Association between fibrinogen-albumin ratio and erectile dysfunction: a retrospective cross-sectional study

Abstract

Objective

The fibrinogen-to-albumin ratio (FAR) is a new marker used in inflammatory processes and is effective in demonstrating associated microvascular damage. The aim of this study was to evaluate the relationship between FAR and erectile dysfunction (ED), a sexual intercourse disorder associated with inflammatory processes and endothelial dysfunction.

Methods

Data from 1876 patients presenting with ED between January 2020 and 2025 were evaluated. A total of 424 patients, 214 in the ED group and 210 in the control group, were included in the study. Anthropometric measurements and biochemical examinations of the patients were recorded. FAR values were compared between the ED group, ED subgroups, and the control group. The cut-off value for the FAR value and the potential correlation between ED severity were examined.

Results

The FAR value was calculated as 89.4 ± 27.3 in the ED group and 67.8 ± 18.8 in the control group (p < 0.001). The optimal cut-off value for FAR was found to be 101.49 with 33% sensitivity and 99% specificity. FAR values were significantly higher in the ED subgroups compared to the control group. A moderate linear correlation was found between IIEF-5 scores and FAR values (r = -0.554, p < 0.001). The FAR value (AUC = 0.724) was found to be more successful in distinguishing between diseased and healthy individuals than CAR (AUC = 0.647), a similar inflammatory marker.

Conclusion

It has been shown that FAR is significantly associated with the presence and severity of ED and can be a reliable and easily accessible marker to support the diagnosis of ED.

Introduction

Erectile dysfunction (ED) is a highly prevalent sexual disorder in men and is defined as the inability to achieve or maintain an erection sufficient for satisfactory sexual performance [1]. ED causes physical and psychological problems in people, causes problems in the relationship between couples and in daily activities, and as a result, it significantly affects the quality of life [2]. Numerous factors associated with the risk of developing ED were identified, including age and comorbidities, vascular endothelial, neuronal, endocrine, and psychological events [3]. The pathogenesis of ED is multifactorial and complex, but vascular ED in particular was shown to be associated with vascular endothelial dysfunction, which limits blood flow and negatively impacts erectile function. Studies on this subject show that inflammatory events cause endothelial dysfunction and play an important role in ED pathophysiology together with atherosclerotic processes [4]. Therefore, ED is not only a sexual health problem but can also be a predictor of other systemic diseases. It is indicated in studies that ED development increases the likelihood of experiencing adverse cardiovascular events and is a predictive factor for cardiovascular diseases [5]. Similarly, a high correlation was found between the degree of ED and glucose metabolism and insulin resistance [6]. For these reasons, early diagnosis is important to investigate the risk factors associated with therapeutic and preventive measures. However, there are a limited number of hematological biomarkers that can be used in the diagnosis of ED, and sufficient support cannot be obtained from these markers in clinical practice.

The most common risk factor for the development of atherosclerosis was shown to be increased production of reactive oxygen species [7]. Therefore, oxidative stress plays an important role in ED pathophysiology. Oxidative stress significantly affects circulating proteins, and albumin and fibrinogen are among the proteins frequently affected and are widely used markers in atherosclerosis-related studies [8]. Fibrinogen-albumin ratio (FAR) is a new indicator based on inflammation and provides more effective clinical results compared to fibrinogen and albümin [9]. Recent studies show that high FAR values are an important risk factor for both arterial and venous ischemia and indicate that they also increase the risk of thrombosis [10]. Considering the relationship between FAR and microcirculation and its role in the development and progression of inflammation, it was shown to have a critical role in cardiovascular diseases such as myocardial infarction, acute decompensated heart failure, and cerebrovascular events [11].

Although many important pathologies involved in ED pathophysiology and associated with FAR were revealed, the relationship between FAR and ED has not yet been evaluated in the literature. In this study, the potential relationship between ED and FAR value will be examined for the first time and it is aimed to calculate the cut-off value that can help in the diagnosis of ED and compare it with other inflammatory markers.

Materials and methods

This retrospective cross-sectional study was completed in the Urology clinic of Ordu University. The study permission was obtained from the Ordu University local ethics committee (No: 2025/48). Data from 1,876 patients who presented to our outpatient clinic with complaints of ED between January 2020 and January 2025 were accessed and evaluated. Those who met the study criteria were included in the patient group. The study was conducted based on the criteria of the Declaration of Helsinki.

First, the patients' sexual and medical characteristics were examined, and attempts were made to distinguish psychogenic from organic ED. Then, 214 patients between the ages of 40 and 70 with an International Index of Erectile Function (IIEF-5) score below 22 were included in the study group. The control group consisted of 210 men who attended the urology outpatient clinic for conditions unrelated to erectile dysfunction, including routine urological evaluation, benign lower urinary tract symptoms, follow-up visits, and other non-inflammatory urological conditions. None of the control subjects reported erectile dysfunction-related complaints. The same exclusion criteria applied to the ED group were also applied to the control group to minimize potential confounding factors. Sociodemographic characteristics of the groups such as age, height, weight, body mass index, and comorbidities such as accompanying cardiac diseases, diabetes mellitus, hypertension, and dyslipidemia were recorded. Biochemical analyses were based on laboratory measurements obtained during routine outpatient evaluation of patients. Blood samples were collected in the morning after overnight fasting as part of the standard diagnostic work-up for erectile dysfunction and were subsequently retrieved from the hospital database during the retrospective review. No additional laboratory tests or blood sampling were performed for the purpose of this study. The fibrinogen-albumin ratio (FAR) value was calculated using fibrinogen (mg/dL) and albumin (g/dL) values, and the CRP-albumin ratio (CAR) value was calculated using C-reactive protein (mg/dL) and albumin values. Additionally, fasting blood glucose (mg/dL), total testosterone (µg/L), creatinine (mg/dL), and sedimentation (mm/h) values of the patients were compared between the groups. Patients in the ED group were divided into subgroups according to their IIEF scores as severe (5–7), moderate (8–11), mild-moderate (12–16), and mild (17–21). ED subgroups were compared first among themselves and then with the control group in terms of FAR and CAR values.

Exclusion criteria from the study included the presence of a psychiatric disease diagnosis and related medication use, neurological disease, endocrine problem, uncontrolled diabetes and related neurological or vascular complications, metabolic syndrome, organ failure, penile disease and history of previous urological surgery, malignancy diagnosis, radiotherapy history, active inflammatory status or inflammatory disease diagnosis, and these patients were not included in the study. Detailed information regarding case selection is shown in Fig. 1.

Fig. 1.

Schematic flow diagram of inclusion and exclusion criteria for our study cohort

Statistical analysis

Statistical analyses were performed using SPSS (SPSS Inc., Chicago, IL), a statistical software program. Sample size was calculated using GPower 3.1 software for a statistical power of 0.90 and an alpha level of 0.05. The Shapiro–Wilk test was used to calculate normality in data distribution. While mean ± standard deviation was used to express numerical variables with normal distribution, median ± interquartile range was used to express data that did not have a normal distribution. Categorical variables were expressed as numbers and percentages. Statistical analyses were performed using the independent samples t-test and one-way ANOVA tests. Pearson correlation analysis was used to determine the relationship between the data. Receiver operating characteristic (ROC) curve analysis was performed to determine the cut-off value for the values, and the AUC value was used to compare the effects between the ratios.

To determine whether FAR was independently associated with ED, a multivariable logistic regression analysis was performed including age, body mass index, diabetes mellitus, hypertension, chronic obstructive pulmonary disease, cardiac disease, dyslipidemia, and FAR as covariates. Prior to model construction, multicollinearity among independent variables was evaluated using tolerance and variance inflation factor (VIF) statistics. Tolerance values > 0.10 and VIF values < 5 were considered indicative of the absence of significant multicollinearity. Outlier analysis was performed using boxplot graphics based on the interquartile range (IQR) method. Observations falling outside Q1 − 1.5 × IQR and Q3 + 1.5 × IQR were classified as outliers. Extreme outliers were defined as observations beyond Q1 − 3 × IQR and Q3 + 3 × IQR.

Results

Demographic characteristics of the groups and comparisons of comorbidities are shown in Table 1. When the mean ages of the ED group and the control group were compared, no significant difference was found between them (60.7 ± 10.5 vs. 57.1 ± 10.1 years; p = 0.61). There was no significant difference between the calculated body mass index (BMI) values of the ED group and the control group (28.46 ± 3.74 vs. 27.16 ± 3.13 kg/m2; p = 0.062). In terms of comorbidities, rates of diabetes mellitus (p = 0.636), hypertension (p = 0.169), heart diseases (p = 0.246), and dyslipidemia (p = 0.805) were found to be similar in the groups.

Table 1.

Anthropometric measurements and comorbidities of patient (ED) and control groups

	ED group (n = 214)	Control (n = 210)	p valueb
Characteristics
Age (year)a	60.7 ± 10.5	57.15 ± 10.1	0.061
Body mass index (kg/m2)a	28.46 ± 3.7	27.16 ± 3.1	0.062
Height (cm)a	174.11 ± 8.11	172.93 ± 6.93	0.467
Weight (kg)a	85.85 ± 10.21	82.09 ± 8.95	0.055
Comorbidities
Diabetes mellitus	83 (39%)	65 (31%)	0.636
Dyslipidemia	45 (21%)	42 (20%)	0.805
Cardiac disease	34 (16%)	25 (12%)	0.246
Hypertension	71 (33%)	61 (29%)	0.169
COPD	58 (27%)	40 (19%)	0.252

0.05 * < 0.001**

^aData are mean ± standard deviation

^bindependent samples t-test

When the groups were compared in terms of biochemical parameters, no significant difference was found between the two groups in terms of serum creatinine (p = 0.064), fasting blood glucose (p = 0.090), total testosterone (p = 0.052), and sedimentation (p = 0.069) measurements. The mean serum fibrinogen value was calculated as 381.8 ± 116.4 mg/dL in the ED group and 306.1 ± 81.5 mg/dL in the control group and it was found to be significantly higher in the ED group (p < 0.001). Serum albumin value was calculated as 4.50 ± 0.3 g/dL in the control group and 4.30 ± 0.3 g/dL in the ED group and it was found to be significantly lower in the ED group (p < 0.001). The mean CRP value was 2.18 ± 1.93 mg/dL in the ED group and 0.85 ± 0.73 mg/dL in the control group (p < 0.001). Details of the biochemical analyses are shown in Table 2.

Table 2.

Comparison of biochemical and hormonal parameters in ED and control groups

	ED group (mean ± SD)	Control (mean ± SD)	p valuea
Glucose (mg/dL)	118.96 ± 20.52	113.64 ± 16.84	0.090
Albumin (g/dL)	4.30 ± 0.30	4.52 ± 0.30	< 0.001**
Total Testosterone (µg/L)	4.21 ± 1.49	4.72 ± 1.81	0.052
Creatinine (mg/dL)	0.88 ± 0.16	0.90 ± 0.12	0.064
CRP (mg/dL)	2.18 ± 1.93	0.85 ± 0.73	< 0.001**
Sedimentation (mm/h)	17.54 ± 12.87	15.91 ± 9.77	0.069
Fibrinogen (mg/dL)	381.89 ± 116.44	306.14 ± 81.51	< 0.001**
Fibrinogen/Albumin ratio	89.45 ± 27.32	67.89 ± 18.81	< 0.001**
CRP/Albumin ratio	0.47 ± 0.42	0.19 ± 0.16	< 0.001**

0.05 * < 0.001**

^aindependent samples t-test

The calculated FAR value was found to be 89.4 ± 27.3 in the ED group and 67.8 ± 18.8 in the control group and it was found to be significantly higher in the ED group (p < 0.001). Outlier value analysis was performed to determine that there was no unexpected data that was significantly different from other data in the groups and was presented in the form of boxplot graphics (Fig. 2). The most appropriate cut-off value of FAR that can be used to distinguish patients with ED from the control group was calculated as 101.49 with 33% sensitivity and 99% specificity. The area under the receiver operating characteristic curve (AUC) value for this value was found to be 0.724 (Fig. 3).

Fig. 2.

Box plot distribution of FAR values ​​in erectile dysfunction and control groups. The horizontal line inside each box represents the median value, while the boxes show the interquartile range. Whiskers represent the minimum and maximum values. Corresponding p-values ​​are available for group comparisons

Fig. 3.

ROC analysis and AUC comparison of FAR and CAR groups

For additional comparative analyses, FAR values were dichotomized according to the median FAR value of the study population (< 75 and ≥ 75). Using this categorization, a significant difference was observed between the proportions of patients with erectile dysfunction and controls across FAR categories (p < 0.001) (Fig. 4).

Fig. 4.

Distribution of erectile dysfunction and control subjects according to FAR categories (< 75 and ≥ 75). The threshold of 75 was based on the median FAR value of the study population and was used for comparative analyses rather than diagnostic classification

When FAR values were compared between the subgroups divided into ED severity according to the IIEF-5 score, no significant difference was found among them (p = 0.071) (Table 3). However, when separate comparisons were made between the ED subgroups and the control group, FAR values were found to be significantly higher than the control group (Table 4).

Table 3.

Comparison of FAR and CAR values ​​among ED subgroups

	Mild (n = 55) (26%)	Mild-Moderate (n = 62) (29%)	Moderate (n = 53) (25%)	Severe (n = 44) (20%)	p valuea
Fibrinogen/Albumin Ratio (FAR)	91.83 ± 15.79	93.11 ± 19.63	100.12 ± 25.24	106.30 ± 27.23	0.071
CRP/Albumin Ratio (CAR)	0.35 ± 0.32	0.56 ± 0.43	0.44 ± 0.44	0.45 ± 0.43	0.095

0.05 * < 0.001**

^aone-way ANOVA

Table 4.

Comparison of Fibrinogen/Albumin Ratio (FAR) and CRP/Albumin Ratio (CAR) values ​​of ED subgroups with the control group

Groups (n = 214)	FAR	Control (n = 210)	p valuea	CAR	Control (n = 210)	p valuea
Mild (n = 55)	91.83 ± 15.79	67.89 ± 18.81	0.045*	0.35 ± 0.32	0.19 ± 0.16	0.023*
Mild-Moderate (n = 62)	93.11 ± 19.63		0.022*	0.56 ± 0.43		< 0.001**
Moderate (n = 53)	100.12 ± 25.24		< 0.001**	0.44 ± 0.44		0.042*
Severe (n = 44)	106.30 ± 27.23		< 0.001**	0.45 ± 0.43		< 0.001**

0.05 * < 0.001**

^aindependent samples t-test

According to the results of the Pearson correlation analysis performed to determine the relationship between IIEF-5 scores and FAR value, a moderate, negative linear relationship was found between the FAR value and the scores (r = −0.554, p < 0.001). A significant relationship was observed between the change in FAR value and the severity of ED (Fig. 5).

Fig. 5.

The relationship between FAR values and IIEF-5 scores in the ED and control groups

When the two groups were compared in terms of CAR value, it was found to be significantly higher in the ED group than in the control group (0.47 ± 0.42 vs. 0.19 ± 0.16, p < 0.001). 33% of patients with ED and 98% of healthy individuals could be detected with a cut-off value of 0.548. The AUC value of CAR was calculated as 0.647. A significant difference was found in terms of CAR value between the ED subgroups and the control group (Table 4).

When the success of the FAR value in distinguishing healthy and sick individuals was compared with the success of the CAR value in terms of AUC values, it was seen that the value of FAR (AUC = 0.724) was higher than the value of CAR (AUC = 0.647) and the FAR value was more successful (Fig. 3).

To evaluate the dose–response relationship between FAR and erectile dysfunction risk, FAR values were categorized into quartiles and analyzed using logistic regression. The lowest FAR quartile was used as the reference category. Compared with the reference quartile, the odds of erectile dysfunction increased progressively across higher FAR quartiles, with ORs of 4.176 (95% CI: 1.54–11.29, p = 0.005), 6.786 (95% CI: 2.50–18.41, p < 0.001), and 9.839 (95% CI: 3.58–27.05, p < 0.001) for the second, third, and fourth quartiles, respectively. The overall model was statistically significant (Omnibus χ² = 26.64, df = 3, p < 0.001), supporting a dose-dependent association between FAR and the presence of erectile dysfunction (Table 5).

Table 5.

Dose–response relationship between FAR quartiles and risk of erectile dysfunction

FAR Quartile	B	S.E	Wald χ2	p value	OR (Exp(B))	95% CI for OR
Q1 (Lowest)	Reference	-	-	-	1.00	Reference
Q2	1.429	0.508	7.93	0.005	4.176	1.54–11.29
Q3	1.915	0.509	14.147	< 0.001	6.786	2.50–18.41
Q4	2.286	0.516	19.641	< 0.001	9.839	3.58–27.05

Model statistics: Omnibus χ² = 26.64, df = 3, p < 0.001

A multivariable logistic regression analysis was performed to identify factors independently associated with erectile dysfunction. The overall model was statistically significant (Omnibus test χ² = 34.820, p < 0.001). The Hosmer–Lemeshow goodness-of-fit test was not statistically significant (p = 0.088), indicating acceptable model fit.

After adjustment for age, BMI, diabetes mellitus, hypertension, chronic obstructive pulmonary disease, cardiac disease, and dyslipidemia, FAR remained independently associated with erectile dysfunction (OR = 1.450, 95% CI: 1.145–1.975, p < 0.001). None of the remaining covariates reached statistical significance (Table 6).

Table 6.

Multivariable logistic regression analysis for factors associated with erectile dysfunction

Variable	B	OR (Exp(B))	95% CI	p value
Age	0.560	1.751	0.587–5.217	0.315
BMI	−0.124	0.883	0.768–1.015	0.081
Diabetes mellitus	0.233	1.262	0.451–3.531	0.657
Hypertension	−0.420	0.657	0.237–1.821	0.420
COPD	−0.782	0.458	0.143–1.460	0.187
Cardiac disease	0.231	1.260	0.351–4.524	0.723
Dyslipidemia	0.411	1.508	0.460–4.947	0.498
FAR	0.372	1.450	1.145–1.195	< 0.001

Omnibus test χ² = 34.820, p < 0.001; Hosmer–Lemeshow test p = 0.088; Nagelkerke R² = 0.234

Multicollinearity diagnostics demonstrated tolerance values ranging from 0.809 to 0.957 and VIF values ranging from 1.044 to 1.237, indicating no evidence of significant multicollinearity among the independent variables. The Nagelkerke R² value was 0.234, indicating that the model explained approximately 23.4% of the variance in erectile dysfunction status. Five-fold internal cross-validation yielded a mean AUC of 0.725 for FAR, which was highly consistent with the original ROC-derived AUC value (0.724).

Discussion

ED is a common health problem that significantly impacts the physical and mental well-being of patients and their partners. The prevalence of ED is increasing worldwide, and it is estimated that approximately 300 million men suffer from the disease [12]. Therefore, there is a need for indices and markers that will facilitate the diagnosis of ED and support current trends. In our study, FAR value was found to be significantly higher in ED patients than in the control group and it was observed that it correlated with the severity of ED according to the IIEF-5 score and could be used as a new marker to support the diagnosis of ED (p < 0.001).

There are several risk factors for ED development, including age, diabetes mellitus (DM), cardiovascular diseases (CVD), hypertension, obesity, chronic inflammatory conditions, and metabolic syndrome. This leads to a very high prevalence of ED. The prevalence of ED, particularly in those over 40 years of age, is reported to be 46.1% in the United States, 42.1%−52.5% in Europe, and 47.4% in China [13]. Endothelial dysfunction is most frequently cited as the key point among the mechanisms advocated for ED development. Reactive oxygen species, which emerge as a result of oxidative stress due to inflammatory events, cause endothelial cell damage and impair the function of cavernous tissue by affecting nitric oxide release [14]. If this condition, which causes endothelial damage, continues, penile vasculopathy develops in the long term and causes loss of penile erection due to decreased blood flow in the cavernous tissue [15]. This condition, which develops in the penile blood vessels, is usually the result of a systemic disorder rather than a localized symptom. Oxidative stress and decreased nitric oxide release resulting from systemic inflammatory events explain the endothelial damage that develops [16]. In the study conducted by Chen et al., it was shown that ED was associated with systemic inflammatory status, inflammation increased the risk of ED, and inflammatory markers were expressed abnormally in these patients [17]. In their study, Zhang et al. showed that systemic inflammatory conditions increased the prevalence of ED and were additionally associated with conditions such as hypertension, DM, CVD, hypercholesterolemia, and mortality rates in ED patients [18]. Moreover, Yüksek et al. emphasized that in addition to the relationship between inflammatory markers and ED, inflammatory disease activities and ED severity were correlated [19]. Therefore, it is thought that inflammation-related circulatory proteins are affected in patients with ED and may be useful in predicting ED.

In addition to its effects on the coagulation mechanism, fibrinogen is a protein that acts as an acute phase reactant associated with inflammation. It increases platelet aggregation by interacting with fibrin degradation products and contributes to thrombus formation. It increases susceptibility to atherosclerotic processes in cardiovascular diseases and was shown to be associated with the development and prevalence of atherosclerosis. High fibrinogen levels were shown to be the cause of thrombosis and vascular damage in cardiovascular diseases and were reported to be an independent predictor associated with a high risk of complications and morbidity [20]. In their study, Ban et al. reported that high fibrinogen levels increased blood viscosity, increased susceptibility to clotting, and promoted thrombosis formation, leading to microcirculatory ischemia and hypoxia, and pointed out that they contributed to the pathogenesis of vascular problems by damaging the endothelial structure [21]. Serum albumin level is a marker that is associated with nutritional status and is generally an acute phase protein and is accepted as a marker of systemic inflammation. Low serum albumin levels lead to the progression of inflammation and increased oxidative damage through the effects of cytokines such as IL-6 and TNF-α, thus leading to the deterioration of endothelial function [22]. Komrokji et al. stated that hypoalbuminemia represented a chronic inflammatory condition and was associated with malignancy and autoimmune disorders and could be used to predict morbidity and mortality [23]. In our study, serum albumin levels were significantly lower and fibrinogen levels were significantly higher in the ED group. Thus, the results were consistent with the literature (p < 0.001). In recent studies, the FAR value has been reported as a reliable and new inflammation-based indicator in predicting disease risk due to the poor prognostic effects of high fibrinogen and low albumin values [24]. In addition to using CRP, albumin, and fibrinogen alone, it was shown to be more successful in showing the inflammatory status and disease activity than ratios such as neutrophil–lymphocyte, monocyte-lymphocyte, platelet-lymphocyte [25]. In addition to its sensitivity to inflammation, FAR was reported to be closely related to the development of ischemia and infarction through endothelial damage due to increased thrombogenic and poor prognostic effects [26]. Studies show that high FAR value is associated with the frequency and severity of coronary artery disease and is also found to be significantly higher in patients with ischemic retinal vein occlusion and cerebral venous thrombosis [27]. Therefore, high FAR value is considered an important risk factor for both arterial and venous ischemia due to its association with dysfunction due to endothelial cell damage. It is also thought that this effect primarily begins in small blood vessels [28]. In studies examining the relationship between patient comorbidities measured by the Charlson comorbidity index and FAR value, it was mentioned that the results showed a positive correlation with a high FAR value, and that new studies were necessary to measure the positive predictive capacity of FAR [29]. In our study, FAR value was found to be significantly higher in the ED group than in the control group (p < 0.001).

Because fibrinogen and albumin levels may be influenced by several cardiometabolic and inflammatory conditions, we additionally adjusted for major comorbidities. The persistence of the association after multivariable adjustment suggests that the relationship between FAR and erectile dysfunction is not solely attributable to differences in comorbidity burden. The most appropriate cut-off value for diagnosing ED was calculated as 101.49 with a sensitivity of 33% and a specificity of 98%. FAR value was found to be similar among subgroups separated according to ED severity (p = 0.071). When the subgroups were compared with the control group, FAR value was found to be statistically significantly higher in all groups (Table 4). A significant relationship was found between the IIEF-5 score and FAR value (r = −0.554, p < 0.001), and ED severity was found to be correlated with our study results (Fig. 5). When compared with CAR value, another current inflammatory marker, FAR was found to be more successful in supporting the diagnosis of ED (Fig. 3).

The ROC analysis identified an optimal FAR cut-off value of 101.49 according to the Youden index. Although this threshold demonstrated excellent specificity (99%), its sensitivity was relatively low (33%), limiting its usefulness as a screening tool for ED. Therefore, FAR should not be considered a standalone diagnostic marker. Nevertheless, the high specificity indicates that markedly elevated FAR levels are strongly associated with the presence of ED and may provide supportive information regarding the inflammatory processes involved in its pathophysiology.

Importantly, the association between FAR and erectile dysfunction remained significant even after adjusting for age, BMI, diabetes mellitus, hypertension, chronic obstructive pulmonary disease, heart disease, and dyslipidemia. This finding supports the hypothesis that FAR may be an independent inflammatory biomarker associated with ED, rather than simply reflecting the effects of concomitant comorbidities (Table 6). The Nagelkerke R² value of 0.234 indicates a moderate explanatory capacity of the model. Given the multifactorial nature of ED, this finding is not unexpected and demonstrates that FAR significantly contributes to the overall risk profile of erectile dysfunction.

Future prospective studies with larger cohorts are required to determine clinically applicable thresholds and to evaluate whether combining FAR with established clinical parameters may improve diagnostic performance.

Limitations

This study has some limitations. Being a single-center study, it makes it difficult to avoid selection bias. It is unknown how the FAR values used will change over time based on variability in possible inflammatory activity. Interventional imaging methods such as penile Doppler ultrasonography could not be recommended for the evaluation of patients due to possible complications and ethical problems. Another limitation is the high number of patients excluded during the screening process. While strict exclusion criteria are necessary to reduce potential confounding factors that could affect inflammatory biomarkers and FAR values, this may have led to selection bias and limited the generalizability of the findings to the broader patient population with ED. Because it is an observational study, the relationship between the FAR value and the results should be approached with caution. The study also used strict exclusion criteria, including metabolic syndrome, uncontrolled diabetes and its complications, neurological diseases, and other conditions known to affect inflammatory biomarkers. While these exclusions are necessary to reduce potential confounding factors, they may have resulted in a more selective study population. Therefore, the findings may be more relevant for patients without these significant comorbidities and should be validated in larger emergency department populations.

Conclusion

In this study, it was found that FAR, as an inflammatory marker, could be an important marker that can help in diagnosing ED and determining the severity of ED. High fibrinogen and low albumin levels suggest ongoing inflammation and oxidative stress. Therefore, FAR may be an important therapeutic target, and this study may pave the way for future studies. FAR demonstrated moderate discriminatory performance and may serve as a complementary biomarker associated with erectile dysfunction. Extended, multicenter, prospective studies are needed on this subject.

Authors’ contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by A.Y., A.C., A.A.A., E.B., A.A. and A.A.. The first draft of the manuscript was written by A.Y. and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Funding

No potential funding was reported by the authors.

Data availability

Data are available from the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

This study was conducted in accordance with the Declaration of Helsinki. Ethical approval was obtained from the Ethics Committee of Ordu University (Decision No: 2025/48).

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.