, DM plus FIB-M and DM plus FIB-H groups had 2.27-fold (adjusted HR: 2.27; 95% CI 1.01–5.12), 2.57-fold (adjusted HR: 2.57; 95% CI 1.12–5.89) and 3.04-fold (adjusted HR: 3.04; 95% CI 1.35–6.82) higher risk of all-cause mortality, respectively (all P < 0.05) (Table 5).

Table 5 Relation of the FIB level and all-cause death in patients with different glycemic metabolism status
Full size table

Discussion

Using a large, real-world, prospective cohort sample, we found that FIB positively correlated with glycemic metabolism in CAD patients undergoing PCI. Moreover, higher FIB levels, analyzed as continuous or categorical variables, were strongly associated with increased risk of long-term all-cause and cardiac mortality. Furthermore, poorer long-term outcomes were also found in diabetic patients, but not in prediabetic patients. Interestingly, when patients were categorized into 9 groups according to both FIB levels and glycemic metabolism status, patients with pre-DM plus high FIB levels, DM plus medium FIB levels and DM plus high FIB levels had increased risk of all-cause mortality than those with NG and low FIB levels. For the first time, our study demonstrated that FIB might affect the long-term prognosis in CAD patients with pre-DM undergoing PCI, and indicated a joint prognostic value of FIB levels and impaired glycemic metabolism on mortality in CAD patients undergoing PCI.

FIB is a crucial glycoprotein consisting of three different polypeptides, which is mainly synthesized in the liver15. Upon action of thrombin, FIB is transformed into fibrin monomer which then crosslinks platelets, increases blood viscosity and ultimately leads to clot formation3. Besides, FIB levels are elevated in response to various chronic inflammatory conditions, including DM, obesity and atherosclerosis7, 12, 16. It is also directly involved in the pathogenesis of atherosclerosis through multiple mechanisms, such as inducing endothelial dysfunction, stimulating smooth muscle cell proliferation and migration, facilitating monocyte or macrophage adhesion and infiltration of atherosclerotic lesions, which will jointly potentiate plaque evolution17.

To date, studies have been conducted on the prognostic value of FIB in different clinical settings. Aside from the positive association with all-cause and CVD mortality in general individuals6, 18, 19, FIB was reported to be an independent risk factor of the occurrence and severity of CAD20. Further, both small sample and large epidemiological studies showed that FIB was associated with worse clinical outcomes in patients with stable CAD10, 21, 22. A recent prospective study from China indicated elevated FIB was also strongly associated with MACE risk in ACS patients, especially when complicated with DM9. Similarly, the present study found FIB had an independent association with long-term all-cause and cardiac mortality in CAD patients undergoing PCI. Instead, the ADVANCE study including 3,865 diabetic patients showed FIB was not an independent predictor of 5-year mortality. It is worth noting that only limited number of patients in the ADVANCE study underwent coronary revascularization, while in our study all patients were treated with PCI [23]. The PRIME study including 926 men aged 50 to 59 without CAD found that FIB was not a risk marker of MI-coronary death. The differences in endpoints and sample size might contribute to the difference in results between this study and ours24. The EPIC-Norfolk study including 16,850 participants who were free of cancer, MI and stroke at baseline found that FIB was not a predictor of all-cause and cardiovascular mortality. However, the data used in the EPIC-Norfolk cohort are rather old and the serum used for measuring FIB and other biomarkers was stored frozen for more than ten years, which might limit the reliability of the results 25. Notably, none of these studies focused only on CAD population. This may be another possible explanation for the controversy between these studies and our findings. In spite of the conflicting findings mentioned above, data from the latest clinical trials confirmed the benefit of anti-inflammatory effect both in patients with chronic coronary disease and acute MI26, 27. Considering the role of FIB as an inflammatory biomarker, additional studies are warranted to further evaluate whether FIB could be helpful to identify high-risk individuals in CAD patients.

Currently, glycemic metabolism abnormality including DM and pre-DM is prevalent in clinical practice, especially in patients with established CAD11. It has been previously demonstrated that DM independently increased the risk of adverse CVD events in CAD patients28. Notably, CAD patients with pre-DM seemed to share similar clinical outcomes with those with normoglycemia10, 29. However, when combined with other disorders, such as dyslipidemia or hypertension, prediabetic patients with CAD were demonstrated to have significantly less favorable prognosis30,31,32. Interestingly, a large-sample observational study recently reported that elevated FIB increased the MACE risk in patients with stable CAD only in the presence of DM and pre-DM, indicating FIB to be valuable for prognostic assessment in prediabetic patients with stable CAD10. However, the combined value of FIB and impaired glycemic metabolism on prediction of mortality in CAD patients undergoing PCI is still unclear. In this study, we observed that among CAD patients undergoing PCI, diabetic individuals with medium or high FIB levels had 2.57-fold and 3.04-fold higher risk of mortality respectively during a median follow-up of 5.1 years. Furthermore, prediabetic patients also had higher mortality risk in the subgroup of high FIB levels, indicating that FIB may be useful for further risk stratification in CAD patients with mild impaired glycemic metabolism after PCI.

Patients with DM were confirmed to have higher levels of plasma FIB12. Chronic mild inflammation is a recognized pathological mechanism of DM33. Elevated FIB existing in diabetic patients aggravates the inflammatory process and the burden of atherosclerosis4, 34. FIB also involves in insulin resistance and impair the normal glycemic regulation13. Moreover, elevated FIB could weaken platelet inhibition with clopidogrel in the presence of DM35. And this effect is mediated through its direct interaction with the GP IIb/IIIa receptor, which is independent from inflammation. Indeed, our study found that the average levels of FIB elevated from NG, pre-DM to DM. Moreover, FIB was also positively associated with glycemic metabolism (HbA1c and FBG) both in CAD patients with or without DM, which was basically consistent with the prior studies9, 10. Collectively, although without established causality, the present study revealed a significant association between FIB and glycemic metabolism, as well as the long-term mortality in CAD patients undergoing PCI. Given the relatively simple and cost-effective test of FIB, these findings encourage its potential value as a biomarker in this specific population to identify high-risk patients, especially in those with DM and pre-DM. Meanwhile, the importance of routine screening for impaired glycemic metabolism also cannot be neglected.

Another issue to be discussed is the potential significance of lowering FIB levels in this specific population. Previous evidence showed the contribution of some lifestyle factors such as smoking, sedentary behavior and unhealthy diet to the elevation of plasma FIB levels36,37,38. On the contrary, exercise training significantly reduced FIB levels and improved cardiorespiratory fitness39. Therefore, lifestyle modification for patients with high FIB levels to achieve clinically favorable outcomes may be a reasonable exploration. In addition to plasmin, several medications such as fibrates are known to reduce FIB levels as an additional effect40. However, to date, no medication can specifically reduce FIB levels in the long run. Future studies are warranted to investigate whether patients could benefit from pharmacologic intervention on the premise that specific drug targeting FIB on a long-term basis is discovered.

This study has some limitations. First, FIB data at baseline was not available in about 40% patients, which might impair the strength of our study. Second, glycemic evaluation to identify new-onset diabetes and prediabetes was not routinely performed during follow-up. Third, information on other coagulation parameters such as prothrombin time (PT), activated partial thromboplastin time (APTT) and thrombin time (TT) were not collected previously. Fourth, the study period was relatively short and nearly 10% of patients were lost to follow-up. A longer follow-up such as ten years would be beneficial. Fifth, due to the observational design, potential confounders cannot be fully controlled.

Conclusions

FIB was independently associated with long-term all-cause and cardiac mortality among CAD patients undergoing PCI, especially in those with DM and Pre-DM. FIB test may help to identify high-risk individuals in this specific population.

Availability of data and materials

Due to ethical restrictions related to the consent given by subjects at the time of study commencement, our datasets are available from the corresponding author upon reasonable request after permission of the Institutional Review Board of Fuwai Hospital.

Abbreviations

FIB:

Fibrinogen

CAD:

Coronary artery disease

PCI:

Percutaneous coronary intervention

DM:

Diabetes mellitus

Pre-DM:

Prediabetes

NG:

Normoglycemia

HbA1c:

Hemoglobin A1c

FBG:

Fasting blood glucose

CVD:

Cardiovascular disease

ACS:

Acute coronary syndrome

LDL-C:

Low-density lipoprotein cholesterol

CI:

Confidence interval

BMI:

Body mass index

CABG:

Coronary artery bypass grafting

LVEF:

Left ventricular ejection fraction

DES:

Drug-eluting stent

ACEI:

Angiotensin-converting enzyme inhibitors

ARB:

Angiotensin II receptor blockers

hs-CRP:

high sensitivity C-reactive protein

TC:

Total cholesterol

TG:

Triglycerides

HDL-C:

High-density lipoprotein cholesterol

MACE:

Major adverse cardiovascular events

MI:

Myocardial infarction

References

  1. 1.

    Koskinas KC, Siontis GCM, Piccolo R, Franzone A, Haynes A, Rat-Wirtzler J, Silber S, Serruys PW, Pilgrim T, Räber L, et al. Impact of diabetic status on outcomes after revascularization with drug-eluting stents in relation to coronary artery disease complexity: patient-level pooled analysis of 6081 patients. Circ Cardiovasc Interv. 2016;9(2):e003255.

    CAS  PubMed  Article  Google Scholar 

  2. 2.

    Lowe GD. Fibrinogen and cardiovascular disease: historical introduction. Eur Heart J. 1995;16:2–5.

    PubMed  Article  Google Scholar 

  3. 3.

    Danesh J, Collins R, Peto R, Lowe GD. Haematocrit, viscosity, erythrocyte sedimentation rate: meta-analyses of prospective studies of coronary heart disease. Eur Heart J. 2000;21(7):515–20.

    CAS  PubMed  Article  Google Scholar 

  4. 4.

    Espinola-Klein C, Rupprecht HJ, Bickel C, Lackner K, Schnabel R, Munzel T, Blankenberg S. Inflammation, atherosclerotic burden and cardiovascular prognosis. Atherosclerosis. 2007;195(2):e126–34.

    CAS  PubMed  Article  Google Scholar 

  5. 5.

    Smith EB. Fibrinogen, fibrin and the arterial wall. Eur Heart J. 1995;16:21.

    Article  Google Scholar 

  6. 6.

    Danesh J, Lewington S, Thompson SG, Lowe GDO, Collins R, Kostis JB, Wilson AC, Folsom AR, Wu K, Benderly M, et al. Plasma fibrinogen level and the risk of major cardiovascular diseases and nonvascular mortality: an individual participant meta-analysis. JAMA. 2005;294(14):1799–809.

    CAS  PubMed  Google Scholar 

  7. 7.

    Stec JJ, Silbershatz H, Tofler GH, Matheney TH, Sutherland P, Lipinska I, Massaro JM, Wilson PF, Muller JE, D’Agostino RB. Association of fibrinogen with cardiovascular risk factors and cardiovascular disease in the Framingham offspring population. Circulation. 2000;102(14):1634–8.

    CAS  PubMed  Article  Google Scholar 

  8. 8.

    Ang L, Behnamfar O, Palakodeti S, Lin F, Pourdjabbar A, Patel MP, Reeves RR, Mahmud E. Elevated baseline serum fibrinogen: effect on 2-year major adverse cardiovascular events following percutaneous coronary intervention. J Am Heart Assoc. 2017;6:11.

    Article  Google Scholar 

  9. 9.

    Zhang L, Xu C, Liu J, Bai X, Li R, Wang L, Zhou J, Wu Y, Yuan Z. Baseline plasma fibrinogen is associated with haemoglobin A1c and 2-year major adverse cardiovascular events following percutaneous coronary intervention in patients with acute coronary syndrome: a single-centre, prospective cohort study. Cardiovasc Diabetol. 2019;18(1):52.

    PubMed  PubMed Central  Article  Google Scholar 

  10. 10.

    Liu S-L, Wu N-Q, Shi H-W, Dong Q, Dong Q-T, Gao Y, Guo Y-L, Li J-J. Fibrinogen is associated with glucose metabolism and cardiovascular outcomes in patients with coronary artery disease. Cardiovasc Diabetol. 2020;19(1):36.

    PubMed  PubMed Central  Article  Google Scholar 

  11. 11.

    Cho NH, Shaw JE, Karuranga S, Huang Y, da Rocha Fernandes JD, Ohlrogge AW, Malanda B. IDF diabetes atlas: global estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes Res Clin Pract. 2018;138:271–81.

    CAS  PubMed  Article  Google Scholar 

  12. 12.

    Barazzoni R, Zanetti M, Davanzo G, Kiwanuka E, Carraro P, Tiengo A, Tessari P. Increased fibrinogen production in type 2 diabetic patients without detectable vascular complications: correlation with plasma glucagon concentrations. J Clin Endocrinol Metab. 2000;85(9):3121–5.

    CAS  PubMed  Article  Google Scholar 

  13. 13.

    Kristiansen OP, Mandrup-Poulsen T. Interleukin-6 and diabetes: the good, the bad, or the indifferent? Diabetes. 2005;54(Suppl 2):S114–24.

    CAS  PubMed  Article  Google Scholar 

  14. 14.

    Classification and Diagnosis of Diabetes. Diabetes Care. 2019;42(Suppl 1):S13–28.

    Google Scholar 

  15. 15.

    Tousoulis D, Papageorgiou N, Androulakis E, Briasoulis A, Antoniades C, Stefanadis C. Fibrinogen and cardiovascular disease: genetics and biomarkers. Blood Rev. 2011;25(6):239–45.

    CAS  PubMed  Article  Google Scholar 

  16. 16.

    Bäck M, Yurdagul A, Tabas I, Öörni K, Kovanen PT. Inflammation and its resolution in atherosclerosis: mediators and therapeutic opportunities. Nat Rev Cardiol. 2019;16(7):389–406.

    PubMed  PubMed Central  Google Scholar 

  17. 17.

    Green D, Foiles N, Chan C, Schreiner PJ, Liu K. Elevated fibrinogen levels and subsequent subclinical atherosclerosis: the CARDIA Study. Atherosclerosis. 2009;202(2):623–31.

    CAS  PubMed  Article  Google Scholar 

  18. 18.

    Yano K, Grove JS, Chen R, Rodriguez BL, Curb JD, Tracy RP. Plasma fibrinogen as a predictor of total and cause-specific mortality in elderly Japanese–American men. Arterioscler Thromb Vasc Biol. 2001;21(6):1065–70.

    CAS  PubMed  Article  Google Scholar 

  19. 19.

    Jenny NS, Yanez ND, Psaty BM, Kuller LH, Hirsch CH, Tracy RP. Inflammation biomarkers and near-term death in older men. Am J Epidemiol. 2007;165(6):684–95.

    PubMed  Article  Google Scholar 

  20. 20.

    Kaptoge S, Di Angelantonio E, Pennells L, Wood AM, White IR, Gao P, Walker M, Thompson A, Sarwar N, Caslake M, et al. C-reactive protein, fibrinogen, and cardiovascular disease prediction. N Engl J Med. 2012;367(14):1310–20.

    PubMed  Article  Google Scholar 

  21. 21.

    Sinning J-M, Bickel C, Messow C-M, Schnabel R, Lubos E, Rupprecht HJ, Espinola-Klein C, Lackner KJ, Tiret L, Münzel T, et al. Impact of C-reactive protein and fibrinogen on cardiovascular prognosis in patients with stable angina pectoris: the AtheroGene study. Eur Heart J. 2006;27(24):2962–8.

    CAS  PubMed  Article  Google Scholar 

  22. 22.

    Mjelva ØR, Svingen GFT, Pedersen EKR, Seifert R, Kvaløy JT, Midttun Ø, Ueland PM, Nordrehaug JE, Nygård O, Nilsen DWT. Fibrinogen and neopterin is associated with future myocardial infarction and total mortality in patients with stable coronary artery disease. Thromb Haemost. 2018;118(4):778–90.

    PubMed  Google Scholar 

  23. 23.

    Lowe G, Woodward M, Hillis G, Rumley A, Li Q, Harrap S, Marre M, Hamet P, Patel A, Poulter N, et al. Circulating inflammatory markers and the risk of vascular complications and mortality in people with type 2 diabetes and cardiovascular disease or risk factors: the ADVANCE study. Diabetes. 2014;63(3):1115–23.

    CAS  PubMed  Article  Google Scholar 

  24. 24.

    Luc G, Bard J-M, Juhan-Vague I, Ferrieres J, Evans A, Amouyel P, Arveiler D, Fruchart J-C, Ducimetiere P. C-reactive protein, interleukin-6, and fibrinogen as predictors of coronary heart disease: the PRIME Study. Arterioscler Thromb Vasc Biol. 2003;23(7):1255–61.

    CAS  PubMed  Article  Google Scholar 

  25. 25.

    Ahmadi-Abhari S, Luben RN, Wareham NJ, Khaw K-T. Seventeen year risk of all-cause and cause-specific mortality associated with C-reactive protein, fibrinogen and leukocyte count in men and women: the EPIC-Norfolk study. Eur J Epidemiol. 2013;28(7):541–50.

    CAS  PubMed  Article  Google Scholar 

  26. 26.

    Tardif J-C, Kouz S, Waters DD, Bertrand OF, Diaz R, Maggioni AP, Pinto FJ, Ibrahim R, Gamra H, Kiwan GS, et al. Efficacy and safety of low-dose colchicine after myocardial infarction. N Engl J Med. 2019;381(26):2497–505.

    CAS  PubMed  Article  Google Scholar 

  27. 27.

    Nidorf SM, Fiolet ATL, Mosterd A, Eikelboom JW, Schut A, Opstal TSJ, The SHK, Xu X-F, Ireland MA, Lenderink T, et al. Colchicine in Patients with Chronic Coronary Disease. N Engl J Med. 2020;383(19):1838–47.

    CAS  PubMed  Article  Google Scholar 

  28. 28.

    Haffner SM, Lehto S, Rönnemaa T, Pyörälä K, Laakso M. Mortality from coronary heart disease in subjects with type 2 diabetes and in nondiabetic subjects with and without prior myocardial infarction. N Engl J Med. 1998;339(4):229–34.

    CAS  PubMed  Article  Google Scholar 

  29. 29.

    Yuan D, Zhang C, Jia S, Jiang L, Xu L, Zhang Y, Xu J, Xu B, Hui R, Gao R, et al. Prediabetes and long-term outcomes in patients with three-vessel coronary artery disease: a large single-center cohort study. J Diabetes Investig. 2021;12(3):409–16.

    PubMed  Article  Google Scholar 

  30. 30.

    Jin J-L, Cao Y-X, Zhang H-W, Sun D, Hua Q, Li Y-F, Guo Y-L, Wu N-Q, Zhu C-G, Gao Y, et al. Lipoprotein(a) and Cardiovascular Outcomes in Patients With Coronary Artery Disease and Prediabetes or Diabetes. Diabetes Care. 2019;42(7):1312–8.

    CAS  PubMed  Article  Google Scholar 

  31. 31.

    Jin J-L, Zhang H-W, Cao Y-X, Liu H-H, Hua Q, Li Y-F, Zhang Y, Guo Y-L, Wu N-Q, Zhu C-G, et al. Long-term prognostic utility of low-density lipoprotein (LDL) triglyceride in real-world patients with coronary artery disease and diabetes or prediabetes. Cardiovasc Diabetol. 2020;19(1):152.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  32. 32.

    Liu H-H, Cao Y-X, Li S, Guo Y-L, Zhu C-G, Wu N-Q, Gao Y, Dong Q-T, Zhao X, Zhang Y, et al. Impacts of prediabetes mellitus alone or plus hypertension on the coronary severity and cardiovascular outcomes. Hypertension. 2018;71(6):1039–46.

    CAS  PubMed  Article  Google Scholar 

  33. 33.

    Odegaard AO, Jacobs DR, Sanchez OA, Goff DC, Reiner AP, Gross MD. Oxidative stress, inflammation, endothelial dysfunction and incidence of type 2 diabetes. Cardiovasc Diabetol. 2016;15:51.

    PubMed  PubMed Central  Article  Google Scholar 

  34. 34.

    Yahagi K, Kolodgie FD, Lutter C, Mori H, Romero ME, Finn AV, Virmani R. Pathology of human coronary and carotid artery atherosclerosis and vascular calcification in diabetes mellitus. Arterioscler Thromb Vasc Biol. 2017;37(2):191–204.

    CAS  PubMed  Article  Google Scholar 

  35. 35.

    Ang L, Palakodeti V, Khalid A, Tsimikas S, Idrees Z, Tran P, Clopton P, Zafar N, Bromberg-Marin G, Keramati S, et al. Elevated plasma fibrinogen and diabetes mellitus are associated with lower inhibition of platelet reactivity with clopidogrel. J Am Coll Cardiol. 2008;52(13):1052–9.

    CAS  PubMed  Article  Google Scholar 

  36. 36.

    Wannamethee SG, Lowe GDO, Shaper AG, Rumley A, Lennon L, Whincup PH. Associations between cigarette smoking, pipe/cigar smoking, and smoking cessation, and haemostatic and inflammatory markers for cardiovascular disease. Eur Heart J. 2005;26(17):1765–73.

    CAS  PubMed  Article  Google Scholar 

  37. 37.

    Howard BJ, Balkau B, Thorp AA, Magliano DJ, Shaw JE, Owen N, Dunstan DW. Associations of overall sitting time and TV viewing time with fibrinogen and C reactive protein: the AusDiab study. Br J Sports Med. 2015;49(4):255–8.

    PubMed  Article  Google Scholar 

  38. 38.

    Miura K, Nakagawa H, Ueshima H, Okayama A, Saitoh S, Curb JD, Rodriguez BL, Sakata K, Okuda N, Yoshita K, et al. Dietary factors related to higher plasma fibrinogen levels of Japanese–Americans in hawaii compared with Japanese in Japan. Arterioscler Thromb Vasc Biol. 2006;26(7):1674–9.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  39. 39.

    Lin X, Zhang X, Guo J, Roberts CK, McKenzie S, Wu W-C, Liu S, Song Y. Effects of exercise training on cardiorespiratory fitness and biomarkers of cardiometabolic health: a systematic review and meta-analysis of randomized controlled trials. J Am Heart Assoc. 2015;4:7.

    Google Scholar 

  40. 40.

    Sahebkar A, Serban M-C, Mikhailidis DP, Toth PP, Muntner P, Ursoniu S, Mosterou S, Glasser S, Martin SS, Jones SR, et al. Head-to-head comparison of statins versus fibrates in reducing plasma fibrinogen concentrations: a systematic review and meta-analysis. Pharmacol Res. 2016;103:236–52.

    CAS  PubMed  Article  Google Scholar 

Download references

Acknowledgements

We are grateful to all staff members for their contribution to the study.

Funding

This work was supported by The National Key Research and Development Program of China (No. 2016YFC1301300, 2016YFC1301301); National Natural Science Foundation of China (No. 81770365).

Author information

Affiliations

  1. State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, No 167, Beilishi Road, Xicheng District, Beijing, 100037, China

    Deshan Yuan, Ping Jiang, Pei Zhu, Sida Jia, Ce Zhang, Yue Liu, Ru Liu, Jingjing Xu, Xiaofang Tang, Xueyan Zhao, Runlin Gao, Yuejin Yang, Bo Xu, Zhan Gao & Jinqing Yuan

Authors
  1. Deshan Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ping Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pei Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sida Jia
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ce Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yue Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Ru Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Jingjing Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Xiaofang Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Xueyan Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Runlin Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Yuejin Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Bo Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Zhan Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Jinqing Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

YDS, ZXY, GRL, YYJ, XB, GZ and YJQ contributed to the conception and design of the work. JP, ZP, JSD, ZC, LY, LR, XJJ and TXF contributed to in data collection and analysis. YDS drafted the manuscript. YJQ critically revised the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Jinqing Yuan.

Ethics declarations

Ethics approval and consent to participate.

The Ethical Review Board of Fuwai Hospital approved the study protocol in accordance with the Declaration of Helsinki, and written informed consent was obtained from all participants.

Consent for publication

The manuscript was approved by all authors for publication.

Competing interests

All authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: Table S1.

Summary of cause of mortality. Figure S1. Restricted cubic splines of FIB levels in relation to relative hazard ratio for all-cause death and cardiac death. Figure S2. Kaplan-Meier analysis for cardiac death according to different FIB levels and glycemic metabolism status. Figure S3. Relations of different glycemic metabolism status and cardiac death in univariate and multivariate survival analysis.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Yuan, D., Jiang, P., Zhu, P. et al. Prognostic value of fibrinogen in patients with coronary artery disease and prediabetes or diabetes following percutaneous coronary intervention: 5-year findings from a large cohort study. Cardiovasc Diabetol 20, 143 (2021). https://doi.org/10.1186/s12933-021-01335-1

Download citation

Keywords

  • Fibrinogen
  • Coronary artery disease
  • Percutaneous coronary intervention
  • Prediabetes
  • Diabetes

Cardiovascular Diabetology

ISSN: 1475-2840