Abstract
Background
Rheumatoid arthritis (RA) is associated with increased cardiovascular disease (CVD) risk, not fully explained by traditional risk factors. Neutrophil extracellular traps (NETs) and regulatory microRNA polymorphisms (miR-SNPs) may contribute to RA-related atherosclerosis through thromboinflammatory mechanisms.
Objectives
To investigate whether miR-SNPs (rs2431697, rs3746444, rs11614913) are associated with subclinical atherosclerosis in RA, and whether NETosis mediates this relationship.
Methods
We genotyped 620 RA patients and 359 healthy controls. Carotid intima-media thickness (CIMT) was assessed in 65 RA patients and 75 controls. Circulating NETosis markers (cfDNA and citH3–DNA) were quantified. Logistic regression models evaluated the association between miR-SNPs and pathological CIMT (pCIMT), accounting for RA status and cardiovascular risk factors.
Results
The rs11614913 C allele was associated with RA susceptibility. pCIMT was significantly more frequent in RA patients than in controls (75.4% vs. 45.3%, p < 0.001). RA-specific associations were observed between pCIMT and both rs2431697 and rs3746444. The rs2431697 T allele was also associated with increased citH3–DNA levels, suggesting enhanced NETosis.
Conclusions
miR-SNPs rs2431697 and rs3746444 are associated with subclinical atherosclerosis in RA and, in the case of rs2431697, preliminary results suggest that NETosis could be an underlying mechanism. These findings support their potential as biomarkers and therapeutic targets to reduce CVD risk in RA.
Supplementary Information
The online version contains supplementary material available at 10.1186/s13075-025-03727-0.
Keywords: Rheumatoid arthritis, MiR-SNPs, NETosis, Atherosclerosis, Cardiovascular risk
Introduction
Rheumatoid arthritis (RA) is a complex multisystem disease that arises from the interaction between a susceptible genetic background and acquired factors, leading to immune system dysregulation. Among the many clinical complications of RA, cardiovascular disease (CVD) (particularly atherosclerosis) accounts for the largest proportion of excess mortality in these patients [1].
Classic cardiovascular risk factors alone do not fully explain the elevated CVD risk observed in RA. Instead, the inflammatory component of CVD, increasingly recognized over the past two decades [2], plays a central role in RA. Large epidemiological studies have shown that, alongside traditional risk factors (history of CVD, diabetes, hyperlipidemia, hypertension and tobacco use), inflammatory mediators, treatments, and disease activity all contribute to the heightened CVD risk in RA [3]. Notably, atherosclerosis is increased even in early RA, and its progression appears to parallel the chronic inflammatory burden [2, 4]. Chronic inflammation compromises vascular wall integrity—an alteration more frequent and occurring earlier in RA patients than in healthy individuals [5]. Thus, RA represents an ideal model for investigating the interplay among autoimmunity, inflammation, and CVD.
Neutrophils, the most abundant white blood cells, are central to host defense and acute inflammation, placing them at the heart of CVD pathophysiology [6]. The discovery of NETosis significantly expanded the known functions of neutrophils [6]. Neutrophil extracellular trap (NET) formation is a conserved antimicrobial mechanism involving the release of nuclear and granular contents in response to sterile or infectious stimuli [7]. However, excessive NETosis promotes thrombosis [8] via a process termed immunothrombosis, which links innate immunity with thrombosis [9]. For example, in COVID-19, NETs have been implicated in multi-organ complications [10]. In RA, NETs may contribute to disease pathology by serving as a source of autoantigens [11]; they are found in both synovial fluid and plasma, and their presence correlates with inflammatory status [12, 13]. Importantly, biological therapies (bDMARDs) reduce not only inflammation and disease activity but also NETosis [1, 14] and CVD risk [1, 14]. However, the specific role of NETosis in CVD development in RA remains underexplored. A study by Pérez-Sánchez et al. showed that NET levels can identify RA patients with early atherosclerosis with high specificity (AUC = 0.79) [15], supporting a thrombo-inflammatory hypothesis for CVD in RA. In fact, the release of NETs in response to various factors, such as cholesterol crystals and platelet cytokines, plays a key role in atherosclerosis. NETs activate IL-1β/TH17 pathways in macrophages within plaques and can damage the endothelium and facilitate leukocyte recruitment, promoting plaque progression and instability [16]. Since PAD4-dependent histone H3 citrullination (citH3) is a key step in chromatin decondensation during NETosis, plasma citH3-DNA complexes provide a relatively specific readout of circulating NETs, whereas cfDNA is a sensitive but non-specific marker of extracellular chromatin [17, 18].
In recent years, regulators of immune and inflammatory responses such as microRNAs (miRNAs) have gained attention. Associations between miRNA polymorphisms (miR-SNPs) and the risk of RA or systemic lupus erythematosus (SLE) have been examined. The most frequently studied miRNAs include miR-146a (rs2910164, rs57095329, rs2431697) [19, 20], miR-196a2 (rs11614913), and miR-499 (rs3746444) [20–22]. miR-146a regulates the NF-κB pathway; miR-196a2 is linked to aging; and miR-499 targets IL-17RB and peptidylarginine deiminase 4 (PAD4), among others [23, 24]. However, data on their genetic contribution to RA remain inconclusive.
In search of novel molecular links among CVD, inflammation, and NETosis, we investigated whether rs2431697, rs3746444, or rs11614913 are associated with subclinical atherosclerosis in RA patients. We focused on rs243167, as our group has previously demonstrated its association with cardiovascular risk in various infectious and non-infectious diseases [25]. On the other hand, we analyzed rs11614913 and rs3746444, since despite being by far the most studied in various ethnic groups in relation to RA risk, the results are contradictory, as they are biased by genetic background and sample size. Furthermore, we evaluated whether NETosis could mediate the association between these miR-SNPs and increased atherosclerotic burden. Understanding these mechanisms could inform new strategies to mitigate CVD risk in RA.
Patients, materials, and methods
Patients and controls
A total of 620 RA patients were recruited from two Spanish hospitals: Morales Meseguer Hospital in Murcia (n = 86) and Reina Sofía Hospital in Córdoba (n = 534). Genetic analyses were performed in all patients, while NET markers and echocardiographic studies were conducted only in patients from the Murcia cohort. All patients were consecutively enrolled over a 36-month period and met the American College of Rheumatology classification criteria for RA[26]. In addition, 359 consecutive healthy blood donors from the Blood Transfusion Center in Murcia, Spain, were included as controls. Written informed consent was obtained from all participants, and the study was approved by the Ethics Committees of both hospitals. Clinical and biological characteristics of the RA patients are summarized in Table 1.
Table 1.
Demographic and clinical data of the rheumatoid arthritis (RA) patients recruited to the study
| Clinical parameters | RA (N = 620) |
|---|---|
| Female, N (%) | 462 (74.5) |
| Age, yr (median ± IQR) | 54.6 ± 12.1 |
| Evolution time, yr (median ± IQR) | 10.5 ± 9.6 |
| DAS28 (mean ± SD) | 3.7 ± 1.7 |
| Rheumatoid Factor positive, N (%) | 311 (50.2) |
| Anti-CCPs antibodies positivity, N (%) | 448 (72.3) |
| Obesity, N (%) | 117 (18.8) |
| BMI (mean ± SD) | 27.7 ± 5.2 |
| Hypertension, N (%) | 149 (24.0) |
| Smoker, N (%) | 157 (25.3) |
| Laboratory parameters | |
| Total cholesterol (mg/dL) (mean ± SD) | 196.7 ± 37.8 |
| HDL-cholesterol (mg/dL) (mean ± SD) | 59.3 ± 21.5 |
| Triglycerides (mg/dL) (mean ± SD) | 107.5 ± 59.9 |
| CRP (mg/dL) (mean ± SD) | 11.0 ± 23.6 |
| ESR (mm/hr) (mean ± SD) | 20.6 ± 16.4 |
| Treatments | |
| Corticosteroids, N (%) | 322 (52.0) |
| Antimalarials, N (%) | 165 (26.6) |
| NSAIDS, N (%) | 360 (58.1) |
| Metotrexate, N (%) | 373 (60.2) |
| Leflunomide, N (%) | 149 (24.0) |
| bDMARDs, N (%) | 215 (34.7) |
DAS: Disease activity score, anti-CCPs Anti-cyclic citrullinated proteins, ESR Erythrocyte sedimentation rate, CRP C reactive protein, RF Rheumatoid factor, NSAIDS non-steroid anti-inflammatory drugs
bDMARDs: Biological disease-modifying antirheumatic drugs; We used the standard medical definition of obesity based on Body Mass Index (BMI) [Overweight: BMI 25.0–29.9 kg/m2; Obesity: BMI ≥ 30.0 kg/m2]
Sample collection and biological parameters
Samples were collected after overnight fasting. Peripheral blood was drawn into EDTA tubes for plasma and genomic DNA extraction. Within 24 h, samples were centrifuged at 2,500 × g for 15 min at room temperature. Plasma was aliquoted and stored at −80 °C until analysis. Genomic DNA was extracted from the buffy coat using the QIAamp DNA Blood Mini Kit and the QIAcube system (Qiagen, Madrid, Spain). DNA quantification was performed with a NanoDrop ND-2000 spectrophotometer (Thermo Fisher Scientific, Madrid, Spain), and DNA samples were stored at −80 °C. Biological parameters were measured using standard clinical laboratory procedures.
miR-SNP genotyping
Polymorphisms in miR-146a (rs2431697), miR-196a2 (rs11614913), and miR-499 (rs3746444) were genotyped using commercial TaqMan assays (Thermo Fisher Scientific, Madrid, Spain): rs2431697 (Assay ID: C__26633319_10); rs11614913 (Assay ID: C__31185852_10); rs3746444 (Assay ID: C__2142612_40).
PCR reactions were prepared in 5 µL volumes containing 0.2 µL of probe, 2.5 µL of Premix Ex Taq 2 × buffer, and 0.3 µL of DNase-free water, which also served as a negative control. Amplifications were performed using a LightCycler 480 system (Roche Farma, Madrid, Spain).
Carotid Intima-Media Thickness (CIMT) Measurement
CIMT, a noninvasive imaging technique, is commonly used to detect carotid artery stenosis or evaluate plaque morphology, among other applications. We used B-mode imaging to analyze wall thickness and plaque morphology. Normal findings were defined as a smooth vascular wall and a CIMT < 0.9 mm, and stenosis < 50% (no visible plaque or narrowing < 50%). Since the result may be influenced by the operator, CIMT was evaluated in 54 patients with RA and 75 healthy controls from the Murcia region by experienced radiologists from Morales Meseguer Hospital (Murcia, Spain). The maximum CIMT value of the common carotid artery was used for analysis. We considered CIMT to be pathological (pCIMT) if it was > 0.90 mm [27] or if plaque was detected by ultrasound.
Quantification of NETosis markers in plasma
NET markers were measured in 86 RA patients and 75 healthy controls, all from Morales Meseguer Hospital (Murcia, Spain). Cell-free double-stranded DNA (cfDNA) was quantified using the fluorescent dye Sytox Green (Thermo Fisher Scientific, Madrid, Spain), following previously described methods [28]. Results are reported in ng/µL. Plasma levels of citrullinated histone H3-DNA complexes (citH3-DNA), a specific marker of NETosis, were measured using a sandwich ELISA, as described previously [29]. Relative concentrations were determined by the difference in optical density (OD) between 405 and 490 nm.
Statistical analysis
Continuous variables are presented as mean ± standard deviation (SD) or as median and interquartile range (IQR), as appropriate. Categorical variables are expressed as absolute frequencies and percentages. Proportions were compared using the Pearson Chi-square test or Fisher’s exact test, as appropriate. Continuous variables were compared using the Mann–Whitney U test or Student’s t-test.
A binary variable for RA status (RA vs. control) was defined. Logistic regression models were used to analyze the association between the dependent variable, CIMT, and miR-SNPs and RA status. To evaluate whether the association between miR-SNPs and CIMT differed between RA patients and controls, an interaction term (RA status × polymorphism) was included in the models, and its significance was assessed using a global (chunk) test.
Three models were constructed:
Model 1: included RA status, polymorphism, and the interaction term.
Model 2: added age and sex to Model 1.
Model 3: further included hypertension, dyslipidemia, and tobacco use. Diabetes was excluded due to complete separation in the data, which prevented maximum likelihood estimation.
A p-value < 0.05 in the global test used to evaluate the interaction term indicated a differential SNP effect on ultrasound findings between RA patients and controls. All analyses were conducted using R version 4.2.3 (R Foundation for Statistical Computing, Vienna, Austria).
Results
Genotypes and RA susceptibility
Table 2 shows the distribution of rs2431697, rs3746444, and rs11614913 genotypes in 620 RA patients and 359 healthy controls. No significant differences in genotype frequencies were found for rs2431697 or rs3746444 between RA patients and controls, suggesting these SNPs do not confer RA susceptibility in this population. However, the C allele of rs11614913, in both homozygous and heterozygous states, was significantly associated with increased risk of RA (Table 2).
Table 2.
rs2431697, rs3746444 and rs11614913 genotypes distribution in healthy controls (HD) and in all rheumatoid arthritis (RA) patients
| Genotypes | HD (N = 359) | RA (N = 620) | P-value* Dominant model | P-value* Recessive model | P-value* Allelic model |
|---|---|---|---|---|---|
| rs2431697 | |||||
| CC, N (%) | 76 (21.2) | 125 (22.3) | 0.680 | 0.510 | 0.511 |
| CT, N (%) | 181 (50.4) | 287 (51.3) | |||
| TT, N (%) | 102 (28.4) | 148 (26.4) | |||
| rs3746444 | |||||
| AA, N (%) | 199 (60.9) | 319 (62.4) | 0.648 | 0.099 | 0.852 |
| AG, N (%) | 119 (36.4) | 166 (32.5) | |||
| GG, N (%) | 9 (2.8) | 26 (5.1) | |||
| rs11614913 | |||||
| CC, N (%) | 108 (33.0) | 234 (43.3) | 0.003 | 0.049 | 0.002 |
| CT, N (%) | 160 (48.9) | 236 (43.6) | |||
| TT, N (%) | 59 (18.0) | 71 (13.1) | |||
*χ2 test
Available genotypes for each rs are shown. Missing genotype rates: rs2431697, HD = 359/359 and RA = 560/620; rs3746444, HD = 327/359 and RA 560/620; rs11614913. HD 327/359 and RA = 541/620. HWE testing: the RA population was in HW equilibrium with frequencies rs2431697, C (0.48) and T (0.52); rs374644, A (0.79) and G (0.21); rs11614913, C (0.65) and T (0.35)
Incidence of Pathological CIMT in RA
CIMT was measured in 54 RA patients and 75 healthy controls, all from Morales Meseguer Hospital (Murcia, Spain). Demographic and cardiovascular risk profiles for these participants are presented in Table 3.
Table 3.
Demographic, clinical and CIMT data of healthy donors (HD) and RA patients
| HD (N = 75) | RA (N = 54) | P-value | |
|---|---|---|---|
| Age, yrs (median ± SD) | 50.9 ± 12.3 | 53.9 ± 11.9 | 0.172 |
| Females, N (%) | 33 (44.0) | 36 (67.0) | 0.011 |
| Hypertension, N (%) | 22 (29.3) | 12 (22.2) | 0.366 |
| Diabetes, N (%) | 4 (5.3) | 6 (11.1) | 0.226 |
| Dyslipidemia, N (%) | 26 (34.7) | 26 (48.2) | 0.124 |
| Smokers, N (%) | 18 (24.3) | 13 (24.1) | 0.974 |
| BMI (median ± SD) | 26.2 ± 4.0 | 27.2 ± 4.1 | 0.162 |
| HDL, mg/dL (median ± SD) | 58.6 ± 14.6 | 62.7 ± 18.3 | 0.177 |
| LDL, mg/dL (median ± SD) | 113.5 ± 27.1 | 108.0 ± 28.4 | 0.273 |
| CIMT máximum (median ± SD) | 0.9 ± 0.2 | 1.7 ± 1.3 | < 0.001 |
| CIMT pathological, N (%) | 34 (45.3) | 46 (85.2) | < 0.001 |
| Plaque, N (%) | 21 (28.0) | 28 (51.9) | 0.006 |
BMI Body mass index, CIMT Carotid intima media thickness
For analysis, we considered the maximum CIMT value at the common carotid artery. CIMT was defined as pathological (pCIMT) when the value exceeded 0.90 mm [27]. As shown in Table 3, pCIMT was significantly more frequent in RA patients than in controls: 49 of 54 RA patients (85.2%) had pCIMT, compared to 34 of 75 controls (45.3%) (p < 0.001).
Mean maximum CIMT values were also significantly higher in RA patients compared to controls (1.7 ± 1.3 mm vs. 0.9 ± 0.2 mm, respectively; p < 0.001). Likewise, the prevalence of carotid plaques was higher in RA patients (51.9%) than in controls (28.0%) (p = 0.006) (Table 3).
Influence of Genotypes on CIMT in RA
We next evaluated whether the studied genotypes were associated with CIMT values in RA patients and controls.
Logistic regression analysis was used to assess the interaction effect between RA status and miR-SNP genotype on the probability of a pathological CIMT. For rs11614913, there were no significant differences in genotype distribution among individuals with pCIMT between RA patients and controls.
However, significant interactions with RA status were observed for both rs2431697 and rs3746444 in predicting pCIMT (Model 1) (Table 4). These interactions remained significant after adjusting for age and sex (Model 2). In the fully adjusted Model 3 (which included age, sex, hypertension, dyslipidemia, and tobacco use) the interaction effects persisted only in RA patients. These findings suggest that the effects of rs2431697 and rs3746444 on atherosclerotic burden may be specific to RA.
Table 4.
Logistic regression models determining the relationship between rs2431697 genotype and CIMT values
| rs2431697 | RA patients (N = 54) | HD (N = 75) | ||||
|---|---|---|---|---|---|---|
| CC | presence of the T allele | CC | presence of the T allele | P-value for the interaction term | ||
| Model 1 | OR | Ref | 6.30 | Ref | 0.33 | 0.007 |
| 95% CI | 1.08–36.65 | 0.11–0.97 | ||||
| Model 2 | OR | Ref | 3.44 | Ref | 0.37 | 0.076 |
| 95% CI | 0.44–26.69 | 0.10–1.38 | ||||
| Model 3 | OR | Ref | 2.44 | Ref | 0.38 | 0.151 |
| 95% CI | 0.30–19.87 | 0.09–1.63 | ||||
| rs3746444 | AA | presence of the G allele | AA | presence of the G allele | ||
| Model 1 | OR | Ref | 2.76 | Ref | 0.39 | 0.076 |
| 95% CI | 0.43–54.20 | 0.14–1.05 | ||||
| Model 2 | OR | Ref | 2.56 | Ref | 0.27 | 0.085 |
| 95% CI | 0.21–30.85 | 0.08–0.94 | ||||
| Model 3 | OR | Ref | 3.76 | Ref | 0.24 | 0.048 |
| 95% CI | 0.29–49.08 | 0.06–0.99 | ||||
Model 1: RA status (RA patient versus blood donor), miR-SNP
Model 2: Model 1 plus age and sex
Model 3: Model 2 plus hypertension, dyslipidemia and tobacco use
Genotypes and NETosis Markers in RA
We then examined whether the presence of risk alleles was associated with elevated NETosis in RA patients. NET markers were measured in 86 RA patients and 75 healthy controls, all from Morales Meseguer Hospital (Murcia, Spain). Plasma cfDNA levels were not significantly associated with any of the miR-SNPs (Fig. 1).
Fig. 1.
NETosis markers in plasma and miR-SNPs genotypes in RA patients. Association of levels of subrogated markers of NETosis, cfDNA (ng/µl) and citH3-DNA complexes (OD 405 nm), in plasma of RA patients in according to rs2431697, rs3746444 and rs1164913 genotypes. citH3-DNA levels were statistically higher in patients carrying the rs2431697-T allele (**p = 0.002)
However, RA patients carrying the rs2431697 T allele showed significantly elevated levels of citH3–DNA complexes compared to non-carriers (OD: CC = 0.128 ± 0.05 vs. CT + TT = 0.184 ± 0.08; p 0.002). Furthermore, analysis of these results stratified by individual genotypes confirmed a significant dose–response relationship of rs2431697 with citH3-DNA complex levels (Figure S1). No significant associations were found between citH3-DNA levels and either the rs3746444 or rs11614913 genotypes (Fig. 1).
Association between CIMT and NETosis in RA
Given the associations between genotype and both CIMT and NETosis, we investigated whether NETosis markers were directly associated with subclinical atherosclerosis in RA.
As shown in Fig. 2, plasma cfDNA levels did not differ significantly between RA patients with pathological CIMT (pCIMT) and those with non-pathological CIMT (nCIMT).
Fig. 2.
Relationship between NETs markers and CIMT in patients with RA. cfDNA was similar in plasma of RA patients regardless of whether CIMT was normal or pathological. In turn, the levels of citH3-DNA complexes were statistically higher in patients with pCIMT vs nCIMT (*p = 0.002)
In contrast, citH3-DNA complex levels were significantly higher in RA patients with pCIMT compared to those with nCIMT [median OD (IQR): 0.166 (0.092–0.409) vs. 0.124 (0.082–0.246); p = 0.021] (Fig. 2).
Discussion
In this study, we show for the first time that two miR-SNPs, rs2431697 and rs3746444, might be associated with subclinical atherosclerosis (pCIMT) in patients with RA, but not in healthy controls. In addition, carriers of the rs2431697 T allele showed higher plasma levels of citH3-DNA complexes. In turn, in a small subgroup of subjects, we observed an association between miR-SNPs and pCIMT that could be mediated by changes in NETosis markers. These results support a model in which the rs2431697 variant modulates the risk of atherosclerosis in RA and suggest a possible role for NETosis in this association.
Several genetic variants have been associated with RA susceptibility and its clinical heterogeneity. However, these associations do not fully explain the variability in clinical outcomes [30]. Functional polymorphisms in microRNA genes (miR-SNPs) have emerged as promising candidates to help bridge this gap [20, 31]. miRNA expression levels in RA-affected tissues have been proposed as potential biomarkers for diagnosis, prognosis, and treatment monitoring [23, 32]. In this study, we investigated whether three miR-SNPs (rs2431697, rs3746444, and rs11614913), previously linked to RA susceptibility, were also associated with subclinical atherosclerosis in RA patients.
Our comparison between RA patients and healthy controls showed that rs11614913 (but not rs2431697 or rs3746444) was associated with RA susceptibility. These findings are partially consistent with previous studies. For example, Toraih et al. reported an association between rs3746444 and increased RA risk, particularly when combined with rs11614913 [22]. Ali et al. also found an association between rs3746444 and RA in a Pakistani population, but not for rs11614913 [21]. Meta-analyses have yielded mixed results [19, 23], and inconsistencies in the association between rs2431697 and RA in European populations have been reported [19, 23, 33]. Notably, the allele frequencies in our cohort were consistent with those reported in Caucasian populations, suggesting that discrepancies between our results and those published may be attributable to differences in ethnicity or sample size.
Next, we assessed the atherosclerotic burden in RA patients. Inflammatory processes in RA contribute significantly to cardiovascular risk, beyond that explained by traditional risk factors. CIMT is a well-validated surrogate marker for subclinical atherosclerosis and is predictive of future cardiovascular events [14, 34–36]. To our knowledge, this is the first study to explore the relationship between miR-SNPs and CIMT in RA.
Our data suggest that rs11614913 is not functionally associated with subclinical atherosclerosis in this population. However, the rs2431697 T allele and rs3746444 G allele were significantly associated with pCIMT in RA patients. Cardiovascular risk in RA has been shown to correlate more closely with cumulative inflammatory burden and flare frequency than with disease duration [37, 38]. Although we could not assess flare frequency in our cohort, no significant differences in DAS28 scores or CRP levels at recruitment were observed between rs2431697 or rs3746444 genotypes (data not shown). Logistic regression revealed that the associations of rs2431697 T and rs3746444 G alleles with pCIMT were specific to RA patients and not observed in healthy controls, even after adjusting for classical cardiovascular risk factors.
As a potential mechanistic link between these miR-SNPs and pCIMT, we investigated NETosis. While its role as a trigger or consequence in autoimmunity remains unclear, NETosis is increasingly recognized as pathogenic in RA [7]. RA patients often display elevated levels of anti-citrullinated protein antibodies (ACPAs) and an increased capacity for NET formation and ROS production [15]. NETs also promote endothelial dysfunction and thrombosis; for example, RA patient serum can activate endothelial cells (HUVECs) via NET-dependent mechanisms [15].
miR-146a is a known negative regulator of the NF-κB pathway [39]. Our previous studies using miR-146a knockout (miR-146a–/–) mice demonstrated its functional role in NETosis-driven thrombosis, both in sterile and infectious contexts. We also showed that reduced miR-146a levels (modulated by the rs2431697 variant) predict cardiovascular events in various settings, including atrial fibrillation [40, 41], community-acquired pneumonia [42], and acute coronary syndrome [28]. The present findings extend these observations to RA, linking rs2431697 with increased NETosis and subclinical atherosclerosis. Furthermore, the cardiovascular risk conferred by rs2431697 appears specific to inflammatory settings, in line with previous studies showing that certain miRNAs become pathogenic only under stress conditions [43].
To date, no functional studies have directly linked rs3746444 to NETosis. However, the G allele has been associated with increased MIR499 transcription and greater cardiovascular disease risk [44]. A recent meta-analysis of 20 studies (n = 9,564 patients and 8,876 controls) found that rs3746444 was associated with heightened CVD susceptibility [45]. These studies focused on circulating miRNA levels and clinical outcomes, underscoring the need for additional mechanistic research. In this sense, although data on rs3746444 and atherosclerosis are limited, Chen et al. validated its functional role by identifying osbpl1a as a target gene potentially linking rs3746444 to HDL metabolism and atherogenesis [46]. While our study did not include functional experiments, the observed association of rs3746444 with pCIMT in RA patients, but not in healthy controls, supports a disease-specific effect. Further research is needed to confirm the causal role of rs3746444 in RA-related atherosclerosis.
In conclusion, our findings support a model in which the rs2431697 variant modulates atherosclerotic risk in RA and we suggest that this could be through a NETosis-dependent mechanism, acting within the inflammatory environment characteristic of the disease. These insights highlight the potential of miR-SNPs as biomarkers and therapeutic targets for reducing cardiovascular risk in RA.
Limitations
The main limitation of our study is the relatively small sample size, particularly in the subgroup analyses for CIMT and NETosis markers. This limited statistical power may have prevented the interaction terms in the regression models from reaching consistent significance across all models.
In our analyses, the direction and magnitude of point estimates were consistent across models, suggesting a higher likelihood of pathological CIMT in RA patients carrying the rs2431697 T or rs3746444 G alleles. In contrast, this association was not observed in healthy controls. These findings support the hypothesis of a disease-specific interaction that may have been underestimated due to sample size constraints. Furthermore, sample size may have influenced the absence of significant differences in genotype frequencies between RA patients and controls for the rs2431697 or rs3746444 genotypes. Post hoc power analysis performed for these miR-SNPs using the allele frequencies observed in RA (n = 620) and controls (n = 359) with a two-tailed α = 0.05 indicated that the study has adequate power to detect moderate or large differences in allele or genotype frequencies, but not subtle differences. Therefore, the absence of statistically significant differences for rs2431697 and rs3746444 should be interpreted as an absence of detectable differences within the statistical limits of our sample, and not as definitive proof that no association exists. Future studies with larger, multicenter cohorts are warranted to validate these associations and further elucidate the functional implications of miR-SNPs in the pathogenesis of RA-related cardiovascular disease.
Supplementary Information
Acknowledgements
We are extremely grateful to all the patients and healthy volunteers who wer involved in the study as well as to the nurses involved in the donation process.
Authors’ contributions
Conceptualization: LR, CM, RGC; Methodology: LR, SCT, NGB, PJGC; Statistical analysis: JHG. SCT, CPS; Clinical support: ES, MFP, IH, JMP, MTH, MLL; Investigation: SCT, MGA, NGB, PJGC; Writing-original draft preparation: RGC, CMG; Writing-review and editing: JHG, CPS, CLP; Supervision: RGC, CMG.
Funding
This study has been funded by Instituto de Salud Carlos III (ISCIII) through the project "PI23/00027" and "PI20/00136" co-funded by the European Union. CPS was funded by a contract from ‘Ramon y Cajal’ program (RYC2021-033828-I) of the Spanish Ministry of Science and Innovation, cofounded by the European Union ‘NextGenerationEU’/PRTR (MCIN/AEI/10.13039/501).
Data availability
No datasets were generated or analysed during the current study.
Declarations
Ethics approval and consent to participate
The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee CEI-CEIm Hospital Morales Meseguer (protocol code CP PI27/0027-CI EST: 08/20, 23 December 2020).
Consent for publication
All authors have read and accepted the revised version of the manuscript for publication.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Laura Reguilón and Jesús H. Gómez contributed equally to this work.
Constantino Martínez and Rocío González-Conejero contributed equally to this work and shared senior authorship.
Contributor Information
Constantino Martínez, Email: constant@um.es.
Rocío González-Conejero, Email: rocio.gonzalez@carm.es.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
No datasets were generated or analysed during the current study.