FABP4 and S100A12, a notable link between inflammatory mediators and cardiovascular risk in rheumatoid arthritis

FABP4 and S100A12, a notable link between inflammatory mediators and cardiovascular risk in rheumatoid arthritis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article FABP4 and S100A12, a notable link between inflammatory mediators and cardiovascular risk in rheumatoid arthritis Fatemeh khoobbakht, Seyed Askar Roghani, Parviz Soufivand, Rezvan Rostampour, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4281885/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: 50% of the deaths of RA patients are due to cardiovascular diseases, and inflammation plays an important role in its pathogenesis. FABP4 and S100A12 are involved as inflammatory mediators in the pathogenesis of CVD. For the first time, in a recent study, we evaluated the association between FABP4 and S100A12 plasma concentrations with cardiovascular risk factors in RA patients . Material and methods: 60 patients with RA (30 newly diagnosed and 30 under treatment) and 30 healthy individuals participated in this study with their personal consent. FABP4 and S100A12 plasma concentrations were measured by ELISA (enzyme-linked immunosorbent assay) method. Using ADVIA 1800 Clinical Chemistry System based on latex-enhanced immunoturbidimetric, HS-CRP concentration was calculated. Results : The FABP4 plasma concentration was significantly elevated in the newly diagnosed and under-treatment RA patients compared to healthy subjects (P <0.001 and P = 0.008, respectively). The plasma levels of S100A12 were remarkably higher in the new case compared to the control groups (P =0.001).There was a significantly positive association between the FABP4 and S100A12 with NT-proBNP(r =0.493, P < 0.001; r =0.445, P < 0.001, respectively). Conclusion: FABP4 and S100A12 correlate with cardiovascular biomarkers in RA patients. Rheumatoid Arthritis CVD FABP4 S100A12 Figures Figure 1 Figure 2 Introduction Rheumatoid arthritis (RA) is a chronic systemic inflammatory disease. Although this disease is autoimmune, the cause is unknown. The initial manifestations of rheumatoid arthritis are pain and symmetrical swelling in the small joints of the hands and feet. Still, large joints such as shoulders, elbows, knees, and ankles are also affected. Morning fatigue, weakness, and joint stiffness lasting more than 30 minutes in these patients are common. Arthritis is associated with progressive disability, premature death, and high socioeconomic costs. RA affects approximately 0.5-1% of the world's population, and women are 2 to 3 times more at risk than men. One of the factors that play a role in the high prevalence of this disease in women is the effects of estrogen on the immune system. Rheumatoid arthritis can occur at any age, but its incidence peaks in the third to fifth decade of life [1-5]. In addition to joints, rheumatoid arthritis causes damage to other tissues and organs, including the heart, kidneys, lungs, digestive system, eyes, skin, and nervous system. Among extra-articular manifestations, cardiovascular disease (CVD) is more common. CVD in RA patients is 1.5-2 times higher than the general population, and 50% of deaths in RA are related to cardiovascular disease, which includes heart failure (HF), ischemic heart disease, pericarditis, myocarditis, and cardiomyopathy[6-13]. The pathogenesis of CVD in RA patients is due to traditional risk factors including hypertension, smoking, dyslipidemia, BMI (body mass index), insulin resistance, hyperhomocysteinemia, physical inactivity, and non-traditional risk factors such as inflammation, increased CRP, some autoantibodies (Rheumatoid factor (RF) and anti-CCP), pro-inflammatory cytokines and side effects of drugs used to treat rheumatoid arthritis. In addition, age is a non-modifiable risk factor for increased cardiovascular disease in rheumatoid arthritis, as blood pressure, lipids, CRP, and inflammation increase with age. Inflammation contributes to the pathogenesis of CVD in RA patients in the following ways: 1) endothelial damage and accelerated formation of atherosclerotic plaques and 2) they contribute to both the initiation and severity of traditional risk factors [7, 14-21]. EULAR (European Union for Rheumatology) advises rheumatoid arthritis patients to check cardiac risk factors every 5 years because these risk factors change the disease activity. In addition, identifying these risk factors can lead to initiating a drug regimen to prevent CVD[7]. CVD risk calculators are used to manage cardiovascular disease in RA patients, although these algorithms are designed for the general population and are often inappropriate and underestimated for RA patients. Currently, there are more than 360 CVD risk prediction models that use cardiac risk factors such as hypertension, smoking, gender, age, and cholesterol. The issue that should be noted is that there is no specific algorithm for RA patients that indicates the risk of CVD in these patients is low or high. Most CVD risk calculators focus on atherosclerosis, but some calculate different types of cardiovascular disease. Using the Systematic Coronary Risk Evaluation (SCORE) calculator, and the Framingham Risk Score (FRS), fatal stroke and heart failure are calculated, respectively[7, 22]. Cytokines and chemokines are involved in the pathogenesis of RA and CVD. FABP4 (fatty acid binding protein 4, is a new adipokine (cytokine produced by fat tissue) that is abundantly found in the synovial tissue, synovial fluid, and serum of RA patients and is involved in the development of a group of cardiovascular disorders such as hypertension, heart failure, and atherosclerosis. FABP4 is widely expressed in a variety of organs, but in RA patients it is uncontrollably produced by M1 macrophages in the synovial, causing cartilage destruction and angiogenesis. Also, this adipokine is known as a cardiovascular biomarker due to its role in causing chronic inflammation [23-26]. Genes responsible for the control of inflammation exert an indispensable role in the pathogenesis of RA. The S100A12 (calgranulin C) protein contributes to both the pathogenesis of RA and a group of cardiovascular diseases, including myocardial infarction, ischemia-reperfusion (IR) injury, and coronary artery disease. This protein is not expressed in healthy synovial tissue, but it exerts its pro-inflammatory effects in RA patients through RAGE, TLR4, and CD36 receptors expressed on macrophages. S100A12 induces the production of inflammatory cytokines and recruits immune cells to the site of inflammation. As a result, the concentration of this protein in the serum and synovial fluid of RA patients increases and is used to monitor the disease [27-31]. Also, the RAGE receptor is located on the heart and blood vessels, and S10012, by binding to it, causes the production of inflammatory cytokines and the filtering of macrophages to the vessel wall, which eventually leads to cardiovascular disorders[30, 31]. In this study, for the first time, we evaluated the association of plasma concentration of FABP4 and S100A12 in the peripheral blood of newly diagnosed and under-treatment patients with the traditional CVD risk factors (age, gender, BMI, smoking, blood pressure, and lipid profile), considering important of inflammation and high prevalence of cardiovascular disease in RA. Material and methods Study population In a recent study, 60 subjects with rheumatoid arthritis were diagnosed by an expert rheumatologist based on the classification criteria of the American College of Rheumatology/The European Alliance of Associations for Rheumatology 2010 (ACR/EULAR 2010), divided into two categories: 30 patients who did not take any drug( newly-diagnosed) and 30 under-treatment patients were from April 2022 to October 2022, referring to Imam Reza Hospital, Kermanshah University of Medical Sciences (KUMS), and as well as 30 healthy subjects after matching for sex and age were evaluated. In the study, all participants with any previous history of chronic disease including metabolic disorder, kidney, pulmonary, cardiovascular disease, and autoimmune, as well as pregnant women and also subjects who were consuming anti-lipid drugs for example Statins, were excluded from the study. The current study was conducted by the Declaration of Helsinki and was followed with approval from the Ethics Committee of KUMS (IR.KUMS.MED.REC.1402.151). Demographic data As previously discussed, in this study, all participants signed a predesigned form with their consent to determine the risk score using several covariates. The data includes gender, age, blood pressure, height, weight, smoking status, comorbidities (diabetes, migraine, kidney disease, and psychological disorders), and family history of heart disease. In study groups, detailed disease modified anti-rheumatoid drug (DMARD) dosage and Demographic information that have been previously studied was shown in Table 1[32]. Table 1 the DMARD dosage, the demographic information, Clinical, Infammatory and Serological Markers of groups study Variables New cases Under-treatment Control P -value Number Age (years) Sex TJC SJC DAS‑28 CRP (mg/L) ESR (mm/hr) Positive RF Positive Anti‑CCP Smoking status Medication Anti‑ HT e (Cozaar®) Antiplatelet MTX a (%) MTX dose (weekly) HCQ b (%) PSL c (%) PSL dose (daily) Other DMARDs d 30 48.80 ± 13.01 Male (n = 5) Female (n = 25) 3.33 ± 3.50 3.20 ± 3.46 3.60 ± 1.16 7.35 ± 6.82 25.76 ± 24.39 (60%), (n = 18) (83.3%), (n = 25) Positive (6.7%) (n = 2) Negative (93.3%) (n = 28) (10%), (n = 3) (10%), (n = 3) 0 0 0 0 0 0 30 49.67 ± 10.51 Male (n = 5) Female (n = 25) 0.23 ± 0.97 0.23 ± 0.97 2.33 ± 0.66 3.33 ± 1.95 17.76 ± 10.91 (46.6%), (n = 14) (43.3%), (n = 13) Positive (10%) (n = 3) Negative (90%) (n = 27) (30%), (n = 9) (13.3%), (n = 4) 100 11.41 100 100 5.33 0 30 48.10 ± 12.07 Male (n = 5) Female (n = 25) 2.23 ± 0.62 Positive (n = 0) Negative (n = 30) 0 0 0 0 0 0 0 0 P < 0.0001 P < 0.0001 P < 0.001 P < 0.001 P = 0.491 Data are Mean ± SEM, TJC: tender joint count, SJC: swollen joint count, a Methotrexate (7.5–25 mg per week), b Hydroxychloroquin (200 mg per day), c Prednisolone (5–10 mg per day), d Disease Modifying Anti-Rheumatic Drug, e Anti-hypertensive drug Plasma sample separation We collected 6 mL of peripheral blood from 60 RA patients (30 newly diagnosed and 30 treated) and 30 healthy controls and poured them into two separate EDTA (ethylene diamine tetra-acetate) tubes. We needed participants' plasma for analysis, so we separated the plasma by centrifuging at 3000g for 10 min and keeping them at -80°C. Enzyme-Linked Immunosorbent Assay (ELISA) According to the Elisa kit instructions, we performed ELISA(enzyme-linked immunosorbent assay) to detect the plasma surface concentration of FABP4 (Podgin, under license from ZellBio GmbH, Iran) (assay range: 1-32 ng/ml) (Cat.NO: PTC-12036-H9648), S100A12 protein (Podgin, under license from ZellBio GmbH, Iran) (assay range: 30-960 pg/ml) (Cat.NO: PTC-13074-H9648) and NT-proBNP (ZellBio GmbH, Germany) (assay range:12.5-400 Pg/ml) (Cat.NO: ZB-11239C-H9648). Measurement of lipid profile and fasting blood sugar (FBS) In tubes containing EDTA (ethylene diamine tetra-acetate), 6 ml peripheral blood samples were spilled after 12 hours of fasting. Lipid profile (Triglyceride, total high-density lipid (HDL), low-density lipid (LDL) cholesterol, high-density lipid (HDL), cholesterol), and FBS were measured with two methods of glucose oxidase-peroxidase (Biosystems, Barcelona, Spain) and enzymatic reactions, respectively. The obtained results were read using a fully automated 7020 chemistry analyzer (Hitachi, Tokyo, Japan) and commercial kits were used to produce these reactions according to the manufacturer’s recommendation. Immunoturbidimetric assay Using ADVIA 1800 Clinical Chemistry System (Siemens, Germany), we measured the concentration of high sensitivity CRP (HS-CRP) in plasma samples based on latex-enhanced immunoturbidimetric (assay range: 0.16-10 mg/L). It was done according to the manufacturer's instructions. Measurement of body mass index (BMI) BMI was calculated for each person using the formula of weight (in kilograms) divided by the square of height (in meters). All participants were classified into three groups based on their BMI: normal weight range (NW): 18.5-24.9, overweight (OW) 25.0-29.9, and obese (OB) ≥ 30 (kg/m2). Calculation of Disease activity score‑28 (DAS‑28) The rheumatologist calculated the disease activity score using the formula DAS28=0/56+0/28 (SJ)+0/70 In (ESR)+0/014GH, (TJ: number of tender joints from 28 joints, SJ: number of swollen joints from 28 joints, GH: global health, ESR: erythrocyte sedimentation rate). Cardiovascular disease risk calculator We used two traditional CVD risk calculators to calculate CVD risk: the Systematic Coronary Risk Evaluation (SCORE), which is based on a set of risk factors including age, sex, systolic blood pressure, smoking, and lipid profile (total cholesterol, LDL (low-density lipid), HDL that evaluates atherosclerosis and other heart failure. Another calculator that we used in the study is FRS. The Framingham algorithm estimates the risk of a heart attack in 10 next years. The risk factors used for this risk score are age, smoking, total cholesterol, HDL (high-density lipid), systolic blood pressure, and antihypertensive drugs. Statistical analysis ANOVA analysis was carried out to compare the three groups. Spearman and Pearson test was conducted to evaluate the correlation between two variables. Kolmogorov-Smirnov (K-S) test was performed to test the normality of the distribution. In all analyses, a P value of <0.05 was indicated as statistically significant. Results were presented as mean ± standard deviation (SD). All analyses were carried out using SPSS software version 24.0 (SPSS, Chicago, IL, USA) and the software GraphPad Prisms® 6.0 (GraphPad Software, La Jolla, California, USA) used for the drawing of the graph. Results The serum levels of lipids profile (LDL, HDL, TG, and cholesterol), HS-CRP, NT-proBNP, FBS, FABP4, and S100A12 The mean serum concentrations of FABP4, S100A12, NT-proBNP, HS-CRP, and HDLin three groups are given in Table 2. Table 2 The mean FABP4, S100A12, NT-proBNP , HS-CRP , and HDL Variables New cases (n=30) Under-treatment (n=30) Control (n=30) P -value FABP4( ng/ml ) 4.62 ± 0.61 3.93 ± 0.87 3.30 ± 1.02 < 0.001 S100A12( pg/ml ) 133.61 ± 23.60 114.87 ± 32.99 107.78 ± 37.66 0.005 NT-proBNP (Pg/ml) 67.61 ± 12.47 61.43 ± 11.99 59.60 ± 10.69 0.016 HS-CRP (mg/L) 7.35 ± 6.82 3.33 ± 1.95 2.23 ± 0.62 < 0.001 SCORE 1.5 11.53 ± 12.19 8.83 ± 6.77 6.13 ± 6.60 0.078 FRS 9.96 ± 11.02 7.20 ± 6.16 4.71 ± 6.07 0.029 HDL (mg/dl) 42.73 ± 10.67 57.86 ± 13.97 44.30 ± 8.82 < 0.001 Data are Mean ± SEM; FABP4: Fatty acid binding protein 4, NT-proBNP: N-terminal pro–B-type natriuretic peptide, HS-CRP: High sensitivity C-reactive protein, HDL: High Density Lipoprotein The comparison of lipids profile, HS-CRP, NT-proBNP, FBS, FABP4, and S100A12 in patients (new case + under-treatment) and control group The serum levels of FABP4, S100A12, HDL, NT-proBNP, and HS-CRP were substantially different between the RA patients (new cases and under-treatment) and healthy individuals ( P < 0.001, P = 0.005, P < 0.001, P = 0.016, P < 0.001, respectively). Still, the serum concentration of TG, cholesterol, LDL, FBS, BPD (diastolic blood pressure), BPS (systolic blood pressure) and BMI were not significantly different between the three groups of the study population ( P = 0.241, P = 0.427, P = 0.105, P = 0.093, P = 0.191, P = 0.593, P = 0.223 respectively). Among CVD risk calculators, FRS was different between the RA patients and healthy subjects While SCORE was not different between the three groups ( P = 0.029 and P = 0.078) (Figure 1). Fig.1 Comparing the plasma levels of FABP4, S100A12, NT-proBNP, and HS-CRP among three groups Fig.1 caption The plasma concentration of FABP4, S100A12, and NT-proBNP was quantified by the sandwich ELISA method. Also, the plasma levels of HS-CRP were read using the ADVIA 1800 Clinical Chemistry System, which was quantified by the Immunoturbidimetric assay. a) Comparing the plasma level of FABP4 among the three groups, which significantly was higher in the newly diagnosed and under-treatment RA patients compared to healthy subjects ( P <0.001 and P = 0.008, respectively). However, there was a remarkable difference between the new case and patient groups ( P = 0.009). b) The graph showed a significant rise of S100A12 in the new case compared to control groups (P =0.001), though there was not a remarkable difference between the under-treatment RA patients compared to healthy subjects ( P =0.322). c) The plasma level of NT-proBNP was substantially higher in newly diagnosed compared to control groups ( P <0.01), Also there was not a remarkable difference between the under-treatment patients and control groups ( P =0.246). d)The Plasma level of HS-CRP was significantly higher in the newly diagnosed and under-treatment RA patients compared to healthy subjects ( P <0.001 and P = 0.013, respectively). However, there was a remarkable difference between the new case and under-treatment RA patient groups ( P = 0.020). Assessment of correlation between variables in patients group (new case + under-treatment) The correlation between the plasma concentration of FABP4 and S100A12 with BMI, LDL/HDL, TG, Cholesterol, FBS, BPS, BPD, NT-proBNP, HS-CRP, DAS-28 and CVD risk calculator( FRS and SCORE) in the patients group (new case + under-treatment) was shown in Table 3. Table 3 Correlation between the plasma level of FABP4 and S100A12 with clinical and laboratory parameters RA patients FABP4 S00A12 BMI TG Chol FBS HDL LDL BPS BPD NT-proBNP HS-CRP SCORE FRS DAS-28 FABP4 r = 1 r = .585 r = .056 r = .055 r = .041 r = .033 r = -.224 r = -.098 r = .0366 r = .077 r = .493 r = .399 r = .277 r = .0297 r = .285 P <.001 p = .674 p = .675 p = .754 p = .801 p = .085 p = .454 p = .004 p = .559 p <.001 p = .002 p = .032 p = .021 p = .027 S100A12 r = .585 r = 1 r = .127 r = .041 r = .122 r = -.032 r = -.183 r = -.190 r = -.026 r = -.220 r = .445 r = .069 r = .017 r =- .004 r = .213 p <.001 p = .333 p = .753 p = .353 p = .809 p = .161 p = .147 p = .841 p = .091 p <.001 p = .599 p = .900 p = .975 p = .102 FABP4: Fatty acid binding protein 4, BMI: Body Mass Index, TG: triglyceride, Chol: Cholesterol, FBS: Fasting Blood Sugar, HDL: High Density Lipoprotein, LDL: Low Density Lipoprotein, BPS: Systolic blood pressure, BPD: Diastolic Blood Pressure, NT-proBNP: N-terminal pro–B-type natriuretic peptide, HS-CRP: High sensitivity C-reactive protein, SCORE: Systematic Coronary Risk Evaluation, FRS: Framingham Risk Score, DAS-28: Disease activity score-28 In the patient group(newly diagnosed and under-treatment), There was a significantly positive correlation between the plasma concentration of FABP4 with S100A12 (r =0.585, P < 0.001), FABP4 with BPS (r =0.366, P = 0.004), FABP4 with NT-proBNP (r =0.493, P < 0.001), FABP4 with HS-CRP (r =0.399, P = 0.002), FABP4 with SCORE (r =0.277, P = 0.032), FABP4 with FRS (r =0.297, P = 0.021) and FABP4 with DAS-28 (r =0.285, P = 0.027), though was a negative correlation between FABP4 with HDL (r =-0.224, P = 0.085) and LDL (r =-0.098, P = 0.454). As well as there was also a significantly positive correlation between the serum levels of S100A12 with NT-proBNP (r =0.445, P < 0.001) and a negative correlation between S100A12 with HDL (r =-0.183, P = 0.161), S100A12 with LDL (r =-0.190, P = 0.147), S100A12 with BPS (r =-0.026, P = 0.841), S100A12 with BPD(r =-0.220, P = 0.091) and S100A12 with FRS (r =-0.004, P = 0.975). Also, in the patient groups, we did not find a significant correlation between BMI, TG, Cholesterol, and FBS with FABP4 and S100A12 (Figure 2). Fig.2 Correlation between plasma levels of FABP4 and S100A12 with different variables in the patient groups Fig.2 caption Correlation analysis was done using Spearman and Pearson correlations. a)In the patient groups, FABP4 plasma concentration was positively a) S100A12 (r =0.585, P < 0.001), b) BPS (r =0.366, P = 0.004), c) NT-proBNP (r =0.493, P < 0.001), d) HS-CRP (r =0.399, P = 0.002), E) SCORE (r =0.277, P = 0.032), F) FRS (r =0.297, P = 0.021) and G) DAS-28 (r =0.285, P = 0.027). H) There was a positive correlation between the S100A12 with NT-proBNP (r =0.445, P < 0.001). Analysis of FABP4 and S100A12 According to CVD Risk Calculators of FRS and SCORE We found in newly diagnosed patients significant differences between FABP4 with FRS ( P = 0.008), FABP4 with SCORE ( P = 0.024), and in patient groups was a positive correlation between FABP4 with FRS ( P = 0.021), FABP4 with SCORE ( P = 0.032), which is mentioned in Table 4. Table 4 Analysis of FABP4 and S100A12 calculated in the Newly Diagnosed Patients, and Under-treatment Patients According to the CVD Risk Calculator of the FRS and SCORE, which into Low, Moderate, and High-risk groups for FRS, and Low-moderate, high, and high-risk groups for SCORE are classified FRS SCORE New cases (n=30) Under-treatment (n=30) Patients(n=60) (New cases+ Under-treatment) FABP4 S100A12 FABP4 S100A12 FABP4 S100A12 P = 0.008 P = 0.517 P = 0.647 P = 0.971 P = 0.021 P = 0.975 P = 0.024 P = 0.618 P = 0.474 P = 0.637 P = 0.032 P = 0.900 FRS classification: (Low risk: FRS 20%), SCORE classification: (under age 50: Lowmoderate risk: SCORE 7.5%), (over age 50: Low-moderate risk: SCORE 10%) Discussion Chemokines and dysregulation of inflammation-controlling genes that lead to the expression of a series of proteins are involved in the migration of leukocytes and the inflammatory process and therefore are suggested to play a role in the pathogenesis of RA. Also, inflammation is a key factor in the pathogenesis of CVD, which is the most common extra-articular manifestation in these patients, leading to the death of half of them. This is the first study demonstrating the correlation between the concentration of plasma FABP4 and S100A12 with traditional CVD risk factors in newly diagnosed and under-treatment RA patients. In our study, baseline serum FABP4 concentrations were significantly higher in newly diagnosed and under-treatment RA patients compared with healthy controls ( P <0.001 and P =0.008, respectively). ). Also, FABP4 plasma concentration was significantly different between under-treatment patients compared with the newly diagnosed RA patients ( P = 0.009). In line with our results, previous studies have reported higher levels of FABP4 in plasma in patients with both early RA and established RA [33-35]. We here show that the levels of FABP4 correlated strongly with HS-CRP in RA patients (r =0.399, P = 0.002), similarly, a correlation between FABP4 and HS-CRP in patients with RA has been reported previously but, Contrary to the results of the study by Shuaishuai Chen, et al, we found a remarkable association between FABP4 and NT-proBNP (r =0.493, P < 0.001)[35]. In previous studies, no significant positive correlation between the plasma levels of FABP4 and RA clinical Parameters, including disease activity score-28 (DAS-28) was observed, interestingly we found a remarkable association between FABP4 and DAS-28(r =0.285, P = 0.027)[34, 36]. Due to the higher plasma levels of FABP4 in patients with RA, We surmise that FABP4 could play a role as a pro-inflammatory factor in the pathogenesis of RA, and the remarkable correlation of FABP4 with DAS-28 indicates the importance of FABP4 as a critical indicator of disease activity in RA, which has scarcely been explored. DAS-28 may increase during the disease, and RA patients were divided into low, moderate, and high disease activity based on the 28-joint disease activity score, so it appears that patients with a consistently high level of disease activity are significantly more at risk of developing cardiovascular diseases[35, 37, 38]. We also tried to evaluate the plasma concentration of S100A12, thus we compared the serum levels of S100A12 between the peripheral blood of both RA and control groups. The results of this research provide interesting information such as that S100A12 was significantly higher in the newly diagnosed compared to the control groups ( P =0.001), however, we did not observe any significant difference between the under-treatment RA patients and healthy subjects ( P =0.322). In line with our investigation, previous studies have reported in rheumatoid arthritis, monocytes are involved in both the initiation and maintenance of inflammation through the production of inflammatory cytokines and S100A12 proteins. S100A12 proteins are abundantly found in the synovial fluid and serum of RA patients, which are strongly increased during persistent inflammation, so they can be used as a better indicator of disease activity than CRP[39, 40]. On the other hand, we confirmed that the serum levels of S100A12 with NT-proBNP were correlated(r =0.445, P < 0.001). NT-proBNP (N-terminal pro-B-type natriuretic peptide) is a marker of cardiac ventricular strain. It is released from cardiac myocytes when stress enters the ventricular vessels' walls and increases the risk of cardiovascular disease. Also, NT-proBNP acts as a serum biomarker to identify heart failure [41-43]. In agreement with this finding[44], plasma NT-proBNP levels in our RA patients were significantly higher than in healthy participants( P = .016). Considering the high plasma concentration of NT-proBNP in RA patients, especially under-treatment patients (61.43 ± 11.99), despite the use of anti-inflammatory drugs, it can be assumed that the risk of cardiovascular disease will increase in these people in the future. However, some investigations did not prove the association between the NT-proBNP level and CVD in RA patients[45, 46]. In the following, to the best of our knowledge, we evaluated the correlation of algorithms that calculate the amount of CVD risk in RA people, including Systematic Coronary Risk Evaluation (SCORE) and Framingham Risk Score (FRS) with FABP4 plasma levels and S100A12 in peripheral blood leukocytes. SCORE calculates the risk of heart disease in the next 10 years[7]. This calculator is not suitable for calculating the CVD risk score in chronic kidney disease, diabetes mellitus, and RA, and people with these diseases are in the very high-risk group for CVD. Therefore, EULAR established the modifying score, which includes a 1.5 Coefficient, to compare chronic inflammatory diseases that increase the mortality rate due to CVD compared to the general population[47]. FRS is the first heart risk score and it can be considered the most common calculator for predicting CVD risk in the next 10 years. The risk factors that FRS is used to calculate CVD include age, sex, total cholesterol, systolic blood pressure, and smoking. This calculator identifies low levels of HDL in men and women as a risk factor. HDL acts as an atheroprotective lipoprotein. Traditional CVD prediction, such as SCORE and FRS, does not consider inflammatory factors and factors that cause CVD risk in RA patients, such as glucocorticoid therapy, abnormal function of lipoproteins, and endothelial dysfunction. [48]. This is the first study to examine the association between plasma levels of FABP4 and S100A12 with SCORE and FRS because these two inflammatory factors play a significant role in cardiovascular diseases. In contradiction to S100A12, FABP4 reveals a significant correlation with SCORE and FRS (r =0.277, P = 0.032; r =0.297, P = 0.021, respectively). Similarly, previous publications have reported an association between FABP4 and CVD [25, 49, 50]. FABP4 increases cholesterol ester accumulation in macrophages and leads to foam cell formation as well as inflammatory responses[25]. Recently, using experimental studies, we investigated the relationship between FABP4 and S100A12 and traditional cardiovascular risk factors, including hypertension, lipid profile, and diabetes. Consistent with a recent study by Rishi J Desai et al., which showed plasma levels of HDL were increased in RA, especially in under-treatment patients after starting disease-modifying antirheumatic drugs (DMARDs), we also reached these results[51]. RA inflammation causes changes in the structure of HDL that lead to stimulation of LDL oxidation and plaque formation. Dysfunctional HDL can further exacerbate LDL metabolic abnormalities and increase the risk of cardiovascular disease[52]. Also, our result showed a significant negative correlation between FABP4 plasma levels with HDL and LDL levels in patients RA (r =-0.224, P = 0.085; r =-0.098, P = 0.454, respectively), which may reflect Dyslipidemia in these patients. This study provides information about the significant positive association between FABP4 and BPS(r =0.366, P = 0.004), as has previously been reported [53, 54]. We could not find a significant association between plasma FABP4 and FBS in our RA patients and healthy subjects, but a recent study conducted by Valéria et al. demonstrates that FABP4 is involved in some aspects of the metabolic syndrome, including the establishment of type 2 diabetes[55]. In the inflammatory setting of RA, HS-CRP is a valuable marker that plays an important role in bone destruction and disease progression. In addition, HS-CRP increases the risk of myocardial infarction, stroke, and peripheral vascular disease in RA patients[56], and FABP4 leads to chronic inflammation in RA. It is worth mentioning that after myocardial infarction, heart failure is the second cause of death in people with rheumatoid arthritis[57, 58]. On the other hand, considering the role of NT-proBNP as a serum biomarker for the diagnosis of heart failure, as well as the role of FABP4 in cardiovascular disorders such as atherosclerosis and other heart failure[24, 25, 41, 42] and the strong positive significant relationship between FABP4 with HS-CRP, and NT-proBNP; We can assume that FABP4 is an inflammatory factor that leads to the pathogenesis of CVD in RA and can be a therapeutic target to reduce disease activity and the risk of heart disease. In the following, we examined the effect of the treatments prescribed to improve rheumatoid arthritis on the results of our study. An important point to note is that in our study, all newly diagnosed patients had not recently used any treatment for rheumatoid arthritis. NSAIDs and GCs are effective in reducing pain, swelling, and stiffness associated with RA. Disease-modifying antirheumatic drugs (DMARDs) are advised as the first line of therapy with moderate to high RA activity, which includes hydroxychloroquine, methotrexate, leflunomide, and sulfasalazine [59, 60]. Previous studies have scarcely explored the effect of NSAIDs and GC on FABP4 and S100A12 in RA patients. However, one of the limitations of our study is that we cannot ignore the possible effects of NSAIDs and GCs on FABP4 and S100A12 in our patients. Furthermore, the results of the study conducted by Foell et al. demonstrated that after MTX therapy in RA patients, the concentration of S100A12 decreased and was nearly undetectable in synovial patients [61]. Also, Witkowski et al. provide interesting information that shows the successful effect of corticosteroids or anti-TNF systemic treatment on S100A12 in rheumatoid arthritis patients, which reduces the expression of this inflammatory factor in synovial and serum levels [62]. Considering the higher plasma levels of FABP4 in patients with RA compared to healthy subjects and the considerable relationship of FABP4 with elevated atherosclerotic diseases by its effect on macrophages, which we have already examined in this research. For the first time, Urushima et al. display that tocilizumab(IL-6R Ab), decreases the level of serum FABP4 in patients with early and established RA[33, 63]. The drugs used in the treatment of RA may increase the risk of cardiovascular diseases in these patients by disrupting the mechanisms of vascular repair or adversely affecting traditional cardiovascular risk factors, such as blood lipid levels. On the other hand, some drugs are associated with a reduction in cardiovascular risk[64, 65]. For example, NSAIDs, as the most widely used drugs for the treatment of patients with RA, cause adverse effects on the digestive system, kidneys, and heart of these patients. Cardiovascular events include stroke, heart failure, high blood pressure, and ultimately death[66]. In addition, glucocorticoids can be considered a double-edged sword, causing cardiovascular complications in rheumatoid arthritis. The American College of Rheumatology and the European Alliance of Associations for Rheumatology recommend the use of glucocorticoids always in a low dose and for the shortest possible time to treat RA[67]. On the other hand, hydroxychloroquine, methotrexate, sulfasalazine, and leflunomide reduce inflammation and cardiovascular events, but cyclosporine can increase blood pressure and play a role in the formation of atherosclerosis[68]. Conclusion Results from our study support a critical association between FABP4 and S100A12 with HS-CRP, NT-ProBNP, DAS-28, lipid profile, and blood pressure in RA patients. Abbreviations RA, Rheumatoid Arthritis ; CVD, Cardiovascular Disease; FABP4, Fatty Acid Binding Protein 4; HS-CRP, High sensitivity C-reactive protein; DAS-28, Disease activity score‑28; FRS, Framingham Risk Score ; SCORE, Systematic Coronary Risk Evaluation ; EULAR, European Alliance of Associations for Rheumatology ; ACR, American college of rheumatology; DMARD, Disease modified anti-rheumatoid drugs ; HF, Heart Failure; BMI, Body mass index; RF, Rheumatoid Factor ; Anti-CCP, Anti–Cyclic Citrullinated Peptide; RAGE, Receptor for Advanced Glycation Endproducts; ELISA, enzyme-linked immunosorbent assay; FBS, Fasting Blood Sugar; LDL, Low-density lipid; HDL, High-density lipid Declarations Acknowledgments We sincerely appreciate for the financial support of the deputy of research and technology of the Kermanshah University of Medical Sciences in this project, as well as the participation of our research assistants in collecting data, writing - review & editing, and conducting experiments. The authors declare that they have no financial or personal interests that affect the data and work reported in this manuscript. Funding Kermanshah University of Medical Sciences participated in this study with financial support (Grant number 4020489). Author Contributions Mahdi Taghadosi and Afsaneh Shamsi: Conceptualization and idea design, Supervision, Funding acquisition, Methodology, and final approval of the article . Fatemeh khoobbakht: obtain data, Investigation, Writing - Original Draft, Resources, Methodology, and Conduction of the experiments . Seyed Askar Roghani: analyzed the data, Conduction of the experiments . Parviz Soufivand: patient diagnosis and provided clinical data . Rezvan Rostampour and Seyedeh Zahra Shahrokhvand: Conduction of the experiments . Data availability Data obtained and/or analyzed during this work are available from the corresponding author on a sensible request. Ethics approval The current research was conducted based on the principles of the Declaration of Helsinki and was followed after the approval of the ethics committee (Approval No: IR.KUMS.MED.REC. 1402.151). Consent to participate All participants signed a predesigned consent form to be informed of the conditions of the study. Consent to publication Not applicable References Díaz-González, F. and M.V. Hernández-Hernández, La artritis reumatoide. Medicina Clínica, 2023. Gravallese, E.M. and G.S. Firestein, Rheumatoid Arthritis—Common Origins, Divergent Mechanisms. New England Journal of Medicine, 2023. 388(6): p. 529-542. Di Matteo, A., J.M. Bathon, and P. Emery, Rheumatoid arthritis. Lancet, 2023. 402(10416): p. 2019-2033. Liao, L., et al., sFlt-1: A double regulator in angiogenesis-related diseases. Current Pharmaceutical Design, 2021. 27(40): p. 4160-4170. Fearon, U., et al., Cellular metabolic adaptations in rheumatoid arthritis and their therapeutic implications. 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Hamm, Role of B-type natriuretic peptide (BNP) and NT-proBNP in clinical routine. Heart, 2006. 92(6): p. 843-849. Heslinga, M., et al., NT-proBNP and sRAGE levels in early rheumatoid arthritis. Scandinavian Journal of Rheumatology, 2023. 52(3): p. 243-249. Wang, M., et al., Rheumatoid arthritis increases the risk of heart failure-current evidence from genome-wide association studies. Frontiers in Endocrinology, 2023. 14: p. 1154271. Tomáš, L.u., et al., Linksventrikuläre Funktion und–Morphologie bei Patienten mit rheumatoider Arthritis. Wiener klinische Wochenschrift, 2013. 125: p. 233-238. Corrales, A., et al., Combined use of QRISK3 and SCORE as predictors of carotid plaques in patients with rheumatoid arthritis. Rheumatology, 2021. 60(6): p. 2801-2807. Jahangiry, L., M.A. Farhangi, and F. Rezaei, Framingham risk score for estimation of 10-years of cardiovascular diseases risk in patients with metabolic syndrome. Journal of Health, Population and Nutrition, 2017. 36: p. 1-6. Hoebaus, C., et al., FABP4 and cardiovascular events in peripheral arterial disease. Angiology, 2018. 69(5): p. 424-430. Egbuche, O., et al., Fatty acid binding protein‐4 and risk of cardiovascular disease: the cardiovascular health study. Journal of the American Heart Association, 2020. 9(7): p. e014070. Desai, R.J., et al., Disease‐Modifying Antirheumatic Drug Use and the Risk of Incident Hyperlipidemia in Patients With Early Rheumatoid Arthritis: A Retrospective Cohort Study. Arthritis care & research, 2015. 67(4): p. 457-466. Yan, J., et al., Dyslipidemia in rheumatoid arthritis: the possible mechanisms. Frontiers in Immunology, 2023. 14: p. 1254753. Ota, H., et al., Elevation of fatty acid-binding protein 4 is predisposed by family history of hypertension and contributes to blood pressure elevation. American journal of hypertension, 2012. 25(10): p. 1124-1130. Tsai, H.-Y., et al., Circulating fatty-acid binding-protein 4 levels predict CV events in patients after coronary interventions. Journal of the Formosan Medical Association, 2021. 120(1): p. 728-736. Lamounier-Zepter, V., et al., Adipocyte fatty acid–binding protein suppresses cardiomyocyte contraction: a new link between obesity and heart disease. Circulation research, 2009. 105(4): p. 326-334. Pope, J.E. and E.H. Choy. C-reactive protein and implications in rheumatoid arthritis and associated comorbidities. in Seminars in arthritis and rheumatism. 2021. Elsevier. Chen, J., L.V. Norling, and D. Cooper, Cardiac dysfunction in rheumatoid arthritis: the role of inflammation. Cells, 2021. 10(4): p. 881. Cozlea, D., et al., The impact of C reactive protein on global cardiovascular risk on patients with coronary artery disease. Current health sciences journal, 2013. 39(4): p. 225. Abbasi, M., et al., Strategies toward rheumatoid arthritis therapy; the old and the new. Journal of cellular physiology, 2019. 234(7): p. 10018-10031. Prasad, P., et al., Rheumatoid arthritis: advances in treatment strategies. Molecular and cellular biochemistry, 2023. 478(1): p. 69-88. Foell, D., et al., Expression of the pro-inflammatory protein S100A12 (EN-RAGE) in rheumatoid and psoriatic arthritis. Rheumatology, 2003. 42(11): p. 1383-1389. Wittkowski, H., et al., Effects of intra-articular corticosteroids and anti-TNF therapy on neutrophil activation in rheumatoid arthritis. Annals of the rheumatic diseases, 2007. 66(8): p. 1020-1025. Boord, J.B., et al., Combined adipocyte-macrophage fatty acid–binding protein deficiency improves metabolism, atherosclerosis, and survival in apolipoprotein E–deficient mice. Circulation, 2004. 110(11): p. 1492-1498. Atzeni, F., et al., Cardiovascular effects of approved drugs for rheumatoid arthritis. Nature Reviews Rheumatology, 2021. 17(5): p. 270-290. England, B.R., et al., Increased cardiovascular risk in rheumatoid arthritis: mechanisms and implications. Bmj, 2018. 361. Crofford, L.J., Use of NSAIDs in treating patients with arthritis. Arthritis research & therapy, 2013. 15: p. 1-10. WJ Bijlsma, J. and F. Buttgereit, Adverse events of glucocorticoids during treatment of rheumatoid arthritis: lessons from cohort and registry studies. Rheumatology, 2016. 55(suppl_2): p. ii3-ii5. Wang, L., Y. Zhang, and S.-Y. Zhang, Immunotherapy for the rheumatoid arthritis-associated coronary artery disease: promise and future. Chinese Medical Journal, 2019. 132(24): p. 2972-2983. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4281885","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":292641934,"identity":"0102abc0-4c70-4975-ac9e-2aff4c78f290","order_by":0,"name":"Fatemeh khoobbakht","email":"","orcid":"","institution":"Immunology Department, Faculty of Medicine, Kermanshah University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Fatemeh","middleName":"","lastName":"khoobbakht","suffix":""},{"id":292641935,"identity":"24fef630-af3f-437a-b653-54b0d04ae7a9","order_by":1,"name":"Seyed Askar 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Sciences","correspondingAuthor":false,"prefix":"","firstName":"Afsaneh","middleName":"","lastName":"Shamsi","suffix":""}],"badges":[],"createdAt":"2024-04-17 12:26:51","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4281885/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4281885/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55329737,"identity":"7e064269-e9b7-46e5-9f13-ecf6c6ce86df","added_by":"auto","created_at":"2024-04-25 19:03:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":129583,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparing the plasma levels of FABP4, S100A12, NT-proBNP, and HS-CRP among three groups\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4281885/v1/69ae48715c61f7dab3bb8cf7.png"},{"id":55329738,"identity":"9ca14885-3ba7-4d5f-b964-f5ee6acf07c7","added_by":"auto","created_at":"2024-04-25 19:03:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":81989,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between plasma levels of FABP4 and S100A12 with different variables in the patient groups\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4281885/v1/592a8eecd1d1c3dcc8dd4f67.png"},{"id":55442179,"identity":"fd78f269-2b08-4d82-8c35-57cc76295142","added_by":"auto","created_at":"2024-04-27 22:33:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1273597,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4281885/v1/038f8041-c501-44fb-8733-4d3bebdb0e53.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"FABP4 and S100A12, a notable link between inflammatory mediators and cardiovascular risk in rheumatoid arthritis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRheumatoid arthritis (RA) is a chronic systemic inflammatory disease. Although this disease is autoimmune, the cause is unknown. The initial manifestations of rheumatoid arthritis are pain and symmetrical swelling in the small joints of the hands and feet. Still, large joints such as shoulders, elbows, knees, and ankles are also affected. Morning fatigue, weakness, and joint stiffness lasting more than 30 minutes in these patients are common. Arthritis is associated with progressive disability, premature death, and high socioeconomic costs. RA affects approximately 0.5-1% of the world's population, and women are 2 to 3 times more at risk than men. One of the factors that play a role in the high prevalence of this disease in women is the effects of estrogen on the immune system. Rheumatoid arthritis can occur at any age, but its incidence peaks in the third to fifth decade of life\u0026nbsp;[1-5].\u003c/p\u003e\n\u003cp\u003eIn addition to joints, rheumatoid arthritis causes damage to other tissues and organs, including the heart, kidneys, lungs, digestive system, eyes, skin, and nervous system. Among extra-articular manifestations, cardiovascular disease (CVD) is more common. CVD in RA patients is 1.5-2 times higher than the general population, and 50% of deaths in RA are related to cardiovascular disease, which includes heart failure (HF), ischemic heart disease, pericarditis, myocarditis, and cardiomyopathy[6-13].\u003c/p\u003e\n\u003cp\u003eThe pathogenesis of CVD in RA patients is due to traditional risk factors including hypertension, smoking, dyslipidemia, BMI (body mass index), insulin resistance, hyperhomocysteinemia, physical inactivity, and non-traditional risk factors such as inflammation, increased CRP, some autoantibodies (Rheumatoid factor (RF) and anti-CCP), pro-inflammatory cytokines and side effects of drugs used to treat rheumatoid arthritis. In addition, age is a non-modifiable risk factor for increased cardiovascular disease in rheumatoid arthritis, as blood pressure, lipids, CRP, and inflammation increase with age. Inflammation contributes to the pathogenesis of CVD in RA patients in the following ways: 1) endothelial damage and accelerated formation of atherosclerotic plaques and 2) they contribute to both the initiation and severity of traditional risk factors\u0026nbsp;[7, 14-21].\u003c/p\u003e\n\u003cp\u003eEULAR (European Union for Rheumatology) advises rheumatoid arthritis patients to check cardiac risk factors every 5 years because these risk factors change the disease activity. In addition, identifying these risk factors can lead to initiating a drug regimen to prevent CVD[7]. CVD risk calculators are used to manage cardiovascular disease in RA patients, although these algorithms are designed for the general population and are often inappropriate and underestimated for RA patients. Currently, there are more than 360 CVD risk prediction models that use cardiac risk factors such as hypertension, smoking, gender, age, and cholesterol. The issue that should be noted is that there is no specific algorithm for RA patients that indicates the risk of CVD in these patients is low or high. Most CVD risk calculators focus on atherosclerosis, but some calculate different types of cardiovascular disease. Using the Systematic Coronary Risk Evaluation (SCORE) calculator, and the Framingham Risk Score (FRS), fatal stroke and heart failure are calculated, respectively[7, 22].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCytokines and chemokines are involved in the pathogenesis of RA and CVD. FABP4 (fatty acid binding protein 4, is a new adipokine (cytokine produced by fat tissue) that is abundantly found in the synovial tissue, synovial fluid, and serum of RA patients and is involved in the development of a group of cardiovascular disorders such as hypertension, heart failure, and atherosclerosis. FABP4 is widely expressed in a variety of organs, but in RA patients it is uncontrollably produced by M1 macrophages in the synovial, causing cartilage destruction and angiogenesis. Also, this adipokine is known as a cardiovascular biomarker due to its role in causing chronic inflammation\u0026nbsp;[23-26].\u003c/p\u003e\n\u003cp\u003eGenes responsible for the control of inflammation exert an indispensable role in the pathogenesis of RA. The S100A12 (calgranulin C) protein contributes to both the pathogenesis of RA and a group of cardiovascular diseases, including myocardial infarction, ischemia-reperfusion (IR) injury, and coronary artery disease. This protein is not expressed in healthy synovial tissue, but it exerts its pro-inflammatory effects in RA patients through RAGE, TLR4, and CD36 receptors expressed on macrophages. S100A12 induces the production of inflammatory cytokines and recruits immune cells to the site of inflammation. As a result, the concentration of this protein in the serum and synovial fluid of RA patients increases and is used to monitor the disease\u0026nbsp;[27-31]. Also, the RAGE receptor is located on the heart and blood vessels, and S10012, by binding to it, causes the production of inflammatory cytokines and the filtering of macrophages to the vessel wall, which eventually leads to cardiovascular disorders[30, 31].\u003c/p\u003e\n\u003cp\u003eIn this study, for the first time, we evaluated the association of plasma concentration of FABP4 and S100A12 in the peripheral blood of newly diagnosed and under-treatment patients with the traditional CVD risk factors (age, gender, BMI, smoking, blood pressure, and lipid profile), considering important of inflammation and high prevalence of cardiovascular disease in RA.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cp\u003e\u003cstrong\u003eStudy population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn a recent study, 60 subjects with rheumatoid arthritis were diagnosed by an expert rheumatologist based on the classification criteria of the American College of Rheumatology/The European Alliance of Associations for Rheumatology 2010 (ACR/EULAR 2010), divided into two categories: 30 patients who did not take any drug( newly-diagnosed) and 30 under-treatment patients were from April 2022 to October 2022, referring to Imam Reza Hospital, Kermanshah University of Medical Sciences (KUMS), and as well as 30 healthy subjects after matching for sex and age were evaluated. In the study, all participants with any previous history of chronic disease including metabolic disorder, kidney, pulmonary, cardiovascular disease, and autoimmune, as well as pregnant women and also subjects who were consuming anti-lipid drugs for example Statins, were excluded from the study. The current study was conducted by the Declaration of Helsinki and was followed with approval from the Ethics Committee of KUMS (IR.KUMS.MED.REC.1402.151).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDemographic data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs previously discussed, in this study, all participants signed a predesigned form with their consent to determine the risk score using several covariates. The data includes gender, age, blood pressure, height, weight, smoking status, comorbidities (diabetes, migraine, kidney disease, and psychological disorders), and family history of heart disease. In study groups, detailed disease modified anti-rheumatoid drug (DMARD) dosage and Demographic information that have been previously studied was shown in Table 1[32].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1 the DMARD dosage, the demographic information, Clinical, Infammatory and Serological Markers of groups study\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"673\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.465081723625556%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.476968796433876%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNew cases\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.13967310549777%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnder-treatment\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.97919762258544%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eControl\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.939078751857355%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.465081723625556%\" valign=\"top\"\u003e\n \u003cp\u003eNumber\u003c/p\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTJC\u003c/p\u003e\n \u003cp\u003eSJC\u003c/p\u003e\n \u003cp\u003eDAS‑28\u003c/p\u003e\n \u003cp\u003eCRP (mg/L)\u003c/p\u003e\n \u003cp\u003eESR (mm/hr)\u003c/p\u003e\n \u003cp\u003ePositive RF\u003c/p\u003e\n \u003cp\u003ePositive Anti‑CCP\u003c/p\u003e\n \u003cp\u003eSmoking status\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMedication\u003c/p\u003e\n \u003cp\u003eAnti‑\u0026nbsp;HT\u003csup\u003ee\u003c/sup\u003e(Cozaar\u0026reg;)\u003c/p\u003e\n \u003cp\u003eAntiplatelet\u003c/p\u003e\n \u003cp\u003eMTX\u003csup\u003ea\u003c/sup\u003e(%)\u003c/p\u003e\n \u003cp\u003eMTX dose (weekly)\u003c/p\u003e\n \u003cp\u003eHCQ\u003csup\u003eb\u003c/sup\u003e(%)\u003c/p\u003e\n \u003cp\u003ePSL\u003csup\u003ec\u003c/sup\u003e(%)\u003c/p\u003e\n \u003cp\u003ePSL dose (daily)\u003c/p\u003e\n \u003cp\u003eOther DMARDs\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.476968796433876%\" valign=\"top\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003cp\u003e48.80 \u0026plusmn; 13.01\u003c/p\u003e\n \u003cp\u003eMale (n = 5)\u003c/p\u003e\n \u003cp\u003eFemale (n = 25)\u003c/p\u003e\n \u003cp\u003e3.33 \u0026plusmn; 3.50\u003c/p\u003e\n \u003cp\u003e3.20 \u0026plusmn; 3.46\u003c/p\u003e\n \u003cp\u003e3.60 \u0026plusmn; 1.16\u003c/p\u003e\n \u003cp\u003e7.35 \u0026plusmn; 6.82\u003c/p\u003e\n \u003cp\u003e25.76 \u0026plusmn; 24.39\u003c/p\u003e\n \u003cp\u003e(60%), (n = 18)\u003c/p\u003e\n \u003cp\u003e(83.3%), (n = 25)\u003c/p\u003e\n \u003cp\u003ePositive (6.7%) (n = 2)\u003c/p\u003e\n \u003cp\u003eNegative (93.3%) (n = 28)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(10%), (n = 3)\u003c/p\u003e\n \u003cp\u003e(10%), (n = 3)\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.13967310549777%\" valign=\"top\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003cp\u003e49.67 \u0026plusmn; 10.51\u003c/p\u003e\n \u003cp\u003eMale (n = 5)\u003c/p\u003e\n \u003cp\u003eFemale (n = 25)\u003c/p\u003e\n \u003cp\u003e0.23 \u0026plusmn; 0.97\u003c/p\u003e\n \u003cp\u003e0.23 \u0026plusmn; 0.97\u003c/p\u003e\n \u003cp\u003e2.33 \u0026plusmn; 0.66\u003c/p\u003e\n \u003cp\u003e3.33 \u0026plusmn; 1.95\u003c/p\u003e\n \u003cp\u003e17.76 \u0026plusmn; 10.91\u003c/p\u003e\n \u003cp\u003e(46.6%), (n = 14)\u003c/p\u003e\n \u003cp\u003e(43.3%), (n = 13)\u003c/p\u003e\n \u003cp\u003ePositive (10%) (n = 3)\u003c/p\u003e\n \u003cp\u003eNegative (90%) (n = 27)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(30%), (n = 9)\u003c/p\u003e\n \u003cp\u003e(13.3%), (n = 4)\u003c/p\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003cp\u003e11.41\u003c/p\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003cp\u003e5.33\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.97919762258544%\" valign=\"top\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003cp\u003e48.10 \u0026plusmn; 12.07\u003c/p\u003e\n \u003cp\u003eMale (n = 5)\u003c/p\u003e\n \u003cp\u003eFemale (n = 25)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.23 \u0026plusmn; 0.62\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ePositive (n = 0)\u003c/p\u003e\n \u003cp\u003eNegative (n = 30)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.939078751857355%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 0.491\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData are Mean \u0026plusmn; SEM, TJC: tender joint count, SJC: swollen joint count, \u003csup\u003ea\u003c/sup\u003eMethotrexate (7.5\u0026ndash;25 mg per week), \u003csup\u003eb\u003c/sup\u003eHydroxychloroquin (200 mg per day), \u003csup\u003ec\u003c/sup\u003ePrednisolone (5\u0026ndash;10 mg per day), \u003csup\u003ed\u003c/sup\u003eDisease Modifying Anti-Rheumatic Drug, \u003csup\u003ee\u003c/sup\u003eAnti-hypertensive drug\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePlasma sample separation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe collected 6 mL of peripheral blood from 60 RA patients (30 newly diagnosed and 30 treated) and 30 healthy controls and poured them into two separate EDTA (ethylene diamine tetra-acetate) tubes. We needed participants\u0026apos; plasma for analysis, so we separated the plasma by centrifuging at 3000g for 10 min and keeping them at -80\u0026deg;C.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEnzyme-Linked Immunosorbent Assay (ELISA)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the Elisa kit instructions, we performed ELISA(enzyme-linked immunosorbent assay)\u0026nbsp;to detect the plasma surface concentration of FABP4 (Podgin, under license from\u0026nbsp;ZellBio GmbH, Iran) (assay range:\u0026nbsp;1-32 ng/ml) (Cat.NO: PTC-12036-H9648), S100A12 protein (Podgin, under license from\u0026nbsp;ZellBio GmbH, Iran) (assay range:\u0026nbsp;30-960 pg/ml) (Cat.NO: PTC-13074-H9648) and\u0026nbsp;NT-proBNP (ZellBio GmbH, Germany) (assay range:12.5-400 Pg/ml) (Cat.NO: ZB-11239C-H9648).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasurement of lipid profile and fasting blood sugar (FBS)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn tubes containing EDTA (ethylene diamine tetra-acetate), 6 ml peripheral blood samples were spilled after 12 hours of fasting. Lipid profile (Triglyceride, total high-density lipid (HDL), low-density lipid (LDL) cholesterol, high-density lipid (HDL), cholesterol), and FBS were measured with two methods of glucose oxidase-peroxidase (Biosystems, Barcelona, Spain) and enzymatic reactions, respectively. The obtained results were read using a fully automated 7020 chemistry analyzer (Hitachi, Tokyo, Japan) and commercial kits were used to produce these reactions according to the manufacturer\u0026rsquo;s recommendation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmunoturbidimetric assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing ADVIA 1800 Clinical Chemistry System (Siemens, Germany), we measured the concentration of high sensitivity CRP (HS-CRP) in plasma samples based on latex-enhanced immunoturbidimetric (assay range: 0.16-10 mg/L). It was done according to the manufacturer\u0026apos;s instructions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasurement of body mass index (BMI)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBMI was calculated for each person using the formula of weight (in kilograms) divided by the square of height (in meters). All participants were classified into three groups based on their BMI: normal weight range (NW): 18.5-24.9, overweight (OW) 25.0-29.9, and obese (OB) \u0026ge; 30 (kg/m2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCalculation of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eDisease activity score‑28 (DAS‑28)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe rheumatologist calculated the disease activity score using the formula DAS28=0/56+0/28 (SJ)+0/70 In (ESR)+0/014GH, (TJ: number of tender joints from 28 joints, SJ: number of swollen joints from 28 joints, GH: global health, ESR: erythrocyte sedimentation rate).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCardiovascular disease risk calculator\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used two traditional CVD risk calculators to calculate CVD risk: the Systematic Coronary Risk Evaluation (SCORE), which is based on a set of risk factors including age, sex, systolic blood pressure, smoking, and lipid profile (total cholesterol, LDL (low-density lipid), HDL that evaluates atherosclerosis and other heart failure.\u003c/p\u003e\n\u003cp\u003eAnother calculator that we used in the study is FRS. The Framingham algorithm estimates the risk of a heart attack in 10 next years. The risk factors used for this risk score are age, smoking, total cholesterol, HDL (high-density lipid), systolic blood pressure, and antihypertensive drugs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eANOVA analysis was carried out to compare the three groups. Spearman and Pearson test was conducted to evaluate the correlation between two variables. Kolmogorov-Smirnov (K-S) test was performed to test the normality of the distribution. In all analyses, a\u0026nbsp;\u003cem\u003eP\u003c/em\u003e value of \u0026lt;0.05 was indicated as statistically significant. Results were presented as mean \u0026plusmn; standard deviation (SD). All analyses were carried out using SPSS software version 24.0 (SPSS, Chicago, IL, USA) and the software GraphPad Prisms\u0026reg; 6.0 (GraphPad Software, La Jolla, California, USA) used for the drawing of the graph.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eThe serum levels of lipids profile (LDL, HDL, TG, and cholesterol), HS-CRP,\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;NT-proBNP,\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;FBS, FABP4, and S100A12\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mean serum concentrations of FABP4, S100A12,\u0026nbsp;NT-proBNP,\u0026nbsp;HS-CRP, and HDLin three groups are given in Table 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 The mean\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFABP4, S100A12,\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;NT-proBNP\u003c/strong\u003e\u003cstrong\u003e,\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;HS-CRP\u003c/strong\u003e\u003cstrong\u003e, and HDL\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.510673234811165%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.211822660098523%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNew cases (n=30)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.182266009852217%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnder-treatment (n=30)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.19704433497537%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eControl (n=30)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.898193760262725%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.510673234811165%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFABP4(\u003c/strong\u003e\u003cstrong\u003eng/ml\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.211822660098523%\" valign=\"top\"\u003e\n \u003cp\u003e4.62 \u0026plusmn; 0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.182266009852217%\" valign=\"top\"\u003e\n \u003cp\u003e3.93 \u0026plusmn; 0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.19704433497537%\" valign=\"top\"\u003e\n \u003cp\u003e3.30 \u0026plusmn; 1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.898193760262725%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026lt;\u0026nbsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.510673234811165%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eS100A12(\u003c/span\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003epg/ml\u003c/span\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.211822660098523%\" valign=\"top\"\u003e\n \u003cp\u003e133.61 \u0026plusmn; 23.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.182266009852217%\" valign=\"top\"\u003e\n \u003cp\u003e114.87 \u0026plusmn; 32.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.19704433497537%\" valign=\"top\"\u003e\n \u003cp\u003e107.78 \u0026plusmn; 37.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.898193760262725%\" valign=\"top\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.510673234811165%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNT-proBNP (Pg/ml)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.211822660098523%\" valign=\"top\"\u003e\n \u003cp\u003e67.61 \u0026plusmn; 12.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.182266009852217%\" valign=\"top\"\u003e\n \u003cp\u003e61.43 \u0026plusmn; 11.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.19704433497537%\" valign=\"top\"\u003e\n \u003cp\u003e59.60 \u0026plusmn; 10.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.898193760262725%\" valign=\"top\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.510673234811165%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHS-CRP (mg/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.211822660098523%\" valign=\"top\"\u003e\n \u003cp\u003e7.35 \u0026plusmn; 6.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.182266009852217%\" valign=\"top\"\u003e\n \u003cp\u003e3.33 \u0026plusmn; 1.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.19704433497537%\" valign=\"top\"\u003e\n \u003cp\u003e2.23 \u0026plusmn; 0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.898193760262725%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026lt;\u0026nbsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.510673234811165%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSCORE\u003c/strong\u003e\u003cstrong\u003e1.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.211822660098523%\" valign=\"top\"\u003e\n \u003cp\u003e11.53 \u0026plusmn; 12.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.182266009852217%\" valign=\"top\"\u003e\n \u003cp\u003e8.83 \u0026plusmn; 6.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.19704433497537%\" valign=\"top\"\u003e\n \u003cp\u003e6.13 \u0026plusmn; 6.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.898193760262725%\" valign=\"top\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.510673234811165%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFRS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.211822660098523%\" valign=\"top\"\u003e\n \u003cp\u003e9.96 \u0026plusmn; 11.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.182266009852217%\" valign=\"top\"\u003e\n \u003cp\u003e7.20 \u0026plusmn; 6.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.19704433497537%\" valign=\"top\"\u003e\n \u003cp\u003e4.71 \u0026plusmn; 6.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.898193760262725%\" valign=\"top\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.510673234811165%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHDL (mg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.211822660098523%\" valign=\"top\"\u003e\n \u003cp\u003e42.73 \u0026plusmn; 10.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.182266009852217%\" valign=\"top\"\u003e\n \u003cp\u003e57.86 \u0026plusmn; 13.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.19704433497537%\" valign=\"top\"\u003e\n \u003cp\u003e44.30 \u0026plusmn; 8.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.898193760262725%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026lt;\u0026nbsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eData are Mean \u0026plusmn; SEM;\u0026nbsp;\u003c/em\u003eFABP4:\u0026nbsp;Fatty acid binding protein 4, NT-proBNP:\u0026nbsp;N-terminal pro\u0026ndash;B-type natriuretic peptide, HS-CRP: High sensitivity C-reactive protein, HDL: High Density Lipoprotein\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe comparison of\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003elipids profile, HS-CRP,\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;NT-proBNP,\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;FBS, FABP4, and S100A12\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ein patients (new case + under-treatment) and control group\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe serum levels of FABP4, S100A12, HDL, NT-proBNP, and HS-CRP were substantially different between the RA patients (new cases and under-treatment) and healthy individuals (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, \u003cem\u003eP\u003c/em\u003e = 0.005, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, \u003cem\u003eP\u003c/em\u003e = 0.016, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, respectively). Still, the serum concentration of TG, cholesterol, LDL, FBS, BPD (diastolic blood pressure), BPS (systolic blood pressure) and BMI were not significantly different between the three groups of the study population (\u003cem\u003eP\u003c/em\u003e = 0.241, \u003cem\u003eP\u003c/em\u003e = 0.427, \u003cem\u003eP\u003c/em\u003e = 0.105, \u003cem\u003eP\u003c/em\u003e = 0.093, \u003cem\u003eP\u003c/em\u003e = 0.191, \u003cem\u003eP\u003c/em\u003e = 0.593,\u003cem\u003e\u0026nbsp;P\u003c/em\u003e = 0.223 respectively). Among CVD risk calculators, FRS was different between the RA patients and healthy subjects While SCORE was not different between the three groups (\u003cem\u003eP\u003c/em\u003e = 0.029 and \u003cem\u003eP\u003c/em\u003e = 0.078) (Figure 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig.1 \u0026nbsp;Comparing the plasma levels of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFABP4, S100A12,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eNT-proBNP,\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eHS-CRP\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;among three groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig.1 caption\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe plasma concentration of FABP4, S100A12, and NT-proBNP was quantified by the sandwich ELISA method. Also, the plasma levels of HS-CRP were read using the ADVIA 1800 Clinical Chemistry System, which was quantified by the Immunoturbidimetric assay. a) Comparing the plasma level of FABP4 among the three groups, which significantly was higher in the newly diagnosed and under-treatment RA patients compared to healthy subjects (\u003cem\u003eP\u003c/em\u003e \u0026lt;0.001 and \u003cem\u003eP\u003c/em\u003e = 0.008, respectively). However, there was a remarkable difference between the new case and patient groups (\u003cem\u003eP\u003c/em\u003e = 0.009). b) The graph showed a significant rise of S100A12 in the new case compared to control groups (P =0.001), though there was not a remarkable difference between the under-treatment RA patients compared to healthy subjects (\u003cem\u003eP\u003c/em\u003e =0.322). c) The plasma level of NT-proBNP was substantially higher in newly diagnosed compared to control groups (\u003cem\u003eP\u003c/em\u003e \u0026lt;0.01), Also there was not a remarkable difference between the under-treatment patients and control groups (\u003cem\u003eP\u003c/em\u003e =0.246). d)The Plasma level of HS-CRP was significantly higher in the newly diagnosed and under-treatment RA patients compared to healthy subjects (\u003cem\u003eP\u003c/em\u003e \u0026lt;0.001 and \u003cem\u003eP\u003c/em\u003e = 0.013, respectively). However, there was a remarkable difference between the new case and under-treatment RA patient groups (\u003cem\u003eP\u003c/em\u003e = 0.020).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssessment of correlation between variables in patients group (new case + under-treatment)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe correlation between the plasma concentration of FABP4 and S100A12 with BMI, LDL/HDL, TG, Cholesterol, FBS, BPS, BPD, NT-proBNP, HS-CRP, DAS-28 and CVD risk calculator( FRS and SCORE) in the patients group (new case + under-treatment) was shown in Table 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e \u003cstrong\u003eCorrelation between the plasma level of FABP4 and S100A12 with clinical and laboratory parameters\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"699\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.166189111747851%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRA patients\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.444126074498567%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFABP4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.444126074498567%\"\u003e\n \u003cp\u003e\u003cstrong\u003eS00A12\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.444126074498567%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.444126074498567%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.444126074498567%\"\u003e\n \u003cp\u003e\u003cstrong\u003eChol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.300859598853868%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFBS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.444126074498567%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHDL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.73352435530086%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLDL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.73352435530086%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBPS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.73352435530086%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBPD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.73352435530086%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNT-proBNP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.73352435530086%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHS-CRP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.73352435530086%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSCORE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.73352435530086%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFRS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.73352435530086%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDAS-28\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.166189111747851%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFABP4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.444126074498567%\"\u003e\n \u003cp\u003er =\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.444126074498567%\"\u003e\n \u003cp\u003er = .585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.444126074498567%\"\u003e\n \u003cp\u003er = .056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.444126074498567%\"\u003e\n \u003cp\u003er = .055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.444126074498567%\"\u003e\n \u003cp\u003er = .041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.300859598853868%\"\u003e\n \u003cp\u003er = .033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.444126074498567%\"\u003e\n \u003cp\u003er = -.224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.73352435530086%\"\u003e\n \u003cp\u003er = -.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.73352435530086%\"\u003e\n \u003cp\u003er = .0366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.73352435530086%\"\u003e\n \u003cp\u003er = .077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.73352435530086%\"\u003e\n \u003cp\u003er = .493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.73352435530086%\"\u003e\n \u003cp\u003er = .399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.73352435530086%\"\u003e\n \u003cp\u003er = .277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.73352435530086%\"\u003e\n \u003cp\u003er = .0297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.73352435530086%\"\u003e\n \u003cp\u003er = .285\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.166189111747851%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.444126074498567%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.444126074498567%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt;.001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.444126074498567%\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e = .674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.444126074498567%\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e = .675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.444126074498567%\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e = .754\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.300859598853868%\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e = .801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.444126074498567%\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e = .085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.73352435530086%\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e = .454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.73352435530086%\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e = .004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.73352435530086%\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e = .559\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.73352435530086%\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt;.001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.73352435530086%\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e = .002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.73352435530086%\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e = .032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.73352435530086%\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e = .021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.73352435530086%\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e = .027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.166189111747851%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eS100A12\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.444126074498567%\"\u003e\n \u003cp\u003er = .585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.444126074498567%\"\u003e\n \u003cp\u003er =\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.444126074498567%\"\u003e\n \u003cp\u003er = .127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.444126074498567%\"\u003e\n \u003cp\u003er = .041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.444126074498567%\"\u003e\n \u003cp\u003er = .122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.300859598853868%\"\u003e\n \u003cp\u003er = -.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.444126074498567%\"\u003e\n \u003cp\u003er = -.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.73352435530086%\"\u003e\n \u003cp\u003er = -.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.73352435530086%\"\u003e\n \u003cp\u003er = -.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.73352435530086%\"\u003e\n \u003cp\u003er = -.220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.73352435530086%\"\u003e\n \u003cp\u003er = .445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.73352435530086%\"\u003e\n \u003cp\u003er = .069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.73352435530086%\"\u003e\n \u003cp\u003er = .017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.73352435530086%\"\u003e\n \u003cp\u003er =- .004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.73352435530086%\"\u003e\n \u003cp\u003er = .213\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.166189111747851%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.444126074498567%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt;.001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.444126074498567%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.444126074498567%\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e = .333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.444126074498567%\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e = .753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.444126074498567%\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e = .353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.300859598853868%\"\u003e\n \u003cp\u003e\u003cem\u003ep\u0026nbsp;\u003c/em\u003e= .809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.444126074498567%\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e = .161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.73352435530086%\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e = .147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.73352435530086%\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e = .841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.73352435530086%\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e = .091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.73352435530086%\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt;.001\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.73352435530086%\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e = .599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.73352435530086%\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e = .900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.73352435530086%\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e = .975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.73352435530086%\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e = .102\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFABP4: Fatty acid binding protein 4, BMI: Body Mass Index, TG: triglyceride, Chol: Cholesterol, FBS: Fasting Blood Sugar, HDL: High Density Lipoprotein, LDL: Low Density Lipoprotein, BPS: Systolic blood pressure, BPD: Diastolic Blood Pressure, NT-proBNP: N-terminal pro\u0026ndash;B-type natriuretic peptide, HS-CRP: High sensitivity C-reactive protein, SCORE: Systematic Coronary Risk Evaluation, FRS: Framingham Risk Score, DAS-28: Disease activity score-28 \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the patient group(newly diagnosed and under-treatment), There was a significantly positive correlation between the plasma concentration of FABP4 with S100A12 (r =0.585, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), FABP4 with BPS (r =0.366, \u003cem\u003eP\u003c/em\u003e = 0.004), FABP4 with NT-proBNP (r =0.493, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), FABP4 with HS-CRP (r =0.399, \u003cem\u003eP\u003c/em\u003e = 0.002), FABP4 with SCORE (r =0.277, \u003cem\u003eP\u003c/em\u003e = 0.032), FABP4 with FRS (r =0.297, \u003cem\u003eP\u003c/em\u003e = 0.021) and FABP4 with DAS-28 (r =0.285, \u003cem\u003eP\u003c/em\u003e = 0.027), though was a negative correlation between FABP4 with HDL (r =-0.224, \u003cem\u003eP\u003c/em\u003e = 0.085) and LDL (r =-0.098, \u003cem\u003eP\u003c/em\u003e = 0.454). As well as there was also a significantly positive correlation between the serum levels of S100A12 with \u0026nbsp;NT-proBNP (r =0.445, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) and a negative correlation between S100A12 with HDL (r =-0.183, \u003cem\u003eP\u003c/em\u003e = 0.161), S100A12 with LDL (r =-0.190, \u003cem\u003eP\u003c/em\u003e = 0.147), S100A12 with BPS (r =-0.026, \u003cem\u003eP\u003c/em\u003e = 0.841), S100A12 with BPD(r =-0.220, \u003cem\u003eP\u003c/em\u003e = 0.091) and S100A12 with FRS (r =-0.004, \u003cem\u003eP\u003c/em\u003e = 0.975). Also, in the patient groups, we did not find a significant correlation between BMI, TG, Cholesterol, and FBS with FABP4 and S100A12 \u0026nbsp;(Figure 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig.2\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp; Correlation between plasma levels of FABP4 and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eS100A12\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;with different variables\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ein the patient groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig.2 caption\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrelation analysis was done using Spearman and Pearson correlations. a)In the patient groups, FABP4 plasma concentration was positively a) S100A12 (r =0.585, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), b) BPS (r =0.366, \u003cem\u003eP\u003c/em\u003e = 0.004), c) NT-proBNP (r =0.493, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), d) HS-CRP (r =0.399, \u003cem\u003eP\u003c/em\u003e = 0.002), E) SCORE (r =0.277, \u003cem\u003eP\u003c/em\u003e = 0.032), F) FRS (r =0.297, \u003cem\u003eP\u003c/em\u003e = 0.021) and G) DAS-28 (r =0.285, \u003cem\u003eP\u003c/em\u003e = 0.027). H) There was a positive correlation between the S100A12 with NT-proBNP (r =0.445, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of FABP4 and S100A12 According to CVD Risk Calculators of FRS and SCORE\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe found in newly diagnosed patients significant differences between FABP4 with FRS (\u003cem\u003eP\u003c/em\u003e = 0.008), FABP4 with SCORE (\u003cem\u003eP\u003c/em\u003e = 0.024), and in patient groups was a positive correlation between FABP4 with FRS (\u003cem\u003eP\u003c/em\u003e = 0.021), FABP4 with SCORE (\u003cem\u003eP\u003c/em\u003e = 0.032), which is mentioned in Table 4.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4 \u0026nbsp;Analysis of FABP4 and S100A12 calculated in the Newly Diagnosed Patients, and Under-treatment Patients According to the CVD Risk Calculator of the FRS and SCORE, which into Low, Moderate, and High-risk groups for FRS, and Low-moderate, high, and high-risk groups for SCORE are classified\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"59.42549371633752%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.28725314183124%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eFRS\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.28725314183124%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSCORE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.342908438061045%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNew cases (n=30)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eUnder-treatment (n=30)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ePatients(n=60)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(New cases+ Under-treatment)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.082585278276483%\" valign=\"top\"\u003e\n \u003cp\u003eFABP4\u003c/p\u003e\n \u003cp\u003eS100A12\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eFABP4\u003c/p\u003e\n \u003cp\u003eS100A12\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eFABP4\u003c/p\u003e\n \u003cp\u003eS100A12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.28725314183124%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 0.008\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 0.517\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 0.647\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 0.971\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 0.021\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 0.975\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.28725314183124%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 0.024\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 0.618\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 0.474\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 0.637\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 0.032\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 0.900\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFRS classification: (Low risk: FRS \u0026lt; 10%, moderate risk: FRS 10\u0026ndash;20%, high risk: FRS \u0026gt; 20%), SCORE classification: (under age 50: Lowmoderate risk: SCORE \u0026lt; 2.5%, high risk: SCORE 2.5\u0026ndash;7.5%, very high risk: SCORE \u0026gt; 7.5%), (over age 50: Low-moderate risk: SCORE \u0026lt; 5%, high risk: SCORE 5\u0026ndash;10%, very high risk: SCORE \u0026gt; 10%)\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eChemokines and dysregulation of inflammation-controlling genes that lead to the expression of a series of proteins are involved in the migration of leukocytes and the inflammatory process and therefore are suggested to play a role in the pathogenesis of RA. Also, inflammation is a key factor in the pathogenesis of CVD, which is the most common extra-articular manifestation in these patients, leading to the death of half of them. This is the first study demonstrating the correlation between the concentration of plasma FABP4 and S100A12 with traditional CVD risk factors in newly diagnosed and under-treatment RA patients.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn our study, baseline serum FABP4 concentrations were significantly higher\u0026nbsp;in newly diagnosed and under-treatment RA patients\u0026nbsp;compared with healthy controls (\u003cem\u003eP\u003c/em\u003e \u0026lt;0.001 and\u0026nbsp;\u003cem\u003eP\u003c/em\u003e =0.008, respectively). ). Also, FABP4 plasma concentration was significantly different between\u0026nbsp;under-treatment patients compared with the newly diagnosed RA patients\u0026nbsp;(\u003cem\u003eP\u003c/em\u003e = 0.009). In line with our results, previous studies have reported higher levels of FABP4 in plasma in patients with both early RA and established RA\u0026nbsp;[33-35]. We here show that the levels of FABP4 correlated strongly with HS-CRP in RA patients (r =0.399,\u0026nbsp;\u003cem\u003eP\u003c/em\u003e = 0.002), similarly, a correlation between FABP4 and HS-CRP in patients with RA has been reported previously but, Contrary to the results of the study by Shuaishuai Chen, et al, we found a remarkable association between FABP4 and NT-proBNP (r =0.493,\u0026nbsp;\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001)[35].\u003c/p\u003e\n\u003cp\u003eIn previous studies, no significant positive correlation between the plasma levels of FABP4 and RA clinical Parameters, including disease activity score-28 (DAS-28) was observed, interestingly we found a remarkable association between FABP4 and DAS-28(r =0.285,\u0026nbsp;\u003cem\u003eP\u003c/em\u003e = 0.027)[34, 36]. Due to the higher plasma levels of FABP4 in patients with RA,\u0026nbsp;We surmise that FABP4 could play a role as a pro-inflammatory factor in the pathogenesis of RA, and the remarkable correlation of FABP4 with DAS-28 indicates the importance of FABP4 as a critical indicator of disease activity in RA, which has scarcely been explored. DAS-28 may increase during the disease, and RA patients were divided into low, moderate, and high disease activity based on the 28-joint disease activity score, so it appears that patients with a consistently high level of disease activity are significantly more at risk of developing cardiovascular diseases[35, 37, 38].\u003c/p\u003e\n\u003cp\u003eWe also tried to evaluate the plasma concentration of S100A12, thus we compared the serum levels of S100A12 between the peripheral blood of both RA and control groups. The results of this research provide interesting information such as that S100A12 was significantly higher in the newly diagnosed compared to the control groups (\u003cem\u003eP\u003c/em\u003e =0.001), however, we did not observe any significant difference between the under-treatment RA patients and healthy subjects (\u003cem\u003eP\u003c/em\u003e =0.322). In line with our investigation, previous studies have reported in rheumatoid arthritis, monocytes are involved in both the initiation and maintenance of inflammation through the production of inflammatory cytokines and S100A12 proteins. S100A12 proteins are abundantly found in the synovial fluid and serum of RA patients, which are strongly increased during persistent inflammation, so they can be used as a better indicator of disease activity than CRP[39, 40]. On the other hand, we confirmed that the serum levels of S100A12 with NT-proBNP were correlated(r =0.445,\u0026nbsp;\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003eNT-proBNP (N-terminal pro-B-type natriuretic peptide) is a marker of cardiac ventricular strain. It is released from cardiac myocytes when stress enters the ventricular vessels' walls and increases the risk of cardiovascular disease. Also, NT-proBNP acts as a serum biomarker to identify heart failure\u0026nbsp;[41-43]. In agreement with this finding[44], plasma NT-proBNP levels in our RA patients were significantly higher than in healthy participants(\u003cem\u003eP\u003c/em\u003e = .016). Considering the high plasma concentration of NT-proBNP in RA patients, especially under-treatment patients\u0026nbsp;(61.43 ± 11.99), despite the use of anti-inflammatory drugs, it can be assumed that the risk of cardiovascular disease will increase in these people in the future. However, some investigations did not prove the association between the NT-proBNP level and CVD in RA patients[45, 46].\u003c/p\u003e\n\u003cp\u003eIn the following, to the best of our knowledge, we evaluated the correlation of algorithms that calculate the amount of CVD risk in RA people, including Systematic Coronary Risk Evaluation (SCORE) and Framingham Risk Score (FRS) with FABP4 plasma levels and S100A12 in peripheral blood leukocytes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSCORE calculates the risk of heart disease in the next 10 years[7]. This calculator is not suitable for calculating the CVD risk score in chronic kidney disease, diabetes mellitus, and RA, and people with these diseases are in the very high-risk group for CVD. Therefore, EULAR established the modifying score, which includes a 1.5 Coefficient, to compare chronic inflammatory diseases that increase the mortality rate due to CVD compared to the general population[47].\u0026nbsp;FRS is the first heart risk score and it can be considered the most common calculator for predicting CVD risk in the next 10 years. The risk factors that FRS is used to calculate CVD include age, sex, total cholesterol, systolic blood pressure, and smoking. This calculator identifies low levels of HDL in men and women as a risk factor. HDL acts as an atheroprotective lipoprotein. Traditional CVD prediction, such as SCORE and FRS, does not consider inflammatory factors and factors that cause CVD risk in RA patients, such as glucocorticoid therapy, abnormal function of lipoproteins, and endothelial dysfunction.\u0026nbsp;[48]. This is the first study to examine the association between plasma levels of FABP4 and S100A12 with SCORE and FRS because these two inflammatory factors play a significant role in cardiovascular diseases. In contradiction to S100A12, FABP4 reveals a significant correlation with SCORE and FRS (r =0.277,\u0026nbsp;\u003cem\u003eP\u003c/em\u003e = 0.032;\u0026nbsp;r =0.297,\u0026nbsp;\u003cem\u003eP\u003c/em\u003e = 0.021, respectively). Similarly, previous publications have reported an association between FABP4 and CVD\u0026nbsp;[25, 49, 50]. FABP4 increases cholesterol ester accumulation in macrophages and leads to foam cell formation as well as inflammatory responses[25].\u003c/p\u003e\n\u003cp\u003eRecently, using experimental studies, we investigated the relationship between FABP4 and S100A12 and traditional cardiovascular risk factors, including hypertension, lipid profile, and diabetes. Consistent with a recent study by Rishi J Desai et al., which showed plasma levels of HDL were increased in RA, especially in under-treatment patients after starting disease-modifying antirheumatic drugs (DMARDs), we also reached these results[51]. RA inflammation causes changes in the structure of HDL that lead to stimulation of LDL oxidation and plaque formation. Dysfunctional HDL can further exacerbate LDL metabolic abnormalities and increase the risk of cardiovascular disease[52]. Also, our result showed a significant negative correlation between FABP4 plasma levels with \u0026nbsp;HDL and LDL levels in patients RA\u0026nbsp;(r =-0.224,\u0026nbsp;\u003cem\u003eP\u003c/em\u003e = 0.085;\u0026nbsp;r =-0.098,\u0026nbsp;\u003cem\u003eP\u003c/em\u003e = 0.454, respectively), which may reflect Dyslipidemia in these patients.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study provides information about the significant positive association between FABP4 and BPS(r =0.366,\u0026nbsp;\u003cem\u003eP\u003c/em\u003e = 0.004), as has previously been reported\u0026nbsp;[53, 54]. We could not find a significant association between plasma FABP4 and FBS in our RA patients and healthy subjects, but a recent study conducted by Valéria et al. demonstrates that FABP4 is involved in some aspects of the metabolic syndrome, including the establishment of type 2 diabetes[55]. In the inflammatory setting of RA, HS-CRP is a valuable marker that plays an important role in bone destruction and disease progression. In addition, HS-CRP increases the risk of myocardial infarction, stroke, and peripheral vascular disease in RA patients[56], and FABP4 leads to chronic inflammation in RA. It is worth mentioning that after myocardial infarction, heart failure is the second cause of death in people with rheumatoid arthritis[57, 58]. On the other hand, considering the role of NT-proBNP as a serum biomarker for the diagnosis of heart failure, as well as the role of FABP4 in cardiovascular disorders such as atherosclerosis and other heart failure[24, 25, 41, 42]\u0026nbsp;and the strong positive significant relationship between FABP4 with HS-CRP, and NT-proBNP; We can assume that FABP4 is an inflammatory factor that leads to the pathogenesis of CVD in RA and can be a therapeutic target to reduce disease activity and the risk of heart disease.\u003c/p\u003e\n\u003cp\u003eIn the following, we examined the effect of the treatments prescribed to improve rheumatoid arthritis on the results of our study. An important point to note is that in our study, all newly diagnosed patients had not recently used any treatment for rheumatoid arthritis.\u0026nbsp;NSAIDs and GCs are effective in reducing pain, swelling, and stiffness associated with RA. Disease-modifying antirheumatic drugs (DMARDs) are advised as the first line of therapy with moderate to high RA activity, which includes hydroxychloroquine, methotrexate, leflunomide, and sulfasalazine\u0026nbsp;[59, 60].\u003c/p\u003e\n\u003cp\u003ePrevious studies have scarcely explored the effect of NSAIDs and GC on FABP4 and S100A12 in RA patients. However, one of the limitations of our study is that we cannot ignore the possible effects of NSAIDs and GCs on FABP4 and S100A12 in our patients. Furthermore, the results of the study conducted by Foell et al. demonstrated that after MTX therapy in RA patients, the concentration of S100A12 decreased and was nearly undetectable in synovial patients\u0026nbsp;[61]. Also, Witkowski et al. provide interesting information that shows the successful effect of corticosteroids or anti-TNF systemic treatment on S100A12 in rheumatoid arthritis patients, which reduces the expression of this inflammatory factor in synovial and serum levels\u0026nbsp;[62]. Considering the higher plasma levels of FABP4 in patients with RA compared to healthy subjects and the considerable relationship of FABP4 with elevated atherosclerotic diseases by its effect on macrophages, which we have already examined in this research. For the first time, Urushima et al. display that tocilizumab(IL-6R Ab), decreases the level of serum FABP4 in patients with early and established RA[33, 63].\u003c/p\u003e\n\u003cp\u003eThe drugs used in the treatment of RA may increase the risk of cardiovascular diseases in these patients by disrupting the mechanisms of vascular repair or adversely affecting traditional cardiovascular risk factors, such as blood lipid levels. On the other hand, some drugs are associated with a reduction in cardiovascular risk[64, 65]. For example, NSAIDs, as the most widely used drugs for the treatment of patients with RA, cause adverse effects on the digestive system, kidneys, and heart of these patients. Cardiovascular events include stroke, heart failure, high blood pressure, and ultimately death[66]. In addition, glucocorticoids can be considered a double-edged sword, causing cardiovascular complications in rheumatoid arthritis. The American College of Rheumatology and the European Alliance of Associations for Rheumatology recommend the use of glucocorticoids always in a low dose and for the shortest possible time to treat RA[67]. On the other hand, hydroxychloroquine, methotrexate, sulfasalazine, and leflunomide reduce inflammation and cardiovascular events, but cyclosporine can increase blood pressure and play a role in the formation of atherosclerosis[68].\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eResults from our study support a critical association between FABP4 and S100A12 with HS-CRP, NT-ProBNP, DAS-28, lipid profile, and blood pressure in RA patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eRA, Rheumatoid Arthritis ; CVD, Cardiovascular Disease; FABP4, Fatty Acid Binding Protein 4; HS-CRP, High sensitivity C-reactive protein; DAS-28, Disease activity score‑28; FRS, Framingham Risk Score ; SCORE, Systematic Coronary Risk Evaluation ; EULAR, European Alliance of Associations for Rheumatology ; ACR, American college of rheumatology; DMARD, Disease modified anti-rheumatoid drugs ; HF, Heart Failure; BMI, Body mass index; RF, Rheumatoid Factor ; Anti-CCP, Anti–Cyclic Citrullinated Peptide; RAGE, Receptor for Advanced Glycation Endproducts; ELISA, enzyme-linked immunosorbent assay; FBS, Fasting Blood Sugar; LDL, Low-density lipid; HDL, High-density lipid\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely appreciate for the financial support of the deputy of research and technology of the Kermanshah University of Medical Sciences in this project, as well as the participation of our research assistants in collecting data, writing - review \u0026amp; editing, and conducting experiments.\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no financial or personal interests that affect the data and work reported in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKermanshah University of Medical Sciences participated in this study with financial support\u0026nbsp;(Grant number 4020489).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMahdi Taghadosi and Afsaneh Shamsi:\u0026nbsp;\u003c/strong\u003eConceptualization and idea design, Supervision, Funding acquisition, Methodology, and final approval of the article\u003cstrong\u003e. Fatemeh khoobbakht:\u0026nbsp;\u003c/strong\u003eobtain data, Investigation, Writing - Original Draft, Resources, Methodology, and Conduction of the experiments\u003cstrong\u003e. Seyed Askar Roghani:\u0026nbsp;\u003c/strong\u003eanalyzed the data, Conduction of the experiments\u003cstrong\u003e. Parviz Soufivand:\u0026nbsp;\u003c/strong\u003epatient diagnosis and provided clinical data\u003cstrong\u003e. Rezvan Rostampour and Seyedeh Zahra Shahrokhvand:\u0026nbsp;\u003c/strong\u003eConduction of the experiments\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData obtained and/or analyzed during this work are available from the corresponding author on a sensible request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe current research was conducted based on the principles of the Declaration of Helsinki and was followed after the approval of the ethics committee\u0026nbsp;(Approval No: IR.KUMS.MED.REC.\u0026nbsp;1402.151).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants signed a predesigned consent form to be informed of the conditions of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eD\u0026iacute;az-Gonz\u0026aacute;lez, F. and M.V. Hern\u0026aacute;ndez-Hern\u0026aacute;ndez, La artritis reumatoide. Medicina Cl\u0026iacute;nica, 2023.\u003c/li\u003e\n \u003cli\u003eGravallese, E.M. and G.S. Firestein, Rheumatoid Arthritis\u0026mdash;Common Origins, Divergent Mechanisms. New England Journal of Medicine, 2023. 388(6): p. 529-542.\u003c/li\u003e\n \u003cli\u003eDi Matteo, A., J.M. Bathon, and P. Emery, Rheumatoid arthritis. Lancet, 2023. 402(10416): p. 2019-2033.\u003c/li\u003e\n \u003cli\u003eLiao, L., et al., sFlt-1: A double regulator in angiogenesis-related diseases. Current Pharmaceutical Design, 2021. 27(40): p. 4160-4170.\u003c/li\u003e\n \u003cli\u003eFearon, U., et al., Cellular metabolic adaptations in rheumatoid arthritis and their therapeutic implications. Nature Reviews Rheumatology, 2022. 18(7): p. 398-414.\u003c/li\u003e\n \u003cli\u003eFigus, F.A., et al., Rheumatoid arthritis: extra-articular manifestations and comorbidities. 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Chinese Medical Journal, 2019. 132(24): p. 2972-2983.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Rheumatoid Arthritis, CVD, FABP4, S100A12","lastPublishedDoi":"10.21203/rs.3.rs-4281885/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4281885/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e 50% of the deaths of RA patients are due to cardiovascular diseases, and inflammation plays an important role in its pathogenesis. FABP4 and S100A12 are involved as inflammatory mediators in the pathogenesis of CVD. For the first time, in a recent study, we evaluated the association between FABP4 and S100A12 plasma concentrations with cardiovascular risk factors in RA patients\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterial and methods:\u003c/strong\u003e 60 patients with RA (30 newly diagnosed and 30 under treatment) and 30 healthy individuals participated in this study with their personal consent. FABP4 and S100A12 plasma concentrations were measured by ELISA (enzyme-linked immunosorbent assay) method. Using ADVIA 1800 Clinical Chemistry System based on latex-enhanced immunoturbidimetric, HS-CRP concentration was calculated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: The FABP4 plasma concentration was significantly elevated in the newly diagnosed and under-treatment RA patients compared to healthy subjects (P \u0026lt;0.001 and P = 0.008, respectively). The plasma levels of S100A12 were remarkably higher in the new case compared to the control groups (P =0.001).There was a significantly positive association between the FABP4 and S100A12 with NT-proBNP(r =0.493, P \u0026lt; 0.001; r =0.445, P \u0026lt; 0.001, respectively).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e FABP4 and S100A12 correlate with cardiovascular biomarkers in RA patients.\u003c/p\u003e","manuscriptTitle":"FABP4 and S100A12, a notable link between inflammatory mediators and cardiovascular risk in rheumatoid arthritis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-25 19:03:17","doi":"10.21203/rs.3.rs-4281885/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b7ff2f85-0a12-4971-a152-3dc657a40b25","owner":[],"postedDate":"April 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-07T11:58:22+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-25 19:03:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4281885","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4281885","identity":"rs-4281885","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}