Prevalence and risk factors of cardiovascular metabolic comorbidities in patients with rheumatoid arthritis: a real-world cross-sectional study

Prevalence and risk factors of cardiovascular metabolic comorbidities in patients with rheumatoid arthritis: a real-world cross-sectional study | 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 Prevalence and risk factors of cardiovascular metabolic comorbidities in patients with rheumatoid arthritis: a real-world cross-sectional study Lina Leng, Quanyi Tang, Jinfeng Zhang, Yaorong Han, Xiaoli Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7751974/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 Objective Rheumatoid arthritis (RA) patients have a significantly increased risk of cardiovascular disease (CVD). The aim of this study is to explore the influencing factors associated with the aggregation of multiple cardiovascular metabolic risk factors in RA patients. Method This study included 1013 RA patients. According to the preset number of cardiovascular risk factors (obesity, dyslipidemia, hypertension and diabetes), patients were divided into three groups: 0, 1 and ≥ 2 risk factor groups. We collected and compared demographic, clinical characteristics, and disease activity indicators of each group. Use multivariate logistic regression analysis to identify factors independently associated with having ≥ 2 risk factors, and conduct sensitivity and subgroup analysis to validate the robustness of the results. Result Among the total population, 368 patients (36.3%) had ≥ 2 cardiovascular risk factors. Multivariate analysis showed that increasing age (OR = 1.023, 95% CI: 1.007–1.039), elevated systolic blood pressure (SBP) (OR = 1.054, 95% CI: 1.042–1.067), elevated body mass index (BMI) (OR = 1.257, 95% CI: 1.194–1.324), elevated triglycerides (TG) (OR = 2.174, 95% CI: 1.620–2.919), elevated fasting blood glucose (FPG) (OR = 1.524, 95% CI: 1.279–1.816), and elevated levels of high-sensitivity C-reactive protein (CRP) (OR = 1.006, 95% CI: 1.002–1.010) are independent risk factors for the aggregation of multiple risk factors. The increase in high-density lipoprotein cholesterol (HDL-C) (OR = 0.170, 95% CI: 0.084–0.344) is a protective factor. The use of statins is significantly associated with high risk burden (OR = 3.404), which may be a confounding effect of their indications for use. Sensitivity analysis confirmed the consistent association of these factors across different risk stratification. Subgroup analysis revealed significant gender differences: CRP was an independent predictor for male patients, while FPG and statin use had a greater impact on female patients. Conclusion In RA patients, traditional cardiovascular risk factors (age, blood pressure, blood lipids, blood glucose) and systemic inflammation (CRP) jointly promote a high burden of cardiovascular metabolic comorbidities. Clinical management should adopt a comprehensive strategy that actively screens and intervenes in traditional metabolic risk factors while controlling inflammation, and should consider gender specific strategies. rheumatoid arthritis cardiovascular disease risk factors comorbidity inflammation dyslipidemia Figures Figure 1 Introduction Rheumatoid Arthritis (RA) is a common chronic systemic autoimmune disease characterized by synovitis and joint destruction. However, extra articular manifestations, especially cardiovascular disease (CVD), have become the leading cause of death in RA patients [ 1 ]. Research has shown that patients with RA have a 2 to 3-fold increased risk of cardiovascular disease and a 1.5-fold increased risk of mortality [ 2 ], while also having a high incidence of heart failure [ 3 ]. The European League Against Rheumatism (EULAR) recommends conducting an annual CVD risk assessment for RA patients [ 4 ], and suggests using a multiplication factor of 1.5 when using a population-based cardiovascular risk calculator to calculate future cardiovascular risk. This increase in risk cannot be fully explained by traditional cardiovascular risk factors (diabetes, hypertension and hyperlipidemia). Chronic systemic inflammation is considered to play a central role in accelerating atherosclerosis [ 5 , 6 ]. There is a profound interaction between inflammation and metabolic disorders. RA related inflammatory factors not only drive joint lesions, but also interfere with lipid metabolism, promote insulin resistance and endothelial dysfunction, thus jointly constituting a unique cardiovascular metabolic risk spectrum [ 7 , 8 ]. Although previous studies have explored the relationship between blood lipid levels, inflammatory markers, and cardiovascular outcomes in RA patients, the results are inconsistent [ 9 – 12 ]. This variability highlights the necessity for further research on how these factors interact and lead to the observed increase in CVD in RA patients. Therefore, identifying specific factors associated with the aggregation of multiple cardiovascular risk factors in RA patients is crucial for implementing precise prevention strategies. This study is based on a large cohort of RA patients and aims to: (1) describe the distribution of cardiovascular risk factors in this population; (2) Compare the clinical characteristics differences between different risk burden groups; (3) Identify factors independently associated with comorbidity burden of high cardiovascular metabolism; (4) Explore the robustness and gender heterogeneity of these associations through sensitivity and subgroup analysis. Method Research Design and Population This study is a cross-sectional analysis that included a total of 1013 RA patients admitted to Xingtai People's Hospital between March 2022 and December 2024. All patients met the RA classification criteria of the American College of Rheumatology (ACR)/EULAR in 2010 [ 13 ]. Exclusion criteria include: age under 18 years old; Accompanied by a history of malignant tumors; Merge severe liver and kidney dysfunction; Combined history of ischemic heart disease, cerebrovascular accident, or peripheral arterial disease; Lack of clinical or laboratory data. The screening process for research participants is shown in Fig. 1 . This study received ethical approval from the Ethics Committee of Xingtai People's Hospital and was conducted in accordance with the ethical principles outlined in the Helsinki Declaration. Obtain written informed consent from all participants. Data Collection Collect the following data through an electronic medical record system: demographic data: gender, age. Lifestyle: Current smoking and drinking habits. Cardiovascular risk factors: Obesity: Body Mass Index (BMI) > 28 kg/m ². Dyslipidemia: Diagnosis based on medical history and lipid profile, specifically: total cholesterol (TC) greater than or equal to 5.2 mmol/L, triglycerides (TG) greater than or equal to 1.7 mmol/L, high-density lipoprotein cholesterol (HDL-C) less than 1.0 mmol/L, low-density lipoprotein cholesterol (LDL-C) greater than or equal to 3.4 mmol/L [ 14 ]. Hypertension: Based on medical history or systolic blood pressure (SBP) ≥ 140 mmHg and/or diastolic blood pressure (DBP) ≥ 90 mmHg. Diabetes mellitus (DM): based on medical history or fasting blood glucose (FPG) ≥ 7.0 mmol/L. Laboratory indicators: TC, TG, HDL-C, LDL-C, atherosclerosis index (AIP = log (TG/HDL-C)), FPG, uric acid (UA). Disease activity indicators: disease duration (MH), erythrocyte sedimentation rate (ESR), high-sensitivity C-reactive protein (CRP), disease activity scores based on 28 joints (DAS28-ESR and DAS28-CRP). Drug therapy: including the use of statins, nonsteroidal anti-inflammatory drugs (NSAIDs), glucocorticoids, disease modifying antirheumatic drugs (DMARDs), and biologics (BDMARDs). Grouping Definition According to the number of the above four cardiovascular risk factors (obesity, dyslipidemia, hypertension, diabetes), patients were divided into three groups: 0 risk factor group (n = 207), 1 risk factor group (n = 438), and ≥ 2 risk factor groups (n = 368). Statistical analysis Perform statistical analysis using IBM SPSS 25.0. Continuous variables that follow a normal distribution are represented as mean ± standard deviation, and inter group comparisons are conducted using analysis of variance; Non normally distributed variables are represented by median (interquartile range), and inter group comparisons are conducted using Kruskal Wallis H test; Categorical variables are expressed in frequency (percentage), and chi square tests are used for inter group comparisons. Afterwards, Bonferroni correction was used for pairwise comparison. Use a multivariate logistic regression model (Enter method) to analyze factors independently associated with having ≥ 2 cardiovascular risk factors (vs. ≤ 1). Variable screening is based on significant differences in clinical importance and univariate analysis (P < 0.1). Collinearity is diagnosed through variance inflation factor (VIF < 10). Perform the following analysis to verify robustness: Sensitivity analysis: Perform binary logistic regression using different risk burden cut-off points (≥ 1 vs. 0: Model A; ≥ 2 vs. ≤ 1: Model B; ≥ 3 vs. ≤ 2: Model C). Subgroup analysis: Perform multivariate logistic regression stratified by gender. P value < 0.05 (bilateral) is considered statistically significant. Results Baseline characteristics of patients A total of 1013 RA patients were included in the analysis, with females accounting for 80.8% and an overall median age of 58.0 years. The disease activity of the entire queue is at a medium to high level (DAS28-ESR median: 4.53). Among cardiovascular risk factors, the prevalence of dyslipidemia was the highest (58.9%), followed by hypertension (28.3%), obesity (12.0%) and diabetes (8.0%). It is worth noting that over one-third (36.3%) of patients have at least 2 cardiovascular risk factors simultaneously. The detailed baseline characteristics are shown in Table 1. Table 1 . Main demographics, cardiovascular risk factors, and disease-related data in RA patients. Characteristics RA (n=1013) Sex, n (%) Female 819 (80.80%) Male 194 (19.20%) Age (year) 58.00 (49.00, 66.50) Current smoker 47 (4.60%) Current drinker 14 (1.40%) Cardiovascular data CV risk factors, n (%) Obesity 122 (12.00%) Dyslipidemia 597 (58.90%) Hypertension 287 (28.30%) Diabetes mellitus 81 (8.00%) Number of CV risk factors, n (%) 0 207 (20.40%) 1 438 (43.20%) 2 277 (27.30%) 3 81 (8.00%) 4 10 (1.00%) Blood pressure (mmHg) Systolic 131.00 (120.00, 144.00) Diastolic 80.00 (72.00, 87.00) BMI (kg/m 2 ) 23.53 (21.22, 26.03) Lipids TC (mmol/L) 4.15 (3.55, 4.69) TG (mmol/L) 1.07 (0.85, 1.38) HDL-C (mmol/L) 1.03 (0.87, 1.19) LDL-C (mmol/L) 2.45 (2.00, 2.90) AIP 0.02 (-0.12, 0.15) Statins, n (%) 66 (6.50%) FPG (mmol/L) 4.90 (4.54, 5.33) UA (umol/L) 248.00 (195.00, 288.00) Disease-related data MH (month) 60.00 (12.00, 120.00) ESR (mm/H) 59.00 (35.00, 87.00) CRP (mg/L) 28.56 (7.48, 47.59) DAS28-ESR 4.53 (3.93, 5.04) DAS28-CRP 3.80 (3.22, 4.30) NSAIDs, % No 489 (48.30%) Yes 524 (51.70%) Glucocorticoids, % No 893 (88.20%) Yes 120 (11.80%) DMARDs, % No 511 (50.40%) Yes 502 (49.60%) Type of DMARDs, % 0 511 (50.40%) 1 367 (36.20%) ≥2 135 (13.3%) BDMARDs, % No 944 (93.20%) Yes 69 (6.80%) Abbreviations: RA rheumatoid arthritis, CV Cardiovascular, BMI body mass index, Obesity , BMI > 30 kg/m 2 , TC total cholesterol, TG triglyceride, HDL-C high-density lipoprotein cholesterol, LDL-C low-density lipoprotein cholesterol, AIP atherogenic index of plasma, FPG fasting plasma glucose, UA uric acid, MH medical history, ESR erythrocyte sedimentation rate, CRP high-sensitivity C-reactive protein, DAS28-ESR disease activity score based on 28 joints using the erythrocyte sedimentation rate, DAS28-CRP disease activity score based on 28 joints using the high-sensitivity C-reactive protein, NSAIDs nonsteroidal anti-inflammatory drugs, DMARDs disease-modifying antirheumatic drugs, BDMARDs biologics. Comparison of Characteristics of Different Cardiovascular Risk Burden Groups As shown in Table 2, compared with the 0 risk factor group, patients with ≥ 1 and ≥ 2 risk factors were older and had a lower proportion of females (both P<0.05). As the number of risk factors increases, the levels of SBP, DBP, BMI, TC, TG, LDL-C, AIP, FPG, UA, CRP, and ESR all show a significant upward trend, while the level of HDL-C decreases significantly (all P<0.001). The disease activity indicators (DAS28-ESR and DAS28-CRP) were also significantly higher in the high-risk group. There were no significant differences in smoking, alcohol consumption, and anti-rheumatic treatment regimens (NSAIDs, glucocorticoids, DMARDs, BDMARDs) among the groups. Table 2 . Differences in demographics, cardiovascular risk factors, and disease-related characteristics according to the number of cardiovascular risk factors in patients with RA. Characteristics Number of cardiovascular risk factors Comparison (Bonferroni-corrected) 0 (n=207) 1 (n=438) ≥2 (n=368) P value 1 versus 0 ≥2 versus 0 Sex, n (%) Female 185 (89.37%) 354 (80.82%) 280 (76.09%) 0.001 0.012 ＜0.001 Male 22 (10.63%) 84 (19.18%) 88 (23.91%) Age (year) 52.00 (41.00, 61.00) 57.00 (48.00, 66.00) 60.50 (54.00, 68.00) ＜0.001 ＜0.001 ＜0.001 Current smoker 6 (2.90%) 21 (4.79%) 20 (5.43%) 0.807 0.524 0.320 Current drinker 2 (0.97%) 6 (1.37%) 6 (1.63%) 0.374 ＞0.999 ＞0.999 SBP (mmHg) 123.00 (115.00, 130.00) 128.00 (117.00, 140.00) 142.00 (131.00, 150.00) ＜0.001 ＜0.001 ＜0.001 DBP (mmHg) 70.00 (66.00, 76.00) 78.00 (71.00, 85.00) 85.00 (77.00, 90.00) ＜0.001 0.044 ＜0.001 BMI (kg/m 2 ) 22.40±2.89 23.32 (20.70, 25.04) 25.50±3.81 ＜0.001 0.066 ＜0.001 TC (mmol/L) 4.15 (3.69, 4.49) 4.08 (3.42, 4.56) 4.25 (3.59, 5.21) ＜0.001 0.414 0.004 TG (mmol/L) 0.93 (0.75, 1.18) 1.04 (0.82, 1.35) 1.21 (0.96, 1.68) ＜0.001 ＜0.001 ＜0.001 HDL-C (mmol/L) 1.14 (1.06, 1.26) 0.98 (0.85, 1.17) 0.95 (0.83, 1.11) ＜0.001 ＜0.001 ＜0.001 LDL-C (mmol/L) 2.28 (1.98, 2.56) 2.41 (1.91, 2.79) 2.63 (2.11, 3.22) ＜0.001 0.086 ＜0.001 AIP -0.11±0.14 0.02 (-0.11, 0.15) 0.11 (-0.02, 0.25) ＜0.001 ＜0.001 ＜0.001 Statins, n (%) 0 (0%) 18 (4.11%) 48 (13.04%) ＜0.001 0.006 ＜0.001 FPG (mmol/L) 4.78 (4.41, 5.07) 4.87 (4.50, 5.24) 5.09 (4.62, 5.89) ＜0.001 0.034 ＜0.001 UA (umol/L) 233.60±68.74 195.00 (158.00, 241.00) 252.00 (202.00, 307.75) ＜0.001 0.118 ＜0.001 MH (month) 48.00 (8.00, 108.00) 60.00 (12.00, 120.00) 76.00 (15.00, 125.25) 0.013 0.084 0.008 ESR (mm/H) 51.00 (28.00, 79.00) 59.50 (35.00, 88.00) 62.20 (40.00, 88.00) 0.001 0.006 ＜0.001 CRP (mg/L) 19.91 (5.44, 37.87) 28.04 (6.54, 49.42) 32.11 (9.65, 52.88) ＜0.001 0.028 ＜0.001 DAS28-ESR 4.51 (3.80, 4.91) 4.53 (3.90, 5.08) 4.60 (4.00, 5.05) 0.054 0.232 0.028 DAS28-CRP 3.78 (3.13, 4.11) 3.74 (3.15, 4.33) 3.81±0.76 0.031 0.616 0.028 NSAIDs, % No 92 (44.44%) 228 (52.05%) 169 (45.92%) 0.103 0.142 ＞0.999 Yes 115 (55.56%) 210 (47.95%) 199 (54.08%) Glucocorticoids, % No 188 (90.82%) 383 (87.44%) 322 (87.50%) 0.412 0.418 0.454 Yes 19 (9.18%) 55 (12.56%) 46 (12.50%) DMARDs, % No 97 (46.86%) 224 (51.14%) 190 (51.63%) 0.508 0.620 0.544 Yes 110 (5314%) 214 (48.86%) 178 (48.37%) Type of DMARDs, % 0 97 (46.86%) 224 (51.14%) 190 (51.63%) 0.848 0.591 ＞0.999 1 81 (39.13%) 156 (35.62%) 130 (35.33%) ≥2 29 (14.01%) 58 (13.24%) 48 (13.04%) BDMARDs, % No 191 (92.27%) 413 (94.29%) 340 (92.39%) 0.476 0.652 ＞0.999 Yes 16 (7.73%) 25 (5.71%) 28 (7.61%) Abbreviations: RA rheumatoid arthritis, SBP systolic blood pressure, DBP diastolic blood pressure, BMI body mass index, TC total cholesterol, TG triglyceride, HDL-C high-density lipoprotein cholesterol, LDL-C low-density lipoprotein cholesterol, AIP atherogenic index of plasma, FPG fasting plasma glucose, UA uric acid, MH medical history, ESR erythrocyte sedimentation rate, CRP high-sensitivity C-reactive protein, DAS28-ESR disease activity score based on 28 joints using the erythrocyte sedimentation rate, DAS28-CRP disease activity score based on 28 joints using the high-sensitivity C-reactive protein, NSAIDs nonsteroidal anti-inflammatory drugs, DMARDs disease-modifying antirheumatic drugs, BDMARDs biologics. Multivariate analysis related to multiple ( ≥ 2) cardiovascular risk factors Multivariate logistic regression analysis (Table 3) showed that after adjusting for other variables, increased levels of age (OR=1.023), SBP (OR=1.054), BMI (OR=1.257), TG (OR=2.174), FPG (OR=1.524), and CRP (OR=1.006) were independently associated with an increased likelihood of having ≥ 2 risk factors. Elevated levels of HDL-C (OR=0.170) are protective factors. The use of statins is significantly positively correlated with high-risk burden (OR=3.404), which may reflect prescription bias (i.e. patients with pre-existing lipid problems or high risk are more likely to be prescribed statins). Table 3. Multivariable logistic regression analysis of factors associated with having ≥2 cardiometabolic comorbidities in patients with RA. Characteristics β OR (95% CI) P Value Age (year) 0.022 1.023 (1.007 - 1.039) 0.005 SBP (mmHg) 0.053 1.054 (1.042 - 1.067) < 0.001 BMI (kg/m²) 0.229 1.257 (1.194 - 1.324) < 0.001 TG (mmol/L) 0.777 2.174 (1.620 - 2.919) < 0.001 HDL-C (mmol/L) -1.772 0.170 (0.084 - 0.344) < 0.001 Statins use 1.225 3.404 (1.754 - 6.609) < 0.001 FPG (mmol/L) 0.421 1.524 (1.279 - 1.816) < 0.001 CRP (mg/L) 0.006 1.006 (1.002 - 1.010) 0.004 Abbreviations: RA rheumatoid arthritis, OR odds ratio , CI confidence interval, SBP systolic blood pressure, BMI body mass index, TG triglyceride, HDL-C high-density lipoprotein cholesterol, FPG fasting plasma glucose, CRP high-sensitivity C-reactive protein. Sensitivity analysis As shown in Table 4, core factors (SBP, BMI, TG, HDL-C, FPG) consistently exhibited strong and consistent associations when using different risk burden cut-off points. Age and CRP have the strongest association in moderate risk burden (Model B). The OR value of statin use cannot be effectively estimated in Model A due to complete separation, and shows significant negative correlation in both Model B and C, once again supporting its explanation as a high-risk biomarker rather than a cause. Table 4. Sensitivity analysis: Associations between identified factors and different cut-off points of cardiometabolic risk burden using binary logistic regression. Characteristics Model A: ≥1 vs 0 Model B: ≥2 vs ≤1 Model C: ≥3 vs ≤2 OR (95% CI) P Value OR (95% CI) P Value OR (95% CI) P Value Age (year) 1.017 (1.001-1.033) 0.033 1.023 (1.007-1.039) 0.005 1.026 (0.998-1.055) 0.070 SBP (mmHg) 1.058 (1.042-1.073) <0.001 1.054 (1.042-1.067) <0.001 1.040 (1.022-1.058) <0.001 BMI (kg/m²) 1.077 (1.017-1.141) 0.011 1.257 (1.194-1.324) <0.001 1.478 (1.348-1.622) <0.001 TG (mmol/L) 4.553 (2.550-8.127) <0.001 2.174 (1.620-2.919) <0.001 1.579 (1.083-2.302) 0.018 HDL-C (mmol/L) 0.027 (0.012-0.060) <0.001 0.170 (0.084-0.344) <0.001 0.248 (0.068-0.896) 0.033 Statins use † 0.997 0.302 (0.155-0.589) <0.001 0.329 (0.147-0.735) 0.007 FPG (mmol/L) 1.346 (1.024-1.769) 0.033 1.524 (1.279-1.816) <0.001 2.014 (1.669-2.430) <0.001 CRP (mg/L) 1.007 (1.001-1.013) 0.029 1.006 (1.002-1.010) 0.004 1.005 (0.999-1.011) 0.080 *† The odds ratio for statin use in Model A could not be effectively estimated (OR: 0.000, 95% CI: 0.000-∞) due to complete separation (i.e., all patients taking this medication had at least one risk factor). Abbreviations: OR odds ratio , CI confidence interval, SBP systolic blood pressure, BMI body mass index, TG triglyceride, HDL-C high-density lipoprotein cholesterol, FPG fasting plasma glucose, CRP high-sensitivity C-reactive protein. Subgroup analysis (stratified by gender) Gender stratification analysis revealed interesting differences (Table 5), with common factors such as age, SBP, BMI, TG, and HDL-C being important influencing factors in both male and female patients. Male specific factors: CRP is a strong independent predictor in male patients (OR=1.013, P=0.012), but not at a significant level in females (P=0.097). Female specific factors: FPG (OR=1.583) and statin use (OR=4.789) have a much greater impact on female patients than on males. Table 5. Subgroup analysis: Multivariable logistic regression results stratified by sex. Characteristics Total population (n=1013) Male (n=194) Female (n=819) OR (95% CI) P Value OR (95% CI) P Value OR (95% CI) P Value Age (year) 1.023 (1.007-1.039) 0.005 1.023 (0.985-1.063) 0.244 1.024 (1.006-1.043) 0.010 SBP (mmHg) 1.054 (1.042-1.067) <0.001 1.079 (1.046-1.114) <0.001 1.052 (1.038-1.066) <0.001 BMI (kg/m²) 1.257 (1.194-1.324) <0.001 1.205 (1.051-1.381) 0.007 1.274 (1.203-1.349) <0.001 TG (mmol/L) 2.174 (1.620-2.919) <0.001 3.438 (1.300-9.093) 0.013 2.151 (1.578-2.934) <0.001 HDL-C (mmol/L) 0.170 (0.084-0.344) <0.001 0.005 (0.000-0.054) <0.001 0.279 (0.130-0.598) 0.001 Statins use 0.302 (0.155-0.589) <0.001 1.294 (0.341-4.917) 0.705 4.789 (2.157-10.635) <0.001 FPG (mmol/L) 1.524 (1.279-1.816) <0.001 1.251 (0.819-1.910) 0.300 1.583 (1.295-1.935) <0.001 CRP (mg/L) 1.006 (1.002-1.010) 0.004 1.013 (1.003-1.023) 0.012 1.004 (0.999-1.009) 0.097 All models were adjusted for other variables listed in the table. Abbreviations: OR odds ratio , CI confidence interval, SBP systolic blood pressure, BMI body mass index, TG triglyceride, HDL-C high-density lipoprotein cholesterol, FPG fasting plasma glucose, CRP high-sensitivity C-reactive protein. Discussion The main findings of this study are as follows: in Chinese RA patients, the burden of multiple cardiovascular metabolic comorbidities (≥ 2 risk factors) is very common, accounting for over one-third (36.3%); High disease activity is significantly correlated with high cardiovascular risk burden; Traditional metabolic factors (blood pressure, obesity, blood lipids, blood glucose) and systemic inflammation (CRP) are independent determinants of multiple risk aggregation; The positive correlation between statin use and high risk reflects rational clinical practice, rather than a causal relationship; There are gender differences in the cardiovascular risk spectrum, with inflammation contributing more to male risk, while metabolic factors (blood glucose) and statin use (as proxy indicators) have a more prominent impact on females. This study confirms the central role of inflammation in cardiovascular metabolic risk in RA patients. As the number of cardiovascular risk factors increases, the levels of inflammatory markers ESR and CRP also show a significant stepwise upward trend. Even after adjusting for traditional risk factors, CRP remains an independent predictor of high-risk burden. However, it is worth noting that ESR did not show independent predictive value in multivariate models. This discovery seems to contradict some traditional views [15], suggesting that it may be due to the different roles played by acute and chronic inflammatory markers in cardiovascular health [16]. CRP, as an acute phase response protein rapidly produced by liver cells under interleukin-6 (IL-6) stimulation, can activate the complement system, induce cell death, and lead to endothelial dysfunction by inhibiting nitric oxide and upregulating endothelial cell adhesion molecules [17]. In addition, it also promotes monocyte recruitment into atherosclerotic plaque, and increases inflammatory response by inducing leukocyte adhesion and migration and the production of reactive oxygen species [18]. Previous studies have shown that CRP levels can independently predict cardiovascular risk in the general population [19]. Epidemiological studies have shown a close association between CRP and IL-6 levels and CV risk [20]. Large observational cohort studies have reported associations between elevated CRP levels in RA and a more atherogenic lipid profile and hyperlipidaemia [21], an increased risk for myocardial infarction (hazard ratio [HR] 2.12 for CRP >10 versus 21.7 mg/L versus <2.6 mg/L) [24], and CV-related death (14% increase for each mg/L and HR of 3.3 for CRP ≥5 mg/L) [25]. As the number of cardiovascular risk factors increases, we observed highly consistent and significant trend changes in blood lipid parameters: TG, TC, LDL-C, and AIP gradually increase, while HDL-C levels significantly decrease (all P trends<0.001). This pattern is consistent with the classical theory that chronic inflammatory states (confirmed by markers such as CRP) are the core drivers of lipid metabolism disorders [26,27]. Inflammatory cytokines such as IL-6 and tumor necrosis factor-α (TNF-α) not only inhibit lipoprotein lipase activity leading to hypertriglyceridemia, but more importantly, induce functional inactivation of HDL, transforming it from a protective granule that is anti-inflammatory, antioxidant, and promotes cholesterol efflux to a dysfunctional pro-inflammatory granule [28,29]. Therefore, the low HDL-C value obtained from routine testing is essentially a comprehensive danger signal: it reflects not only a decrease in quantity, but also implies the loss of its protective function and even the acquisition of harmful properties [30]. This explains why low HDL-C is a stronger independent risk predictor than high LDL-C in our multivariate model. Therefore, there is an urgent need to change the cardiovascular risk management of RA patients, that is, from simply focusing on the numerical management of LDL-C to comprehensively evaluating the inflammatory load (such as CRP), AIP and functional lipoprotein indicators. It is worth noting that the use of statins is significantly associated with high risk (OR=3.404, 95% CI: 1.754-6.609), reflecting significant prescription bias where statins are reasonably prescribed to clinically identified high-risk patients. This finding is not contradictory to the protective effect of statins in primary cardiovascular prevention, but rather highlights the importance of identifying individuals in the RA population who require statin intervention. In addition, the increase of FPG, BMI, and SBP are independent predictive factors strongly associated with multiple comorbidity risk in this study, and their robustness has been consistently confirmed in sensitivity analysis of different risk stratification models. In terms of mechanism, these factors together form an interwoven metabolic disorder network. Elevated FPG is a core marker of insulin resistance and impaired beta cell function, and its pathogenic mechanism goes far beyond simple glucose metabolism abnormalities. Hyperglycemia directly damages vascular endothelial function and aggravates systemic micro inflammation by promoting the formation of advanced glycation end products (AGEs), inducing oxidative stress and activating protein kinase C (PKC) pathway, which has synergistic effects with RA's own inflammatory background and accelerates atherosclerosis [31]. Elevated BMI, especially visceral fat accumulation, causes adipose tissue to transform from an energy storage organ to an active endocrine organ, secreting large amounts of adipokines such as leptin, resistin, IL-6, TNF-α, and pro-inflammatory cytokines [32]. These factors not only directly aggravate systemic inflammation, but also promote endothelial dysfunction, which is a key initial step in the pathophysiology of atherosclerosis [33]. The increase of SBP itself is a direct reflection of the abnormal high pressure and shear stress sustained by the vascular wall, which will lead to endothelial dysfunction, vascular wall remodeling and decreased compliance, thus making it easier to form atherosclerotic plaque [34]. Our results are consistent with the studies [35,11]. This indicates that these traditional risk factors still maintain independent and strong predictive power, emphasizing the importance of traditional risk management while managing inflammation. The discovery of gender differences has significant clinical implications. In male patients, CRP is a stronger independent predictor (OR=1.013, p=0.012), while the effect of FPG is not significant. This may be due to the biological differences in immune response, hormone levels (such as the lack of protective effects of estrogen), and fat distribution between genders [36]. The impact of FPG and statin use is more prominent in female patients, suggesting that female RA patients may be more sensitive to metabolic disorders. This calls for gender specific risk assessment and intervention strategies in clinical management. This study has several limitations. Firstly, cross-sectional design cannot infer causal relationships, and prospective cohort studies are needed in the future to verify the association between these factors and ultimate cardiovascular events. Secondly, the data comes from a single center, which may lead to selection bias and limit the extrapolation of its results. Thirdly, some potential confounding factors such as dietary structure, physical activity level, specific medication dosage, and treatment compliance were not included in the analysis. Finally, we excluded patients with atherosclerotic cardiovascular disease (ASCVD), which makes our conclusions more applicable to the primary prevention scenario, but may not be applicable to the patient population. Conclusion In summary, this study suggests that cardiovascular risk management in RA patients is a complex issue involving multiple aspects of inflammation and metabolism. Through early identification, comprehensive intervention and gender stratification strategy, it is expected to reduce the cardiovascular incidence rate and mortality of this vulnerable population and improve its long-term prognosis. Abbreviations RA rheumatoid arthritis CVD cardiovascular disease SBP systolic blood pressure BMI body mass index TG triglycerides FPG fasting blood glucose CRP high-sensitivity C-reactive protein HDL-C high-density lipoprotein cholesterol EULAR European League Against Rheumatism ACR American College of Rheumatology TC total cholesterol TG triglycerides LDL-C low-density lipoprotein cholesterol DBP diastolic blood pressure DM diabetes mellitus AIP atherosclerosis index UA uric acid MH disease duration ESR erythrocyte sedimentation rate CRP high-sensitivity C-reactive protein DAS28 disease activity scores based on 28 joints NSAIDs nonsteroidal anti-inflammatory drugs DMARDs disease modifying antirheumatic drugs BDMARDs Biologics OR odds ratio CI confidence interval IL-6 interleukin-6 TNF-α tumor necrosis factor-α AGEs advanced glycation end products PKC protein kinase C ASCVD atherosclerotic cardiovascular disease Declarations Acknowledgements The author thanks Xingtai People's Hospital for providing data on this platform and thanks all participants for their selfless dedication. Author contributions LL: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Supervision, Validation, Visualization, Writing-original draft, Writing-review & editing. QT: Conceptualization, Data curation, Investigation, Methodology, Software, Visualization, Writing-original draft. JZ: Conceptualization, Investigation, Methodology, Software, Visualization. YH: Conceptualization, Investigation, Methodology, Software, Visualization. XL: Conceptualization, Funding acquisition, Methodology, Project administration, Resources, Supervision, Visualization, Writing-review & editing. Funding Statement This study was supported by Key R&D Projects in Xingtai City (No. 2025ZC074). Availability of data and materials The datasets analyzed during the current study are available from the corresponding author on reasonable request. Ethics approval This study received approval from the Research Ethics Committee of Xingtai People’s Hospital (approval number: 2025[031]). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study. Consent for publication Not applicable. Competing interests The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. 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Myasoedova E, Crowson CS, Kremers HM, Roger VL, Fitz-Gibbon PD, Therneau TM, Gabriel SE. Lipid paradox in rheumatoid arthritis: the impact of serum lipid measures and systemic inflammation on the risk of cardiovascular disease. Ann Rheum Dis. 2011;70(3):482–7. doi: 10.1136/ard.2010.135871 . Choy E, Ganeshalingam K, Semb AG, Szekanecz Z, Nurmohamed MT. Cardiovascular risk in rheumatoid arthritis: recent advances in the understanding of the pivotal role of inflammation, risk predictors and the impact of treatment. Rheumatology (Oxford). 2014;53(12):2143–54. doi: 10.1093/rheumatology/keu224 . Gonzalez-Gay MA, Gonzalez-Juanatey C, Lopez-Diaz MJ, Piñeiro A, Garcia-Porrua C, Miranda-Filloy JA, Ollier WE, Martin J, Llorca J. HLA-DRB1 and persistent chronic inflammation contribute to cardiovascular events and cardiovascular mortality in patients with rheumatoid arthritis. Arthritis Rheum. 2007;57(1):125–32. doi: 10.1002/art.22482 . Danesh J, Kaptoge S, Mann AG, Sarwar N, Wood A, Angleman SB, Wensley F, Higgins JP, Lennon L, Eiriksdottir G, et al. Long-term interleukin-6 levels and subsequent risk of coronary heart disease: Two new prospective studies and a systematic review. PLoS Med. 2008;5:e78. doi: 10.1371/journal.pmed.0050078 . Emerging Risk Factors Collaboration. Kaptoge S, Di Angelantonio E, Lowe G, Pepys MB, Thompson SG, Collins R, Danesh J. C-reactive protein concentration and risk of coronary heart disease, stroke, and mortality: An individual participant meta-analysis. Lancet. 2010;375:132–140. doi: 10.1016/s0140-6736(09)61717-7 . Raterman HG, Levels H, Voskuyl AE, Lems WF, Dijkmans BA, Nurmohamed MT. HDL protein composition alters from proatherogenic into less atherogenic and proinflammatory in rheumatoid arthritis patients responding to rituximab. Ann Rheum Dis. 2013;72(4):560–5. doi: 10.1136/annrheumdis-2011-201228 . Jamnitski A, Levels JH, van den Oever IA, Nurmohamed MT. High-density lipoprotein profiling changes in patients with rheumatoid arthritis treated with tumor necrosis factor inhibitors: A cohort study. J Rheumatol. 2013;40(6):825–30. doi: 10.3899/jrheum.121358 . Behl T, Kaur I, Sehgal A, Zengin G, Brisc C, Brisc MC, Munteanu MA, Nistor-Cseppento DC, Bungau S. The Lipid Paradox as a Metabolic Checkpoint and Its Therapeutic Significance in Ameliorating the Associated Cardiovascular Risks in Rheumatoid Arthritis Patients. Int J Mol Sci. 2020;21(24):9505. doi: 10.3390/ijms21249505 . Rohm TV, Meier DT, Olefsky JM, Donath MY. Inflammation in obesity, diabetes, and related disorders. Immunity. 2022;55(1):31–55. doi: 10.1016/j.immuni.2021.12.013 . Moroni L, Farina N, Dagna L. Obesity and its role in the management of rheumatoid and psoriatic arthritis. Clin Rheumatol. 2020;39(4):1039–1047. doi: 10.1007/s10067-020-04963-2 . Weber BN, Giles JT, Liao KP. Shared inflammatory pathways of rheumatoid arthritis and atherosclerotic cardiovascular disease. Nat Rev Rheumatol. 2023;19(7):417–28. doi: 10.1038/s41584-023-00969-7 . Myasoedova E, Crowson CS, Green AB, Matteson EL, Gabriel SE. Longterm blood pressure variability in patients with rheumatoid arthritis and its effect on cardiovascular events and all-cause mortality in RA: a population-based comparative cohort study. J Rheumatol. 2014;41(8):1638–44. doi: 10.3899/jrheum.131170 . Pamies A, Vallvé JC, Paredes S. New Cardiovascular Risk Biomarkers in Rheumatoid Arthritis: Implications and Clinical Utility-A Narrative Review. Biomedicines. 2025;13(4):870. doi: 10.3390/biomedicines13040870 . Raadsen R, Hansildaar R, van Kuijk AWR, Nurmohamed MT. Male rheumatoid arthritis patients at substantially higher risk for cardiovascular mortality in comparison to women. Semin Arthritis Rheum. 2023;62:152233. doi: 10.1016/j.semarthrit.2023.152233 . Additional Declarations No competing interests reported. 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1","display":"","copyAsset":false,"role":"figure","size":133693,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of participant selection.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7751974/v1/4fbb6ee3f5c70a10e3e8a801.jpeg"},{"id":101943215,"identity":"970ef2e4-a8b8-46a9-8616-849195c590bd","added_by":"auto","created_at":"2026-02-05 09:41:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1297699,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7751974/v1/9220de55-971c-4d40-847a-0da571cce8a1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prevalence and risk factors of cardiovascular metabolic comorbidities in patients with rheumatoid arthritis: a real-world cross-sectional study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRheumatoid Arthritis (RA) is a common chronic systemic autoimmune disease characterized by synovitis and joint destruction. However, extra articular manifestations, especially cardiovascular disease (CVD), have become the leading cause of death in RA patients [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Research has shown that patients with RA have a 2 to 3-fold increased risk of cardiovascular disease and a 1.5-fold increased risk of mortality [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], while also having a high incidence of heart failure [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The European League Against Rheumatism (EULAR) recommends conducting an annual CVD risk assessment for RA patients [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], and suggests using a multiplication factor of 1.5 when using a population-based cardiovascular risk calculator to calculate future cardiovascular risk. This increase in risk cannot be fully explained by traditional cardiovascular risk factors (diabetes, hypertension and hyperlipidemia). Chronic systemic inflammation is considered to play a central role in accelerating atherosclerosis [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThere is a profound interaction between inflammation and metabolic disorders. RA related inflammatory factors not only drive joint lesions, but also interfere with lipid metabolism, promote insulin resistance and endothelial dysfunction, thus jointly constituting a unique cardiovascular metabolic risk spectrum [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Although previous studies have explored the relationship between blood lipid levels, inflammatory markers, and cardiovascular outcomes in RA patients, the results are inconsistent [\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This variability highlights the necessity for further research on how these factors interact and lead to the observed increase in CVD in RA patients. Therefore, identifying specific factors associated with the aggregation of multiple cardiovascular risk factors in RA patients is crucial for implementing precise prevention strategies.\u003c/p\u003e\u003cp\u003eThis study is based on a large cohort of RA patients and aims to: (1) describe the distribution of cardiovascular risk factors in this population; (2) Compare the clinical characteristics differences between different risk burden groups; (3) Identify factors independently associated with comorbidity burden of high cardiovascular metabolism; (4) Explore the robustness and gender heterogeneity of these associations through sensitivity and subgroup analysis.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eResearch Design and Population\u003c/h2\u003e\u003cp\u003eThis study is a cross-sectional analysis that included a total of 1013 RA patients admitted to Xingtai People's Hospital between March 2022 and December 2024. All patients met the RA classification criteria of the American College of Rheumatology (ACR)/EULAR in 2010 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Exclusion criteria include: age under 18 years old; Accompanied by a history of malignant tumors; Merge severe liver and kidney dysfunction; Combined history of ischemic heart disease, cerebrovascular accident, or peripheral arterial disease; Lack of clinical or laboratory data. The screening process for research participants is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. This study received ethical approval from the Ethics Committee of Xingtai People's Hospital and was conducted in accordance with the ethical principles outlined in the Helsinki Declaration. Obtain written informed consent from all participants.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eCollect the following data through an electronic medical record system: demographic data: gender, age. Lifestyle: Current smoking and drinking habits. Cardiovascular risk factors: Obesity: Body Mass Index (BMI)\u0026thinsp;\u0026gt;\u0026thinsp;28 kg/m \u0026sup2;. Dyslipidemia: Diagnosis based on medical history and lipid profile, specifically: total cholesterol (TC) greater than or equal to 5.2 mmol/L, triglycerides (TG) greater than or equal to 1.7 mmol/L, high-density lipoprotein cholesterol (HDL-C) less than 1.0 mmol/L, low-density lipoprotein cholesterol (LDL-C) greater than or equal to 3.4 mmol/L [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Hypertension: Based on medical history or systolic blood pressure (SBP)\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg and/or diastolic blood pressure (DBP)\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg. Diabetes mellitus (DM): based on medical history or fasting blood glucose (FPG)\u0026thinsp;\u0026ge;\u0026thinsp;7.0 mmol/L. Laboratory indicators: TC, TG, HDL-C, LDL-C, atherosclerosis index (AIP\u0026thinsp;=\u0026thinsp;log (TG/HDL-C)), FPG, uric acid (UA). Disease activity indicators: disease duration (MH), erythrocyte sedimentation rate (ESR), high-sensitivity C-reactive protein (CRP), disease activity scores based on 28 joints (DAS28-ESR and DAS28-CRP). Drug therapy: including the use of statins, nonsteroidal anti-inflammatory drugs (NSAIDs), glucocorticoids, disease modifying antirheumatic drugs (DMARDs), and biologics (BDMARDs).\u003c/p\u003e\n\u003ch3\u003eGrouping Definition\u003c/h3\u003e\n\u003cp\u003eAccording to the number of the above four cardiovascular risk factors (obesity, dyslipidemia, hypertension, diabetes), patients were divided into three groups: 0 risk factor group (n\u0026thinsp;=\u0026thinsp;207), 1 risk factor group (n\u0026thinsp;=\u0026thinsp;438), and \u0026ge;\u0026thinsp;2 risk factor groups (n\u0026thinsp;=\u0026thinsp;368).\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003ePerform statistical analysis using IBM SPSS 25.0. Continuous variables that follow a normal distribution are represented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, and inter group comparisons are conducted using analysis of variance; Non normally distributed variables are represented by median (interquartile range), and inter group comparisons are conducted using Kruskal Wallis H test; Categorical variables are expressed in frequency (percentage), and chi square tests are used for inter group comparisons. Afterwards, Bonferroni correction was used for pairwise comparison. Use a multivariate logistic regression model (Enter method) to analyze factors independently associated with having\u0026thinsp;\u0026ge;\u0026thinsp;2 cardiovascular risk factors (vs. \u0026le; 1). Variable screening is based on significant differences in clinical importance and univariate analysis (P\u0026thinsp;\u0026lt;\u0026thinsp;0.1). Collinearity is diagnosed through variance inflation factor (VIF\u0026thinsp;\u0026lt;\u0026thinsp;10). Perform the following analysis to verify robustness: Sensitivity analysis: Perform binary logistic regression using different risk burden cut-off points (\u0026ge;\u0026thinsp;1 vs. 0: Model A; \u0026ge; 2 vs. \u0026le; 1: Model B; \u0026ge; 3 vs. \u0026le; 2: Model C). Subgroup analysis: Perform multivariate logistic regression stratified by gender. P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (bilateral) is considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBaseline characteristics of patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 1013 RA patients were included in the analysis, with females accounting for 80.8% and an overall median age of 58.0 years. The disease activity of the entire queue is at a medium to high level (DAS28-ESR median: 4.53). Among cardiovascular risk factors, the prevalence of dyslipidemia was the highest (58.9%), followed by hypertension (28.3%), obesity (12.0%) and diabetes (8.0%). It is worth noting that over one-third (36.3%) of patients have at least 2 cardiovascular risk factors simultaneously. The detailed baseline characteristics are shown in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e. Main demographics, cardiovascular risk factors, and disease-related data in RA patients.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRA (n=1013)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eSex, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e819 (80.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e194 (19.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge (year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e58.00 (49.00, 66.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCurrent smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e47 (4.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCurrent drinker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14 (1.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCardiovascular data\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eCV risk factors, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eObesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e122 (12.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Dyslipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e597 (58.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e287 (28.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e81 (8.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eNumber of CV risk factors, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e207 (20.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e438 (43.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e277 (27.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e81 (8.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10 (1.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eBlood pressure (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSystolic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e131.00 (120.00, 144.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDiastolic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e80.00 (72.00, 87.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23.53 (21.22, 26.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eLipids\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTC (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.15 (3.55, 4.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.07 (0.85, 1.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHDL-C (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.03 (0.87, 1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLDL-C (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.45 (2.00, 2.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAIP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.02 (-0.12, 0.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStatins, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e66 (6.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFPG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.90 (4.54, 5.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUA (umol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e248.00 (195.00, 288.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDisease-related data\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMH (month)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60.00 (12.00, 120.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eESR (mm/H)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e59.00 (35.00, 87.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCRP (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28.56 (7.48, 47.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDAS28-ESR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.53 (3.93, 5.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDAS28-CRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.80 (3.22, 4.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eNSAIDs, %\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e489 (48.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e524 (51.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eGlucocorticoids, %\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e893 (88.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e120 (11.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eDMARDs, %\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e511 (50.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e502 (49.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eType of DMARDs, %\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e511 (50.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e367 (36.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e≥2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e135 (13.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eBDMARDs, %\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e944 (93.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e69 (6.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u0026nbsp;\u003c/strong\u003e\u003cem\u003eRA\u003c/em\u003e rheumatoid arthritis, \u003cem\u003eCV\u003c/em\u003e Cardiovascular, \u003cem\u003eBMI\u003c/em\u003e body mass index, \u003cem\u003eObesity\u003c/em\u003e, BMI \u0026gt; 30 kg/m\u003csup\u003e2\u003c/sup\u003e, \u003cem\u003eTC\u003c/em\u003e total cholesterol, \u003cem\u003eTG\u003c/em\u003e triglyceride, \u003cem\u003eHDL-C\u003c/em\u003e high-density lipoprotein cholesterol, \u003cem\u003eLDL-C\u003c/em\u003e low-density lipoprotein cholesterol, \u003cem\u003eAIP\u003c/em\u003e atherogenic index of plasma, \u003cem\u003eFPG\u003c/em\u003e fasting plasma glucose, \u003cem\u003eUA\u003c/em\u003e uric acid, \u003cem\u003eMH\u003c/em\u003e medical history, \u003cem\u003eESR\u003c/em\u003e erythrocyte sedimentation rate, \u003cem\u003eCRP\u003c/em\u003e high-sensitivity C-reactive protein, \u003cem\u003eDAS28-ESR\u003c/em\u003e disease activity score based on 28 joints using the erythrocyte sedimentation rate, \u003cem\u003eDAS28-CRP\u0026nbsp;\u003c/em\u003edisease activity score based on 28 joints using the high-sensitivity C-reactive protein, \u003cem\u003eNSAIDs\u0026nbsp;\u003c/em\u003enonsteroidal anti-inflammatory drugs, \u003cem\u003eDMARDs\u003c/em\u003e disease-modifying antirheumatic drugs, \u003cem\u003eBDMARDs\u003c/em\u003e biologics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparison of Characteristics of Different Cardiovascular Risk Burden Groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Table 2, compared with the 0 risk factor group, patients with\u0026nbsp;≥\u0026nbsp;1 and\u0026nbsp;≥\u0026nbsp;2 risk factors were older and had a lower proportion of females (both P\u0026lt;0.05). As the number of risk factors increases, the levels of SBP, DBP, BMI, TC, TG, LDL-C, AIP, FPG, UA, CRP, and ESR all show a significant upward trend, while the level of HDL-C decreases significantly (all P\u0026lt;0.001). The disease activity indicators (DAS28-ESR and DAS28-CRP) were also significantly higher in the high-risk group. There were no significant differences in smoking, alcohol consumption, and anti-rheumatic treatment regimens (NSAIDs, glucocorticoids, DMARDs, BDMARDs) among the groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e. Differences in demographics, cardiovascular risk factors, and disease-related characteristics according to the number of cardiovascular risk factors in patients with RA.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"612\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of cardiovascular risk factors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eComparison (Bonferroni-corrected)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (n=207)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (n=438)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e≥2 (n=368)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 versus 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e≥2 versus 0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003eSex, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e185 (89.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e354 (80.82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e280 (76.09%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22 (10.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e84 (19.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e88 (23.91%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge (year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e52.00 (41.00, 61.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e57.00 (48.00, 66.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60.50 (54.00, 68.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCurrent smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6 (2.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21 (4.79%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20 (5.43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.807\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.320\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCurrent drinker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (0.97%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6 (1.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6 (1.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e＞0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e＞0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e123.00 (115.00, 130.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e128.00 (117.00, 140.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e142.00 (131.00, 150.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e70.00 (66.00, 76.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e78.00 (71.00, 85.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e85.00 (77.00, 90.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBMI\u0026nbsp;(kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22.40±2.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23.32 (20.70, 25.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25.50±3.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTC (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.15 (3.69, 4.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.08 (3.42, 4.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.25 (3.59, 5.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.93 (0.75, 1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.04 (0.82, 1.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.21 (0.96, 1.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHDL-C (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.14 (1.06, 1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.98 (0.85, 1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.95 (0.83, 1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLDL-C (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.28 (1.98, 2.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.41 (1.91, 2.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.63 (2.11, 3.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAIP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.11±0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.02 (-0.11, 0.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.11 (-0.02, 0.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStatins, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18 (4.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e48 (13.04%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFPG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.78 (4.41, 5.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.87 (4.50, 5.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.09 (4.62, 5.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUA (umol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e233.60±68.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e195.00 (158.00, 241.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e252.00 (202.00, 307.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMH (month)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e48.00 (8.00, 108.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60.00 (12.00, 120.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e76.00 (15.00, 125.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eESR (mm/H)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e51.00 (28.00, 79.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e59.50 (35.00, 88.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e62.20 (40.00, 88.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCRP (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19.91 (5.44, 37.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28.04 (6.54, 49.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32.11 (9.65, 52.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDAS28-ESR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.51 (3.80, 4.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.53 (3.90, 5.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.60 (4.00, 5.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDAS28-CRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.78 (3.13, 4.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.74 (3.15, 4.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.81±0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003eNSAIDs, %\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e92 (44.44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e228 (52.05%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e169 (45.92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e＞0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e115 (55.56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e210 (47.95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e199 (54.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003eGlucocorticoids, %\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e188 (90.82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e383 (87.44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e322 (87.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.454\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19 (9.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e55 (12.56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e46 (12.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003eDMARDs, %\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e97 (46.86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e224 (51.14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e190 (51.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.508\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.620\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.544\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e110 (5314%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e214 (48.86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e178 (48.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\n \u003cp\u003eType of DMARDs, %\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e97 (46.86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e224 (51.14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e190 (51.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e0.848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e0.591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e＞0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e81 (39.13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e156 (35.62%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e130 (35.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e≥2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e29 (14.01%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e58 (13.24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e48 (13.04%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003eBDMARDs, %\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e191 (92.27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e413 (94.29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e340 (92.39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.652\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e＞0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16 (7.73%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25 (5.71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28 (7.61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u0026nbsp;\u003c/strong\u003e\u003cem\u003eRA\u003c/em\u003e rheumatoid arthritis,\u003cem\u003e\u0026nbsp;SBP\u003c/em\u003e systolic blood pressure, \u003cem\u003eDBP\u0026nbsp;\u003c/em\u003ediastolic blood pressure, \u003cem\u003eBMI\u003c/em\u003e body mass index,\u0026nbsp;\u003cem\u003eTC\u003c/em\u003e total cholesterol, \u003cem\u003eTG\u003c/em\u003e triglyceride, \u003cem\u003eHDL-C\u003c/em\u003e high-density lipoprotein cholesterol, \u003cem\u003eLDL-C\u003c/em\u003e low-density lipoprotein cholesterol,\u0026nbsp;\u003cem\u003eAIP\u003c/em\u003e atherogenic index of plasma,\u0026nbsp;\u003cem\u003eFPG\u003c/em\u003e fasting plasma glucose, \u003cem\u003eUA\u003c/em\u003e uric acid,\u0026nbsp;\u003cem\u003eMH\u003c/em\u003e medical history, \u003cem\u003eESR\u003c/em\u003e erythrocyte sedimentation rate, \u003cem\u003eCRP\u003c/em\u003e high-sensitivity C-reactive protein,\u0026nbsp;\u003cem\u003eDAS28-ESR\u003c/em\u003e disease activity score based on 28 joints using the erythrocyte sedimentation rate,\u0026nbsp;\u003cem\u003eDAS28-CRP\u0026nbsp;\u003c/em\u003edisease activity score based on 28 joints using the\u0026nbsp;high-sensitivity C-reactive protein,\u0026nbsp;\u003cem\u003eNSAIDs\u0026nbsp;\u003c/em\u003enonsteroidal anti-inflammatory drugs, \u003cem\u003eDMARDs\u003c/em\u003e disease-modifying antirheumatic drugs, \u003cem\u003eBDMARDs\u003c/em\u003e biologics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultivariate analysis related to multiple (\u003c/strong\u003e\u003cstrong\u003e≥\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;2) cardiovascular risk factors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMultivariate logistic regression analysis (Table 3) showed that after adjusting for other variables, increased levels of age (OR=1.023), SBP (OR=1.054), BMI (OR=1.257), TG (OR=2.174), FPG (OR=1.524), and CRP (OR=1.006) were independently associated with an increased likelihood of having\u0026nbsp;≥\u0026nbsp;2 risk factors. Elevated levels of HDL-C (OR=0.170) are protective factors. The use of statins is significantly positively correlated with high-risk burden (OR=3.404), which may reflect prescription bias (i.e. patients with pre-existing lipid problems or high risk are more likely to be prescribed statins).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Multivariable logistic regression analysis of factors associated with having ≥2 cardiometabolic comorbidities in patients with RA.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eβ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd 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 valign=\"top\"\u003e\n \u003cp\u003eAge (year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.023 (1.007 - 1.039)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.054 (1.042 - 1.067)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBMI (kg/m²)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.257 (1.194 - 1.324)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.777\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.174 (1.620 - 2.919)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHDL-C (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.170 (0.084 - 0.344)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStatins use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.404 (1.754 - 6.609)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFPG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.524 (1.279 - 1.816)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCRP (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.006 (1.002 - 1.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations:\u0026nbsp;\u003cem\u003eRA\u003c/em\u003e rheumatoid arthritis,\u003cem\u003e\u0026nbsp;OR\u0026nbsp;\u003c/em\u003eodds ratio\u003cem\u003e, CI\u0026nbsp;\u003c/em\u003econfidence interval,\u003cem\u003e\u0026nbsp;SBP\u003c/em\u003e systolic blood pressure, \u003cem\u003eBMI\u003c/em\u003e body mass index,\u0026nbsp;\u003cem\u003eTG\u003c/em\u003e triglyceride, \u003cem\u003eHDL-C\u003c/em\u003e high-density lipoprotein cholesterol, \u003cem\u003eFPG\u003c/em\u003e fasting plasma glucose,\u0026nbsp;\u003cem\u003eCRP\u003c/em\u003e high-sensitivity C-reactive protein.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSensitivity analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Table 4, core factors (SBP, BMI, TG, HDL-C, FPG) consistently exhibited strong and consistent associations when using different risk burden cut-off points. Age and CRP have the strongest association in moderate risk burden (Model B). The OR value of statin use cannot be effectively estimated in Model A due to complete separation, and shows significant negative correlation in both Model B and C, once again supporting its explanation as a high-risk biomarker rather than a cause.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u0026nbsp;\u003c/strong\u003eSensitivity analysis: Associations between identified factors and different cut-off points of cardiometabolic risk burden using binary logistic regression.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"593\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel A: ≥1 vs 0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel B: ≥2 vs ≤1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel C: ≥3 vs ≤2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd 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 \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd 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 \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd 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 valign=\"top\"\u003e\n \u003cp\u003eAge (year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.017 (1.001-1.033)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.023 (1.007-1.039)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.026 (0.998-1.055)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.058 (1.042-1.073)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.054 (1.042-1.067)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.040 (1.022-1.058)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBMI (kg/m²)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.077 (1.017-1.141)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.257 (1.194-1.324)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.478 (1.348-1.622)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.553 (2.550-8.127)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.174 (1.620-2.919)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.579 (1.083-2.302)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHDL-C (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.027 (0.012-0.060)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.170 (0.084-0.344)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.248 (0.068-0.896)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStatins use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e†\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.302 (0.155-0.589)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.329 (0.147-0.735)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFPG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.346 (1.024-1.769)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.524 (1.279-1.816)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.014 (1.669-2.430)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCRP (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.007 (1.001-1.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.006 (1.002-1.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.005 (0.999-1.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*† The odds ratio for statin use in Model A could not be effectively estimated (OR: 0.000, 95% CI: 0.000-∞) due to complete separation (i.e., all patients taking this medication had at least one risk factor).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAbbreviations:\u0026nbsp;\u003cem\u003eOR\u0026nbsp;\u003c/em\u003eodds ratio\u003cem\u003e, CI\u0026nbsp;\u003c/em\u003econfidence interval,\u003cem\u003e\u0026nbsp;SBP\u003c/em\u003e systolic blood pressure, \u003cem\u003eBMI\u003c/em\u003e body mass index,\u0026nbsp;\u003cem\u003eTG\u003c/em\u003e triglyceride, \u003cem\u003eHDL-C\u003c/em\u003e high-density lipoprotein cholesterol, \u003cem\u003eFPG\u003c/em\u003e fasting plasma glucose,\u0026nbsp;\u003cem\u003eCRP\u003c/em\u003e high-sensitivity C-reactive protein.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubgroup analysis (stratified by gender)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGender stratification analysis revealed interesting differences (Table 5), with common factors such as age, SBP, BMI, TG, and HDL-C being important influencing factors in both male and female patients. Male specific factors: CRP is a strong independent predictor in male patients (OR=1.013, P=0.012), but not at a significant level in females (P=0.097). Female specific factors: FPG (OR=1.583) and statin use (OR=4.789) have a much greater impact on female patients than on males.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5.\u0026nbsp;\u003c/strong\u003eSubgroup analysis: Multivariable logistic regression results stratified by sex.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"655\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal population (n=1013)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale (n=194)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale (n=819)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\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 \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\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 \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\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 valign=\"top\"\u003e\n \u003cp\u003eAge (year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.023 (1.007-1.039)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.023 (0.985-1.063)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.024 (1.006-1.043)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.054 (1.042-1.067)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.079 (1.046-1.114)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.052 (1.038-1.066)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBMI (kg/m²)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.257 (1.194-1.324)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.205 (1.051-1.381)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.274 (1.203-1.349)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.174 (1.620-2.919)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.438 (1.300-9.093)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.151 (1.578-2.934)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHDL-C (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.170 (0.084-0.344)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.005 (0.000-0.054)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.279 (0.130-0.598)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStatins use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.302 (0.155-0.589)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.294 (0.341-4.917)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.789 (2.157-10.635)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFPG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.524 (1.279-1.816)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.251 (0.819-1.910)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.583 (1.295-1.935)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCRP (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.006 (1.002-1.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.013 (1.003-1.023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.004 (0.999-1.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAll models were adjusted for other variables listed in the table.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAbbreviations:\u0026nbsp;\u003cem\u003eOR\u0026nbsp;\u003c/em\u003eodds ratio\u003cem\u003e, CI\u0026nbsp;\u003c/em\u003econfidence interval,\u003cem\u003e\u0026nbsp;SBP\u003c/em\u003e systolic blood pressure, \u003cem\u003eBMI\u003c/em\u003e body mass index,\u0026nbsp;\u003cem\u003eTG\u003c/em\u003e triglyceride, \u003cem\u003eHDL-C\u003c/em\u003e high-density lipoprotein cholesterol, \u003cem\u003eFPG\u003c/em\u003e fasting plasma glucose,\u0026nbsp;\u003cem\u003eCRP\u003c/em\u003e high-sensitivity C-reactive protein.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe main findings of this study are as follows: in Chinese RA patients, the burden of multiple cardiovascular metabolic comorbidities (≥\u0026nbsp;2 risk factors) is very common, accounting for over one-third (36.3%); High disease activity is significantly correlated with high cardiovascular risk burden; Traditional metabolic factors (blood pressure, obesity, blood lipids, blood glucose) and systemic inflammation (CRP) are independent determinants of multiple risk aggregation; The positive correlation between statin use and high risk reflects rational clinical practice, rather than a causal relationship; There are gender differences in the cardiovascular risk spectrum, with inflammation contributing more to male risk, while metabolic factors (blood glucose) and statin use (as proxy indicators) have a more prominent impact on females.\u003c/p\u003e\n\u003cp\u003eThis study confirms the central role of inflammation in cardiovascular metabolic risk in RA patients. As the number of cardiovascular risk factors increases, the levels of inflammatory markers ESR and CRP also show a significant stepwise upward trend. Even after adjusting for traditional risk factors, CRP remains an independent predictor of high-risk burden. However, it is worth noting that ESR did not show independent predictive value in multivariate models. This discovery seems to contradict some traditional views [15], suggesting that it may be due to the different roles played by acute and chronic inflammatory markers in cardiovascular health [16]. CRP, as an acute phase response protein rapidly produced by liver cells under interleukin-6 (IL-6) \u0026nbsp;stimulation, can activate the complement system, induce cell death, and lead to endothelial dysfunction by inhibiting nitric oxide and upregulating endothelial cell adhesion molecules [17]. In addition, it also promotes monocyte recruitment into atherosclerotic plaque, and increases inflammatory response by inducing leukocyte adhesion and migration and the production of reactive oxygen species [18]. Previous studies have shown that CRP levels can independently predict cardiovascular risk in the general population [19]. Epidemiological studies have shown a close association between CRP and IL-6 levels and CV risk [20]. Large observational cohort studies have reported associations between elevated CRP levels in RA and a more atherogenic lipid profile and hyperlipidaemia [21], an increased risk for myocardial infarction (hazard ratio [HR] 2.12 for CRP \u0026gt;10 versus \u0026lt;1 mg/L) [22], heart failure (HR 1.25 per 100 mg/L increase in CRP) [23], stroke (HR 2.02 for CRP \u0026gt;21.7 mg/L versus \u0026lt;2.6 mg/L) [24], and CV-related death (14% increase for each mg/L and HR of 3.3 for CRP\u0026nbsp;≥5 mg/L) [25].\u003c/p\u003e\n\u003cp\u003eAs the number of cardiovascular risk factors increases, we observed highly consistent and significant trend changes in blood lipid parameters: TG, TC, LDL-C, and AIP gradually increase, while HDL-C levels significantly decrease (all P trends\u0026lt;0.001). This pattern is consistent with the classical theory that chronic inflammatory states (confirmed by markers such as CRP) are the core drivers of lipid metabolism disorders [26,27]. Inflammatory cytokines such as IL-6 and tumor necrosis factor-α (TNF-α) not only inhibit lipoprotein lipase activity leading to hypertriglyceridemia, but more importantly, induce functional inactivation of HDL, transforming it from a protective granule that is anti-inflammatory, antioxidant, and promotes cholesterol efflux to a dysfunctional pro-inflammatory granule [28,29]. Therefore, the low HDL-C value obtained from routine testing is essentially a comprehensive danger signal: it reflects not only a decrease in quantity, but also implies the loss of its protective function and even the acquisition of harmful properties [30]. This explains why low HDL-C is a stronger independent risk predictor than high LDL-C in our multivariate model. Therefore, there is an urgent need to change the cardiovascular risk management of RA patients, that is, from simply focusing on the numerical management of LDL-C to comprehensively evaluating the inflammatory load (such as CRP), AIP and functional lipoprotein indicators.\u003c/p\u003e\n\u003cp\u003eIt is worth noting that the use of statins is significantly associated with high risk (OR=3.404, 95% CI: 1.754-6.609), reflecting significant prescription bias where statins are reasonably prescribed to clinically identified high-risk patients. This finding is not contradictory to the protective effect of statins in primary cardiovascular prevention, but rather highlights the importance of identifying individuals in the RA population who require statin intervention. In addition, the increase of FPG, BMI, and SBP are independent predictive factors strongly associated with multiple comorbidity risk in this study, and their robustness has been consistently confirmed in sensitivity analysis of different risk stratification models. In terms of mechanism, these factors together form an interwoven metabolic disorder network. Elevated FPG is a core marker of insulin resistance and impaired beta cell function, and its pathogenic mechanism goes far beyond simple glucose metabolism abnormalities. Hyperglycemia directly damages vascular endothelial function and aggravates systemic micro inflammation by promoting the formation of advanced glycation end products (AGEs), inducing oxidative stress and activating protein kinase C (PKC) pathway, which has synergistic effects with RA's own inflammatory background and accelerates atherosclerosis [31]. Elevated BMI, especially visceral fat accumulation, causes adipose tissue to transform from an energy storage organ to an active endocrine organ, secreting large amounts of adipokines such as leptin, resistin, IL-6, TNF-α, and pro-inflammatory cytokines [32]. These factors not only directly aggravate systemic inflammation, but also promote endothelial dysfunction, which is a key initial step in the pathophysiology of atherosclerosis [33]. The increase of SBP itself is a direct reflection of the abnormal high pressure and shear stress sustained by the vascular wall, which will lead to endothelial dysfunction, vascular wall remodeling and decreased compliance, thus making it easier to form atherosclerotic plaque [34]. Our results are consistent with the studies [35,11]. This indicates that these traditional risk factors still maintain independent and strong predictive power, emphasizing the importance of traditional risk management while managing inflammation.\u003c/p\u003e\n\u003cp\u003eThe discovery of gender differences has significant clinical implications. In male patients, CRP is a stronger independent predictor (OR=1.013, p=0.012), while the effect of FPG is not significant. This may be due to the biological differences in immune response, hormone levels (such as the lack of protective effects of estrogen), and fat distribution between genders [36]. The impact of FPG and statin use is more prominent in female patients, suggesting that female RA patients may be more sensitive to metabolic disorders. This calls for gender specific risk assessment and intervention strategies in clinical management.\u003c/p\u003e\n\u003cp\u003eThis study has several limitations. Firstly, cross-sectional design cannot infer causal relationships, and prospective cohort studies are needed in the future to verify the association between these factors and ultimate cardiovascular events. Secondly, the data comes from a single center, which may lead to selection bias and limit the extrapolation of its results. Thirdly, some potential confounding factors such as dietary structure, physical activity level, specific medication dosage, and treatment compliance were not included in the analysis. Finally, we excluded patients with atherosclerotic cardiovascular disease (ASCVD), which makes our conclusions more applicable to the primary prevention scenario, but may not be applicable to the patient population.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, this study suggests that cardiovascular risk management in RA patients is a complex issue involving multiple aspects of inflammation and metabolism. Through early identification, comprehensive intervention and gender stratification strategy, it is expected to reduce the cardiovascular incidence rate and mortality of this vulnerable population and improve its long-term prognosis.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eRA\u003c/p\u003e\n\u003cp\u003erheumatoid arthritis\u003c/p\u003e\n\u003cp\u003eCVD\u003c/p\u003e\n\u003cp\u003ecardiovascular disease\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSBP\u003c/p\u003e\n\u003cp\u003esystolic blood pressure\u003c/p\u003e\n\u003cp\u003eBMI\u003c/p\u003e\n\u003cp\u003ebody mass index\u003c/p\u003e\n\u003cp\u003eTG\u003c/p\u003e\n\u003cp\u003etriglycerides\u003c/p\u003e\n\u003cp\u003eFPG\u003c/p\u003e\n\u003cp\u003efasting blood glucose\u003c/p\u003e\n\u003cp\u003eCRP\u003c/p\u003e\n\u003cp\u003ehigh-sensitivity C-reactive protein\u003c/p\u003e\n\u003cp\u003eHDL-C\u003c/p\u003e\n\u003cp\u003ehigh-density lipoprotein cholesterol\u003c/p\u003e\n\u003cp\u003eEULAR\u003c/p\u003e\n\u003cp\u003eEuropean League Against Rheumatism\u003c/p\u003e\n\u003cp\u003eACR\u003c/p\u003e\n\u003cp\u003eAmerican College of Rheumatology\u003c/p\u003e\n\u003cp\u003eTC\u003c/p\u003e\n\u003cp\u003etotal cholesterol\u003c/p\u003e\n\u003cp\u003eTG\u003c/p\u003e\n\u003cp\u003etriglycerides\u003c/p\u003e\n\u003cp\u003eLDL-C\u003c/p\u003e\n\u003cp\u003elow-density lipoprotein cholesterol\u003c/p\u003e\n\u003cp\u003eDBP\u003c/p\u003e\n\u003cp\u003ediastolic blood pressure\u003c/p\u003e\n\u003cp\u003eDM\u003c/p\u003e\n\u003cp\u003ediabetes mellitus\u003c/p\u003e\n\u003cp\u003eAIP\u003c/p\u003e\n\u003cp\u003eatherosclerosis index\u003c/p\u003e\n\u003cp\u003eUA\u003c/p\u003e\n\u003cp\u003euric acid\u003c/p\u003e\n\u003cp\u003eMH\u003c/p\u003e\n\u003cp\u003edisease duration\u003c/p\u003e\n\u003cp\u003eESR\u003c/p\u003e\n\u003cp\u003eerythrocyte sedimentation rate\u003c/p\u003e\n\u003cp\u003eCRP\u003c/p\u003e\n\u003cp\u003ehigh-sensitivity C-reactive protein\u003c/p\u003e\n\u003cp\u003eDAS28\u003c/p\u003e\n\u003cp\u003edisease activity scores based on 28 joints\u003c/p\u003e\n\u003cp\u003eNSAIDs\u003c/p\u003e\n\u003cp\u003enonsteroidal anti-inflammatory drugs\u003c/p\u003e\n\u003cp\u003eDMARDs\u003c/p\u003e\n\u003cp\u003edisease modifying antirheumatic drugs\u003c/p\u003e\n\u003cp\u003eBDMARDs\u003c/p\u003e\n\u003cp\u003eBiologics\u003c/p\u003e\n\u003cp\u003eOR\u003c/p\u003e\n\u003cp\u003eodds ratio\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCI\u003c/p\u003e\n\u003cp\u003econfidence interval\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIL-6\u003c/p\u003e\n\u003cp\u003einterleukin-6\u003c/p\u003e\n\u003cp\u003eTNF-α\u003c/p\u003e\n\u003cp\u003etumor necrosis factor-α\u003c/p\u003e\n\u003cp\u003eAGEs\u003c/p\u003e\n\u003cp\u003eadvanced glycation end products\u003c/p\u003e\n\u003cp\u003ePKC\u003c/p\u003e\n\u003cp\u003eprotein kinase C\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eASCVD\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eatherosclerotic cardiovascular disease\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author thanks Xingtai People\u0026apos;s Hospital for providing data on this platform and thanks all participants for their selfless dedication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLL: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Supervision, Validation, Visualization, Writing-original draft, Writing-review \u0026amp; editing. QT: Conceptualization, Data curation, Investigation, Methodology, Software, Visualization, Writing-original draft. JZ: Conceptualization, Investigation, Methodology, Software, Visualization. YH: Conceptualization, Investigation, Methodology, Software, Visualization. XL: Conceptualization, Funding acquisition, Methodology, Project administration, Resources, Supervision, Visualization, Writing-review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Key R\u0026amp;D Projects in Xingtai City (No. 2025ZC074).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received approval from the Research Ethics Committee of Xingtai People\u0026rsquo;s Hospital (approval number: 2025[031]). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePublisher\u0026rsquo;s note\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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J Rheumatol. 2014;41(8):1638\u0026ndash;44. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3899/jrheum.131170\u003c/span\u003e\u003cspan address=\"10.3899/jrheum.131170\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePamies A, Vallv\u0026eacute; JC, Paredes S. New Cardiovascular Risk Biomarkers in Rheumatoid Arthritis: Implications and Clinical Utility-A Narrative Review. Biomedicines. 2025;13(4):870. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/biomedicines13040870\u003c/span\u003e\u003cspan address=\"10.3390/biomedicines13040870\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRaadsen R, Hansildaar R, van Kuijk AWR, Nurmohamed MT. Male rheumatoid arthritis patients at substantially higher risk for cardiovascular mortality in comparison to women. Semin Arthritis Rheum. 2023;62:152233. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.semarthrit.2023.152233\u003c/span\u003e\u003cspan address=\"10.1016/j.semarthrit.2023.152233\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\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, cardiovascular disease, risk factors, comorbidity, inflammation, dyslipidemia","lastPublishedDoi":"10.21203/rs.3.rs-7751974/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7751974/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eRheumatoid arthritis (RA) patients have a significantly increased risk of cardiovascular disease (CVD). The aim of this study is to explore the influencing factors associated with the aggregation of multiple cardiovascular metabolic risk factors in RA patients.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e\u003cp\u003eThis study included 1013 RA patients. According to the preset number of cardiovascular risk factors (obesity, dyslipidemia, hypertension and diabetes), patients were divided into three groups: 0, 1 and \u0026ge;\u0026thinsp;2 risk factor groups. We collected and compared demographic, clinical characteristics, and disease activity indicators of each group. Use multivariate logistic regression analysis to identify factors independently associated with having\u0026thinsp;\u0026ge;\u0026thinsp;2 risk factors, and conduct sensitivity and subgroup analysis to validate the robustness of the results.\u003c/p\u003e\u003ch2\u003eResult\u003c/h2\u003e\u003cp\u003eAmong the total population, 368 patients (36.3%) had\u0026thinsp;\u0026ge;\u0026thinsp;2 cardiovascular risk factors. Multivariate analysis showed that increasing age (OR\u0026thinsp;=\u0026thinsp;1.023, 95% CI: 1.007\u0026ndash;1.039), elevated systolic blood pressure (SBP) (OR\u0026thinsp;=\u0026thinsp;1.054, 95% CI: 1.042\u0026ndash;1.067), elevated body mass index (BMI) (OR\u0026thinsp;=\u0026thinsp;1.257, 95% CI: 1.194\u0026ndash;1.324), elevated triglycerides (TG) (OR\u0026thinsp;=\u0026thinsp;2.174, 95% CI: 1.620\u0026ndash;2.919), elevated fasting blood glucose (FPG) (OR\u0026thinsp;=\u0026thinsp;1.524, 95% CI: 1.279\u0026ndash;1.816), and elevated levels of high-sensitivity C-reactive protein (CRP) (OR\u0026thinsp;=\u0026thinsp;1.006, 95% CI: 1.002\u0026ndash;1.010) are independent risk factors for the aggregation of multiple risk factors. The increase in high-density lipoprotein cholesterol (HDL-C) (OR\u0026thinsp;=\u0026thinsp;0.170, 95% CI: 0.084\u0026ndash;0.344) is a protective factor. The use of statins is significantly associated with high risk burden (OR\u0026thinsp;=\u0026thinsp;3.404), which may be a confounding effect of their indications for use. Sensitivity analysis confirmed the consistent association of these factors across different risk stratification. Subgroup analysis revealed significant gender differences: CRP was an independent predictor for male patients, while FPG and statin use had a greater impact on female patients.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eIn RA patients, traditional cardiovascular risk factors (age, blood pressure, blood lipids, blood glucose) and systemic inflammation (CRP) jointly promote a high burden of cardiovascular metabolic comorbidities. Clinical management should adopt a comprehensive strategy that actively screens and intervenes in traditional metabolic risk factors while controlling inflammation, and should consider gender specific strategies.\u003c/p\u003e","manuscriptTitle":"Prevalence and risk factors of cardiovascular metabolic comorbidities in patients with rheumatoid arthritis: a real-world cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-23 11:44:39","doi":"10.21203/rs.3.rs-7751974/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":"a54206b2-04ae-461b-83ef-81b2d1ed7b17","owner":[],"postedDate":"November 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-04T21:23:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-23 11:44:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7751974","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7751974","identity":"rs-7751974","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}