Abstract
Background
Familial Mediterranean fever (FMF) is a monogenic autoinflammatory condition accompanied with periodic attacks of fever. The clinical utility of some novel inflammatory parameters has not been well explored in FMF. Hence, the aim of this study was to explore the accuracy of a variety of inflammatory indexes in patients with an FMF-attack-free period and without a complication of amyloidosis.
Methods
This cross-sectional study included a total of 114 patients with FMF (of them, 43.8% were men) and 97 controls (of them, 43.3% were men). Complete blood count, albumin, fibrinogen, C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), and serum amyloid A (SAA) were measured. Other parameters were calculated [i.e., platelet-albumin ratio (PAR), HALP score, systemic immune-inflammation index (SII), and pan-immune-inflammation value (PIV)]. Receiver operating characteristic (ROC) curve analysis was used to test the discriminative ability of each marker between patients with FMF and those without FMF.
Results
The area under the ROC curve for fibrinogen [AUC = 0.710, 95% CI (0.644–0.770)], CRP [AUC = 0.780 (0.718–0.834)], fibrinogen/albumin [AUC = 0.722 (0.657–0.782)], and CRP/albumin [AUC = 0.782 (0.720–0.836)] indicated satisfactory clinical accuracy. The AUCs for SAA [AUC = 0.844 (0.788–0.890)], SAA/albumin [AUC = 0.856 (0.801-0.900)], and PLT/SAA [AUC = 0.810 (0.750–0.861)] indicated good clinical accuracy. There was no difference between AUCs for SAA and SAA/albumin (P = 0.073), whereas the AUCs for these 2 parameters were significantly higher than the AUC for PLT/SAA (P = 0.033 and P = 0.005, respectively).
Conclusion
SAA and SAA/albumin ratio are the most reliable biomarkers in discriminating patients with FMF from healthy individuals.
Keywords: Acute phase reactants, Amyloidosis, Autoinflammatory diseases, Serum amyloid A
Introduction
Familial Mediterranean fever (FMF) is a monogenic autoinflammatory condition accompanied with periodic attacks of fever [1]. This disease is more prevalent among Turks, Armenians, non-Ashkenazi Jews, and Arabs [2]. The mutations in the Mediterranean fever gene (MEFV) are the main culprit of FMF [3]. MEFV is responsible for coding the pyrin protein, which favors the regulation of inflammatory processes. When this protein is mutated, the activation of caspase 1 occurs, with consequent secretion of interleukins (ILs), acute-phase reactants, and leukocytes, thus contributing to systemic inflammation [2].
The majority of patients (more than 90%) experience the disease before 20 years old. Serositis (e.g., peritonitis and pericarditis) and monoarticular arthritis are also features of FMF [1], but the most serious complication of FMF is AA amyloidosis, which is characterized by the accumulation of serum amyloid A (SAA) proteins in tissues [4].
The gold standard for FMF treatment is colchicine. Not only does this medication suppress the inflammation, but it also prevents the onset of amyloidosis. However, for individuals with FMF who do not respond to colchicine, anti-IL-1 therapy (e.g., anakinra, rilonacept, and canakinumab) is the treatment alternative [3].
Although individuals with FMF are usually without symptoms between the attacks, the monitoring of markers of inflammation in the attack-free period is of great importance [1]. Acute-phase reactants, which are proteins synthesized by the liver, can be reliable indicators for monitoring low-grade inflammation in the FMF-attack-free period. Some of these markers of inflammation can serve as predictors of the severity of the disease and the risk of amyloidosis [2, 5].
Although C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR) are routinely used to monitor the disease, SAA was shown to be superior over CRP and ESR in attack-free periods [4, 5]. However, SAA is not cost-effective and is not available in all clinical settings [5]. Some novel inflammatory indexes have been emerging, thus showing promising results regarding their diagnostic and prognostic role in FMF and other inflammatory diseases [2, 6–12]. It is suggested they may reflect systemic inflammation better than any single biomarker [2, 6–12].
Pan-immune-inflammation value (PIV) is a novel parameter for systemic inflammation [2]. It is a blood-derived biomarker, easily available and inexpensive, that is calculated from immune cell subpopulations of neutrophils, monocytes, lymphocytes, and platelets [2]. Another composite index that reflects systemic inflammation is the HALP score derived from haemoglobin, albumin, lymphocytes, and platelets [10]. The HALP score has not been yet examined in FMF. The systemic immune-inflammation index (SII) includes neutrophils, lymphocytes, and platelets in its algorithm [8, 9, 11]. It was shown reliable in the diagnosis of sacroiliitis in patients with FMF [8]. This composite index was also shown to be a reliable predictor of adverse outcomes in pregnant women with FMF [9]. Platelets (PLT) are not only attributed to coagulability and thrombosis [13] but also accelerate inflammatory responses by the production of proinflammatory cytokines [14]. Serum albumin is a negative acute-phase reactant used to monitor nutritional status. Albumin also exhibits anti-inflammatory and antioxidant features. Lower albumin levels are related to higher systemic inflammation levels [14]. The platelet-albumin (PLT/albumin) ratio is proposed as another parameter of the inflammatory state and an alternative indicator for the disease activity of some autoinflammatory disorders [14, 15]. To the best of our knowledge, the PLT/albumin ratio, SII/albumin, and PIV/albumin have not yet been examined in FMF.
The present study aimed to investigate whether conventional and novel inflammatory biomarkers, as well as their ratios with albumin, can identify subclinical inflammation in FMF patients during attack-free periods, specifically in those without amyloidosis. Since the clinical utility of several novel inflammatory parameters has not yet been explored in FMF, we sought to assess the accuracy of a variety of inflammatory indexes in this setting. For this purpose, we compared FMF patients with healthy controls, rather than with other inflammatory conditions, because our primary focus was on characterizing persistent inflammation in FMF rather than establishing differential diagnostic markers.
Patients and methods
This retrospective, cross-sectional study included a total of 114 patients with FMF (of them, 43.8% were men) and 97 controls (of them, 43.3% were men). The study was approved by the Recep Tayyip Erdogan University Institutional Ethics Committee and was conducted following the Declaration of Helsinki principles (Approval number: 2024/107, Approval date: 16.05.2024). The diagnosis of FMF was established according to Tel-Hashomer criteria [16]. Patients with genetically confirmed FMF were enrolled. All patients were receiving colchicine therapy at the time of the study. Six patients in our cohort were under IL-1 blockade (four on anakinra and two on canakinumab); however, due to the small number and potential to introduce heterogeneity, they were not included in the final analysis.
The criteria for the inclusion of patients in the study group were as follows: individuals diagnosed with attack-free FMF older than 18 years. The attack-free period was regarded as if at least 2 weeks from the end of an FMF attack period had passed, according to acute-phase reactants, the clinical symptoms, and the physical examination of these patients.
Patients being in the active phase of the FMF, acute/chronic infections, acute/chronic renal failure, amyloidosis, individuals with autoinflammatory diseases other than FMF, pregnancy, and those with malignancies or neurological diseases were excluded from the study. Moreover, patients who received nonsteroidal anti-inflammatory medications and anticoagulant therapy were also excluded from the study. The control group encompassed healthy volunteers free of any signs of acute/chronic infection and who did not use any medications.
Blood samples were obtained after at least 8 h of overnight fasting. A complete blood count was determined on the automatic analyzer (Sysmex XN 9000, Kobe, Japan). Serum levels of total proteins, albumin, and CRP were measured on Roche Cobas 6000 c 501, Mannheim, Germany. Plasma fibrinogen was determined on Sysmex CA-660 hemostasis analyzer, Kobe, Japan. SAA was measured by ELISA. Other parameters were calculated. The platelet-albumin ratio (PAR) is obtained by dividing the platelet count by the serum albumin levels [14]. The HALP score was calculated by using the following formula: haemoglobin × albumin × lymphocyte count / PLT count [10, 11]. The systemic immune-inflammation index (SII) was determined as following: neutrophil count × PLT count / lymphocyte count [10, 11]. The PIV was calculated as follows: (neutrophil count × PLT count × monocyte count) / lymphocyte count [2].
Statistical analysis
After testing the data distribution with the Kolmogorov-Smirnov test, the continuous variables were compared with the Mann-Whitney test. Categorical variables were tested using the chi-square test for contingency tables. Data were expressed as median (interquartile range) for skewed variables. Categorical variables were expressed as absolute frequencies. To identify factors associated with the presence of FMF as a dichotomous dependent variable, stepwise binary logistic regression was used with an enter selection criterion. In the logistic regression analysis, the data were presented as odds ratios (OR) and 95% confidence intervals (95% CI). The explained variation of the dependent variable was indicated by the Nagelkerke R2 value.
Receiver operating characteristic (ROC) curve analysis was used to test the discriminative ability of each marker between patients with FMF and those without FMF. The area under the ROC curve (AUC) between 0.5 and 0.7 could indicate that the diagnostic test has low accuracy; between 0.7 and 0.8 the diagnostic test has satisfactory accuracy; between 0.8 and 0.9, the diagnostic test has good accuracy; and an AUC above 0.9 indicates excellent accuracy of the diagnostic test. ROC curves with areas higher than 0.8 were compared. These data were given as AUCs, 95% Confidence intervals, standard errors (SE) and differences between curves. All statistical analyses were performed using SPSS Statistical package version 23 software (Chicago, Illinois, USA). ROC curves were compared using MedCalc Statistical Software Ltd. (Ostend, Belgium). All statistical tests were considered significant if the significance level P was below 0.05.
Results
The clinical characteristics of the 211 subjects are listed in Table 1. Patients with FMF had a lower BMI than the control group. The prevalence of hypertensive patients was higher in FMF patients than in control subjects.
Table 1.
Demographic and clinical data in studied population
| Control group (n = 97) | Familial Mediterranean Fever (FMF) (n = 114) | P | |
|---|---|---|---|
| N (male/female) | 97 (42/55) | 114 (50/64) | 0.935 |
| Age, years | 38 (30–47) | 34 (24–44) | 0.054 |
| BMI, kg/m2 | 27.1 (24.6–30.0) | 25.6 (23.1–30.0) | 0.039 |
| Diabetes (Yes/No) | 0/97 | 2/112 | 0.190 |
| Hypertension (Yes/No) | 0/97 | 8/106 | 0.008 |
| Smoking habits (Yes/No) | 27/70 | 26/88 | 0.401 |
| Alcohol consumption (Yes/No) | 3/94 | 0/114 | 0.059 |
| Duration of illness (years) | - | 12.0 (5.0–20.0) | - |
The median disease duration was 12 years (range: 5–20), as shown in Table 1. All patients were on colchicine treatment, while none of the included patients received IL-1 blockade. Family history of FMF was present in the majority of patients, while absent in 50 individuals. The distribution of MEFV mutations in our cohort was as follows: M694V homozygous in 13.9% of patients, M694V heterozygous in 28.5%, M680I heterozygous in 26.3%, and M680I homozygous in 2.2%. The remaining patients carried a variety of other heterozygous or homozygous variants, including R202Q, V726A, A761H, G148G, P369S/A408G, E148Q, F479L, and R408Q.
Table 2 shows the laboratory markers in the tested population. The concentrations of fibrinogen, CRP, SAA, SII, PIV, fibrinogen/albumin, CRP/albumin, SAA/albumin, SII/albumin, PIV/albumin, and PLT/albumin were significantly higher in the patients with FMF. Also, WBC, monocyte and neutrophil counts were higher in patients than in the controls. Levels of albumin, PLT/SAA, and HALP score were lower in the patients than in the controls.
Table 2.
Laboratory markers in the studied groups
| Control group (n = 97) | Familial Mediterranean Fever (FMF) (n = 114) | P | |
|---|---|---|---|
| Glucose, mg/dL | 90.0 (86.0–95.0) | 90.5 (84.0–96.0) | 0.593 |
| Total proteins, g/dL | 7.5 (7.4–7.8) | 7.6 (7.3–7.8) | 0.825 |
| Albumin, g/dL | 4.7 (4.5–4.9) | 4.5 (4.3–4.7) | < 0.001 |
| Fibrinogen, mg/dL | 271 (240–317) | 343 (265–403) | < 0.001 |
| CRP, mg/L | 1.5 (0.7-3.0) | 6.6 (2.5–10.0) | < 0.001 |
| SAA, mg/L | 7.2 (4.6–10.0) | 16.0 (11.0–20.0) | < 0.001 |
| WBC, cell/µL | 6570 (5450–7980) | 7210 (5930–8470) | 0.015 |
| Lymphocytes count | 1970 (1670–2370) | 2080 (1660–2540) | 0.413 |
| Monocytes count | 380 (320–480) | 430 (350–520) | 0.012 |
| Neutrophil count | 3700 (3110–4980) | 4400 (3330–5500) | 0.034 |
| Platelet count, 10³/µL | 257 (212–298) | 270 (234–316) | 0.097 |
| ESR (mm/h) | 8 (4–12) | 8 (4–18) | 0.328 |
| SII | 4.6 (3.5–6.5) | 5.5 (3.9–7.5) | 0.026 |
| PIV | 176 (121–302) | 240 (156–342) | 0.004 |
| Fibrinogen/Albumin | 60.00 (50.83–67.56) | 77.05 (59.57–92.73) | < 0.001 |
| CRP/Albumin | 0.34 (0.14–0.67) | 1.41 (0.53–2.33) | < 0.001 |
| SAA/Albumin | 1.54 (0.96–2.10) | 3.47 (2.45–4.55) | < 0.001 |
| SII/Albumin | 9.76 (7.14–14.15) | 11.88 (9.34–16.40) | 0.005 |
| PIV/Albumin | 36.68 (26.67–65.49) | 53.09 (35.12–77.64) | 0.001 |
| PLT/Albumin | 5.50 (4.53–6.38) | 5.93 (5.12–6.86) | 0.007 |
| PLT/SAA | 3.66 (2.33–5.51) | 1.70 (1.25–2.27) | < 0.001 |
| HALP score | 53.6 (42.9–69.0) | 49.0 (36.5–59.2) | 0.012 |
The data were expressed as median (interquartile range) and compared with the Mann-Whitney U test. Abbreviations: CRP, C-reactive protein; SAA, Serum amyloid A; WBC, White blood cell; PLT, Platelet; ESR, Erythrocyte sedimentation rate; SII, Systemic immune-inflammation index; PIV, Pan-immune-inflammation value; HALP score, Haemoglobin, albumin, lymphocyte, and platelet score
Binary logistic regression analysis was used to assess the associations between FMF and markers that differed significantly between the groups studied (Table 3). Fibrinogen, CRP, SAA, WBC, neutrophil count, SII, PIV, fibrinogen/albumin, CRP/albumin, SAA/albumin, SII/albumin, PIV/albumin, and PLT/albumin were positively associated with FMF. When each of these markers-fibrinogen, CRP, SAA, WBC, neutrophil count, SII, PIV, fibrinogen/albumin, CRP/albumin, SAA/albumin, SII/albumin, PIV/albumin, and PLT/albumin-increased by 1 unit, the probability of the presence of FMF increased by 1%, 16.7%, 16.7%, 22.1%, 24.6%, 9.1%, 21.6%, 4.4%, 97.6%, 2.046 times, 4.8%, 1% and 33.6%, respectively. Nagelkerke R2 showed that each predictor in univariable analysis, fibrinogen, CRP, SAA, WBC, neutrophil count, SII, PIV, fibrinogen/albumin, CRP/albumin, SAA/albumin, SII/albumin, PIV/albumin, and PLT/albumin could explain the variation in FMF presence by 16.8%, 18.0%, 29.8%, 4.3%, 3.9%, 3.2%, 3.9%, 20.1%, 18.1%, 31.1%, 4.5%, 4.9%, 4.9%, and 5.9%, respectively.
Table 3.
Odds ratios (OR) after univariate and multivariate logistic regression analysis for parameters predicting abilities towards FMF
| Predictors | Unadjusted OR (95% CI) |
P | Nagelkerke R 2 |
|---|---|---|---|
| Age | 0.978 (0.956-1.000) | 0.055 | 0.024 |
| BMI | 0.949 (0.900-1.000) | 0.050 | 0.025 |
| Albumin | 0.351 (0.158–0.740) | 0.010 | 0.057 |
| Fibrinogen | 1.010 (1.006–1.014) | < 0.001 | 0.168 |
| CRP | 1.167 (1.090–1.250) | < 0.001 | 0.180 |
| SAA | 1.167 (1.106–1.231) | < 0.001 | 0.298 |
| WBC | 1.221 (1.045–1.426) | 0.012 | 0.043 |
| Monocytes count | 1.247 (0.997–1.560) | 0.054 | 0.024 |
| Neutrophil count | 1.246 (1.040–1.494) | 0.017 | 0.039 |
| SII | 1.091 (1.002–1.189) | 0.046 | 0.032 |
| PIV | 1.216 (1.021–1.448) | 0.029 | 0.039 |
| Fibrinogen/Albumin | 1.044 (1.027–1.062) | < 0.001 | 0.201 |
| CRP/Albumin | 1.976 (1.456–2.681) | < 0.001 | 0.181 |
| SAA/Albumin | 2.046 (1.596–2.623) | < 0.001 | 0.311 |
| SII/Albumin | 1.048 (1.007–1.092) | 0.021 | 0.045 |
| PIV/Albumin | 1.010 (1.002–1.018) | 0.017 | 0.049 |
| PLT/Albumin | 1.336 (1.100-1.622) | 0.004 | 0.058 |
| PLT/SAA | 0.478 (0.379–0.602) | < 0.001 | 0.360 |
| HALP score | 0.980 (0.966–0.995) | 0.008 | 0.054 |
Abbreviations: BMI, Body mass index; CRP, C-reactive protein; SAA, Serum amyloid A; WBC, White blood cell; PLT, Platelet; ESR, Erythrocyte sedimentation rate; SII, Systemic immune-inflammation index; PIV, Pan-immune-inflammation value; HALP score, Haemoglobin, albumin, lymphocyte, and platelet score
Albumin, PLT, and HALP score were negatively associated with the presence of FMF. When each of these markers-albumin, PLT and HALP score - decreased by one unit, the probability of the presence of FMF increased by 64.9%, 52.2%, and 2%, respectively. Nagelkerke R2 showed that each predictor, albumin, PLT and HALP score, could explain the variation in the occurrence of FMF by 5.7%, 36% and 5.4%, respectively.
ROC analysis was used to discriminate subjects with FMF from those who did not have it (Table 4). The calculated AUCs for tested predictors (ranking from 0.5 to 0.7) indicated that the clinical accuracy of each diagnostic test (albumin, WBC, lymphocyte count, monocytes count, neutrophil count, platelet count, SII, PIV, SII/albumin, PIV/albumin, PLT/albumin, and HALP score) was low. The discriminatory abilities for fibrinogen, CRP, fibrinogen/albumin, and CRP/albumin (all AUCs between 0.7 and 0.8) could suggest satisfactory accuracy for each diagnostic tool.
Table 4.
ROC analysis for single marker discriminatory abilities towards FMF presence
| Predictors | AUC (95%CI) |
SE | Sensitivity | Specificity | P |
|---|---|---|---|---|---|
| Albumin | 0.641 (0.572–0.705) | 0.037 | 42 | 81 | < 0.001 |
| Fibrinogen | 0.710 (0.644–0.770) | 0.035 | 46 | 95 | < 0.001 |
| CRP | 0.780 (0.718–0.834) | 0.033 | 60 | 88 | < 0.001 |
| SAA | 0.844 (0.788–0.890) | 0.028 | 79 | 85 | < 0.001 |
| WBC | 0.597 (0.524–0.664) | 0.040 | 85 | 34 | 0.014 |
| Lymphocytes count | 0.533 (0.463–0.601) | 0.010 | 57 | 54 | 0.414 |
| Monocytes count | 0.601 (0.531–0.667) | 0.040 | 63 | 57 | 0.011 |
| Neutrophil count | 0.585 (0.515–0.652) | 0.040 | 63 | 55 | 0.032 |
| Platelet count | 0.566 (0.496–0.634) | 0.040 | 80 | 35 | 0.095 |
| SII | 0.589 (0.519–0.656) | 0.039 | 63 | 55 | 0.024 |
| PIV | 0.614 (0.544–0.680) | 0.039 | 65 | 59 | 0.004 |
| Fibrinogen/Albumin | 0.722 (0.657–0.782) | 0.035 | 49 | 93 | < 0.001 |
| CRP/Albumin | 0.782 (0.720–0.836) | 0.032 | 59 | 91 | < 0.001 |
| SAA/Albumin | 0.856 (0.801-0.900) | 0.027 | 82 | 84 | < 0.001 |
| SII/Albumin | 0.611 (0.542–0.677) | 0.039 | 61 | 61 | 0.004 |
| PIV/Albumin | 0.629 (0.560–0.694) | 0.039 | 66 | 60 | < 0.001 |
| PLT/Albumin | 0.608 (0.539–0.647) | 0.039 | 87 | 34 | 0.005 |
| PLT/SAA | 0.810 (0.750–0.861) | 0.031 | 84 | 72 | < 0.001 |
| HALP score | 0.600 (0.530–0.667) | 0.039 | 73 | 46 | 0.010 |
Abbreviations: CRP, C-reactive protein; SAA, Serum amyloid A; WBC, White blood cell; PLT, Platelet; ESR, Erythrocyte sedimentation rate; SII, Systemic immune-inflammation index; PIV, Pan-immune-inflammation value; HALP score, Haemoglobin, albumin, lymphocyte, and platelet score
The AUCs for SAA, SAA/albumin, and PLT/SAA were between 0.8 and 0.9, indicating good accuracy for each diagnostic tool to discriminate FMF patients from controls. In addition, these three markers had the highest sensitivity and specificity compared to the others.
Our further aim was to compare the AUCs for SAA, SAA/albumin, and PLT/SAA (Table 5). The AUCs for SAA and SAA/albumin did not differ significantly (P = 0.073). In contrast, the AUC for SAA was significantly higher than the AUC for PLT/SAA (P = 0.033), and the AUC for SAA/albumin was significantly higher than the AUC for PLT/SAA (P = 0.005).
Table 5.
Comparisons of AUCs demonstrated good accuracy to discriminate patients and controls
| Difference between AUCs | 95% Confidence Interval | SE | P | |
|---|---|---|---|---|
| SAA vs. SAA/Albumin | 0.0120 | -0.00114–0.0252 | 0.0067 | 0.073 |
| SAA vs. PLT/SAA | 0.0340 | 0.00275–0.0652 | 0.0159 | 0.033 |
| SAA/Albumin vs. PLT/SAA | 0.0460 | 0.0136–0.0784 | 0.0165 | 0.005 |
Abbreviations: SAA, Serum amyloid A; PLT, platelet
Discussion
To the best of our knowledge, the current study is the first that investigated a wide range of inflammatory biomarkers, i.e., conventional and novel ones, in order to find the most reliable one in the discrimination of patients with FMF from healthy individuals. We have examined some novel indexes that were not examined in FMF before (such as HALP score, SII/albumin, PIV/albumin, and PLT/albumin ratio). However, the results of this study show that these indexes are not superior to SAA in the identification of patients with FMF.
Previous studies have shown that some acute-phase reactants can be reliable indicators for monitoring subclinical inflammation even in the FMF-attack-free period [2, 17, 18]. In our current study, except for albumin, PLT/SAA, and HALP score, which were found to be lower, patients with FMF exhibited higher levels of almost all other examined markers of inflammation as compared with the healthy control group. However, the clinical accuracy of the majority of examined diagnostic tests was low, with AUCs for tested predictors lower than 0.7. The AUCs for fibrinogen, CRP, fibrinogen/albumin, and CRP/albumin were between 0.7 and 0.8, thus suggesting satisfactory accuracy for each of the mentioned diagnostic tools concerning their discriminatory abilities towards FMF presence. Importantly, the AUCs for SAA, SAA/albumin, and PLT/SAA were between 0.8 and 0.9, indicating good accuracy for each diagnostic tool to discriminate FMF patients from controls. These three markers had the highest sensitivity and specificity for FMF presence compared to the others. By comparing the AUCs for these parameters, it was shown that there was no difference between AUCs for SAA and SAA/albumin. These 2 parameters exhibited comparable AUCs and good sensitivity and specificity (79 vs. 85% for SAA and 82 vs. 84% for SAA/albumin, respectively). On the contrary, the AUCs for these 2 parameters were significantly higher than the AUC for PLT/SAA. These findings suggest that SAA is still the most reliable biomarker in FMF.
The obtained results of the current study are in line with Çakan et al. [5], who proposed SAA as the most sensitive method for the identification of occult inflammation in the FMF-attack-free period. SAA was shown to be a more reliable parameter than CRP for monitoring the activity of various autoinflammatory diseases, including FMF [19]. SAA plays an important role in the autoinflammatory cascade of FMF. This protein enhances the expression of interleukins (e.g., IL-6, IL-1β), prostaglandin E 2 (PGE2), and cyclooxygenase-2 (COX-2) by activation of extracellular signal-regulated kinase 1/2 (ERK1/2), nuclear factor kappa B (NF-κB), and p38 mitogen-activated protein kinase (p38MAPK) pathways [1]. On the other hand, interleukins favor the synthesis of SAA. Therefore, the measurement of SAA could be of utmost importance, even in the FMF-attack-free phases due to sustained subclinical inflammation [19].
However, given the fact that SAA is not available in non-specialized centers [19] and since it is more expensive than routinely and easily measured CRP, fibrinogen, fibrinogen/albumin, and CRP/albumin, the results of the current study show that the latter ones could be used with satisfactory accuracy towards FMF presence, with higher sensitivity but lower specificity than SAA. This is especially important in low-income countries where resources are limited. Onder et al. [6] reported a higher AUC for the fibrinogen/albumin ratio than for fibrinogen and albumin alone for detecting subclinical inflammation in FMF. However, the authors showed that the CRP/albumin ratio was not superior to CRP for detecting FMF presence [7]. These 2 latter studies included a comparable number of patients with FMF as our current study, but they did not examine SAA or any novel inflammatory biomarker that our current study did [6, 7]. Other examined inflammatory biomarkers in our current study are cost-effective and easily available via routine laboratory diagnostics. However, these inflammatory biomarkers have not shown their reliability for identification of patients with FMF.
Our findings demonstrate that SAA and SAA/albumin ratios retain discriminative capacity even during attack-free periods of FMF. This is clinically meaningful because subclinical inflammation persisting in these phases is closely associated with long-term complications, particularly amyloidosis. Therefore, assessment of these biomarkers may assist clinicians in detecting persistent low-grade inflammation, identifying patients who require closer monitoring, and guiding the optimization of anti-inflammatory therapy. In this way, the clinical relevance of our results extends beyond statistical discrimination and offers a practical tool for the management of FMF patients in routine practice.
In our patients, M694V and M680I mutations predominated, in line with previous reports associating these variants with a more severe disease course. However, as the primary aim of this study was to investigate inflammatory biomarkers during attack-free periods, we did not stratify biomarker analyses according to genotype. Future studies with larger samples are warranted to clarify the potential associations between specific MEFV variants and inflammatory profiles.
Several limitations of the present study should be acknowledged. First, the cross-sectional design does not allow conclusions regarding causality between biomarkers and FMF. Second, all inflammatory parameters were measured only once during the attack-free period, limiting our ability to evaluate longitudinal changes. Third, we did not perform stratified analyses according to treatment or genotype due to sample size restrictions, and we excluded patients with active clinical manifestations. Thus, our study does not address the predictive value of inflammatory indices for specific clinical findings such as sacroiliitis or erysipelas. In addition, patients with amyloidosis or other chronic inflammatory conditions such as IBD or rheumatic diseases were excluded; therefore, our results cannot be extrapolated to differential diagnostic settings. Future prospective studies with larger and more diverse patient cohorts are warranted to address these aspects. Although detailed clinical data such as parental consanguinity, age at first diagnosis, diagnostic delay and its causes, and appendectomy history were available, these were not analyzed further as they were outside the scope of this biomarker-focused study.
Finally, while subclinical inflammation is widely recognized as a key determinant in the long-term care of FMF patients, the exact threshold predictive of adverse outcomes such as amyloidosis remains unclear. Because patients with amyloidosis were excluded by design, we were not able to examine this relationship directly. Future prospective studies incorporating detailed family history and long-term follow-up are needed to establish the prognostic significance of persistent subclinical inflammation in predicting complications, particularly amyloidosis.
Conclusion
Of the broad spectrum of examined inflammatory biomarkers, SAA, SAA/albumin, and PLT/SAA showed good clinical accuracy for FMF presence, whereas fibrinogen, CRP, fibrinogen/albumin, and CRP/albumin exhibited satisfactory accuracy concerning the discriminatory abilities towards FMF presence in the attack-free period. Other examined inflammatory parameters did not show satisfactory clinical accuracy concerning the FMF presence. Follow-up studies with large sample sizes are necessary to confirm the obtained results.
Acknowledgements
The authors from Serbia were partially funded by the Ministry of Science, Technological Development and Innovation, Republic of Serbia, through two Grant Agreements with the University of Belgrade – Faculty of Pharmacy, No. 451-03-136/2025-03/200161 and No. 451-03-137/2025-03/200161.
Author contributions
F.M., B.K., and O.C. designed the study, conducted the experiment; A.K., wrote the manuscript; A.N., and V.C.K. performed data the analysis, O.C. and B.K. participated in the sample and clinical data collection. A.K. and F.M. helped optimize the research and proofread the paper and revised the manuscript. All authors helped with the final approval of the version.
Funding
No financial support was received for this study.
Data availability
The data sets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
The study was approved by the Recep Tayyip Erdogan University Institutional Ethics Committee and was conducted following the Declaration of Helsinki principles (Approval number: 2024/107, Approval date: 16.05.2024). Since this study has a retrospective design, it does not require a participant consent form.
Disclosures
None.
Competing interests
The authors declare no competing interests.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data sets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.