Predicting the likelihood of cardiovascular disease in patients with rheumatoid arthritis: development and Validation of a Novel Model

Predicting the likelihood of cardiovascular disease in patients with rheumatoid arthritis: development and Validation of a Novel Model | 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 Predicting the likelihood of cardiovascular disease in patients with rheumatoid arthritis: development and Validation of a Novel Model Min Feng, Fanxing Meng, Jiali Yu, Yanlin Wang, Yan Qin, Yuhan Jia, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2914034/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Patients with rheumatoid arthritis (RA) have increased mortality and morbidity due to cardiovascular disease (CVD). Distinguishing RA with CVD, RA with CVD risk factors and patients with RA only (pure RA), is still a challenge. The study aimed to develop a better model to predict the likelihood of CVD in RA. Methods Pure RA (n = 402), RA with CVD risk factors (n = 394), and RA with CVD (n = 201) were ultimately recruited into the study and their peripheral bloods were collected to measure the levels of routine examination indicators, vascular endothelial growth factor (VEGF) and immune cells. Univariate analysis, the least absolute shrinkage and selection operator (LASSO), the random forest (RF) and the logistic regression models (LR) were employed to screen characteristic variables between each two groups, and individualized nomograms were further established to more conveniently predict the likelihood of CVD in RA. Results Univariate analysis showed that the levels of WBC, BUN, creatinine, CK, LDH and VEGF were significantly elevated in RA with CVD, as well as serum TC, TG, LDL, ApoB100 and ApoE, while ApoA1 and HDL/CHOL were decreased. Furthermore, the ratio of Treg cells, exhibiting excellent separation performance, in RA with CVD was significantly lower than that in other groups, while the ratio of Th1/Th2/NK and Treg were significantly elevated. LASSO, RF and LR models were also used to find the risk factors for CVD in RA. Through the final selected indicators screened by three machine learning models and univariate analysis, a convenient nomogram was established for predicting CVD risk in RA. Conclusions Serum lipids, lipoproteins, and Treg cells have been identified as risk factors for CVD in patients with RA, and three nomograms combining various risk factors were constructed and were used for individualized prediction of CVD in patients with RA (pure RA and/or with CVD risk factors). rheumatoid arthritis cardiovascular disease VEGF regulatory T cells prediction Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease characterized by symmetry joint impairment and dysfunction, which has turned into a worldwide public health challenge due to its high disability rate and socioeconomic burden on society [ 1 – 3 ]. The global prevalence of RA is about 0.5-1.0%, while the prevalence of RA in Chinese people is around 0.32 to 0.36%, and RA is not only more common in women (3:1), but they also are usually sicker than men [ 4 ]. So far, although the contribution of genetic predisposition and environmental factors to the pathogenesis of RA has been partially confirmed, the etiology of RA needs to be further explored [ 5 ]. In addition, due to that RA remains often under-prioritized in the early stage, especially in the underprivileged families, patients with RA often arrive at hospital with irreversible joint dysfunction and disability, and are often accompanied by multiple comorbidities that lead to poor clinical outcomes, such as cardiovascular disease (CVD), interstitial pneumonia and anemia [ 6 ]. CVD nowadays has been regarded as the number one cause of morbidity and mortality in the world [ 7 ], and its incidence has elevated by about one third over the past decade [ 8 ], seriously affecting people's quality of life and increase their psychological and economic burden [ 7 , 9 ]. Alarmingly, the prevalence of CVD in autoimmune diseases has been greatly underestimated in recent decades [ 10 ]. Result from a large observational cohort showed that patients with RA experience CV events 1.5-2 fold than the general population, including coronary heart disease (CHD), myocardial infarction (MI), stroke and heart failure [ 11 – 13 ]. Despite the availability of excellent treatment strategy, CV events cause more than half of all deaths in RA patients compared with the healthy population, cancer and respiratory diseases [ 10 , 14 , 15 ]. Consistent with the general population, traditional CVD risk factors, such as hypertension, dyslipidemia, diabetes and smoking and so on, also play an important role in the occurrence and progress of CVD in RA patients [ 16 , 17 ]. In general, the lipid profile that induces the occurrence of CVD is usually high total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C) and low level of high-density lipoprotein cholesterol (HDL-C). In addition, mounting evidence suggests that the chronic inflammatory state of RA patients from the early stage also holds the position of an dominant player contributing to the increased morbidity and progression of CV complications, explaining the premature onset of cardiovascular disease in RA [ 18 , 19 ]. Elevated C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR) have been shown to be involved in the development of CVD [ 20 ]. And pro-inflammatory cells and cytokines implicated in RA also contribute to CVD. In addition, endothelial dysfunction and some medications are also regarded as the key inducer of CVD. Lack of attention to the disease in RA, especially in developing countries, leads to a higher incidence of comorbidities, especially CVD. As CV events is hidden in the early stage, it is often ignored by people, resulting in irreversible consequences. Therefore, an RA specific CVD risk assessment tool needs to be developed to facilitate clinicians to take preventive measures. In general, we can prevent or alleviate the risk of CVD using existing screening methods and interventions. Multiple cardiovascular risk models have been built to predict the likelihood of CVD in individuals, such as Framingham risk score (FRS), the Systematic Coronary Risk Evaluation (SCORE), and 2013 American College of Cardiology/American Heart Association CVD Risk Score, but these models tend to underestimate the risk of CVD in RA patients due to their limited indicators, resulting in some individuals cannot be accurately identified [ 21 ]. Therefore, hypertension, dyslipidemia, smoking, obesity, immune molecules and other biological indicators need to be explored to refine the CVD risk assessment model for RA patients so that timely preventive strategies can be adopted. In this study, a variety of machine learning methods were used to incorporate blood routine index, serological indicators and multiple lymphocyte subsets to explore the factors influencing the occurrence of CVD in RA, and predictive nomograms were established to identify RA with CVD, RA with CVD risk factors, and patients with RA only (pure RA). Materials and methods Study design and population The retrospective study recruited 1535 RA patients (1030 females, 505 males, median age 57.58 ± 9.84 years) who met the 2010 American College of Rheumatology/European League Against Rheumatology classification criteria for RA and were diagnosed by 2 clinicians at the Second Hospital of Shanxi Medical University from January 2016 to December 2019. Disease activity of RA was evaluated according to the disease activity score 28-erythrocyte sedimentation rate (DAS28-ESR) [ 22 ]. Individuals with one or more of the following conditions were excluded from the study: 1) under 18 years of age (n = 64); 2) with pregnancy (n = 28); 3) with neoplastic diseases (n = 52); 4) with other autoimmune diseases (n = 113); 5) with a history of CVD (n = 94); 6) patients who have been prescribed lipid-lowering drugs or non-steroidal anti-inflammatory drugs (NSAIDs) or who have used relatively high doses of corticosteroids for a long time (n = 187). Out of the remaining 997 RA patients, 402 were pure RA, 394 were RA with CVD risk factors (including hypertension or diabetes or dyslipidemia or fatty liver disease), and 201 were RA with CVD. The category of CVD includes coronary heart disease, coronary atherosclerosis, heart failure, myocardial infarction, and other peripheral vascular diseases. The medical ethics committee of the Second Hospital of Shanxi Medical University has approved the study (2016KY007), and written informed consents were obtained from all patients. Demographic and laboratory data All clinical characteristics of the patients, such as age, sex, body mass index (BMI), smoking, disease duration, and the quantities of tender and swollen joints, were obtained from the medical records of our hospital. In addition, data of routine examination biomarker were also recorded, including blood routine examination [white blood cells (WBC), red blood cells (RBC), hemoglobin (Hb), platelet (PLT), the number and percentage of lymphocyte (LYMP) and neutrophile granulocyte (NEUT)], liver and kidney function indicators [albumin (ALB), alkaline phosphatase (ALP), alanine aminotransferase (ALT), aspartate aminotransferase (AST), glutamyl transferase (GGT), urea, creatinine, cystatin C (CYSC), α1 microglobulin (α1-MG), β2 microglobulin (β2-MG), complement C1q, creatine kinase (CK), and lactate dehydrogenase (LDH)], and lipid indicators, including total cholesterol (CHOL), triglycerides (TG), high-density lipoprotein (HDL), low-density lipoprotein (LDL), apolipoprotein A1 (ApoA1), apolipoprotein B100 (ApoB100), apolipoprotein E (ApoE), lipoprotein α (LP(α)) and high-density lipoprotein/cholesterol (HDL/CHOL). Erythrocyte sedimentation rate (ESR), C-reactive protein (CRP) and vascular endothelial growth factor (VEGF) level were also measured. Lymphocyte subsets Peripheral blood specimens were collected from RA patients on the morning of the first day after admission and before medication. CD3+/CD4+/CD8 + T cells were identified by anti-CD3 /CD4/CD8/CD45 antibodies and B/NK cells were labeled by anti-CD3/CD16/CD56/CD45/CD19 antibodies. For Th1/Th2/Th17 cells, we need to stimulate the cells with Ionomycin and PMA for 6 h and then label them with anti-CD4/IFN-γ/IL-4/IL-17 antibodies. In addition, anti-CD4/CD25/FoxP3 antibodies were employed for the identification of Treg cells. Statistical analysis SPSS22.0 and R package were used to analyze the data. One-way ANOVA was used to analyze differences in continuous variables, which was recorded as mean ± standard deviation (SD), among the four groups. Dichotomous variables were compared by the Chi-square test. P value < 0.05 indicated statistical significance between groups. In the study, in addition to univariate analysis, the least absolute shrinkage and selection operator (LASSO), the random forest (RF) and the logistic regression models (LR) were also employed to screen characteristic variables between each two groups (pure RA and/or RA + CVD risk factors vs. RA + CVD). By removing the missing values, a total of 628 patients’ data (241 pure RA, 259 RA + CVD risk factors, 500 RA without CVD, 128 RA + CVD) were finally included in these machine learning (ML) models for analysis. These patients were randomly selected and divided into a training set (75%) for calculation of parameters and construction of models, and a validation set (25%) for model testing and evaluation, where the four groups’ proportions in each set were equal. Ten-fold cross-validation was used in the training set, that is, 9/10 of the data was used to fit the model in turn, and the remaining 1/10 of the data was used for validation, and the process was performed 10 times in total. Finally, the model was tested by the validation set. The step was repeated 10 times randomly (i.e., we completed 10 resampling iterations) so as to obtain the stable result. To ensure the fairness of these models, we used the same data segmentation and repetition to evaluate these models each time. In the process of 10 repetitions, the model with the maximum area under the receiver operating characteristic curve (AU-ROC) was considered to be the best model. Metrics with the top 10 variable important for the projection value (VIP) or weight size were considered as feature variables. The selection criteria of the final important variable was that the indicator had discriminative significance in the three machine learning algorithms and univariate analysis at the same time. Multivariate logistic regression was then used to analyze these screened final significant variables to generate individualized predictive nomograms. Results Clinical characteristics and demographic data of participants Among 997 RA patients, 402 were pure RA (279 females, 123 males, 55.06 ± 12.88 years), 394 were RA with CVD risk factors (262 females, 132 males, 55.13 ± 11.43 years), i.e., 796 were RA without CVD (541 females, 255 males, 55.10 ± 12.18 years) and 201 were RA with CVD (134 females, 67 males, 56.77 ± 8.71 years), of which 126 were coronary heart disease, 41 coronary atherosclerosis, 34 blood clots. No significant differences were observed in age, sex, BMI, disease duration and smoking history among the four groups. Results of univariate analysis showed that levels of VEGF, WBC and AST in RA patients with CVD were significantly higher than those in pure RA and RA without CVD, respectively. LYMP in pure RA and level of TBIL in RA with CVD risk factors were significantly lower than those in RA with CVD. In addition, the serum contents of urea, creatinine, CK, LDH, TC, TG, LDL, ApoB100 and ApoE in RA with CVD were significantly higher than those in pure RA and/or RA with CVD risk factors, while ApoA1 level was reduced ( P < 0.05). Furthermore, the ratio of HDL/CHOL in RA with CVD was obviously elevated compared with RA with CVD risk factors and RA without CVD. The clinical characteristics and demographic data of 997 RA patients were summarized in Table 1 . The alluvial plot (Fig. 1 ) showed the distribution of patients across the disease groups, gender, DAS28-ESR and RA-related autoantibodies. After removing the missing values, remaining 628 patients in all three groups were randomly assigned to a training set (75%) consisted of 181 pure RA, 194 RA with CVD risk factors and 96 RA with CVD, and a validation set (25%) consisted of 60 pure RA, 65 RA with CVD risk factors and 32 RA with CVD. Figure 2 exhibited the study design and analysis scheme. Table 1 Comparison of clinical characteristics and demographic data among pure RA, RA with CVD risk factors, and RA with CVD. Pure RA a (n = 402) RA + cause b (n = 394) RA without CVD a+b (n = 796) RA with CVD c (n = 201) P a vs c P b vs c P a+b vs c Clinical characteristics Age (years) 55.06 ± 12.88 55.13 ± 11.43 55.10 ± 12.18 56.77 ± 8.71 0.158 0.056 Gender: female/male 279/123 262/132 541/255 134/67 0.495 0.967 0.725 Disease duration (years) 10.50 ± 5.25 10.35 ± 3.50 10.42 ± 3.87 10.80 ± 2.55 0.158 0.246 BMI (kg/m 2 ) 23.11 ± 3.05 22.87 ± 3.16 23.04 ± 3.08 23.22 ± 2.57 0.214 0.164 Smoker (%) 64 (15.92) 58 (14.72) 122 (15.33) 29 (14.43) 0.632 0.924 0.751 DAS28-ESR 5.26 ± 1.25 5.17 ± 1.13 5.23 ± 1.18 5.34 ± 1.21 0.213 0.245 Hypertension (%) - 221 (56.10) 221 (27.76) - diabetes (%) - 125 (31.73) 125 (15.70) - hyperlipidemia (%) - 112 (28.43) 112 (14.07) - Fatty liver disease (%) - 163 (41.37) 163 (20.48) - Coronary heart disease (%) - - - 126 (62.69) coronary atherosclerosis(%) - - - 41 (20.40) Blood clots(%) - - - 34 (16.91) ANA (+), n (%) 198 (49.25) 188 (47.72) 386 (48.49) 93 (46.27) ACPA (+), n (%) 211 (52.49) 199 (50.51) 410 (51.51) 96 (47.76) RF (+), n (%) 217 (53.98) 199 (50.51) 416 (52.26) 106 (52.74) APF (+), n (%) 175 (43.53) 174 (44.16) 349 (43.84) 91 (45.27) AKA (+), n (%) 209 (51.99) 199 (50.51) 408 (51.26) 90 (44.78) MCV (+), n (%) 219 (54.48) 198 (50.25) 417(52.39) 92 (45.78) Laboratory data VEGF (pg/ml) 207.06 ± 155.21 564.09 ± 322.31 383.78 ± 308.88 520.89 ± 450.70 < 0.001 < 0.001 0.001 WBC (*10 9 /L) 7.29 ± 2.83 7.69 ± 3.03 7.49 ± 2.94 7.90 ± 2.95 0.014 0.857 0.032 RBC (*10 12 /L) 4.15 ± 0.55 4.17 ± 0.57 4.16 ± 0.56 4.18 ± 0.56 0.987 0.904 HB (g/L) 118.19 ± 20.21 117.62 ± 20.60 117.91 ± 20.39 120.68 ± 19.49 0.211 0.091 PLT (*10 9 /L) 292.60 ± 117.06 302.76 ± 106.13 297.64 ± 111.81 285.87 ± 104.77 0.068 0.139 LYMP (*10 9 /L) 1.70 ± 0.73 1.87 ± 0.75 1.78 ± 0.75 1.87 ± 0.91 0.049 0.538 0.328 NEUT (*10 9 /L) 4.94 ± 2.44 5.14 ± 2.66 5.04 ± 2.55 5.33 ± 2.62 0.186 0.101 LYMP(%) 24.73 ± 9.20 25.94 ± 9.44 25.33 ± 9.33 24.82 ± 9.10 0.135 0.866 NEUT(%) 65.65 ± 11.68 64.69 ± 11.63 65.17 ± 11.66 65.59 ± 10.89 0.348 0.885 ESR (mm/h) 55.03 ± 37.33 57.18 ± 37.35 56.09 ± 37.33 52.92 ± 35.99 0.382 0.300 CRP (mg/L) 34.78 ± 44.24 34.96 ± 44.04 34.86 ± 44.11 31.29 ± 42.95 0.488 0.305 ALB (g/L) 36.28 ± 5.15 36.21 ± 4.82 36.25 ± 4.98 36.38 ± 5.97 0.861 0.589 ALP (U/L) 98.86 ± 49.02 98.13 ± 39.94 98.49 ± 44.73 101.48 ± 42.94 0.665 0.392 ALT (U/L) 19.07 ± 14.07 20.05 ± 16.91 19.56 ± 15.55 25.73 ± 43.13 0.228 0.088 AST (U/L) 19.61 ± 10.60 20.00 ± 9.36 19.80 ± 10.00 24.82 ± 36.33 0.009 0.092 0.005 TBIL (umol/L) 10.91 ± 4.54 9.97 ± 4.31 10.44 ± 4.45 11.07 ± 5.52 0.943 0.024 0.126 DBIL (umol/L) 2.14 ± 0.95 2.03 ± 1.43 2.08 ± 1.21 2.13 ± 1.22 0.348 0.605 0.838 GGT (U/L) 33.59 ± 36.69 31.34 ± 29.37 32.46 ± 33.22 39.88 ± 51.22 0.312 0.199 BUN (mmol/L) 5.25 ± 1.87 5.29 ± 1.86 5.27 ± 1.87 5.92 ± 2.16 0.002 0.005 < 0.001 creatinine (umol/L) 55.87 ± 16.92 55.59 ± 15.94 55.73 ± 16.43 61.69 ± 19.68 0.001 0.001 < 0.001 cystatin C (mg/L) 0.95 ± 0.63 0.94 ± 0.50 0.94 ± 0.57 1.05 ± 0.79 0.272 0.209 α1-MG (mg/L) 27.53 ± 9.52 27.33 ± 9.32 27.42 ± 9.41 29.59 ± 12.05 0.479 0.226 β2-MG (mg/L) 2.65 ± 1.52 2.50 ± 0.94 2.57 ± 1.27 2.77 ± 1.41 0.553 0.500 C1q (mg/L) 225.25 ± 53.32 233.30 ± 59.69 229.39 ± 56.78 229.86 ± 63.20 0.299 0.634 CK (U/L) 44.63 ± 25.73 43.46 ± 21.88 44.04 ± 23.87 69.06 ± 75.68 0.014 0.011 0.002 LDH (U/L) 195.37 ± 52.84 199.04 ± 53.49 197.20 ± 53.16 216.86 ± 56.21 < 0.001 < 0.001 < 0.001 TC (mmol/L) 4.02 ± 0.83 4.20 ± 0.80 4.11 ± 0.82 7.06 ± 32.01 < 0.001 0.016 < 0.001 TG (mmol/L) 1.16 ± 0.51 1.30 ± 0.59 1.23 ± 0.56 1.62 ± 1.11 < 0.001 0.003 < 0.001 HDL (mmol/L) 1.16 ± 0.32 1.20 ± 0.34 1.18 ± 0.33 1.24 ± 0.38 0.081 0.082 LDL (mmol/L) 2.31 ± 0.61 2.22 ± 0.61 2.26 ± 0.61 2.59 ± 0.90 0.015 < 0.001 < 0.001 ApoA1 (g/L) 1.30 ± 2.08 1.45 ± 2.91 1.38 ± 2.55 1.27 ± 0.33 0.013 0.014 0.002 ApoB100 (g/L) 0.77 ± 1.00 0.72 ± 0.20 0.74 ± 0.70 0.83 ± 0.28 < 0.001 < 0.001 < 0.001 ApoE (mg/L) 40.31 ± 13.48 38.80 ± 14.31 39.51 ± 13.93 47.62 ± 26.09 0.041 < 0.001 < 0.001 LP(α) (mg/dL) 34.37 ± 30.06 37.18 ± 32.65 35.85 ± 31.46 39.43 ± 30.82 0.070 0.064 HDL/CHOL (%) 29.01 ± 7.74 30.12 ± 8.22 29.58 ± 8.00 27.86 ± 8.29 0.089 0.002 0.002 RA, rheumatoid arthritis; BMI, body mass index; CVD, cardiovascular disease; ANA, anti-nuclear antibodies; ACPA, anti-citrullinated protein antibodies; RF, rheumatoid factor; APF, antiperinuclear factor; AKA, anti-keratin antibodies; MCV, anti-mutated citrullinated vimentin antibodies; VEGF, vascular endothelial growth factor; WBC, white blood cell; HB, hemoglobin; PLT, platelet; LYMP, lymphocyte; NEUT, neutrophile granulocyte; ESR, erythrocyte sedimentation rate; CRP, C-reactive protein; ALB, albumin; ALP, alkaline phosphatase; ALT, alanine aminotransferase; AST, aspartate aminotransferase; GGT, glutamyl transpeptidase; BUN, serum urea nitrogen; α1-MG, α1 microglobulin; β2-MG, β2 microglobulin; CK, creatine kinase; LDH, lactate dehydrogenase; TC, total cholesterol; TG, triglycerides; HDL, high-density lipoprotein cholesterol; LDL, low-density lipoprotein cholesterol; ApoA1, apolipoprotein A1; ApoB100, apolipoprotein B100; ApoE, apolipoprotein E; LP(α), lipoprotein α; HDL/CHOL, high-density lipoprotein/cholesterol Comparison of lymphocyte among pure RA, RA with CVD risk factors and RA with CVD In terms of immune cells, the results showed that the absolute counts of CD3 + T, NK, CD4 + T, CD8 + T and TBNK cells were significantly reduced in the pure RA compared with RA with CVD, as did the proportion and absolute count of Th1 cells, Th2 cells count, and the ratio of Th1/Th2/NK and Treg, respectively. The ratio of NK and Treg cells was obviously lower in RA with CVD risk factors. In addition, the count of NK cells, the ratios of Th1/Th2/NK and Treg in RA without CVD were statistically lower than these indicators in RA with CVD, respectively. Notably, the percentage of Treg cells in pure RA, RA with CVD risk factor, and the overall population of both groups were significantly higher than that in RA with CVD. The specific values of the results are described in Table 2 . Table 2 Characteristics of lymphocyte subpopulations in pure RA, RA with CVD risk factors, and RA with CVD Pure RA a (n = 402) RA + cause b (n = 394) RA without CVD a+b (n = 796) RA with CVD c (n = 201) P a vs c P b vs c P a+b vs c Lymphocyte cells T (cell/µL) 1220.88 ± 554.29 1372.98 ± 580.18 1296.17 ± 571.98 1395.08 ± 723.81 0.013 0.655 0.191 B (cell/µL) 202.84 ± 155.54 240.98 ± 179.98 221.72 ± 169.06 238.23 ± 203.93 0.220 0.979 0.649 NK (cell/µL) 233.95 ± 166.50 249.58 ± 167.99 241.69 ± 167.32 280.41 ± 194.12 0.003 0.281 0.006 CD4 + T (cell/µL) 748.71 ± 375.73 845.27 ± 406.35 796.51 ± 393.91 876.37 ± 527.81 0.035 0.804 0.215 CD8 + T (cell/µL) 430.14 ± 229.54 480.86 ± 240.31 455.25 ± 236.16 483.16 ± 268.34 0.043 0.719 0.263 CD4 + /CD8 + T 1.98 ± 0.99 1.96 ± 0.93 1.97 ± 0.96 2.05 ± 1.07 0.702 0.400 TBNK (cell/µL) 1689.17 ± 728.03 1905.92 ± 766.90 1796.45 ± 754.88 1961.19 ± 954.30 0.001 0.943 0.051 T cells (%) 71.86 ± 8.99 71.92 ± 8.70 71.89 ± 8.84 70.87 ± 9.52 0.484 0.243 B cells (%) 11.78 ± 6.31 12.24 ± 6.23 12.01 ± 6.27 11.71 ± 6.24 0.389 0.540 NK cells (%) 14.36 ± 8.28 13.82 ± 8.05 14.09 ± 8.17 15.38 ± 9.10 0.191 0.106 CD4 + T cells (%) 43.67 ± 9.39 43.64 ± 8.53 43.66 ± 8.97 43.59 ± 9.69 0.944 0.870 CD8 + T cells (%) 25.49 ± 8.47 25.54 ± 8.37 25.51 ± 8.42 24.97 ± 8.96 0.501 0.248 TBNK (%) 98.03 ± 1.08 97.97 ± 1.04 98.00 ± 1.06 97.94 ± 1.17 0.553 0.549 Th1 (cell/µL) 105.76 ± 89.17 137.30 ± 90.47 121.39 ± 91.14 163.58 ± 178.92 0.001 0.598 0.181 Th2 (cell/µL) 7.90 ± 6.07 8.75 ± 5.92 8.32 ± 6.01 9.44 ± 8.00 0.040 1.000 0.126 Th17 (cell/µL) 7.00 ± 5.81 7.65 ± 6.62 7.32 ± 6.23 7.56 ± 6.55 0.152 0.921 Treg (cell/µL) 33.72 ± 24.89 37.92 ± 29.56 35.80 ± 27.37 38.41 ± 55.61 0.845 0.100 0.199 Th1 cells (%) 14.93 ± 9.84 17.07 ± 9.60 15.99 ± 9.78 17.55 ± 12.11 0.042 0.558 0.314 Th2 cells (%) 1.23 ± 3.17 1.08 ± 0.59 1.16 ± 2.29 1.08 ± 0.71 0.901 0.753 Th17 cells (%) 0.98 ± 0.65 0.96 ± 0.71 0.97 ± 0.68 0.93 ± 0.84 0.336 0.180 Treg cells (%) 4.61 ± 2.30 4.58 ± 2.58 4.60 ± 2.44 4.13 ± 2.50 0.009 0.043 0.003 Th1/Th2 18.03 ± 16.67 20.70 ± 17.73 19.35 ± 17.25 22.93 ± 27.61 0.277 0.642 0.510 Th17/Treg 0.32 ± 0.93 0.26 ± 0.22 0.29 ± 0.68 0.36 ± 1.12 0.784 0.490 Th1/Treg 4.10 ± 3.42 4.81 ± 3.90 4.45 ± 3.68 5.84 ± 6.18 0.002 0.303 0.016 Th2/Treg 0.30 ± 0.37 0.29 ± 0.21 0.30 ± 0.30 2.28 ± 27.77 0.032 0.085 0.009 B/Treg 7.81 ± 7.00 8.41 ± 8.23 8.11 ± 7.64 18.59 ± 142.02 0.183 0.087 NK/Treg 9.84 ± 9.06 9.19 ± 8.25 9.52 ± 8.67 15.88 ± 58.77 0.048 0.029 0.006 Machine learning models applied to pure RA, RA with CVD risk factors and RA with CVD After removing missing values, the data of remaining 628 RA patients (241 pure RA, 259 RA with CVD risk factors, 128 RA with CVD) were entered into ML algorithms. Three groups of patients were randomly divided into training set and validation set at a ratio of 3:1. LASSO, RF and LR models were established to identify RA with CVD from pure RA and/or RA with CVD risk factors and filter the corresponding valuable indicators. When identifying the pure RA and the RA with CVD group, the range of AUC of LASSO, RF and LR were 0.79 to 0.91, 0.84 to 0.93, and 0.77 to 0.86, respectively. When distinguishing the RA with CVD from RA with CVD risk factors, the range of AUC of LASSO, RF and LR were 0.61 to 0.77, 0.76 to 0.94, and 0.61 to 0.73, respectively. And when distinguishing RA with CVD from RA without CVD, the range of AUC of LASSO, RF and LR were 0.70 to 0.79, 0.76 to 0.88, and 0.62 to 0.80, respectively (Fig. 3 ). Supplementary Table 1 displayed the top 20 important indicators of the three models when they had the best identifiable performance. Performance ability of the screened important indicators in the diagnosis of RA with CVD Indicators that were simultaneously significant in LASSO, RF, LR and univariate analysis were considered as important indicators. When comparing pure RA patients and RA with CVD, a total of 6 indicators were screened, namely ApoA1, TG, TC, BUN, Th1/Treg and ApoE. LDL, Treg% and TC were considered as important indicators when comparing RA with CVD risk factors and RA with CVD. In addition, in comparison of RA with or without CVD, 6 indicators including TG, LDL, Treg%, TC, CK and ApoE were screened out (Fig. 4). Establishment of a risk prediction model for CVD in individuals with RA Based on the above results, multivariable logistic regression analysis was used to screen for independent predictors, and nomograms were established. Figure 5 showed the score of each predictor, the personal total score and the predicted risk of occurrence CVD for RA. This nomogram (Fig. 5 A) contains 4 independent predictors, TG, TC, BUN and Th1/Treg, when predicting the risk of CVD development from pure RA patients. And LDL and Treg% serve as the independent predictors in the nomogram which predicts the risk of CVD development from RA with CVD risk factors (Fig. 5 B). In addition, the nomogram includes 5 independent predictors, namely TG, LDL, Treg%, CK and ApoE, when predicting the risk of CVD development from all RA patients (Fig. 5 C). These nomograms exhibit good clinical usefulness. Discussion The incidence of CVD, which includes coronary artery disease (CAD) or coronary heart disease (CHD), peripheral artery disease (PAD), cerebrovascular disease and aortic atherosclerosis [ 23 ], is increasing year by year in both developing and developed countries [ 24 ]. It is largely understood that the initiation, progression, and complications of CVD are complex phenomena involving the interplay of the irrational diet, reduced physical activity, abnormal metabolism, dyslipidemia, excessive consumption of alcohol and tobacco and immune system disorders [ 25 ]. In particular, dyslipidemia, which is characterized by elevated levels of plasma TG, LDL, very low-density lipoprotein cholesterol (VLDL-C) and reduced HDL, is confirmed to promote the development of atherosclerotic plaques and occurrence of CVD, posing a serious threat to humans health. Furthermore, several exchangeable apolipoproteins, such as ApoA1, ApoB100, ApoE, and LP(α), have also been shown to play important roles in modulating plasma and cellular lipid metabolism and homeostasis, consequently affecting the emergence of CVD [ 26 ]. RA is a complex systemic autoimmune disease accompanied by poor prognosis, which is characterized by an increased morbidity and mortality of CVD. This increased risk of CVD is apparent even before the clinical diagnosis of RA: subjects with RA have a 1.5-2.0 fold risk to develop CVD than individuals without RA [ 27 ]. And the phenomenon could be attribute to shared inflammatory underpinnings (such as elevated CRP, ESR, rheumatoid factor, and anti-citrullinated protein antibodies), aberrant initiation of an immune response, abnormal changes of serum lipids and lipoproteins, endothelial dysfunction and the use of steroids [ 28 , 29 ]. As is commonly acknowledged, the inflammatory properties inherent in RA not only have the capacity to modify the structure and function of lipoproteins, but also exert direct or indirect effects on vascular architecture [ 30 , 31 ]. Patients diagnosed with RA should be closely monitored for risk factors for CVD, as recommended by EULAR guidelines [ 32 ]. More research, hence, is imperative to fully characterize the intricate mechanism underlying the development of CVD in RA, to screen for characteristic differential biomarkers, and use them to construct and evaluate risk assessment models, prevention and treatment strategies that are tailored specifically for RA [ 31 ]. In the retrospective study, we used real-world data in conjunction with traditional CVD risk factors and RA-related indicators to construct a more robust predictive model of CVD risk in RA patients. The main results of our study showed that the levels of WBC, urea, creatinine, CK, LDH and VEGF were significantly elevated in RA with CVD, as well as serum TC, TG, LDL, ApoB100 and ApoE, while ApoA1 and HDL/CHOL were decreased. VEGF has been reported to promote the occurrence of microvessels in atherosclerotic plaques and increase the possibility of intracatheral hemorrhage and thrombosis. Our results showed that VEGF levels were significantly higher in RA patients with CVD than in pure RA, but lower than that in RA with CVD risk factors, which we hypothesized was because VEGF may play a more crucial role in the process of atherosclerosis than CVD has already occurred. CD4 + CD25 + FOXP3 + Treg cells, recently, has received increasing attention in inflammation and immune regulation. Given that both innate and adaptive immune responses are involved in the initiation and development of CVD, suppressive Treg cells may be crucial for maintaining cardiovascular homeostasis [ 33 , 34 ]. The ratio of Treg cells, in the current study, in RA with CVD was significantly lower than that in other groups, while the ratio of Th1/Treg, Th2/Treg and NK/Treg were significantly elevated, exhibiting excellent separation performance. Nowadays, machine learning are developing rapidly and has been successfully used to diagnose diseases and predict disease onset [ 35 , 36 ]. Various risk calculators, to date, have been utilized to predict the risk of CVD developing, however, none of them seem to perform as effectively in RA patients as they do in the general population [ 37 ]. Numerous scholars, hence, have endeavored to devise and authenticate CVD risk assessment models specifically for RA. An enhanced and recalibrated version of the European Systematic Coronary Risk Evaluation algorithm [ 38 ] (SCORE) was devised for RA patients in the Netherlands [ 39 ]. The novel score incorporated measures of inflammatory disease activity and reevaluated traditional risk factors, yet demonstrated only marginal improvement over its predecessor when applied to individuals with RA. Solomon et al. also built an expanded CVD risk prediction score for RA patients (ERS-RA), which incorporated both traditional CVD risk factors and RA-related indicators such as inflammatory disease activity, disease course, daily prednisone dosage and so on [ 40 ]. Despite its comprehensive nature, the ERS-RA still failed to outperform FRS in external validation. However, as the FRS solely encompasses some conventional risk factors, it may potentially underestimate the CVD risk in patients with RA. Risk models for patients with RA may require the inclusion of factors that differ from or supplement those used in the general population. Recent research has shown that multiple biochemical markers can be analyzed together to create a comprehensive index. Therefore, we then established a novel model that included blood routine index, serum lipid, VEGF and lymphocyte subpopulations, to estimate CVD risk in RA accurately and comprehensively. In this study, LASSO, RF and LR models were used to find the predictive factors for CVD in RA, and ApoA1, TG, TC, BUN, Th1/Treg and ApoE (pure RA vs. RA with CVD), LDL, Treg% and TC (RA with CVD risk factors vs. RA with CVD), and TG, LDL, Treg%, TC, CK and ApoE (RA without CVD vs. RA with CVD) were screened out, respectively. Through the final selected indicators, a convenient nomogram was established for predicting CVD risk in RA, which will greatly promote the individualized risk prediction of CVD in clinical RA patients, providing a very convenient suggestion for clinical practice. The advantage of this study is that it is the first time to integrate serum lipids, lipoproteins and lymphocytes to assess the risk of CVD in RA, providing a certain reference for the prediction and diagnosis of CVD. The information provided in this study can help physicians make more reasonable treatment plans for patients, improve their goal-oriented medical outcomes, and improve the quality of life of patients with RA. Our study, however, still had several limitations. First, this study was performed in a single hospital and the size of the recruited population was limited. Additionally, a lag may be existed in the diagnosis of CVD in the RA. That is, CVD is present before RA. Third, this study had the inevitable limitations of a retrospective study. A prospective study would be required to avoid the interference of these factors. Conclusion In conclusion, serum lipids, lipoproteins, and Treg cells have been identified as risk factors for CVD in patients with RA, and three nomograms were constructed to predict the development of CVD in patients with RA in different backgrounds (pure RA and/or with CVD risk factors). Our study shows that integrated ipidomic profiling and lymphocyte subset analysis is a promising approach to predict the probability of CVD in RA. Abbreviations RA: Rheumatoid arthritis; CVD: Cardiovascular disease; VEGF: Vascular endothelial growth factor; TC: Total cholesterol; TG: Triglyceride; HDL-C: High-density lipoprotein cholesterol; LDL-C: Low-density lipoprotein cholesterol; ApoA1: Apolipoprotein A1; ApoB100: Apolipoprotein B100; ApoE: Apolipoprotein E. Declarations Acknowledgements We are grateful to all members who participated in the study. Author contributions JL and HG designed the work. MF performed the writing of the article. MF and F Meng were responsible for data collation and analysis. JY, YW, YQ, YJ and GJ completed the analysis of data. ZL and CG polished the article. All authors approved the submitted version. Funding We would like to acknowledge the financial support from the Scientifific research project of Shanxi Provincial Health Commission (2019044), the Research Project Supported by Shanxi Scholarship Council of China (2020-191), the Science and Technology Innovation Project of Shanxi Province (2020SYS08), the Project of Central Guides Local Science and Technology Development Funds (YDZJSX2022C031) and the Foundation of Shanxi Key Laboratory for immunomicroecology (202104010910012). Availability of data and materials The data analyzed in this study are available from the corresponding author upon reasonable request. Ethics approval and consent to participate The study was reviewed and approved by the ethics committee of the Second Hospital of Shanxi Medical University. Each participant in this study provided written informed consent. Consent for publication Informed consent was obtained from all participants for publication. Competing interests The authors declare no competing interests. Author details 1 Department of Rheumatology, the Second Hospital of Shanxi Medical University, Taiyuan, Shanxi, China; 2 Shanxi Medical University, Taiyuan, Shanxi, China; 3 Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA. 4 Division of Nephrology, Department of Medicine, Shenzhen Baoan Shiyan People’s Hospital, Shenzhen, Guangdong, China; Min Feng and Fanxing Meng contributed to this work equally *Correspondence: Jing Luo, [email protected] ; and Hui Guo, [email protected] References McInnes IB, Schett G. The pathogenesis of rheumatoid arthritis. N Engl J Med. 2011;365(23):2205-19. Scott DL, Wolfe F, Huizinga TW. Rheumatoid arthritis. Lancet. 2010;376(9746):1094-108. Kitas GD, Gabriel SE. Cardiovascular disease in rheumatoid arthritis: state of the art and future perspectives. Ann Rheum Dis.2011;70(1):8-14. Van Vollenhoven RF. 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Ridker PM, Danielson E, Fonseca FAH, Genest J, Gotto AM, Kastelein JJP, et al. Rosuvastatin to Prevent Vascular Events in Men and Women with Elevated C-Reactive Protein. N Engl J Med. 2008;359(21): 2195-207. Varga Z, Sabzwari SRA, Vargova V. Cardiovascular Risk of Nonsteroidal Anti-Inflammatory Drugs: An Under-Recognized Public Health Issue. Cureus. 2017;9(4):e1144. Olvera Lopez E, Ballard BD, Jan A. Cardiovascular Disease. 2022 Aug 8. Schnabel RB, Yin X, Larson MG, Yamamoto JF, Fontes JD, Kathiresan S, et al. Multiple inflammatory biomarkers in relation to cardiovascular events and mortality in the community. Arterioscler Thromb Vasc Biol. 2013;33(7):1728-33. Del Giudice M, Gangestad SW. Rethinking IL-6 and CRP: Why they are more than inflammatory biomarkers, and why it matters. Brain Behav Immun. 2018;70:61-75. Su X, Peng D. The exchangeable apolipoproteins in lipid metabolism and obesity. Clin Chim Acta. 2020;503:128-35. Maradit-Kremers H, Crowson CS, Nicola PJ, Ballman KV, Roger VL, Jacobsen SJ, et al. Increased unrecognized coronary heart disease and sudden deaths in rheumatoid arthritis: a population-based cohort study. Arthritis Rheum. 2005;52(2):402-11. England BR, Thiele GM, Anderson DR, Mikuls TR. Increased cardiovascular risk in rheumatoid arthritis: mechanisms and implications. BMJ. 2018;361:k1036. Kremers HM, Nicola PJ, Crowson CS, Ballman KV, Gabriel SE. Prognostic importance of low body mass index in relation to cardiovascular mortality in rheumatoid arthritis. Arthritis Rheum. 2004;50(11):3450-7. Toms TE, Symmons DP, Kitas GD. Dyslipidaemia in rheumatoid arthritis: the role of inflammation, drugs, lifestyle and genetic factors. Curr Vasc Pharmacol. 2010; 8:301-26. Blum A, Adawi M. Rheumatoid arthritis (RA) and cardiovascular disease. Autoimmun Rev. 2019;18(7):679-90. Li M, Wang X, Fu W, He S, Li D, Ke Q. CD4+CD25+Foxp3+ regulatory T cells protect endothelial function impaired by oxidized low density lipoprotein via the KLF-2 transcription factor. Cell Physiol Biochem. 2011;28(4):639-48. Libby P, Lichtman AH, Hansson GK. Immune effector mechanisms implicated in atherosclerosis: from mice to humans. Immunity. 2013;38(6):1092-104. Meng X, Yang J, Dong M, Zhang K, Tu E, Gao Q, et al. Regulatory T cells in cardiovascular diseases. Nat Rev Cardiol. 2016;13(3):167-79. Chen J, Remulla D, Nguyen JH, Dua A, Liu Y, Dasgupta P, et al. Current status of artificial intelligence applications in Urology and its potential to influence clinical practice. BJU Int. 2019;124:567-77. Liang H, Tsui BY, Ni H, Valentim CCS, Baxter SL, Liu G, et al. Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence. Nat Med. 2019;25:433-8. Wei T, Yang B, Liu H, Xin F, Fu L. Development and validation of a nomogram to predict coronary heart disease in patients with rheumatoid arthritis in northern China. Aging (Albany NY). 2020;12(4):3190-204. Conroy RM, Pyörälä K, Fitzgerald AP, Sans S, Menotti A, De Backer G, et al. Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project. Eur Heart J. 2003;24(11):987-1003. Arts EE, Popa CD, Den Broeder AA, Donders R, Sandoo A, Toms T, et al. Prediction of cardiovascular risk in rheumatoid arthritis: performance of original and adapted SCORE algorithms. Ann Rheum Dis. 2016;75(4):674-80. Solomon DH, Greenberg J, Curtis JR, Liu M, Farkouh ME, Tsao P, et al. Derivation and internal validation of an expanded cardiovascular risk prediction score for rheumatoid arthritis: a Consortium of Rheumatology Researchers of North America Registry Study. Arthritis Rheumatol. 2015;67(8):1995-2003. 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02:48:46","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2914034/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2914034/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":37385825,"identity":"2525eef3-e943-4d27-b896-5516ead52320","added_by":"auto","created_at":"2023-05-23 14:41:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":799237,"visible":true,"origin":"","legend":"\u003cp\u003eAlluvial plot exhibiting the number of individuals crossing over gender, disease groups, DAS28-ESR and RA-related autoantibodies such as anti-nuclear antibodies (ANA), anti-citrullinated protein antibodies (ACPA), rheumatoid factor (RF), antiperinuclear factor (APF), anti-keratin antibodies (AKA) and anti-mutated citrullinated vimentin antibodies (MCV).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-2914034/v1/7ca7119e21460ea9604c13c3.png"},{"id":37385827,"identity":"1578ce06-bec2-4187-9a89-c52c723d5280","added_by":"auto","created_at":"2023-05-23 14:41:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":193592,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart for the inclusion and exclusion of RA patients and the experimental design and analysis in this study. \u003cem\u003eRA\u003c/em\u003e rheumatoid arthritis, \u003cem\u003eCVD\u003c/em\u003e cardiovascular disease, \u003cem\u003eLASSO\u003c/em\u003e least absolute shrinkage and selection operator, \u003cem\u003eRF\u003c/em\u003erandom forest, \u003cem\u003eLR\u003c/em\u003e logistic regression models\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-2914034/v1/998f92ff2fa99c04802b0ac9.png"},{"id":37385828,"identity":"17c878a5-8c11-4b61-bd21-53e7afaa0b4b","added_by":"auto","created_at":"2023-05-23 14:41:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1812325,"visible":true,"origin":"","legend":"\u003cp\u003eAreas under ROC curve (AUC) of LASSO, RF and LR machine learning models in ten resampling iterations. The maximum of AUC of the ROC curve of LASSO, RF and LR. (A, D) pure RA vs. RA with CVD; (B, E) RA with CVD risk factors vs. RA with CVD; (C, F) RA without CVD vs. RA with CVD.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-2914034/v1/bfb485396f2b88184723e358.png"},{"id":37385829,"identity":"2040e1bf-f899-4aa3-8ea7-a562ea1ecee1","added_by":"auto","created_at":"2023-05-23 14:41:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":360466,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-2914034/v1/84d9e85482804e8bc342e188.png"},{"id":37385830,"identity":"936d71f4-a91e-4f04-be81-3ab7da96f5e4","added_by":"auto","created_at":"2023-05-23 14:41:55","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":451317,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram of CVD in RA patients with different backgrounds(A) pure RA (B) RA with CVD risk factors (C) RA without CVD. TC, total cholesterol; TG, triglycerides; BUN, blood urea nitrogen; Treg, regulatory T cells; LDL, low-density lipoprotein cholesterol; CK, creatine kinase; ApoE, Apoprotein E.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-2914034/v1/7fe5131d356d4ed9228cd651.png"},{"id":40095350,"identity":"df6077f4-59b8-4db0-b699-830e784a2321","added_by":"auto","created_at":"2023-07-16 23:10:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1611346,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2914034/v1/4e91e924-1160-418e-a675-cdcc243c2757.pdf"},{"id":37387018,"identity":"bd1290e0-31cd-4203-b46e-c76945bfd10e","added_by":"auto","created_at":"2023-05-23 14:49:55","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17607,"visible":true,"origin":"","legend":"","description":"","filename":"renamed4182f.docx","url":"https://assets-eu.researchsquare.com/files/rs-2914034/v1/c286428312344a27334a77fa.docx"}],"financialInterests":"","formattedTitle":"Predicting the likelihood of cardiovascular disease in patients with rheumatoid arthritis: development and Validation of a Novel Model","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRheumatoid arthritis (RA) is a chronic systemic autoimmune disease characterized by symmetry joint impairment and dysfunction, which has turned into a worldwide public health challenge due to its high disability rate and socioeconomic burden on society [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The global prevalence of RA is about 0.5-1.0%, while the prevalence of RA in Chinese people is around 0.32 to 0.36%, and RA is not only more common in women (3:1), but they also are usually sicker than men [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. So far, although the contribution of genetic predisposition and environmental factors to the pathogenesis of RA has been partially confirmed, the etiology of RA needs to be further explored [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In addition, due to that RA remains often under-prioritized in the early stage, especially in the underprivileged families, patients with RA often arrive at hospital with irreversible joint dysfunction and disability, and are often accompanied by multiple comorbidities that lead to poor clinical outcomes, such as cardiovascular disease (CVD), interstitial pneumonia and anemia [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCVD nowadays has been regarded as the number one cause of morbidity and mortality in the world [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], and its incidence has elevated by about one third over the past decade [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], seriously affecting people's quality of life and increase their psychological and economic burden [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Alarmingly, the prevalence of CVD in autoimmune diseases has been greatly underestimated in recent decades [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Result from a large observational cohort showed that patients with RA experience CV events 1.5-2 fold than the general population, including coronary heart disease (CHD), myocardial infarction (MI), stroke and heart failure [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Despite the availability of excellent treatment strategy, CV events cause more than half of all deaths in RA patients compared with the healthy population, cancer and respiratory diseases [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Consistent with the general population, traditional CVD risk factors, such as hypertension, dyslipidemia, diabetes and smoking and so on, also play an important role in the occurrence and progress of CVD in RA patients [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In general, the lipid profile that induces the occurrence of CVD is usually high total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C) and low level of high-density lipoprotein cholesterol (HDL-C). In addition, mounting evidence suggests that the chronic inflammatory state of RA patients from the early stage also holds the position of an dominant player contributing to the increased morbidity and progression of CV complications, explaining the premature onset of cardiovascular disease in RA [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Elevated C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR) have been shown to be involved in the development of CVD [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. And pro-inflammatory cells and cytokines implicated in RA also contribute to CVD. In addition, endothelial dysfunction and some medications are also regarded as the key inducer of CVD.\u003c/p\u003e \u003cp\u003eLack of attention to the disease in RA, especially in developing countries, leads to a higher incidence of comorbidities, especially CVD. As CV events is hidden in the early stage, it is often ignored by people, resulting in irreversible consequences. Therefore, an RA specific CVD risk assessment tool needs to be developed to facilitate clinicians to take preventive measures. In general, we can prevent or alleviate the risk of CVD using existing screening methods and interventions. Multiple cardiovascular risk models have been built to predict the likelihood of CVD in individuals, such as Framingham risk score (FRS), the Systematic Coronary Risk Evaluation (SCORE), and 2013 American College of Cardiology/American Heart Association CVD Risk Score, but these models tend to underestimate the risk of CVD in RA patients due to their limited indicators, resulting in some individuals cannot be accurately identified [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Therefore, hypertension, dyslipidemia, smoking, obesity, immune molecules and other biological indicators need to be explored to refine the CVD risk assessment model for RA patients so that timely preventive strategies can be adopted.\u003c/p\u003e \u003cp\u003eIn this study, a variety of machine learning methods were used to incorporate blood routine index, serological indicators and multiple lymphocyte subsets to explore the factors influencing the occurrence of CVD in RA, and predictive nomograms were established to identify RA with CVD, RA with CVD risk factors, and patients with RA only (pure RA).\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and population\u003c/h2\u003e \u003cp\u003eThe retrospective study recruited 1535 RA patients (1030 females, 505 males, median age 57.58\u0026thinsp;\u0026plusmn;\u0026thinsp;9.84 years) who met the 2010 American College of Rheumatology/European League Against Rheumatology classification criteria for RA and were diagnosed by 2 clinicians at the Second Hospital of Shanxi Medical University from January 2016 to December 2019. Disease activity of RA was evaluated according to the disease activity score 28-erythrocyte sedimentation rate (DAS28-ESR) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Individuals with one or more of the following conditions were excluded from the study: 1) under 18 years of age (n\u0026thinsp;=\u0026thinsp;64); 2) with pregnancy (n\u0026thinsp;=\u0026thinsp;28); 3) with neoplastic diseases (n\u0026thinsp;=\u0026thinsp;52); 4) with other autoimmune diseases (n\u0026thinsp;=\u0026thinsp;113); 5) with a history of CVD (n\u0026thinsp;=\u0026thinsp;94); 6) patients who have been prescribed lipid-lowering drugs or non-steroidal anti-inflammatory drugs (NSAIDs) or who have used relatively high doses of corticosteroids for a long time (n\u0026thinsp;=\u0026thinsp;187). Out of the remaining 997 RA patients, 402 were pure RA, 394 were RA with CVD risk factors (including hypertension or diabetes or dyslipidemia or fatty liver disease), and 201 were RA with CVD. The category of CVD includes coronary heart disease, coronary atherosclerosis, heart failure, myocardial infarction, and other peripheral vascular diseases. The medical ethics committee of the Second Hospital of Shanxi Medical University has approved the study (2016KY007), and written informed consents were obtained from all patients.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDemographic and laboratory data\u003c/h3\u003e\n\u003cp\u003eAll clinical characteristics of the patients, such as age, sex, body mass index (BMI), smoking, disease duration, and the quantities of tender and swollen joints, were obtained from the medical records of our hospital. In addition, data of routine examination biomarker were also recorded, including blood routine examination [white blood cells (WBC), red blood cells (RBC), hemoglobin (Hb), platelet (PLT), the number and percentage of lymphocyte (LYMP) and neutrophile granulocyte (NEUT)], liver and kidney function indicators [albumin (ALB), alkaline phosphatase (ALP), alanine aminotransferase (ALT), aspartate aminotransferase (AST), glutamyl transferase (GGT), urea, creatinine, cystatin C (CYSC), α1 microglobulin (α1-MG), β2 microglobulin (β2-MG), complement C1q, creatine kinase (CK), and lactate dehydrogenase (LDH)], and lipid indicators, including total cholesterol (CHOL), triglycerides (TG), high-density lipoprotein (HDL), low-density lipoprotein (LDL), apolipoprotein A1 (ApoA1), apolipoprotein B100 (ApoB100), apolipoprotein E (ApoE), lipoprotein α (LP(α)) and high-density lipoprotein/cholesterol (HDL/CHOL).\u003c/p\u003e \u003cp\u003eErythrocyte sedimentation rate (ESR), C-reactive protein (CRP) and vascular endothelial growth factor (VEGF) level were also measured.\u003c/p\u003e\n\u003ch3\u003eLymphocyte subsets\u003c/h3\u003e\n\u003cp\u003ePeripheral blood specimens were collected from RA patients on the morning of the first day after admission and before medication. CD3+/CD4+/CD8\u0026thinsp;+\u0026thinsp;T cells were identified by anti-CD3 /CD4/CD8/CD45 antibodies and B/NK cells were labeled by anti-CD3/CD16/CD56/CD45/CD19 antibodies. For Th1/Th2/Th17 cells, we need to stimulate the cells with Ionomycin and PMA for 6 h and then label them with anti-CD4/IFN-γ/IL-4/IL-17 antibodies. In addition, anti-CD4/CD25/FoxP3 antibodies were employed for the identification of Treg cells.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eSPSS22.0 and R package were used to analyze the data. One-way ANOVA was used to analyze differences in continuous variables, which was recorded as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), among the four groups. Dichotomous variables were compared by the Chi-square test. \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicated statistical significance between groups. In the study, in addition to univariate analysis, the least absolute shrinkage and selection operator (LASSO), the random forest (RF) and the logistic regression models (LR) were also employed to screen characteristic variables between each two groups (pure RA and/or RA\u0026thinsp;+\u0026thinsp;CVD risk factors vs. RA\u0026thinsp;+\u0026thinsp;CVD). By removing the missing values, a total of 628 patients\u0026rsquo; data (241 pure RA, 259 RA\u0026thinsp;+\u0026thinsp;CVD risk factors, 500 RA without CVD, 128 RA\u0026thinsp;+\u0026thinsp;CVD) were finally included in these machine learning (ML) models for analysis. These patients were randomly selected and divided into a training set (75%) for calculation of parameters and construction of models, and a validation set (25%) for model testing and evaluation, where the four groups\u0026rsquo; proportions in each set were equal. Ten-fold cross-validation was used in the training set, that is, 9/10 of the data was used to fit the model in turn, and the remaining 1/10 of the data was used for validation, and the process was performed 10 times in total. Finally, the model was tested by the validation set. The step was repeated 10 times randomly (i.e., we completed 10 resampling iterations) so as to obtain the stable result. To ensure the fairness of these models, we used the same data segmentation and repetition to evaluate these models each time. In the process of 10 repetitions, the model with the maximum area under the receiver operating characteristic curve (AU-ROC) was considered to be the best model. Metrics with the top 10 variable important for the projection value (VIP) or weight size were considered as feature variables. The selection criteria of the final important variable was that the indicator had discriminative significance in the three machine learning algorithms and univariate analysis at the same time. Multivariate logistic regression was then used to analyze these screened final significant variables to generate individualized predictive nomograms.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eClinical characteristics and demographic data of participants\u003c/h2\u003e \u003cp\u003eAmong 997 RA patients, 402 were pure RA (279 females, 123 males, 55.06\u0026thinsp;\u0026plusmn;\u0026thinsp;12.88 years), 394 were RA with CVD risk factors (262 females, 132 males, 55.13\u0026thinsp;\u0026plusmn;\u0026thinsp;11.43 years), i.e., 796 were RA without CVD (541 females, 255 males, 55.10\u0026thinsp;\u0026plusmn;\u0026thinsp;12.18 years) and 201 were RA with CVD (134 females, 67 males, 56.77\u0026thinsp;\u0026plusmn;\u0026thinsp;8.71 years), of which 126 were coronary heart disease, 41 coronary atherosclerosis, 34 blood clots. No significant differences were observed in age, sex, BMI, disease duration and smoking history among the four groups. Results of univariate analysis showed that levels of VEGF, WBC and AST in RA patients with CVD were significantly higher than those in pure RA and RA without CVD, respectively. LYMP in pure RA and level of TBIL in RA with CVD risk factors were significantly lower than those in RA with CVD. In addition, the serum contents of urea, creatinine, CK, LDH, TC, TG, LDL, ApoB100 and ApoE in RA with CVD were significantly higher than those in pure RA and/or RA with CVD risk factors, while ApoA1 level was reduced (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Furthermore, the ratio of HDL/CHOL in RA with CVD was obviously elevated compared with RA with CVD risk factors and RA without CVD. The clinical characteristics and demographic data of 997 RA patients were summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The alluvial plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) showed the distribution of patients across the disease groups, gender, DAS28-ESR and RA-related autoantibodies. After removing the missing values, remaining 628 patients in all three groups were randomly assigned to a training set (75%) consisted of 181 pure RA, 194 RA with CVD risk factors and 96 RA with CVD, and a validation set (25%) consisted of 60 pure RA, 65 RA with CVD risk factors and 32 RA with CVD. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e exhibited the study design and analysis scheme.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of clinical characteristics and demographic data among pure RA, RA with CVD risk factors, and RA with CVD.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePure RA\u003csup\u003ea\u003c/sup\u003e (n\u0026thinsp;=\u0026thinsp;402)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRA\u0026thinsp;+\u0026thinsp;cause\u003csup\u003eb\u003c/sup\u003e (n\u0026thinsp;=\u0026thinsp;394)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRA without CVD\u003csup\u003ea+b\u003c/sup\u003e (n\u0026thinsp;=\u0026thinsp;796)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRA with CVD\u003csup\u003ec\u003c/sup\u003e (n\u0026thinsp;=\u0026thinsp;201)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003ea vs c\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eb vs c\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003ea+b vs c\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.06\u0026thinsp;\u0026plusmn;\u0026thinsp;12.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.13\u0026thinsp;\u0026plusmn;\u0026thinsp;11.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55.10\u0026thinsp;\u0026plusmn;\u0026thinsp;12.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56.77\u0026thinsp;\u0026plusmn;\u0026thinsp;8.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender: female/male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e279/123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e262/132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e541/255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e134/67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.725\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease duration (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.50\u0026thinsp;\u0026plusmn;\u0026thinsp;5.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.35\u0026thinsp;\u0026plusmn;\u0026thinsp;3.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.42\u0026thinsp;\u0026plusmn;\u0026thinsp;3.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.80\u0026thinsp;\u0026plusmn;\u0026thinsp;2.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.246\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.11\u0026thinsp;\u0026plusmn;\u0026thinsp;3.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.87\u0026thinsp;\u0026plusmn;\u0026thinsp;3.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.04\u0026thinsp;\u0026plusmn;\u0026thinsp;3.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.22\u0026thinsp;\u0026plusmn;\u0026thinsp;2.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.164\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoker (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64 (15.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58 (14.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e122 (15.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29 (14.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.751\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDAS28-ESR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.26\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.17\u0026thinsp;\u0026plusmn;\u0026thinsp;1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.23\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.34\u0026thinsp;\u0026plusmn;\u0026thinsp;1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.245\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e221 (56.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e221 (27.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ediabetes (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e125 (31.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e125 (15.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehyperlipidemia (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112 (28.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e112 (14.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFatty liver disease (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e163 (41.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e163 (20.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoronary heart disease (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e126 (62.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecoronary atherosclerosis(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41 (20.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood clots(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34 (16.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANA (+), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e198 (49.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e188 (47.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e386 (48.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e93 (46.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACPA (+), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e211 (52.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e199 (50.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e410 (51.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e96 (47.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRF (+), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e217 (53.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e199 (50.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e416 (52.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e106 (52.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPF (+), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e175 (43.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e174 (44.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e349 (43.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e91 (45.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAKA (+), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e209 (51.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e199 (50.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e408 (51.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90 (44.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCV (+), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e219 (54.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e198 (50.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e417(52.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e92 (45.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaboratory data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVEGF (pg/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e207.06\u0026thinsp;\u0026plusmn;\u0026thinsp;155.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e564.09\u0026thinsp;\u0026plusmn;\u0026thinsp;322.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e383.78\u0026thinsp;\u0026plusmn;\u0026thinsp;308.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e520.89\u0026thinsp;\u0026plusmn;\u0026thinsp;450.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC (*10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.29\u0026thinsp;\u0026plusmn;\u0026thinsp;2.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.69\u0026thinsp;\u0026plusmn;\u0026thinsp;3.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.49\u0026thinsp;\u0026plusmn;\u0026thinsp;2.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.90\u0026thinsp;\u0026plusmn;\u0026thinsp;2.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRBC (*10\u003csup\u003e12\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.904\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHB (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e118.19\u0026thinsp;\u0026plusmn;\u0026thinsp;20.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e117.62\u0026thinsp;\u0026plusmn;\u0026thinsp;20.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e117.91\u0026thinsp;\u0026plusmn;\u0026thinsp;20.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e120.68\u0026thinsp;\u0026plusmn;\u0026thinsp;19.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT (*10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e292.60\u0026thinsp;\u0026plusmn;\u0026thinsp;117.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e302.76\u0026thinsp;\u0026plusmn;\u0026thinsp;106.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e297.64\u0026thinsp;\u0026plusmn;\u0026thinsp;111.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e285.87\u0026thinsp;\u0026plusmn;\u0026thinsp;104.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLYMP (*10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.328\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNEUT (*10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.94\u0026thinsp;\u0026plusmn;\u0026thinsp;2.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.14\u0026thinsp;\u0026plusmn;\u0026thinsp;2.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.04\u0026thinsp;\u0026plusmn;\u0026thinsp;2.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.33\u0026thinsp;\u0026plusmn;\u0026thinsp;2.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLYMP(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.73\u0026thinsp;\u0026plusmn;\u0026thinsp;9.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.94\u0026thinsp;\u0026plusmn;\u0026thinsp;9.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.33\u0026thinsp;\u0026plusmn;\u0026thinsp;9.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.82\u0026thinsp;\u0026plusmn;\u0026thinsp;9.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.866\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNEUT(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.65\u0026thinsp;\u0026plusmn;\u0026thinsp;11.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.69\u0026thinsp;\u0026plusmn;\u0026thinsp;11.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65.17\u0026thinsp;\u0026plusmn;\u0026thinsp;11.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e65.59\u0026thinsp;\u0026plusmn;\u0026thinsp;10.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESR (mm/h)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.03\u0026thinsp;\u0026plusmn;\u0026thinsp;37.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.18\u0026thinsp;\u0026plusmn;\u0026thinsp;37.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.09\u0026thinsp;\u0026plusmn;\u0026thinsp;37.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52.92\u0026thinsp;\u0026plusmn;\u0026thinsp;35.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.300\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.78\u0026thinsp;\u0026plusmn;\u0026thinsp;44.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.96\u0026thinsp;\u0026plusmn;\u0026thinsp;44.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.86\u0026thinsp;\u0026plusmn;\u0026thinsp;44.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.29\u0026thinsp;\u0026plusmn;\u0026thinsp;42.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.305\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALB (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.28\u0026thinsp;\u0026plusmn;\u0026thinsp;5.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.21\u0026thinsp;\u0026plusmn;\u0026thinsp;4.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.25\u0026thinsp;\u0026plusmn;\u0026thinsp;4.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.38\u0026thinsp;\u0026plusmn;\u0026thinsp;5.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.589\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALP (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98.86\u0026thinsp;\u0026plusmn;\u0026thinsp;49.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98.13\u0026thinsp;\u0026plusmn;\u0026thinsp;39.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98.49\u0026thinsp;\u0026plusmn;\u0026thinsp;44.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e101.48\u0026thinsp;\u0026plusmn;\u0026thinsp;42.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.392\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.07\u0026thinsp;\u0026plusmn;\u0026thinsp;14.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.05\u0026thinsp;\u0026plusmn;\u0026thinsp;16.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.56\u0026thinsp;\u0026plusmn;\u0026thinsp;15.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.73\u0026thinsp;\u0026plusmn;\u0026thinsp;43.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.61\u0026thinsp;\u0026plusmn;\u0026thinsp;10.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.00\u0026thinsp;\u0026plusmn;\u0026thinsp;9.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.80\u0026thinsp;\u0026plusmn;\u0026thinsp;10.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.82\u0026thinsp;\u0026plusmn;\u0026thinsp;36.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTBIL (umol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.91\u0026thinsp;\u0026plusmn;\u0026thinsp;4.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.97\u0026thinsp;\u0026plusmn;\u0026thinsp;4.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.44\u0026thinsp;\u0026plusmn;\u0026thinsp;4.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.07\u0026thinsp;\u0026plusmn;\u0026thinsp;5.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBIL (umol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.03\u0026thinsp;\u0026plusmn;\u0026thinsp;1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.08\u0026thinsp;\u0026plusmn;\u0026thinsp;1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.13\u0026thinsp;\u0026plusmn;\u0026thinsp;1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.838\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGGT (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.59\u0026thinsp;\u0026plusmn;\u0026thinsp;36.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.34\u0026thinsp;\u0026plusmn;\u0026thinsp;29.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.46\u0026thinsp;\u0026plusmn;\u0026thinsp;33.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39.88\u0026thinsp;\u0026plusmn;\u0026thinsp;51.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.25\u0026thinsp;\u0026plusmn;\u0026thinsp;1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.29\u0026thinsp;\u0026plusmn;\u0026thinsp;1.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.27\u0026thinsp;\u0026plusmn;\u0026thinsp;1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.92\u0026thinsp;\u0026plusmn;\u0026thinsp;2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecreatinine (umol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.87\u0026thinsp;\u0026plusmn;\u0026thinsp;16.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.59\u0026thinsp;\u0026plusmn;\u0026thinsp;15.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55.73\u0026thinsp;\u0026plusmn;\u0026thinsp;16.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61.69\u0026thinsp;\u0026plusmn;\u0026thinsp;19.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecystatin C (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eα1-MG (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.53\u0026thinsp;\u0026plusmn;\u0026thinsp;9.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.33\u0026thinsp;\u0026plusmn;\u0026thinsp;9.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.42\u0026thinsp;\u0026plusmn;\u0026thinsp;9.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.59\u0026thinsp;\u0026plusmn;\u0026thinsp;12.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.226\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ2-MG (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.65\u0026thinsp;\u0026plusmn;\u0026thinsp;1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.57\u0026thinsp;\u0026plusmn;\u0026thinsp;1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.77\u0026thinsp;\u0026plusmn;\u0026thinsp;1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC1q (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e225.25\u0026thinsp;\u0026plusmn;\u0026thinsp;53.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e233.30\u0026thinsp;\u0026plusmn;\u0026thinsp;59.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e229.39\u0026thinsp;\u0026plusmn;\u0026thinsp;56.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e229.86\u0026thinsp;\u0026plusmn;\u0026thinsp;63.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.634\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCK (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.63\u0026thinsp;\u0026plusmn;\u0026thinsp;25.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.46\u0026thinsp;\u0026plusmn;\u0026thinsp;21.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.04\u0026thinsp;\u0026plusmn;\u0026thinsp;23.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69.06\u0026thinsp;\u0026plusmn;\u0026thinsp;75.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDH (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e195.37\u0026thinsp;\u0026plusmn;\u0026thinsp;52.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e199.04\u0026thinsp;\u0026plusmn;\u0026thinsp;53.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e197.20\u0026thinsp;\u0026plusmn;\u0026thinsp;53.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e216.86\u0026thinsp;\u0026plusmn;\u0026thinsp;56.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.06\u0026thinsp;\u0026plusmn;\u0026thinsp;32.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.62\u0026thinsp;\u0026plusmn;\u0026thinsp;1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApoA1 (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.30\u0026thinsp;\u0026plusmn;\u0026thinsp;2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.45\u0026thinsp;\u0026plusmn;\u0026thinsp;2.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.38\u0026thinsp;\u0026plusmn;\u0026thinsp;2.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApoB100 (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.77\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApoE (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.31\u0026thinsp;\u0026plusmn;\u0026thinsp;13.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.80\u0026thinsp;\u0026plusmn;\u0026thinsp;14.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.51\u0026thinsp;\u0026plusmn;\u0026thinsp;13.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47.62\u0026thinsp;\u0026plusmn;\u0026thinsp;26.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLP(α) (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.37\u0026thinsp;\u0026plusmn;\u0026thinsp;30.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.18\u0026thinsp;\u0026plusmn;\u0026thinsp;32.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.85\u0026thinsp;\u0026plusmn;\u0026thinsp;31.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39.43\u0026thinsp;\u0026plusmn;\u0026thinsp;30.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL/CHOL (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.01\u0026thinsp;\u0026plusmn;\u0026thinsp;7.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.12\u0026thinsp;\u0026plusmn;\u0026thinsp;8.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.58\u0026thinsp;\u0026plusmn;\u0026thinsp;8.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.86\u0026thinsp;\u0026plusmn;\u0026thinsp;8.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eRA, rheumatoid arthritis; BMI, body mass index; CVD, cardiovascular disease; ANA, anti-nuclear antibodies; ACPA, anti-citrullinated protein antibodies; RF, rheumatoid factor; APF, antiperinuclear factor; AKA, anti-keratin antibodies; MCV, anti-mutated citrullinated vimentin antibodies; VEGF, vascular endothelial growth factor; WBC, white blood cell; HB, hemoglobin; PLT, platelet; LYMP, lymphocyte; NEUT, neutrophile granulocyte; ESR, erythrocyte sedimentation rate; CRP, C-reactive protein; ALB, albumin; ALP, alkaline phosphatase; ALT, alanine aminotransferase; AST, aspartate aminotransferase; GGT, glutamyl transpeptidase; BUN, serum urea nitrogen; α1-MG, α1 microglobulin; β2-MG, β2 microglobulin; CK, creatine kinase; LDH, lactate dehydrogenase; TC, total cholesterol; TG, triglycerides; HDL, high-density lipoprotein cholesterol; LDL, low-density lipoprotein cholesterol; ApoA1, apolipoprotein A1; ApoB100, apolipoprotein B100; ApoE, apolipoprotein E; LP(α), lipoprotein α; HDL/CHOL, high-density lipoprotein/cholesterol\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eComparison of lymphocyte among pure RA, RA with CVD risk factors and RA with CVD\u003c/h3\u003e\n\u003cp\u003eIn terms of immune cells, the results showed that the absolute counts of CD3\u0026thinsp;+\u0026thinsp;T, NK, CD4\u0026thinsp;+\u0026thinsp;T, CD8\u0026thinsp;+\u0026thinsp;T and TBNK cells were significantly reduced in the pure RA compared with RA with CVD, as did the proportion and absolute count of Th1 cells, Th2 cells count, and the ratio of Th1/Th2/NK and Treg, respectively. The ratio of NK and Treg cells was obviously lower in RA with CVD risk factors. In addition, the count of NK cells, the ratios of Th1/Th2/NK and Treg in RA without CVD were statistically lower than these indicators in RA with CVD, respectively. Notably, the percentage of Treg cells in pure RA, RA with CVD risk factor, and the overall population of both groups were significantly higher than that in RA with CVD. The specific values of the results are described in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of lymphocyte subpopulations in pure RA, RA with CVD risk factors, and RA with CVD\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePure RA\u003csup\u003ea\u003c/sup\u003e (n\u0026thinsp;=\u0026thinsp;402)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRA\u0026thinsp;+\u0026thinsp;cause\u003csup\u003eb\u003c/sup\u003e (n\u0026thinsp;=\u0026thinsp;394)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRA without CVD\u003csup\u003ea+b\u003c/sup\u003e (n\u0026thinsp;=\u0026thinsp;796)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRA with CVD\u003csup\u003ec\u003c/sup\u003e (n\u0026thinsp;=\u0026thinsp;201)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003ea vs c\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eb vs c\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003ea+b vs c\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocyte cells\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT (cell/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1220.88\u0026thinsp;\u0026plusmn;\u0026thinsp;554.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1372.98\u0026thinsp;\u0026plusmn;\u0026thinsp;580.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1296.17\u0026thinsp;\u0026plusmn;\u0026thinsp;571.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1395.08\u0026thinsp;\u0026plusmn;\u0026thinsp;723.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB (cell/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e202.84\u0026thinsp;\u0026plusmn;\u0026thinsp;155.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e240.98\u0026thinsp;\u0026plusmn;\u0026thinsp;179.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e221.72\u0026thinsp;\u0026plusmn;\u0026thinsp;169.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e238.23\u0026thinsp;\u0026plusmn;\u0026thinsp;203.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.649\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNK (cell/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e233.95\u0026thinsp;\u0026plusmn;\u0026thinsp;166.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e249.58\u0026thinsp;\u0026plusmn;\u0026thinsp;167.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e241.69\u0026thinsp;\u0026plusmn;\u0026thinsp;167.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e280.41\u0026thinsp;\u0026plusmn;\u0026thinsp;194.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD4\u003csup\u003e+\u003c/sup\u003eT (cell/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e748.71\u0026thinsp;\u0026plusmn;\u0026thinsp;375.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e845.27\u0026thinsp;\u0026plusmn;\u0026thinsp;406.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e796.51\u0026thinsp;\u0026plusmn;\u0026thinsp;393.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e876.37\u0026thinsp;\u0026plusmn;\u0026thinsp;527.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD8\u003csup\u003e+\u003c/sup\u003eT (cell/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e430.14\u0026thinsp;\u0026plusmn;\u0026thinsp;229.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e480.86\u0026thinsp;\u0026plusmn;\u0026thinsp;240.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e455.25\u0026thinsp;\u0026plusmn;\u0026thinsp;236.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e483.16\u0026thinsp;\u0026plusmn;\u0026thinsp;268.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.263\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD4\u003csup\u003e+\u003c/sup\u003e/CD8\u003csup\u003e+\u003c/sup\u003e T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e2.05\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.400\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTBNK (cell/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1689.17\u0026thinsp;\u0026plusmn;\u0026thinsp;728.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1905.92\u0026thinsp;\u0026plusmn;\u0026thinsp;766.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1796.45\u0026thinsp;\u0026plusmn;\u0026thinsp;754.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1961.19\u0026thinsp;\u0026plusmn;\u0026thinsp;954.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT cells (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e71.86\u0026thinsp;\u0026plusmn;\u0026thinsp;8.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e71.92\u0026thinsp;\u0026plusmn;\u0026thinsp;8.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e71.89\u0026thinsp;\u0026plusmn;\u0026thinsp;8.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e70.87\u0026thinsp;\u0026plusmn;\u0026thinsp;9.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.243\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB cells (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e11.78\u0026thinsp;\u0026plusmn;\u0026thinsp;6.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e12.24\u0026thinsp;\u0026plusmn;\u0026thinsp;6.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e12.01\u0026thinsp;\u0026plusmn;\u0026thinsp;6.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e11.71\u0026thinsp;\u0026plusmn;\u0026thinsp;6.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.540\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNK cells (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e14.36\u0026thinsp;\u0026plusmn;\u0026thinsp;8.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e13.82\u0026thinsp;\u0026plusmn;\u0026thinsp;8.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e14.09\u0026thinsp;\u0026plusmn;\u0026thinsp;8.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e15.38\u0026thinsp;\u0026plusmn;\u0026thinsp;9.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD4\u003csup\u003e+\u003c/sup\u003eT cells (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e43.67\u0026thinsp;\u0026plusmn;\u0026thinsp;9.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e43.64\u0026thinsp;\u0026plusmn;\u0026thinsp;8.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e43.66\u0026thinsp;\u0026plusmn;\u0026thinsp;8.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e43.59\u0026thinsp;\u0026plusmn;\u0026thinsp;9.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.870\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD8\u003csup\u003e+\u003c/sup\u003eT cells (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e25.49\u0026thinsp;\u0026plusmn;\u0026thinsp;8.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e25.54\u0026thinsp;\u0026plusmn;\u0026thinsp;8.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e25.51\u0026thinsp;\u0026plusmn;\u0026thinsp;8.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e24.97\u0026thinsp;\u0026plusmn;\u0026thinsp;8.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.248\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTBNK (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e98.03\u0026thinsp;\u0026plusmn;\u0026thinsp;1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e97.97\u0026thinsp;\u0026plusmn;\u0026thinsp;1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e98.00\u0026thinsp;\u0026plusmn;\u0026thinsp;1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e97.94\u0026thinsp;\u0026plusmn;\u0026thinsp;1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.549\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTh1 (cell/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e105.76\u0026thinsp;\u0026plusmn;\u0026thinsp;89.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e137.30\u0026thinsp;\u0026plusmn;\u0026thinsp;90.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e121.39\u0026thinsp;\u0026plusmn;\u0026thinsp;91.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e163.58\u0026thinsp;\u0026plusmn;\u0026thinsp;178.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTh2 (cell/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e7.90\u0026thinsp;\u0026plusmn;\u0026thinsp;6.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e8.75\u0026thinsp;\u0026plusmn;\u0026thinsp;5.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e8.32\u0026thinsp;\u0026plusmn;\u0026thinsp;6.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e9.44\u0026thinsp;\u0026plusmn;\u0026thinsp;8.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTh17 (cell/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e7.00\u0026thinsp;\u0026plusmn;\u0026thinsp;5.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e7.65\u0026thinsp;\u0026plusmn;\u0026thinsp;6.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e7.32\u0026thinsp;\u0026plusmn;\u0026thinsp;6.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e7.56\u0026thinsp;\u0026plusmn;\u0026thinsp;6.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.921\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreg (cell/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e33.72\u0026thinsp;\u0026plusmn;\u0026thinsp;24.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e37.92\u0026thinsp;\u0026plusmn;\u0026thinsp;29.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e35.80\u0026thinsp;\u0026plusmn;\u0026thinsp;27.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e38.41\u0026thinsp;\u0026plusmn;\u0026thinsp;55.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTh1 cells (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e14.93\u0026thinsp;\u0026plusmn;\u0026thinsp;9.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e17.07\u0026thinsp;\u0026plusmn;\u0026thinsp;9.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e15.99\u0026thinsp;\u0026plusmn;\u0026thinsp;9.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e17.55\u0026thinsp;\u0026plusmn;\u0026thinsp;12.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.314\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTh2 cells (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.23\u0026thinsp;\u0026plusmn;\u0026thinsp;3.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.16\u0026thinsp;\u0026plusmn;\u0026thinsp;2.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.753\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTh17 cells (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.180\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreg cells (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e4.61\u0026thinsp;\u0026plusmn;\u0026thinsp;2.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4.58\u0026thinsp;\u0026plusmn;\u0026thinsp;2.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e4.60\u0026thinsp;\u0026plusmn;\u0026thinsp;2.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e4.13\u0026thinsp;\u0026plusmn;\u0026thinsp;2.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTh1/Th2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e18.03\u0026thinsp;\u0026plusmn;\u0026thinsp;16.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e20.70\u0026thinsp;\u0026plusmn;\u0026thinsp;17.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e19.35\u0026thinsp;\u0026plusmn;\u0026thinsp;17.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e22.93\u0026thinsp;\u0026plusmn;\u0026thinsp;27.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.510\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTh17/Treg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.36\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.490\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTh1/Treg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e4.10\u0026thinsp;\u0026plusmn;\u0026thinsp;3.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4.81\u0026thinsp;\u0026plusmn;\u0026thinsp;3.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e4.45\u0026thinsp;\u0026plusmn;\u0026thinsp;3.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e5.84\u0026thinsp;\u0026plusmn;\u0026thinsp;6.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTh2/Treg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e2.28\u0026thinsp;\u0026plusmn;\u0026thinsp;27.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB/Treg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e7.81\u0026thinsp;\u0026plusmn;\u0026thinsp;7.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e8.41\u0026thinsp;\u0026plusmn;\u0026thinsp;8.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e8.11\u0026thinsp;\u0026plusmn;\u0026thinsp;7.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e18.59\u0026thinsp;\u0026plusmn;\u0026thinsp;142.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNK/Treg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e9.84\u0026thinsp;\u0026plusmn;\u0026thinsp;9.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e9.19\u0026thinsp;\u0026plusmn;\u0026thinsp;8.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e9.52\u0026thinsp;\u0026plusmn;\u0026thinsp;8.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e15.88\u0026thinsp;\u0026plusmn;\u0026thinsp;58.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eMachine learning models applied to pure RA, RA with CVD risk factors and RA with CVD\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAfter removing missing values, the data of remaining 628 RA patients (241 pure RA, 259 RA with CVD risk factors, 128 RA with CVD) were entered into ML algorithms. Three groups of patients were randomly divided into training set and validation set at a ratio of 3:1. LASSO, RF and LR models were established to identify RA with CVD from pure RA and/or RA with CVD risk factors and filter the corresponding valuable indicators. When identifying the pure RA and the RA with CVD group, the range of AUC of LASSO, RF and LR were 0.79 to 0.91, 0.84 to 0.93, and 0.77 to 0.86, respectively. When distinguishing the RA with CVD from RA with CVD risk factors, the range of AUC of LASSO, RF and LR were 0.61 to 0.77, 0.76 to 0.94, and 0.61 to 0.73, respectively. And when distinguishing RA with CVD from RA without CVD, the range of AUC of LASSO, RF and LR were 0.70 to 0.79, 0.76 to 0.88, and 0.62 to 0.80, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Supplementary Table\u0026nbsp;1 displayed the top 20 important indicators of the three models when they had the best identifiable performance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003ePerformance ability of the screened important indicators in the diagnosis of RA with CVD\u003c/h3\u003e\n\u003cp\u003eIndicators that were simultaneously significant in LASSO, RF, LR and univariate analysis were considered as important indicators. When comparing pure RA patients and RA with CVD, a total of 6 indicators were screened, namely ApoA1, TG, TC, BUN, Th1/Treg and ApoE. LDL, Treg% and TC were considered as important indicators when comparing RA with CVD risk factors and RA with CVD. In addition, in comparison of RA with or without CVD, 6 indicators including TG, LDL, Treg%, TC, CK and ApoE were screened out (Fig.\u0026nbsp;4).\u003c/p\u003e\n\u003ch3\u003eEstablishment of a risk prediction model for CVD in individuals with RA\u003c/h3\u003e\n\u003cp\u003eBased on the above results, multivariable logistic regression analysis was used to screen for independent predictors, and nomograms were established. Figure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e showed the score of each predictor, the personal total score and the predicted risk of occurrence CVD for RA. This nomogram (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA) contains 4 independent predictors, TG, TC, BUN and Th1/Treg, when predicting the risk of CVD development from pure RA patients. And LDL and Treg% serve as the independent predictors in the nomogram which predicts the risk of CVD development from RA with CVD risk factors (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB). In addition, the nomogram includes 5 independent predictors, namely TG, LDL, Treg%, CK and ApoE, when predicting the risk of CVD development from all RA patients (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eC). These nomograms exhibit good clinical usefulness.\u003c/p\u003e\n"},{"header":"Discussion","content":"\u003cp\u003eThe incidence of CVD, which includes coronary artery disease (CAD) or coronary heart disease (CHD), peripheral artery disease (PAD), cerebrovascular disease and aortic atherosclerosis [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], is increasing year by year in both developing and developed countries [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. It is largely understood that the initiation, progression, and complications of CVD are complex phenomena involving the interplay of the irrational diet, reduced physical activity, abnormal metabolism, dyslipidemia, excessive consumption of alcohol and tobacco and immune system disorders [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In particular, dyslipidemia, which is characterized by elevated levels of plasma TG, LDL, very low-density lipoprotein cholesterol (VLDL-C) and reduced HDL, is confirmed to promote the development of atherosclerotic plaques and occurrence of CVD, posing a serious threat to humans health. Furthermore, several exchangeable apolipoproteins, such as ApoA1, ApoB100, ApoE, and LP(α), have also been shown to play important roles in modulating plasma and cellular lipid metabolism and homeostasis, consequently affecting the emergence of CVD [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRA is a complex systemic autoimmune disease accompanied by poor prognosis, which is characterized by an increased morbidity and mortality of CVD. This increased risk of CVD is apparent even before the clinical diagnosis of RA: subjects with RA have a 1.5-2.0 fold risk to develop CVD than individuals without RA [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. And the phenomenon could be attribute to shared inflammatory underpinnings (such as elevated CRP, ESR, rheumatoid factor, and anti-citrullinated protein antibodies), aberrant initiation of an immune response, abnormal changes of serum lipids and lipoproteins, endothelial dysfunction and the use of steroids [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. As is commonly acknowledged, the inflammatory properties inherent in RA not only have the capacity to modify the structure and function of lipoproteins, but also exert direct or indirect effects on vascular architecture [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Patients diagnosed with RA should be closely monitored for risk factors for CVD, as recommended by EULAR guidelines [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. More research, hence, is imperative to fully characterize the intricate mechanism underlying the development of CVD in RA, to screen for characteristic differential biomarkers, and use them to construct and evaluate risk assessment models, prevention and treatment strategies that are tailored specifically for RA [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the retrospective study, we used real-world data in conjunction with traditional CVD risk factors and RA-related indicators to construct a more robust predictive model of CVD risk in RA patients. The main results of our study showed that the levels of WBC, urea, creatinine, CK, LDH and VEGF were significantly elevated in RA with CVD, as well as serum TC, TG, LDL, ApoB100 and ApoE, while ApoA1 and HDL/CHOL were decreased. VEGF has been reported to promote the occurrence of microvessels in atherosclerotic plaques and increase the possibility of intracatheral hemorrhage and thrombosis. Our results showed that VEGF levels were significantly higher in RA patients with CVD than in pure RA, but lower than that in RA with CVD risk factors, which we hypothesized was because VEGF may play a more crucial role in the process of atherosclerosis than CVD has already occurred. CD4\u003csup\u003e+\u003c/sup\u003eCD25\u003csup\u003e+\u003c/sup\u003eFOXP3\u003csup\u003e+\u003c/sup\u003e Treg cells, recently, has received increasing attention in inflammation and immune regulation. Given that both innate and adaptive immune responses are involved in the initiation and development of CVD, suppressive Treg cells may be crucial for maintaining cardiovascular homeostasis [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The ratio of Treg cells, in the current study, in RA with CVD was significantly lower than that in other groups, while the ratio of Th1/Treg, Th2/Treg and NK/Treg were significantly elevated, exhibiting excellent separation performance.\u003c/p\u003e \u003cp\u003eNowadays, machine learning are developing rapidly and has been successfully used to diagnose diseases and predict disease onset [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Various risk calculators, to date, have been utilized to predict the risk of CVD developing, however, none of them seem to perform as effectively in RA patients as they do in the general population [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Numerous scholars, hence, have endeavored to devise and authenticate CVD risk assessment models specifically for RA. An enhanced and recalibrated version of the European Systematic Coronary Risk Evaluation algorithm [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] (SCORE) was devised for RA patients in the Netherlands [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The novel score incorporated measures of inflammatory disease activity and reevaluated traditional risk factors, yet demonstrated only marginal improvement over its predecessor when applied to individuals with RA. Solomon et al. also built an expanded CVD risk prediction score for RA patients (ERS-RA), which incorporated both traditional CVD risk factors and RA-related indicators such as inflammatory disease activity, disease course, daily prednisone dosage and so on [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Despite its comprehensive nature, the ERS-RA still failed to outperform FRS in external validation. However, as the FRS solely encompasses some conventional risk factors, it may potentially underestimate the CVD risk in patients with RA. Risk models for patients with RA may require the inclusion of factors that differ from or supplement those used in the general population. Recent research has shown that multiple biochemical markers can be analyzed together to create a comprehensive index. Therefore, we then established a novel model that included blood routine index, serum lipid, VEGF and lymphocyte subpopulations, to estimate CVD risk in RA accurately and comprehensively. In this study, LASSO, RF and LR models were used to find the predictive factors for CVD in RA, and ApoA1, TG, TC, BUN, Th1/Treg and ApoE (pure RA vs. RA with CVD), LDL, Treg% and TC (RA with CVD risk factors vs. RA with CVD), and TG, LDL, Treg%, TC, CK and ApoE (RA without CVD vs. RA with CVD) were screened out, respectively. Through the final selected indicators, a convenient nomogram was established for predicting CVD risk in RA, which will greatly promote the individualized risk prediction of CVD in clinical RA patients, providing a very convenient suggestion for clinical practice.\u003c/p\u003e \u003cp\u003eThe advantage of this study is that it is the first time to integrate serum lipids, lipoproteins and lymphocytes to assess the risk of CVD in RA, providing a certain reference for the prediction and diagnosis of CVD. The information provided in this study can help physicians make more reasonable treatment plans for patients, improve their goal-oriented medical outcomes, and improve the quality of life of patients with RA. Our study, however, still had several limitations. First, this study was performed in a single hospital and the size of the recruited population was limited. Additionally, a lag may be existed in the diagnosis of CVD in the RA. That is, CVD is present before RA. Third, this study had the inevitable limitations of a retrospective study. A prospective study would be required to avoid the interference of these factors.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, serum lipids, lipoproteins, and Treg cells have been identified as risk factors for CVD in patients with RA, and three nomograms were constructed to predict the development of CVD in patients with RA in different backgrounds (pure RA and/or with CVD risk factors). Our study shows that integrated ipidomic profiling and lymphocyte subset analysis is a promising approach to predict the probability of CVD in RA.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eRA: Rheumatoid arthritis; CVD: Cardiovascular disease; VEGF: Vascular endothelial growth factor; TC: Total cholesterol; TG: Triglyceride; HDL-C: High-density lipoprotein cholesterol; LDL-C: Low-density lipoprotein cholesterol; ApoA1: Apolipoprotein A1; ApoB100: Apolipoprotein B100; ApoE: Apolipoprotein E.\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to all members who participated in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJL and HG designed the work. MF performed the writing of the article. MF and F Meng were responsible for data collation and analysis. JY, YW, YQ, YJ and GJ completed the analysis of data. ZL and CG polished the article. All authors approved the submitted version. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge the financial support from the Scientifific research project of Shanxi Provincial Health Commission (2019044), the Research Project Supported by Shanxi Scholarship Council of China (2020-191), the Science and Technology Innovation Project of Shanxi Province (2020SYS08), the Project of Central Guides Local Science and Technology Development Funds (YDZJSX2022C031) and the Foundation of Shanxi Key Laboratory for immunomicroecology (202104010910012).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data analyzed in this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was reviewed and approved by the ethics committee of the Second Hospital of Shanxi Medical University. Each participant in this study provided written informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all participants for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e \u003c/strong\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eDepartment of Rheumatology, the Second Hospital of Shanxi Medical University, Taiyuan, Shanxi, China; \u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003eShanxi Medical University, Taiyuan, Shanxi, China; \u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u003c/sup\u003eDepartment of Pathology, Brigham and Women\u0026rsquo;s Hospital, Harvard Medical School, Boston, MA, USA.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e4\u003c/sup\u003eDivision of Nephrology, Department of Medicine, Shenzhen Baoan Shiyan People\u0026rsquo;s Hospital, Shenzhen, Guangdong, China; \u003c/p\u003e\n\u003cp\u003eMin Feng and Fanxing Meng contributed to this work equally\u003c/p\u003e\n\u003cp\u003e*Correspondence: Jing Luo, [email protected]; and Hui Guo, [email protected] \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMcInnes IB, Schett G. 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Cardiovascular risk factors predicting cardiac events are different in patients with rheumatoid arthritis, psoriatic arthritis, and psoriasis. Semin Arthritis Rheum. 2018;48(3):367-73.\u003c/li\u003e\n\u003cli\u003eEngland BR, Thiele GM, Anderson DR, Mikuls TR. Increased cardiovascular risk in rheumatoid arthritis: mechanisms and implications. BMJ. 2018;361:k1036.\u003c/li\u003e\n\u003cli\u003eAvouac J, Amrouche F, Meune C, Rey G, Kahan A, Allanore Y. Mortality profile of patients with rheumatoid arthritis in France and its change in 10 years. Semin Arthritis Rheum. 2017;46(5): 537-43.\u003c/li\u003e\n\u003cli\u003eSymmons DPM, Gabriel SE. Epidemiology of CVD in rheumatic disease, with a focus on RA and SLE. Nat Rev Rheumatol. 2011;7(7):399-408. \u003c/li\u003e\n\u003cli\u003eKisiel B, Kruszewski R, Juszkiewicz A, Klos K, Tlustochowicz M, Thustochowicz W. Prevalence of atherosclerosis in diabetic and non-diabetic patients with rheumatoid arthritis. Pak J Med Sci. 2015;31(5):1078-83.\u003c/li\u003e\n\u003cli\u003eRuscitti P, Cipriani P, Masedu F, Romano S, Berardicurti O, Liakouli V, et al. Increased cardiovascular events and subclinical atherosclerosis in rheumatoid arthritis patients: 1 year prospective single centre study. PLoS One. 2017;12(1):e0170108.\u003c/li\u003e\n\u003cli\u003eCasta\u0026ntilde;eda S, Vicente-Rabaneda EF, Garc\u0026iacute;a-Casta\u0026ntilde;eda N, Prieto-Pe\u0026ntilde;a D, Dessein PH, Gonz\u0026aacute;lez-Gay MA. Unmet needs in the management of cardiovascular risk in inflammatory joint diseases. Expert Rev Clin Immunol. 2020;16(1):23-36.\u003c/li\u003e\n\u003cli\u003eHansson GK. Inflammation, atherosclerosis, and coronary artery disease. N Engl J Med. 2005;352(16):1685-95.\u003c/li\u003e\n\u003cli\u003eGiles JT, Post WS, Blumenthal RS, Polak J, Petri M, Gelber AC, et al. Longitudinal predictors of progression of carotid atherosclerosis in rheumatoid arthritis. Arthritis Rheum. 2011;63(11):3216-25. \u003c/li\u003e\n\u003cli\u003eRidker PM, Danielson E, Fonseca FAH, Genest J, Gotto AM, Kastelein JJP, et al. Rosuvastatin to Prevent Vascular Events in Men and Women with Elevated C-Reactive Protein. N Engl J Med. 2008;359(21): 2195-207.\u003c/li\u003e\n\u003cli\u003eVarga Z, Sabzwari SRA, Vargova V. Cardiovascular Risk of Nonsteroidal Anti-Inflammatory Drugs: An Under-Recognized Public Health Issue. Cureus. 2017;9(4):e1144. \u003c/li\u003e\n\u003cli\u003eOlvera Lopez E, Ballard BD, Jan A. Cardiovascular Disease. 2022 Aug 8. \u003c/li\u003e\n\u003cli\u003eSchnabel RB, Yin X, Larson MG, Yamamoto JF, Fontes JD, Kathiresan S, et al. Multiple inflammatory biomarkers in relation to cardiovascular events and mortality in the community. Arterioscler Thromb Vasc Biol. 2013;33(7):1728-33.\u003c/li\u003e\n\u003cli\u003eDel Giudice M, Gangestad SW. Rethinking IL-6 and CRP: Why they are more than inflammatory biomarkers, and why it matters. Brain Behav Immun. 2018;70:61-75.\u003c/li\u003e\n\u003cli\u003eSu X, Peng D. The exchangeable apolipoproteins in lipid metabolism and obesity. Clin Chim Acta. 2020;503:128-35. \u003c/li\u003e\n\u003cli\u003eMaradit-Kremers H, Crowson CS, Nicola PJ, Ballman KV, Roger VL, Jacobsen SJ, et al. Increased unrecognized coronary heart disease and sudden deaths in rheumatoid arthritis: a population-based cohort study. Arthritis Rheum. 2005;52(2):402-11. \u003c/li\u003e\n\u003cli\u003eEngland BR, Thiele GM, Anderson DR, Mikuls TR. Increased cardiovascular risk in rheumatoid arthritis: mechanisms and implications. BMJ. 2018;361:k1036. \u003c/li\u003e\n\u003cli\u003eKremers HM, Nicola PJ, Crowson CS, Ballman KV, Gabriel SE. Prognostic importance of low body mass index in relation to cardiovascular mortality in rheumatoid arthritis. Arthritis Rheum. 2004;50(11):3450-7. \u003c/li\u003e\n\u003cli\u003eToms TE, Symmons DP, Kitas GD. Dyslipidaemia in rheumatoid arthritis: the role of inflammation, drugs, lifestyle and genetic factors. Curr Vasc Pharmacol. 2010; 8:301-26.\u003c/li\u003e\n\u003cli\u003eBlum A, Adawi M. Rheumatoid arthritis (RA) and cardiovascular disease. Autoimmun Rev. 2019;18(7):679-90. \u003c/li\u003e\n\u003cli\u003eLi M, Wang X, Fu W, He S, Li D, Ke Q. CD4+CD25+Foxp3+ regulatory T cells protect endothelial function impaired by oxidized low density lipoprotein via the KLF-2 transcription factor. Cell Physiol Biochem. 2011;28(4):639-48. \u003c/li\u003e\n\u003cli\u003eLibby P, Lichtman AH, Hansson GK. Immune effector mechanisms implicated in atherosclerosis: from mice to humans. Immunity. 2013;38(6):1092-104.\u003c/li\u003e\n\u003cli\u003eMeng X, Yang J, Dong M, Zhang K, Tu E, Gao Q, et al. Regulatory T cells in cardiovascular diseases. Nat Rev Cardiol. 2016;13(3):167-79.\u003c/li\u003e\n\u003cli\u003eChen J, Remulla D, Nguyen JH, Dua A, Liu Y, Dasgupta P, et al. Current status of artificial intelligence applications in Urology and its potential to influence clinical practice. BJU Int. 2019;124:567-77.\u003c/li\u003e\n\u003cli\u003eLiang H, Tsui BY, Ni H, Valentim CCS, Baxter SL, Liu G, et al. Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence. Nat Med. 2019;25:433-8.\u003c/li\u003e\n\u003cli\u003eWei T, Yang B, Liu H, Xin F, Fu L. Development and validation of a nomogram to predict coronary heart disease in patients with rheumatoid arthritis in northern China. Aging (Albany NY). 2020;12(4):3190-204. \u003c/li\u003e\n\u003cli\u003eConroy RM, Py\u0026ouml;r\u0026auml;l\u0026auml; K, Fitzgerald AP, Sans S, Menotti A, De Backer G, et al. Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project. Eur Heart J. 2003;24(11):987-1003.\u003c/li\u003e\n\u003cli\u003eArts EE, Popa CD, Den Broeder AA, Donders R, Sandoo A, Toms T, et al. Prediction of cardiovascular risk in rheumatoid arthritis: performance of original and adapted SCORE algorithms. Ann Rheum Dis. 2016;75(4):674-80. \u003c/li\u003e\n\u003cli\u003eSolomon DH, Greenberg J, Curtis JR, Liu M, Farkouh ME, Tsao P, et al. Derivation and internal validation of an expanded cardiovascular risk prediction score for rheumatoid arthritis: a Consortium of Rheumatology Researchers of North America Registry Study. Arthritis Rheumatol. 2015;67(8):1995-2003.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"rheumatoid arthritis, cardiovascular disease, VEGF, regulatory T cells, prediction","lastPublishedDoi":"10.21203/rs.3.rs-2914034/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2914034/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cb\u003eBackground\u003c/b\u003e Patients with rheumatoid arthritis (RA) have increased mortality and morbidity due to cardiovascular disease (CVD). Distinguishing RA with CVD, RA with CVD risk factors and patients with RA only (pure RA), is still a challenge. The study aimed to develop a better model to predict the likelihood of CVD in RA.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMethods\u003c/b\u003e Pure RA (n\u0026thinsp;=\u0026thinsp;402), RA with CVD risk factors (n\u0026thinsp;=\u0026thinsp;394), and RA with CVD (n\u0026thinsp;=\u0026thinsp;201) were ultimately recruited into the study and their peripheral bloods were collected to measure the levels of routine examination indicators, vascular endothelial growth factor (VEGF) and immune cells. Univariate analysis, the least absolute shrinkage and selection operator (LASSO), the random forest (RF) and the logistic regression models (LR) were employed to screen characteristic variables between each two groups, and individualized nomograms were further established to more conveniently predict the likelihood of CVD in RA.\u003c/p\u003e \u003cp\u003e \u003cb\u003eResults\u003c/b\u003e Univariate analysis showed that the levels of WBC, BUN, creatinine, CK, LDH and VEGF were significantly elevated in RA with CVD, as well as serum TC, TG, LDL, ApoB100 and ApoE, while ApoA1 and HDL/CHOL were decreased. Furthermore, the ratio of Treg cells, exhibiting excellent separation performance, in RA with CVD was significantly lower than that in other groups, while the ratio of Th1/Th2/NK and Treg were significantly elevated. LASSO, RF and LR models were also used to find the risk factors for CVD in RA. Through the final selected indicators screened by three machine learning models and univariate analysis, a convenient nomogram was established for predicting CVD risk in RA.\u003c/p\u003e \u003cp\u003e \u003cb\u003eConclusions\u003c/b\u003e Serum lipids, lipoproteins, and Treg cells have been identified as risk factors for CVD in patients with RA, and three nomograms combining various risk factors were constructed and were used for individualized prediction of CVD in patients with RA (pure RA and/or with CVD risk factors).\u003c/p\u003e","manuscriptTitle":"Predicting the likelihood of cardiovascular disease in patients with rheumatoid arthritis: development and Validation of a Novel Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-05-23 14:41:50","doi":"10.21203/rs.3.rs-2914034/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":"bb686c45-6ab4-4afd-8a3c-b144a370a5c5","owner":[],"postedDate":"May 23rd, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-07-16T23:10:14+00:00","versionOfRecord":[],"versionCreatedAt":"2023-05-23 14:41:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2914034","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2914034","identity":"rs-2914034","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}